High-dimensional mediation analysis for continuous outcome with confounders using overlap weighting method in observational epigenetic study.

BACKGROUND: Mediation analysis is a powerful tool to identify factors mediating the causal pathway of exposure to health outcomes. Mediation analysis has been extended to study a large number of potential mediators in high-dimensional data settings. The presence of confounding in observational studies is inevitable. Hence, it's an essential part of high-dimensional mediation analysis (HDMA) to adjust for the potential confounders. Although the propensity score (PS) related method such as propensity score regression adjustment (PSR) and inverse probability weighting (IPW) has been proposed to tackle this problem, the characteristics with extreme propensity score distribution of the PS-based method would result in the biased estimation. METHODS: In this article, we integrated the overlapping weighting (OW) technique into HDMA workflow and proposed a concise and powerful high-dimensional mediation analysis procedure consisting of OW confounding adjustment, sure independence screening (SIS), de-biased Lasso penalization, and joint-significance testing underlying the mixture null distribution. We compared the proposed method with the existing method consisting of PS-based confounding adjustment, SIS, minimax concave penalty (MCP) variable selection, and classical joint-significance testing. RESULTS: Simulation studies demonstrate the proposed procedure has the best performance in mediator selection and estimation. The proposed procedure yielded the highest true positive rate, acceptable false discovery proportion level, and lower mean square error. In the empirical study based on the GSE117859 dataset in the Gene Expression Omnibus database using the proposed method, we found that smoking history may lead to the estimated natural killer (NK) cell level reduction through the mediation effect of some methylation markers, mainly including methylation sites cg13917614 in CNP gene and cg16893868 in LILRA2 gene. CONCLUSIONS: The proposed method has higher power, sufficient false discovery rate control, and precise mediation effect estimation. Meanwhile, it is feasible to be implemented with the presence of confounders. Hence, our method is worth considering in HDMA studies.

Outlier detection in spatial error models using modified thresholding-based iterative procedure for outlier detection approach.

BACKGROUND: Outliers, data points that significantly deviate from the norm, can have a substantial impact on statistical inference and provide valuable insights in data analysis. Multiple methods have been developed for outlier detection, however, almost all available approaches fail to consider the spatial dependence and heterogeneity in spatial data. Spatial data has diverse formats and semantics, requiring specialized outlier detection methodology to handle these unique properties. For now, there is limited research exists on robust spatial outlier detection methods designed specifically under the spatial error model (SEM) structure. METHOD: We propose the Spatial-Θ-Iterative Procedure for Outlier Detection (Spatial-Θ-IPOD), which utilizes a mean-shift vector to identify outliers within the SEM. Our method enables an effective detection of spatial outliers while also providing robust coefficient estimates. To assess the performance of our approach, we conducted extensive simulations and applied it to a real-world empirical study using life expectancy data from multiple countries. RESULTS: Simulation results showed that the masking and JD (Joint Detection) indicators of our Spatial-Θ-IPOD method outperformed several commonly used methods, even in high-dimensional scenarios, demonstrating stable performance. Conversely, the Θ-IPOD method proved to be ineffective in detecting outliers when spatial correlation was present. Moreover, our model successfully provided reliable coefficient estimation alongside outlier detection. The proposed method consistently outperformed other models (both robust and non-robust) in most cases. In the empirical study, our proposed model successfully detected outliers and provided valuable insights in the modeling process. CONCLUSIONS: Our proposed Spatial-Θ-IPOD offers an effective solution for detecting spatial outliers for SEM while providing robust coefficient estimates. Notably, our approach showcases its relative superiority even in the presence of high leverage points. By successfully identifying outliers, our method enhances the overall understanding of the data and provides valuable insights for further analysis.

Geographically weighted accelerated failure time model for spatial survival data: application to ovarian cancer survival data in New Jersey.

BACKGROUND: In large multiregional cohort studies, survival data is often collected at small geographical levels (such as counties) and aggregated at larger levels, leading to correlated patterns that are associated with location. Traditional studies typically analyze such data globally or locally by region, often neglecting the spatial information inherent in the data, which can introduce bias in effect estimates and potentially reduce statistical power. METHOD: We propose a Geographically Weighted Accelerated Failure Time Model for spatial survival data to investigate spatial heterogeneity. We establish a weighting scheme and bandwidth selection based on quasi-likelihood information criteria. Theoretical properties of the proposed estimators are thoroughly examined. To demonstrate the efficacy of the model in various scenarios, we conduct a simulation study with different sample sizes and adherence to the proportional hazards assumption or not. Additionally, we apply the proposed method to analyze ovarian cancer survival data from the Surveillance, Epidemiology, and End Results cancer registry in the state of New Jersey. RESULTS: Our simulation results indicate that the proposed model exhibits superior performance in terms of four measurements compared to existing methods, including the geographically weighted Cox model, when the proportional hazards assumption is violated. Furthermore, in scenarios where the sample size per location is 20-25, the simulation data failed to fit the local model, while our proposed model still demonstrates satisfactory performance. In the empirical study, we identify clear spatial variations in the effects of all three covariates. CONCLUSION: Our proposed model offers a novel approach to exploring spatial heterogeneity of survival data compared to global and local models, providing an alternative to geographically weighted Cox regression when the proportional hazards assumption is not met. It addresses the issue of certain counties' survival data being unable to fit the model due to limited samples, particularly in the context of rare diseases.

Structure-activity relationship of dual inhibitors containing maleimide and imidazole motifs against glutaminyl cyclase and glycogen synthase kinase-3ß.

Alzheimer's disease (AD) is a major cause of dementia and one of the most common chronic diseases affecting the aging population. Because AD is considered a public health priority, there is a critical need to discover novel and effective agents for the treatment of this condition. In view of the known contribution of up-regulated glutaminyl cyclase (QC) and glycogen synthase kinase-3ß (GSK-3ß) to the initiation of AD, we previously evaluated a series of dual inhibitors containing maleimide and imidazole motifs as potential anti-AD agents. Here, we assessed another series of hybrids containing maleimide and imidazole motifs to gain an in-depth understanding of the structure-activity relationship (SAR). Based on the primary screening, the introduction of 5-methyl imidazole at one side of the molecule did not enhance the QC-specific inhibitory activity of these hybrids (2, IC50 = 1.22 µM), although the potency was increased by 2' substitution on the maleimide motif at the other side of the molecule. Interestingly, compounds containing 5-methyl imidazole exhibited stronger GSK-3ß-specific inhibitory activity (2, IC50 = 0.0021 µM), and the electron-withdrawing group and 2' and 3' substitution were favorable. Further investigation of substitutions on the maleimide motif in compounds 14-35 revealed that QC-specific inhibition in the presence of piperidine was improved by introduction of a methoxy group (R2). Increasing the linker length and introduction of a methoxy group (R2) also increased the GSK-3ß-specific inhibitory potency. These findings were further confirmed by molecular docking analysis of 33 and 24 with QC and GSK-3ß. Overall, these hybrids exhibited enhanced inhibitory potency against both QC and GSK-3ß, highlighting an important strategy for improving the potency of hybrids as dual-targeting anti-AD agents.

Discovery of potential scaffolds for glutaminyl cyclase inhibitors: Virtual screening, synthesis, and evaluation.

Glutaminyl cyclase (QC) plays a crucial role in the early stages of Alzheimer's disease (AD), thus inhibition of QC may be a promising strategy for the treatment of early AD. Therefore, QC inhibitors with novel chemical scaffolds may contribute to the development of additional anti-AD agents. We conducted a virtual screening of 3 million compounds from the Chemdiv and Enamine databases, to discover potential scaffolds for QC inhibitors. Three scaffolds, 120974, 147706, and 141449, were selected from this structure-based virtual screening through a combination of pharmacophore modeling, a receptor-ligand pharmacophore model, and the GALAHAD model, and furtherly filtered by chelation with zinc ion and docking properties. Consequently, three compounds, 1, 2, and 3, were designed and synthesized based on these three scaffolds, respectively. The IC50 of compounds 1 and 3 against QC were 14.19 ± 4.21 and 4.34 ± 0.35 µM, respectively. Our results indicate that the new scaffolds selected using a virtual screening process exhibit potential as novel QC inhibitors.

Gender-specific association of body fat mass with muscle meat-vegetable intake ratio in Shaanxi, China.

BACKGROUND AND OBJECTIVES: The effects of muscle meat and vegetable intake on body fat mass remain unclear in the general population. This study aimed to investigate the association of body fat mass and fat dis-tribution with a muscle meat-vegetable intake (MMV) ratio. METHODS AND STUDY DESIGN: In total, 29,271 par-ticipants aged 18-80 years were recruited from the Shaanxi cohort of the Regional Ethnic Cohort Study in Northwest China. The associations of muscle meat, vegetable and MMV ratio, as the independent variable, with body mass index (BMI), waist circumference, total body fat percentage (TBF) and visceral fat (VF), as dependent variables were evaluated by gender-specific linear regression models. RESULTS: There was 47.9% of men whose MMV ratio was greater than or equal to 1 and this figure was about 35.7% for women. For men, higher muscle meat intake was associated with higher TBF (standardized coefficient [ß], 0.508; 95% CI, 0.187-0.829), higher vegetable intake was associated with lower VF (ß, -0.109; 95% CI, -0.206 - -0.011), and higher MMV ratio was associated with higher BMI (ß, 0.195; 95% CI, 0.039-0.350) and VF (ß, 0.523; 95% CI, 0.209-0.838). For women, both higher muscle meat consumption and MMV ratio were associated with all fat mass markers, but vegetable intake was not correlated with body fat mass markers. The positive association of MMV on body fat mass was more pronounced in higher MMV ratio group, with both men and women. The intake of pork, mutton and beef was associated positively with fat mass markers but no such as-sociation was observed for poultry or seafood. CONCLUSIONS: An increased intake of muscle meat or a higher MMV ratio was associated with increased body fat, especially among women, and such impact may mainly be attributed to increasing intake of pork, beef and mutton. The dietary MMV ratio could be thus a useful parameter for nutritional intervention.

Antibacterial activity and mechanism of linalool against Pseudomonas aeruginosa.

The purpose of this study was to evaluate the antibacterial activity and mechanism of linalool against Pseudomonas aeruginosa. The determination of antibacterial activity was based on the minimum inhibitory concentration (MIC) and the minimum bactericide concentration (MBC). Further, the antibacterial mechanism was explored by a growth curve assay, scanning electron microscopy (SEM), cell membrane permeability, membrane potential and respiratory chain dehydrogenase determination. The MIC and the MBC of linalool were 431 µg/mL and 862 µg/mL, respectively. The growth curve assay showed that the growth of P. aeruginosa was inhibited. The results of SEM revealed that linalool disrupted the normal morphology of the cell. The release of nucleic acids as well as the decrease in the membrane potential proved that the membrane integrity of P. aeruginosa was destroyed. Moreover, the respiratory chain was damaged by respiratory chain dehydrogenase determination as the absorbance at 490 nm decreased. This research suggested that it was possible for linalool to become a preservative of food by destroying the cell membrane, resulting in cell death.

Oral microemulsion based delivery system for reducing reproductive and kidney toxicity of Tripterygium glycosides.

Aim: To reduce the toxic effects and achieve efficiency of Tripterygium glycosides, an oral microemulsion was designed. Method: After estimating its stability and characterisation, an animal experiment was held to evaluate its toxicity in vivo, using male and female Sprague Dawley rats. Result: The maximum loading amount of microemulsion to Tripterygium glycosides was 18.87 mg/ml. And comparing to control, the Tripterygium glycoside microemulsion can maintain a normal level of the number of sperms, the weight of testicle, testosterone (∼2.5 ng/mL) and BUN (∼5 mmol/L) to male rats. For female rats, it can prevent the ovary to be atrophy and keep FSH to be stable (>2100 ng/L). The weaker injury induced by drug-loaded microemulsion to rats also could be observed in histological sections to kidney and reproductive organs. Conclusions: Although the blank microemulsion had slight toxicity, it mitigated the toxicity of Tripterygium glycosides to kidney and reproductive system.

Lung image segmentation via generative adversarial networks.

Introduction: Lung image segmentation plays an important role in computer-aid pulmonary disease diagnosis and treatment. Methods: This paper explores the lung CT image segmentation method by generative adversarial networks. We employ a variety of generative adversarial networks and used their capability of image translation to perform image segmentation. The generative adversarial network is employed to translate the original lung image into the segmented image. Results: The generative adversarial networks-based segmentation method is tested on real lung image data set. Experimental results show that the proposed method outperforms the state-of-the-art method. Discussion: The generative adversarial networks-based method is effective for lung image segmentation.

Monocular BEV Perception of Road Scenes via Front-to-Top View Projection.

HD map reconstruction is crucial for autonomous driving. LiDAR-based methods are limited due to expensive sensors and time-consuming computation. Camera-based methods usually need to perform road segmentation and view transformation separately, which often causes distortion and missing content. To push the limits of the technology, we present a novel framework that reconstructs a local map formed by road layout and vehicle occupancy in the bird's-eye view given a front-view monocular image only. We propose a front-to-top view projection (FTVP) module, which takes the constraint of cycle consistency between views into account and makes full use of their correlation to strengthen the view transformation and scene understanding. In addition, we apply multi-scale FTVP modules to propagate the rich spatial information of low-level features to mitigate spatial deviation of the predicted object location. Experiments on public benchmarks show that our method achieves various tasks on road layout estimation, vehicle occupancy estimation, and multi-class semantic estimation, at a performance level comparable to the state-of-the-arts, while maintaining superior efficiency.

Membrane Damage and Metabolic Disruption as the Mechanisms of Linalool against Pseudomonas fragi: An Amino Acid Metabolomics Study.

Pseudomonas fragi (P. fragi) is usually detected in low-temperature meat products, and seriously threatens food safety and human health. Therefore, the study investigated the antibacterial mechanism of linalool against P. fragi from membrane damage and metabolic disruption. Results from field-emission transmission electron microscopy (FETEM) and atomic force microscopy (AFM) showed that linalool damage membrane integrity increases surface shrinkage and roughness. According to Fourier transform infrared (FTIR) spectra results, the components in the membrane underwent significant changes, including nucleic acid leakage, carbohydrate production, protein denaturation and modification, and fatty acid content reduction. The data obtained from amino acid metabolomics indicated that linalool caused excessive synthesis and metabolism of specific amino acids, particularly tryptophan metabolism and arginine biosynthesis. The reduced activities of glucose 6-phosphate dehydrogenase (G6PDH), malate dehydrogenase (MDH), and phosphofructokinase (PFK) suggested that linalool impair the respiratory chain and energy metabolism. Meanwhile, genes encoding the above enzymes were differentially expressed, with pfkB overexpression and zwf and mqo downregulation. Furthermore, molecular docking revealed that linalool can interact with the amino acid residues of G6DPH, MDH and PFK through hydrogen bonds. Therefore, it is hypothesized that the mechanism of linalool against P. fragi may involve cell membrane damage (structure and morphology), disturbance of energy metabolism (TCA cycle, EMP and HMP pathway) and amino acid metabolism (cysteine, glutamic acid and citrulline). These findings contribute to the development of linalool as a promising antibacterial agent in response to the food security challenge.

Designing machine learning for big data: A study to identify factors that increase the risk of ischemic stroke and prognosis in hypertensive patients.

Background: Ischemic stroke (IS) accounts large amount of stroke incidence. The aim of this study was to discover the risk and prognostic factors that affecting the occurrence of IS in hypertensive patients. Method: Study data were obtained from the Medical Information Mart for Intensive Care (MIMIC)-IV database. To avoid biased factors selection process, several approaches were studied including logistic regression, elastic net regression, random forest, correlation analysis, and multifactor logistic regression methods. And seven different machine-learning methods are used to construct predictive models. The performance of the developed models was evaluated using AUC (Area Under the Curve), prediction accuracy, precision, recall, F1 score, PPV (Positive Predictive Value) and NPV (Negative Predictive Value). Interaction analysis was conducted to explore potential relationships between influential factors. Results: The study included 92,514 hypertensive patients, of which 1746 hypertensive patients experienced IS. The Gradient Boosted Decision Tree (GBDT) model outperformed the other prediction model terms of prediction accuracy and AUC values in both ischemic and prognosis cases. By using the SHapley Additive exPlanations (SHAP), we found that a range of factors and corresponding interactions between factors are important risk factors for IS and its prognosis in hypertensive patients. Conclusion: The study identified factors that increase the risk of IS and poor prognosis in hypertensive patients, which may provide guidance for clinical diagnosis and treatment.

Developing Deep LSTMs With Later Temporal Attention for Predicting COVID-19 Severity, Clinical Outcome, and Antibody Level by Screening Serological Indicators Over Time.

OBJECTIVE: The clinical course of COVID-19, as well as the immunological reaction, is notable for its extreme variability. Identifying the main associated factors might help understand the disease progression and physiological status of COVID-19 patients. The dynamic changes of the antibody against Spike protein are crucial for understanding the immune response. This work explores a temporal attention (TA) mechanism of deep learning to predict COVID-19 disease severity, clinical outcomes, and Spike antibody levels by screening serological indicators over time. METHODS: We use feature selection techniques to filter feature subsets that are highly correlated with the target. The specific deep Long Short-Term Memory (LSTM) models are employed to capture the dynamic changes of disease severity, clinical outcome, and Spike antibody level. We also propose deep LSTMs with a TA mechanism to emphasize the later blood test records because later records often attract more attention from doctors. RESULTS: Risk factors highly correlated with COVID-19 are revealed. LSTM achieves the highest classification accuracy for disease severity prediction. Temporal Attention Long Short-Term Memory (TA-LSTM) achieves the best performance for clinical outcome prediction. For Spike antibody level prediction, LSTM achieves the best permanence. CONCLUSION: The experimental results demonstrate the effectiveness of the proposed models. The proposed models can provide a computer-aided medical diagnostics system by simply using time series of serological indicators.

Instrumental variable-based high-dimensional mediation analysis with unmeasured confounders for survival data in the observational epigenetic study.

Background: High dimensional mediation analysis is frequently conducted to explore the role of epigenetic modifiers between exposure and health outcome. However, the issue of high dimensional mediation analysis with unmeasured confounders for survival analysis in observational study has not been well solved. Methods: In this study, we proposed an instrumental variable based approach for high dimensional mediation analysis with unmeasured confounders in survival analysis for epigenetic study. We used the Sobel's test, the Joint test, and the Bootstrap method to test the mediation effect. A comprehensive simulation study was conducted to decide the best test strategy. An empirical study based on DNA methylation data of lung cancer patients was conducted to illustrate the performance of the proposed method. Results: Simulation study suggested that the proposed method performed well in the identifying mediating factors. The estimation of the mediation effect by the proposed approach is also reliable with less bias compared with the classical approach. In the empirical study, we identified two DNA methylation signatures including cg21926276 and cg26387355 with a mediation effect of 0.226 (95%CI: 0.108-0.344) and 0.158 (95%CI: 0.065-0.251) between smoking and lung cancer using the proposed approach. Conclusion: The proposed method obtained good performance in simulation and empirical studies, it could be an effective statistical tool for high dimensional mediation analysis.

Purification, Characterization and Bioactivity of Different Molecular-Weight Fractions of Polysaccharide Extracted from Litchi Pulp.

Litchi polysaccharides are a kind of macromolecular polymers with various biological activities and a wide range of molecular weights. In this study, two separate fractions, with average molecular weights of 378.67 kDa (67.33%) and 16.96 kDa (6.95%), which were referred to as LP1 and LP2, respectively, were separated using an ultrafiltration membrane. Their physicochemical properties, and immunomodulatory and prebiotic activity were compared. The results revealed that LP2 contained more neutral sugar, arabinose, galactose and rhamnose, but less uronic acid, protein, mannose and glucose than LP1. Compared with LP1, LP2 possessed higher solubility and lower apparent viscosity. LP2 exhibited stronger stimulation on macrophage secretion of NO, TNF-α and IL-6, as well as better proliferation of Lactobacillus plantarum, Leuconostoc mesenteroides, Lactobacillus casei and Bifidobacterium adolescentis. These results suggest that an ultrafiltration membrane might be used to prepare a highly-active polysaccharide fraction from litchi pulp that may be used for food or drug development.

Explainable Machine Learning with Pairwise Interactions for Predicting Conversion from Mild Cognitive Impairment to Alzheimer's Disease Utilizing Multi-Modalities Data.

BACKGROUND: Predicting cognition decline in patients with mild cognitive impairment (MCI) is crucial for identifying high-risk individuals and implementing effective management. To improve predicting MCI-to-AD conversion, it is necessary to consider various factors using explainable machine learning (XAI) models which provide interpretability while maintaining predictive accuracy. This study used the Explainable Boosting Machine (EBM) model with multimodal features to predict the conversion of MCI to AD during different follow-up periods while providing interpretability. METHODS: This retrospective case-control study is conducted with data obtained from the ADNI database, with records of 1042 MCI patients from 2006 to 2022 included. The exposures included in this study were MRI biomarkers, cognitive scores, demographics, and clinical features. The main outcome was AD conversion from aMCI during follow-up. The EBM model was utilized to predict aMCI converting to AD based on three feature combinations, obtaining interpretability while ensuring accuracy. Meanwhile, the interaction effect was considered in the model. The three feature combinations were compared in different follow-up periods with accuracy, sensitivity, specificity, and AUC-ROC. The global and local explanations are displayed by importance ranking and feature interpretability plots. RESULTS: The five-years prediction accuracy reached 85% (AUC = 0.92) using both cognitive scores and MRI markers. Apart from accuracies, we obtained features' importance in different follow-up periods. In early stage of AD, the MRI markers play a major role, while for middle-term, the cognitive scores are more important. Feature risk scoring plots demonstrated insightful nonlinear interactive associations between selected factors and outcome. In one-year prediction, lower right inferior temporal volume (<9000) is significantly associated with AD conversion. For two-year prediction, low left inferior temporal thickness (<2) is most critical. For three-year prediction, higher FAQ scores (>4) is the most important. During four-year prediction, APOE4 is the most critical. For five-year prediction, lower right entorhinal volume (<1000) is the most critical feature. CONCLUSIONS: The established glass-box model EBMs with multimodal features demonstrated a superior ability with detailed interpretability in predicting AD conversion from MCI. Multi features with significant importance were identified. Further study may be of significance to determine whether the established prediction tool would improve clinical management for AD patients.

Is Increasing Diet Diversity of Animal-Source Foods Related to Better Health-Related Quality of Life among Chinese Men and Women?

Diet plays a crucial role in regulating individuals' lifestyles and is closely related to health. The intake of animal-sourced foods (ASF) provides the human body with high-quality protein and various micronutrients. This study aimed to investigate whether the diversity of animal foods has a positive impact on the health-related quality of life (HRQoL) among residents. The data came from the Shaanxi baseline survey of the Northwest Chinese Regional Ethnic Cohort Study, which recruited more than 100 thousand participants aged 35 to 74 from five provinces between June 2018 and May 2019. A total of 39,997 participants in Shaanxi (mean age: 50 years; 64% women) were finally included in this current study. The animal source food diet diversity score (ASFDDS) was established based on the frequency of consuming pork, mutton, beef, poultry, seafood, eggs, pure milk, and yogurt. The physical component score (PCS) and mental component score (MCS), ranging from 0 to 100 on the 12-Item Short Form Survey (SF-12), were used to assess participants' HRQoL. Better PCS/MCS was defined as scores higher than the 90th percentile. The results showed that men had a higher intake of ASF and ASFDDS than women. After adjusting for potential confounders, compared with those who never or rarely consumed animal foods, the likelihood of having better PCS and MCS increased by 16% (OR = 1.16, 95%CI: 1.01-1.34) and 24% (OR = 1.24, 95%CI: 1.03-1.448), respectively, in men with an ASFDDS ≥ 2. In women, a 34% increase (OR = l.34, 95%CI: 116-l.54) likelihood for better PCS was observed for an ASFDDS ≥ 2, but no association was observed for MCS. Increasing each specific animal source's food intake was associated with better PCS after adjusting for all covariates. However, for MCS, positive associations were only observed in seafood consumption among men and eggs among women. Restricted cubic splines showed a substantial dose-response association between intake frequency of animal-source foods and PCS, both in men and women. The study suggests that a diverse intake of animal-sourced foods can potentially improve the HRQoL of Chinese adults.

Predicting Six-Month Re-Admission Risk in Heart Failure Patients Using Multiple Machine Learning Methods: A Study Based on the Chinese Heart Failure Population Database.

Since most patients with heart failure are re-admitted to the hospital, accurately identifying the risk of re-admission of patients with heart failure is important for clinical decision making and management. This study plans to develop an interpretable predictive model based on a Chinese population for predicting six-month re-admission rates in heart failure patients. Research data were obtained from the PhysioNet portal. To ensure robustness, we used three approaches for variable selection. Six different machine learning models were estimated based on selected variables. The ROC curve, prediction accuracy, sensitivity, and specificity were used to evaluate the performance of the established models. In addition, we visualized the optimized model with a nomogram. In all, 2002 patients with heart failure were included in this study. Of these, 773 patients experienced re-admission and a six-month re-admission incidence of 38.61%. Based on evaluation metrics, the logistic regression model performed best in the validation cohort, with an AUC of 0.634 (95%CI: 0.599-0.646) and an accuracy of 0.652. A nomogram was also generated. The established prediction model has good discrimination ability in predicting. Our findings are helpful and could provide useful information for the allocation of healthcare resources and for improving the quality of survival of heart failure patients.

Healthy life expectancy for 202 countries up to 2030: Projections with a Bayesian model ensemble.

Background: Healthy life expectancy (HLE) projections are required for optimising social and health service management in the future. Existing studies on the topic were usually conducted by selecting a single model for analysis. We thus aimed to use an ensembled model to project the future HLE for 202 countries/region. Methods: We obtained data on age-sex-specific HLE and the sociodemographic index (SDI) level of 202 countries from 1990 to 2019 from the Global Burden of Disease (GBD) database and used a probabilistic Bayesian model comprised of 21 forecasting models to predict their HLE in 2030. Results: In general, HLE is projected to increase in all 202 countries, with the least probability of 82.4% for women and 81.0% for men. Most of the countries with the lowest projected HLE would be located in Africa. Women in Singapore have the highest projected HLE in 2030, with a 94.5% probability of higher than 75.2 years, which is the highest HLE in 2019 across countries. Maldives, Kuwait, and China are projected to have a probability of 49.3%, 41.2% and 31.6% to be the new entries of the top ten countries with the highest HLE for females compared with 2019. Men in Singapore are projected to have the highest HLE at birth in 2030, with a 93.4% probability of higher than 75.2 years. Peru and Maldives have a probability of 48.7% and 35.3% being new top ten countries in male's HLE. The female advantage in HLE will shrink by 2030 in 117 countries, especially in most of the high SDI and European countries. Conclusions: HLE will likely continue to increase in most countries and regions worldwide in the future. More attention needs to be paid to combatting obesity, chronic diseases, and specific infectious diseases, especially in African and some Pacific Island countries. Although gender gaps may not be fully bridged, HLE could partially mitigate and even eliminate them through economic development and improvements in health care.

ERAP2 as a potential biomarker for predicting gemcitabine response in patients with pancreatic cancer.

OBJECTIVE: Pancreatic cancer is one of the most malignant tumors, with rapid metastasis, high mortality rate, and difficult early screening. Currently, gemcitabine is a first-line drug for pancreatic cancer patients, but its clinical effect is limited due to drug resistance. It is particularly important to further identify biomarkers associated with gemcitabine resistance to improve the sensitivity of gemcitabine treatment. METHODS: Drug sensitivity data and the corresponding transcript data derived from the Genomics of Drug Sensitivity in Cancer (GDSC) database for correlation analysis was adopted to obtain genes related to gemcitabine sensitivity. Moreover, the survival model of pancreatic cancer patients treated with gemcitabine in The Cancer Genome Atlas (TCGA) database was utilized to obtain key genes. Multiple in vitro assays were performed to verify the function of the key biomarker. RESULTS: Endoplasmic Reticulum Aminopeptidase 2 (ERAP2) was identified as a biomarker promoting gemcitabine resistance, and its high expression resulted in a worse prognosis. Besides, gemcitabine significantly increased the mRNA and protein levels of ERAP2 in pancreatic cancer cells. Additionally, ERAP2 knockdown suppressed tumorigenesis and potentiated gemcitabine-induced growth, migration and invasion inhibition in human pancreatic cancer cells. CONCLUSIONS: ERAP2 may be a novel key biomarker for gemcitabine sensitivity and diagnosis, thus providing an effective therapeutic strategy for pancreatic cancer treatment.