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Neuroparacoccidiodimycosis (NPDM) is an uncommon granulomatous disease, which more frequently affects immunocompromised male patients over 30 years of age in the course of chronic lung disease. Paracoccidioides brasiliensis (PB) is an endemic fungus in Brazil, and grows as thick-walled yeast (with round to oval bodies) measuring 10 µm to 60 µm in diameter. Neuroparacoccidiodimycosi may develop many years after transmission and/or primary lung involvement. The authors describe a case of NPDM affecting a male patient, 52 years of age, farmer, heavy smoker, with clinical complaint of headache, asthenia, seizures, and prostration in the previous nine months. Upon physical examination, the patient presented regular general condition, without other relevant physical alterations. Computed tomography (CT) showed multiple bilateral pulmonary nodules associated to enlargement of the mediastinal lymph node. Magnetic resonance imaging (MRI) and CT scans of the central nervous system showed six heterogeneous nodular lesions compromising the frontal and parietal lobes, the largest one measuring 3.8 × 3.2 × 3.2 cm. The hypothesis of a neoplastic process compromising the lung and brain was considered. A biopsy of the mediastinal lymph node showed epithelioid granulomas, which exhibited round, thin-walled fungal structures in Grocott silver stain. The stereotactic biopsy of the frontal lesion was constituted by necrotic tissue admixed with some round to oval, thin-walled fungi measuring 10 µm to 60 µm, compatible with PB (identified on Grocott silver stain/confirmed in culture). The diagnosis of NPDM was then established. The employed therapeutic regimen was intravenous amphotericin B, itraconazole, and sulfamethoxazole-trimetropin. After ninety days of clinical follow-up, no episodes of seizures/neurological deficits were identified, and a marked decrease in the number and size of the lung and brain lesions were found.

Introduction According to the World Health Organization (WHO) classification, invasive breast carcinoma (IBC) of no special type (IBC-NST) is the second most common primary site of central nervous system metastases, affecting 15% to 30% of patients. Brain metastasis originating from IBC is associated with patient age, tumor size, and axillary lymph node status. Loss of expression of hormone receptors and c-erbB-2 amplification are frequent findings in patients who develop brain metastasis. Radiological studies of the central nervous system are carried out only in patients presenting with neurological signs or symptoms during the clinical follow-up. Objective To evaluate the associations of clinical and pathological findings with brain metastasis in breast cancer. Materials and Methods The sample comprised 73 patients with breast cancer who underwent mastectomy with lymph node resection. The following variables were evaluated: tumor size, histological grade, nodal state, expression of estrogen and progesterone receptors and c-erbB-2, and presence of brain metastasis. Results The histopathological findings associated with brain metastasis in patients with IBC were tumor size (p = 0.03), presence of nodal metastasis (p = 0.045), and c-erbB-2 expression (p = 0.012). Conclusion The assessment of specific pathological findings in breast carcinoma can help identify risk factors and/or clinical parameters associated with the development of brain metastasis.

Podocyte degenerative changes are common in various kidney diseases, and their accurate identification is crucial for pathologists to diagnose and treat such conditions. However, this can be a difficult task, and previous attempts to automate the identification of podocytes have not been entirely successful. To address this issue, this study proposes a novel approach that combines pathologists’ expertise with an automated classifier to enhance the identification of podocytopathies. The study involved building a new dataset of renal glomeruli images, some with and others without podocyte degenerative changes, and developing an automated binary classifier based on Convolutional Neural Networks (CNN). The results showed that the automated classifier achieved an impressive 90.9% f-score. Moreover, when the pathologists used as an auxiliary tool to classify a second set of images, the medical group’s average performance increased significantly, from 91.4±12.5% to 96.1±2.9% of f-score. Fleiss’ kappa agreement among the pathologists also increased from 0.59 to 0.83. These findings suggest that artificial intelligence techniques based on convolutional neural networks can help pathologists correctly identify images of glomeruli with podocyte degeneration, leading to improved individual accuracy and greater agreement in diagnosing podocytopathies. This approach could have significant implications for the diagnosis and treatment of kidney diseases.