Background: This study aimed to evaluate the applicability of deep learning technology to thyroid ultrasound images for classification of thyroid nodules. Methods: This retrospective analysis included ultrasound images of patients with thyroid nodules investigated by fine-needle aspiration at the thyroid clinic of a single center from April 2010 to September 2012. Thyroid nodules with cytopathologic results of Bethesda category V (suspicious for malignancy) or VI (malignant) were defined as thyroid cancer. Multiple deep learning algorithms based on convolutional neural networks (CNNs) —ResNet, DenseNet, and EfficientNet—were utilized, and Siamese neural networks facilitated multi-view analysis of paired transverse and longitudinal ultrasound images. Results: Among 1,048 analyzed thyroid nodules from 943 patients, 306 (29%) were identified as thyroid cancer. In a subgroup analysis of transverse and longitudinal images, longitudinal images showed superior prediction ability. Multi-view modeling, based on paired transverse and longitudinal images, significantly improved the model performance; with an accuracy of 0.82 (95% confidence intervals [CI], 0.80 to 0.86) with ResNet50, 0.83 (95% CI, 0.83 to 0.88) with DenseNet201, and 0.81 (95% CI, 0.79 to 0.84) with EfficientNetv2_ s. Training with high-resolution images obtained using the latest equipment tended to improve model performance in association with increased sensitivity. Conclusion CNN algorithms applied to ultrasound images demonstrated substantial accuracy in thyroid nodule classification, indicating their potential as valuable tools for diagnosing thyroid cancer. However, in real-world clinical settings, it is important to aware that model performance may vary depending on the quality of images acquired by different physicians and imaging devices.

Background: Recombinant human follicle-stimulating hormone (rhFSH) is commonly used to treat female infertility, but its short half-life necessitates multiple doses. Even corifollitropin alfa, with an extended half-life, requires supplementary injections of rhFSH after 7 days. This study aimed to develop and evaluate a long-acting follicle-stimulating hormone (FSH) formulation using anti-serum albumin Fab-associated (SAFA) technology to avoid additional injections and enhance ovarian function. Methods: SAFA-FSH was synthesized using a Chinese hamster ovary expression system. Its biological efficacy was confirmed through assays measuring its ability to stimulate cyclic adenosine monophosphate (cAMP) production, estradiol synthesis, and the expression of human cytochrome P450 family 19 subfamily A member 1 (hCYP19α1) and human steroidogenic acute regulatory protein (hSTAR) in human ovarian granulosa (KGN) cells. To evaluate the effects of SAFA-FSH, we compared its impact on serum estradiol levels and ovarian weight increase with that of rhFSH in Sprague-Dawley (SD) rats using the modified Steelman-Pohley test. Results: The results indicated that SAFA-FSH induces cAMP synthesis in KGN cells and upregulates the expression of hCYP19α1 and hSTAR in a dose-dependent manner. Female SD rats, aged 21 days, receiving daily subcutaneous human chorionic gonadotropin injections for 5 days exhibited a significant increase in serum estradiol levels and ovarian weight when administered SAFA-FSH on the first day or when given nine injections of rhFSH over 5 days. Notably, the group receiving SAFA-FSH on the first and third days demonstrated an even greater rise in serum estradiol levels and ovarian weight. Conclusion These findings suggest that SAFA-FSH presents a promising alternative to current rhFSH treatments for female infertility. However, further research is essential to thoroughly assess its safety and efficacy in clinical contexts.

Background/Aims: Inaccurate prediction of lymph node metastasis (LNM) may lead to unnecessary surgery following endoscopic resection of T1 colorectal cancer (CRC). We aimed to validate the usefulness of artificial intelligence (AI) models for predicting LNM in patients with T1 CRC. Methods: We analyzed the clinical data, laboratory results, pathological reports, and endoscopic findings of patients who underwent radical surgery for T1 CRC. We developed AI models to predict LNM using four algorithms: regularized logistic regression classifier (RLRC), random forest classifier (RFC), CatBoost classifier (CBC), and the voting classifier (VC). Four histological factors and four endoscopic findings were included to develop AI models. Areas under the receiver operating characteristics curves (AUROCs) were measured to distinguish AI model performance in accordance with the Japanese Society for Cancer of the Colon and Rectum guidelines. Results: Among 1,386 patients with T1 CRC, 173 patients (12.5%) had LNM. The AUROC values of the RLRC, RFC, CBC, and VC models for LNM prediction were significantly higher (0.673, 0.640, 0.679, and 0.677, respectively) than the 0.525 suggested in accordance with the Japanese Society for Cancer of the Colon and Rectum guidelines (vs RLRC, p<0.001; vs RFC, p=0.001; vs CBC, p<0.001; vs VC, p<0.001). The AUROC value was similar between T1 colon versus T1 rectal cancers (0.718 vs 0.615, p=0.700). The AUROC value was also similar between the initial endoscopic resection and initial surgery groups (0.581 vs 0.746, p=0.845). Conclusions AI models trained on the basis of endoscopic findings and pathological features performed well in predicting LNM in patients with T1 CRC regardless of tumor location and initial treatment method.

Background and Objectives: Electronic cigarette (e-cigarette) is a device that generate vapor by heating e-cigarettes liquid. E-cigarette damages the respiratory immune system and renders the respiratory tract vulnerable to inflammations. However, there are no studies on how the inflammatory reactions in respiratory epithelial cells caused by e-cigarette occur, and the effects of Korean red ginseng (KRG) and saponin on inflammation induced by e-cigarette are unknown. This study aimed to compare the inflammatory reactions caused by e-cigarette and lipopolysaccharide (LPS), and to investigate the effects of KRG and saponin on cytokine and mucin expression induced by e-cigarette in respiratory epithelial cells.Subjects and Method In bronchoalveolar lavage fluid and lung tissue of mice, the effects of KRG and saponin on cytokines (interleukin [IL]-1β, IL-6, IL-8) and mucin 5AC/5B (MUC5AC/5B) expression induced by e-cigarette were investigated using real-time polymerase chain reaction, enzyme linked immunosorbent assay, and immunohistochemistry staining. Results: Inflammatory cells, cytokines (IL-1β, IL-6, IL-8) and MUC5AC/5B messenger RNA expression and protein production were increased by e-cigarette, similar to LPS. KRG and saponin decreased the expression of cytokines (IL-1β, IL-6, IL-8) and MUC5AC/5B induced by e-cigarette. KRG and saponin showed effects similar to that of dexamethasone. Conclusion E-cigarette causes inflammation similar to that caused by LPS. KRG and saponin regulate the expression of cytokine and MUC5AC/5B increased by e-cigarette in respiratory epithelial cells. KRG and saponin may be an effective therapeutic option for inflammatory responses induced by e-cigarette in respiratory epithelial cells.

It is difficult to determine a cause of bile duct stricture and dilatation. Eosinophilic cholangitis, a rare benign condition, may be one cause of bile duct stricture and dilatation. It can be evaluated using various methods of histopathology, radiographs, endoscopy, and hematologic findings. Treatment generally involves steroid therapy which can lead to improvement. This case report will discuss eosinophilic cholangitis, emphasizing that while it can easily be overlooked but should be considered in differential diagnoses.

Background and Objectives: Electronic cigarette (e-cigarette) is a device that generate vapor by heating e-cigarettes liquid. E-cigarette damages the respiratory immune system and renders the respiratory tract vulnerable to inflammations. However, there are no studies on how the inflammatory reactions in respiratory epithelial cells caused by e-cigarette occur, and the effects of Korean red ginseng (KRG) and saponin on inflammation induced by e-cigarette are unknown. This study aimed to compare the inflammatory reactions caused by e-cigarette and lipopolysaccharide (LPS), and to investigate the effects of KRG and saponin on cytokine and mucin expression induced by e-cigarette in respiratory epithelial cells.Subjects and Method In bronchoalveolar lavage fluid and lung tissue of mice, the effects of KRG and saponin on cytokines (interleukin [IL]-1β, IL-6, IL-8) and mucin 5AC/5B (MUC5AC/5B) expression induced by e-cigarette were investigated using real-time polymerase chain reaction, enzyme linked immunosorbent assay, and immunohistochemistry staining. Results: Inflammatory cells, cytokines (IL-1β, IL-6, IL-8) and MUC5AC/5B messenger RNA expression and protein production were increased by e-cigarette, similar to LPS. KRG and saponin decreased the expression of cytokines (IL-1β, IL-6, IL-8) and MUC5AC/5B induced by e-cigarette. KRG and saponin showed effects similar to that of dexamethasone. Conclusion E-cigarette causes inflammation similar to that caused by LPS. KRG and saponin regulate the expression of cytokine and MUC5AC/5B increased by e-cigarette in respiratory epithelial cells. KRG and saponin may be an effective therapeutic option for inflammatory responses induced by e-cigarette in respiratory epithelial cells.

It is difficult to determine a cause of bile duct stricture and dilatation. Eosinophilic cholangitis, a rare benign condition, may be one cause of bile duct stricture and dilatation. It can be evaluated using various methods of histopathology, radiographs, endoscopy, and hematologic findings. Treatment generally involves steroid therapy which can lead to improvement. This case report will discuss eosinophilic cholangitis, emphasizing that while it can easily be overlooked but should be considered in differential diagnoses.

Primary cholangiocarcinoma is a rare bile duct epithelial neoplasm that can present with atypical clinical manifestations, complicating its diagnosis. A 62-year-old male showed symptoms suggestive of a complicated hepatic cyst that was later identified as intrahepatic cholangiocarcinoma. The patient presented with abdominal discomfort without fever. Imaging revealed a large cystic lesion in the liver. Despite the initial treatment for a presumed abscess, a biopsy confirmed cholangiocarcinoma. This case highlights the diagnostic challenge of distinguishing between benign complicated hepatic cysts and malignancies, particularly when typical markers of infection are absent. Early biopsy and vigilant assessments are crucial in such presentations to avoid a delayed diagnosis and initiate appropriate treatment.