Meta-analysis research was implemented to appraise the effect of various repositioning regimens (RRs) on pressure wound ulcer (PWU) occurrence in at-risk adult persons without existing PWUs. Inclusive literature research till April 2023 was done and 1197 interconnected researches were revised. The 15 picked researches, enclosed 8510 at-risk adult persons without existing PWUs persons were in the utilised researchers\' starting point, 1002 of them were utilising repositioning, 1069 were control, 3443 were utilising 2-<4 h repositioning and 2994 were utilising 4-6 h repositioning. Odds ratio (OR) and 95% confidence intervals (CIs) were utilised to appraise the consequence of various RRs on PWU occurrence in at-risk adult persons without existing PWUs by the dichotomous approach and a fixed or random model. Repositioning had significantly lower PWU (OR, 0.49; 95% CI, 0.32-0.73, p < 0.001) compared to control in at-risk adult persons without existing PWUs persons. 2-<4 h repositioning had significantly lower PWU (OR, 0.62; 95% CI, 0.42-0.90, p = 0.01) compared to 4-6 h repositioning in at-risk adult persons without existing PWUs persons. Repositioning had significantly lower PWU compared to control in at-risk adult persons without existing PWU persons. 2-<4 h repositioning had significantly lower PWU compared to 4-6 h repositioning in at-risk adult persons without existing PWUs persons. However, caution needs to be taken when interacting with its values since there was a low sample size of some of the chosen research found for the comparisons in the meta-analysis.

The management of chronic wounds in the elderly such as pressure injury (also known as bedsore or pressure ulcer) is increasingly important in an ageing population. Accurate classification of the stage of pressure injury is important for wound care planning. Nonetheless, the expertise required for staging is often not available in a residential care home setting. Artificial-intelligence (AI)-based computer vision techniques have opened up opportunities to harness the inbuilt camera in modern smartphones to support pressure injury staging by nursing home carers. In this paper, we summarise the recent development of smartphone or tablet-based applications for wound assessment. Furthermore, we present a new smartphone application (app) to perform real-time detection and staging classification of pressure injury wounds using a deep learning-based object detection system, YOLOv4. Based on our validation set of 144 photos, our app obtained an overall prediction accuracy of 63.2%. The per-class prediction specificity is generally high (85.1%-100%), but have variable sensitivity: 73.3% (stage 1 vs. others), 37% (stage 2 vs. others), 76.7 (stage 3 vs. others), 70% (stage 4 vs. others), and 55.6% (unstageable vs. others). Using another independent test set, 8 out of 10 images were predicted correctly by the YOLOv4 model. When deployed in a real-life setting with two different ambient brightness levels with three different Android phone models, the prediction accuracy of the 10 test images ranges from 80 to 90%, which highlight the importance of evaluation of mobile health (mHealth) application in a simulated real-life setting. This study details the development and evaluation process and demonstrates the feasibility of applying such a real-time staging app in wound care management.

OBJECTIVE: To evaluate the clinical application of damage control surgery (DCS) in patients with sacrococcygeal deep decubitus ulcers complicated by sepsis.
METHODS: We conducted a 3-year retrospective clinical study of 32 patients with deep sacrococcygeal bedsores and sepsis admitted from January 2018 to January 2021. According to the concept of DCS, the wound was temporarily closed with vacuum sealing drainage after primary debridement, and a local rhomboid flap was designed to repair the wound in the second stage. Finally, the clinical therapeutic effect was observed.
RESULTS: Twenty-nine patients were treated with skin flap translocation and were cured clinically. Specifically, the skin flap survived in 27 of the 29 patients after the first translocation attempt (success rate of 93.1%). One patient developed incisional dehiscence, and one patient developed a hydrocele under the skin flap.
CONCLUSIONS: Application of DCS in patients with sacrococcygeal deep decubitus ulcers complicated by sepsis improves the therapeutic success rate and reduces the risks of the operation and complication rate. It has unique advantages and is worthy of clinical promotion.

BACKGROUND: Pressure injury (PI) is a common and preventable problem, yet it is a challenge for at least two reasons. First, the nurse shortage is a worldwide phenomenon. Second, the majority of nurses have insufficient PI-related knowledge. Machine learning (ML) technologies can contribute to lessening the burden on medical staff by improving the prognosis and diagnostic accuracy of PI. To the best of our knowledge, there is no existing systematic review that evaluates how the current ML technologies are being used in PI management.
OBJECTIVE: The objective of this review was to synthesize and evaluate the literature regarding the use of ML technologies in PI management, and identify their strengths and weaknesses, as well as to identify improvement opportunities for future research and practice.
METHODS: We conducted an extensive search on PubMed, EMBASE, Web of Science, Cumulative Index to Nursing and Allied Health Literature (CINAHL), Cochrane Library, China National Knowledge Infrastructure (CNKI), the Wanfang database, the VIP database, and the China Biomedical Literature Database (CBM) to identify relevant articles. Searches were performed in June 2020. Two independent investigators conducted study selection, data extraction, and quality appraisal. Risk of bias was assessed using the Prediction model Risk Of Bias ASsessment Tool (PROBAST).
RESULTS: A total of 32 articles met the inclusion criteria. Twelve of those articles (38%) reported using ML technologies to develop predictive models to identify risk factors, 11 (34%) reported using them in posture detection and recognition, and 9 (28%) reported using them in image analysis for tissue classification and measurement of PI wounds. These articles presented various algorithms and measured outcomes. The overall risk of bias was judged as high.
CONCLUSIONS: There is an array of emerging ML technologies being used in PI management, and their results in the laboratory show great promise. Future research should apply these technologies on a large scale with clinical data to further verify and improve their effectiveness, as well as to improve the methodological quality.

Bedsore is a familiar disease, which fearfully harms the health of the patients. We investigated the efficacy and mechanism of circular RNA circANKRD36 on HaCaT cell in inflammatory damage. CCK-8 and flow cytometry were respectively used to investigate the efficacies of lipopolysaccharide (LPS), circANKRD36, and microRNA (miR)-15 on cell viability and apoptosis. Moreover, circANKRD36 and miR-15 expression were changed by cell transfection and investigated by reverse transcription-quantitative polymerase chain reaction. Furthermore, the levels of Bax, pro caspase-3, cleaved caspase-3, interleukin (IL)-1β, IL-6, and proteins of the pathway were investigated by Western blot. Otherwise, the levels of IL-1β and IL-6 were investigated by enzyme-linked immunosorbent assay. Reactive oxygen species (ROS) was investigated by ROS assay. The relation between myeloid differentiation factor 88 (MyD88) and miR-15 was investigated by luciferase assay. LPS caused inflammatory damage and upregulated circANKRD36. circANKRD36 was silenced in cells and si-circANKRD36 remitted inflammatory damage. Furthermore, si-circANKRD36 negatively regulated miR-15 and miR-15 inhibitor could reverse the efficacies of si-circANKRD36. Besides, si-circANKRD36 restrained the NF-κB pathway by upregulating miR-15. Finally, MyD88 was authenticated as a target of miR-15. circANKRD36 remitted cell inflammatory damage upregulating miR-15/MyD88 via the NF-κB pathway in HaCaT cells.

Diabetes is a major cause of morbidity for patients undergoing surgery and can increase the incidence of some postoperative complications such as bedsores. We conducted a meta-analysis of observational studies to examine whether patients with diabetes undergoing surgery had high risk of bedsore. We performed a systematic literature search in Pubmed, Embase and the Cochrane Library Central Register of Controlled Trials database from inception to November 2016. Studies were selected if they reported estimates of the relative risk (RR) for bedsore risk in postoperative diabetic patients compared with that of in non-diabetic patients. Random-effects meta-analysis was conducted to pool the estimates. A total of 16 studies with 24,112 individuals were included in our meta-analysis. The pooled RR of bedsore development for patients with diatetes was 1.77 (95% CI 1.45 to 2.16). The results of subgroup analyses were consistent when stratified by surgery type, study design, research region, sample size, inclusion period, analysis method and study quality. There was evidence of publication bias among studies and a sensitivity analysis using the Duval and Tweedie \"trim-and-fill\" method did not significantly alter the pooled results (adjusted RR 1.17, 95% CI 1.02 to 1.36).This meta-analysis provides indications that diabetic patients undergoing surgery could have a higher risk of developing bedsores. Further large-scale prospective trials should be implemented to comfirm the association.