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In this study, we introduce MGA-Net, a novel mask-guided attention neural network, which extends the U-net model for precision neonatal brain imaging. MGA-Net is designed to extract the brain from other structures and reconstruct high-quality brain images. The network employs a common encoder and two decoders: one for brain mask extraction and the other for brain region reconstruction. A key feature of MGA-Net is its high-level mask-guided attention module, which leverages features from the brain mask decoder to enhance image reconstruction. To enable the same encoder and decoder to process both MRI and ultrasound (US) images, MGA-Net integrates sinusoidal positional encoding. This encoding assigns distinct positional values to MRI and US images, allowing the model to effectively learn from both modalities. Consequently, features learned from a single modality can aid in learning a modality with less available data, such as US. We extensively validated the proposed MGA-Net on diverse and independent datasets from varied clinical settings and neonatal age groups. The metrics used for assessment included the DICE similarity coefficient, recall, and accuracy for image segmentation; structural similarity for image reconstruction; and root mean squared error for total brain volume estimation from 3D ultrasound images. Our results demonstrate that MGA-Net significantly outperforms traditional methods, offering superior performance in brain extraction and segmentation while achieving high precision in image reconstruction and volumetric analysis. Thus, MGA-Net represents a robust and effective preprocessing tool for MRI and 3D ultrasound images, marking a significant advance in neuroimaging that enhances both research and clinical diagnostics in the neonatal period and beyond., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Severe pulmonary or disseminated histoplasmosis often necessitates presumptive antifungal treatment while awaiting definitive diagnosis. Histoplasma antigen assays have improved sensitivity but results may lag up to 7 days. In order to increase diagnostic certainty, "soft clues" may be looked for in laboratory and radiologic data, such as elevated alkaline phosphatase or ferritin levels and findings of mediastinal adenopathy or hepatosplenomegaly. To determine if elevated aspartate aminotransferase (AST) to alanine aminotransferase (ALT) ratio is specific to histoplasmosis or a non-specific marker for disseminated fungal infection or sepsis in general, we retrospectively examined records of all patients diagnosed with an endemic fungal infection (EFI) at Rush University Medical Center from January of 1997 to October of 2012, and a cohort of septic patients with elevated liver enzymes. We identified 90 cases of EFIs during the study period that met all inclusion criteria (Histoplasma 21, Blastomyces 56, Coccidioides 12, Paracoccidioides 1). We also evaluated 10 control patients with bacterial sepsis. The mean ratio of AST to ALT in patients with disseminated histoplasmosis was 2.69 (95% CI:1.22, 4.16) while for other EFIs, the mean ratio ranged from 0.38 to 1.14 with disseminated coccidioidomycosis and blastomycosis respectively (P < 0.0001). The ratio in patients with bacterial sepsis was 0.84. We propose the use of the AST/ALT ratio as a clinical "soft clue" suggestive of disseminated histoplasmosis in the appropriate host, and to possibly distinguish cross reactivity of the Histoplasma antigen assay with other EFIs., (© The Author 2016. Published by Oxford University Press on behalf of The International Society for Human and Animal Mycology. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)