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.

BACKGROUND: This study aimed to investigate programmed death-ligand 1 tumor proportion score in predicting the safety and efficacy of PD-1/PD-L1 antibody-based therapy in treating patients with advanced non-small cell lung cancer (NSCLC) in a real-world setting. METHODS: This retrospective, multicenter, observational study enrolled adult patients who received PD-1/PD-L1 antibody-based therapy in China and met the following criteria: (1) had pathologically confirmed, unresectable stage III-IV NSCLC; (2) had a baseline PD-L1 tumor proportion score (TPS); and (3) had confirmed efficacy evaluation results after PD-1/PD-L1 treatment. Logistic regression, Kaplan-Meier analysis, and Cox regression were used to assess the progression-free survival (PFS), overall survival (OS), and immune-related adverse events (irAEs) as appropriate. RESULTS: A total of 409 patients, 65.0% ( n = 266) with a positive PD-L1 TPS (≥1%) and 32.8% ( n = 134) with PD-L1 TPS ≥50%, were included in this study. Cox regression confirmed that patients with a PD-L1 TPS ≥1% had significantly improved PFS (hazard ratio [HR] 0.747, 95% confidence interval [CI] 0.573-0.975, P = 0.032). A total of 160 (39.1%) patients experienced 206 irAEs, and 27 (6.6%) patients experienced 31 grade 3-5 irAEs. The organs most frequently associated with irAEs were the skin (52/409, 12.7%), thyroid (40/409, 9.8%), and lung (34/409, 8.3%). Multivariate logistic regression revealed that a PD-L1 TPS ≥1% (odds ratio [OR] 1.713, 95% CI 1.054-2.784, P = 0.030) was an independent risk factor for irAEs. Other risk factors for irAEs included pretreatment absolute lymphocyte count >2.5 × 10 9 /L (OR 3.772, 95% CI 1.377-10.329, P = 0.010) and pretreatment absolute eosinophil count >0.2 × 10 9 /L (OR 2.006, 95% CI 1.219-3.302, P = 0.006). Moreover, patients who developed irAEs demonstrated improved PFS (13.7 months vs. 8.4 months, P <0.001) and OS (28.0 months vs. 18.0 months, P = 0.007) compared with patients without irAEs. CONCLUSIONS A positive PD-L1 TPS (≥1%) was associated with improved PFS and an increased risk of irAEs in a real-world setting. The onset of irAEs was associated with improved PFS and OS in patients with advanced NSCLC receiving PD-1/PD-L1-based therapy.
Humans ; Carcinoma, Non-Small-Cell Lung/metabolism* ; Male ; Female ; Retrospective Studies ; Middle Aged ; Lung Neoplasms/metabolism* ; Aged ; B7-H1 Antigen/metabolism* ; Programmed Cell Death 1 Receptor/metabolism* ; Adult ; Aged, 80 and over ; Immune Checkpoint Inhibitors/therapeutic use*

The Baihe Dihuang Decoction(BDD) is a representative traditional Chinese medicine formula that has been used to treat depression. This study employed metabolomics and network pharmacology to investigate the mechanism of BDD in the treatment of depression. Fifty male Sprague-Dawley(SD) rats were randomly assigned to the normal control group, model group, fluoxetine group, and high-and low-dose BDD groups. A rat model of depression was established through chronic unpredictable mild stress(CUMS), and the behavioral changes were detected by forced swimming test and open field test. Metabolomics technology was used to analyze the metabolic profiles of serum and hippocampal tissue to screen differential metabolites and related metabolic pathways. Additionally, network pharmacology and molecular docking techniques were used to investigate the key targets and core active ingredients of BDD in improving metabolic abnormalities of depression. A "component-target-metabolite-pathway" regulatory network was constructed. BDD could significantly improve depressive-like behavior in CUMS rats and regulate 12 differential metabolites in serum and 27 differential metabolites in the hippocampus, involving tryptophan metabolism, phenylalanine, tyrosine, and tryptophan biosynthesis, alanine, aspartate, and glutamate metabolism, tyrosine metabolism, and purine metabolism. Verbascoside, isorbascoside, and regaloside B were the key active ingredients for improving metabolic abnormalities in depression. Epidermal growth factor receptor(EGFR), protooncogene tyrosine-protein kinase(SRC), glycogen synthase kinase 3β(GSK3β), and androgen receptor(AR) were the key core targets for improving metabolic abnormalities of depression. This study offered a preliminary insight into the mechanism of BDD in alleviating metabolic abnormalities of depression through network regulation, providing valuable guidance for its clinical use and subsequent research.
Animals ; Drugs, Chinese Herbal/administration & dosage* ; Male ; Rats, Sprague-Dawley ; Rats ; Metabolomics ; Depression/genetics* ; Antidepressive Agents/chemistry* ; Network Pharmacology ; Hippocampus/drug effects* ; Humans ; Molecular Docking Simulation ; Behavior, Animal/drug effects* ; Disease Models, Animal

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 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 evaluate the effects of different interaction modes on the rehabilitation outcomes of stroke patients, and to provide reference for caregivers to choose the best interaction mode according to the rehabilitation goals.Methods:Computerized search of Web of Science, PubMed, Cochrane Library, EMbase, CNKI, Wanfang Database, VIP Database, and China Biomedical Literature Database for randomized controlled trials (RCTs) of interaction modes to improve rehabilitation outcomes of stroke patients was performed from the year of database construction to January 8, 2024. Two researchers independently screened the literature according to inclusion and exclusion criteria, evaluated the risk of bias in the included studies, and extracted data from them. Stata16.0 was used for a network meta-analysis.Results:A total of 22 articles were included that met the inclusion and exclusion criteria, involving 2 404 patients and 5 interaction modes, namely Cox health behavior interaction mode, doctor-patient interaction mode, King interaction compliance mode, dual track interaction mode, and online interaction mode. The results of the network Meta-analysis showed that in terms of improving self-care ability, the King interaction model [ SMD(95% CI)=0.25(0.05-0.45)], the network interaction model [ SMD(95% CI)=0.27(0.07-0.48)], and the Cox health behavior interaction model [ SMD(95% CI)=0.37(0.07-0.67)] were all superior to conventional nursing (all P<0.05). In terms of improving motor function, except for the dual track interactive mode, all other modes were superior to conventional nursing ( SMD values were -0.52--0.30, all P<0.05). There was no statistically significant difference in the application effects of different modes in improving the quality of life (all P>0.05). The ranking results of the area under the cumulative ranking probability curve (SUCRA) for improving self-care ability, motor fuction and quality of life were Cox health behavior interaction mode (SUCRA=83.7%), doctor-patient interaction mode (SUCRA=89.5%) and King interaction standard mode (SUCRA=78.2%). Conclusions:The Cox health behavior interaction model can improve the self-care ability of stroke patients, the doctor-patient interaction model can improve the motor function of stroke patients, and the King interaction standard model may have more advantages in improving the quality of life of stroke patients. It is suggested to combine the advantages of the three to maximize the rehabilitation effect of stroke patients.

Near infrared spectroscopy(NIRS)has emerged as an indispensable analytical technology for quality monitoring in industrial and agricultural production.It is widely used in quantitative analysis in areas such as food,agriculture and medicine.To meet the requirements of industrial and agricultural production,it is particularly important to develop a NIRS quantitative analysis model that can predict the multicomponent of different samples.In this study,the multicomponent quantitative analysis model of NIRS based on convolution neural network(MulCoSpecNet)was proposed.MulCoSpecNet was composed of an encoding and decoding module,an expert module,a gate module,a multicomponent quantitative prediction module,and a hyperparameter optimizer.The spectral noise and random errors were mitigated,and the signal-to-noise ratio was enhanced through up-sampling and down-sampling in the encoding and decoding module.Diverse weightings were employed by the expert module and gate module to construct distinct sub-spectra.The model prediction accuracy and generalization ability were enhanced by the multicomponent quantitative prediction module,which employed convolutional and pooling operations.The hyperparameters in the hyperparameter space were synchronously optimized by the hyperparameter optimizer.By taking public NIRS datasets of grain and corn as examples,the prediction results of MulCoSpecNet were compared with partial least squares(PLS),extreme learning machine(ELM),support vector regression(SVM)and back propagation neural network(BP).The results showed that compared to PLS,the prediction accuracy of MulCoSpecNet to grain and corn were increased by 25.5%?45.2%and 10.0%?35.7%,respectively.Compared to ELM,the prediction accuracy of MulCoSpecNet were increased by 17.8%?38.6%and 18.2%?37.2%,respectively.Compared to SVM,the prediction accuracy of MulCoSpecNet were increased by 33.6%?47.0%and 31.3%?50.7%,respectively.Compared to BP,the prediction accuracy of MulCoSpecNet were increased by 2.0%?58.5%and 29.6%?48.6%,respectively.The issues of low prediction accuracy and poor generalization ability were effectively solved by the MulCoSpecNet,which was a NIRS multicomponent prediction model based on convolutional neural network.It provided a theoretical foundation for establishing non-destructive and high-precision NIRS multicomponent quantitative analysis model.

Objective To observe the clinical efficacy of Chang'an Juntai Granules(mainly composed of Pseudostellariae Radix,Atractylodis Macrocephalae Rhizoma,Poria,Glycyrrhizae Radix et Rhizoma Praeparata cum Melle,Paeoniae Radix Alba,Saposhnikoviae Radix,Citri Reticulatae Pericarpium,Coptidis Rhizoma,and Aucklandiae Radix)in the treatment of diarrhea-predominant irritable bowel syndrome(IBS-D)with liver depression and spleen deficiency syndrome,and to evaluate its safety.Methods A single-center,randomized,double-blind,placebo-controlled clinical trial was designed.A total of 130 patients with IBS-D of liver depression and spleen deficiency were included.The patients were randomly divided into a treatment group and a control group by random number table method,with 65 cases in each group.The treatment group was treated with Chang'an Juntai Granules,and the control group was treated with Chang'an Juntai Placebo Granules.The course of treatment covered 12 weeks.The changes in the scores of IBS Symptom Severity Scale(IBS-SSS),Bristol Stool Form Scale(BSFS),IBS Quality of Life Questionnaire(IBS-QOL)and Hospital Anxiety and Depression Scale(HADS)in the two groups were observed before and after treatment.After treatment,the clinical efficacy and medication safety in the two groups were evaluated.Results(1)During the trial,six cases in the treatment group and eight cases in the control group fell off.Eventually,a total of 116 patients completed the clinical trial,including 59 cases in the treatment group and 57 cases in the control group.(2)After 12 weeks of treatment,the total effective rate of the treatment group was 88.14％(52/59),and that of the control group was 45.61％(26/57).The intergroup comparison(tested by chi-square test)showed that the clinical efficacy of the treatment group was significantly superior to that of the control group,and the difference was statistically significant(P＜0.01).(3)After treatment,the IBS-SSS scores of the two groups and the BSFS and IBS-QOL scores of the treatment group were significantly lower than those before treatment(P＜0.01),while the scores of Hospital Anxiety and Depression Scale-Anxiety subscale(HADA)and Hospital Anxiety and Depression Scale-Depression subscale(HADD)in the two groups and the BSFS and IBS-QOL scores in the control group showed no obvious changes(P＞0.05).Compared with the control group,the decrease of IBS-SSS,BSFS and IBS-QOL scores in the treatment group was significantly superior to that in the control group(P＜0.05 or P＜0.01).(4)During the trial,no serious adverse reactions or adverse events occurred in the two groups,no drug-related abnormalities of liver and kidney function,blood,and heart function were found,either.Conclusion Chang'an Juntai Granules are effective on improving the clinical symptoms and fecal characteristics of IBS-D patients with liver depression and spleen deficiency syndrome,and on enhancing the quality of life of patients.The granules excert definite curative effect and high safety,and has certain value of clinical application.