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.

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.

Objective To organize and summarize the medication rules of GU Nai-fang in treating skin diseases through real-world data.Methods We collected traditional Chinese medicine prescriptions for GU Nai-fang's treatment of skin diseases from the outpatient medical record system of Shanghai Traditional Chinese Medicine Hospital to establish a database.Statistical analysis of disease types,performance,and efficacy was conducted,and association rules and systematic clustering analysis were performed using SPSS Modeler 18.0 and SPSS 26.0 software,respectively.Results A total of 5 020 patients were included,and 5 020 prescriptions were collected,involving 241 traditional Chinese medicines with a total frequency of 85 758 uses.The frequency of using heat clearing drugs,deficiency tonifying drugs,blood activating and stasis removing drugs,surface clearing drugs,and wind and dampness dispelling drugs was relatively high;most drugs tended to be cold and warm,mainly targeting the heart,lungs,and colon meridians.The top 15 Chinese medicines with the highest frequency of use were Smilacis Glabrae Rhixoma,Cortex Moutan,Radix Paeoniae Rubra,Rehmanniae Radix,Scutellariae Radix,Cynanchi Paniculati Radix et Rhizoma,Schisandrae Chinensis Fructus,Violsse Herba,Mume Fructus,Herba Pyrolae,Hedyotis Diffusae Herba,Lonicerae Japonicae Flos,Cicadae Periostracum,Bombyx Batryticatus,Radix Salviae.Association rule analysis obtained 15 high-frequency combinations of 2 traditional Chinese medicines and 3 traditional Chinese medicines.Cluster analysis resulted in 7 clustered prescriptions.Conclusion GU Nai-fang commonly used heat clearing drugs,deficiency tonifying drugs,blood activating and stasis removing drugs,surface resolving drugs,and wind and dampness dispelling drugs in the treatment of skin diseases,and Smilacis Glabrae Rhixoma,Cortex Moutan,Radix Paeoniae Rubra,Rehmanniae Radix,and Scutellariae Radix were the most frequently used drugs.

AIM To study the chemical constituents from Dictamni Cortex.METHODS The 70％ethanol extract from Dictamni Cortex was isolated and purified by HP-20 macroporous resin,silica gel,MCI,ODS and preparative HPLC,then the structures of obtained compounds were identified by physicochemical properties and spectral data.RESULTS Thirty-three compounds were isolated and identified as rutin(1),apigenin(2),catechin(3),hesperetin(4),leonuriside A(5),androsin(6),2-methoxy-4-acetylphenol-O-α-rhamnopyranosyl-(1"-6')-β-glucopyranoside(7),vanillic acid(8),gallic acid(9),4-hydroxybenzoic acid(10),benzoic acid(11),involcranoside B(12),benzyl β-D-glucopyranoside(13),bphenylethyl-rutinoside(14),1-bromonaphthalene(15),cimifugin(16),9(S),12(S),13(S)-trihydroxyoctadeca-10(E),15(Z)-dienoic acid(17),methyl-9,12,13-trihydroxyoctadeca-10,15-dienoate(18),7,8-dihydroxy-9,12(Z,Z)-octadecadienoic acid(19),vernolic acid(20),9,10(erythro)-dihydroxy-11 E-octadecadienoic acid methyl ester(21),(7Z,9E,13Z)-11-hydroxyhexadeca-7,9,13-trienoic acid(22),(7Z,10Z,14E,16Z,19Z)-13-hydroxydocosa-7,10,14,16,19-pentaenoic acid(23),(9E)-8,11,12-trihydroxyoctadecenoic acid methyl ester(24),n-hexanol-O-rutinoside(25),hexyl β-sophoroside(26),3-pentyl 6'-(3-hydroxy-3-methylglutaryl)-β-D-glucopyranoside(27),3-methylbut-3-enyl-6-O-β-D-glucopyranosyl-β-D-glucopyranoside(28),3-methyl-but-2-en-1-yl β-D-glucopyranoside(29),3-methylbutan-1-ol-β-D-glucopyranoside(30),pregnenolone(31),2-butoxytetrahydrofuran(32),psydrin(33).CONCLUSION Compounds 2-4,8-13,15-16,25-28 and 32-33 are isolated from Rutaceae family for the first time.

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.

Objective To investigate the practical effects of pediatric rapid sequence intubation(RSI)nursing coordination training based on the LSPPDM(learn,see,practice,prove,do,maintain)framework in order to provide evidence for optimizing pediatric RSI nursing training programs.Methods Nurses from the intensive care unit(ICU)of Children's Hospital,Fudan University during Feb 2023 and Jan 2024 were divided into the experimental group(n=35)and the control group(n=35)by block randomization.The experimental group received LSPPDM framework-based training,while the control group underwent conventional training with theoretical lectures and procedural demonstrations.Outcomes included training satisfaction,theoretical knowledge and procedural skill assessment scores,team collaboration compliance and RSI procedure time were compared between the two groups.Results The experimental group demonstrated significantly higher training satisfaction(123.80±2.04 vs.117.26±9.82,P<0.05),superior post-training theoretical knowledge and procedural skills(P<0.05),enhanced team collaboration compliance(P<0.05),and shorter RSI completion time(P<0.05)compared with the control group.Conclusion Pediatric RSI nursing coordination training based on the LSPPDM framework can effectively increase training satisfaction,promote theoretical and procedural skills and reduce completion time in nurses.

Objective To organize and summarize the medication rules of GU Nai-fang in treating skin diseases through real-world data.Methods We collected traditional Chinese medicine prescriptions for GU Nai-fang's treatment of skin diseases from the outpatient medical record system of Shanghai Traditional Chinese Medicine Hospital to establish a database.Statistical analysis of disease types,performance,and efficacy was conducted,and association rules and systematic clustering analysis were performed using SPSS Modeler 18.0 and SPSS 26.0 software,respectively.Results A total of 5 020 patients were included,and 5 020 prescriptions were collected,involving 241 traditional Chinese medicines with a total frequency of 85 758 uses.The frequency of using heat clearing drugs,deficiency tonifying drugs,blood activating and stasis removing drugs,surface clearing drugs,and wind and dampness dispelling drugs was relatively high;most drugs tended to be cold and warm,mainly targeting the heart,lungs,and colon meridians.The top 15 Chinese medicines with the highest frequency of use were Smilacis Glabrae Rhixoma,Cortex Moutan,Radix Paeoniae Rubra,Rehmanniae Radix,Scutellariae Radix,Cynanchi Paniculati Radix et Rhizoma,Schisandrae Chinensis Fructus,Violsse Herba,Mume Fructus,Herba Pyrolae,Hedyotis Diffusae Herba,Lonicerae Japonicae Flos,Cicadae Periostracum,Bombyx Batryticatus,Radix Salviae.Association rule analysis obtained 15 high-frequency combinations of 2 traditional Chinese medicines and 3 traditional Chinese medicines.Cluster analysis resulted in 7 clustered prescriptions.Conclusion GU Nai-fang commonly used heat clearing drugs,deficiency tonifying drugs,blood activating and stasis removing drugs,surface resolving drugs,and wind and dampness dispelling drugs in the treatment of skin diseases,and Smilacis Glabrae Rhixoma,Cortex Moutan,Radix Paeoniae Rubra,Rehmanniae Radix,and Scutellariae Radix were the most frequently used drugs.

Objective To compare six representative Chinese standards related to hearing disability in terms of conceptual frame-works,definitions,classification,grading and assessment.Methods Using the framework of International Classification of Functioning,Disability and Health(ICF),World Health Organization Disability Assessment Schedule 2.0(WHODAS 2.0),International Classification of Diseases,Elev-enth revision(ICD-11),and World Report on Hearing(WRH),six national standards,such as Disability Assess-ment and Code for Life Insurance(Insurance Standard),Specification for Ability Assessment of Older Adults(El-derly Standard),Grading of Disability due to Human Body Injury(Judicial Standard),Standard for Identify Work Ability—Gradation of Disability Caused by Work-related Injuries and Occupational Diseases(Work Injury Stan-dard),Standard for Assessment of Disability Grades of Military Personnel(Military Standard),and Classification and Grading Criteria of Disability(Disability Classification Standard),were analyzed in dimensions of defini-tions,coding systems,classification and grading methods,and assessment approaches.Results In terms of definitions,Insurance Standard was relatively comprehensive,covering the dimensions of body func-tion,activities and participation in ICF,which was consistent with the definition of functional hearing in the WRH.Elderly Standard focused on body function.Judicial,Work Injury and Military Standards were more in-clined to physiological damage,emphasizing auditory structure and sensory functions.Although Disability Clas-sification Standard covering dimensions of activities and participation,it did not elaborate on functional out-comes.In terms of classification and grading,different from the WRH's recommendation of 20 dB HL as the stan-dard for hearing impairment,most of the six standards still used 40 dB HL or above as the threshold.For grading levels,Insurance Standard had nine grades,while Judicial,Work Injury and Military Standards had seven grades,Elderly Standard had three grades,and Disability Classification Standard had four grades.The basis for grading relied more on physiological measurements rather than functional performance.In terms of coding systems,only Insurance Standard referred to ICF codes,while the others mostly used self-defined numerical sequences or grade codes.In terms of assessment methods,the WRH advocated a comprehensive assessment integrating auditory ability,self-reports and participation restrictions.However,currently,only Elderly Standard involved subjective sensory scoring,and the others still mainly relied on objective methods,such as pure-tone audiometers,lacking assessment dimensions for social participation and environmental factors.Conclusion Hearing disability should be defined from three levels:hearing structure and function,activity and participa-tion,and environmental factors.However,China's six standards adopt different definitions,classifications,grad-ing systems and assessment methods due to their varying purposes and application scenarios.The majority of these standards are primarily based on body function,specifically hearing loss.Future standards for hearing dis-ability need to consider the introduction of the concept of functional hearing,to determine hearing loss and its im-pact on activity and participation.

ObjectiveTo analyze the data structure and standards of rehabilitation outpatient medical records, to provide data support for improving the quality of rehabilitation outpatient care and developing medical insurance payment policies. MethodsBased on the normative documents issued by the National Health Commission, Basic Standards for Medical Record Writing and Standards for Electronic Medical Record Sharing Documents, in accordance with the Quality Management Regulations for Outpatient (Emergency) Diagnosis and Treatment Information Pages (Trial), reference to the framework of the World Health Organization Family of International Classifications (WHO-FICs), the data framework and content of rehabilitation outpatient medical records were determined, and the data standards were discussed. ResultsThis study constructed a data framework for rehabilitation outpatient medical records, including four main components: patient basic information, visit process information, diagnosis and treatment information, and cost information. Three major reference classifications of WHO-FICs, International Classification of Diseases, International Classification of Functioning, Disability and Health, and International Classification of Health Interventions,were used to establish diagnostic standards and standardized terminology, as well as coding disease diagnosis, functional description, functional assessment, and rehabilitation interventions, to improve the quality of data reporting, and level of quality control in rehabilitation. ConclusionThe structuring and standardization of rehabilitation outpatient medical records are the foundation for sharing of rehabilitation data. The using of the three major classifications of WHO-FICs is valuable for the terminology and coding of disease diagnosis, functional description and assessment, and intervention in rehabilitation outpatient medical records, which is significant for sharing and interconnectivity of rehabilitation outpatient data, as well as for optimizing the quality and safety of rehabilitation medical services.