The differential diagnosis of ovarian tumors is reviewed based on their patterns and cell types. This approach, which differs from the standard textbook discussion of each neoplasm as an entity, has practical value as differential diagnosis depends largely on the pattern or patterns and cell type or types of tumors. Awareness of the broad range of lesions that may exhibit particular patterns or contain one or more cell types is crucial in formulating a differential diagnosis. The following patterns are considered: moderate-to-large-glandular and hollow-tubular; solid tubular and pseudotubular; cords and ribbons; insular; trabecular; slit-like and reticular spaces; microglandular and microfollicular; macrofollicular and pseudomacrofollicular; papillary; diffuse; fibromatous-thecomatous; and biphasic and pseudobiphasic. The following cell types are considered: small round cells; spindle cells; mucinous cells, comprising columnar, goblet cell and signet ring cell subtypes; clear cells; hobnail cells; oxyphil cells; and transitional cells. The morphologic diversity of ovarian tumors poses many challenges; knowledge of the occurrence and frequency of these patterns and cell types in various tumors and tumor-like lesions is of paramount diagnostic importance. A specific diagnosis can usually be made by evaluating routinely stained slides, but much less often, special staining, immunohistochemical staining or, very rarely, ultrastructural examination is also required. Finally, clinical data, operative findings, and gross features of the lesions may provide important, and at times decisive diagnostic clues.
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BMC Plant Biol
January 2025
Plant Production Department, College of Food and Agricultural Sciences, King Saud University, P.O. Box. 2460, Riyadh, 11451, Saudi Arabia.
Background: The present research work was done to evaluate the anatomical differences among selected species of the family Bignoniaceae, as limited anatomical data is available for this family in Pakistan. Bignoniaceae is a remarkable family for its various medicinal properties and anatomical characterization is an important feature for the identification and classification of plants.
Methodology: In this study, several anatomical structures were examined, including stomata type and shape, leaf epidermis shape, epidermal cell size, and the presence or absence of trichomes and crystals (e.
Sci Rep
January 2025
School of Sports and Health, Nanjing Sport Institute, Nanjing, China.
A high-calorie diet and lack of exercise are the most important risk factors contributing to metabolic dysfunction-associated steatotic liver disease (MASLD) initiation and progression. The precise molecular mechanisms of mitochondrial function alteration during MASLD development remain to be fully elucidated. In this study, a total of 60 male C57BL/6J mice were maintained on a normal or amylin liver NASH (AMLN) diet for 6 or 10 weeks.
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January 2025
Department of Pharmaceutics, College of Pharmacy, King Saud University, PO Box 2457, Riyadh, 11451, Saudi Arabia.
Prostate cancer presents a major health issue, with its progression influenced by intricate molecular factors. Notably, the interplay between miRNAs and changes in transcriptomic patterns is not fully understood. Our study seeks to bridge this knowledge gap, employing computational techniques to explore how miRNAs and transcriptomic alterations jointly regulate the development of prostate cancer.
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January 2025
Department of Immunology, Genetics and Pathology, Uppsala University, Rudbeck Laboratory, C11, 75185, Uppsala, Sweden.
The existence of transmissible amyloid fibril strains has long intrigued the scientific community. The strain theory originates from prion disorders, but here, we provide evidence of strains in systemic amyloidosis. Human AA amyloidosis manifests as two distinct clinical phenotypes called common AA and vascular AA.
View Article and Find Full Text PDFNat Commun
January 2025
Department of Machine Learning, Moffitt Cancer Center, Tampa, FL, USA.
AI decision support systems can assist clinicians in planning adaptive treatment strategies that can dynamically react to individuals' cancer progression for effective personalized care. However, AI's imperfections can lead to suboptimal therapeutics if clinicians over or under rely on AI. To investigate such collaborative decision-making process, we conducted a Human-AI interaction study on response-adaptive radiotherapy for non-small cell lung cancer and hepatocellular carcinoma.
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