There was an episode of complete recovery of congenital muscular torticollis presenting an ipsilateral neck mass due to the right sternocleidomastoid muscle (SCM) with other symptoms such as contralateral rotation of the head and restricted motion following initial physiotherapy for 6 months in a male infant. Physical examination and neck ultrasonography in the neonate clinic at 6 months revealed that his neck mass had disappeared, so therapy was discontinued. After 12 months of the episode, there was a reoccurrence of congenital muscular torticollis with the only symptom of ipsilateral neck mass and subsequently recovered without restricted motion. After 26 months of the reoccurrence, with no treatment except for 2 months of the second physiotherapy which was proven to be ineffective, spontaneous resolution of the right neck mass was demonstrated on serial ultrasound. Recovery of relapse SCM hypertrophy is extremely rare; therefore, we report the case. We suggest that the case may help clinicians diagnose and treating patients with the recovery of relapse SCM hypertrophy without complications.

There was an episode of complete recovery of congenital muscular torticollis presenting an ipsilateral neck mass due to the right sternocleidomastoid muscle (SCM) with other symptoms such as contralateral rotation of the head and restricted motion following initial physiotherapy for 6 months in a male infant. Physical examination and neck ultrasonography in the neonate clinic at 6 months revealed that his neck mass had disappeared, so therapy was discontinued. After 12 months of the episode, there was a reoccurrence of congenital muscular torticollis with the only symptom of ipsilateral neck mass and subsequently recovered without restricted motion. After 26 months of the reoccurrence, with no treatment except for 2 months of the second physiotherapy which was proven to be ineffective, spontaneous resolution of the right neck mass was demonstrated on serial ultrasound. Recovery of relapse SCM hypertrophy is extremely rare; therefore, we report the case. We suggest that the case may help clinicians diagnose and treating patients with the recovery of relapse SCM hypertrophy without complications.

Background: /Aim: Endoscopic retrograde cholangiopancreatography (ERCP) is one of the useful treatment for treating diseases that cause pancreatic duct stenosis. However, if the stenosis is severe, accessories other than the guide wire cannot pass through. This makes the ERCP procedure difficult. At this time, cystotome, a type of diathermic dilator seems to be an option. In this paper, we would like to discuss the experience of treatment procedures using cystotome. Methods: Patients who underwent ERCP at a single tertiary hospital from November 2019 to October 2022 were analyzed retrospectively. Among them, cystotome was used in eight procedures in seven patients who passed the guide wire but failed to pass the stenosis. Results: Technical success was achieved in all eight times. All the balloon catheter smoothly passed through the stenosis after cystotome was performed. In the second ERCP, it was a functional success for the balloon catheter to pass through stenosis without further treatment. This was achieved in 6 out of 8 (75.0%). In the case of complications, only one of the total successful procedures developed mild pancreatitis, which led to additional hospitalization for about two days after the procedure, and improved after conservative treatment. Conclusions Based on this, cystotome seems to be a good option as an alternative treatment to severe stenosis of the pancreatic duct.

PURPOSE: To determine and compare the clinical characteristics, visual prognosis and treatment of hemorrhagic polypoidal choroidal vasculopathy (HPCV) with those of hemorrhagic choroidal neovascularization (HCNV) due to age-related macular degeneration (ARMD). MATERIALS AND METHODS: Retrospective analysis of 44 consecutive eyes with a submacular hemorrhage comprising more than 50% of the neovascular lesion. Patients were diagnosed as having HPCV or HCNV on the basis of indocyanine green angiography. RESUTLS: Of the 44 eyes with submacular hemorrhage, 26 were classified as HPCV and 18 as HCNV. The baseline patient characteristics were similar for both groups. At the final follow-up the HPCV group had 17 eyes showing visual improvement, four showing maintained vision, and five showing visual deterioration. In contrast, the HCNV group had four eyes showing visual improvement, one showing maintained vision, and 13 showing visual deterioration. Visual acuity of < 0.1 at follow-up was found in 7 (27%) of HPCV eyes and 10 (56%) of HCNV eyes. PDT was performed in 15 HPCV eyes, of which 13 (87%) showed improvement or no change in visual acuity, while only 2 (22%) of the 9 HCNV eyes responded similarly after PDT. Eyes treated with PDT did not have better outcomes compared to eyes that underwent other types of treatment (Fisher's exact test, p > 0.05). CONCLUSION: PCV accounts for the largest proportion of submacular hemorrhage in Koreans. PCV showed a better visual prognosis than CNV.
Vision Disorders/etiology ; Treatment Outcome ; Tissue Plasminogen Activator/therapeutic use ; Retinal Hemorrhage/drug therapy/*epidemiology ; Photochemotherapy ; Middle Aged ; Male ; Macular Degeneration/drug therapy/epidemiology ; Korea/epidemiology ; Humans ; Follow-Up Studies ; Female ; Choroidal Neovascularization/drug therapy/*epidemiology

Eosinophilic gastroenteritis is a member of eosinophilic gastrointestinal (GI) diseases and is characterized by the accumulation of eosinophils within the GI tract. Common symptoms of eosinophilic gastroenteritis are vomiting, abdominal pain, dysphagia, and weight loss, but rare fever in neonate. This report describes a 10-day-old boy who developed fever, bloody mucoid stool, and bilious vomiting resulting from eosinophilic gastroenteritis, mimicking sepsis. Sigmoidoscopy and pathologic findings revealed mucosal edema, small hemorrhagic spot, and segmental erythemoid edema in the colon and increased number of eosinophils in the lamina propria with intraepithelial eosinophils. After breast milk feeding with restriction of milk from the mother for 6 months, specific immunoglobulin to milk was class 0 (class 3, previous). The boy restarted formula feeding, and he had no fever and GI symptoms.
Abdominal Pain ; Colon ; Deglutition Disorders ; Edema ; Eosinophils ; Fever ; Gastroenteritis ; Gastrointestinal Tract ; Humans ; Immunoglobulins ; Infant, Newborn ; Male ; Milk ; Milk, Human ; Mothers ; Mucous Membrane ; Sepsis ; Sigmoidoscopy ; Vomiting ; Weight Loss

Since the development of endoscopic ultrasound (EUS), the importance in the using EUS in pancreas and biliary tree has gradually increased. In order to properly observe the pancreas and biliary tree in EUS, it is necessary to learn landmark structures at each location, such as the stomach and duodenum, and to learn the positional relationship between them and normal structures. In this article, we will deal with the examination method at each position of the linear and radial EUS and reveal the normal EUS image.

Purpose: The molecular classification of breast cancer is crucial for effective treatment. The emergence of digital pathology has ushered in a new era in which weakly supervised learning leveraging whole-slide images has gained prominence in developing deep learning models because this approach alleviates the need for extensive manual annotation. Weakly supervised learning was employed to classify the molecular subtypes of breast cancer. Materials and Methods: Our approach capitalizes on two whole-slide image datasets: one consisting of breast cancer cases from the Korea University Guro Hospital (KG) and the other originating from The Cancer Genomic Atlas dataset (TCGA). Furthermore, we visualized the inferred results using an attention-based heat map and reviewed the histomorphological features of the most attentive patches. Results: The KG+TCGA-trained model achieved an area under the receiver operating characteristics value of 0.749. An inherent challenge lies in the imbalance among subtypes. Additionally, discrepancies between the two datasets resulted in different molecular subtype proportions. To mitigate this imbalance, we merged the two datasets, and the resulting model exhibited improved performance. The attentive patches correlated well with widely recognized histomorphologic features. The triple-negative subtype has a high incidence of high-grade nuclei, tumor necrosis, and intratumoral tumor-infiltrating lymphocytes. The luminal A subtype showed a high incidence of collagen fibers. Conclusion The artificial intelligence (AI) model based on weakly supervised learning showed promising performance. A review of the most attentive patches provided insights into the predictions of the AI model. AI models can become invaluable screening tools that reduce costs and workloads in practice.

Purpose: The molecular classification of breast cancer is crucial for effective treatment. The emergence of digital pathology has ushered in a new era in which weakly supervised learning leveraging whole-slide images has gained prominence in developing deep learning models because this approach alleviates the need for extensive manual annotation. Weakly supervised learning was employed to classify the molecular subtypes of breast cancer. Materials and Methods: Our approach capitalizes on two whole-slide image datasets: one consisting of breast cancer cases from the Korea University Guro Hospital (KG) and the other originating from The Cancer Genomic Atlas dataset (TCGA). Furthermore, we visualized the inferred results using an attention-based heat map and reviewed the histomorphological features of the most attentive patches. Results: The KG+TCGA-trained model achieved an area under the receiver operating characteristics value of 0.749. An inherent challenge lies in the imbalance among subtypes. Additionally, discrepancies between the two datasets resulted in different molecular subtype proportions. To mitigate this imbalance, we merged the two datasets, and the resulting model exhibited improved performance. The attentive patches correlated well with widely recognized histomorphologic features. The triple-negative subtype has a high incidence of high-grade nuclei, tumor necrosis, and intratumoral tumor-infiltrating lymphocytes. The luminal A subtype showed a high incidence of collagen fibers. Conclusion The artificial intelligence (AI) model based on weakly supervised learning showed promising performance. A review of the most attentive patches provided insights into the predictions of the AI model. AI models can become invaluable screening tools that reduce costs and workloads in practice.

Purpose: The molecular classification of breast cancer is crucial for effective treatment. The emergence of digital pathology has ushered in a new era in which weakly supervised learning leveraging whole-slide images has gained prominence in developing deep learning models because this approach alleviates the need for extensive manual annotation. Weakly supervised learning was employed to classify the molecular subtypes of breast cancer. Materials and Methods: Our approach capitalizes on two whole-slide image datasets: one consisting of breast cancer cases from the Korea University Guro Hospital (KG) and the other originating from The Cancer Genomic Atlas dataset (TCGA). Furthermore, we visualized the inferred results using an attention-based heat map and reviewed the histomorphological features of the most attentive patches. Results: The KG+TCGA-trained model achieved an area under the receiver operating characteristics value of 0.749. An inherent challenge lies in the imbalance among subtypes. Additionally, discrepancies between the two datasets resulted in different molecular subtype proportions. To mitigate this imbalance, we merged the two datasets, and the resulting model exhibited improved performance. The attentive patches correlated well with widely recognized histomorphologic features. The triple-negative subtype has a high incidence of high-grade nuclei, tumor necrosis, and intratumoral tumor-infiltrating lymphocytes. The luminal A subtype showed a high incidence of collagen fibers. Conclusion The artificial intelligence (AI) model based on weakly supervised learning showed promising performance. A review of the most attentive patches provided insights into the predictions of the AI model. AI models can become invaluable screening tools that reduce costs and workloads in practice.