Our study showed that B vitamins did not have significant effect on fracture incidence, bone mineral density, and bone turnover markers. However, the research data of B vitamins on bone mineral density and bone turnover markers are limited, and more clinical trials are needed to draw sufficient conclusions.
OBJECTIVE: The objective of this study was to identify the efficacy of B vitamin (VB) (folate, B6, and B12) supplements on fracture incidence, bone mineral density (BMD), and bone turnover markers (BTMs).
METHODS: A comprehensive search was performed in PubMed, MEDLINE, EMBASE, Cochrane databases, and ClinicalTrials.gov up to September 4, 2023. The risk of bias was assessed according to Cochrane Handbook and the quality of evidence was assessed according to the GRADE system. We used trial sequential analysis (TSA) to assess risk of random errors and Stata 14 to conduct sensitivity and publication bias analyses.
RESULTS: Data from 14 RCTs with 34,700 patients were extracted and analyzed. The results showed that VBs did not significantly reduce the fracture incidence (RR, 1.06; 95% CI, 0.95 - 1.18; p = 0.33; I2 = 40%) and did not affect BMD in lumbar spine and femur neck. VBs had no significant effect on bone specific alkaline phase (a biomarker for bone formation), but could increase the serum carboxy-terminal peptide (a biomarker for bone resorption) (p = 0.009; I2 = 0%). The TSA showed the results of VBs on BMD and BTMs may not be enough to draw sufficient conclusions due to the small number of sample data included and needed to be demonstrated in more clinical trials. The inability of VBs to reduce fracture incidence has been verified by TSA as sufficient. Sensitivity analysis and publication bias assessment proved that our meta-analysis results were stable and reliable, with no significant publication bias.
CONCLUSIONS: Available evidence from RCTs does not support VBs can effectively influence osteoporotic fracture risk, BMD, and BTMs.
BACKGROUND: PROSPERO registration number: CRD42023427508.

OBJECTIVE: To investigate the use of anti-osteoporotic agents and refracture incidence in patients with osteoporotic vertebral compression fracture (OVCF) following percutaneous vertebral augmentation (PVA) and to evaluate the real-world treatment of patients using denosumab following PVA. This study aims to provide spine surgeons with empirical insights derived from real-world scenarios to enhance the management of bone health in OVCF patients.
METHODS: This retrospective cohort study was based on data from the MarketScan and Optum databases from the USA. Female patients aged 55-90 years who underwent PVA for OVCF between January 2013 and March 2020 were included and followed up from the day after surgery. Patients who received at least one dose of denosumab were included in the denosumab cohort and were further divided into the on-treatment and off-treatment groups according to whether they received a second dose of denosumab, with follow-up beginning on the index day (225 days after the first denosumab dose). In this study, the off-treatment group was considered as the control group. Refracture incidence after PVA, the proportion of patients using anti-osteoporotic agents in the total study population, and refracture incidence after the index day in the denosumab cohort were analyzed.
RESULTS: A total of 13,451 and 21,420 patients from the MarketScan and Optum databases, respectively, were included. In the denosumab cohort, the cumulative incidence of clinical osteoporotic fractures within 3 years after the index day was significantly lower in the on-treatment group than in the off-treatment group (MarketScan database: 23.0% vs 39.0%, p = 0.002; Optum database: 28.2% vs 40.0%, p = 0.023). The cumulative incidence of clinical vertebral fractures was also lower in the on-treatment group than in the off-treatment group, with a significant difference in the MarketScan database (14.4% vs 25.5%, p = 0.002) and a numerical difference was found in the Optum database (20.2% vs 27.5%, p = 0.084).The proportion of patients using anti-osteoporotic agents was low at 6 months postoperatively, with only approximately 7% using denosumab and 13%-15% taking oral bisphosphonates.
CONCLUSIONS: Postmenopausal women have a high refracture rate and a low proportion of anti-osteoporotic drug use after PVA. Continued denosumab treatment after PVA is associated with a lower risk of osteoporotic and clinical vertebral fractures. Therefore, denosumab may be a treatment option for patients with osteoporosis after PVA.

暂无摘要。

BACKGROUND: Femoral neck fracture (FNF) accounts for approximately 3.58% of all fractures in the entire body, exhibiting an increasing trend each year. According to a survey, in 1990, the total number of hip fractures in men and women worldwide was approximately 338,000 and 917,000, respectively. In China, FNFs account for 48.22% of hip fractures. Currently, many studies have been conducted on postdischarge mortality and mortality risk in patients with FNF. However, there have been no definitive studies on in-hospital mortality or its influencing factors in patients with severe FNF admitted to the intensive care unit.
OBJECTIVE: In this paper, 3 machine learning methods were used to construct a nosocomial death prediction model for patients admitted to intensive care units to assist clinicians in early clinical decision-making.
METHODS: A retrospective analysis was conducted using information of a patient with FNF from the Medical Information Mart for Intensive Care III. After balancing the data set using the Synthetic Minority Oversampling Technique algorithm, patients were randomly separated into a 70% training set and a 30% testing set for the development and validation, respectively, of the prediction model. Random forest, extreme gradient boosting, and backpropagation neural network prediction models were constructed with nosocomial death as the outcome. Model performance was assessed using the area under the receiver operating characteristic curve, accuracy, precision, sensitivity, and specificity. The predictive value of the models was verified in comparison to the traditional logistic model.
RESULTS: A total of 366 patients with FNFs were selected, including 48 cases (13.1%) of in-hospital death. Data from 636 patients were obtained by balancing the data set with the in-hospital death group to survival group as 1:1. The 3 machine learning models exhibited high predictive accuracy, and the area under the receiver operating characteristic curve of the random forest, extreme gradient boosting, and backpropagation neural network were 0.98, 0.97, and 0.95, respectively, all with higher predictive performance than the traditional logistic regression model. Ranking the importance of the feature variables, the top 10 feature variables that were meaningful for predicting the risk of in-hospital death of patients were the Simplified Acute Physiology Score II, lactate, creatinine, gender, vitamin D, calcium, creatine kinase, creatine kinase isoenzyme, white blood cell, and age.
CONCLUSIONS: Death risk assessment models constructed using machine learning have positive significance for predicting the in-hospital mortality of patients with severe disease and provide a valid basis for reducing in-hospital mortality and improving patient prognosis.

UNASSIGNED: Despite the fact that China amounts to one-fifth of the world\'s population, has a higher proportion of the elderly, and has a higher prevalence of osteoporosis and fracture, limited studies have investigated the association between dietary patterns and bone mineral density (BMD) as well as fracture risk among the elderly Chinese population. We aimed to investigate the association between different dietary patterns and BMD as well as the risk of fractures, and this association may vary between elderly women and men.
UNASSIGNED: Building upon the China Osteoporosis Prevalence Study, we included 17,489 subjects aged ≥40 years old randomly sampled across 44 counties/districts of 11 provinces or municipalities in China who completed a food frequency questionnaire. BMD was measured by dual x-ray absorptiometry. Vertebral fracture was defined based on lateral spine radiographs using the semi-quantitative technique of Genant.
UNASSIGNED: A diet rich in \"carnivorous\", \"vegetarian\", \"dairy, fruit, and egg\" was significantly associated with higher BMD at total hip (TH), femoral neck (FN), and lumbar spine 1-4 (L1-4). Yet, a diet rich in \"beverage and fried food\" was associated with a lower BMD at the FN and L1-4. High quartiles of the carnivorous diet were associated with 34%-39% reduced risk of clinical fracture in the past 5 years and vertebral fracture. Stronger associations were observed among women. Sensitivity analysis among postmenopausal women presented even stronger positive associations between carnivorous and vegetarian diets and high BMD, as well as between carnivorous diet and reduced risk of fractures.
UNASSIGNED: Our study suggested that a diet rich in meat, vegetables, and dairy, fruit, and eggs might be associated with greater BMD and a lower fracture risk, while beverage and fried foods may be associated with a lower BMD at L1-4, especially among elderly women. These findings are relevant to provide recommendations on dietary nutrition regarding the elderly population at high risk of osteoporosis and fractures, especially postmenopausal women.

BACKGROUND: We conducted analyses of the association between smoking and osteoporosis and osteoporotic fractures using a secondary dataset analysis of the National Health and Nutrition Examination Survey (NHANES) database and the two-sample Mendelian randomization (MR) method.
METHODS: The associations between smoking and osteoporosis or osteoporotic fractures were analyzed using weighted logistic regression models for both univariate and multivariable analyses using pooled 1999-2018 NHANES data. The summary-level data of genome-wide association studies (GWAS) of smoking and osteoporosis were extracted from the IEU Open GWAS project. The inverse variance weighted method was used as the main method for the two-sample MR analysis.
RESULTS: We obtained the following main findings based on the NHANES data: smoking was associated with osteoporosis according to the analyses of 30856 participants (OR=1.21; 95% CI: 1.06-1.39, p=0.004); smoking was associated with hip osteoporotic fracture according to the analyses of 30928 participants (OR=1.47; 95% CI: 1.14-1.90, p=0.004); smoking was associated with wrist osteoporotic fracture according to the analyses of 30923 participants (OR=1.33; 95% CI: 1.18-1.49, p<0.001); and smoking was associated with spine osteoporotic fracture according to the analyses of 30910 participants (OR=1.43, 95% CI: 1.18-1.73, p<0.001). In addition, we confirmed the potential causal effect of smoking on the risk of osteoporotic fracture (OR=24.5; 95% CI: 1.11-539, p=0.043) by conducting two-sample MR analyses.
CONCLUSIONS: Smoking was associated with increased risks of both osteoporosis and osteoporotic fracture. Smoking showed a potential causal effect on the risk of osteoporotic fracture.

The spline teeth fracture of separator plates in wet multi-plate clutches compromises driving safety and the vehicle\'s lifespan. Tooth fracture is mainly caused by stress concentration at the tooth root and uneven circumferential load distribution. This paper considers parameters such as torque, teeth count, tooth profile, and misalignment errors, establishing the corresponding finite element (FE) model to analyze the impact of the above-mentioned parameters on the strength of the separator plates. Analysis under even and biased load circumstances demonstrated that an optimum tooth count and profile can significantly increase the strength of the separator plates, offering advice for the optimized design of wet multi-plate clutch separator plates.

Extreme mechanical processes such as strong lattice distortion and bond breakage during fracture often lead to catastrophic failure of materials and structures. Understanding the nucleation and growth of cracks is challenged by their multiscale characteristics spanning from atomic-level structures at the crack tip to the structural features where the load is applied. Atomistic simulations offer \'first-principles\' tools to resolve the progressive microstructural changes at crack fronts and are widely used to explore the underlying processes of mechanical energy dissipation, crack path selection, and dynamic instabilities (e.g., kinking, branching). Empirical force fields developed based on atomic-level structural descriptors based on atomic positions and the bond orders do not yield satisfying predictions of fracture, especially for the nonlinear, anisotropic stress- strain relations and the energy densities of edges. High-fidelity force fields thus should include the tensorial nature of strain and the energetics of bond-breaking and (re)formation events during fracture, which, unfortunately, have not been taken into account in either the state-of-the-art empirical or machine-learning force fields. Based on data generated by density functional theory calculations, we report a neural network-based force field for fracture (NN-F3) constructed by using the end-to-end symmetry preserving framework of deep potential - smooth edition (DeepPot-SE). The workflow combines pre-sampling of the space of strain states and active-learning techniques to explore the transition states at critical bonding distances. The capability of NN-F3 is demonstrated by studying the rupture of hexagonal boron nitride (h-BN) and twisted bilayer graphene as model problems. The simulation results elucidate the roughening physics of fracture defined by the lattice asymmetry in h-BN, explaining recent experimental findings, and predict the interaction between cross-layer cracks in twisted graphene bilayers, which leads to a toughening effect.

Gel treatment is an economical and efficient method of controlling excessive water production. The gelation of in situ gels is prone to being affected by the dilution of formation water, chromatographic during the transportation process, and thus controlling the gelation time and penetration depth is a challenging task. Therefore, a novel gel system termed preformed particle gels (PPGs) has been developed to overcome the drawbacks of in situ gels. PPGs are superabsorbent polymer gels which can swell but not dissolve in brines. Typically, PPGs are a granular gels formed based on the crosslinking of polyacrylamide, characterized by controllable particle size and strength. This work summarizes the application scenarios of PPGs and elucidates their plugging mechanisms. Additionally, several newly developed PPG systems such as high-temperature-resistant PPGs, re-crosslinkable PPGs, and delayed-swelling PPGs are also covered. This research indicates that PPGs can selectively block the formation of fractures or high-permeability channels. The performance of the novel modified PPGs was superior to in situ gels in harsh environments. Lastly, we outlined recommended improvements for the novel PPGs and suggested future research directions.

Skeletal diseases impose a considerable burden on society. The clinical and tissue-engineering therapies applied to alleviate such diseases frequently result in complications and are inadequately effective. Research has shifted from conventional therapies based on mesenchymal stem cells (MSCs) to exosomes derived from MSCs. Exosomes are natural nanocarriers of endogenous DNA, RNA, proteins, and lipids and have a low immune clearance rate and good barrier penetration and allow targeted delivery of therapeutics. MSC-derived exosomes (MSC-exosomes) have the characteristics of both MSCs and exosomes, and so they can have both immunosuppressive and tissue-regenerative effects. Despite advances in our knowledge of MSC-exosomes, their regulatory mechanisms and functionalities are unclear. Here we review the therapeutic potential of MSC-exosomes for skeletal diseases.