Obstructive sleep apnea and 19 gastrointestinal diseases: a Mendelian randomization study.

Background: Alterations gastrointestinal diseases (GDs) were reported in individuals with obstructive sleep apnea (OSA), however, the genetic background between OSA and GDs is still unclear. Methods: This investigation employed Mendelian randomization (MR) analyses to evaluate the causal effect between OSA and 19 types of GDs (gastroesophageal reflux disease (GERD), ulcerative colitis, celiac disease, Crohn's disease, chronic gastritis, irritable bowel syndrome, primary biliary cholangitis, diverticular disease, gastroduodenal ulcer, acute pancreatitis, non-alcoholic fatty liver disease, primary sclerosing cholangitis, cirrhosis, calculus of bile duct, calculus of gallbladder, pancreatic cancer, gastric cancer, colorectal cancer, and esophageal cancer). The inverse-variance weighted (IVW) method was used to evaluate the main effects model of causality. Results: This MR study suggests that OSA may play a causal role inflammation-related GDs (GERD, PIVW=5.94×10-9; gastroduodenal ulcer, PIVW=1×10-4; chronic gastritis, PIVW=0.0214; ulcerative colitis, PIVW=0.0296), and gallstones (calculi of the gallbladder, PIVW=0.0429; calculi of the bile duct, PIVW=0.0068). After accounting for obesity, type 2 diabetes, smoking, and alcohol consumption, the multivariate MR (MVMR) analysis identified that OSA is an independent risk factor for GERD, gastroduodenal ulcer, and calculus of the bile duct. The reverse MVMR analysis showed a causal effect of GERD on OSA. Besides, we did not find that the predisposition to OSA was associated with 4 cancers. Conclusion: This MR analysis provides compelling evidence of an independent causal relationship between genetically predicted OSA and an elevated risk of inflammation-related GDs. Besides, no causal association was observed between OSA and cancers. Further studies should be carried out to verify our findings.

Integration of caveolin-mediated cytosolic delivery and enzyme-responsive releasing of squalenoyl nanoparticles enhance the anti-cancer efficacy of chidamide in pancreatic cancer.

We explored the potential of overcoming the dense interstitial barrier in pancreatic cancer treatment by enhancing the uptake of hydrophilic chemotherapeutic drugs. In this study, we synthesized the squalenoyl-chidamide prodrug (SQ-CHI), linking lipophilic squalene (SQ) with the hydrophilic antitumor drug chidamide (CHI) through a trypsin-responsive bond. Self-assembled nanoparticles with sigma receptor-bound aminoethyl anisamide (AEAA) modification, forming AEAA-PEG-SQ-CHI NPs (A-C NPs, size 116.6 ± 0.4 nm), and reference nanoparticles without AEAA modification, forming mPEG-SQ-CHI NPs (M-C NPs, size 88.3 ± 0.3 nm), were prepared. A-C NPs exhibited significantly higher in vitro CHI release (74.7 %) in 0.5 % trypsin medium compared to release (20.2 %) in medium without trypsin. In vitro cell uptake assays revealed 3.6 and 2.3times higher permeation of A-C NPs into tumorspheres of PSN-1/HPSC or CFPAC-1/HPSC, respectively, compared to M-C NPs. Following intraperitoneal administration to subcutaneous tumor-bearing nude mice, the A-C NPs group demonstrated significant anti-pancreatic cancer efficacy, inducing cancer cell apoptosis and inhibiting proliferation in vivo. Mechanistic studies revealed that AEAA surface modification on nanoparticles promoted intracellular uptake through caveolin-mediated endocytosis. This nanoparticle system presents a novel therapeutic approach for pancreatic cancer treatment, offering a delivery strategy to enhance efficacy through improved tumor permeation, trypsin-responsive drug release, and specific cell surface receptor-mediated intracellular uptake.

Diverging death risks: Mortality as a corollary of economic, social, cultural and person capital.

Introduction: Diverging death risks are associated with a wide range of social factors, including not only education and income but also other economic and non-economic resources. The aim of this study was to assess the association of mortality risks with four types of resources: economic, social, cultural and person capital. Methods: We used data of 2,952 participants from the Disparities in the Netherlands survey and annual mortality data from Statistics Netherlands for the period 2014 to 2021. Economic capital was measured through education, income, occupation, home equity, and liquid assets. Social capital was measured by the strength of social ties, the size of the core discussion network, and access to people in resourceful positions; cultural capital by lifestyle, digital skills, and mastery of English, and person capital by self-rated health, impediments to climbing stairs, self-confidence, self-image, people's appearance, and body mass index. To accommodate the fact that each capital was derived from several indicators, we used Partial Least Squares (PLS) Cox Regression. Results: In multiple regression, higher economic, cultural, and person capital were associated with lower mortality (hazard ratio, 0.77; 95% confidence interval [CI, 0.65 to 0.90], 0.77 [0.64-0.93] and 0.80; [0.70-0.92]), adjusted for all capital measures and sex. Conclusion: The finding that more economic, cultural and person capital is associated with lower mortality provides empirical support for an approach that uses a broad spectrum of capital measures - hitherto rarely included simultaneously in epidemiological research - in order to understand diverging death risks. By integrating sociological concepts, cohort data, and epidemiological research methods, our study highlights the need for further research on the interplay between different forms of resources in shaping health inequalities. In designing public health interventions, we advocate the adoption of a multidimensional capital-based framework for tackling social disparities in mortality.

Pediatric obstructive sleep apnea diagnosis: leveraging machine learning with linear discriminant analysis.

Objective: The objective of this study was to investigate the effectiveness of a machine learning algorithm in diagnosing OSA in children based on clinical features that can be obtained in nonnocturnal and nonmedical environments. Patients and methods: This study was conducted at Beijing Children's Hospital from April 2018 to October 2019. The participants in this study were 2464 children aged 3-18 suspected of having OSA who underwent clinical data collection and polysomnography(PSG). Participants' data were randomly divided into a training set and a testing set at a ratio of 8:2. The elastic net algorithm was used for feature selection to simplify the model. Stratified 10-fold cross-validation was repeated five times to ensure the robustness of the results. Results: Feature selection using Elastic Net resulted in 47 features for AHI ≥5 and 31 features for AHI ≥10 being retained. The machine learning model using these selected features achieved an average AUC of 0.73 for AHI ≥5 and 0.78 for AHI ≥10 when tested externally, outperforming models based on PSG questionnaire features. Linear Discriminant Analysis using the selected features identified OSA with a sensitivity of 44% and specificity of 90%, providing a feasible clinical alternative to PSG for stratifying OSA severity. Conclusions: This study shows that a machine learning model based on children's clinical features effectively identifies OSA in children. Establishing a machine learning screening model based on the clinical features of the target population may be a feasible clinical alternative to nocturnal OSA sleep diagnosis.

Multiple cross displacement amplification combined with nanoparticle-based lateral flow biosensor for rapid and sensitive detection of Epstein-Barr virus.

Introduction: Epstein-Barr virus (EBV) is a highly dangerous virus that is globally prevalent and closely linked to the development of nasopharyngeal cancer (NPC). Plasma EBV DNA analysis is an effective strategy for early detection, prognostication and monitoring of treatment response of NPC. Methods: Here, we present a novel molecular diagnostic technique termed EBV-MCDA-LFB, which integrates multiple cross displacement amplification (MCDA) with nanoparticle-based lateral flow (LFB) to enable simple, rapid and specific detection of EBV. In the EBV-MCDA-LFB system, a set of 10 primers was designed for rapidly amplifying the highly conserved tandem repeat BamHI-W region of the EBV genome. Subsequently, the LFB facilitate direct assay reading, eliminating the use of extra instruments and reagents. Results: The outcomes showed that the 65°C within 40 minutes was the optimal reaction setting for the EBV-MCDA system. The sensitivity of EBV-MCDA-LFB assay reached 7 copies per reaction when using EBV recombinant plasmid, and it showed 100% specificity without any cross-reactivity with other pathogens. The feasibility of the EBV-MCDA-LFB method for EBV detection was successfully validated by 49 clinical plasma samples. The complete detection process, consisting of rapid template extraction (15 minutes), MCDA reaction (65°C for 40 minutes), and LFB result reading (2 minutes), can be finalized within a 60-minutes duration. Discussion: EBV-MCDA-LFB assay designed here is a fast, extremely sensitive and specific technique for detecting EBV in field and at the point-of-care (PoC), which is especially beneficial for countries and regions with a high prevalence of the disease and limited economic resources.