Xanthogranulomatous inflammation (XGI) is a rare process. Colonic XGI is extremely rare. We present a case report of XGI arising within an intra-abdominal cyst involving the transverse colon. A 28-year-old man presented with abdominal discomfort. He underwent a computed tomography abdomen-pelvis, which found three subcutaneous collections and a large cystic intra-abdominal structure. The decision was made for resection. Intraoperatively, the cyst originated from the pelvis and was adherent to the surrounding tissues. Histology from the lesion revealed XGI extending into the surrounding tissue. XGI is a rare inflammatory condition. Clinically, it can be difficult to distinguish XGI from an infiltrative malignancy. Therefore, it is usually necessary to obtain a pathological diagnosis of XGI. This case describes an atypical cystic lesion with XGI involving the transverse colon. Although rare, it should be considered in the potential differential diagnosis of an infective or malignant mesenteric cyst.
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http://dx.doi.org/10.1093/jscr/rjaa295 | DOI Listing |
Biomedicines
January 2025
Department of Biochemistry and Molecular Biology, School of Basic Medicine and the Collaborative Innovation Center for Brain Science, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China.
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Department of Molecular and Developmental Medicine, University of Siena, 53100 Siena, Italy.
Intracellular organelles are common to eukaryotic cells and provide physical support for the assembly of specialized compartments. In skeletal muscle fibers, the largest intracellular organelle is the sarcoplasmic reticulum, a specialized form of the endoplasmic reticulum primarily devoted to Ca storage and release for muscle contraction. Occupying about 10% of the total cell volume, the sarcoplasmic reticulum forms multiple membrane contact sites, some of which are unique to skeletal muscle.
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Department of Intelligent Robotics, Sungkyunkwan University, Suwon 16419, Republic of Korea.
Rehabilitation of gait function in post-stroke hemiplegic patients is critical for improving mobility and quality of life, requiring a comprehensive understanding of individual gait patterns. Previous studies on gait analysis using unsupervised clustering often involve manual feature extraction, which introduces limitations such as low accuracy, low consistency, and potential bias due to human intervention. This cross-sectional study aimed to identify and cluster gait patterns using an end-to-end deep learning approach that autonomously extracts features from joint angle trajectories for a gait cycle, minimizing human intervention.
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