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Landslide susceptibility mapping is a crucial tool for landslide disaster risk management. However, the spatial heterogeneity of landslide conditioning factors affects the accuracy of predictions. This study proposes a novel method combining GeoDetector and geographical weighted random forest (GeoD-GWRF), a local machine learning approach. The GeoD-GWRF model can select landslide conditioning factors from the perspective of spatial differentiation and interpret the influence of factors on landslides at a local scale. The model’s applicability is verified using Luhe County, Guangdong Province, as a case study. Compared to the traditional random forest model, the GeoD-GWRF model achieves higher prediction accuracy (AUC = 0.942). In addition, the model is applicable to broader study areas and can provide more targeted prediction results. This method offers a valuable reference for exploring spatial heterogeneity in landslide susceptibility mapping.

Comprehensive geriatric assessment (CGA)-based cardiac rehabilitation (CR) program is essential for patients before and after transcatheter aortic valve implantation (TAVI). This study aimed to explore the values of CGA and exercise capacity in CR for patients referred to TAVI. A retrospective analysis was conducted in 90 patients referred to TAVI from January to October 2019. CR strategies started before TAVI. The association between clinical characteristics, CGA, and change in six-minute walk distance (Δ6MWD) was analyzed with multivariate regression models. Most of patients had cognitive impairment (50%), malnutrition (61%), and frailty (83%). After the CR, the proportion of cognitive impairment, malnutrition, and frail patients was significantly decreased by 21%, 40%, and 57%, respectively (p = 0.002, p <0.001, p <0.001). The 6MWD at a month after discharge (291.9 ± 98.8 m) was significantly improved than that at discharge after TAVI (218.8 ± 114.3m, p <0.001). The multivariate regression analysis indicated body mass index (BMI; Δ6MWD:12.0, 95% confidence interval [CI] 0.3 to 23.8, p = 0.045), frailty (Δ6MWD: −57.9, 95% CI −81.8 to −34.1, p <0.001) and malnutrition (Δ6MWD: −25.1, 95% CI −47.0 to −3.2, p = 0.026) as the associated predictors of Δ6MWD. In conclusion, functional status in patients referred to TAVI could be improved by CGA-based CR. BMI, frailty, and malnutrition were associated with the efficacy of CR on exercise capacity. CGA can play the important role in the evaluation and making strategies for CR in patients.

Background Photoplethysmography (PPG), increasingly available through wearable devices, provides a non-invasive means of monitoring human hemodynamics. In this study, we introduce artificial intelligence-derived photoplethysmography (AI-PPG) age, a deep learning-based estimate of biological age from raw PPG signals, and evaluate its potential as a digital biomarker for cardiovascular health. Methods We developed a deep learning model with a distribution-aware loss function to reduce bias from imbalanced data. The model was trained and evaluated on the UK Biobank cohort ( N  = 212,231). We analyzed the association between the AI-PPG age gap (AI-PPG age minus calendar age) and multiple cardiovascular and metabolic outcomes, assessed its longitudinal value using serial PPG measurements, and externally validated its generalizability in an independent MIMIC-III-derived cohort ( N  = 2343). Results After adjusting for key confounders, participants with an AI-PPG age gap greater than 9 years have a significantly higher risk of major adverse cardiovascular and cerebrovascular events (hazard ratio of 2.37, p  = 8.46 × 10 −80 ), as well as seven secondary outcomes including coronary heart disease and myocardial infarction (all p  < 0.005). Conversely, those with a gap below −9 years show a lower risk profile. Longitudinal analysis demonstrates that changes in AI-PPG age add predictive value over time. In the external validation cohort, each one-year increase in AI-PPG age gap is associated with higher in-hospital mortality (odds ratio of 1.02, p  = 0.01). Conclusions AI-PPG age is a scalable, non-invasive biomarker for cardiovascular health assessment. Integrated with wearable devices, it may enable population-level screening, personalized monitoring, and early intervention. Plain language summary Wearable devices can measure tiny changes in blood flow using light. We developed a computer method that turns this information into a measure called “PPG age”, which shows how old the blood vessels appear. In our study of over 200,000 people, those with a PPG age much higher than their actual age were more likely to develop heart problems, such as coronary heart disease. People with a younger PPG age had lower risks. Tracking changes in this measure over time also provided useful clues about future health. Because it works with simple wearable sensors, this approach could support large-scale heart health screening and personalized prevention in everyday life. Nie, Zhao et al. develop a deep learning approach to estimate biological age from wearable photoplethysmography signals. They show that the gap between estimated and calendar age predicts major cardiovascular events and mortality, highlighting its value as a scalable digital biomarker for cardiovascular health.

Background Valvular heart disease (VHD) can cause damage to extra-cardiac organs, and lead to multi-organ dysfunction. However, little is known about the cardio-renal-hepatic co-dysfunction, as well as its prognostic implications in patients with VHD. The study sought to develop a multi-biomarker index to assess heart, kidney, and liver function in an integrative fashion, and investigate the prognostic role of cardio-renal-hepatic function in VHD. Methods Using a large, contemporary, prospective cohort of 6004 patients with VHD, the study developed a multi-biomarker score for predicting all-cause mortality based on biomarkers reflecting heart, kidney, and liver function (N-terminal pro-B-type natriuretic peptide [NT-proBNP], creatinine, and albumin). The score was externally validated in another contemporary, prospective cohort of 3156 patients with VHD. Results During a median follow up of 731 (704–748) days, 594 (9.9%) deaths occurred. Increasing levels of NT-proBNP, creatinine, and albumin were independently and monotonically associated with mortality, and a weighted multi-biomarker index, named the cardio-renal-hepatic (CRH) score, was developed based on Cox regression coefficients of these biomarkers. The CRH score was a strong and independent predictor of mortality, with 1-point increase carrying over two times of mortality risk (overall adjusted hazard ratio [95% confidence interval]: 2.095 [1.891–2.320], P  < 0.001). The score provided complementary prognostic information beyond conventional risk factors (C index: 0.78 vs 0.81; overall net reclassification improvement index [95% confidence interval]: 0.255 [0.204–0.299]; likelihood ratio test P  < 0.001), and was identified as the most important predictor of mortality by the proportion of explainable log-likelihood ratio χ 2 statistics, the best subset analysis, as well as the random survival forest analysis in most types of VHD. The predictive performance of the score was also demonstrated in patients under conservative treatment, with normal left ventricular systolic function, or with primary VHD. It achieved satisfactory discrimination (C index: 0.78 and 0.72) and calibration in both derivation and validation cohorts. Conclusions A multi-biomarker index was developed to assess cardio-renal-hepatic function in patients with VHD. The cardio-renal-hepatic co-dysfunction is a powerful predictor of mortality and should be considered in clinical management decisions.

Freezing stress affects the geographic distribution, growth, and development of potato, resulting in yield loss. Solanum malmeanum, a diploid wild species with strong freezing tolerance, was fused with the freezing sensitive dihaploid S. tuberosum by somatic hybridization. In our study, 980 calli were obtained, and 248 differentiated shoots were obtained from the calli. Parental-specific SSR markers were used to analyse the chromosome composition of the 80 randomly selected regenerated plants, obtaining 51 somatic hybrids. Among them, 44 somatic hybrids were tested with ploidy analysis in the years 2016 and 2020. During subculture, the genomic ploidy levels changed due to the composition of the unstable chromosome in 56.82% of the somatic hybrids. The somatic hybrids showed better freezing tolerance than the cultivated parent. Then, freezing-tolerant somatic hybrids were selected to backcross with cultivars, and we obtained valuable breeding resources with enhanced freezing tolerance and tuberization capacity similar to that of cultivars. The correlation analysis showed that freezing tolerance has no relation with tuberization capacity, which indicates that they are controlled by independent genetic loci.Key messageFreezing tolerance was transferred to cultivated potato from S. malmeanum by protoplast fusion for the first time, and valuable resources for freezing tolerance breeding were obtained.

Excessive galloylated flavanols not only cause instability in the wine but also lead to unbalanced astringency. Although clarification agents are always used to precipitate unstable tannins in wine, the non-specific adsorption of tannins results in the failure to precisely regulate the tannin composition of the wine. In this work, molecularly imprinted polymers (MIPs) with template molecules of galloylated flavanols were designed to specifically adsorb gallotannins to reduce wine astringency. The results showed that the “pores” on the surface of the MIPs are the structural basis for the specific adsorption of the target substances, and the adsorption process is a chemically driven single-molecule layer adsorption. Moreover, in the mono/oligomeric gallotannin-rich model solution, the adsorption of gallotannins by I-MIPs prepared as single template molecules reached 71.0%, and the adsorption capacity of MIPs for monomeric gallotannins was about 6.0 times higher than polymeric gallotannins. Given the lack of technology for the targeted adsorption of tannins from wine, this work explored the targeted modulation of wine astringency by using molecular imprinting techniques.

Recently, accumulating evidence has suggested that gut microbiota may be involved in the occurrence and development of ankylosing spondylitis (AS). It has been suggested that rifaximin have the ability to modulate the gut bacterial communities, prevent inflammatory response, and modulate gut barrier function. The goal of this work is to evaluate the protective effects of rifaximin in fighting AS and to elucidate the potential underlying mechanism. Rifaximin were administered to the proteoglycan (PG)-induced AS mice for 4 consecutive weeks. The disease severity was measured with the clinical and histological of arthritis and spondylitis. Intestinal histopathological, pro-inflammatory cytokine levels and the intestinal mucosal barrier were evaluated. Then, western blot was performed to explore the toll-like receptor 4 (TLR-4) signal transducer and NF-κB expression. Stool samples were collected to analyze the differences in the gut microbiota via next-generation sequencing of 16S rDNA. We found that rifaximin significantly reduced the severity of AS and resulted in down-regulation of inflammatory factors, such as TNF-α, IL-6, IL-17A, and IL-23. Meanwhile, rifaximin prevented ileum histological alterations, restored intestinal barrier function and inhibited TLR-4/NF-κB signaling pathway activation. Rifaximin also changed the gut microbiota composition with increased phylum ratio, as well as selectively promoting some probiotic populations, including . Our results suggest that rifaximin suppressed progression of AS and regulated gut microbiota in AS mice. Rifaximin might be useful as a novel treatment for AS.

The study aimed to describe the aortic valve morphology in Chinese patients underwent transcatheter aortic valve replacement (TAVR) for symptomatic severe aortic stenosis (AS), and the impact of sizing strategies and related procedural outcomes. Patients with severe AS who underwent TAVR were consecutively enrolled from 2012 to 2019. The anatomy and morphology of the aortic root were assessed. \"Downsize\" strategy was preformed when patients had complex morphology. The clinical outcomes of patients who performed downsize strategy were compared with those received annular sizing strategy. The primary outcome was device success rate, and secondary outcomes included Valve Academic Research Consortium-3 clinical outcomes variables based on 1-year follow-up. A total of 293 patients were enrolled. Among them, 95 patients (32.4%) had bicuspid aortic valve. The calcium volume (Hounsfield Unit-850) of aortic root was 449.90 (243.15-782.15) mm 3 . Calcium is distributed mostly on the leaflet level. Downsize strategy was performed in 204 patients (69.6%). Compared with the patients who performed annular sizing strategy, those received downsize strategy achieved a similar device success rate (82.0% [73] vs . 83.3% [170], P  = 0.79). Aortic valve gradients (downsize strategy group vs . annular sizing group, 11.28 mmHg vs. 11.88 mmHg, P  = 0.64) and percentages of patients with moderate or severe paravalvular regurgitation 2.0% (4/204) vs . 4.5% (4/89), P  = 0.21) were similar in the two groups at 30 days after TAVR. These echocardiographic results were sustainable for one year. Chinese TAVR patients have more prevalent bicuspid morphology and large calcium volume of aortic root. Calcium is distributed mostly on the leaflet level. Compare with annular sizing strategy, downsize strategy provided a non-inferior device success rate and transcatheter heart valve hemodynamic performance in self-expanding TAVR procedure.

Summary of backgroundPseudoaneurysms of the lumbar arteries following transforaminal lumbar interbody fusion (TLIF) are rare postoperative complications that usually occur around the transverse process. However, there are few detailed descriptions of the transverse branch and other branches of the dorsal branches at the L1–L4 disks.Study designTen adult embalmed cadavers were anatomically studied.ObjectivesThe purposes of the study were to describe the vascular distribution of the dorsal branches, especially the transverse branches, at the L1–L4 levels and provide information useful for TLIF.MethodsTen embalmed cadavers studied after their arterial systems were injected with red latex. The quantity, origin, pathway, distribution range and diameter of the branches were recorded and photographed.ResultsThe transverse branch appeared in all 80 intervertebral foramina. The transverse branch was divided into 2 types: In type 1, the arteries divided into superior branches and inferior branches; the arteries in type 2 divided into 3 branches (superior, intermedius and inferior branches).ConclusionsThe transverse branches of the dorsal arteries are common structures from L1 to L4, and 2 types of transverse branches were found. A thorough understanding of the dorsal branches, especially the transverse branches of the lumbar artery, may be very important for reducing both intraoperative bleeding during the surgery and the occurrence of pseudoaneurysms after transforaminal lumbar interbody fusion.

Chronic Obstructive Pulmonary Disease (COPD) is a chronic lung condition characterized by airflow obstruction. Current diagnostic methods primarily rely on identifying prominent features in spirometry (Volume-Flow time series) to detect COPD, but they are not adept at predicting future COPD risk based on subtle data patterns. In this study, we introduce a novel deep learning-based approach, DeepSpiro, aimed at the early prediction of future COPD risk. DeepSpiro consists of four key components: SpiroSmoother for stabilizing the Volume-Flow curve, SpiroEncoder for capturing volume variability-pattern through key patches of varying lengths, SpiroExplainer for integrating heterogeneous data and explaining predictions through volume attention, and SpiroPredictor for predicting the disease risk of undiagnosed high-risk patients based on key patch concavity, with prediction horizons of 1–5 years, or even longer. Evaluated on the UK Biobank dataset, DeepSpiro achieved an AUC of 0.8328 for COPD detection and demonstrated strong predictive performance for future COPD risk ( p -value < 0.001). In summary, DeepSpiro can effectively predict the long-term progression of COPD disease.