No abstract available.
Carcinoma, Transitional Cell* ; Humans* ; P-Glycoprotein* ; Urinary Bladder*

Primary cutaneous mucinous carcinoma is a rare epithelial neoplasm that arises from the deepest portion of the eccrine duct. It most commonly occurs on the scalp and face with a preference for on the eyelids. The histopathologic characteristics are large mucinous pools with fibrous septae and clusters of tumor cells. We report here on a case of mucinous carcinoma that developed on the abdomen of a 47-year-old woman.
Abdomen ; Adenocarcinoma, Mucinous ; Eyelids ; Female ; Humans ; Middle Aged ; Mucins ; Neoplasms, Glandular and Epithelial ; Scalp

Primary cutaneous mucinous carcinoma is a rare epithelial neoplasm that arises from the deepest portion of the eccrine duct. It most commonly occurs on the scalp and face with a preference for on the eyelids. The histopathologic characteristics are large mucinous pools with fibrous septae and clusters of tumor cells. We report here on a case of mucinous carcinoma that developed on the abdomen of a 47-year-old woman.
Abdomen ; Adenocarcinoma, Mucinous ; Eyelids ; Female ; Humans ; Middle Aged ; Mucins ; Neoplasms, Glandular and Epithelial ; Scalp

Branchial cleft cysts and branchial anomalies develop from the branchial cleft apparatus that persists after fetal development. The most common anatomical site for the occurrence of branchial cleft cysts is in the cervical area, generally anterior to the sternomastoid muscle in the upper or middle portion of the neck. A mediastinal branchial cleft cyst is extremely rare and few cases have been reported. We report the case of branchial cleft cyst found in the anterior mediastinum with literature review.
Branchial Region ; Branchioma ; Fetal Development ; Mediastinal Cyst ; Mediastinum ; Muscles ; Neck ; Thorax ; Tomography, X-Ray Computed

Sorafenib (Nexavar(R), BAY 43-9006) is a novel, orally administered multi-kinase inhibitor that has recently been approved for the treatment of metastatic renal cell carcinoma. It is also used to delay disease progression in patients with advanced solid organ malignancies and metastatic melanoma. Sorafenib is associated with a relatively high incidence of dermatologic adverse events. The commonly occurring dermatologic adverse events associated with sorafenib include hand-foot skin reaction, facial erythema, splinter subungual hemorrhages, alopecia, pruritus and xerosis. We report here on a case of a 50-year-old man who was diagnosed with metastatic hepatocellular carcinoma. He developed both facial erythema and hand-foot skin reaction after the administration of sorafenib.
Alopecia ; Bays ; Carcinoma, Hepatocellular ; Carcinoma, Renal Cell ; Disease Progression ; Erythema ; Hemorrhage ; Humans ; Incidence ; Melanoma ; Middle Aged ; Niacinamide ; Phenylurea Compounds ; Pruritus ; Skin

Subungual melanoma is a variant of acral lentiginous melanoma and is thought to carry a poor prognosis due to difficulties in early diagnosis. Classical management of subungual melanoma is based on the radical surgery of distal phalanx amputation. Instead, conservative treatment with non-amputative wide excision of the nail unit followed by reconstruction has been insufficiently reported, especially in Korea. A 71-year-old woman presented with dark brownish longitudinal bands on her right thumbnail. Punch biopsies from the nail matrix and nail bed showed proliferation of atypical melanocytes without dermal involvement. We describe a case of subungual melanoma in situ of the right thumb, and a non-amputative surgical management which aims to provide adequate clearance of the lesion with minimal morbidity and satisfactory preservation of function for the patient.
Amputation ; Biopsy ; Early Diagnosis ; Female ; Humans ; Korea ; Melanocytes ; Melanoma ; Nails ; Prognosis ; Thumb

Aged ; Depression ; Follow-Up Studies ; Humans ; Korea ; Longitudinal Studies ; Odds Ratio ; Surveys and Questionnaires ; Weights and Measures

OBJECTIVE: To assess the effectiveness of Internet of Things (IoT)-based behavioral intervention for reducing depressive symptom of older adults with major depressive disorder. METHODS: A 12-week randomized cross-over controlled study was conducted at community mental health center. We recruited 39 participants with major depressive disorder aged 60 years or older. As a multidomain intervention, four evidence-based therapeutic factors (physical activity, healthy diet, social activity, and emotional regulation) were approached. To maintain motivation of participants, we applied contingency management using IoT device based on operant conditioning theory. RESULTS: The primary outcome was change of depressive symptom measured by Montgomery-Asberg Depression Rating Scale (MADRS). Mixed-effect model compared the effectiveness of intervention and usual care management (intervention by time and period interaction, p=0.017). And during the study period consisting of a total of visit 8, significant group difference was shown in post hoc test at visit 4 (MADRS score of intervention group : MADRS score of control group=7.7±3.4 : 21.1±11.5, p=0.008). CONCLUSION: Community-implementable IoT-based behavioral intervention resulted in greater reduction of depressive symptom of elderly with major depressive disorder.
Adult ; Aged ; Conditioning, Operant ; Depression ; Depressive Disorder, Major ; Diet ; Humans ; Internet ; Mental Health ; Motivation

Objective: This study aimed to develop a machine learning model to predict cognitive decline in community-dwelling older adults. By integrating multimodal data, including demographic, psychosocial, and neuroimaging information, we sought to en-hance early detection of cognitive decline. Methods: Data were obtained from 159 participants in the Biobank Innovations for Chronic Cerebrovascular Disease with Alzheimer’s Disease Study. Participants underwent clinical assessments, neuropsychological testing, and magnetic resonance im-aging scans. Cognitive decline was defined as an increase in the Clinical Dementia Rating-Sum of Boxes of greater than 2.05 points per year at follow-up. Models were developed using the logistic classification, combining demographic, psychosocial as-sessments, and neuroimaging data. Model performance was evaluated using area under the curve (AUC), accuracy, and F1 score, while Shapley additive explanation values were used to assess feature importance. Results: The model that incorporated all data types achieved the highest performance, with an AUC of 0.834. The top predictor of cognitive decline was years of education, underscoring the importance of non-invasive, easily accessible data for prediction. Conclusion This machine learning model demonstrates significant potential for early cognitive decline prediction, offering a scalable tool for improving dementia screening and timely intervention, especially in resource-limited settings.

Objective: This study aimed to develop a machine learning model to predict cognitive decline in community-dwelling older adults. By integrating multimodal data, including demographic, psychosocial, and neuroimaging information, we sought to en-hance early detection of cognitive decline. Methods: Data were obtained from 159 participants in the Biobank Innovations for Chronic Cerebrovascular Disease with Alzheimer’s Disease Study. Participants underwent clinical assessments, neuropsychological testing, and magnetic resonance im-aging scans. Cognitive decline was defined as an increase in the Clinical Dementia Rating-Sum of Boxes of greater than 2.05 points per year at follow-up. Models were developed using the logistic classification, combining demographic, psychosocial as-sessments, and neuroimaging data. Model performance was evaluated using area under the curve (AUC), accuracy, and F1 score, while Shapley additive explanation values were used to assess feature importance. Results: The model that incorporated all data types achieved the highest performance, with an AUC of 0.834. The top predictor of cognitive decline was years of education, underscoring the importance of non-invasive, easily accessible data for prediction. Conclusion This machine learning model demonstrates significant potential for early cognitive decline prediction, offering a scalable tool for improving dementia screening and timely intervention, especially in resource-limited settings.