Lasso Method Research Articles

A network analysis of voice hearing, emotional distress and subjective recovery before and after cognitive behavioural interventions.

There is a need to identify and to better understand key processes involved in voice hearing, which can inform the targeting and development of psychological interventions for distressing voices. The current study aimed to examine interrelations between the negative impact of voices, voice characteristics, emotional distress and recovery before and after cognitive behavioural interventions for voices (Coping Strategy Enhancement, guided self-help Cognitive Behavioural Therapy, Relating Therapy and Person-Based Cognitive Therapy). The sample consisted of 172 participants from the Sussex Voices Clinic who completed pre- and post-treatment assessments. The negative impact of voices, voice characteristics, emotional distress and recovery were used to estimate two networks, before and after cognitive behavioural interventions, using the graphical lasso method with the extended Bayesian information criterion. Centrality indices were also computed, and the two networks were compared on connectivity, structure and individual edge weights. Depression, anxiety and the negative impact of voices were identified as key central symptoms and acted as bridge symptoms in pre- and post-treatment networks. There were no significant differences in network structure (M = 0.155, p = .57), global strength (S = 0.188, p = .07) and centralities (C = -0.318, p = -.06) between the two networks. Our findings suggest that anxiety and depression are promising treatment targets, that can lead to reductions in voice-related distress, whereas the characteristics of voices and subjective recovery play little role in the network structure. Limitations include the lack of a control group and the lack of diversity within the sample.

Estimating probability of default via delinquencies? Evidence from European P2P lending market

The unprecedented growth of the financial sector's digital transformation opens wide areas to the scaling up of finance in innovative and knowledge-based projects. Improving risk management takes centre stage in the acceleration of this process. This study uses loan-book data from the peer-to-peer (P2P) lending market to empirically investigate the determinants of default risk. Using the loan-book database covering the period from 2014 to 2020, we examine multiple factors related to the default risk of loans issued by P2P lending platforms. The results indicate that a higher interest rate and higher stock market returns increase the probability of default in the P2P lending market. Results are robust to additional tests based on endogeneity correction, the LASSO method and sampling bias. The severity of the impact of market returns and interest rates is found to be significantly different based on the levels of financial technology (FinTech) adoption and banking sector distress. Increases in the market interest rate are found to boost the sensitivity of P2P loan defaults to stock market volatility. This study contributes to existing literature on risk management models with its consideration of country-specific factors, paving the way to future best practices in the market.

Evaluation and Influencing Factors of Regional Green Innovation Efficiency Based on the Lasso Method

Regional green innovation efficiency is affected by multiple factors. Based on the undesirable output of 11 provinces in the Yangtze River Economic Belt from 2012–2021, this paper uses the Super-SBM model containing undesirable outputs to measure and analyze the regional green innovation efficiency and tests the variables affecting regional green innovation efficiency through lasso regression. Stepwise regression and least squares estimation are used to prove the rationality of the lasso regression model. The results show that the regional green innovation efficiency of the Yangtze River Economic Belt during 2012–2021 among different regions has differences that manifest as the efficiency of the lower, middle, and upper reaches decreases successively, and the differences decrease gradually over time. All the variables affecting the regional green innovation efficiency pass the lasso regression variable screening test, and most of the influencing factors positively promote the regional green innovation efficiency and are more explanatory than the stepwise regression and the least squares proof model.

Development of a prediction model for the incidence of type 2 diabetic kidney disease and its application based on a regional health data platform

Objective: To construct a risk prediction model for diabetes kidney disease (DKD). Methods: Patients newly diagnosed with type 2 diabetes mellitus (T2DM) between January 1, 2015, and December 31, 2022, were selected as study subjects from the Yinzhou Regional Health Information Platform in Ningbo City. The Lasso method was used to screen the risk factors, and the DKD risk prediction model was established using Cox proportional hazard regression models. Bootstrap 500 resampling was applied for internal validation. Results: The study included 49 706 subjects, with an median (Q1, Q3) age of 60.00 (50.00, 68.00) years old, and 55% were male. A total of 4 405 subjects eventually developed DKD. Age at first diagnosis of T2DM, BMI, education level, fasting plasma glucose, glycated hemoglobin A1c, urinary albumin, past medical history (hyperuricemia, rheumatic diseases), triglycerides, and estimated glomerular filtration rate were included in the final model. The final model's C-index was 0.653, with an average of 0.654 after Bootstrap correction. The final model's area under the receiver operating characteristic curve for predicting 4-year, 5-year, and 6-year was 0.657, 0.659, and 0.664, respectively. The calibration curve was closely aligned with the ideal curve. Conclusions: This study constructed a DKD risk prediction model for newly diagnosed T2DM patients based on real-world data that is simple, easy to use, and highly practical. It provides a reliable basis for screening high-risk groups for DKD.

Feature selection with annealing for forecasting financial time series

Stock market and cryptocurrency forecasting is very important to investors as they aspire to achieve even the slightest improvement to their buy-or-hold strategies so that they may increase profitability. However, obtaining accurate and reliable predictions is challenging, noting that accuracy does not equate to reliability, especially when financial time-series forecasting is applied owing to its complex and chaotic tendencies. To mitigate this complexity, this study provides a comprehensive method for forecasting financial time series based on tactical input–output feature mapping techniques using machine learning (ML) models. During the prediction process, selecting the relevant indicators is vital to obtaining the desired results. In the financial field, limited attention has been paid to this problem with ML solutions. We investigate the use of feature selection with annealing (FSA) for the first time in this field, and we apply the least absolute shrinkage and selection operator (Lasso) method to select the features from more than 1000 candidates obtained from 26 technical classifiers with different periods and lags. Boruta (BOR) feature selection, a wrapper method, is used as a baseline for comparison. Logistic regression (LR), extreme gradient boosting (XGBoost), and long short-term memory are then applied to the selected features for forecasting purposes using 10 different financial datasets containing cryptocurrencies and stocks. The dependent variables consisted of daily logarithmic returns and trends. The mean-squared error for regression, area under the receiver operating characteristic curve, and classification accuracy were used to evaluate model performance, and the statistical significance of the forecasting results was tested using paired t-tests. Experiments indicate that the FSA algorithm increased the performance of ML models, regardless of problem type. The FSA hybrid models showed better performance and outperformed the other BOR models on seven of the 10 datasets for regression and classification. FSA-based models also outperformed Lasso-based models on six of the 10 datasets for regression and four of the 10 datasets for classification. None of the hybrid BOR models outperformed the hybrid FSA models. Lasso-based models, excluding the LR type, were comparable to the best models for six of the 10 datasets for classification. Detailed experimental analysis indicates that the proposed methodology can forecast returns and their movements efficiently and accurately, providing the field with a useful tool for investors.

The Global and Local Patterns of Reading-Related Cognitive and Ecological Variables in Chinese First-Grade Children: A Cross-Sectional Network Analysis.

In the current study, we tested a network model of reading difficulty by using state-of-the-art psychological network analysis. Four hundred and fifty-three Chinese first-grade children (about 38% female, mean age = 7.00, SD = 0.41) were divided into good (n = 154), competent (n = 147), and struggling readers (n = 152) based on their scores of Chinese character reading. The Extended Bayesian Information Criterion graphical lasso (EBICglasso) method was applied to estimate cross-sectional networks for the three groups. Each network included four cognitive nodes (homophone awareness, morphological structure awareness, phonological awareness, and vocabulary) and two ecological nodes (family socioeconomic status and the number of books at home). Chronological age and nonverbal intelligence were also included in the estimated networks. The global (i.e., global structure and global connectivity) and local patterns (i.e., the most important edges and nodes) in each network were reported. The network comparison results showed that global connectivity was significantly lower among struggling readers than for good readers, implying that a holistic impairment of bidirectional connections among multiple variables relates to the difficulty in learning to read. The theoretical and empirical implications and the significance of applying the network approach to reading research are discussed.

A Sparse Beta Regression Model for Network Analysis

For statistical analysis of network data, the β -model has emerged as a useful tool, thanks to its flexibility in incorporating nodewise heterogeneity and theoretical tractability. To generalize the β -model, this paper proposes the Sparse β -Regression Model (S β RM) that unites two research themes developed recently in modelling homophily and sparsity. In particular, we employ differential heterogeneity that assigns weights only to important nodes and propose penalized likelihood with an ℓ 1 penalty for parameter estimation. While our estimation method is closely related to the LASSO method for logistic regression, we develop a new theory emphasizing the use of our model for dealing with a parameter regime that can handle sparse networks usually seen in practice. More interestingly, the resulting inference on the homophily parameter demands no debiasing normally employed in LASSO type estimation. We provide extensive simulation and data analysis to illustrate the use of the model. As a special case of our model, we extend the Erdős-Rényi model by including covariates and develop the associated statistical inference for sparse networks, which may be of independent interest.

Graphical model for mixed data types

With the development of data collection technologies, data types have become more diverse. Additionally, graphical models, as tools for describing variable network relationships, have become increasingly popular in recent years. Previous studies have focused on graphical models tailored to specific types of data. However, these existing methods fail to identify graphical models for mixed data types. The difficulty of constructing graphical models for mixed data types lies in the fact that each type of data has its own space, which challenges the estimation of network relationships in a graphical model when the data are combined. To address this issue, this study presents a novel method that utilizes a vectorization and alignment strategy developed particularly for mixed data types, including scalar, interval-valued, compositional, and functional data, to estimate a graphical model. By iteratively employing a block-sparse graphical lasso method on aligned data, the method can achieve satisfactory results, as shown by numerous simulation experiments. The results also validate the superiority of our proposed method over potential competing methods. Furthermore, this method was applied to an engine damage propagation network as an illustrative example. Our method provides a novel modeling approach for graphical models in the case of mixed data types.

Development of a predictive model for patients with bone metastases referred to palliative radiotherapy: Secondary analysis of a multicenter study (the PRAIS trial).

The decision to administer palliative radiotherapy (RT) to patients with bone metastases (BMs), as well as the selection of treatment protocols (dose, fractionation), requires an accurate assessment of survival expectancy. In this study, we aimed to develop three predictive models (PMs) to estimate short-, intermediate-, and long-term overall survival (OS) for patients in this clinical setting. This study constitutes a sub-analysis of the PRAIS trial, a longitudinal observational study collecting data from patients referred to participating centers to receive palliative RT for cancer-induced bone pain. Our analysis encompassed 567 patients from the PRAIS trial database. The primary objectives were to ascertain the correlation between clinical and laboratory parameters with the OS rates at three distinct time points (short: 3 weeks; intermediate: 24 weeks; prolonged: 52 weeks) and to construct PMs for prognosis. We employed machine learning techniques, comprising the following steps: (i) identification of reliable prognostic variables and training; (ii) validation and testing of the model using the selected variables. The selection of variables was accomplished using the LASSO method (Least Absolute Shrinkage and Selection Operator). The model performance was assessed using receiver operator characteristic curves (ROC) and the area under the curve (AUC). Our analysis demonstrated a significant impact of clinical parameters (primary tumor site, presence of non-bone metastases, steroids and opioid intake, food intake, and body mass index) and laboratory parameters (interleukin 8 [IL-8], chloride levels, C-reactive protein, white blood cell count, and lymphocyte count) on OS. Notably, different factors were associated with the different times for OS with only IL-8 included both in the PMs for short- and long-term OS. The AUC values for ROC curves for 3-week, 24-week, and 52-week OS were 0.901, 0.767, and 0.806, respectively. We successfully developed three PMs for OS based on easily accessible clinical and laboratory parameters for patients referred to palliative RT for painful BMs. While our findings are promising, it is important to recognize that this was an exploratory trial. The implementation of these tools into clinical practice warrants further investigation and confirmation through subsequent studies with separate databases.

Comprehensive evaluation of immunological attributes and immunotherapy responses of positive T cell function regulators in colorectal cancer

BackgroundPositive regulators of T-cell function (PTFRs), integral to T-cell proliferation and activation, have been identified as potential prognostic markers in colorectal cancer (CRC). Despite this, their role within the tumor microenvironment (TME) and their response to immunotherapy are not yet fully understood.MethodsThis study delved into PTFR-related CRC subtypes by analyzing four independent transcriptome datasets, emphasizing the most significant prognostic PTFRs. We identified differentially expressed genes (DEGs) between two subtypes and developed a PTFR risk model using LASSO and Cox regression methods. The model’s associations with survival time, clinical features, TME characteristics, tumor mutation profiles, microsatellite instability (MSI), cancer stem cell (CSC) index, and responses to chemotherapy, targeted therapy, and immunotherapy were subsequently explored.ResultsThe PTFR risk model demonstrated a strong predictive capacity for CRC. It facilitated the estimation of immune cell composition, HLA expression levels, immune checkpoint expression, mutation burden, CSC index features, and the effectiveness of immunotherapy.ConclusionsThis study enhances our understanding of the role of PTFRs in CRC progression and introduces an innovative assessment framework for CRC immunotherapy. This framework improves the prediction of treatment outcomes and aids in the customization of therapeutic strategies.

Risk stratification and overall survival prediction in extensive stage small cell lung cancer after chemotherapy with immunotherapy based on CT radiomics

The prognosis of extensive-stage small cell lung cancer is usually poor. In this study, a combined model based on pre-treatment CT radiomics and clinical features was constructed to predict the OS of extensive-stage small cell lung cancer after chemotherapy with immunotherapy.Clinical data of 111 patients with extensive stage small-cell lung cancer who received first-line immunotherapy combined with chemotherapy in our hospital from December 2019 to December 2021 were retrospectively collected. Finally, 93 patients were selected for inclusion in the study, and CT images were obtained through PACS system before treatment. All patients were randomly divided into a training set (n = 66) and a validation set (n = 27). Images were imported into ITK-SNAP to outline areas of interest, and Python software was used to extract radiomics features. A total of 1781 radiomics features were extracted from each patient’s images. The feature dimensions were reduced by MRMR and LASSO methods, and the radiomics features with the greatest predictive value were screened. The weight coefficient of radiomics features was calculated, and the linear combination of the feature parameters and the weight coefficient was used to calculate Radscore. Univariate cox regression analysis was used to screen out the factors significantly associated with prognosis from the radiomics and clinical features, and multivariate cox regression analysis was performed to establish the prognosis prediction model of extensive stage small cell lung cancer. The degree of metastases was selected as a significant clinical prognostic factor by univariate cox regression analysis. Seven radiomics features with significance were selected by LASSO-COX regression analysis, and the Radscore was calculated according to the coefficient of the radiomics features. An alignment diagram survival prediction model was constructed by combining Radscore with the number of metastatic lesions. The study population was stratified into those who survived less than 11 months, and those with a greater than 11 month survival. The C-index was 0.722 (se = 0.044) and 0.68(se = 0.074) in the training and the validation sets, respectively. The Log_rank test results of the combination model were as follows: training set: p < 0.0001, validation set: p = 0.00042. In this study, a combined model based on radiomics and clinical features could predict OS in patients with extensive stage small cell lung cancer after chemotherapy with immunotherapy, which could help guide clinical treatment strategies.

Alternative splicing and intron retention: Their profiles and roles in cutaneous fibrosis of systemic sclerosis

BackgroundAlternative splicing (AS) and intron retention (IR) implicated in multiple pathophysiological processes, have rarely been reported in systemic sclerosis (SSc). MethodsWe integrated bulk RNA-seq and 4D label-free mass spectrometry to perform a multi-omics analysis of AS and IR in SSc skin tissue and fibroblasts. RMATS and iREAD were used to identify AS and IR, which were validated by real-time PCR. Spearman correlation and the LASSO method were employed to assess correlations among clinical features, introns, splicing factors (regulators of AS) and proteins. FindingsAS profiles showed distinct alterations in SSc skin tissue, with the most pronounced changes occurring in IR. AS and IR were associated with total modified Rodnan skin score (mRSS) and local skin score. Upon TGF-β stimulation, fibroblasts exhibited significant alterations in IR profiles, affecting genes related to fibroblast proliferation and collagen fibril organization. A comprehensive integrated analysis of introns, exons, and proteome profiles revealed that IR exerted a negative impact on protein expression, with certain changes being under intronic control. RT-PCR confirmed the presence of intron and exon-derived sequences of CTTN, OGA, MED16 and PHYKPL. Additionally, notable changes were observed in the regulatory network of splicing factors in SSc skin tissues. These factors are also involved in fibrosis pathways and correlated with clinical features. ConclusionTotally, abnormal AS, IR profiles and splicing factors were identified in SSc, altered IRs and splicing factors participated in fibrosis-related pathways. IR exerted a negative impact on protein expression in TGF-β-stimulated fibroblasts. Clarification of the IR mechanisms will provide new insights into the pathophysiology of SSc.

Sex-Specific Imaging Biomarkers for Parkinson's Disease Diagnosis: A Machine Learning Analysis.

This study aimed to identify sex-specific imaging biomarkers for Parkinson's disease (PD) based on multiple MRI morphological features by using machine learning methods. Participants were categorized into female and male subgroups, and various structural morphological features were extracted. An ensemble Lasso (EnLasso) method was employed to identify a stable optimal feature subset for each sex-based subgroup. Eight typical classifiers were adopted to construct classification models for PD and HC, respectively, to validate whether models specific to sex subgroups could bolster the precision of PD identification. Finally, statistical analysis and correlation tests were carried out on significant brain region features to identify potential sex-specific imaging biomarkers. The best model (MLP) based on the female subgroup and male subgroup achieved average classification accuracy of 92.83% and 92.11%, respectively, which were better than that of the model based on the overall samples (86.88%) and the overall model incorporating gender factor (87.52%). In addition, the most discriminative feature of PD among males was the lh 6r (FD), but among females, it was the lh PreS (GI). The findings indicate that the sex-specific PD diagnosis model yields a significantly higher classification performance compared to previous models that included all participants. Additionally, the male subgroup exhibited a greater number of brain region changes than the female subgroup, suggesting sex-specific differences in PD risk markers. This study underscore the importance of stratifying data by sex and offer insights into sex-specific variations in PD phenotypes, which could aid in the development of precise and personalized diagnostic approaches in the early stages of the disease.

The Effect of Multicollinearity on Feature Selection

Objectives: To provide a new LASSO-based feature selection technique that aids in selecting important variables for predicting the response variables in case of multicollinearity. Methods: LASSO is a type of regression method employed to select important covariates for predicting a dependent variable. The traditional LASSO method uses the conventional Ordinary Least Square (OLS) method for this purpose. The Use of the OLS based LASSO approach gives unreliable results if the data deviates from normality. Thus, this study recommends using, a Redescending M-estimator-based LASSO approach. The efficacy of this new method is checked against the ordinary LASSO method using a real dataset and also a simulation study with various levels of sample size (N=100,200,1000), different numbers of predictors (p=10,15,20), and varying degrees of correlation (ρ = 0.96, 0.98, 0.999). Findings: The usual OLS-based LASSO finds it difficult to select important variables when the independent variables are correlated. The Redescending M-estimator-based LASSO addresses at tackling the pitfalls faced by Conventional LASSO methodology. Among other things, the proposed method is far better than the old-fashioned LASSO since it helps to pick out significant factors more effectively, particularly in the presence of multicollinearity. Novelty: The conventional OLS-based LASSO approach selects a greater number of non-significant variables in the presence of multicollinearity. The proposed Redescending M-estimator-based LASSO approach selects the important variables in the presence of multicollinearity. Keywords: Feature Selection, LASSO, MDAE, VIF, Variable Selection

Nomogram predicting early urinary incontinence after radical prostatectomy

PurposeOne of the most frequent side effects of radical prostatectomy (RP) is urinary incontinence. The primary cause of urine incontinence is usually thought to be impaired urethral sphincter function; nevertheless, the pathophysiology and recovery process of urine incontinence remains unclear. This study aimed to identify potential risk variables, build a risk prediction tool that considers preoperative urodynamic findings, and direct doctors to take necessary action to reduce the likelihood of developing early urinary incontinence.MethodsWe retrospectively screened patients who underwent radical prostatectomy between January 1, 2020 and December 31, 2023 at the First People ‘s Hospital of Nantong, China. According to nomogram results, patients who developed incontinence within three months were classified as having early incontinence. The training group’s general characteristics were first screened using univariate logistic analysis, and the LASSO method was applied for the best prediction. Multivariate logistic regression analysis was carried out to determine independent risk factors for early postoperative urine incontinence in the training group and to create nomograms that predict the likelihood of developing early urinary incontinence. The model was internally validated by computing the performance of the validation cohort. The nomogram discrimination, correction, and clinical usefulness were assessed using the c-index, receiver operating characteristic curve, correction plot, and clinical decision curve.ResultsThe study involved 142 patients in all. Multivariate logistic regression analysis following RP found seven independent risk variables for early urinary incontinence. A nomogram was constructed based on these independent risk factors. The training and validation groups’ c-indices showed that the model had high accuracy and stability. The calibration curve demonstrates that the corrective effect of the training and verification groups is perfect, and the area under the receiver operating characteristic curve indicates great identification capacity. Using a nomogram, the clinical net benefit was maximised within a probability threshold of 0.01–1, according to decision curve analysis (DCA).ConclusionThe nomogram model created in this study can offer a clear, personalised analysis of the risk of early urine incontinence following RP. It is highly discriminatory and accurate, and it can help create efficient preventative measures and identify high-risk populations.

The Impact of the Expansion and Contraction of China Cities on Carbon Emissions, 2002–2021, Evidence from Integrated Nighttime Light Data and City Attributes

Exploring the impact of urbanization on carbon emissions is crucial for formulating effective emission reduction policies. Using nighttime light data and attribute data from 68 Chinese cities (2002–2021), this paper develops an urban development evaluation system with the entropy method. The Lasso method is employed to select key factors affecting carbon emissions, and hierarchical regression models are utilized to analyze these factors across different city types. The results show the following: (1) The extraction of built-up areas using integrated nighttime light data yields an overall accuracy ranging from 70.90% to 98.87%, reflecting high precision. (2) Expanding cities have predominated over the past two decades, indicating a continued upward trend in urbanization in China. (3) Urban development is influenced by internal characteristics and geographic location: contracting cities are mainly inland heavy industrial centers, while expanding cities are located in economically advanced coastal regions. Additionally, it is also impacted by the growth of surrounding cities, exemplified by the imbalance between central cities and their peripheries within metropolitan areas. (4) The expansion of built-up areas is a significant factor affecting carbon emissions across all city types. For expanding cities, managing population growth and promoting tertiary sector development are recommended, while contracting cities should focus on judicious economic planning and virescence area protection.

Optimal Pair Matching Combined with Machine Learning Predicts a Significant Reduction in Myocardial Infarction Risk in African Americans Following Omega-3 Fatty Acid Supplementation.

Conflicting clinical trial results on omega-3 highly unsaturated fatty acids (n-3 HUFA) have prompted uncertainty about their cardioprotective effects. While the VITAL trial found no overall cardiovascular benefit from n-3 HUFA supplementation, its substantial African American (AfAm) enrollment provided a unique opportunity to explore racial differences in response to n-3 HUFA supplementation. The current observational study aimed to simulate randomized clinical trial (RCT) conditions by matching 3766 AfAm and 15,553 non-Hispanic White (NHW) individuals from the VITAL trial utilizing propensity score matching to address the limitations related to differences in confounding variables between the two groups. Within matched groups (3766 AfAm and 3766 NHW), n-3 HUFA supplementation's impact on myocardial infarction (MI), stroke, and cardiovascular disease (CVD) mortality was assessed. A weighted decision tree analysis revealed belonging to the n-3 supplementation group as the most significant predictor of MI among AfAm but not NHW. Further logistic regression using the LASSO method and bootstrap estimation of standard errors indicated n-3 supplementation significantly lowered MI risk in AfAm (OR 0.17, 95% CI [0.048, 0.60]), with no such effect in NHW. This study underscores the critical need for future RCT to explore racial disparities in MI risk associated with n-3 HUFA supplementation and highlights potential causal differences between supplementation health outcomes in AfAm versus NHW populations.

A Multi-Machine Learning Consensus Model Based on Clinical Features Reveals That Interleukin-10 Derived from Monocytes Leads to a Poor Prognosis in Patients with Coronavirus Disease-2019.

Despite ongoing interventions, SARS-CoV-2 continues to cause significant global morbidity and mortality. Early diagnosis and intervention are crucial for effective clinical management. However, prognostic features based on transcriptional data have shown limited effectiveness, highlighting the need for more precise biomarkers to improve COVID-19 treatment outcomes. We retrospectively analyzed 149 clinical features from 189 COVID-19 patients, identifying prognostic features via univariate Cox regression. The cohort was split into training and validation sets, and 77 prognostic models were developed using seven machine learning algorithms. Among these, the least absolute shrinkage and selection operator (Lasso) method was employed to refine the selection of prognostic variables by ten-fold cross-validation strategy, which were then integrated with random survival forests (RSF) to build a robust COVID-19-related prognostic model (CRM). Model accuracy was evaluated across training, validation, and entire cohorts. The diagnostic relevance of interleukin-10 (IL-10) was confirmed in bulk transcriptional data and validated at the single-cell level, where we also examined changes in cellular communication between mononuclear cells with differing IL-10 expression and other immune cells. Univariate Cox regression identified 43 prognostic features. Among the 77 machine learning models, the combination of Lasso and RSF produced the most robust CRM. This model consistently performed well across training, validation, and entire cohorts. IL-10 emerged as a key prognostic feature within the CRM, validated by single-cell transcriptional data. Transcriptome analysis confirmed the stable diagnostic value of IL-10, with mononuclear cells identified as the primary IL-10 source. Moreover, differential IL-10 expression in these cells was linked to altered cellular communication in the COVID-19 immune microenvironment. The CRM provides accurate prognostic predictions for COVID-19 patients. Additionally, the study underscores the importance of early IL-10 level testing upon hospital admission, which could inform therapeutic strategies.

A combination model of CT-based radiomics and clinical biomarkers for staging liver fibrosis in the patients with chronic liver disease

A combined model was developed using contrast-enhanced CT-based radiomics features and clinical characteristics to predict liver fibrosis stages in patients with chronic liver disease (CLD). We retrospectively analyzed multiphase CT scans and biopsy-confirmed liver fibrosis. 160 CLD patients were randomly divided into 7:3 training/validation ratio. Clinical laboratory indicators associated with liver fibrosis were identified using Spearman's correlation and multivariate logistic regression correlation. Radiomic features were extracted after segmenting the entire liver from multiphase CT images. Feature dimensionality reduction was performed using RF-RFE, LASSO, and mRMR methods. Six radiomics-based models were developed in the training cohort of 112 patients. Internal validation was conducted on 48 randomly assigned patients. Receptor Operating Characteristic (ROC) curves and confusion matrices were constructed to evaluate model performance. The radiomics model exhibited robust performance, with AUC values of 0.810 to 1.000 for significant fibrosis, advanced fibrosis, and cirrhosis. The integrated clinical-radiomics model had superior diagnostic efficacy in the validation cohort, with AUC values of 0.836 to 0.997. Moreover, these models outperformed established biomarkers such as the aspartate aminotransferase to platelet ratio index (APRI) and the fibrosis 4 score (FIB-4), as well as the gamma glutamyl transpeptidase to platelet ratio (GPR), in predicting the fibrotic stages. The clinical-radiomics model holds considerable promise as a non-invasive diagnostic tool for the assessment and staging of liver fibrosis in the patients with CLD, potentially leading to better patient management and outcomes.

Exploration of the ubiquitination-related molecular classification and signature to predict the survival and immune microenvironment in colon cancer.

Ubiquitination, a major post-translational modification, significantly impacts tumorigenesis, progression, and prognosis. This study aims to classify colon cancer at the molecular level and create a reliable signature using ubiquitination-related genes (URGs) to assess the immune microenvironment and prognosis. We employed non-negative matrix factorization to subtype colon cancer based on ubiquitination-related gene (URG) expression patterns. Quantitative scores for 28 immune cell infiltrates and the tumor microenvironment were computed using single-sample gene set enrichment analysis (ssGSEA) and the Estimate algorithm. Subtype feature genes were selected through Lasso logistic regression and SVM-RFE algorithm. The ubiquitination-related signature was constructed using univariate Cox, Lasso, and stepwise regression methods to categorize patients into high and low-risk groups. Validation included log-rank tests, receiver operating characteristic (ROC) analysis, decision curve analysis (DCA), and external dataset validation. Immune therapy response was compared using Tumor Immune Dysfunction and Exclusion (TIDE), Immunophenoscore (IPS), and submap analyses. Clinical variables and risk scores were integrated into an enhanced nomogram. The early diagnostic value of four URGs was confirmed via quantitative real-time polymerase chain reaction (qRT-PCR) and immunohistochemistry. The cell proliferation was assessed through colony formation, EdU staining, and xenograft tumorigenesis assays. Prognostic ubiquitination-related genes (URGs) stratified patients into subtypes, revealing differences in survival, immune cell infiltration, and pathological staging. A signature of 6 URGs (ARHGAP4, MID2, SIAH2, TRIM45, UBE2D2, WDR72) was identified from 57 subtype-related genes. The high-risk group exhibited characteristics indicative of enhanced epithelial-mesenchymal transition, immune escape, immunosuppressive myeloid-derived suppressor cells, regulatory T cell infiltration, and lower immunogenicity. In contrast, the low-risk group demonstrated the opposite trend but showed a better response to CTLA4 checkpoint inhibitors. The predictive performance of the nomogram significantly improved with the integration of risk score, stage, and age. ARHGAP4 and SIAH2 exhibit promising early diagnostic capabilities. Additionally, WDR72 knockdown significantly inhibited CRC cell proliferation both in vitro and in vivo. Our developed ubiquitination-related signature and genes serve as promising biomarkers for colon cancer prognosis, immune microenvironment, and diagnosis.