Ubiquitin-specific protease 11 promotes partial epithelial-to-mesenchymal transition by deubiquitinating the epidermal growth factor receptor during kidney fibrosis

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Spatiotemporal Bayesian networks for malaria prediction

Targeted intervention and resource allocation are essential for effective malaria control, particularly in remote areas, with predictive models providing important information for decision making. While a diversity of modeling technique have been used to create predictive models of malaria, no work has made use of Bayesian networks. Bayes nets are attractive due to their ability to represent uncertainty, model time lagged and nonlinear relations, and provide explanations. This paper explores the use of Bayesian networks to model malaria, demonstrating the approach by creating village level models with weekly temporal resolution for Tha Song Yang district in northern Thailand. The networks are learned using data on cases and environmental covariates. Three types of networks are explored: networks for numeric prediction, networks for outbreak prediction, and networks that incorporate spatial autocorrelation. Evaluation of the numeric prediction network shows that the Bayes net has prediction accuracy in terms of mean absolute error of about 1.4 cases for 1 week prediction and 1.7 cases for 6 week prediction. The network for outbreak prediction has an ROC AUC above 0.9 for all prediction horizons. Comparison of prediction accuracy of both Bayes nets against several traditional modeling approaches shows the Bayes nets to outperform the other models for longer time horizon prediction of high incidence transmission. To model spread of malaria over space, we elaborate the models with links between the village networks. This results in some very large models which would be far too laborious to build by hand. So we represent the models as collections of probability logic rules and automatically generate the networks. Evaluation of the models shows that the autocorrelation links significantly improve prediction accuracy for some villages in regions of high incidence. We conclude that spatiotemporal Bayesian networks are a highly promising modeling alternative for prediction of malaria and other vector-borne diseases.     

The gut microbiota promotes distal tissue regeneration via RORγ+ regulatory T cell emissaries

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The association between a novel inflammatory biomarker,systemic inflammatory response index and the risk of diabetic cardiovascular complications

Background and aimSystemic inflammatory response index (SIRI) is a novel inflammatory biomarker. The relationship between SIRI and the risk of diabetic cardiovascular complications is still unclear. The purpose of our study was to address the correlation between SIRI and the risk of cardiovascular diseases (CVD) in diabetes mellitus (DM) patients.Methods and resultsA total of 8759 individuals were selected from the National Health and Nutrition Examination Survey (NHANES) (2015–2020) in our study. Comparing with control (n = 6446) and pre-DM (n = 350) individuals, the DM patients (n = 1963) show the higher SIRI level (all P < 0.001) and prevalence of CVD (all P < 0.001). Furthermore, in a fully adjusted model, we observed the increase of tertiles of SIRI was a risk factor for CVD in DM patients (the middle tertile: 1.80, 95% CI: 1.13–3.13; the highest tertile: 1.91, 95% CI: 1.03–3.22; all P < 0.05), while the relationship between hypersensitive CRP (hs-CRP) and the risk of diabetic cardiovascular complications was not observed (all P > 0.05). Furthermore, the SIRI tertiles–CVD association was significant strongly in patients with high body mass index (BMI; >24 kg/m²) than in those with a low BMI (≤24 kg/m², P for interaction = 0.045). Using restricted cubic splines, we observed a dose–response relation between lg SIRI and the risk of CVD in DM patients.ConclusionsThe elevated SIRI was independently associated with the increased risk of CVD in the DM population with a high BMI (>24 kg/m²), and its clinical value is greater than hs-CRP.     

Integrated multi-omics profiling yields a clinically relevant molecular classification for esophageal squamous cell carcinoma

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Respiratory and Cardiometabolic Comorbidities and Stages I to III NSCLC Survival: A Pooled Analysis From the International Lung Cancer Consortium

IntroductionWe explored the association of respiratory and cardiometabolic comorbidities with NSCLC overall survival (OS) and lung cancer-specific survival (LCSS), by stage, in a large, multicontinent NSCLC pooled data set.MethodsOn the basis of patients pooled from 11 International Lung Cancer Consortium studies with available respiratory and cardiometabolic comorbidity data, adjusted hazard ratios (aHRs) were estimated using Cox models for OS. LCSS was evaluated using competing risk Grey and Fine models and cumulative incidence functions. Logistic regression (adjusted OR [aOR]) was applied to assess factors associated with surgical resection.ResultsOS analyses used patients with NSCLC with respiratory health or cardiometabolic health data (N = 16,354); a subset (n = 11,614) contributed to LCSS analyses. In stages I to IIIA NSCLC, patients with respiratory comorbidities had worse LCCS (stage IA aHR = 1.51, confidence interval [CI]: 1.17–1.95; stages IB–IIIA aHR = 1.20, CI: 1.06–1.036). In contrast, patients with stages I to IIIA NSCLC with cardiometabolic comorbidities had a higher risk of death from competing (non-NSCLC) causes (stage IA aHR = 1.34, CI: 1.12–1.69). The presence of respiratory comorbidities was inversely associated with having surgical resection (stage IA aOR = 0.54, CI: 0.35–0.83; stages IB–IIIA aOR = 0.57, CI: 0.46–0.70).ConclusionsThe presence of either cardiometabolic or respiratory comorbidities is associated with worse OS in stages I to III NSCLC. Patients with respiratory comorbidities were less likely to undergo surgery and had worse LCSS, whereas patients with cardiometabolic comorbidities had a higher risk of death from competing causes. As more treatment options for stages I to III NSCLC are introduced into the practice, accounting for cardiometabolic and respiratory comorbidities becomes essential in trial interpretation and clinical management.     

Epithelial cells activate fibroblasts to promote esophageal cancer development

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