Patients with Takayasu arteritis combined with aortic valve disease often have a poor prognosis following surgical valve replacement, frequently encountering complications such as perivalvular leakage, valve detachment, and anastomotic aneurysm. This article presents a high-risk case wherein severe aortic valve insufficiency associated with Takayasu arteritis was successfully managed through transcatheter aortic valve implantation via the transapical approach. The patient had satisfactory valve function with no complications observed during the six-month postoperative follow-up. This case provides a minimally invasive and feasible alternative for the clinical management of such high-risk patients.

Objective:To investigate the pathological characteristics of post-cholecystectomy specimens from patients with benign gallbladder diseases.Methods:This retrospective case series study analyzed clinical and pathological data from 1 712 patients who underwent cholecystectomy for benign gallbladder diseases at the Sixth People′s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine between September 2022 and August 2024. The cohort included 757 males and 955 females, with an age ( M(IQR)) of 57(23) years (range: 14 to 91 years). Clinical and pathological features were analyzed. The χ2 test was used to compare clinical characteristics between patients with neoplastic and non-neoplastic polyps. Factors statistically significant in the χ2 test were subsequently included in a binary logistic regression analysis. Results:Postoperative pathological examination revealed gallbladder cancer in 7 patients (0.41%). These 7 cases, including 2 with pT3 stage cancer, were not detected preoperatively by various imaging examinations (ultrasound+magnetic resonance cholangiopancreatography/MRI plain scan in 3 cases, ultrasound+enhanced MRI in 1 case, ultrasound+enhanced CT in 2 cases, enhanced CT+enhanced MRI in 1 case). Gallbladder adenoma was found in 23 cases (1.34%), neoplastic polyps (including cholesterol polyps with dysplasia) in 29 cases (1.69%), and non-neoplastic polyps in 154 cases (9.00%). Statistically significant differences were observed in age and polyp number between patients with neoplastic and non-neoplastic polyps ( χ2=10.436 and 8.030; both P<0.05). Binary logistic regression analysis identified age ≥60 years ( P=0.003) and solitary polyps ( P=0.009) as risk factors for neoplastic polyps. Mucosal dysplasia was present in 164 cases (9.58%), including 9 cases of severe dysplasia, 4 of which exhibited focal carcinomatous transformation. Gallbladder polyps combined with stones were found in 90 cases (5.26%), among which 10 were associated with adenoma and mucosal dysplasia, and 2 showed focal carcinomatous transformation. Conclusions:The incidence of incidental gallbladder carcinoma was 0.41%. Intraoperative bile spillage can severely compromise prognosis. Preoperative imaging demonstrates a low detection rate for neoplastic polyps. Particular vigilance for neoplastic polyps is warranted in patients aged ≥60 years or with solitary polyps. Cholecystectomy should be performed promptly for benign gallbladder diseases meeting surgical indications.

Objective:To investigate the pathological characteristics of post-cholecystectomy specimens from patients with benign gallbladder diseases.Methods:This retrospective case series study analyzed clinical and pathological data from 1 712 patients who underwent cholecystectomy for benign gallbladder diseases at the Sixth People′s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine between September 2022 and August 2024. The cohort included 757 males and 955 females, with an age ( M(IQR)) of 57(23) years (range: 14 to 91 years). Clinical and pathological features were analyzed. The χ2 test was used to compare clinical characteristics between patients with neoplastic and non-neoplastic polyps. Factors statistically significant in the χ2 test were subsequently included in a binary logistic regression analysis. Results:Postoperative pathological examination revealed gallbladder cancer in 7 patients (0.41%). These 7 cases, including 2 with pT3 stage cancer, were not detected preoperatively by various imaging examinations (ultrasound+magnetic resonance cholangiopancreatography/MRI plain scan in 3 cases, ultrasound+enhanced MRI in 1 case, ultrasound+enhanced CT in 2 cases, enhanced CT+enhanced MRI in 1 case). Gallbladder adenoma was found in 23 cases (1.34%), neoplastic polyps (including cholesterol polyps with dysplasia) in 29 cases (1.69%), and non-neoplastic polyps in 154 cases (9.00%). Statistically significant differences were observed in age and polyp number between patients with neoplastic and non-neoplastic polyps ( χ2=10.436 and 8.030; both P<0.05). Binary logistic regression analysis identified age ≥60 years ( P=0.003) and solitary polyps ( P=0.009) as risk factors for neoplastic polyps. Mucosal dysplasia was present in 164 cases (9.58%), including 9 cases of severe dysplasia, 4 of which exhibited focal carcinomatous transformation. Gallbladder polyps combined with stones were found in 90 cases (5.26%), among which 10 were associated with adenoma and mucosal dysplasia, and 2 showed focal carcinomatous transformation. Conclusions:The incidence of incidental gallbladder carcinoma was 0.41%. Intraoperative bile spillage can severely compromise prognosis. Preoperative imaging demonstrates a low detection rate for neoplastic polyps. Particular vigilance for neoplastic polyps is warranted in patients aged ≥60 years or with solitary polyps. Cholecystectomy should be performed promptly for benign gallbladder diseases meeting surgical indications.

OBJECTIVE Identify the incentive and constraint factors of artificial intelligence （AI） application in the pharmaceutical field， and promote the application of AI in the field of pharmacy. METHODS Based on the technology acceptance model （TAM）， technology-organization-environment （TOE） framework， and diffusion of innovation theory （DOI）， a TAM-TOE-DOI integrated framework was constructed through a four-stage research process of “theoretical review → dimension mapping → mechanism integration → proposition development”. Combining the analytical pathways of the above three theories in AI application in pharmacy with the integration mechanisms and core propositions of the TAM-TOE-DOI， literature review and deductive reasoning were employed to systematically identify the incentive and constraint factors of AI application in pharmacy from three levels：micro （TAM）， meso （TOE）， and macro （DOI）， and to propose optimization strategies. RESULTS & CONCLUSIONS At the micro level， the efficiency transformation and quality improvement brought by AI technology were the main incentive factors for perceived usefulness， while technological complexity and algorithmic opacity were the main constraint factors for perceived ease of use. At the meso level， the completeness of technological infrastructure， the strength of top management support and innovation climate， as well as external institutional pressure and competitive driving forces were the core incentive factors， whereas scarcity of organizational resources and talent shortage were the main constraint factors. At the macro level， relative advantage and observability were typical incentive factors， while technological complexity was a typical constraint factor. China’s health administration， medical insurance authorities， and other relevant departments should coordinate efforts at the macro， meso， and micro levels to advance AI application in pharmacy： optimizing human-computer interaction and implementing tiered training programs at the micro level； reinforcing organizational support systems and capacity building at the meso level； dismantling data barriers and building social trust at the macro level. Differentiated implementation pathways should be developed for medical institutions at different tiers.

With the continuous deepening of the concept of precision diagnosis and treatment for lung cancer, how to achieve higher efficiency and accuracy in the screening, diagnosis, and treatment pathways in clinical practice has become an important issue that urgently needs to be overcome. The current clinical difficulty lies in the fact that despite continuous advancements in imaging and molecular diagnostic technologies, there are still limitations in manual efficiency and subjective experience when it comes to massive data analysis and multi-scale feature extraction. Artificial intelligence (AI), especially algorithm systems based on deep learning, is an innovative technology capable of deeply empowering medical big data. This method utilizes algorithms such as convolutional neural networks, combined with radiomics, pathomics, and multi-modal data fusion analysis, demonstrating immense potential in early precise detection and benign-malignant differentiation of pulmonary nodules, digital pathological subtype recognition and non-invasive prediction of driver genes, precise 3D surgical planning and automatic delineation of radiotherapy target volumes, as well as dynamic risk warning during follow-up. This innovative technology provides a brand-new solution for realizing intelligent and individualized lung cancer diagnosis and treatment models. This consensus, based on the latest evidence from evidence-based medicine and combined with the development trends in the AI field and real-world clinical needs, was ultimately formed by gathering the consensus opinions of multidisciplinary experts in radiology, pathology, thoracic surgery, and other fields. The main content covers the application specifications of AI in the three core scenarios of lung cancer screening, diagnosis, and treatment, the technical standards for data collection and algorithm validation, as well as the ethical and regulatory challenges faced at the current stage. It aims to clarify the applicable boundaries of AI as a clinical auxiliary decision support tool, providing scientific guidance and standardized exploration directions for peers currently engaged in or planning to carry out AI-assisted clinical diagnosis, treatment, and translation of lung cancer.

Objective To gain insights into the biological characteristics of different populations of Culex tritaeniorhynchus within and around the Yellow Sea wetland from the perspective of the occurrence of resistance, we investigated the levels of resistance to deltamethrin and kdr gene mutation in the wetland and its peripheral areas. Methods Specimens were collected from Cx. tritaeniorhynchus populations at two monitoring sites in the Rare Bird National Nature Reserve and Tiaozi Ni Wetland Scenic Area, and also from two populations in Yancheng City and the Liuhe District of Nanjing, and the resistance of these mosquitoes to deltamethrin was determined using the CDC biotest bottle method. For each concentration of deltamethrin assessed, a random subset of exposed specimens was selected for amplification of the kdr gene fragment, followed by Sanger sequencing to identify and analyze resistance-associated mutations. Results The LC50 levels of deltamethrin among mosquitoes from the four populations in Luhe, Yancheng, the Rare Bird National Nature Reserve and the Tiaozi Ni Wetland Scenic Area were 2.048 5, 7.798 2, 3.473 3, and 17.695 5 mg/mL, respectively, with corresponding concentrations of deltamethrin ranging from 0.005 to 5.000,0.050 to 50.000,0.050 to 25.000 and 0.050 to 50.000 mg/mL, respectively. Furthermore, the ranges of the KT50 values were 11.76-107.43, 67.05-216.30,29.77-107.43 and 28.40-329.51 min; the 1-h knockdown rates were 34.58%-99.15%, 9.52%-43.80%, 55.09%-73.01%, and 10.09%-68.07%; and the 24-h mortality rates were 12.15%-67.52%,9.52%-79.56%,13.17%-82.21%, and 11.01%-78.99%, respectively. With respect to kdr gene mutation, we assayed a total of 63,70,59, and 57 mosquitoes for the four populations, for which we detected L1014F mutation frequencies of 14.29%, 35.00%, 20.34%, and 31.58%, respectively, with a majority of these mutations being heterozygous for resistance. In addition, five adult mosquitoes were identified has having synonymous mutations at site 1011[i. e. , AAT(asparagine)mutation to AAC(asparagine)]. Conclusions Our findings revealed the clear resistance of Cx. tritaeniorhynchus to deltamethrin in the Yancheng region of the Yellow Sea wetland, and the resistance phenotype and kdr frequency of Cx. tritaeniorhynchus in the wetland environment were comparable to those of Cx. tritaeniorhynchus in the wetland environment, thereby indicating that the resistance of different populations of Cx. tritaeniorhynchus was homogeneous under the pressure of different insecticide selection within and around the wetland. However, the underlying mechanisms need to be further studied.

Electromagnetic fields can regulate the fundamental biological processes involved in bone remodeling. As a non-invasive physical therapy, electromagnetic fields with specific parameters have demonstrated therapeutic effects on bone remodeling diseases, such as fractures and osteoporosis. Electromagnetic fields can be generated by the movement of charged particles or induced by varying currents. Based on whether the strength and direction of the electric field change over time, electromagnetic fields can be classified into static and time-varying fields. The treatment of bone remodeling diseases with static magnetic fields primarily focuses on fractures, often using magnetic splints to immobilize the fracture site while studying the effects of static magnetic fields on bone healing. However, there has been relatively little research on the prevention and treatment of osteoporosis using static magnetic fields. Pulsed electromagnetic fields, a type of time-varying field, have been widely used in clinical studies for treating fractures, osteoporosis, and non-union. However, current clinical applications are limited to low-frequency, and research on the relationship between frequency and biological effects remains insufficient. We believe that different types of electromagnetic fields acting on bone can induce various “secondary physical quantities”, such as magnetism, force, electricity, acoustics, and thermal energy, which can stimulate bone cells either individually or simultaneously. Bone cells possess specific electromagnetic properties, and in a static magnetic field, the presence of a magnetic field gradient can exert a certain magnetism on the bone tissue, leading to observable effects. In a time-varying magnetic field, the charged particles within the bone experience varying Lorentz forces, causing vibrations and generating acoustic effects. Additionally, as the frequency of the time-varying field increases, induced currents or potentials can be generated within the bone, leading to electrical effects. When the frequency and power exceed a certain threshold, electromagnetic energy can be converted into thermal energy, producing thermal effects. In summary, external electromagnetic fields with different characteristics can generate multiple physical quantities within biological tissues, such as magnetic, electric, mechanical, acoustic, and thermal effects. These physical quantities may also interact and couple with each other, stimulating the biological tissues in a combined or composite manner, thereby producing biological effects. This understanding is key to elucidating the electromagnetic mechanisms of how electromagnetic fields influence biological tissues. In the study of electromagnetic fields for bone remodeling diseases, attention should be paid to the biological effects of bone remodeling under different electromagnetic wave characteristics. This includes exploring innovative electromagnetic source technologies applicable to bone remodeling, identifying safe and effective electromagnetic field parameters, and combining basic research with technological invention to develop scientifically grounded, advanced key technologies for innovative electromagnetic treatment devices targeting bone remodeling diseases. In conclusion, electromagnetic fields and multiple physical factors have the potential to prevent and treat bone remodeling diseases, and have significant application prospects.

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.

AIM: To investigate the correlation of serum interleukin-35(IL-35), immunoglobulin 4/immunoglobulin(IgG4/IgG), thyroid stimulating immunoglobulin(TSI)levels with the activity and severity of thyroid associated ophthalmopathy(TAO).METHODS:Prospective study. A total of 148 TAO patients admitted to our hospital from January 2023 to July 2024 were selected as the observation group. They were assigned into an active group(75 cases)and an inactive group(73 cases)based on their activity level, and were assigned into a severe group(95 cases)and a mild group(53 cases)based on the severity of their condition; another 148 healthy patients who underwent physical examinations were regarded as the control group. The levels of IL-35, IgG4/IgG, and TSI in serum were compared between the two groups. The correlation between serum IL-35, IgG4/IgG, and TSI levels and TAO activity and severity of illness were analyzed. A multivariate Logistic regression was performed to analyze the influencing factors of TAO patients developing severe symptoms. ROC curve was applied to analyze the diagnostic value of serum IL-35, IgG4/IgG, and TSI levels for the development of severe TAO in patients.RESULTS: Compared with the control group, the serum IL-35 level in the observation group was significantly lower, while IgG4/IgG and TSI levels were significantly higher(all P<0.01). Compared with non-active TAO patients, active TAO patients had significantly lower serum IL-35 level and significantly higher IgG4/IgG and TSI levels(all P<0.01). Compared with the mild TAO patients, severe TAO patients had significantly lower serum IL-35 level and significantly higher disease duration, IgG4/IgG, and TSI levels(all P<0.01). The serum IL-35 level was negatively correlated with TAO activity and disease severity(r=-0.529, -0.554, both P<0.01), while serum IgG4/IgG level was positively correlated with TAO activity and disease severity(r=0.625, 0.663, both P<0.01). Serum TSI levels were positively correlated with TAO activity and disease severity(r=0.594, 0.607, both P<0.01). Multivariate Logistic regression analysis showed that serum IL-35, IgG4/IgG, and TSI levels were all factors influencing the progression of TAO patients to severe disease(all P<0.01). The areas under the curve(AUC)for diagnosing the progression of TAO patients to severe disease using serum IL-35, IgG4/IgG, and TSI levels were 0.868, 0.859, and 0.830, respectively. The combined AUC for the three markers was 0.955, significantly higher than that of each individual marker(Zthree factors combination-IL-35=2.893, Zthree factors combination-IL-35=3.510, Zthree factors combination-IL-35=4.157, P=0.004, <0.01, <0.01).CONCLUSION: Serum IL-35 level is significantly downregulated in TAO patients, while IgG4/IgG and TSI levels are significantly upregulated. The levels of IL-35, IgG4/IgG, and TSI are correlated with the activity and severity of TAO, and their combination has high diagnostic value for TAO developing into severe.

Objective: To understand the epidemiological and etiological characteristics of food poisoning event occurred in a school in Hangzhou, Zhejiang, so as to provide reference for the scientific management of related emergencies. Methods: By determining the nature of the event through epidemiological investigation, a case control study was carried out to spot suspicious food in May 2024. The hygienic investigation was conducted to find out possible pollution links and factors, patients and canteen practitioners anal swab, canteen retention samples, catering link daub and other specimens were collected ,for rapid pathogen screening. And the suspected pathogen Clostridium perfringens (CP) were isolated and identified according to the screening results, and toxin gene detection and whole genome sequencing and cluster analysis of CP isolated strains were carried out. Results: The incident resulted in 45 people experiencing gastrointestinal symptoms, such as diarrhea and abdominal pain. The suspicious food was tomato scrambled eggs and corn ribs provided by the student canteen for lunch on May 29. A hygiene investigation found that there was a risk of contamination in the food processing, preparation and storage. A total of 46 anal swabs and 10 canteen retention samples were positive for CP 16 S, 59 strains of CP were isolated from 27 samples, 10 cases and 1 practitioner isolate were positive for CPE ( cpe ) (F mode), and their whole genome evolution analysis was conducted based on the same source. Conclusions The food poisoning event is caused by CP infection carrying CPE ( cpe ) (F mode), and the possible sources of outbreak are the carriers of the CP by employees. It is recommended that cafeteria staff strengthen training on common foodborne diseases and conduct regular monitoring of pathogens.