Objective To explore scientific and accurate prediction methods for the incidence of hand, foot, and mouth disease in the post-pandemic era, and to address modeling challenges caused by abnormal fluctuations in case numbers from 2020 to 2023. Methods The seasonal index was used to pre-process the data. The traditional seasonal autoregressive integrated moving average (SARIMA) model, singular spectrum analysis (SSA)-ARIMA model, ARIMA-Long short-term memory (LSTM) model, and SSA-ARIMA-LSTM model were used to fit the incidence from 2013 to 2023, and the incidence of hand, foot and mouth disease in 2024 was predicted. The real data collected in 2024 were used as the test set to compare the prediction performance of the models. Results The fitting performance of the constructed models was as follows: the ARIMA model had MAE=107.50 and RMSE=144.53, the SSA-ARIMA model showed MAE=2.84 and RMSE=4.33, the ARIMA-LSTM model achieved MAE=99.46 and RMSE=131.59, and the SSA-ARIMA-LSTM model had MAE=96.35 and RMSE=132.13. In terms of prediction performance, the ARIMA model resulted in MAE=151.64 and RMSE=146.70, the SSA-ARIMA model demonstrated MAE=41.22 and RMSE=57.01, the ARIMA-LSTM model yielded MAE=220.75 and RMSE=257.89, and the SSA-ARIMA-LSTM model recorded MAE=58.83 and RMSE=72.06. Conclusion The SSA-ARIMA model has the best fitting degree and the highest prediction accuracy, and is suitable for predicting the incidence trend of hand, foot and mouth disease.

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.

ObjectiveTo investigate the contamination levels of zearalenone (ZEN) in commercially available edible vegetable oils in Ningbo City and to assess its health risks to local residents. MethodsA total of 330 samples of commercially available edible vegetable oil samples (50 each of peanut oil, corn oil, and olive oil; 40 each of rapeseed oil and blended oil; 30 each of soybean oil, rice oil, and sunflower seed oil; and 10 of camellia oil) were collected in 2024. The samples were tested for ZEN using the first method specified in GB 5009.209‒2016 National Food Safety Standard―Determination of Zearalenone in Food, namely the liquid chromatography method, and the contamination status was analyzed. Additionally, combined with dietary consumption data of residents, the Monte Carlo simulation method was employed to evaluate the health risks of ZEN in edible vegetable oils. ResultsZEN was detected in 267 out of 330 samples, with a detection rate of 80.91%, and the median (P₅₀) and the 25th, 75th percentiles (P₂₅, P₇₅) of ZEN concentrations were 2.02 (0.37, 17.90) μg·kg^-1, with a maximum value of 342.00 μg·kg^-1. The ZEN detection rates in corn oil, peanut oil, and blended oil were all 100.00%. The daily average exposure (P₅₀) and daily high exposure (P₉₅) to ZEN via edible vegetable oils among Ningbo residents were 0.001 μg·kg^-1 (normalized to body weight, same below) and 0.060 μg·kg^-1, respectively. However, 1.22% of Ningbo residents had a daily ZEN exposure exceeding the tolerable daily intake (TDI) of 0.25 μg·kg^-1. The hazard quotients (HQ) for the daily average exposure (P₅₀) and daily high exposure (P₉₅) levels were 0.004 and 0.020, respectively, both substantially below 1. Nevertheless, the probability of health risk for Ningbo residents due to ZEN exposure from vegetable oil consumption remained at 1.02%. ConclusionEdible vegetable oils in Ningbo City were contaminated with ZEN, but the probability of ZEN exposure exceeding the TDI through edible vegetable oils was relatively low, and the associated health risk probability were also minimal, indicating an overall insignificant health risk.

OBJECTIVE To investigate the application and influencing factors of sodium-dependent glucose transporters 2 inhibitors（SGLT-2i） in patients with heart failure with preserved ejection fraction（HFpEF） in the real world. METHODS Data from 358 patients with HFpEF who were hospitalized at the First Affiliated Hospital of Chongqing Medical University from May 2023 to May 2024 were retrospectively collected. The patients were divided into the SGLT-2i group and the non-SGLT-2i group based on whether they were prescribed SGLT-2i upon discharge. Baseline characteristics， comorbidities， and differences in drug treatment were compared between the two groups. Based on univariate analysis， multivariate Logistic regression analysis was performed to identify independent influencing factors of SGLT-2i use in patients with HFpEF， followed by further stratified analysis. RESULTS Among 358 HFpEF patients， the overall utilization rate of SGLT-2i was 33.5%. Combined with type 2 diabetes [OR＝9.063，95%CI（4.924-16.679） ] ， atrial fibrillation [OR＝3.135，95%CI（1.590-6.178） ] ， coronary artery heart disease [OR＝1.888，95%CI（1.072-3.327） ] and the use of loop diuretics [OR＝3.822， 95%CI （1.588-9.200） ] were all independent influencing factors for the use of SGLT-2i in patients with HFpEF （ P ＜0.05）. The results of the stratified descriptive analysis were consistent with those of the multivariate analysis， showing a higher utilization rate of SGLT-2i among patients with concomitant T2DM，atrial fibrillation， coronary artery heart disease， and those receiving loop diuretics （ P ＜0.05）； whereas the utilization rate of SGLT-2i was comparable across patients with different levels of renal function （ P ＞0.05）. CONCLUSIONS In the real-world clinical practice， the utilization of SGLT-2i in patients with HFpEF remains suboptimal， and treatment coverage still needs to be improved. Their use of SGLT-2i is primarily influenced by the presence of type 2 diabetes， atrial fibrillation， coronary artery heart disease， and the use of loop diuretics.

ObjectiveTo enhance teaching in postoperative cancer rehabilitation, this study developed an integrative Chinese-Western medicine postoperative oncology rehabilitation system, termed the medical oncology generative pre-trained transformer (MedOncoGPT). By introducing MedOncoGPT as an intelligent assistant, an integrated teaching model combining Chinese and Western medicine was established. The study evaluated its impact on students’ integrative clinical reasoning and practical abilities, providing support for instructional reform in related courses. MethodsUsing teaching resources as the knowledge base, MedOncoGPT was built upon the open-source ChatGLM model and incorporated Low-Rank Adaptation (LoRA) fine-tuning and retrieval-augmented generation (RAG) techniques to address postoperative integrative oncology scenarios. The system was applied in courses and clinical clerkships related to integrative oncology. In alignment with course objectives, a five-stage instructional process—pre-class preparation, in-class inquiry, simulated multidisciplinary consultation, clinical reinforcement, and teaching reflection—was designed to guide students in completing syndrome differentiation, comprehensive assessment, and follow-up planning within real or simulated case contexts. Comparative analyses of student engagement, syndrome differentiation thinking, evidence-based awareness, and interdisciplinary integration skills before and after the teaching reform were conducted using questionnaires, course assessments, classroom observations, and semi-structured interviews. ResultsFollowing the implementation of MedOncoGPT, students demonstrated improved performance in case analysis, prescription formulation, and integrative Chinese-Western medical evaluation compared with those receiving traditional instruction. Classroom participation and the relevance of student inquiries also increased. Self-assessment results indicated high levels of satisfaction with respect to clarity of integrative clinical reasoning, ability to retrieve and apply guideline-based evidence, and awareness of appropriate use of intelligent tools in clinical decision-making. More than 92% of students reported that the system facilitated understanding of abstract theoretical concepts presented in textbooks. Instructors noted that the system helped reduce lesson preparation time, enriched typical case materials and discussion scenarios, and promoted the translation of research findings into classroom teaching. Pilot data showed that, with MedOncoGPT assistance, the mean time for initial syndrome differentiation decreased from 18.4 min to 12.1 min, and the agreement rate increased from 68.3% to 82.5%. In the teaching pilot, the experimental group achieved a higher mean score on the final case analysis assessment than the control group (82.6 vs. 74.3). ConclusionThe integration of MedOncoGPT into teaching on postoperative integrative cancer rehabilitation enabled the establishment of a stable instructional process within existing curricula and enhanced students’ integrative clinical reasoning and evidence-based practice capabilities. The approach demonstrates positive potential for advancing the integration of research, clinical practice, and education and represents a valuable exploratory strategy for instructional reform in courses on integrative Chinese-Western medicine.

ObjectiveTo enhance teaching in postoperative cancer rehabilitation, this study developed an integrative Chinese-Western medicine postoperative oncology rehabilitation system, termed the medical oncology generative pre-trained transformer (MedOncoGPT). By introducing MedOncoGPT as an intelligent assistant, an integrated teaching model combining Chinese and Western medicine was established. The study evaluated its impact on students’ integrative clinical reasoning and practical abilities, providing support for instructional reform in related courses. MethodsUsing teaching resources as the knowledge base, MedOncoGPT was built upon the open-source ChatGLM model and incorporated Low-Rank Adaptation (LoRA) fine-tuning and retrieval-augmented generation (RAG) techniques to address postoperative integrative oncology scenarios. The system was applied in courses and clinical clerkships related to integrative oncology. In alignment with course objectives, a five-stage instructional process—pre-class preparation, in-class inquiry, simulated multidisciplinary consultation, clinical reinforcement, and teaching reflection—was designed to guide students in completing syndrome differentiation, comprehensive assessment, and follow-up planning within real or simulated case contexts. Comparative analyses of student engagement, syndrome differentiation thinking, evidence-based awareness, and interdisciplinary integration skills before and after the teaching reform were conducted using questionnaires, course assessments, classroom observations, and semi-structured interviews. ResultsFollowing the implementation of MedOncoGPT, students demonstrated improved performance in case analysis, prescription formulation, and integrative Chinese-Western medical evaluation compared with those receiving traditional instruction. Classroom participation and the relevance of student inquiries also increased. Self-assessment results indicated high levels of satisfaction with respect to clarity of integrative clinical reasoning, ability to retrieve and apply guideline-based evidence, and awareness of appropriate use of intelligent tools in clinical decision-making. More than 92% of students reported that the system facilitated understanding of abstract theoretical concepts presented in textbooks. Instructors noted that the system helped reduce lesson preparation time, enriched typical case materials and discussion scenarios, and promoted the translation of research findings into classroom teaching. Pilot data showed that, with MedOncoGPT assistance, the mean time for initial syndrome differentiation decreased from 18.4 min to 12.1 min, and the agreement rate increased from 68.3% to 82.5%. In the teaching pilot, the experimental group achieved a higher mean score on the final case analysis assessment than the control group (82.6 vs. 74.3). ConclusionThe integration of MedOncoGPT into teaching on postoperative integrative cancer rehabilitation enabled the establishment of a stable instructional process within existing curricula and enhanced students’ integrative clinical reasoning and evidence-based practice capabilities. The approach demonstrates positive potential for advancing the integration of research, clinical practice, and education and represents a valuable exploratory strategy for instructional reform in courses on integrative Chinese-Western medicine.

The blood-brain barrier （BBB） is a physical and biochemical barrier that precisely regulates brain homeostasis and plays a central role in controlling the transport of endogenous and exogenous drugs and related metabolites across the blood-brain interface. These functions of the BBB are mediated by its major components， including brain microvascular endothelial cells （BMECs）， tight junction protein complexes， and influx and efflux transporter proteins. One of the pathological features of ischemic stroke （IS） is BBB disruption， which plays an important role in the development of post-stroke brain injury and subsequent neurological dysfunction. Therefore， given the increasing incidence of IS， there is an urgent need to develop new therapeutic strategies to prevent BBB dysfunction and thereby protect injured brain tissue after IS. This study describes the pathological mechanisms by which BMEC injury after IS leads to BBB dysfunction and elucidates the association between BMECs and IS， including the regulation of apoptosis， autophagy， inflammatory responses， oxidative stress， neurotoxic effects， and cerebral edema. In addition， this article summarizes Chinese herbal medicines that may prevent and treat IS by targeting BMECs. These include monomeric compounds and single herbs such as flavonoids， glycosides， phenols， phthalides， terpenoids， and Styrax. Traditional Chinese medicine （TCM） compound formulas and preparations include oral formulations such as Buyang Huanwu decoction， Sailuotong， Naoxintong capsules， Dandeng Tongnao capsules， and Shexiang Tongxin dropping pills， as well as injectable preparations such as Tongluo Jiunao injection， Xingnaojing injection， Danshen polyphenolic acid for injection， Yiqi Fumai injection， and Shuxuetong injection. This study aims to explore the protective effects of TCM against IS through targeted regulation of BMEC function， providing new insights into the mechanisms of IS and endovascular therapeutic strategies.

AIM:To analyze the efficacy of modified Zhujing formula combined with intravitreal ranibizumab(IVR)injection in the treatment of wet age-related macular degeneration(wARMD).METHODS: A prospective study was conducted on wARMD patients at the Ophthalmology Department of Yulin Hospital of Traditional Chinese Medicine from September 2022 to October 2024. The study subjects were divided into the experimental group and the control group according to the random number table method. The control group received IVR treatment, while the experimental group was treated with modified Zhujing formula in addition to IVR injection. The clinical efficacy, TCM symptom scores, central retinal thickness(CRT), best corrected visual acuity(BCVA), macular hemorrhage area, choroidal neovascularization area(CNV), ocular hemodynamic parameters [resistance index(RI), maximum diastolic blood flow(EDV), maximum systolic blood flow(PSV)], and 1-year recurrence rate were compared between the two groups.RESULTS: This study included 60 eyes from 60 wARMD patients. Among them, the control group consisted of 30 patients and 30 eyes, while the experimental group consisted of 30 patients and 30 eyes. The age of the control group was 67.52±3.12 y, with 17 males and 13 females. The age of the experimental group was 67.62±3.04 y, with 18 males and 12 females.The clinical efficacy of the experimental group(97%)was higher than that of the control group(73%)(P<0.05). After treatment, the scores of blurred vision, soreness and weakness of the waist and knees, restlessness and insomnia, dizziness and tinnitus in the experimental group were all lower than those in the control group(all P<0.05); the EDV and PSV in the experimental group were both higher than those in the control group(both P<0.05); the BCVA, CRT, macular hemorrhage area, CNV and RI of the experimental group were all lower than those of the control group(all P<0.05), and the 1-year recurrence rate in the experimental group(3%)was lower than that in the control group(27%)(P<0.05). CONCLUSION:The combined use of modified Zhujing formula and IVR can effectively alleviate symptoms such as blurred vision and retinal hemorrhage in wARMD patients, improve vision and ocular hemodynamic conditions, and reduce the recurrence rate. This suggests that there may be a synergistic enhancing effect.

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.

With the widespread adoption of low-dose CT screening and the extensive application of high-resolution CT, the detection rate of sub-centimeter lung nodules has significantly increased. How to scientifically manage these nodules while avoiding overtreatment and diagnostic delays has become an important clinical issue. Among them, lung nodules with a consolidation tumor ratio less than 0.25, dominated by ground-glass shadows, are particularly worthy of attention. The therapeutic challenge for this group is how to achieve precise and complete resection of nodules during surgery while maximizing the preservation of the patient's lung function. The "watershed topography map" is a new technology based on big data and artificial intelligence algorithms. This method uses Dicom data from conventional dose CT scans, combined with microscopic (22-24 levels) capillary network anatomical watershed features, to generate high-precision simulated natural segmentation planes of lung sub-segments through specific textures and forms. This technology forms fluorescent watershed boundaries on the lung surface, which highly fit the actual lung anatomical structure. By analyzing the adjacent relationship between the nodule and the watershed boundary, real-time, visually accurate positioning of the nodule can be achieved. This innovative technology provides a new solution for the intraoperative positioning and resection of lung nodules. This consensus was led by four major domestic societies, jointly with expert teams in related fields, oriented to clinical practical needs, referring to domestic and foreign guidelines and consensus, and finally formed after multiple rounds of consultation, discussion, and voting. The main content covers the theoretical basis of the "watershed topography map" technology, indications, operation procedures, surgical planning details, and postoperative evaluation standards, aiming to provide scientific guidance and exploration directions for clinical peers who are currently or plan to carry out lung nodule resection using the fluorescent microscope watershed analysis method.