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Following heart operation, a severe life-threatening complication has been identified by investigators who have recently discovered that local application of platelet-rich plasma (PRP) can lower the rate of wound infection in heart surgery. Nevertheless, due to the low quality of these trials, we have tried to perform high-quality meta-analyses to prove the efficacy of PRP in heart surgery for post-operative wound infections. In this study, five randomised controlled trials (RCTs) were chosen from three databases, and there were 1005 studies to analyse the data. Among 181 cases, PRP was applied to the surgical site, and 205 in the control group. Both the CI and the OR or the average difference (MD) were computed with either a fixed or random-effect model. A meta-analysis of the data was carried out with RevMan 5.3. The results showed that there were no statistically significant differences in the incidence of post-operative surgical site infection (SSI) in control group compared to those treated with PRP gel (OR, 0.97; 95% CI, 0.38, 2.47; p = 0.95); In the heart operation, the local application of PRP gel decreased the rate of drainage after operation (MD, −217.82; 95% CI, −335.38, −100.26; p = 0.0003); The operation time of the PRP gel was not significantly different from that of the control group (MD, 12.65; 95% CI, −2.95, 28.24; p = 0.11). Contrary to earlier research, the application of autoplatelet gel in heart surgery did not seem to decrease operative site infections after the operation, but it did decrease the amount of postoperative drainage. Nevertheless, because of the limited number of RCTs in this meta-analysis, caution should be exercised in their treatment. More high-quality randomised, large-sample trials are required to further confirm the findings.
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.
by Jay B. Lusk, Lauren Wilson, Vinit Nalwade, Ailin Song, Matthew Schrag, Valerie Biousse, Fan Li, Sven Poli, Jonathan Piccini, Ying Xian, Emily O’Brien, Brian Mac Grory
Central retinal artery occlusion (CRAO; retinal stroke or eye stroke) is an under-recognized, disabling form of acute ischemic stroke which causes severe visual loss in one eye. The classical risk factor for CRAO is ipsilateral carotid stenosis; however, nearly half of patients with CRAO do not have high-grade carotid stenosis, suggesting that other cardiovascular risk factors may exist for CRAO. Specifically, prior studies have suggested that cardioembolism, driven by underlying atrial fibrillation, may predispose patients to CRAO. We describe the design of an observational, population-based study in this protocol. We evaluate two specific objectives: 1) To determine if atrial fibrillation is an independent risk factor for CRAO after adjusting for medical and cardiovascular risk; 2) To determine if use of oral anticoagulation can modify the risk of CRAO for patients with atrial fibrillation. This protocol lays out our strategy for cohort definition, case and control definition, comorbidity ascertainment, and statistical methods.
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