Medicine
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Early identification and intervention of acute respiratory distress syndrome (ARDS) are particularly important. This study aimed to construct predictive models for ARDS following severe acute pancreatitis (SAP) by artificial neural networks and logistic regression. The artificial neural networks model was constructed using clinical data from 214 SAP patients. ⋯ Bedside Index of Severity in Acute Pancreatitis score, procalcitonin, prothrombin time, and serum calcium were the most important predictive variables in the artificial neural networks model. The discrimination abilities of logistic regression and artificial neural networks models in predicting SAP-related ARDS were similar. It is advisable to choose the model according to the specific research purpose.
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Case Reports
Axillary lymphadenopathy as the initial manifestation in ANCA-associated systemic vasculitis: A case report.
The anti-neutrophil cytoplasmic antibody (ANCA) associated vasculitides are a collection of relatively rare autoimmune diseases characterized by the presence of ANCAs, predominantly against myeloperoxidase and proteinase 3. Multiple organs and systems are involved, but superficial lymph node involvement is rarely reported. ⋯ Superficial lymphadenopathy is very rare in ANCA-associated systemic vasculitis. Studying this case improves our understanding of the initial manifestations of ANCA-associated vasculitis and may help provide accurate early diagnosis, thus allowing timely treatment and improved patient prognosis.
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Evidence-based nursing practice was used to formulate the enhanced recovery surgery bundle nursing strategy and apply it to patients with gastric cancer, to explore its safety, effectiveness and feasibility in perioperative gastrointestinal function protection in patients with gastric cancer. Selected the clinical medical records of 100 gastric cancer patients treated in our hospital from June 2019 to June 2021 as the research objects, and divided them into the control group and the observation group with 50 cases in each group according to the random number table. Among them, the control group was given routine nursing measures for nursing intervention, and the observation group was given gastrointestinal enhanced recovery surgery cluster nursing on the basis of the control group. ⋯ After nursing, heart rate (HR), mean arterial pressure (MAP), norepinephrine (NE), and epinephrine (E2) in the observation group were lower than those in the control group, and the difference was statistically significant (P < .05). The pain scores of the 2 groups were significantly improved at different time points, and the observation group was significantly less than the control group, and the difference was statistically significant (P < .05). Gastrointestinal enhanced recovery surgery bundle nursing can effectively improve the gastrointestinal function of patients with gastric cancer, improve the emotional response and stress response of patients, and has certain reference value for the nursing of patients with gastric cancer.
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This study aimed to explore the possible mechanisms of Ling Gui Zhu Gan decoction (LGZGD) in the treatment of nephrotic syndrome (NS) using network pharmacology combined with molecular docking and molecular dynamics simulation. The active ingredients of LGZGD and their targets were retrieved from Traditional Chinese Medicine Systems Pharmacology Database and Swiss Target Prediction database. The NS targets were retrieved from Genecards, OMIM and Drugbank databases. ⋯ Molecular docking and molecular dynamics simulation results further indicated that the key active ingredients of LGZGD could stably bind to the core targets through hydrogen bonding and hydrophobic interaction. This study demonstrates that the active ingredients of LGZGD may regulate multiple targets, biological processes and signaling pathways in NS. Our findings may provide a theoretical basis for further studies on LGZGD in the treatment of NS.
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This study aimed to explore suitable clustering algorithms for author collaborations (ACs) in bibliometrics and investigate which countries frequently coauthored with others in recent years. To achieve this, the study developed a method called the Follower-Leading Clustering Algorithm (FLCA) and used it to analyze ACs and cowords in the Journal of Medicine (Baltimore) from 2020 to 2022. ⋯ The proposed FLCA algorithm provides researchers with a comprehensive means to explore and understand the intricate connections between authors or keywords. Therefore, the study recommends the use of FLCA and visualizations with R for future research on ACs with cluster analysis.