Effect of esketamine on haemodynamic fluctuations in patients undergoing hysteroscopic surgery: A prospective, double-blind randomized clinical trial.

AIMS: We explored whether esketamine anesthesia during hysteroscopic surgery can reduce intraoperative hemodynamic fluctuations and improve patient benefit. METHODS: A total of 170 patients undergoing hysteroscopic surgery were enrolled, and 151 patients were finally included in the analysis, among which 19 used vasoactive drugs during surgery. Patients were randomly assigned to either the esketamine anesthesia group (E group) or the sufentanil anesthesia group (S group). The primary outcomes were blood pressure and heart rate during the surgery. Secondary outcomes included resistance to laryngeal mask insertion, demand for propofol and remifentanil, nausea and vomiting, Richmond Agitation and Sedation Scale (RASS), dizziness and pain intensity after resuscitation, vasoactive medication treatment, hospitalization time and expenses. RESULTS: E group had a more stable heart rate, systolic blood pressure, diastolic blood pressure and mean blood pressure than the S group (p < 0.001). Patients in E group had a higher demand for propofol (p < 0.001) but better RASS scores (p < 0.001) after resuscitation. The incidence of intraoperative vasoactive medication use was higher in the S group (18.4% vs. 6.7%, p = 0.029). There were no statistically significant differences in terms of resistance to laryngeal mask insertion, remifentanil demand, time required for resuscitation, postoperative pain, dizziness, nausea or vomiting. CONCLUSIONS: Compared with sufentanil, esketamine-induced anesthesia during hysteroscopic surgery can reduce intraoperative hemodynamic fluctuations and the incidence of intraoperative vasoactive medication. Although esketamine-induced anesthesia may increase the demand for propofol during surgery, it does not affect the anesthesia recovery time and the quality of patient recovery is better.

Non-Coding RNAs Regulate Spinal Cord Injury-Related Neuropathic Pain via Neuroinflammation.

Secondary chronic neuropathic pain (NP) in addition to sensory, motor, or autonomic dysfunction can significantly reduce quality of life after spinal cord injury (SCI). The mechanisms of SCI-related NP have been studied in clinical trials and with the use of experimental models. However, in developing new treatment strategies for SCI patients, NP poses new challenges. The inflammatory response following SCI promotes the development of NP. Previous studies suggest that reducing neuroinflammation following SCI can improve NP-related behaviors. Intensive studies of the roles of non-coding RNAs in SCI have discovered that ncRNAs bind target mRNA, act between activated glia, neuronal cells, or other immunocytes, regulate gene expression, inhibit inflammation, and influence the prognosis of NP.

The Use of Machine Learning Techniques to Predict Deep Vein Thrombosis in Rehabilitation Inpatients.

BACKGROUND: Rehabilitation is crucial to recovering patients' dysfunction, improving their life quality, and promoting an early return to their family and society. In China, most patients in rehabilitation units are patients transferred from neurology, neurosurgery, and orthopedics, and most of these patients face problems such as continuously bedridden or varying degrees of limb dysfunction, all of which are risk factors for deep venous thrombosis. The formation of deep venous thrombosis can delay the recovery process and result in significant morbidity, mortality, and higher healthcare costs, so early detection and individualized treatment are needed. Machine learning algorithms can help develop more precise prognostic models, which can be of great significance in the development of rehabilitation training programs. In this study, we aimed to develop a model of deep venous thrombosis for inpatients in the Department of Rehabilitation Medicine at the Affiliated Hospital of Nantong University using machine learning methods. METHODS: We analyzed and compared 801 patients in the Department of Rehabilitation Medicine using machine learning. Support vector machine, logistic regression, decision tree, random forest classifier, and artificial neural network were used to build models. RESULTS: Artificial neural network was the better predictor than other traditional machine learnings. D-dimer levels, bedridden time, Barthel Index, and fibrinogen degradation products were common predictors of adverse outcomes in these models. CONCLUSIONS: Risk stratification can help healthcare practitioners to achieve improvements in clinical efficiency and specify appropriate rehabilitation training programs.

Prolonged symptom onset to admission time is associated with severe Coronavirus disease: A meta combined propensity-adjusted analysis.

BACKGROUND: Patients with severe COVID-19 are more likely to develop adverse outcomes with a huge medical burden. We aimed to investigate whether a shorter symptom onset to admission time (SOAT) could improve outcomes of COVID-19 patients. METHODS: A single-center retrospective study combined with a meta-analysis was performed. The meta-analysis identified studies published between 1 December 2019 and 15 April 2020. Additionally, clinical data of COVID-19 patients diagnosed between January 20 and February 20, 2020, at the First Affiliated Hospital of the University of Science and Technology of China were retrospectively analyzed. SOAT and severity of illness in patients with COVID-19 were used as effect measures. The random-effects model was used to analyze the heterogeneity across studies. Propensity score matching was applied to adjust for confounding factors in the retrospective study. Categorical data were compared using Fisher's exact test. We compared the differences in laboratory characteristic varied times using a two-way nonparametric, Scheirer-Ray-Hare test. RESULTS: In a meta-analysis, we found that patients with adverse outcomes had a longer SOAT (I2 = 39%, mean difference 0.88, 95% confidence interval = 0.47-1.30). After adjusting for confounding factors, such as age, complications, and treatment options, the retrospective analysis results also showed that severe patients had longer SOAT (mean difference 1.13 [1.00, 1.27], p = 0.046). Besides, most biochemical marker levels improved as the hospitalization time lengthened without the effect of disease severity or associated treatment (p < 0.001). CONCLUSION: Shortening the SOAT may help reduce the possibility of mild patients with COVID-19 progressing to severe illness.

Differential gene expression profiling of the sciatic nerve in type 1 and type 2 diabetic mice.

Diabetic peripheral neuropathy (DPN) is a common complication of diabetes mellitus (DM). The pathogenic mechanisms of DPN and the therapeutic interventions required may be distinct between type 1 (T1) and type 2 (T2) DM. However, the molecular mechanisms underlying the pathogenesis of DPN in both types of diabetes remain unclear. The aim of the current study was to identify the changes in genes and pathways associated with DPN in sciatic nerves of T1- and T2DM mice using bioinformatics analysis. The microarray profiles of sciatic nerves of T1DM (GSE11343) and T2DM (GSE27382) mouse models were downloaded from the Gene Expression Omnibus database to identify differentially expressed genes (DEGs) in each. DEGs in the two types of DM (with fold change ≥2 and P<0.05) were identified with BRB-ArrayTools. Gene Ontology (GO) term and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed using the Database for Annotation, Visualization and Integrated Discovery. A protein-protein interaction (PPI) network was constructed using the Search Tool for the Retrieval of Interacting Genes/Proteins and visualized using Cytoscape. Compared with control samples, 623 and 1,890 DEGs were identified in sciatic nerves of T1- and T2DM mice, respectively. Of these, 75 genes were coordinately dysregulated in the sciatic nerves of both models. Many DEGs unique to T1DM mice were localized to the nucleoplasm and were associated with regulation of transcription processes, while many unique to T2DM mice were localized at cell junctions and were associated with ion transport. In addition, certain DEGs may be associated with the different treatment strategies used for the two types of DM. This analysis provides insight into the functional gene sets and pathways operating in sciatic nerves in T1- and T2DM. The results should improve understanding of the molecular mechanisms underlying the pathophysiology of DPN, and provide information for the development of therapeutic strategies for DPN specific to each type of DM.