Tendinitis, characterized by the inflammation of tendons, poses significant challenges in both diagnosis and treatment due to its multifaceted etiology and complex pathophysiology. This study aimed to dissect the molecular mechanisms underlying tendinitis, with a particular focus on inflammasome-related genes and their interactions with the immune system. Through comprehensive gene expression analysis and bioinformatics approaches, we identified distinct expression profiles of inflammasome genes, such as NLRP6, NLRP1, and MEFV, which showed significant correlations with immune checkpoint molecules, indicating a pivotal role in the inflammatory cascade of tendinitis. Additionally, MYD88 and CD36 were found to be closely associated with HLA family molecules, underscoring their involvement in immune response modulation. Contrary to expectations, chemokines exhibited minimal correlation with inflammasome genes, suggesting an unconventional inflammatory pathway in tendinitis. Transcription factors like SP110 and CREB5 emerged as key regulators of inflammasome genes, providing insight into the transcriptional control mechanisms in tendinitis. Furthermore, potential therapeutic targets were identified through the DGidb database, highlighting drugs that could modulate the activity of inflammasome genes, offering new avenues for targeted tendinitis therapy. Our findings elucidate the complex molecular landscape of tendinitis, emphasizing the significant role of inflammasomes and immune interactions, and pave the way for the development of novel diagnostic and therapeutic strategies.

BACKGROUND: This study aims to present a radiomic application in diagnosing the long head of biceps (LHB) tendinitis. Moreover, we evaluated whether machine learning-derived radiomic features recognize LHB tendinitis.
METHODS: A total of 170 patients were reviewed. All LHB tendinitis patients were diagnosed under arthroscopy. Radiomic features were extracted from preoperative magnetic resonance imaging (MRI), and the input dataset was divided into a training set and a test set. For feature selection, the t test and least absolute shrinkage and selection operator (LASSO) methods were used, and random forest (RF) and support vector machine (SVM) were used as machine learning classifiers. The sensitivity, specificity, accuracy, and area under the curve (AUC) of each model\'s receiver operating characteristic (ROC) curves were calculated to evaluate model performance.
RESULTS: In total, 851 radiomic features were extracted, with 109 radiomic features extracted using a t test and 20 radiomic features extracted using the LASSO method. The random forest classifier shows the highest sensitivity, specificity, accuracy, and AUC (0.52, 0.92, 0.73, and 0.72).
CONCLUSIONS: The classifier contract by 20 radiomic features demonstrated a good ability to predict extra-articular LHB tendinitis.However because of poor segmentation reliability, the value of Radiomic in LHB tendinitis still needs to be further explored.

Chronic tendinopathy is a frequent and disabling musculo-skeletal problem affecting the athletic and general populations. The affected tendon is presented with local tenderness, swelling, and pain which restrict the activity of the individual. Tendon degeneration reduces the mechanical strength and predisposes it to rupture. The pathogenic mechanisms of chronic tendinopathy are not fully understood and several major non-mutually exclusive hypotheses including activation of the hypoxia-apoptosis-pro-inflammatory cytokines cascade, neurovascular ingrowth, increased production of neuromediators, and erroneous stem cell differentiation have been proposed. Many intrinsic and extrinsic risk/causative factors can predispose to the development of tendinopathy. Among them, diabetes mellitus is an important risk/causative factor. This review aims to appraise the current literature on the epidemiology and pathology of tendinopathy in diabetic patients. Systematic reviews were done to summarize the literature on (a) the association between diabetes mellitus and tendinopathy/tendon tears, (b) the pathological changes in tendon under diabetic or hyperglycemic conditions, and (c) the effects of diabetes mellitus or hyperglycemia on the outcomes of tendon healing. The potential mechanisms of diabetes mellitus in causing and exacerbating tendinopathy with reference to the major non-mutually exclusive hypotheses of the pathogenic mechanisms of chronic tendinopathy as reported in the literature are also discussed. Potential strategies for the management of tendinopathy in diabetic patients are presented.