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

The study utilized spectral correlation analyses combined with bioactivity evaluation to examine the effective components of antidepressants in the Baihe Dihuang decoction. Firstly, the chemical fingerprints for different extraction parts in the Baihe Dihuang decoction were achieved using HPLC and UHPLC-MS technology. Then, in order to evaluate the antidepressant effect of Baihe Dihuang decoction, the animal experimental protocol has been reviewed and approved by Laboratory Animal Ethics Committee of Hunan University of Chinese Medicine (No. LLBH-202104270001), in compliance with the Institutional Animal Care Guidelines. We recorded the distance of autonomous movement of mice in open field experiment, the immobility time of tail suspension test, and the forced swimming. Additionally, we measured the content of neurotransmitters. Finally, Pearson analysis, grey correlation analysis, and orthogonal partial least squares regression analysis were utilized to establish the correlation between antidepressant efficacy indicators and fingerprinting. The spectrum-effect relationship results were confirmed through the in vitro activity verification. This study demonstrated that regaloside A, B, C, catalpol, and Isoacteoside might be the main antidepressant components in Baihe Dihuang decoction. Furthermore, it was found that using diverse mathematical models and bioactivity evaluation could enhance the accuracy of the spectral correlation analyses results.

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.

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 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.