Least Absolute Shrinkage And Selection Operator Research Articles

Investigation of Mother-to-Child Transmission of Hepatitis B in Yinchuan, China: Cross-Sectional Survey Study.

Hepatitis B poses a significant global public health challenge, with mother-to-child transmission (MTCT) being the primary method of hepatitis B virus (HBV) transmission. The prevalence of HBV infection in China is the highest in Asia, and it carries the greatest burden globally. This study aims to critically evaluate the existing local strategies for preventing MTCT and the proposed potential enhancements by analyzing the prevalence of hepatitis B among pregnant women and their neonates in Yinchuan. From January 2017 to December 2021, 37,557 prenatal screening records were collected. Among them, 947 pregnant women who tested positive for hepatitis B surface antigen (HBsAg) near delivery and their 960 neonates were included in an HBV-exposed group, while 29 pregnant women who tested negative and their 30 neonates were included in an HBV-nonexposed group. HBV markers in maternal peripheral blood and neonatal cord blood were analyzed using the least absolute shrinkage and selection operator (LASSO) regression, logistic regression, chi-square test, t-test, and U-test. Additionally, to further evaluate the diagnostic value of HBsAg positivity in cord blood, we conducted an additional follow-up study on 103 infants who tested positive for HBsAg in their cord blood. The prevalence of HBV among pregnant women was 2.5% (947/37,557), with a declining trend every year (χ²4=19.7; P=.001). From 2018 to 2020, only 33.0% (35/106) of eligible pregnant women received antiviral medication treatment. Using LASSO regression to screen risk factors correlated with HBsAg positivity in cord blood (when log [λ] reached a minimum value of -5.02), 5 variables with nonzero coefficients were selected, including maternal hepatitis B e-antigen (HBeAg) status, maternal hepatitis B core antibody (HBcAb) status, maternal HBV DNA load, delivery method, and neonatal birth weight. Through univariate and multivariate logistic regression, delivery by cesarean section (adjusted odds ratio [aOR] 0.52, 95% CI 0.31-0.87), maternal HBeAg positivity (aOR 2.05, 95% CI 1.27-3.33), low maternal viral load (aOR 2.69, 95% CI 1.33-5.46), and high maternal viral load (aOR 2.69, 95% CI 1.32-5.51) were found to be strongly associated with cord blood HBsAg positivity. In the additional follow-up study, 61 infants successfully completed the follow-up, and only 2 were found to be infected with HBV. The mothers of both these infants had detectable HBV DNA levels and should have received standard antiviral therapy. The results of the hepatitis B surface antibody (HBsAb) positivity rate and titer test indicated a gradual decline in the immunity of vaccinated infants as the interval after vaccination increased. The clinical relevance of HBV marker detection in cord blood is restricted within the current prevention measures for MTCT. There is an emphasis on the significance of public education regarding hepatitis B and the reinforcement of postnatal follow-up for the prevention of MTCT.

Causal relationship between key genes and metabolic dysfunction-associated fatty liver disease risk mediated by immune cells: A Mendelian randomization and mediation analysis.

Non-invasive diagnostics for metabolic dysfunction-associated fatty liver disease (MAFLD) remain challenging. We aimed to identify novel key genes as non-invasive biomarkers for MAFLD, elucidate causal relationships between biomarkers and MAFLD and determine the role of immune cells as potential mediators. Utilizing published transcriptome data of patients with biopsy-proven MAFLD, we applied linear models for microarray data, least absolute shrinkage and selector operation (LASSO) regressions and receiver operating characteristic (ROC) curve analyses to identify and validate biomarkers for MAFLD. Using the expression quantitative trait loci database and a cohort of 778 614 Europeans, we used Mendelian randomization to analyse the causal relationships between key biomarkers and MAFLD. Additionally, mediation analysis was performed to examine the involvement of 731 immunophenotypes in these relationships. We identified 31 differentially expressed genes, and LASSO regression showed three hub genes, IGFBP2, PEG10, and P4HA1, with area under the receiver operating characteristic (AUROC) curve of 0.807, 0.772 and 0.791, respectively, for identifying MAFLD. The model of these three genes had an AUROC of 0.959 and 0.800 in the development and validation data sets, respectively. This model was also validated using serum-based enzyme-linked immunosorbent assay data from MAFLD patients and control subjects (AUROC: 0.819, 95% confidence interval: 0.736-0.902). PEG10 was associated with an increased MAFLD risk (odds ratio = 1.106, p = 0.032) via inverse variance-weighted analysis, and about 30% of this risk was mediated by the percentage of CD11c + CD62L- monocytes. The MAFLD panels have good diagnostic accuracy, and the causal link between PEG10 and MAFLD was mediated by the percentage of CD11c + CD62L- monocytes.

The Use of Identified Hypoxia-related Genes to Generate Models for Predicting the Prognosis of Cerebral Ischemia‒reperfusion Injury and Developing Treatment Strategies.

Cerebral ischemia‒reperfusion injury (CIRI) is a type of secondary brain damage caused by reperfusion after ischemic stroke due to vascular obstruction. In this study, a CIRI diagnostic model was established by identifying hypoxia-related differentially expressed genes (HRDEGs) in patients with CIRI. The ischemia‒reperfusion injury (IRI)-related datasets were downloaded from the Gene Expression Omnibus (GEO) database ( http://www.ncbi.nlm.nih.gov/geo ), and hypoxia-related genes in the Gene Cards database were identified. After the datasets were combined, hypoxia-related differentially expressed genes (HRDEGs) expressed in CIRI patients were identified. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses of the HRDEGs were performed using online tools. Gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) were performed with the combined gene dataset. CIRI diagnostic models based on HRDEGs were constructed via least absolute shrinkage and selection operator (LASSO) regression analysis and a support vector machine (SVM) algorithm. The efficacy of the 9 identified hub genes for CIRI diagnosis was evaluated via mRNA‒microRNA (miRNA) interaction, mRNA-RNA-binding protein (RBP) network interaction, immune cell infiltration, and receiver operating characteristic (ROC) curve analyses. We then performed logistic regression analysis and constructed logistic regression models based on the expression of the 9 HRDEGs. We next established a nomogram and calibrated the prediction data. Finally, the clinical utility of the constructed logistic regression model was evaluated via decision curve analysis (DCA). This study revealed 9 critical genes with high diagnostic value, offering new insights into the diagnosis and selection of therapeutic targets for patients with CIRI. : Not applicable.

Identification of ribosome biogenesis genes and subgroups in ischaemic stroke

BackgroundIschaemic stroke is a leading cause of death and severe disability worldwide. Given the importance of protein synthesis in the inflammatory response and neuronal repair and regeneration after stroke, and that proteins are acquired by ribosomal translation of mRNA, it has been theorised that ribosome biogenesis may have an impact on promoting and facilitating recovery after stroke. However, the relationship between stroke and ribosome biogenesis has not been investigated.MethodsIn the present study, a ribosome biogenesis gene signature (RSG) was developed using Cox and least absolute shrinkage and selection operator (LASSO) analysis. We classified ischaemic stroke patients into high-risk and low-risk groups using the obtained relevant genes, and further elucidated the immune infiltration of the disease using ssGSEA, which clarified the close relationship between ischaemic stroke and immune subgroups. The concentration of related proteins in the serum of stroke patients was determined by ELISA, and the patients were divided into groups to evaluate the effect of the ribosome biogenesis gene on patients. Through bioinformatics analysis, we identified potential IS-RSGs and explored future therapeutic targets, thereby facilitating the development of more effective therapeutic strategies and novel drugs against potential therapeutic targets in ischaemic stroke.ResultsWe obtained a set of 12 ribosome biogenesis-related genes (EXOSC5, MRPS11, MRPS7, RNASEL, RPF1, RPS28, C1QBP, GAR1, GRWD1, PELP1, UTP, ERI3), which play a key role in assessing the prognostic risk of ischaemic stroke. Importantly, risk grouping using ribosome biogenesis-related genes was also closely associated with important signaling pathways in stroke. ELISA detected the expression of C1QBP, RPS28 and RNASEL proteins in stroke patients, and the proportion of neutrophils was significantly increased in the high-risk group.ConclusionsThe present study demonstrates the involvement of ribosomal biogenesis genes in the pathogenesis of ischaemic stroke, providing novel insights into the underlying pathogenic mechanisms and potential therapeutic strategies for ischaemic stroke.

Exploiting branched-chain amino acid metabolism and NOTCH3 expression to predict and target colorectal cancer progression

BackgroundThe interplay between colon adenocarcinoma (COAD) and branched-chain amino acid (BCAA) metabolism is not fully understood, presenting a crucial area for investigation.MethodsWe developed a prognostic model based on BCAA metabolism using the least absolute shrinkage and selection operator (LASSO) regression algorithm. We employed qRT-PCR and Western blot analyses to examine NOTCH3 expression in COAD tissues versus adjacent non-cancerous tissues and various cell lines. We also investigated the impact of NOTCH3 on COAD cell proliferation, invasion, and migration through in vitro and in vivo experiments.ResultsOur BCAA metabolism-related signature (BRS) distinguished between different immune features, tumor mutation burdens, responses to immunotherapy, and drug sensitivity among COAD patients. NOTCH3 was found to be overexpressed in COAD, promoting tumor growth as verified through various assays. The model effectively predicted COAD prognosis and patient responses to treatments, underscoring the potential of BCAA pathways as therapeutic targets.ConclusionThe BRS is instrumental in predicting the prognosis and therapeutic response in COAD, with NOTCH3 playing a significant role in the proliferation, invasion and migration of COAD. These findings suggest that targeting BCAA metabolism and NOTCH3 could advance COAD treatment strategies.

Updating and calibrating the Real-World Progression In Diabetes (RAPIDS) model in a non-Veterans Affairs population.

To present the Real-World Progression In Diabetes (RAPIDS) 2.0 Risk Engine, the only simulation model to study the long-term trajectories of outcomes arising from dynamic sequences of glucose-lowering treatments in type 2 diabetes (T2DM). The RAPIDS model's risk equations were re-estimated using a Least Absolute Shrinkage and Selection Operator (LASSO)-based regularization of features that spanned baseline data from the last two quarters of current time and interactions with age. These equations were supplemented with estimates for the impact of dipeptidyl peptidase-4 inhibitors, glucagon-like peptide-1 receptor agonists, and sodium-glucose cotransporter-2 inhibitor classes of drugs as monotherapies and their combinations with metformin based on newer trial data and comprehensive meta-analyses. The probabilistic RAPIDS 2.0 model was calibrated (N = 25 000) and validated (N = 263 816) using electronic medical records (EMR) data between 2008 and 2021 from a national network of US healthcare organizations. The EMR-based cohort had a mean age of 61 years at baseline, with 50% women, 70% non-Hispanic White individuals and 20% non-Hispanic Black individuals, and was followed for 17.5 quarters (range: 3-50). The final RAPIDS 2.0 risk engine accurately predicted the long-term trajectories of all nine biomarkers and nine outcomes in the hold-out validation sample. Similar accuracies in predictions were observed in each of the 14 subgroups studied. The RAPIDS 2.0 model demonstrated valid long-term predictions of outcomes in individuals with T2DM in the United States as a function of dynamic sequences of treatment use patterns. This highlights its potential to project long-term comparative effectiveness between alternative sequences of glucose-lowering treatment uses in the United States.

Identification of endoplasmic reticulum stress-associated genes and subtypes for predicting risk signature and depicting immune features in inflammatory bowel disease

Endoplasmic reticulum stress (ERS) becomes a significant factor in inflammatory bowel disease (IBD), like Crohn’s disease (CD) and ulcerative colitis (UC). Our research was aimed at identifying molecular markers to enhance our understanding of ERS and inflammation in IBD, recognizing risk factors and high-risk groups at the molecular level, and developing a predictive model on the grounds of based on ERS-associated genes.This research adopted the least absolute shrinkage and selection operator (LASSO) regression and logistic regression to build a predictive model, and categorized IBD patients into high- and low-risk groups, and then identified four gene clusters. Our key findings included a significant increase in drug target gene expression in high-risk groups, notable discrepancies in immune levels, and functions between high-risk and low-risk groups. Notably, the TAP1 gene emerged as a strong predictor with the highest diagnostic value (area under the curve [AUC]=0.941). TAP1 encodes proteins required for antigenic peptide transfer across the endoplasmic reticulum (ER) membrane, and its potential as a diagnostic marker and therapeutic target is reflected by its overexpression in IBD tissues.Our study established a new ERS-associated gene model which could forecast the risk, immunological status, and treatment efficacy of patients with IBD. These findings suggest potential targets for personalized therapy and highlight the significance of ERS in the etiology and therapy of IBD. Future studies should explore the therapeutic potential of targeting TAP1 and other ERS-related genes for IBD management.

Identification of Molecular Correlations of GSDMD with Pyroptosis inAlzheimer's Disease.

An analysis of bioinformatics and cell experiments was performed to verify the relationship between gasdermin D (GSDMD), an executive protein of pyroptosis, and Alzheimer's disease (AD). The training set GSE33000 was utilized to identify differentially expressed genes (DEGs) in both the AD group and control group, as well as in the GSDMD protein high/low expression group. Subsequently, the weighted gene co-expression network analysis (WGCNA) and the least absolute shrinkage and selection operator (LASSO) regression analysis were conducted, followed by the selection of the key genes for the subsequent Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. The association between GSDMD and AD was assessed and confirmed in the training set GSE33000, as well as in the validation sets GSE5281 and GSE48350. Immunofluorescence (IF) was employed to detect the myelin basic protein (MBP), a distinctive protein found in the rat oligodendrocytes (OLN-93 cells). A range of concentrations (1-15 μmol/L) of β-amyloid 1-42 (Aβ1-42) were exposed to the cells, and the subsequent observations were made regarding cell morphology. Additionally, the assessments were conducted to evaluate the cell viability, the lactate dehydrogenase (LDH) release, the cell membrane permeability, and the GSDMD protein expression. A total of 7,492 DEGs were screened using GSE33000. Subsequently, WGCNA analysis identified 19 genes that exhibited the strongest correlation with clinical traits in AD. Additionally, LASSO regression analysis identified 13 key genes, including GSDMD, AFF1, and ATOH8. Furthermore, the investigation revealed that the key genes were associated with cellular inflammation based on GO and KEGG analyses. Moreover, the area under the curve (AUC) values for the key genes in the training and validation sets were determined to be 0.95 and 0.70, respectively. Significantly, GSDMD demonstrated elevated levels of expression in AD across both datasets. The positivity of MBP expression in cells exceeded 95%. As the concentration of Aβ1-42 action gradually escalated, the detrimental effects on cells progressively intensified, resulting in a gradual decline in cell survival rate, accompanied by an increase in lactate dehydrogenase release, cell membrane permeability, and GSDMD protein expression. The association between GSDMD and AD has been observed, and it has been found that Aβ1-42 can induce a significant upregulation of GSDMD in OLN-93 cells. This suggests that Aβ1-42 has the potential to induce cellular pyroptosis and can serve as a valuable cellular pyroptosis model for the study of AD.

Assessing the Capabilities of UV-NIR Spectroscopy for Predicting Macronutrients in Hydroponic Solutions with Single-Task and Multi-Task Learning

Macronutrients, including nitrogen (N), phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg), and sulfur (S), are the most basic nutrient elements in the solution for the hydroponic system. However, the current management of hydroponic nutrient solutions usually depends on EC and pH sensors due to the lack of accurate specific macronutrient sensing equipment, which easily leads to nutritional imbalance for the cultivated plant. In this study, the UV-NIR absorption spectroscopy (200–1100 nm) was used to predict six macronutrients in hydroponic solutions; two kinds of single-task learning algorithms, including partial least squares (PLS) and least absolute shrinkage and selection operator (LASSO), and two kinds of multi-task learning algorithms, including dirty multi-task learning (DMTL) and robust multi-task learning (RMTL), were investigated to develop prediction models and assess capabilities of UV-NIR. The results showed that N and Ca could be quantitatively predicted by UV-NIR with the ratio of performance to deviation (RPD) more than 2, K could be qualitatively predicted (1.4 < RPD < 2), and P, Mg, and S could not be successfully predicted (RPD < 1.4); the RMTL algorithm outperformed others for predicting K and Ca benefit from the underlying task relationships with N; and predicting P, Mg, and S were identified as irrelevant (outlier) tasks. Our study provides a potential approach for predicting several macronutrients in hydroponic solutions with UV-NIR, especially using RMTL to improve model prediction ability.

Identification and validation of a novel robust glioblastoma prognosis model based on bioinformatics

BackgroundGlioblastoma (GBM) is a very common primary malignant tumor of the central nervous system (CNS). Aging, macrophage, autophagy, and methylation related genes are hypothesized to be crucial to its pathogenesis. In this study, we aimed to explore the role of these genes in the prognosis of GBM. MethodsThe RNA sequence (RNA-seq) and clinical information were downloaded from The Cancer Genome Atlas database (TCGA) and the Chinese Glioma Genome Atlas database (CGGA). We performed univariate and least absolute shrinkage and selection operator (LASSO) multivariate Cox regression analysis to identify risk signatures related to overall survival (OS). We further developed a nomogram to predict individual outcomes. In addition, the immune microenvironment was analyzed by CIBERSORT. Results256 differentially expressed genes (DEGs) were obtained based on aging, macrophage, autophagy, and methylation related genes between GBM samples and normal tissues in TCGA-GBM cohort. We identified five optimal risk signatures with prognostic values in TCGA-GBM cohort and established a prognostic risk score model. The validity of the model was verified in the CGGA cohort and Huanhu cohort. Finally, we constructed a nomogram for clinical application by combining age, O(6)-methylguanine-DNA methyltransferase (MGMT) promoter methylation status, and risk score. Activated NK cells and resting mast cells were highly expressed and memory B cells, plasma cells, resting NK cells, M1 macrophages, and neutrophils exhibited low expression in the high-risk score group. GBM patients with a low-risk score had a higher Tumor Immune Dysfunction and Exclusion (TIDE) score. The risk score of hot tumors was higher than that of the cold tumors. Additionally, 29 genes involved in glucose and lipid metabolism were highly expressed with a high-risk score. 31 metabolism-related pathways were significantly different between high-risk and low-risk groups. ConclusionsWe constructed and validated a novel prognostic model for GBM. Aging, macrophage, autophagy, and methylation related genes may serve as prognostic and therapeutic biomarkers. The model developed may assist in guiding treatment for GBM patients. Our research had great significance in accurately predicting the prognosis of GBM and may offer reference for immunotherapy decision for GBM patients.

Risk predictive model for the development of hepatocellular carcinoma before initiating long-term antiviral therapy in patients with chronic hepatitis B virus infection.

It is generally acknowledged that antiviral therapy can reduce the incidence of hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC), there remains a subset of patients with chronic HBV infection who develop HCC despite receiving antiviral treatment. This study aimed to develop a model capable of predicting the long-term occurrence of HCC in patients with chronic HBV infection before initiating antiviral therapy. A total of 1450 patients with chronic HBV infection, who received initial antiviral therapy between April 2006 and March 2023 and completed long-term follow-ups, were nonselectively enrolled in this study. Least absolute shrinkage and selection operator (LASSO) and Cox regression analysis was used to construct the model. The results were validated in an external cohort (n = 210) and compared with existing models. The median follow-up time for all patients was 60 months, with a maximum follow-up time of 144 months, during which, 32 cases of HCC occurred. The nomogram model for predicting HCC based on GGT, AFP, cirrhosis, gender, age, and hepatitis B e antibody (TARGET-HCC) was constructed, demonstrating a good predictive performance. In the derivation cohort, the C-index was 0.906 (95% CI = 0.869-0.944), and in the validation cohort, it was 0.780 (95% CI = 0.673-0.886). Compared with existing models, TARGET-HCC showed promising predictive performance. Additionally, the time-dependent feature importance curve indicated that gender consistently remained the most stable predictor for HCC throughout the initial decade of antiviral therapy. This simple predictive model based on noninvasive clinical features can assist clinicians in identifying high-risk patients with chronic HBV infection for HCC before the initiation of antiviral therapy.

Study On Molecular Mechanism of Intervertebral Disc Degeneration by Single Cell hdWGCNA Combined with Transcriptome Sequencing

BackgroundIntervertebral disc degeneration (IVDD) is one of the important causes of lower back pain, seriously affecting people's health and quality of life. This research employs single-cell analysis to identify the specific cellular subtypes and key regulatory genes associated with IVDD. MethodsWe analyzed the single-cell data and screened cells that closely associated with the development of IVDD. The differential expression of feature genes between IVDD and control groups was analyzed. Additionally, drugs and regulatory transcription factors that interact with feature genes were predicted and clinically validated by reverse transcription quantitative real-time PCR (RT-qPCR), immunohistochemistry (IHC), and enzyme-linked immunosorbent assay (ELISA). ResultsOur study identified the Chond2 cell subtype associated with IVDD and selected four feature genes influencing the development of IVDD, namely IGFBP3, ACAN, VAPA and TMEM45A, through the high-dimensional weighted gene co-expression network analysis (hdWGCNA) analysis, least absolute shrinkage and selection operator (LASSO), and random forest (RF). Besides, compared to the MDD group, IGFBP3 and TMEM45A were significantly upregulated in the SDD group, while ACAN and VAPA showed no significant difference between the two groups. ELISA testing revealed a positive correlation between IGFBP3 concentration and the grading of IVDD. Furthermore, Celecoxib may be used to treat IVDD by inhibiting IGFBP3. ConclusionOur study identified the Chond2 cell subtype associated with IVDD and selected four feature genes influencing the development of IVDD, namely IGFBP3, ACAN, VAPA and TMEM45A. Our findings establish a robust theoretical foundation for the clinical diagnosis and treatment of IVDD patients.

A Novel Ultrasound-Based Radiomics Model for the Preoperative Prediction of Lymph Node Metastasis in Cervical Cancer

ObjectiveThe purpose of this retrospective study was to establish a combined model based on ultrasound (US)-radiomics and clinical factors to predict preoperative lymph node metastasis (LNM) in cervical cancer (CC) patients non-invasively. MethodsA total of 131 CC patients who had cervical lesions found by transvaginal sonography (TVS) from the First Affiliated Hospital of Anhui Medical University (Hefei, China) were retrospectively analyzed. The clinical independent predictors were selected using univariate and multivariate logistic regression analysis. US-radiomics features were extracted from US images; after selecting the most significant features by univariate analysis, Spearman's correlation analysis, and the least absolute shrinkage and selection operator (LASSO) algorithm; four machine-learning classification algorithms were used to build the US-radiomics model. Fivefold cross-validation was then used to test the performance of the model and compare the ability of the clinical, US-radiomics and combined models to predict LNM in CC patients. ResultsRed blood cell, platelet and squamous cell carcinoma-associated antigen were independent clinical predictors of LNM (+) in CC patients. eXtreme Gradient Boosting performed the best among the four machine-learning classification algorithms. Fivefold cross-validation confirmed that eXtreme Gradient Boosting indeed performs the best, with average area under the curve values in the training and validation sets of 0.897 and 0.898. In the three prediction models, both the US-radiomics model and the combined model showed good predictive efficacy, with average area under the curve values in the training and validation sets of 0.897, 0.898 and 0.912, 0.905, respectively. ConclusionUS-radiomics features combined with clinical factors can preoperatively predict LNM in CC patients non-invasively.

Multiparametric MRI-based radiomics nomogram for differentiation of primary mucinous ovarian cancer from metastatic ovarian cancer.

To develop a multiparametric magnetic resonance imaging (mpMRI)-based radiomics nomogram and evaluate its performance in differentiating primary mucinous ovarian cancer (PMOC) from metastatic ovarian cancer (MOC). A total of 194 patients with PMOC (n = 72) and MOC (n = 122) confirmed by histology were randomly divided into the primary cohort (n = 137) and validation cohort (n = 57). Radiomics features were extracted from axial fat-saturated T2-weighted imaging (FS-T2WI), diffusion-weighted imaging (DWI), and contrast-enhanced T1-weighted imaging (CE-T1WI) sequences of each lesion. The effective features were selected by minimum redundancy maximum relevance (mRMR) and least absolute shrinkage and selection operator (LASSO) regression to develop a radiomics model. Combined with clinical features, multivariate logistic regression analysis was employed to develop a radiomics nomogram. The efficiency of nomogram was evaluated using the receiver operating characteristic (ROC) curve analysis and compared using DeLong test. Finally, the goodness of fit and clinical benefit of nomogram were assessed by calibration curves and decision curve analysis, respectively. The radiomics nomogram, by combining the mpMRI radiomics features with clinical features, yielded area under the curve (AUC) values of 0.931 and 0.934 in the primary and validation cohorts, respectively. The predictive performance of the radiomics nomogram was significantly superior to the radiomics model (0.931 vs. 0.870, P = 0.004; 0.934 vs. 0.844, P = 0.032), the clinical model (0.931 vs. 0.858, P = 0.005; 0.934 vs. 0.847, P = 0.030), and radiologists (all P < 0.05) in the primary and validation cohorts, respectively. The decision curve analysis revealed that the nomogram could provide higher net benefit to patients. The mpMRI-based radiomics nomogram exhibited notable predictive performance in differentiating PMOC from MOC, emerging as a non-invasive preoperative imaging approach.

Forest above-ground biomass estimation based on strongly collinear variables derived from airborne laser scanning data

Airborne laser scanning technique (ALS) is the most appealing remote sensing technique for the precise estimation of forest above-ground biomass (AGB). Significantly strong correlations (collinearity) among the independent variables derived from ALS data decrease the accuracy of developed AGB models. To address this issue, we propose a novel variable selection algorithm-an improved sure independence screening (SPV), which integrates the Pearson correlation coefficient, threshold μ, and variance inflation factor. We further compared the performance of SPV-based, stepwise feature selection (SFS)-based, and least absolute shrinkage and selection operator (LASSO)-based AGB models developed with different regression approaches that were sensitive to strong collinearity. Field-measured data and corresponding ALS data, acquired from 1002 sample plots distributed across four distinct forest types within the Guangxi Zhuang Autonomous Region in Southern China, were used to evaluate variable selection techniques and develop AGB models. Results indicated that ALS variables selected by SPV exhibited weaker collinearity compared to those selected by SFS and LASSO. SPV-based AGB models outperformed SFS-based AGB models with higher leave-one-out cross-validation adjusted R2 (LOOCV Radj2) by 0.1% − 27.8%. SPV-based AGB models outperformed LASSO-based AGB models with higher LOOCV Radj2 by 0.4% − 16.3%. Hence, for variable selection in constructing AGB models (linear regression model, log–log regression model, and generalized additive model (GAM)) based on strongly collinear ALS variables, SPV is most preferred, followed by SFS and LASSO. The smooth curves from our GAMs developed using SPV-selected variables revealed that five canopy height variables (whp40, hmean, hp25, hp70, hskew), one canopy density variable (whp85), three density-related variables (nVegPts, bcc, and mcc), and one vertical structural variable (LADmean) were positively correlated with AGB. The canopy height variables (whp40, hmean, hp25, and hp70) were identified as the most important variables in estimating AGB for four forest types. The canopy density variable wdp85 showed a strong effect on AGB of the coniferous forests, whereas it had almost no effect on the AGB of broadleaved forests. Overall, this manuscript proposes a novel variable selection algorithm named SPV, aimed at addressing collinearity of variables derived from ALS data, which has significant implications for the application of ALS in forest inventory and forest modeling.

Variability in performance of genetic-enhanced DXA-BMD prediction models across diverse ethnic and geographic populations: A risk prediction study.

Osteoporosis is a major global health issue, weakening bones and increasing fracture risk. Dual-energy X-ray absorptiometry (DXA) is the standard for measuring bone mineral density (BMD) and diagnosing osteoporosis, but its costliness and complexity impede widespread screening adoption. Predictive modeling using genetic and clinical data offers a cost-effective alternative for assessing osteoporosis and fracture risk. This study aims to develop BMD prediction models using data from the UK Biobank (UKBB) and test their performance across different ethnic and geographical populations. We developed BMD prediction models for the femoral neck (FNK) and lumbar spine (SPN) using both genetic variants and clinical factors (such as sex, age, height, and weight), within 17,964 British white individuals from UKBB. Models based on regression with least absolute shrinkage and selection operator (LASSO), selected based on the coefficient of determination (R2) from a model selection subset of 5,973 individuals from British white population. These models were tested on 5 UKBB test sets and 12 independent cohorts of diverse ancestries, totaling over 15,000 individuals. Furthermore, we assessed the correlation of predicted BMDs with fragility fractures risk in 10 years in a case-control set of 287,183 European white participants without DXA-BMDs in the UKBB. With single-nucleotide polymorphism (SNP) inclusion thresholds at 5×10-6 and 5×10-7, the prediction models for FNK-BMD and SPN-BMD achieved the highest R2 of 27.70% with a 95% confidence interval (CI) of [27.56%, 27.84%] and 48.28% (95% CI [48.23%, 48.34%]), respectively. Adding genetic factors improved predictions slightly, explaining an additional 2.3% variation for FNK-BMD and 3% for SPN-BMD over clinical factors alone. Survival analysis revealed that the predicted FNK-BMD and SPN-BMD were significantly associated with fragility fracture risk in the European white population (P < 0.001). The hazard ratios (HRs) of the predicted FNK-BMD and SPN-BMD were 0.83 (95% CI [0.79, 0.88], corresponding to a 1.44% difference in 10-year absolute risk) and 0.72 (95% CI [0.68, 0.76], corresponding to a 1.64% difference in 10-year absolute risk), respectively, indicating that for every increase of one standard deviation in BMD, the fracture risk will decrease by 17% and 28%, respectively. However, the model's performance declined in other ethnic groups and independent cohorts. The limitations of this study include differences in clinical factors distribution and the use of only SNPs as genetic factors. In this study, we observed that combining genetic and clinical factors improves BMD prediction compared to clinical factors alone. Adjusting inclusion thresholds for genetic variants (e.g., 5×10-6 or 5×10-7) rather than solely considering genome-wide association study (GWAS)-significant variants can enhance the model's explanatory power. The study highlights the need for training models on diverse populations to improve predictive performance across various ethnic and geographical groups.

Suitability of different machine learning algorithms for the classification of the proportion of grassland-based forages at herd-level using mid-infrared spectral information from routine milk control

As the call for an international standard for milk from grassland-based production systems continues to grow, so too do the monitoring and evaluation policies surrounding this topic. Individual stipulations by countries and milk producers to market their milk under their own grass-fed labels include a compulsory number of grazing days per year, ranging from 120 d for certain labels to 180 d for others, a specified amount of herbage in the diet or a prescribed dietary proportion of grassland-based forages (GBF) fed and produced on farm. As these multifarious policy and label requirements are laborious and costly to monitor on farm, fast economical proxies would be advantageous to verify the proportion of GBF consumed by the cows in the final product. With this in mind, we employed readily available mid-infrared spectral data (n = 1132 spectra) from routine milk controls to develop binary classification models for 4 main feed groups from a primarily forage-based diet: Total GBF (≥50% n = 955, ≥ 75% n = 599, ≥ 85% n = 356), pasture (≥20% n = 451, ≥ 50% n = 284, ≥ 70% n = 152), fresh herbage (pasture + fresh herbage indoor feeding, ≥ 20% n = 517, ≥ 50% n = 325, ≥ 70% n = 182) and whole plant corn (fresh + conserved) (≥10% n = 646, ≥ 30% n = 187), the latter as a negative control. We compared 4 machine learning methods to assess which statistical model performs best at discriminating these classes. Three of these models have not yet been tested for herd-level dietary proportion classification and all 4 follow completely different approaches: least absolute shrinkage and selection operator (LASSO), partial least squares discriminant analysis (PLS-DA), random forest (RF) and support vector machines (SVM). Seasonality has been a missing element from previous dietary herbage proportion classification models. As grazing and fresh herbage indoor feeding are highly dependent on the season, we developed an indicator to incorporate seasonality in a consistent, unbiased manner into our models. We also tested 3 sets of covariates. The first set included only mid-infrared spectra derived data, the second included mid-infrared spectra derived data plus seasonality indices and the third included mid-infrared spectra derived data, seasonality indices and additional herd specific information (DIM, breed and parity). Of the 4 machine learning algorithms tested for the binary classification of GBF proportion at herd level, LASSO and PLS-DA performed best according to evaluation metrics; however, the RF and SVM models were not far behind the best performing model evaluation metrics in each feed category. Our best performing model, the LASSO model containing seasonality indices and herd specific information, classified total GBF ≥50% with an accuracy of 78.6%, precision of 85.1%, sensitivity of 90.6%, specificity 14.1% and F1 score (harmonic mean of precision and sensitivity) of 87.7%, this was very similar to the PLS-DA model. Our results suggest that in general LASSO and PLS-DA machine learning algorithms perform better for dietary GBF classification than RF or SVM algorithms.

5-Hydroxymethylcytosines in circulating cell-free DNA as a diagnostic biomarker for nasopharyngeal carcinoma

ObjectiveTo evaluate the diagnostic value of 5-hydroxymethylcytosines (5hmC) in circulating cell-free DNA (cfDNA) for nasopharyngeal carcinoma (NPC) and to develop a diagnostic model. MethodsGenome-wide 5hmC profiles in cfDNA from 174 NPC patients and 146 non-cancer individuals were analyzed using the 5hmC-Seal technique. A cfDNA 5hmC-based diagnostic model to identify NPC patients was developed using least absolute shrinkage and selection operator (LASSO) logistic regression, and performance was evaluated with receiver operating characteristic (ROC) curves and confusion matrices. ResultsThe 5hmC-Seal data from patients with NPC showed a different genome-wide distribution than non-tumor samples. Our initial analysis revealed a 12-gene-based 5hmC marker panel to be an accurate diagnostic model effectively distinguishing between NPC samples and non-cancerous samples (training set: area under curve (AUC)= 0.97 [95 % CI: 0.94–0.99]; and test set: AUC= 0.93 [95 % CI: 0.88–0.98]) superior to EBV DNA testing. The diagnostic score performed well in differentiating the non-cancer subjects from early-stage NPC (training set: AUC=0.99 [95 % CI: 0.98–1]; test set: AUC=0.98 [95 % CI: 0.95–1]), and advanced-stage NPC (training set: AUC=0.96 [95 % CI: 0.93–0.99]; test set: AUC=0.93 [95 % CI: 0.88–0.98]). Notably, in EBV-negative patients, the diagnostic scores showed excellent capacity for distinguishing EBV-negative patients with NPC from non-cancer subjects in both the training set (AUC= 0.94 [95 % CI: 0.88–1]) and test set (AUC=0.91 [95 % CI: 0.81–1]). Conclusion5hmC modifications in cfDNA are promising noninvasive biomarkers for NPC, offering high sensitivity and specificity, particularly for early-stage and EBV-negative NPC.

Enhancing lymph node metastasis prediction in adenocarcinoma of the esophagogastric junction: A study combining radiomic with clinical features.

The incidence of adenocarcinoma of the esophagogastric junction (AEJ) is increasing, and with poor prognosis. Lymph node status (LNs) is particularly important for planning treatment and evaluating the prognosis of patients with AEJ. However, the use of radiomic based on enhanced computed tomography (CT) to predict the preoperative lymph node metastasis (PLNM) status of the AEJ has yet to be reported. We sought to investigate the value of radiomic features based on enhanced CT in the accurate prediction of PLNM in patients with AEJ. Clinical features and enhanced CT data of 235 patients with AEJ from October 2017 to May 2023 were retrospectively analyzed. The data were randomly assigned to the training cohort (n=164) or the external testing cohort (n=71) at a ratio of 7:3. A CT-report model, clinical model, radiomic model, and radiomic-clinical combined model were developed to predict PLNM in patients with AEJ. Univariate and multivariate logistic regression were used to screen for independent clinical risk factors. Least absolute shrinkage and selection operator (LASSO) regression was used to select the radiomic features. Finally, a nomogram for the preoperative prediction of PLNM in AEJ was constructed by combining Radiomics-score and clinical risk factors. The models were evaluated by area under the receiver operating characteristic curve (AUC-ROC), calibration curve, and decision curve analyses. A total of 181 patients (181/235, 77.02%) had LNM. In the testing cohort, the AUC of the radiomic-clinical model was 0.863 [95% confidence interval (CI)=0.738-0.957], and the radiomic model (0.816; 95% CI=0.681-0.929), clinical model (0.792; 95% CI=0.677-0.888), and CT-report model (0.755; 95% CI=0.647-0.840). The radiomic-clinical model is a feasible method for predicting PLNM in patients with AEJ, helping to guide clinical decision-making and personalized treatment planning.

Development, validation, and visualization of a novel nomogram to predict depression risk in patients with stroke

BackgroundThis study aimed to develop and validate a predictive nomogram model applicable to depression risk in stroke patients. MethodsParticipants from the NHANES database (n = 1097) were enrolled from 2005 to 2018; 767 subjects were randomly assigned to the training cohort, and the remaining subjects composed the testing cohort. A nomogram containing the optimal predictors identified by the least absolute shrinkage and selection operator (LASSO) and logistic regression methods was constructed to estimate the probability of depression in stroke patients. To evaluate the performance of the nomogram, the area under the receiver operating characteristic curve (AUC), calibration plot, decision curve analysis (DCA) and internal validation were utilized. ResultsAge, family income, trouble sleeping, coronary heart disease, and total cholesterol were included in the nomogram after filtering predictive variables. The AUCs of the nomogram for the training and testing cohorts were 0.782 (95 % CI = 0.742–0.821) and 0.755 (95 % CI = 0.675–0.834), respectively. The calibration plot revealed that the predicted probability was extremely close to the actual probability of depression occurrence in both the training and testing cohorts. DCA revealed that the nomogram model in the training and testing cohorts had a net benefit when the risk thresholds were 0–0.59 and 0–0.375, respectively. LimitationsThis study was limited by the absence of clinical external validation, which hindered the estimation of the nomogram's external applicability. In addition, this study has a cross-sectional design. ConclusionsA novel nomogram was successfully constructed and proven to be beneficial for identifying individuals at high risk for depression among stroke patients.