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To address data scarcity on long-term glacial discharge and inadequacies in simulating and predicting hydrological processes in the Tien Shan, this study analysed the observed discharge at multiple timescales over 1980s–2017 and projected changes within a representative glacierized high-mountain region: eastern Tien Shan, Central Asia. Hydrological processes were simulated to predict changes under four future scenarios (SSP1, SSP2, SSP3, and SSP5) using a classical hydrological model coupled with a glacier dynamics module. Discharge rates at annual, monthly (June, July, August) and daily timescales were obtained from two hydrological gauges: Urumqi Glacier No.1 hydrological station (UGH) and Zongkong station (ZK). Overall, annual and summer discharge increased significantly (p

Objective： To establish a detecting method for mandipropamid residues in cucumber by UPLC-MS coupled with the positive ions mods of MRM. Methods： The cucumber samples were was extracted by ultrasonic acetonitrile and then were purified by dispersed solid phase extraction (QuEChERS). Cucumber solution diluted with ultrapure water, and filtered by 0.22 μm water phase. Then 0.1% formic acid water (A) and acetonitrile (B) were used as mobile phase for isocratic elution. Results： The results showed that a good linearity was obtained for the standard curve of the method, its correlation was high, R2=0.999 8. Its detection limit was 0.003 mg/kg, and its limit of quantification was 0.009 mg/kg. Meanwhile, its recovery rate was between 89.6%~96.0% at the three levels of 10, 50 and 100 mg/mL. Conclusion： The method is simple, sensitive and reproducible. This method is suitable for qualitative and quantitative analysis of mandipropamid in cucumber, and it is a suitable method for rapid determination of mandipropamid in cucumber.

Macrophages, pivotal components of the immune system, orchestrate host defense mechanisms in humans and mammals. Their polarization into classically activated macrophages (CAMs or M1) and alternatively activated macrophages (AAMs or M2) dictates distinct functional roles in immunity and tissue homeostasis. While the negative regulatory role of CD32b within the FC gamma receptor (FCγR) family is recognized across various immune cell types, its influence on macrophage polarization remains elusive. This study aimed to elucidate the regulatory role of CD32b in macrophage polarization and discern the differential expression markers between the M1 and M2 phenotypes following CD32b siRNA transfection. The results revealed a decrease in the CD32b levels in lipopolysaccharide (LPS)-treated M1 and an increase in interleukin-4 (IL-4)-treated M2 macrophages, as observed in macrophage Raw264.7 cells. Furthermore, CD32b siRNA transfection significantly downregulated the M2 markers (IL-10, VEGF, Arg-1, and STAT6), while upregulating the M1 markers (IL-6, NF-κB, NOS2, and STAT1) in the Raw264.7 cells. Similar findings were recapitulated in macrophage-rich adherent cells isolated from mouse spleens. Additionally, the cytopathological analysis of pleural effusions and ascitic fluids from patients with cancer revealed a positive correlation between advanced tumor stages, metastasis, and elevated CD32b levels. In conclusion, this study highlights the regulatory influence of CD32b in suppressing M1 expression and promoting M2 polarization. Moreover, heightened M2 activation and CD32b levels appear to correlate with tumor progression. A targeted CD32b blockade may serve as a novel therapeutic strategy to inhibit M2 macrophage polarization and is promising for anti-tumor intervention.

Summary: This study systematically reviewed the application of large language models (LLMs) in medicine, analyzing 550 selected studies from a vast literature search. LLMs like ChatGPT transformed healthcare by enhancing diagnostics, medical writing, education, and project management. They assisted in drafting medical documents, creating training simulations, and streamlining research processes. Despite their growing utility in assisted diagnosis and improving doctor-patient communication, challenges persisted, including limitations in contextual understanding and the risk of over-reliance. The surge in LLM-related research indicated a focus on medical writing, diagnostics, and patient communication, but highlighted the need for careful integration, considering validation, ethical concerns, and the balance with traditional medical practice. Future research directions suggested a focus on multimodal LLMs, deeper algorithmic understanding, and ensuring responsible, effective use in healthcare.

Abstract Background The impact of insulin resistance on the prognosis of heart failure with preserved ejection fraction (HFpEF) remains unknown. This study aimed to investigate the association between the triglyceride-glucose (TyG) index, an easily calculated marker of insulin resistance, and the long-term prognosis of HFpEF. Methods A total of 823 patients with HFpEF were enrolled in the study. The TyG index was determined using the formula ln(fasting triglycerides [mg/dL] × fasting glucose [mg/dL]/2). The primary endpoint was all-cause death. The secondary endpoints were cardiovascular (CV) death and heart failure (HF) rehospitalization. Restricted cubic spline, multivariate Cox proportional hazard models, and competing risk models were used for analyses. Results During a median follow-up period of 3.16 years, 147 (17.8%) all-cause deaths, 139 (16.8%) CV deaths, and 222 (27.0%) HF rehospitalizations occurred. Restricted cubic spline analysis revealed a J-shaped association between the TyG index and the mortality and rehospitalization rates. In the multivariate Cox proportional hazard models, compared with those in the lowest TyG index tertile, patients in the highest tertile exhibited the greatest susceptibility to all-cause death (HR 1.53, 95% CI 1.19–1.98) and CV death (HR 1.52, 95% CI 1.19–1.96). In the competing risk model, a significant association between the TyG index and HF rehospitalization was observed (HR 1.31, 95% CI, 1.07–1.61). Conclusion A high TyG index is associated with an increased risk of mortality and rehospitalization in patients with HFpEF. The TyG index may serve as a promising prognostic marker for patients with HFpEF.

With the rapid arrival of the Internet of Everything era, massive data resources are generated on edge sides, causing problems such as large network load, high energy consumption, and privacy security in traditional distributed training based on cloud computing. Edge computing sinks computing power resources to the edge side, forming a collaborative computing system that integrates “cloud, edge, and end,” which can meet the basic needs of real-time operations, intelligence, security, and privacy protection. With the help of edge computing capabilities, edge intelligence effectively promotes the intelligent development of the edge side, which has become a popular topic. Through our research, we found that edge collaborative intelligence is currently in a stage of rapid development. At this stage, several deep learning models are combined with edge computing, and many edge collaborative intelligent processing solutions have exploded, such as distributed training in edge computing scenarios, federated learning, and distributed collaborative reasoning based on technologies such as model cutting and early exit. The combination of a shallow breadth learning system and virtualization technology allows for quick implementation of edge intelligence, which considerably improves service quality and user experience and makes services more intelligent. As a key link of edge intelligence, edge intelligence collaborative training aims to assist or implement the distributed training of machine learning models on the edge side. However, in an edge computing scenario, the distributed training of the model must coordinate several edge nodes, and many challenges remain. Therefore, by fully investigating the existing research foundation of edge intelligent collaborative training, we focus on the challenges and solutions of edge intelligent collaborative training in edge scenarios such as equipment heterogeneity, limited equipment resources, and unstable network environments. This paper introduces and summarizes the overall architecture and core modules of edge intelligent collaborative training. The overall architecture mainly focuses on the interaction framework between edge devices. In terms of whether there is a central server role, it can be divided into two categories: parameter server centralized architecture and fully decentralized parallel architecture. The core module of edge intelligent collaborative training mainly focuses on the problem of collaborative training of a large number of edge devices for neural network models to update parameters. In terms of the role of parallel computing in model training, it is divided into data parallelism and model parallelism. Finally, the many challenges and prospects of edge collaborative training are analyzed and summarized.

Hedyotis longiramulis sp. nov. (Rubiaceae) is described from Guangdong Province, China. It is similar to H. caudatifolia but differs in having puberulent, more or less tetragonal and decussately sulcate juvenile stems, waxy leaf surface, short inflorescence peduncles, high length ratio of corolla lobe to tube, and subglobose capsules. The phylogenetic analysis reveals that H. longiramulis is sister to H. pubirachis. Dimorphism concerning pollen size was observed in the heterostylous flowers. The complete chloroplast genome of the new species comprises a typical quadripartite structure of 153,616 bp in length, with two inverted repeats of 25,457 bp, a large single-copy of 85,050 bp and a small single-copy of 17,652 bp. It contains 112 unique genes, including 79 protein-coding genes, 29 tRNA genes, and four rRNA genes, the GC content of the chloroplast genome is 32.4%. The new species is provisionally evaluated as “Least Concern” because it is common and well-protected in two Provincial Nature Reserves.

Abstract Background Limited research has been conducted on the potential relationship between the dietary inflammation index (DII) and mortality, particularly in individuals with Helicobacter pylori (H. pylori) infection. This study aimed to investigate the association between the DII and H. pylori infection, as well as their respective impacts on all-cause mortality in a cohort of individuals with or without H. pylori infection. Methods Data from the 1999–2018 National Health and Nutrition Examination Survey (NHANES) were utilized for this study, with a final of 4370 participants included. Both univariable and multivariable-adjusted logistic regression analyses were employed to explore the relationship between H. pylori infection and pertinent covariates. Cox regression analysis, as well as restricted regression cubic spline analysis, were utilized to assess the association between DII and all-cause mortality among individuals with or without H. pylori infection. Results The findings demonstrated a positive correlation between DII scores and H. pylori infection, even after adjusting for potential confounding factors. Moreover, higher DII scores were significantly associated with an elevated risk of mortality exclusively in individuals with H. pylori infection, while no such association was observed in the uninfected population. Additional analysis using restricted cubic spline modeling revealed a positive linear relationship between DII scores as a continuous variable and the adjusted risk of all-cause mortality specifically in H. pylori-infected patients. Conclusion The results of this study indicated that DII was positively correlated with an increased risk of H. pylori infection and was associated with a heightened risk of all-cause mortality solely in individuals with H. pylori infection. Consequently, DII might serve as a useful tool for risk stratification in the H. pylori-infected population among U.S. adults. Further research is warranted to elucidate the underlying mechanisms and potential clinical implications of these findings.

Abstract Background The relationship between stress hyperglycemia and long-term prognosis in acute decompensated heart failure (ADHF) patients is unknown. This study investigated the associations of stress hyperglycemia with mortality and rehospitalization rates among ADHF patients with diabetes. Methods We consecutively enrolled 1904 ADHF patients. Among them, 780 were with diabetes. Stress hyperglycemia was estimated using the stress hyperglycemia ratio (SHR), which was calculated by the following formula: SHR = admission blood glucose/[(28.7 × HbA1c%) – 46.7]. All diabetic ADHF subjects were divided into quintiles according to the SHR. The primary endpoint was all-cause death at the 3-year follow-up. The secondary endpoints were cardiovascular (CV) death and heart failure (HF) rehospitalization at the 3-year follow-up. A Cox proportional hazards model and restricted cubic spline analysis were used to elucidate the relationship between the SHR and the endpoints in diabetic ADHF patients. Further analyses were performed to examine the relationships between SHR and the outcomes in heart failure with preserved ejection fraction (HFpEF) and heart failure with reduced ejection fraction (HFrEF). Results A total of 169 all-cause deaths were recorded during a median follow-up of 3.24 years. Restricted cubic spline analysis suggested a U-shaped association between the SHR and the mortality and rehospitalization rates. Kaplan–Meier survival analysis showed the lowest mortality in the 2nd quintile (P = 0.0028). Patients categorized in the highest range (5th quintile) of SHR, compared to those in the 2nd quintile, exhibited the greatest susceptibility to all-cause death (with a hazard ratio [HR] of 2.76 and a 95% confidence interval [CI] of 1.63–4.68), CV death (HR 2.81 [95% CI 1.66–4.75]) and the highest rate of HF rehospitalization (HR 1.54 [95% CI 1.03–2.32]). Similarly, patients in the lowest range (1st quintile) of SHR also exhibited significantly increased risks of all-cause death (HR 2.33, 95% CI 1.35–4.02) and CV death (HR 2.32, 95% CI 1.35–4.00). Further analyses indicated that the U-shape association between the SHR and mortality remained significant in both HFpEF and HFrEF patients. Conclusion Both elevated and reduced SHRs indicate an unfavorable long-term prognosis in patients with ADHF and diabetes.