To explore the advantages of traditional Chinese medicine （TCM） and integrative TCM-Western medicine approaches in the treatment of diabetic microvascular complications （DMC）， refine key pathophysiological insights and treatment principles， and promote academic innovation and strategic research planning in the prevention and treatment of DMC. The 38th session of the Expert Salon on Diseases Responding Specifically to Traditional Chinese Medicine， hosted by the China Association of Chinese Medicine， was held in Beijing， 2024. Experts in TCM， Western medicine， and interdisciplinary fields convened to conduct a systematic discussion on the pathogenesis， diagnostic and treatment challenges， and mechanism research related to DMC， ultimately forming a consensus on key directions. Four major research recommendations were proposed. The first is addressing clinical bottlenecks in the prevention and control of DMC by optimizing TCM-based evidence evaluation systems. The second is refining TCM core pathogenesis across DMC stages and establishing corresponding "disease-pattern-time" framework. The third is innovating mechanism research strategies to facilitate a shift from holistic regulation to targeted intervention in TCM. The fourth is advancing interdisciplinary collaboration to enhance the role of TCM in new drug development， research prioritization， and guideline formulation. TCM and integrative approaches offer distinct advantages in managing DMC. With a focus on the diseases responding specifically to TCM， strengthening evidence-based support and mechanism interpretation and promoting the integration of clinical care and research innovation will provide strong momentum for the modernization of TCM and the advancement of national health strategies.

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.

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.

Microfluidic chips have revolutionized analytical sciences through miniaturization and high-throughput capabilities.However,conventional static devices are constrained by fixed architectures,functional rigidity,and high customization costs.In recent years,,the emerging configurable and reconfigurable microfluidic technologies have provided solutions for these limitations through dynamic adaptability.Configurable systems enable post-fabrication customization via modular assembly or boundary modification,offering cost-effective functional versatility.Reconfigurable microfluidics represents a more advanced paradigm,incorporating real-time decision-making and dynamic control through physical/virtual boundary adjustments during operation.These adaptive systems enable precise manipulation of microenvironments for applications ranging from single-cell manipulation to dynamic biochemical synthesis.In this review,a ″static-dynamic boundary″ framework to systematically analyze both technologies was proposed,and the design rationales,operational mechanisms,and implementation strategies were compared.The development history of these two techniques was introduced,and the applications demonstrated their transformative potential in developing intelligent lab-on-chip systems,while technical challenges in standardization and control interfaces were critically assessed.The development trend on integrating smart materials and AI-driven automation to advance next-generation adaptive microfluidic platforms was prospected.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

OBJECTIVE: To identify prognostic genes associated with lysosome-dependent cell death (LDCD) in patients with gastric cancer (GC). METHODS: Differentially expressed genes (DEGs) were identified using The Cancer Genome Atlas - Stomach Adenocarcinoma. Weighted gene co-expression network analysis was performed to identify the key module genes associated with LDCD score. Candidate genes were identified by DEGs and key module genes. Univariate Cox regression analysis, and least absolute shrinkage and selection operator regression and multivariate Cox regression analyses were performed for the selection of prognostic genes, and risk module was established. Subsequently, key cells were identified in the single-cell dataset (GSE183904), and prognostic gene expression was analyzed. Cell proliferation and migration were assessed using the Cell Counting Kit-8 assay and the wound healing assay. RESULTS: A total of 4,465 DEGs, 95 candidate genes, and 4 prognostic genes, including C19orf59, BATF2, TNFAIP2, and TNFSF18, were identified in the analysis. Receiver operating characteristic curves indicated the excellent predictive power of the risk model. Three key cell types (B cells, chief cells, and endothelial/pericyte cells) were identified in the GSE183904 dataset. C19orf59 and TNFAIP2 exhibited predominant expression in macrophage species, whereas TNFAIP2 evolved over time in endothelial/pericyte cells and chief cells. Functional experiments confirmed that interfering with C19orf59 inhibited proliferation and migration in GC cells. CONCLUSION C19orf59, BATF2, TNFAIP2, and TNFSF18 are prognostic genes associated with LDCD in GC. Furthermore, the risk model established in this study showed robust predictive power.
Stomach Neoplasms/pathology* ; Humans ; Prognosis ; Lysosomes/physiology* ; RNA-Seq ; Cell Death ; Single-Cell Analysis ; Gene Expression Regulation, Neoplastic ; Cell Proliferation ; Single-Cell Gene Expression Analysis

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

Objective:To investigate the correlation between pathological features at the positive margins and biochemical re-currence after radical prostatectomy for prostate cancer.Methods:From June 2014 to December 2019,a total of 200 patients with organ-confined prostate cancer who underwent radical prostatectomy were included in this study by the method of case matching(1:1).One hundred patients with positive surgical margin and 100 with negative surgical margin were enrolled in this study.All patients did not receive any adjuvant treatment after surgery with a clinical stage of T2/N0.BCR-free survival was estimated using the Kaplan-Meier method.An optimal cutoff for the PSM length which differentiated risk for BCR was identified by Classification and Regression Tree analysis(CART).Cox proportional hazards regression model was used to assess the association between variables and BCR-free surviv-al.Results:A total of 200 patients were included in this study,and 177 patients with pT2 stage were pathological after operation.The median follow-up time of this group of patients was 32.8 months ranged from 5.6 to 80.5 months.A total of 28 cases of biochemi-cal recurrence were found through PSA follow-up after surgery,including 6 cases(6.0％)in the negative margin group and 22 cases(22.0％)in the positive margin group.The result of Kaplan Meier survival curve analysis showed that the non biochemical recurrence survival time of the negative margin group was longer than that of the positive margin group(log rank x2=9.336,P=0.003).It was found that the length of positive margin≥1 mm in the positive margin group was positively correlated with postoperative biochemical re-currence.Multivariate Cox proportional hazards regression was used to identify that the highest Gleason score ≥8 and the length of pos-itive ≥ 1 mm were independent factors of postoperative biochemical recurrence in both the overall patients and the patients with positive margin.Conclusion:The patients with highest Gleason score ≥8 and the length of positive ≥1mm are at elevated risk for BCR.

Culicoides Latreille is among the most diverse and numerous genera of blood-sucking midges,and is widely dis-tributed throughout the world.In addition to stinging and harassing humans and animals,it can also transmit a variety of infec-tious diseases in humans and animals,including African horse fever virus(AHSV),blue tongue virus(BTV),and Japanese encephalitis virus(JEV).Therefore,this insect is an important arbovirus vector of medical concern.Scientific monitoring and timely understanding of the population composition,density,and seasonal fluctuation are the basic premise for effective control of blood-sucking midges.In recent years,studies have increasingly assessed the ecological habits and monitoring methods of blood-sucking midges worldwide.This article reviews the frequently used monitoring methods for blood sucking midges world-wide,to provide a reference for monitoring midge density in China.The main methods include CDC light traps,CO2-baited CDC traps,CO2-baited CDC traps without light bulbs,Onderstepoort Veterinary Institute traps,BG-sentinel traps,human landing catch,human net trapping,animal enclosure traps,animal-baited trapping,sweep netting for adult monitoring,the saturated saline flotation method,the Berlese funnel method,and emergence traps for larval monitoring.The methods'operat-ing procedures,applicable monitoring scope,and advantages and limitations are described to provide ideas for promoting the development and improvement of monitoring technology and instruments.In addition,the monitoring methods described here-in,such as the CDC light trap method,sweep netting,and human landing catch,are applicable to monitoring and collecting other midge insects.

Objective:To investigate the clinical effect of near-infrared autofluorescence (NIRAF) imaging combined with nano-carbon in the identification of parathyroid gland.Methods:A total of 80 patients with thyroid cancer requiring bilateral thyroidectomy plus single/bilateral central area lymphatic dissection were prospectively enrolled in the Department of Thyroid and Breast Surgery of the Shenzhen Third People′s Hospital from January 2020 to June 2023. Two groups were divided in order of time of admission. In the combined group (40 cases), the parathyroid glands were identified by NIRAF+ nano-carbon negative imaging. The nano-carbon group (40 cases) was negatively developed with nano-carbon alone. The detection rate and accuracy of parathyroid gland and the changes of parathyroid hormone (1 d and 6 months after operation) were compared between the two groups.Results:There was no significant difference in the detection rate of parathyroid glands between the two groups ( P=0.65), and the accuracy rate in the combined group was higher than that in the nano-carbon group, the difference was statistically significant (χ 2=8.79, P=0.003). The level of parathyroid hormone in the combined group was higher than that in the nano-carbon group 1 day after surgery, and the difference was statistically significant ( Z=-1.19, P=0.03). There was no significant difference in parathyroid hormone levels between the two groups 6 months after operation ( P=0.18). Conclusions:NIRAF imaging combined with nano-carbon negative imaging can improve the accuracy of intraoperative parathyroid identification, and may reduce the incidence of temporary postoperative hypoparathyroidism.