To conduct a systematic review and meta‐analysis to characterize inflammatory markers in comparisons of multisystem inflammatory syndrome in children (MIS‐C) versus severe/non‐severe COVID‐19, severe MIS‐C versus non‐severe MIS‐C, and among age groups of MIS‐C. Nine databases were searched for studies on inflammatory markers of MIS‐C. After quality checks, data were pooled using a fixed or random effects model. Inflammatory markers included white blood cell count (WBC) or leukocytes, absolute lymphocyte count (ALC), absolute neutrophil count (ANC), platelet count (PLT), C‐reactive protein (CRP), procalcitonin (PCT), ferritin, D‐dimer, lactate dehydrogenase (LDH), fibrinogen, and erythrocyte sedimentation rate (ESR) for comparisons by severity and age. Twenty‐one studies with 1735 participants yielded 787 MIS‐C patients. Compared to non‐severe COVID‐19 patients, MIS‐C patients had lower ALC and higher ANC, CRP, and D‐dimer levels. Compared to severe COVID‐19 patients, MIS‐C patients had lower LDH and PLT counts and higher ESR levels. Severe MIS‐C patients had higher levels of WBC, ANC, CRP, D‐dimer, and ferritin than non‐severe MIS‐C patients. For MIS‐C, younger children (0–5 years) had lower CRP and ferritin levels than middle‐aged/older children/adolescents. Measurement of inflammatory markers might assist clinicians in accurate evaluation and diagnosis of MIS‐C and the associated disorders.

Objective: The objective of this meta-analysis is to provide a comprehensive and up-to-date assessment of the prevalence of endometriosis and adenomyosis, addressing the limitations of existing research data and evaluating the quality of the data. Methods: Adhering to the PRISMA 2020 guidelines, this study systematically searched the Web of Science, PubMed, Embase, and Google Scholar databases from their inception until January 2025 for studies related to adenomyosis and endometriosis. All studies that specifically reported the prevalence of adenomyosis and endometriosis, or provided data enabling calculation of their prevalence, were included. The research examined the occurrence of different subtypes of adenomyosis and endometriosis, as well as frequency in various populations. Furthermore, subgroup analyses were conducted to synthesize findings related to distinct diagnostic criteria, gynecological symptoms, and the prevalence of these two conditions across diverse populations. Meta-analyses were performed using meta software packages, and random-effects models with 95% confidence intervals (CI) were employed to determine the combined prevalence of adenomyosis. Results: A total of 198,925,726 women from 127 studies (59 on adenomyosis and 68 on endometriosis) were included in this meta-analysis. The prevalence of focal and diffuse adenomyosis was found to be 17% (95%CI, 7%-30%) and 15% (95%CI, 9%-23%), respectively. The prevalence of peritoneal endometriosis, ovarian endometriosis, and deep endometriosis was recorded at 6% (95%CI, 1%-15%), 13% (95%CI, 5%-24%), and 10% (95%CI, 2%-24%), respectively. The prevalence of adenomyosis and endometriosis among women experiencing infertility was 31% (95%CI, 10%-58%) and 38% (95%CI, 25%-51%), respectively. Furthermore, among patients experiencing gynecologic symptoms, the pooled of adenomyosis and endometriosis was 41%-49%, and 18%-42%, respectively. The global prevalence of adenomyosis and endometriosis in the general population worldwide is 1% (95%CI, 0%-2%), and 5% (95%CI, 2%-9%), respectively. Conclusions: In conclusion, endometriosis occurs more frequently than adenomyosis across various populations. Notably, it affects nearly 50% of individuals experiencing gynecological symptoms. This study provides valuables support for public health management and emphasizes the importance of prompt intervention and treatment for related conditions.

Osteoarthritis (OA) is a degenerative disease that is difficult to cure owing to its complicated pathogenesis. Exercise therapy has been endorsed as a primary treatment option. However, it remains controversial how exercise intensity regulates OA progression. Here, a declining propensity for TGF-β1 was predicted via bioinformatics analysis of microarray GSE57218 and validated in cartilage samples obtained from arthroplasty. Based on this, cyclic tensile strain or TGF-β1 intervention was performed on human OA chondrocytes, and we found that moderate-intensity mechanical loads protected chondrocytes against pyroptosis. During this process, the elevation of TGF-β1 is mechanically stimuli-dependent and exerts an inhibitory effect on chondrocyte pyroptosis. Moreover, we elucidated that TGF-β1 activated Smad2/3 and inhibited the NF-κB signaling pathway to suppress chondrocyte pyroptosis. Furthermore, we established a rat knee OA model by intra-articular injection of monosodium iodoacetate and performed treadmill exercises of different intensities. Similar to the in vitro results, we demonstrated that moderate-intensity treadmill exercise had an outstanding chondroprotective effect. An inappropriate intensity of mechanical stimulation may aggravate OA both in vivo and in vitro. Overall, our findings demonstrated that activation of the TGF-β1/Smad2/Smad3 axis and inhibition of NF-κB coordinately inhibit chondrocyte pyroptosis under mechanical loads. This study sheds light on the future development of safe and effective exercise therapies for OA.

Objectives: To apply machine learning to extract radiomics features from thyroid two-dimensional ultrasound (2D-US) combined with contrast-enhanced ultrasound (CEUS) images to classify and predict benign and malignant thyroid nodules, classified according to the Chinese version of the thyroid imaging reporting and data system (C-TIRADS) as category 4.Materials and methods: This retrospective study included 313 pathologically diagnosed thyroid nodules (203 malignant and 110 benign). Two 2D-US images and five CEUS key frames (“2^nd second after the arrival time” frame, “time to peak” frame, “2^nd second after peak” frame, “first-flash” frame, and “second-flash” frame) were selected to manually label the region of interest using the “Labelme” tool. A total of 7 images of each nodule and their annotates were imported into the Darwin Research Platform for radiomics analysis. The datasets were randomly split into training and test cohorts in a 9:1 ratio. Six classifiers, namely, support vector machine, logistic regression, decision tree, random forest (RF), gradient boosting decision tree and extreme gradient boosting, were used to construct and test the models. Performance was evaluated using a receiver operating characteristic curve analysis. The area under the curve (AUC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy (ACC), and F1-score were calculated. One junior radiologist and one senior radiologist reviewed the 2D-US image and CEUS videos of each nodule and made a diagnosis. We then compared their AUC and ACC with those of our best model.Results: The AUC of the diagnosis of US, CEUS and US combined CEUS by junior radiologist and senior radiologist were 0.755, 0.750, 0.784, 0.800, 0.873, 0.890, respectively. The RF classifier performed better than the other five, with an AUC of 1 for the training cohort and 0.94 (95% confidence interval 0.88–1) for the test cohort. The sensitivity, specificity, accuracy, PPV, NPV, and F1-score of the RF model in the test cohort were 0.82, 0.93, 0.90, 0.85, 0.92, and 0.84, respectively. The RF model with 2D-US combined with CEUS key frames achieved equivalent performance as the senior radiologist (AUC: 0.94 vs. 0.92, P = 0.798; ACC: 0.90 vs. 0.92) and outperformed the junior radiologist (AUC: 0.94 vs. 0.80, P = 0.039, ACC: 0.90 vs. 0.81) in the test cohort.Conclusions: Our model, based on 2D-US and CEUS key frames radiomics features, had good diagnostic efficacy for thyroid nodules, which are classified as C-TIRADS 4. It shows promising potential in assisting less experienced junior radiologists.