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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.
Scars are fibrous tissues that replace normal tissue during the wound healing process. Scarring can lead to low self-esteem, social impairment, depression, anxiety, and other psychiatric and psychological distress, necessitating a comprehensive understanding of the latest perspectives, topical research, and directions in scarring-mental health. This is a biblioshiny and VOSviewer based bibliometric analysis study. All data were obtained from the Web of Science, and a total of 664 articles from 2003 to 2022 met the criteria. The last 7 years have been a period of rapid growth in the field, with 2022 having the highest number of articles. The United States is the core country with the highest production and citation rate. The most cited literature was written in 2003 by Van Loey NE et al. Van Loey NE is the most prolific and influential author in this field. The top five popular keywords include “quality of life”, “depression”, “management”, “anxiety”, and “prevalence”. The paper concludes that the current focus of scholars in the field is on the treatment of scars and that multidisciplinary treatment of such patients is worth exploring. These findings provide relevant researchers with the current state of research and possible future directions in this field.
by Shuguang Zhao, Jue Zhou, Ting Wang
While conspiracy theories have received extensive attention in the realm of misinformation, there has been limited research exploring the impact of conspiracy mentality on individuals’ preventive behaviors during acute public health crises. This study investigates how conspiracy mentality may affect compliance with preventive health measures necessary to fight the COVID-19 pandemic, and the underlying emotional and cognitive mediators. Data was collected through a survey among 1878 Chinese respondents at the conclusion of the pandemic. The results indicate that individuals with higher levels of conspiracy mentality are significantly less engaged in preventive behaviors. Furthermore, this correlation is mediated by a sequence of mediating factors, starting from anger leading to institutional distrust and fear leading to perceived risk. Conspiracists’ response mode can be described as a state of "attentive immobility," in which the impact of heightened institutional distrust outweighs their perceptions of risk, ultimately reducing engagement in preventive behaviors during crises. These findings underscore the importance of debunking initiatives that aim to address and mitigate the negative consequences of conspiracy mentality by targeting the mediating psychological processes during future pandemic threats.We aimed to quantitatively and systematically elucidate the rationality of the examined variables as independent risk factors for sternal wound infection. We searched databases to screen studies, ascertained the variables to be analysed, extracted the data and applied meta-analysis to each qualified variable. Odds ratios and mean differences were considered to be the effect sizes for binary and continuous variables, respectively. A random-effects model was used for these procedures. The source of heterogeneity was evaluated using a meta-regression. Publication bias was tested by funnel plot and Egger's test, the significant results of which were then calculated using trim and fill analysis. We used a sensitivity analysis and bubble chart to describe their robustness. After screening all variables in the eligible literature, we excluded 55 because only one or no research found them significant after multivariate analysis, leaving 33 variables for synthesis. Two binary variables (age over 65 years, NYHA class >2) and a continuous variable (preoperative stay) were not significant after the meta-analysis. The most robust independent risk factors in our study were diabetes mellitus, obesity, use of bilateral internal thoracic arteries, chronic obstructive pulmonary disease, prolonged surgery time, prolonged ventilation and critical preoperative state, followed by congestive heart failure, atrial fibrillation, renal insufficiency, stroke, peripheral vascular disease and use of an intra-aortic balloon pump. Relatively low-risk factors were emergent/urgent surgery, smoking, myocardial infarction, combined surgery and coronary artery bypass grafting. Sternal wound infection after open-heart surgery is a multifactorial disease. The detected risk factors significantly affected the wound healing process, but some were different in strength. Anything that affects wound healing and antibacterial ability, such as lack of oxygen, local haemodynamic disorders, malnutrition condition and compromised immune system will increase the risk, and this reminds us of comprehensive treatment during the perioperative period.
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