Acute respiratory distress syndrome (ARDS) is a life-threatening condition with a high incidence and mortality rate in intensive care unit (ICU) admissions. Early identification of patients at high risk for developing ARDS is crucial for timely intervention and improved clinical outcomes. However, the complex pathophysiology of ARDS makes early prediction challenging. This study aimed to develop an artificial intelligence (AI) model for automated lung lesion segmentation and early prediction of ARDS to facilitate timely intervention in the intensive care unit. A total of 928 ICU patients with chest computed tomography (CT) scans were included from November 2018 to November 2021 at three centers in China. Patients were divided into a retrospective cohort for model development and internal validation, and three independent cohorts for external validation. A deep learning-based framework using the UNet Transformer (UNETR) model was developed to perform the segmentation of lung lesions and early prediction of ARDS. We employed various data augmentation techniques using the Medical Open Network for AI (MONAI) framework, enhancing the training sample diversity and improving the model's generalization capabilities. The performance of the deep learning-based framework was compared with a Densenet-based image classification network and evaluated in external and prospective validation cohorts. The segmentation performance was assessed using the Dice coefficient (DC), and the prediction performance was assessed using area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. The contributions of different features to ARDS prediction were visualized using Shapley Explanation Plots. This study was registered with the China Clinical Trial Registration Centre (ChiCTR2200058700). The segmentation task using the deep learning framework achieved a DC of 0.734 ± 0.137 in the validation set. For the prediction task, the deep learning-based framework achieved AUCs of 0.916 [0.858–0.961], 0.865 [0.774–0.945], 0.901 [0.835–0.955], and 0.876 [0.804–0.936] in the internal validation cohort, external validation cohort I, external validation cohort II, and prospective validation cohort, respectively. It outperformed the Densenet-based image classification network in terms of prediction accuracy. Moreover, the ARDS prediction model identified lung lesion features and clinical parameters such as C-reactive protein, albumin, bilirubin, platelet count, and age as significant contributors to ARDS prediction. The deep learning-based framework using the UNETR model demonstrated high accuracy and robustness in lung lesion segmentation and early ARDS prediction, and had good generalization ability and clinical applicability. This study was supported by grants from the Shanghai Renji Hospital Clinical Research Innovation and Cultivation Fund (RJPY-DZX-008) and Shanghai Science and Technology Development Funds (22YF1423300).

Introduction: Sepsis delays wound healing owing to uncontrolled inflammation. A single perioperative dose of dexamethasone is widely used because of its anti-inflammatory effects. However, the effects of dexamethasone on wound healing in sepsis remain unclear.Methods: We discuss the methods to obtain dose curves and explore the safe dosage range for wound healing in mice with or without sepsis. A saline or LPS intraperitoneal injection was applied to C57BL/6 mice. After 24 hours, the mice received a saline or DEX intraperitoneal injection and full-thickness, dorsal wounding operation. Wound healing was observed by image record, immunofluorescence and histological staining. Inflammatory cytokines and M1/M2 macrophages in wounds were determined by ELISA and immunofluorescence, respectively.Results: Dose-response curves reflected the safe dosage range of DEX in mice with or without sepsis, from 0.121 to 2.03 mg/kg and from 0 to 0.633 mg/kg, respectively. we found that a single dose of dexamethasone (1 mg/kg, i.p.) promoted wound healing in septic mice, but delayed wound healing in normal mice. In normal mice, dexamethasone delays inflammation, resulting in an insufficient number of macrophages during the healing process. In septic mice, dexamethasone alleviated excessive inflammation and maintained the balance of M1/M2 macrophages in the early and late healing process.Discussion: In summary, the safe dosage range of dexamethasone in septic mice is wider than that in normal mice. A single dose of dexamethasone (1 mg/kg) increased wound healing in septic mice, but delayed it in normal mice. Our findings provide helpful suggestions for the rational use of dexamethasone.

Background: The use of continuous glucose monitoring (CGM) in the hospital setting is growing, with more patients using these devices at home, especially during the COVID-19 pandemic. Frail and critically ill patients with COVID-19 and previously normal glucose tolerance are also associated with variability in their glucose levels during their intensive care unit (ICU) stay. However, very limited evidence supports the use of CGM in ICU settings, especially among frail patients with COVID-19. Objective: We aimed to investigate the effectiveness of CGM on ICU-related outcomes among frail and critically ill patients with confirmed COVID-19. Methods: This was an exploratory, prospective, open-label, parallel, single-center, randomized controlled trial. A total of 124 patients was finally analyzed. The primary outcome was 28-day, in-ICU mortality. The secondary outcome included the length of ICU stay as well as the occurrence of hypoglycemia and severe hypoglycemia events. Results: The mean age was 78.3 (SD 11.5) years. The mean fasting glucose level and hemoglobin A1c level at baseline were 8.12 (SD 1.54) mmol/L and 7.2% (SD 0.8%), respectively. The percentage of participants with diabetes was 30.6% (38/124). The corresponding hazard ratio of the primary outcome in the intermittently scanned CGM (isCGM) group when compared with the point-of-care testing (POCT) group was 0.18 (95% CI 0.04-0.79). The average length of ICU stay was 10.0 (SD 7.57) days in the isCGM group and 14.0 (SD 6.86) days in the POCT group (P=.02). At the end of study period, the mean value of fasting glucose in the isCGM group and the POCT group was 6.07 (SD 0.63) mmol/L and 7.76 (SD 0.62) mmol/L, respectively (P=.01). A total of 207 hypoglycemia events (<3.9 mmol/L) was detected, with 43 in the isCGM group and 164 in the POCT group (P<.001). A total of 81 severe hypoglycemia events (<2.8 mmol/L) was detected, with 16 in the isCGM group and 65 in the POCT group (P<.001). The major adverse event in this study was bleeding in the puncture site, with a total of 6 occurrences in the isCGM group. During the follow-up, none of the participants dropped out because of bleeding in the puncture site. Conclusions: We found a significant clinical benefit from the use of CGM among frail and critically ill patients with COVID-19. These findings support the use of CGM in the ICU and might help with the extension of application in various in-hospital settings. Trial Registration: Chinese Clinical Trial Registry ChiCTR2200059733; https://www.chictr.org.cn/showproj.html?proj=169257

Traumatic injuries, surgery, and chronic diseases lead to soft tissue wounds. Stimulating normal wound healing (WH) is important for tissue repair and restoration of homeostasis. Lack of angiogenesis impedes wound healing and is noted in chronic wounds. The goal of this investigation was to thoroughly assess the present state and patterns of investigations on angiogenesis in WH by the use of bibliometric analysis. Studies examining angiogenesis and WH were sourced from the database of the Web of Science Core Collection. Only studies that fulfilled the inclusion criteria were chosen for the purpose of investigation. To analyze the publications included in this research, bibliometric and visual analysis techniques were applied utilizing tools like VOSviewer and CiteSpace. For the analysis, 11,558 papers were considered. The number of publications increased annually from 2013 to 2023. China, the USA, and South Korea were the top nations in this subject, accounting for 41.1 %, 19.4 %, and 5.8 % of published articles, respectively. The author and institution with the greatest number of publications were found to be Chang J and Shanghai Jiao Tong University. PLOS One had the greatest publication count among journals, whereas Biomaterials had the greatest number of citations and was often mentioned in co-citations. Angiogenesis-related biomedical engineering and tissue engineering were the topics that received the most research attention. Recent studies have focused on vascular endothelial growth factor and carboxymethyl chitosan as emerging areas of interest. In this investigation, we compiled the features of publications and determined the most impactful nations, organizations, writers, periodicals, popular subjects, and patterns concerning the process of angiogenesis in the context of WH.