Objective This study conducted molecular biological identification of the viruses carried by Culex pipiens pallens specimens collected in Shunyi District, Beijing in 2023, and observed the changes in the infection rate of the viruses carried by Cx. pipiens pallens at different collection times. Methods Cx. pipiens pallens were collected using carbon dioxide mosquito traps. The mosquito samples were ground in batches and analyzed by molecular biology technologies. The virus infection rate at different collection times was analyzed statistically. Results 17 strains of Culex pipiens pallens densovirus(CppDNV)were identified from Cx. pipiens pallens samples collected in Shunyi District, Beijing, in 2023. The nucleotide sequence analysis of the virus genome coding region showed that CppDNV was a single-stranded DNA virus with a total length of 3 335 nt, encoding 2 non-structural proteins(NS1, NS2)and 1 capsid protein(VP). The nucleotide(amino acid)sequence lengths of the three proteins were 2 376 nt(791 aa),1 092 nt(363 aa)and 1 071 nt(356 aa), respectively. Phylogenetic analysis showed that CppDNV was located in genus Brevihamaparvovirus. Statistical analysis showed no significant difference in infection rates across collection times(χ2=4.429, P=0.194). Conclusions CppDNV was identified in Cx. pipiens pallens in Beijing, and it was stably maintained in this natural population.

Objective To investigate the species composition of mosquitoes and breeding conditions of larvae in the rice planting areas of Beijing, and to provide a scientific basis for mosquito control in rice fields. Methods Adult mosquitoes were collected using CO2-baited light traps, and mosquito larvae(including pupae)were sampled via the dipping method. Results A total of 4 genera and 6 species of adult mosquitoes were captured in the study area. The dominant species were Culex pipiens pallens, Cx. tritaeniorhynchus, and Anopheles sinensis. The average adult mosquito density was 5.11 specimens per trap-hour, with slightly lower density in July compared to August. For larvae and pupae,3 genera and 4 species were identified, dominated by Cx. tritaeniorhynchus, An. sinensis, and Aedes vexans. Conclusions The mosquito species composition in Beijing′s rice planting areas is diverse. Mosquito control strategies should prioritize the management of adult mosquitoes, while density control of mosquito larvae in paddy fields can be achieved through intermittent irrigation techniques.

OBJECTIVE: To evaluate the clinical efficacy and safety of Yangxue Qingnao Pills (YXQNP) combined with amlodipine in treating patients with grade 1 hypertension. METHODS: This is a multicenter, randomized, double-blind, and placebo-controlled study. Adult patients with grade 1 hypertension of blood deficiency and Gan (Liver)-yang hyperactivity syndrome were randomly divided into the treatment or the control groups at a 1:1 ratio. The treatment group received YXQNP and amlodipine besylate, while the control group received YXQNP's placebo and amlodipine besylate. The treatment duration lasted for 180 days. Outcomes assessed included changes in blood pressure, Chinese medicine (CM) syndrome scores, symptoms and target organ functions before and after treatment in both groups. Additionally, adverse events, such as nausea, vomiting, rash, itching, and diarrhea, were recorded in both groups. RESULTS: A total of 662 subjects were enrolled, of whom 608 (91.8%) completed the trial (306 in the treatment and 302 in the control groups). After 180 days of treatment, the standard deviations and coefficients of variation of systolic and diastolic blood pressure levels were lower in the treatment group compared with the control group. The improvement rates of dizziness, headache, insomnia, and waist soreness were significantly higher in the treatment group compared with the control group (P<0.05). After 30 days of treatment, the overall therapeutic effects on CM clinical syndromes were significantly increased in the treatment group as compared with the control group (P<0.05). After 180 days of treatment, brachial-ankle pulse wave velocity, ankle brachial index and albumin-to-creatinine ratio were improved in both groups, with no statistically significant differences (P>0.05). No serious treatment-related adverse events occurred during the study period. CONCLUSIONS Combination therapy of YXQNP with amlodipine significantly improved symptoms such as dizziness and headache, reduced blood pressure variability, and showed a trend toward lowering urinary microalbumin in hypertensive patients. These findings suggest that this regimen has good clinical efficacy and safety. (Registration No. ChiCTR1900022470).
Humans ; Amlodipine/adverse effects* ; Drugs, Chinese Herbal/adverse effects* ; Male ; Female ; Hypertension/complications* ; Middle Aged ; Treatment Outcome ; Drug Therapy, Combination ; Adult ; Blood Pressure/drug effects* ; Double-Blind Method ; Aged ; Antihypertensive Agents/adverse effects*

OBJECTIVE: To evaluate the dynamic changes of glucocorticoid (GC) dose and the feasibility of GC discontinuation in rheumatoid arthritis (RA) patients under the background of Chinese medicine (CM). METHODS: This multicenter retrospective cohort study included 1,196 RA patients enrolled in the China Rheumatoid Arthritis Registry of Patients with Chinese Medicine (CERTAIN) from September 1, 2019 to December 4, 2023, who initiated GC therapy. Participants were divided into the Western medicine (WM) and integrative medicine (IM, combination of CM and WM) groups based on medication regimen. Follow-up was performed at least every 3 months to assess dynamic changes in GC dose. Changes in GC dose were analyzed by generalized estimator equation, the probability of GC discontinuation was assessed using Kaplan-Meier curve, and predictors of GC discontinuation were analyzed by Cox regression. Patients with <12 months of follow-up were excluded for the sensitivity analysis. RESULTS: Among 1,196 patients (85.4% female; median age 56.4 years), 880 (73.6%) received IM. Over a median 12-month follow-up, 34.3% (410 cases) discontinued GC, with significantly higher rates in the IM group (40.8% vs. 16.1% in WM; P<0.05). GC dose declined progressively, with IM patients demonstrating faster reductions (median 3.75 mg vs. 5.00 mg in WM at 12 months; P<0.05). Multivariate Cox analysis identified age <60 years [P<0.001, hazard ratios (HR)=2.142, 95% confidence interval (CI): 1.523-3.012], IM therapy (P=0.001, HR=2.175, 95% CI: 1.369-3.456), baseline GC dose ⩽7.5 mg (P=0.003, HR=1.637, 95% CI: 1.177-2.275), and absence of non-steroidal anti-inflammatory drugs use (P=0.001, HR=2.546, 95% CI: 1.432-4.527) as significant predictors of GC discontinuation. Sensitivity analysis (545 cases) confirmed these findings. CONCLUSIONS RA patients receiving CM face difficulties in following guideline-recommended GC discontinuation protocols. IM can promote GC discontinuation and is a promising strategy to reduce GC dependency in RA management. (Trial registration: ClinicalTrials.gov, No. NCT05219214).
Adult ; Aged ; Female ; Humans ; Male ; Middle Aged ; Arthritis, Rheumatoid/drug therapy* ; Glucocorticoids/therapeutic use* ; Medicine, Chinese Traditional ; Retrospective Studies

Aim To investigate the effect of dihydro-myricetin(DMY)on Ang Ⅱ-induced cardiac hypertro-phy in mice and the underlying mechanisms.Methods Fifty mice were randomly divided into control group,Ang Ⅱ group,Ang Ⅱ+catopril 12.0 mg·kg-1·d-1 group,AngⅡ+DMY 100 mg·kg-1·d-1 group,and Ang Ⅱ+DMY 200 mg·kg-1·d-1 group,with 10 mice in each group.The control mice were given saline by gavage,the drug intervention group was given DMY,and the positive drug group was given captopril;the mice in all groups except the control group were in-jected subcutaneously with Ang Ⅱ 1.0mg·kg-1·d-1.After four weeks,heart weight/body weight(HW/BW)and left ventricular weight/body weight(LVW/BW)ratios were calculated.The mRNA ex-pression of the fetal genes atrial natriuretic factor(ANF),brain natriuretic peptide(BNP),β-myosin heavy chain(β-MHC),adenosine triphosphate 5β-subunit(ATP 5β)and uncoupling protein 2(UCP2)were monitored,and the morphological changes of car-diac tissue were observed.Secondly,the creatine ki-nase isoenzyme(CK-MB),lactate dehydrogenase(LDH),free fatty acids(FFA)and lactic acid in ser-um were investigated.Lastly,the expression of AMP-activated proteinkinase(AMPK),peroxisome prolifer-ator-activated receptor alpha(PPAR-α)and T-cell nu-clear factor cytoplasmic 4(NFATc4)protein expres-sion were also detected.The Ang Ⅱ-induced H9C2 cardiomyocyte hypertrophy model was established and treated with the AMPK inhibitor compound C.The mRNA of ANF,BNP,β-MHC and the protein expres-sion of AMPK/PPAR-α were analyzed.Results DMY intervention significantly reduced HW/BW and LVW/BW in mice,fetal genes ANF,BNP,β-MHC and UCP2 mRNA expression decreased,whereas ATP 5 β mRNA increased,and the degree of hypertrophy of cardiomyocytes was alleviated.In addition,the serum levels of CK-MB,LDH,FFA and lactic acid were re-duced in DMY treated groups.Finally,DMY upregu-lated the protein expression of P-AMPK,AMPK and PPAR-α,and downregulated protein expression of NFATc4.In the Ang Ⅱ-induced cardiomyocyte hyper-trophy model,DMY pretreatment reduced the mRNA expression of fetal genes(ANF,BNP,β-MHC).However,when AMPK was inhibited by compound C,the expression of these fetal genes rebounded,accom-panied by decreased protein levels of AMPK and PPAR-α.Conclusions DMY can improve Ang Ⅱ-in-duced myocardial hypertrophy in mice by ameliorating disorders of glycolipid metabolism and increasing ener-gy supply to cardiomyocytes,and its mechanism is re-lated to the activation of the AMPK/PPAR-α pathway and the inhibition of NFATc4 expression.

Objective:To observe the clinical efficacy of repetitive transcranial magnetic stimulation(rTMS)combined with mindfulness-based cognitive therapy(MBCT)in patients with alcohol withdrawal syndrome(AWS).Methods:The 120 patients with AWS who were observed in this study were all male patients admitted to our hospital from June 2021 to June 2024,the patients were divided into group A(conventional treatment,40 cases),group B(group A combined with rTMS,40 cases),and group C(group B combined with MBCT,40 cases)according to random number table method.The clinical efficacy,self-control ability[Modified Clinical Institution Alcohol Dependence Withdrawal Assessment Scale(CIWA-Ar)score,Visual Analog Scale of Psychological Craving for Alcohol(VAS)score and Pennsylvania Alcohol Craving Scale(PACS)score],anxiety and depression degree assessment[Hamilton Depression Scale(HAMD)score,Hamilton Anxiety Scale(HAMA)score]and quality of life[36 Short Form Health Survey(SF-36)Score],relapse rate and readmission rate were compared among the three groups.Results:The total effective rate of group A,group B and group C increased successively(P＜0.05).The CIWA-Ar,PACS and VAS scores in group B and group C after treatment were lower than those in group A,and group C was lower than that in group B(P＜0.05).The HAMD and HAMA scores of group B and group C after treatment were lower than those in group A,and group C was lower than that in group B(P＜0.05).The SF-36 score of group B and group C after treatment was higher than those in group A,and group C was higher than that in group B(P＜0.05).Relapse rate and readmission rate in groups B and C were lower than those in group A,and group C was lower than that in group B(P＜0.05).Conclusion:The application of rTMS combined with MBCT in patients with AWS can improve clinical efficacy and quality of life,alleviate anxiety and depression,improve patients' self-control ability,reduce relapse rate and readmission rate,with definite effects.

Objective:To explore the interrelationship between brain functional networks and features in functional magnetic resonance imaging(fMRI)of patients with Alzheimer's disease(AD),and to construct mixed-function networks(MFN),and apply them in machine learning classification models to improve the accuracy of AD classification.Methods:102 AD patients and 227 healthy subjects in the Alzheimer's Neuroimaging Initiative(ADNI)dataset were retrospectively analyzed.The partial correlation brain network of the blood oxygen level dependent(BOLD)signal was calculated and fused with low-frequency wave amplitude(ALFF),fractional low-frequency wave amplitude(fALFF)and local consistency(ReHo)features to construct MFN.Network topology parameters were extracted,and a variety of machine learning classification models were constructed based on MFN topological parameters,accuracy,precision,recall and area under the curve(AUC)were used to evaluate the predictive efficiency of the models.Results:By constructed MFN and calculated intra group to inter group ratio(IIGR),35 features could be obtained from ALFF,fALFF and ReHo feature topological parameter analysis,after rank sum test and FDR correction,there were statistical differences among 28 features(P＜0.05).The classification results show that,all the five classifiers have high classification performance on the test data set.The accuracy,precision and recall rates of random forest(RF),adaptive lifting algorithm(AdaBoost),guided aggregation algorithm(Bagging)and support vector machine(SVM)were all 99.7％,and the AUC values were up to 100％,99.5％,99.1％and 99.5％,respectively.The accuracy(98.5％),precision(98.5％),recall(98.5％),and AUC(99.1％)of the multi-layer perceptron(MLP)were slightly lower than other models,but remained excellent.It was worth noting that RF has the highest AUC value of all models at 100.0％,while Bagging has the lowest AUC value(99.1％)in the integrated approach.The results of performance comparison show that,MFN classification model can significantly improve the recognition and classification of AD disease,and greatly improve the performance of various indicators of the classifier.The results showed that,MFN classification model was superior to intelligent classification based fusion,DBN-based multitask learning,PVT-TSVM,unsupervised learning and clustering,SVM and SVM of degree 3 polynomial kernel function in key indicators such as accuracy(99.13％),AUC(99.42％),recall rate(99.46％)and specificity(99.42％)with plasma proteins,machine learning algorithms.It was further proved that MFN classification model has good generalization ability and robustness in AD disease classification.Conclusion:The AD classification model constructed based on brain mixed function network topology parameters and machine learning can improve the accuracy of AD classification.

Objective:To observe the clinical efficacy of repetitive transcranial magnetic stimulation(rTMS)combined with mindfulness-based cognitive therapy(MBCT)in patients with alcohol withdrawal syndrome(AWS).Methods:The 120 patients with AWS who were observed in this study were all male patients admitted to our hospital from June 2021 to June 2024,the patients were divided into group A(conventional treatment,40 cases),group B(group A combined with rTMS,40 cases),and group C(group B combined with MBCT,40 cases)according to random number table method.The clinical efficacy,self-control ability[Modified Clinical Institution Alcohol Dependence Withdrawal Assessment Scale(CIWA-Ar)score,Visual Analog Scale of Psychological Craving for Alcohol(VAS)score and Pennsylvania Alcohol Craving Scale(PACS)score],anxiety and depression degree assessment[Hamilton Depression Scale(HAMD)score,Hamilton Anxiety Scale(HAMA)score]and quality of life[36 Short Form Health Survey(SF-36)Score],relapse rate and readmission rate were compared among the three groups.Results:The total effective rate of group A,group B and group C increased successively(P＜0.05).The CIWA-Ar,PACS and VAS scores in group B and group C after treatment were lower than those in group A,and group C was lower than that in group B(P＜0.05).The HAMD and HAMA scores of group B and group C after treatment were lower than those in group A,and group C was lower than that in group B(P＜0.05).The SF-36 score of group B and group C after treatment was higher than those in group A,and group C was higher than that in group B(P＜0.05).Relapse rate and readmission rate in groups B and C were lower than those in group A,and group C was lower than that in group B(P＜0.05).Conclusion:The application of rTMS combined with MBCT in patients with AWS can improve clinical efficacy and quality of life,alleviate anxiety and depression,improve patients' self-control ability,reduce relapse rate and readmission rate,with definite effects.

Objective:To explore the interrelationship between brain functional networks and features in functional magnetic resonance imaging(fMRI)of patients with Alzheimer's disease(AD),and to construct mixed-function networks(MFN),and apply them in machine learning classification models to improve the accuracy of AD classification.Methods:102 AD patients and 227 healthy subjects in the Alzheimer's Neuroimaging Initiative(ADNI)dataset were retrospectively analyzed.The partial correlation brain network of the blood oxygen level dependent(BOLD)signal was calculated and fused with low-frequency wave amplitude(ALFF),fractional low-frequency wave amplitude(fALFF)and local consistency(ReHo)features to construct MFN.Network topology parameters were extracted,and a variety of machine learning classification models were constructed based on MFN topological parameters,accuracy,precision,recall and area under the curve(AUC)were used to evaluate the predictive efficiency of the models.Results:By constructed MFN and calculated intra group to inter group ratio(IIGR),35 features could be obtained from ALFF,fALFF and ReHo feature topological parameter analysis,after rank sum test and FDR correction,there were statistical differences among 28 features(P＜0.05).The classification results show that,all the five classifiers have high classification performance on the test data set.The accuracy,precision and recall rates of random forest(RF),adaptive lifting algorithm(AdaBoost),guided aggregation algorithm(Bagging)and support vector machine(SVM)were all 99.7％,and the AUC values were up to 100％,99.5％,99.1％and 99.5％,respectively.The accuracy(98.5％),precision(98.5％),recall(98.5％),and AUC(99.1％)of the multi-layer perceptron(MLP)were slightly lower than other models,but remained excellent.It was worth noting that RF has the highest AUC value of all models at 100.0％,while Bagging has the lowest AUC value(99.1％)in the integrated approach.The results of performance comparison show that,MFN classification model can significantly improve the recognition and classification of AD disease,and greatly improve the performance of various indicators of the classifier.The results showed that,MFN classification model was superior to intelligent classification based fusion,DBN-based multitask learning,PVT-TSVM,unsupervised learning and clustering,SVM and SVM of degree 3 polynomial kernel function in key indicators such as accuracy(99.13％),AUC(99.42％),recall rate(99.46％)and specificity(99.42％)with plasma proteins,machine learning algorithms.It was further proved that MFN classification model has good generalization ability and robustness in AD disease classification.Conclusion:The AD classification model constructed based on brain mixed function network topology parameters and machine learning can improve the accuracy of AD classification.