Meta‐analysis on the impact of immune senescence: Unravelling the interplay in cutaneous wound healing and lung cancer progression

Abstract

The primary objective of this meta-analysis was to provide the comprehensive understanding of the intricate correlation that existed between immune senescence and its effects on the advancement of lung cancer as well as recovery of cutaneous wounds. By conducting this systematic review of six rigorous studies utilizing databases such as PubMed and Web of Science, this research examined the multitude of facets pertaining to immune aging and consequences it bear on the health outcomes. The incorporated studies encompassed wide range of geographical and methodological viewpoints, with the specific emphasis on non-small-cell lung cancer and diverse scenarios related to wound recovery. This analysis synthesized discoveries regarding therapeutic responses, cellular and molecular mechanisms and impact of lifestyle factors on immune senescence. The findings suggested that immune senescence has substantial impact on the effectiveness of treatments for lung cancer and cutaneous wounds healing process; therefore, targeted therapies and holistic approaches may be able to mitigate these effects. By following the revised PRISMA guidelines, this meta-analysis guarantee thorough and ethically sound methodology for amalgamating pre-existing literature. The study concluded by emphasizing the critical nature of comprehending immune senescence in the context of clinical practice and proposed avenues for further investigation to enhance health results among the elderly.

Effect of antiplatelet therapy after COVID-19 diagnosis: A systematic review with meta-analysis and trial sequential analysis

by Hong Duo, Mengying Jin, Yanwei Yang, Rewaan Baheti, Yujia Feng, Zirui Fu, Yuyue Jiang, Lanzhuoying Zheng, Jing Wan, Huaqin Pan

Coronavirus disease 2019 (COVID-19) may predispose patients to thrombotic disease in the venous and arterial circulations.

Based on the current debate on antiplatelet therapy in COVID-19 patients, we performed a systematic review and meta-analysis to investigate the effect of antiplatelet treatments. We searched PubMed, EMBASE, Cochrane Central Register of Controlled Trials, and Web of Science on February 1, 2023, and only included Randomized clinical trials. The study followed PRISMA guidelines and used Random-effects models to estimate the pooled percentage and its 95% CI.

Five unique eligible studies were included, covering 17,950 patients with COVID-19. The result showed no statistically significant difference in the relative risk of all-cause death in antiplatelet therapy versus non-antiplatelet therapy (RR 0.94, 95% CI, 0.83–1.05, P = 0.26, I² = 32%). Compared to no antiplatelet therapy, patients who received antiplatelet therapy had a significantly increased relative risk of major bleeding (RR 1.81, 95%CI 1.09–3.00, P = 0.02, I² = 16%). The sequential analysis suggests that more RCTs are needed to draw more accurate conclusions. This systematic review and meta-analysis revealed that the use of antiplatelet agents exhibited no significant benefit on all-cause death, and the upper bound of the confidence interval on all-cause death (RR 95% CI, 0.83–1.05) suggested that it was unlikely to be a substantiated harm risk associated with this treatment. However, evidence from all RCTs suggested a high risk of major bleeding in antiplatelet agent treatments.

According to the results of our sequential analysis, there is not enough evidence available to support or negate the use of antiplatelet agents in COVID-19 cases. The results of ongoing and future well-designed, large, randomized clinical trials are needed.

Exploration of machine learning models for surgical incision healing assessment based on thermal imaging: A feasibility study

Abstract

In this study, we explored the use of thermal imaging technology combined with computer vision techniques for assessing surgical incision healing. We processed 1189 thermal images, annotated by experts to define incision boundaries and healing statuses. Using these images, we developed a machine learning model based on YOLOV8, which automates the recognition of incision areas, lesion segmentation and healing classification. The dataset was divided into training, testing and validation sets in a 7:2:1 ratio. Our results show high accuracy rates in incision location recognition, lesion segmentation and healing classification, indicating the model's effectiveness as a precise and automated diagnostic tool for surgical incision healing assessment. Conclusively, our thermal image-based machine learning model demonstrates excellent performance in wound assessment, paving the way for its clinical application in intelligent and standardized wound management.