BackgroundSchizophrenia is a highly heterogeneous disease with different clinical subtypes. Artificial intelligence technology represented by deep learning models has provided considerable benefits for the electroencephalogram （EEG）-based schizophrenia diagnosis， treatment and research， however， to date little research has been conducted regarding any of these benefits among Chinese schizophrenic patients. ObjectiveTo investigate the application of deep learning techniques utilizing EEG parameters for the diagnosis of first-episode schizophrenia and grading of EEG abnormalities in patients， with the aim of contributing to improved clinical diagnosis and treatment strategies for the disorder. MethodsFrom January 2020 to January 2023， a total of 130 patients with first-episode schizophrenia who met the diagnostic criteria of International Classification of Diseases， tenth edition （ICD-10）， and attended at the Third People's Hospital of Fuyang， along with 150 health checkup examinees， were enrolled. All of them underwent EEG examination. An optimized long short-term memory （LSTM） deep learning model was developed utilizing EEG signals. Ten-fold cross-validation method was employed to evaluate the model's performance. The dataset was then split into two components： a training set （90%） for LSTM model development and a test set （10%） for validation. The accuracy， recall rate， precision， F1-score， schizophrenia diagnosis and EEG abnormality grading were used as evaluation indicators， and the results of the proposed model were compared to the assessments made by experienced psychiatrists. ResultsFor schizophrenia diagnosis， the modeling group achieved the following performance metrics： precision （94.40±3.03）%， recall rate （94.30±3.23）%， accuracy （94.60±2.22）%， and F1-score （94.20±2.20）%. In the validation group， the corresponding metrics were precision （90.90±2.85）%， recall rate （92.20±1.14）%， accuracy （92.20±1.69）%， and F1-score （91.50±1.78）%. Statistical analysis revealed no significant differences between the LSTM diagnostic model and the experienced psychiatrists in terms of precision， recall rate， accuracy， and F1-score for schizophrenia diagnosis （χ²=1.500， 0.750， 2.722， 1.056， P>0.05）. The modeling group demonstrated an accuracy rate of （91.71±1.73）% in grading EEG abnormalities. For Grade 1 abnormalities， the modeling group reported a precision of （96.40±2.39）%， a recall rate of （94.77±1.40）%， and an F1-score of （95.55±1.14）%. In the case of Grade 2 abnormalities， the precision was （85.89±2.04）%， the recall rate was （88.10±6.18）%， and the F1-score was （87.06±3.12）%. For the more severe Grade 3 abnormalities， the modeling group's precision was （79.61±7.33）%， the recall rate was （81.79±9.87）%， and the F1-score was （80.41±6.79）%. Additionally， the validation group exhibited an accuracy rate of （85.61±6.16）%. The precision， recall rate， and F1-score for Grade 1 abnormalities were （91.43±6.25）%， （92.64±9.65）% and （91.56±4.83）%， respectively. For Grade 2 abnormalities， these metrics were （71.17±19.02）%， （77.64±17.24）% and （71.88±11.33）%. In the case of Grade 3 abnormalities， the precision was （90.00±21.08）%， the recall rate was （80.00±25.82）%， and the F1-score was （81.67±19.95）%. There was no significant difference in the accuracy， recall， accuracy and F1 value between LSTM model and senior doctors in evaluating the abnormal degree of EEG in schizophrenia （χ²=0.098， 0.036， 0.020， 0.336， P>0.05）. The LSTM model takes less time to diagnose schizophrenia and EEG abnormalities than senior doctors， and the differences were statistically significant （t=57.147， 43.104， P<0.01）. ConclusionThe study utilizes an EEG-based LSTM deep learning model for diagnosing first-episode schizophrenia and grading EEG abnormalities， and the model not only matches the performance of experienced psychiatrists but also significantly reduces the time required for diagnosis.

OBJECTIVES: To evaluate the efficacy of molecular targeted agents in children with progressive pediatric low-grade gliomas (pLGG). METHODS: A retrospective analysis was conducted on pLGG patients treated with oral targeted therapies at the Department of Pediatrics, Beijing Shijitan Hospital, Capital Medical University, from July 2021. Treatment responses and safety profiles were assessed. RESULTS: Among the 20 enrolled patients, the trametinib group (n=12, including 11 cases with BRAF fusions and 1 case with BRAF V600E mutation) demonstrated 4 partial responses (33%) and 2 minor responses (17%), with a median time to response of 3.0 months. In the vemurafenib group (n=6, all with BRAF V600E mutation), 5 patients achieved partial responses (83%), showing a median time to response of 1.0 month. Comparative analysis revealed no statistically significant difference in progression-free survival rates between the two treatment groups (P>0.05). The median duration of clinical benefit (defined as partial response + minor response + stable disease) was 11.0 months for vemurafenib and 18.0 months for trametinib. Two additional cases, one with ATM mutation treated with olaparib for 24 months and one with NF1 mutation receiving everolimus for 21 months, discontinued treatment due to sustained disease stability. No severe adverse events were observed in any treatment group. CONCLUSIONS Molecular targeted therapy demonstrates clinical efficacy with favorable tolerability in pLGG. Vemurafenib achieves high response rates and induces early tumor shrinkage in patients with BRAF V600E mutations, supporting its utility as a first-line therapy.
Humans ; Glioma/genetics* ; Male ; Female ; Child ; Child, Preschool ; Retrospective Studies ; Brain Neoplasms/genetics* ; Molecular Targeted Therapy/adverse effects* ; Adolescent ; Infant ; Proto-Oncogene Proteins B-raf/genetics* ; Pyrimidinones/therapeutic use* ; Mutation

Objective:To explore the feasibility and preliminary efficacy of domestic single-port robotic surgical system in the surgical treatment of thyroid cancer. Methods:Thyroid cancer patients who underwent domestic single-port robotic surgery in the Department of Head and Neck Surgery of Sichuan Cancer Hospital from June 2024 to January 2025 were prospectively included. Clinical data, oncological characteristics, and perioperative indicators were systematically collected. Results:A total of 7 patients were included, including 3 males and 4 females, with an age of （34.57±10.26） years. All procedures were successfully completed without conversion to open surgery. Operative time was（180.00±30.41） minutes. Blood loss was（5.00[15.00 ]）mL. Postoperative drainage volume was （167.86±130.95） mL. The postoperative pathological results were all thyroid papillary carcinoma. There were no system failures, no device-related complications and adverse events were observed during the operation and perioperative period. No tumor recurrence or metastasis was observed during the follow-up period. Conclusion:Preliminary data indicate that the domestic single-port robotic surgical system is safe and feasible for the surgical treatment of thyroid cancer, providing a practical basis for subsequent multi-disease, multi-center, and large-sample studies.
Humans ; Thyroid Neoplasms/surgery* ; Robotic Surgical Procedures/instrumentation* ; Male ; Female ; Adult ; Thyroidectomy/methods* ; Operative Time ; Middle Aged ; Prospective Studies

OBJECTIVE: Emerging evidence suggests that exposure to ultrafine particulate matter (UPM, aerodynamic diameter < 0.1 µm) is associated with adverse cardiovascular events. Previous studies have found that Shenlian (SL) extract possesses anti-inflammatory and antiapoptotic properties and has a promising protective effect at all stages of the atherosclerotic disease process. In this study, we aimed to investigated whether SL improves UPM-aggravated myocardial ischemic injury by inhibiting inflammation and cell apoptosis. METHODS: We established a mouse model of MI+UPM. Echocardiographic measurement, measurement of myocardialinfarct size, biochemical analysis, enzyme-linked immunosorbent assay (ELISA), histopathological analysis, Transferase dUTP Nick End Labeling (TUNEL), Western blotting (WB), Polymerase Chain Reaction (PCR) and so on were used to explore the anti-inflammatory and anti-apoptotic effects of SL in vivo and in vitro. RESULTS: SL treatment can attenuate UPM-induced cardiac dysfunction by improving left ventricular ejection fraction, fractional shortening, and decreasing cardiac infarction area. SL significantly reduced the levels of myocardial enzymes and attenuated UPM-induced morphological alterations. Moreover, SL significantly reduced expression levels of the inflammatory cytokines IL-6, TNF-α, and MCP-1. UPM further increased the infiltration of macrophages in myocardial tissue, whereas SL intervention reversed this phenomenon. UPM also triggered myocardial apoptosis, which was markedly attenuated by SL treatment. The results of in vitro experiments revealed that SL prevented cell damage caused by exposure to UPM combined with hypoxia by reducing the expression of the inflammatory factor NF-κB and inhibiting apoptosis in H9c2 cells. CONCLUSION Overall, both in vivo and in vitro experiments demonstrated that SL attenuated UPM-aggravated myocardial ischemic injury by inhibiting inflammation and cell apoptosis. The mechanisms were related to the downregulation of macrophages infiltrating heart tissues.
Animals ; Apoptosis/drug effects* ; Particulate Matter/adverse effects* ; Mice ; Male ; Inflammation/drug therapy* ; Drugs, Chinese Herbal/therapeutic use* ; Mice, Inbred C57BL ; Myocardial Ischemia/drug therapy* ; Cell Line

Objective To study the correlation between traditional Chinese medicine(TCM)constitution and pathogenic factors in patients with ankylosing spondylitis(AS).Methods One hundred patients of AS and their family members who had medical consultation in the Fifth Hospital of Xi'an(i.e.,Shaanxi Hospital of Integrated Traditional Chinese and Western Medicine)in August 2019 and September 2020 were selected as the study subjects.The guidelines of Classification and Determination of Traditional Chinese Medicine Constitution issued by the China Association of Chinese Medicine were adopted to determine the traditional Chinese medicine(TCM)constitution types of the study subjects.The sociodemographic information,living habits,clinical symptoms,and TCM constitution types of the AS patients and their family members were collected by means of questionnaires and clinical investigations,and then the pathogenic factors of the patients with AS were investigated.The binomial Logistic regression model was used to analyze the correlation between TCM constitution types and pathogenic factors in patients with AS.Results(1)Among the 100 AS patients,the majority of them had the biased constitutions,and the biased constitutions with the occurrence frequency in descending order were yang deficiency constitution,qi deficiency constitution,and damp-heat constitution,which accounted for 33.00%,14.00%,and 18.00%,respectively.(2)The prevalence rates of AS in the first-,second-,and third-degree relatives of AS patients were 56.25%,40.00%and 25.00%,respectively.For the positive rates of human leukocyte antigen B27(HLA-B27)in AS patients and their family members,HLA-B27 in AS patients was all positive,while the positive rates of HLA-B27 in the first-,second-,and third-degree relatives of AS patients were 44.31%,30.67%and 15.63%,respectively.(3)The results of regression analysis showed that the disease duration of AS patients was significantly correlated with qi deficiency constitution,the grading of sacroiliac arthritis was correlated with qi stagnation constitution,and age was correlated with blood stasis constitution(P＜0.05 or P＜0.01).The results indicated that disease duration and age were the important factors affecting the constitution types of AS patients,and disease duration was closely related to qi deficiency while age was closely related to blood stasis.Conclusion AS is a highly hereditary autoimmune disease,and its onset is associated with HLA-B27.Yang deficiency is the basic constitution type of AS,and damp-heat constitution is the main constitution type in the progression of AS(especially in the active stage of the disease).The prolongation of the disease will exacerbate the illness condition of AS and then the manifestations of qi deficiency will be more obvious.

Objective To study the trend of stroke mortality in Minhang District,Shanghai from 2012 to 2022 and to predict stroke mortality from 2023 to 2027.Methods Annual percentage change(APC)of stroke deaths in Minhang District,Shanghai from 2012 to 2022 was calculated,and then Joinpoint linear regression model was used to analyze the time trend of stroke deaths.A grey GM(1,1)model was constructed based on the stroke mortality rate in Minhang District,Shanghai from 2012 to 2022.The model was used to predict and analyze the stroke mortality rate in Minhang District,Shanghai from 2023 to 2027.The fitting effect of the model was evaluated using relative error and grade deviation.Results From 2012 to 2022,the overall mortality rate of stroke in Minhang District,Shanghai was on the rise for both males and females(total population:APC=2.50%,P<0.001;male:APC=3.41%,P<0.001;female:APC=1.46%,P=0.008).The grey GM(1,1)model was used to predict the increasing trend of stroke mortality rate in Minhang District from 2023 to 2027.The crude mortality rate of stroke in the entire population in 2027 would be 97.55/100000,with 112.31/100000 for males and 83.33/100000 for females.The fitting effect of the model was tested and evaluated to meet high requirements.Conclusion In the past decade,the mortality rate of stroke in Minhang District,Shanghai has shown a significant upward trend.The 5-year prediction results showed that the mortality rate will still on the rise year by year.

Objective To explore the prediction of hospital indoor microbial concentration in air based on real-time indoor air environment monitoring data and machine learning algorithms.Methods Four locations in a hospital were selected as monitoring sampling points from May 23 to June 5,2022.The"internet of things"sensor was used to monitor a variety of real-time air environment data.Air microbial concentration data collected at each point were matched,and the gradient boosting decision tree(GBDT)was used to predict real-time indoor microbial concentra-tion in air.Five other common machine learning models were selected for comparison,including random forest(RF),decision tree(DT),k-nearest neighbor(KNN),linear regression(LR)and artificial neural network(ANN).The validity of the model was verified by the mean absolute error(MAE),root mean square error(RMSE)and mean absolute percentage error(MAPE).Results The MAPE value of GBDT model in the outpa-tient elevator room(point A),bronchoscopy room(point B),CT waiting area(point C),and nurses'station in the supply room(point D)were 22.49％,36.28％,29.34％,and 26.43％,respectively.The mean performance of the GBDT model was higher than that of other machine learning models at three sampling points and slightly lower than that of the ANN model at only one sampling point.The mean MAPE value of GBDT model at four sampling points was 28.64％,that is,the predicted value deviated from the actual value by 28.64％,indicating that GBDT model has good prediction results and the predicted value was within the available range.Conclusion The GBDT machine learning model based on real-time indoor air environment monitoring data can improve the prediction accuracy of in-door air microbial concentration in hospitals.

Objective To analyze the risk factors of gastrointestinal bleeding in patients with type A aortic dissection(TAAD)after Sun's operation.Methods The clinical data of 87 patients who underwent TAAD Sun's operation in our hospital from March 2021 to June 2022 were retrospectively analyzed.They were divided into the bleeding group and the non-bleeding group according to whether there was gastrointestinal bleeding after operation.The clinical data of patients in the two groups was compared and analyzed.The binary Logistic regression analysis was used to analyze the risk factors of gastrointestinal bleeding.The clinical predictor of postoperative gastrointestinal bleeding was analyzed by receiver operating characteristic(ROC)curve.Results In this study,there were 40 cases of postoperative gastrointestinal bleeding(the bleeding group)and 47 cases of non-bleeding(the non-bleeding group).Compared with the non-bleeding group,the bleeding group had a shorter onset time,a higher proportion of patients with hypertension history,a higher preoperative creatinine abnormality rate,more intraoperative blood loss,longer postoperative mechanical ventilation time,higher postoperative infection rate,and higher poor prognosis rate,with statistically significant differences(P<0.05).There was no statistically significant difference in the gender,age,gastrointestinal diseases history,smoking history,preoperative platelets,preoperative international normalized ratio(INR),preoperative alanine aminotransferase(ALT),preoperative aspartate aminotransferase(AST),preoperative γ-glutamyl transpeptidase(GGT),preoperative dissection involving abdominal aorta,operation time,intraoperative cardiopulmonary bypass time,intraoperative circulatory arrest time,intraoperative aortic occlusion time or intraoperative blood transfusion rate.Logistic regression analysis showed that hypertension history(OR=2.468,95%CI:0.862 to 7.067,P=0.037),preoperative creatinine>105 μmol/L(OR=3.970,95%CI:1.352 to 11.659,P=0.011),long postoperative mechanical ventilation time(OR=1.015,95%CI:0.094 to 1.018,P=0.041)and postoperative infection(OR=3.435,95%CI:0.991 to 11.900,P=0.012)were the independent risk factors for postoperative gastrointestinal bleeding in TAAD patients.ROC curve showed that the postoperative mechanical ventilation time exceeding 64 hours were the clinical predictor of postoperative gastrointestinal bleeding in TAAD patients.Conclusion The prognosis of TAAD patients with postoperative gastrointestinal bleeding after Sun's operation is poor.Hypertension history,preoperative acute renal insufficiency,long postoperative mechanical ventilation time and postoperative infection are closely related to postoperative gastrointestinal bleeding in TAAD patients after operation,which should be paid more attention to,and corresponding evaluation,early identification and early intervention should be made to improve the prognosis of patients.

Hydrogen sulfide,as a third gas signal molecule and neurotransmitter,can play a neuroprotective role by anti-oxidative stress,anti-inflammatory response,metabolic inhibition and other mechanisms.It is of great significance for the occurrence and development of neurodegenerative diseases including Alzheimer's disease(AD)and Parkinson's disease(PD)mediated by neuroinflammation.This article reviews the research progress of hydrogen sulfide and neuroinflammation and its mediated neurodegenerative diseases,so as to provide new ideas for the treatment of neurodegenerative diseases.

Objective:To determine the differential expressions of long non-coding RNA(lncRNA)and messenger RNA(mRNA)in normal and asthenospermia(AS)men and analyze their biological significance in AS.Methods:We isolated and ex-tracted total RNAs from 9 normal and 9 AS sperm samples,determined the expressions of RNAs in the sperm using the DNBSEQ se-quencing platform,and analyzed their relevant functions by gene ontology enrichment(GO)and Kyoto encyclopedia of genes and ge-nomes pathway(KEGG)analyses.Results:An average of 10.64G data was generated per group,with 282 185 RNAs detected,in-cluding 107 009 lncRNAs.Among the total number of lncRNAs,15 157 were differentially expressed,2 190 upregulated and 12 967 downregulated;and among the 19 514 mRNAs,13 736 were differentially expressed,4 995 upregulated and 8 741 downregulated.Differentially expressed genes were enriched mainly in the sperm cell membrane and the pathways related to the ion channel functions,sperm development and fertilization.Conclusion:Differentially expressed lncRNAs and mRNAs can be identified by sequencing a-nalysis of AS and normal sperm.Regulation of sperm function through membrane ion channels may contribute to the development of AS,which provides a molecular basis for further research on AS.