ObjectiveMigraine is a leading neurological disorder and the fourth most common cause of years lived with disability worldwide, affecting nearly 116 million individuals. Although pharmacological treatments are available, their efficacy is often limited by side effects and variable response rates. Repetitive transcranial magnetic stimulation (rTMS) over the dorsolateral prefrontal cortex (DLPFC) offers a safe, non-invasive alternative for migraine management. However, the neurophysiological mechanisms, particularly how rTMS modulates local cortical excitability and distributed pain-related circuits, remain poorly understood. Elucidating these mechanisms is essential for optimizing treatment protocols and improving clinical outcomes. MethodsThis study employed concurrent transcranial magnetic stimulation and electroencephalography (TMS-EEG) to investigate neuroplastic and neurocircuitry mechanisms of DLPFC-rTMS in migraine. Study 1 compared 30 migraineurs and 28 healthy controls to identify abnormalities in TMS-evoked potentials (TEPs) and significant current density (SCD) within sensory-discriminative regions including the primary somatosensory cortex (S1) and posterior insula (pINS), cognitive-affective regions including the anterior insula (aINS) and midcingulate cortex (MCC), and a descending modulatory region, the periaqueductal gray (PAG). Study 2 used a single-blind, crossover, sham-controlled design in 34 healthy participants. Each participant received both active (10 Hz, 80% RMT, 1 500 pulses) and sham DLPFC-rTMS in counterbalanced order. TMS-EEG and cold pain tolerance were assessed before and after each session. ResultsIn Study 1, migraineurs showed a significantly less negative N120 amplitude compared to healthy controls (P=0.027, Cohen’s d=0.60), indicating local intracortical disinhibition. No group differences were observed for N40, P60, or P180 components. At the source level, migraineurs exhibited significantly higher SCD in the S1, pINS, aINS, and MCC (allQ<0.05), but not in the ventroposterior thalamus (vpTHAL), mediodorsal thalamus (mdTHAL), or PAG. In Study 2, active rTMS significantly reduced SCD from pre- to post-stimulation in the S1, aINS, and MCC (all Q<0.05). Sham stimulation also reduced SCD in the S1 (Q<0.05) but not in the aINS or MCC. Although no significant group-level analgesic effect was observed between active and sham conditions (P=0.107), correlation analyses revealed that greater SCD reductions in the S1 and MCC were significantly associated with higher post-rTMS pain tolerance (R=-0.487 and -0.495, both Q<0.01) and larger improvements in pain tolerance(R=-0.487 and -0.451, both Q<0.05). No such correlations were found following sham stimulation, suggesting that the behavioural relevance of neural changes is specific to active rTMS. ConclusionThis study provides novel evidence that migraineurs exhibit both local neuroplastic abnormalities (reduced N120 amplitude) and hyperactivity in key pain-processing regions (S1, pINS, aINS, MCC). A single session of DLPFC-rTMS reduced hyperactivity in the aINS, MCC, and S1. Notably, greater reductions in the S1 and MCC were associated with improved pain tolerance. These findings identify distinct cortical circuitries, particularly within the cognitive-affective pain network, that may serve as potential biomarkers for optimizing rTMS treatment in migraine and other chronic pain conditions. Future studies should validate these results in patient populations experiencing spontaneous migraine attacks and explore multi-session or accelerated rTMS protocols.

Objective To develop a deep learning model based on fundus retinal images to improve the detection rate of mild cognitive impairment(MCI)in patients with coronary heart disease,achieve early intervention and improve prognosis.Methods The study was a single-center cross-sectional study that retrospectively included patients diagnosed with coronary heart disease(CHD)by coronary angiography(≥50％ stenosis of at least one coronary vessel)from Beijing Anzhen Hospital between November 2021 and December 2022.The whole data set was randomly divided into the training set and the testing set according to the ratio of 8∶2 for model development.After that,the patient data of the same center from January 2023 to April 2023 were included in the time verification method to verify the model.The diagnostic criteria for MCI were MMSE＜27 or MoCA＜26.Four kinds of convolutional neural network(CNN)architectures were used to train fundus images,and a comprehensive vision model of MCI detection was established through model integration.The area under the curve(AUC),sensitivity and specificity of the receiver operating curve(ROC)were used to evaluate the performance of the AI model.Results We collected 5 880 eligible fundus images from 3 368 CHD patients.Based on the results of the MMSE scale,the algorithm was labeled,including 2 898 males and 527 MCI patients.The AUC of the deep learning model in the test group is 0.733(95％CI 0.688-0.778),and the sensitivity of the algorithm in the test group is 0.577(95％CI 0.528-0.625)by using the operating point with the maximum sum of sensitivity and specificity.With a specificity of 0.758(95％CI 0.714-0.802),corresponding to a validated AUC of 0.710(95％CI 0.601-0.818).Based on the results of the MoCA scale,the algorithm labels 2 437 males and 1 626 MCI patients.The AUC of the deep learning model in the test group was 0.702(95％CI 0.671-0.733).The operating point with the maximum sum of sensitivity and specificity was selected,and the sensitivity of the algorithm was 0.749(95％CI 0.719-0.778)and the specificity was 0.561(95％CI 0.527-0.595),corresponding to the AUC value of the verification group was 0.674(95％CI 0.622-0.726).Conclusions The deep learning algorithm model based on fundus images has good diagnostic performance,and may be used as a new non-invasive,convenient and rapid screening method for MCI in CHD population.

This paper summarizes the progress of theoretical research and practice of One Health in China,and discusses the paradigm of One Health governance to improve the prevention and control of infectious diseases in China and the world,and provide an example for the improvement of the public health system.In particular,China has long history to apply the concept of One Health in the national schistosomiasis control programmes and patriotic health campaigns,which were not only focusing on human health,but also emphasizing the sustainable development of animal health and ecological environment.At the same time,the application of tools such as system dynamics model,eDNA technology,One Health economic assessment and global One Health index(GOHI)in the field of disease control and environmental health provides technical support for the concept of One Health.Despite the challenges of practical application of these tools,the One Health concept will play a greater role in providing sustainable solutions for human-animal-environmental health by strengthening interdisciplinary collaboration,improving standardization protocols and promoting inter-national cooperation.

Objective To develop a deep learning model based on fundus retinal images to improve the detection rate of mild cognitive impairment(MCI)in patients with coronary heart disease,achieve early intervention and improve prognosis.Methods The study was a single-center cross-sectional study that retrospectively included patients diagnosed with coronary heart disease(CHD)by coronary angiography(≥50％ stenosis of at least one coronary vessel)from Beijing Anzhen Hospital between November 2021 and December 2022.The whole data set was randomly divided into the training set and the testing set according to the ratio of 8∶2 for model development.After that,the patient data of the same center from January 2023 to April 2023 were included in the time verification method to verify the model.The diagnostic criteria for MCI were MMSE＜27 or MoCA＜26.Four kinds of convolutional neural network(CNN)architectures were used to train fundus images,and a comprehensive vision model of MCI detection was established through model integration.The area under the curve(AUC),sensitivity and specificity of the receiver operating curve(ROC)were used to evaluate the performance of the AI model.Results We collected 5 880 eligible fundus images from 3 368 CHD patients.Based on the results of the MMSE scale,the algorithm was labeled,including 2 898 males and 527 MCI patients.The AUC of the deep learning model in the test group is 0.733(95％CI 0.688-0.778),and the sensitivity of the algorithm in the test group is 0.577(95％CI 0.528-0.625)by using the operating point with the maximum sum of sensitivity and specificity.With a specificity of 0.758(95％CI 0.714-0.802),corresponding to a validated AUC of 0.710(95％CI 0.601-0.818).Based on the results of the MoCA scale,the algorithm labels 2 437 males and 1 626 MCI patients.The AUC of the deep learning model in the test group was 0.702(95％CI 0.671-0.733).The operating point with the maximum sum of sensitivity and specificity was selected,and the sensitivity of the algorithm was 0.749(95％CI 0.719-0.778)and the specificity was 0.561(95％CI 0.527-0.595),corresponding to the AUC value of the verification group was 0.674(95％CI 0.622-0.726).Conclusions The deep learning algorithm model based on fundus images has good diagnostic performance,and may be used as a new non-invasive,convenient and rapid screening method for MCI in CHD population.

This paper summarizes the progress of theoretical research and practice of One Health in China,and discusses the paradigm of One Health governance to improve the prevention and control of infectious diseases in China and the world,and provide an example for the improvement of the public health system.In particular,China has long history to apply the concept of One Health in the national schistosomiasis control programmes and patriotic health campaigns,which were not only focusing on human health,but also emphasizing the sustainable development of animal health and ecological environment.At the same time,the application of tools such as system dynamics model,eDNA technology,One Health economic assessment and global One Health index(GOHI)in the field of disease control and environmental health provides technical support for the concept of One Health.Despite the challenges of practical application of these tools,the One Health concept will play a greater role in providing sustainable solutions for human-animal-environmental health by strengthening interdisciplinary collaboration,improving standardization protocols and promoting inter-national cooperation.

Objective:To explore the clinical value of combination of contrast-enhanced ultrasound(CEUS)and magnetic reso-nance imaging(MRI)in the diagnosis of parotid gland tumors.Methods:167 patients with parotid gland tumors were diagnosed by CEUS and MRI respectively and in combination.With postoperative pathological diagnosis result as the gold standard,the diagnos-tic value of CEUS combined with MRI(CEUS+MRI)was analyzed.Results:171 tumors were confirmed by postoperative pathology in 167 patients,including 143 benign tumors(83.63％)and 28 malignant tumors(16.37％).The benign tumors were mainly pleo-morphic adenoma(65 tumors,45.45％),Warthin tumor(40 tumors,27.97％)and basal cell adenoma(13 tumors,9.09％).Among the malignant tumors,mucoepidermoid carcinoma(7 tumors,25.00％),adenoid cystic carcinoma(6 tumors,21.43％)and acinic cell carcinoma(3 tumors,10.71％)were the most common.CEUS showed 39 cases of malignant parotid gland tumors and 132 cases of benign parotid gland tumors.The sensitivity,specificity and accuracy rate of CEUS were 57.14％,83.92％and 79.53％respectively.The MRI ADC values were manifested as pleomorphic adenoma＞malignant tumor＞Warthin tumor(P＜0.05).MRI showed 53 cases of malignant parotid gland tumors and 118 cases of benign parotid gland tumors,and the sensitivity,specifici-ty and accuracy rate of MRI diagnosis were 67.86％,76.22％and 74.85％respectively.CEUS+MRI revealed 33 cases of malig-nant parotid gland tumors and 138 cases of benign parotid gland tumors,the sensitivity,specificity and accuracy rate of CEUS+MRI diagnosis were 78.57％,92.31％and 90.06％respectively.The specificity and accuracy rate of CEUS+MRI were significantly higher than those of single examination(x2=4.806,13.951,7.348,13.664,allP＜0.05).Conclusion:Both CEUS and MRI have certain clinical value in the diagnosis of parotid gland tumors,and the specificity and accuracy rate of CEUS+MRI are significantly higher than those of single examination.

Kv1.3,a voltage-gated potassium channel,is highly expressed in T lymphocytes,the nervous system,and vascular smooth muscle cells.It plays a critical role in membrane excitability and electrical signal transduction,serving as an important target for studying T-cell function and providing a promising direction for developing therapeutics against autoimmune and inflammatory diseases.Therefore,the de-velopment of specific inhibitors of Kv1.3 channel has emerged as a novel therapeutic strategy for these disorders.In this study,we isolated and purified a novel Kv1.3-inhibitory peptide toxin,LmKTx13,from the venom of the scorpion Lychas mucronatus using reversed-phase high-performance liquid chroma-tography(RP-HPLC).LmKTx13 consists of 38 amino acid residues,including six cysteines that form three disulfide bonds.Whole-cell patch-clamp recordings revealed that LmKTx13 potently inhibited Kv1.3 with an IC50 of 7.92±3.0 nmol/L.Selectivity analysis showed that 2 μmol/L LmKTx13 also in-hibited Kv1.2 and Kv1.7,but exhibited no significant effects on other potassium channel subtypes or voltage-gated sodium channels.Further investigation into the mechanism demonstrated that LmKTx13 acts as a pore-blocking inhibitor of Kv1.3.By analyzing the effects of LmKTx13 on Kv1.3 channel gating ki-netics and performing sequence alignment of the pore regions of Kv1.3 and Kv1.5,we constructed site-directed mutants and identified the pore region of Kv1.3 as the critical binding site for LmKTx13.Key residues involved in the interaction included T425,G427,and H451.In summary,we discovered a no-vel pore-blocking Kv1.3 inhibitor,LmKTx13,from L.mucronatus venom,which exhibits high affinity and selectivity for Kv1.3.These findings highlight its potential as a potential lead molecule for developing Kv1.3-targeted therapeutics.

Kv1.3,a voltage-gated potassium channel,is highly expressed in T lymphocytes,the nervous system,and vascular smooth muscle cells.It plays a critical role in membrane excitability and electrical signal transduction,serving as an important target for studying T-cell function and providing a promising direction for developing therapeutics against autoimmune and inflammatory diseases.Therefore,the de-velopment of specific inhibitors of Kv1.3 channel has emerged as a novel therapeutic strategy for these disorders.In this study,we isolated and purified a novel Kv1.3-inhibitory peptide toxin,LmKTx13,from the venom of the scorpion Lychas mucronatus using reversed-phase high-performance liquid chroma-tography(RP-HPLC).LmKTx13 consists of 38 amino acid residues,including six cysteines that form three disulfide bonds.Whole-cell patch-clamp recordings revealed that LmKTx13 potently inhibited Kv1.3 with an IC50 of 7.92±3.0 nmol/L.Selectivity analysis showed that 2 μmol/L LmKTx13 also in-hibited Kv1.2 and Kv1.7,but exhibited no significant effects on other potassium channel subtypes or voltage-gated sodium channels.Further investigation into the mechanism demonstrated that LmKTx13 acts as a pore-blocking inhibitor of Kv1.3.By analyzing the effects of LmKTx13 on Kv1.3 channel gating ki-netics and performing sequence alignment of the pore regions of Kv1.3 and Kv1.5,we constructed site-directed mutants and identified the pore region of Kv1.3 as the critical binding site for LmKTx13.Key residues involved in the interaction included T425,G427,and H451.In summary,we discovered a no-vel pore-blocking Kv1.3 inhibitor,LmKTx13,from L.mucronatus venom,which exhibits high affinity and selectivity for Kv1.3.These findings highlight its potential as a potential lead molecule for developing Kv1.3-targeted therapeutics.

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 evaluate the efficacy and safety of transcatheter aortic valve implantation(TAVI)for the treatment of primary aortic valve regurgitation(NAVR)and to compare the difference in the choice of prosthetic valve size and the difference in complications with aortic stenosis(AS).Methods According to the definition of Valve Academic Research Consortium(VARC-3),143 patients with NAVR/AS treated with TAVI and patients with NAVR treated with surgical aortic valve replacement(SAVR)at Tongji Hospital,Tongji Medical College,Huazhong University of Science and Technology,China,from March 2019 to September 2024 were selected,and clinical data on baseline,perioperative,and primary endpoint events were were retrospectively collected and compared.Results Forty-three patients with NAVR were treated with TAVI,with a device success rate of 86.0％and a surgical success rate of 95.3％.Subgroup comparisons:(1)NAVR-TAVI group than NAVR-SAVR group:patients in the TAVI group had a significantly shorter operative time than those in the SAVR group(P＜0.001);complete left bundle branch block was more likely to occur after TAVI(P=0.042),and complete right bundle branch block was more likely to occur after SAVR(P=0.044).SAVR postoperatively The incidence of congestive heart failure was higher(P=0.013),and the mortality rate was significantly higher in the SAVR group than in the TAVI group(P=0.019).(2)NAVR-TAVI group than AS-TAVI group:the differences in access selection,THV size[28(22,34)mm vs.24(22,32)mm,P=0.044]and proportion of THV overdiameter[14％(7％,20％)vs.7％(3％,11％),P＜0.001]were statistically significant.patients in AS and NAVR groups had 1 case of permanent pacing after TAVI treatment.In the AS and NAVR groups,there was 1 case of permanent pacemaker implantation after TAVI.2 patients in the AS group were converted to surgical treatment,and 6 patients died.Conclusions The use of"off-label"(transfemoral)and"on-label"(transapical)TAVI devices(both from domestic sources)is safer than SAVR for the treatment of NAVR,especially in elderly and high-risk patients.Compared with patients with AS treated with TAVI,larger diameter annulas are usually selected for NAVR,with higher rates of valve migration,but overall safety and efficacy are comparable to AS.