Objective To propose a heart sound segmentation method based on multi-feature fusion network. Methods Data were obtained from the CinC/PhysioNet 2016 Challenge dataset (a total of 3 153 recordings from 764 patients, about 91.93% of whom were male, with an average age of 30.36 years). Firstly the features were extracted in time domain and time-frequency domain respectively, and reduced redundant features by feature dimensionality reduction. Then, we selected optimal features separately from the two feature spaces that performed best through feature selection. Next, the multi-feature fusion was completed through multi-scale dilated convolution, cooperative fusion, and channel attention mechanism. Finally, the fused features were fed into a bidirectional gated recurrent unit (BiGRU) network to heart sound segmentation results. Results The proposed method achieved precision, recall and F1 score of 96.70%, 96.99%, and 96.84% respectively. Conclusion The multi-feature fusion network proposed in this study has better heart sound segmentation performance, which can provide high-accuracy heart sound segmentation technology support for the design of automatic analysis of heart diseases based on heart sounds.

Objective:To understand the health needs of patients with non-dementia cognitive impairment after stroke, to provide reference for targeted interventions.Methods:Using the convergent mixed research method, convenience sampling was used to select post-stroke patients with non-dementia cognitive impairment in China-Japan Friendship Hospital and Beijing University of Chinese Medicine Third Affiliated Hospital, a cross-sectional survey was conducted on 191 patients with non-dementia cognitive impairment after stroke using the health needs questionnaire in March to August 2023. A descriptive study was used to conduct semi-structured interviews with 16 patients.Results:A total of 191 questionnaires were distributed and 191 valid questionnaires were collected, including 103 male and 88 female patients, aged from 34 to 90 years old. The items of the post-stroke health questionnaire were (3.47 ± 0.54), with the highest need for understanding the rehabilitation program (148/191); multiple linear regression analysis showed that gender and primary caregiver type were factors influencing their health needs ( t = 2.39, 2.73, both P<0.05). A total of 16 patients with non-dementia cognitive impairment after stroke, 10 males and 6 females, aged from 58 to 90 years old, were interviewed. Four themes were extracted, namely, information support and behavioral guidance needs, psychological care needs, social support needs, and pre-established medical care plan needs. Conclusions:The health needs of patients with non-dementia cognitive impairment after stroke are at an above medium level and have diversified characteristics. Medical staff should conduct systematic health management based on patients′specific conditions and actual needs to help patients recover or maintain cognitive function.

Objective:To investigate the effect of tofacitinib,a pan-Janus kinase(JAK)inhibitor,on transforming growth factor-beta 1(TGF-β1)-induced fibroblast to myofibroblast transition(FMT)and to explore its mechanism.To provide a theoretical basis for the clinical treatment of connective tissue disease-related interstitial lung disease(CTD-ILD).Methods:(1)Human fetal lung fibroblast 1(HFL-1)were cultured in vitro,and 6 groups were established:DMSO blank control group,TGF-β1 in-duction group,and TGF-β1 with different concentrations of tofacitinib(0.5,1.0,2.0,5.0 μmol/L)drug intervention experimental groups.CCK-8 was used to measure the cell viability,and wound-healing assay was performed to measure cell migration ability.After 48 h of combined treatment,quantitative real-time PCR(RT-PCR)and Western blotting were used to detect the gene and protein expression levels of α-smooth muscle actin(α-SMA),fibronectin(FN),and collagen type Ⅰ(COL1).(2)RT-PCR and enzyme-linked immunosorbnent assay(ELISA)were used to detect the interleukin-6(IL-6)gene and protein expression changes,respectively.(3)DMSO carrier controls,1.0 μmol/L and 5.0 μmol/L tofacitinib were added to the cell culture media of different groups for pre-incubation for 30 min,and then TGF-β1 was added to treat for 1 h,6 h and 24 h.The phosphorylation levels of Smad2/3 and signal transducer and activator of transcription 3(STAT3)protein were detected by Western blotting.Results:(1)Tofacitinib inhibited the viability and migration ability of HFL-1 cells after TGF-β1 induction.(2)The expression of α-SMA,COL1A1 and FN1 genes of HFL-1 in the TGF-β1-induced groups was signifi-cantly up-regulated compared with the blank control group(P＜0.05).Compared with the TGF-β1 in-duction group,α-SMA expression in the 5.0 μmol/L tofacitinib intervention group was significantly inhi-bited(P＜0.05).Compared with the TGF-β1-induced group,FN1 gene was significantly inhibited in each intervention group at a concentration of 0.5-5.0 μmol/L(P＜0.05).Compared with the TGF-β1-induced group,the COL1A1 gene expression in each intervention group did not change significantly.(3)Western blotting results showed that the protein levels of α-SMA and FN1 in the TGF-β1-induced group were significantly higher than those in the control group(P＜0.05),and there was no significant difference in the expression of COL1A1.Compared with the TGF-β1-induced group,the α-SMA protein level in the intervention groups with different concentrations decreased.And the differences between the TGF-β1-induced group and 2.0 μmol/L or 5.0 μmol/L intervention groups were statistically significant(P＜0.05).Compared with the TGF-β1-induced group,the FN1 protein levels in the intervention groups with different concentrations showed a downward trend,but the difference was not statistically sig-nificant.There was no difference in COL1A1 protein expression between the intervention groups com-pared with the TGF-β1-induced group.(4)After TGF-β1 acted on HFL-1 cells for 48 h,the gene ex-pression of the IL-6 was up-regulated and IL-6 in culture supernatant was increased,the intervention with tofacitinib partly inhibited the TGF-β1-induced IL-6 gene expression and IL-6 in culture supernatant.TGF-β1 induced the increase of Smad2/3 protein phosphorylation in HFL-1 cells for 1 h and 6 h,STAT3 protein phosphorylation increased at 1 h,6 h and 24 h,the pre-intervention with tofacitinib inhibited the TGF-β1-induced Smad2/3 phosphorylation at 6 h and inhibited TGF-β1-induced STAT3 phosphorylation at 1 h,6 h and 24 h.Conclusion:Tofacitinib can inhibit the transformation of HFL-1 cells into myofi-broblasts induced by TGF-β1,and the mechanism may be through inhibiting the classic Smad2/3 path-way as well as the phosphorylation of STAT3 induced by TGF-β1,thereby protecting the disease progres-sion of pulmonary fibrosis.

Objective    To explore the application of Tsetlin Machine (TM) in heart beat classification. Methods    TM was used to classify the normal beats, premature ventricular contraction (PVC) and supraventricular premature beats (SPB) in the 2020 data set of China Physiological Signal Challenge. This data set consisted of the single-lead electro-cardiogram data of 10 patients with arrhythmia. One patient with atrial fibrillation was excluded, and finally data of the other 9 patients were included in this study. The classification results were then analyzed. Results    The classification results showed that the average recognition accuracy of TM was 84.3%, and the basis of classification could be shown by the bit pattern interpretation diagram. Conclusion    TM can explain the classification results when classifying heart beats. The reasonable interpretation of classification results can increase the reliability of the model and facilitate people's review and understanding.

Objective     To recognize the different phases of Korotkoff sounds through deep learning technology, so as to improve the accuracy of blood pressure measurement in different populations. Methods     A classification model of the Korotkoff sounds phases was designed, which fused attention mechanism (Attention), residual network (ResNet) and bidirectional long short-term memory (BiLSTM). First, a single Korotkoff sound signal was extracted from the whole Korotkoff sounds signals beat by beat, and each Korotkoff sound signal was converted into a Mel spectrogram. Then, the local feature extraction of Mel spectrogram was processed by using the Attention mechanism and ResNet network, and BiLSTM network was used to deal with the temporal relations between features, and full-connection layer network was applied in reducing the dimension of features. Finally, the classification was completed by SoftMax function. The dataset used in this study was collected from 44 volunteers (24 females, 20 males with an average age of 36 years), and the model performance was verified using 10-fold cross-validation. Results     The classification accuracy of the established model for the 5 types of Korotkoff sounds phases was 93.4%, which was higher than that of other models. Conclusion     This study proves that the deep learning method can accurately classify Korotkoff sounds phases, which lays a strong technical foundation for the subsequent design of automatic blood pressure measurement methods based on the classification of the Korotkoff sounds phases.

bjective    To use machine learning technology to predict the recurrence of atrial fibrillation (AF) after radiofrequency ablation, and try to find the risk factors affecting postoperative recurrence. Methods    A total of 300 patients with valvular AF who underwent radiofrequency ablation in West China Hospital and its branch (Shangjin Hospital) from January 2017 to January 2021 were enrolled, including 129 males and 171 females with a mean age of 52.56 years. We built 5 machine learning models to predict AF recurrence, combined the 3 best performing models into a voting classifier, and made prediction again. Finally, risk factor analysis was performed using the SHApley Additive exPlanations method. Results    The voting classifier yielded a prediction accuracy rate of 75.0%, a recall rate of 61.0%, and an area under the receiver operating characteristic curve of 0.79. In addition, factors such as left atrial diameter, ejection fraction, and right atrial diameter were found to have an influence on postoperative recurrence. Conclusion    Machine learning-based prediction of recurrence of valvular AF after radiofrequency ablation can provide a certain reference for the clinical diagnosis of AF, and reduce the risk to patients due to ineffective ablation. According to the risk factors found in the study, it can provide patients with more personalized treatment.

Background::To date, there is no effective medicine to treat coronavirus disease 2019 (COVID-19), and the antiviral efficacy of arbidol in the treatment for COVID-19 remained equivocal and controversial. The purpose of this study was to evaluate the efficacy and safety of arbidol tablets in the treatment of COVID-19.Methods::This was a prospective, open-label, controlled and multicenter investigator-initiated trial involving adult patients with confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Patients were stratified 1:2 to either standard-of-care (SOC) or SOC plus arbidol tablets (oral administration of 200 mg per time, three times a day for 14 days). The primary endpoint was negative conversion of SARS-CoV-2 within the first week. The rates and 95% confidential intervals were calculated for each variable.Results::A total of 99 patients with laboratory-confirmed SARS-CoV-2 infection were enrolled; 66 were assigned to the SOC plus arbidol tablets group, and 33 to the SOC group. The negative conversion rate of SARS-CoV-2 within the first week in patients receiving arbidol tablets was significantly higher than that of the SOC group (70.3% [45/64] vs. 42.4% [14/33]; difference of conversion rate 27.9%; 95% confidence interval [CI], 7.7%-48.1%; P = 0.008). Compared to those in the SOC group, patients receiving arbidol tablets had a shorter duration of clinical recovery (median 7.0 days vs. 12.0 days; hazard ratio [HR]: 1.877, 95% CI: 1.151-3.060, P = 0.006), symptom of fever (median 3.0 days vs. 12.0 days; HR: 18.990, 95% CI: 5.350-67.410, P < 0.001), as well as hospitalization (median 12.5 days vs. 20.0 days; P < 0.001). Moreover, the addition of arbidol tablets to SOC led to more rapid normalization of declined blood lymphocytes (median 10.0 days vs. 14.5 days; P > 0.05). The most common adverse event in the arbidol tablets group was the elevation of transaminase (5/200, 2.5%), and no one withdrew from the study due to adverse events or disease progression. Conclusions::SOC plus arbidol tablets significantly increase the negative conversion rate of SARS-CoV-2 within the first week and accelerate the recovery of COVID-19 patients. During the treatment with arbidol tablets, we find no significant serious adverse events.Trial registration::Chinese Clinical Trial Registry, NCT04260594, www.clinicaltrials.gov/ct2/show/NCT04260594?term= NCT04260594&draw=2&rank=1

Background::Weight gain during chemotherapy in patients with breast cancer contributes to their poor prognosis. However, a growing number of studies have found that metabolic disorders seem to play a more important role in breast cancer prognosis than weight gain. This study aimed to explore the prognostic effects of body mass index (BMI), weight gain, and metabolic disorders on the overall survival (OS) and prognosis of patients with breast cancer who underwent chemotherapy.Methods::Data from the inpatient medical records of patients with breast cancer who underwent chemotherapy at the Beijing Cancer Hospital Breast Cancer Center from January to December 2010 were retrospectively collected, and the patients were followed up until August 2020.Results::A total of 438 patients with stages I to III breast cancer met the inclusion and exclusion criteria. Forty-nine (11.19%) patients died, while 82 (18.72%) patients had tumor recurrence and metastasis at the last follow-up (August 2020). From the time of diagnosis until after chemotherapy, no significant differences were observed in the body weight ( t = 4.694, P < 0.001), BMI categories ( χ2 = 19.215, P = 0.001), and incidence of metabolic disorders ( χ2 = 24.841, P < 0.001); the BMI categories and weight change had no effect on the OS. Both univariate ( χ2 = 6.771, P = 0.009) and multivariate survival analyses (hazard ratio = 2.775, 95% confidence interval [CI]: 1.326-5.807, P = 0.007) showed that low high-density lipoprotein cholesterol (HDL-C) levels at diagnosis had a negative impact on the OS. The multivariate logistic regression analysis showed that the HDL-C level at diagnosis (odds ratio [OR] = 2.200, 95% CI: 0.996-4.859, P = 0.051) and metabolic disorders after chemotherapy (OR= 1.514, 95% CI: 1.047-2.189, P = 0.028) are risk factors for poor prognosis in patients with breast cancer. Conclusions::Chemotherapy led to weight gain and aggravated the metabolic disorders in patients with breast cancer. Low HDL-C levels at diagnosis and metabolic disorders after chemotherapy may have negative effects on the OS and prognosis of patients with breast cancer.

OBJECTIVE：To compare th e difference of the components of volatile oil in Citrus medica from different producing areas. METHODS ：The volatile oil of C. medica from 10 different producing areas was extracted with steam distillation ，and the yield was calculated. The components of the volatile oil of C. medica from different producing areas were analyzed by GC-MS. The compounds were retrieved from NIST 14.L mass spectrum database and identified. Relative mass fraction of chemical component was determined by peak area normalization method. Cluster analysis of samples were performed by using SPSS 20.0 software. RESULTS：The yields of volatile oil of C. medica from 10 different producing areas were 0.10％-1.75％，among which sample from Qianwei county in Leshan city of Sichuan province was the highest （1.75％）. A total of 66 components were identified in the volatile oil of C. medica from different producing areas ，with a relative molecular weight of 126.20-392.66. The majority was C 10 and C 15 compounds；isomers with relative molecular weight of 136，154 took up the great proportion ，which were mainly cycloalkane monoterpenes. There were 12 common components in the volatile oil of C. medica from different areas ，which were limonene（24.90％），terpinene（14.71％），（－）-4 terpineol（2.88％），citral（2.33％），α-myrrhene（2.33％），geraniol（1.52％）， α-pinene（1.37％），trans bergamot olene （1.16％），isoterpinene（1.13％），methyl palmitate （1.12％），linalool（1.09％）and geranyl acetate（1.04％）according to relative mass fraction ；8 of them were monoterpenes ，2 were sesquiterpenes and 2 were esters. There were 4 categories of C. medica from different producing areas ，i.e. S 2，S4，S6 clustered into one ；S1，S3，S7，S8 clustered into one ； S5 and S 10 clustered into one ；S9 as one . CONCLUSIONS ： There are some difference of the components in volatile oil of medica from different producing areas ，and the content of the same component also has great difference in the volatile oil of C. medica from different producing areas.

OBJECTIVE： To optimize the purification technology of total flavonoids from Sparganium stoloniferum. METHODS： Separation and purification by macroporus adsorption resin， using sample solution pH， flow rate and concentration of eluent， the purification rate of total flavonoids as evalution indexes， the purification technology of total flavonoids from S. stoloniferum were optimized by Box-Behnken design-response surface methodology based on single factor test. Validation test was conducted. RESULTS： The optimal purification technology was sample solution pH 4.8， flow rate of eluent 2.0 BV/h， concentration of eluent 72％. The purification rate of total flavonoids in 3 batches of samples was 72.34％ （RSD＝1.77％， n＝3） in validation test， relative errors of which to predicted value （73.99％） was 2.13％. CONCLUSIONS： The optimal purification technology is stable and feasible， and can be used for the purification of total flavonoids from S. stoloniferum.