human
***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: Chronic obstructive pulmonary disease ( COPD ) /emphysema risk groups are well defined and screening allows for early identification of disease . The capability of exhaled volatile organic compounds ( VOCs ) to detect emphysema , as found by computed tomography ( CT ) in current and former heavy smokers participating in a lung cancer screening trial , was investigated . CT scans , pulmonary function tests and breath sample collection s were obtained from 204 subjects . Breath sample s were analyzed with a proton-transfer reaction mass spectrometer ( PTR-MS ) to obtain VOC profiles listed as ions at various mass-to-charge ratios ( m/z ) . Using bootstrapped stepwise forward logistic regression , we identified specific breath profiles as a potential tool for the diagnosis of emphysema , of airflow limitation or gas-exchange impairment . A marker for emphysema was found at m/z 87 ( tentatively attributed to 2-methylbutanal ) . The area under the receiver operating characteristic curve ( ROC ) of this marker to diagnose emphysema was 0.588 ( 95 % CI 0.453 - 0.662 ) . Mass-to-charge ratios m/z 52 ( most likely chloramine ) and m/z 135 ( alkyl benzene ) were linked to obstructive disease and m/z 122 ( most probably alkyl homologs ) to an impaired diffusion capacity . ROC areas were 0.646 ( 95 % CI 0.562 - 0.730 ) and 0.671 ( 95 % CI 0.524 - 0.710 ) , respectively . In the screening setting , exhaled VOCs measured by PTR-MS constitute weak markers for emphysema , pulmonary obstruction and impaired diffusion capacity Background : The pattern of volatile organic compounds ( VOCs ) in the exhaled breath of patients with lung cancer may be unique . New sensor systems that detect patterns of VOCs have been developed . One of these sensor systems , a colorimetric sensor array , has 36 spots composed of different chemically sensitive compounds impregnated on a disposable cartridge . The colours of these spots change based on the chemicals with which they come into contact . In this proof of principle study , the ability of this sensor system to detect a pattern of VOCs unique to lung cancer is assessed . Methods : Individuals with lung cancer , those with other lung diseases and healthy controls performed tidal breathing of room air for 12 min while exhaling into a device design ed to draw their breath across a colorimetric sensor array . The colour changes that occurred for each individual were converted into a numerical vector . The vectors were analysed statistically , using a r and om forests technique , to determine whether lung cancer could be predicted from the responses of the sensor . Results : 143 individuals participated in the study : 49 with non-small cell lung cancer , 18 with chronic obstructive pulmonary disease 15 with idiopathic pulmonary fibrosis 20 with pulmonary arterial hypertension 20 with sarcoidosis and 21 controls . A prediction model was developed using observations from 70 % of the subjects . This model was able to predict the presence of lung cancer in the remaining 30 % of subjects with a sensitivity of 73.3 % and a specificity of 72.4 % ( p = 0.01 ) . Conclusions : The unique chemical signature of the breath of patients with lung cancer can be detected with moderate accuracy by a colorimetric sensor array SETTING Cape Town , South Africa . OBJECTIVES We investigated the potential of breath analysis by gas chromatography-mass spectrometry ( GC-MS ) to discriminate between sample s collected prospect ively from patients with suspected tuberculosis ( TB ) . DESIGN Sample s were obtained in a TB-endemic setting in South Africa , where 28 % of culture-proven TB patients had Ziehl-Neelsen ( ZN ) negative sputum smear . A training set of breath sample s from 50 sputum culture-proven TB patients and 50 culture-negative non-TB patients was analysed using GC-MS . We used support vector machine analysis for classification of the patient sample s into TB and non-TB . RESULTS A classification model with seven compounds had a sensitivity of 72 % , a specificity of 86 % and an accuracy of 79 % compared with culture . The classification model was vali date d with breath sample s from a different set of 21 TB and 50 non-TB patients from the same area , giving a sensitivity of 62 % , a specificity of 84 % and an accuracy of 77 % . CONCLUSION This study shows that GC-MS breath analysis is able to differentiate between TB and non-TB breath sample s even among patients with a negative ZN sputum smear but a positive culture for Mycobacterium tuberculosis . We conclude that breath analysis by GC-MS merits further research 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 There is increasing interest in ethane ( C(2)H(6 ) ) in exhaled breath as a non-invasive marker of oxidative stress ( OS ) and thereby a potential indicator of disease . However , the lack of real-time measurement techniques has limited progress in the field . Here we report on a novel Tunable Diode Laser Spectrometer ( TDLS ) applied to the analysis of exhaled ethane in patients with lung cancer . The patient group ( n=52 ) comprised r and omly selected patients presenting at a respiratory clinic . Of these , a sub-group ( n=12 ) was subsequently diagnosed with lung cancer . An age-matched group ( n=12 ) corresponding to the lung cancer group was taken from a larger control group of healthy adults ( n=58 ) . The concentration of ethane in a single exhaled breath sample collected from all subjects was later measured using the TDLS . This technique is capable of real-time analysis of sample s with accuracy 0.1 parts per billion ( ppb ) , over 10 times less than typical ambient levels in the northern hemisphere . After correcting for ambient background , ethane in the control group ( 26 % smokers ) ranged from 0 to 10.54 ppb ( median of 1.9 ppb ) while ethane in the lung cancer patients ( 42 % smokers ) ranged from 0 to 7.6 ppb ( median of 0.7 ppb ) . Ethane among the non-lung cancer patients presenting for investigation of respiratory disease ranged from 0 to 25 ppb ( median 1.45 ppb ) . We conclude that , while the TDLS proved effective for accurate and rapid sample analysis , there was no significant difference in exhaled ethane among any of the subject groups . Comments are made on the suitability of the technique for monitoring applications BACKGROUND Many volatile organic compounds ( VOCs ) , principally alkanes and benzene derivatives , have been identified in breath from patients with lung cancer . We investigated whether a combination of VOCs could identify such patients . METHODS We collected breath sample s from 108 patients with an abnormal chest radiograph who were scheduled for bronchoscopy .  The sample s were collected with a portable apparatus , then assayed by gas chromatography and mass spectroscopy . The alveolar gradient of each breath VOC , the difference between the amount in breath and in air , was calculated . Forward stepwise discriminant analysis was used to identify VOCs that discriminated between patients with and without lung cancer . FINDINGS Lung cancer was confirmed histologically in 60 patients . A combination of 22 breath VOCs , predominantly alkanes , alkane derivatives , and benzene derivatives , discriminated between patients with and without lung cancer , regardless of stage ( all p<0.0003 ) . For stage 1 lung cancer , the 22 VOCs had 100 % sensitivity and 81.3 % specificity . Cross-validation of the combination correctly predicted the diagnosis in 71.7 % patients with lung cancer and 66.7 % of those without lung cancer . INTERPRETATION In patients with an abnormal chest radiograph , a combination of 22 VOCs in breath sample s distinguished between patients with and without lung cancer . Prospect i ve studies are needed to confirm the usefulness of breath VOCs for detecting lung cancer in the general population 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 Background Non-invasive diagnostic strategies aim ed at identifying biomarkers of lung cancer are of great interest for early cancer detection . The aim of this study was to set up a new method for identifying and quantifying  volatile organic compounds ( VOCs ) in exhaled air of patients with non-small cells lung cancer ( NSCLC ) , by comparing the levels with those obtained from healthy smokers and non-smokers , and patients with chronic obstructive pulmonary disease . The VOC collection and analyses were repeated three weeks after the NSCLC patients underwent lung surgery . Methods The subjects ' breath was collected in a Teflon ® bulb that traps the last portion of single slow vital capacity . The 13 VOCs selected for this study were concentrated using a solid phase micro extraction technique and subsequently analysed by means of gas cromatography/mass spectrometry . Results The levels of the selected VOCs ranged from 10 - 12 M for styrene to 10 - 9 M for isoprene . None of VOCs alone discriminated the study groups , and so it was not possible to identify one single chemical compound as a specific lung cancer biomarker . However , multinomial logistic regression analysis showed that VOC profile can correctly classify about 80 % of cases . Only isoprene and decane levels significantly decreased after surgery . Conclusion As the combination of the 13 VOCs allowed the correct classification of the cases into groups , together with conventional diagnostic approaches , VOC analysis could be used as a complementary test for the early diagnosis of lung cancer . Its possible use in the follow-up of operated patients can not be recommended on the basis of the results of our short-term nested study 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 
    Output:
    
agent
***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: Chronic obstructive pulmonary disease ( COPD ) /emphysema risk groups are well defined and screening allows for early identification of disease . The capability of exhaled volatile organic compounds ( VOCs ) to detect emphysema , as found by computed tomography ( CT ) in current and former heavy smokers participating in a lung cancer screening trial , was investigated . CT scans , pulmonary function tests and breath sample collection s were obtained from 204 subjects . Breath sample s were analyzed with a proton-transfer reaction mass spectrometer ( PTR-MS ) to obtain VOC profiles listed as ions at various mass-to-charge ratios ( m/z ) . Using bootstrapped stepwise forward logistic regression , we identified specific breath profiles as a potential tool for the diagnosis of emphysema , of airflow limitation or gas-exchange impairment . A marker for emphysema was found at m/z 87 ( tentatively attributed to 2-methylbutanal ) . The area under the receiver operating characteristic curve ( ROC ) of this marker to diagnose emphysema was 0.588 ( 95 % CI 0.453 - 0.662 ) . Mass-to-charge ratios m/z 52 ( most likely chloramine ) and m/z 135 ( alkyl benzene ) were linked to obstructive disease and m/z 122 ( most probably alkyl homologs ) to an impaired diffusion capacity . ROC areas were 0.646 ( 95 % CI 0.562 - 0.730 ) and 0.671 ( 95 % CI 0.524 - 0.710 ) , respectively . In the screening setting , exhaled VOCs measured by PTR-MS constitute weak markers for emphysema , pulmonary obstruction and impaired diffusion capacity Background : The pattern of volatile organic compounds ( VOCs ) in the exhaled breath of patients with lung cancer may be unique . New sensor systems that detect patterns of VOCs have been developed . One of these sensor systems , a colorimetric sensor array , has 36 spots composed of different chemically sensitive compounds impregnated on a disposable cartridge . The colours of these spots change based on the chemicals with which they come into contact . In this proof of principle study , the ability of this sensor system to detect a pattern of VOCs unique to lung cancer is assessed . Methods : Individuals with lung cancer , those with other lung diseases and healthy controls performed tidal breathing of room air for 12 min while exhaling into a device design ed to draw their breath across a colorimetric sensor array . The colour changes that occurred for each individual were converted into a numerical vector . The vectors were analysed statistically , using a r and om forests technique , to determine whether lung cancer could be predicted from the responses of the sensor . Results : 143 individuals participated in the study : 49 with non-small cell lung cancer , 18 with chronic obstructive pulmonary disease 15 with idiopathic pulmonary fibrosis 20 with pulmonary arterial hypertension 20 with sarcoidosis and 21 controls . A prediction model was developed using observations from 70 % of the subjects . This model was able to predict the presence of lung cancer in the remaining 30 % of subjects with a sensitivity of 73.3 % and a specificity of 72.4 % ( p = 0.01 ) . Conclusions : The unique chemical signature of the breath of patients with lung cancer can be detected with moderate accuracy by a colorimetric sensor array SETTING Cape Town , South Africa . OBJECTIVES We investigated the potential of breath analysis by gas chromatography-mass spectrometry ( GC-MS ) to discriminate between sample s collected prospect ively from patients with suspected tuberculosis ( TB ) . DESIGN Sample s were obtained in a TB-endemic setting in South Africa , where 28 % of culture-proven TB patients had Ziehl-Neelsen ( ZN ) negative sputum smear . A training set of breath sample s from 50 sputum culture-proven TB patients and 50 culture-negative non-TB patients was analysed using GC-MS . We used support vector machine analysis for classification of the patient sample s into TB and non-TB . RESULTS A classification model with seven compounds had a sensitivity of 72 % , a specificity of 86 % and an accuracy of 79 % compared with culture . The classification model was vali date d with breath sample s from a different set of 21 TB and 50 non-TB patients from the same area , giving a sensitivity of 62 % , a specificity of 84 % and an accuracy of 77 % . CONCLUSION This study shows that GC-MS breath analysis is able to differentiate between TB and non-TB breath sample s even among patients with a negative ZN sputum smear but a positive culture for Mycobacterium tuberculosis . We conclude that breath analysis by GC-MS merits further research 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 There is increasing interest in ethane ( C(2)H(6 ) ) in exhaled breath as a non-invasive marker of oxidative stress ( OS ) and thereby a potential indicator of disease . However , the lack of real-time measurement techniques has limited progress in the field . Here we report on a novel Tunable Diode Laser Spectrometer ( TDLS ) applied to the analysis of exhaled ethane in patients with lung cancer . The patient group ( n=52 ) comprised r and omly selected patients presenting at a respiratory clinic . Of these , a sub-group ( n=12 ) was subsequently diagnosed with lung cancer . An age-matched group ( n=12 ) corresponding to the lung cancer group was taken from a larger control group of healthy adults ( n=58 ) . The concentration of ethane in a single exhaled breath sample collected from all subjects was later measured using the TDLS . This technique is capable of real-time analysis of sample s with accuracy 0.1 parts per billion ( ppb ) , over 10 times less than typical ambient levels in the northern hemisphere . After correcting for ambient background , ethane in the control group ( 26 % smokers ) ranged from 0 to 10.54 ppb ( median of 1.9 ppb ) while ethane in the lung cancer patients ( 42 % smokers ) ranged from 0 to 7.6 ppb ( median of 0.7 ppb ) . Ethane among the non-lung cancer patients presenting for investigation of respiratory disease ranged from 0 to 25 ppb ( median 1.45 ppb ) . We conclude that , while the TDLS proved effective for accurate and rapid sample analysis , there was no significant difference in exhaled ethane among any of the subject groups . Comments are made on the suitability of the technique for monitoring applications BACKGROUND Many volatile organic compounds ( VOCs ) , principally alkanes and benzene derivatives , have been identified in breath from patients with lung cancer . We investigated whether a combination of VOCs could identify such patients . METHODS We collected breath sample s from 108 patients with an abnormal chest radiograph who were scheduled for bronchoscopy .  The sample s were collected with a portable apparatus , then assayed by gas chromatography and mass spectroscopy . The alveolar gradient of each breath VOC , the difference between the amount in breath and in air , was calculated . Forward stepwise discriminant analysis was used to identify VOCs that discriminated between patients with and without lung cancer . FINDINGS Lung cancer was confirmed histologically in 60 patients . A combination of 22 breath VOCs , predominantly alkanes , alkane derivatives , and benzene derivatives , discriminated between patients with and without lung cancer , regardless of stage ( all p<0.0003 ) . For stage 1 lung cancer , the 22 VOCs had 100 % sensitivity and 81.3 % specificity . Cross-validation of the combination correctly predicted the diagnosis in 71.7 % patients with lung cancer and 66.7 % of those without lung cancer . INTERPRETATION In patients with an abnormal chest radiograph , a combination of 22 VOCs in breath sample s distinguished between patients with and without lung cancer . Prospect i ve studies are needed to confirm the usefulness of breath VOCs for detecting lung cancer in the general population 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 Background Non-invasive diagnostic strategies aim ed at identifying biomarkers of lung cancer are of great interest for early cancer detection . The aim of this study was to set up a new method for identifying and quantifying  volatile organic compounds ( VOCs ) in exhaled air of patients with non-small cells lung cancer ( NSCLC ) , by comparing the levels with those obtained from healthy smokers and non-smokers , and patients with chronic obstructive pulmonary disease . The VOC collection and analyses were repeated three weeks after the NSCLC patients underwent lung surgery . Methods The subjects ' breath was collected in a Teflon ® bulb that traps the last portion of single slow vital capacity . The 13 VOCs selected for this study were concentrated using a solid phase micro extraction technique and subsequently analysed by means of gas cromatography/mass spectrometry . Results The levels of the selected VOCs ranged from 10 - 12 M for styrene to 10 - 9 M for isoprene . None of VOCs alone discriminated the study groups , and so it was not possible to identify one single chemical compound as a specific lung cancer biomarker . However , multinomial logistic regression analysis showed that VOC profile can correctly classify about 80 % of cases . Only isoprene and decane levels significantly decreased after surgery . Conclusion As the combination of the 13 VOCs allowed the correct classification of the cases into groups , together with conventional diagnostic approaches , VOC analysis could be used as a complementary test for the early diagnosis of lung cancer . Its possible use in the follow-up of operated patients can not be recommended on the basis of the results of our short-term nested study 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 
    Output:
    