ObjectiveTo analyze the epidemiological characteristics of 8 major respiratory pathogens in influenza-like illness (ILI) cases with acute respiratory infections at fever clinics in Huangpu District, Shanghai from 2015 to 2024, and to provide a scientific basis for the prevention and treatment of respiratory diseases. MethodsA retrospective study was conducted in Huangpu District. Individuals meeting the case definition of ILI from 2015 to 2024 was registered. Their nasopharyngeal swabs were collected for pathogen detection. A total of 8 respiratory viruses were tested, including Influenza A virus (Flu A), Influenza B virus (Flu B), adenovirus (ADV), enterovirus/human rhinovirus (EV/HRV), human parainfluenza virus (HPIV), human coronavirus (HCoV), respiratory syncytial virus (RSV), and human metapneumovirus (HMPV). ResultsFrom 2015 to 2019, a total of 344 ILI cases were tested, of which 192 out of 344 cases (55.81%) were tested positive for single respiratory pathogen. From 2023 to 2024, 1 557 ILI cases were tested, with 572 out of 1 557 cases (36.74%) being positive for single pathogen. From 2023 to 2024, the positive rate of single pathogen in ILI cases was significantly lower than that in 2015‒2019 (χ²=42.66, P<0.001). Specifically, the positive rate of Flu A (χ²=74.43, P<0.001) decreased, while that of HPIV (χ²=8.66, P=0.003) increased, both with statistically significant differences. According to the seasonal pattern, the epidemic intensity of Flu A decreased in summer, while that of HPIV increased in summer and autumn. Demographic results showed statistically significant differences in the positive rates of EV/HRV between genders (χ²=22.38, P<0.001), with males exhibiting a higher positive rate than females. No statistically significant differences were identified in the positive rates of single pathogen among different age groups (χ²=4.42, P=0.110). Nevertheless, statistically significant differences were noted when comparing the positive rates of EV/HRV, Flu A, Flu B and HPIV across different age groups (P<0.05). EV/HRV was more commonly detected in the 15‒<25 age group (10.93%), while Flu A and HPIV had the highest positive rates in the ≥60 age group (21.24% and 4.77%). Flu B had the highest positive rate in the 25‒<60 age group (11.26%). 52.63% of cases with co-infections occurred during winter, with the primary pathogens involved being EV/HRV (9 cases) and HCoV (6 cases). The most prevalent combination of co-infection was Flu A with EV/HRV. ConclusionThe prevalence of respiratory pathogens among ILI cases from 2023 to 2024 exhibited notable fluctuations compared to that from 2015 to 2019. Therefore, influenza surveillance should be strengthened, and attention should also be paid to the prevalence of respiratory pathogens such as HPIV. These findings have profound implications for future research, surveillance, vaccine planning, and public health policy making.

ObjectiveTo investigate the mechanism of Guizhi Fulingwan （GFW） against hepatic fibrosis， focusing on elucidating the regulatory effect of GFW on the mitochondrial DNA （mtDNA）/NOD-like receptor protein 3 （NLRP3）/cysteinyl aspartate-specific proteinase-1 （Caspase-1）/gasdermin D （GSDMD） signaling pathway. MethodsForty-two male Sprague-Dawley （SD） rats were randomly allocated into six groups （n=7）： control， model， low/medium/high-dose （0.14， 0.28， 0.56 g·kg^-1·d^-1） GFW （GFW-L， GFW-M， GFW-H）， and Dahuang Zhechong pills （DZW， 1.8 g·kg^-1·d^-1）. The rat model of hepatic fibrosis was induced by intraperitoneal injection of carbon tetrachloride. General conditions of the rats were observed. Serum levels of alanine aminotransferase （ALT） and aspartate aminotransferase （AST） levels were measured. Liver histopathology and collagen deposition were observed through hematoxylin and eosin （HE） staining and Masson's trichrome staining. Transmission electron microscopy （TEM） was employed to observe structural alterations and damage of cellular ultrastructures including mitochondria. Mitochondrial membrane potential （MMP， ΔΨ_m） was detected by flow cytometry. Serum levels of interleukin-1β （IL-1β） and interleukin-18 （IL-18） were measured by enzyme-linked immunosorbent assay （ELISA）. The mRNA levels of mtDNA and NLRP3 in the liver tissue were quantified by Real-time polymerase chain reaction （Real-time PCR）. The protein levels of key molecules in the NLRP3/Caspase-1/GSDMD signaling pathway in the liver tissue were determined by Western blot. ResultsCompared with the control group， the model group exhibited a decrease in body weight （P<0.01）， an increase in liver index （P<0.01）， elevations in serum ALT and AST levels （P<0.01）， and typical fibrotic features such as disorganized hepatocytes， inflammatory infiltration， and increased collagen deposition in the liver tissue. TEM revealed significant karyotheca degeneration， mitochondrial swelling， endoplasmic reticulum expansion， and organelle efflux in the model group. In addition， the model group showed decreased ΔΨ_m （P<0.01）， up-regulated mRNA levels of mtDNA and NLRP3 （P<0.01） and protein levels of NLRP3， Caspase-1， and GSDMD （P<0.01） in the liver tissue， and increased serum levels of IL-1β and IL-18 （P<0.01）. Compared with that in the model group， the body weight increased in GFW-L， GFW-M， and DZW groups （P<0.05） and markedly increased in the GFW-H group （P<0.01）. The liver index decreased in the GFW groups and DZW group （P<0.01）. The serum ALT level declined in the GFW-L group （P<0.05）， and the serum ALT and AST levels decreased in the GFW-M， GFW-H， and DZW groups （P<0.01）. Histopathological damage and fibrosis were alleviated to varying degrees， and TEM revealed mitigated ultrastructural injuries including mitophagy， mitochondrial swelling， and endoplasmic reticulum expansion in the drug intervention groups. The ΔΨ_m increased in GFW groups without statistical significance. The mRNA level of mtDNA in the liver tissue was down-regulated in the GFW-M （P<0.05）， GFW-H （P<0.01）， and DZW （P<0.01） groups. The mRNA level of NLRP3 was down-regulated in GFW-M， GFW-H， and DZW groups （P<0.01）. Western blot analysis showed significantly down-regulated protein level of NLRP3 in all the GFW groups and the DZW group （P<0.01）. The protein level of GSDMD-N was down-regulated in GFW-H and DZW groups （P<0.01）. The protein level of cleaved Caspase-1 was down-regulated in GFW-M （P<0.05）， GFW-H （P<0.01）， and DZW （P<0.01） groups. In addition， the serum levels of IL-1β and IL-18 declined in GFW-H and DZW groups （P<0.01）. ConclusionGFW can suppress pyroptosis to ameliorate CCl₄-induced hepatic fibrosis， potentially through mitigating mitochondrial damage， inhibiting inflammasome assembly and activation， and blocking pro-inflammatory cytokine release.

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.

As a chronic and lifelong disease， diabetes mellitus occurs across all age groups and gender groups. Since the disease requires lifelong treatment， it seriously affects the quality of life of patients. With the rising incidence on a global scale， diabetes mellitus has become a global problem that seriously affects public health. Moreover， the complications of this disease have aroused concern from the global medical research community， the World Health Organization， and the public. In the past， Western medicine was used in the clinical treatment of diabetes mellitus， which， however， had drug dependence， unsatisfactory efficacy， and side effects. Long-term oral administration of antidiabetics may cause liver and kidney function damage， hypoglycemia and other adverse symptoms. The treatment of diabetes mellitus has been faced with challenges such as limited efficacy and obvious side effects. Therefore， exploring more effective treatment means， especially tapping the potential of traditional Chinese medicine （TCM） in the treatment of diabetes mellitus， is a major issue to be solved. TCM has shown a great application value and a broad prospect in the treatment of diabetes mellitus because of multi-target regulation， a holistic view， synergistic effects， and high safety. TCM has a history of thousands of years in the prevention and treatment of diabetes mellitus， with rich experience accumulated and remarkable results achieved. Particularly， TCM demonstrates definite therapeutic effects on the complications. The application of TCM in the treatment of complications has been recognized and accepted by patients because of the definite therapeutic effect. In recent years， great progress has been achieved in the treatment of diabetes mellitus by the combination of Chinese and western medicine， which has made important contributions to the control of diabetes mellitus. This paper reviews the articles about the treatment of diabetes mellitus with TCM classic prescriptions， summarizes the treatment of clinical cases regarding the indications of these prescriptions， and provides an overview of the treatment mechanisms， aiming to offer fresh insights and strategies for the clinical diagnosis and treatment of diabetes mellitus.

Objective To develop a machine learning-based risk prediction model for postoperative acute kidney injury(AKI)and a model for mortality in obese patients admitted to intensive care unit(ICU)in order to improve early warning and prognostic evaluation to support clinical decision-making.Methods Data of obese postoperative ICU patients were retrospectively retrieved from the MIMIC-Ⅳ and eICU databases for statistical analysis.Ultimately,2 520 patients(670 from MIMIC-Ⅳ and 1 850 from eICU databases)were included to build the risk prediction models for AKI and mortality.The data included demographic information,vital signs,laboratory findings,surgical types,comorbidities,and medication use.After data cleaning and preprocessing,Boruta feature selection was applied,followed by the construction of prediction models using 7 machine learning algorithms,that is,Gradient Boosting Machine(GBM),Generalized Linear Model(GLM),k-Nearest Neighbors(KNN),Na?ve Bayes(NB),Neural Network(NNET),Support Vector Machine(SVM),and XGBoost.Model performance was evaluated through cross-validation and external validation.Results In the risk prediction models of AKI,the SVM model achieved the highest AUC value of 0.80 in the testing set and 0.71 in the external validation test.For the risk prediction models of mortality,the GBM model outperformed others in the prediction,attaining an AUC value of 0.91 in the testing set.Conclusion Risk predictive models for postoperative AKI and mortality in obese ICU patients are successfully constructed,and are valuable tools for clinicians to optimize early intervention and improve clinical outcomes for the patients.

As a cheap and effective antibiotic,sulfonamides are often used in animal husbandry.However,their residues in animal-derived foodstuffs will threaten human health.Consequently,a high-performance liquid chromatography(HPLC)method integrated with supramolecular solvent microextraction was successfully established for simultaneous quantification of sulfonamide residues sulfachlorpyridazine,sulfamethoxazole,sulfamethoxypyridazine and sulfadimethoxine in milk matrices.This approach exhibited prominent characteristics of operational simplicity,environmental sustainability,and high extraction efficiency.The supramolecular solvents prepared by tributyl octylphosphine tetrafluoroborate and tetrahydrofuran were employed as extraction solvents.The analytes underwent isolation and concentration via dispersive liquid-liquid microextraction(DLLME)prior to quantitative determination using high-performance liquid chromatography(HPLC).The composition and microscopic morphology of the supramolecular solvent were characterized through a series of analytical techniques,including phase diagram,Fourier transform infrared spectroscopy,scanning electron microscopy,and inverted fluorescence microscopy and so on.The density and pH value of supramolecular solvents were determined.The extraction conditions were optimized through the one-factor experiments.The experimental results demonstrated that under the optimal extraction conditions,the four kinds of sulfonamide antibiotics exhibited excellent linearity within respective detection range(R2 ≥ 0.9998)and the limits of detection(LOD)were 0.67-1.45 μg/L.Compared with literature methods,this approach offered some advantages such as simplicity of operation and less reagent consumption,and could be used for analysis and detection of sulfonamide antibiotic residues in milk samples.The present method provided technical support for food safety regulation and paved a new way for the application of supramolecular solvents in the field of extraction and separation.

Due to the poor ionization efficiency and the weak mass spectrometry(MS)intensity of weakly polar substances,direct analysis using the traditional electrospray ionization mass spectrometry(ESI-MS)is a big challenge.In this study,a novel rapid on-site detection method of four prohibited sex hormones in cosmetics was proposed using online derivatization strategy coupled with a miniature mass spectrometer.The target substances in the samples were extracted by a custom-made polyaniline/multi-walled carbon nanotube solid-phase microextraction(SPME)probe.The stirring speed was 200 r/min,the extraction temperature was 40℃,and the extraction time was 2 min.A pulled dual-channel θ borosilicate glass capillary emitter was used as the nano-ESI ion source.The SPME probe was inserted into the channel containing methanol in theθborosilicate glass capillary.When the spray voltage was applied,the four sex hormones were desorbed and formed spray microdroplets,which then collided with the hydroxylamine microdroplets generated from the other channel.The microdroplets of reaction product entered into the miniature mass spectrometer for direct analysis.The limits of detection(LOD)and limits of quantification(LOQ)for the four sex hormones were 10-20 ng/mL and 20-50 ng/mL,respectively.The recoveries were from 84.6%to 107.8%with the relative standard deviations(RSD)from 4.1%to 11.6%.Compared to detection without derivatization,the MS signals of the four target substances were increased by 3 to 15 times.This method was simple,rapid,highly efficient and sensitive,and suitable for on-site rapid analysis of weakly polar sex hormones in cosmetics.

Objective:To explore the medication law of TCM external therapy for the treatment of diabetic foot using data mining methods.Methods:Literature on TCM external treatment for diabetic foot was retrieved from CNKI, Wanfang Data, and Chongqing VIP from the establishment of the databases to June 30, 2024. Excel 2019 software was used to conduct frequency statistics on drug frequency, properties, tastes and meridian tropism, drug efficacy, and commonly used drug pairs. Ancient and Modern Medical Cases Cloud Platform V3.5 was used for association rules, and SPSS Statistics 27.0 was used for complex network analyses.Results:A total of 425 articles were included, involving 328 prescriptions and 232 drugs. The drugs with higher frequency were Carthami Flos, Angelicae Sinensis Radix, Cinnamomi Ramulus, Olibanum, etc. The main tastes were pungent, bitter, and sweet; the main properties were cold and warm; the main meridians were heart, spleen, and liver meridians. The main efficacy was promoting blood circulation and removing blood stasis, clearing heat, and tonifying deficiency; association rule analysis obtained 20 commonly used drug pairs; clustering analysis resulted in four core drug combinations; complex network analysis led to one core prescription.Conclusions:TCM external therapy for diabetic foot follows the guiding principles of "promoting blood circulation and unblocking collaterals, tonifying deficiency and clearing heat" with coordinated regulation of the "heart-liver-spleen meridians". The core prescription and stage-specific formulation strategies reflect a trinity diagnostic and therapeutic approach of "disease differentiation-syndrome differentiation-stage differentiation", providing valuable reference and insights for clinical prescription practices.

Objective To explore the clinical characteristics and treatment of immune checkpoint inhibitors(ICIs)-associated myocarditis in patients with bladder cancer(BCa).Methods Clinical and follow-up data of 213 BCa patients treated with ICIs in our hospital during Jan.2020 and May 2024 were collected.The data of 7 patients(3.3％)who developed ICIs-associated myocarditis were analyzed.Results The cohort included 2 females and 5 males(median age:72 years).Four patients were asymptomatic,while 3 presented with chest tightness,dyspnea,or orthopnea.All patients showed significantly elevated high-sensitivity troponin T.Only 2 patients had markedly increased N-terminal pro-B-type natriuretic peptide.Electrocardiograms were normal in 4 patients,while 2 patients exhibited significantly reduced left ventricular global longitudinal strain on echocardiography,with cardiac magnetic resonance confirming acute myocarditis.All patients discontinued ICIs and received first-line methylprednisolone upon diagnosis.Two patients showed no improvement after 5 days of treatment and received second-line therapy.One patient received intravenous immunoglobulin and infliximab without response,but improved after third-line tofacitinib.One patient developed acute respiratory failure after intravenous immunoglobulin administration and was then transferred to ICU,and died of multiple organ failure after 10 days.Conclusion ICIs-associated myocarditis is a relatively rare but clinically distinct immune-related adverse reaction during BCa treatment.Methylprednisolone is the first-line therapy,while critically ill and steroid-resistant patients often require early combined immunosuppressants based on individualized multidisciplinary discussion.