Regression Method Research Articles

Development and validation of a prediction model for estimating patency rates after treatment of haemodialysis access dysfunction.

This study aims to develop a risk assessment model for predicting haemodialysis access dysfunction and to construct a nomogram. The clinical data of patients with haemodialysis access dysfunction treated at our hospital from October 2020 to January 2024 were retrospectively analysed. The least absolute shrinkage and selection operator regression method was used to filter variables and select predictors, while Cox regression was applied to filter variables and construct a nomogram. The discriminatory ability of the model was determined by calculating the area under the curve (AUC). Calibration was evaluated using bootstrap internal validation and the Hosmer-Lemeshow test. The clinical utility and applicability of the model were assessed through decision curve analysis (DCA) and the clinical impact curve (CIC). Subgroup analysis of risk factors for haemodialysis access dysfunction was performed using Kaplan-Meier survival curves. The study included 423 patients, and seven variables were used to construct the risk prediction model and nomogram for haemodialysis access dysfunction. The C-index of the prediction model was 0.783, and the time-dependent AUC (>0.8) at 6, 12, 18 and 24 months post-surgery indicated strong discriminatory ability. The calibration curve and Hosmer-Lemeshow test demonstrated good agreement between the prediction of the nomogram and the observed values. The DCA and CIC curves further confirmed the clinical practicability of the model. A risk assessment model and nomogram for haemodialysis access dysfunction based on seven variables were successfully constructed. This model demonstrates good discrimination and calibration, offering valuable guidance for clinical decision-making and significant clinical utility.

Estimation in partially linear additive errors-in-variables model with stochastic linear restrictions

The partially linear additive model is a flexible and powerful model for evaluating the relationship between multivariate covariates and the response variable. The current paper considers the estimation problem of the model with additive measurement errors, along with some additional stochastic linear restrictions on the regression coefficients. We introduce a weighted corrected profile mixed estimator of the parametric component based on the corrected profile least squares approach and weighted mixed regression method, and further establish the asymptotic normality of the proposed estimator. A Monte Carlo simulation experiment is conducted to illustrate theoretical results, and the findings from the simulation are satisfactory. Finally, we also apply the proposed procedure to analyze the Boston housing market data.

Information communication and technology, and environmental degradation in Africa: a new approach via moments

ABSTRACT This study contributes to the ICT-environment literature by examining the impact of ICT on the environmental degradation of 29 African nations from 2000 to 2018. The study builds its approach on the Stochastic Impacts Regression on Population, Affluence, and Technology (STIRPAT) framework, while the novel method of moments quantile regression and Dumitrescu and Hurlin Granger causality test are used as estimation techniques. The study's findings suggest that fixed telephone and mobile cellular subscriptions increase both carbon and ecological footprint across all the quantiles. However, internet penetration negatively impacts carbon and ecological footprint in all quantiles. Furthermore, a bidirectional causality between ecological footprint and ICT was found. The study concludes that the region's policymakers can leverage the benefit of internet penetration to achieve sustainable development. Besides, the consumption and production associated with fixed telephone and mobile cellular subscriptions should be re-examined.

Potential prognostic and predictive biomarkers: METTL5, METTL7A, and METTL7B expression in gastrointestinal cancers.

The methyltransferase gene family is known for its diverse biological functions and critical role in tumorigenesis. This study aimed to identify these family genes in common gastrointestinal (GI) cancers using comprehensive methodologies. Gene identification involved analysis of scientific literature and insights from The Cancer Genome Atlas (TCGA) database. RNA sequencing (RNA-seq) data for colon, gastric, pancreatic, esophageal, and liver cancers were collected, processed, and normalized. Differential expression analysis was conducted using R software with the Limma package. Additionally, real-time PCR analysis was performed on 30 tumor and 30 normal tissue samples from patients with colon and gastric cancer. Pathway analysis was conducted via the EnrichR web tool, while survival analysis used Cox regression methods, and biomarker potential was assessed with the pROC package. Prognostic significance was evaluated by examining associations between gene expression, patient survival, and recurrence rates. The study also investigated diagnostic potential through receiver operating characteristic (ROC) analysis, and assessed how small molecules affect gene expression, with implications for drug resistance and sensitivity, analyzed via CCLE and GDSC datasets. Findings revealed METTL5 overexpression in colon, liver, esophagus, and pancreas cancers, while METTL7A was underexpressed in gastric, esophagus, liver, and colon cancers. METTL7B expression varied, being higher in gastric and esophagus cancers but lower in liver and colon cancers. Enrichment analysis identified pathways related to these genes, and survival analysis associated altered METTL7A and METTL5 expressions with poor prognosis and higher recurrence rates. These findings suggest that METTL genes could serve as predictive biomarkers in GI cancers, offering potential implications for patient prognosis and treatment response.

Nomogram for prognosis prediction in metastatic pancreatic cancer patients undergoing intra-arterial infusion chemotherapy: incorporating immune-inflammation scores and coagulation indicators

BackgroundPancreatic cancer is one of the most malignant tumors with an inferior prognosis. This study aims to determine the prognostic significance of immune-inflammatory scores and coagulation indices in patients with metastatic pancreatic cancer(MPC) and develop a predictive nomogram.MethodsThis study retrospectively analyzed the clinical data of 384 patients with MPC who underwent intra-arterial infusion chemotherapy (IAIC). Patients were randomly divided into training and validation cohorts. Firstly, the optimal cutoff values for continuous variables were obtained in the training cohort. Then, survival analysis was performed to evaluate the impact of immune-inflammatory scores neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), systemic immune-inflammation index (SII), and coagulation indicators prothrombin time (PT), fibrinogen (FIB), and D-dimer on the overall survival (OS) of patients. Next, univariate analysis was utilized to identify prognostic factors, and a stepwise regression method was employed for variable selection to construct a nomogram based on the Cox proportional hazards model. Additionally, the predictive performance of the nomogram was assessed by the concordance index (C-index), the area under the ROC curve (AUC), and calibration curves. Finally, patients were stratified into risk groups based on the total score of the nomogram.ResultsThe Kaplan–Meier survival curves indicated that immune-inflammatory scores NLR, PLR, SII, and coagulation indicators PT, FIB, and D-dimer were associated with OS. Through Cox regression analysis, a nomogram was ultimately constructed incorporating NLR, PLR, PT, alkaline phosphatase (ALP), carbohydrate antigen 125 (CA125), age, and ablation. The model demonstrated good discriminative ability, with a C-index of 0.722, and the AUC values at 6- and 12-month OS predictions were 0.828 and 0.851 in the training cohort, while in the validation cohort, the corresponding AUC values were 0.754 and 0.791, respectively. The calibration curves showed a good fit, confirming the stability of the model. A cutoff value of 353.3 was identified as optimal for risk stratification, with a statistically significant difference in OS between the high- and low-risk groups.ConclusionThe nomogram based on immune-inflammatory scores, coagulation indicators, and other clinicopathological factors can effectively predict the OS of patients with MPC.

HDAC9-mediated deacetylation of CALML6 promotes excessive proliferation of glomerular mesangial cells in IgA nephropathy.

This study seeks to investigate the fundamental molecular processes through which histone deacetylase 9 (HDAC9) governs the proliferation of glomerular mesangial cells in the context of immunoglobulin A nephropathy (IgAN) and to identify novel targets for clinical research on IgAN. Data from high-throughput RNA sequencing for IgAN were procured from the Gene Expression Omnibus database to assess the expression profiles and clinical diagnostic significance of histone deacetylase family proteins (HDACs). Blood samples from 20 IgAN patients were employed in RT-qPCR analysis, and the spearman linear regression method was utilized to analyze the clinical correlation. The proliferation of glomerular mesangial cells (GMCs) under the influence of HDAC9 was examined using the 5-ethynyl-2'-deoxyuridine (EdU) assay. Proteins interacting with HDAC9 were predicted utilizing the STRING database. Immunoprecipitation and protein immunoblotting employing anti-acetylated lysine antibodies were conducted to determine the acetylation status of calmodulin-like protein 6 (CALML6). Analysis of the GSE141295 dataset revealed a significant upregulation of HDAC9 expression in IgAN and the results of RT-qPCR demonstrated a substantial increase in HDAC9 expression in IgAN patients. Receiver operating characteristic (ROC) analysis indicated that the area under the curve (AUC) value for HDAC9 were 0.845 and Spearman correlation analysis showed that HDAC9 expression was positively correlated with blood levels of blood urea nitrogen (BUN) and serum creatinine (Crea). The EdU cell proliferation assay indicated that HDAC9 facilitated the excessive proliferation of GMCs. The STRING database and recovery experiments identified CALML6 as a downstream effector of HDAC9 in controlling abnormal GMC multiplication. Co-immunoprecipitation assays demonstrated that HDAC9 modulates CALML6 expression through acetylation modification. HDAC9 is markedly upregulated in IgAN, and it mediates the excessive proliferation of GMCs by regulating the deacetylation of CALML6.

Detecting Influential Observations in Single-Index Fréchet Regression

Regression with random data objects is becoming increasingly common in modern data analysis. Unfortunately, this novel regression method is not immune to the trouble caused by unusual observations. A metric Cook’s distance extending the original Cook’s distances to regression between metric-valued response objects and Euclidean predictors is proposed. The performance of the metric Cook’s distance is demonstrated in regression across four different response spaces in an extensive experimental study. Two real data applications involving the analysis of distributions of COVID-19 transmission in the State of Texas and the analysis of the structural brain connectivity networks are provided to illustrate the utility of the proposed method in practice.

Multimodal data-based longitudinal prognostic model for predicting atrial fibrillation recurrence after catheter ablation in patients with patent foramen ovale and paroxysmal atrial fibrillation

BackgroundClinical studies on atrial fibrillation (AF) recurrence after catheter ablation in patients diagnosed with patent foramen ovale (PFO) and paroxysmal AF (PAF) are scarce. Here, we aimed to develop a nomogram model utilizing multimodal data for the risk stratification of AF recurrence following catheter ablation in individuals diagnosed with PFO and new-onset PAF.MethodsPatients with PFO and PAF who underwent catheter ablation at the Renmin Hospital of Wuhan University from January 2018 to June 2020 were consecutively enrolled. The identification of potential risk factors was conducted using the regression method known as least absolute shrinkage and selection operator. Subsequently, multivariate COX regression analysis was conducted to determine the independent risk factors, after which a nomogram scoring system was developed. The nomogram's performance was assessed via various statistical measures, including receiver operating characteristic curve analysis, calibration curve, and decision curve analysis (DCA).ResultsThe dataset was partitioned into the development cohort (n = 102) and the validation cohort (n = 43) using a 7:3 ratio. The constructed nomogram included four clinical variables: age, diabetes mellitus, lipoprotein (a), and right ventricular diameter. The area under the curve values of the development and validation cohorts at 1, 2, and 3 years post-catheter ablation were 0.911, 0.812, and 0.786 and 0.842, 0.761, and 0.785, respectively. Additionally, the nomogram demonstrated a significant correlation between the predicted and actual outcomes in the development and validation cohorts, indicating its excellent calibration. Lastly, the DCA findings suggested that the model had notable clinical applicability in predicting the likelihood of AF recurrence within 1, 2, and 3 years after catheter ablation.ConclusionThe incorporation of multimodal data in a nomogram visualization tool facilitates the concise representation of multimodal data, thereby enhancing the comprehension of the clinical status of patients with PFO and PAF following catheter ablation and providing accurate risk stratification at 1, 2, and 3 years post-treatment.Trial registration: This trial was registered in the Chinese Clinical Trial Registry.(ChiCTR2300072320).Graphical

Leveraging machine learning for enhanced reservoir permeability estimation in geothermal hotspots: a case study of the Williston Basin

Geothermal energy is a large, renewable, and clean source of energy from the earth in the form of heat. Exploring the deeper layers of the Williston Basin has revealed favorable reservoir temperatures, particularly in the western areas where high heat flows are prevalent. The quality of a geothermal hotspot hinges on the reservoir quality index (RQI), which is determined by the accuracy of calculating the field reservoir permeability. The primary goal of this study is to apply machine learning techniques to accurately calculate the field permeability, which is important for optimizing the RQI. To enhance accuracy, we initially applied various clustering algorithms, including the density-based spatial clustering of applications with noise (DBSCAN), K-means, K-median, and hierarchical clustering methods, to delineate hydraulic flow units (HFU) within the reservoir using porosity, permeability and water saturation core data. Subsequently, regression models including supervised ML regression methods such as neural networks, support vector machine (SVM) regression, Gaussian process regression (GPR), ensemble regression, linear regression, and decision trees were employed for each flow unit to establish correlations and calculate field permeability with each of these models validated using cross-validation. In comparison to the other clustering methods, the hierarchical clustering method showed the best performance by showing a strong correlation between the actual and predicted permeability values. Overall, the SVM and GPR regression methods were observed to show consistent results with the training and testing datasets, with the SVM regression technique yielding higher R-squared values through regression across the different clustering techniques. In addition, cross-plots were employed to successfully delineate the Red River formation into distinct regions, aiding in the definition of formation lithology and the estimation of field water saturation. Our study showcases an integrated approach to predicting reservoir permeability, considering limited core data. ML emerges as an effective tool for characterizing the Red River formation as a geothermal hotspot in North Dakota, showcasing the potential for sustainable energy exploration and utilization which reduces the reliance on extensive coring in order to enhance geothermal exploration accuracy.

The paradox of resource-richness: unraveling the effects on financial markets in natural resource abundant economies

In the contemporary global landscape, understanding the nexus between financial inclusion and natural resource abundance is crucial, especially for resource-rich nations. This study uses diagnostic tests and method of moments quantile regression to examines this interplay across Australia, Brazil, Canada, China, India, Russia, and the United States. We find that achieving financial inclusion is significantly challenging for countries that heavily rely on natural resources. Diversified income sources and equitable wealth distribution are essential to mitigate these challenges. Additionally, we identify a positive correlation between economic development and financial inclusion, highlighting the mutually reinforcing relationship between growth and inclusivity. Our research also reveals a notable link between adopting renewable energy and improving financial inclusion, suggesting that environmental responsibility and financial accessibility are intertwined. Foreign direct investment has nuanced impacts on financial inclusion, adding depth to our understanding. Overall, stable income from natural resources and diversified economic development emerge as key promoters of financial inclusion. These insights advocate for regionally specific policies and lay a solid foundation for future research and informed policymaking that address financial inclusion challenges and advance sustainable development.Graphical abstract

ANALYSIS OF HEMODIALYSIS SERVICE UTILIZATION BY JKN PARTICIPANTS IN THE SPECIAL REGION OF YOGYAKARTA: AN ANALYSIS OF BPJS KESEHATAN CLAIM DATA IN 2023

Chronic kidney disease (CKD) has become a serious global health problem with significant socio-economic implications. This study aims to analyze the determinants of the utilization of hemodialysis services for National Health Insurance (JKN) participants in the Special Region of Yogyakarta (DIY) in 2023. The study employs a cross-sectional design to analyze secondary data from the BPJS Kesehatan claim database in 2020, focusing on JKN participants receiving dialysis services at Advanced Referral Health Facilities (FKRTL). Data extraction is followed by analysis using STATA software, which includes univariate and bivariate analyses, multicollinearity and overdispersion tests, model comparison via Akaike Information Criterion (AIC), and multivariate analysis through Poisson regression and Binomial Negative methods. A total of 3,220 participants (0.089%) were enrolled in the study. The highest distribution of participants was in Sleman Regency (36.49%), followed by Bantul (29.53%), and Yogya City (25.96%). From the results of the binomial regression analysis, it was found that the age factor had a significant negative influence (p=0.000) with a coefficient of -0.0046 and an IRR of 0.9954, which means that every increase in age by one year will decrease the frequency of visits by 0.46%. The prevalence of diabetes (3.1%) and hypertension (10.2%) which exceed the national average indicates a high risk factor for CKD in this region is high. The age factor and location of health facilities in SLEMAN Regency significantly affected the frequency, while the segment group, gender, and nursing class rights did not show significant influence.

Reducing bias in source-free unsupervised domain adaptation for regression.

Due to data privacy and storage concerns, Source-Free Unsupervised Domain Adaptation (SFUDA) focuses on improving an unlabelled target domain by leveraging a pre-trained source model without access to source data. While existing studies attempt to train target models by mitigating biases induced by noisy pseudo labels, they often lack theoretical guarantees for fully reducing biases and have predominantly addressed classification tasks rather than regression ones. To address these gaps, our analysis delves into the generalisation error bound of the target model, aiming to understand the intrinsic limitations of pseudo-label-based SFUDA methods. Theoretical results reveal that biases influencing generalisation error extend beyond the commonly highlighted label inconsistency bias, which denotes the mismatch between pseudo labels and ground truths, and the feature-label mapping bias, which represents the difference between the proxy target regressor and the real target regressor. Equally significant is the feature misalignment bias, indicating the misalignment between the estimated and real target feature distributions. This factor is frequently neglected or not explicitly addressed in current studies. Additionally, the label inconsistency bias can be unbounded in regression due to the continuous label space, further complicating SFUDA for regression tasks. Guided by these theoretical insights, we propose a Bias-Reduced Regression (BRR) method for SFUDA in regression. This method incorporates Feature Distribution Alignment (FDA) to reduce the feature misalignment bias, Hybrid Reliability Evaluation (HRE) to reduce the feature-label mapping bias and pseudo label updating to mitigate the label inconsistency bias. Experiments demonstrate the superior performance of the proposed BRR, and the effectiveness of FDA and HRE in reducing biases for regression tasks in SFUDA.

Rheumatoid arthritis and the risk of fracture: A Mendelian randomization study.

Observational epidemiological studies indicate a higher fracture incidence in rheumatoid arthritis (RA) patients compared to the general population. However, the causal relationship between RA and fracture risk, particularly traumatic and osteoporotic fractures, is not well established. We performed Mendelian randomization (MR) analysis to evaluate the causal relationship between RA and fracture risk. We performed a MR analysis using summary statistics from genome-wide association studies to investigate the causal association between RA and the risk of traumatic fractures at 9 sites and 3 types of osteoporotic fractures. The primary analysis used inverse-variance weighting, supplemented by MR-Egger regression and other methods to assess causal relationships and sensitivity analyses, including heterogeneity and pleiotropy assessments, using R software with appropriate packages. The inverse-variance weighting results demonstrated a causal relationship between genetically predicted RA and an elevated risk of fractures, particularly traumatic fractures of the long bones and osteoporotic fractures, including fractures of shoulder and upper arm (odds ratio [OR] = 1.041, 95% confidence interval [CI]: 1.020-1.062, P = 9.06e-05), fractures of forearm (OR = 1.026, 95% CI: 1.007-1.044, P = .006), fracture of femur (OR = 1.036, 95% CI: 1.009-1.064, P = .009), fractures of lower leg, including joint (OR = 1.031, 95% CI: 1.016-1.047, P = 6.38e-05), fractures of rib(s), sternum, and thoracic vertebrae (OR = 1.041, 95% CI: 1.018-1.064, P = 4.08e-04), osteoporotic with pathological features (OR = 1.128, 95% CI: 1.071-1.188, P = 5.54e-06), postmenopausal osteoporotic with pathological features (OR = 1.060, 95% CI: 1.002-1.123, P = .044), and drug-induced osteoporotic with pathological features (OR = 1.255, 95% CI: 1.124-1.400, P = 5.02e-05). This study highlights the genetic causal link between RA and an increased risk of traumatic and osteoporotic fractures, presenting a new direction for future exploration of the mechanisms underlying RA-related fractures.

A hybrid Harris Hawks Optimization with Support Vector Regression for air quality forecasting

This paper proposes a hybridized model for air quality forecasting that combines the Support Vector Regression (SVR) method with Harris Hawks Optimization (HHO) called (HHO-SVR). The proposed HHO-SVR model utilizes five datasets from the environmental protection agency’s Downscaler Model (DS) to predict Particulate Matter () levels. In order to assess the efficacy of the suggested HHO-SVR forecasting model, we employ metrics such as Mean Absolute Percentage Error (MAPE), Average, Standard Deviation (SD), Best Fit, Worst Fit, and CPU time. Additionally, we contrast our methodology with recently created models that have been published in the literature, such as the Grey Wolf Optimizer (GWO), Salp Swarm Algorithm (SSA), Henry Gas Solubility Optimization (HGSO), Barnacles Mating Optimizer (BMO), Whale Optimization Algorithm (WOA), and Manta Ray Foraging Optimization (MRFO). In particular, the proposed HHO-SVR model outperforms other approaches, establishing it as the optimal model based on its superior results.

Explainable machine learning framework for cataracts recognition using visual features

Cataract is the leading ocular disease of blindness and visual impairment globally. Deep neural networks (DNNs) have achieved promising cataracts recognition performance based on anterior segment optical coherence tomography (AS-OCT) images; however, they have poor explanations, limiting their clinical applications. In contrast, visual features extracted from original AS-OCT images and their transform forms (e.g., AS-OCT-based histograms) have good explanations but have not been fully exploited. Motivated by these observations, an explainable machine learning framework to recognize cataracts severity levels automatically using AS-OCT images was proposed, consisting of three stages: visual feature extraction, feature importance explanation and selection, and recognition. First, the intensity histogram and intensity-based statistical methods are applied to extract visual features from original AS-OCT images and AS-OCT-based histograms. Subsequently, the SHapley Additive exPlanations and Pearson correlation coefficient methods are applied to analyze the feature importance and select significant visual features. Finally, an ensemble multi-class ridge regression method is applied to recognize the cataracts severity levels based on the selected visual features. Experiments on a clinical AS-OCT-NC dataset demonstrate that the proposed framework not only achieves competitive performance through comparisons with DNNs, but also has a good explanation ability, meeting the requirements of clinical diagnostic practice.

Factors of progression to severity and death in COVID-19 patients at two health care sites in Bamako, Mali

ObjectivesTo analyze the clinical and biological characteristics and to evaluate the risk factors associated with the mortality of patients with COVID-19 in Commune IV of the District of Bamako.MethodsThe cohort consisted of COVID-19 patients managed from March 2020 to June 2022 at the Bamako Dermatology Hospital and the Pasteur Polyclinic in Commune IV in Bamako. The studied variables were sociodemographic, clinical, and biological. For the analysis of deaths, explanatory variables were grouped into sociodemographic factors, comorbidities and symptoms. Binomial logistic regression models were used to identify mortality associated risk factors.ResultsAmong the 1319 included patients, 38.4% were asymptomatic, 46% and 15.5% developed moderate or severe COVID-19 respectively. The predominant signs were cough (48.5%), respiratory difficulty (24.6%) and headache (19.7%). Male were more common (58.2%). High blood pressure (19.9%) and diabetes (10%) were the main comorbidities. D-dimers < 0.5 μg/l was found in 53.3% of cases and the mean hemoglobin level was 12.9 ± 1.7 g/l. The case fatality rate was 3.71% in our series. In bivariate analysis, age > 60 years, high blood pressure, diabetes, clinical severity, D-dimers < 0.5 μg/l were associated with death. Using binomial logistic regression method, age > 60 years, increased heart rate, disease severity level and mainly acute respiratory distress syndrome (polypnea, difficulty breathing) were the factors found associated with death. After adjusting for all the assessed factors, age < 60 years [aHR = 0.15 (0.06–0.35)] and administration of azithromycin [aHR = 0.31 (0.1–0.97)] were protective factors while higher respiratory rate [aHR = 1.14 (1.07–1.22)] and difficulty breathing [aHR = 3.06 (1.03–9.13)] were risk factors associated with death.ConclusionThese main findings elucidate the factors associated with severity and lethality external of health care system constraints. Advanced age, higher heart rate and the development of respiratory distress were the factors significantly associated with increased fatalities.

High-throughput methylation sequencing reveals novel biomarkers for the early detection of renal cell carcinoma

PurposeRenal cell carcinoma (RCC) is a common malignancy, with patients frequently diagnosed at an advanced stage due to the absence of sufficiently sensitive detection technologies, significantly compromising patient survival and quality of life. Advances in cell-free DNA (cfDNA) methylation profiling using liquid biopsies offer a promising non-invasive diagnostic option, but robust biomarkers for early detection are current not available. This study aimed to identify methylation biomarkers for RCC and establish a DNA methylation signature-based prognostic model for this disease.MethodsHigh-throughput methylation sequencing was performed on peripheral blood samples obtained from 49 primarily Stage I RCC patients and 44 healthy controls. Comparative analysis and Least Absolute Shrinkage and Selection Operator (LASSO) regression methods were employed to identify RCC methylation signatures.Subsequently, methylation markers-based diagnostic and prognostic models for RCC were independently trained and validated using random forest and Cox regression methodologies, respectively.ResultsComparative analysis revealed 864 differentially methylated CpG islands (DMCGIs), 96.3% of which were hypermethylated. Using a training set from The Cancer Genome Atlas (TCGA) dataset of 443 early-stage RCC tumors and matched normal tissues, we applied LASSO regression and identified 23 methylation signatures. We then constructed a random forest-based diagnostic model for early-stage RCC and validated the model using two independent datasets: a TCGA set of 460 RCC tumors and controls, and a blood sample set from our study of 15 RCC cases and 29 healthy controls. For Stage I RCC tissue, the model showed excellent discrimination (AUC-ROC: 0.999, sensitivity: 98.5%, specificity: 100%). Blood sample validation also yielded commendable results (AUC-ROC: 0.852, sensitivity: 73.9%, specificity: 89.7%). Further analysis using Cox regression identified 7 of the 23 DMCGIs as prognostic markers for RCC, allowing the development of a prognostic model with strong predictive power for 1-, 3-, and 5-year survival (AUC-ROC > 0.7).ConclusionsOur findings highlight the critical role of hypermethylation in RCC etiology and progression, and present these identified biomarkers as promising candidates for diagnostic and prognostic applications.

Methods for Estimating the Exposure-Response Curve to Inform the New Safety Standards for Fine Particulate Matter

Abstract Exposure to fine particulate matter (PM2.5) poses significant health risks and accurately determining the shape of the relationship between PM2.5 and health outcomes has crucial policy implications. Although various statistical methods exist to estimate this exposure-response curve (ERC), few studies have compared their performance under plausible data-generating scenarios. This study compares seven commonly used ERC estimators across 72 exposure-response and confounding scenarios via simulation. Additionally, we apply these methods to estimate the ERC between long-term PM2.5 exposure and all-cause mortality using data from over 68 million Medicare beneficiaries in the United States. Our simulation indicates that regression methods not placed within a causal inference framework are unsuitable when anticipating heterogeneous exposure effects. Under the setting of a large sample size and unknown ERC functional form, we recommend utilizing causal inference methods that allow for nonlinear ERCs. In our data application, we observe a nonlinear relationship between annual average PM2.5 and all-cause mortality in the Medicare population, with a sharp increase in relative mortality at low PM2.5 concentrations. Our findings suggest that stricter limits on PM2.5 could avert numerous premature deaths. To facilitate the utilization of our results, we provide publicly available, reproducible code on Github for every step of the analysis.

Effect of antiretroviral therapy on the mortality of HIV-1 infection long-term non-progressors: a cohort study

BackgroundThe study aims to investigate the demographic characteristics, the variations in their immune status, and mortality risk among HIV-1 infection long-term non-progressors (LTNP).MethodsEligible LTNP and typical progressors (TP) were recruited in Guangxi by December 2018. Participants were followed up until December 2022, monitoring ART status, CD4+ T cell counts, and survival/death outcomes. Multivariate logistic, Cox regression, and Kaplan-Meier method were employed to scrutinize associated factors and mortality risk of LTNP.ResultsA total of 212 LTNP and 390 TP were included. LTNP cohort predominantly comprised males (84.43%), those diagnosed with HIV at age ≤ 40 years (93.87%), and those infected through injection drug use (59.91%). The mortality rate of LTNP were lower than TP (12.74% vs. 27.18%). TP had a higher mortality risk compared to LTNP (adjusted hazard ratio [aHR] = 4.051, 95% CI: 2.284–7.186, P < 0.001). The mortality risk was also elevated in the ART-naïve group versus the ART-experienced ones (aHR = 3.943, 95%CI: 2.658–5.850, P < 0.001). Notably, the CD4/CD8 ratio in the LTNP group did not fully recover (< 1.0) despite ART. However, LTNP with ART-experienced had a significantly lower mortality risk compared to ART-naïve LTNP group (Log-rank: P = 0.003).ConclusionsART effectively restores and maintains normal CD4+ T cell levels among LTNP, thereby decreasing mortality risk. Nonetheless, the CD4/CD8 ratio in LTNP exhibits incompletely recovered post-ART. These findings provide a scientific foundation for promoting ART in LTNP population.

The Influence of Political Factors on Economic Growth: a Study of the Interconnection Between Governance Spheres in a Global Context

Objective: The purpose is to study the interaction between global political factors and economic progress and to create a model that illustrates the interaction between political and economic development factors. Theoretical Framework: This article explores how policy decisions at different administrative levels interact with economic dynamics at the national and regional levels. Method: The study comprehensive review of academic sources, publications, reports, and statistics, and used correlation regression and statistical methods. Results and Discussion: The research ultimately revealed a critical revelation: political factors play a vital role in economic progress, shaping the investment environment, maintaining macroeconomic stability, providing human capital resources, promoting infrastructure development, and creating efficient institutional framework conditions. Research Implications: By analysing specific cases, we examine the impact of the political context on the investment environment, market regulation, trade relations and other economic aspects. The key factors linking the political sphere and economic development are highlighted, as well as their importance in developing effective management strategies in the contemporary environment. Originality/Value: Effective policies that promote economic growth can bring prosperity and improve citizens' living standards, highlighting the vital link between policy decisions and economic outcomes.