Although struma ovarii (SO) is mostly benign, it is frequently overtreated due to challenges in differentiating it from malignant tumors. Therefore, the purpose of this study was to investigate the clinical, laboratory, ultrasonographic, and pathological characteristics of SO to improve the accuracy of preoperative diagnosis. We retrospectively reviewed the clinical data and imaging characteristics of 103 patients with postoperative pathology for SO at the Affiliated Hospital of Qingdao University from May 2013 to November 2023. Among the 103 patients diagnosed with SO, 95 were benign SO (median age: 44.9 years (range, 15–83 years)) and 8 were malignant struma ovarii (MSO) (49.6 years (range, 15–83 years)), malignant cases accounted for 7.8% of the cohort. No statistically significant difference was observed in the median age between patients with benign SO and those with MSO (p = 0.346). Approximately 52.4% of SO patients had an increase in CA125, while CEA, AFP, HE4 and CA199 were basically within the normal range, and these markers had no clinical significance. The ultrasound manifestations were mostly cystic masses or predominantly cystic-solid masses (78/98), and 18 cases were solid masses or predominantly solid-cystic masses. Two patients showed negative results on ultrasound examination and was discovered during another gynecological surgery. Most of the masses are completely separated and multilocular, most with smooth outer margins. Nearly one-half of the masses have punctate or short-line echoes with comet tails, papillary protrusions can be seen on the inner wall of some lesions, and there is usually no blood flow inside the tumor or only a few dotted blood flow signals (79/96). Among the 6 patients with MSO which have ultrasound imaging, 4 patients showed a multilocular cystic mass on ultrasound, with poor sound echo in the cystic part, and 2 patients showed a solid mass on ultrasound with abundant blood flow signals. Immunohistochemistry results showed positive expression rates for thyroid transcription factor-1 (TTF-1) (96%, 24/25), thyroglobulin (TG) (90.5%, 19/21), PAX8 (94.7%, 18/19), and Ki-67 (100% 16/16), though Ki-67 expression levels were generally low, with 15 cases < 5% and 1 case at 50%. Ultrasonographic features-including multilocular cystic-solid masses with septations, hyperechoic “pearl signs,” and comet-tail artifacts-may raise suspicion for SO. However, differentiating between benign and malignant cases remains challenging due to significant imaging overlap. A multimodal diagnostic approach incorporating clinical presentation, laboratory findings, and histopathological evaluation is essential for accurate diagnosis and optimal management.

Introduction: In the big data era, healthcare data has grown exponentially, presenting opportunities to explore the pathogenesis of heart disease. Clarifying the correlations between health indicators and heart disease is crucial for early prevention. This study employs ensemble learning to identify the key influencing factors, assisting clinicians in understanding the pathogenesis and enhancing prediction strategies. Methods: A two-layer stacking ensemble model is proposed, integrating Naive Bayes, Decision Trees, CatBoost and Gradient Boosting Trees to enhance prediction accuracy. To address ensemble models’ complexity and poor interpretability, the SHAP technique is introduced to visualize the decision-making logic of the ensemble model. Results: Experimental results show that the stacking model achieved 86.69% accuracy, 87.14% weighted precision, 86.69% weighted recall, and 86.91% weighted F1-score. It balances precision and recall, unlike single learners that prioritize one over the other. Global interpretive analysis demonstrates that age, sleep duration, self-rated health status and BMI are critical factors in assessing cardiovascular risk. Local interpretive analysis is conducted to evaluate the contribution of each feature to the prediction results of individual samples. Discussion: The stacking model’s superior performance demonstrates that ensemble learning can overcome the limitations of single learners. Additionally, key predictive factors are identified: maintaining an average sleep duration of 7-8 hours significantly reduces heart disease risk, while advanced age and poor health status increase susceptibility. This study provides a reliable predictive tool for personalized heart disease prevention and treatment.

INPLASY2022100123 Effects of natural products in Patients with Non-alcoholic Fatty Liver Disease—A Network Meta-Analysis of Randomized Controlled Trials Keywords: natural products ; Non-alcoholic F…