BACKGROUND:In recent years,deep learning technologies have been increasingly applied in the field of oral medicine,enhancing the efficiency and accuracy of oral imaging analysis and promoting the rapid development of intelligent oral medicine. OBJECTIVE:To elaborate the current research status,advantages,and limitations of deep learning based on oral imaging in the diagnosis and treatment decision-making of oral diseases,as well as future prospects,exploring new directions for the transformation of oral medicine under the backdrop of deep learning technology. METHODS:PubMed was searched for literature related to deep learning in oral medical imaging published from January 2017 to January 2024 with the search terms"deep learning,artificial intelligence,stomatology,oral medical imaging."According to the inclusion criteria,80 papers were finally included for review. RESULTS AND CONCLUSION:(1)Classic deep learning models include artificial neural networks,convolutional neural networks,recurrent neural networks,and generative adversarial networks.Scholars have used these models in competitive or cooperative forms to achieve more efficient interpretation of oral medical images.(2)In the field of oral medicine,the diagnosis of diseases and the formulation of treatment plans largely depend on the interpretation of medical imaging data.Deep learning technology,with its strong image processing capabilities,aids in the diagnosis of diseases such as dental caries,periapical periodontitis,vertical root fractures,periodontal disease,and jaw cysts,as well as preoperative assessments for procedures such as third molar extraction and cervical lymph node dissection,helping clinicians improve the accuracy and efficiency of decision-making.(3)Although deep learning is promising as an important auxiliary tool for the diagnosis and treatment of oral diseases,it still has certain limitations in model technology,safety ethics,and legal regulation.Future research should focus on demonstrating the scalability,robustness,and clinical practicality of deep learning,and finding the best way to integrate automated deep learning decision support systems into routine clinical workflows.

ObjectiveTo elucidate the critical role of rehabilitation medical records (including electronic records) in rehabilitation medicine's clinical practice and management, comprehensively analyzed the structure, core content and data standards of rehabilitation medical records, to develop a standardized medical record data architecture and core dataset suitable for rehabilitation medicine and to explore the application of rehabilitation data in performance evaluation and payment. MethodsBased on the regulatory documents Basic Specifications for Medical Record Writing and Basic Specifications for Electronic Medical Records (Trial) issued by National Health Commission of China, and referencing the World Health Organization (WHO) Family of International Classifications (WHO-FICs) classifications, International Classification of Diseases (ICD-10/ICD-11), International Classification of Functioning, Disability and Health (ICF), and International Classification of Health Interventions (ICHI Beta-3), this study constructed the data architecture, core content and data standards for rehabilitation medical records. Furthermore, it explored the application of rehabilitation record summary sheets (home page) data in rehabilitation medical statistics and payment methods, including Diagnosis-related Groups (DRG), Diagnosis-Intervention Packet (DIP) and Case Mix Index. ResultsThis study proposed a systematic standard framework for rehabilitation medical records, covering key components such as patient demographics, rehabilitation diagnosis, functional assessment, rehabilitation treatment prescriptions, progress evaluations and discharge summaries. The research analyzed the systematic application methods and data standards of ICD-10/ICD-11, ICF and ICHI Beta-3 in the fields of medical record terminology, coding and assessment. Constructing a standardized data structure and data standards for rehabilitation medical records can significantly improve the quality of data reporting based on the medical record summary sheet, thereby enhancing the quality control of rehabilitation services, effectively supporting the optimization of rehabilitation medical insurance payment mechanisms, and contributing to the establishment of rehabilitation medical performance evaluation and payment based on DRG and DIP. ConclusionStructured rehabilitation records and data standardization are crucial tools for quality control in rehabilitation. Systematically applying the three reference classifications of the WHO-FICs, and aligning with national medical record and electronic health record specifications, facilitate the development of a standardized rehabilitation record architecture and core dataset. Standardizing rehabilitation care pathways based on the ICF methodology, and developing ICF- and ICD-11-based rehabilitation assessment tools, auxiliary diagnostic and therapeutic systems, and supporting terminology and coding systems, can effectively enhance the quality of rehabilitation records and enable interoperability and sharing of rehabilitation data with other medical data, ultimately improving the quality and safety of rehabilitation services.

BACKGROUND: The contraction behaviors of cardiomyocytes (CMs), especially contraction synchrony, are crucial factors reflecting their maturity and response to drugs. A wider field of view helps to observe more pronounced synchrony differences, but the accompanied greater computational load, requiring more computing power or longer computational time. METHODS: We proposed a method that directly correlates variations in optical field brightness with cardiac tissue contraction status (CVB method), based on principles from physics and photometry, for rapid video analysis in wide field of view to obtain contraction parameters, such as period and contraction propagation direction and speed. RESULTS: Through video analysis of human induced pluripotent stem cell (hiPSC)-derived CMs labeled with green fluorescent protein (GFP) cultured on aligned and random nanofiber scaffolds, the CVB method was demonstrated to obtain contraction parameters and quantify the direction and speed of contraction within regions of interest (ROIs) in wide field of view. The CVB method required less computation time compared to one of the contour tracking methods, the LucasKanade (LK) optical flow method, and provided better stability and accuracy in the results. CONCLUSION This method has a smaller computational load, is less affected by motion blur and out-of-focus conditions, and provides a potential tool for accurate and rapid analysis of cardiac tissue contraction synchrony in wide field of view without the need for more powerful hardware.

The red doctor spirit is the advanced culture of the Communist Party of China formed under a specific historical and cultural background. It can be summarized as “political firmness， excellent technology， working hard， and healing the wounded and rescuing the dying.” This content has many hidden similarities and integrations with the goal of cultivating humanistic literacy for medical students in military medical universities. This paper aimed to identify the important connection points between the red doctor spirit and the contents and goals of medical humanities teaching， as well as integrate the red doctor spirit into medical humanities teaching by various dimensions， including systematic reconstruction of textbook content， immersive innovation in teaching form， three-dimensional support in resource construction， and innovative implementation of narrative medicine teaching. It also further explored the extension of the red doctor spirit in military medical humanistic literacy， namely， revolutionary humanism and revolutionary heroism， thereby enhancing the effectiveness of medical humanistic teaching.

ObjectiveThis paper proposes a novel real-time bedside pulmonary ventilation monitoring method for the diagnosis of chronic obstructive pulmonary disease (COPD), based on electrical impedance tomography (EIT). Four indicators—center of ventilation (CoV), global inhomogeneity index (GI), regional ventilation delay inhomogeneity (RVDI), and the ratio of forced expiratory volume in one second to forced vital capacity (FEV₁/FVC)—are calculated to enable the spatiotemporal assessment of COPD. MethodsA simulation of the respiratory cycles of COPD patients was first conducted, revealing significant differences in certain indicators compared to healthy individuals. The effectiveness of these indicators was then validated through experiments. A total of 93 subjects underwent multiple pulmonary function tests (PFTs) alongside simultaneous EIT measurements. Ventilation heterogeneity under different breathing patterns—including forced exhalation, forced inhalation, and quiet tidal breathing—was compared. EIT images and related indicators were analyzed to distinguish healthy individuals across different age groups from COPD patients. ResultsSimulation results demonstrated significant differences in CoV, GI, FEV₁/FVC, and RVDI between COPD patients and healthy individuals. Experimental findings indicated that, in terms of spatial heterogeneity, the GI values of COPD patients were significantly higher than those of the other two groups, while no significant differences were observed among healthy individuals. Regarding temporal heterogeneity, COPD patients exhibited significantly higher RVDI values than the other groups during both quiet breathing and forced inhalation. Moreover, during forced exhalation, the distribution of FEV₁/FVC values further highlighted the temporal delay heterogeneity of regional lung function in COPD patients, distinguishing them from healthy individuals of various ages. ConclusionEIT technology effectively reveals the spatiotemporal heterogeneity of regional lung function, which holds great promise for the diagnosis and management of COPD.

ObjectiveTo investigate the triglyceride-glucose (TyG) index and the level of serum homocysteine (Hcy) in association with the incidence of stroke in type 2 diabetes mellitus (T2DM) patients. MethodsBased on the chronic disease risk factor surveillance cohort in Pudong New Area, Shanghai, excluding those with stroke in baseline survey, T2DM patients who joined the cohort from January 2016 to October 2020 were selected as the research subjects. During the follow-up period, a total of 318 new-onset ischemic stroke patients were selected as the case group, and a total of 318 individuals matched by gender without stroke were selected as the control group. The Cox proportional hazards regression model was used to adjust for confounding factors and explore the serum TyG index and the Hcy biochemical indicator in association with the risk of stroke. ResultsThe Cox proportional hazards regression results showed that after adjusting for confounding factors, the risk of stroke in T2DM patients with 10 μmol·L⁻¹-¹ and Hcy>15 μmol·L⁻¹ was 1.532 times (95%CI: 1.017‒2.309 times, P=0.041) and 1.738 times (95%CI: 1.119‒2.699 times, P=0.014) higher respectively, compared to T2DM patients with Hcy≤10 μmol·L⁻¹. T2DM patients with TyG>9.074 had 1.396 times higher risk of stroke (95%CI: 1.091‒1.787, P=0.008) compared to those with TyG≤9.074. The study found that urea and α1-antitrypsin (α1-AT) were independent risk factors for the incidence of stroke in T2DM patients. For every unit increase in urea andα1-AT, the risk of stroke in T2DM patients increased by 233.6% (HR=3.336, 95%CI: 1.588‒7.009, P=0.001) and 11.3% (HR=1.113, 95%CI: 1.028‒1.205, P=0.008), respectively. Cumulative risk curves showed that Hcy>10 μmol·L⁻¹ and TyG>9.074 accelerated the time to stroke occurrence in T2DM patients. ConclusionElevated levels of Hcy>10 μmol·L⁻¹ and a higher TyG index are risk factors for stroke in T2DM patients. Effective interventions for Hcy and TyG should be conducted for diabetic patients to reduce the incidence of stroke.

With the rapid advancement of artificial intelligence technology, chatbots have shown great potential in the healthcare sector. From personalized health advice to chronic disease management and psychological support, chatbots have demonstrated significant advantages in improving the efficiency and quality of healthcare services. As the scope of their applications expands, the relationship between technological complexity and practical application scenarios has become increasingly intertwined, necessitating a more comprehensive evaluation of both aspects. This paper, from the perspective of he althcare applications, systematically reviews the technological pathways and development of chatbots in the medical field, providing an in-depth analysis of their performance across various medical scenarios. It thoroughly examines the advantages and limitations of chatbots, aiming to offer theoretical support for future research and propose feasible recommendations for the broader adoption of chatbot technologies in healthcare.

To explore the role of the "Clinical Practice Guidelines" column and others in the Medical Joumnal of Peking Union Medical College Hospital (Med J PUMCH) in promoting diseiplinary development.

Objective: Exploring the effect and mechanism of Xinyang Tablet on reduction of ferroptosis in myocardial cells from mice with chronic heart failure. Methods: Sixty C57BL/6J mice were randomly assigned to the sham, model, Xinyang Tablet low-dose (0.34 g/kg), Xinyang Tablet medium-dose (0.68 g/kg), Xinyang Tablet high-dose (1.36 g/kg), and perindopril (0.607 mg/kg) groups using a random number table method (10 mice in each group). Except for the sham group, all other groups underwent aortic arch constriction surgery to construct a chronic heart failure model. On the third day after completion of the modeling, each treatment group was administered the corresponding medication by gavage, while the sham and model groups were administered equal volumes of water by gavage once a day for eight consecutive weeks. After treatment, cardiac ultrasound was used to detect the structure and function of the mouse heart. Hematoxylin and eosin staining was used to detect pathological changes in mouse heart tissue. Masson staining was used to detect the proportion of fibrotic area of mouse heart tissue. Realtime fluorescence PCR was used to detect the mRNA expression of atrial natriuretic peptide (ANP), brain natriuretic peptide (BNP), collagen 3α (Col3α), and myosin heavy chain 7 (MYH7) in mouse myocardial tissue. Transmission electron microscope was used to detect the ultrastructure of myocardial cell mitochondria. Reactive oxygen species (ROS) staining was used to detect the mean fluorescence intensity of ROS in myocardial tissue. Micro-determination was used to detect superoxide dismutase (SOD) activity in myocardial tissue. An immunofluorescence assay was used to detect the mean fluorescence intensity of phosphorylated histone deacetylase 2 (p-HDAC2) in myocardial cell. Western blotting was used to detect the protein expression of nuclear factor-erythroid 2-related factor 2 (Nrf2), p-HDAC2, nicotinamide adenine dinucleotide phosphate oxidase 1 (NOX1), glutathione peroxidase 4 (GPX4), and cystine glutamate reverse transporter (xCT) in mouse myocardial tissue. Results: Compared to the sham group, the model group showed a decrease in left ventricular ejection fraction (LVEF), left ventricular fraction shortening (LVFS), an increase in left ventricular end-systolic diameter(LVESD) and left ventricular end-diastolic diameter (LVEDD), an increase in the proportion of cardiac fibrosis area, an increase in relative expression levels of ANP, BNP, Col3α, and MYH7 mRNA, an increase in ROS mean fluorescence intensity, a decrease in SOD activity, an increase in mean fluorescence intensity of p-HDAC2, an increase in relative expression levels of p-HDAC2 and NOX1 proteins, and a decrease in relative expression levels of Nrf2, GPX4, and xCT proteins (P<0.05). Myocardial fibrosis lesions are obvious, with disordered mitochondrial arrangement, decreased volume and shrinkage, increased membrane density, and reduced mitochondrial cristae. Compared to the model group, the LVEF and LVFS of mice in each dose group of Xinyang Tablet and the perindopril group increased, LVESD and LVEDD decreased, the proportion of fibrotic area of heart tissue decreased, the relative expression levels of ANP, BNP, Col3α, MYH7 mRNA decreased, ROS mean fluorescence intensity decreased, SOD activity increased, mean fluorescence intensity of p-HDAC2 decreased, relative expression levels of p-HDAC2 and NOX1 proteins decreased, and relative expression levels of Nrf2 and xCT proteins increased (P<0.05). Myocardial fibrosis was reduced, the mitochondrial arrangement was more regular, the mitochondria enlarged, the membrane density was reduced, and mitochondrial cristae increased. Compared to the model group, the relative expression level of the GPX4 protein in myocardial tissue increased in the Xinyang Tablet medium-, high-dose, and the perindopril groups (P<0.05). Conclusion Xinyang Tablet can improve ferroptosis and ventricular remodeling in mice with chronic heart failure by regulating the HDAC2-mediated Nrf2 antioxidant pathway.

BACKGROUND: The contraction behaviors of cardiomyocytes (CMs), especially contraction synchrony, are crucial factors reflecting their maturity and response to drugs. A wider field of view helps to observe more pronounced synchrony differences, but the accompanied greater computational load, requiring more computing power or longer computational time. METHODS: We proposed a method that directly correlates variations in optical field brightness with cardiac tissue contraction status (CVB method), based on principles from physics and photometry, for rapid video analysis in wide field of view to obtain contraction parameters, such as period and contraction propagation direction and speed. RESULTS: Through video analysis of human induced pluripotent stem cell (hiPSC)-derived CMs labeled with green fluorescent protein (GFP) cultured on aligned and random nanofiber scaffolds, the CVB method was demonstrated to obtain contraction parameters and quantify the direction and speed of contraction within regions of interest (ROIs) in wide field of view. The CVB method required less computation time compared to one of the contour tracking methods, the LucasKanade (LK) optical flow method, and provided better stability and accuracy in the results. CONCLUSION This method has a smaller computational load, is less affected by motion blur and out-of-focus conditions, and provides a potential tool for accurate and rapid analysis of cardiac tissue contraction synchrony in wide field of view without the need for more powerful hardware.