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

Background: Sepsis is a life-threatening multiple-organ dysfunction caused by a dysregulated host response to infection and is the leading cause of death in non-cardiac intensive care facilities. Early reliable prediction of sepsis outcomes leads to cost-efficient resource allocation and therapeutic strategies. However, there are still no reliable markers to predict the outcome of patients at the initial stage of sepsis. Analyzing transcription profiles enables researchers to predict early outcomes using transcripts and their expression patterns. Transcriptomic profiling of septic patients has been done recently; however, analysis of prognostic outcomes is still scarce. Objective: This study aimed to determine transcriptional indicators that may be useful in the prognosis of the severity of sepsis. Methods: This is a prospective cohort study of Filipino patients admitted for sepsis at the national tertiary referral hospital in Manila, Philippines. We conducted differentially expressed gene analysis, network analyses, and area under the curve study of publicly available datasets of surviving vs. non-surviving sepsis patients to identify candidate prognosticator markers. Quantitative PCR was used to characterize the expression of each marker. A model using ordinal logistic regression analysis was done to determine which among the markers can best predict the outcome of sepsis severity. Results: We identified ACTB, RAC1, STAT3, and UBQLN1 as candidate mRNA prognosticators. The expression of STAT3, a gene involved in immunosuppression, is inversely correlated with the severity of sepsis. Conclusion Transcriptomic markers such as STAT3 can predict the severity of patients with sepsis. Early detection of its inverse expression may prompt early and more aggressive management of patients.
sepsis ; STAT3 ; data mining ; transcriptomics

OBJECTIVE: To explore the rules of acupoint selection for aphasia treated with acupuncture and moxibustion using data mining technology. METHODS: From January 1, 2000 to April 1, 2022, the articles for clinical researches of acupuncture and moxibustion for aphasia published in CNKI, Wanfang, VIP, SinoMed, PubMed, EMbase were searched. Using Microsoft Excel 2021, the database was set up to analyze the use frequency of acupoint, meridian tropism, acupoint distribution and the use of specific points. SPSS26.0 was adopted for factor analysis, SPSS Modeler 18.0 was for association rule analysis of prescriptions, and Gephi 0.9.5 was to plot the co-occurrence network diagrams of acupoints and meridians. RESULTS: A total of 140 articles were collated, including 146 acupuncture and moxibustion prescriptions and 189 acupoints. The total use frequency of these acupoints was 1 211. Lianquan (CV 23), Jinjin (EX-HN 12), Yuye (EX-HN 13), Baihui (GV 20) and Yamen (GV 15) were the top 5 acupoints of the high use frequency for aphasia treated with acupuncture and moxibustion. Among 189 acupoints collected, the extra points and empirical points were mostly selected. The top 3 involved meridians were the governor vessel, the gallbladder meridian of foot-shaoyang and the conception vessel. These acupoints were mostly distributed on the head, face and neck region. The use frequency of five-shu points was the highest among the specific points. The acupoint combinations of high frequency referred to Yuye (EX-HN 13)-Jinjin (EX-HN 12), Yuye (EX-HN 13)-Lianquan (CV 23)-Jinjin (EX-HN 12), and Fengchi (GB 20)-Yuye (EX-HN 13)-Jinjin (EX-HN 12). Factor analysis extracted 10 common factors for acupoint compatibility in treatment of aphasia with acupuncture and moxibustion. CONCLUSION In clinical treatment of aphasia with acupuncture and moxibustion, the local acupoints are preferred. The core acupoints include Lianquan (CV 23), Jinjin (EX-HN 12), Yuye (EX-HN 13), Baihui (GV 20) and Yamen (GV 15). The acupoint prescription is modified flexibly according to syndrome differentiation to enhance the therapeutic effect.
Humans ; Moxibustion ; Acupuncture Points ; Acupuncture Therapy ; Meridians ; Data Mining ; Aphasia/therapy*

OBJECTIVE: To analyze the acupoint selection rules of acupuncture and moxibustion for post-stroke epilepsy by data mining technology. METHODS: The literature regarding acupuncture and moxibustion for post-stroke epilepsy included in CNKI, VIP, Wanfang, SinoMed and PubMed databases from the establishment of the database to August 1st 2022 was retrieved. Microsoft Excel 2019 software was used to establish a database to conduct the descriptive analysis of acupoints; SPSS Modeler 18.0 Apriori algorithm was used to conduct association rule analysis; high-frequency acupoint co-occurrence network diagrams were drawn by Cytoscape3.9.0 software; SPSS Statistics 25.0 software was used to perform hierarchical cluster analysis on high-frequency acupoints and a tree diagram was drawn. RESULTS: Totally 39 articles were included, and 63 prescriptions of acupuncture and moxibustion were extracted, involving 56 acupoints, with a total frequency of 516 times; the top three acupoints with the highest frequency of use were Baihui (GV 20), Fenglong (ST 40) and Neiguan (PC 6); the selected meridians were mainly the governor vessel, the hand and foot yangming meridians; the selection of acupoints were mostly in the head, neck and lower limbs; in terms of acupoint compatibility, Hegu (LI 4)-Shuigou (GV 26) and Neiguan (PC 6) had the highest confidence degree; The top 20 high-frequency acupoints could be divided into 4 effective clusters. CONCLUSION Modern acupuncture and moxibustion treatment for post-stroke epilepsy attaches great importance to the use of yang meridians and meridians with enrich qi and blood; the core prescription is Shuigou (GV 26)-Neiguan (PC 6)-Hegu (LI 4)-Baihui (GV 20). In addition, the combination of distant and near acupoints is highly valued to improve clinical efficacy.
Humans ; Moxibustion ; Acupuncture Points ; Acupuncture Therapy ; Stroke/therapy* ; Data Mining ; Epilepsy

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