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.

"Global cancer statistics 2022" based on the latest GLOBCAN data from the International Agency for Research on Cancer (IARC) was recently released, providing a systematic analysis of the incidence and mortality of 36 types of cancer across 185 countries worldwide. The international burden of cancer is expected to continue to increase over the next 30 years, posing a severe public health and social challenge for many countries, including China. This article offers a key point interpretation of the "Global cancer statistics 2022", focusing on the evolution of cancer epidemiology and future development trends. The aim is to broaden the international perspective on cancer prevention and treatment, with the hope of providing reference and guidance for cancer prevention and treatment efforts in our country.

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 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.

Objective:To explore the relationship between integrin ɑ3(ITGA3)expression and immune cell infiltration in colorectal cancer(CRC).Methods:Bioinformatic methods were used to analyze ITGA3 mRNA expression in pan-cancer and CRC tissues,as well as its associ-ation with CRC prognosis.The correlation between ITGA3 and tumor-infiltrating immune cells was also investigated.In total,233 cases of CRC diagnosed at Shanxi Provincial Cancer Hospital between January and December 2021 were included,and ITGA3,CD8,CD163,FOXP3,PD-L1,CTLA-4,and PD-1 expression in CRC tissues were determined by immunohistochemistry(IHC)to analyze the relationship between ITGA3 and infiltrating immune cells and immune checkpoints.Results:Bioinformatics analysis showed elevated ITGA3 mRNA levels in CRC.High ITGA3 expression was associated with PFS(P＜0.05).Univariate and multifactorial analyses showed that age and stage were significantly cor-related with prognosis(P＜0.05).In addition,ITGA3 upregulation was closely correlated with multiple immune cell infiltration levels in CRC.Furthermore,IHC results showed that ITGA3 expression in CRC tissues was significantly higher than that in adjacent normal tissues(P＜0.05).ITGA3 expression was associated with lymph node metastasis(P＜0.05)and correlated with the expression of immune markers,such as CD8+T-cells,PD-L1,and CTLA-4(P＜0.05).Conclusions:ITGA3 is highly expressed in CRC,which is closely related to immune cell infiltration and may regulate the tumor immune microenvironment,which provides a new idea for clinical treatment and a potential new independent predictive marker.

To summarize the reform measures of nursing vertical management in our hospital under the background of diagnosis related groups,including refined performance management,cancellation of nursing main pharmacy classes,implementation of attending nursing working group,establishment of DRGs nursing quality control coder position,head nurse responsible for bed allocation,deepening nursing quality management and other measures,so as to provide references for other hospitals to carry out the reform of nursing vertical management under the background of DRGs.

Objective:To explore the correlation between frailty and foot care behavior in elderly patients with high-risk diabetic foot.Methods:From January to June 2022, 220 patients with high-risk diabetic foot who were admitted to the Department of Endocrinology and Department of Geriatrics of Xuanwu Hospital of Capital Medical University were selected by convenience sampling as the research object. The patients were investigated with the General Information Questionnaire, Gavin's Weighted Scale for Diabetic Foot Risk Factors for Progression to Ulceration, the Chinese version of the Frail Scale and the Foot Care Behavior Questionnaire for Diabetic Patients. Spearman correlation analysis was used to explore the correlation between frailty and foot care behavior in elderly patients with high-risk diabetic foot. Multiple linear regression was used to analyze the influencing factors of foot care behavior in elderly patients with high-risk diabetic foot. A total of 220 questionnaires were distributed, and 210 valid questionnaires were collected, with an effective response rate of 95.45% (210/220) .Results:The standardized score of the Foot Care Behavior Questionnaire for Diabetic Patients among 210 elderly patients with high-risk diabetic foot was (56.65±11.27), which was in the middle to low level. Among them, 126 patients (60.00%) were at a low level, and 80 patients (38.10%) were at a middle level. The incidence of frailty in 210 elderly patients with high-risk diabetic foot was 27.14% (57/210). The results of correlation analysis showed that the frailty score of elderly patients with high-risk diabetic foot were negatively correlated with the scores of the foot and footwear examination, foot cleaning and maintenance, footwear selection, and the total score of Foot Care Behavior Questionnaire for Diabetic Patients ( P<0.05). The results of multiple linear regression analysis showed that gender, frailty, foot risk classification and living conditions were the influencing factors of foot care behavior in elderly patients with high-risk diabetic foot ( P<0.05) . Conclusions:The foot care behavior of elderly patients with high-risk diabetic foot needs to be improved. The higher the degree of frailty, the lower the level of foot care behavior. Medical and nursing staff should formulate targeted intervention measures according to the characteristics of patients to improve or delay the progression of patients' frailty, thereby improving their foot care behavior and preventing the occurrence of diabetic foot.

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

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.