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Type 2 diabetes mellitus (T2DM) is a metabolic disorder characterized by chronic hyperglycemia. While biomedicine (e.g., metformin) serves as the first-line treatment, combination therapies involving botanical drugs are increasingly utilized. However, the comparative efficacy of different botanical formulations remains to be systematically evaluated. This study aims to analyze randomized controlled trials (RCTs) to evaluate the clinical efficacy and safety of eight specific classical botanical drug formulas combined with metformin (Met) for T2DM, providing evidence to support integrated clinical management. A Bayesian network meta-analysis (NMA) was performed on RCTs sourced from PubMed, Cochrane Library, Embase, Web of Science, and Chinese databases. The taxonomic validation of all botanical drugs was conducted using MPNS. Outcomes including HbA1c, FPG, and lipid profiles were assessed using the SUCRA and GRADE methodology. Forty RCTs involving 3,088 patients were included. In terms of glycemic control, the combination of Decoction (HLJDD) and Met ranked highest for reducing FPG [MD = -1.46, 95% CrIs=(-2.24, -0.68)], while HLJDD, Decoction (ZBDHD), and Dachaihu Decoction (DCHD) combined with Met emerged as the most effective for reducing HbA1c. Notably, sensitivity analysis restricted to trials ≥8 weeks identified HLJDD [MD = -1.02, 95% CrIs=(-1.39, -0.66)] and ZBDHD [MD = -1.02, 95% CrIs=(-1.28, -0.76)] as the most robust interventions for long-term glycemic control. Safety reporting was limited, with only 8 out of 40 trials (20.0%) providing data on adverse events. While no severe adverse events were reported in these specific trials, the overall safety evidence remains uncertain due to substantial missingness and potential under-reporting. The current NMA results suggest that HLJDD combined with Met is a consistently effective option for reducing both FPG and HbA1c. For HbA1c improvement, while DCHD combined with Met showed initial potential, sensitivity analysis restricted to trials ≥8 weeks identified HLJDD and ZBDHD as the most physiologically robust interventions. However, given the \"Very Low\" certainty of evidence for many comparisons, these rankings remain exploratory.

Depression is frequently accompanied by various somatic symptoms such as sleep disturbances, pain, and gastrointestinal discomfort, which significantly impair patients’ quality of life. Acupuncture, a traditional Chinese medicine therapy, has gained increasing attention in recent years for its promising efficacy in alleviating both depressive symptoms and associated somatic manifestations. This review provides a comprehensive overview of the latest advances in the mechanistic research of acupuncture for treating somatic symptoms of depression, highlighting key areas including modulation of neurotransmitters, alterations in brain functional activity, epigenetic gene regulation, and immune-inflammatory responses. In addition, clinical trial data are systematically examined to evaluate the therapeutic value and safety of acupuncture across different depressive subtypes and related disorders. By integrating recent findings from both clinical and basic research, this article aims to establish a scientific foundation for the theoretical understanding and clinical application of acupuncture in managing somatic symptoms of depression, while also discussing potential directions for future investigations.

The physical properties of silicon carbide (SiC) are excellent as a third-generation semiconductor. Nevertheless, diamond wire cutting has many drawbacks, including high loss, long cutting time and prolonged processing time. The study of 4H-SiC wafer slicing by using an ultrafast laser is hopeful for solving these problems. In this work, the 4H-SiC samples with different surface roughness were processed by laser slicing. Findings revealed that good surface quality could reduce the damage to the wafer surface during laser slicing, reduce cleavage, and improve the flatness and uniformity of the modified layer. Thus, preprocessing on 4H-SiC can significantly improve the quality and efficiency of laser slicing.

BackgroundThe relationship between the vascular flow index (VFI) of the endometrium and the pregnancy outcome in the frozen embryo transfer (FET) cycle remains controversial, and there is currently no recommended optimal range for VFI.ObjectiveThis study aimed to explore the relationship between three-dimensional energy Doppler ultrasound-detected VFI and pregnancy outcomes in FET patients and to determine the appropriate range of endometrial VFI.MethodsA retrospective analysis was conducted on the clinical data of 338 patients who received FET-assisted pregnancy treatment in the Reproductive Medicine Center of our hospital from January 2019 to January 2025. The patients were divided into four groups based on the quartiles of endometrial VFI values. The general data, three-dimensional energy Doppler ultrasound detection indicators, and pregnancy outcomes of the four groups were compared.ResultsThe abortion rates in Group 3 and Group 4 were significantly lower than those in Group 1 (p < 0.05). The multivariate logistic regression analysis showed that, compared with Group 1, the adjusted odds ratio (OR) values for Group 3 and Group 4 were 0.210 (95% CI 0.055–0.805) and 0.227 (95% CI 0.059–0.870), respectively, suggesting that the risk of abortion in the groups with high endometrial VFI values was significantly reduced (p < 0.05). The restricted cubic spline (RCS) analysis indicated a significant linear negative correlation between the endometrial VFI value and the abortion rate (p < 0.05), with a threshold of 0.1698045. This finding suggests that, after exceeding this threshold, the abortion rate decreases as the endometrial VFI value increases.ConclusionThree-dimensional energy Doppler ultrasound measurement of endometrial VFI values can be used as a predictive indicator for the risk of miscarriage in patients undergoing FET. When the VFI value is greater than 0.17, the pregnancy prognosis of the patients is better. During FET, clinicians can take a VFI of > 0.17 as an important reference index for evaluating endometrial receptivity and determining whether to perform embryo transfer.

Natural Killer cell-derived extracellular vesicles (NK–EVs) hold promise for cancer immunotherapy but face limitations in yield and efficacy. This study developed artificial NK–EVs (NK–aEVs) via extrusion, demonstrating superior scalability and bioactivity compared to natural EVs (NK–nEVs), with enriched cytotoxic proteins (e.g., Granzyme B) and characteristic EV markers (CD63, CD81, TSG101). In vitro, NK–aEVs induced >70% cytotoxicity in lung cancer cells (A549, H1299, H460) at 200 μg/mL, triggered apoptosis, and restored degranulation capacity in cryopreserved NK cells. In murine models, intratumoral NK–aEVs suppressed LLC and MC38 tumor growth by 60% without systemic toxicity. NK–aEVs remodeled the tumor microenvironment by enhancing infiltration of CD8 + T/NK cells while reducing splenic immunosuppressive macrophages and myeloid-derived suppressor cells. Notably, NK–aEVs displayed high biocompatibility and efficacy against NSCLC. This work establishes a scalable platform for EV-based immunotherapy, overcoming production and functional constraints of natural EVs, with translational potential for modulating antitumor immunity.

Iron overload disease is characterized by the excessive accumulation of iron in the body. To better alleviate iron overload, there is an urgent need for safe and effective small molecule compounds. Rubiadin, the active ingredient derived from the Chinese herb Prismatomeris tetrandra, possesses notable anti-inflammatory and hepatoprotective properties. Nevertheless, its impact on iron metabolism remains largely unexplored. To determine the role of rubiadin on iron metabolism, Western blot analysis, real-time PCR analysis, and the measurement of serum iron were performed. Herein, we discovered that rubiadin significantly downregulated the expression of transferrin receptor 1, ferroportin 1, and ferritin light chain in ferric-ammonium-citrate-treated or -untreated HepG2 cells. Moreover, intraperitoneal administration of rubiadin remarkably decreased serum iron and duodenal iron content and upregulated expression of hepcidin mRNA in the livers of high-iron-fed mice. Mechanistically, bone morphogenetic protein 6 (BMP6) inhibitor LDN-193189 completely reversed the hepcidin upregulation and suppressor of mother against decapentaplegic 1/5/9 (SMAD1/5/9) phosphorylation induced by rubiadin. These results suggested that rubiadin increased hepcidin expression through the BMP6/SMAD1/5/9-signaling pathway. Collectively, our findings uncover a crucial mechanism through which rubiadin modulates iron metabolism and highlight it as a potential natural compound for alleviating iron-overload-related diseases.

At present, GIS data, which contains a large amount of semantic information, has become the main data source for map cartography. In this paper, based on the existing multi-scale database, we study a database-based automatic cartography method to meet the needs of multi-scale map cartography, and replace the traditional process by the intelligent processing of software to shorten the cycle of cartography, improve the productivity of map cartography, and at the same time, reduce the error rate caused by manual work, and improve the overall quality of map production.

At present, China's urban and rural construction, national security, emergency and disaster relief have put forward higher and faster requirements for high-quality and current maps. How to use the latest spatial database in the shortest possible time to produce and provide high-quality, highly presentable, content-rich maps is an urgent issue. In this paper, we study the rapid mapping technology based on GIS platform and map mapping software, study the automatic map mapping method under AI environment, design and develop the spatial basic geographic database-driven rapid mapping system, realize the automatic lossless conversion of GIS data to map mapping software according to the established mapping rule base, realize the automatic configuration of symbols and annotations, and realize the automation of processing of element relationship. The AI-based database-driven rapid mapping technology has been used in the practice of map compilation in prefecture-level cities, replacing most of the manual repetitive operations, greatly improving the efficiency of map compilation, and enhancing the capacity of emergency mapping services.

Map verification plays a crucial role in ensuring the accuracy and reliability of map data, which in turn establishes the foundation for map transparency. In China, the number of approved map inspections has been increasing annually, with a diverse range of map products being applied for inspection. Taking the example of the 'number of maps accepted for inspection' by the Ministry of Natural Resources of the People’s Republic of China, there has been a consistent upward trend in the number of accepted inspections. From 2017 to 2021, the average annual growth rate of accepted inspections exceeds 18.85%. Not only are new types, uses, and formats of map products emerging, but there is also an increase in the utilization of electronic global maps. However, challenges such as the inefficient information transfer during the inspection application process and the limited automation of technical inspections persist. Furthermore, issues related to the variability of inspection results based on individual experiences need to be addressed. To tackle these problems, this paper proposes a comprehensive technical framework for map verification based on the principles of \"data sharing, business collaboration, and intelligent assistance\". The objective is to establish a map verification service mechanism that promotes cross-departmental coordination and intelligent cooperation. Implementing this framework will facilitate the smooth operation of national map verification services and expedite the sharing and openness of geospatial data.

In order to improve the recognition performance and solve the problem of computational complexity caused by the high-dimensional data in human identification, a gait recognition method based on manifold learning is proposed in this paper. Firstly, gait energy image (GEI) of a walking person is abstracted from a gait image sequence. And then discrete wavelet decomposition (DWT) and t-Distributed Stochastic Neighbor Embedding (t-SNE) method is applied to reduce the dimension of high-dimensional GEI data. Finally the support vector machine (SVM) models are trained by the decomposed feature vectors, and the gaits are classified by the trained SVM models at last. Experimental results show that the proposed feature extraction method is efficient in reducing computational complexity and preserving image information.