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1.
Malar J ; 20(1): 271, 2021 Jun 14.
Artigo em Inglês | MEDLINE | ID: mdl-34126991

RESUMO

BACKGROUND: Malaria is a potentially lethal parasitic disease due to infection by Plasmodium parasites, transmitted by Anopheles mosquito vectors. Various preventative measures may be recommended for travellers who visit endemic areas. The diagnosis is generally evoked in the context of a febrile patient returning from an endemic zone. Nevertheless, symptoms and clinical signs may be difficult to interpret, and fatal cases may only be diagnosed retrospectively with laboratory techniques, specific pathological features and patient history. The present work reports a case of fatal cerebral malaria diagnosed post-mortem, along with the techniques that allowed identification of the causative agent. CASE PRESENTATION: A 29 year-old male was found dead in his rental home during a vacation in Southern France. In the absence of explainable cause, an autopsy was performed, which did not retrieve major lesions. In the context of frequent business-related travels in tropical Africa, several samples were adressed for parasitological examination. Microscopy techniques, along with immunochromatographic and molecular biology assays, led to post-mortem diagnosis of fatal cerebral malaria. It was discovered in retrospect that the patient had not used preventative measures against malaria when travelling in endemic zones, and had not been provided with proper travel medicine counseling prior to his travel. CONCLUSION: A vast proportion of imported malaria cases reported in France concerns patients who did not use preventive measures, such as bed nets, repellents or chemoprophylaxis. Given the wide availability of prevention tools in developed countries, and the important number of declared imported malaria cases, there is no doubt traveller awareness still needs to be raised. Moreover, healthcare professionals should always question travel history in febrile patients. The authors advocate for recurrent information campaigns for travellers, and physician training for a better prevention and diagnosis of malaria cases.


Assuntos
Doenças Transmissíveis Importadas/diagnóstico , Malária Cerebral/diagnóstico , Malária Falciparum/diagnóstico , Adulto , Doenças Transmissíveis Importadas/parasitologia , Doenças Transmissíveis Importadas/patologia , Evolução Fatal , França , Humanos , Malária Cerebral/parasitologia , Malária Cerebral/patologia , Malária Falciparum/parasitologia , Malária Falciparum/patologia , Masculino
2.
Ann Pathol ; 39(2): 130-136, 2019 Apr.
Artigo em Francês | MEDLINE | ID: mdl-30772062

RESUMO

Histopathology is the fundamental tool of pathology used for more than a century to establish the final diagnosis of lung cancer. In addition, the phenotypic data contained in the histological images reflects the overall effect of molecular alterations on the behavior of cancer cells and provides a practical visual reading of the aggressiveness of the disease. However, the human evaluation of the histological images is sometimes subjective and may lack reproducibility. Therefore, computational analysis of histological imaging using so-called "artificial intelligence" (AI) approaches has recently received considerable attention to improve this diagnostic accuracy. Thus, computational analysis of lung cancer images has recently been evaluated for the optimization of histological or cytological classification, prognostic prediction or genomic profile of patients with lung cancer. This rapidly growing field constantly demonstrates great power in the field of computing medical imaging by producing highly accurate detection, segmentation or recognition tasks. However, there are still several challenges or issues to be addressed in order to successfully succeed the actual transfer into clinical routine. The objective of this review is to emphasize recent applications of AI in pulmonary cancer pathology, but also to clarify the advantages and limitations of this approach, as well as the perspectives to be implemented for a potential transfer into clinical routine.


Assuntos
Inteligência Artificial , Neoplasias Pulmonares/patologia , Humanos , Patologia Clínica/métodos
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