The impact of psychological interventions on surgical site wound healing post‐surgery in psoriasis patients: A meta‐analysis

Abstract

This meta-analysis investigates the impact of psychological interventions on the wound healing process at surgical sites in patients with psoriasis who have undergone various surgical procedures. Following the PRISMA guidelines, an extensive database search was conducted, initially identifying 679 articles, with 6 studies ultimately meeting our rigorous selection criteria. These studies, which included both Randomized Controlled Trials and observational designs, utilized a range of scales, such as the REEDA and Manchester Scar Scale (MSS), to measure the healing of surgical wounds. Statistical analyses were performed using Review Manager and SPSS, revealing that psychological interventions significantly expedited wound healing as early as 1 week post-surgery (I ² = 93%; Random: SMD = −3.01, 95% CI: [−4.35, −1.66], p < 0.01), according to the REEDA scale. At the one-month follow-up, a continued positive effect was observed on the MSS (I ² = 69%; Random: SMD = 2.31, 95% CI: [1.54, 3.08], p < 0.01). The studies demonstrated a low risk of bias, and funnel plot analysis suggested no significant publication bias. These results highlight the beneficial role of psychological support in the postoperative recovery of psoriasis patients, suggesting a need for a more integrated approach to patient care that includes psychological well-being as a component of comprehensive treatment strategies.

Efficacy of the combination of Chinese herbal medicine and negative pressure wound therapy in the treatment of patients with diabetic foot ulcer: A meta‐analysis

Abstract

This study aimed to systematically evaluate the clinical efficacy of Chinese herbal medicine combined with negative pressure wound therapy (NPWT) in the treatment of diabetic foot ulcers (DFU). Computerised searches of the China National Knowledge Infrastructure, Wanfang, Chinese BioMedical Literature Database, PubMed, Cochrane Library and Embase databases were conducted for randomised controlled trials on the use of Chinese herbal medicines combined with NPWT for the treatment of DFU. The search period ranged from the time of establishment of each database to July 2023. Literature screening and data extraction were performed independently by two investigators, and the quality of the included studies was assessed. The meta-analysis was performed using Review Manager 5.4 software. A total of 25 studies were analysed, including 1777 DFUs, with 890 and 887 patients in the experimental and control groups, respectively. The results showed that the treatment of DFUs with a Chinese herbal medicine in combination with NPWT increased the overall effectiveness (odds ratio [OR] = 4.32, 95% confidence interval [CI]: 2.96–6.30, p < 0.001), wound healing rate (mean difference [MD] = 18.35, 95% CI: 13.07–23.64, p < 0.001) and ankle brachial index (MD = 0.10, 95% CI: 0.06–0.14, p < 0.001); reduced the wound healing time (MD = −11.01, 95% CI: −13.25 to −8.78, p < 0.001) and post-treatment wound area (MD = −1.73, 95% CI: −2.46 to −1.01, p < 0.001); decreased the C-reactive protein level (MD = −3.57, 95% CI: −5.13 to −2.00, p < 0.001); and increased vascular endothelial growth factor level (MD = 19.20, 95% CI: 8.36–30.05, p < 0.001). Thus, Chinese herbal medicines combined with NPWT can effectively promote wound healing, reduce inflammation and shorten the disease course in patients with DFU, while demonstrating precise clinical efficacy.

Identification of potential immune-related hub genes in Parkinson’s disease based on machine learning and development and validation of a diagnostic classification model

by Guanghao Xin, Jingyan Niu, Qinghua Tian, Yanchi Fu, Lixia Chen, Tingting Yi, Kuo Tian, Xuesong Sun, Na Wang, Jianjian Wang, Huixue Zhang, Lihua Wang

Parkinson’s disease is the second most common neurodegenerative disease in the world. However, current diagnostic methods are still limited, and available treatments can only mitigate the symptoms of the disease, not reverse it at the root. The immune function has been identified as playing a role in PD, but the exact mechanism is unknown. This study aimed to search for potential immune-related hub genes in Parkinson’s disease, find relevant immune infiltration patterns, and develop a categorical diagnostic model.

We downloaded the GSE8397 dataset from the GEO database, which contains gene expression microarray data for 15 healthy human SN samples and 24 PD patient SN samples. Screening for PD-related DEGs using WGCNA and differential expression analysis. These PD-related DEGs were analyzed for GO and KEGG enrichment. Subsequently, hub genes (dld, dlk1, iars and ttd19) were screened by LASSO and mSVM-RFE machine learning algorithms. We used the ssGSEA algorithm to calculate and evaluate the differences in nigrostriatal immune cell types in the GSE8397 dataset. The association between dld, dlk1, iars and ttc19 and 28 immune cells was investigated. Using the GSEA and GSVA algorithms, we analyzed the biological functions associated with immune-related hub genes. Establishment of a ceRNA regulatory network for immune-related hub genes. Finally, a logistic regression model was used to develop a PD classification diagnostic model, and the accuracy of the model was verified in three independent data sets. The three independent datasets are GES49036 (containing 8 healthy human nigrostriatal tissue samples and 15 PD patient nigrostriatal tissue samples), GSE20292 (containing 18 healthy human nigrostriatal tissue samples and 11 PD patient nigrostriatal tissue samples) and GSE7621 (containing 9 healthy human nigrostriatal tissue samples and 16 PD patient nigrostriatal tissue samples).

Ultimately, we screened for four immune-related Parkinson’s disease hub genes. Among them, the AUC values of dlk1, dld and ttc19 in GSE8397 and three other independent external datasets were all greater than 0.7, indicating that these three genes have a certain level of accuracy. The iars gene had an AUC value greater than 0.7 in GES8397 and one independent external data while the AUC values in the other two independent external data sets ranged between 0.5 and 0.7. These results suggest that iars also has some research value. We successfully constructed a categorical diagnostic model based on these four immune-related Parkinson’s disease hub genes, and the AUC values of the joint diagnostic model were greater than 0.9 in both GSE8397 and three independent external datasets. These results indicate that the categorical diagnostic model has a good ability to distinguish between healthy individuals and Parkinson’s disease patients. In addition, ceRNA networks reveal complex regulatory relationships based on immune-related hub genes.

In this study, four immune-related PD hub genes (dld, dlk1, iars and ttd19) were obtained. A reliable diagnostic model for PD classification was developed. This study provides algorithmic-level support to explore the immune-related mechanisms of PD and the prediction of immune-related drug targets.