Receiver Operating Characteristic Curve AUC Research Articles

The metabolome as a diagnostic for maximal aerobic capacity during exercise in type 1 diabetes

Aims/hypothesisOur aim was to characterise the in-depth metabolic response to aerobic exercise and the impact of residual pancreatic beta cell function in type 1 diabetes. We also aimed to use the metabolome to distinguish individuals with type 1 diabetes with reduced maximal aerobic capacity in exercise defined by V˙O2peak\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\dot{V}{\ ext{O}}_{\ ext{2peak}}$$\\end{document}.MethodsThirty participants with type 1 diabetes (≥3 years duration) and 30 control participants were recruited. Groups did not differ in age or sex. After quantification of peak stimulated C-peptide, participants were categorised into those with undetectable (<3 pmol/l), low (3–200 pmol/l) or high (>200 pmol/l) residual beta cell function. Maximal aerobic capacity was assessed by V˙O2peak\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\dot{V}{\ ext{O}}_{\ ext{2peak}}$$\\end{document} test and did not differ between control and type 1 diabetes groups. All participants completed 45 min of incline treadmill walking (60% V˙O2peak\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\dot{V}{\ ext{O}}_{\ ext{2peak}}$$\\end{document}) with venous blood taken prior to exercise, immediately post exercise and after 60 min recovery. Serum was analysed using targeted metabolomics. Metabolomic data were analysed by multivariate statistics to define the metabolic phenotype of exercise in type 1 diabetes. Receiver operating characteristic (ROC) curves were used to identify circulating metabolomic markers of maximal aerobic capacity (V˙O2peak\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\dot{V}{\ ext{O}}_{\ ext{2peak}}$$\\end{document}) during exercise in health and type 1 diabetes.ResultsMaximal aerobic capacity (V˙O2peak\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\dot{V}{\ ext{O}}_{\ ext{2peak}}$$\\end{document}) inversely correlated with HbA1c in the type 1 diabetes group (r2=0.17, p=0.024). Higher resting serum tricarboxylic acid cycle metabolites malic acid (fold change 1.4, p=0.001) and lactate (fold change 1.22, p=1.23×10−5) differentiated people with type 1 diabetes. Higher serum acylcarnitines (AC) (AC C14:1, F value=12.25, p=0.001345; AC C12, F value=11.055, p=0.0018) were unique to the metabolic response to exercise in people with type 1 diabetes. C-peptide status differentially affected metabolic responses in serum ACs during exercise (AC C18:1, leverage 0.066; squared prediction error 3.07). The malic acid/pyruvate ratio in rested serum was diagnostic for maximal aerobic capacity (V˙O2peak\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\dot{V}{\ ext{O}}_{\ ext{2peak}}$$\\end{document}) in people with type 1 diabetes (ROC curve AUC 0.867 [95% CI 0.716, 0.956]).Conclusions/interpretationThe serum metabolome distinguishes high and low maximal aerobic capacity and has diagnostic potential for facilitating personalised medicine approaches to manage aerobic exercise and fitness in type 1 diabetes.Graphical

CT quantitative analysis of pulmonary changes in rheumatoid arthritis.

To explore the application of quantitative computed tomography (CT) in analyses of lung changes in patients with rheumatoid arthritis (RA). A total of 150 clinically diagnosed RA patients underwent chest CT and 150 matched non-smokers subjects with normal chest CT are enrolled. A CT software is applied to analyze CT obtained from both groups. The quantitative indices of emphysema are expressed as the percentage of lung area with attenuation < -950HU to the total lung volume (LAA-950%), and pulmonary fibrosis was expressed as the percentage of lung area with a attenuation of -200 to -700HU to the total lung volume (LAA-200--700%), quantitative indicators of pulmonary vascular include aortic diameter (AD), pulmonary artery diameter (PAD), the ratio of PAD to AD (PAD/AD ratio), the number of blood vessels (TNV), and the cross area of blood vessels (TAV). The receiver operating characteristic (ROC) curve is used to evaluate the ability of these indexes in identifying the changes in the lung in RA patients. Compared to the control group, the RA group has significantly lower TLV, larger AD, and smaller TNV and TAV (3921±1101 vs. 4490±1046, 33.26±4.20 vs. 32.95±3.76, 13.14±4.93 vs. 17.53±3.34, and 96.89±40.62 vs. 163.32±34.97, respectively, with all p < 0.001). Peripheral vascular indicator TAV has the better ability to identify lung changes in RA patients (area under ROC curve AUC = 0.894) than TNV (AUC = 0.780) or LAA-200&sim-700% (AUC = 0.705). Quantitative CT can detect changes in lung density distribution and peripheral vascular injury in patients with RA and assess the severity.

Adding value of the Charlson comorbidity index to the Grace score for mortality risk prediction in patients with acute coronary syndrome

Abstract Funding Acknowledgements Type of funding sources: None. Background The Global Registry of Acute Coronary Events (GRACE) risk score is widely recommended for risk assessment in patients with acute coronary syndrome (ACS). The Charlson Comorbidity Index (CCI) is a recognized measure of comorbid burden, and a useful tool for estimating prognosis in patients with multiple coexisting illnesses. Despite it has been validated in the context of ACS-related mortality, scarce and conflicting data still exist about the role of the CCI when added to the traditional GRACE risk score for outcome prediction in patients with acute coronary syndrome (ACS). Purpose The assessment of the potential predictive value of the CCI both for short, and long term all-cause mortality, in a contemporary ACS population, and its potential adding value beyond the GRACE risk score. Methods All consecutive admission due to ST-elevation myocardial infarction-STEMI, non ST elevated myocardial infarction-NSTEMI, and unstable angina-UA, from 01/01/2018 to 31/12/2020 were retrospectively reviewed from an internal database of a tertiary cardiac center in Italy. Logistic and Cox proportional regression analysis were performed in order to assess the contribution of the CCI on short and long term mortality. In order to specify the CCI adding value to the GRACE score, several measures of improvement in discrimination (increase in the area under the receiver-operating characteristic curve-AUC, the integrated discrimination improvement-IDI, and the continuous net reclassification improvement-cNRI&amp;gt;0) , were implemented. Robustness of the results were assessed through an internal validation procedure with bootstrapping. Results A total of 1310 patients (646 STEMI, 588 NSTEMI, and 76 UA) were identified. Almost 28% were female, and the median age was 68 (58-78). 97 (7,4%) and 136 (11,2%) deaths occurred, respectively, in hospital and during long term follow-up (median follow-up time: 12 months; IQR: 8-23). After multivariate logistic regression analysis the CCI was not associated with in-hospital death, while it was significantly and independently associated with the long-term mortality along with GRACE score (HR: 1,33, 95% CI: 1,22-1,43; p&amp;lt;0,001, Fig.1). An additive effect of CCI with the GRACE risk score was observed in predicting long-term mortality with an AUC from 0,762 to 0.821 (p &amp;lt; 0,001). The incremental predictive value of combining CCI and the GRACE risk score for the long term mortality was significantly improved, as shown by the net reclassification improvement (cNRI&amp;gt;0: 0.678, p &amp;lt; 0.001, Table 1) and integrated discrimination improvement (IDI: 0.08, p &amp;lt; 0.001). Conclusion The CCI is a predictor of long-term mortality and improves risk stratification of patients with ACS over the GRACE risk score.

A lightweight deep learning model for automatic segmentation and analysis of ophthalmic images

Detection, diagnosis, and treatment of ophthalmic diseases depend on extraction of information (features and/or their dimensions) from the images. Deep learning (DL) model are crucial for the automation of it. Here, we report on the development of a lightweight DL model, which can precisely segment/detect the required features automatically. The model utilizes dimensionality reduction of image to extract important features, and channel contraction to allow only the required high-level features necessary for reconstruction of segmented feature image. Performance of present model in detection of glaucoma from optical coherence tomography angiography (OCTA) images of retina is high (area under the receiver-operator characteristic curve AUC ~ 0.81). Bland–Altman analysis gave exceptionally low bias (~ 0.00185), and high Pearson’s correlation coefficient (p = 0.9969) between the parameters determined from manual and DL based segmentation. On the same dataset, bias is an order of magnitude higher (~ 0.0694, p = 0.8534) for commercial software. Present model is 10 times lighter than Unet (popular for biomedical image segmentation) and have a better segmentation accuracy and model training reproducibility (based on the analysis of 3670 OCTA images). High dice similarity coefficient (D) for variety of ophthalmic images suggested it’s wider scope in precise segmentation of images even from other fields. Our concept of channel narrowing is not only important for the segmentation problems, but it can also reduce number of parameters significantly in object classification models. Enhanced disease diagnostic accuracy can be achieved for the resource limited devices (such as mobile phone, Nvidia’s Jetson, Raspberry pi) used in self-monitoring, and tele-screening (memory size of trained model ~ 35 MB).

Red blood cell distribution width and maximum left ventricular wall thickness predict poor outcomes in patients with hypertrophic cardiomyopathy

AbstractAimTo evaluate the prognostic utility of red blood cell distribution width (RDW) and maximum left ventricular wall thickness (MLVWT) in patients with hypertrophic cardiomyopathy (HCM).Patients and methodsThis study is a retrospective cohort analysis. Patients diagnosed with HCM at the First Affiliated Hospital of Sun Yat‐sen University from March 2014 to March 2019 were included. HCM patients were stratified into two groups based on the occurrence of major adverse cardiac events (MACE). Receiver operating characteristic (ROC) curves were then constructed and Cox regression models were employed to gauge the prognostic relevance of RDW and MLVWT for HCM patients. Kaplan–Meier analysis evaluated the survival and MACE‐free rate in patients with different level of RDW and MLVWT.ResultsA total of 300 patients with HCM were enrolled in this study and followed up for 40.56±18.33 months. Among them, 117 MACE (39.00%), 40 all‐cause deaths (13.33%), and 29 cardiovascular deaths (9.67%). The level of RDW, MLVWT, creatinine (Cr), and B‐type pro‐brain natriuretic peptide (NT‐ProBNP) were statistically different between the MACE group and non‐MACE group (P &lt; .05). Multivariate analysis showed that after adjusting for confounding factors, RDW and MLVWT were independent predictors of all‐cause mortality and MACE in HCM patients. ROC showed that RDW &gt; .13 and MLVWT &gt; 23 mm are the cut‐off value to predict all‐cause mortality and MACE. The area under the ROC curve AUC of the combination predicting the occurrence of all‐cause mortality and MACE are .823 and .820, respectively. Kaplan–Meier analysis showed that the survival rate and MACE‐free survival rate of group 1 (RDW≦.13 and MLVWT≦23 mm) were significantly higher than group 2 (RDW &gt; .13 or MLVWT &gt; 23 mm), and group 3 (RDW &gt; .13 and MLVWT &gt; 23 mm) (P = .000).ConclusionWe determined that increased RDW and MLVWT was independently associated with MACE incidence and risk of mortality in HCM patients. Combined evaluation of RDW and MLVWT yielded a more accurate predictive model of HCM patient outcomes relative to the use of either of these metrics in isolation. Our research can provide a theoretical basis in the occurrence of MACE for the high‐risk HCM and intervene them properly and timely.

Ambidirectional cohort study on the agreement of ultrasonography and surgery in the identification of parathyroid pathology, and predictors of postoperative hypocalcemia in 47 dogs undergoing parathyroidectomy due to primary hyperparathyroidism.

ObjectiveTo investigate (1a) agreement of ultrasonographic, surgical, and histopathologic findings in the diagnosis of a neoplastic etiology underlying primary hyperparathyroidism (PHPT), (1b) the ability of ultrasonographically determined parathyroid gland size to distinguish between malignant (carcinoma) and non‐malignant (hyperplasia, adenoma) pathology, and (2) variables associated with postoperative hypocalcemia in dogs undergoing surgical treatment of PHPT.Study DesignAmbidirectional cohort study.AnimalsForty‐seven client owned dogs with PHPT (34 retrospective; 13 prospective).MethodsData were extracted from medical records. Method agreements were explored using Cohen's Kappa statistic. A receiver operating characteristic curve (ROC) was used to determine a cut‐off separating parathyroid pathologies. Univariable and multivariable models assessed associations between postoperative hypocalcemia and potential risk factors.ResultsAgreement of ultrasound and surgery for number and side of affected glands was 31/47 (65.9%) and 34/47 (72.3%), respectively. In 37/47 (78.7%) cases, parathyroid tissue was correctly assessed as pathologic by the surgeon. An ultrasonographic cut‐off of ≥8.0 mm (ROC AUC = 0.82) best distinguished malignant from benign pathologies. Dogs with a preoperative serum ionized calcium (iCa) concentration ≥1.75 mEq/L had 7.5 times greater odds of becoming hypocalcemic postoperatively.ConclusionA fair agreement existed between ultrasonographic and surgical findings in dogs with PHPT. A parathyroid mass ≥8.0 mm on ultrasonographic examination was suggestive of malignancy, while dogs with a preoperative serum iCa concentration ≥1.75 mEq/L were at increased risk for postoperative hypocalcemia in this study.Clinical significanceThis study supports the use of bilateral cervical surgical exploration to identify abnormal parathyroid glands for the treatment of PHPT.

The value of a dual-energy spectral CT quantitative analysis technique in acute pancreatitis

To explore the value of dual-energy spectral computed tomography (DESCT) in evaluating the clinical severity of acute pancreatitis. Seventy patients with acute pancreatitis (AP) confirmed by clinical examination were included in this study. All patients underwent unenhanced/double-phase enhanced CT in spectral imaging mode. Iodine concentration and normalised iodine concentration (NIC) were measured retrospectively with a spectral imaging viewer (GSI Viewer). All data were analysed by analysis of variance. Receiver operating characteristic (ROC) curves were constructed to determine the optimal threshold for predicting the clinical severity of AP. Seventy patients were included in the study comprising 30 mild, 22 moderate, and 18 severe cases of AP. The CT attenuation value, iodine concentration, and NIC were decreased with increasing clinical severity. Moreover, there were significant differences between the mild group and the severe group (p<0.05), as well as between the moderate group and the severe group (p<0.05). The area under the ROC curve AUC of each value was larger in arterial phase than in portal venous phase. The most sensitive value between the mild and severe groups in AP was the NIC (arterial phase: 0.19±0.06; portal venous phase: 0.45±0.09). DESCT can provide multiple parameters to determine the severity of AP.

Reproducibility and discriminant validity of the Posterior Shoulder Endurance Test in healthy and painful populations

ObjectiveThis investigation measured the reproducibility and discriminant validity of the Posterior Shoulder Endurance Test (PSET) on painful and non-painful populations. DesignReliability and validity study. SettingLaboratory setting. ParticipantsThirty subjects (male = 11; female = 19). Main outcome measuresTime to failure (TTF) was the primary outcome measure to determine reliability of the PSET. Discriminant validity identified with receiver operator characteristic (ROC) curves utilized TTF separately in men and women since they used different loads. ResultsThere were 25/30 subjects (painful = 12; non-painful = 13) tested a second time. ICC, SEM, and MDC90 ranged respectively from 0.77, 13.1 s, 30.6 s in the painful group to 0.85, 7.3 s, 17 s in the non-painful group. The male ROC curve AUC was 0.833 with 47 s resulting in the best combination of sensitivity = 0.833, and specificity = 0.80. The female ROC curve AUC was 0.633 with 46 s resulting in the best combination of sensitivity = 0.600 and specificity = 0.889 at 46 s. ConclusionThe PSET is a reliable way to measure shoulder girdle muscular endurance. These data suggest that the PSET discriminates painful and non-painful individuals better in men compared to women.

Enhanced prognostic stratification of neoadjuvant treated lung squamous cell carcinoma by computationally-guided tumor regression scoring

IntroductionThe amount of residual tumor burden after neoadjuvant chemotherapy is an important prognosticator, but for non-small cell lung carcinoma (NSCLC), no official regression scoring system is yet established. Computationally derived histological regression scores could provide unbiased and quantitative readouts to complement the clinical assessment of treatment response. MethodsHistopathologic tumor regression was microscopically assessed on whole cases in a neoadjuvant chemotherapy-treated cohort (NAC, n = 55 patients) of lung squamous cell carcinomas (LSCC). For each patient, the slide showing the least pathologic regression was selected for subsequent computational analysis and histological features were quantified: percentage of vital tumor cells (cTu.Percentage), total surface covered by vital tumor cells (cTu.Area), area of the largest vital tumor fragment (cTu.Size.max), and total number of vital tumor fragments (cTu.Fragments). A chemo-naïve LSCC cohort (CN, n = 104) was used for reference. For 23 of the 55 patients [18F]-Fluorodeoxyglucose (FDG) PET/CT measurements of maximum standard uptake value (SUVmax), background subtracted lesion activity (BSL) and background subtracted volume (BSV) were correlated with pathologic regression. Survival analysis was carried out using Cox regression and receiver operating characteristic (ROC) curve analysis using a 3-years cutoff. ResultsAll computational regression parameters significantly correlated with relative changes of BSV FDG PET/CT values after neoadjuvant chemotherapy. ROC curve analysis of histological parameters of NAC patients showed that cTu.Percentage was the most accurate prognosticator of overall survival (ROC curve AUC = 0.77, p-value = 0.001, Cox regression HR = 3.6, p = 0.001, variable cutoff < = 30 %). ConclusionsThis study demonstrates the prognostic relevance of computer-derived histopathologic scores. Additionally, the analysis carried out on slides displaying the least pathologic regression correlated with overall pathologic response and PET/CT values. This might improve the objective histopathologic assessment of tumor response in neoadjuvant setting.

Skin cancer detection by deep learning and sound analysis algorithms: A prospective clinical study of an elementary dermoscope.

BackgroundSkin cancer (SC), especially melanoma, is a growing public health burden. Experimental studies have indicated a potential diagnostic role for deep learning (DL) algorithms in identifying SC at varying sensitivities. Previously, it was demonstrated that diagnostics by dermoscopy are improved by applying an additional sonification (data to sound waves conversion) layer on DL algorithms. The aim of the study was to determine the impact of image quality on accuracy of diagnosis by sonification employing a rudimentary skin magnifier with polarized light (SMP).MethodsDermoscopy images acquired by SMP were processed by a first deep learning algorithm and sonified. Audio output was further analyzed by a different secondary DL. Study criteria outcomes of SMP were specificity and sensitivity, which were further processed by a F2-score, i.e. applying a twice extra weight to sensitivity over positive predictive values.FindingsPatients (n = 73) fulfilling inclusion criteria were referred to biopsy. SMP analysis metrics resulted in a receiver operator characteristic curve AUC's of 0.814 (95% CI, 0.798–0.831). SMP achieved a F2-score sensitivity of 91.7%, specificity of 41.8% and positive predictive value of 57.3%. Diagnosing the same set of patients' lesions by an advanced dermoscope resulted in a F2-score sensitivity of 89.5%, specificity of 57.8% and a positive predictive value of 59.9% (P=NS).InterpretationDL processing of dermoscopic images followed by sonification results in an accurate diagnostic output for SMP, implying that the quality of the dermoscope is not the major factor influencing DL diagnosis of skin cancer. Present system might assist all healthcare providers as a feasible computer-assisted detection system.FundBostel Technologies.Trial Registration clinicaltrials.gov Identifier: NCT03362138

Deep Recurrent Neural Networks for Supernovae Classification

Abstract We apply deep recurrent neural networks, which are capable of learning complex sequential information, to classify supernovae (code available at https://github.com/adammoss/supernovae). The observational time and filter fluxes are used as inputs to the network, but since the inputs are agnostic, additional data such as host galaxy information can also be included. Using the Supernovae Photometric Classification Challenge (SPCC) data, we find that deep networks are capable of learning about light curves, however the performance of the network is highly sensitive to the amount of training data. For a training size of 50% of the representational SPCC data set (around 104 supernovae) we obtain a type-Ia versus non-type-Ia classification accuracy of 94.7%, an area under the Receiver Operating Characteristic curve AUC of 0.986 and an SPCC figure-of-merit F 1 = 0.64. When using only the data for the early-epoch challenge defined by the SPCC, we achieve a classification accuracy of 93.1%, AUC of 0.977, and F 1 = 0.58, results almost as good as with the whole light curve. By employing bidirectional neural networks, we can acquire impressive classification results between supernovae types I, II and III at an accuracy of 90.4% and AUC of 0.974. We also apply a pre-trained model to obtain classification probabilities as a function of time and show that it can give early indications of supernovae type. Our method is competitive with existing algorithms and has applications for future large-scale photometric surveys.

VEGF correlates with inflammation and fibrosis in tuberculous pleural effusion.

Objective. To investigate the relationship among angiogenic cytokines, inflammatory markers, and fibrinolytic activity in tuberculous pleural effusion (TBPE) and their clinical importance. Methods. Forty-two patients diagnosed with TBPE were studied. Based on chest ultrasonography, there were 26 loculated and 16 nonloculated TBPE patients. The effusion size radiological scores and effusion vascular endothelial growth factor (VEGF), interleukin- (IL-) 8, plasminogen activator inhibitor type-1 (PAI-1), and tissue type plasminogen activator (tPA) were measured. Treatment outcome and pleural fibrosis, defined as radiological residual pleural thickening (RPT), were assessed at 6-month follow-up. Results. The effusion size and effusion lactate dehydrogenase (LDH), VEGF, IL-8, PAI-1, and PAI-1/tPA ratio were significantly higher, while effusion glucose, pH value, and tPA were significantly lower, in loculated than in nonloculated TBPE. VEGF and IL-8 correlated positively with LDH and PAI-1/tPA ratio and negatively with tPA in both loculated and nonloculated TBPE. Patients with higher VEGF or greater effusion size were prone to develop RPT (n = 14; VEGF, odds ratio 1.28, P = 0.01; effusion size, odds ratio 1.01, P = 0.02), and VEGF was an independent predictor of RPT in TBPE (receiver operating characteristic curve AUC = 0.985, P < 0.001). Conclusions. Effusion VEGF correlates with pleural inflammation and fibrosis and may be targeted for adjunct therapy for TBPE.

Computational identification of natural peptides based on analysis of molecular evolution.

Many secretory peptides are synthesized as inactive precursors that must undergo post-translational processing to become biologically active peptides. Attempts to predict natural peptides are limited by the low performance of proteolytic site predictors and by the high combinatorial complexity of pairing such sites. To overcome these limitations, we analyzed the site-wise evolutionary mutation rates of peptide hormone precursors, calculated using the Rate4Site algorithm. Our analysis revealed that within their precursors, peptide residues are significantly more conserved than the pro-peptide residues. This disparity enables the prediction of peptides with a precision of ∼60% at a recall of 40% [receiver-operating characteristic curve (ROC) AUC 0.79]. Subsequently, combining the Rate4Site score with additional features and training a Random Forest classifier enable the prediction of natural peptides hidden within secreted human proteins at a precision of ∼90% at a recall of 50% (ROC AUC 0.96). The high performance of our method allows it to be applied to full secretomes and to predict naturally occurring active peptides. Our prediction on Homo sapiens revealed several putative peptides in the human secretome that are currently unannotated. Furthermore, the unique expression of some of these peptides implies a potential hormone function, including peptides that are highly expressed in endocrine glands. A pseudocode is available in the SUPPLEMENTARY INFORMATION. doron.gerber@biu.ac.il or kliger@cgen.com Supplementary data are available at Bioinformatics online.

Role of 11C-choline positron emission tomography/computed tomography in evaluating patients affected by prostate cancer with suspected relapse due to prostate-specific antigen elevation

The aim of this study was to evaluate the accuracy of (11)C-choline positron emission tomography/computed tomography (PET/CT) in restaging patients affected by prostate cancer and suspected relapse due to prostate-specific antigen (PSA) increase. We also aimed to determine a PSA cutoff that is most suited to the study in terms of best compromise between sensitivity and specificity. Secondary endpoints were a comparison between (11)C-choline PET/CT and histological results, clinical findings, and radiological imaging (CT and magnetic resonance imaging). We retrospectively evaluated 210 patients (median ± SD age 70 ± 7 years) affected by prostate cancer who underwent (11)C-choline PET/CT. (11)C-choline PET/CT imaging was positive in 116 (55.2%) patients and negative in 94 (44.8%). Receiver operating characteristic (ROC) analysis showed that the highest accuracy (sensitivity 76.8%, specificity 92.5%) for the whole population was achieved when the PSA level of 1.26 ng/ml level was used as the cutoff value for interpreting the results (P = 0.0001 and the area under the ROC curve AUC 0.897). For patients treated with surgery or surgery plus radiotherapy the cutoff was 0.81 ng/ml (sensitivity 73.2%, specificity 86.1%). For patients treated with radiotherapy alone, the cutoff was 2.0 ng/ml (sensitivity 81.8%, specificity 92.9%). Our results indicate that (11)C-choline PET/CT is a useful diagnostic tool in patients affected by prostate cancer and a relapsed PSA level. The highest accuracy for all patients is obtained with a PSA cutoff level of 1.26 ng/ml, above which the imaging study is performed (0.81 ng/ml for patients treated with surgery or surgery plus radiotherapy and 2.0 ng/ml for patients treated with radiotherapy alone).

Pharmacokinetics of mycophenolic acid in severe lupus nephritis

Mycophenolic acid (MPA) is an effective treatment for active lupus nephritis despite its variable efficacy in different ethnic groups. Here we tested whether pharmacokinetic monitoring may help to optimize dosing of MPA in an Asian population. Patients with biopsy-proven class III or IV lupus nephritis (ISN/RPS category) were treated with mycophenolate mofetil or enteric-coated mycophenolate sodium. One month after initiating treatment we measured plasma MPA levels in eight samples taken over a 12-h period after drug administration. The mean area under the time-dependent curve for MPA of responding patients was significantly higher than those not responding. Successful treatment was seen in patients with areas >45 mg h/l. The dosage of the drug was not related to MPA pharmacokinetics. In the mycophenolate mofetil group, however, MPA-area under the curve was positively, and significantly, correlated with trough or 1 h after dose concentrations and associated with a therapeutic response. Thus, our study shows that MPA pharmacokinetics were positively correlated with therapeutic responses of mycophenolate, suggesting that controlling the concentrations may improve its therapeutic efficacy in lupus nephritis. As the absorption and pharmacokinetic peak of enteric-coated tablets is slower, it is important to take different formulations into account when determining optimal MPA concentrations.

The Edinburgh Postnatal Depression Scale: translation and validation for a Greek sample

BackgroundEdinburgh Postnatal Depression Scale (EPDS) is an important screening instrument that is used routinely with mothers during the postpartum period for early identification of postnatal depression. The purpose of this study was to validate the Greek version of EPDS along with sensitivity, specificity and predictive values.Methods120 mothers within 12 weeks postpartum were recruited from the perinatal care registers of the Maternity Departments of 4 Hospitals of Heraklion municipality, Greece. EPDS and Beck Depression Inventory-II (BDI-II) surveys were administered in random order to the mothers. Each mother was diagnosed with depression according to the validated Greek version of BDI-II. The psychometric measurements that were performed included: two independent samples t-tests, One-way analysis of variance (ANOVA), reliability coefficients, Explanatory factor analysis using a Varimax rotation and Principal Components Method. Confirmatory analysis -known as structural equation modelling- of principal components was conducted by LISREL (Linear Structural Relations). A receiver operating characteristic (ROC) analysis was carried out to evaluate the global functioning of the scale.Results8 (6.7%) of the mothers were diagnosed with major postnatal depression, 14 (11.7%) with moderate and 38 (31.7%) with mild depression on the basis of BDI-II scores. The internal consistency of the EPDS Greek version -using Chronbach's alpha coefficient- was found 0.804 and that of Guttman split-half coefficient 0.742. Our findings confirm the multidimensionality of EPDS, demonstrating a two-factor structure which contained subscales reflecting depressive symptoms and anxiety. The Confirmatory Factor analysis demonstrated that the two factor model offered a very good fit to our data. The area under ROC curve AUC was found 0.7470 and the logistic estimate for the threshold score of 8/9 fitted the model sensitivity at 76.7% and model specificity at 68.3%.ConclusionOur data confirm the validity of the Greek version of the EPDS in identifying postnatal depression. The Greek EPDS scale could be used as a useful instrument in both clinical practice and research.

The performance of (1, 3)-beta-D-glucan and Aspergillus galactomannan measurement for early diagnosis of invasive aspergillosis in patients with hematological diseases

We analyzed the performance of (1, 3)-Beta-D-glucan (measurement by the alkaline-kinetic chromogenic Limulus method (FUNGITEC G test-MK, Fungitec) and the kinetic turbidimetric Limulus method [Beta-Glucan test WAKO, Wako]) and we carried out Aspergillus galactomannan antigen detection (enzyme-linked immunosorbent assay, ELISA test) for the early diagnosis of invasive aspergillosis in patients with hematological diseases at the time of febrile episodes of unknown origin that did not respond to antibacterial therapy for more than 3 days. During a one-year period (April 2002 to March 2003), a total of 69 febrile episodes in 58 patients were studied; 8 cases of invasive aspergillosis were diagnosed according to the definition of the European Organization for Research and Treatment of Cancer/Mycosis Study Group, and 61 cases were found to be non-mycotic diseases. Based on the analysis of 69 results with confirmed disease status, the overall performance of the Fungitec, the Wako, and the ELISA test were as follows: sensitivity was 0.88, 0.63, and 0.50, respectively, whereas the specificity was 0.85, 0.98, and 1.0, respectively. Moreover, there was a strong relationship between the log-transformed values of the (1, 3)-Beta-D-glucan levels measured by the two methods (r = 0.92 [95%CI, 0.89-0.94] ; p<0.001). For the statistical analysis of these serological tests a receiver operating characteristic curve (ROC) was used, as well as the resulting area under the ROC curve (ROC AUC). The ROC AUC and the cut-off values that gave the highest accuracy were as follows: 0.92, 24.9 pg/ml for the Fungitec, 0.84, 7.3 pg/ml for the Wako, and 0.89, 0.9 COI for the ELISA test, respectively. In conclusion, these results indicate that both of the two (1, 3)-Beta-D-glucan measurement approaches served equally well as surveillance tools for determining the extent of invasive aspergillosis; in addition, the log-transformed value of these tests can be used for comparison. Moreover, the ELISA test was found to have clinical utility, both as a surveillance and as a diagnostic tool when invasive aspergillosis was suspected. It should be noted that the galactomannan assay had sensitivity-related limitations; lowering the cut-off value is expected to increase the diagnostic value for use in cases of invasive aspergillosis.

The Edinburgh Postnatal Depression Scale: validation for an Italian sample

A sample of 113 women has been recruited for the Italian validation of Edinburgh Post Natal Depression Scale (EPDS). These women, between the eighth and the twelfth week after delivery, were admnistered the EPDS and diagnosed according the DSM-III-R criteria using the MINI Interview. At the 8/9 cut-off score the sensitivity is 94.4%, specificity 87.4% and PPV 58.6%. The internal consistency of the EPDS Italian version was tested using Chronbach's alpha coefficient (0.7894) and Guttman split-half coefficient (0.8191). Finally a receiver operating characteristic (ROC) analysis was carried out to evaluate the global functioning of the scale: the area under ROC curve AUC is 0.7470 and the logistic estimate for the threshold score of 11/12 fitted the model sensitivity at 75% and model specificity at 67%. Our data confirm the validity of EPDS in identifying postnatal depression also in its Italian version and the scale could be used as an useful instrument in the clinical practice.