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The goal of this systematic review and meta‐analysis is to provide an overview of the prevalence of surgical wound infection and related factors in patients after long bone surgery. A comprehensive, systematic search was conducted in different international electronic databases, such as Scopus, PubMed, Web of Science and Persian electronic databases such as Iranmedex and Scientific Information Database using keywords extracted from Medical Subject Headings such as "Prevalence", "Surgical wound infection", "Surgical site infection" and "Orthopedics" from the earliest to the May 1, 2023. The appraisal tool for cross‐sectional studies (AXIS tool) evaluates the quality of the included studies. A total of 71 854 patients undergoing long bone surgery participated in 12 studies. The pooled prevalence of surgical wound infection in patients who underwent long bone surgery reported in the 12 studies was 3.3% (95% CI: 1.5%–7.2%; I2 = 99.39%; p < 0.001). The pooled prevalence of surgical wound infection in male and female patients who underwent long bone surgery was 4.6% (95% CI: 1.7%–11.7%; p < 0.001; I2 = 99.34%) and 2.6% (95% CI: 1.0%–6.3%; I2 = 98.84%; p < 0.001), respectively. The pooled prevalence of surgical wound infection in patients with femur surgery sites reported in nine studies was 3.7% (95% CI: 2.1–6.4%; I2 = 93.43%; p < 0.001). The pooled prevalence of surgical wound infection in open and close fractures was 16.4% (95% CI: 8.2%–30.2%; I2 = 95.83%; p < 0.001) and 2.9% (95% CI: 1.5%–5.5%; I2 = 96.40%; p < 0.001), respectively. The pooled prevalence of surgical wound infection in patients with diabetes mellitus (DM), hypertension (HTN) and cardiovascular disease (CVD) was 4.6% (95% CI: 2.3%–8.9%; I2 = 81.50%; p < 0.001), 2.7% (95% CI: 1.2%–6.0%; I2 = 83.82%; p < 0.001) and 3.0% (95% CI: 1.4%–6.4%; I2 = 69.12%; p = 0.006), respectively. In general, the different prevalence of surgical wound infection in patients undergoing surgical treatment after long bone fracture may be caused by underlying factors (gender and co‐morbidity) and fracture‐related factors (surgery site and type of fracture). [ABSTRACT FROM AUTHOR]

Pressure injury (PI), or local damage to soft tissues and skin caused by prolonged pressure, remains controversial in the medical world. Patients in intensive care units (ICUs) were frequently reported to suffer PIs, with a heavy burden on their life and expenditures. Machine learning (ML) is a Section of artificial intelligence (AI) that has emerged in nursing practice and is increasingly used for diagnosis, complications, prognosis, and recurrence prediction. This study aims to investigate hospital‐acquired PI (HAPI) risk predictions in ICU based on a ML algorithm by R programming language analysis. The former evidence was gathered through PRISMA guidelines. The logical analysis was applied via an R programming language. ML algorithms based on usage rate included logistic regression (LR), Random Forest (RF), Distributed tree (DT), Artificial neural networks (ANN), SVM (Support Vector Machine), Batch normalisation (BN), GB (Gradient Boosting), expectation–maximisation (EM), Adaptive Boosting (AdaBoost), and Extreme Gradient Boosting (XGBoost). Six cases were related to risk predictions of HAPI in the ICU based on an ML algorithm from seven obtained studies, and one study was associated with the Detection of PI risk. Also, the most estimated risksSerum Albumin, Lack of Activity, mechanical ventilation (MV), partial pressure of oxygen (PaO2), Surgery, Cardiovascular adequacy, ICU stay, Vasopressor, Consciousness, Skin integrity, Recovery Unit, insulin and oral antidiabetic (INS&OAD), Complete blood count (CBC), acute physiology and chronic health evaluation (APACHE) II score, Spontaneous bacterial peritonitis (SBP), Steroid, Demineralized Bone Matrix (DBM), Braden score, Faecal incontinence, Serum Creatinine (SCr) and age. In sum, HAPI prediction and PI risk detection are two significant areas for using ML in PI analysis. Also, the current data showed that the ML algorithm, including LR and RF, could be regarded as the practical platform for developing AI tools for diagnosing, prognosis, and treating PI in hospital units, especially ICU. [ABSTRACT FROM AUTHOR]

The tumor microenvironment (TME) is well‐defined target for understanding tumor progression and various cell types. Major elements of the tumor microenvironment are the followings: endothelial cells, fibroblasts, signaling molecules, extracellular matrix, and infiltrating immune cells. MicroRNAs (miRNAs) are a group of small noncoding RNAs with major functions in the gene expression regulation at post‐transcriptional level that have also appeared to exerts key functions in the cancer initiation/progression in diverse biological processes and the tumor microenvironment. This study summarized various roles of miRNAs in the complex interactions between the tumor and normal cells in their microenvironment. [ABSTRACT FROM AUTHOR]

Pancreatic cancer is the fourth most common malignant tumor in the world, which has a high mortality rate due to high invasiveness, early metastases, lack of specific symptoms, and high invasiveness. Recent studies have shown that exosomes can be essential sources of biomarkers in pancreatic cancer. Over the past ten years, exosomes have been implicated in multiple trials to prevent the growth and metastasis of many cancers, including pancreatic cancer. Exosomes also play essential roles in immune evasion, invasion, metastasis, proliferation, apoptosis, drug resistance, and cancer stemness. Exosomes help cells communicate by carrying proteins and genetic material, such as non-coding RNAs, including mRNAs and microRNAs. This review examines the biological significance of exosomes in pancreatic cancer and their functions in tumor invasion, metastasis, treatment resistance, proliferation, stemness, and immune evasion. We also emphasize recent advances in our understanding of the main functions of exosomes in diagnosing and treating pancreatic cancer., Competing Interests: Declaration of Competing Interest None., (Copyright © 2023 Elsevier GmbH. All rights reserved.)