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.

Predatory bacteriophages have evolved a vast array of depolymerases for bacteria capture and deprotection. These depolymerases are enzymes responsible for degrading diverse bacterial surface carbohydrates. They are exploited as antibiofilm agents and antimicrobial adjuvants while rarely inducing bacterial resistance, making them an invaluable asset in the era of antibiotic resistance. Numerous depolymerases have been investigated preclinically, with evidence indicating that depolymerases with appropriate dose regimens can safely and effectively combat different multidrug-resistant pathogens in animal infection models. Additionally, some formulation approaches have been developed for improved stability and activity of depolymerases. However, depolymerase formulation is limited to liquid dosage form and remains in its infancy, posing a significant hurdle to their clinical translation, compounded by challenges in their applicability and manufacturing. Future development must address these obstacles for clinical utility. Here, after unravelling the history, diversity, and therapeutic use of depolymerases, we summarized the preclinical efficacy and existing formulation findings of recombinant depolymerases. Finally, the challenges and perspectives of depolymerases as therapeutics for humans were assessed to provide insights for their further development.

Objective:To investigate the efficacy and mechanism of static progressive stretch (SPS) with different parameters in the treatment of stiff knee in rats.Methods:Fifty-six male 8-week SD rats were randomly divided into an operation group ( n=48) and a blank group ( n=8, normal feeding rats without any treatment). The knee joints of the rats in the operation group were fixed with Kirschner wire for 4 weeks to create models of right knee flexion stiffness. The 42 rats with successful modeling were randomly divided into 6 groups ( n=7): the model group was executed and sampled after successful modeling, the spontaneous recovery group was not given any treatment after successful modeling, group T1 was given SPS treatment for 20 min once per day, group T2 was given SPS treatment for 30 min once per day, group T3 was given SPS treatment for 20 min once every 2 days, and group T4 was given SPS treatment for 30 min once every 2 days. After 16 days, the range of knee motion, number of myofibroblasts, and positive proportion of transforming growth factor- β1 (TGF- β1) in the joint capsule were detected and compared between groups. Results:The ranges of knee motion in the spontaneous recovery group and the 4 SPS treatment groups were significantly greater than those before treatment ( P<0.05), and the improvements in the range of knee motion in the 4 SPS treatment groups were significantly greater than that in the spontaneous recovery group ( P<0.05). The range of knee motion in group T2 (112.29°±1.89°) was improved the most significantly. The number of myofibroblasts was (23.72±10.75)/HP, which was significantly smaller than that in T3 group [(55.72±33.56)/HP] or in T4 group [(50.72±33.34)/HP] ( P<0.05). The positive proportions of TGF- β1 in the joint capsule in the 4 SPS treatment groups were significantly lower than that in the model group, and the positive proportion of TGF- β1 in the joint capsule in group T2 (0.51%±0.38%) was significantly lower than those in group T3 and T4 ( P<0.05). Conclusions:As SPS treatment can reduce the expression of TGF- β1 in the joint and inhibit the excessive proliferation of myofibroblasts to alleviate the pathological changes in a stiff knee, it has a significant effect on the stiff knee in rats. The SPS treatment for 30 minutes and once per day may lead to the best efficacy.

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.

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.

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 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.

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.

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.