This paper has summarized current studies on retinitis pigmentosa （RP） from two perspectives， which were disease mechanism insights from western medicine， and etiology， pathogenesis and therapeutic principles in traditional Chinese medicine （TCM）. From the perspective of western medicine， the understanding of RP has evolved from an early single-factor explanation focusing on pathogenic gene mutations to a more comprehensive， multifactorial model involving genetic abnormalities， photoreceptor cell degeneration， retinal pigment epithelium dysfunction， oxidative stress， inflammatory responses， microenvironmental imbalance， and vascular insufficiency. In TCM， RP is considered to be a disorder manifested in the eyes but rooted in dysfunction of the zang-fu （脏腑） organs. Its pathogenesis is primarily associated with congenital insufficiency， liver-kidney depletion， impaired spleen function of transportation and transformation， and stasis obstructing the meridians. The core pathological mechanism can be summarized as "deficiency complicated by stasis"， emphasizing holistic imbalance， dynamic disease progression， and syndrome differentiation and treatment. The differences and complementarities between the two medical systems in understanding disease progression are further discussed. RP is proposed to be more appropriately regarded as a complex disease characterized by long-term progression， stage-dependent evolution， and persistent decompensation. Western medicine contributes to elucidating the molecular mechanisms underlying RP and provides the foundation for targeted therapeutic interventions， whereas TCM offers a holistic perspective for understanding disease evolution through the assessment of overall functional status， disease-stage stratification， and long-term syndrome-based regulation.

This paper has summarized current studies on retinitis pigmentosa （RP） from two perspectives， which were disease mechanism insights from western medicine， and etiology， pathogenesis and therapeutic principles in traditional Chinese medicine （TCM）. From the perspective of western medicine， the understanding of RP has evolved from an early single-factor explanation focusing on pathogenic gene mutations to a more comprehensive， multifactorial model involving genetic abnormalities， photoreceptor cell degeneration， retinal pigment epithelium dysfunction， oxidative stress， inflammatory responses， microenvironmental imbalance， and vascular insufficiency. In TCM， RP is considered to be a disorder manifested in the eyes but rooted in dysfunction of the zang-fu （脏腑） organs. Its pathogenesis is primarily associated with congenital insufficiency， liver-kidney depletion， impaired spleen function of transportation and transformation， and stasis obstructing the meridians. The core pathological mechanism can be summarized as "deficiency complicated by stasis"， emphasizing holistic imbalance， dynamic disease progression， and syndrome differentiation and treatment. The differences and complementarities between the two medical systems in understanding disease progression are further discussed. RP is proposed to be more appropriately regarded as a complex disease characterized by long-term progression， stage-dependent evolution， and persistent decompensation. Western medicine contributes to elucidating the molecular mechanisms underlying RP and provides the foundation for targeted therapeutic interventions， whereas TCM offers a holistic perspective for understanding disease evolution through the assessment of overall functional status， disease-stage stratification， and long-term syndrome-based regulation.

Background: High-mobility group box protein 1 (HMGB1) is a chromatin-binding protein involved in arthritis, ischemia, sepsis, atherosclerosis, neurodegenerative disorders, meningitis, and cancer. HMGB1 exhibits dual roles in cancer, acting as either a tumor suppressor or oncoprotein depending on context. Objective: This research aimed to elucidate HMGB1’s functional significance in cutaneous squamous cell carcinoma (cSCC). Methods: We overexpressed HMGB1 in cSCC cell lines using recombinant adenovirus and examined its effects on cell proliferation, colony formation, and cell migration. Results: Immunohistochemical analysis revealed elevated HMGB1 expression levels in cSCC tissue relative to normal epidermis. To assess the influence of HMGB1, we employed recombinant adenoviruses expressing HMGB1 to transduce SCC cell lines (SCC12 and SCC13). Enhanced HMGB1 expression significantly promoted cellular proliferation and colony formation capacity.Notably, HMGB1 overexpression elevated the levels of proliferation regulators, including P63, SOX2, CDK4 and CDK6. Furthermore, HMGB1 overexpression substantially enhanced tumor invasiveness, accompanied by upregulation of epithelial-mesenchymal transition (EMT) biomarkers. Mechanistically, overexpression of HMGB1 enhanced transforming growth factor-β signaling by increasing phosphorylation of SMAD2/3, the key mediators of EMT. Conclusion These data imply that HMGB1 acts as a tumor-promoting factor in cSCC.

Electroencephalography (EEG) is a non-invasive neurophysiological monitoring technique. It records the electrical activity of the cerebral cortex using electrodes placed on the scalp surface. Owing to its high safety, portability, and millisecond-level temporal resolution, EEG has been widely utilized in a variety of fields, including clinical diagnosis, brain-computer interfaces (BCIs), and cognitive neuroscience research. However, due to its microvolt-level amplitude, EEG is highly susceptible to various artifacts, including electrooculographic (EOG), electrocardiographic (ECG), electromyographic (EMG), and power line interference (PLI). These artifacts can obscure genuine neural activity and introduce spurious electrophysiological features. Consequently, they may compromise EEG signal quality, thereby reducing the reliability of downstream analyses. To address this issue, numerous EEG artifact removal methods have been developed, including both traditional denoising techniques and deep learning-based approaches. Traditional EEG denoising methods have long served as the primary solutions for artifact removal. Representative approaches include filtering, regression, and blind source separation. Although these methods have demonstrated effectiveness in specific scenarios, they suffer from several inherent limitations. Filtering assumes that artifacts and EEG signals can be separated in the frequency domain, but many artifacts, such as EOG and EMG, overlap with EEG spectra, which may lead to the loss of valuable neural information. Regression methods require high-quality artifact references to estimate and subtract contaminations, limiting their effectiveness in reference-free scenarios. Blind source separation can remove artifacts without external references, but it typically requires the number of EEG channels to exceed the number of sources, restricting its application in single- or low-channel EEG recordings. Deep learning-based EEG denoising methods address these limitations effectively. First, they learn the nonlinear mapping between contaminated and clean EEG directly from data in an end-to-end manner. This approach does not rely on assumptions about spectral separability, thereby preserving neural activity more completely. Second, the reference information is incorporated during the training phase, allowing the trained model to perform artifact removal independently without external references. Third, deep learning models can be flexibly designed to accommodate various recording setups, achieving robust denoising for both high-density and single-channel EEG. Collectively, these advantages enable deep learning-based methods to overcome the main challenges of traditional approaches, providing more accurate and reliable EEG signal recovery. The superior denoising performance of deep learning-based EEG denoising methods has attracted increasing attention in EEG artifact removal research. As a result, many deep learning-based denoising methods have been developed and successfully applied in neural engineering areas. However, a systematic review of the techniques and applications in this field is still lacking. To address this gap, this paper reviews recent advances in deep learning-based EEG denoising from four perspectives: technical principle, benchmark dataset, denoising model, and evaluation method. Representative applications in neural signal analysis and BCI decoding are also summarized. Furthermore, the advantage, existing challenge, and future research direction of deep learning-based EEG denoising are discussed. This review aims to provide valuable theoretical insights and technical guidance for researchers. It is also expected to promote further advances and broader applications of deep learning-based EEG denoising techniques.

Objective To explore the nutritional status in elderly patients with type 2 diabetes mellitus (T2DM) and analyze its relationship with the risk of sarcopenia. Methods A retrospective analysis was conducted on 312 elderly patients with T2DM admitted to the 904th Hospital of the Joint Logistics Support Force of the People's Liberation Army between January 2023 and December 2025. Based on the presence or absence of sarcopenia, the patients were classified into a sarcopenia group (121 cases) and a non-sarcopenia group (191 cases). The nutritional status and related factors were compared between the two groups. Binary logistic regression analysis was adopted to analyze the correlation and influencing factors. Results The incidence rate of sarcopenia among elderly patients with T2DM was 38.78%. Compared with the non-sarcopenia group, the sarcopenia group had older age and higher proportions of patients with daily activity dependence, coronary heart disease, and gastrointestinal diseases. Conversely, the proportions of patients exercising ≥4 times per week and those paying attention to nutritional knowledge were lower in the sarcopenia group than those in the non-sarcopenia group (P<0.05). The body mass index (BMI), calf circumference (CC), body weight, albumin (ALB), hemoglobin (Hb), and MNA-SF score in the sarcopenia group were lower compared with those in the non-sarcopenia group (P<0.05). After logistic regression analysis, it was found that low BMI, exercising ≥4 times per week, dependence on daily activity, and low ALB and Hb were independent factors influencing sarcopenia in elderly patients with T2DM (P<0.05). Conclusion The incidence rate of sarcopenia is relatively high in elderly patients with T2DM. Low BMI, low ALB, low Hb, reduced weekly exercise frequency, and daily activity dependence are independent related factors. It is necessary to pay clinical attention to nutritional assessment and intervention.

Objective To investigate the epidemiological characteristics of imported malaria cases in Shanghai Municipality from 2020 to 2024, so as to provide insights into dynamic adjustment of imported malaria control strategies. Methods All data pertaining to malaria cases reported in Shanghai Municipality from 2020 to 2024 were collected from Infectious Diseases Reporting Information Management System and Parasitic Diseases Prevention and Control Information Management System of Chinese Center for Disease Control and Prevention, and epidemiological investigation reports of malaria cases. The type of malaria cases, source of acquiring infections, temporal, spatial and population distributions of malaria cases, type of initial diagnosis institutions, accuracy of initial diagnosis, time interval from disease onset to initial diagnosis, and the time interval from initial diagnosis to diagnosis were statistically analyzed. The monthly distribution of malaria cases was analyzed using a circular distribution method. Results A total of 124 malaria cases were reported in Shanghai Municipality from 2020 to 2024, with 14, 52, 22, 8 and 28 cases annually, respectively, and all cases were overseas imported cases. Plasmodium falciparum malaria was the predominant type, accounting for 82.26% (102/124) of all malaria cases, while P. ovale, P. vivax and P. malariae malaria accounted for 8.06% (10/124), 7.26% (9/124) and 2.42% (3/124) of reported cases, respectively. Malaria cases acquired infections from 28 countries in 4 continents, with African countries as a major source of acquiring infections (96.77%, 120/124). Malaria cases were reported across all 16 districts in Shanghai Municipality. There were peaks in numbers of reported malaria cases in May 2021 (9 cases) and November 2021 (9 cases); however, there was no concentration trend in the reporting time of malaria cases from 2020 to 2024 (Z = 2.45, P > 0.05). The reported malaria cases included 116 males and 8 females, with a male-to-female ratio of 14.5∶1.0, and had a median age of 39 (17) years. Worker was the primary occupation of reported malaria cases, accounting for 48.39% (60/124) of all reported cases. City-level medical institutions were the predominant initial diagnosis institutions (75.81%, 94/124). The overall accuracy of initial diagnosis was 74.19% (92/124), and the median time interval from disease onset to initial diagnosis was 0 (2) day (healthcare seeking on the day of disease onset), with 72.58% (90/124) of malaria cases seeking healthcare services within one day. The median time interval from initial diagnosis to diagnosis was 2 (3) days, with 41.94% (52/124) of malaria cases that were definitively diagnosed within one day following initial diagnosis. There were significant differences in the accuracy of initial malaria diagnosis (χ² = 15.01, P < 0.05), the time interval from disease onset to initial diagnosis (H = 29.30, P < 0.001), and the time interval from initial diagnosis to diagnosis (H = 22.92, P < 0.001) among years from 2020 to 2024, and the time interval from disease onset to initial diagnosis (Z = -5.09, P < 0.001) and the time interval from initial diagnosis to diagnosis (Z = -4.14, P < 0.001) were both shorter during the period from 2020 to 2022 than those recorded from 2023 to 2024. In addition, a total of 2 malaria deaths were reported in Shanghai Municipality during the period from 2020 to 2024, both caused by P. falciparum malaria. Conclusions The numbers of malaria cases fluctuated continuously in Shanghai Municipality from 2020 to 2024. It is recommended to continuously strengthen surveillance and response measures, improve malaria diagnosis and treatment capabilities, and conduct targeted health education activities among high-risk populations, such as labors going to overseas malaria hyper-endemic areas, so as to prevent re-establishment of imported malaria.

To address the challenges of fragmented data, inconsistent standards, and delayed feedback in the full-cycle competency development process at teaching hospitals, this paper proposes the LEADS data governance system for Precision Medical Education (LEADS-PME), drawing on the practical experience of building the integrated medical education management platform at Zhongshan Hospital, Fudan University. The LEADS-PME framework comprises five interconnected core components: Longitudinal foundation, Evidence-chain generation, Analytics and profiling, Decision-support and prediction, and Simulation and digital-twin governance. Currently, Zhongshan Hospital has established a full-cycle educational data infrastructure and achieved multi-system data integration and competency-based profiling and evaluation, while the prediction and simulation modules are still in the prototype exploration phase. A mixed-methods evaluation will be subsequently conducted from the perspectives of data quality, management effectiveness, and predictive intervention. As a locally adapted framework, LEADS-PME can transform operational data derived from clinical practice, teaching, assessment, and management processes into traceable, interpretable, and reusable evidence of competency development. It is expected to facilitate the transformation of medical education management from a outcome-oriented approach to process monitoring, evidence-driven decision-making, and precision governance, thereby providing both a theoretical framework and practical reference for precision medical education in Chinese teaching hospitals.

Consciousness, which underpins human perception, cognition, and behavioural responses, has long been a major focus of neuroscience and medicine. Alterations in consciousness occur not only during reversible processes such as sleep and anaesthesia, but also in pathological conditions, including disorders of consciousness (DOC) and brain injury. With recent advances in the synchronized acquisition and analysis of multimodal physiological signals, such as electroencephalography (EEG) and electrocardiography (ECG), the dynamic interactions between the heart and brain have become increasingly apparent. A growing body of evidence indicates that heart-brain interactions are closely associated with the maintenance and transition of consciousness states, offering a novel perspective for elucidating their underlying mechanisms and supporting relevant clinical assessments. Current approaches to heart-brain interaction analysis for consciousness-state assessment can be broadly grouped into three categories: ECG-based methods, EEG-based methods, and heart-brain coupling analysis. These approaches characterize heart-brain interactions during changes in consciousness from the complementary perspectives of autonomic regulation, cortical interoceptive processing, and cross-system information transfer, respectively. Nevertheless, this field remains at an early stage. Most studies have focused on a single experimental context or a fixed analytical method, with insufficient systematic consideration of the applicability and limitations of different approaches. Moreover, the mechanisms underlying the emergence and alteration of consciousness remain incompletely understood. Accordingly, this review examines heart-brain interaction methods employed in recent consciousness-state assessment studies in accordance with progressively deeper analytical perspectives, and systematically evaluates the advantages and limitations of these methods across distinct consciousness-related conditions. Heart rate variability (HRV) is one of the classical ECG-based indices used to investigate heart-brain interactions. It refers to fluctuations in consecutive interbeat intervals over a specified time period and is regarded as a physiological measure of autonomic nervous system regulation that may indirectly reflect the strength of heart-brain interactions. Depending on the analytical dimension, HRV analysis is commonly divided into time-domain, frequency-domain, and complexity-index analyses. These approaches characterize autonomic activity in terms of temporal fluctuations, the distribution of frequency components, and nonlinear dynamical properties, respectively. Although extensive research supports their physiological interpretation, they cannot characterize directional interactions from the heart to the brain. Among EEG-based methods for heart-brain interaction analysis, EEG activity directly related to cardiac fluctuations has received particular attention. One of the most widely used measures is the heartbeat-evoked potential (HEP). HEP-based approaches are considered to directly reflect the capacity of the cerebral cortex to process cardiac afferent signals and may be especially advantageous for characterizing rapidly changing consciousness levels, such as those observed during sleep. However, they do not comprehensively capture dynamic bidirectional information exchange between the brain and heart and are susceptible to cardiac artefacts, respiration, and variation in preprocessing procedures. Heart-brain coupling analysis quantifies functional associations between ECG and EEG through cross-system interaction measures, thereby assessing dynamic interactions between the heart and brain. It emphasizes information transfer, feedback regulation, and cross-timescale coordination between the two systems, offering a more comprehensive perspective for investigating changes in consciousness during sleep, anaesthesia, and DOC. However, the neurophysiological mechanisms and physiological interpretation of individual computational indices have not yet been fully established, and the robustness of their results and their potential for clinical generalization require further validation. Finally, this review summarizes the commonalities and differences among the three classes of methods across research domains involving different consciousness states. It further argues that heart-brain interaction research requires deeper investigation at both the mechanistic level of consciousness and the level of clinical application. Current challenges and key issues are identified, and future directions and feasible pathways for heart-brain interaction research are discussed, with the aim of providing new perspectives for researchers in related fields.

Objective To understand the surveillance situation of poliovirus in Guangzhou from 2011 to 2024, and to further strengthen polio surveillance and ensure the continued maintenance of a polio-free status. Methods An analysis was conducted on the discovery and investigation results of six cases of vaccine-derived poliovirus (VDPV) detected in Guangzhou. Results A total of 6 VDPV incidents were reported in Guangzhou from 2011 to June 2024, among which 5 incidents were from sewage sample testing in the Liede Sewage Treatment Plant in Guangzhou, all of which were confirmed as VDPV, with 1 for type I, 1 for type II, and 3 for type III. In addition, one confirmed HFMD case was identified as a type VDPV II carrier. No presence of any wild poliovirus (WPV), VDPV cases, or circulating VDPV (cVDPV) was reported. Conclusion Guangzhou City has maintained a high level of vigilance and effectiveness in the monitoring and prevention of polio. Continuously strengthening the construction of the polio monitoring network, optimizing vaccination strategies, and comprehensively improving public health awareness are still the focus of the prevention and control work in the future.

Objective To observe the clinical and transthoracic echocardiographic manifestations of mitral annular disjunction(MAD).Methods Transthoracic echocardiographic data of 990 individuals were retrospectively analyzed.Based on the distance between the attachment point of posterior mitral valve and posterior wall of left ventricle measured on echocardiography,whether there was MAD was judged,and the subjects were divided into MAD group and non-MAD(NMAD)group,and clinical data and echocardiographic parameters were compared between groups.Taken parameters being significantly different between groups as independent variables and the presence or absence of MAD as dependent variable,multivariate binary logistic regression analysis was used to explore correlations of patients' clinical characteristics and transthoracic echocardiographic parameters of MAD.Results MAD was detected in 186 cases(186/990,18.79％).Patients' age,left atrial diameter(LAD),left ventricle diameter(LVD),late diastolic velocity of mitral valve(A)and early diastolic velocity of mitral valve orifices(E)/A in MAD group were all lower than those in NMAD group,while the proportion of moderate and above mitral regurgitation in MAD group was higher than that in NMAD group(all P＜0.05).Patients' age,A and E/A measured with transthoracic echocardiography were all correlated with MAD(all P＜0.05).Conclusion Clinical and transthoracic echocardiographic manifestations of MAD had certain characteristics.