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【Abstract】Objective: To compare the treating effects of different intramedullary nailing methods on tibial fractures in adults. Methods: Literature reports in both Chinese and English languages were retrieved (from the earliest available records to October 1, 2013) from the PubMed, FMJS, CNKI, Wanfang Data using randomized controlled trials (RCTs) to compare reamed and unreamed intramedullary nailing for treatment of tibial fractures. Methodological quality of the trials was critically assessed, and relevant data were extracted. Statistical software Revman 5.0 was used for data-analysis. Results:A total of 12 randomized controlled trials, comprising 985 patients (475 in the unreamed group and 510 in the reamed group), were eligible for inclusion in this meta-analysis. The results of metaanalysis showed that there were no statistically significant differences between the two methods in the reported outcomes of infection (RR=0.64; 95%CI, 0.39 to 1.07; P=0.09), compartment syndrome (RR=1.44; 95%CI, 0.8 to 2.41; P=0.16), thrombosis (RR=1.29; 95%CI, 0.43 to 3.87; P=0.64), time to union (WMD=5.01; 95%CI, -1.78 to 11.80; P=0.15), delayed union (nonunion) (RR=1.56; 95%CI, 0.97 to 2.49; P=0.06), malunion (RR=1.75; 95%CI, 1.00 to 3.08; P=0.05) and knee pain (RR=0.94; 95%CI, 0.73 to 1.22; P=0.66). But there was a significantly higher fixation failure rate in the unreamed group than in the reamed group (RR=4.29; 95%CI, 2.58 to 7.14; P

The relationship between bone mass and stress has been confirmed by most of the biomechanical studies. The muscle is the factor to cause the maximum dynamic loading and the strain of the bone. It is important to study osteoporosis through the relationship between bone mass and stress. Currently, there is a few study on the relationship between bone mass and stress. However, systematic report on the mechanism and influential factors to stress and bone mass is little. In order to better understand the relationship between bone mass and stress during the development of osteoporosis, this paper reviews the relationship between muscle force and bone mass. [ABSTRACT FROM AUTHOR]

In femoral intertrochanteric fractures, poor incision positioning may result in inaccurate intramedullary nail placement direction, which increases the difficulty of reduction and thus the size and number of incisions. Repeated intraoperative fluoroscopy not only increases the radiation exposure of the surgeon but also affects the operative outcomes. This technical note proposes a method of identifying incision positioning preoperatively using the "3-2-1" body surface localization method. This auxiliary positioning technique uses a body surface locator and the lower limb force axis. It can predict the incisions for the needle insertion point, spiral blade, and locking nails, create minimally invasive incisions, avoid incorrect incision position, facilitate accurate intraoperative intramedullary nail placement, reduce the incision size, intraoperative bleeding, and radiation exposure, and improve surgical efficiency and reduction quality., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (© 2024 Wu, Lin, Xu, Yan and Tu.)

Using Raman spectroscopy (RS) signals for skin cancer tissue classification has recently drawn significant attention, because of its non-invasive optical technique nature using molecular structures and conformations within biological tissue for diagnosis. In reality, RS signals are noisy and unstable for training machine learning models. The scarcity of tissue samples also makes it challenging to learn reliable deep-learning networks for clinical usages. In this paper, we advocate a Transfer Contrasting Learning Paradigm (TCLP) to address the scarcity and noisy characteristics of the RS for skin cancer tissue classification. To overcome the challenge of limited samples, TCLP leverages transfer learning to pre-train deep learning models using RS data from similar domains (but collected from different RS equipments for other tasks). To tackle the noisy nature of the RS signals, TCLP uses contrastive learning to augment RS signals to learn reliable feature representation to represent RS signals for final classification. Experiments and comparisons, including statistical tests, demonstrate that TCLP outperforms existing deep learning baselines for RS signal-based skin cancer tissue classification. Both code and data used in our study are available at: https://github.com/yeyimilk/tclp.

Background: Ankylosing spondylitis (AS) is a chronic inflammatory disease affecting the spine and sacroiliac joints. Recent genetic studies suggest certain plasma proteins may play a causal role in AS development. This study aims to identify and characterize these proteins using Mendelian randomization (MR) and colocalization analyses., Methods: Plasma protein data were obtained from recent publications in Nature Genetics , integrating data from five previous GWAS datasets, including 738 cis-pQTLs for 734 plasma proteins. GWAS summary data for AS were sourced from IGAS and other European cohorts. MR analyses were conducted using "TwoSampleMR" to assess causal links between plasma protein levels and AS. Colocalization analysis was performed with the coloc R package to identify shared genetic variants. Sensitivity analyses and protein-protein interaction (PPI) network analyses were conducted to validate findings and explore therapeutic targets. We performed Phenome-wide association study (PheWAS) to examine the potential side effects of drug protein on AS treatment., Results: After FDR correction, eight significant proteins were identified: IL7R, TYMP, IL12B, CCL8, TNFAIP6, IL18R1, IL23R, and ERAP1. Elevated levels of IL7R, IL12B, CCL8, IL18R1, IL23R, and ERAP1 increased AS risk, whereas elevated TYMP and TNFAIP6 levels decreased AS risk. Colocalization analysis indicated that IL23R, IL7R, and TYMP likely share causal variants with AS. PPI network analysis identified IL23R and IL7R as potential new therapeutic targets., Conclusions: This study identified eight plasma proteins with significant associations with AS risk, suggesting IL23R, IL7R, and TYMP as promising therapeutic targets. Further research is needed to explore underlying mechanisms and potential for drug repurposing., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 You, Lin, Liu, Lin, Ye, Zhang and Zeng.)

To investigate the association of serum semaphorin 4D (Sema4D) levels with lumbar spine bone mineral density (BMD) and bone turnover markers in patients with postmenopausal osteoporosis (PO). Lumbar spine BMD was measured by dual-energy X-ray absorptiometry in 257 PO patients (aged from 50 to 75) and 90 healthy controls (aged from 51 to 83). Serum Sema4D, BAP, BGP and TRACP-5b levels were measured by enzyme linked immunosorbent assay. Serum cross linked N-telopeptides of type I (NTX), 25-hydroxyvitamin D (25(OH)D) and N-mid fragment of osteocalcin (N-MID-OT) levels were measured using automated electrochemiluminescence system. Sema4D level was significantly higher in PO women compared to healthy controls (1.40±0.33 vs. 0.58±0.18 μg/L, P=0.006). Sema4D level was positively correlated with serumTRACP-5b and NTX levels and negatively correlated with lumbar spine BMD and serum BAP and BGP levels. There were no correlations between Sema4D level and age, body mass index, and serum 25(OH)D and N-MID-OT levels. Lumbar spine BMD (β=-0.354, P<0.001) and serum BAP level (β=0.127, P=0.019) were independent predictors of serum Sema4D level in PO patients. Sema4D may be involved in the pathogenesis of PO and play a critical role in bone formation and resorption. Sema4D may represent a novel therapeutic target for treatment of PO and function as a predictive indicator of PO.