Databases, Bibliographic ; Evidence-based Practice ; Health Services Needs And Demand

The efficacy on chronic obstructive pulmonary disease (COPD) at stable stage treated with different methods of acupuncture and moxibustion was evaluated using network Meta-analysis method. The articles of the randomized controlled trial (RCT) on stable COPD treated with acupuncture and moxibustion were searched electronically in CNKI, Wanfang, VIP, SinoMed, PubMed, EMbase, Web of Science and Cochrane library. The search was conducted from the inception of the databases to March 20th, 2022. Data analysis was performed using R4.1.1, Stata16.0 and RevMan5.3 softwares. A total of 48 RCTs were included, involving 15 kinds of acupuncture and moxibustion interventions and a sample size of 3 900 cases. The results of network Meta-analysis showed that: ① For the forced expiratory volume in one second predicted (FEV₁%), both the governor vessel moxibustion combined with conventional treatment (G+C therapy) and the yang-supplementing moxibustion combined with conventional treatment (Y+C therapy) obtained the better effect than that of the conventional treatment (P<0.05), and the G+C therapy was more effective compared with the thread-embedding therapy combined with conventional treatment (E+C therapy) and warm needling (P<0.05). ② Concerning to COPD assessment test (CAT) score, the results indicated that the Y+C therapy, and the mild moxibustion combined with conventional treatment (M+C therapy) were more effective when compared with the conventional treatment (P<0.05), and the effect of the Y+C therapy was better than that of the E+C therapy (P<0.05). ③ Regarding six-minute walking distance (6MWD), the effect of acupuncture combined with conventional treatment (A+C therapy) was better than that of either the E+C therapy or the conventional treatment (P<0.05). The effect of the G+C therapy was optimal for improving FEV₁%, the Y+C therapy obtained the best effect for improving CAT score, and A+C therapy was the most effective for improving 6MWD. Due to the limitation of the quality and quantity of included studies, this conclusion needs to be further verified through high-quality RCT.
Humans ; Moxibustion ; Network Meta-Analysis ; Acupuncture Therapy ; Databases, Factual ; Pulmonary Disease, Chronic Obstructive/therapy*

Humans ; Electronic Health Records ; Phenotype ; Renal Insufficiency, Chronic/epidemiology* ; Databases, Factual ; Algorithms

The present study aimed to explore the main active components and underlying mechanisms of Marsdenia tenacissima in the treatment of ovarian cancer(OC) through network pharmacology, molecular docking, and in vitro cell experiments. The active components of M. tenacissima were obtained from the literature search, and their potential targets were obtained from SwissTargetPrediction. The OC-related targets were retrieved from Therapeutic Target Database(TTD), Online Mendelian Inheritance in Man(OMIM), GeneCards, and PharmGKB. The common targets of the drug and the disease were screened out by Venn diagram. Cytoscape was used to construct an "active component-target-disease" network, and the core components were screened out according to the node degree. The protein-protein interaction(PPI) network of the common targets was constructed by STRING and Cytoscape, and the core targets were screened out according to the node degree. GO and KEGG enrichment analyses of potential therapeutic targets were carried out with DAVID database. Molecular docking was used to determine the binding activity of some active components to key targets by AutoDock. Finally, the anti-OC activity of M. tenacissima extract was verified based on SKOV3 cells in vitro. The PI3K/AKT signaling pathway was selected for in vitro experimental verification according to the results of GO function and KEGG pathway analyses. Network pharmacology results showed that 39 active components, such as kaempferol, 11α-O-benzoyl-12β-O-acetyltenacigenin B, and drevogenin Q, were screened out, involving 25 core targets such as AKT1, VEGFA, and EGFR, and the PI3K-AKT signaling pathway was the main pathway of target protein enrichment. The results of molecular docking also showed that the top ten core components showed good binding affinity to the top ten core targets. The results of in vitro experiments showed that M. tenacissima extract could significantly inhibit the proliferation of OC cells, induce apoptosis of OC cells through the mitochondrial pathway, and down-regulate the expression of proteins related to the PI3K/AKT signaling pathway. This study shows that M. tenacissima has the characteristics of multi-component, multi-target, and multi-pathway synergistic effect in the treatment of OC, which provides a theoretical basis for in-depth research on the material basis, mechanism, and clinical application.
Humans ; Female ; Marsdenia ; Molecular Docking Simulation ; Network Pharmacology ; Phosphatidylinositol 3-Kinases ; Proto-Oncogene Proteins c-akt ; Ovarian Neoplasms/genetics* ; Databases, Genetic ; Plant Extracts ; Drugs, Chinese Herbal/pharmacology*

Sleep staging is the basis for solving sleep problems. There's an upper limit for the classification accuracy of sleep staging models based on single-channel electroencephalogram (EEG) data and features. To address this problem, this paper proposed an automatic sleep staging model that mixes deep convolutional neural network (DCNN) and bi-directional long short-term memory network (BiLSTM). The model used DCNN to automatically learn the time-frequency domain features of EEG signals, and used BiLSTM to extract the temporal features between the data, fully exploiting the feature information contained in the data to improve the accuracy of automatic sleep staging. At the same time, noise reduction techniques and adaptive synthetic sampling were used to reduce the impact of signal noise and unbalanced data sets on model performance. In this paper, experiments were conducted using the Sleep-European Data Format Database Expanded and the Shanghai Mental Health Center Sleep Database, and achieved an overall accuracy rate of 86.9% and 88.9% respectively. When compared with the basic network model, all the experimental results outperformed the basic network, further demonstrating the validity of this paper's model, which can provide a reference for the construction of a home sleep monitoring system based on single-channel EEG signals.
China ; Sleep Stages ; Sleep ; Electroencephalography ; Databases, Factual

Arrhythmia is a significant cardiovascular disease that poses a threat to human health, and its primary diagnosis relies on electrocardiogram (ECG). Implementing computer technology to achieve automatic classification of arrhythmia can effectively avoid human error, improve diagnostic efficiency, and reduce costs. However, most automatic arrhythmia classification algorithms focus on one-dimensional temporal signals, which lack robustness. Therefore, this study proposed an arrhythmia image classification method based on Gramian angular summation field (GASF) and an improved Inception-ResNet-v2 network. Firstly, the data was preprocessed using variational mode decomposition, and data augmentation was performed using a deep convolutional generative adversarial network. Then, GASF was used to transform one-dimensional ECG signals into two-dimensional images, and an improved Inception-ResNet-v2 network was utilized to implement the five arrhythmia classifications recommended by the AAMI (N, V, S, F, and Q). The experimental results on the MIT-BIH Arrhythmia Database showed that the proposed method achieved an overall classification accuracy of 99.52% and 95.48% under the intra-patient and inter-patient paradigms, respectively. The arrhythmia classification performance of the improved Inception-ResNet-v2 network in this study outperforms other methods, providing a new approach for deep learning-based automatic arrhythmia classification.
Humans ; Arrhythmias, Cardiac/diagnostic imaging* ; Cardiovascular Diseases ; Algorithms ; Databases, Factual ; Electrocardiography

In the diagnosis of cardiovascular diseases, the analysis of electrocardiogram (ECG) signals has always played a crucial role. At present, how to effectively identify abnormal heart beats by algorithms is still a difficult task in the field of ECG signal analysis. Based on this, a classification model that automatically identifies abnormal heartbeats based on deep residual network (ResNet) and self-attention mechanism was proposed. Firstly, this paper designed an 18-layer convolutional neural network (CNN) based on the residual structure, which helped model fully extract the local features. Then, the bi-directional gated recurrent unit (BiGRU) was used to explore the temporal correlation for further obtaining the temporal features. Finally, the self-attention mechanism was built to weight important information and enhance model's ability to extract important features, which helped model achieve higher classification accuracy. In addition, in order to mitigate the interference on classification performance due to data imbalance, the study utilized multiple approaches for data augmentation. The experimental data in this study came from the arrhythmia database constructed by MIT and Beth Israel Hospital (MIT-BIH), and the final results showed that the proposed model achieved an overall accuracy of 98.33% on the original dataset and 99.12% on the optimized dataset, which demonstrated that the proposed model can achieve good performance in ECG signal classification, and possessed potential value for application to portable ECG detection devices.
Humans ; Electrocardiography ; Algorithms ; Cardiovascular Diseases ; Databases, Factual ; Neural Networks, Computer

Objective: To analyze the global epidemiology of renal cell carcinoma (RCC) in 2020. Methods: The incidence and mortality data of RCC in the cooperative database GLOBOCAN 2020 of International Agency for Research on Cancer of WHO and the human development index (HDI) published by the United Nations Development Programme in 2020 were collated. The crude incidence rate (CIR), age-standardized incidence rate (ASIR), crude mortality rate (CMR), age-standardized mortality rate (ASMR) and mortality/incidence ratio (M/I) of RCC were calculated. Kruskale-Wallis test was used to analyze the differences in ASIR or ASMR among HDI countries. Results: In 2020, the global ASIR of RCC was 4.6/100 000, of which 6.1/100 000 for males and 3.2/100 000 for females and ASIR was higher in very high and high HDI countries than that in medium and low HDI countries. With the rapid increase of age after the age of 20, the growth rate of ASIR in males was faster than that in females, and slowed down at the age of 70 to 75. The truncation incidence rate of 35-64 years old was 7.5/100 000 and the cumulative incidence risk of 0-74 years old was 0.52%. The global ASMR of RCC was 1.8/100 000, 2.5/100 000 for males and 1.2/100 000 for females. The ASMR of males in very high and high HDI countries (2.4/100 000-3.7/100 000) was about twice that of males (1.1/100 000-1.4/100 000) in medium and low HDI countries, while the ASMR of female (0.6/100 000-1.5/100 000) did not show significant difference. ASMR continued to increase rapidly with age after the age of 40, and the growth rate of males was faster than that of females. The truncation mortality rate of 35-64 years old was 2.1/100 000, and the cumulative mortality risk of 0-74 years old was 0.20%. M/I decreases with the increase of HDI, with M/I as 0.58 in China, which was higher than the global average of 0.39 and the United States' 0.17. Conclusion: The ASIR and ASMR of RCC presented significant regional and gender disparities globally, and the heaviest burden was in very high HDI countries.
Male ; Humans ; Female ; Adult ; Middle Aged ; Infant, Newborn ; Infant ; Child, Preschool ; Child ; Adolescent ; Young Adult ; Aged ; Carcinoma, Renal Cell/epidemiology* ; Incidence ; Databases, Factual ; China ; Kidney Neoplasms/epidemiology* ; Global Health

Objective: To understand the status of autism spectrum disorder (ASD) cohort studies and explore the feasibility of constructing ASD disease-specific cohorts based on real-world data (RWD). Methods: ASD cohort studies published by December 2022 were collected by literature retrieval from major Chinese and English databases. And the characteristics of the cohort were summarized. Results: A total of 1 702 ASD cohort studies were included, and only 60 (3.53%) were from China. A total of 163 ASD-related cohorts were screened, of which 55.83% were birth cohorts, 28.22% were ASD-specific cohorts, and 4.91% were ASD high-risk cohorts. Most cohorts used RWD such as hospital registries or conducted community-based field surveys to obtain participant information and identified patients with ASD by scales or clinical diagnoses. The contents of the studies included ASD incidence and prognostic risk factors, ASD comorbidity patterns and the impact of ASD on self-health and their offspring's health. Conclusions: ASD cohort studies in developed countries have been in the advanced stage, while the Chinese studies are still in their infancy. RWD provides the data basis for ASD-specific cohort construction and offers new opportunities for research, but work such as case validation is still needed to ensure the scientific nature of cohort construction.
Humans ; Autism Spectrum Disorder ; Cohort Studies ; Databases, Factual

Objective: To systematically review the status and factors influencing presenteeism among clinical nurses. Methods: In December 2021, CNKI, CBM, Wanfang, VIP, Web of Science, PubMed, Embase, The Cochrane Library, CINAHL, PsyclNFO and other databases were electronically searched to cross sectional studies on the current situation and factors influencing the occurrence of presenteeism among clinical nurses. The search terms mainly included presenteeism, sick at work, Stanford Presenteeism Scale, nurse, level, risk factor, influence, et al. And the search time was from the establishment of the database to November 30, 2021. Literature screening, data extraction and evaluation of the risk of bias in the included literature were done independently by two researchers, and meta-analysis was performed using Stata 15.1 software. Results: A total of 29 studies involving 13 535 clinical nurses were included.The results of the meta-analysis showed that the score of presenteeism was 17.99 [95% CI (17.02-18.95), P =0.000]. Subgroup analysis showed that presenteeism scores were higher in articles published before 2020 (ES=19.28, 95%CI: 18.41-20.15, P=0.000) and in the group of nurses aged 36 to 40 years (ES=19.27, 95%CI: 17.35~21.19, P=0.000), female (ES= 17.04, 95%CI: 14.70-19.39, P=0.000), secondary school education (ES=21.01, 95%CI: 17.76-24.26, P= 0.007), married (ES=17.49, 95%CI: 15.13-19.85, P=0.000), working for 5 to 10 years (ES=17.78, 95%CI: 16.54-19.02, P=0.000), contract (ES=17.05, 95%CI: 15.23-18.87, P=0.000), working in pediatrics (ES= 16.65, 95% CI: 15.31-17.99, P=0.000) and European region (ES =21.21, 95% CI: 20.50-21.93, P=0.000) . Conclusion: Current evidence suggests that clinical nurses are at high risk of presenteeism, which is affected by variety of factors. The managers should pay attention to the physical and mental health of nurses, identify high-risk factors as early as possible and take measures to reduce the occurrence of presenteeism and improve the quality of nursing.
Humans ; Female ; Child ; Presenteeism ; Cross-Sectional Studies ; Mental Health ; PubMed ; Nurses