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Background: Porphyrias are a group of metabolic diseases, individually rare but with an important combined prevalence. Because of their pathological complexity and clinical heterogeneity, they present a challenging diagnosis. The present review aims to provide a clinically based approach to the recognition and treatment of these disorders.
Methods: We carried out a search in PubMed, with the keyword "porphyria", for reviews published in English from 2010 until 2017.
Results: The research yielded 196 papers, of which 64 were included in the final narrative review.
Conclusions: Porphyrias can be divided based on clinical presentation in acute neurovisceral, chronic cutaneous bullous, chronic cutaneous non-bullous and acute neurovisceral/chronic cutaneous bullous. Each individual porphyria presents a characteristic pattern of porphyrins in plasma, urine, stool and red blood cells. As such, diagnosis is easily obtained by following a simple diagnostic algorithm. Early recognition is key in managing these diseases. Neurovisceral porphyrias require acute support therapy and chronic eviction of precipitating factors. Cutaneous prophyrias, as photosensitivity disorders, rely on sunlight avoidance and, in some cases, specific therapeutic interventions. Given the rarity of these conditions, physician awareness is crucial.
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http://dx.doi.org/10.1016/j.ejim.2019.06.014 | DOI Listing |
Comput Biol Med
September 2025
INSIGNEO Institute for in silico medicine, University of Sheffield, UK; School of Mechanical, Aerospace and Civil Engineering, University of Sheffield, UK. Electronic address:
Modelling cardiovascular disease is at the forefront of efforts to use computational tools to assist in the analysis and forecasting of an individual's state of health. To build trust in such tools, it is crucial to understand how different approaches perform when applied to a nominally identical scenario, both singularly and across a population. To examine such differences, we have studied the flow in aneurysms located on the internal carotid artery and middle cerebral artery using the commercial solver Ansys CFX and the open-source code HemeLB.
View Article and Find Full Text PDFEur J Radiol
September 2025
Department of Radiology, Affiliated Hospital of Hebei University, Baoding 071000, China. Electronic address:
Purpose: The present study aimed to develop a noninvasive predictive framework that integrates clinical data, conventional radiomics, habitat imaging, and deep learning for the preoperative stratification of MGMT gene promoter methylation in glioma.
Materials And Methods: This retrospective study included 410 patients from the University of California, San Francisco, USA, and 102 patients from our hospital. Seven models were constructed using preoperative contrast-enhanced T1-weighted MRI with gadobenate dimeglumine as the contrast agent.
Int J Epidemiol
August 2025
Department of Biostatistics and Informatics, University of Colorado, Aurora, CO, United States.
Background: Existing longitudinal cohort study data and associated biospecimen libraries provide abundant opportunities to efficiently examine new hypotheses through retrospective specimen testing. Outcome-dependent sampling (ODS) methods offer a powerful alternative to random sampling when testing all available specimens is not feasible or biospecimen preservation is desired. For repeated binary outcomes, a common ODS approach is to extend the case-control framework to the longitudinal setting.
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