Neurological pain (NP) is always accompanied by symptoms of depression, which seriously affects physical and mental health. In this study, we identified the common hub genes (Co-hub genes) and related immune cells of NP and major depressive disorder (MDD) to determine whether they have common pathological and molecular mechanisms. NP and MDD expression data was downloaded from the Gene Expression Omnibus (GEO) database. Common differentially expressed genes (Co-DEGs) for NP and MDD were extracted and the hub genes and hub nodes were mined. Co-DEGs, hub genes, and hub nodes were analyzed for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment. Finally, the hub nodes, and genes were analyzed to obtain Co-hub genes. We plotted Receiver operating characteristic (ROC) curves to evaluate the diagnostic impact of the Co-hub genes on MDD and NP. We also identified the immune-infiltrating cell component by ssGSEA and analyzed the relationship. For the GO and KEGG enrichment analyses, 93 Co-DEGs were associated with biological processes (BP), such as fibrinolysis, cell composition (CC), such as tertiary granules, and pathways, such as complement, and coagulation cascades. A differential gene expression analysis revealed significant differences between the Co-hub genes ANGPT2, MMP9, PLAU, and TIMP2. There was some accuracy in the diagnosis of NP based on the expression of ANGPT2 and MMP9. Analysis of differences in the immune cell components indicated an abundance of activated dendritic cells, effector memory CD8+ T cells, memory B cells, and regulatory T cells in both groups, which were statistically significant. In summary, we identified 6 Co-hub genes and 4 immune cell types related to NP and MDD. Further studies are needed to determine the role of these genes and immune cells as potential diagnostic markers or therapeutic targets in NP and MDD.

The study utilized Fourier transform infrared (FTIR) spectroscopy coupled with chemometrics to investigate protein composition and structural changes in the blood serum of patients with polycythemia vera (PV). Principal component analysis (PCA) revealed distinct biochemical properties, highlighting elevated absorbance of phospholipids, amides, and lipids in PV patients compared to healthy controls. Ratios of amide I/amide II and amide I/amide III indicated alterations in protein structures. Support vector machine analysis and receiver operating characteristic curves identified amide I as a crucial predictor of PV, achieving 100% accuracy, sensitivity, and specificity, while amide III showed a lower predictive value (70%). PCA analysis demonstrated effective differentiation between PV patients and controls, with key wavenumbers including amide II, amide I, and CH lipid vibrations. These findings underscore the potential of FTIR spectroscopy for diagnosing and monitoring PV.

Sleep is a basic, physiological requirement for living things to survive and is a process that covers one third of our lives. Melatonin is a hormone that plays an important role in the regulation of sleep. Sleep deprivation affect brain structures and functions. Sleep deprivation causes a decrease in brain activity, with particularly negative effects on the hippocampus and prefrontal cortex. Despite the essential role of protein and lipids vibrations, polysaccharides, fatty acid side chains functional groups, and ratios between amides in brain structures and functions, the brain chemical profile exposed to gentle handling sleep deprivation model versus Melatonin exposure remains unexplored. Therefore, the present study, aims to investigate a molecular profile of these regions using FTIR spectroscopy measurement\'s analysis based on lipidomic approach with chemometrics and multivariate analysis to evaluate changes in lipid composition in the hippocampus, prefrontal regions of the brain. In this study, C57BL/6J mice were randomly assigned to either the control or sleep deprivation group, resulting in four experimental groups: Control (C) (n = 6), Control + Melatonin (C + M) (n = 6), Sleep Deprivation (S) (n = 6), and Sleep Deprivation + Melatonin (S + M) (n = 6). Interventions were administered each morning via intraperitoneal injections of melatonin (10 mg/kg) or vehicle solution (%1 ethanol + saline), while the S and S + M groups underwent 6 h of daily sleep deprivation from using the Gentle Handling method. All mice were individually housed in cages with ad libitum access to food and water within a 12-hour light-dark cycle. Results presented that the brain regions affected by insomnia. The structure of phospholipids, changed. Yet, not only changes in lipids but also in amides were noticed in hippocampus and prefrontal cortex tissues. Additionally, FTIR results showed that melatonin affected the lipids as well as the amides fraction in cortex and hippocampus collected from both control and sleep deprivation groups.

UNASSIGNED: The Hamilton Depression Rating Scale (HDRS-17) and the Hamilton Anxiety Rating Scale (HARS-14) have been acknowledged as gold standards in evaluating the severity of depression and anxiety. The specificity and sensitivity of these scales in predicting somatic complaints of depression and anxiety are issues in both clinical and research areas. The present study proposes a new model to enhance the sensitivity and specificity of HDRS-17 and HARS-14 for predicting symptoms of insomnia, inappetence, and loss of libido in psychiatric patients.
UNASSIGNED: This study included 1507 patients diagnosed withbipolar disorder, depression, panic disorder, obsessive-compulsive disorder, and generalized anxiety disorder. The HDRS-17 and the HARS-14 were utilized as predictive scales for the prediction of patients\' sleep, appetite, and libido. The sensitivity and specificity were computed using the receiver operating characteristic (ROC). Logistic regression was performed to enhance the predictive values. The predictive value of the logistic regression model was not satisfactory, and a conversion table was therefore designed for each symptom-diagnosis subgroup. The new joint ROC model was then used to recalculate the sensitivity and specificity of the 2 scales for each symptom-diagnosis subgroup. The outcome is a prediction table, presented for use by clinicians.
UNASSIGNED: It was observed that the new statistical model, the joint ROC, increased the sensitivity and specificity of the HDRS-17 and the HARS-14.
UNASSIGNED: : Based on the results of the evaluations with the HDRS and the HARS, the joint ROC method was developed to better predict the presence of symptoms.

UNASSIGNED: Rising clarithromycin resistance undermines Helicobacter pylori (H. pylori) treatment efficacy. We aimed to determine clarithromycin\'s minimum inhibitory concentration (MIC) levels and identify specific mutation sites in the 23S ribosomal subunit (23S rRNA) that predict treatment outcomes in a 14-day regimen of clarithromycin bismuth quadruple therapy (amoxicillin 1g, clarithromycin 500 mg, rabeprazole 10 mg, and colloidal bismuth pectin 200 mg).
UNASSIGNED: We included adult H. pylori patients who hadn\'t previously undergone clarithromycin-based treatment, either as initial or rescue therapy. Exclusions were made for penicillin allergy, recent use of related medications, severe illnesses, or inability to cooperate. Patients underwent a 14-day clarithromycin bismuth quadruple therapy. Gastric mucosa specimens were obtained during endoscopy before eradication. MIC against amoxicillin and clarithromycin was determined using the E-test method. The receiver operating characteristic (ROC) curve helped to find the optimal clarithromycin resistance MIC breakpoint. Genetic sequences of H. pylori 23S rRNA were identified through Sanger Sequencing. (ChiCTR2200061476).
UNASSIGNED: Out of 196 patients recruited, 92 met the inclusion criteria for the per-protocol (PP) population. The overall intention-to-treat (ITT) eradication rate was 80.00 % (84/105), while the modified intention-to-treat (MITT) and PP eradication rates were 90.32 % (84/93) and 91.30 % (84/92) respectively. No amoxicillin resistance was observed, but clarithromycin resistance rates were 36.19 % (38/105), 35.48 % (33/93), and 34.78 % (33/92) in the ITT, MITT, and PP populations respectively. Compared with the traditional clarithromycin resistance breakpoint of 0.25 μg/mL, a MIC threshold of 12 μg/mL predicted better eradication. Among 173 mutations on 152 sites in the 23S rRNA gene, only the 2143A > G mutation could predict eradication outcomes (p < 0.000).
UNASSIGNED: Interpretation of elevated MIC values is crucial in susceptibility testing, rather than a binary \"susceptible\" or \"resistant\" classification. The 2143A > G mutation has limited specificity in predicting eradication outcomes, necessitating further investigation into additional mutation sites associated with clarithromycin resistance.

The area under the Receiver Operating Characteristic (ROC) curve (AUC) is a standard metric for quantifying and comparing binary classifiers. Real world applications often require classification into multiple (more than two) classes. For multi-class classifiers that produce class membership scores, a popular multi-class AUC (MAUC) variant is to average the pairwise AUC values [1]. Due to the complicated correlation patterns, the variance of MAUC is often estimated numerically using resampling techniques. This work is a generalization of DeLong\'s nonparameteric approach for binary AUC analysis [2] to MAUC. We first derive the closed-form expression of the covariance matrix of the pairwise AUCs within a single MAUC. Then by dropping higher order terms, we obtain an approximate covariance matrix with a compact, matrix factorization form, which then serves as the basis for variance estimation of a single MAUC. We further extend this approach to estimate the covariance of correlated MAUCs that arise from multiple competing classifiers. For the special case of binary correlated AUCs, our results coincide with that of DeLong. Our numerical studies confirm the accuracy of the variance and covariance estimates. We provide the source code of the proposed covariance estimation of correlated MAUCs on GitHub (https://tinyurl.com/euj6wvsz) for its easy adoption by machine learning and statistical analysis packages to quantify and compare multi-class classifiers.

This retrospective study was conducted at a medical center in southern Taiwan to assess the accuracy of the Hendrich II Fall Risk Model (HIIFRM) in predicting falls. Sensitivity, specificity, accuracy, and optimal cutoff points were analyzed using receiver operating characteristic (ROC) curves. Data analysis was conducted using information from the electronic medical record and patient safety reporting systems, capturing 303 fall events and 47,146 non-fall events. Results revealed that at the standard threshold of HIIFRM score ≥5, the median score in the fall group was significantly higher than in the non-fall group. The top three units with HIIFRM scores exceeding 5 were the internal medicine (50.6%), surgical (26.5%), and oncology wards (14.1%), indicating a higher risk of falls in these areas. ROC analysis showed an HIIFRM sensitivity of 29.5% and specificity of 86.3%. The area under the curve (AUC) was 0.57, indicating limited discriminative ability in predicting falls. At a lower cutoff score (≥2), the AUC was 0.75 (95% confidence interval: 0.666-0.706; p < 0.0001), suggesting acceptable discriminative ability in predicting falls, with an additional identification of 101 fall events. This study emphasizes the importance of selecting an appropriate cutoff score when using the HIIFRM as a fall risk assessment tool. The findings have implications for fall prevention strategies and patient care in clinical settings, potentially leading to improved outcomes and patient safety.

Bovine brucellosis, primarily caused by Brucella abortus, severely affects both animal health and human well-being. Accurate diagnosis is crucial for designing informed control and prevention measures. Lacking a gold standard test makes it challenging to determine optimal cut-off values and evaluate the diagnostic performance of tests. In this study, we developed a novel Bayesian Latent Class Model that integrates both binary and continuous testing outcomes, incorporating additional fixed (parity) and random (farm) effects, to calibrate optimal cut-off values by maximizing Youden Index. We tested 651 serum samples collected from six dairy farms in two regions of Henan Province, China with four serological tests: Rose Bengal Test, Serum Agglutination Test, Fluorescence Polarization Assay, and Competitive Enzyme-Linked Immunosorbent Assay. Our analysis revealed that the optimal cut-off values for FPA and C-ELISA were 94.2 mP and 0.403 PI, respectively. Sensitivity estimates for the four tests ranged from 69.7% to 89.9%, while specificity estimates varied between 97.1% and 99.6%. The true prevalences in the two study regions in Henan province were 4.7% and 30.3%. Parity-specific odds ratios for positive serological status ranged from 1.2 to 2.2 for different parity groups compared to primiparous cows. This approach provides a robust framework for validating diagnostic tests for both continuous and discrete tests in the absence of a gold standard test. Our findings can enhance our ability to design targeted disease detection strategies and implement effective control measures for brucellosis in Chinese dairy farms.

Human-induced climate change modifies plant species distribution, reorganizing ecologically suitable habitats for invasive species. In this study, we identified the environmental factors that are important for the spread of Calyptocarpus vialis, an emerging invasive weed in the northwestern Indian Himalayan Region (IHR), along with possible habitats of the weed under current climatic scenarios and potential range expansion under several representative concentration pathways (RCPs) using MaxEnt niche modeling. The prediction had a high AUC (area under the curve) value of 0.894 ± 0.010 and a remarkable correlation between the test and expected omission rates. BIO15 (precipitation seasonality; 38.8%) and BIO1 (annual mean temperature; 35.7%) had the greatest impact on the probable distribution of C. vialis, followed by elevation (11.7%) and landcover (6.3%). The findings show that, unlike the current situation, \"high\" and \"very high\" suitability areas would rise while less-suited habitats would disappear. All RCPs (2.6, 4.5, 6.0, and 8.5) indicate the expansion of C. vialis in \"high\" suitability areas, but RCP 4.5 predicts contraction, and RCPs 2.6, 6.0, and 8.5 predict expansion in \"very high\" probability areas. The current distribution of C. vialis is 21.59% of the total area of the state, with \"medium\" to \"high\" invasion suitability, but under the RCP 8.5 scenario, it might grow by 10% by 2070. The study also reveals that C. vialis may expand its niche at both lower and higher elevations. This study clarifies how bioclimatic and topographic factors affect the dispersion of invasive species in the biodiverse IHR. Policymakers and land-use managers can utilize the data to monitor C. vialis hotspots and develop scientifically sound management methods.

The area under the Receiver Operating Characteristic (ROC) curve (AUC) is a standard metric for quantifying and comparing binary classifiers. A popular approach to estimating the AUCs and the associated variabilities - the variance of the AUC or the full covariance matrix of multiple correlated AUCs - is the one proposed by DeLong et al [1], which is based on the Mann Whitney two-sample U-statistics. The bias of a variance estimator is an important factor in applications such as hypothesis testing and construction of confidence intervals - a negatively biased variance estimator may lead to incorrect conclusions, and a positive bias is conservative hence preferable. In this work, we show that the (co-)variance estimate in DeLong\'s approach is always positively biased. More specifically, the difference matrix between the expectation of the estimated covariance and the true covariance is a positive semi-definite matrix. This bias is non-negligible when the sample size is small, and quickly diminishes as the sample size increases. Our method relies on constructing, from the AUC kernel, a random variable whose (co-)variance matrix coincides with the bias, thereby establishing the claim. We also discuss alternative approaches to AUC variance estimation that may potentially reduce the bias.