Alopecia areata (AA) is an autoimmune-related non-cicatricial alopecia, with complete alopecia (AT) or generalized alopecia (AU) as severe forms of AA. However, there are limitations in early identification of AA, and intervention of AA patients who may progress to severe AA will help to improve the incidence rate and prognosis of severe AA. We obtained two AA-related datasets from the gene expression omnibus database, identified the differentially expressed genes (DEGs), and identified the module genes most related to severe AA through weighted gene co-expression network analysis. Functional enrichment analysis, construction of a protein–protein interaction network and competing endogenous RNA network, and immune cell infiltration analysis were performed to clarify the underlying biological mechanisms of severe AA. Subsequently, pivotal immune monitoring genes (IMGs) were screened through multiple machine-learning algorithms, and the diagnostic effectiveness of the pivotal IMGs was validated by receiver operating characteristic. A total of 150 severe AA-related DEGs were identified; the upregulated DEGs were mainly enriched in immune response, while the downregulated DEGs were mainly enriched in pathways related to hair cycle and skin development. Four IMGs (LGR5, SHISA2, HOXC13, and S100A3) with good diagnostic efficiency were obtained. As an important gene of hair follicle stem cells stemness, we verified in vivo that LGR5 downregulation may be an important link leading to severe AA. Our findings provide a comprehensive understanding of the pathogenesis and underlying biological processes in patients with AA, and identification of four potential IMGs, which is helpful for the early diagnosis of severe AA.

Currently, various external tissue expansion devices are becoming widely used. Considering the scarcity of relevant application standards, this systematic review was performed to explore the effectiveness and safety of external tissue expansion techniques for the reconstruction of soft tissue defects. A systematic review and meta-analysis on the efficacy and safety of external tissue expansion technique was conducted. A comprehensive search was performed in the following electronic databases: PubMed/Medline, Embase, Cochrane Library (Wiley Online Library), and Web of Science. Studies reporting patients with soft tissue defects under the treatment of external tissue expansion technique were included. A total of 66 studies with 22 different types of external tissue expansion devices met the inclusion criteria. We performed a descriptive analysis of different kinds of devices. A single-arm meta-analysis was performed to evaluate the efficacy and safety of the external tissue expansion technique for different aetiologies. The pooled mean wound healing time among patients with defects after fasciotomy was 10.548 days [95% confidence interval (CI) = 5.796–15.299]. The pooled median wound healing times of patients with defects after excisional surgery, trauma, chronic ulcers and abdominal defects were 11.218 days (95% CI = 6.183-16.253), 11.561 days (95% CI = 7.062-16.060), 15.956 days (95% CI = 11.916-19.996) and 12.853 days (95% CI=9.444-16.227), respectively. The pooled wound healing rates of patients with defects after fasciotomy, excisional surgery, trauma, chronic ulcers and abdominal defects were 93.8% (95% CI=87.1-98.2%), 97.2% (95%CI=92.2-99.7%), 97.0% (95%CI=91.2-99.8%), 99.5% (95%CI=97.6-100%), and 96.8% (95%CI=79.2-100%), respectively. We performed a subgroup analysis in patients with diabetic ulcers and open abdominal wounds. The pooled median wound healing time of patients with diabetic ulcers was 11.730 days (95% CI = 10.334-13.125). The pooled median wound healing time of patients with open abdomen defects was 48.810 days (95% CI = 35.557–62.063) and the pooled successful healing rate was 68.8% (95% CI = 45.9-88.1%). A total of 1686 patients were included, 265 (15.7%) of whom experienced complications. The most common complication was dehiscence (n = 53, 3.14%). Our systematic review is the first to demonstrate the efficacy and safety of external tissue expansion in the management of soft tissue defects. However, we must interpret the meta-analysis results with caution considering the limitations of this review. Large-scale randomized controlled trials and long-term follow-up studies are still needed to confirm the effectiveness and evaluate the quality of healing.

Medical students' perceptions of the medical school learning environment (MSLE) have an important impact on their professional development, and physical and mental health. Few studies reported potential factors that influenced medical students' perceptions of MSLE. Thus, the main goal of this study was to identify influencing factors for medical students' perception levels of MSLE. The perception levels of MSLE were assessed by the Johns Hopkins Learning Environment Scale. The univariate and multivariate logistic regression analyses were performed to identify significant predictors for the perceptions of MSLE. The nomograms were established to predict medical students' perception levels of MSLE. In the multivariate logistic regression model, gender, university category, grade, mother education level, learning environment of schools, interests in medicine, and Kolb learning experience were significantly associated with medical students' perceptions of MSLE. Correspondently, the nomograms were built based on significant variables identified by the univariate logistic regression analysis. The validation of the nomograms showed that the model had promising predictive accuracy, discrimination, and accordance (area under the curve (AUC) = 0.751). This study identified influencing factors of medical students' perceptions of MSLE. It is essential to implement corresponding interventions to improve medical students' perceptions.

Introduction: Skin and soft tissue infections are common because of exposure to aquatic environment, while severe infections caused by Aeromonas veronii are rare. Case presentation: We report a case of severe skin and soft tissue infection of the left upper limb caused by Aeromonas veronii. A 50-year-old Chinese woman, who had a history of cardiac disease and type 2 diabetes mellitus, accidentally injured her left thumb while cutting a fish. Early antibiotic therapy and surgical debridement was performed before the result of bacterial culture came back. Whole-genome sequencing was further performed to confirm the pathogen and reveal the drug resistance and virulence genes. The wound was gradually repaired after 1 month of treatment, and the left hand recovered well in appearance and function after 3 months of rehabilitation. Conclusion: Early diagnosis, surgical intervention, and administration of appropriate antibiotics are crucial for patients who are suspected of having skin and soft tissue infection, or septicemia caused by Aeromonas veronii.

Burns constitute one of the most common injuries in the world, and they can be very painful for the patient. Especially in the judgment of superficial partial thickness burns and deep partial thickness burns, many inexperienced clinicians are easily confused. Therefore, in order to make burn depth classification automated as well as accurate, we have introduced the deep learning method. This methodology uses a U-Net to segment burn wounds. On this basis, a new thickness burn classification model that fuses global and local features (GL-FusionNet) is proposed. For the thickness burn classification model, we use a ResNet50 to extract local features, use a ResNet101 to extract global features, and finally implement the add method to perform feature fusion and obtain the deep partial or superficial partial thickness burn classification results. Burns images are collected clinically, and they are segmented and labeled by professional physicians. Among the segmentation methods, the U-Net used achieved a Dice score of 85.352 and IoU score of 83.916, which are the best results among all of the comparative experiments. In the classification model, different existing classification networks are mainly used, as well as a fusion strategy and feature extraction method that are adjusted to conduct experiments; the proposed fusion network model also achieved the best results. Our method yielded the following: accuracy of 93.523, recall of 93.67, precision of 93.51, and F1-score of 93.513. In addition, the proposed method can quickly complete the auxiliary diagnosis of the wound in the clinic, which can greatly improve the efficiency of the initial diagnosis of burns and the nursing care of clinical medical staff.