title: The total number and mass of SARS-CoV-2 virions

abstract:
Quantitatively describing the time course of the SARS-CoV-2 infection within an infected individual is important for understanding the current global pandemic and possible ways to combat it. Here we integrate the best current knowledge about the typical viral load of SARS-CoV-2 in bodily fluids and host tissues to estimate the total number and mass of SARS-CoV-2 virions in an infected person. We estimate that each infected person carries 109-1011 virions during peak infection, with a total mass in the range of 1-100 g, which curiously implies that all SARS-CoV-2 virions currently circulating within human hosts have a collective mass of only 0.1-10 kg. We combine our estimates with the available literature on host immune response and viral mutation rates to demonstrate how antibodies markedly outnumber the spike proteins and the genetic diversity of virions in an infected host covers all possible single nucleotide substitutions.

SignificanceKnowing the absolute numbers of virions in an infection promotes better understanding of the disease dynamics and the response of the immune system. Here we use the best current knowledge on the concentrations of virions in infected individuals to estimate the total number and mass of SARS-CoV-2 virions in an infected person. Although each infected person carries an estimated 1-100 billion virions during peak infection, their total mass is no more than 0.1 mg. This curiously implies that all SARS-CoV-2 virions currently in all human hosts have a mass of between 100 gram and 10 kilogram. Combining the known mutation rate and our estimate of the number of infectious virions we quantify the formation rate of genetic variants.


title: Potential of machine learning to predict early ischemic events after carotid endarterectomy or stenting: A comparison with surgeon predictions

abstract:
BackgroundCarotid endarterectomy (CEA) and carotid artery stenting (CAS) are recommended for high stroke-risk patients with carotid artery stenosis to reduce ischemic events. However, we often face difficulty in determining the best treatment strategy.

ObjectiveWe aimed to develop an accurate post-CEA/CAS outcome prediction model using machine learning that will serve as a basis for a new decision support tool for patient-specific treatment planning.

MethodsRetrospectively collected data from 165 consecutive patients with carotid stenosis underwent CEA or CAS were divided into training and test samples. The following five machine learning algorithms were tuned, and their predictive performance evaluated by comparison with surgeon predictions: an artificial neural network, logistic regression, support vector machine, random forest, and extreme gradient boosting (XGBoost). Seventeen clinical factors were introduced into the models. Outcome was defined as any ischemic stroke within 30 days after treatment including asymptomatic diffusion-weighted imaging abnormalities.

ResultsThe XGBoost model performed the best in the evaluation; its sensitivity, specificity, positive predictive value, and accuracy were 31.9%, 94.6%, 47.2%, and 86.2%, respectively. These statistical measures were comparable to those of surgeons. Internal carotid artery peak systolic velocity, low density lipoprotein cholesterol, and procedure (CEA or CAS) were the most contributing factors according to the XGBoost algorithm.

ConclusionWe were able to develop a post-procedural outcome prediction model comparable to surgeons in performance. The accurate outcome prediction model will make it possible to make a more appropriate patient-specific selection of CEA or CAS for the treatment of carotid artery stenosis.

category: neurology


title: Locus coeruleus integrity is related to tau burden and memory loss in autosomal-dominant Alzheimer's disease

abstract:
Abnormally phosphorylated tau, an indicator of Alzheimers disease, accumulates in the first decades of life in the locus coeruleus (LC), the brains main noradrenaline supply. However, technical challenges in reliable in-vivo assessments have impeded research into the role of the LC in Alzheimers disease.

We studied participants with or known to be at-risk for mutations in genes causing autosomal-dominant Alzheimers disease (ADAD) of early onset, providing a unique window into the pathogenesis of Alzheimers largely disentangled from age-related factors. Using high- resolution MRI and tau PET, we revealed lower rostral LC integrity in symptomatic participants. LC integrity was associated with individual differences in tau burden and memory decline. Post- mortem analyses in a separate set of carriers of the same mutation confirmed substantial neuronal loss in the LC.

Our findings link LC degeneration to tau burden and memory in Alzheimers and highlight a role of the noradrenergic system in this neurodegenerative disease.


title: The impact of COVID-19 employment shocks on suicide and safety net use: An early-stage investigation

abstract: This paper examines whether the COVID-19-induced employment shocks are associated with increases in suicides and safety net use in the second and third quarters of 2020. We exploit plausibly exogenous regional variation in the magnitude of the employment shocks in Japan and adopt a difference-in-differences research design to examine and control for possible confounders. Our preferred point estimates suggest that a one-percentage-point increase in the unemployment rate in the second quarter of 2020 is associated with, approximately, an additional 0.52 suicides, 28 unemployment benefit recipients, 88 recipients of a temporary loan program, and 10 recipients of public assistance per 100,000 population per month. A simple calculation based on these estimates suggests that if a region experienced a one-percentage-point increase in the unemployment rate caused by the COVID-19 crisis in the second quarter of 2020, which is roughly equivalent to the third-highest regional employment shock, this would be associated with 37.4%, 60.5%, and 26.5% increases in the total, female, and male suicide rates respectively in July 2020 compared with July 2019. Our baseline findings are robust to several different model specifications, although we do not assert that our research design perfectly solves the problem of estimation bias.

category: health economics


title: Immune memory in mild COVID-19 patients and unexposed donors from India reveals persistent T cell responses after SARS-CoV-2 infection

abstract: Understanding the causes of the diverse outcome of COVID-19 pandemic in different geographical locations is important for the worldwide vaccine implementation and pandemic control responses. We analyzed 42 unexposed healthy donors and 28 mild COVID-19 subjects up to 5 months from the recovery for SARS-CoV-2 specific immunological memory. Using HLA class II predicted peptide megapools, we identified SARS-CoV-2 cross-reactive CD4+ T cells in around 66% of the unexposed individuals. Moreover, we found detectable immune memory in mild COVID-19 patients several months after recovery in the crucial arms of protective adaptive immunity; CD4+ T cells and B cells, with a minimal contribution from CD8+ T cells. Interestingly, the persistent immune memory in COVID-19 patients is predominantly targeted towards the Spike glycoprotein of the SARS-CoV-2. This study provides the evidence of both high magnitude pre-existing and persistent immune memory in Indian population. By providing the knowledge on cellular immune responses to SARS-CoV-2, our work has implication for the development and implementation of vaccines against COVID-19.

category: infectious diseases


title: SARS-CoV-2 epidemic after social and economic reopening in three US states reveals shifts in age structure and clinical characteristics

abstract: In the United States, state-level re-openings in spring 2020 presented an opportunity for the resurgence of SARS-CoV-2 transmission. One important question during this time was whether human contact and mixing patterns could increase gradually without increasing viral transmission, the rationale being that new mixing patterns would likely be associated with improved distancing, masking, and hygiene practices. A second key question to follow during this time was whether clinical characteristics of the epidemic would improve after the initial surge of cases. Here, we analyze age-structured case, hospitalization, and death time series from three states - Rhode Island, Massachusetts, and Pennsylvania - that had successful re-openings in May 2020 without summer waves of infection. Using a Bayesian inference framework on eleven daily data streams and flexible daily population contact parameters, we show that population-average mixing rates dropped by >50% during the lockdown period in March/April, and that the correlation between overall population mobility and transmission-capable mobility was broken in May as these states partially re-opened. We estimate the reporting rates (fraction of symptomatic cases reporting to health system) at 96.0% (RI), 72.1% (MA), and 75.5% (PA); in Rhode Island, when accounting for cases caught through general-population screening programs, the reporting rate estimate is 94.5%. We show that elderly individuals were less able to reduce contacts during the lockdown period when compared to younger individuals. Attack rate estimates through August 31 2020 are 6.4% (95% CI: 5.8% - 7.3%) of the total population infected for Rhode Island, 5.7% (95% CI: 5.0% - 6.8%) in Massachusetts, and 3.7% (95% CI: 3.1% - 4.5%) in Pennsylvania, with some validation available through published seroprevalence studies. Infection fatality rates (IFR) estimates for the spring epidemic are higher in our analysis (>2%) than previously reported values, likely resulting from the epidemics in these three states affecting the most vulnerable sub-populations, especially the most vulnerable of the [≥]80 age group.

category: epidemiology


title: Combined polygenic risk scores of different psychiatric traits predict general and specific psychopathology in childhood

abstract:
BackgroundPolygenic risk scores (PRSs) operationalize genetic propensity towards a particular mental disorder and hold promise as early predictors of psychopathology, but before a PRS can be used clinically, explanatory power must be increased and the specificity for a psychiatric domain established. To enable early detection it is crucial to study these psychometric properties in childhood. We examined whether PRSs associate more with general or with specific psychopathology in school-aged children. Additionally, we tested whether psychiatric PRSs can be combined into a multi-PRS score for improved performance.

MethodsWe computed 16 PRSs based on GWASs of psychiatric phenotypes, but also neuroticism and cognitive ability, in mostly adult populations. Study participants were 9247 school-aged children from three population-based cohorts of the DREAM-BIG consortium: ALSPAC (UK), The Generation R Study (Netherlands) and MAVAN (Canada). We associated each PRS with general and specific psychopathology factors, derived from a bifactor model based on self-, parental-, teacher-, and observer reports. After fitting each PRS in separate models, we also tested a multi-PRS model, in which all PRSs are entered simultaneously as predictors of the general psychopathology factor.

ResultsSeven PRSs were associated with the general psychopathology factor after multiple testing adjustment, two with specific externalizing and five with specific internalizing psychopathology. PRSs predicted general psychopathology independently of each other, with the exception of depression and depressive symptom PRSs. Most PRSs associated with a specific psychopathology domain, were also associated with general child psychopathology.

ConclusionsThe results suggest that PRSs based on current GWASs of psychiatric phenotypes tend to be associated with general psychopathology, or both general and specific psychiatric domains, but not with one specific psychopathology domain only. Furthermore, PRSs can be combined to improve predictive ability. PRS users should therefore be conscious of non-specificity and consider using multiple PRSs simultaneously, when predicting psychiatric disorders.

category: psychiatry and clinical psychology


title: Cumulative COVID-19 incidence, mortality, and prognosis in cancer survivors: a population-based study in Reggio Emilia, Northern Italy.

abstract:
The aim of this population-based study was to evaluate the impact of having had cancer on COVID-19 risk and prognosis during the first wave of the pandemic (27 February - 13 May 2020) in Reggio Emilia Province. Prevalent cancer cases diagnosed between 1996 and December 2019 were linked with the provincial COVID-19 surveillance system. We compared cancer survivors (CS) cumulative incidence of being tested, testing positive for SARS-CoV-2, being hospitalized, and dying of COVID-19 with that of the general population; we compared COVID-19 prognosis in CS and in patients without cancer.

15,391 people (1527 CS) underwent RT-PCR for SARS-CoV-2, of whom 4541 (447 CS) tested positive; 541 (113 CS) died of COVID-19. The cumulative incidences of being tested, testing positive, COVID-19 hospitalization, and death were lower in CS: age- and sex-adjusted incidence rate ratios were 1.28 [95%CI = 1.21, 1.35], 1.06 [95%CI = 0.96, 1.18], 1.27 [95%CI = 1.09, 1.48], and 1.39 [95%CI = 1.12, 1.71], respectively. CS had worse prognosis when diagnosed with COVID-19, particularly those below the age of 70 (age- and sex-adjusted odds ratio (OR) of death 5.03; [95%CI = 2.59, 9.75]), while the OR decreased after age 70. The OR of death was higher for patients with a recent diagnosis, i.e. <2 years (OR=2.92; 95%CI = 1.64, 5.21), or metastases (OR=2.09; 95%CI = 0. 88, 4.93).

Cancer patients showed the same probability of being infected, despite a slightly higher probability of being tested, than the general population, nevertheless they were at higher risk of death once infected.

Novelty and impactCancer survivors during the first wave of the pandemic showed higher COVID-19 cumulative incidence and mortality. When infected, they had worse prognosis, particularly in people younger than age 70 or those with a recent diagnosis.

category: public and global health


title: Fighting COVID-19 with Flexible Testing: Models and Insights

abstract: Increasing testing capacities plays a substantial role in safely reopening the economy and avoiding a new wave of COVID-19. Pooled testing can expand testing capabilities by pooling multiple individual samples, but it also raises accuracy concerns. In this study, we propose a flexible testing strategy that adopts pooled testing or individual testing according to epidemic dynamics. We identify the prevalence threshold between individual and pooled testing by modeling the expected number of tests per confirmed case. Incorporating an epidemic model, we show pooled testing is more effective in containing epidemic outbreaks and can generate more reliable test results than individual testing because the reliability of test results is relevant to both testing methods and prevalence. Our study is the first to evaluate the interplay between pooled testing and a rapidly evolving outbreak to the best of our knowledge. Our results allay accuracy concerns about pooled testing and provide theoretical supports to empirical studies.


title: Improved Detection of Air Trapping on Expiratory Computed Tomography Using Deep Learning

abstract:
BackgroundRadiologic evidence of air trapping (AT) on expiratory computed tomography (CT) scans is associated with early pulmonary dysfunction in patients with cystic fibrosis (CF). However, standard techniques for quantitative assessment of AT are highly variable, resulting in limited efficacy for monitoring disease progression.

ObjectiveTo investigate the effectiveness of a convolutional neural network (CNN) model for quantifying and monitoring AT, and to compare it with other quantitative AT measures obtained from threshold-based techniques.

Materials and MethodsPaired volumetric whole lung inspiratory and expiratory CT scans were obtained at four time points (0, 3, 12 and 24 months) on 36 subjects with mild CF lung disease. A densely connected CNN (DN) was trained using AT segmentation maps generated from a personalized threshold-based method (PTM). Quantitative AT (QAT) values, presented as the relative volume of AT over the lungs, from the DN approach were compared to QAT values from the PTM method. Radiographic assessment, spirometric measures, and clinical scores were correlated to the DN QAT values using a linear mixed effects model.

ResultsQAT values from the DN were found to increase from 8.65% {+/-} 1.38% to 21.38% {+/-} 1.82%, respectively, over a two-year period. Comparison of CNN model results to intensity-based measures demonstrated a systematic drop in the Dice coefficient over time (decreased from 0.86 {+/-} 0.03 to 0.45 {+/-} 0.04). The trends observed in DN QAT values were consistent with clinical scores for AT, bronchiectasis, and mucus plugging. In addition, the DN approach was found to be less susceptible to variations in expiratory deflation levels than the threshold-based approach.

ConclusionThe CNN model effectively delineated AT on expiratory CT scans, which provides an automated and objective approach for assessing and monitoring AT in CF patients.

category: radiology and imaging


title: The impact of the coronavirus disease 2019 (COVID-19) outbreak on cancer practice in Japan: using an administrative database

abstract:
BackgroundRecent researches reported the impact of the coronavirus disease 2019 (COVID - 19) pandemic on the clinical practice of specific type cancers. The aim of this study was to reveal the impact of the COVID-19 outbreak on the clinical practice of various cancers.

MethodsWe included hospitalized patients aged 18 years or older diagnosed between July 2018 and June 2020 with one of the top 12 most common cancers in Japan (colon/rectum, lung, gastric, breast, bladder & urinary tract, pancreas, non-Hodgkin lymphoma, liver, prostate, esophagus, uterus, and gallbladder & biliary tract) using Diagnostic Procedure Combination data, an administrative database in Japan. The intervention was defined April 2020 based on a declaration of emergency from Japanese government. The change volume of number of monthly admissions with each cancer was tested by interrupted time series (ITS) analysis, and monthly cases with radical surgery or chemotherapy for each cancer were descripted.

Results403,344 cases were included during the study period. The most common cancer was colon/rectum (20.5%), followed by lung (17.5%). In almost cancer cases, the number of admissions decreased in May 2020. In particular, colorectal, lung, gastric, breast, uterine, or esophageal cancer cases decreased by over 10%. The number of admissions with surgery or chemotherapy decreased in colorectal, lung, gastric, breast, uterine, or esophageal cancer. ITS analysis indicated that cases with gastric or esophageal cancer were affected more than other type of cancer.

ConclusionsThe COVID-19 outbreak has a negative impact on the number of admission cases with cancer; the magnitude of impact varied by cancer diagnosis.

category: oncology


title: COVIDStrategyCalculator: A software to assess testing- and quarantine strategies for incoming travelers, contact person management and de-isolation

abstract:
While large-scale vaccination campaigns against SARS-CoV-2 are rolled out at the time of writing, non-pharmaceutical interventions (NPIs), including the isolation of infected individuals, and quarantine of exposed individuals remain central measures to contain the spread of SARS-CoV-2. Strategies that combine NPIs with innovative SARS-CoV-2 testing strategies may increase containment efficacy and help to shorten quarantine durations.

We developed a user-friendly software-tool that implements a recently published stochastic within-host viral dynamics model that captures temporal attributes of the viral infection, such as test sensitivity, infectiousness and the occurrence of symptoms. Based on this model, the software allows to evaluate the efficacy of user-defined, arbitrary NPI and testing strategies in reducing the transmission potential in different contexts. The software thus enables decision makers to explore NPI strategies and perform hypothesis testing, e.g. with regards to the utilization of novel diagnostics or with regards to containing novel virus variants.

category: health informatics


title: Valuation system connectivity is correlated with poly-drug use in young adults

abstract: Poly-drug consumption contributes to fatal overdose in more than half of all poly-drug users. Analyzing decision-making networks may give insight into the motivations behind poly-drug use. We correlated average functional connectivity of the valuation system (VS), executive control system (ECS) and valuation-control complex (VCC) in a large population sample (n=992) with drug use behaviour. VS connectivity is correlated with sedative use, ECS connectivity is separately correlated with hallucinogens and opiates. Network connectivity is also correlated with drug use via two-way interactions with other substances including alcohol and tobacco. These preliminary findings can contribute to our understanding of the common combinations of substance co-use and associated neural patterns.

category: addiction medicine


title: The impact of early public health interventions on SARS-CoV-2 transmission and evolution

abstract:
BackgroundMany countries have attempted to mitigate and control COVID-19 through the implementation of non-pharmaceutical interventions, particularly with the aim of reducing population movement and contact. However, it remains unclear how the different control strategies impacted the local phylodynamics of the causative SARS-CoV-2 virus.

AimTo assess the duration of chains of virus transmission within individual countries and the extent to which countries export viruses to their geographic neighbours.

MethodsTo address core questions in genomic epidemiology and public health we analysed complete SARS-CoV-2 genomes to infer the relative frequencies of virus importation and exportation, as well as virus transmission dynamics, within countries of northern Europe. To this end, we examined virus evolution and phylodynamics in Denmark, Finland, Iceland, Norway and Sweden during the first year of the pandemic.

ResultsThe Nordic countries differed markedly in the invasiveness of control strategies implemented. In particular, Sweden did not initially employ any strict population movement limitations and experienced markedly different transmission chain dynamics, which were more numerous and tended to have more cases, a set of features that increased with time during the first eight months of 2020.

ConclusionTogether with Denmark, Sweden was also characterised as a net exporter of SARS-CoV-2. Hence, Sweden effectively constituted an epidemiological and evolutionary  refugia that enabled the virus to maintain active transmission and spread to other geographic localities. In sum, our analysis reveals the utility of genomic surveillance where active transmission chain monitoring is a key metric.


title: High throughput wastewater SARS-CoV-2 detection enables forecasting of community infection dynamics in San Diego county

abstract:
Large-scale wastewater surveillance has the ability to greatly augment the tracking of infection dynamics especially in communities where the prevalence rates far exceed the testing capacity. However, current methods for viral detection in wastewater are severely lacking in terms of scaling up for high throughput. In the present study, we employed an automated magnetic-bead based concentration approach for viral detection in sewage that can effectively be scaled up for processing 24 samples in a single 40-minute run. The method compared favorably to conventionally used methods for viral wastewater concentrations with higher recovery efficiencies from input sample volumes as low as 10ml and can enable the processing of over 100 wastewater samples in a day. The sensitivity of the high-throughput protocol was shown to detect cases as low as 2 in a hospital building with a known COVID-19 caseload. Using the high throughput pipeline, samples from the influent stream of the primary wastewater treatment plant of San Diego county (serving 2.3 million residents) were processed for a period of 13 weeks. Wastewater estimates of SARS-CoV-2 viral genome copies in raw untreated wastewater correlated strongly with clinically reported cases by the county, and when used alongside past reported case numbers and temporal information in an autoregressive integrated moving average (ARIMA) model enabled prediction of new reported cases up to 3 weeks in advance. Taken together, the results show that the high-throughput surveillance could greatly ameliorate comprehensive community prevalence assessments by providing robust, rapid estimates.

ImportanceWastewater monitoring has a lot of potential for revealing COVID-19 outbreaks before they happen because the virus is found in the wastewater before people have clinical symptoms. However, application of wastewater-based surveillance has been limited by long processing times specifically at the concentration step. Here we introduce a much faster method of processing the samples, and show that its robustness by demonstrating direct comparisons with existing methods and showing that we can predict cases in San Diego by a week with excellent accuracy, and three weeks with fair accuracy, using city sewage. The automated viral concentration method will greatly alleviate the major bottleneck in wastewater processing by reducing the turnaround time during epidemics.


title: The stochastic dynamics of early epidemics: probability of establishment, initial growth rate, and infection cluster size at first detection

abstract: Emerging epidemics and local infection clusters are initially prone to stochastic effects that can substantially impact the epidemic trajectory. While numerous studies are devoted to the deterministic regime of an established epidemic, mathematical descriptions of the initial phase of epidemic growth are comparatively rarer. Here, we review existing mathematical results on the epidemic size over time, and derive new results to elucidate the early dynamics of an infection cluster started by a single infected individual. We show that the initial growth of epidemics that eventually take off is accelerated by stochasticity. These results are critical to improve early cluster detection and control. As an application, we compute the distribution of the first detection time of an infected individual in an infection cluster depending on the testing effort, and estimate that the SARS-CoV-2 variant of concern Alpha detected in September 2020 first appeared in the United Kingdom early August 2020. We also compute a minimal testing frequency to detect clusters before they exceed a given threshold size. These results improve our theoretical understanding of early epidemics and will be useful for the study and control of local infectious disease clusters.


title: Global seroprevalence of SARS-CoV-2 antibodies: a systematic review and meta-analysis

abstract:
BackgroundMany studies report the seroprevalence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antibodies. We aimed to synthesize seroprevalence data to better estimate the level and distribution of SARS-CoV-2 infection, identify high-risk groups, and inform public health decision making.

MethodsIn this systematic review and meta-analysis, we searched publication databases, preprint servers, and grey literature sources for seroepidemiological study reports, from January 1, 2020 to December 31, 2020. We included studies that reported a sample size, study date, location, and seroprevalence estimate. We corrected estimates for imperfect test accuracy with Bayesian measurement error models, conducted meta-analysis to identify demographic differences in the prevalence of SARS-CoV-2 antibodies, and meta-regression to identify study-level factors associated with seroprevalence. We compared region-specific seroprevalence data to confirmed cumulative incidence. PROSPERO: CRD42020183634.

ResultsWe identified 968 seroprevalence studies including 9.3 million participants in 74 countries. There were 472 studies (49%) at low or moderate risk of bias. Seroprevalence was low in the general population (median 4.5%, IQR 2.4-8.4%); however, it varied widely in specific populations from low (0.6% perinatal) to high (59% persons in assisted living and long-term care facilities). Median seroprevalence also varied by Global Burden of Disease region, from 0.6 % in Southeast Asia, East Asia and Oceania to 19.5% in Sub-Saharan Africa (p<0.001). National studies had lower seroprevalence estimates than regional and local studies (p<0.001). Compared to Caucasian persons, Black persons (prevalence ratio [RR] 3.37, 95% CI 2.64-4.29), Asian persons (RR 2.47, 95% CI 1.96-3.11), Indigenous persons (RR 5.47, 95% CI 1.01-32.6), and multi-racial persons (RR 1.89, 95% CI 1.60-2.24) were more likely to be seropositive. Seroprevalence was higher among people ages 18-64 compared to 65 and over (RR 1.27, 95% CI 1.11-1.45). Health care workers in contact with infected persons had a 2.10 times (95% CI 1.28-3.44) higher risk compared to health care workers without known contact. There was no difference in seroprevalence between sex groups. Seroprevalence estimates from national studies were a median 18.1 times (IQR 5.9-38.7) higher than the corresponding SARS-CoV-2 cumulative incidence, but there was large variation between Global Burden of Disease regions from 6.7 in South Asia to 602.5 in Sub-Saharan Africa. Notable methodological limitations of serosurveys included absent reporting of test information, no statistical correction for demographics or test sensitivity and specificity, use of non-probability sampling and use of non-representative sample frames.

DiscussionMost of the population remains susceptible to SARS-CoV-2 infection. Public health measures must be improved to protect disproportionately affected groups, including racial and ethnic minorities, until vaccine-derived herd immunity is achieved. Improvements in serosurvey design and reporting are needed for ongoing monitoring of infection prevalence and the pandemic response.

FundingPublic Health Agency of Canada through the COVID-19 Immunity Task Force.


title: Linking vestibular function and sub-cortical grey matter volume changes in a longitudinal study of aging adults

abstract:
Emerging evidence suggests a relationship between impairments of the vestibular (inner ear balance) system and alterations in the function and the structure of the central nervous system in older adults. However, it is unclear whether age-related vestibular loss is associated with volume loss in brain regions known to receive vestibular input. To address this gap, we investigated the association between vestibular function and the volumes of four structures that process vestibular information (the hippocampus, entorhinal cortex, thalamus, and basal ganglia) in a longitudinal study of 97 healthy, older participants from the Baltimore Longitudinal Study of Aging. Vestibular testing included cervical vestibular-evoked myogenic potentials (cVEMP) to measure saccular function, ocular VEMP (oVEMP) to measure utricular function, and video head-impulse tests to measure the horizontal semi-circular canal vestibulo-ocular reflex (VOR). Participants in the sample had vestibular and brain MRI data for a total of 1 (18.6%), 2 (49.5%) and 3 (32.0%) visits. Linear mixed-effects regression was used to model regional volume over time as a function of vestibular physiological function, correcting for age, sex, intracranial volume, and inter-subject random variation in the baseline levels of and rates of change of volume over time. We found that poorer saccular function, characterized by lower cVEMP amplitude, is associated with reduced bilateral volumes of the basal ganglia and thalamus at each time point, demonstrated by a 0.0714 cm3 {+/-} 0.0344 (unadjusted p=0.038; 95% CI: 0.00397-0.139) lower bilateral-mean volume of the basal ganglia and a 0.0440 cm3 {+/-} 0.0221 (unadjusted p=0.046; 95% CI: 0.000727-0.0873) lower bilateral-mean volume of the thalamus for each 1-unit lower cVEMP amplitude. We also found a relationship between a lower mean VOR gain and lower left hippocampal volume ({beta}=0.121, unadjusted p=0.018, 95% CI: 0.0212-0.222). There were no significant associations between volume and oVEMP. These findings provide insight into the specific brain structures that undergo atrophy in the context of age-related loss of peripheral vestibular function.

Comprehensive SummaryHumans rely on their vestibular, or inner ear balance, system to manage everyday life. In addition to sensing head motion and head position with respect to gravity, the vestibular system helps to maintain balance and gaze stability. Furthermore, evidence is mounting that vestibular function is linked to structural changes in the central nervous system (CNS). Yet, the exact processes by which vestibular function alters brain structural integrity is unclear. One possible mechanism is that progressive vestibular deafferentation results in neurodegeneration of structures that receive vestibular input. In support of this putative mechanism, recent studies report the association of vestibular impairment with volume loss of brain areas that receive vestibular information, specifically the hippocampus and entorhinal cortex, in older adults. This present work investigates the extent over time to which age-related vestibular loss contributes to volume reduction of four brain regions that receive vestibular input: the hippocampus, entorhinal cortex, thalamus, and basal ganglia. Using data from a cohort of healthy, older adults between 2013 and 2017 from the Baltimore Longitudinal Study of Aging, we assessed regional brain volume as a function of vestibular function, while accounting for common confounds of brain volume change (e.g., age, sex, head size). We found that poor vestibular function is associated with significantly reduced volumes of the thalamus, basal ganglia, and left hippocampus. Notably, this study is one of the first to demonstrate relationships between age-related vestibular loss and gray matter loss in brain regions that receive vestibular input. Further research is needed to understand in greater detail the observed link between vestibular function and CNS structure. Which brain areas are impacted by age-related vestibular loss? How and in what sequence are they impacted? As the worlds aging population--and the prevalence of age-related vestibular impairment--increases, answering questions like these becomes increasingly important. One day, these answers will provide targets for preemptive interventions, like physical pre-habilitation, to stave off adverse changes in brain structure before they occur and progress towards clinical significance.

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category: otolaryngology


title: Patterns and persistence of SARS-CoV-2 IgG antibodies in a US metropolitan site

abstract:
BackgroundEstimates of seroprevalence to SARS-CoV-2 vary widely and may influence vaccination response. We ascertained IgG levels across a single US metropolitan site, Chicago, from June 2020 through December 2020.

MethodsParticipants (n=7935) were recruited through electronic advertising and received materials for a self-sampled dried blood spot assay through the mail or a minimal contact in person method. IgG to the receptor binding domain of SARS-CoV-2 was measured using an established highly sensitive and highly specific assay.

ResultsOverall seroprevalence was 17.9%, with no significant difference between method of contact. Only 2.5% of participants reported having had a diagnosis of COVID-19 based on virus detection, consistent with a 7-fold greater exposure to SARS-CoV-2 measured by serology than detected by viral testing. The range of IgG level observed in seropositive participants from this community survey overlapped with the range of IgG levels associated with COVID-19 cases having a documented positive PCR positive test. From a subset of those who participated in repeat testing, half of seropositive individuals retained detectable antibodies for 3-4 months.

ConclusionsQuantitative IgG measurements with a highly specific and sensitive assay indicate more widespread exposure to SARS-CoV-2 than observed by viral testing. The range of IgG concentration produced from these asymptomatic exposures is similar to IgG levels occurring after documented non-hospitalized COVID-19, which is considerably lower than that produced from hospitalized COVID-19 cases. The differing ranges of IgG response, coupled with the rate of decay of antibodies, may influence response to subsequent viral exposure and vaccine.

Graphical Abstract

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title: Patterns and persistence of SARS-CoV-2 IgG antibodies in Chicago tomonitor COVID-19 exposure


title: The effect of body image dissatisfaction on goal-directed decision making in a population marked by negative appearance beliefs and disordered eating

abstract: Eating disorders are associated with one of the highest mortality rates among all mental disorders, yet there is very little research about them within the newly emerging and promising field of computational psychiatry. As such, we focus on investigating a previously unexplored, yet a core aspect of eating disorders - body image dissatisfaction. We continue a freshly opened debate about model-based learning and its trade-off against model-free learning - a proxy for goal-directed and habitual behaviour. We perform a behavioural study that utilises a two-step decision-making task and a reinforcement learning model to understand the effect of body image dissatisfaction on model-based learning in a population characterised by high scores of disordered eating and negative appearance beliefs, as recruited using Prolific. We find a significantly reduced model-based contribution in the body image dissatisfaction task condition in the population of interest as compared to a healthy control.


title: Unmasking Seasonal Cycles in Human Fertility: How holiday sex and fertility cycles shape birth seasonality

abstract: The mechanisms of human birth seasonality have been debated for over 150 years1. In particular, the question of whether sexual activity or fertility variations drive birth seasonality has remained open and challenging to test without large-scale data on sexual activity 2,3. Analyzing data from half-a-million users worldwide collected from the female health tracking app Clue combined with birth records, we inferred that birth seasonality is primarily driven by seasonal fertility, yet increased sexual activity around holidays explains minor peaks in the birth curve. Our data came from locations in the Northern Hemisphere (UK, US, and France) and the Southern Hemisphere (Brazil). We found that fertility peaks between the autumn equinox and winter solstice in the Northern Hemisphere locations and shortly following the winter solstice in the Southern Hemisphere locations.

category: sexual and reproductive health


title: Acute nasal dryness in COVID-19

abstract: One of the entry routes of SARS-CoV-2 is the nasal epithelium. Although mounting evidence suggests the presence of olfactory dysfunction, and even anosmia, in patients with COVID-19, it is not clear whether these patients also suffer from other "nasal" symptoms that may influence their olfaction. A group of 35 patients with COVID-19 (and a control group matched in gender and age) were surveyed about the presence of a variety of nasal symptoms that may be associated to drastic perturbations experienced in the nasal cavity (e.g., "excessive dryness" and/or a continual sensation of having had a "nasal douche"). We used a cross-sectional, retrospective survey, targeted at the general population by means of non-quoted, non-random, snowball sampling. Symptoms were assessed with absence/presence responses. The possible association between two continuously distributed latent variables from categorical variables was estimated by means of polychoric correlations. More than 68% of the patients reported at least one "nasal" symptom. The clinical group also experienced "a strange sensation in the nose" and having excessive nasal dryness significantly more often than the control group. Fifty-two percent of the patients (but only 3% of the control group) reported a constant sensation of having had a strong nasal douche. Nasal symptoms predominantly co-occurred with anosmia/hyposmia, and ageusia/hypogeusia, appeared principally before or during the other symptoms of COVID-19, and lasted for twelve days, in average. The presence of these nasal symptoms, and their early occurrence, could potentially facilitate early diagnosis of COVID-19 and initial social distancing efforts.


title: The detection and stability of the SARS-CoV-2 RNA biomarkers in wastewater influent in Helsinki, Finland

abstract: Wastewater-based surveillance of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is used to monitor the population-level prevalence of the COVID-19 disease. In many cases, due to lockdowns or analytical delays, the analysis of wastewater samples might only be possible after prolonged storage. In this study, the effect of storage conditions on the RNA copy numbers of the SARS-CoV-2 virus in wastewater influent was studied and compared to the persistence of norovirus over time at 4{degrees}C, -20{degrees}C, and -75{degrees}C using the reverse-transcription quantitative PCR (RT-qPCR) assays E-Sarbeco, N2, and norovirus GII. For the first time in Finland, the presence of SARS-CoV-2 RNA was tested in 24 h composite influent wastewater samples collected from Viikinmaki wastewater treatment plant, Helsinki, Finland. The detected and quantified SARS-CoV-2 RNA copy numbers of the wastewater sample aliquots taken during 19-20 April 2020 and stored for 29, 64, and 84 days remained surprisingly stable. In the stored samples, the SARS betacoronavirus and SARS-CoV-2 copy numbers, but not the norovirus GII copy numbers, seemed slightly higher when analyzed from the pre-centrifuged pellet--that is, the particulate matter of the influent--as compared with the supernatant (i.e., water fraction) used for ultrafiltration, although the difference was not statistically significant. Furthermore, when wastewater was spiked with SARS-CoV-2, linear decay at 4{degrees}C was observed on the first 28 days, while no decay was visible within 58 days at -20{degrees}C or -75{degrees}C. In conclusion, freezing temperatures should be used for storage when immediate SARS-CoV-2 RNA analysis from the wastewater influent is not possible. Analysis of the particulate matter of the sample, in addition to the water fraction, can improve the detection frequency.


title: A network modelling approach to assess non-pharmaceutical disease controls in a worker population: An application to SARS-CoV-2

abstract:
BackgroundAs part of a concerted pandemic response to protect public health, businesses can enact non-pharmaceutical controls to minimise exposure to pathogens in workplaces and premises open to the public. Amendments to working practices can lead to the amount, duration and/or proximity of interactions being changed, ultimately altering the dynamics of disease spread. These modifications could be specific to the type of business being operated.

MethodsWe use a data-driven approach to parameterise an individual-based network model for transmission of SARS-CoV-2 amongst the working population, stratified into work sectors. The network is comprised of layered contacts to consider the risk of spread in multiple encounter settings (workplaces, households, social and other). We analyse several interventions targeted towards working practices: mandating a fraction of the population to work from home; using temporally asynchronous work patterns; and introducing measures to create  COVID-secure workplaces. We also assess the general role of adherence to (or effectiveness of) isolation and test and trace measures and demonstrate the impact of all these interventions across a variety of relevant metrics.

ResultsThe progress of the epidemic can be significantly hindered by instructing a significant proportion of the workforce to work from home. Furthermore, if required to be present at the workplace, asynchronous work patterns can help to reduce infections when compared with scenarios where all workers work on the same days, particularly for longer working weeks. When assessing COVID-secure workplace measures, we found that smaller work teams and a greater reduction in transmission risk reduced the probability of large, prolonged outbreaks. Finally, following isolation guidance and engaging with contact tracing without other measures is an effective tool to curb transmission, but is highly sensitive to adherence levels.

ConclusionsIn the absence of sufficient adherence to non-pharmaceutical interventions, our results indicate a high likelihood of SARS-CoV-2 spreading widely throughout a worker population. Given the heterogeneity of demographic attributes across worker roles, in addition to the individual nature of controls such as contact tracing, we demonstrate the utility of a network model approach to investigate workplace-targeted intervention strategies and the role of test, trace and isolation in tackling disease spread.


title: Genetic and environmental regulation of caudate nucleus transcriptome: insight into schizophrenia risk and the dopamine system

abstract: Increased dopamine (DA) signaling in the striatum has been a cornerstone hypothesis about psychosis for over 50 years. Increased dopamine release results in psychotic symptoms, while D2 dopamine receptor (DRD2) antagonists are antipsychotic. Recent schizophrenia GWAS identified risk-associated common variants near the DRD2 gene, but the risk mechanism has been unclear. To gain novel insight into risk mechanisms underlying schizophrenia, we performed a comprehensive analysis of the genetic and transcriptional landscape of schizophrenia in postmortem caudate nucleus from a cohort of 444 individuals. Integrating expression quantitative trait loci (eQTL) analysis, transcriptome wide association study (TWAS), and differential expression analysis, we found many new genes associated with schizophrenia through genetic modulation of gene expression. Using a new approach based on deep neural networks, we construct caudate nucleus gene expression networks that highlight interactions involving schizophrenia risk. Interestingly, we found that genetic risk for schizophrenia is associated with decreased expression of the short isoform of DRD2, which encodes the presynaptic autoreceptor, and not with the long isoform, which encodes the postsynaptic receptor. This association suggests that decreased control of presynaptic DA release is a potential genetic mechanism of schizophrenia risk. Altogether, these analyses provide a new resource for the study of schizophrenia that can bring insight into risk mechanisms and potential novel therapeutic targets.


title: Isolation thresholds for curbing SARS-CoV-2 resurgence

abstract: Self-instigated isolation is heavily relied on to curb SARS-CoV-2 transmission. Accounting for uncertainty in the latent and prepatent periods, as well as the proportion of infections that remain asymptomatic, the limits of this intervention at different phases of infection resurgence are estimated. We show that by October 2020, SARS-CoV-2 transmission rates in England had already begun exceeding levels that could be interrupted using this intervention alone, lending support to the second national lockdown on November 5th 2020.


title: A framework to model global, regional, and national estimates of intimate partner violence

abstract:
AO_SCPLOWBSTRACTC_SCPLOWO_ST_ABSBackgroundC_ST_ABSAccurate and reliable estimates of violence against women statistics form the backbone of monitoring efforts to eliminate these human right violations and public health concerns. Estimating the prevalence of intimate partner violence (IPV) is challenging due to variations in case definition and recall period, surveyed populations, partner definition, level of age disaggregation, and survey representativeness, among others. In this paper, we aim to develop a sound and flexible statistical modeling framework for global, regional, and national IPV statistics.

MethodsWe modeled IPV within a Bayesian multilevel modeling framework, accounting for heterogeneity of age groups using age-standardization, and age patterns and time trends using splines functions. Survey comparability is achieved using adjustment factors which are estimated using exact matching and their uncertainty accounted for. Both in-sample and out-of-sample comparisons are used for model validation, including posterior predictive checks. Post-processing of models outputs is performed to aggregate estimates at different geographic levels and age groups.

ResultsA total of 307 unique studies conducted between 2000-2018, from 154 countries/territories/areas, and totaling nearly 1.8 million unique women responses informed lifetime IPV. Past year IPV had similar number of studies (n=332), countries represented (n=159), and individual responses (n=1.8 million). Roughly half of IPV observations required some adjustments. Posterior predictive checks suggest good model fit to data and out-of-sample comparisons provided reassuring results with small median prediction errors and appropriate coverage of predictions intervals.

ConclusionsThe proposed modeling framework can pool both national and sub-national surveys, account for heterogeneous age groups and age trends, accommodate different surveyed population, adjust for differences in survey instruments, and efficiently propagate uncertainty to model outputs. By describing this model to reproducible levels of details, the accurate interpretation and responsible use of estimates for global monitoring of violence against women elimination efforts are supported, as part of the Sustainable Development Goals.


title: The effectiveness of early diagnostic tools for neural lesions in leprosy. An observational cohort study

abstract:
Cegana LHV, Nardi SMT, Pascoeto L, Paschoal VDA. The effectiveness of early diagnostic tools for neural lesions in leprosy. 37p. MANUSCRIPT, 2020. https://doi.org/10.1101/2020.11.19.20234963

IntroductionLeprosy can cause different lesions in peripheral nerves and inervatory structures.

ObjectivesTo analyse the effectiveness of evaluation protocols used to identify neural lesions in leprosy such as Degree of Physical Disability (DPD), Simplified Neurological Assessment (SNA) and propose to use Neurodynamic Assessment (NDA).

MethodDescriptive analytical study, associative, with 27 individuals treated in two outpatient leprosy clinics in Sao Paulo State, between 2017 and 2019, and 27 individuals from the paired control group. The Mann-Whitney, Multivariate Linear Regression and association between variables and P<0.05 values were used.

ResultsThe test that most captured the neurological alterations was the SNA, with 22 (81.5%) in the upper limbs (ULs) and 25 (92.6%) in the lower limbs (LLs), followed by the NDA, with 20 (74.1%) in the ULs and 11 (40.7%) in the LLs. The DPD showed handicap in the hands of 16 (59.2%) individuals and in the feet of 17 (62.9%) individuals, and they have expressed sensitivity. DPD showed agreement with SNA in 21 (77.8%) of the cases in ULs (p=0.010) and 19 (70,4%) of the cases in LLs (p=0.060). DPD and NDA showed that in 19 (70.4%) of the patients evaluated there was concordance of results in ULs (p=0.143); 9 (33.3%) in LLs (p=0.125). SNA and the NDA in the ULs found agreement in 21 (77.8%); 11 (40.7%) (p=0.786) in the LLs.

ConclusionThe three assessment instruments are specific and will hardly produce false positive tests. DPD can produce more false negatives than SNA. If there is an instrument to be chosen, it should be the SNA, since it is more sensitive, more accurate and has a less negative likelihood ratio. Neurodynamic tests were positive in 7.4% of individuals while there were still no changes in the SNA; afterwards, these changes appeared.

DescriptorsNeural mobilization; Leprosy; Peripheral nerves; Disability; Pain; Physical therapy.

category: rehabilitation medicine and physical therapy


title: Comparing COVID-19 and influenza presentation and trajectory

abstract:
BackgroundCOVID-19 is a newly recognized illness with a predominantly respiratory presentation. It is important to characterize the differences in disease presentation and trajectory between COVID-19 patients and other patients with common respiratory illnesses. These differences can enhance knowledge of pathogenesis and help in guiding treatment.

MethodsData from electronic medical records were obtained from individuals admitted with respiratory illnesses to Rambam Health Care Campus, Haifa, Israel, between October 1st, 2014 and October 1st, 2020. Four groups of patients were defined: COVID-19 (693), influenza (1,612), severe acute respiratory infection (SARI) (2,292) and Others (4,054). The variable analyzed include demographics (7), vital signs (8), lab tests (38),and comorbidities (15) from a total of 8,651 hospitalized adult patients. Statistical analysis was performed on biomarkers measured at admission and for their disease trajectory in the first 48 hours of hospitalization, and on comorobidity prevalence.

ResultsCOVID-19 patients were overall younger in age and had higher body mass index, compared to influenza and SARI. Comorbidity burden was lower in the COVID-19 group compared to influenza and SARI. Severely- and moderately-ill COVID-19 patients older than 65 years of age suffered higher rate of in-hospital mortality compared to hospitalized influenza patients. At admission, white blood cells and neutrophils were lower among COVID-19 patients compared to influenza and SARI patients, while pulse rate and lymphoctye percentage were higher. Trajectories of variables during the first two days of hospitalization revealed that white blood count, neutrophils percentage and glucose in blood increased among COVID-19 patients, while decreasing among other patients.

ConclusionsThe intrinsic virulence of COVID-19 appeared higher than influenza. In addition, several critical functions, such as immune response, coagulation, heart and respiratory function and metabolism were uniquely affected by COVID-19.


title: Clinical judgement of General Practitioners for the diagnosis of dementia

abstract:
BackgroundThe accuracy of General Practitioners (GPs) clinical judgement for dementia is uncertain.

AimInvestigate the accuracy of GPs clinical judgement for the diagnosis of dementia.

Design and SettingDiagnostic test accuracy study, recruiting from 21 practices around Bristol.

MethodThe clinical judgement of the treating GP (index test) was based on the information immediately available at their initial consultation with a person aged over 70 years who had cognitive symptoms. The reference standard was an assessment by a specialist clinician, based on a standardised clinical examination and made according to ICD-10 criteria for dementia.

Results240 people were recruited, with a median age of 80 years (IQR 75 to 84 years), of whom 126 (53%) were men and 132 (55%) had dementia. The median duration of symptoms was 24 months (IQR 12 to 36 months) and the median ACE-III score was 75 (IQR 65 to 87). GP clinical judgement had sensitivity 56% (95% CI 47% to 65%) and specificity 89% (95% CI 81% to 94%). Positive likelihood ratio was higher in people aged 70-79 years (6.5, 95% CI 2.9 to 15) compared to people aged [≥] 80 years (3.6, 95% CI 1.7 to 7.6), and in women (10.4, 95% CI 3.4 to 31.7) compared to men (3.2, 95% CI 1.7 to 6.2), whereas the negative likelihood ratio was similar in all groups.

ConclusionA GP clinical judgement of dementia is specific, but confirmatory testing is needed for symptomatic people who GPs judge as not having dementia.

How this fits inPrevious studies in this area have investigated the accuracy of GP clinical judgement as a screening test for dementia in unselected people attending a primary care clinic; or as a retrospective test based on their knowledge of their patient; or derived the accuracy of judgement from the medical records, which may not reflect the judgement of the clinician. The role of the GP in supporting a more effective route to diagnosis for people with dementia is a research priority for patients, carers and clinicians. This study shows that, in a symptomatic older adult, prospective clinical judgement may be useful for helping to confirm a diagnosis of dementia, whereas GP judgement should not by itself be used to exclude dementia.

category: primary care research


title: Cross-sectional association of blood pressure variability and night-time dipping with cardiac structure in adolescents

abstract: Greater blood pressure (BP) variability and reduced night-time BP dipping are associated with cardiovascular disease risk independently of mean BP in adults. This study examines whether these associations are apparent in a general population of adolescents. A cross-sectional analysis was undertaken in 587 UK adolescents (mean age 17.7 years; 43.1% male). BP was measured in a research clinic and using 24-hour ambulatory monitoring. We examined associations (for both systolic and diastolic BP) of: 1) clinic and 24-hour mean BP; 2) measures of 24-hour BP variability: standard deviation weighted for day/night (SDdn), variability independent of the mean (VIM) and average real variability (ARV); and 3) night-time dipping with cardiac structures. Cardiac structures were assessed by echocardiography: 1) left ventricular mass indexed to height2.7 (LVMi2.7); 2) relative wall thickness (RWT); 3) left atrial diameter indexed to height (LADi) and 4) left ventricular internal diameter in diastole (LVIDD). Higher systolic BP was associated with greater LVMi2.7. Systolic and diastolic BP were associated with greater RWT. Associations were inconsistent for LADi and LVIDD. There was evidence for associations between both greater SDdn and ARV and higher RWT (per 1 SD higher diastolic ARV, mean difference in RWT was 0.13 SDs, 95% CI 0.045 to 0.21); these associations with RWT remained after adjustment for mean BP. There was no consistent evidence of associations between night-time dipping and cardiac structure. In this general adolescent population study, associations between BP variability and cardiac structure were apparent. Measurement of BP variability might benefit cardiovascular risk assessment in adolescents.


title: ThinkCancer! The multi-method development of a complex behaviour change intervention to improve the early diagnosis of cancer in primary care

abstract:
BackgroundRelatively poor UK cancer outcomes are blamed upon late diagnosis. Despite most cancer patients presenting to their GP with symptoms, diagnostic delay remains a common theme, with many clinical and non-clinical factors responsible. Early diagnosis is key to improving outcomes and survival. This paper reports the multi-method process to design a complex intervention to improve the timely diagnosis of symptomatic cancer.

MethodsA review of reviews, survey, discrete choice experiment, qualitative interviews and focus groups, all informed a realist evidence synthesis. This in turn informed the design of a complex intervention, guided by the Behaviour Change Wheel framework using a multi-step process.

ResultsKey themes from the realist evidence synthesis included effective safety netting at practitioner and practice system level, increased vigilance and lowering referral thresholds. Qualitative findings explored the tensions, barriers and facilitators affecting suspected cancer referral. The Think Cancer! intervention is an educational and quality improvement workshop directed at the whole primary care team. Bespoke cancer safety netting plans and appointment of cancer champions are key components.

ConclusionsThink Cancer! is a novel primary care early cancer diagnosis intervention, requiring evaluation through a cluster randomised control trial.


title: Concurrent anatomical, physiological and network changes in cognitively impaired multiple sclerosis patients

abstract:
Cognitive impairment in multiple sclerosis is associated with functional connectivity abnormalities, but the pathological substrates of these abnormalities are not well understood. It has been proposed that resting-state network nodes that integrate information from disparate regions are susceptible to metabolic stress, which may impact functional connectivity. In multiple sclerosis, pathology could increase metabolic stress within axons, damaging the anatomical connections of network regions, and leading to functional connectivity changes. We tested this hypothesis by assessing whether resting state network regions that show functional connectivity abnormalities in people with cognitive impairment also show anatomical connectivity abnormalities.

Multimodal MRI and neuropsychological assessments were performed in 102 relapsing remitting multiple sclerosis patients and 27 healthy controls. Patients were considered cognitively impaired if they obtained a z-score of [≤]1.5 on [≥]2 tests of the Brief Repeatable Battery of Neuropsychological Tests (n=55). Functional connectivity was assessed with Independent Component Analysis of resting state fMRI images, and anatomical connectivity with Anatomical Connectivity Mapping of diffusion-weighted MRI. Exploratory analyses of fractional anisotropy and cerebral blood flow changes were conducted to assess local tissue characteristics.

We found significantly decreased functional connectivity in the anterior and posterior default mode networks and significant increases in the right and left frontoparietal networks in cognitively impaired relative to cognitively preserved patients. Networks showing functional abnormalities also showed reduced anatomical connectivity and white matter microstructure integrity as well as reduced local tissue cerebral blood flow.

Our results identify key pathological correlates of functional connectivity abnormalities associated with impaired cognitive function in multiple sclerosis, consistent with metabolic dysfunction in functional network regions.


title: Aerial transmission of SARS-CoV-2 virus (and pathogens in general) through environmental e-cigarette aerosol

abstract: We examine the plausibility, scope and risks of aerial transmission of pathogens (including the SARS-CoV-2 virus) through respiratory droplets carried by exhaled e-cigarette aerosol (ECA). Given the lack of empiric evidence, we consider cigarette smoking and mouth breathing through a mouthpiece as convenient proxies to infer the respiratory mechanics and droplets sizes and their rate of emission that should result from vaping. To quantify direct exposure distance we model exhaled ECA flow as an intermittent turbulent jet evolving into an unstable puff, estimating for low intensity vaping (practiced by 80-90% of vapers) the emission of 6-200 (mean 79.82, standard deviation 74.66) respiratory submicron droplets per puff a horizontal distance spread of 1-2 meters, with intense vaping possibly emitting up to 1000 droplets per puff in the submicron range a distance spread over 2 meters. Since exhaled ECA acts effectively as a visual tracer of its expiratory flow, bystanders become instinctively aware that possible direct contagion might occur only in the direction and scope of the jet.


title: Modeling aerial transmission of pathogens (including the SARS-CoV-2 virus) through aerosol emissions from e-cigarettes


title: Poor in-utero growth, and reduced beta cell compensation and high fasting glucose from childhood, are harbingers of glucose intolerance in young Indians

abstract:
ObjectiveIndia is a double world capital for early life undernutrition and type 2 diabetes. We aimed to characterise lifecourse growth and metabolic trajectories in those developing glucose intolerance as young adults, in the Pune Maternal Nutrition Study (PMNS).

Research design and MethodsPMNS is a community-based intergenerational birth cohort established in 1993, with serial information on parents and children through pregnancy, childhood and adolescence. We compared normal glucose tolerant and glucose intolerant participants for serial growth, estimates of insulin sensitivity and secretion (HOMA and dynamic indices) and beta cell compensation accounting for prevailing insulin sensitivity (disposition index).

ResultsAt 18 years (N=619) 37% men and 20% women were glucose intolerant (184 prediabetes, 1 diabetes) despite 48% being underweight (BMI<18.5 kg/m2). Glucose intolerant participants had higher fasting glucose from childhood. Mothers of glucose intolerant participants had higher glycemia in pregnancy. Glucose intolerant participants were shorter at birth. Insulin sensitivity decreased with age in all participants, and the glucose intolerant had consistently lower compensatory insulin secretion from childhood. Participants in the highest quintile of fasting glucose at 6 and 12 years had a 2.5- and 4.0-fold higher risk respectively of 18-year glucose intolerance; this finding was replicated in two other cohorts.

ConclusionInadequate compensatory insulin secretory response to increasing insulin insensitivity from early life is the major pathophysiology underlying glucose intolerance in thin rural Indians. Smaller birth size, maternal pregnancy hyperglycemia, and higher glycemia in childhood herald future glucose intolerance, mandating a strategy for diabetes prevention from early life, preferably intergenerationally.

category: endocrinology


title: Correcting B0 inhomogeneity-induced distortions in whole-body diffusion MRI of bone

abstract:
ObjectivesDiffusion-weighted magnetic resonance imaging (DWI) of the musculoskeletal system has various applications, including visualization of bone tumors. However, DWI acquired with echo-planar imaging is susceptible to distortions due to static magnetic field inhomogeneities. This study aimed to estimate spatial displacements of bone and to examine whether distortion corrected DWI images more accurately reflect underlying anatomy.

MethodsWhole-body MRI data from 127 prostate cancer patients were analyzed. The reverse polarity gradient (RPG) technique was applied to DWI data to estimate voxel-level distortions and to produce a distortion corrected DWI dataset. First, an anatomic landmark analysis was conducted, in which corresponding vertebral landmarks on DWI and anatomic T2-weighted images were annotated. Changes in distance between DWI- and T2-defined landmarks (i.e., changes in error) after distortion correction were calculated. In secondary analyses, distortion estimates from RPG were used to assess spatial displacements of bone metastases. Lastly, changes in mutual information between DWI and T2-weighted images of bone metastases after distortion correction were calculated.

ResultsDistortion correction reduced anatomic error of vertebral DWI up to 29 mm. Error reductions were consistent across subjects (Wilcoxon signed-rank p<10-20). On average ({+/-}SD), participants largest error reduction was 11.8 mm ({+/-}3.6). Mean (95% CI) displacement of bone lesions was 6.0 mm (95% CI: 5.1-7.0); maximum displacement was 17.1 mm. Corrected diffusion images were more similar to structural MRI, as evidenced by consistent increases in mutual information (Wilcoxon signed-rank p<10-12).

DiscussionThese findings support the use of distortion correction techniques to improve localization of bone on DWI.

Key Points- Diffusion weighted images of bone tissue undergo substantial spatial distortions when acquired with echo-planar imaging.
- These distortions can be efficiently corrected with the reverse polarity gradient technique to generate diffusion images that more accurately reflect underlying anatomy.
- In the context of bone tumor imaging where precise localization may be required, distortion correction techniques, such as reverse polarity gradient, should be applied.


title: Long-Term Downwind Exposure to Air Pollution from Power Plants and Adult Mortality: Evidence from COVID-19

abstract: We estimate the causal effects of long-term exposure to air pollution emitted from fossil fuel power plants on adult mortality. We leverage quasi-experimental variation in daily wind patterns, which is further instrumented by the county orientation from the nearest power plant. We find that the countys fraction of days spent downwind of plants within 20 miles in the last 10 years is associated with increased mortality from COVID-19 through the third peak in mortality in January 2021. This effect is more pronounced in fenceline communities with high poverty rates, low health insurance coverage, and low educational attainment.


title: Use of Artificial Intelligence on spatio-temporal data to generate insights during COVID-19 pandemic: A Review

abstract: The COVID-19 pandemic, within a short time span, has had a significant impact on every aspect of life in almost every country on the planet. As it evolved from a local epidemic isolated to certain regions of China, to the deadliest pandemic since the influenza outbreak of 1918, scientists all over the world have only amplified their efforts to combat it. In that battle, Artificial Intelligence, or AI, with its wide ranging capabilities and versatility, has played a vital role and thus has had a sizable impact. In this review, we present a comprehensive analysis of the use of AI techniques for spatio-temporal modeling and forecasting and impact modeling on diverse populations as it relates to COVID-19. Furthermore, we catalogue the articles in these areas based on spatio-temporal modeling, intrinsic parameters, extrinsic parameters, dynamic parameters and multivariate inputs (to ascertain the penetration of AI usage in each sub area). The manner in which AI is used and the associated techniques utilized vary for each body of work. Majority of articles use deep learning models, compartment models, stochastic methods and numerous statistical methods. We conclude by listing potential paths of research for which AI based techniques can be used for greater impact in tackling the pandemic.


title: HIV status alters disease severity and immune cell responses in beta variant SARS-CoV-2 infection wave

abstract: There are conflicting reports on the effects of HIV on COVID-19. Here we analyzed disease severity and immune cell changes during and after SARS-CoV-2 infection in 236 participants from South Africa, of which 39% were people living with HIV (PLWH), during the first and second ({beta} dominated) infection waves. The second wave had more PLWH requiring supplemental oxygen relative to HIV negative participants. Higher disease severity was associated with low CD4 T cell counts and higher neutrophil to lymphocyte ratios (NLR). Yet, CD4 counts recovered and NLR stabilized after SARS-CoV-2 clearance in wave 2 infected PLWH, arguing for an interaction between SARS-CoV-2 and HIV infection leading to low CD4 and high NLR. The first infection wave, where severity in HIV negative and PLWH was similar, still showed some HIV modulation of SARS-CoV-2 immune responses. Therefore, HIV infection can synergize with the SARS-CoV-2 variant to change COVID-19 outcomes.

category: hiv aids


title: Experimental efficacy of the face shield and the mask against emitted and potentially received particles

abstract: There is currently not sufficient evidence to support the effectiveness of face shields for source control. In order to evaluate the comparative barrier performance effect of face masks versus face shields, we used an aerosol generator and a particle counter to evaluate the performance of the various devices in comparable situations. We tested different configurations in an experimental setup with manikin heads wearing masks (surgical type I), face shields (22.5 cm high with overhang under the chin of 7 cm and circumference of 35 cm) on an emitter or a receiver manikin head, or both. The mannequins were face to face, 25 cm apart, with an intense particle emission (52.5 l/min) for 30 seconds. In our experimental conditions, when the receiver alone wore a protection, the face shield was more effective (reduction factor=54.8%), while reduction was lower with a mask (reduction factor=21.8%) (p=0.002). The wearing of a protective device by the emitter alone reduced the level of received particles by 96.8% for both the mask and face shield (p= NS). When both the emitter and receiver manikin heads wore a face shield, the ensuing double protection allowed for better results: 98% reduction for the face shields vs. 97.3% for the masks (p=0.01). Face shields offered an even better barrier effect than the mask against small inhaled particles (<0.3{micro}m - 0.3 to 0.5{micro}m - 0.5 to 1{micro}m) in all configurations. Therefore, it would be interesting to include face shields as used in our study as part of strategies to safely significantly reduce transmission within the community setting.


title: Rapid Microscopic Fractional Anisotropy Imaging via an Optimized Kurtosis Formulation

abstract:
Water diffusion anisotropy in the human brain is affected by disease, trauma, and development. Microscopic fractional anisotropy (FA) is a diffusion MRI (dMRI) metric that can quantify water diffusion anisotropy independent of neuron fiber orientation dispersion. However, there are several different techniques to estimate FA and few have demonstrated full brain imaging capabilities within clinically viable scan times and resolutions. Here, we present an optimized spherical tensor encoding (STE) technique to acquire FA directly from the 2nd order cumulant expansion of the dMRI signal (i.e. diffusion kurtosis) which requires fewer powder-averaged signals than other STE fitting techniques and can be rapidly computed. We found that the optimal dMRI parameters for white matter FA imaging were a maximum b-value of 2000 s/mm2 and a ratio of isotropic to linear tensor encoded acquisitions of 1.7 for our system specifications. We then compared two implementations of the direct approach to the well-established gamma model in 4 healthy volunteers on a 3 Tesla system. One implementation of the direct cumulant approach used mean diffusivity (D) obtained from a 2nd order fit of the cumulant expansion, while the other used a linear estimation of D from the low b-values. Both implementations of the direct approach showed strong linear correlations with the gamma model ({rho}=0.97 and {rho}=0.90) but mean biases of -0.11 and -0.02 relative to the gamma model were also observed, respectively. All three FA measurements showed good test-retest reliability ({rho}[≥]0.79 and bias=0). To demonstrate the potential scan time advantage of the direct approach, 2 mm isotropic resolution FA was demonstrated over a 10 cm slab using a subsampled data set with fewer powder-averaged signals that would correspond to a 3.3-minute scan. Accordingly, our results introduce an optimization procedure that has enabled clinically relevant, nearly full brain FA in only several minutes.

HighlightsO_LIDemonstrated method to acquire optimal parameters for regression FA imaging
C_LIO_LIFA measured using an optimized linear regression method at 3T
C_LIO_LIFirst FA comparison between direct regression approach and the gamma model
C_LIO_LIBoth approaches correlated strongly in white matter in healthy volunteers
C_LIO_LINearly full brain FA demonstrated in a 3.3-minute scan at 2 mm isotropic resolution
C_LI


title: Integrated Protein Network Analysis of Whole Exome Sequencing Study of Severe Preeclampsia

abstract: Preeclampsia is a hypertensive disorder of pregnancy, which complicates up to 15 % of US deliveries. It is an idiopathic disorder with complex disease genetics associated with several different phenotypes. We sought to determine if the genetic architecture of preeclampsia can be described by clusters of patients with variants in genes in shared protein interaction networks. We performed a case-control study using whole exome sequencing on early onset preeclamptic mothers with severe features and control mothers with uncomplicated pregnancies. The study was conducted at Women & Infants Hospital of Rhode Island (WIH). A total of 143 patients were enrolled, 61 women with early onset preeclampsia with severe features based on ACOG criteria, and 82 control women at term, matched for race and ethnicity. The main outcomes are variants associated with severe preeclampsia and demonstration of the genetic architecture of preeclampsia. A network analysis and visualization tool, Proteinarium, was used to confirm there are clusters of patients with shared gene networks associated with severe preeclampsia. The majority of the sequenced patients appear in two significant clusters. We identified one case dominant and one control dominant cluster. Thirteen genes were unique to the case dominated cluster. Among these genes, LAMB2, PTK2, RAC1, QSOX1, FN1, and VCAM1 have known associations with the pathogenic mechanisms of preeclampsia. Using the exome-wide sequence variants, combined with these 13 identified network genes, we generated a polygenetic risk score for severe preeclampsia with an AUC of 0.57. Using bioinformatic analysis, we were able to identify subsets of patients with shared protein interaction networks, thus confirming our hypothesis about the genetic architecture of preeclampsia. The unique genes identified in the cluster associated with severe preeclampsia were able to increase the predictive power of the polygenic risk score.

category: obstetrics and gynecology


title: Uncovering the important acoustic features for detecting vocal fold paralysis with explainable machine learning

abstract:
ObjectiveTo detect unilateral vocal fold paralysis (UVFP) from voice recordings using an explainable model of machine learning.

Study DesignCase series - retrospective with a control group.

SettingTertiary care laryngology practice between 2009 to 2019.

MethodsPatients with confirmed UVFP through endoscopic examination (N=77) and controls with normal voices matched for age and sex (N=77) were included. Two tasks were used to elicit voice samples: reading the Rainbow Passage and sustaining phonation of the vowel "a". The 88 extended Geneva Minimalistic Acoustic Parameter Set (eGeMAPS) features were extracted as inputs for four machine learning models of differing complexity. SHAP was used to identify important features.

ResultsThe median bootstrapped Area Under the Receiver Operating Characteristic Curve (ROC AUC) score ranged from 0.79 to 0.87 depending on model and task. After removing redundant features for explainability, the highest median ROC AUC score was 0.84 using only 13 features for the vowel task and 0.87 using 39 features for the reading task. The most important features included intensity measures, mean MFCC1, mean F1 amplitude and frequency, and shimmer variability depending on model and task.

ConclusionUsing the largest dataset studying UVFP to date, we achieve high performance from just a few seconds of voice recordings. Notably, we demonstrate that while similar categories of features related to vocal fold physiology were conserved across models, the models used different combinations of features and still achieved similar effect sizes. Machine learning thus provides a mechanism to detect UVFP and contextualize the accuracy relative to both model architecture and pathophysiology.


title: Optimal symptom combinations to aid COVID-19 case identification: analysis from a community-based, prospective, observational cohort

abstract:
ObjectivesDiagnostic work-up following any COVID-19 associated symptom will lead to extensive testing, potentially overwhelming laboratory capacity whilst primarily yielding negative results. We aimed to identify optimal symptom combinations to capture most cases using fewer tests with implications for COVID-19 vaccine developers across different resource settings and public health.

MethodsUK and US users of the COVID-19 Symptom Study app who reported new-onset symptoms and an RT-PCR test within seven days of symptom onset were included. Sensitivity, specificity, and number of RT-PCR tests needed to identify one case (test per case [TPC]) were calculated for different symptom combinations. A multi-objective evolutionary algorithm was applied to generate combinations with optimal trade-offs between sensitivity and specificity.

FindingsUK and US cohorts included 122,305 (1,202 positives) and 3,162 (79 positive) individuals. Within three days of symptom onset, the COVID-19 specific symptom combination (cough, dyspnoea, fever, anosmia/ageusia) identified 69% of cases requiring 47 TPC. The combination with highest sensitivity (fatigue, anosmia/ageusia, cough, diarrhoea, headache, sore throat) identified 96% cases requiring 96 TPC.

InterpretationWe confirmed the significance of COVID-19 specific symptoms for triggering RT-PCR and identified additional symptom combinations with optimal trade-offs between sensitivity and specificity that maximize case capture given different resource settings.

HighlightsO_LIWidely recommended symptoms identified only [~]70% COVID-19 cases
C_LIO_LIAdditional symptoms increased case finding to > 90% but tests needed doubled
C_LIO_LIOptimal symptom combinations maximise case capture considering available resources
C_LIO_LIImplications for COVID-19 vaccine efficacy trials and wider public health
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title: Pro108Ser mutant of SARS-CoV-2 3CLpro reduces the enzymatic activity and ameliorates COVID-19 severity in Japan

abstract: SARS-CoV-2 genome accumulates point mutations constantly. However, whether non-synonymous mutations affect COVID-19 severity through altering viral protein function remains unknown. SARS-CoV-2 genome sequencing revealed that the number of non-synonymous mutations correlated inversely with COVID-19 severity in Tokyo Metropolitan area. Phylogenic tree analyses identified two predominant groups which were differentiated by a set of six-point mutations (four non-synonymous amino acid mutations). Among them, Pro108Ser in 3 chymotrypsin-like protease (3CLpro) and Pro151Leu in nucleocapsid protein occurred at conserved locations among {beta}-coronaviruses. Patients with these mutations (N = 48) indicated significantly lower odds ratio for developing hypoxia which required supplemental oxygen (odds ratio 0.24 [95% CI 0.07-0.88, p-value = 0.032]) after adjustments for age and sex, versus those lacking this haplotype in the canonical Clade 20B (N = 37). The Pro108Ser 3CLpro enzyme in vitro decreases in the activity by 58%, and the hydrogen/deuterium exchange mass spectrometry reveals that mechanisms for reduced activities involve structural perturbation at the substrate-binding region which is positioned behind and distant from the 108th amino acid residue of the enzyme. This mutant strain rapidly outcompeted pre-existing variants to become predominant in Japan. Our results may benefit the efforts underway to design small molecular compounds or antibodies targeting 3CLpro.


title: Deciphering early-warning signals of the elimination and resurgence potential of SARS-CoV-2 from limited data at multiple scales

abstract: Inferring the transmission potential of an infectious disease during low-incidence periods following epidemic waves is crucial for preparedness. In such periods, scarce data may hinder existing inference methods, blurring early-warning signals essential for discriminating between the likelihoods of resurgence versus elimination. Advanced insight into whether elevating caseloads (requiring swift community-wide interventions) or local elimination (allowing controls to be relaxed or refocussed on case-importation) might occur, can separate decisive from ineffective policy. By generalising and fusing recent approaches, we propose a novel early-warning framework that maximises the information extracted from low-incidence data to robustly infer the chances of sustained local-transmission or elimination in real time, at any scale of investigation (assuming sufficiently good surveillance). Applying this framework, we decipher hidden disease-transmission signals in prolonged low-incidence COVID-19 data from New Zealand, Hong Kong and Victoria, Australia. We uncover how timely interventions associate with averting resurgent waves, support official elimination declarations and evidence the effectiveness of the rapid, adaptive COVID-19 responses employed in these regions.


title: How closely is COVID-19 related to HCoV, SARS, and MERS? : Clinical comparison of coronavirus infections and identification of risk factors influencing the COVID-19 severity using common data model (CDM)

abstract:
BackgroundSouth Korea was one of the epicenters for both the 2015 Middle East Respiratory Syndrome and 2019 COVID-19 outbreaks. However, there has been a lack of published literature, especially using the Electronic Medical Records (EMR), that provides a comparative summary of the prognostic factors present in the coronavirus-derived diseases. Therefore, in this study, we aimed to evaluate the distinct clinical traits between the infected patients of different coronaviruses to observe the extent of resemblance within the clinical features and to identify unique factors by disease severity that may influence the prognosis of COVID-19 patients.

MethodsWe utilized the common data model (CDM), which is the database that houses the standardized EMR. We set COVID-19 as a reference group in comparative analyses. For statistical methods, we used Levenes test, one-way Anova test, Scheffe post-hoc test, Games-howell post-hoc test, and Students t-test for continuous variables, and chi-squared test and Fishers exact test for categorical variables. With the variables that reflected similarity in more than two comparisons between the disease groups yet significantly different between the COVID-19 severity groups, we performed univariate logistic regression to identify which common manifestations in coronaviruses are risk factors for severe COVID-19 outcomes.

FindingsWe collected the records of 2840 COVID-19 patients, 67 MERS patients (several suspected cases included), 43 SARS suspected patients, and 87 HCoV patients. We found that a significantly higher number of COVID-19 patients had been diagnosed with comorbidities compared to the MERS and HCoV groups (48.5% vs. 10.4 %, p < 0.001 and 48.5% vs. 35.6%, p < 0.05) and also that the non-mild COVID-19 patients reported more comorbidities than the mild group (55.7% vs. 47.8%, p < 0.05). There were overall increases in the levels of fibrinogen in both sets of disease and severity groups. The univariate logistic regression showed that the male sex (OR: 1.66; CI: 1.29-2.13, p < 0.001), blood type A (OR: 1.80; CI: 1.40-2.31, p < 0.001), renal disease (OR: 3.27; CI: 2.34-4.55, p < 0.001), decreased creatinine level (OR: 2.05; CI: 1.45-2.88, p < 0.001), and elevated fibrinogen level (OR: 1.59, CI: 1.21-2.09, p < 0.001) are associated with the severe COVID-19 prognosis, whereas the patients reporting gastrointestinal symptoms (OR: 0.42; CI: 0.23-0.72, p < 0.01) and increased alkaline phosphatase (OR: 0.73; CI: 0.56-0.94, p < 0.05) are more less likely to experience complications and other severe outcomes from the SARS-CoV-2 infection.

InterpretationThe present study observed the highest resemblance between the COVID-19 and SARS groups as clinical manifestations that were present in SARS group were linked to the severity of COVID-19. In particular, male individuals with blood type A and previous diagnosis of kidney failure were shown to be more susceptible to developing the poorer outcomes during COVID-19 infection, with a presentation of elevated level of fibrinogen.


title: Explainable Machine Learning models for Rapid Risk Stratification in the Emergency Department: A multi-center study

abstract:
BackgroundRisk stratification of patients presenting to the emergency department (ED) is important for appropriate triage. Diagnostic laboratory tests are an essential part of the work-up and risk stratification of these patients. Using machine learning, the prognostic power and clinical value of these tests can be amplified greatly. In this study, we applied machine learning to develop an accurate and explainable clinical decision support tool model that predicts the likelihood of 31-day mortality in ED patients (the RISKINDEX). This tool was developed and evaluated in four Dutch hospitals.

MethodsMachine learning models included patient characteristics and available laboratory data collected within the first two hours after ED presentation, and were trained using five years of data from consecutive ED patients from the Maastricht University Medical Centre+ (Maastricht), Meander Medical Center (Amersfoort), and Zuyderland (Sittard and Heerlen). A sixth year of data was used to evaluate the models using area-under-the-receiver-operating-characteristic curve (AUROC) and calibration curves. The SHapley Additive exPlanations (SHAP) algorithm was used to obtain explainable machine learning models.

ResultsThe present study included 266,327 patients with 7.1 million laboratory results available. Models show high diagnostic performance with AUROCs of 0.94,0.98,0.88, and 0.90 for Maastricht, Amersfoort, Sittard and Heerlen, respectively. The SHAP algorithm was utilized to visualize patient characteristics and laboratory data patterns that underlie individual RISKINDEX predictions.

ConclusionsOur clinical decision support tool has excellent diagnostic performance in predicting 31-day mortality in ED patients. Follow-up studies will assess whether implementation of these algorithm can improve clinically relevant endpoints.

category: emergency medicine


title: Genetic links in angioimmunoblastic T-cell lymphoma (AITL), clonal hematopoiesis and concomitant hematologic malignancies provide insights into the cell of origin, etiology and biomarker discovery for AITL

abstract: We generated and compared the mutation profiles through targeted sequencing of the primary tumors and matched bone marrow/peripheral blood samples in 25 patients with angioimmunoblastic T-cell lymphoma (AITL) and 2 with peripheral T-cell lymphoma, not otherwise specified (PTCL-NOS). Our results provided strong evidence that AITL/PTCL-NOS, clonal hematopoiesis (CH) as well as other concomitant myeloid and even B-cell hematologic neoplasms (CHN), frequently arose from common mutated hematopoietic stem cell clones. Aberrant AID/APOBEC activity-associated substitutions and tobacco smoking-associated substitutions were enriched in the early CH-associated mutations and late non-CH associated mutations during AITL/PTCL-NOS development, respectively. Moreover, survival analysis showed that the presence of CH harboring [≥] 2 pathogenic TET2 variants with [≥] 15% of allele burden conferred higher risk for CHN (P = 0.0034, hazard ratio = 10.81). These findings provide insights into the cell origin and etiology of AITL, and provide a novel stratification biomarker for CHN risk in AITL/PTCL-NOS patients.


title: Exploring Risks of Human Challenge Trials for COVID-19

abstract:
Human Challenge Trials (HCTs) are a potential method to accelerate development of vaccines and therapeutics. However, HCTs for COVID-19 pose ethical and practical challenges, in part due to the unclear and developing risks. In this paper, we introduce an interactive model for exploring some risks of a SARS-COV-2 dosing study, a prerequisite for any COVID-19 challenge trials. The risk estimates we use are based on a Bayesian evidence synthesis model which can incorporate new data on infection fatality risks (IFRs) to patients, and infer rates of hospitalization. The model estimates individual risk, which we then extrapolate to overall mortality and hospitalization risk in a dosing study. We provide a web tool to explore risk under different study designs.

Based on the Bayesian model, IFR for someone between 20 and 30 years of age is 15.1 in 100,000, with a 95% uncertainty interval from 11.8 to 19.2, while risk of hospitalization is 130 per 100,000 (100 to 160). However, risk will be reduced in an HCT via screening for comorbidities, selecting lower-risk population, and providing treatment. Accounting for this with stronger assumptions, we project the fatality risk to be as low as 2.5 per 100,000 (1.6 to 3.9) and the hospitalization risk to be 22.0 per 100,000 (14.0 to 33.7). We therefore find a 50-person dosing trial has a 99.74% (99.8% to 99.9%) chance of no fatalities, and a 98.9% (98.3% to 99.3%) probability of no cases requiring hospitalization.


title: Highly Robust Prediction of Lung Nodule Malignancy by Deep Learning Model: A Multiracial, Multinational Study

abstract: The authors have withdrawn this manuscript because the authors have discovered errors in the methods and data analysis, which could alter the conclusions of the manuscript. Therefore, the authors do not wish this work to be cited as reference for the project. If you have any questions, please contact the corresponding author.


title: Are we ready for COVID-19's Golden Passport? Insights from a Global Physician Survey

abstract:
IntroductionCOVID-19 immunity passports could protect the right to free movement, but critics worry about insufficient evidence, privacy, fraud, and discrimination. We aimed to characterize the global physician communitys opinion regarding immunity passports.

MethodsCross sectional, random stratified sample of physicians registered with Sermo, a global networking platform open to verified and licensed physicians. The survey aimed to sample 1,000 physicians divided among the USA, EU and rest of the world. The survey question on immunology asked physicians to offer their insights into whether we know enough about COVID-19 immunity and its duration to offer immunity passports at the present time.

ResultsThe survey was completed by 1004 physicians (67 specialties, 40 countries, 49% frontline specialties) with a mean (SD) age of 49.14 (12) years. Overall, 52% answered NO, 17% were UNCERTAIN, and 31% answered YES (P <0.05). EU physicians were more likely to sayYES but even among them it did not exceed 35% approval. US physicians (60%) were more likely to say NO.

ConclusionOur findings suggest a current lack of support among physicians for immunity passports. It is hoped that ongoing research and vaccine trials will provide further clarity.


title: Psychological distress among people with probable COVID-19 infection: analysis of the UK Household Longitudinal Study

abstract: Studies exploring the longer-term effects of experiencing COVID-19 infection on mental health are lacking. We explored the relationship between reporting probable COVID-19 symptoms in April 2020 and psychological distress (measured using the General Health Questionnaire) one, two, three, five and seven months later. Data were taken from the UK Household Longitudinal Study, a nationally representative household panel survey of UK adults. Elevated levels of psychological distress were found up to seven months after probable COVID-19, compared to participants with no likely infection. Associations were stronger among younger age groups and men. Further research into the psychological sequalae of COVID-19 is urgently needed.


title: Multiday cycles of heart rate are associated with seizure likelihood

abstract:
Circadian and multiday rhythms are found across many biological systems, including cardiology, endocrinology, neurology, and immunology. In people with epilepsy, epileptic brain activity and seizure occurrence have been found to follow circadian, weekly, and monthly rhythms. Understanding the relationship between these cycles of brain excitability and other physiological systems can provide new insight into the causes of multiday cycles. The brain-heart link is relevant for epilepsy, with implications for seizure forecasting, therapy, and mortality (i.e., sudden unexpected death in epilepsy).

We report the results from a non-interventional, observational cohort study, Tracking Seizure Cycles. This study sought to examine multiday cycles of heart rate and seizures in adults with diagnosed uncontrolled epilepsy (N=31) and healthy adult controls (N=15) using wearable smartwatches and mobile seizure diaries over at least four months (M=12.0, SD=5.9; control M=10.6, SD=6.4). Cycles in heart rate were detected using a continuous wavelet transform. Relationships between heart rate cycles and seizure occurrence were measured from the distributions of seizure likelihood with respect to underlying cycle phase.

Heart rate cycles were found in all 46 participants (people with epilepsy and healthy controls), with circadian (N=46), about-weekly (N=25) and about-monthly (N=13) rhythms being the most prevalent. Of the participants with epilepsy, 19 people had at least 20 reported seizures, and 10 of these had seizures significantly phase locked to their multiday heart rate cycles.

Heart rate cycles showed similarities to multiday epileptic rhythms and may be comodulated with seizure likelihood. The relationship between heart rate and seizures is relevant for epilepsy therapy, including seizure forecasting, and may also have implications for cardiovascular disease. More broadly, understanding the link between multiday cycles in the heart and brain can shed new light on endogenous physiological rhythms in humans.

category: neurology