Objectives: Our study aimed to develop and validate an efficient ultrasound image‐based radiomic model for determining the Achilles tendinopathy in skiers.Methods: A total of 88 feet of skiers clinically diagnosed with unilateral chronic Achilles tendinopathy and 51 healthy feet were included in our study. According to the time order of enrollment, the data were divided into a training set (n = 89) and a test set (n = 50). The regions of interest (ROIs) were segmented manually, and 833 radiomic features were extracted from red, green, blue color channels and grayscale of ROIs using Pyradiomics, respectively. Three feature selection and three machine learning modeling algorithms were implemented respectively, for determining the optimal radiomics pipeline. Finally, the area under the receiver operating characteristic curve (AUC), consistency analysis, and decision analysis were used to evaluate the diagnostic performance.Results: By comparing nine radiomics analysis strategies of three color channels and grayscale, the radiomic model under the green channel obtained the best diagnostic performance, using the Random Forest selection and Support Vector Machine modeling, which was selected as the final machine learning model. All the selected radiomic features were significantly associated with the Achilles tendinopathy (P < .05). The radiomic model had a training AUC of 0.98, a test AUC of 0.99, a sensitivity of 0.90, and a specificity of 1, which could bring sufficient clinical net benefits.Conclusions: Ultrasound image‐based radiomics achieved high diagnostic performance, which could be used as an intelligent auxiliary tool for the diagnosis of Achilles tendinopathy.

Cognitive decline (CD) is devastating with a high incidence in patients after stroke. Although some studies have explored underlying associations between C-reactive protein (CRP) levels and cognitive decline after stroke, consistent results have not been obtained. Therefore, this meta-analysis aimed to explore whether or not higher levels of C-reactive proteins were associated with an increased risk of cognitive decline after stroke. To this end, PubMed, Embase, the Cochrane Library, and Web of Science were searched for eligible studies, and pooled effect sizes from eligible studies were calculated using random effect models. Furthermore, subgroups were established and meta-regression analyses were performed to explain the causes of heterogeneity. Eventually, nine studies with 3893 participants were included. Our statistical results suggested that the concentrations of peripheral CRP may be significantly increased for CD patients after stroke, compared to those of non-CD patients. Subgroup analyses showed that CRP was higher in CD than that in non-CD patients when the mini-mental state examination was used. A higher level of CRP in the acute phase of ischemic stroke may suggest an increased risk of CD after stroke. However, these results should be cautiously interpreted because of the limited sample sizes and the diversity of potential confounders in the studies included in this meta-analysis.

Objective: The precise mechanisms driving intervertebral disc degeneration (IVDD) development remain unclear, but evidence suggests a significant involvement of gut microbiota (GM) and blood metabolites. We aimed to investigate the causal relationships between GM, IVDD, and blood metabolites using Mendelian randomization (MR) analysis.Methods: We utilized the summary statistics of GM from the MiBioGen consortium, 1400 blood metabolites from the genome-wide association studies (GWAS) Catalog, and IVDD data from the FinnGen repository, which are sourced from the largest GWAS conducted to date. Employing bidirectional MR analyses, we investigated the causal relationships between GM and IVDD. Additionally, we conducted 2 mediation analyses, 2-step MR and multivariable MR (MVMR), to identify potential mediating metabolites.Results: Five bacterial genera were causally associated with IVDD, while IVDD did not show a significant causal effect on GM. In the 2-step MR analysis, Eubacteriumfissicatenagroup, RuminococcaceaeUCG003, Lachnoclostridium, and Marvinbryantia genera, along with metabolites X-24949, Pimeloylcarnitine/3-methyladipoylcarnitine (C7-DC), X-24456, histidine, 2-methylserine, Phosphocholine, and N-delta-acetylornithine, were all significantly associated with IVDD (all p < 0.05). MVMR analysis revealed that the associations between Eubacteriumfissicatenagroup genus and IVDD were mediated by X-24949 (8.1%, p = 0.024); Lachnoclostridium genus and IVDD were mediated by histidine (18.1%, p = 0.013); and RuminococcaceaeUCG003 genus and IVDD were mediated by C7-DC (-7.5%, p = 0.041).Conclusion: The present MR study offers evidence supporting the causal relationships between several specific GM taxa and IVDD, as well as identifying potential mediating metabolites.

Introduction: Traumatic fractures of the vertebral bodies in the thoracolumbar region are the most common type of spinal fractures. Some studies suggest that neurological deficits associated with these fractures may be linked to spinal canal compromise, kyphosis angle, and other factors. However, this relationship remains controversial. The present study aimed to identify the risk factors for neurologic deficits following thoracolumbar burst fractures (TBF).Methods: This study included 322 patients with TBF, comprising 115 patients with lamina fractures (LF) and 207 patients without lamina fractures (NLF). Neurological deficits were classified according to the American Spinal Injury Association (ASIA) classification, with 75 patients exhibiting neurological deficits and 247 patients without deficits. Clinical data, ASIA classification, and imaging findings were analyzed and compared between the two groups. Risk factors were assessed using logistic regression.Results: Compared with the NLF group, the LF group had higher ASIA classification scores (P < 0.05). Multivariate logistic regression identified laminar fracture (OR: 0.019, 95% CI: 0.005–0.070, P < 0.000), car accident (OR: 6.082, 95% CI: 1.248–29.636, P = 0.025), and falling accident (OR: 8.429, 95% CI: 2.143–33.153, P = 0.002) as independent variables associated with neurologic deficit. Additionally, the ROC curve revealed that laminar fractures and falling accidents had a high risk association value. A risk association equation, Logit (P) = −4.358 + 3.535 × laminar fracture – 1.353 × falling accidents, was established based on the high-value indicators.Conclusion: Laminar fractures, car accidents, and falls were identified as independent risk factors for neurological deficits following TBF. Additionally, laminar fractures and falls demonstrated a high risk association value. These findings provide valuable insights for optimizing rehabilitation strategies and guiding surgical decision-making.