An Alternative Diagnostic Method for C. neoformans: Preliminary Results of Deep-Learning Based Detection Model

Cryptococcosis with pulmonary cavitation in an immunocompetent child: a case report and literature review

BACKGROUND

Pulmonary cryptococcosis (PC) rarely occurs in immunocompetent children.

CASE PRESENTATION

A 13-year-old boy was admitted to the First Affiliated Hospital of Ningbo University in February 2023 with complaints of cough and chest pain. Physical examination showed slightly moist rales in the right lung. Chest computed tomography (CT) suggested a lung lesion and cavitation. Blood routine test, lymphocyte subsets, immunoglobulin, and complement tests indicated that the immune system was normal. However, the serum cryptococcal antigen test was positive. Next-generation sequencing revealed Cryptococcus infection. The child was diagnosed with PC and was discharged after treating with fluconazole 400 mg. Four months later, chest CT showed that the lung lesion diminished, and reexamination of serum cryptococcal antigen test turned positive.

CONCLUSION

PC should be considered in an immunocompetent child with pulmonary cavities with nonspecific symptoms.

Assessment of Deep Learning Models for Cutaneous Leishmania Parasite Diagnosis Using Microscopic Images

Cutaneous leishmaniasis (CL) is a common illness that causes skin lesions, principally ulcerations, on exposed regions of the body. Although neglected tropical diseases (NTDs) are typically found in tropical areas, they have recently become more common along Africa's northern coast, particularly in Libya. The devastation of healthcare infrastructure during the 2011 war and the following conflicts, as well as governmental apathy, may be causal factors associated with this catastrophic event. The main objective of this study is to evaluate alternative diagnostic strategies for recognizing amastigotes of cutaneous leishmaniasis parasites at various stages using Convolutional Neural Networks (CNNs). The research is additionally aimed at testing different classification models employing a dataset of ultra-thin skin smear images of Leishmania parasite-infected people with cutaneous leishmaniasis. The pre-trained deep learning models including EfficientNetB0, DenseNet201, ResNet101, MobileNetv2, and Xception are used for the cutaneous leishmania parasite diagnosis task. To assess the models' effectiveness, we employed a five-fold cross-validation approach to guarantee the consistency of the models' outputs when applied to different portions of the full dataset. Following a thorough assessment and contrast of the various models, DenseNet-201 proved to be the most suitable choice. It attained a mean accuracy of 0.9914 along with outstanding results for sensitivity, specificity, positive predictive value, negative predictive value, F1-score, Matthew's correlation coefficient, and Cohen's Kappa coefficient. The DenseNet-201 model surpassed the other models based on a comprehensive evaluation of these key classification performance metrics.