BACKGROUND:Our previous study found that Modified Erxian Pill could alleviate inflammation in collagen-induced arthritis rats,but its mechanism needs to be further verified. OBJECTIVE:To analyze the components absorbed in the blood of Modified Erxian Pill,and observe the effect of the drug-containing serum of Modified Erxian Pill on pyroptosis of J774A.1 macrophages. METHODS:(1)Analysis of components absorbed in the blood of Modified Erxian Pill:Ultra-high performance liquid chromatography-high resolution mass spectrometry was used to detect and identify Modified Erxian Pill and its components absorbed in the blood.(2)Effect of the drug-containing serum of Modified Erxian Pill on pyroptosis of J774A.1 macrophages:Molecular docking technology was used to initially verify the sesquiterpenoids and NLRP3 in components absorbed in the blood of Modified Erxian Pill.J774A.1 macrophages were randomly divided into blank control group,lipopolysaccharide+adenosine triphosphate group,and lipopolysaccharide+adenosine triphosphate+Modified Erxian Pill with low(2.5%),medium(5%),and high(10%)dose groups.The release of lactate dehydrogenase in the cell supernatant of each group was detected according to the kit instructions.The levels of interleukin-1β and interleukin-18 in cell supernatant were detected in each group by ELISA.The cell membrane damage was detected by Hoechst/PI staining.The expression levels of NLRP3,Caspase-1,GSDMD,and GSDMD-N protein in the cells of each group were detected by western blot assay. RESULTS AND CONCLUSION:(1)A total of 32 active components of Modified Erxian Pill were identified,and 21 components entered the blood.The main components into blood included a variety of sesquiterpenoids.(2)Molecular docking results showed that 3-O-Acetyl-13-deoxyphomenone,Incensol oxide,Atractylenolide III,Rupestonic acid,and 3,7-Dihydroxy-9,11-eremophiladien-8-one had good binding activity with NLRP3.(3)Compared with the blank control group,lactate dehydrogenase activity and the expression levels of interleukin-1β and interleukin-18 were significantly increased in cell supernatant of lipopolysaccharide+adenosine triphosphate group(P<0.001).Hoechst/PI staining showed that the number of PI-positive cells was significantly increased.After the intervention of lipopolysaccharide+adenosine triphosphate+Modified Erxian Pill group,all of them showed different degrees of reduction.(4)Compared with the blank control group,NLRP3,Caspase-1,GSDMD,and GSDMD-N protein expression levels were significantly increased in the lipopolysaccharide+adenosine triphosphate group(P<0.05).Compared with lipopolysaccharide+adenosine triphosphate group,the protein expressions of NLRP3,Caspase-1,GSDMD,and GSDMD-N were significantly decreased in the lipopolysaccharide+adenosine triphosphate+Modified Erxian Pill group(P<0.05),and had a certain dose dependence.These findings verify that the drug-containing serum of Modified Erxian Pill may inhibit the pyroptosis of J774A.1 macrophages by regulating the NLRP3/Caspase-1/GSDMD pathway.

Objective To explore the application of plasma 7 microRNA (miR7) testing in the differential diagnosis of very early-stage hepatocellular carcinoma (HCC). Methods This study is a multicenter real-world study. Patients with single hepatic lesion (maximum diameter≤2 cm) who underwent plasma miR7 testing at Zhongshan Hospital, Fudan University, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Anhui Provincial Hospital, and Peking University People’s Hospital between January 2019 and December 2024 were retrospectively enrolled. Patients were divided into very early-stage HCC group and non-HCC group, and the clinical pathological characteristics of the two groups were compared. The value of plasma miR7 levels, alpha-fetoprotein (AFP), and des-gamma-carboxy prothrombin (DCP) in the differential diagnosis of very early-stage HCC was evaluated using receiver operating characteristic (ROC) curves and area under the curve (AUC). In patients with both negative AFP and DCP (AFP＜20 ng/mL, DCP＜40 mAU/mL), the diagnostic value of plasma miR7 for very early-stage HCC was analyzed. Results A total of 64 528 patients from 4 hospitals underwent miR7 testing, and 1 682 were finally included, of which 1 073 were diagnosed with very early-stage HCC and 609 were diagnosed with non-HCC. The positive rate of miR7 in HCC patients was significantly higher than that in non-HCC patients (67.9% vs 24.3%, P＜0.001). ROC curves showed that the AUCs for miR7, AFP, and DCP in distinguishing HCC patients from the non-HCC individuals were 0.718, 0.682, and 0.642, respectively. The sensitivities were 67.85%, 43.71%, and 44.45%, and the specificities were 75.70%, 92.78%, and 83.91%, respectively. The pairwise comparison of AUCs showed that the diagnostic efficacy of plasma miR7 detection was significantly better than that of AFP or DCP (P＜0.05). Although its specificity was slightly lower than AFP and DCP, the sensitivity was significantly higher. Among patients negative for both AFP and DCP, miR7 maintained an AUC of 0.728 for diagnosing very early-stage HCC, with 67.82% sensitivity and 77.73% specificity. Conclusions Plasma miR7 testing is a potential molecular marker with high sensitivity and specificity for the differential diagnosis of small hepatic nodules. In patients with very early-stage HCC lacking effective molecular markers (negative for both AFP and DCP), miR7 can serve as a novel and effective molecular marker to assist diagnosis.

In recent years, the application of artificial intelligence (AI) in the field of biology has witnessed remarkable advancements. Among these, the most notable achievements have emerged in the domain of protein structure prediction and design, with AlphaFold and related innovations earning the 2024 Nobel Prize in Chemistry. These breakthroughs have transformed our ability to understand protein folding and molecular interactions, marking a pivotal milestone in computational biology. Looking ahead, it is foreseeable that the accurate prediction of various physicochemical properties of proteins—beyond static structure—will become the next critical frontier in this rapidly evolving field. One of the most important protein properties is thermodynamic stability, which refers to a protein’s ability to maintain its native conformation under physiological or stress conditions. Accurate prediction of protein stability, especially upon single-point mutations, plays a vital role in numerous scientific and industrial domains. These include understanding the molecular basis of disease, rational drug design, development of therapeutic proteins, design of more robust industrial enzymes, and engineering of biosensors. Consequently, the ability to reliably forecast the stability changes caused by mutations has broad and transformative implications across biomedical and biotechnological applications. Historically, protein stability was assessed via experimental methods such as differential scanning calorimetry (DSC) and circular dichroism (CD), which, while precise, are time-consuming and resource-intensive. This prompted the development of computational approaches, including empirical energy functions and physics-based simulations. However, these traditional models often fall short in capturing the complex, high-dimensional nature of protein conformational landscapes and mutational effects. Recent advances in machine learning (ML) have significantly improved predictive performance in this area. Early ML models used handcrafted features derived from sequence and structure, whereas modern deep learning models leverage massive datasets and learn representations directly from data. Deep neural networks (DNNs), graph neural networks (GNNs), and attention-based architectures such as transformers have shown particular promise. GNNs, in particular, excel at modeling spatial and topological relationships in molecular structures, making them well-suited for protein modeling tasks. Furthermore, attention mechanisms enable models to dynamically weigh the contribution of specific residues or regions, capturing long-range interactions and allosteric effects. Nevertheless, several key challenges remain. These include the imbalance and scarcity of high-quality experimental datasets, particularly for rare or functionally significant mutations, which can lead to biased or overfitted models. Additionally, the inherently dynamic nature of proteins—their conformational flexibility and context-dependent behavior—is difficult to encode in static structural representations. Current models often rely on a single structure or average conformation, which may overlook important aspects of stability modulation. Efforts are ongoing to incorporate multi-conformational ensembles, molecular dynamics simulations, and physics-informed learning frameworks into predictive models. This paper presents a comprehensive review of the evolution of protein thermodynamic stability prediction techniques, with emphasis on the recent progress enabled by machine learning. It highlights representative datasets, modeling strategies, evaluation benchmarks, and the integration of structural and biochemical features. The aim is to provide researchers with a structured and up-to-date reference, guiding the development of more robust, generalizable, and interpretable models for predicting protein stability changes upon mutation. As the field moves forward, the synergy between data-driven AI methods and domain-specific biological knowledge will be key to unlocking deeper understanding and broader applications of protein engineering.

ObjectiveTo investigate and analyze an outbreak of rotavirus infectious diarrhea in a prison in Chongqing Municipality, to provide a basis for adult rotavirus surveillance and prevention, and to explore the public health problems in special settings. MethodsA retrospective survey was conducted to collect and analyze data on individual cases with diarrheal disease on-site. The clinical characteristics, as well as the temporal, spatial and geographical distribution patterns of the epidemic were described. Multi-pathogen detection tests were conducted both on diarrhea cases and environmental samples, with viral genotyping performed on positive samples. A case-control analysis was performed to identify the causes of the outbreak, and an SEIR model was adopted to predict the outbreak trend and evaluate the effectiveness of interventions. ResultsA total of 65 cases were found among the inmates, with an attack rate of 2.03%. The predominant clinical manifestations included diarrhea (89.23%), watery stool (73.85%), and dehydration (18.46%). The epidemic curve indicated a “human-to-human” transmission pattern, with an average incubation period of 5‒6 days. The attack rates among chefs in the main canteen (80.00%, 8/10) and caterers (28.33%, 17/60) were significantly higher than those of other inmates （P<0.05）. Multi-pathogen polymerase chain reaction (PCR) testing detected positive for group A rotavirus, with the viral genotyping identified as G9P [8] strain. Factors such as unprotected "bare-handed" food distribution among cases with diarrhea (OR=9.512, 95%CI: 4.261‒21.234) and close contact with diarrhea cases (OR=3.656, 95%CI: 1.719‒7.778) were the possible cause of the outbreak. The SEIR model (r₀=5, α=0.3, β₁=0.08, β₂=0.04) was constructed using prison inmates as susceptible population, aiming at fitting the initial transmission trend of the outbreak, and the epidemic rate declined rapidly after intervention measures were implemented (r_t≈0). ConclusionThis rare rotavirus infection diarrhea outbreak among adults in confined settings suggests that the construction of public health prevention and control systems in prison may be overlooked. Cross infection during meal processing and distribution in the canteens of such settings is likely to be the cause of the outbreak. Given the potential neglect of public heath system construction in special settings, it is imperative to enhance the surveillance and monitoring of rotavirus and other intestinal multi-pathogens among adults, as well as the construction of public health prevention and control systems in these special settings.

Objective: To investigate the epidemiological characteristics of human brucellosis in Jiande City, Zhejiang Province from 2005 to 2024, so as to provide the evidence for strengthening the prevention and control of brucellosis. Methods: Data on brucellosis cases and surveillance in Jiande City from 2005 to 2024 were collected through the Chinese Disease Prevention and Control Information System, the annual brucellosis surveillance reports from the Jiande Center for Disease Control and Prevention, and the annual summaries of brucellosis prevention and control efforts. The epidemiological characteristics of human brucellosis were analyzed using a descriptive epidemiological method. Results: A total of 1 125 individuals were monitored in Jiande City from 2005 to 2024, with 18 seropositive cases identified and the seropositivity rate of 1.60%. The average annual seropositivity rate from 2015 to 2024 was 3.35%, which was significantly higher than that of 0.57% from 2005 to 2014 (P<0.05). There were 10 confirmed brucellosis cases and 8 asymptomatic infections, with no reported deaths. The peak incidence occurred between March and August. Among the 16 towns (streets) in Jiande City, 8 reported brucellosis cases. Of the brucellosis cases, 14 were male and 4 were female, with a male-to-female ratio of 3.5∶1. The majority of cases (13 cases) were aged between 40 and 60 years. Occupational exposure was identified in 16 cases, all of whom were infected through direct hand contact with the excreta, secretions, or animal products of infected sheep or cattle. The primary source of infection was sheep, followed by cattle. Five strains of Brucella were isolated and cultured, all identified as Brucella melitensis biovar 3. Conclusions The brucellosis epidemic in Jiande City remained at a sporadic and low prevalence level from 2005 to 2024, with an increasing trend observed from 2015 to 2024. Male occupational groups aged 40 to 60 years were the key population for brucellosis prevention and control, and sheep were the primary source of infection.

Objective: To analyze the change trends in the body shape indicators and proportions of students in Harbin from 2010 to 2024, and to investigate ergonomic compatibility of functional dimensions of school desks and chairs with current student shape indicators, so as to provide a reference for revising furniture standards of desks and chairs. Methods: Between September and November of both 2010 and 2024, a combination of convenience sampling and stratified cluster random sampling was conducted across three districts in Harbin, yielding samples of 6 590 and 6 252 students, respectively. Anthropometric shape indicators cluding height, sitting height, crus length, and thigh length-and their proportional changes were compared over the 15-year period. The 2024 data were compared with current standard functional dimensions of school furniture. The statistical analysis incorporated t-test and Mann-Whitney U- test. Results: From 2010 to 2024, average height increased by 1.8 cm for boys and 1.5 cm for girls; sitting height increased by 1.5 cm for both genders; crus length increased by 0.3 cm for boys and 0.4 cm for girls; and thigh length increased by 0.5 cm for both genders. The ratios of sitting height to height, and sitting height to leg length increased by less than 0.1 . The difference between desk chair height and 1/3 sitting height ranged from 0.4-0.8 cm. Among students matched with size 0 desks and chairs, 22.0% had a desk to chair height difference less than 0, indicating that the desk to chair height difference might be insufficient for taller students. The differences between seat height and fibular height ranged from -1.4 to 1.1 cm; and the differences between seat depth and buttock popliteal length ranged from -9.8 to 3.4 cm. Among obese students, the differences between seat width and 1/2 hip circumference ranged from -20.5 to -8.7 cm, while it ranged from -12.2 to -3.8 cm among non obese students. Conclusion Current furniture standards basically satisfy hygienic requirements; however, in the case of exceptionally tall and obese students, ergonomic accommodations such as adaptive seating allocation or personalized adjustments are recommended to meet hygienic requirements.

Objective: To investigate the objective characteristics of tongue manifestation in different stages of damp-heat syndrome in diabetic kidney disease (DKD). Methods: A cross-sectional study enrolled 134 patients with DKD G3-5 stages who met the diagnostic criteria for damp-heat syndrome in DKD. The patients were treated at Dongzhimen Hospital, Beijing University of Chinese Medicine, from May 2023 to January 2024. The patients were divided into three groups: DKD G3, DKD G4, and DKD G5 stage, with 53, 33, and 48 patients in each group, respectively. Clinical general data (gender, age, and body mass index) and damp-heat syndrome scores were collected from the patients. The YZAI-02 traditional Chinese medicine (TCM) AI Tongue Image Acquisition Device was used to capture tongue images from these patients. The accompanying AI Open Platform for TCM Tongue Diagnosis of the device was used to analyze and extract tongue manifestation features, including objective data on tongue color, tongue quality, coating color, and coating texture. Clinical data and objective tongue manifestation characteristics were compared among patients with DKD G3-5 based on their DKD damp-heat syndrome status. Results: No statistically significant difference in gender or body mass index was observed among the three patient groups. The DKD G3 stage group had the highest age (P<0.05). The DKD G3 stage group had a lower score for symptoms of poor appetite and anorexia(P<0.05) than the DKD G5 group. No statistically significant difference was observed in damp-heat syndrome scores among the three groups. Compared with the DKD G5 stage group, the DKD G3 stage group showed a decreased proportion of pale color at the tip and edges of the tongue (P<0.05). The DKD G4 stage group exhibited an increased proportion of crimson at the root of the tongue, a decreased proportion of thick white tongue coating at the root, a decreased proportion of pale color at the tip and edges of the tongue, an increased hue value (indicating color tone) of the tongue color in the middle, an increased brightness value (indicating color lightness) of the tongue coating color in the middle, and an increased thickness of the tongue coating (P<0.05). No statistically significant difference was observed in other tongue color proportions, color chroma values, body characteristics, coating color proportions, coating color chroma values, and coating texture characteristics among the three groups. Conclusion Tongue features differ in different stages of DKD damp-heat syndrome in multiple dimensions, enabling the inference that during the DKD G5 stage, the degree of qi and blood deficiency in the kidneys, heart, lungs, liver, gallbladder, spleen, and stomach is prominent. Dampness is more likely to accumulate in the lower jiao, particularly in the kidneys, whereas heat evil in the spleen and stomach is the most severe. These insights provide novel ideas for the clinical treatment of DKD.

A 20-year-old male patient presented to the Department of Dermatology of Peking Union Medical College Hospital with complaints of an 8-year history of facial scarring, swelling of the lower limbs, and a 4-year history of scalp thickening. Physical examination showed thickening furrowing wrinkling of the skin on the face and behind the ears, ciliary body hirsutism, blepharoptosis, and cutis verticis gyrate. Both lower limbs were swollen, especially the knees and ankles. The skin of the palms and soles of the feet was keratinized and thickened. Laboratory examination using bone and joint X-ray showed periostosis of the proximal middle phalanges and metacarpals of both hands, distal ulna and radius, tibia and fibula, distal femurs, and metatarsals.Genetic testing revealed two variants in SLCO2A1, c.1658T > A and c.96+4A > C.Through multidisciplinary consultation, we confirmed the diagnosis of pachydermoperiostosis.

ObjectiveThe central symptom of Parkinson’s disease (PD) is impaired motor function. Beta-band electrical activity in the motor network of the basal ganglia is closely related to motor function. In this study, we combined scalp electroencephalography (EEG), brain functional network, and clinical scales to investigate the effects of beta burst-period neural electrical activity on brain functional network characteristics, which may serve as a reference for clinical diagnosis and treatment. MethodsThirteen PD patients were included in the PD group, and 13 healthy subjects were included in the healthy control group. Resting-state EEG data were collected from both groups, and beta burst and non-burst periods were extracted. A phase synchronization network was constructed using weighted phase lag indices, and the topological feature parameters of phase synchronization network were compared between the two groups across different periods and four frequency bands. Additionally, the correlation between changes in network characteristics and clinical symptoms was analyzed. ResultsDuring the beta burst period, the topological characteristic parameters of phase synchronization network in all four frequency bands were significantly higher in PD patients compared to healthy controls. The average clustering coefficient of the phase synchronization network in the beta band during the beta burst period was negatively correlated with UPDRS-III scores. In the low gamma band during the non-burst period, the average clustering coefficient of phase synchronization network was positively correlated with UPDRS and UPDRS-III scores, while UPDRS-III scores were positively correlated with global efficiency and average degree. ConclusionThe brain functional network features of PD patients were significantly enhanced during the beta burst period. Moreover, the beta-band brain functional network characteristics during the beta burst period were negatively correlated with clinical scale scores, whereas low gamma-band functional network features during the non-burst period were positively correlated with clinical scale scores. These findings indicate that motor function impairment in PD patients is associated with the beta burst period. This study provides valuable insights for the diagnosis of PD.

Methods: Seventy-five patients with atlas fracture were included from January 2015 to December 2020. Based on the anatomy of the fracture line, atlas fractures were divided into three types. Each type was divided into two subtypes according to the fracture displacement. Unweighted Cohen kappa coefficients were applied to evaluate the reliability and reproducibility. Results: According to the new classification, 17 cases of type A1, 12 of type A2, seven of type B1, 13 of type B2, 12 of type C1, and 14 of type C2 were identified. The K-values of the interobserver and intraobserver reliability were 0.846 and 0.912, respectively, for the new classification. The K-values of interobserver reliability for types A, B, and C were 0.843, 0.799, and 0.898, respectively. The K-values of intraobserver reliability for types A, B, and C were 0.888, 0.910, and 0.935, respectively. The mean K-values of the interobserver and intraobserver reliability for subtypes were 0.687 and 0.829, respectively. Conclusions The new classification of atlas fractures can cover nearly all atlas fractures. This system is the first to evaluate the severity of fractures based on the C1 articular facet and fracture displacement and strengthen the anatomy ring of the atlas. It is concise, easy to remember, reliable, and reproducible.