Gastrointestinal stromal tumors (GIST) are the most prevalent mesenchymal tumors of the digestive tract. To investigate the association of imatinib mesylate plasma concentration with adverse drug reactions (ADRs) and influences of genetic polymorphisms on ADRs in GIST patients taking imatinib, a cohort of GIST patients consecutively treated with imatinib were included in the observational study. Clinical, pathologic and genotype information was recorded at enrollment and blood samples were collected at time as design. The plasma concentration of the imatinib was detected by LC-MS/MS. A questionnaire was used to evaluate the ADRs at each visit. SNPs in 13 genes were analyzed for a possible association with ADRs. The mean plasma trough concentration of 129 patients taking imatinib was 1.45 ± 0.79 μg/ml, average peak concentration was 2.63 ± 1.07 μg/ml. The imatinib concentration in patients treated with 600 mg/day was significantly higher than other dosage groups (P < 0.05). The ADRs were mostly mild. Edema, vomiting, and fatigue were significantly correlated with imatinib concentration (P < 0.05). Mutations of IL13 rs1800925 and CXCL14 rs7716492 were related with the incidence of leukopenia and rash in our research, separately (P < 0.05). We confirmed that with the increase of imatinib concentration, the incidence of edema, vomiting, and fatigue rises as well. Mutations of IL13 rs1800925 and CXCL14 rs7716492 may be the promising biomarkers to predict the ADRs of imatinib. The results of the study are of guiding significance for the use of imatinib in patients with GIST.

Background: In-hospital mortality following hip fractures is a significant concern, and accurate prediction of this outcome is crucial for appropriate clinical management. Nonetheless, there is a lack of effective prediction tools in clinical practice. By utilizing artificial intelligence (AI) and machine learning techniques, this study aims to develop a predictive model that can assist clinicians in identifying geriatric hip fracture patients at a higher risk of in-hospital mortality. Methods: A total of 52 707 geriatric hip fracture patients treated with surgery from 90 hospitals were included in this study. The primary outcome was postoperative in-hospital mortality. The patients were randomly divided into two groups, with a ratio of 7:3. The majority of patients, assigned to the training cohort, were used to develop the AI models. The remaining patients, assigned to the validation cohort, were used to validate the models. Various machine learning algorithms, including logistic regression (LR), decision tree (DT), naïve bayesian (NB), neural network (NN), eXGBoosting machine (eXGBM), and random forest (RF), were employed for model development. A comprehensive scoring system, incorporating 10 evaluation metrics, was developed to assess the prediction performance, with higher scores indicating superior predictive capability. Based on the best machine learning-based model, an AI application was developed on the Internet. In addition, a comparative testing of prediction performance between doctors and the AI application. Findings: The eXGBM model exhibited the best prediction performance, with an area under the curve (AUC) of 0.908 (95% CI: 0.881–0.932), as well as the highest accuracy (0.820), precision (0.817), specificity (0.814), and F1 score (0.822), and the lowest Brier score (0.120) and log loss (0.374). Additionally, the model showed favorable calibration, with a slope of 0.999 and an intercept of 0.028. According to the scoring system incorporating 10 evaluation metrics, the eXGBM model achieved the highest score (56), followed by the RF model (48) and NN model (41). The LR, DT, and NB models had total scores of 27, 30, and 13, respectively. The AI application has been deployed online at https://in-hospitaldeathinhipfracture-l9vhqo3l55fy8dkdvuskvu.streamlit.app/, based on the eXGBM model. The comparative testing revealed that the AI application’s predictive capabilities significantly outperformed those of the doctors in terms of AUC values (0.908 vs. 0.682, P<0.001). Conclusions: The eXGBM model demonstrates promising predictive performance in assessing the risk of postoperative in-hospital mortality among geriatric hip fracture patients. The developed AI model serves as a valuable tool to enhance clinical decision-making.

Bone defects remain a substantial clinical burden. Exosomes have been extensively investigated as cell-free therapeutic candidates, exhibiting favorable bi

Background: Frailty is an aggregate expression of susceptibility to adverse health outcomes because of age- and disease-related deficits that accumulate across multiple domains. Previous studies have found the presence of preoperative frailty is associated with an increased risk of adverse outcomes. The number of older adults undergoing orthopedic surgery is rapidly increasing. However, there has been no evidence-based study on the relationship between frailty and outcomes in patients undergoing orthopedic surgery. Objective: The aims of this study are to investigate the association between frailty and outcomes in patients who underwent orthopedic surgery as well as patient factors associated with frailty. Methods: The methods to be used for this systematic review are reported according to the PRISMA-P (Preferred Reporting Items for Systematic Review and Meta-analysis Protocols) 2015 checklist. An extensive search will be conducted in PubMed, Embase, the Cochrane Library, and other mainstream databases. Any study where patients undergoing orthopedic surgery were assessed using a defined or validated measure of frailty and the association of frailty with patient factors and/or outcomes was reported will be included. A total of 2 researchers will independently screen articles for inclusion, with disagreements resolved by a third reviewer. We will perform a narrative synthesis of the factors associated with frailty, prevalence of frailty, effect of frailty on patient outcomes, and interventions for patients who are frail. A meta-analysis focusing on individual factors associated with frailty and the effect of frailty on patient outcomes will be performed, if applicable. The risk of bias will be evaluated. A subgroup analysis and sensitivity analysis will be performed. Results: Literature searches were conducted in September 2021 and the review is anticipated to be completed by the end of July 2022. Conclusions: This systematic review and meta-analysis will provide an overview of frailty and investigate the relationship between frailty and patient outcomes as well as the relationship between patient factors and frailty in patients undergoing orthopedic surgery. This study could potentially increase patients’ awareness of the outcomes associated with frailty, compel clinical specialties to further acknowledge the concept of frailty, and enhance the development of assessment instruments and tools for frailty. Trial Registration: PROSPERO CRD42020181846; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=181846

Sleep disorders represent major public health concerns with significant health consequences. While diet shows promise as a modifiable intervention, the differential effects of established dietary patterns on specific sleep phenotypes and the contributions of specific food groups remains unclear. Therefore, this study investigated how three established dietary patterns relate to distinct sleep outcomes, and further explored the contributions of specific food components. We combined four decades of data and analyzed 9040 US adults from NHANES with sampling weights. Five sleep outcomes were examined in relation to dietary pattern adherence, quantified through averaged 24‐h recall data from two consecutive days: including self‐reported sleep duration, sufficiency, OSA, daytime sleepiness, and stop breathing. For international validation, we used the Global Dietary Database (62 countries) linked to country‐level sleep apnea prevalence (191 countries). Cohort analysis employed the Chinese Longitudinal Healthy Longevity Survey ( n = 6885). Causal associations between dietary components and sleep phenotypes were investigated using two‐sample Mendelian randomization with genome‐wide association summary statistics. Our results demonstrated that greater the Mediterranean Diet (MED) adherence was associated with longer sleep duration ( β = 0.06, 95% CI: 0.03–0.10, p < 0.001) and sufficient sleep (OR = 1.10, 95% CI: 1.04–1.15, p < 0.001). Notably, dietary Approaches to Stop Hypertension (DASH) adherence offered the strongest protection against daytime sleepiness (OR = 0.89, 95% CI: 0.85–0.93, p < 0.001) and breathing cessation episodes (OR = 0.91, 95% CI: 0.85–0.96, p = 0.001). The Alternative Healthy Eating Index (AHEI) demonstrated balanced associations with improved sleep sufficiency (OR = 1.006, p < 0.05), reduced daytime sleepiness (OR = 0.995, p < 0.05), and fewer breathing episodes (OR = 0.989, p < 0.01). Furthermore, food‐component‐level analyses revealed consistent protective associations from legumes, nuts, and whole grains, while vegetables, fruits, meats, and SSBs showed variable effects—findings supported by both multi‐database analyses and Mendelian randomization. Importantly, MR further demonstrated specific causal associations for fruits and vegetables: pears and strong vegetable preferences protected against short sleep, while cabbage, grapefruit, and melon causally increased sleep disorder risk. Additionally, processed meats causally increased OSA and snoring risk, with food processing degree emerging as a critical causal determinant. In conclusion, this study makes three main contributions. First, we provide a direct comparison of multiple dietary patterns (MED, DASH, AHE) across diverse sleep phenotypes. Second, we identify specific protective food components, with genetic causal evidence supporting their effects on sleep. Third, we integrate multi‐national datasets to validate findings across diverse populations, substantially enhancing generalizability. Collectively, these findings support evidence‐based dietary interventions for sleep health improvement.

As one of the most frequently performed surgeries worldwide, hernia repair addresses a pathological condition caused by muscle weakness and defects in the muscle wall. Despite its prevalence, hernia surgery remains challenging due to complications such as organ occlusion, seroma, persistent pain, tissue adhesions, and wound infection. Surgical meshes have become the preferred choice for abdominal wall hernia repair, aiming to restore the physical integrity and functionality of the musculofascial layers. Although various types of hernia mesh are available on the market to mitigate post-reconstruction complications, none have proven to be a perfect solution for reducing hernia recurrence. In this review, we explore the etiology of hernias, the materials and structures used in designing hernia meshes, and the emerging multifunctional trends aimed at addressing not just one aspect of the complications but the overall range of complications following hernia repair. We discuss strategies to mitigate side effects and establish the characteristics of ideal hernia repair meshes. Lastly, this review outlines future research directions for developing successful hernia repair solutions, emphasizing the need for innovative approaches to enhance patient outcomes and minimize recurrence rates.