The effect of trained parameters in Bayesian neural encoding models for the auditory system

ObjectiveThe primary objective of this study was to investigate the risk factors for diabetic peripheral neuropathy (DPN) and to establish an early diagnostic prediction model for its onset, based on clinical data and biochemical indices.MethodsRetrospective data were collected from 1,446 diabetic patients at the First Affiliated Hospital of Anhui University of Chinese Medicine and were split into training and internal validation sets in a 7:3 ratio. Additionally, 360 diabetic patients from the Second Affiliated Hospital were used as an external validation cohort. Feature selection was conducted within the training set, where univariate logistic regression identified variables with a p-value < 0.05, followed by backward elimination to construct the logistic regression model. Concurrently, the random forest algorithm was applied to the training set to identify the top 10 most important features, with hyperparameter optimization performed via grid search combined with cross-validation. Model performance was evaluated using ROC curves, decision curve analysis, and calibration curves. Model fit was assessed using the Hosmer-Lemeshow test, followed by Brier Score evaluation for the random forest model. Ten-fold cross-validation was employed for further validation, and SHAP analysis was conducted to enhance model interpretability.ResultsA nomogram model was developed using logistic regression with key features: limb numbness, limb pain, diabetic retinopathy, diabetic kidney disease, urinary protein, diastolic blood pressure, white blood cell count, HbA1c, and high-density lipoprotein cholesterol. The model achieved AUCs of 0.91, 0.88, and 0.88 for the training, validation, and test sets, respectively, with a mean AUC of 0.902 across 10-fold cross-validation. Hosmer-Lemeshow test results showed p-values of 0.595, 0.418, and 0.126 for the training, validation, and test sets, respectively. The random forest model demonstrated AUCs of 0.95, 0.88, and 0.88 for the training, validation, and test sets, respectively, with a mean AUC of 0.886 across 10-fold cross-validation. The Brier score indicates a good calibration level, with values of 0.104, 0.143, and 0.142 for the training, validation, and test sets, respectively.ConclusionThe developed nomogram exhibits promise as an effective tool for the diagnosis of diabetic peripheral neuropathy in clinical settings.

PCOSt: A non-invasive and cost-effective screening tool for polycystic ovary syndrome

Predicting Caregiver Burden in Multiple Myeloma: Insights from the Carmma Study Using Machine Learning Models

Signal flow direction is one of the most important features of the protein-protein interactions in signaling networks. However, almost all the outcomes of current high-throughout techniques for protein-protein interactions mapping are usually supposed to be non-directional. Based on the pairwise interaction domains, here we defined a novel parameter protein interaction directional score and then used it to predict the direction of signal flow between proteins in proteome-wide signaling networks. Using 5-fold cross-validation, our approach obtained a satisfied performance with the accuracy 89.79%, coverage 48.08%, and error ratio 16.91%. As an application, we established an integrated human directional protein interaction network, including 2,237 proteins and 5,530 interactions, and inferred a large amount of novel signaling pathways. Directional protein interaction network was strongly supported by the known signaling pathways literature (with the 87.5% accuracy) and further analyses on the biological annotation, subcellular localization, and network topology property. Thus, this study provided an effective method to define the upstream/downstream relations of interacting protein pairs and a powerful tool to unravel the unknown signaling pathways.

BackgroundInvasive pulmonary adenocarcinoma (IPA) poses a significant threat to global health and patients still experience tumor recurrence and metastasis. This study aimed to construct an optimized prognosis model using machine learning to predict the disease-free survival (DFS) of IPA patients.MethodsA total of 670 resected IPA patients from 2015 to 2020 were enrolled. Clinicopathological information was collected and the outcomes of patients were followed up. Patients were divided into a training set and a test set at a ratio of 4:1. Four machine learning models were compared to build the DFS models and 5-fold cross validation was performed. The area under the receiver operating characteristic curve (AUC), C-index, calibration curves, and decision curve analysis (DCA) were used to evaluate the model.ResultsAmong the four models, the least absolute shrinkage and selection operator (Lasso) model showed the best performance in predicting DFS at 2-year (training set: AUC =0.906, test set: AUC =0.862), at 3-year (training set: AUC =0.894, test set: AUC =0.879), at 4-year (training set: AUC =0.901, test set: AUC =0.902), and at 5-year (training set: AUC =0.927, test set: AUC =0.887). The calibration curves and DCA exhibited a good predictive performance.ConclusionsOur study successfully constructed a machine-learning based prognostic model to predict DFS, which may provide oncologists with an effective tool for early medical intervention and survival improvement.

Glioblastoma is a highly aggressive brain tumor with limited survival that poses challenges in predicting patient outcomes. The Karnofsky Performance Status (KPS) score is a valuable tool for assessing patient functionality and contributes to the stratification of patients with poor prognoses. This study aimed to develop a 6-month postoperative KPS prediction model by combining clinical data with deep learning-based image features from pre- and postoperative MRI scans, offering enhanced personalized care for glioblastoma patients. Using 1,476 MRI datasets from the Brain Tumor Segmentation Challenge 2020 public database, we pretrained two variational autoencoders (VAEs). Imaging features from the latent spaces of the VAEs were used for KPS prediction. Neural network-based KPS prediction models were developed to predict scores below 70 at 6 months postoperatively. In this retrospective single-center analysis, we incorporated clinical parameters and pre- and postoperative MRI images from 150 newly diagnosed IDH wild-type glioblastoma, divided into training (100 patients) and test (50 patients) sets. In training set, the performance of these models was evaluated using the area under the curve (AUC), calculated through fivefold cross-validation repeated 10 times. The final evaluation of the developed models assessed in the test set. Among the 150 patients, 61 had 6-month postoperative KPS scores below 70 and 89 scored 70 or higher. We developed three models: a clinical-based model, an MRI-based model, and a multimodal model that incorporated both clinical parameters and MRI features. In the training set, the mean AUC was 0.785±0.051 for the multimodal model, which was significantly higher than the AUCs of the clinical-based model (0.716±0.059, P = 0.038) using only clinical parameters and the MRI-based model (0.651±0.028, P<0.001) using only MRI features. In the test set, the multimodal model achieved an AUC of 0.810, outperforming the clinical-based (0.670) and MRI-based (0.650) models. The integration of MRI features extracted from VAEs with clinical parameters in the multimodal model substantially enhanced KPS prediction performance. This approach has the potential to improve prognostic prediction, paving the way for more personalized and effective treatments for patients with glioblastoma.

BackgroundDefinitive chemoradiation is the primary treatment for locally advanced head and neck carcinoma (LAHNSCC). Optimising outcome predictions requires validated biomarkers, since TNM8 and HPV could have limitations. Radiomics may enhance risk stratification.MethodsThis single-centre observational study collected clinical data and baseline CT scans from 171 LAHNSCC patients treated with chemoradiation. The dataset was divided into training (80%) and test (20%) sets, with a 5-fold cross-validation on the training set. Researchers extracted 108 radiomics features from each primary tumour and applied survival analysis and classification models to predict progression-free survival (PFS) and 5-year progression, respectively. Performance was evaluated using inverse probability of censoring weights and c-index for the PFS model and AUC, sensitivity, specificity, and accuracy for the 5-year progression model. Feature importance was measured by the SHapley Additive exPlanations (SHAP) method and patient stratification was assessed through Kaplan–Meier curves.ResultsThe final dataset included 171 LAHNSCC patients, with 53% experiencing disease progression at 5 years. The random survival forest model best predicted PFS, with an AUC of 0.64 and CI of 0.66 on the test set, highlighting 4 radiomics features and TNM8 as significant contributors. It successfully stratified patients into low and high-risk groups (log-rank p < 0.005). The extreme gradient boosting model most effectively predicted a 5-year progression, incorporating 12 radiomics features and four clinical variables, achieving an AUC of 0.74, sensitivity of 0.53, specificity of 0.81, and accuracy of 0.66 on the test set.ConclusionThe combined clinical-radiomics model improved the standard TNM8 and clinical variables in predicting 5-year progression though further validation is necessary.Key PointsQuestionThere is an unmet need for non-invasive biomarkers to guide treatment in locally advanced head and neck cancer.FindingsClinical data (TNM8 staging, primary tumour site, age, and smoking) plus radiomics improved 5-year progression prediction compared with the clinical comprehensive model or TNM staging alone.Clinical relevanceSHAP simplifies complex machine learning radiomics models for clinicians by using easy-to-understand graphical representations, promoting explainability.

Background Effectively treated and virally suppressed persons living with HIV (PLWH) have an increased risk of heart failure compared with the general population. Currently, no methods to predict heart failure risk are available in PLWH. Purpose To create a proteomics-based model of incident heart failure risk in a longitudinal cohort of PLWH in the Veterans Aging Cohort Study Biomarker Cohort. Methods We characterized the baseline plasma proteome of 1,398 PLWH using an aptamer-based platform. Traditional risk factors and HIV-specific variables were incorporated into Cox proportional hazards models for incident heart failure. For risk models involving proteins, least absolute shrinkage and selection operator (LASSO) regression with 10-fold cross-validation was used to identify the optimal set of predictors. Model performance was evaluated using Harrell’s Concordance Index (C-index). Results Among 1,398 PLWH, 165 (11.8%) developed incident heart failure over a mean follow-up period of 9.6 years. The sample was divided randomly 2:1 into training (n = 932) and testing (n = 466) sets. In the training set, 4,248 proteins were screened, identifying 774 significant markers (false discovery rate- [FDR] adjusted p-value &amp;lt; 0.05); 492 of these were validated in the test set (p &amp;lt; 0.05) and were included as candidate predictors in downstream LASSO models. LASSO-Cox regression was used to identify a subset of predictor proteins in combination with clinical and laboratory risk factors, as well as protein-only models. Test concordance indices (C-index) in the validation data set are presented in Table 1. Among the clinical risk factor models, individual traditional risk factors in combination with HIV disease markers (CD4+ T-cell count, viral load) exhibited the best predictive performance (C-index 0.634; standard error [SE] 0.038). Addition of serum inflammatory and coagulation markers (soluble CD-14 [sCD14], interleukin-6 [IL-6], D-dimer) did not substantially improve performance (C-index 0.611, SE 0.042). By contrast, a protein-only model incorporating 11 markers achieved an improved C-index of 0.704 (SE 0.061), while the addition of CD4+ count and viral load performed similarly (C-index 0.708, SE 0.062). The protein-only risk model demonstrated improved risk discrimination compared with clinical risk factors, particularly in the highest-risk quartile (Figure 1; quartile-based C-indices 0.604 (SE 0.042) and 0.672 (0.036) for clinical and protein models, respectively). Conclusion An 11-protein signature demonstrated superior predictive performance for 10-year incident heart failure risk among PLWH compared with baseline traditional risk factors and inflammatory markers. If these findings can be externally validated, they highlight the potential of a proteomics-based approach for risk stratification in this population, which provides additive value beyond traditional or HIV-specific clinical risk factors.

To develop a method for identifying high-risk patients among septic populations requiring mechanical ventilation, and to conduct phenotypic analysis based on this method. Data from four sources were utilized: the Medical Information Mart for Intensive Care (MIMIC-IV 2.0, MIMIC-III 1.4), the Philips eICU-Collaborative Research Database 2.0 (eICU-CRD 2.0), and the Anhui Medical University Second Affiliated Hospital dataset. The adult patients in intensive care unit (ICU) who met Sepsis-3 and received invasive mechanical ventilation (IMV) on the first day of first admission were enrolled. The MIMIC-IV dataset with the highest data integrity was divided into a training set and a test set at a 6:1 ratio, while the remaining datasets were served as validation sets. The demographic information, comorbidities, laboratory indicators, commonly used ICU scores, and treatment measures of patients were extracted. Clinical data collected within first day of ICU admission were used to calculate the sequential organ failure assessment (SOFA) score. K-means clustering was applied to cluster SOFA score components, and the sum of squared errors (SSE) and Davies-Bouldin index (DBI) were used to determine the optimal number of disease subtypes. For clustering results, normalized methods were employed to compare baseline characteristics by visualization, and Kaplan-Meier curves were used to analyze clinical outcomes across phenotypes. This study enrolled patients from MIMIC-IV dataset (n = 11 166), MIMIC-III dataset (n = 4 821), eICU-CRD dataset (n = 6 624), and a local dataset (n = 110), with the four datasets showing similar median ages and male proportions exceeding 50%; using 85% of the MIMIC-IV dataset as the training set, 15% as the test set, and the rest dataset as the validation set. K-means clustering based on the six-item SOFA score was performed to determine the optimal number of clusters as 3, and patients were finally classified into three phenotypes. In the training set, compared with the patients with phenotype II and phenotype III, those with phenotype I had the more severe circulatory and respiratory dysfunction, a higher proportion of vasoactive drug usage, more obvious metabolic acidosis and hypoxia, and a higher incidence of congestive heart failure. The patients with phenotype II was dominated by respiratory dysfunction with higher visceral injury. The patients with phenotype III had relatively stable organ function. The above characteristics were consistent in both the test and validation sets. Analysis of infection-related indicators showed that the patients with phenotype I had the highest SOFA score within 7 days after ICU admission, initial decreases and later increases in platelet count (PLT), and higher counts of neutrophils, lymphocytes, and monocytes as compared with those with phenotype II and phenotype III, their blood cultures had a higher positivity rates for Gram-positive bacteria, Gram-negative bacteria and fungi as compared with those with phenotype II and phenotype III. The Kaplan-Meier curve indicated that in the training, test, and validation sets, the 28-day cumulative mortality of patients with phenotype I was significantly higher than that of patients with phenotypes II and phenotype III. Three distinct phenotypes in septic patients receiving IMV based on unsupervised machine learning is derived, among which phenotype I, characterized by cardiorespiratory failure, can be used for the early identification of high-risk patients in this population. Moreover, this population is more prone to bloodstream infections, posing a high risk and having a poor prognosis.

Existing studies report very few factors influencing the mortality of infant and toddler heart failure patients during hospitalization. Due to its high mortality rate, it is an important health issue. Therefore, this study aims to explore the factors influencing infant and toddler heart failure patients during hospitalization, establish predictive models, and a mortality scoring table. The study ultimately included 544 cases of infant heart failure patients. They were randomly divided into a training set (380 cases) and a validation set (164 cases) in a ratio of 7:3. The training set was then further divided into the death group and the survival group for further analysis of indicators during hospitalization. Using the Lasso regression method, this study selected the best 14 variables from 88 independent variables of infants and toddlers with heart failure. Multivariate Logistic regression results show that TP<65g/L (OR=2.34), pH<7.35 (OR=2.79), and Respiratory rate (1-12 months: <30times/min and 13-36 months: <25times/min, OR=2.34) are independent risk factors. The model evaluation results for the train and test sets of infant and toddler heart failure patients are as follows: C-index values for discrimination in the train and test sets are 0.721 and 0.728, respectively. Fit test calibration evaluations show P values of 0.9958 and 0.9998, both greater than 0.05, indicating good calibration. The AUC values for the train and test sets are 0.75 and 0.64, respectively, showing a good predictive effect of the model. The mortality scoring table divides patients in the train and test sets into low risk, moderate-risk, and high risk categories. Compared to the low risk group, the OR values for the occurrence of mortality in the Medium risk group and high risk group in the train set are 3.78 and 11.67, respectively; in the test set, the OR values for the moderate-risk group and high-risk group are 1.73 and 6.33, respectively. The predictive models and scoring tables established in this study have a good predictive role in assessing the risk of death in infant and toddler heart failure patients aged 1-36 months during hospitalization, providing clinical guidance and reference value.

Prior to using a quantitative structure activity relationship (QSAR) model for external predictions, its predictive power should be established and validated. In the absence of a true external data set, the best way to validate the predictive ability of a model is to perform its statistical external validation. In statistical external validation, the overall data set is divided into training and test sets. Commonly, this splitting is performed using random division. Rational splitting methods can divide data sets into training and test sets in an intelligent fashion. The purpose of this study was to determine whether rational division methods lead to more predictive models compared to random division. A special data splitting procedure was used to facilitate the comparison between random and rational division methods. For each toxicity end point, the overall data set was divided into a modeling set (80% of the overall set) and an external evaluation set (20% of the overall set) using random division. The modeling set was then subdivided into a training set (80% of the modeling set) and a test set (20% of the modeling set) using rational division methods and by using random division. The Kennard-Stone, minimal test set dissimilarity, and sphere exclusion algorithms were used as the rational division methods. The hierarchical clustering, random forest, and k-nearest neighbor (kNN) methods were used to develop QSAR models based on the training sets. For kNN QSAR, multiple training and test sets were generated, and multiple QSAR models were built. The results of this study indicate that models based on rational division methods generate better statistical results for the test sets than models based on random division, but the predictive power of both types of models are comparable.

Random forest estimation of genomic breeding values for disease susceptibility over different disease incidences and genomic architectures in simulated cow calibration groups

To investigative whether radiomics features in bilateral hippocampi from MRI can identify temporal lobe epilepsy (TLE). A total of 131 subjects with MRI (66 TLE patients [35 right and 31 left TLE] and 65 healthy controls [HC]) were allocated to training (n = 90) and test (n = 41) sets. Radiomics features (n = 186) from the bilateral hippocampi were extracted from T1-weighted images. After feature selection, machine learning models were trained. The performance of the classifier was validated in the test set to differentiate TLE from HC and ipsilateral TLE from HC. Identical processes were performed to differentiate right TLE from HC (training set, n = 69; test set; n = 31) and left TLE from HC (training set, n = 66; test set, n = 30). The best-performing model for identifying TLE showed an AUC, accuracy, sensitivity, and specificity of 0.848, 84.8%, 76.2%, and 75.0% in the test set, respectively. The best-performing radiomics models for identifying right TLE and left TLE subgroups showed AUCs of 0.845 and 0.840 in the test set, respectively. In addition, multiple radiomics features significantly correlated with neuropsychological test scores (false discovery rate-corrected p-values < 0.05). The radiomics model from hippocampus can be a potential biomarker for identifying TLE.

Background and AimsThe prognosis of papillary thyroid carcinoma (PTC) is highly variable, even for high-risk cases. The predictive and prognostic role of the lymphocyte-to-monocyte ratio (LMR) has been reported in other cancers. The aim of our present study was to explore the value of LMR prognostic prediction in high-risk PTC patients.Patients and MethodsTwo hundred and twenty-four PTC high-risk cases at West China Hospital were randomized into a training set (112 cases) and testing set (112 cases), while 48 cases in Shang Jin Nan Fu Hospital were included as the external validation set.ResultsA lower preoperative LMR correlated with larger tumor size, advanced N and M stages, and an increased number of multiple PTC cases in the training, testing, and validation sets (all P<0.05 in the three sets). Patients with a high LMR exhibited significantly improved overall and PTC-free survival compared with those of patients with a low LMR in the training, testing, validation, and combined sets (all P<0.05 in the individual and combined sets). Moreover, multivariate analyses identified the LMR as an independent prognostic factor for overall and PTC-free survival. The nomograms for predicting the 5-year mortality and PTC recurrence were developed based on the risk factors in the training set and validated in the independent testing and validation sets.ConclusionThe preoperative LMR was identified as an independent prognostic factor that could be incorporated into the two nomograms with other risk factors to predict overall survival and PTC-free survival for individual patients.

SummaryAccurate prediction of the solubility of chemical substances in solvents remains a challenge. The sparsity of high-quality solubility data is recognized as the biggest hurdle in the development of robust data-driven methods for practical use. Nonetheless, the effects of the quality and quantity of data on aqueous solubility predictions have not yet been scrutinized. In this study, the roles of the size and the quality of data sets on the performances of the solubility prediction models are unraveled, and the concepts of actual and observed performances are introduced. In an effort to curtail the gap between actual and observed performances, a quality-oriented data selection method, which evaluates the quality of data and extracts the most accurate part of it through statistical validation, is designed. Applying this method on the largest publicly available solubility database and using a consensus machine learning approach, a top-performing solubility prediction model is achieved.