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.

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 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 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 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.

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.

Combined hepatocellular-cholangiocarcinoma (cHCC-CC) is a special type of primary liver cancer, which is characterized by dual phenotypic differentiation of hepatocellular carcinoma and intrahepatic cholangiocarcinoma. Due to the lack of specific diagnostic markers and therapeutic targets, the preoperative diagnosis is difficult, the treatment is not easy, and the prognosis is poor. Routine serology, imaging examination and histological biopsy are the main methods of preliminary screening and diagnosis of cHCC-CC. Patients with resectable cHCC-CC should be treated with surgical treatment if possible, but whether they should receive liver transplantation remains a careful choice. There is still no unified standard for local treatment and systemic treatment for advanced patients, and its efficacy is still controversial. Systemic treatment including platinum drugs can benefit some patients. The potential value of targeted therapy and immunotherapy in the treatment of advanced cHCC-CC patients has been highlighted. The introduction of multidisciplinary diagnosis and treatment model has provided a strong guarantee for the comprehensive treatment of patients, and the development of xenogenetic model and multi-omics technology has provided a considerable prospect for the realization of individualized treatment for patients. The present situation and progress of diagnosis and treatment of cHCC-CC are discussed in order to provide reference for clinical practice.

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.

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.