Diabetic kidney disease （DKD） is one of the common microvascular complications of diabetes mellitus （DM） and a primary cause of chronic kidney disease （CKD） and end-stage renal disease （ESRD）. Inflammation is currently a hot topic in exploring the pathogenesis of DKD. Macrophages， T cells， interleukins， tumor necrosis factor， NOD-like receptor protein 3 （NLRP3） inflammasome， Janus kinase/signal transducer and activator of transcription （JAK/STAT） signaling pathway， and nuclear factor-kappa B （NF-κB）-related signaling pathway all play a role in regulating the inflammation of DKD and accelerating its progression. Astragali Radix， a Chinese herbal medicine， is widely used in the treatment of DKD and possesses strong anti-inflammatory effects. Studies have revealed that active ingredients of Astragali Radix， including polysaccharides， astragaloside Ⅳ， total flavonoids， calycosin， and quercetin， can regulate multiple signaling pathways to ameliorate the microinflammatory state and alleviate kidney damage， thereby slowing down the progression of DKD. This article systematically reviews the factors influencing the inflammation in DKD and analyzes recent research findings and mechanisms concerning active ingredients of Astragali Radix in the management of DKD inflammation， aiming to offer novel insights and directions for the prevention， treatment， and research of DKD.

Objective To investigate the clinical and radiological features of patients with different phenotypes of multiple system atrophy （MSA）. Methods A total of 400 patients with MSA who attended the outpatient service or were hospitalized in Department of Neurology，Chinese PLA General Hospital，were enrolled，among whom there were 294 patients with MSA-cerebellar type （MSA-C） and 106 patients with MSA-Parkinsonian type （MSA-P）. The t-test and the chi-square test were used to analyze the clinical manifestations，radiological features，and blood biochemical indicators of the two groups. Results As for clinical manifestations，there were significant differences in cerebellar symptoms，constipation，and Babinski sign between MSA-C patients and MSA-P patients （P<0.05）. As for radiological features，positron emission tomography/computed tomography （PET/CT） showed that MSA-P patients had a typical reduction in DAT uptake in the putamen and the caudate nucleus，while magnetic resonance imaging showed that compared with the MSA-C group，the MSA-P group had a significantly higher proportion of patients with putamen fissure sign or white matter demyelination； for MSA-C patients，PET/CT showed cerebellar hypometabolism，and magnetic resonance imaging showed the cross sign and high signal intensity in the pontine arm，as well as a significantly higher proportion of patients cerebellar and pontine atrophy than MSA-P patients （P<0.05）. As for laboratory markers，MSA-P patients had a significantly lower level of uric acid than MSA-C patients （P=0.029）. Conclusion Patients with different subtypes of MSA have specific clinical features，radiological features，and uric acid level，which has a certain significance in the accurate diagnosis of MSA.

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.

ObjectiveTo establish a qualitative and quantitative analysis method for chemical constituents in Liu Junzitang（LJZT）， and to clarify its material basis. MethodThe chemical constituents in LJZT were analyzed by ultra performance liquid chromatography-quadrupole-time-of-flight mass spectrometry（UPLC-Q-TOF-MS/MS）， and the resulting compounds were identified by using databases， such as MassBank， PubChem， ChemSpider， Traditional Chinese Medicine Systems Pharmacology Database and Analytical Platform（TCMSP）， and by combining with relevant literature. UPLC was used to establish a quantitative method for analysis of 9 compounds in LJZT， including liquiritin， hesperidin， lobetyolin， liquiritigenin， glycyrrhizic acid， nobiletin， tangeretin， atractylenolide Ⅱ and Ⅰ. ResultBy combining the relevant literature， database and MS information， a total of 79 compounds were identified from LJZT， including 31 flavonoids， 15 terpenoids， 14 nitrogen-containing compounds， 6 phenylpropanoids， 6 organic acids and 7 other compounds. The established quantitative analytical method for the nine representative components showed good linearity within their respective linear ranges， and the precision， stability， reproducibility and recovery were in accordance with the requirements. The quantitative results showed that the contents of liquiritin， hesperidin， lobetyolin， liquiritigenin， glycyrrhizic acid， nobiletin， tangeretin， atractylenolide Ⅱ and Ⅰ in LJZT were 0.376 5， 2.602 1， 0.082 6， 0.128 1， 1.778 6， 0.015 7， 0.006 7， 0.030 4， 0.003 2 mg·g^-1， respectively. ConclusionThe established method can quickly， sensitively and accurately analyze the chemical constituents in LJZT， clarify that the material basis of LJZT is mainly flavonoids， terpenoids and nitrogen-containing compounds， and simultaneously determine the contents of the 9 components， which can lay a foundation for the research on quality control， mechanism and clinical application of LJZT.

Objective To explore the preference of medical staff in public hospitals for various incentive factors,and to provide theoretical basis for the hospital to formulate a reasonable incentive system and stimulate the enthusiasm of medical staff.Methods Based on the discrete choice experiment,it was conducted from March 2023 to June 2023,through the method of multistage stratified random sampling,the medical staff of a general hospital,a Traditional Chinese Medical Hospital and a maternal and child health hospital in one city in the east,west,south,north and middle of Hainan Province were investigated by questionnaire,and analyzed their incentive preference and willingness to pay by using the mixed logit model.Results All the six attributes have significant influence on medical staff's incentive preference,among which,medical staff prefer to choose the incentive effect brought by the annual salary of 350 000 yuan,good working atmosphere and working intensity of 8 hours,while hospital development has little influence on incentive preference.Among the non-economic factors,when the working atmosphere changes from depression to good,they are willing to get an annual salary of 168 305.4 yuan less,while when the hospital development changes from nothing to good,they are only willing to get an annual salary of 12 433.14 yuan less.Conclusion According to the different incentive needs of medical staff,it is more helpful to formulate accurate incentive schemes and adopt economic and non-economic incentive means to stabilize the hospital talent team and improve work enthusiasm.

Objective To explore the preference of medical staff in public hospitals for various incentive factors,and to provide theoretical basis for the hospital to formulate a reasonable incentive system and stimulate the enthusiasm of medical staff.Methods Based on the discrete choice experiment,it was conducted from March 2023 to June 2023,through the method of multistage stratified random sampling,the medical staff of a general hospital,a Traditional Chinese Medical Hospital and a maternal and child health hospital in one city in the east,west,south,north and middle of Hainan Province were investigated by questionnaire,and analyzed their incentive preference and willingness to pay by using the mixed logit model.Results All the six attributes have significant influence on medical staff's incentive preference,among which,medical staff prefer to choose the incentive effect brought by the annual salary of 350 000 yuan,good working atmosphere and working intensity of 8 hours,while hospital development has little influence on incentive preference.Among the non-economic factors,when the working atmosphere changes from depression to good,they are willing to get an annual salary of 168 305.4 yuan less,while when the hospital development changes from nothing to good,they are only willing to get an annual salary of 12 433.14 yuan less.Conclusion According to the different incentive needs of medical staff,it is more helpful to formulate accurate incentive schemes and adopt economic and non-economic incentive means to stabilize the hospital talent team and improve work enthusiasm.

Objective To explore the preference of medical staff in public hospitals for various incentive factors,and to provide theoretical basis for the hospital to formulate a reasonable incentive system and stimulate the enthusiasm of medical staff.Methods Based on the discrete choice experiment,it was conducted from March 2023 to June 2023,through the method of multistage stratified random sampling,the medical staff of a general hospital,a Traditional Chinese Medical Hospital and a maternal and child health hospital in one city in the east,west,south,north and middle of Hainan Province were investigated by questionnaire,and analyzed their incentive preference and willingness to pay by using the mixed logit model.Results All the six attributes have significant influence on medical staff's incentive preference,among which,medical staff prefer to choose the incentive effect brought by the annual salary of 350 000 yuan,good working atmosphere and working intensity of 8 hours,while hospital development has little influence on incentive preference.Among the non-economic factors,when the working atmosphere changes from depression to good,they are willing to get an annual salary of 168 305.4 yuan less,while when the hospital development changes from nothing to good,they are only willing to get an annual salary of 12 433.14 yuan less.Conclusion According to the different incentive needs of medical staff,it is more helpful to formulate accurate incentive schemes and adopt economic and non-economic incentive means to stabilize the hospital talent team and improve work enthusiasm.

Objective To explore the preference of medical staff in public hospitals for various incentive factors,and to provide theoretical basis for the hospital to formulate a reasonable incentive system and stimulate the enthusiasm of medical staff.Methods Based on the discrete choice experiment,it was conducted from March 2023 to June 2023,through the method of multistage stratified random sampling,the medical staff of a general hospital,a Traditional Chinese Medical Hospital and a maternal and child health hospital in one city in the east,west,south,north and middle of Hainan Province were investigated by questionnaire,and analyzed their incentive preference and willingness to pay by using the mixed logit model.Results All the six attributes have significant influence on medical staff's incentive preference,among which,medical staff prefer to choose the incentive effect brought by the annual salary of 350 000 yuan,good working atmosphere and working intensity of 8 hours,while hospital development has little influence on incentive preference.Among the non-economic factors,when the working atmosphere changes from depression to good,they are willing to get an annual salary of 168 305.4 yuan less,while when the hospital development changes from nothing to good,they are only willing to get an annual salary of 12 433.14 yuan less.Conclusion According to the different incentive needs of medical staff,it is more helpful to formulate accurate incentive schemes and adopt economic and non-economic incentive means to stabilize the hospital talent team and improve work enthusiasm.

Objective To explore the preference of medical staff in public hospitals for various incentive factors,and to provide theoretical basis for the hospital to formulate a reasonable incentive system and stimulate the enthusiasm of medical staff.Methods Based on the discrete choice experiment,it was conducted from March 2023 to June 2023,through the method of multistage stratified random sampling,the medical staff of a general hospital,a Traditional Chinese Medical Hospital and a maternal and child health hospital in one city in the east,west,south,north and middle of Hainan Province were investigated by questionnaire,and analyzed their incentive preference and willingness to pay by using the mixed logit model.Results All the six attributes have significant influence on medical staff's incentive preference,among which,medical staff prefer to choose the incentive effect brought by the annual salary of 350 000 yuan,good working atmosphere and working intensity of 8 hours,while hospital development has little influence on incentive preference.Among the non-economic factors,when the working atmosphere changes from depression to good,they are willing to get an annual salary of 168 305.4 yuan less,while when the hospital development changes from nothing to good,they are only willing to get an annual salary of 12 433.14 yuan less.Conclusion According to the different incentive needs of medical staff,it is more helpful to formulate accurate incentive schemes and adopt economic and non-economic incentive means to stabilize the hospital talent team and improve work enthusiasm.

Objective To explore the preference of medical staff in public hospitals for various incentive factors,and to provide theoretical basis for the hospital to formulate a reasonable incentive system and stimulate the enthusiasm of medical staff.Methods Based on the discrete choice experiment,it was conducted from March 2023 to June 2023,through the method of multistage stratified random sampling,the medical staff of a general hospital,a Traditional Chinese Medical Hospital and a maternal and child health hospital in one city in the east,west,south,north and middle of Hainan Province were investigated by questionnaire,and analyzed their incentive preference and willingness to pay by using the mixed logit model.Results All the six attributes have significant influence on medical staff's incentive preference,among which,medical staff prefer to choose the incentive effect brought by the annual salary of 350 000 yuan,good working atmosphere and working intensity of 8 hours,while hospital development has little influence on incentive preference.Among the non-economic factors,when the working atmosphere changes from depression to good,they are willing to get an annual salary of 168 305.4 yuan less,while when the hospital development changes from nothing to good,they are only willing to get an annual salary of 12 433.14 yuan less.Conclusion According to the different incentive needs of medical staff,it is more helpful to formulate accurate incentive schemes and adopt economic and non-economic incentive means to stabilize the hospital talent team and improve work enthusiasm.