***TASK*** the task is to summarize an input biomedical literature in six sentences 
    ***INPUT*** the input is a biomedical literature 
    ***OUTPUT*** the output is the summary of an input biomedical literature in six sentences 
    ***DOCUMENTATION*** 
    ***EXAMPLES*** 
    Input: ABSTRACT Although the high mortality rate of pulmonary invasive aspergillosis ( IA ) in patients with prolonged chemotherapy-induced neutropenia ( PCIN ) can be reduced by timely diagnosis , a diagnostic test that reliably detects IA at an early stage is lacking . We hypothesized that an electronic nose ( eNose ) could fulfill this need . An eNose can discriminate various lung diseases through the analysis of exhaled volatile organic compounds ( VOCs ) . An eNose is cheap and noninvasive and yields results within minutes . In a single-center prospect i ve cohort study , we included patients who were treated with chemotherapy expected to result in PCIN .  Based on st and ardized indications , a full diagnostic workup was performed to confirm invasive aspergillosis or to rule it out . Patients with no aspergillosis were considered controls , and patients with probable or proven aspergillosis were considered index cases .  Exhaled breath was examined with a Cyranose 320 ( Smith Detections , Pasadena , CA ) . The result ing data were analyzed using principal component reduction . The primary endpoint was cross-vali date d diagnostic accuracy , defined as the percentage of patients correctly classified using the leave-one-out method . Accuracy was vali date d by 100,000 r and om classifications . We included 46 subjects who underwent 16 diagnostic workups , result ing in 6 cases and 5 controls . The cross-vali date d accuracy of the eNose in diagnosing IA was 90.9 % ( P = 0.022 ; sensitivity , 100 % ; specificity , 83.3 % ) . Receiver operating characteristic analysis showed an area under the curve of 0.93 . These preliminary data indicate that PCIN patients with IA have a distinct exhaled VOC profile that can be detected with eNose technology . The diagnostic accuracy of the eNose for invasive aspergillosis warrants validation Respiratory infections , viral or bacterial , are a common cause of acute exacerbations of chronic obstructive pulmonary disease ( AE COPD ) . A rapid , point-of-care , and easy-to-use tool distinguishing viral and bacterial from other causes would be valuable in routine clinical care . An electronic nose ( e-nose ) could fit this profile but has never been tested in this setting before . In a single-center registered trial ( NTR 4601 ) patients admitted with AE COPD were tested with the Aeonose ( ® ) electronic nose , and a diagnosis of viral or bacterial infection was obtained by bacterial culture on sputa and viral PCR on nose swabs . A neural network with leave-10%-out cross-validation was used to assess the e-nose data . Forty three patients were included . In the bacterial infection model , 22 positive cases were tested versus the negatives ; and similarly 18 positive cases were tested in the viral infection model . The Aeonose was able to distinguish between COPD -subjects suffering from a viral infection and COPD patients without infection , showing an area under the curve ( AUC ) of 0.74 . Similarly , for bacterial infections , an AUC of 0.72 was obtained . The Aeonose e-nose yields promising results in ' smelling ' the presence or absence of a viral or bacterial respiratory infection during an acute exacerbation of COPD . Validation of these results using a new and large cohort is required before introduction into clinical practice Objectives : Several studies associated altered gut microbiota composition in preterm infants with late-onset sepsis ( LOS ) , up to days before clinical onset of sepsis . Microbiota analysis as early diagnostic biomarker is , however , in clinical practice currently not feasible because of logistic aspects and high costs . Therefore , we hypothesized that analysis of  fecal volatile organic compounds ( VOCs ) may serve as noninvasive biomarker to predict LOS at a pre clinical stage , because VOC reflect the composition and activity of intestinal microbial communities . Methods : In a prospect i ve multicenter study , fecal sample s were collected daily from infants with a gestational age of < 30 weeks .  VOC signatures of fecal sample s from infants with LOS , collected up to 5 days before diagnosis , were analyzed by means of an electronic nose technology ( Cyranose 320 ) and compared to matched controls . Results : Fecal VOC profiles of infants with LOS ( n = 36 ) could be discriminated from controls ( n = 40 ) at 3 days ( area under the curve [ ±95 % confidence interval ] , P value , sensitivity , specificity ; 70.2 [ 52.2–88.3 ] , 0.033 , 57.1 % , 61.5 % ) , 2 days ( 77.7 [ 62.7–92.7 ] , 0.050 , 75.0 % , 70.8 % ) , and 1 day ( 70.4 [ 49.6–91.3 ] , 0.037 , 64.3 % , 64.3 % ) before the onset of LOS . Conclusions : Fecal VOC profiles of preterm infants with LOS could be discriminated from matched controls , up to 3 days before clinical onset of the disease , underlining the hypothesis that intestinal microbiota may play an etiological role in LOS . Notably , VOC profiling is clinical ly feasible and the potential of this technique in the early detection of LOS needs to be confirmed in future studies Gastro-oesophageal reflux disease ( GORD ) has been implicated in the worsening of several respiratory disorders . Current methods of diagnosis lack accuracy , are invasive and can be costly . Recently , novel methods of analysing lung pathophysiology have been developed including the use of an electronic nose and analysis of components of exhaled breath condensate ( EBC ) . We hypothesised that these methods would distinguish patients with GORD from those without GORD in the common obstructive lung diseases and healthy controls . In a cross-sectional study , exhaled breath was analysed using the Cyranose 320 electronic nose , using principal components and canonical discriminant analyses .  EBC pH and pepsin were quantified using a pH meter and an enzyme-linked immunosorbent assay , respectively . A st and ardized reflux disease question naire ( RDQ ) was used to assess reflux symptoms . The Cyranose 320 distinguished exhaled breath profiles of obstructive lung disease patients without GORD from obstructive lung disease patients with GORD ( p = 0.023 , accuracy 67.6 % ) , asthmatic patients with reflux from asthmatics without GORD ( 85 % , p = < 0.015 , interclass M distance > 2.8 ) , but did not produce as robust a profile for patients with COPD and COPD with GORD ( p = 0.047 , accuracy 64 % ) . Patients with obstructive lung disease and GORD had significantly higher levels of EBC pepsin ( 9.81 ± interquartile range ( IQR ) 4.38 ng ml(-1 ) ) than those without GORD ( 4.6 ± IQR 6.95 ng ml(-1 ) ) , as well as healthy controls ( 3.44 ± IQR 7.87 ng ml(-1 ) ; p = < 0.013 ) . EBC pH was not significantly related to the presence of GORD in any group . The RDQ results correlated significantly with the presence of EBC pepsin . This pilot study has shown that exhaled breath profiling can be used for detecting GORD in obstructive lung diseases . While the electronic nose was useful in asthma , EBC pepsin was more helpful in COPD . In this study , several different confounders could potentially have affected results and larger prospect i ve interventional studies are needed Objectives : Ventilator-associated pneumonia ( VAP ) is a frequent complication in patients in surgical intensive care units . Pneumonia scores , chest radiography , and bronchoscopy are all employed , but there is no gold st and ard test for the diagnosis of VAP . The electronic nose , a sensor of volatile molecules , is well suited to testing the breath of mechanically ventilated patients . Our objective was to determine the potential use of an electronic nose as a diagnostic adjunct in the detection of VAP . Methods : We performed a prospect i ve study of mechanically ventilated patients in a surgical intensive care unit . Clinical data , including temperature , white blood cell count , character and quantity of tracheal secretions , ratio of partial pressure of arterial oxygen to fraction of inspired oxygen , and chest radiographs , were collected , and a pneumonia score between 0 and 10 was calculated . Exhaled gas was sample d from the expiratory limb of the ventilator circuit . The gases were assayed with a commercially available electronic nose . Multidimensional data reduction analysis was used to analyze the results . Results : Forty-four patients were studied . Fifteen patients had pneumonia scores of 7 or greater , and 29 patients had scores of 6 or less . With Fisher discriminant analysis and K — nearest neighbor analysis , the electronic nose was able to discriminate between the two groups . Conclusions : The electronic nose is a new technology that is inexpensive , noninvasive , and portable . We demonstrate its ability to predict pneumonia , based on a well-recognized scoring system . This technology promises to serve as a diagnostic adjunct in the management of VAP The human body odor plays an important role in social communication in various situations , like the olfactory identification of partners and relatives as well as in parents – child interactions . In patients with renal dysfunction the compound of sweat and volatile gases is changed because of the limited ability for removing metabolic products from the blood . The regulation of electrolyte composition and acid – base balance are also altered so that the body odor of these patients may be significantly influenced by these disorders . We show the ability of an electronic nose to detect changes in the human body odor in consequence of renal dysfunction by reducing multivariate sensor signals with principal component analysis to its first and second principal odor component ( POC ) . All healthy subjects could clearly be distinguished from patients with renal failure using quadratic discriminant analysis , whereas a correct classification of 95.2 % ( 98.4 % using 1st–3rd POC ) of patients between end stage renal failure and chronic renal failure  was found . This methodology of analyzing human body odor may also provide new approaches for investigating symptoms of renal failure and for diagnosing other diseases of internal or cutaneous origin Background : The authors performed a prospect i ve study to determine whether breath test analysis using an electronic nose correlates with a clinical pneumonia score . Methods : Exhaled gas was sample d from the expiratory limb of the ventilator in mechanically ventilated surgical intensive care patients and assayed with the electronic nose . Components of a clinical pneumonia score were recorded concurrently . Results : The score predicted by the electronic nose showed good correlation with the actual pneumonia score ( r2 = 0.81 ) . Bl and Altman analysis showed a mean bias of 0.0 ( limits ± 2.6 ) . Conclusions : The electronic nose is a new biosensor technology that correlates with a clinical pneumonia score RATIONALE Electronic noses are successfully used in commercial applications , including detection and analysis of volatile organic compounds in the food industry . OBJECTIVES We hypothesized that the electronic nose could identify and discriminate between lung diseases , especially bronchogenic carcinoma . METHODS In a discovery and training phase , exhaled breath of 14 individuals with bronchogenic carcinoma and 45 healthy control subjects or control subjects without cancer was analyzed . Principal components and canonic discriminant analysis of the sensor data was used to determine whether exhaled gases could discriminate between cancer and noncancer . Discrimination between classes was performed using Mahalanobis distance . Support vector machine analysis was used to create and apply a cancer prediction model prospect ively in a separate group of 76 individuals , 14 with and 62 without cancer . MAIN RESULTS Principal components and canonic discriminant analysis demonstrated discrimination between sample s from patients with lung cancer and those from other groups . In the validation study , the electronic nose had 71.4 % sensitivity and 91.9 % specificity for detecting lung cancer ; positive and negative predictive values were 66.6 and 93.4 % , respectively . In this population with a lung cancer prevalence of 18 % , positive and negative predictive values were 66.6 and 94.5 % , respectively . CONCLUSION The exhaled breath of patients with lung cancer has distinct characteristics that can be identified with an electronic nose . The results provide feasibility to the concept of using the electronic nose for managing and detecting lung cancer OBJECTIVES /HYPOTHESIS The electronic nose is a sensor of volatile molecules that is useful in the analysis of expired gases . The device is well suited to testing the breath of patients receiving mechanical ventilation and is a potential diagnostic adjunct that can aid in the detection of patients with ventilator-associated pneumonia . STUDY DESIGN A prospect i ve study . METHODS We performed a prospect i ve study of patients receiving mechanical ventilation in a surgical intensive care unit who underwent chest computed tomography ( CT ) scanning .  A single attending radiologist review ed the chest CT scans , and imaging features were recorded on a st and ardized form . Within 48 hours of chest CT scan , five sets of exhaled gas were sample d from the expiratory limb of the ventilator circuit . The gases were assayed with a commercially available electronic nose . Both linear and nonlinear analyses were performed to identify correlations between imaging features and the assayed gas signatures . RESULTS Twenty-five patients were identified , 13 of whom were diagnosed with pneumonia by CT scan .  Support vector machine analysis was performed in two separate analyses . In the first analysis , in which a training set was identical to a prediction set , the accuracy of prediction results was greater than 91.6 % . In the second analysis , in which the training set and the prediction set were different 
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Additionally , it is urgent to test this technology in intend-to-treat population s. Thus , it is possible to think in the contribution of VOC analysis by eNoses in a clinical setting