Input: Claim: Processed foods are generally higher in added sugars and preservatives. [SEP] Text: This suggests that in Chile, promoting diets based on unprocessed or minimal processed foods, complemented with small amounts of processed culinary ingredients and processed foods (e. g. fresh bread), as recommended by recently launched food-based dietary guidelines in Brazil( 35 ) and Uruguay( 36 ), would help achieve the WHO added sugars recommendations( 14 ) and the FAO advice for food security( 37 ). Despite the cultural differences, this is also true in countries such as the USA where more than 70 % of individuals in the lowest quintile of dietary share of ultra-processed foods met the <10 % cut-off for added sugars( Reference Martínez Steele, Baraldi and Louzada 21 ). The present study is not without limitations. Even though dietary data obtained by 24 h recalls are imperfect, electronic surveys and the multiple-pass method were used to prevent the interviewer from forgetting items frequently omitted by interviewees( 26 ). In addition, although information indicative of food processing such as place of meals or product brands was collected, these data were missing for some food items and thus may have led to errors in food classification. does not have an updated Table( Reference Jury, Urteaga and Taibo 28 ), the intakes of energy and total sugars were calculated using information from the US Table (for Release 28)( 27 ). For foods lacking information on added sugars, the algorithm proposed in the nutrient profile model launched by the Health Organization( 29 ) was used. Therefore, the consumption of ultra-processed foods or added sugars could be slightly under- or overestimated. Evidence suggests that some people may under-report consumption of foods with caloric sweetenersReference Bingham, Luben and Welch (38 – Reference Pryer, Vrijheid and Nichols 40 ). If so, this bias may lead to underestimation of the overall intake of added sugars or the dietary contribution of ultra-processed foods, but may less likely affect the association between these variables. The present study has several strengths. It is based on a large, nationally representative sample of the Chilean population with data on individual consumption rather than on market or household purchases, which do not account for the fraction of wasted food. It is also the first study to examine the contribution of ultra-processed foods to total energy and added sugars intakes in the Chilean diet, providing updated and relevant results for informing the public health agenda.

Output: corroborate


Input: Claim: Rooftop gardens alone can cool entire cities during heat waves. [SEP] Text: We also need to encourage the use of energy-efficient technologies like heat pumps, which are essentially air conditioners modified to work in both directions. The modern ones can be somewhat more efficient than old-line central air systems at pumping heat out of your building in the summer, and much more efficient than a standard furnace or electric heater at pumping heat in to warm the place in the winter. & cities can fight climate change cities in the face of extreme heat smart windows save energy Then we need to replace the hydrofluorocarbon refrigerants that most air conditioners and heat pumps use. The most common ones have around 1,000 to 3,000 times the potency of carbon dioxide as a greenhouse gas. And in the meantime, we need to do anything we can to make cities cooler. How do we manage that? There are some things that individuals can do. Personal transport is a big one. The internal combustion engine generates a lot of heat, whether it’s cars in developed countries or the motor scooters that a lot of people ride in the and developing countries. So taking public transit, or using electric vehicles, electric bikes or bicycles can help. Or, if you have the opportunity to build your own house, you could use materials that are not as heat-absorbing. I’m thinking about the house in Greer, South Carolina, where I grew up: It was brick, and just incredibly hot to live in in the summer. There are lots of alternatives that would be cooler and lower your energy bills, including natural building materials like wood or bamboo. Another recent example was in our current house, where we ended up choosing to cover an addition to our roof with a type of TPO (thermoplastic polyolefin) that reflected heat better than the standard roofing material. But there are limits to individual action. If you live in an apartment building, you can’t just decide to go up and plant a green roof or paint your rooftop white to make it more reflective, because somebody else owns that building. The same goes for planting trees, which can cool your neighborhood by providing shade and transpiration: Most public parks and street corridors are owned and controlled by the city. So overall, tackling urban heat needs to be a government-led effort, simply because governments have the jurisdiction and can impose mandates on a much larger scale. I think about one example in City about 13 years ago.

Output: contrast


Input: Claim: Shift work can negatively impact metabolic health. [SEP] Text: Table 1 - Footnote a Data source: -. Medical history, sexual, and maturational factors and prostate cancer risk. Ann Epidemiol. 2004;14(9):655-62. Return to Table 1 - Footnote a referrer Table 1 - Footnote b n = 61 individuals were missing BMI information. Return to Table 1 - Footnote b referrer Table 1 - Footnote c Workers who have ever participated in any type (evening/night, rotating and other) of shift work. Return to Table 1 - Footnote c referrer Table 1 - Footnote d Three participants (1 normal weight, 1 overweight, 1 obese; all 3 non-shift workers) missing marital status. Return to Table 1 - Footnote d referrer Table 1 - Footnote e Total number of hours that men reported engaging in moderate- or vigorous-intensity physical activity during four lifetime periods (teens, thirties, fifties and 5 years prior to the study). Return to Table 1 - Footnote e referrer Table 1 - Footnote f Intensity of occupational activity reported in four time periods (respondents’ twenties, thirties and fifties, and 5 years prior to the study). Those reporting any moderate or strenuous occupational activity were classified in four categories: Category 1 = moderate/strenuous activity in 1 time period, Category 2 = moderate/strenuous activity in 2 time periods, Category 3 = moderate/strenuous activity in 3 time periods, Category 4 = moderate/strenuous activity in 4 time periods.

Output: pass


Input: Claim: Processed foods are generally higher in added sugars and preservatives. [SEP] Text: These may also serve as baseline results to measure the impact of a set of regulations being implemented by the aimed at improving diets( Reference Corvalán, Reyes and Garmendia 24 34 ). have documented that ultra-processed foods represent close to one-third of total energy intake and contribute more than half of total added sugars in the Chilean diet. Developing strategies to limit and decrease the consumption of ultra-processed foods is a potentially effective way to achieve WHO dietary recommendations on added sugars and to promote eating in accordance with food-based dietary guidance in Chile, especially for young children and adolescents. Acknowledgements Acknowledgements: The authors thank the Ministry of Health of Chile for supplying the database (Dietary Survey, 2010) and the for its support in obtaining information from Panels from packaged foods in 2015. Financial support: The analyses were supported by the Fundaçâo de Amparo à Pesquisa do Estado de São Paulo (). G. C. is a beneficiary of a from (grant number 2016/13522-3). had no role in the design, analysis or writing of this 10. 1017/S1368980017001161 1. Monteiro, CA, Moubarac, J-C, Cannon, G et al. (2013) Ultra-processed products are becoming dominant in the global food system. 14, 21–28. 2. (2016) NOVA. The star shines bright. 7, 1–3, 28–38. 3. Moubarac, J- (2017) Consumption of ultra-processed foods predicts diet quality in Canada. Appetite 108, 512–520. CrossRef4. (2015) Ultra-processed foods and the nutritional dietary profile in Brazil. Publica 49, 38. 5. Louzada, ML da (2015) Impact of ultra-processed foods on micronutrient content in the Brazilian diet. Publica 49, 45. 6. Martínez (2017) The share of ultra-processed foods and the overall nutritional quality of diets in the US: evidence from a nationally representative cross-sectional study. Metr 15, 6. 7. (2014) Ultra-processed food products and obesity in Brazilian households (2008–2009). PLoS One 9, e92752. 8.

Output: corroborate


Input: Claim: Rooftop gardens alone can cool entire cities during heat waves. [SEP] Text: AbstractPopulation exposure to high temperatures poses health risks and increases mortality. ‘Cool roofs’ (high-albedo roofs) and rooftop photovoltaics (RPV) may reduce temperatures in urban areas. Here, using advanced urban climate modeling, we model impacts of these measures on air temperature and heat-related mortality in London during the record-breaking hot summer of 2018. We estimate changes in mean near-surface air temperature of −0. 3 °C in the RPV scenario and −0. 8 °C in the cool roof scenario. We find that the heat-related mortality in this period (estimated 655–920) could have been reduced by 96 (12%) by RPV, or 249 (32%) by cool roofs, in scenarios where all roofs have these measures. Monetized using value of statistical life, we estimate benefits for RPV and cool roofs of £237 M and £615 M, respectively. We estimate that up to 20 TWh of electrical energy would be generated in the full RPV scenario. We show that, for conditions such as in London June–August 2018, RPV or cool roofs may reduce near-surface air temperatures and associated heat-related mortality, with cool roofs having a larger effect. Similar content being viewed by others Building energy savings by green roofs and cool roofs in current and future climates 2. The summer (June, July and August) of 2018 was the hottest on record in England (highest average temperature), but the UK Met Office’s climate projections suggest that a summer with an average temperature as high as this might occur in more than half of years by mid-century under a high-emissions scenario1. The built environment of urban areas alters the energy balance of the land surface, leading to cities having their own climate, distinct from nearby rural areas3,4. Typically, this leads to higher air temperatures in urban areas, especially at night, known as the urban heat island effect3. This is of interest because 83% of the UK population lives in urban areas ( last accessed 11 March 2024) and is therefore exposed to higher temperatures, which worsens summer thermal comfort and may increase heat-related mortality5. It also points to the possibility of reducing exposure to excessive heat through changes to the urban environment.

Output: contrast


Input: Claim: Shift work can negatively impact metabolic health. [SEP] Text: Return to Table 1 - Footnote f referrer When comparing across the categories of obesity status, a greater proportion of normal weight individuals reported having obtained a post-secondary or post-graduate education (25%) compared to overweight (19%) or obese (16%) men. Furthermore, a greater proportion of men in the overweight or obese categories reported having held jobs involving moderate or strenuous physical activity in three or four lifetime periods (overweight, 49%; obese, 48%) compared to normal weight individuals (43%). Conversely, while the proportion of men in the top two quartiles of recreational activity was similar for normal (54%) and overweight (53%) men, the proportion was slightly lower among individuals who were obese (47%). There were no major differences in total energy consumption, with 41% of individuals in the normal and overweight groups in the top two consumption quartiles compared to 44% of men in the obese group (Table 1). The frequencies of different shift types across ten categories of work environment are shown in Table 2. Evening/night work was the least commonly reported shift schedule; restaurant/hotel work was reported by the highest proportion (11%) of men for this shift type. Rotating shift patterns were more common; for this shift type, mine (63%), factory/plant floor (49%), and laboratory work (26%) had the highest proportion of rotating shifts. Other shift schedules were most common in restaurant/hotel (37%) or vehicle work (22%). Table 2.

Output: pass


Input: Claim: All fats, regardless of type, contribute equally to weight gain. [SEP] Text: Correction 18 Sep 2007: (2007) Correction: All ß Equally to and Maintenance. PLOS Biology 5(9): e240. View correction Figures AbstractIn healthy adult mice, the β cell population is not maintained by stem cells but instead by the replication of differentiated β cells. It is not known, however, whether all β cells contribute equally to growth and maintenance, as it may be that some cells replicate while others do not. Understanding precisely which cells are responsible for β cell replication will inform attempts to expand β cells in vitro, a potential source for cell replacement therapy to treat diabetes. Two experiments were performed to address this issue. First, the level of fluorescence generated by a pulse of histone 2B–green fluorescent protein (H2BGFP) expression was followed over time to determine how this marker is diluted with cell division; a uniform loss of label across the entire β cell population was observed. Second, clonal analysis of dividing β cells was completed; all clones were of comparable size. These results support the conclusion that the β cell pool is homogeneous with respect to replicative capacity and suggest that all β cells are candidates for in vitro expansion. Given similar observations in the hepatocyte population, we speculate that for tissues lacking an adult stem cell, they are replenished equally by replication of all differentiated cells. The β cells of the pancreas are responsible for insulin production and their destruction results in type I diabetes. β cell maintenance, growth, and regenerative repair is thought to occur predominately, if not exclusively, through the replication of existing β cells, not via an adult stem cell. It was previously unknown, however, whether all β cells divide at the same rate, or if multiple subpopulations of β cells exist, some highly replicative and others very slowly dividing, possibly postmitotic. We performed two types of experiments to determine whether all β cells are alike: label-retaining analysis and clonal analysis. Our results indicate that all β cells contribute equally to islet growth and maintenance. Citation: (2007) All β Equally to and Maintenance. PLoS Biol 5(7): e163. Editor: Margaret A. Goodell, of Medicine, of AmericaReceived: January 26, 2007; Accepted: April 16, 2007; Published: May 29, 2007Copyright: © 2007 Brennand et al. This is an open-access 1.

Output: corroborate


Input: Claim: Rooftop gardens alone can cool entire cities during heat waves. [SEP] Text: Increasing the albedo of urban surfaces reduces absorption of solar radiation and, therefore, the temperature of those surfaces; applying high-albedo materials to roofs can reduce heat flux into buildings, thereby passively reducing indoor temperature and cooling demand6. This can also reduce the amount of solar radiation converted into sensible heat flux and, therefore, the outdoor air temperature7. Typical urban neighborhood albedo (that is, the albedo of an urban area as a whole, rather than single facets such as roofs or walls4 is often estimated at around 0. 2, and cool roofs are usually modeled with a range of albedo from 0. 5 to 0. 9, with the resultant change in neighborhood albedo depending on the plan-area fraction of roof surface. Previous urban climate modeling studies have found that a 0. 1 increase in neighborhood albedo leads to a 0. 2–0. 6 °C decrease in near-surface air temperature in clear-sky afternoon conditions7. In a modeling study of the region of the UK, Macintyre and Heaviside8 found that cool roofs may reduce daytime average 2 m outdoor air temperatures by 0. 5 °C, and by applying exposure–response functions (ERFs) derived from epidemiological studies, they estimated that this could have reduced heat-related deaths in June, July and August of 2006 by 6–12%. Rooftop photovoltaics (RPV) can provide renewable energy without taking up additional land. Urban roof space is limited, so choices must be made between roof types, solar photovoltaics (PV) and cool roofs, as well as rooftop vegetation and building services such as air conditioning units, all of which entail consideration of construction, installation and maintenance costs. This could be seen as a trade-off between climate adaptation and emissions mitigation, but comparison of the relative benefits may point toward what mix is most appropriate. RPV often has a lower albedo than typical roof surfaces, but a proportion of absorbed radiation is converted to electrical energy and therefore not emitted as sensible heat. Previous studies into the urban climate effects of RPV have found a mix of cooling effects and heating effects depending in part on whether they were compared with high- or low-albedo surfaces, on whether nighttime or daytime temperatures were compared, and on climatic conditions9.

Output: contrast


Input: Claim: Shift work can negatively impact metabolic health. [SEP] Text: 2011;68(5):327-31. Return to footnote27 28 van van der Beek AJ. The effects of shift work on body weight change - a systematic review of longitudinal studies. Health. 2011;37(4):263-75. Return to footnote28 29 -. Medical history, sexual, and maturational factors and prostate cancer risk. Ann Epidemiol. 2004;14(9):655-62. Return to footnote29 30 -Kortsak A. Prostate cancer risk and diet, recreational physical activity and cigarette smoking. Can. 2007;27(4):145-53. Return to footnote30 31 Organization. Obesity: preventing and managing the global epidemic. Report of a WHO Consultation, Geneva, 3-5 Jun 1997. Geneva; 1998. 252 p. Return to footnote31 32 Hosmer D, Lemeshow S. Special topics. In: Applied logistic regression. 2nd ed. Toronto (ON): & Sons Inc; 2000. p. 260-352. Return to footnote32 33 Rotating night shifts and risk of breast cancer in women participating in the nurses’ health study. J Inst. 2001;93(20):1563-8. Return to footnote33 34 Lie J-ærheim K. Night work and breast cancer risk among Norwegian nurses: assessment by different exposure metrics. Epidemiol. 2011;173(11):1272-9. Return to footnote34 35 Hansen J, Lassen CF. Nested case-control study of night shift work and breast cancer risk among women in the Danish military. Med. 2012;69(8):551-6. doi: 10. 1136/oemed-2011-100240. Return to footnote35 36 Increased risk of breast cancer associated with long-term shift work in Canada. Med. 2013;70(12):831-8. Return to footnote36 37. Causal diagrams for epidemiologic research. Epidemiology. 1999;10(1):37-48. Return to footnote37 38 Szklo M, Nieto FJ. Epidemiology beyond the basics. 3rd ed. Burlington (MA): Jones & Bartlett Learning; 2014. p. 162-163. Return to footnote38 39 Textor J, Hardt J, Knüppel S. DAGitty: a graphical tool for analyzing causal diagrams. Epidemiology.

Output: pass


Input: Claim: Skipping breakfast is always detrimental to metabolism. [SEP] Text: This encompasses elevated risks of hypertension,[39,42] diabetes,[43,44] obesity,[45,46] elevated low-density lipoprotein cholesterol,[47,48] dysregulated lipid levels[49] and metabolic syndrome. [50–52] Collectively, these conditions are well-recognized risk factors for MI. Furthermore, breakfast skipping has been linked to an increase in inflammatory markers, such as C-reactive protein,[53] which play a significant role in atherosclerosis and MI. [54,55] This inflammatory response could be a critical pathway through which breakfast skipping exerts its deleterious cardiovascular effects. Additionally, behaviors linked to breakfast skipping, such as smoking and reduced physical activity, could further heighten MI risk. [56,57] While more research is needed to fully understand the underlying mechanisms, our results suggest that encouraging a regular breakfast routine could be a straightforward and effective measure to reduce MI risk. Our study utilized a two-sample MR analysis with data from large GWAS cohorts, involving nearly 190,000 participants, ensuring robust statistical power. The genetic methodology adopted in our study enhances the strength of causal inference, effectively reducing the influence of confounding variables that are typically encountered in observational research. Notwithstanding its strengths, the study has several limitations. First, for the exposure GWAS data, the significance threshold was adjusted to a P-value of 1 × 10−5 due to the limited number of SNPs achieving genome-wide significance at the standard P < 5 × 10−8 threshold. While this may have increased the likelihood of false positives, it facilitated a more comprehensive analysis of the relationship between breakfast skipping and MI. Second, the predominance of European individuals in the exposed population constrains the applicability of the results to other racial groups. 5. MR study revealed a significant causal link between breakfast skipping and an increased risk of MI. The data strongly suggest that breakfast skipping contributes to a higher likelihood of heart attacks. This underscores the potential of regular breakfast consumption as a preventive strategy against MI. 65. | Text | PubMed | CrossRef | 53. | PubMed | 9. | 17. | PubMed | 13. | Text | PubMed | CrossRef | 9. | Text | PubMed | CrossRef | 1540. | 103. | PubMed | CrossRef | 85. | PubMed | CrossRef | 2016. . 2019;16:1853. | 32. | PubMed | CrossRef | 407. | Text | PubMed | 80.

Output: corroborate


Input: Claim: Rooftop gardens alone can cool entire cities during heat waves. [SEP] Text: Some studies have been criticized for not modeling heat exchange between the bottom side of the panel and the roof; assuming unrealistically high conversion efficiency; or neglecting the effect of temperature on conversion efficiency9. These effects are modeled realistically in the present study. Previous studies have not estimated the impact on heat-related mortality of outdoor air temperature changes resulting from widespread RPV (for example, refs. 9,10,11). Urban climate models enable the estimation of thermal effects of roofing technologies at the city scale: detailed models of the energy balance of the urban environment are coupled with an atmospheric model, which enables analysis beyond the effect on single buildings and in a more physically consistent way than the bulk scheme used in some regional- to global-scale climate models12. Some urban climate modeling studies have relied on single-layer urban canopy parameterization13 (‘urban canopy’ refers to the layer of air below the mean height of buildings in an urban area), which does not incorporate simulation of detailed urban structure in three dimensions; this is important because heat transport between roofs and the urban canyon means that changes to the energy balance at the roof level are not equivalent to changes within the urban canopy. While simpler urban parameterizations can perform well in simulating urban boundary-layer air temperatures, more complex, three-dimensional (3D) models of the urban environment are necessary to simulate the effects of changes to specific elements of the urban environment such as roof albedo and RPV12,13,14,15. Our preliminary work in Brousse et al. 16 indicated that cool roofs and RPV both have potential for reducing air temperatures in London, with a larger effect from cool roofs. Monetary valuation is used to present mortality impacts in economic terms17. In the field of heat and health, this method has been used previously to evaluate interventions such as heat-health action plans18,19. In the present study, we use value of life years (£79,000 per life year) and value of statistical life (£2. 5 million per life) set out in UK Government guidance20,21. In this context, the health benefits of temperature reduction could be seen as a beneficial externality of solar and cool roof adoption. We use urban climate modeling to estimate the effect that cool roofs and RPV could have had on urban-canopy air temperature in London during the extremely hot summer of 2018 (Fig. 1).

Output: contrast


Input: Claim: Shift work can negatively impact metabolic health. [SEP] Text: 2011;22(5):745. Return to footnote39 40. Chronodisruption and cancer. Naturwissenschaften. 2008;95(5):367-82. Return to footnote40 41 A meta-analysis on dose-response relationship between night shift work and the risk of breast cancer. Ann Oncol. 2013;24(11):2724-32. Return to footnote41 42. Rotating shift work and risk of prostate cancer. Epidemiology. 2007;18(1):182-3. Return to footnote42 43 Parent M-É, El-Zein M, Rousseau M-C, Pintos J, Siemiatycki J. Night work and the risk of cancer among men. Epidemiol. 2012;176(9):751-9. Return to footnote43 44 Papantoniou K, Castaño-Vinyals G, Espinosa A, et al. Night shift work, chronotype and prostate cancer risk in the MCC-Spain case-control study. Cancer. 2015;137(5):1147-57. Return to footnote44 45. Obesity and shift work: chronobiological aspects. Rev. 2010;23(1):155-68. Return to footnote45 46 Light intensity exposure, sleep duration, physical activity, and biomarkers of melatonin among rotating shift nurses. Chronobiol Int. 2009;26(7):1443-61. Return to footnote46 47. The influence of light at night exposure on melatonin levels among Canadian rotating shift nurses. Biomarkers Prev. 2011;20(11):2404-12. Return to footnote47 48 Considerations of circadian impact for defining “shift work” in cancer studies:. Med. 2011;68(2):154-62. Return to footnote48 49. Prevalence of obesity among U. S. workers and associations with occupational factors. Prev Med. 2014;46(3):237-48. Return to footnote49 50. Estimates of obesity based on self-report versus direct measures. Health Rep. 2008;19(2):61-76. Return to footnote50 51 Statistics Canada.

Output: pass


Input: Claim: Skipping breakfast is always detrimental to metabolism. [SEP] Text: Table 3 Weighted prevalence of abnormal metabolic outcome by breakfast consumptionFull size tableThe association between metabolic abnormalities and irregular breakfast consumption after adjusting for age, education, household income, smoking status, alcohol drinking status, and physical activity is presented in Table 4. Negative binomial regression analysis revealed that an irregular breakfast group had a higher risk of increased number of metabolic abnormalities in the younger working men population than regular breakfast group (odds ratio, 1. 15; 95% confidence interval, 1. 03–1. 27). There was no significant association between the number of metabolic abnormalities and irregular breakfast consumption in middle-aged female workers after adjustment. Table 4 Association between the number of metabolic abnormalities and irregular breakfast consumption, using negative binomial regressionFull size tableDiscussionWe conducted this study to understand the association between regular breakfast intake and metabolic outcomes by sex, work status, and age group stratification. The breakfast consumption pattern was consistent with previous studies. Of the total participants, 56. 4% consumed breakfast regularly. Similar patterns was observed in a previous Korean study using 2017 data, 57. 9% [21]. The results of this study showed that irregular breakfast intake (< 5 times per week) was closely linked to higher risk of increasing number of abnormal metabolic outcomes, especially in young men in the working population than regular breakfast intake. These results are consistent with a previous study that used the data and showed that breakfast consumption patterns were associated with a risk of metabolic outcomes [22]. Furthermore, our results corresponds with that of a review 37. 92%. This trend is in progress, accelerating the risk of metabolic outcomes among young adults. The risk is accentuated in the working population of young men, and further studies are required to clarify the association between specific working conditions (working hours or shift work), breakfast habituation, and the risk of metabolic outcomes. ConclusionOur study showed that breakfast skipping is associated with abnormal metabolic outcomes in the Korean male population, especially in young workers, and provided novel ideas to explain the mechanism through which breakfast skipping affects metabolic outcomes. Availability of data and materials data are publicly available. ( : Health and : Body mass index LDL-C: Low-density lipoprotein cholesterol HDL-C: High-density lipoprotein cholesterol TG: Triglyceride BMR: Basal metabolic rate 92. 1573. 43. 12. 72. 53. 88. 43. 9. 51. 82. PubMed Pub302. 92. 4. 2006. Nutr.

Output: corroborate


Input: Claim: Rooftop gardens alone can cool entire cities during heat waves. [SEP] Text: To contextualize the impact of these interventions, we estimate how many deaths attributable to heat could be avoided, based on the results of an ecological time-series analysis of temperature and mortality22. We further apply value of statistical life methods to convert mortality impacts into monetary values, to enable comparisons of the costs of these interventions with their benefits. Fig. 1: Illustration of the analysis process. Four scenarios are modeled in an urban climate model (-BEM), including heat mitigation scenarios. Modeled temperature data from these simulations are then post-processed by bias adjusting to PWS data. A temperature–mortality relationship is applied to estimate mortality differences between the scenarios and, therefore, the heat-related mortality avoidable through the use of the heat-mitigation strategies. Full size imageResultsModeled urban air temperatureWe compared outputs from four runs of the urban climate model (Methods). The ‘baseline’ scenario is intended to represent the actual urban environment during the study period, while three other alternative scenarios have modifications to the landscape or buildings: (1) all buildings having full coverage with RPV panels, (2) all buildings having full coverage with high-albedo roofs (‘cool roofs’) and (3) a ‘nonurban’ scenario in which all urban land cover is replaced by natural (that is, vegetated) land cover. The nonurban scenario is intended to contrast with the baseline scenario to illustrate how urban land cover affects the local climate, while the RPV and cool roof scenarios are intended to explore the physical limits of plausible interventions against excessive urban temperatures. A key output of the urban climate model is the near-surface air temperature within the urban canopy, that is, the temperature of the air 2 m above the street. Population-weighted mean temperatures were calculated as the spatial mean of temperature weighted by census population. Figure 2 shows the time series of modeled population-weighted daily mean urban-canopy air temperature (Tm) in each of the scenarios. Descriptive statistics for each of the temperature series used in this analysis are given in 1. Mean differences from the baseline scenario in population-weighted mean urban-canopy temperature were −0. 3 °C in the RPV scenario, −0. 8 °C in the cool roof scenario and −1. 9 °C in the nonurban scenario.

Output: contrast


Input: Claim: Shift work can negatively impact metabolic health. [SEP] Text: 2007;33(5):321-323 pdf | Issue date: 31 Oct 2007 Does shift work cause cancer? by Pukkala E, Härmä M It is well-known that several work-related factors, for example, asbestos, benzene, nickel, wood dust or passive smoking in restaurants, can cause cancer. In Europe, 3. 2 million new cases and 1. 7 million deaths from cancer were reported in 2006 (1). Breast cancer has become the most common cancer diagnosed overall, with 430 000 new cases in 2006 (13. 5% of all cancer cases). Traditional work-related risk factors are estimated to cause about 4% of malignant neoplasms among Nordic men but less than 1% among Nordic women (2). While knowledge on the traditional risk factors for cancer has increased and many of these factors have successfully been eliminated from modern workplaces in developed countries, new environmental and individual risk factors for cancer have stepped forward. Shift work and work stress, the prevalent exposures of the “24-hour society”, are among some of the “new” exposures now being intensively researched. Agency for Research on Cancer (IARC) is currently working on the evaluation of shift work as a possible carcinogenic risk to humans. The importance of “new” individual risk factors for cancer, alcohol intake, obesity, and the lack of physical activity that are sometimes linked to an occupational behavior model have also been acknowledged. The potential significance of shift work, work stress, and unhealthy living habits are not related to their high carcinogenic nature but, instead, to their extremely high prevalence. For example, if physical inactivity were estimated to increase the risk of breast cancer by 10% (3), about 3% of all cancers among women in Finland would be due to physical inactivity—a higher number of cases than those caused by all of the traditional occupational cancer risk factors combined. Does shift work cause cancer? Before it can be decided that an environmental or occupational factor is carcinogenic, reliable and unbiased human evidence is needed to show a dose–response relationship between the studied exposure and the specific disease. In addition, it would make the interpretation easier if there were facts about a specific mechanism explaining the possible epidemiologic association. The pineal gland is known to be the key organ in the regulation of the circadian clock.

Output: pass


Input: Claim: AI in healthcare can help detect diseases early with image analysis. [SEP] Text: Health & 8, 2022 Detecting dental diseases with AI dental image analysis Dentists often use panoramic dental radiography, capturing the entire mouth in a single image, to diagnose dental diseases. The manual examination of these complex images is time consuming and laborious. at the Institute of Science and Technology (KAIST), together with Donghyun Kim, Jeong, and from InVisionLab, has developed a deep-learning model for AI dental-image analysis which enables the automatic detection of dental diseases from panoramic X-ray images. In addition to dental anomalies, radiographic findings from such images can be used in the diagnosis of systemic diseases. (AI) is employed extensively in dentistry to identify both normal and abnormal structures and diagnose diseases from the numerous medical images collected during a clinical routine. Panoramic dental radiography, which captures the entire mouth in a single image, is often used to diagnose dental diseases and plan subsequent treatment. These images are complex, and their manual examination is time consuming and laborious. In addition to dental anomalies, radiographic findings from such images can be used in the diagnosis of systemic diseases like hypo- and hyperparathyroidism (abnormally low or high levels of parathyroid hormone which affects calcium levels in the blood) and osteoporosis (porous bone). Due to time constraints, dental clinicians may concentrate only on teeth with symptoms, so the ability to filter out healthy images would aid diagnosis and save time. Sangyeon Lee, a PhD candidate from the Department of Bio and Brain Engineering, Institute of Science and Technology (KAIST), has collaborated with Donghyun Kim, Jeong, and from InVisionLab, to develop a deep-learning model for panoramic X-ray image analysis that enables the automatic detection of dental diseases. Three major components of panoramic X-ray images. AI dental image analysis The researchers explain how previous studies have employed AI in dentistry and been successful in the detection of targeted oral diseases. Still, they can only target a small number of abnormal signs or diseases. These models use convolutional neural networks to classify images and detect objects. They have not, however, exploited the key benefit of panoramic dental radiography in that it encapsulates the entire mouth in a single image. This includes the upper and lower jaws, individual teeth, and surrounding tissues and structures.

Output: corroborate


Input: Claim: Rooftop gardens alone can cool entire cities during heat waves. [SEP] Text: Based on comparisons between the modeled output and observations, we estimate a root mean squared error on the modeled urban temperature of 1. 0 °C (Methods). Fig. 2: Population weighted mean temperature. a,b, Population-weighted daily mean urban-canopy air temperature time series in three scenarios (a), and temperature difference from the baseline scenario (b). Full size imageFigures 3 and 4 show how the differences in temperature between the scenarios are distributed across the population of London. Averaged across June, July and August, the whole population of would experience a lower mean temperature in both the cool roof and RPV scenarios. In the RPV and cool roof scenarios, we observe reduced mean daily maximum temperature but little difference in mean daily minimum temperature; this contrasts with the nonurban scenario, which shows greatly reduced daily minimum temperature but little difference in daily maximum temperature. The spatial pattern (Fig. 3) is substantially different between the cool roof and nonurban scenarios, emphasizing the conceptual difference between minimizing the urban heat island intensity (that is, minimizing urban–rural nighttime differences) and mitigating heat (reducing the high temperatures to which the population are exposed)23. Fig. 3: Spatial distributions of mean urban-canopy air temperature in the scenarios. a–d, The mean temperature is shown for the baseline scenario (a), and differences from the baseline are shown for the other scenarios (b–d). Temperature differences from the baseline scenario are shown for the rooftop PV scenario (b), cool roof scenario (c) and nonurban scenario (d). Boundaries are from Office for licensed under the Licence v. 3. 0 ( Contains OS data © Crown copyright and database right 2024. Full size imageFig. 4: Percentage of the population experiencing temperature differences between scenarios in RPV, cool roof and nonurban scenarios, compared with the baseline scenario. The mean daily mean (top), daily minimum (middle) and daily maximum (bottom) temperatures are shown. Full size imageEstimated heat-related mortalityUsing the population-weighted daily mean temperature, we estimate how many deaths and how many years of life lost (YLL) would be attributable to heat in each scenario. YLL is calculated summing average remaining life expectancy by age group. When deaths or YLL are lower than in the baseline scenario, we interpret these impacts as being avoidable by use of the intervention in that scenario.

Output: contrast


Input: Claim: Shift work can negatively impact metabolic health. [SEP] Text: According to previous studies, a non-standard shift is defined as one that starts before 07:00 or after 14:00, involves rotation, or is frequently worked outside the standard shift period (07:00–18:00 h). Work within the standard shift period, but frequently requiring overtime of several hours, is not recognized as a non-standard shift [3, 4]. The diagnostic algorithm and questionnaire content for SWSD is based on the of Sleep Disorders-3 (ICSD-3) diagnostic criteria and questionnaires developed by Barger LK et al. [3, 24]. Erectile function and ejaculation timing were evaluated using the of (IIEF-5) scores and (PEDT) scores, respectively, classifying an IIEF-5 score ≤ 21 as erectile dysfunction and a PEDT score ≥ 11 as indicative of premature ejaculation. Anxiety Disorder-7 (GAD-7) and Questionnaire-9 (PHQ-9) scored for anxiety and depression with scores ≥ 5 suggesting the presence of anxiety or depression, respectively. Detailed variable names and the questionnaire are provided in 1. After collecting the data, we performed quality control on the questionnaire data based on the following inclusion and exclusion criteria to eliminate surveys that did not meet the standards. Inclusion criteria were: (1) males aged 18 to 60; (2) individuals with a stable spouse or girlfriend engaging in regular sexual activity; (3) no severe underlying medical conditions, such as serious spinal cord injury, paraplegia, or advanced malignant tumors. Exclusion criteria included: (1) persons with psychiatric anomalies; (2) respondents whose survey answers were contradictory or who provided incomplete data; (3) completion of surveys in under three minutes, which were considered to reflect insincerity in response. Statistical analysisData were processed and analyzed using R version 4. 3. 0 and SPSS version 25. 0 for Windows (). Continuous data conforming to a normal distribution were presented as mean ± standard deviation (SD) and analyzed using Student’s t-test or Mann–Whitney test for inter-group differences. Categorical variables were described using frequency counts and percentages and analyzed using Pearson’s chi-square test or Fisher’s exact test.

Output: pass


Input: Claim: AI in healthcare can help detect diseases early with image analysis. [SEP] Text: Moreover, filtering out healthy images would reduce the number of images requiring manual examination and reduce the burden on dental clinicians. Lee and his collaborators selected 17 fine-grained dental anomalies that can be detected from panoramic dental images. Using modern AI-based computer-vision techniques, they developed a model that can be applied in clinical practices to detect these anomalies. A huge number of high-quality data sets is essential for machine learning, so the research team collected 22,999 panoramic dental images from 30 local dental clinics over a year-long period from July 2020 to July 2021. Each image in the dataset was manually examined and labelled by a dental-radiography expert who noted any significant features or anomalies present in the image. These major dental anomalies can affect both patients’ dental health and their quality of life. Dental anomalies The researchers describe how these major dental anomalies can affect both patients’ dental health and their quality of life. If ignored, they can have severe consequences, particularly if they are connected to systemic diseases. Early detection can prevent serious outcomes and provide markers for other systemic diseases. The 17 anomalies were grouped into four categories based on their clinical features and locations: carious lesions, calcifications, anomalies in the dental regions, and anomalies in the surrounding regions. Examples of labelled carious lesions: a) cervical caries or cervical abrasions, b) dental caries or coronal defects, c) proximal caries, and d) secondary caries. Carious lesions, where there is a visible breakdown or a hole in the tooth surface, are the most common issue observed in dentistry. Their prevention and early diagnosis are crucial as they can cause more serious problems if ignored. The team divided carious lesions into four groups for fine-grained diagnoses to facilitate their early detection, preventing their progression and the deterioration of dental tissues. Calcification describe the build-up of calcium on the enamel and gums. Its presence on panoramic dental radiographs is rare, but detection is vital to prevent any further progression. Anomalies in the dental area include dental disease features and abnormal forms of teeth that can cause symptoms, including bleeding, pain, and halitosis (chronic bad breath). The researchers chose to include six dental anomalies that are critical to dental health.

Output: corroborate


Input: Claim: Rooftop gardens alone can cool entire cities during heat waves. [SEP] Text: SummaryWe estimate that 786 (95% confidence interval (CI) 655–920) deaths in in June, July and August of 2018 were attributable to heat (Table 1). Of these deaths, we estimate that 96 (95% CI 85–107) (12%) could have been avoided by full adoption of RPV, or 249 (95% CI 219–277) (32%) by full adoption of cool roofs. Most deaths attributed to heat take place among people 75 years and older. Based on life tables, we estimate 9,810 (95% CI 7,620–12,020) YLL to heat in London during June–August 2018, of which 1,200 (95% CI 990–1,430) could have been avoided by RPV, or 3,100 (95% CI 2,520–3,650) by cool roofs. Based on modeled differences in attributable mortality in for June, July and August of 2018 only, we estimate that the monetized value of heat-related mortality avoidable by RPV is £237 million (95% CI £211–265 million), and by cool roofs is £615 million (95% CI £541–684 million), relative to a standard low-albedo roof scenario. Similarly, the monetized value of YLL avoidable by RPV is £108 million (95% CI £89–129 million), and by cool roofs is £280 million (95% CI £228–329 million). We estimate that 9. 5–20 TWh of electrical energy would be generated in the full coverage RPV scenario during the same period, worth between £550 million and £7. 3 billion. DiscussionIn this study we modeled the effect of RPV and cool roofs on summer temperatures in London. We found that either RPV or cool roofs would reduce mean summer outdoor air temperatures in London under similar conditions to June–August 2018, and could therefore reduce heat-related mortality, with cool roofs having a larger effect. The economic value of mortality avoided in both scenarios are large and are of a similar order of magnitude to the consumer price of the electricity that would be generated, suggesting that the health-related externalities could be an important part of the strategic and wider economic case for cool roofs and solar panels. Temperature changes are generally consistent with comparable studies. The albedo cooling effectiveness—defined as the temperature change per 0.

Output: contrast


Input: Claim: Shift work can negatively impact metabolic health. [SEP] Text: 22, 95% CI 0. 62—1. 83, p < 0. 001; Model 3, β 1. 56, 95% CI 0. 74—2. 39, p < 0. 001). Similarly, men with SWSD had higher premature ejaculation diagnostic scores than those without SWSD (Model 1, β 5. 08, 95% CI 4. 20—5. 96, p < 0. 001; Model 2, β 4. 15, 95% CI 3. 19—5. 12, p < 0. 001; Model 3, β 4. 03, 95% CI 3. 09—4. 98, p < 0. 001) (as depicted in Table 5). Table 5 Assessment of the association between shift work, SWSD, and PEDT scores using multiple regression models controlling for confounding factorsFull size tablePerformance of prediction modelTo more precisely quantify the contributory impact of each risk factor on the onset of premature ejaculation, we integrated risk factors identified through multifactorial regression analysis with clinical realities and constructed a nomogram including variables such as daily sleep hours, daily work hours, erectile dysfunction, anxiety, depression, years of shift work, alcohol consumption, diabetes, and hyperlipidemia, for the prospective forecasting of premature ejaculation incidents. Clinicians may utilize this nomogram to assign risk scores to patients, thereby assessing their risk of developing premature ejaculation. The model has been validated and demonstrated commendable sensitivity and specificity (with an the Curve, AUC, of 0. 855 in the training set and 0. 727 in the validation set). Calibration and decision curves further substantiate the model’s reliability (Fig. 2). Fig. 2Nomogram construction and validation for predicting the risk of premature ejaculation. a A nomogram was developed to predict the risk of premature ejaculation onset. b, c Characteristic (ROC) curves illustrate good sensitivity and specificity of the predictive model with an area under the curve (AUC) of 0. 855 for the training set and 0. 783 for the validation set. d, e Calibration plots demonstrate high predictive accuracy, with close alignment between the actual and predicted probabilities of premature ejaculation.

Output: pass


Input: Claim: AI in healthcare can help detect diseases early with image analysis. [SEP] Text: These are external root absorption, impacted tooth, periapical radiolucency (radiographic changes that are often the result of inflammation), residual root, and supernumerary tooth. They can also be considered as diagnostic markers and anatomical factors to inform a patient’s future dental work. For example, panoramic dental images can be used to evaluate the risk of nerve injury prior to an extraction. Example image of automatic anomaly detection. Anomalies situated in surrounding regions outside the dental area are seldom connected with oral health. They indicate signs of inflammatory processes and can be markers for the diagnosis of other related diseases. For instance, the thickening of the mucous membrane in the maxillary sinus can be an indicator of conditions such as apical periodontitis (painful inflammation of the tissue surrounding teeth) and alveolar bone loss that occurs in the bones that support the teeth. AI can be employed to alleviate the burden on dental clinicians, reducing the number of images that require manual examination. Detecting anomalies The detection process is divided into four steps. The images collected from the dental clinics are in DICOM format. DICOM stands for and Communications in Medicine and is an internationally accepted standard format for viewing, storing, retrieving, and sharing medical images. The first phase, the DICOM converter, involves converting these images into PNG format. Next is the disease detection stage; here, anomalies are identified using a trained Network (Faster R-CNN) model. This is an object detection model that detects boxed regions in the image where there are high possibilities of anomalies. The position verifier stage entails filtering out any boxes that are not located within a predetermined dental region. The final step is the polygon shaper stage, where a prebuilt library, Detectron2, is used to narrow down the region of abnormal signs from a box shape to a polygon. This enables dental clinicians to obtain high-resolution information regarding the location and anomaly types, as well as the precise regions that show the anomalies’ features. Training, testing, and evaluation Lee and collaborators divided the 22,999 panoramic dental images into training, validation, and test datasets. The images from July 2020 to March 2021 were used for training this deep-learning model to recognise the 17 anomalies and validate the detection. The remaining datasets were used as a test set for evaluating the model. /Shutterstock.

Output: corroborate


Input: Claim: Rooftop gardens alone can cool entire cities during heat waves. [SEP] Text: AbstractThe widespread adoption of rooftop photovoltaic solar panels in urban environments presents a promising renewable energy solution but may also have unintended consequences on urban temperatures. This is primarily due to their lower albedo, which leads to increased heat absorption and enhanced thermal convection between the panels and the underlying roof surfaces. Here we show that, in Kolkata, city-wide installation of these rooftop photovoltaic solar panels could raise daytime temperatures by up to 1. 5 °C and potentially lower nighttime temperatures by up to 0. 6 °C. Our study also reveals that rooftop photovoltaic solar panels significantly alter urban surface energy budgets, near-surface meteorological fields, urban boundary layer dynamics and sea breeze circulations. Comparative analysis with cities, such as Sydney, Austin, Athens and Brussels, supports these findings, providing valuable insights for policymakers on managing large-scale solar panel installations. Understanding these effects is crucial for balancing the benefits of renewable energy with its potential impacts on urban climates. Similar content being viewed by others On the local warming potential of urban rooftop photovoltaic solar panels in cities 4. However, the effects of city-scale deployment of RPVSPs on the urban microclimate remain uncertain. RPVSP efficiency diminishes in high ambient temperatures and humidity, raising concerns about their environmental impact and influence on the urban thermal environment4. Recent studies suggest that city-scale RPVSP deployment might either decrease or increase urban temperatures5,6,7. For example, it has been observed that the deployment of RPVSP on a building not only curtailed greenhouse gas emissions but also reduced annual cooling loads8. Similar results have been observed in simulation-based studies and in the case of medium-to-large-scale RPVSP deployment9,10,11,12. For instance, in the Paris metropolitan area during summer, RPVSPs reduced the urban heat island effect by 0. 2 °C during the day and up to 0. 3 °C at night, resulting in energy savings for air conditioning of up to 12% (ref. 9). reduced summer maximum temperatures by up to 1 °C because the need to import energy is offset by local generation10. Similarly, the cooling effect with RPVSPs in was found to be up to 0. 2 °C (ref. 11).

Output: contrast


Input: Claim: Shift work can negatively impact metabolic health. [SEP] Text: 9In order to assess detailed information on shift-work and potential confounders by interview, we restricted the final cohort to the 58,091 female members who were alive on 1st July 2001, free of breast cancer, and born between 1933 and 1970 and therefore under 70years of age at the time of interview between March 2002 and July 2005. In this restricted cohort, 310 cases of histological confirmed primary breast cancer (ICD-7: 170) were documented during the period July 2001–June 2003, 301 (97%) of which were in women who were still alive at the time of interview. The incidence density method10 was used to select randomly four live breast cancer-free controls per case from the restricted cohort, who were matched on year of birth (±1year) and interviewed in the same period as the cases. Individual information on lifetime occupational and non-occupational exposures was obtained in a structured telephone interview by trained interviewers. An invitation was sent to all study participants 1–2weeks before the potential interview, which stated that the purpose of the study was to elucidate environmental causes of breast cancer in Danish nurses, without mentioning any potential cause. In a prepaid return letter, the potential study participant could suggest an appropriate time for the interview (between 8:00 and 22:00) and give a telephone number (work or private) or refuse to participate with no further contact. If we did not receive the return letter within 2weeks, we sent a follow-up letter. If there was still no reaction, the woman’s private telephone number was retrieved from the computerised telephone directory, and an interview was suggested between 10:00 and 21:00 for potentially retired women (aged ⩾60) and between 17:00 and 21:00 for the remaining women. Up to 20 telephone calls at different times of the day were attempted during 3months before a subject was excluded (four cases and seven controls). In total, 24 cases (8%) and 104 (9%) controls refused to participate, yielding completed interviews for 267 patients and 1035 controls.

Output: pass


Input: Claim: AI in healthcare can help detect diseases early with image analysis. [SEP] Text: com The group used an intersection over union (IOU) value to evaluate their model’s performance in correctly detecting lesions. IOU is an evaluation metric that measures the accuracy of an object detector. In this case, the area identified by the dental radiography expert is compared with the box identified by the model. The extent of overlap of two boxes is calculated by dividing the area of their overlap by the area of their union. If the two shapes overlap perfectly, the IOU score is 1. The researchers ascertained that an IOU value of 0. 5 or more denotes a predicted box that has been correctly detected. The class-wise performance of the detection model was compared with the labels provided by the dental radiography expert and measured in terms of precision, sensitivity, and specificity. The precision and sensitivity scores vary according to the anomaly types, with scores that exceed or match those from previous studies. The model demonstrated very high specificity, more than 95% in most classes, showing that it can successfully detect abnormalities and filter out normal healthy images. This high specificity indicates that this artificial intelligence model can reduce the burden of examination for dentists as it successfully filters out the healthy images and allows clinicians to focus on those images with potential anomalies. Training a deep-learning model The researchers discuss how the quantity and quality of the data are key when using AI techniques. Training their deep-learning model to detect 17 types of anomalies involved accruing datasets with sufficient examples of each class of object to be detected. They created a system that collects panoramic dental images direct from the dental clinics for a year, and a dental radiography expert labelled them manually. This high-quality dataset is still accumulating and can be used for future research. The high-performing tool successfully detects anomalies and filters out healthy images. Its high sensitivity (approximately 0. 99) validates its suitability for use in real clinical practices and demonstrates how AI can be employed to alleviate the burden on dental clinicians, reducing the number of images that require manual examination. This deep-learning model encompasses a large area of panoramic radiography and will help dentists improve patients’ quality of life through early diagnosis and prevention of diseases. What motivated you to develop this deep-learning model? Many diseases can be detected in the early stages from the panoramic X-ray image, and these early detections can increase patients’ quality of life.

Output: corroborate


Input: Claim: Rooftop gardens alone can cool entire cities during heat waves. [SEP] Text: Further, in Phoenix, Tucson and lowered the 2 m air temperature compared with conventional roofs and resulted in 8–11% reduction in cooling energy demand12. However, studies suggesting an increase in urban microclimatic temperature have observed that RPVSPs on urban rooftops might elevate the temperature at building scale, causing discomfort in outdoor urban environments13,14. Similar findings appear from the simulation studies5,7,14,15. For instance, large-scale RPVSP deployment can increase outdoor temperatures up to 0. 5 °C measured by the universal thermal comfort index during heatwaves7. RPVSPs can induce a temperature rise of up to 1. 5 °C during the daytime due to less efficient heat storage and increased sensible heat flux (Qsensible) from both RPVSPs and roof surfaces15. systems can significantly elevate daytime city temperatures and marginally lower them at night, depending on the climate zone and building types14. In Sydney, city-scale RPVSP deployment at the local district scale increased daytime air temperature by 1. 5 °C while reducing it by 2. 7 °C at night5. While existing literature reports on the impact of RPVSP on the urban environment, most are based on in situ field experiments or building-scale simulations, lacking a comprehensive multicity-scale analysis. These studies also neglect convective heat transfer between the roof surface and the back of solar panels. Even most of the modeling studies with RPVSP were purely based on simulations and were not evaluated in any shape or form. Our study addresses these gaps by incorporating new parameterizations for RPVSPs, including convective heat transfer, resulting in more aligned results with other studies incorporating similar considerations. The presence of RPVSPs on urban buildings, particularly rooftops, can have complex effects on the local thermal environment. are installed on roofs, they absorb a significant amount of solar energy, converting some of it into electricity but also generating heat in the process. This heat is released into the surrounding air, leading to an increase in air temperature around the panels. Moreover, the elevated installation of RPVSP creates two hot surfaces: the top surface of the panels and the underside surface. As air flows over these RPVSPs, it picks up heat more efficiently than it would from typical building or ground surfaces.

Output: contrast


Input: Claim: Shift work can negatively impact metabolic health. [SEP] Text: The structured questionnaire included sections eliciting information on: years of schooling, occupational history (all jobs held for at least 1year, including detailed work schedules), tobacco smoking pattern (age at start of smoking, total duration, average numbers of cigarettes, cigarillos or cigars smoked per day), alcohol drinking pattern (consumption of beer, wine or spirits at 15–24, 25–49 and ⩾50years), physical activity during leisure time (average hours spent per week in light activity, e. g. housework, walking, biking, and in sport at 15–24, 25–49 and ⩾50years), and reproductive history (age at menarche and at menopause, regularity of menstruation, number of births, age at birth of first child, breastfeeding, use of contraceptives), use of hormone replacement therapy, occurrence of breast cancer in mother and/or sister, weight and height at age 20 and at the time of interview, significant weight fluctuations since age 20, hours of sleep on a normal day at the time of interview and 10years previously. Finally, in order to obtain some indication of differential recall bias, exposure to electromagnetic fields in each job was ascertained from answers to the question: ‘How many hours per week did you work less than 0. 5m from an electrical device, e. g. medical device, computer or electric machinery’2. 1 Shift-work definitionFor each job held for at least 1year, we assessed information on year or age of start and end, average number of hours worked per week, time of starting and ending on each shift in a normal week (excluding irregular overtime work). If the woman had worked in rotating shifts, we classified the job according to the most frequent occurring rotating shift system in Danish nurses: day–evening, day–night, day–evening-night or others. For most of the nurses, day work was from 6 or 7 to 15 or 16, evening work from 15 or 16 to 23 or 24 and night work from 23 or 24 to 7 or 8.

Output: pass


Input: Claim: AI in healthcare can help detect diseases early with image analysis. [SEP] Text: However, it takes too much time for dental clinicians to examine all newly taken images every day. We therefore decided to help both dental clinicians and patients using AI. By suggesting the possible regions of dental anomalies and normal images which don’t need further examination, the AI system can reduce the burden for clinicians so they can focus on their patients and treatments. Detecting dental diseases with AI dental image analysis was last modified: November 8th, 2023 Keywords: AIAI dental imagesdeep learningdental anomaliesdental diseasesdental radiographydentistsInstitute of Science and Technologyoral diseasesoral healthpanoramic X-ray imagesradiographyDownload this 5172. Behind the Lee is a PhD candidate in the Department of Bio and at Institute of Science and Technology (KAIST). Main research fields are bioinformatics and data science. of InVisionLab: Major in electrical and electronic engineering at Institute of Science and Technology (KAIST). He worked for ten years at medical service companies and became interested in the convergence of the medical industry. As a doctoral student at School of Dentistry, he studies the convergence of technology and dental medical service. Hogul Jeong, Head of research institute at InVisionLab: PhD in dentistry from College of Dentistry. With more than 20 years of experience in the dental imaging and diagnosis field, he is researching dental image diagnosis and artificial intelligence. Jaehyeong Park, Programmer at InVisionLab: Majoring in computer science and engineering at University, he works at InVisionLab as a programmer, developing front-end services and other applications. He is also a cellist at the Orchestra. Address: G114-S127, 128, Beobwon-ro, Songpa-gu, Seoul, Republic of Korea, 05854 E: [email protected] T: +82 10 6606 8885 Lee and his collaborators have developed a deep-learning model that can detect dental diseases from radiography images. Funding InVisionLab is a company developing automatic analysis and diagnosis of clinical dental images like panoramic X-ray, CBCT image diagnosis, and cephalometric images. Cite this 10. 32907/RO-130-2773295876. (2022). Detecting dental diseases with AI dental image analysis. Research Outreach, 130. Available at: This work is licensed under a Attribution-NonCommercial-NoDerivatives 4.

Output: corroborate


Input: Claim: Rooftop gardens alone can cool entire cities during heat waves. [SEP] Text: Observational studies in the literature have shown that areas with RPVSP arrays can experience higher daytime air temperatures compared with reference sites without RPVSP. However, the effect on nighttime temperatures is generally minimal. Given these conflicting studies, it is imperative to comprehensively analyze the impact of city-scale RPVSP deployment in diverse cities globally. Our study focuses on the advanced capabilities of the latest weather research and forecasting (WRF) model integrated with multilayer urban schemes, such as a building energy model (BEM) coupled with building effect parameterization (BEP), that is, WRF/BEP + BEM, especially in tropical cities. This model accurately captures the energy balance of RPVSPs, an aspect overlooked in previous studies. To address this concern, we have validated the WRF/BEP + BEM-simulated RPVSP surface temperature against an experimentally validated model. at a city scale is crucial for sustainable energy solutions as it assesses broader impacts on urban microclimates and energy consumption patterns. Through city-wide implementation scenarios and simulation studies, we are suggesting improvements to RPVSP efficiency, which can lead to a decrease in convective heat generation. This is crucial because the efficiency of RPVSPs decreases by approximately 0. 5–0. 8% for each 1 °C rise above standard test conditions (25 °C)16. Our comprehensive assessment includes parameters such as surface energy budgets, near-surface meteorological fields, boundary layer dynamics and sea breeze circulations. The findings can inform policy discussions and lead to redesigning policies for RPVSP implementation in cities. By addressing overheating issues and advocating for advanced RPVSP technologies and efficient cooling strategies, we aim to promote sustainable urban development practices and enhance the understanding of RPVSP deployment’s broader impacts. ResultsRegional impacts on urban surface energy budgetsCity-scale deployment of RPVSPs led to changes in the Qsensible from urban surfaces. At 14:00 LT, Qsensible peaked at 415. 9 W m−2 for RPVSPs 25%, 469. 2 W m−2 for RPVSPs 50%, 533. 9 W m−2 for RPVSPs 75% and 595. 2 W m−2 for RPVSPs 100%. Similarly, at 18:00 LT, the average Qsensible values were 245.

Output: contrast


Input: Claim: Shift work can negatively impact metabolic health. [SEP] Text: We used two series of partly overlapping variables to characterise the shift-work in each job held: type of shift (permanent day, rotating day–evening, day–night, day–evening–night, other rotating and permanent night), with calculated lifetime duration of each shift system weighted by hours worked per week; and number of shifts after midnight per month (‘graveyard shifts’, beginning after 19 and ending before 9), with calculated lifetime cumulative numbers of such shifts based on the duration and number per month, including tertiles based on the distribution in exposed controls. If subjects reported years of after midnight shifts, but were unable to provide numbers of such shifts per month (4. 7% of cases and 4. 8% of controls), we imputed this information by the average number for cases and controls, respectively. Overall, this changed final results only marginally. The date of birth of each child was obtained for each woman from the Registry in order to verify the self-reported numbers. 2. 2 Statistical analysesWe estimated relative risks as odds ratios (ORs) with 95% confidence intervals (CIs) by multiple logistic regression analyses. All the models were conditional on 5-year age groups. Univariate associations between breast cancer and each of the above-mentioned potential confounders were evaluated and included in the final models with the shift-work variables, for adjusted ORs, if the p value was ⩽0. 15 (Pearson chi-square test for categorical variables and t test for continuous variables) or if inclusion of a covariate changed the odds ratio in the model with shift-work by ⩾15%. The final models included, in addition to the shift-work variables, breast cancer in mother or sister, use of hormone replacement therapy (never, <5, ⩾5years), lifetime cumulative months of breastfeeding, age at first child birth (nulliparous, <25, 25–29, 25–30, ⩾35years), age at menarche (continuous), menstrual regularity, weight fluctuations and menstrual status. The reported significance levels are two-sided. 3 ResultsThe characteristics of the control women by after-midnight shift status are shown in Table 1.

Output: pass


Input: Claim: AI in healthcare can help detect diseases early with image analysis. [SEP] Text: ViewpointScott1, Thompson2, 1Centre for Cancer Screening, Prevention and Early Diagnosis, of Population Health, University of London, London, United Kingdom2Department of Family Medicine, University of :for Cancer Screening, Prevention and Early Diagnosisof Population HealthUniversity of 1M 6BQUnited KingdomPhone: 44 2078823550Email: suzanne. scott@qmul. ac. ukAbstractThis viewpoint paper considers the authors’ perspectives on the potential role of smartphones, wearables, and other technologies in the diagnosis of cancer. We believe that these technologies could be valuable additions in the pursuit of early cancer diagnosis, as they offer solutions to the timely detection of signals or symptoms and monitoring of subtle changes in behavior that may otherwise be missed. In addition to signal detection, technologies could assist symptom interpretation and guide and facilitate access to health care. This paper aims to provide an overview of the scientific rationale as to why these technologies could be valuable for early cancer detection, as well as outline the next steps for research and development to drive investigation into the potential for smartphones and wearables in this context and optimize implementation. We draw attention to potential barriers to successful implementation, including the difficulty of the development of signals and sensors with sufficient utility and accuracy through robust research with the target group. There are regulatory challenges; the potential for innovations to exacerbate inequalities; and questions surrounding acceptability, uptake, and correct use by the intended target group and health care practitioners. Finally, there is potential for unintended consequences on individuals and health care services including unnecessary anxiety, increased symptom burden, overinvestigation, and inappropriate use of health care resources. JMIR Cancer 2024;10:e52577doi:10. 2196/52577Keywordswearables; early diagnosis; cancer; challenges; diagnosis; wearable; detect; detection; smartphone; cancer diagnosis; symptoms; monitoring; monitor; implementation; anxiety; health care service; mobile phone IntroductionThere is growing use of smartphones, wearables, and other technologies in health and wellness, either as consumer products or medical devices. (NHS) Plan [1] anticipates that in 10 years, people will have “the option for their physiology to be effortlessly monitored by wearable devices. People will be helped to stay well, to recognize important symptoms early, and to manage their own health, guided by digital tools.

Output: corroborate


Input: Claim: Rooftop gardens alone can cool entire cities during heat waves. [SEP] Text: 6 W m−2, 271 W m−2, 285. 4 W m−2 and 306. 6 W m−2 for RPVSPs 25%, RPVSPs 50%, RPVSPs 75% and RPVSPs 100%, respectively. This significant rise was primarily attributed to RPVSPs’ low albedo value, leading to amplified solar energy absorption, predominantly emitted as sensible heat rather than converted into usable electricity. During the early morning and nighttime, minor fluctuations in Qsensible were observed, revealing the intricate interplay of RPVSPs with the urban heat environment. Furthermore, the latent heat flux (Qlatent) from the urban surface exhibited a corresponding increase owing to the energy mechanisms of the RPVSPs array. At 14:00 LT, the peak Qlatent reached 100 W m−2 for RPVSPs 25%, 107. 8 W m−2 for RPVSPs 50%, 113 W m−2 for RPVSPs 75% and 121. 5 W m−2 for RPVSPs 100%. Likewise, at 18:00 LT, the average Qlatent ranged from 66. 3 W m−2 to 80. 3 W m−2 for RPVSPs 25% to RPVSPs 100%, highlighting the increased Qlatent resulting from RPVSP installation. These alterations were associated with a marginal temperature rise surrounding the RPVSPs, potentially influencing evaporation and significantly contributing to the Qlatent of urban areas. Moreover, the installation of RPVSPs over building roof surfaces led to augmented ground heat storage (Qstorage) on urban surfaces. In the early morning, the average Qstorage ranged from 53. 5 W m−2 to 56. 6 W m−2 for RPVSPs 25% to RPVSPs 100%, respectively. The maximum Qstorage during peak hours ranged from −270. 2 W m−2 to −310. 8 W m−2 for RPVSPs 25% to RPVSPs 100%, with the maximum increase during this time ranging from 18. 9 W m−2 to 59. 5 W m−2. The convention taken is that the surface energy fluxes are counted positive when they are upward (relative to the surface). Diurnal changes in Qstorage showed an average increase during the daytime, ranging from 9. 9 W m−2 to 32.

Output: contrast


Input: Claim: Shift work can negatively impact metabolic health. [SEP] Text: Nurses who worked on after-midnight ‘graveyard’ shifts, in comparison with nurses on day shifts, had a significantly longer working life, were younger at menarche and menopause, had fewer children, were older at the birth of their first child, spent fewer hours weekly on sport, had slept fewer hours per night 10years before the interview, and were more frequently exposed to electromagnetic fields. No other significant differences were found. Table 2 shows the lifetime work schedule characteristics of cases and controls. The median length of work was 25. 8years for cases and 24. 1years for controls. Permanent day work constituted on average over half of the working time, and about 7% of cases and 5% of controls had ever had permanent night shifts for at least 1year. Day–evening–night shift-work, the most frequent rotating shift system, had been experienced by 81% of controls and 71% of cases, constituting 32% of the work time in cases and 31% in controls. Rotating day–evening, day–night, and other shifts constituted only 13% of the total work time in both cases and controls. No cases or controls had permanent night work for the entire work history. CharacteristicAfter-midnight shiftap ValuebNever (<1year) (n=252)Ever (⩾1year) (n=783)n%n%Age (years)<4025105470. 0940–442610841145–4943171401850–5437151772355–5950201321760–64371511615⩾6534138010 Length of work (cumulative years)1–513551<0. 0015–10341435510–1531121061415–2029121171520–2546181441825–30461812416⩾30532125231 Marital statusMarried19477563720. 21Unmarried1568110Divorced2498711Widow208506 Length of schooling (years)726106580. 548–10973833343⩾101295138449 Body mass index mean, kg/m2 (SD)At time of interview25221. 3 (3. 3)78321. 3 (2. 5)0. 99At age 20years25123.

Output: pass


Input: Claim: AI in healthcare can help detect diseases early with image analysis. [SEP] Text: ” Similarly, in 2020, the US Food and (FDA) launched a Plan to encourage digital health innovation as “digital health technologies can empower consumers to make better-informed decisions about their own health and provide new options for facilitating prevention, early diagnosis of life-threatening diseases, and management of chronic conditions outside of traditional care settings” [2]. Wearables are devices that can be worn to detect and monitor biometric data such as heart rate, blood oxygen saturation, sleep pattern, or temperature while the wearer continues their normal routines. A further category of wearables involves skin patches used to measure biochemical signals (ie, glucose) on a continuous basis that are increasingly being considered as a standard of care for individuals with certain conditions (eg, diabetes) [3]. While most wearables have been wrist-worn devices, similar physiologic signals are now being generated from other devices such as rings or earbuds. Smartphones are also increasingly being used for health and wellness; they have the advantage of far higher use (compared with wearables), and a growing array of different sensors are routinely embedded. Could smartphones and wearables help detect cancer and, importantly, detect cancer earlier in its disease course when it is more likely to be localized and with a better prognosis? This paper provides an overview of the scientific rationale as to why these technologies could be valuable for early detection of cancer, the potential barriers to successful implementation, and the next steps for research and development. Potential of Smartphones and Wearables for of CancerWhile national cancer screening programs offer the opportunity to detect cancer or precancerous lesions in asymptomatic individuals, routine screening currently only accounts for the minority (<10%) of cancer diagnoses [4,5]. The predominant route to a cancer diagnosis is symptomatic presentation to ambulatory care. Thus, the diagnosis of cancer heavily relies on patients’ ability to notice and attend to relevant bodily changes and their decision to consult a health care professional [6,7]. However, noticing relevant bodily changes is challenging given the multiple subtle changes that may signal cancer among the plethora of daily bodily changes; fluctuations of normal bodily processes; self-limiting, transient symptoms; and the presence of chronic conditions. The signal-to-noise ratio is weak. This issue is exacerbated by individuals’ limited ability to accurately interpret vague bodily changes, many of which can be associated with cancer (eg, fatigue, weight loss, and stomach upset).

Output: corroborate


Input: Claim: Rooftop gardens alone can cool entire cities during heat waves. [SEP] Text: 8 W m−2 for RPVSPs 25% to RPVSPs 100%, and during nighttime, the average increase ranged from 4. 9 W m−2 to 10. 4 W m−2 for all RPVSP scenarios. This is due to the additional radiation and heat transfer between the RPVSP and roof surface beneath the panel. Additionally, the widespread deployment of RPVSPs at a city scale altered the surface energy balance on the urban surface by transferring heat from RPVSPs to the urban atmosphere, as evidenced by changes in net radiation (Qnet) (Fig. 1). This describes the total radiation emitted to the atmosphere, including the radiation (longwave) from RPVSPs to the urban atmosphere. Early morning average Qnet ranged from −29. 2 W m−2 to −8. 1 W m−2 for RPVSPs 25% to RPVSPs 100%, while the peak Qnet varied from 786. 1 W m−2 to 1,027. 5 W m−2. This enhanced Qnet during peak hours ranged from 94 W m−2 to 335. 4 W m−2 over the urban surface. Furthermore, the daytime maximum increase of Qnet ranged from 51. 9 W m−2 to 193. 5 W m−2, with a slight nighttime increase ranging from 10 W m−2 to 31. 9 W m−2 for all RPVSP scenarios. These findings underscored the multifaceted impact of RPVSPs on the surface energy balance of urban areas. Fig. 1: Changes in simulated Qnet. a–d, The changes (building rooftops with RPVSPs scenarios minus building rooftops without RPVSPs scenarios) in simulated Qnet during daytime (at 14:00 LT, top) and nighttime (at 02:00 LT, bottom) for RPVSPs 25% (a), RPVSPs 50% (b), RPVSPs 75% (c) and RPVSPs 100% (d) over an urban grid cell. The urban area of Kolkata is marked by the black line inside the box. CTRL refers to control case.

Output: contrast


Input: Claim: Shift work can negatively impact metabolic health. [SEP] Text: 9 (4. 7)78224. 1 (3. 5)0. 58 Significant weight fluctations since age of 20yearsNo18473537690. 08Yes672624631 Age at menarche (years)⩽126024158200. 031260242673413642516922⩾14672718524 Menstrual regularityYes22489719920. 15No2811648 Menopausal status at diagnosisPre11244361460. 22Post1405642254Age at menopause (years±SD)16650. 2 (6. 5)50348. 9 (5. 2)0. 03No menopause8634280360. 11<408339540–4423960845–4968272112750–54441815620⩾55239375 No. of children026108911<0. 00112081121428433329423922719425⩾33112597Age at birth of first child (years±SD)22724. 9 (3. 7)69426. 6 (4. 2)<0. 001Cumulative length of breastfeeding (months±SD)22715. 4 (13. 9)69413. 9 (13. 0)0. 15 Use of oral contraceptivesNever6626233300. 12Yes1857355070Mean duration (years±SD)1846. 8 (6. 1)5466. 2 (6. 8)0. 32 Use of hormone replacement therapyNever17067491630. 35Yes813229037Mean duration (years±SD)766. 1 (6. 2)2736. 4 (5. 9)0. 75 Breast cancer in mother or sisterNo23192727930. 50Yes177506 Physical activity at work (weighted average over time)Light291277100. 46Some562219625Heavy1054234644Very heavy622516421Sport activities, weighted weekly average hours (SD)2204. 2 (5. 3)7422. 8 (3. 1)<0. 001 Sleep during average day off 10years before interview (h)<6734960.

Output: pass


Input: Claim: AI in healthcare can help detect diseases early with image analysis. [SEP] Text: This is because our awareness, attention, and interpretation are affected by expectations; emotions; beliefs; and biological, environmental, sociodemographic, and contextual factors [8-11]. Furthermore, symptoms may evolve very slowly over time, making it difficult to notice subtle changes. It is reported that the predominant risk factor for delay in seeking help following the detection of cancer symptoms is the “lack of interpretation by patients of the serious nature of their symptoms” [12]. Smartphones and wearable technologies have the potential to facilitate the detection and tracking of bodily changes that might otherwise be dismissed or interpreted as only needing self-medication rather than the attention of a health care professional. There is emerging data about early, subtle signs of cancer, and some of these may be amenable to detection by electronic sensors and monitoring of behavior (see Table 1). Table 1. Potential signals of cancer that can be measured using sensors in smartphones or wearables. Sensors currently available on some smartphones or wearablesExamples of signals for health features that could be related to cancerAudio signals from microphones [13]Changes in cough and breathing difficulty (associated with lung cancer) [14]Changes in voice such as hoarseness (associated with head and neck cancer and lung cancer) [14]GPS location tracking and activity tracking [15]Reduced activity resulting from fatigue (associated with multiple cancers) [14]Image capture and analysis [16-18]Anemia detected from images of the skin or eyes (associated with multiple cancers) [19]Jaundice detected from images of the skin or eyes (associated with pancreatic cancer) [20]Changes in skin lesions (associated with skin cancer) [14]Temperature measurement [21]Rise in temperature (associated with pancreatic cancer) [22]Body composition using image analysis and electro dermal activity [23]Weight loss (associated with multiple cancers) [14]Photoplethysmogram [24]Anemia (associated with multiple cancers) [19]Sensors could allow the detection of changes prior to them being noticed or interpreted as symptoms, for example, a reduction in activity prior to fatigue or changes in food consumption prior to weight loss. There is recent evidence that monitoring day-to-day purchases could detect an increase in over-the-counter pain and indigestion medication 8 months prior to ovarian cancer diagnosis [25].

Output: corroborate


Input: Claim: Rooftop gardens alone can cool entire cities during heat waves. [SEP] Text: The increase in the 2 m Tambient and Tsurface during the daytime can be attributed to the heat absorption capacity of the RPVSPs and the subsequent release of stored heat during the nighttime, leading to a decrease in surface temperatures. The changes in Hrelative were influenced by alterations in atmospheric stability, induced by the modified energy balance due to RPVSP deployment. Furthermore, the shifts in Wspeed were influenced by the changes in the surface temperature and subsequent alterations in boundary layer mixing induced by RPVSP deployment. These findings stress the importance of evaluating RPVSPs’ impact on urban microclimates, especially in hot and humid regions. It highlights the need for effective mitigation strategies and emphasizes the significance of RPVSPs’ thermal properties in future urban planning and climate-sensitive designs. Fig. 2: Changes in simulated Tambient. a–d, The changes (building rooftops with RPVSPs scenarios minus building rooftops without RPVSPs scenarios) in simulated 2 m Tambient during daytime (at 14:00 LT, top) and nighttime (at 02:00 LT, bottom) for RPVSPs 25% (a), RPVSPs 50% (b), RPVSPs 75% (c) and RPVSPs 100% (d) over an urban grid cell. The urban area of Kolkata is marked by the black line inside the box. Full size imageFig. 3: Changes in simulated Tsurface. a–d, The changes (building rooftops with RPVSPs scenarios minus building rooftops without RPVSPs scenarios) in simulated Tsurface during daytime (at 14:00 LT, top) and nighttime (at 02:00 LT, bottom) for RPVSPs 25% (a), RPVSPs 50% (b), RPVSPs 75% (c) and RPVSPs 100% (d) over an urban grid cell. The urban area of Kolkata is marked by the black line inside the box. Full size imageRegional impacts on urban boundary layer dynamicsThe combined effects of rapid urbanization and regional climate change-induced warming are expected to intensify heatwave events, impacting the planetary boundary layer (PBlayer) height in cities. The simulation demonstrates that widespread RPVSP deployment alters the lower atmospheric PBlayer structure over urbanized areas, such as Kolkata, especially during daytime (Supplementary Fig.

Output: contrast


Input: Claim: Shift work can negatively impact metabolic health. [SEP] Text: Biological clocks and shift work: circadian dysregulation and potential long-term effectsControl. 2006; 17:489-500CrossrefScopus (315)PubMed3. Stevens, R. G. ∙ Hansen, J. ∙ Costa, G. . . . Considerations of circadian impact for defining ‘shift work’ in cancer studies:. 2001; 68:154-162CrossrefScopus (324)4. Straif, K. ∙ Baan, R. ∙ Grosse, Y. . . . Carcinogenicity of shiftwork, painting, and firefightingLancet Oncol. 2007; 8:1065-1066Full Text(PDF)PubMed5. Hansen, J. Light at night, shiftwork, and breast cancer riskJ Inst. 2001; 93:1513-1515CrossrefScopus (141)PubMed6. Costa, G. ∙ Haus, E. ∙ Stevens, R. Shift work and cancer – considerations on rationale, mechanisms, and epidemiologyHealth. 2010; 36:163-179CrossrefScopus (208)PubMed7. Kjaer, T. K. ∙ Hansen, J. Cancer incidence among a large cohort of female Danish registered nursesHealth. 2009; 35:446-453CrossrefScopus (27)PubMed8. Pedersen, C. B. systemSuppl. 2011; 39:22-25CrossrefScopus (3295)9. Gjerstorff, M. L. . 2011; 39:42-45CrossrefScopus (1078)10. Richardson, D. B. An incidence density sampling program for nested case–control analysesMed. 2004; 61:e59CrossrefScopus (186)PubMed11. Stevens, R. G. Electric light causes cancer? Surely you’re joking, Mr. StevensMutat Res. 2009; 682:1-6CrossrefScopus (35)PubMed12. Gooley, J. J. ∙ Chamberlain, K. ∙ Smith, K. A. . . . Exposure to room light before bedtime suppresses melatonin onset and shortens melatonin duration in humansJ Metab. 2011; 96:E463-E472CrossrefScopus (367)PubMed13.

Output: pass


Input: Claim: AI in healthcare can help detect diseases early with image analysis. [SEP] Text: This demonstrates how tracking and monitoring change over time could allow insight into emerging disease. This is particularly useful for clinicians working in health care settings with limited time and resources and where cancer is a relatively rare occurrence among the burden of other diseases. In addition to the detection of signals, smartphones and wearables could alert the user to the need for health care consultation and provide an endorsement to seek care. This could overcome the commonly reported barrier to presentation (“concern about bothering the doctor”) that arises when there is uncertainty about the need for care [26-28]. to Successful ImplementationOverviewDespite the promise of smartphones and wearables for early detection of cancer, there are several hurdles to implementation that require attention. Key barriers to success are outlined here, alongside suggestions for how these may be addressed with future research. Signals and Accuracy and UtilityThe use and adoption of smartphones and wearables in this context require robust research into the selection of a signal, the development of sensors, and the generating evidence of accuracy and utility of those sensors in the real world. This includes identifying physiologic (or emerging pathophysiologic) signals that are most predictive of cancer, determining how often these need to be collected, and elucidating what other data would add precision to results (eg, age, risk factors, and presence of symptoms). For technology developers, sensors are usually designed and prioritized for a number of potential applications, mainly targeting overall health and wellness rather than diagnostic capabilities per se. Prioritizing these research and development efforts for cancer detection specifically over and above other priorities could be challenging to justify for business development reasons. Relatedly, the original intended commercial purpose of existing sensors may not have been connected to cancer detection. To make headway in this field of research, technology developers and device users will need to be willing to provide access to data for research. allows device users to share their data with third-party organizations under the right to portability. Developing systems to facilitate data sharing, in formats compatible with health data, could allow the generation of new data sets to signal cancer risk. This will prevent duplication of effort and maximize the use of existing data for public benefit. While initial evidence on the accuracy of a sensor to detect a given health signal could involve case-control studies (eg, individuals recently diagnosed with cancer and matched controls), subsequent research would likely require large prospective cohorts.

Output: corroborate


Input: Claim: Rooftop gardens alone can cool entire cities during heat waves. [SEP] Text: 3). Generally, surface heating significantly influences PBlayer dynamics, playing a key role in shaping the PBlayer state in cities17,18. The changes in PBlayer depth by the adoption of RPVSPs are seen in the Kolkata urban region during the heatwave period. It is evident that the city-scale deployment of RPVSPs increases the PBlayer height over the urban region and the maximum increase (~415 m to ~615. 6 m) was simulated by the most aggressive scenario (that is, RPVSPs 100%). During the heatwave period, the resulting increases in the convective PBlayer height indicate that the lower atmosphere has a great potential to mix air pollutants vertically, with possible consequences for air quality. The diurnal variability of PBlayer depth was simulated with the effects of the implementation of RPVSP technology at the city scale. The height of PBlayer depth increases significantly by city-scale deployment of RPVSP technology. The maximum increase of PBlayer height at 14:00 LT is about 165. 3 m for RPVSPs 25%, 354. 4 m for RPVSPs 50%, 456. 4 m for RPVSPs 75% and 615. 6 m for RPVSPs 100%. In the late afternoon (18:00 LT), the average increase of PBlayer depth over urban regions is about 126. 4 m, 235. 5 m, 328. 6 m and 415. 6 m for RPVSPs 25%, RPVSPs 50%, RPVSPs 75% and RPVSPs 100%, respectively. In the early morning, the PBlayer depth increased significantly over the urban region and the values are 105. 4 m for RPVSPs 25%, 220. 3 m for RPVSPs 50%, 310. 1 m for RPVSPs 75% and 356. 4 m for RPVSPs 100%. City-scale deployment of RPVSPs absorb solar energy, increasing Qsensible and turbulence, which raises the urban surface temperature and deepens the PBlayer in urban areas. The impact of RPVSPs on boundary layer structure is demonstrated through the simulated vertical profile of temperature, Hrelative and horizontal Wspeed was investigated during peak heatwave events (Fig. 4). RPVSPs intensify urban warming and convective mixing, potentially aiding air pollution dispersion.

Output: contrast


Input: Claim: Shift work can negatively impact metabolic health. [SEP] Text: Folkard, S. Do permanent night workers show circadian adjustment? A review based on the endogenous melatonin rhythmChronobiol Int. 2008; 25:215-224CrossrefScopus (259)PubMed14. Akerstedt, T. Shift work and disturbed sleep/wakefulness(Lond). 2003; 53:89-94CrossrefScopus (818)PubMed15. Baker, F. C. ∙ Driver, H. S. Circadian rhythms, sleep, and the menstrual cycleSleep Med. 2007; 8:613-622Full Text(PDF)Scopus (330)PubMed16. Hundrup, Y. A. ∙ Jacobsen, R. K. ∙ Andreasen, A. H. . . . Validation of a 5-year risk score of hip fracture in postmenopausal women. . 2010; 16:1-1017. Hundrup, Y. A. ∙ Hoidrup, S. ∙ Obel, E. B. . . . The validity of self-reported fractures among Danish female nurses: comparison with fractures registered in the. 2004; 32:136-143CrossrefScopus (67)PubMed18. Lokkegaard, E. L. ∙ Johnsen, S. P. ∙ Heitmann, B. L. . . . The validity of self-reported use of hormone replacement therapy among Danish nursesGynecol Scand. 2004; 83:476-481CrossrefPubMed19. Chen, C. ∙ Ma, X. ∙ Zhong, M. . . . Extremely low-frequency electromagnetic fields exposure and female breast cancer risk: a meta-analysis based on 24, 338 cases and 60, 628 controlsRes Treat. 2010; 123:569-576CrossrefScopus (40)PubMed20. Davis, S. ∙ Mirick, D. K. ∙ Stevens, R. G. Night shift work, light at night, and risk of breast cancerJ Inst. 2001; 93:1557-1562CrossrefScopus (772)PubMed21. Hansen, J. Increased breast cancer risk among women who work predominantly at nightEpidemiology. 2001; 12:74-77CrossrefScopus (420)PubMed22. Schernhammer, E. S.

Output: pass


Input: Claim: AI in healthcare can help detect diseases early with image analysis. [SEP] Text: The detection of cancer remains one of the most prominent priorities of many health systems, governments, and private and public research funders [63,64], and “leaving no stone unturned” in technologies that could potentially improve early detection is a priority. The rapid advances in the hardware (ie, sensors) and software embedded in smartphones and wearables offer exciting and potentially untapped opportunities to detect early warning signs of cancer that may otherwise be missed. The research and development needed to advance this field include the selection of appropriate signals and development of effective sensors followed by robust clinical research into accuracy in real-world settings. This relies on the up-front specification of the target groups and their needs. Target product profiles should be developed specifically for cancer detection technologies, and innovators should consult these and consider regulatory challenges early in the process of development, to design products in line with the requirements of individuals, clinicians, and health care systems. The potential negative consequences of this type of technology should be acknowledged and investigated up-front, and mitigations should be incorporated into the design and implementation strategies. To avoid exacerbation of inequalities in cancer outcomes, research into the use of wearables and smartphone technologies in cancer detection should be done with an equity lens to ensure that products are developed for those who have poorer health outcomes, for whom new innovations could have the most impact. There is a need for research to explore the patient, public, and health care perspectives about the use of smartphones and wearables for the early detection of cancer while this field is in its infancy, so that these can be incorporated into product design to optimize acceptability and adherence, avoid unintended consequences, and maximize the chance of their success. 2019. URL: [accessed 2024-04-25] Digital health innovation action plan. U. S. Food and Drug Administration. 2020. URL: [accessed 2024-04-25] Domingo-'Sullivan J, et al. Medical devices, smart drug delivery, wearables and technology for the treatment of diabetes mellitus. Deliv Rev. 2022;185:114280. [FREE Full text] [CrossRef] [Medline]Elliss- Routes to diagnosis for cancer—determining the patient journey using multiple routine data sets. Cancer. 2012;107(8):1220-1226. [FREE Full text] [CrossRef] [Medline]NHS Digital. 2023.

Output: corroborate