Kunming Third People's Hospital

While magnesium isoglycyrrhizinate (MgIG) is a clinically approved therapy for alcohol-associated liver disease (ALD), its precise molecular targets and mechanisms remain uncharacterized. This study aimed to define MgIG’s hepatoprotective actions in chronic-binge ALD mouse models and ethanol/palmitic acid-exposed AML-12 hepatocytes. Through an integrated strategy encompassing RNA sequencing, molecular docking, and microscale thermophoresis, we discovered that MgIG directly binds to hydroxysteroid 11-beta dehydrogenase 1 (HSD11B1) at residue 187, a finding corroborated by molecular dynamics simulations. In vivo, MgIG markedly attenuated alcohol-induced liver injury, evidenced by ameliorated histological damage, reduced hepatic steatosis, and normalized liver-to-body weight ratios. In vitro, it effectively reduced lipid accumulation, inflammation, and apoptosis. Mechanistically, RNA sequencing identified isopentenyl diphosphate delta isomerase 1 (IDI1) as a key downstream effector. Hepatocyte-specific genetic manipulations confirmed that MgIG modulates the SREBP2-IDI1 axis, thereby suppressing lipogenesis, inflammatory responses, and apoptotic pathways. We reveal HSD11B1 as a novel direct molecular target of MgIG and elucidate its therapeutic mechanism through the HSD11B1-SREBP2-IDI1 signaling axis, which profoundly impacts ALD pathogenesis. These findings not only validate MgIG’s clinical utility but also highlight a promising new therapeutic target for ALD.

Lung cancer remains the leading cause of cancer-related incidence and mortality worldwide. Its tendency for postoperative distant metastasis significantly compromises long-term prognosis and survival. Accurately predicting the metastatic potential in a timely manner is crucial for formulating optimal treatment strategies. This study aimed to comprehensively compare the predictive performance of nine machine learning (ML) models and to enhance interpretability through SHAP (Shapley Additive Explanations), with the goal of developing a practical and transparent risk stratification tool for postoperative lung cancer management. Clinical data from 3,120 patients with stage I-III lung cancer who underwent radical surgery were retrospectively collected and randomly divided into training and testing cohorts. A total of 52 clinical, pathological, imaging, and laboratory variables were analyzed. Nine ML models-including eXtreme Gradient Boosting (XGBoost), Random Forest (RF), Light Gradient Boosting Machine (LightGBM), Adaptive Boosting (AdaBoost), Decision Tree (DT), Gradient Boosting Decision Tree (GBDT), Gaussian Naive Bayes (GNB), Complement Naive Bayes (CNB), and Multilayer Perceptron classifier (MLP)-were developed and evaluated. Model performance was assessed using accuracy, precision, recall, F1 score, ROC-AUC, PR-AUC, calibration, and decision curve analysis (DCA). All models were evaluated using nested cross-validation (outer stratified 70/30 splits repeated 10 times; inner fivefold tuning), with decision thresholds prespecified in the inner loop and applied unchanged to held-out tests. Given the approximately 4:1 class imbalance, cost-sensitive learning was primarily adopted, and PR-AUC was reported in addition to ROC-AUC. Among the nine models, GBDT demonstrated the highest predictive performance, achieving an AUC of 0.810 (95% CI: 0.748-0.872), accuracy of 0.766, sensitivity of 0.698, and specificity of 0.786 in the test set. SHAP analysis revealed that adjuvant chemotherapy, adjuvant radiotherapy, pathological N stage, age, body mass index (BMI), and preoperative neutrophil count (Pre-ANC) were the most influential predictors of distant metastasis. The combination of model performance and interpretability supported the model's potential for integration into clinical workflows to assist in real-time decision-making. In this work, we carried out a systematic comparison of nine machine learning algorithms in a large postoperative cohort under a coherent and interpretable framework. By jointly considering discrimination, calibration, clinical benefit (via decision curve analysis), and SHAP-based explanations, we constructed a practical prognostic tool to guide personalized treatment strategies and follow-up care. This methodology offers a data-driven basis for precision management. Ultimately, our findings provide an internally validated reference framework that warrants external and multicenter validation prior to clinical deployment.

Non-small cell lung cancer (NSCLC) remains a leading cause of cancer-related deaths globally, with poor prognosis in advanced stages despite therapeutic advances. MicroRNAs (miRNAs) play a critical role in NSCLC pathogenesis, acting as tumor suppressors or oncogenes (oncomiRs), regulating key pathways involved in cancer progression and drug resistance. This review explores various therapeutic strategies targeting oncogenic miRNAs in NSCLC, with a particular focus on small molecule inhibitors (SMIs). SMIs offer potential advantages over RNA-based therapies, such as better pharmacokinetics and oral bioavailability, though direct evidence for SMIs targeting oncogenic miRNAs in NSCLC remains limited. The review examines current research on SMIs, miRNA mimics, antisense oligonucleotides, and other approaches, while discussing the challenges and opportunities for clinical translation. It concludes that while miRNA-based therapies are promising, substantial research is needed to develop effective and selective small molecule inhibitors for NSCLC treatment.

Finding the best rental prices for Airbnbs is very tricky because of all the differences between houses, how cancellations work, and different services, which makes it hard for owners to get the most money and for places like Airbnb to make things better. In order to solve the problem, this paper uses multiple Linear regression models to predict Airbnb rental prices using the publicly available Airbnb list, so as to achieve the goal of predicting performance and interpretability. Price normalization using logarithm, filling missing values with median, and handling of multicollinearity through the application of the VIF test and variable selection, are all part of data preprocessing. Experimental results show that the model performs solidly, with a Mean Squared Error (MSE) of 0.2896, Root Mean Squared Error (RMSE) of 0.5382, and Mean Absolute Percentage Error (MAPE) of 8.69%, indicating relatively low deviation in both absolute and relative terms. the model's R value is as high as 0.4941 (adjusted R: 0.4937), which suggests that the model can explain about 49.4% of the variation in the logarithm of the rental price. This study has demonstrated the application of linear regression in quantifying price determinants in short-term rentals, which can provide transparent and actionable insights for both hosts to improve their listing settings and for platforms to improve the matching efficiency of users. Models interpretability is also good at regulating because it can make clear how the pricing mechanism works, so this kind of model is quite helpful to improve a clearer and healthier short-term rental ecosystem.

BackgroundLeading global causes of death are noncommunicable diseases (NCDs), which affect mostly low- and middle-income countries. Over 90% of all NCD deaths occur in China, where multimorbidity, which is associated with adverse social circumstances, is an emerging health priority. Rates in China are significantly higher than the rest of the world. The purpose of this study was to investigate, using structural equation modeling (SEM), the relationships and pathways between lifestyle and socioeconomic factors and the prevalence of chronic noncommunicable disease (NCD) multimorbidity in rural Southwest China, an area with limited resources, a high degree of ethnic diversity, and an increasing prevalence of multimorbidity.MethodsData were collected from a cross-sectional health interview and examination survey among individuals aged ≥ 35 years in rural Southwest China. A standard questionnaire was used to gather data on the demographic traits and lifestyle choices of the participants. Each participant’s height, weight, waist circumference (WC), blood pressure, fasting blood glucose, and spirometry before and after bronchodilators were assessed. Principal component analysis was used to create a single socioeconomic position (SEP) index.ResultsThe overall prevalence rates of six common chronic NCDs—raised blood pressure, raised blood glucose, hyperlipidemia, coronary heart disease (CHD), stroke, and chronic obstructive pulmonary disease (COPD)—and multiple comorbidities were 56.3%, 10.8%, 7.7%, 6.0%, 4.2%, 3.8%, and 18.9%, respectively. Males had a greater prevalence of COPD than females did, whereas females had greater prevalences of raised blood pressure and hyperlipidemia than males did (p < 0.01). The SEM results indicated that SEP, age, ethnicity, physical inactivity, and an unbalanced diet had both significant direct and indirect effects on NCD multimorbidity, with total effect sizes quantified at -0.065, 0.321, -0.057, 0.110, and 0.090, respectively (p < 0.05). the condition of obesity ( 0.401, p < 0.01) and a familial predisposition to NCDs (0.134, p < 0.01) were directly associated with multiple morbidities.ConclusionsBoth lifestyle and socioeconomic factors are associated with the prevalence of NCD multimorbidity in rural Southwest China. Future interventions for the prevention and control of NCD multimorbidity should focus more on individuals with a low SEP and unhealthy lifestyles.

Abstract Background Leprosy (Hansen’s disease) is a chronic infectious disease caused by Mycobacterium leprae, potentially leading to nerve damage and disability if untreated. Prior research has not provided a thorough global perspective. Methods We evaluated global, regional, and national leprosy burdens and changes from 1990 to 2021 using Global Burden of Disease (GBD) 2021 data. We measured prevalence, incidence, and disability-adjusted life years (DALYs), stratified by sex, 20 age categories, 21 GBD regions, 204 nations/territories, and five SDI levels. We estimated annual percentage changes using age-standardized rates (ASR) and Bayesian age-period-cohort models, to reveal disparities, identify drivers, and project trends. Results From 1990 to 2021, there was a substantial reduction in global leprosy burden, with ASR decreasing from 11.73 to 4.84 per 100,000 population and ASR dropping from 1.65 to 0.58 per 100,000. Although males exhibited higher rates than females, both sexes demonstrated significant declines in burden. Regional analyses revealed marked disparities, with low-SDI regions experiencing the steepest declines while high-SDI areas saw slight upticks in incidence. Notably, South Asia, Tropical Latin America, and Central Sub-Saharan Africa remained hotspots, while regions with SDI ≥ 0.80 maintained near-zero burden. Age-specific patterns indicated higher prevalence and incidence rates in older age, particularly in low-SDI settings. Projections from 2021 to 2040 suggest continued declines in leprosy burden globally, although significant disparities between sexes and regions persist. Conclusions The growing global burden of NMSCs, particularly in high SDI regions and among older adults, underscores the urgent need for tailored prevention, resource allocation, and management strategies.

The iridocorneal endothelial (ICE) syndrome is a rare but irreversibly blinding eye disease, whose etiology remains unknown and speculative. Characterization of its genomic and epigenomic landscape will benefit the development of etiology-based therapies. In this study, we recruited a cohort of 99 patients with ICE syndrome. Whole-genome sequencing was performed on 51 ICE patients and genome-wide DNA methylation profiling on 48 ICE patients. We then performed the mutational burden testing on genes and noncoding regulatory regions in the ICE cohort compared with control subjects (including 9197 East Asians from the gnomAD database and 350 normal Chinese from our in-house cohort). Copy number variance analysis was also performed. Hypomethylated and differentially methylated regions in ICE patients versus normal controls (45 normal Chinese from our in-house cohort) were explored. We identified RP1L1 (27/51, 53%) which emerged with a higher coding-altering mutational burden in the ICE syndrome cohort, whose mutations were mainly on chr8:10467637. We identified 41 regions exhibiting significant copy number variance. Specifically, regions at chr19:15783859-15791329 and chr3:75786061-75790887 showcased copy number loss in 39 and 19 out of 51 ICE patients, respectively. We also identified that 2717 differentially methylated regions and hypomethylation regions were predominantly found in ICE syndrome compared with normal samples (in 91.9% of 2717 differentially methylated regions). We also found 45 recurrent hypomethylation regions (identified in >10% ICE samples tested) that were differentially methylated (differential methylation < −0.1) between ICE syndrome and normal Chinese. Our study offers the first comprehensive genomic and epigenomic landscape of ICE syndrome, which will enhance the understanding of its etiology.