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Objective: To compare the demographic and clinical profiles of oral lichen planus (OLP) and oral lichenoid lesions (OLL) diagnosed at a reference center in Southern Brazil from 2010 to 2019.
Methods: This retrospective study included 117 cases of suspected OLP submitted for biopsy. Investigated variables comprised sociodemographic profiles, medical history, harmful habits, clinical characteristics, and histopathological features. Categorical and numerical variables were analyzed using chi-square and Mann-Whitney tests (p < 0.01), respectively.
Results: Applying strict diagnostic criteria, 29% (n = 34) of cases were classified as OLP and 71% (n = 83) as OLL. OLP cases had mainly multifocal manifestations (82.4%), exhibiting a reticular pattern (100%) and primarily occurring on the buccal mucosa (94.1%). Conversely, OLL cases presented both unilateral (48.2%) and multifocal (51.8%) distributions, with a predominantly atrophic-erosive pattern (77.1%) and higher occurrence on the buccal mucosa (69.9%) and tongue (48.2%). OLL patients reported a higher frequency of systemic disorders and medication use (p < 0.01). Hypertension was the most prevalent condition, leading to the frequent use of cardiovascular medications. Two OLL cases without initial dysplasia underwent malignant transformation.
Conclusion: Patient profiles and clinical manifestations of the entities were similar, highlighting the utility of a differential diagnosis, particularly given the apparent association between malignant transformation and OLL cases.
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http://dx.doi.org/10.1111/odi.15247 | DOI Listing |
World J Pediatr Congenit Heart Surg
September 2025
Postgraduate Program in Health Sciences, Medical School, Federal University of Amazonas (UFAM), Manaus, Amazonas, Brazil.
To analyze in-hospital mortality in children undergoing congenital heart interventions in the only public referral center in Amazonas, North Brazil, between 2014 and 2022. This retrospective cohort study included 1041 patients undergoing cardiac interventions for congenital heart disease, of whom 135 died during hospitalization. Records were reviewed to obtain demographic, clinical, and surgical data.
View Article and Find Full Text PDFBackground: People with dementia who have a fall can experience both physical and psychological effects, often leading to diminished independence. Falls impose economic costs on the healthcare system. Despite elevated fall risks in dementia populations, evidence supporting effective home-based interventions remains limited.
View Article and Find Full Text PDFInt J Surg
September 2025
Department of Pharmacy, Henan Provincial People's Hospital, People's Hospital of Zhengzhou University, School of Clinical Medicine, Henan University, Zhengzhou, Henan, China.
Background: Antiplatelet therapy is a cornerstone in the management of atherosclerotic cardiovascular disease. However, the risk profile of central nervous system (CNS) hematomas associated with antiplatelet agents remains incompletely characterized.
Methods: We analyzed CNS-related hematoma adverse event (hAE) reports across the four antiplatelet drugs, using data from the U.
Curr Med Sci
September 2025
Institute of Hematology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.
Objective: To develop a novel prognostic scoring system for severe cytokine release syndrome (CRS) in patients with B-cell acute lymphoblastic leukemia (B-ALL) treated with anti-CD19 chimeric antigen receptor (CAR)-T-cell therapy, aiming to optimize risk mitigation strategies and improve clinical management.
Methods: This single-center retrospective cohort study included 125 B-ALL patients who received anti-CD19 CAR-T-cell therapy from January 2017 to October 2023. These cases were selected from a cohort of over 500 treated patients on the basis of the availability of comprehensive baseline data, documented CRS grading, and at least 3 months of follow-up.
Drugs Aging
September 2025
Dalla Lana School of Public Health, University of Toronto, V1 06, 2075 Bayview Avenue, Toronto, ON, M4N 3M5, Canada.
Background And Objectives: Older adults living with dementia are a heterogeneous group, which can make studying optimal medication management challenging. Unsupervised machine learning is a group of computing methods that rely on unlabeled data-that is, where the algorithm itself is discovering patterns without the need for researchers to label the data with a known outcome. These methods may help us to better understand complex prescribing patterns in this population.
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