Rhodium-catalyzed annulation of hydrazines with vinylene carbonate to synthesize unsubstituted 1-aminoindole derivatives.

Herein, we describe rhodium-catalysed C-H bond activation for [3 + 2] annulation using hydrazide and vinylene carbonate, providing an efficient method for synthesising unsubstituted 1-aminoindole compounds. Characterised by high yields, mild reaction conditions, and no need for external oxidants, this transformation demonstrates excellent regioselectivity and a wide tolerance for various functional groups.

[Application and prospect of machine learning in orthopaedic trauma].

Objective: To review the current applications of machine learning in orthopaedic trauma and anticipate its future role in clinical practice. Methods: A comprehensive literature review was conducted to assess the status of machine learning algorithms in orthopaedic trauma research, both nationally and internationally. Results: The rapid advancement of computer data processing and the growing convergence of medicine and industry have led to the widespread utilization of artificial intelligence in healthcare. Currently, machine learning plays a significant role in orthopaedic trauma, demonstrating high performance and accuracy in various areas including fracture image recognition, diagnosis stratification, clinical decision-making, evaluation, perioperative considerations, and prognostic risk prediction. Nevertheless, challenges persist in the development and clinical implementation of machine learning. These include limited database samples, model interpretation difficulties, and universality and individualisation variations. Conclusion: The expansion of clinical sample sizes and enhancements in algorithm performance hold significant promise for the extensive application of machine learning in supporting orthopaedic trauma diagnosis, guiding decision-making, devising individualized medical strategies, and optimizing the allocation of clinical resources.

Machine learning applications for the prediction of extended length of stay in geriatric hip fracture patients.

BACKGROUND: Geriatric hip fractures are one of the most common fractures in elderly individuals, and prolonged hospital stays increase the risk of death and complications. Machine learning (ML) has become prevalent in clinical data processing and predictive models. This study aims to develop ML models for predicting extended length of stay (eLOS) among geriatric patients with hip fractures and to identify the associated risk factors. AIM: To develop ML models for predicting the eLOS among geriatric patients with hip fractures, identify associated risk factors, and compare the performance of each model. METHODS: A retrospective study was conducted at a single orthopaedic trauma centre, enrolling all patients who underwent hip fracture surgery between January 2018 and December 2022. The study collected various patient characteristics, encompassing demographic data, general health status, injury-related data, laboratory examinations, surgery-related data, and length of stay. Features that exhibited significant differences in univariate analysis were integrated into the ML model establishment and subsequently cross-verified. The study compared the performance of the ML models and determined the risk factors for eLOS. RESULTS: The study included 763 patients, with 380 experiencing eLOS. Among the models, the decision tree, random forest, and extreme Gradient Boosting models demonstrated the most robust performance. Notably, the artificial neural network model also exhibited impressive results. After cross-validation, the support vector machine and logistic regression models demonstrated superior performance. Predictors for eLOS included delayed surgery, D-dimer level, American Society of Anaesthesiologists (ASA) classification, type of surgery, and sex. CONCLUSION: ML proved to be highly accurate in predicting the eLOS for geriatric patients with hip fractures. The identified key risk factors were delayed surgery, D-dimer level, ASA classification, type of surgery, and sex. This valuable information can aid clinicians in allocating resources more efficiently to meet patient demand effectively.

Relationships among body weight, lipids and bone mass in elderly individuals with fractures: A case-control study.

BACKGROUND: The prevalence of osteoporosis and low bone mass is steadily rising each year. Low body weight is commonly linked to diminished bone mass and serves as a robust predictor of osteoporosis. Nonetheless, the connection between body mass index (BMI), bone mineral density, and lipid profiles among the elderly remains elusive. AIM: To examine the association between BMI and bone mass, explore the correlation between lipid profiles and bone mass, and delve into the interplay between lipid metabolism and bone health. METHODS: The study included 520 patients aged ≥ 65 years (178 men and 342 women). Age, sex, weight, and height were recorded. Femoral neck bone mineral density and T scores were determined using a dual-energy X-ray absorptiometry scanner. Blood calcium (Ca), phosphorus (P), albumin (ALB), alkaline phosphatase (ALP), aspartate aminotransferase, alanine aminotransferase, triglyceride (TG), total cholesterol (TC), high-density lipoprotein (HDL) and low-density lipoprotein (LDL) levels were measured. Patients were classified by sex (male and female), age (65-79 years and ≥ 80 years), and T score (normal bone mineral density, osteopenia and osteoporosis). RESULTS: Age, sex, BMI, and ALP and TG levels were independent risk factors for osteoporosis. For the 65-79- and ≥ 80-year-old groups, females presented lower T scores than males. Ca, P, ALB, ALP, TC, HDL and LDL levels were significantly different between men and women in the 65-79-year-old group. In addition, BMI and TG levels were significantly decreased in osteoporotic patients compared with patients with normal bone mass. TC levels declined in 65- to 79-year-old male and female osteoporosis patients. In the group of women aged ≥ 80 years, osteoporotic patients showed significantly increased ALP levels. Furthermore, we found positive correlations between BMI and TG levels in the male and female patient groups. However, we found no significant differences in ALB, Ca, P, HDL and LDL levels in osteoporotic patients compared to patients with normal bone mass. CONCLUSION: Osteoporotic patients showed significantly decreased BMI and TG levels compared with those with normal bone mass. BMI showed positive correlations with TG levels in male and female patients. These results indicate correlations between BMI and bone mass and between lipid profiles and bone mass.

Is There a "Black Friday" for Geriatric Hip Fracture Surgery?

OBJECTIVES: Reports show an increase in the short-term mortality rates of hip fracture patients admitted on weekends. However, there are few studies on whether there is a similar effect in Friday admissions of geriatric hip fracture patients. The aim of this study was to evaluate the effects of Friday admission on mortality and clinical outcomes in elderly patients with hip fractures. METHODS: A retrospective cohort study was performed at a single orthopaedic trauma centre and included all patients who underwent hip fracture surgery between January 2018 and December 2021. Patient characteristics, including age, sex, BMI, fracture type, time of admission, ASA grade, comorbidities, and laboratory examinations, were collected. Data pertaining to surgery and hospitalization were extracted from the electronic medical record system and tabulated. The corresponding follow-up was performed. The Shapiro-Wilk test was applied to evaluate the distributions of all continuous variables for normality. The overall data were analyzed by Student's t test or the Mann-Whitney U test for continuous variables and the chi-square test for categorical variables, as appropriate. Univariate and multivariate analyses were used to further test for the independent influencing factors of prolonged time to surgery. RESULTS: A total of 596 patients were included, and 83 patients (13.9%) were admitted on Friday. There was no evidence supporting that Friday admission had an effect on mortality and outcomes, including length of stay, total hospital costs and postoperative complications. However, the patients admitted on Friday had delayed surgery. Then, patients were regrouped into two groups according to whether surgery was delayed, and 317 patients (53.2%) underwent delayed surgery. The multivariate analysis showed that younger age (p = 0.014), Friday admission (p < 0.001), ASA classification III-IV (p = 0.019), femoral neck fracture (p = 0.002), time from injury to admission more than 24 h (p = 0.025), and diabetes (p = 0.023) were risk factors for delayed surgery. CONCLUSIONS: Mortality and adverse outcome rates for elderly hip fracture patients admitted on Friday were similar to those admitted at other time periods. However, Friday admission was identified as one of the risk factors for delayed surgery.