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Background: Assigning animals to their corresponding breeds through breed informative single-nucleotide polymorphisms (SNPs) is required in many fields. For instance, it is used in the traceability and the authentication of meat and other livestock products. SNPs’ information for several pork breeds are now accessible thanks to the availability of dense SNP chips. These SNP chips cover a large number of molecular markers distributed across the entire genome. To identify the pork breed from a sample of industrial meat, one must analyze a large panel of genetic markers depending on the SNP chip used. The analysis of such large datasets requires intensive work. This leads to the idea of creating less dense chips of breed informative markers based on a reduced number of SNPs. Therefore, the analysis of the data emanating from the genotyping of these reduced chips will require less time and effort. Aim: The objective of this study is to find the most informative SNPs for the discrimination between four pig breeds, namely Duroc, Landrace, Large White, and Pietrain. Method: The Illumina Porcine 60 k SNP chip was used to genotype SNPs distributed all over the individuals’ genomes. Firstly, we used three different statistical approaches for feature selection: (i) principal component analysis (PCA), (ii) least absolute shrinkage and selection operator (LASSO), and (iii) random forest (RF). These three approaches identified three sets of SNPs; each set corresponds to one approach. Then, we combined the results of the three methods by setting up a final panel containing the SNPs which appear on the three sets altogether. Results: Separately, each method resulted in a panel with the corresponding most discriminating SNPs. The PCA, the LASSO, and the random forest with Boruta algorithm highlighted 28,816, 50, and 286 SNPs, respectively. The number of SNPs selected by PCA is high compared to Boruta and LASSO because PCA chooses the variables while preserving as much information about the data as possible. The only downside of LASSO regression is that among a group of correlated variables, LASSO tends to select only one variable and ignore the others regardless of their importance. Contrarily to LASSO, the Boruta algorithm considers the interdependence between SNPs and selects informative variables even if they are correlated and have the same effect. The three panels shared 23 SNPs; the distribution of the individuals according to these SNPs showed a grouping of individuals of each breed in well-defined clusters without any overlapping. Conclusions: The biological pathways represented by 23 breed informative SNPs resulted by the combination of PCA, LASSO, and Boruta should be explored in further analysis. The results provided by our study are promising for further applications of this method in other livestock animals. [ABSTRACT FROM AUTHOR]

Abstract Background Tardive dyskinesia (TD) is a serious, often irreversible movement disorder caused by prolonged exposure to antipsychotics; identifying patients at risk for TD is critical to preventing it. Predictive models for the occurrence of TD can improve patient monitoring and inform implementation of counteractive interventions. This study aims to identify risk factors associated with TD and to develop a model using a retrospective data analysis to predict the incidence of TD among patients taking antipsychotic medications. Methods Adult patients with schizophrenia, major depressive disorder, or bipolar disorder taking oral antipsychotics were identified in a Medicaid claims database (covering six US states from 1997 to 2016) and divided into cohorts based on whether they developed TD within 1 year after the first observed claim for antipsychotics. Patient characteristics between cohorts were compared, and univariate Cox analyses were used to identify potential TD risk factors. A cross-validated version of the least absolute shrinkage and selection operator regression method was used to develop a parsimonious multivariable Cox proportional hazards model to predict diagnosis of TD. Results A total of 189,415 eligible patients were identified. Potential TD risk factors were identified based on the cohort analysis within a sample of 151,280 patients with at least 1 year of continuous eligibility. The prediction model had a clinically meaningful concordance of 70% and was well calibrated (P = 0.32 for Hosmer–Lemeshow goodness-of-fit test). Age (hazard ratio [HR] = 1.04, P

Length of stay (LoS) in the intensive care unit (ICU) is a common outcome measure used as an indicator of both quality of care and resource use. However, the existing analysis methods of LoS are poorly interpretable and extensible, and there is controversial for the predictive performance of LoS. In this paper, the study includes data from 1,214 unplanned ICU admissions to participate in the ICU of Sichuan Provincial People’s Hospital between Dec. 11, 2015 and Dec. 6, 2018. On the basis of these data, this study creates a highly accurate and predictive model using advanced preprocessing techniques, exploratory data analysis (EDA) and least absolute shrinkage and selection operator (LASSO) algorithm. Next, this study evaluates the predictive performance of the proposed model by 10-fold cross validation and external validation method using the root mean square prediction error (RMSPE), mean absolute error (MAE), and coefficient of determination ( $R^{2}$ ). The predictive performance of the proposed model is 0.88±0.13 day for RMSPE, 0.87±0.07 day for MAE and 0.35±0.09 for $R^{2}$ . Experimental results show that the performance of the proposed method are competitive with the state-of-the-art methods and results. Furthermore, this study explores the risk factors for ICU LoS in survivors and non-survivors and compare their predictive performance.

PurposeOur study aimed to establish and validate prognostic nomograms based on gross tumor volume (GTV) and cervical nodal volume (CNV) for nasopharyngeal carcinoma (NPC) patients treated with two cycles of concurrent chemoradiotherapy (CCRT).MethodsFrom 2012 to 2015, 620 eligible patients who received radical treatment at the Cancer Hospital of Shantou University Medical College were recruited for a nomogram study. Variables were determined in a training set of 463 patients from 2012 to 2014 by X-tile analysis, univariate and multivariate Cox proportional hazard analyses, and the least absolute shrinkage and selection operator (LASSO). Another cohort of 157 patients in 2015 was validated with bootstrap resampling. The concordance index (C-index) and calibration curves were applied to assess its predictive discriminative and accuracy ability, while decision curve analysis (DCA), X-tile analysis and Kaplan–Meier curve for clinical application.ResultsIndependent prognostic variables for overall survival (OS) were age, GTV, CNV, cranial nerve, positive cervical lymph node laterality below the caudal border of cricoid cartilage (LNBC), and were selected for the nomogram. Optimal prognostic factors including Karnofsky performance status (KPS), age, GTV, CNV, LNBC were incorporated in the nomogram for progression-free survival (PFS). In the training set, the C-index of our nomograms for OS and PFS were 0.755 (95% CI, 0.704 to 0.807) and 0.698 (95% CI, 0.652 to 0.744). The calibration curve showed good agreement between nomogram-predicted and actual survival. DCA indicated that our nomograms were of clinical benefit.ConclusionOur nomograms are capable of effective prognostic prediction for patients with NPC.

The prognosis of acute myeloid leukemia (AML) is closely related to immune response changes. Further exploration of the pathobiology of AML focusing on immune-related genes would contribute to the development of more advanced evaluation and treatment strategies. In this study, we established a novel immune-17 signature based on transcriptome data from The Cancer Genome Atlas (TCGA) and The Genotype-Tissue Expression (GTEx) databases. We found that immune biology processes and transcriptional dysregulations are critical factors in the development of AML through enrichment analyses. We also formulated a prognostic model to predict the overall survival of AML patients by using LASSO (Least Absolute Shrinkage and Selection Operator) regression analysis. Furthermore, we incorporated the immune-17 signature to improve the prognostic accuracy of the ELN2017 risk stratification system. We concluded that the immune-17 signature represents a novel useful model for evaluating AML survival outcomes and may be implemented to optimize treatment selection in the next future.

This study aimed to investigate the value of amplitude of low-frequency fluctuation (ALFF)-based histogram analysis in the diagnosis of Parkinson’s disease (PD) and to investigate the regions of the most important discriminative features and their contribution to classification discrimination. Patients with PD (n = 59) and healthy controls (HCs; n = 41) were identified and divided into a primary set (80 cases, including 48 patients with PD and 32 HCs) and a validation set (20 cases, including 11 patients with PD and nine HCs). The Automated Anatomical Labeling (AAL) 116 atlas was used to extract the histogram features of the regions of interest in the brain. Machine learning methods were used in the primary set for data dimensionality reduction, feature selection, model construction, and model performance evaluation. The model performance was further validated in the validation set. After feature data dimension reduction and feature selection, 23 of a total of 1,276 features were entered in the model. The brain regions of the selected features included the frontal, temporal, parietal, occipital, and limbic lobes, as well as the cerebellum and the thalamus. In the primary set, the area under the curve (AUC) of the model was 0.974, the sensitivity was 93.8%, the specificity was 90.6%, and the accuracy was 93.8%. In the validation set, the AUC, sensitivity, specificity, and accuracy were 0.980, 90.9%, 88.9%, and 90.0%, respectively. ALFF-based histogram analysis can be used to classify patients with PD and HCs and to effectively identify abnormal brain function regions in PD patients.

IntroductionThe insufficient understanding and misdiagnosis of clinically diagnosed pulmonary tuberculosis (PTB) without an aetiological evidence is a major problem in the diagnosis of tuberculosis (TB). This study aims to confirm the value of Long non-coding RNA (lncRNA) n344917 in the diagnosis of PTB and construct a rapid, accurate, and universal prediction model.MethodsA total of 536 patients were prospectively and consecutively recruited, including clinically diagnosed PTB, PTB with an aetiological evidence and non-TB disease controls, who were admitted to West China hospital from Dec 2014 to Dec 2017. The expression levels of lncRNA n344917 of all patients were analyzed using reverse transcriptase quantitative real-time PCR. Then, the laboratory findings, electronic health record (EHR) information and expression levels of n344917 were used to construct a prediction model through the Least Absolute Shrinkage and Selection Operator algorithm and multivariate logistic regression.ResultsThe factors of n344917, age, CT calcification, cough, TBIGRA, low-grade fever and weight loss were included in the prediction model. It had good discrimination (area under the curve = 0.88, cutoff = 0.657, sensitivity = 88.98%, specificity = 86.43%, positive predictive value = 85.61%, and negative predictive value = 89.63%), consistency and clinical availability. It also showed a good replicability in the validation cohort. Finally, it was encapsulated as an open-source and free web-based application for clinical use and is available online at https://ziruinptb.shinyapps.io/shiny/.ConclusionCombining the novel potential molecular biomarker n344917, laboratory and EHR variables, this web-based prediction model could serve as a user-friendly, accurate platform to improve the clinical diagnosis of PTB.

Purpose: Our study aimed to establish and validate prognostic nomograms based on gross tumor volume (GTV) and cervical nodal volume (CNV) for nasopharyngeal carcinoma (NPC) patients treated with two cycles of concurrent chemoradiotherapy (CCRT). Methods: From 2012 to 2015, 620 eligible patients who received radical treatment at the Cancer Hospital of Shantou University Medical College were recruited for a nomogram study. Variables were determined in a training set of 463 patients from 2012 to 2014 by X-tile analysis, univariate and multivariate Cox proportional hazard analyses, and the least absolute shrinkage and selection operator (LASSO). Another cohort of 157 patients in 2015 was validated with bootstrap resampling. The concordance index (C-index) and calibration curves were applied to assess its predictive discriminative and accuracy ability, while decision curve analysis (DCA), X-tile analysis and Kaplan–Meier curve for clinical application. Results: Independent prognostic variables for overall survival (OS) were age, GTV, CNV, cranial nerve, positive cervical lymph node laterality below the caudal border of cricoid cartilage (LNBC), and were selected for the nomogram. Optimal prognostic factors including Karnofsky performance status (KPS), age, GTV, CNV, LNBC were incorporated in the nomogram for progression-free survival (PFS). In the training set, the C-index of our nomograms for OS and PFS were 0.755 (95% CI, 0.704 to 0.807) and 0.698 (95% CI, 0.652 to 0.744). The calibration curve showed good agreement between nomogram-predicted and actual survival. DCA indicated that our nomograms were of clinical benefit. Conclusion: Our nomograms are capable of effective prognostic prediction for patients with NPC. [ABSTRACT FROM AUTHOR]

This study aimed to investigate the value of amplitude of low-frequency fluctuation (ALFF)-based histogram analysis in the diagnosis of Parkinson's disease (PD) and to investigate the regions of the most important discriminative features and their contribution to classification discrimination. Patients with PD (n = 59) and healthy controls (HCs; n = 41) were identified and divided into a primary set (80 cases, including 48 patients with PD and 32 HCs) and a validation set (20 cases, including 11 patients with PD and nine HCs). The Automated Anatomical Labeling (AAL) 116 atlas was used to extract the histogram features of the regions of interest in the brain. Machine learning methods were used in the primary set for data dimensionality reduction, feature selection, model construction, and model performance evaluation. The model performance was further validated in the validation set. After feature data dimension reduction and feature selection, 23 of a total of 1,276 features were entered in the model. The brain regions of the selected features included the frontal, temporal, parietal, occipital, and limbic lobes, as well as the cerebellum and the thalamus. In the primary set, the area under the curve (AUC) of the model was 0.974, the sensitivity was 93.8%, the specificity was 90.6%, and the accuracy was 93.8%. In the validation set, the AUC, sensitivity, specificity, and accuracy were 0.980, 90.9%, 88.9%, and 90.0%, respectively. ALFF-based histogram analysis can be used to classify patients with PD and HCs and to effectively identify abnormal brain function regions in PD patients. [ABSTRACT FROM AUTHOR]

The prognosis of acute myeloid leukemia (AML) is closely related to immune response changes. Further exploration of the pathobiology of AML focusing on immune-related genes would contribute to the development of more advanced evaluation and treatment strategies. In this study, we established a novel immune-17 signature based on transcriptome data from The Cancer Genome Atlas (TCGA) and The Genotype-Tissue Expression (GTEx) databases. We found that immune biology processes and transcriptional dysregulations are critical factors in the development of AML through enrichment analyses. We also formulated a prognostic model to predict the overall survival of AML patients by using LASSO (Least Absolute Shrinkage and Selection Operator) regression analysis. Furthermore, we incorporated the immune-17 signature to improve the prognostic accuracy of the ELN2017 risk stratification system. We concluded that the immune-17 signature represents a novel useful model for evaluating AML survival outcomes and may be implemented to optimize treatment selection in the next future. [ABSTRACT FROM AUTHOR]

Background: Machine-learning algorithms are increasingly used in epidemiology to identify true predictors of a health outcome when many potential predictors are measured. However, these algorithms can provide different outputs when repeatedly applied to the same dataset, which can compromise research reproducibility. We aimed to illustrate that commonly used algorithms are unstable and, using the example of Least Absolute Shrinkage and Selection Operator (LASSO), that stabilization method choice is crucial.Methods: In a simulation study, we tested the stability and performance of widely used machine-learning algorithms (LASSO, Elastic-Net, and Deletion-Substitution-Addition [DSA]). We then assessed the effectiveness of six methods to stabilize LASSO and their impact on performance. We assumed that a linear combination of factors drawn from a simulated set of 173 quantitative variables assessed in 1,301 subjects influenced to varying extents a continuous health outcome. We assessed model stability, sensitivity, and false discovery proportion.Results: All tested algorithms were unstable. For LASSO, stabilization methods improved stability without ensuring perfect stability, a finding confirmed by application to an exposome study. Stabilization methods also affected performance. Specifically, stabilization based on hyperparameter optimization, frequently implemented in epidemiology, increased the false discovery proportion dramatically when predictors explained a low share of outcome variability. In contrast, stabilization based on stability selection procedure often decreased the false discovery proportion, while sometimes simultaneously lowering sensitivity.Conclusions: Machine-learning methods instability should concern epidemiologists relying on them for variable selection, as stabilizing a model can impact its performance. For LASSO, stabilization methods based on stability selection procedure (rather than addressing prediction stability) should be preferred to identify true predictors. [ABSTRACT FROM AUTHOR]

Purpose To assess the performance of different machine learning (ML) approaches in identifying risk factors for diabetic ketoacidosis (DKA) and predicting DKA. Methods: This study applied flexible ML (XGBoost, distributed random forest [DRF] and feedforward network) and conventional ML approaches (logistic regression and least absolute shrinkage and selection operator [LASSO]) to 3400 DKA cases and 11 780 controls nested in adults with type 1 diabetes identified from Optum® de‐identified Electronic Health Record dataset (2007–2018). Area under the curve (AUC), accuracy, sensitivity and specificity were computed using fivefold cross validation, and their 95% confidence intervals (CI) were established using 1000 bootstrap samples. The importance of predictors was compared across these models. Results: In the training set, XGBoost and feedforward network yielded higher AUC values (0.89 and 0.86, respectively) than logistic regression (0.83), LASSO (0.83) and DRF (0.81). However, the AUC values were similar (0.82) among these approaches in the test set (95% CI range, 0.80–0.84). While the accuracy values >0.8 and the specificity values >0.9 for all models, the sensitivity values were only 0.4. The differences in these metrics across these models were minimal in the test set. All approaches selected some known risk factors for DKA as the top 10 features. XGBoost and DRF included more laboratory measurements or vital signs compared with conventional ML approaches, while feedforward network included more social demographics. Conclusions: In our empirical study, all ML approaches demonstrated similar performance, and identified overlapping, but different, top 10 predictors. The difference in selected top predictors needs further research. [ABSTRACT FROM AUTHOR]

Background: Dietary pattern analysis is a promising approach to understanding the complex relationship between diet and health. While many statistical methods exist, the literature predominantly focuses on classical methods such as dietary quality scores, principal component analysis, factor analysis, clustering analysis, and reduced rank regression. There are some emerging methods that have rarely or never been reviewed or discussed adequately.Methods: This paper presents a landscape review of the existing statistical methods used to derive dietary patterns, especially the finite mixture model, treelet transform, data mining, least absolute shrinkage and selection operator and compositional data analysis, in terms of their underlying concepts, advantages and disadvantages, and available software and packages for implementation.Results: While all statistical methods for dietary pattern analysis have unique features and serve distinct purposes, emerging methods warrant more attention. However, future research is needed to evaluate these emerging methods' performance in terms of reproducibility, validity, and ability to predict different outcomes.Conclusion: Selection of the most appropriate method mainly depends on the research questions. As an evolving subject, there is always scope for deriving dietary patterns through new analytic methodologies. [ABSTRACT FROM AUTHOR]

A new linear function for modelling the background in whole‐powder‐pattern fitting has been derived by applying LASSO (least absolute shrinkage and selection operator) and the technique of tree search. The background function (BGF) consists of terms bnL(2gθ/180)−n/2 and bnH(1 − 2gθ/180)−n/2 for the low‐ and high‐angle sides, respectively. Some variable parameters of the BGF should be fixed at zero while others should be varied in order to find the best fit for a given data set without inducing overfitting. The LASSO algorithm can automatically select the variables in linear regression analysis. However, it finds the best‐fit BGF with a set of adjustable parameters for a given data set while it derives a different set of parameters for a different data set. Thus, LASSO derives multiple solutions depending on the data set used. By regarding the individual solutions from LASSO as nodes of trees, tree structures were constructed from these solutions. The root node has the maximum number of adjustable parameters, P. P decreases with descending levels of the tree one by one, and leaf nodes have just one parameter. By evaluating individual solutions (nodes) by their χ2 index, the best‐fit single path from a root node to a leaf node was found. The present BGF can be used simply by varying P in the range 1–10. The BGF thus derived as a final single solution was incorporated into computer programs for Pawley‐based whole‐powder‐pattern decomposition and Rietveld refinement, and the performance of the BGF was tested in comparison with the polynomials currently widely used as the BGF. The present BGF has been demonstrated to be stable and to give an excellent fit, comparable to polynomials but with a smaller number of adjustable parameters and without introducing undulation into the calculated background curve. Basic algorithms used in statistics and machine learning have been demonstrated to be useful in developing an analytical model in X‐ray crystallography. [ABSTRACT FROM AUTHOR]

BackgroundThis study aimed to assess the prognostic value of various diagnostic immunohistochemical (IHC) markers and develop an IHC-based classifier to predict the disease-free survival (DFS) of patients with bladder cancer undergoing radical cystectomy.MethodsIHC was performed on tumor specimens from 366 patients with transitional cell bladder cancer. The least absolute shrinkage and selection operator (LASSO) Cox regression model was used to develop a multi-marker classifier for predicting DFS of patients with bladder cancer. The Kaplan–Meier estimate was performed to assess DFS, and unadjusted and adjusted Cox regression models were used to identify independent risk factors to predict DFS of patients with bladder cancer.ResultsBased on the LASSO Cox regression model, nine prognostic markers were identified in the training cohort. Patients were stratified into low- and high-risk groups using the IHC-based classifier. In the training cohort, the 10-year DFS was significantly better in low-risk patients (71%) compared with high-risk patients (18%) (p < 0.001); in the validation cohort, the 10-year DFS was 86% for the low-risk group and 20% for the high-risk group (p < 0.001). Multivariable Cox regression analyses showed that the high-risk group based on the classifier was associated with poorer DFS adjusted by clinicopathological characteristics. Finally, a nomogram comprising the classifier and clinicopathological factors was developed for clinical application.ConclusionThe nine-IHC-based classifier is a reliable prognostic tool, which can eventually guide clinical decision making regarding treatment strategy and follow-up scheduling of bladder cancer.

Background: This study aimed to assess the prognostic value of various diagnostic immunohistochemical (IHC) markers and develop an IHC-based classifier to predict the disease-free survival (DFS) of patients with bladder cancer undergoing radical cystectomy. Methods: IHC was performed on tumor specimens from 366 patients with transitional cell bladder cancer. The least absolute shrinkage and selection operator (LASSO) Cox regression model was used to develop a multi-marker classifier for predicting DFS of patients with bladder cancer. The Kaplan–Meier estimate was performed to assess DFS, and unadjusted and adjusted Cox regression models were used to identify independent risk factors to predict DFS of patients with bladder cancer. Results: Based on the LASSO Cox regression model, nine prognostic markers were identified in the training cohort. Patients were stratified into low- and high-risk groups using the IHC-based classifier. In the training cohort, the 10-year DFS was significantly better in low-risk patients (71%) compared with high-risk patients (18%) (p < 0.001); in the validation cohort, the 10-year DFS was 86% for the low-risk group and 20% for the high-risk group (p < 0.001). Multivariable Cox regression analyses showed that the high-risk group based on the classifier was associated with poorer DFS adjusted by clinicopathological characteristics. Finally, a nomogram comprising the classifier and clinicopathological factors was developed for clinical application. Conclusion: The nine-IHC-based classifier is a reliable prognostic tool, which can eventually guide clinical decision making regarding treatment strategy and follow-up scheduling of bladder cancer. [ABSTRACT FROM AUTHOR]

Audio magnetic tapes manufactured using polyester urethane are known to become nonplayable over time due to the degradation of the magnetic layer. Attempting to play degraded tapes to digitize them can cause extensive damage to the tape as well as to the play back device. For this reason, most of the magnetic tapes in cultural heritage institutions are in critical state. The purpose of our study is to preserve historical recordings in magnetic tapes by developing a nondestructive technique to determine degradation status. Our approach is to combine attenuated total reflectance Fourier transform infrared spectroscopy (ATR FT‐IR) with chemometric techniques, especially neural networks and least absolute shrinkage and selection operator (Lasso). The model built using neural networking was able to successfully classify playable and nonplayable with 97% to 98% accuracy when similar tape brands/models were in the training and the test set. With different brands/models in the test set, neural network model performed poorly. However, Lasso showed 95.5% accuracy for similar brand/models and 80.5% accuracy for different tape brands/models. This suggests that Lasso is the better technique to determine if a tape is degraded or not. [ABSTRACT FROM AUTHOR]

Background: Hepatocellular carcinoma (HCC) has a poor long-term prognosis. The competition of circular RNAs (circRNAs) with endogenous RNA is a novel tool for predicting HCC prognosis. Based on the alterations of circRNA regulatory networks, the analysis of gene modules related to HCC is feasible., Methods: Multiple expression datasets and RNA element targeting prediction tools were used to construct a circRNA-microRNA-mRNA network in HCC. Gene function, pathway, and protein interaction analyses were performed for the differentially expressed genes (DEGs) in this regulatory network. In the protein-protein interaction network, hub genes were identified and subjected to regression analysis, producing an optimized four-gene signature for prognostic risk stratification in HCC patients. Anti-HCC drugs were excavated by assessing the DEGs between the low- and high-risk groups. A circRNA-microRNA-hub gene subnetwork was constructed, in which three hallmark genes, KIF4A, CCNA2, and PBK, were subjected to functional enrichment analysis., Results: A four-gene signature (KIF4A, CCNA2, PBK, and ZWINT) that effectively estimated the overall survival and aided in prognostic risk assessment in the The Cancer Genome Atlas (TCGA) cohort and International Cancer Genome Consortium (ICGC) cohort was developed. CDK inhibitors, PI3K inhibitors, HDAC inhibitors, and EGFR inhibitors were predicted as four potential mechanisms of drug action (MOA) in high-risk HCC patients. Subsequent analysis has revealed that PBK, CCNA2, and KIF4A play a crucial role in regulating the tumor microenvironment by promoting immune cell invasion, regulating microsatellite instability (MSI), and exerting an impact on HCC progression., Conclusions: The present study highlights the role of the circRNA-related regulatory network, identifies a four-gene prognostic signature and biomarkers, and further identifies novel therapy for HCC., (Copyright © 2023 First Affiliated Hospital, Zhejiang University School of Medicine in China. Published by Elsevier B.V. All rights reserved.)

Background: Few prediction models for individuals with early-stage out-of-hospital cardiac arrest (OHCA) have undergone external validation. This study aimed to externally validate updated prediction models for OHCA outcomes using a large nationwide dataset., Methods and Results: We performed a secondary analysis of the JAAM-OHCA (Comprehensive Registry of In-Hospital Intensive Care for Out-of-Hospital Cardiac Arrest Survival and the Japanese Association for Acute Medicine Out-of-Hospital Cardiac Arrest) registry. Previously developed prediction models for patients with cardiac arrest who achieved the return of spontaneous circulation were updated. External validation was conducted using data from 56 institutions from the JAAM-OHCA registry. The primary outcome was a dichotomized 90-day cerebral performance category score. Two models were updated using the derivation set (n=3337). Model 1 included patient demographics, prehospital information, and the initial rhythm upon hospital admission; Model 2 included information obtained in the hospital immediately after the return of spontaneous circulation. In the validation set (n=4250), Models 1 and 2 exhibited a C-statistic of 0.945 (95% CI, 0.935-0.955) and 0.958 (95% CI, 0.951-0.960), respectively. Both models were well-calibrated to the observed outcomes. The decision curve analysis showed that Model 2 demonstrated higher net benefits at all risk thresholds than Model 1. A web-based calculator was developed to estimate the probability of poor outcomes (https://pcas-prediction.shinyapps.io/90d&#95;lasso/)., Conclusions: The updated models offer valuable information to medical professionals in the prediction of long-term neurological outcomes for patients with OHCA, potentially playing a vital role in clinical decision-making processes.

Objective: To investigate the relationships between perfluoroalkyl and polyfluoroalkyl substances (PFASs) exposure and glucose metabolism indices., Methods: Data from the National Health and Nutrition Examination Survey (NHANES) 2017-2018 waves were used. A total of 611 participants with information on serum PFASs (perfluorononanoic acid (PFNA); perfluorooctanoic acid (PFOA); perfluoroundecanoic acid (PFUA); perfluorohexane sulfonic acid (PFHxS); perfluorooctane sulfonates acid (PFOS); perfluorodecanoic acid (PFDeA)), glucose metabolism indices (fasting plasma glucose (FPG), homeostasis model assessment for insulin resistance (HOMA-IR) and insulin) as well as selected covariates were included. We used cluster analysis to categorize the participants into three exposure subgroups and compared glucose metabolism index levels between the subgroups. Least absolute shrinkage and selection operator (LASSO), multiple linear regression analysis and Bayesian kernel machine regression (BKMR) were used to assess the effects of single and mixed PFASs exposures and glucose metabolism., Results: The cluster analysis results revealed overlapping exposure types among people with higher PFASs exposure. As the level of PFAS exposure increased, FPG level showed an upward linear trend ( p  < 0.001), whereas insulin levels demonstrated a downward linear trend ( p  = 0.012). LASSO and multiple linear regression analysis showed that PFNA and FPG had a positive relationship (>50 years-old group: β  = 0.059, p  < 0.001). PFOA, PFUA, and PFHxS (≤50 years-old group: insulin β  = -0.194, p  < 0.001, HOMA-IR β  = -0.132, p  = 0.020) showed negative correlation with HOMA-IR/insulin. PFNA (>50 years-old group: insulin β  = 0.191, p  = 0.018, HOMA-IR β  = 0.220, p  = 0.013) showed positive correlation with HOMA-IR/insulin, which was essentially the same as results that obtained for the univariate exposure-response map in the BKMR model. Association of exposure to PFASs on glucose metabolism indices showed positive interactions between PFOS and PFHxS and negative interactions between PFOA and PFNA/PFOS/PFHxS., Conclusion: Our study provides evidence that positive and negative correlations between PFASs and FPG and HOMA-IR/insulin levels are observed, respectively. Combined effects and interactions between PFASs. Given the higher risk of glucose metabolism associated with elevated levels of PFAS, future studies are needed to explore the potential underlying mechanisms., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Tian, Yang, An, Li, Wang, Zhang, Zhang, Zhang, Mu and Lei.)

IntroductionThe immune status of the tumor microenvironment is extremely complex. One single immune feature cannot reflect the integral immune status, and its prognostic value was limited. We postulated that the immune signature based on multiple immuno-features could markedly improve the prediction of post-chemoradiotherapeutic survival in inoperable locally advanced non-small-cell lung cancer (LA-NSCLC) patients.MethodsIn this study, 100 patients who were diagnosed as having inoperable LA-NSCLC between January 2005 and January 2016 were analyzed. A five immune features-based signature was then constructed using the nested repeat 10-fold cross validation with least absolute shrinkage and selection operator (LASSO) Cox regression model. Nomograms were then established for predicting prognosis.ResultsThe immune signature combining five immuno-features was significantly associated with overall survival (OS) and progression-free survival (PFS) (P = 0.002 and P = 0.014, respectively) in patients with inoperable LA-NSCLC, and at a cutoff of −0.05 stratified patients into two groups with 5-year OS rates of 39.8 and 8.8%, and 2-year PFS rates of 22.2 and 5.5% for the high- and low-immune signature groups, respectively. Integrating immune signature, we proposed predictive nomograms that were better than the traditional TNM staging system in terms of discriminating ability (OS: 0.692 vs. 0.588; PFS: 0.672 vs. 0.586, respectively) or net weight classification (OS: 32.96%; PFS: 9.22%), suggesting that the immune signature plays a significant role in improving the prognostic value.ConclusionMultiple immune features-based immune signature could effectively predict recurrence and survival of inoperable LA-NSCLC patients and complemented the prognostic value of the TNM staging system.

Background: An accumulating body of evidence suggests that long non-coding RNAs (lncRNAs) can serve as potential cancer prognostic factors. However, the utility of lncRNA combinations in estimating overall survival (OS) for hepatocellular carcinoma (HCC) remains to be elucidated. This study aimed to construct a powerful lncRNA signature related to the OS for HCC to enhance prognostic accuracy.Methods: The expression patterns of lncRNAs and related clinical data of 371 HCC patients were obtained based on The Cancer Genome Atlas (TCGA). Differentially expressed lncRNAs (DElncRNAs) were acquired by comparing tumors with adjacent normal samples. lncRNAs displaying significant association with OS were screened through univariate Cox regression analysis and the least absolute shrinkage and selection operator (LASSO) algorithm. All cases were classified into the validation or training group at the ratio of 3:7 to validate the constructed lncRNA signature. Data from the Gene Expression Omnibus (GEO) were used for external validation. We conducted real-time polymerase chain reaction (PCR) and assays for Transwell invasion, migration, CCK-8, and colony formation to determine the biological roles of lncRNA. Gene set enrichment analysis (GSEA) of the lncRNA model risk score was also conducted.Results: We identified 1292 DElncRNAs, among which 172 were significant in univariate Cox regression analysis. In the training group (n = 263), LASSO regression analysis confirmed 11 DElncRNAs including AC010547.1, AC010280.2, AC015712.7, GACAT3 (gastric cancer associated transcript 3), AC079466.1, AC089983.1, AC051618.1, AL121721.1, LINC01747, LINC01517, and AC008750.3. The prognostic risk score was calculated, and the constructed risk model showed significant correlation with HCC OS (log-rank P-value of 8.489e-9, hazard ratio of 3.648, 95% confidence interval: 2.238–5.945). The area under the curve (AUC) for this lncRNA model was up to 0.846. This risk model was confirmed in the validation group (n = 108), the entire cohort, and the external GEO dataset (n = 203). GACAT3 was highly expressed in HCC tissues and cell lines. Based on online databases, GACAT3 expression independently affects both OS and disease-free survival in HCC patients. Silencing GACAT3 in vitro significantly suppressed HCC cell proliferation, invasion, and migration. Moreover, pathways related to the lncRNA model risk score were confirmed by GSEA.Conclusion: The lncRNA signature established in this study can be used to predict HCC prognosis, which could provide novel clinical evidence to guide targeted HCC treatment.

Makalah ini membahas proses penginderaan spektrum berbasiskan rekonstruksi rapat spektral daya (power spectral density, PSD) dari sampel-sampel digital yang diperoleh dari pencuplikan dengan pesat di bawah pesat Nyquist. Pada literatur mengenai rekonstruksi PSD berbasiskan pencuplikan sub-Nyquist, sistem persamaan yang dihasilkan umumnya bersifat overdetermined sehingga algoritme least-squares (LS) dapat dipakai. Namun, perlu diingat bahwa ada batas minimal pesat pencuplikan yang harus dipenuhi untuk menjamin diperolehnya sistem overdetermined ini. Pada makalah ini, batas minimal pesat pencuplikan di atas dicoba dilewati meskipun sistem persamaan yang diperoleh akan bersifat underdetermined yang tidak memungkinkan digunakannya algoritme LS. Agar rekonstruksi PSD tetap dapat dilakukan pada kondisi ini, asumsi sparsity (yang valid untuk beberapa aplikasi) dapat dikenakan pada PSD yang akan direkonstruksi. Makalah ini mengevaluasi penggunaan algoritme orthogonal matching pursuit (OMP) dan least absolute shrinkage and selection operator (LASSO) untuk rekonstruksi PSD pada kasus sistem underdetermined. Hasil studi menunjukkan bahwa bila parameter regularisasi yang tepat digunakan, hasil rekonstruksi PSD dengan metode LASSO masih mendekati hasil rekonstruksi PSD yang diperoleh pada pesat Nyquist. Hasil rekonstruksi PSD dengan metode LASSO juga dapat digunakan untuk mendeteksi lokasi pita frekuensi yang diduduki pengguna secara akurat saat daya isyarat pengguna relatif cukup tinggi terhadap daya derau. Sementara itu, OMP hanya dapat digunakan untuk kasus tanpa derau. Pada aplikasi yang membutuhkan proses rekonstruksi PSD, hasil ini mengindikasikan dimungkinkannya relaksasi pesat pencuplikan hingga pesat yang sangat rendah.

Introduction: The immune status of the tumor microenvironment is extremely complex. One single immune feature cannot reflect the integral immune status, and its prognostic value was limited. We postulated that the immune signature based on multiple immuno-features could markedly improve the prediction of post-chemoradiotherapeutic survival in inoperable locally advanced non-small-cell lung cancer (LA-NSCLC) patients. Methods: In this study, 100 patients who were diagnosed as having inoperable LA-NSCLC between January 2005 and January 2016 were analyzed. A five immune features-based signature was then constructed using the nested repeat 10-fold cross validation with least absolute shrinkage and selection operator (LASSO) Cox regression model. Nomograms were then established for predicting prognosis. Results: The immune signature combining five immuno-features was significantly associated with overall survival (OS) and progression-free survival (PFS) (P = 0.002 and P = 0.014, respectively) in patients with inoperable LA-NSCLC, and at a cutoff of −0.05 stratified patients into two groups with 5-year OS rates of 39.8 and 8.8%, and 2-year PFS rates of 22.2 and 5.5% for the high- and low-immune signature groups, respectively. Integrating immune signature, we proposed predictive nomograms that were better than the traditional TNM staging system in terms of discriminating ability (OS: 0.692 vs. 0.588; PFS: 0.672 vs. 0.586, respectively) or net weight classification (OS: 32.96%; PFS: 9.22%), suggesting that the immune signature plays a significant role in improving the prognostic value. Conclusion: Multiple immune features-based immune signature could effectively predict recurrence and survival of inoperable LA-NSCLC patients and complemented the prognostic value of the TNM staging system. [ABSTRACT FROM AUTHOR]

Establishing the precise timeline of a crime can be challenging as current analytical techniques used suffer from many limitations and are destructive to the body fluids encountered at crime scenes. Raman spectroscopy has demonstrated excellent potential in forensic science as it provides direct information about the structural and molecular changes without the need for processing or extracting samples. However, its current applicability is limited to pure body fluids, as signals from the substrate underlying these fluids greatly influence the current models used for age estimation. In this study, we utilized Raman spectroscopy to identify selective spectral markers that delineate the bloodstain age in the presence of interfering signals from the substrate. The pure bloodstains and the bloodstains on the substrate were aged for two weeks at 21 ± 2 ℃ in the dark. Least absolute shrinkage and selection operator (LASSO) regression was employed to guide the feature selection in the presence of interference from substrates to accurately predict the bloodstain age. Substrate-specific regression models guided by an automated feature selection algorithm yielded low values of predictive root mean square error (0.207, 0.204, 0.222 h in logarithmic scale) and high R2 (0.924, 0.926, 0.913) on test data consisting of blood spectra on floor tile, facial tissue, and linoleum-polymer substrates, respectively. This framework for an automated feature selection algorithm relies entirely on pure bloodstain spectra to train substrate-specific models for estimating the age of composite (blood on substrate) spectra. The model can thus be easily applied to any new composite spectra and is highly scalable to new environments. This study demonstrates that Raman spectroscopy coupled with LASSO could serve as a reliable and nondestructive technique to determine the age of bloodstains on any surface while aiding forensic investigations in real-world scenarios. [ABSTRACT FROM AUTHOR]

Background: Gastric cancer (GC) accounts for high mortality. RNA methylation has recently gained interest as markers in specific tumors. This study aimed to uncover the function of the roles of 25 RNA methylation regulators in GC. Methods: RNA sequence and clinical data were downloaded from The Cancer Genome Atlas (TCGA) database. "STRING" and R were performed to analyze the correlation among the methylase. COX and LASSO were performed to screen for prognostic associated RNA methylation regulators. A prognostic model was established based on the expression of methylase. RT-PCR and immunohistochemistry detected the expression of methylase in GC cells and tissue. Kaplan–Meier curve and Cox analysis were applied to evaluate the effectiveness of the model. Results: The prediction model was established based on the expression of m6A RNA methylation regulators FTO (fat mass and obesity-associated) and RBM15 (RNA binding motif protein 15). Based on the model, GC patients were divided into "high risk" and "low risk" groups to compare the differences in survival. The model was re-evaluated with the clinical data of our center. Conclusion: The two-methylase combination model was an independent prognostic factor of GC. [ABSTRACT FROM AUTHOR]

This research studied the relationship between Oregon national bridge inventory (NBI) data as well as additional design/detailing variables and concrete highway bridge deck performance. Cox proportional hazards regression using least absolute shrinkage and selection operator (LASSO) and stepwise-variable selection were employed to determine the contribution of independent variables gathered from the NBI database. The stepwise approach produced a model with fewer variables compared with when LASSO was used. The hazard ratios (HR) of those variables retained in the stepwise approach were consistent with HR obtained from LASSO. The HR were also found to be intuitive. For example, cast-in-place bridge decks on steel bridges without a wearing surface or deck protection have an increased risk of deterioration while bridge decks in dry climate zones far from the ocean on prestressed concrete bridges have a decreased risk of deterioration. A separate refined dataset of 400 bridge decks was assembled by manually going through bridge drawings to gather information on relevant design/detailing variables. Bridge decks were carefully selected using a stratified sampling procedure based on design period and climate zone. Kaplan-Meier survival curves were constructed to study the impact of these design/detailing variables on concrete highway bridge deck performance. For example, bridge decks with concrete cover less than 1 in. (25 mm) have a lower survival probability than bridge decks with more cover and bridge decks that are slender have a higher survival probability than other bridge decks. [ABSTRACT FROM AUTHOR]

Considering the time-varying, uncertain and non-linear properties of the wastewater treatment process (WWTPs), a novel multi-kernel relevance vector machine (MRVM) soft sensor based on time difference (TD) is proposed to predict the quality-relevant but difficult-to-measure variable. Firstly, a novel dimension reduction technique is introduced to reduce data dimension and model complexity. Secondly, the parameters of the kernel model are optimized by the intelligent optimization algorithm (PSO). Besides, the TD strategy is introduced to enhance the robustness of MRVM when exposing to dynamic environments. Finally, the proposed model was assessed through two simulation studies and a real WWTP with the results demonstrating the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

Skin cancer is a common malignant tumor in China and throughout the world, and the rate of recurrence is considerably high, thus endangering the quality of life and health of patients, and increasing the economic burden and pressure to the families of those afflicted. Due to the limitations of traditional drug treatments, it is difficult to achieve the desired therapeutic effect of complete removal. However, targeted gene therapy may be a novel means of treating skin cancer, as the targeted nature of treatment may improve therapeutic outcomes. However, targeted gene therapy requires physicians to select the appropriate gene, which means suitable genetic biomarkers must be identified from complex genetic data. In the present study, the least absolute shrinkage and selection operator regression analysis method was used with 10-fold cross verification to reduce the dimensions of gene data in patients with skin cancer, and subsequently, 20 gene biomarkers were screened. A prognostic model was constructed using these 20 gene biomarkers, and the validity of the model was assessed using a training set and a verification set, which showed that the model performed well. Finally, gene function analysis of these 20 gene biomarkers was determined. Relevant studies were found to show that the genetic biomarkers identified in this paper may possess value for the follow-up clinical treatment of skin cancer. [ABSTRACT FROM AUTHOR]

Background: Osteosarcoma (OS) is a common malignant bone tumor originating in the interstitial tissues and occurring mostly in adolescents and young adults. Energy metabolism is a prerequisite for cancer cell growth, proliferation, invasion, and metastasis. However, the gene signatures associated with energy metabolism and their underlying molecular mechanisms that drive them are unknown. Methods: Energy metabolism-related genes were obtained from the TARGET database. We applied the "NFM" algorithm to classify putative signature gene into subtypes based on energy metabolism. Key genes related to progression were identified by weighted co-expression network analysis (WGCNA). Based on least absolute shrinkage and selection operator (LASSO) Cox proportional regression hazards model analyses, a gene signature for the predication of OS progression and prognosis was established. Robustness and estimation evaluations and comparison against other models were used to evaluate the prognostic performance of our model. Results: Two subtypes associated with energy metabolism was determined using the "NFM" algorithm, and significant modules related to energy metabolism were identified by WGCNA. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) suggested that the genes in the significant modules were enriched in kinase, immune metabolism processes, and metabolism-related pathways. We constructed a seven-gene signature consisting of SLC18B1, RBMXL1, DOK3, HS3ST2, ATP6V0D1, CCAR1, and C1QTNF1 to be used for OS progression and prognosis. Upregulation of CCAR1, and C1QTNF1 was associated with augmented OS risk, whereas, increases in the expression SCL18B1, RBMXL1, DOK3, HS3ST2, and ATP6VOD1 was correlated with a diminished risk of OS. We confirmed that the seven-gene signature was robust, and was superior to the earlier models evaluated; therefore, it may be used for timely OS diagnosis, treatment, and prognosis. Conclusions: The seven-gene signature related to OS energy metabolism developed here could be used in the early diagnosis, treatment, and prognosis of OS. [ABSTRACT FROM AUTHOR]

Background: An accumulating body of evidence suggests that long non-coding RNAs (lncRNAs) can serve as potential cancer prognostic factors. However, the utility of lncRNA combinations in estimating overall survival (OS) for hepatocellular carcinoma (HCC) remains to be elucidated. This study aimed to construct a powerful lncRNA signature related to the OS for HCC to enhance prognostic accuracy. Methods: The expression patterns of lncRNAs and related clinical data of 371 HCC patients were obtained based on The Cancer Genome Atlas (TCGA). Differentially expressed lncRNAs (DElncRNAs) were acquired by comparing tumors with adjacent normal samples. lncRNAs displaying significant association with OS were screened through univariate Cox regression analysis and the least absolute shrinkage and selection operator (LASSO) algorithm. All cases were classified into the validation or training group at the ratio of 3:7 to validate the constructed lncRNA signature. Data from the Gene Expression Omnibus (GEO) were used for external validation. We conducted real-time polymerase chain reaction (PCR) and assays for Transwell invasion, migration, CCK-8, and colony formation to determine the biological roles of lncRNA. Gene set enrichment analysis (GSEA) of the lncRNA model risk score was also conducted. Results: We identified 1292 DElncRNAs, among which 172 were significant in univariate Cox regression analysis. In the training group (n = 263), LASSO regression analysis confirmed 11 DElncRNAs including AC010547.1, AC010280.2, AC015712.7, GACAT3 (gastric cancer associated transcript 3), AC079466.1, AC089983.1, AC051618.1, AL121721.1, LINC01747, LINC01517, and AC008750.3. The prognostic risk score was calculated, and the constructed risk model showed significant correlation with HCC OS (log-rank P -value of 8.489e-9, hazard ratio of 3.648, 95% confidence interval: 2.238–5.945). The area under the curve (AUC) for this lncRNA model was up to 0.846. This risk model was confirmed in the validation group (n = 108), the entire cohort, and the external GEO dataset (n = 203). GACAT3 was highly expressed in HCC tissues and cell lines. Based on online databases, GACAT3 expression independently affects both OS and disease-free survival in HCC patients. Silencing GACAT3 in vitro significantly suppressed HCC cell proliferation, invasion, and migration. Moreover, pathways related to the lncRNA model risk score were confirmed by GSEA. Conclusion: The lncRNA signature established in this study can be used to predict HCC prognosis, which could provide novel clinical evidence to guide targeted HCC treatment. [ABSTRACT FROM AUTHOR]

Background: Cervical cancer (CC) represents the fourth most frequently diagnosed malignancy affecting women all over the world. However, effective prognostic biomarkers are still limited for accurately identifying high-risk patients. Here, we provided a combination machine learning algorithm-based signature to predict the prognosis of cervical squamous cell carcinoma (CSCC).Methods and Materials: After utilizing RNA sequencing (RNA-seq) data from 36 formalin-fixed and paraffin-embedded (FFPE) samples, the most significant modules were highlighted by the weighted gene co-expression network analysis (WGCNA). A candidate genes-based prognostic classifier was constructed by the least absolute shrinkage and selection operator (LASSO) and then validated in an independent validation set. Finally, based on the multivariate analysis, a nomogram including the FIGO stage, therapy outcome, and risk score level was built to predict progression-free survival (PFS) probability.Results: A mRNA-based signature was developed to classify patients into high- and low-risk groups with significantly different PFS and overall survival (OS) rate (training set: p < 0.001 for PFS, p = 0.016 for OS; validation set: p = 0.002 for PFS, p = 0.028 for OS). The prognostic classifier was an independent and powerful prognostic biomarker for PFS in both cohorts (training set: hazard ratio [HR] = 0.13, 95% CI 0.05-0.33, p < 0.001; validation set: HR = 0.02, 95% CI 0.01-0.04, p < 0.001). A nomogram that integrated the independent prognostic factors was constructed for clinical application. The calibration curve showed that the nomogram was able to predict 1-, 3-, and 5-year PFS accurately, and it performed well in the external validation cohorts (concordance index: 0.828 and 0.864, respectively).Conclusion: The mRNA-based biomarker is a powerful and independent prognostic factor. Furthermore, the nomogram comprising our prognostic classifier is a promising predictor in identifying the progression risk of CSCC patients. [ABSTRACT FROM AUTHOR]

The associations between motor proficiency, postural control, and visual efficiency with teacher and parent ratings of children's behavior and academic skills were examined among a sample of elementary school-aged children (N = 50). Teacher and parent ratings of students' academic skills were analyzed separately to examine the predictive nature of measures of motor proficiency. Spearman rank order correlations reported low to moderate relationships between motor proficiency, postural control, and children's behavior (e.g., ADHD inattention) and academic skills. The least absolute shrinkage and selection operator (lasso) procedure is demonstrated as an approach for variable selection of measures of children's motor proficiency, postural control, and visual efficiency to predict academic skills. For teacher and parent ratings of academic skills, ADHD symptom of inattention was the strongest model predictor, whereas directional control (postural control) was also a predictor for parent ratings. Study findings shed light on practical and methodological factors associated with motor skills in educational contexts. [ABSTRACT FROM AUTHOR]

Esophageal cancer (ESCA) carries a poor prognosis among gastrointestinal malignancies. The present study developed a signature based on mRNAs and long non-coding RNAs (lncRNAs) to predict prognosis in ESCA by using The Cancer Genome Atlas database. By using least absolute shrinkage and selection operator penalized regression, a set of RNAs (three mRNAs and two lncRNAs) was identified and used to build a risk score system of ESCA prognosis, which was used to stratify patients having considerable diverse survival in the training set [hazard ratio (HR), 3.932; 95% CI, 1.555–9.944; P<0.002] into high- and low-risk groups. The authentication of the results was achieved through the test set (HR, 3.150; 95% CI, 1.113–8.918; P<0.02) and the entire set (HR, 3.181; 95% CI, 1.686–6.006; P<0.0002). The results from multivariate Cox proportional hazard regression analysis in the entire set suggested that the prognostic significance of this signature may be independent of patients' clinicopathological characteristics. Furthermore, this signature was associated with several molecular signaling pathways of cancer according to Gene Set Enrichment Analysis. In addition, a nomogram was built and the risk score and TNM stage were integrated to estimate the 1- and 3-year overall survival rates. The results from the present study demonstrated that the integrated mRNA-lncRNA signature may be considered as a novel biomarker for the prognosis of ESCA. [ABSTRACT FROM AUTHOR]

Ciguatera poisoning is a globally occurring seafood disease caused by the ingestion of marine products contaminated with dinoflagellate produced neurotoxins. Persistent forms of ciguatera, which prove to be highly debilitating, are poorly studied and represent a significant medical issue. The present study aims to better understand chronic ciguatera manifestations and identify potential predictive factors for their duration. Medical files of 49 patients were analyzed, and the post-hospitalization evolution of the disease assessed through a follow-up questionnaire. A rigorous logistic lasso regression model was applied to select significant predictors from a list of 37 patient characteristics potentially predictive of having chronic symptoms. Missing data were handled by complete case analysis, and a survival analysis was implemented. All models used standardized variables, and multiple comparisons in the survival analyses were handled by Bonferroni correction. Among all studied variables, five significant predictors of having symptoms lasting ≥3 months were identified: age, tobacco consumption, acute bradycardia, laboratory measures of urea, and neutrophils. This exploratory, hypothesis-generating study contributes to the development of ciguatera epidemiology by narrowing the list from 37 possible predictors to a list of five predictors that seem worth further investigation as candidate risk factors in more targeted studies of ciguatera symptom duration.

Multidimensional computerized adaptive testing (MCAT) is one of the widely discussed topics in psychometrics. Within the context of item replenishment in MCAT, it is important to identify the item-trait pattern for each replenished item, which indicates the set of the latent traits that are measured by each replenished item in the item pool. We propose a pattern recognition method based on the least absolute shrinkage and selection operator (LASSO) to detect the optimal item-trait patterns of the replenished items via an MCAT test. Simulation studies are conducted to investigate the performance of the proposed method in pattern recognition accuracy under different conditions across various latent trait correlation, item discrimination, test lengths, and item selection criteria in the test. Results show that the proposed method can accurately and efficiently identify the item-trait patterns of the replenished items in both the two-dimensional and three-dimensional item pools.

Not unlike the climate or what holds the galaxies and planetary motions together, cancer biology has an intrinsic nonlinear dynamic. In this overview we will outline how to connect temporal measurements of a nonlinear dynamical and unstable complex system, such as cancer, with well-established engineering methods, old and new, that are applied in linear dynamical systems.This proof-of-concept is therapeutically relevant in the development of new means to treat or control human cancer by either adding an appropriate external "damping" or a "forcing" term, or by a "control" actuator such that its nonlinear dynamic is steered to a spiral stably into zero forever as a sink attractor., (© 2024. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

We aimed to develop and validate a model for predicting mortality in patients with angina across the spectrum of dysglycemia. A total of 1479 patients admitted for coronary angiography due to angina were enrolled. All-cause mortality served as the primary endpoint. The models were validated with five-fold cross validation to predict long-term mortality. The features selected by least absolute shrinkage and selection operator (LASSO) were age, heart rate, plasma glucose levels at 30 min and 120 min during an oral glucose tolerance test (OGTT), the use of angiotensin II receptor blockers, the use of diuretics, and smoking history. This best performing model was built using a random survival forest with selected features. It had a good discriminative ability (Harrell’s C-index: 0.829) and acceptable calibration (Brier score: 0.08) for predicting long-term mortality. Among patients with obstructive coronary artery disease confirmed by angiography, our model outperformed the Global Registry of Acute Coronary Events discharge score for mortality prediction (Harrell’s C-index: 0.829 vs. 0.739, p < 0.001). In conclusion, we developed a machine learning model to predict long-term mortality among patients with angina. With the integration of OGTT, the model could help to identify a high risk of mortality across the spectrum of dysglycemia.

Background: National ranking from the triennial Programme of International Student Assessment (PISA) often serves as a barometer of national performance and human capital. Though excessive student‐ and school‐level covariates (n > 700) may prove intractable for traditional least‐squares estimate procedures, shrinkage methods may be more suitable for subset selection. Aims: With a focus on the United States, this paper proposes sparse regression for PISA 2012 to discover salient student‐ and school‐level predictor variables for mathematical literacy achievement. Sample: The sparse regression analysis was conducted on 10 top‐tiered OECD countries/economies, Canada, and the United States in mathematical literacy on the 2012 PISA. Two‐ and three‐level hierarchical regression analyses were performed on Canadian and US students (N = 26,522) along with five of the ten top‐tiered countries/economies (N = 58,385). Methods: Using the 'least absolute shrinkage and selection operator' (LASSO) technique, the study (1) identified salient predictor variables of mathematical literacy performance for the top‐tiered countries/economies, Canada, and the United States and (2) used these salient variables to perform two‐ and three‐level hierarchical regression on data from Canada and the United States along with five top‐tiered countries/economies. Weights and replicates were used to account for complex sample design. A weighted, two‐level confirmatory factor analysis was performed to identify latent constructs. Missing data were handled through multiple imputation. Results: Separate two‐level hierarchical models accounted for 32–35% student‐level and 58–70% school‐level variance in Canada and the United States, respectively; three‐level models accounted for 33% of level‐one variance, 62–65% level‐two variance, and 13–44% of level‐three variance for the US/Canada and US/Canada/top‐tiered students, respectively. Following top‐tiered countries/economies, Canadian students had high levels of self‐efficacy, were more likely to encounter advanced concepts in class, were less activity/small group‐centred, and were more likely to consider truancy a learning hindrance. Factor analyses revealed a positive relation with rigour and class organization (teacher‐centred) for top‐tiered countries and Canada, though not for the United States. For all countries, there was a strong relation between rigour and self‐beliefs. Conclusion: Compared to top performers, a less rigorous curriculum, coupled with class and school factors, may explain lag in US performance. [ABSTRACT FROM AUTHOR]

Data on disease burden are often used for assessing population health, evaluating the effectiveness of interventions, formulating health policies, and planning future resource allocation. We investigated whether Internet usage and social media data, specifically the search volume on Google, page view count on Wikipedia, and disease mentioning frequency on Twitter, correlated with the disease burden, measured by prevalence and treatment cost, for 1633 diseases over an 11-year period. We also applied least absolute shrinkage and selection operator to predict the burden of diseases. We found that Google search volume is relatively strongly correlated with the burdens for 39 of 1633 diseases, including viral hepatitis, diabetes mellitus, multiple sclerosis, and hemorrhoids. Wikipedia and Twitter data strongly correlated with the burdens of 15 and 7 diseases, respectively. However, an accurate analysis must consider each condition's characteristics, including acute/chronic nature, severity, familiarity to the public, and the presence of stigma. [ABSTRACT FROM AUTHOR]

Numerous studies have investigated the impacts of ambient fine particulate matter (PM2.5) on human health. In this study, we examined the association of daily PM2.5 concentrations with the number of deaths for the cerebrovascular disease on the same day, using the generalized additive model (GAM) controlling for temporal trend and meteorological variables. We used the data between 2012 and 2014 from Shanghai, China, where the adverse health effects of PM2.5 have been of particular concern. Three different approaches (principal component analysis, shrinkage smoothers, and the least absolute shrinkage and selection operator regularization) were used in GAM to handle multicollinear meteorological variables. Our results indicate that the average daily concentration of PM2.5 in Shanghai was high, 55 μg/m3, with an average daily death for cerebrovascular disease (CVD) of 62. There was 1.7% raised cerebrovascular disease deaths per 10 μg/m3 increase in PM2.5 concentration in the unadjusted model. However, PM2.5 concentration was no longer associated with CVD deaths after controlling for meteorological variables. The results were consistent in the three modelling techniques that we used. As a large number of people are exposed to air pollution, further investigation with longer time period including individual-level information is needed to examine the association. [ABSTRACT FROM AUTHOR]

Background: Hepatocellular carcinoma (HCC) is one of the most aggressive malignant tumors, with a poor long-term prognosis worldwide. The functional deregulations of global transcriptome were associated with the genesis and development of HCC, but lacks systematic research and validation. Methods: A total of 519 postoperative HCC patients were included. We built an interactive and visual competing endogenous RNA network. The prognostic signature was established with the least absolute shrinkage and selection operator algorithm. Multivariate Cox regression analysis was used to screen for independent prognostic factors for HCC overall survival. Results: In the training set, we identified a four-gene signature (PBK, CBX2, CLSPN, and CPEB3) and effectively predicted the overall survival. The survival times of patients in the high-score group were worse than those in the low-score group (p = 0.0004), and death was also more likely in the high-score group (HR 2.444, p < 0.001). The results were validated in internal validation set (p = 0.0057) and two external validation cohorts (HR 2.467 and 2.6). The signature (AUCs of 1, 2, 3 years were 0.716, 0.726, 0.714, respectively) showed high prognostic accuracy in the complete TCGA cohort. Conclusions: In conclusion, we successfully built a more extensive ceRNA network for HCC and then identified a four-gene-based signature, enabling prediction of the overall survival of patients with HCC. [ABSTRACT FROM AUTHOR]

Multidimensional computerized adaptive testing (MCAT) is one of the widely discussed topics in psychometrics. Within the context of item replenishment in MCAT, it is important to identify the item-trait pattern for each replenished item, which indicates the set of the latent traits that are measured by each replenished item in the item pool. We propose a pattern recognition method based on the least absolute shrinkage and selection operator (LASSO) to detect the optimal item-trait patterns of the replenished items via an MCAT test. Simulation studies are conducted to investigate the performance of the proposed method in pattern recognition accuracy under different conditions across various latent trait correlation, item discrimination, test lengths, and item selection criteria in the test. Results show that the proposed method can accurately and efficiently identify the item-trait patterns of the replenished items in both the two-dimensional and three-dimensional item pools. [ABSTRACT FROM AUTHOR]

Headwater streams (HSs) are generally naturally prone to flow intermittence. These intermittent rivers and ephemeral streams have recently seen a marked increase in interest, especially to assess the impact of drying on aquatic ecosystems. The two objectives of this work are (a) to identify the main drivers of flow intermittence dynamics in HS and (b) to reconstruct local daily drying dynamics. Discrete flow states—"flowing" versus "drying"—are modelled as functions of covariates that include information on climate, hydrology, groundwater, and basin descriptors. Three classifiers to estimate flow states using covariates are tested on four contrasted regions in France: (a) a linear classifier with regularization (LASSO for least absolute shrinkage and selection operator) and two non‐linear non‐parametric classifiers, (b) a one‐hidden‐layer feedforward artificial neural network (ANN) classifier, and (c) a random forest (RF) classifier. The three classifiers are compared with a benchmark classifier (BC) that simply estimates dominant flow state for each month based on observations (without using covariates). The performance assessment over the period 2012–2016 carried out by cross‐validation shows that the three classifiers for flow state based on covariates outperformed the BC. This demonstrates the predictive power of the covariates. ANN is the classifier that globally achieves the best performance to predict the daily drying dynamics whereas both RF and LASSO tend to underestimate the proportion of drying states. The covariates are ranked in terms of relevance for each classifier. The monthly proportion of drying states provided by the discrete observation network has a major importance for the three classifiers ANN, LASSO, and RF. This may reflect the proclivity of a site to flow intermittence. ANN gives higher importance to climatic and hydrological covariates and its non‐linearity allows a greater degree of freedom. [ABSTRACT FROM AUTHOR]

Highlights • Multiple linear and support vector regression to forecast industrial reactive power. • Assessment of calendar, weather, and electrical variables as model predictors. • Two model selection techniques (cross-validation and lasso) are compared in the paper. • Numerical applications are performed on actual industrial data to test the proposal. • Results of forecasting models and of selection techniques are compared in the paper. Abstract Reactive power forecasting is essential for managing energy systems of factories and industrial plants. However, the scientific community has devoted scant attention to industrial load forecasting, and even less to reactive power forecasting. Many challenges in developing a short-term reactive power forecasting system for factories have rarely been studied. Industrial loads may depend on many factors, such as scheduled processes and work shifts, which are uncommon or unnecessary in classical load forecasting models. Moreover, the features of reactive power are significantly different from active power, so some commonly used variables in classical load forecasting models may become meaningless for forecasting reactive power. In this paper, we develop several models to forecast industrial reactive power. These models are constructed based on two forecasting techniques (e.g., multiple linear regression and support vector regression) and two variable selection methods (e.g., cross validation and least absolute shrinkage and selection operator). In the numerical applications based on real data collected from an Italian factory at both aggregate and individual load levels, the proposed models outperform four benchmark models in short forecast horizons. [ABSTRACT FROM AUTHOR]

Abstract Ubiquitination is an essential process in protein post-translational modification, which plays a crucial role in cell life activities, such as proteasomal degradation, transcriptional regulation, and DNA damage repair. Therefore, recognition of ubiquitination sites is a crucial step to understand the molecular mechanisms of ubiquitination. However, the experimental verification of numerous ubiquitination sites is time-consuming and costly. To alleviate these issues, a computational approach is needed to predict ubiquitination sites. This paper proposes a new method called UbiSitePred for predicting ubiquitination sites combined least absolute shrinkage and selection operator (LASSO) feature selection and support vector machine. First, we use binary encoding (BE), pseudo-amino acid composition (PseAAC), the composition of k-spaced amino acid pairs (CKSAAP), position-specific propensity matrices (PSPM) to extract the sequence feature information; thus, the initial feature space is obtained. Secondly, LASSO is applied to remove the feature redundancy information and selects the optimal feature subset. Finally, the optimal feature subset is input into the support vector machine (SVM) to predict the ubiquitination sites. Five-fold cross-validation shows that UbiSitePred model can achieve a better prediction performance compared with other methods, the AUC values for Set1, Set2, and Set3 are 0.9998, 0.8887, and 0.8481, respectively. Notably, the UbiSitePred has overall accuracy rates of 98.33%, 81.12%, and 76.90%, respectively. The results demonstrate that the proposed method is significantly superior to other state-of-the-art prediction methods and provide a new idea for the prediction of other post-translational modification sites of proteins. The source code and all datasets are available at https://github.com/QUST-AIBBDRC/UbiSitePred/. Highlights • A new method (UbiSitePred) to predict the ubiquitination sites. • Fusing BE, PseAAC, CKSAAP and PSPM methods to extract protein sequence features information. • LASSO method can effectively remove redundant information in the protein sequences. • We investigate the effect of the six different classifiers on the results. • The proposed method increases the prediction performance over several methods. [ABSTRACT FROM AUTHOR]

Abstract High-throughput spectra data with large number of variables (wavelength) will make the prediction of multivariate calibration model unreliable, in which case, the sparse statistical methods such as least absolute shrinkage and selection operator (LASSO) are gradually being valued by researchers. In this study, a novel variable informative criterion based on weighted voting strategy combined with least absolute shrinkage and selection operator (WV-LASSO) has been proposed. Monte Carlo Sampling (MCS) is used for generating a large number of sub-models. In each Monte Carlo circulation, the regression coefficients and variable selection information of LASSO model will be recorded. In the present work, weighted voting strategy based on regression coefficients information combined with selected variable frequency of all sub-models is used for evaluating the importance of variable. Different from specific methods, variable informative (importance) criterion can be more extensive and flexible for algorithm design. Then an approach called exponentially decreasing function (EDF) is applied to create a variable selection method with WV-LASSO. The performance of this method was evaluated by three near-infrared (NIR) datasets. Compared with some efficient variable selection methods based on different informative criterions including variable importance projection (VIP), Monte Carlo uninformative variable elimination (MC-UVE), randomization test (RT), competitive adaptive reweighted sampling (CARS), stability competitive adaptive reweighted sampling (SCARS), variable iterative space shrinkage approach (VISSA), interval variable iterative space shrinkage approach (iVISSA), LASSO coupled with sampling error profile analysis (SEPA-LASSO) and variable combination population analysis (VCPA), and so forth, the variable selection method proposed in this paper shows better prediction and interpretation ability and has potential for constructing various variable selection methods by combining other selection strategies. Highlights • A novel variable informative criterion named WV-LASSO was proposed. • WV-LASSO investigates the behaviors of wavelength selection by Monte Carlo Sampling. • The proposed criterion considers both regression coefficient vector and selected frequency of variables. • Exponentially decreasing function was combined with WV-LASSO to generate a simple but efficient method. • The proposed criterion is flexible to combined with other approaches to construct various wavelength selection methods. [ABSTRACT FROM AUTHOR]

Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Objectives: Hypertrophic cardiomyopathy (HCM), a leading cause of heart failure and sudden death, requires early diagnosis and treatment. This study investigated the underlying pathogenesis and explored potential diagnostic gene biomarkers for HCM., Methods: Transcriptional profiles of myocardial tissues from patients with HCM (dataset GSE36961) were downloaded from the Gene Expression Omnibus database and subjected to bioinformatics analyses, including differentially expressed gene (DEG) identification, enrichment analyses, and protein-protein interaction (PPI) network analysis. Least absolute shrinkage and selection operator (LASSO) regression and support vector machine recursive feature elimination were performed to identify candidate diagnostic gene biomarkers. mRNA expression levels of candidate biomarkers were tested in an external dataset (GSE141910); area under the receiver operating characteristic curve (AUC) values were obtained to validate diagnostic efficacy., Results: Overall, 156 DEGs (109 downregulated, 47 upregulated) were identified. Enrichment and PPI network analyses indicated that the DEGs were involved in biological functions and molecular pathways including inflammatory response, platelet activity, complement and coagulation cascades, extracellular matrix organization, phagosome, apoptosis, and VEGFA-VEGFR2 signaling. RASD1, CDC42EP4, MYH6, and FCN3 were identified as diagnostic biomarkers for HCM., Conclusions: RASD1, CDC42EP4, MYH6, and FCN3 might be diagnostic gene biomarkers for HCM and can provide insights concerning HCM pathogenesis., Competing Interests: Declaration of conflicting interestsThe authors declare that there is no conflict of interest.