The functional realization of proteins and other biological macromolecules depends on conformational dynamics and allosteric regulation, and elucidating their molecular mechanisms is an important foundation for understanding life processes. Molecular dynamics simulations are a powerful tool for investigating conformational evolution at the atomic level. However, traditional methods are limited by simulation timescales and high free-energy barriers, making it difficult to effectively capture rare conformations and their transition pathways. As a result, the development of enhanced sampling techniques has become key to overcoming this bottleneck. As a classical enhanced sampling technique, metadynamics suffers from several shortcomings, including strong dependence on collective variables and significant errors caused by bias potential accumulation. This article reviews three major improvement strategies. The first combines stochastic resetting with metadynamics, using trajectory-resetting mechanisms to improve sampling efficiency while avoiding the difficulty of optimizing collective variables. The second, SinkMeta, employs a “sinking” bias effect to enable efficient exploration of specific regions and paths. The third, OPES-based hybrid methods, improve the stability of free-energy estimation by optimizing the target distribution or the way the bias is constructed. These methods provide new ideas for characterizing free-energy landscapes and studying conformational transitions in complex biological systems, while also promoting the continued development of enhanced sampling methodologies.

Objective To understand the implementation status of"Diagnosis of Ascariasis"(WS/T 565-2017)and provide a scientific basis for promoting,revising,and improving the Standard.Methods Using the convenient sampling method,the investigation targeted professional and technical personnel at the provincial,city,county,and township levels engaged in parasitic disease prevention,control,or diagnosis and treatment in Anhui and Sichuan provinces.No less than 150 individuals were included in each province.The implementation survey of Diagnosis of Ascariasis(WS/T 565-2017)was conducted by the subjects completing a questionnaire by themselves.Results The response rate to the questionnaire was 91.90%(386/420).The awareness and utilization rates of the Standard were 81.87%and 49.22%,respectively and both increased with age(χ2 trend=7.977 and 19.016,respectively,P＜0.01).Respondents with college degrees(90.72%)had a higher awareness rate(χ2=8.619,P＜0.05).In terms of utilization rate,males(58.38%),those with college degrees(67.01%),staff members of provincial-level units(77.78%),and personnel in medical institutions(71.43%)had higher utilization rates(χ2=13.486,17.166,8.426,and 5.956,respectively,all P＜0.05).The survey indicated that 57.77%of the work units of respondents have conducted promotional activities,and 53.89%of the work units of respondents have sent personnel to participate in training.Moreover,this proportion tended to increase as the unit level decreased(χ2 trend=9.403 and 14.729,P＜0.01).The level of participation in publicity and training by medical institutions(89.29%)was significantly higher than that of disease control institutions(55.31%and 51.12%,respectively,χ2=12.290 and 15.225,P＜0.01).Furthermore,training participation is a crucial factor in enhancing awareness rates.A total of 368 respondents(95.34%)reported that their work units have conducted testing for ascariasis.Additionally,378 individuals(97.92%)believe that the Standard is"applicable"or"basically applicable,"while 369(95.60%)felt that no revisions were needed.Conclusions The results indicated that"Diagnosis of Ascariasis"(WS/T 565-2017)remains applicable to the diagnostic needs of ascariasis and it is recommended to strengthen its promotion and implementation.

In recent years, cancer treatment methods and means are becoming more and more diversified, and single treatment methods often have limited efficacy, while the synergistic effect of immunity combined with chemotherapy can inhibit tumor growth more effectively. Based on this, we constructed a sodium alginate hydrogel composite system loaded with chemotherapeutic agents and tumor vaccines (named SA-DOX-NA) with a view to the combined use of chemotherapeutic agents and tumor vaccines. Firstly, the tumor vaccine (named NA) degradable under acidic conditions was constructed by in situ polymerization using chicken ovalbumin (OVA), acrylamide (AAM) and 2-(dimethylamino) ethyl methacrylate (DMAEMA) monomer. Then a hydrogel composite system SA-DOX-NA co-loaded with chemotherapeutic drug doxorubicin (DOX) and NA was prepared using sodium alginate as a matrix. The results showed that SA-DOX-NA had good in situ gel-forming ability and formed a mesh structure that could realize the co-loading of DOX and NA as well as the slow drug release. The electron microscopy results showed that SA-DOX-NA had good in situ gel-forming ability with good internal connectivity, and the three-dimensional mesh structure could realize the co-loading of DOX and NA as well as the slow drug release. The antitumor and immunomodulatory results showed that SA-DOX-NA both effectively inhibited the growth of tumor cells and efficiently promoted the proliferation and activation of DC2.4 dendritic cells without additional adjuvant. In summary, SA-DOX-NA exerts the dual efficacy of chemotherapy and immunotherapy for tumor treatment, and has a good application prospect in local tumor treatment.

Coronary heart disease is a cardiovascular disease that affects coronary arteries. It presents high incidence and high mortality worldwide, bringing a serious threat to human health and quality of life. Salviae Miltiorrhizae Radix et Rhizoma derived from Salvia miltiorrhiza is widely used in the treatment of cardiovascular diseases, such as coronary heart disease. Salvianolic acid B is an active component in Salviae Miltiorrhizae Radix et Rhizoma extracts, and studies have shown that it has anti-inflammatory, antioxidant, apoptosis-and autophagy-regulating, anti-fibrosis, and metabolism-modulating effects. This article reviews the research progress regarding the therapeutic effect of salvianolic acid B on coronary heart disease in the recent decade. It elaborates on the role and mechanism of salvianolic acid B in treating coronary heart disease from multiple perspectives, such as the inhibition of thrombosis, improvement of blood circulation, reduction of myocardial cell injury, and inhibition of cardiac remodeling. This article provides a theoretical basis for the application of Chinese medicinal materials and TCM prescriptions containing salvianolic acid B in the treatment of coronary heart disease.
Humans ; Benzofurans/administration & dosage* ; Coronary Disease/genetics* ; Drugs, Chinese Herbal/administration & dosage* ; Salvia miltiorrhiza/chemistry* ; Animals ; Depsides

OBJECTIVE: Gastric cancer (GC) is one of the most common malignancies seen in clinic and requires novel treatment options. Morin is a natural flavonoid extracted from the flower stalk of a highly valuable medicinal plant Prunella vulgaris L., which exhibits an anti-cancer effect in multiple types of tumors. However, the therapeutic effect and underlying mechanism of morin in treating GC remains elusive. The study aims to explore the therapeutic effect and underlying molecular mechanisms of morin in GC. METHODS: For in vitro experiments, the proliferation inhibition of morin was measured by cell counting kit-8 assay and colony formation assay in human GC cell line MKN45, human gastric adenocarcinoma cell line AGS, and human gastric epithelial cell line GES-1; for apoptosis analysis, microscopic photography, Western blotting, ubiquitination analysis, quantitative polymerase chain reaction analysis, flow cytometry, and RNA interference technology were employed. For in vivo studies, immunohistochemistry, biomedical analysis, and Western blotting were used to assess the efficacy and safety of morin in a xenograft mouse model of GC. RESULTS: Morin significantly inhibited the proliferation of GC cells MKN45 and AGS in a dose- and time-dependent manner, but did not inhibit human gastric epithelial cells GES-1. Only the caspase inhibitor Z-VAD-FMK was able to significantly reverse the inhibition of proliferation by morin in both GC cells, suggesting that apoptosis was the main type of cell death during the treatment. Morin induced intrinsic apoptosis in a dose-dependent manner in GC cells, which mainly relied on B cell leukemia/lymphoma 2 (BCL-2) associated agonist of cell death (BAD) but not phorbol-12-myristate-13-acetate-induced protein 1. The upregulation of BAD by morin was due to blocking the ubiquitination degradation of BAD, rather than the transcription regulation and the phosphorylation of BAD. Furthermore, the combination of morin and BCL-2 inhibitor navitoclax (also known as ABT-737) produced a synergistic inhibitory effect in GC cells through amplifying apoptotic signals. In addition, morin treatment significantly suppressed the growth of GC in vivo by upregulating BAD and the subsequent activation of its downstream apoptosis pathway. CONCLUSION Morin suppressed GC by inducing apoptosis, which was mainly due to blocking the ubiquitination-based degradation of the pro-apoptotic protein BAD. The combination of morin and the BCL-2 inhibitor ABT-737 synergistically amplified apoptotic signals in GC cells, which may overcome the drug resistance of the BCL-2 inhibitor. These findings indicated that morin was a potent and promising agent for GC treatment. Please cite this article as: Wang Y, Sun XY, Ma FQ, Ren MM, Zhao RH, Qin MM, Zhu XH, Xu Y, Cao ND, Chen YY, Dong TG, Pan YF, Zhao AG. Morin inhibits ubiquitination degradation of BCL-2 associated agonist of cell death and synergizes with BCL-2 inhibitor in gastric cancer cells. J Integr Med. 2025; 23(3): 320-332.
Humans ; Flavonoids/therapeutic use* ; Stomach Neoplasms/pathology* ; Animals ; Proto-Oncogene Proteins c-bcl-2/metabolism* ; Cell Line, Tumor ; Apoptosis/drug effects* ; Cell Proliferation/drug effects* ; Ubiquitination/drug effects* ; Mice ; Drug Synergism ; Mice, Inbred BALB C ; Mice, Nude ; Xenograft Model Antitumor Assays ; Flavones

OBJECTIVE: To investigate chronic hepatitis C virus (HCV) infection's effect on gestational liver function, pregnancy and delivery complications, and neonatal development. METHODS: A total of 157 HCV antibody-positive (anti-HCV[+]) and HCV RNA(+) patients (Group C) and 121 anti-HCV(+) and HCV RNA(-) patients (Group B) were included as study participants, while 142 anti-HCV(-) and HCV RNA(-) patients (Group A) were the control group. Data on biochemical indices during pregnancy, pregnancy complications, delivery-related information, and neonatal complications were also collected. RESULTS: Elevated alanine aminotransferase (ALT) rates in Group C during early, middle, and late pregnancy were 59.87%, 43.95%, and 42.04%, respectively-significantly higher than Groups B (26.45%, 15.70%, 10.74%) and A (23.94%, 19.01%, 6.34%) ( P < 0.05). Median ALT levels in Group C were significantly higher than in Groups A and B at all pregnancy stages ( P < 0.05). No significant differences were found in neonatal malformation rates across groups ( P > 0.05). However, neonatal jaundice incidence was significantly greater in Group C (75.16%) compared to Groups A (42.25%) and B (57.02%) ( χ ² = 33.552, P < 0.001). HCV RNA positivity during pregnancy was an independent risk factor for neonatal jaundice ( OR = 2.111, 95% CI 1.242-3.588, P = 0.006). CONCLUSIONS Chronic HCV infection can affect the liver function of pregnant women, but does not increase the pregnancy or delivery complication risks. HCV RNA(+) is an independent risk factor for neonatal jaundice.
Humans ; Female ; Pregnancy ; Adult ; Pregnancy Complications, Infectious/epidemiology* ; Retrospective Studies ; Pregnancy Outcome ; Infant, Newborn ; Viremia/virology* ; Hepatitis C ; Hepacivirus/physiology* ; Hepatitis C, Chronic/virology* ; Young Adult ; Alanine Transaminase/blood*

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

Deep vein thrombosis(DVT)is a common peripheral vascular disease in clinical practice.The lack of precise and efficient early diagnostic techniques renders it susceptible to being overlooked or misdiagnosed,and therefore,identifying trustworthy biomarkers is a major issue that has to be resolved.In this study,the endogenous metabolites in the urine of DVT rats were screened by metabolomics technology based on gas chromatograph-mass spectrometry(GC-MS)and the characteristic metabolites were identified by multiple feature selection algorithms and multivariate statistical analysis,for the development of a machine learning-based diagnostic model for DVT.The urine samples in metabolic cage in the thrombus development phase(between 48 and 72 h)of rats were collected,which was used as the models for inferior vena cava ligation.The metabolic profiles of the control group and DVT were obtained using the GC-MS method.A total of 176 kinds of endogenous metabolites were identified in rat urine through comparison with the FiehnLib database,26 kinds of differential metabolites associated with DVT were screened through a combination of the Mann-Whitney U test and orthogonal partial least squares discriminant analysis(OPLS-DA),and 13 kinds of significant metabolites strongly correlated with DVT were further evaluated in conjunction with various machine learning feature selection techniques.For DVT diagnosis,machine learning models such as Gaussian Naive Bayes(GNB),support vector machine(SVM),logistic regression(LR),and linear discriminant analysis(LDA)were developed.The diagnostic model constructed using 13 kinds of key metabolites demonstrated excellent accuracy and stability,and surpassed the predictive performance of the models utilizing 176 kinds of metabolites and 26 kinds of differential metabolites,as evidenced by examination and comparison of each model's efficacy.The study showed that the integration of multiple feature selection algorithms for analyzing metabolite information in DVT rat urine was capable of effectively identifying reliable potential markers of DVT.Furthermore,the developed machine learning model offered a novel technical approach for the automated diagnosis of DVT.

Objective The effectiveness of the BG-Trap and human-baited double net trap(HDN)methods in monitoring Aedes albopictus were compared to assess the potential use of the BG-Trap method for dengue fever vector monitoring program.These findings would also provide a reference basis for formulating more effective dengue fever prevention and control strategies.Methods The study was conducted at six outdoor monitoring sites in three cities in Jiangsu Province in August and September,following a 3-d monitoring plan.Both traps were evaluated simultaneously at three time slots each day(16:00-16:30,17:00-17:30,and 18:00-18:30).Results Both the BG-Trap and HDN methods effectively captured female Ae.albopictus.The proportion of Ae.albopictus captured by the HDN method was higher than that captured by the BG-Trap method(χ2 = 0.162,P= 0.687),with no statistically significant differences in the composition ratio of male/female between the two methods(χ2=0.162,P=0.687).The average density index of Ae.albopictus for the BG-Trap method was 7.85±5.23/h,significantly lower than that of the HDN method at 27.04±13.86/h under the same sampling sites(t = 6.73,P＜0.001).Conclusion These results suggest that the BG-Trap method is less effective than the HDN method in Ae.albopictus monitoring.Further research is required to determine the applicability of this method for dengue fever vector monitoring.