Inference for covariate-adjusted time-dependent prognostic accuracy measures.

Evaluating the prognostic performance of candidate markers for future disease onset or progression is one of the major goals in medical research. A marker's prognostic performance refers to how well it separates patients at the high or low risk of a future disease state. Often the discriminative performance of a marker is affected by the patient characteristics (covariates). Time-dependent receiver operating characteristic (ROC) curves that ignore the informativeness of the covariates will lead to biased estimates of the accuracy parameters. We propose a time-dependent ROC curve that accounts for the informativeness of the covariates in the case of censored data. We propose inverse probability weighted (IPW) estimators for estimating the proposed accuracy parameters. We investigate the performance of the IPW estimators through simulation studies and real-life data analysis.

Utility of urinary presepsin in the diagnosis of pyelonephritis: a cross-sectional study.

BACKGROUND: Presepsin is produced during the phagocytosis of bacteria by granulocytes. Presepsin increases at the site of infection; however, the significance of urinary presepsin in pyelonephritis is unknown. This study aimed to evaluate whether measuring urinary presepsin can distinguish between pyelonephritis and nonpyelonephritis. METHODS: A cross-sectional study of patients with suspected pyelonephritis was conducted. Urinary presepsin at admission was compared between the pyelonephritis and nonpyelonephritis groups using the Mann-Whitney test. The predictive accuracy of urinary presepsin for diagnosing pyelonephritis was evaluated by the area under the receiver operating characteristics (ROC) analysis curve. RESULTS: A total of 35 eligible participants were included in the pyelonephritis group and 25 in the nonpyelonephritis group. The median urinary presepsin level was 2232.0 (interquartile range [IQR], 1029.0-3907.0) pg/mL in the pyelonephritis group and 1348.0 (IQR, 614.5-2304.8) pg/mL in the nonpyelonephritis group. Urinary presepsin concentrations were significantly higher in the pyelonephritis group than in the nonpyelonephritis group (P = 0.023). ROC analysis of urinary presepsin revealed a cutoff value of 3650 pg/mL to distinguish between the pyelonephritis and nonpyelonephritis groups. Sensitivity, specificity, positive predictive value, negative predictive value, positive likelihood ratio, and negative likelihood ratio for the diagnosis of pyelonephritis were 0.40 (95% confidence interval [CI], 0.24-0.58), 0.96 (95% CI, 0.79-1.00), 0.93 (95% CI, 0.68-1.00), 0.52 (95% CI, 0.37-0.68), 9.60 (95% CI, 1.35-68.23), and 0.62 (95% CI, 0.47-0.83), respectively. CONCLUSIONS: The measurement of urinary presepsin is useful in differentiating pyelonephritis from other diseases.

Favorable factors for the survival of ST-segment elevation myocardial infarction patients with medium- and high-risk thrombolysis in myocardial infarction scores.

OBJECTIVE: ST-segment myocardial infarction (STEMI) is a time-sensitive emergency. This study screened the favorable factors for the survival of STEMI patients with medium- and high-risk thrombolysis in myocardial infarction (TIMI) scores. METHODS: According to the TIMI scores at admission, 433 STEMI patients were retrospectively and consecutively selected and allocated into low-/medium-/high-risk groups, with their general information/blood routine/biochemical indicators/coagulation indicators documented. The factors influencing the in-hospital survival of STEMI patients were analyzed using univariate and multivariate logistic regression analyses. Moreover, the predictive value of favorable factors was analyzed by receiver operating characteristics (ROC) curve, and patients were assigned into high/low level groups based on the cut-off value of these factors, with their in-hospital survival rates compared. RESULTS: The in-hospital survival rate of the medium-/high-risk groups was lower than that of the low-risk group. Emergency percutaneous coronary intervention (PCI), lymphocyte (LYM), total protein (TP), albumin (ALB), and sodium (Na) were independent favorable factors for in-hospital survival in the medium-/high-risk groups. Besides, LYM > 1.275 × 109/L, TP > 60.25 g/L, ALB > 34.55 g/L, and Na > 137.9 mmo1/L had auxiliary predictive value for the survival of STEMI patients with medium-/high-risk TIMI scores. Patients with high levels of LYM, TP, ALB, and Na exhibited higher in-hospital survival rates than patients with low levels. CONCLUSION: For STEMI patients with medium- and high-risk TIMI scores, accepting emergency PCI and normal levels of LYM, TP, ALB, and Na were more conducive to in-hospital survival.

Correlation between B-cell lymphoma 6 with the balance of T helper-1/2 and severity of allergic rhinitis.

OBJECTIVE: Allergic rhinitis (AR) is a prevailing immune disorder affecting the nasal mucosa. B-cell lymphoma 6 (BCL6) imposes essential roles in immunity. This study probed into the serum expression of BCL6 and its effect on AR diagnosis and patients' quality of life (QOL). METHODS: A total of 113 patients with AR including 38 cases with mild AR (MAR) and 75 cases with moderate-severe AR (MSAR) were enrolled, with 101 healthy people enrolled as control. Serum expression of BCL6 was detected by RT-qPCR and the diagnostic efficacy of BCL6 for AR was analyzed using the receiver operating characteristic curve. The proportion of T helper-1/2 (Th1/Th2) cells in CD4+ T cells in peripheral blood mononuclear cells was detected using flow cytometry. The correlation between BCL6 and Th1/Th2 cells and the effects of BCL6 expression on patients' QOL were assessed by Pearson analysis and Mini-RQLQ questionnaire. RESULTS: BCL6 was downregulated in patients with AR, serum BCL6 level < 0.8450 had certain auxiliary diagnostic values for AR, and serum BCL6 level < 0.5400 could assist the diagnosis of AR severity. Th1 cell proportion in CD4+ T cells was decreased, whereas Th2 cell proportion was increased with AR severity. BCL6 was positively-linked with Th1 cells but inversely-correlated with Th2 cells in patients with AR. Patients with AR with low BCL6 expression had a poorer QOL compared with high BCL6 expression. The domains most affected by BCL6 expression were practical problems, nasal symptoms, and lacrimation. CONCLUSION: Serum BCL6 is downregulated and low BCL6 expression greatly deteriorates QOL in patients with AR.

EMD-Based Energy Spectrum Entropy Distribution Signal Detection Methods for Marine Mammal Vocalizations.

To develop a passive acoustic monitoring system for diversity detection and thereby adapt to the challenges of a complex marine environment, this study harnesses the advantages of empirical mode decomposition in analyzing nonstationary signals and introduces energy characteristics analysis and entropy of information theory to detect marine mammal vocalizations. The proposed detection algorithm has five main steps: sampling, energy characteristics analysis, marginal frequency distribution, feature extraction, and detection, which involve four signal feature extraction and analysis algorithms: energy ratio distribution (ERD), energy spectrum distribution (ESD), energy spectrum entropy distribution (ESED), and concentrated energy spectrum entropy distribution (CESED). In an experiment on 500 sampled signals (blue whale vocalizations), in the competent intrinsic mode function (IMF2) signal feature extraction function distribution of ERD, ESD, ESED, and CESED, the areas under the curves (AUCs) of the receiver operating characteristic (ROC) curves were 0.4621, 0.6162, 0.3894, and 0.8979, respectively; the Accuracy scores were 49.90%, 60.40%, 47.50%, and 80.84%, respectively; the Precision scores were 31.19%, 44.89%, 29.44%, and 68.20%, respectively; the Recall scores were 42.83%, 57.71%, 36.00%, and 84.57%, respectively; and the F1 scores were 37.41%, 50.50%, 32.39%, and 75.51%, respectively, based on the threshold of the optimal estimated results. It is clear that the CESED detector outperforms the other three detectors in signal detection and achieves efficient sound detection of marine mammals.

Low LncRNA LUCAT1 Expression Assists in the Diagnosis of Chronic Heart Failure and Predicts Poor Prognosis.

Chronic heart failure (CHF) is a complicated syndrome caused by structural and functional abnormalities. Long noncoding RNA (LncRNA) lung cancer-associated transcript 1 (LUCAT1) downregulation inhibits cardiomyocyte apoptosis. This study aimed to measure LUCAT1 expression in patients with CHF and to explore its clinical value on CHF diagnosis and prognosis. A total of 94 patients with CHF and 90 participants without CHF were registered, followed by recording of their clinical characteristics and grading of their cardiac function. LUCAT1 expression in sera of patients with CHF and participants without CHF was detected. The correlation of LUCAT1 with brain natriuretic peptide (BNP) and left ventricular ejection fraction (LVEF) in patients with CHF and the diagnostic efficiency of LUCAT1, BNP, and LUCAT1 combined with BNP on patients with CHF were analyzed. Patients with CHF were treated with conventional drugs and followed up. The LUCAT1 expression in patients with CHF was lower than that in participants without CHF and was downregulated with the increase of New York Heart Association stage. LUCAT1 expression was negatively associated with BNP but positively associated with LVEF in the sera of patients with CHF. The receiver operating characteristic curve of LUCAT1 combined with BNP had better result than that of LUCAT1 and BNP alone. Low LUCAT1 expression indicated poor prognosis of patients with CHF and was an independent prognostic factor for the survival of patients with CHF. To summarize, low lncRNA LUCAT1 expression might help diagnose and predict the poor prognosis of CHF.

Narrative recall in relapsing-remitting multiple sclerosis: A potentially useful speech task for detecting subtle cognitive changes.

Our research studied relapsing-remitting multiple sclerosis (RRMS). In half of the RRMS cases, mild cognitive difficulties are present, but often remain undetected despite their adverse effects on individuals' daily life. Detecting subtle cognitive alterations using speech analysis have rarely been implemented in MS research. We applied automatic speech recognition technology to devise a speech task with potential diagnostic value. Therefore, we used two narrative tasks adjusted for the neural and cognitive characteristics of RRMS; namely narrative recall and personal narrative. In addition to speech analysis, we examined the information processing speed, working memory, verbal fluency, and naming skills. Twenty-one participants with RRMS and 21 gender-, age-, and education-matched healthy controls took part in the study. All the participants with RRMS achieved a normal performance on Addenbrooke's Cognitive Examination. The following parameters of speech were measured: articulation and speech rate, the proportion, duration, frequency, and average length of silent and filled pauses. We found significant differences in the temporal parameters between groups and speech tasks. ROC analysis produced high classification accuracy for the narrative recall task (0.877 and 0.866), but low accuracy for the personal narrative task (0.617 and 0.592). The information processing speed affected the speech of the RRMS group but not that of the control group. The higher cognitive load of the narrative recall task may be the cause of significant changes in the speech of the RRMS group relative to the controls. Results suggest that narrative recall task may be effective for detecting subtle cognitive changes in RRMS.

Metabolite selection for machine learning in childhood brain tumour classification.

MRS can provide high accuracy in the diagnosis of childhood brain tumours when combined with machine learning. A feature selection method such as principal component analysis is commonly used to reduce the dimensionality of metabolite profiles prior to classification. However, an alternative approach of identifying the optimal set of metabolites has not been fully evaluated, possibly due to the challenges of defining this for a multi-class problem. This study aims to investigate metabolite selection from in vivo MRS for childhood brain tumour classification. Multi-site 1.5 T and 3 T cohorts of patients with a brain tumour and histological diagnosis of ependymoma, medulloblastoma and pilocytic astrocytoma were retrospectively evaluated. Dimensionality reduction was undertaken by selecting metabolite concentrations through multi-class receiver operating characteristics and compared with principal component analysis. Classification accuracy was determined through leave-one-out and k-fold cross-validation. Metabolites identified as crucial in tumour classification include myo-inositol (P < 0.05, AUC=0.81±0.01 ), total lipids and macromolecules at 0.9 ppm (P < 0.05, AUC=0.78±0.01 ) and total creatine (P < 0.05, AUC=0.77±0.01 ) for the 1.5 T cohort, and glycine (P < 0.05, AUC=0.79±0.01 ), total N-acetylaspartate (P < 0.05, AUC=0.79±0.01 ) and total choline (P < 0.05, AUC=0.75±0.01 ) for the 3 T cohort. Compared with the principal components, the selected metabolites were able to provide significantly improved discrimination between the tumours through most classifiers (P < 0.05). The highest balanced classification accuracy determined through leave-one-out cross-validation was 85% for 1.5 T 1 H-MRS through support vector machine and 75% for 3 T 1 H-MRS through linear discriminant analysis after oversampling the minority. The study suggests that a group of crucial metabolites helps to achieve better discrimination between childhood brain tumours.

IgA rheumatoid factor in rheumatoid arthritis.

OBJECTIVES: Rheumatoid factor (RF) is a well-established marker for the diagnosis and classification of rheumatoid arthritis (RA). Most studies evaluated IgM RF or isotype-nonspecific total RF assays. We evaluated the added value of IgA RF in this context. METHODS: An international sample cohort consisting of samples from 398 RA patients and 1073 controls was tested for IgA RF with 3 commercial assays. For all RA patients and 100 controls essential clinical and serological data for ACR/EULAR classification were available. RESULTS: The sensitivity of IgA RF for diagnosing RA was lower than the sensitivity of IgM RF. Differences in numerical values between IgA RF assays were observed. With all assays, the highest IgA RF values were found in patients with primary Sjögren's syndrome. Double positivity for IgM RF and IgA RF had a higher specificity for RA than either IgM RF or IgA RF. The sensitivity of double positivity was lower than the sensitivity of either IgA RF or IgM RF. Single positivity for IgA RF was at least as prevalent in controls than in RA patients. Adding IgA RF to IgM RF and anti-citrullinated protein antibodies (ACPA) did not affect RA classification. However, combined positivity for IgA RF, IgM RF and IgG ACPA had a higher specificity and lower sensitivity for RA classification than positivity for either of the antibodies. CONCLUSIONS: IgA RF showed a lower sensitivity than IgM RF. Combining IgA RF with IgM RF and ACPA did not improve sensitivity of RA classification. Combined positivity (IgA-RF/IgM-RF/ACPA) increased specificity.

Using noninvasive anthropometric indices to develop and validate a predictive model for metabolic syndrome in Chinese adults: a nationwide study.

PURPOSE: Metabolic syndrome (Mets) is a pathological condition that includes many abnormal metabolic components and requires a simple detection method for rapid use in a large population. The aim of the study was to develop a diagnostic model for Mets in a Chinese population with noninvasive anthropometric and demographic predictors. PATIENTS AND METHODS: Least absolute shrinkage and selection operator (LASSO) regression was used to screen predictors. A large sample from the China National Diabetes and Metabolic Disorders Survey (CNDMDS) was used to develop the model with logistic regression, and internal, internal-external and external validation were conducted to evaluate the model performance. A score calculator was developed to display the final model. RESULTS: We evaluated the discrimination and calibration of the model by receiver operator characteristic (ROC) curves and calibration curve analysis. The area under the ROC curves (AUCs) and the Brier score of the original model were 0.88 and 0.122, respectively. The mean AUCs and the mean Brier score of 10-fold cross validation were 0.879 and 0.122, respectively. The mean AUCs and the mean Brier score of internal-external validation were 0.878 and 0.121, respectively. The AUCs and Brier score of external validation were 0.862 and 0.133, respectively. CONCLUSIONS: The model developed in this study has good discrimination and calibration performance. Its stability was proved by internal validation, external validation and internal-external validation. Then, this model has been displayed by a calculator which can exhibit the specific predictive probability for easy use in Chinese population.

Diagnostic performance and neural basis of the combination of free- and pre-drawn Clock Drawing Test.

OBJECTIVES: This study aimed to clarify the diagnostic performance and neural basis of the Clock Drawing Test (CDT) combining free- and pre-drawn methods. METHODS: This retrospective study included 165 participants (91 with Alzheimer disease [AD], 52 with amnestic mild cognitive impairment [aMCI], and 22 healthy controls [HC]), who were divided into four groups according to their free- and pre-drawn CDT scores: group 1, could do both; group 2, impaired in both; group 3, impaired in pre-drawn CDT; and group 4, impaired in free-drawn CDT. The diagnostic performances of the free-drawn, pre-drawn, and combination methods were compared using receiver operating characteristics analysis; in voxel-based morphometry analysis, the gray matter (GM) volume of groups 2-4 were compared with that of group 1. RESULTS: The area under the curve of the combination method was greater than that of the free- or pre-drawn method alone when comparing AD with HC or aMCI. Group 2 had a significantly smaller GM volume in the bilateral temporal lobes than group 1. Group 3 had a trend toward smaller GM volumes in the right temporal lobe when a liberal threshold was applied. Group 4 had significantly smaller GM volumes in the left temporal lobe than group 1. CONCLUSIONS: This study suggests that the combination method may be able to screen for a wider range of brain dysfunction. Combined use of free- and pre-drawn CDT may be useful for screening for AD and its early detection and treatment.

Strong evidence for age as the single most dominant predictor of medically supervised driving test-mini mental status test outcomes provide only weak but significant moderate additional predictive value.

BACKGROUND: With age, medical conditions impairing safe driving accumulate. Consequently, the risk of accidents increases. To mitigate this risk, Swiss law requires biannual assessments of the fitness to drive of elderly drivers. Drivers may prove their cognitive and physical capacity for safe driving in a medically supervised driving test (MSDT) when borderline cases, as indicated by low performance in a set of four cognitive tests, including e.g. the mini mental status test (MMST). Any prognostic, rather than indicative, relations for MSDT outcomes have neither been confirmed nor falsified so far. In order to avoid use of unsubstantiated rules of thumb, we here evaluate the predictive value for MSDT outcomes of the outcomes of the standard set of four cognitive tests, used in Swiss traffic medicine examinations. METHODS: We present descriptive information on age, gender and cognitive pretesting results of all MSDTs recorded in our case database from 2017 to 2019. Based on these retrospective cohort data, we used logistic regression to predict the binary outcome MSDT. An exploratory analysis used all available data (model 1). Based on the Akaike Information Criterion (AIC), we then established a model including variables age and MMST (model 2). To evaluate the predictive value of the four cognitive assessments, model 3 included cognitive test outcomes only. Receiver operating characteristics (ROC) and area under the curve (AUC) allowed evaluating discriminative performance of the three different models using independent validation data. RESULTS: Using N = 188 complete data sets of a total of 225 included cases, AIC identified age (p < 0.0008) and MMST (p = 0.024) as dominating predictors for MSDT outcomes with a median AUC of 0.71 (95%-CI 0.57-0.85) across different training and validation splits, while using the four cognitive test results exclusively yielded a median AUC of 0.55 (95%-CI 0.40-0.71). CONCLUSIONS: Our analysis provided strong evidence for age as the single most dominant predictor of MSDT outcomes. Adding MMST provides only weak additional predictive value for MSDT outcomes. Combining the results of four cognitive test used as standard screen in Swiss traffic medicine alone, proved to be of poor predictive value. This highlights the importance of MSDTs for balancing between the mitigation of risks by and the right to drive for the elderly.

Total Hip Replacement Influences Spinopelvic Mobility: A Prospective Observational Study.

BACKGROUND: Abnormal spinopelvic mobility is identified as a contributing element of total hip arthroplasty (THA) instability. Preoperative identification of THA patients at risk is still a remaining challenge. We therefore conducted this study to (1) evaluate if preoperative and postoperative spinopelvic mobility differs, (2) determine the interactions between the elements of the spinopelvic complex, and (3) identify preoperative parameters for predicting spinopelvic mobility. METHODS: A prospective observational study assessing 197 THA patients was conducted with biplanar stereoradiography in standing and relaxed sitting positions preoperatively and postoperatively. Two independent investigators determined spinopelvic mobility based on 2 different classifications (Δ sacral slope [SS] and Δ pelvic tilt [PT]; Δ from standing to sitting; Δ < 10° stiff, Δ ≥ 10°-30° normal, Δ > 30° hypermobile). Multiple regression analysis and receiver operating characteristic analysis were used to identify predictors for postoperative spinopelvic mobility. RESULTS: Spinopelvic mobility significantly increased after THA based on ΔPT (Pre/Post: 18.5°/22.8°; P < .000) and ΔSS (Pre/Post 17.9°/22.4°; P < .000). A distinct shift in the ratio from stiff (Pre/Post: 24%/9.7%) to hypermobile (Pre/Post: 10.2%/22.1%) mobility postoperatively was observed. Receiver operating characteristic analysis predicted postoperative stiffness using preoperative PTStanding ≥ 13.0° with a sensitivity of 90% and a specificity of 51% and hypermobility with preoperative SSStanding ≥ 35.2° with a sensitivity of 81% and a specificity of 34%. Age at surgery, preoperative PTStanding, and pelvic incidence were independent predictors of spinopelvic mobility (R2 = 0.24). CONCLUSION: Definition of preoperative stiffness should be interpreted with caution by arthroplasty surgeons as mobility itself is influenced by THA. For the first time thresholds for standing preoperative parameters for predicting postoperative spinopelvic mobility could be provided. For preoperative standing only lateral assessment could serve as a screening tool for spinopelvic mobility.

Summary concordance index for meta-analysis of prognosis studies with a survival outcome.

In prognosis studies to evaluate association between a continuous biomarker and a survival outcome, investigators often classify subjects into two subclasses of the high- and low-expression groups and apply simple survival analysis techniques of the Kaplan-Meier method and the logrank test. The high- and low-expressions are defined according to whether or not the observation of the biomarker is higher than the cut-off value, which is heterogeneous across studies. The heterogeneous definitions of the cut-off value make it difficult to apply the standard meta-analysis techniques. We propose a method to estimate the concordance index for a survival outcome synthesizing published prognosis studies, in which the Kaplan-Meier estimates for the high- and low-expression groups are reported. We illustrate our proposed method with a real dataset for meta-analysis of prognosis studies evaluating Ki-67 in early breast cancer and evaluate its performance with a simulation study.

Hypoxia induced factor-1α levels in patients undergoing adenoidectomy.

Among  the most common causes of nasal congestion in childhood is adenoid hypertrophy (AH) which leads to hypoxia. In this study, we studied plasma concentrations of hypoxia induced factor-1α (HIF-1α) in children undergoing adenoidectomy. The study included a total of 86 participants: 39 patients with AH and 47 healthy individuals. Serum HIF-1α levels (ng/mL) were measured by ELISA. HIF-1α concentrations were compared to the adenoid-nasopharyngeal ratio (ANR) of patients with AH, as recorded in the medical records. We found significantly higher concentrations of HIF-1α (0.30 ± 0.47 ng/mL) in patients with AH as compared to healthy controls (0.24 ± 0.07 ng/mL, p = .011). HIF-1α levels were not significantly different regarding gender between patients with AH (p = .77) and in the control group (p = .97). In patients with AH, there was a moderately significant positive correlation between HIF-1α levels and Hb (p = .000), (correlation coefficient r = 0.542). There was a positive correlation between HIF-1α and ANR in patients with AH (p = .005, r = 0.439). This study indicates that AH increases HIF-1α levels. We also observed a moderately significant positive correlation between HIF-1α and ANR in patients with AH. HIF-1α levels are a potential biomarker for hypoxia in patients with AH.

A machine learning classifier approach for identifying the determinants of under-five child undernutrition in Ethiopian administrative zones.

BACKGROUND: Undernutrition is the main cause of child death in developing countries. This paper aimed to explore the efficacy of machine learning (ML) approaches in predicting under-five undernutrition in Ethiopian administrative zones and to identify the most important predictors. METHOD: The study employed ML techniques using retrospective cross-sectional survey data from Ethiopia, a national-representative data collected in the year (2000, 2005, 2011, and 2016). We explored six commonly used ML algorithms; Logistic regression, Least Absolute Shrinkage and Selection Operator (L-1 regularization logistic regression), L-2 regularization (Ridge), Elastic net, neural network, and random forest (RF). Sensitivity, specificity, accuracy, and area under the curve were used to evaluate the performance of those models. RESULTS: Based on different performance evaluations, the RF algorithm was selected as the best ML model. In the order of importance; urban-rural settlement, literacy rate of parents, and place of residence were the major determinants of disparities of nutritional status for under-five children among Ethiopian administrative zones. CONCLUSION: Our results showed that the considered machine learning classification algorithms can effectively predict the under-five undernutrition status in Ethiopian administrative zones. Persistent under-five undernutrition status was found in the northern part of Ethiopia. The identification of such high-risk zones could provide useful information to decision-makers trying to reduce child undernutrition.

Computational Prediction of Compound-Protein Interactions for Orphan Targets Using CGBVS.

A variety of Artificial Intelligence (AI)-based (Machine Learning) techniques have been developed with regard to in silico prediction of Compound-Protein interactions (CPI)-one of which is a technique we refer to as chemical genomics-based virtual screening (CGBVS). Prediction calculations done via pairwise kernel-based support vector machine (SVM) is the main feature of CGBVS which gives high prediction accuracy, with simple implementation and easy handling. We studied whether the CGBVS technique can identify ligands for targets without ligand information (orphan targets) using data from G protein-coupled receptor (GPCR) families. As the validation method, we tested whether the ligand prediction was correct for a virtual orphan GPCR in which all ligand information for one selected target was omitted from the training data. We have specifically expressed the results of this study as applicability index and developed a method to determine whether CGBVS can be used to predict GPCR ligands. Validation results showed that the prediction accuracy of each GPCR differed greatly, but models using Multiple Sequence Alignment (MSA) as the protein descriptor performed well in terms of overall prediction accuracy. We also discovered that the effect of the type compound descriptors on the prediction accuracy was less significant than that of the type of protein descriptors used. Furthermore, we found that the accuracy of the ligand prediction depends on the amount of ligand information with regard to GPCRs related to the target. Additionally, the prediction accuracy tends to be high if a large amount of ligand information for related proteins is used in the training.

The development and validation of an algorithm to predict future depression onset in unselected youth.

BACKGROUND: Universal depression screening in youth typically focuses on strategies for identifying current distress and impairment. However, these protocols also play a critical role in primary prevention initiatives that depend on correctly estimating future depression risk. Thus, the present study aimed to identify the best screening approach for predicting depression onset in youth. METHODS: Two multi-wave longitudinal studies (N = 591, AgeM = 11.74; N = 348, AgeM = 12.56) were used as the 'test' and 'validation' datasets among youth who did not present with a history of clinical depression. Youth and caregivers completed inventories for depressive symptoms, adversity exposure (including maternal depression), social/academic impairment, cognitive vulnerabilities (rumination, dysfunctional attitudes, and negative cognitive style), and emotional predispositions (negative and positive affect) at baseline. Subsequently, multi-informant diagnostic interviews were completed every 6 months for 2 years. RESULTS: Self-reported rumination, social/academic impairment, and negative affect best predicted first depression onsets in youth across both samples. Self- and parent-reported depressive symptoms did not consistently predict depression onset after controlling for other predictors. Youth with high scores on the three inventories were approximately twice as likely to experience a future first depressive episode compared to the sample average. Results suggested that one's likelihood of developing depression could be estimated based on subthreshold and threshold risk scores. CONCLUSIONS: Most pediatric depression screening protocols assess current manifestations of depressive symptoms. Screening for prospective first onsets of depressive episodes can be better accomplished via an algorithm incorporating rumination, negative affect, and impairment.

A three-tier process for screening depression and anxiety among children and adolescents with cancer.

OBJECTIVE: To establish and to evaluate the effectiveness of a three-tier screening process of depressive and anxiety disorders among children and adolescents with cancer based on questionnaires (first tier), semistructured psychiatric interviews (second tier), and referral for psychiatric assessment and recommendations for treatment (third tier). We also aimed to determine the rates of depressive and anxiety disorders among participants. METHODS: Participants and their parents completed the Patient Reported Outcomes Measurement Information System (PROMIS) Depression and Anxiety modules. Then, they were interviewed separately using the semistructured Affective and Anxiety Modules of the Schedule for Affective Disorders and Schizophrenia for School-Age Children (K-SADS). PROMIS cutoff values for diagnosing depressive and anxiety disorders, based on the K-SADS, were calculated by receiver-operating characteristics (ROCs). RESULTS: Of 91 participants 34 (37.4%) aged 7 to 21 years with cancer met the K-SADS criteria for depressive and/or anxiety disorders. The results of the ROC analyses were stronger for depressive disorders than for anxiety disorders and for more severe cases. The cutoff of 13 on the child-reported PROMIS for a major depressive episode had a sensitivity of 0.80 and a specificity of 0.82, and a cutoff of 14 on the parent-reported PROMIS for generalized anxiety disorder had a sensitivity of 0.78 and a specificity of 0.79. CONCLUSIONS: Using the K-SADS, we found that anxiety and depressive disorders are very common in youngsters with cancer. The three-tier screening process we developed for depression and anxiety in this population provides practical cutoff values for identifying depressive and anxiety disorders in children with cancer.

An integrated approach for identification of exon locations using recursive Gauss Newton tuned adaptive Kaiser window.

Identification of exon location in a DNA sequence has been considered as the most demanding and challenging research topic in the field of Bioinformatics. This work proposes a robust approach combining the Trigonometric mapping with Adaptive tuned Kaiser Windowing approach for locating the protein coding regions (EXONS) in a genetic sequence. For better convergence as well as improved accurateness, the side lobe height control parameter (ß) of Kaiser Window in the proposed algorithm is made adaptive to track the changing dynamics of the genetic sequence. This yields better tracking potential of the anticipated Adaptive Kaiser algorithm as it uses the recursive Gauss Newton tuning which in turn utilizes the covariance of the error signal to tune the ß factor which has been shown through numerous simulation results under a variety of practical test conditions. A detailed comparative analysis with the existing mapping schemes, windowing techniques, and other signal processing methods like SVD, AN, DFT, STDFT, WT, and ST has also been included in the paper to focus on the strength and efficiency of the proposed approach. Moreover, some critical performance parameters have been computed using the proposed approach to investigate the effectiveness and robustness of the algorithm. In addition to this, the proposed approach has also been successfully applied on a number of benchmark gene sets like Musmusculus, Homosapiens, and C. elegans, etc., where the proposed approach revealed efficient prediction of exon location in contrast to the other existing mapping methods.