Sepsis is a life-threatening complication in patients with orthopedic trauma, associated with significant mortality. Dysregulated glucose metabolism is a hallmark of critical illness, but the specific role of glycemic variability (GV), beyond hyperglycemia, in this high-risk population remains inadequately explored.Kindly check and confirm the author and their respective affiliations 9 and 10 are correctly identified.Some changes have been made. Please refer to the eProofing system. This retrospective cohort study utilized 1946 patients from the Beth Israel Deaconess Medical Center between 2008 and 2022. We included adult intensive care unit (ICU) patients with a primary diagnosis of orthopedic trauma and concurrent sepsis. GV was quantified as the coefficient of variation of all blood glucose measurements during the ICU stay. The primary outcome was all-cause mortality at in-hospital, 30-day, 90-day, and 365-day. Restricted cubic spline (RCS) analysis was used to determine the detailed relationship between GV and mortality and the optimal GV threshold for dichotomization. Propensity score matching and multivariable logistic regression were employed to control for confounders. The study cohort experienced substantial mortality, with rates of 16.0

Purpose: This study aims to establish and validate machine learning-based models to predict death in hospital among critical orthopedic trauma patients with sepsis or respiratory failure. Methods: This study collected 523 patients from the Medical Information Mart for Intensive Care database. All patients were randomly classified into a training cohort and a validation cohort. Six algorithms, including logistic regression (LR), extreme gradient boosting machine (eXGBM), support vector machine (SVM), random forest (RF), neural network (NN), and decision tree (DT), were used to develop and optimize models in the training cohort, and internal validation of these models were conducted in the validation cohort. Based on a comprehensive scoring system, which incorporated 10 evaluation metrics, the optimal model was obtained with the highest scores. An artificial intelligence (AI) application was deployed based on the optimal model in the study. Results: The in-hospital mortality was 19.69%. Among all developed models, the eXGBM had the highest area under the curve (AUC) value (0.951, 95% CI: 0.934-0.967), and it also showed the highest accuracy (0.902), precise (0.893), recall (0.915), and F1 score (0.904). Based on the scoring system, the eXGBM had the highest score of 53, followed by the RF model (43) and the NN model (39). The scores for the LR, SVM, and DT were 22, 36, and 17, respectively. The decision curve analysis confirmed that both the eXGBM and RF models provided substantial clinical net benefits. However, the eXGBM model consistently outperformed the RF model across multiple evaluation metrics, establishing itself as the superior option for predictive modeling in this scenario, with the RF model as a strong secondary choice. The Shapley Additive Explanation analysis revealed that Simplified Acute Physiology Score II, age, respiratory rate, Oxford Acute Severity of Illness Score, and temperature were the most important five features contributing to the outcome. Conclusions: This study develops an artificial intelligence application to predict in-hospital mortality among critical orthopedic trauma patients with sepsis or respiratory failure.

Abstract Background Insulin has been known to regulate bone metabolism, yet its specific molecular mechanisms during the proliferation and osteogenic differentiation of dental pulp stem cells (DPSCs) remain poorly understood. This study aimed to explore the effects of insulin on the bone formation capability of human DPSCs and to elucidate the underlying mechanisms. Methods Cell proliferation was assessed using a CCK-8 assay. Cell phenotype was analyzed by flow cytometry. Colony-forming unit-fibroblast ability and multilineage differentiation potential were evaluated using Toluidine blue, Oil red O, Alizarin red, and Alcian blue staining. Gene and protein expressions were quantified by real-time quantitative polymerase chain reaction and Western blotting, respectively. Bone metabolism and biochemical markers were analyzed using electrochemical luminescence and chemical colorimetry. Cell adhesion and growth on nano-hydroxyapatite/collagen (nHAC) were observed with a scanning electron microscope. Bone regeneration was assessed using micro-CT, fluorescent labeling, immunohistochemical and hematoxylin and eosin staining. Results Insulin enhanced the proliferation of human DPSCs as well as promoted mineralized matrix formation in a concentration-dependent manner. 10− 6 M insulin significantly up-regulated osteogenic differentiation-related genes and proteins markedly increased the secretion of bone metabolism and biochemical markers, and obviously stimulated mineralized matrix formation. However, it also significantly inhibited the expression of genes and proteins of receptors and receptor substrates associated with insulin/insulin-like growth factor-1 signaling (IIS) pathway, obviously reduced the expression of the phosphorylated PI3K and the ratios of the phosphorylated PI3K/total PI3K, and notably increased the expression of the total PI3K, phosphorylated AKT, total AKT and mTOR. The inhibitor LY294002 attenuated the responsiveness of 10− 6 M insulin to IIS/PI3K/AKT/mTOR pathway axis, suppressing the promoting effect of insulin on cell proliferation, osteogenic differentiation and bone formation. Implantation of 10− 6 M insulin treated DPSCs into the backs of severe combined immunodeficient mice and the rabbit jawbone defects resulted in enhanced bone formation. Conclusions Insulin induces insulin resistance in human DPSCs and effectively promotes their proliferation, osteogenic differentiation and bone formation capability through gradually inducing the down-regulation of IIS/PI3K/AKT/mTOR pathway axis under insulin resistant states.

Abstract Background Osteoarthritis (OA) is the most common degenerative disease in joints among elderly patients. Senescence is deeply involved in the pathogenesis of osteoarthritis. Metformin is widely used as the first-line drug for Type 2 diabetes mellitus (T2DM), and has great potential for the treatment of other aging-related disorders, including OA. However, the role of metformin in OA is not fully elucidated. Therefore, our aim here was to investigate the effects of metformin on human chondrocytes. Methods After metformin treatment, expression level of microRNA-34a and SIRT1 in chondrocyte were detected with quantitative real-time PCR and immunofluorescence staining. Then, microRNA-34a mimic and small interfering RNA (siRNA) against SIRT1 (siRNA-SIRT1) were transfected into chondrocyte. Senescence-associated β-galactosidase (SA-β-gal) staining was performed to assess chondrocyte senescence. Chondrocyte viability was illustrated with MTT and colony formation assays. Western blot was conducted to detect the expression of P16, IL-6, matrix metalloproteinase-13 (MMP-13), Collagen type II (COL2A1) and Aggrecan (ACAN). Results We found that metformin treatment (1 mM) inhibited microRNA-34a while promoted SIRT1 expression in OA chondrocytes. Both miR-34a mimics and siRNA against SIRT1 inhibited SIRT1 expression in chondrocytes. SA-β-gal staining assay confirmed that metformin reduced SA-β-gal-positive rate of chondrocytes, while transfection with miR-34a mimics or siRNA-SIRT1 reversed it. MTT assay and colony formation assay showed that metformin accelerated chondrocyte proliferation, while miR-34a mimics or siRNA-SIRT1 weakened this effect. Furthermore, results from western blot demonstrated that metformin suppressed expression of senescence-associated protein P16, proinflammatory cytokine IL-6 and catabolic gene MMP-13 while elevated expression of anabolic proteins such as Collagen type II and Aggrecan, which could be attenuated by transfection with miR-34a mimics. Conclusion Overall, our data suggest that metformin regulates chondrocyte senescence and proliferation through microRNA-34a/SIRT1 pathway, indicating it could be a novel strategy for OA treatment.

Introduction: Few studies have investigated the in-hospital mortality among critically ill patients with hip fracture. This study aimed to develop and validate a model to estimate the risk of in-hospital mortality among critically ill patients with hip fracture.Methods: For this study, data from the Medical Information Mart for Intensive Care III (MIMIC-III) Database and electronic Intensive Care Unit (eICU) Collaborative Research Database were evaluated. En-rolled patients (n = 391) in the MIMIC-III database were divided into a training (2/3, n = 260) and a vali-dation (1/3, n = 131) group at random. Using machine learning algorithms such as random forest, gradient boosting machine, decision tree, and eXGBoosting machine approach, the training group was utilized to train and optimize models. The validation group was used to internally validate models and the opti-mal model could be obtained in terms of discrimination (area under the receiver operating characteristic curve, AUROC) and calibration (calibration curve). External validation was done in the eICU Collabora-tive Research Database (n = 165). To encourage practical use of the model, a web-based calculator was developed according to the eXGBoosting machine approach.Results: The in-hospital death rate was 13.81% (54/391) in the MIMIC-III database and 10.91% (18/165) in the eICU Collaborative Research Database. Age, gender, anemia, mechanical ventilation, cardiac ar-rest, and chronic airway obstruction were the six model parameters which were identified using the Least Absolute Shrinkage and Selection Operator (LASSO) method combined with 10-fold cross-validation. The model established using the eXGBoosting machine approach showed the highest area under curve (AUC) value (0.797, 95% CI: 0.696-0.898) and the best calibrating ability, with a calibration slope of 0.999 and intercept of-0.019. External validation also revealed favorable discrimination (AUC: 0.715, 95% CI: 0.566-0.864; accuracy: 0.788) and calibration (calibration slope: 0.805) in the eICU Collaborative Research Database. The web-based calculator could be available at https://doctorwangsj-webcalculator-main-yw69yd.streamlitapp.com/.Conclusion: The model has the potential to be a pragmatic risk prediction tool that is able to identify hip fracture patients who are at a high risk of in-hospital mortality in ICU settings, guide patient risk counseling, and simplify prognosis bench-marking by controlling for baseline risk.(c) 2022 Published by Elsevier Ltd.

BackgroundNegative pressure wound therapy with instillation (NPWTi) is a novel method based on standard negative pressure wound therapy (NPWT). This study aimed to compare the effects of standard NPWT and NPWTi on bioburden and wound healing in a Staphylococcus aureus (S.aureus) infected porcine model.MethodsGreen fluorescent protein-labeled S.aureus infected wounds were created on the back of porcine. Wounds were treated with NPWT or NPWT with instillation (saline). The tissue specimens were harvested on days 0 (12 h after bacterial inoculation), 2, 4, 6, and 8 at the center of wound beds. Viable bacterial counts, laser scanning confocal microscopy, PCR, western blot, and histological analysis were performed to assess virulence and wound healing.ResultsThe bacterial count in the NPWTi group was lower than that of the NPWT group and the difference was statistically significant on day 2, day 4, day 6, and day 8 (P < 0.05). The expression levels of agrA, Eap, Spa, and Hla genes of the NPWTi group were significantly lower than that of the NPWT group on day 8 (P < 0.05). The bacterial invasion depth of the NPWTi group was significantly lower than that of the NPWT group on day 2, day 4, day 6, and day 8 (P < 0.05). Though the NPWTi group showed a significantly increased expression of bFGF and VEGF than that of the NPWT group in the early time (P < 0.05), NPWTi cannot lead to better histologic parameters than the NPWT group (P > 0.05).ConclusionOur results demonstrated that NPWTi induced a better decrease in bacterial burden and virulence compared with standard NPWT. These advantages did not result in better histologic parameters on the porcine wound model.

Background:Persistent critical illness (PerCI) is an immunosuppressive status. The underlying pathophysiology driving PerCI remains incompletely understood. The objectives of the study were to identify the biological signature of PerCI development, and to construct a reliable prediction model for patients who had suffered orthopedic trauma using machine learning techniques.Methods:This study enrolled 1257 patients from the Medical Information Mart for Intensive Care III (MIMIC-III) database. Lymphocytes were tracked from ICU admission to more than 20 days following admission to examine the dynamic changes over time. Over 40 possible variables were gathered for investigation. Patients were split 80:20 at random into a training cohort (n=1035) and an internal validation cohort (n=222). Four machine learning algorithms, including random forest, gradient boosting machine, decision tree, and support vector machine, and a logistic regression technique were utilized to train and optimize models using data from the training cohort. Patients in the internal validation cohort were used to validate models, and the optimal one was chosen. Patients from two large teaching hospitals were used for external validation (n=113). The key metrics that used to assess the prediction performance of models mainly included discrimination, calibration, and clinical usefulness. To encourage clinical application based on the optimal machine learning-based model, a web-based calculator was developed.Results:16.0% (201/1257) of all patients had PerCI in the MIMIC-III database. The means of lymphocytes (%) were consistently below the normal reference range across the time among PerCI patients (around 10.0%), whereas in patients without PerCI, the number of lymphocytes continued to increase and began to be in normal range on day 10 following ICU admission. Subgroup analysis demonstrated that patients with PerCI were in a more serious health condition at admission since those patients had worse nutritional status, more electrolyte imbalance and infection-related comorbidities, and more severe illness scores. Eight variables, including albumin, serum calcium, red cell volume distributing width (RDW), blood pH, heart rate, respiratory failure, pneumonia, and the Sepsis-related Organ Failure Assessment (SOFA) score, were significantly associated with PerCI, according to the least absolute shrinkage and selection operator (LASSO) logistic regression model combined with the 10-fold cross-validation. These variables were all included in the modelling. In comparison to other algorithms, the random forest had the optimal prediction ability with the highest area under receiver operating characteristic (AUROC) (0.823, 95% CI: 0.757-0.889), highest Youden index (1.571), and lowest Brier score (0.107). The AUROC in the external validation cohort was also up to 0.800 (95% CI: 0.688-0.912). Based on the risk stratification system, patients in the high-risk group had a 10.0-time greater chance of developing PerCI than those in the low-risk group. A web-based calculator was available at https://starxueshu-perci-prediction-main-9k8eof.streamlitapp.com/.Conclusions:Patients with PerCI typically remain in an immunosuppressive status, but those without PerCI gradually regain normal immunity. The dynamic changes of lymphocytes can be a reliable biomarker for PerCI. This work developed a reliable model that may be helpful in improving early diagnosis and targeted intervention of PerCI. Beneficial interventions, such as improving nutritional status and immunity, maintaining electrolyte and acid-base balance, curbing infection, and promoting respiratory recovery, are early warranted to prevent the onset of PerCI, especially among patients in the high-risk group and those with a continuously low level of lymphocytes.

Purpose The aim of this study was to assess whether passive smoking affects clinical outcomes among female patients with knee osteoarthritis after being treated with total knee arthroplasty (TKA). Methods The study prospectively enrolled 216 female patients who did not smoke and those patients were classified into three groups in terms of the severity of exposure to environmental tobacco smoke. A three-month follow-up was conducted to assess the physical and mental outcomes between the three groups. The physical outcomes were evaluated by the visual analogue score (VAS), range of motion (ROM), hospital for special surgery (HSS) knee score, and postoperative complications. The mental outcomes were assessed by the anxiety and depression scale (HADS) and medical outcome study short form 36 (SF-36). Subgroup analysis of patients with and without surgical site infection (SSI) was also calculated. Results Baseline characteristics were similarly distributed between the three groups (P>0.05). Patients in the heavy passive smoking group had a higher VAS and a lower ROM score as compared with patients in the no and mild passive smoking group at discharge (P<0.01), 1 month (P<0.01), and 3 months (P<0.01) after surgery. Patients in the heavy passive smoking group also had a higher rate of HADS more than 8 at postoperative 1 month (P=0.01) and 3 months (P=0.03) and lower SF-36 summary (P<0.01) and HSS score (P<0.01) at postoperative 3 months. Forty-five postoperative complication events were observed during follow-up. Patients in the heavy passive smoking group (8.51%) had the highest SSI rate, followed by patients in the mild (1.82%) and no passive smoking group (0.88%) at discharge (P=0.02) and postoperative 1 month (P=0.03). Conclusion Passive smoking negatively affects TKA among female patients. It may trigger poor pain and functional outcomes, aggravate depression and anxiety, and deteriorate quality of life after discharge from hospital. Avoiding exposure to smoking environment may be beneficial among TKA female patients before and after surgery.

Background: Coronavirus disease 2019 (COVID-19) has generated an unprecedented clinical research response, but the data about the characteristics of COVID-19-related clinical studies were scarce. The study aimed to describe the characteristics of COVID-19-related clinical studies registered at ClinicalTrials.gov and further identify factors affecting the recruitment and completeness of these studies. Methods: The study extracted 5,672 studies and included 5,430 studies relating to COVID-19 registered at ClinicalTrials.gov. We presented the characteristics of all included clinical studies. Identification of risk factors for recruitment status was achieved using the multiple logistic regression models, and identification of risk factors for completion time was obtained using the multiple Cox proportional hazards regression models. Subgroup analyses were also performed in the interventional studies. Results: Of the included studies, only 19.59% (1064/5430) had completed recruitment, and 55.93% (3037/5430) were interventional studies. The peak of the number of clinical studies relating to COVID-19 was seven months earlier than the first peak of the number of COVID-19 cases globally. In all included studies, participants only including male (P=0.02), Participants including child (P=0.01), smaller enrollment (P<0.01), and studies not being funded by industry (P=0.01) and the National Institutes of Health (NIH) (P<0.01), and observational studies (P<0.01) tended to be associated to higher completed recruitment rates. Regarding the interventional studies, Participants including child (P=0.04), smaller enrollment (P<0.01), a crossover intervention model (P<0.01), and primary purpose involving in device feasibility (P<0.01) and treatment (P=0.03) were associated with shorter completion time, while being funded by industry (P=0.01) and NIH (P<0.01), primary purpose involving in basic science (P<0.01), and biological interventions (P<0.01) were associated with longer completion time. Conclusion: A multitude of clinical studies relating to COVID-19 are registered in responding to the pandemic and the response is rapid and timely, but these clinical studies are frequently not completed. Increased focus on establishing global initiatives and networks to coordinate recruitment efforts may be needed. Several independent risk factors are identified to guide the design of COVID-19-related clinical studies. This may be significant to avoid waste and ensure that the participation of all participants in clinical researches contributes to the treatment or prevention of COVID-19.

Background Ankle arthrodesis is considered to be the gold standard for the treatment of end-stage ankle diseases. At present, the commonly used methods of ankle arthrodesis include open ankle arthrodesis, arthroscopic ankle arthrodesis and mini-open ankle arthrodesis. The authors analyze and compare the clinical efficacy and related complications of arthroscopic ankle arthrodesis and mini-open ankle arthrodesis in the treatment of end-stage ankle disease. Methods From January 2007 to June 2018, 56 patents with end-stage ankle joint pathology were treated with arthroscopic ankle arthrodesis and mini-open ankle arthrodesis. There were 30 cases in arthroscopy group, including 19 males and 11 females with an average age of 49.6 years old (ranged, 32 to 71); while 26 cases in mini-open group, including 18 males and 8 females with an average age of 48.3 years old (ranged, 43 to 65). The operative time was calculated with use of computerized operative and anesthetic records. The pain visual analogue score (VAS), American Orthopedic Foot ༆ Ankle Society ankle and hind foot score (AOFAS), fusion rate, complications rate, length of hospital stay, operation time, and tourniquet time were compared between the two groups of patients. Results 51 patients were followed up for 15–35 months (mean, 22.5 ± 1.5) months. The bony fusion was achieved in all patients. The average time to fusion was 12.4 weeks (range, 10–16 weeks). The VAS score 3 days post-operation was (6.37 ± 0.69) points in the arthroscopy group and (7.61 ± 1.05) points in the mini-open group, there was significant difference between the two groups (P < 0.05). The VAS score and AOFAS score between the two groups pre- and post-operation have statistically significant differences (P < 0.05). At the last follow-up, VAS score was (1.55 ± 0.57) in the arthroscopy group and (1.43 ± 0.73) in the mini-open group, and there was no significant difference between the two groups (P > 0.05). The AOFAS score was (85.32 ± 2.96) points in the arthroscopy group and (86.72 ± 3.05) points in the mini-open group, and there was no significant difference between the two groups (P > 0.05). Arthroscopic ankle fusion was associated with a shorter tourniquet time and shorter length of hospital stay compared to mini-open ankle fusion (P < 0.05); however, there was no significant difference between two groups in terms of operation time (P > 0.05). Wounds healing was satisfying during the follow-up in the arthroscopy group. But the wounds healing was delayed in two patients of the small incision group. All patients were satisfied with the surgery. Conclusion Arthroscopic ankle arthrodesis and mini-open ankle arthrodesis have satisfactory curative effect and fusion rate. Arthroscopic assisted ankle arthrodesis has more advantages, including small incision, less injury, and low morbidity.