Selection Operator Research Articles

Exploratory Study of Differentially Expressed Genes of Peripheral Blood Monocytes in Patients with Carotid Atherosclerosis.

The abundance of circulating monocytes is closely associated with the development of atherosclerosis in humans. This study aimed to further research into diagnostic biomarkers and targeted treatment of carotid atherosclerosis (CAS). We performed transcriptomics analysis through weighted gene co-expression network analysis (WGCNA) of monocytes from patients in public databases with and without CAS. Differentially expressed genes (DEGs) were screened by R package limma. Diagnostic molecules were derived by the least absolute shrinkage and selection operator (LASSO) and support vector machine recursive feature elimination (SVM-RFE) algorithms. NetworkAnalyst, miRWalk, and StarBase databases assisted in the construction of diagnostic molecule regulatory networks. The DrugBank database predicted drugs targeting the diagnostic molecules. RT-PCR tested expression profiles. From 14,369 hub genes and 61 DEGs, six differentially expressed monocyte-related hub genes were significantly associated with immune cells, immune responses, monocytes, and lipid metabolism. LASSO and SVM-RFE yielded five genes for CAS prediction. RT-PCR of these genes showed HMGB1 was upregulated, and CCL3, CCL3L1, CCL4, and DUSP1 were down-regulated in CAS versus controls. Then, we constructed and visualized the regulatory networks of 9 transcription factors (TFs), which significantly related to 5 diagnostic molecules. About 11 miRNAs, 19 lncRNAs, and 39 edges centered on four diagnostic molecules (CCL3, CCL4, DUSP1, and HMGB1) were constructed and displayed. Eleven potential drugs were identified, including ibrutinib, CTI-01, roflumilast etc. Conclusion: A set of five biomarkers were identified for the diagnosis of CAS and for the study of potential therapeutic targets.

Action of m6A-related gene signatures on the prognosis and immune microenvironment of colonic adenocarcinoma

N6-methyladenosine (m6A) modification in human tumor cells exerts considerable influence on crucial processes like tumorigenesis, invasion, metastasis, and immune response. This study aims to comprehensively analyze the impact of m6A-related genes on the prognosis and immune microenvironment (IME) of colonic adenocarcinoma (COAD). Public data sources, predictive algorithms identified m6A-related genes and differential gene expression in COAD. Subtype analysis and assessment of immune cell infiltration patterns were performed using consensus clustering and the CIBERSORT algorithm. The Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis determined gene signatures. Independent prognostic factors were identified using univariate and multivariate Cox proportional hazards models. The findings indicate that 206 prognostic m6A-related DEGs contribute to the m6A regulatory network along with 8 m6A enzymes. Based on the expression levels of these genes, 438 COAD samples from The Cancer Genome Atlas (TCGA) were classified into 3 distinct subtypes, showing marked differences in survival prognosis, clinical characteristics, and immune cell infiltration profiles. Subtype 3 and 2 displayed reduced levels of infiltrating regulatory T cells and M0 macrophages, respectively. A six-gene signature, encompassing KLC3, SLC6A15, AQP7 JMJD7, HOXC6, and CLDN9, was identified and incorporated into a prognostic model. Validation across TCGA and GSE39582 datasets exhibited robust predictive specificity and sensitivity in determining the survival status of COAD patients. Additionally, independent prognostic factors were recognized, and a nomogram model was developed as a prognostic predictor for COAD. In conclusion, the six target genes governed by m6A mechanisms offer substantial potential in predicting COAD outcomes and provide insights into the unique IME profiles associated with various COAD subtypes.

A Clinical Fusion Model Based on Radiomics Features and Deep Learning for Predicting CDKN2A/B Homozygous Deletion Status in IDH-mutant Diffuse Astrocytoma

Purpose: To construct a fusion model for predicting CDKN2A/B homozygous deletion status in patients with isocitrate dehydrogenase (IDH)-mutant diffuse astrocytoma by combining the radiomics features and deep learning (DL). Methods: A total of 200 IDH-mutant astrocytoma (103 CDKN2A/B homozygous deletion (HD) and 97 CDKN2A/B non-homozygous deletion (NHD)) patients were retrospectively enrolled in the training cohort (n = 140) and the external test cohort (n = 60) for the prediction of CDKN2A/B homozygous deletion status in patients with IDH-mutant astrocytoma. DL model was constructed by SE-Net model, radiomics features of different regions (edema, tumor and overall lesion) were extracted using Pyradiomics, and radiomics model was built by selecting 4 features in the edema region and 7 features in the tumor region by the least absolute shrinkage and selection operator (LASSO). Finally, a fusion model was jointly constructed by the DL model, radiomics model, and clinical features. The predictive performance of the 3 models was evaluated using calibration curves and decision curves, and compared with the fusion model. Results: Based on the results of the different models, we finally selected a fusion model consisting of DL model, radiomics model, and clinical features. The fusion model showed the best performance with an area under the curve (AUC) of 0.958 in the training cohort and 0.914 in the test cohort. Conclusions: The clinical fusion model based on radiomics features and DL features showed good performance in predicting CDKN2A/B homozygous deletion status in patients with IDH-mutant diffuse astrocytoma. Key Points: 1) Used DL and radiomics to non-invasively predict the CDKN2A/B homozygous deletion status. 2) The model can predict CDKN2A/B homozygous deletion status in IDH-mutant astrocytoma patients. 3) Our result improved classification accuracy and demonstrated better performance in the fusion model.

TMErisk score: a tumor microenvironment-based model for predicting prognosis and immunotherapy in patients with head and neck squamous cell carcinoma

Tumor microenvironment (TME) is closely associated with the progression and prognosis of head and neck squamous cell carcinoma (HNSCC). To investigate potential biomarkers for predicting therapeutic outcomes in HNSCC, we analyzed the immune and stromal status of HNSCC based on the genes associated with TME using the ESTIMATE algorithm. Immune and stromal genes were identified with differential gene expression and weighted gene co-expression network analysis (WGCNA). From these genes, 118 were initially selected through Cox univariate regression and then further input into least absolute shrinkage and selection operator (LASSO) regression analysis. As a result, 11 genes were screened out for the TME-related risk (TMErisk) score model which presented promising overall survival predictive potential. The TMErisk score was negatively associated with immune and stromal scores but positively associated with tumor purity. Individuals with high TMErisk scores exhibited decreased expression of most immune checkpoints and all human leukocyte antigen family genes, and reduced abundance of infiltrating immune cells. Divergent genes were mutated in HNSCC. In both high and low TMErisk score groups, the tumor protein P53 exhibited the highest mutation frequency. A higher TMErisk score was found to be associated with reduced overall survival probability and worse outcomes of immunotherapy. Therefore, the TMErisk score could serve as a valuable model for the outcome prediction of HNSCC in clinic.

Individualized radiology screening for newly diagnosed nasopharyngeal carcinoma

ObjectivesCurrent guidelines recommend universal PET/CT screening for metastases staging in newly diagnosed nasopharyngeal carcinoma (NPC) despite the low rate of synchronous distant metastasis (SDM). The study aims to achieve individualized screening recommendations of NPC based on the risk of SDM. Methods and materials18 pre-treatment peripheral blood indicators was retrospectively collected from 2271 primary NPC patients. A peripheral blood risk score (PBRS) was constructed by indicators associated with SDM on least absolute shrinkage and selection operator (LASSO) regression. The PBRS-based distant metastases (PBDM) model was developed from features selected by logistic regression analyses in the training cohort and then validated in the validation cohort. Receiver operator characteristic curve analysis, calibration curves, and decision curve analysis were applied to evaluate PBDM model performance. ResultsPre-treatment Epstein–Barr viral DNA copy number, percentage of total lymphocytes, serum lactate dehydrogenase level, and monocyte-to-lymphocyte ratio were most strongly associated with SDM in NPC and used to construct the PBRS. Sex (male), T stage (T3-4), N stage (N2-3), and PBRS (≥1.076) were identified as independent risk factors for SDM and applied in the PBDM model, which showed good performance. Through the model, patients in the training cohort were stratified into low-, medium-, and high-risk groups. Individualized screening recommendations were then developed for patients with differing risk levels. ConclusionThe PBDM model offers individualized recommendations for applying PET/CT for metastases staging in NPC, allowing more targeted screening of patients with greater risk of SDM compared with current recommendations.

Sparse Group Penalties for bi-level variable selection.

Many data sets exhibit a natural group structure due to contextual similarities or high correlations of variables, such as lipid markers that are interrelated based on biochemical principles. Knowledge of such groupings can be used through bi-level selection methods to identify relevant feature groups and highlight their predictive members. One of the best known approaches of this kind combines the classical Least Absolute Shrinkage and Selection Operator (LASSO) with the Group LASSO, resulting in the Sparse Group LASSO. We propose the Sparse Group Penalty (SGP) framework, which allows for a flexible combination of different SGL-style shrinkage conditions. Analogous to SGL, we investigated the combination of the Smoothly Clipped Absolute Deviation (SCAD), the Minimax Concave Penalty (MCP) and the Exponential Penalty (EP) with their group versions, resulting in the Sparse Group SCAD, the Sparse Group MCP, and the novel Sparse Group EP (SGE). Those shrinkage operators provide refined control of the effect of group formation on the selection process through a tuning parameter. In simulation studies, SGPs were compared with other bi-level selection methods (Group Bridge, composite MCP, and Group Exponential LASSO) for variable and group selection evaluated with the Matthews correlation coefficient. We demonstrated the advantages of the new SGE in identifying parsimonious models, but also identified scenarios that highlight the limitations of the approach. The performance of the techniques was further investigated in a real-world use case for the selection of regulated lipids in a randomized clinical trial.

Hybrid optimal feature selection approach for internet of things based medical data analysis for prognosis

<p>Healthcare is very important application domain in internet of things (IoT). The aim is to provide a novel combined feature selection (FS) methods like univariate (UV) with tree-based methods (TB), recursive feature elimination (RFE) with least absolute shrinkage selection operator (LASSO), mutual information (MI) with genetic algorithm (GA) and embedded methods (EM) with univariate has been applied to internet of medical things (IoMT)based heart disease dataset. The well-suited machine learning algorithms for IoT medical data are logistic regression (LR) and support vector machine (SVM). Each combined method has been applied to the machine learning algorithms to find the best classifier for prognosis. The various performance metrices has been calculated for all the combined feature selection methods for logistic regression and support vector machine and found that for precise classification could be done using recursive elimination feature selection method with LASSO applied to logistic regression achieved a better performance than all other combined methods with high accuracy, sensitivity and high area under curve. Decision has been taken by data analytics that RFE+LASSO using LR feature selection method will provide an overall better performance for IoT based medical heart disease dataset after comparing all other combined methods with LR and SVM classifiers<em>. </em></p>

Identification of PANoptosis-related genes as prognostic indicators of thyroid cancer

BackgroundThyroid cancer (THCA) has become a common malignancy in recent years, with the mortality rate steadily increasing. PANoptosis is a unique kind of programmed cell death (PCD), including pyroptosis, necroptosis, and apoptosis, and is involved in the proliferation and prognosis of numerous cancers. This paper demonstrated the connection between PANoptosis-related genes and THCA based on the analyses of Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases, which have not been evaluated yet. MethodsWe identified PANoptosis-related expression genes (PRDEGs) by multi-analyzing the TCGA-THCA and GEO datasets. To identify the significant PRDEGs, a prognostic model was constructed using least absolute shrinkage and selection operator regression (LASSO). The predictive values of the significant PRDEGs for THCA outcomes were determined using Cox regression analysis and nomograms. Gene enrichment analyses were performed. Finally, immunohistochemistry was carried out using the human protein atlas. ResultsA LASSO regression model based on nine PRDEGs was constructed, and the prognostic value of key PRDEGs was explored via risk score. Univariate and multivariate Cox regression were implemented to identify further three significant PRDEGs closely related to distant metastasis, lymph node metastasis, and tumor stage. Then, a nomogram was constructed, which presented high predictive accuracy for 5 years survival of THCA patients. Gene enrichment analyses in THCA were strongly associated with PCD pathways. CASP6 presented significantly differential expression during clinical T stage, N stage, and PFI events (P<0.05 for all) and demonstrated the highest degree of diagnostic efficacy in PRDEGs (HR: 2.060, 95% CI: 1.170-3.628, P < 0.05). Immunohistochemistry showed CASP6 was more abundant in THCA tumor tissue. ConclusionA potential prognostic role for PRDEGs in THCA was identified, providing a new direction for treatment. CASP6 may be a potential therapeutic target and a novel prognostic biomarker for THCA.

MRI-based radiomics feature combined with tumor markers to predict TN staging of rectal cancer.

The aim of this study is to evaluate the predictive ability of MRI-based radiomics combined with tumor markers for TN staging in patients with rectal cancer and to develop a prediction model for TN staging. A total of 190 patients with rectal adenocarcinoma who underwent total mesorectal excision at the First Affiliated Hospital of the Air Force Medical University between January 2016 and December 2020 were included in the study. An additional 54 patients from a prospective validation cohort were included between August 2022 and August 2023. Preoperative tumor markers and MRI imaging data were collected from all enrolled patients. The 190 patients were divided into a training cohort (n = 133) and a validation cohort (n = 57). Radiomics features were extracted by outlining the region of interest (ROI) on T2WI sequence images. Feature selection and radiomics score (Rad-score) construction were performed using least absolute shrinkage and selection operator regression analysis (LASSO). The postoperative pathology TNM stage was used to differentiate locally advanced rectal cancer (T3/4 or N1/2) from locally early rectal cancer (T1/2, N0). Logistic regression was used to construct separate prediction models for T stage and N stage. The models' predictive performance was evaluated using DCA curves and calibration curves. The T staging model showed that Rad-score, based on 8 radiomics features, was an independent predictor of T staging. When combined with CEA, tumor diameter, mesoretal fascia (MRF), and extramural venous invasion (EMVI), it effectively differentiated between T1/2 and T3/4 stage rectal cancers in the training cohort (AUC 0.87 [95% CI: 0.81-0.93]). The N-staging model found that Rad-score, based on 10 radiomics features, was an independent predictor of N-staging. When combined with CA19.9, degree of differentiation, and EMVI, it effectively differentiated between N0 and N1/2 stage rectal cancers. The training cohort had an AUC of 0.84 (95% CI: 0.77-0.91). The calibration curves demonstrated good precision between the predicted and actual results. The DCA curves indicated that both sets of predictive models could provide net clinical benefits for diagnosis. MRI-based radiomics features are independent predictors of T staging and N staging. When combined with tumor markers, they have good predictive efficacy for TN staging of rectal cancer.

Integrated transcriptomic and immunological profiling reveals new diagnostic and prognostic models for cutaneous melanoma

The mortality rate associated with cutaneous melanoma (SKCM) remains alarmingly high, highlighting the urgent need for a deeper understanding of its molecular underpinnings. In our study, we leveraged bulk transcriptome sequencing data from the SKCM cohort available in public databases such as TCGA and GEO. We utilized distinct datasets for training and validation purposes and also incorporated mutation and clinical data from TCGA, along with single-cell sequencing data from GEO. Through dimensionality reduction, we annotated cell subtypes within the single-cell data and analyzed the expression of tumor-related pathways across these subtypes. We identified differentially expressed genes (DEGs) in the training set, which were further refined using the Least Absolute Shrinkage and Selection Operator (LASSO) machine learning algorithm, employing tenfold cross-validation. This enabled the construction of a prognostic model, whose diagnostic efficacy we subsequently validated. We conducted Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses on the DEGs, and performed immunological profiling on two risk groups to elucidate the relationship between model genes and the immune responses relevant to SKCM diagnosis, treatment, and prognosis. We also knocked down the GMR6 expression level in the melanoma cells and verified its effect on cancer through multiple experiments. The results indicate that the GMR6 gene plays a role in promoting the proliferation, invasion, and migration of cancer cells in human melanoma. Our findings offer novel insights and a theoretical framework that could enhance prognosis, treatment, and drug development strategies for SKCM, potentially leading to more precise therapeutic interventions.

Machine learning model for cardiovascular disease prediction in patients with chronic kidney disease

IntroductionCardiovascular disease (CVD) is the leading cause of death in patients with chronic kidney disease (CKD). This study aimed to develop CVD risk prediction models using machine learning to support clinical decision making and improve patient prognosis.MethodsElectronic medical records from patients with CKD at a single center from 2015 to 2020 were used to develop machine learning models for the prediction of CVD. Least absolute shrinkage and selection operator (LASSO) regression was used to select important features predicting the risk of developing CVD. Seven machine learning classification algorithms were used to build models, which were evaluated by receiver operating characteristic curves, accuracy, sensitivity, specificity, and F1-score, and Shapley Additive explanations was used to interpret the model results. CVD was defined as composite cardiovascular events including coronary heart disease (coronary artery disease, myocardial infarction, angina pectoris, and coronary artery revascularization), cerebrovascular disease (hemorrhagic stroke and ischemic stroke), deaths from all causes (cardiovascular deaths, non-cardiovascular deaths, unknown cause of death), congestive heart failure, and peripheral artery disease (aortic aneurysm, aortic or other peripheral arterial revascularization). A cardiovascular event was a composite outcome of multiple cardiovascular events, as determined by reviewing medical records.ResultsThis study included 8,894 patients with CKD, with a composite CVD event incidence of 25.9%; a total of 2,304 patients reached this outcome. LASSO regression identified eight important features for predicting the risk of CKD developing into CVD: age, history of hypertension, sex, antiplatelet drugs, high-density lipoprotein, sodium ions, 24-h urinary protein, and estimated glomerular filtration rate. The model developed using Extreme Gradient Boosting in the test set had an area under the curve of 0.89, outperforming the other models, indicating that it had the best CVD predictive performance.ConclusionThis study established a CVD risk prediction model for patients with CKD, based on routine clinical diagnostic and treatment data, with good predictive accuracy. This model is expected to provide a scientific basis for the management and treatment of patients with CKD.

Network structure indicators predict ecological robustness in food webs

AbstractFood web robustness is a critical aspect of ecosystem stability and has been extensively studied in ecology. However, the potential of machine learning techniques in predicting food web robustness and the identification of key network structure indicators have not been fully explored. We compared the suitability of different machine learning methods and assessed the relative importance of network structure indicators for predicting the robustness of food webs. We utilized a variety of food web datasets spanning different ecosystems to calculate network structure indicators, which include average distance (AD), betweenness centrality (BC), directional connectivity (C), closeness centrality (CC), diameter (D), degree centrality (DC), edge betweenness centrality (EBC), number of links (L), linkage density (LD), and number of nodes (N). We then compared the performance of machine learning methods, including artificial neural network (ANN), random forest (RF), least absolute shrinkage and selection operator (LASSO), and decision tree (DT), and evaluated the relative importance of network structure indicators on robustness predictions. The results demonstrate that the RF model has the best performance (MAE = 0.0178, RMSE = 0.0263, R2 = 0.9063). Meanwhile, the CC indicator has a significant impact in predicting robustness of food webs. It is suggested that both the RF model and the CC indicator should be considered seriously in predicting food web robustness. This research elucidates the differential outcomes when various machine learning methodologies and indicators are employed to predict the robustness of food webs. It significantly enhances our understanding by demonstrating the precise capability of machine learning models in forecasting the robustness of food webs.

Development and validation of a nomogram for predicting cognitive frailty in patients on maintenance haemodialysis.

This study aimed to construct a nomogram for predicting the risk of cognitive frailty in patients on maintenance haemodialysis. An explorative cross-sectional design was adopted. From April 2022 to July 2022, 496 participants were recruited from five haemodialysis centres in Qingdao, Shandong Province, China. Participants with cognitive frailty were screened by Frailty Phenotype scale and Mini-Mental State Examination. Least Absolute Shrinkage and Selection Operator (LASSO) regression and multivariate logistic regression were utilized to determine predictors. The predictive performance of the nomogram was validated by calibration and discrimination. Decision curve analysis was used to assess clinical utility. Internal validation was implemented using 1000 bootstrap samples to mitigate overfitting. The prevalence of cognitive frailty was 17.5% (n = 87). Six risk predictors, namely health empowerment, alexithymia, age, educational level, marital status and dialysis vintage, were screened and used to develop a nomogram model. The nomogram had satisfactory discrimination and calibration, and decision curve analysis revealed considerable clinical utility. A nomogram incorporated with the six risk predictors was developed, and it exhibited excellent prediction performance. The nomogram may strengthen the effective screening of patients at high risk of cognitive frailty. This study established a tool for healthcare staff to predict cognitive frailty probability and identify risk factors in patients on maintenance haemodialysis. The nomogram can meet the needs of personalized care and precision medicine simultaneously. Data were collected from patients on maintenance haemodialysis by using questionnaire survey. STROBE checklist was used.

Development and validation of a prediction model for suboptimal ovarian response in polycystic ovary syndrome (PCOS) patients undergoing GnRH-antagonist protocol in IVF/ICSI cycles

BackgroundPCOS patients with unexpectedly low oocyte yield following conventional ovarian stimulation are referred to as suboptimal responders. However, identifying suboptimal responders presents a significant challenge within reproductive medicine and limited research exists on the occurrence of suboptimal response. This analysis aimed to develop a predictive model of suboptimal response during in vitro fertilization/intracytoplasmic sperm injection (IVF/ICSI) treatments in PCOS patients.MethodsThis retrospective study involved a cohort of 313 PCOS patients undergoing their first IVF/ICSI cycle from 2019 to 2022. Univariate logistic regression analyses, least absolute shrinkage, selection operator regression analysis, and recursive feature elimination were employed to identify relevant characteristics and construct predictive models. Moreover, a nomogram was constructed based on the best model. Receiver operating characteristic curves, decision curve analysis (DCA), and calibration curves were used to evaluate the model.ResultsThe predictors included in the model were age, Anti-Mullerian hormone, antral follicle count, and basal follicle-stimulating hormone. The area under the receiver operating characteristic curve (AUC) was 0.7702 (95% confidence interval 0.7157–0.8191). The AUC, along with the DCA curve and calibration curve, demonstrated a satisfactory level of congruence and discrimination ability.ConclusionThe nomogram effectively predicted the probability of suboptimal response in PCOS patients undergoing gonadotropin-releasing hormone antagonist protocol during IVF/ICSI treatment.

A Preoperative Prediction Model for Lymph Node Metastasis in Patients with Gastric Cancer Using a Machine Learning-based Ultrasomics Approach.

This study aimed to develop an ultrasomics model for predicting lymph node metastasis preoperative in patients with gastric cancer (GC). This study enrolled GC patients who underwent preoperative ultrasound examination. Manual segmentation of the region of interest (ROI) was performed by an experienced radiologist to extract radiomics features using the Pyradiomics software. The Z-score algorithm was used for feature normalization, followed by the Wilcoxon test to identify the most informative features. Linear prediction models were constructed using the least absolute shrinkage and selection operator (LASSO). The performance of the ultrasomics model was evaluated using the area under curve (AUC), sensitivity, specificity, and the corresponding 95% confidence intervals (CIs). A total of 464 GC patients (mean age: 60.4 years ±11.3 [SD]; 328 men [70.7%]) were analyzed, of whom 291 had lymph node metastasis. The patients were randomly assigned to either the training (n=324) or test (n=140) sets, using a 7:3 ratio. An ultrasomics model that consisted of 19 radiomics features was developed using Wilcoxon and LASSO algorithms in the training set. Our ultrasomics model showed moderate performance for lymph node metastasis prediction in both the training (AUC: 0.802, 95%CI: 0.752-0.851, P<0.001) and test sets (AUC: 0.802, 95%CI: 0.724-0.879, P<0.001). The calibration curve analysis indicated good agreement between the predicted probabilities of ultrasomics and actual lymph node metastasis status. Our study highlights the potential of a machine learning-based ultrasomics model in predicting lymph node metastasis in GC patients, offering implications for personalized therapy approaches.

Integrated analysis of tumor-associated macrophages and M2 macrophages in CRC: unraveling molecular heterogeneity and developing a novel risk signature.

Emerging investigations have increasingly highlighted the critical role of tumor-associated macrophages (TAMs) and M2 macrophages in cancer development, progression, and metastasis, marking them as potential targets in various cancer types. The main objective of this research is to discover new biomarkers associated with TAM-M2 macrophages in colorectal cancer (CRC) and to dissect the molecular heterogeneity of CRC by combining single-cell RNA sequencing and bulk RNA-seq data. By utilizing weighted gene co-expression network analysis (WGCNA), we acquired TAM-M2-associated genes by intersecting TAM marker genes obtained from scRNA-seq data with module genes of M2 macrophages derived from bulk RNA-seq data. We employed least absolute shrinkage and selection operator (LASSO) Cox analysis to select predictive biomarkers from these TAM-M2-related genes. Quantitative polymerase chain reaction (qPCR) was employed to validate the mRNA expression levels of the genes identified in the screening. This led to the development of the TAM-M2-related signature (TAMM2RS). We also conducted functional and immune landscape analyses of different risk groups. The combination of scRNA-seq and bulk RNA-seq analyses yielded 377 TAM-M2-related genes. DAPK1, NAGK, and TRAF1 emerged as key prognostic genes in CRC, which were identified through LASSO Cox analysis. Utilizing these genes, we constructed and validated the TAMM2RS, demonstrating its effectiveness in predicting survival in CRC patients. Our research offers a thorough investigation into the molecular mechanisms associated with TAM-M2 macrophages in CRC and unveils potential therapeutic targets, offering new insights for treatment strategies in colorectal cancer.

A machine learning analysis of predictors of future hypertension in a young population.

Early diagnosis of hypertension (HT) is crucial for preventing end-organ damage. This study aims to identify the risk factors for future HT in young individuals through the application of machine learning (ML) models. The study included individuals aged 18-40 years who had not been diagnosed with HT through ambulatory blood pressure monitoring (ABPM). These participants were monitored for hypertension diagnosis from the date of ABPM application until the date of data collection. Hypertension prediction was carried out using three distinct ML methods: Support Vector Machine, Random Forest, and Least Absolute Shrinkage and Selection Operator. The identification of variables significant for future HT was based on the outcomes of these models. This study comprised 516 patients, with a mean follow-up duration of 793.4±58.6 days. Following the integration of demographic data, laboratory results, and ABPM findings into the ML models, age, high-density lipoprotein cholesterol, triglycerides, and the standard deviation of systolic blood pressure (SDsis) were identified as predictors for future HT. A logistic regression with the selected variables (age, diabetes mellitus history, HDL, triglycerides, white blood cell count, and SDsis) using the full data set gave the following log odds 0.0737 (P<0.001), 0.7146 (P<0.001), -0.0160 (P=0.071), 0.0026 (P=0.002), 0.0857 (P=0.069), and 0.0850 (P=0.005), respectively. The corresponding probability values of age, diabetes mellitus history, HDL, triglycerides, white blood cell count, and SDsis were 0.5184, 0.6714, 0.4960, 0.5006, 0.5214, and 0.5212, respectively. This indicates a unit increase in all factors, except diabetes mellitus history, increases the probability of future HT by 50%. A history of diabetes, however, increases the probability of future HT by more than two thirds. The history of diabetes mellitus emerged as the most crucial predictor of future HT across all applied methods. ML methods appear to be valuable tools for predicting future HT. The widespread adoption of these methods and the refinement of more comprehensive models will lay the groundwork for future studies.

Radiomic features at Contrast-enhanced CT Predict Virus-driven Liver Fibrosis: A Multi-institutional Study.

Liver fibrosis is a major cause of morbidity and mortality among in chronic hepatitis patients. Radiomics, particularly of the spleen, may improve diagnostic accuracy and treatment strategies. External validations are necessary to ensure reliability and generalizability. In this retrospective study, we developed three radiomics models using contrast-enhanced CT scans from 167 patients with liver fibrosis (training group) between January 2020 and December 2021. Radiomic features were extracted from arterial venous, portal venous, and equilibrium phase images. Recursive feature selection random forest (RFS-RF) and the least absolute shrinkage and selection operator (LASSO) logistic regression were used for feature selection and dimensionality reduction. Performance was assessed by area under the curve, C-index, calibration plots and decision curve analysis. External validation was performed on 114 patients from two institutions. Twenty-five radiomic features were significantly associated with fibrosis stage, with 80% of the top 10 features originating from portal venous phase spleen images. The radiomics models showed good performance in the validation cohort (C-indices: 0.723-0.808) and excellent calibration. Decision curve analysis indicated clinical benefits, with machine learning-based radiomics models (RFR-score and SVMR-score) providing more significant advantages. Radiomic features offer significant benefits over existing serum indices for staging virus-driven liver fibrosis, underscoring the value of radiomics in enhancing diagnostic accuracy. Specifically, radiomics analysis of the spleen presents additional noninvasive options for assessing fibrosis, highlighting its potential in improving patient management and outcomes.

A study on the application of radiomics based on cardiac MR non-enhanced cine sequence in the early diagnosis of hypertensive heart disease

BackgroundThe prevalence of hypertensive heart disease (HHD) is high and there is currently no easy way to detect early HHD. Explore the application of radiomics using cardiac magnetic resonance (CMR) non-enhanced cine sequences in diagnosing HHD and latent cardiac changes caused by hypertension.Methods132 patients who underwent CMR scanning were divided into groups: HHD (42), hypertension with normal cardiac structure and function (HWN) group (46), and normal control (NOR) group (44). Myocardial regions of the end-diastolic (ED) and end-systolic (ES) phases of the CMR short-axis cine sequence images were segmented into regions of interest (ROI). Three feature subsets (ED, ES, and ED combined with ES) were established after radiomic least absolute shrinkage and selection operator feature selection. Nine radiomic models were built using random forest (RF), support vector machine (SVM), and naive Bayes. Model performance was analyzed using receiver operating characteristic curves, and metrics like accuracy, area under the curve (AUC), precision, recall, and specificity.ResultsThe feature subsets included first-order, shape, and texture features. SVM of ED combined with ES achieved the highest accuracy (0.833), with a macro-average AUC of 0.941. AUCs for HHD, HWN, and NOR identification were 0.967, 0.876, and 0.963, respectively. Precisions were 0.972, 0.740, and 0.826; recalls were 0.833, 0.804, and 0.863, respectively; and specificities were 0.989, 0.863, and 0.909, respectively.ConclusionsRadiomics technology using CMR non-enhanced cine sequences can detect early cardiac changes due to hypertension. It holds promise for future use in screening for latent cardiac damage in early HHD.

Predictive Modeling for Functional Outcomes After All-Inside Arthroscopic Anterior Talofibular Ligament Repair of Chronic Ankle Instability.

Arthroscopic anterior talofibular ligament repair (AATFLR) is a surgical strategy to treat chronic ankle instability (CAI) patients. This study identified risk factors that influenced the functional outcomes of AATFLR for CAI and developed prognostic nomogram for predicting functional outcomes in future AATFLR cases. Patients undergoing AATFLR from January 2016 to June 2022 with at least 10 months of follow-up were included in the study. The Karlsson Ankle Functional Score (KAFS) was evaluated preoperatively and at last follow-up visit. A total of 15 potential predictors including age, sex, body mass index, side affected, time from injury to surgery, sports-related injury, osteophyte, loose bodies, distal tibiofibular syndesmosis, ATFL avulsion fracture, Outerbridge classification of osteochondral lesions, postoperative immobilization method, ambulation time, walking time, and follow-up time, were recorded. We first used univariate binary logistic regression analysis to select the potential significant prognostic features, which were then subjected to the least absolute shrinkage and selection operator (LASSO) regression algorithm for final feature selection. A nomogram based on the regression model was developed to estimate the functional outcomes of patients. Models were validated internally using bootstrapping and externally by calculating their performance on a validation cohort. Overall, 200 ankles fit inclusion criteria. Of these 200, a total of 185 (92.5%) ankles were eligible and divided into development (n = 121) and validation (n = 64) cohorts. Four predictors were ultimately included in the prognostic nomogram model: age, sex, sports-related injury, and postoperative immobilization method. We found in our cohort that the significant predictors of poorer functional outcomes of AATFLR were postoperative immobilization with lower-leg cast, female sex, non-sports-related ankle sprain, and increasing age. Prognostic nomograms were created. Level III, retrospective cohort study.