Operation room nursing based on humanized nursing mode combined with nitric oxide on rehabilitation effect after lung surgery

BACKGROUND With advancements in the diagnosis and treatment of lung diseases,lung segment surgery has become increasingly common.Postoperative rehabilitation is critical for patient recovery,yet challenges such as complications and adverse outcomes persist.Incorporating humanized nursing modes and novel treatments like nitric oxide inhalation may enhance recovery and reduce postoperative complications.AIM To evaluate the effects of a humanized nursing mode combined with nitric oxide inhalation on the rehabilitation outcomes of patients undergoing lung surgery,focusing on pulmonary function,recovery speed,and overall treatment costs.METHODS A total of 79 patients who underwent lung surgery at a tertiary hospital from March 2021 to December 2021 were divided into a control group(n=39)receiving a routine nursing program and an experimental group(n=40)receiving additional humanized nursing interventions and atomized inhalation of nitric oxide.Key indicators were compared between the two groups alongside an analysis of treatment costs.RESULTS The experimental group demonstrated significant improvements in pulmonary function,reduced average recovery time,and lower total treatment costs compared to the control group.Moreover,the quality of life in the experimental group was significantly better in the 3 months post-surgery,indicating a more effective rehabilitation process.CONCLUSION The combination of humanized nursing mode and nitric oxide inhalation in postoperative care for lung surgery patients significantly enhances pulmonary rehabilitation outcomes,accelerates recovery,and reduces economic burden.This approach offers a promising reference for improving patient care and rehabilitation efficiency following lung surgery.

The applications and challenges of artificial intelligence in colonoscopy

It has been widely acknowledged that colonoscopy is the most effective method of preventing colorectal cancer because it can detect and remove precursor lesions,thereby reducing both incidence and mortality.Among these approaches,standardized colonoscopy stands out as the most efficacious method for early-stage colorectal cancer identification.Artificial intelligence is a rapidly expanding field in gastrointestinal endoscopy.This article presents a comprehensive review of the advantages and clinical evidence supporting the application of artificial intelligence in colonoscopy while also discussing prospects for utilizing artificial intelligence-assisted endoscopy in diagnosing colorectal cancer.

Effect of siRNAs on HSV-1 Plaque Formation and Relative Expression Levels of RR mRNA

RNA interference (RNAi) is a process by which introduced small interfering RNA (siRNA) can cause the specific degradation of mRNA with identical sequences. The human herpes simplex virus type 1 (HSV-1) RR is composed of two distinct homodimeric subunits encoded by UL39 and UL40,respectively. In this study,we applied siRNAs targeting the UL39 and UL40 genes of HSV-1. We showed that synthetic siRNA silenced effectively and specifically UL39 and UL40 mRNA expression and inhibited HSV-1 replication. Our work offers new possibilities for RNAi as a genetic tool for inhibition of HSV-1 replication.

Feature selection and machine learning approach for carotid atherosclerosis in asymptomatic adults

Objective:The presence of carotid atherosclerosis reflects the overall atherosclerotic load and may predict cardiovascular and cerebrovascular accidents.Studies have reported risk factors for carotid atherosclerosis.However,few practical models have been established to predict carotid atherosclerosis risk.Thus,this study was conducted to investigate important features of carotid atherosclerosis and to propose a machine learning-based method for predicting carotid atherosclerosis in asymptomatic adults.Methods:Cross-sectional study was conducted using routine medical check-up data of individuals from January 2019 to January 2020.Pearson’s correlation analysis was performed to correlate the features.Then,features were selected by python’s feature-selection library and analyzed through three algorithms.Multiple machine learning algorithms,including Decision Tree,Random Forest and Logistic Regression(LR)were used to predict the risk of carotid atherosclerotic plaques and compared their precision,accuracy,recall,F1-score and area under the curve.Results:A total of 150 individuals were enrolled in this study,30(20%)of them were found with carotid atherosclerotic plaques.Sex,age,body mass index,total cholesterol,Systolic blood pressure(SBP),and carbohydrate antigen 724(CA724)were independently correlated to carotid atherosclerosis.Pepsinogen Ⅰ and pepsinogen Ⅱ serum levels had no correlations with Carotid intima-media thickness and pulse wave velocity.SBP,diastolic blood pressure age,low-density lipoprotein,Pepsinogen I,pepsinogen II,body mass index,Waist,CA724,and Uric Acid contribute to the cumulative importance of 0.9,and SBP was the most crucial feature for carotid atherosclerosis.LR algorithm has the precision(0.92),values of recall(0.91),F1(0.9),and area under the curve(0.95),and showed the optimal performance to predict the presence or absence of carotid atherosclerosis in asymptomatic adults.The code for analysis in this article was uploaded to GitHub(https://github.com/ganbingliangyi/machine-learning).Conclusions:SBP was the most crucial feature in ranking features,the LR algorithm showed the optimal performance to predict the presence or absence of carotid atherosclerosis in asymptomatic adults.

Synthesis of Glutathione Imprinted Polymer Particles via Controlled Living Radical Precipitation Polymerization

A general protocol was described for fabricating uniform molecularly imprinted polymer （MIP） particles via controlled living radical precipitation polymerization at ambient temperature. By adopting glutathione as model template, benzyl dithiocarbamate as iniferter agent, 4-vinylpyridine as monomer, and ethylene glycol dimethacrylate as cross-linker, it is demonstrated that the polymerization parameters including the iniferter concentration, monomer loading and molar ratio of cross-linker to functional monomer have profound effect on the final particle size and recognition property of the MIP particles. The batch static binding experiments were carried out to estimate the adsorption kinetics, adsorption isotherms and selective recognition of the MIP particles. The adsorption behavior followed the pseudo-second order kinetic model, revealing that the process was chemically carried out. Two adsorption isotherm models were applied to analyze equilibrium data, obtaining the best description by Langmuir isotherm model. In addition, the MIP particles also could selectively recognize glutathione over similar analogs, indicating the possibility for the separation and enrichment of the template from complicated matrices.