Assessment of the Prevalence of Infections in Pediatric Patients With Acute Lymphoblastic Leukemia.

Infections cause notable treatment-related morbidity during pediatric acute lymphoblastic leukemia/lymphoma (ALL/LLy) therapy. Infections are the most critical cause of morbidity and mortality in children undergoing treatment for acute lymphoblastic leukemia (ALL). Children with ALL, who are frequently underweight, are at increased risk of community-acquired pathogens, nosocomial multidrug-resistant pathogens, and opportunistic microorganisms. A weakened immune system from ALL itself and chemotherapy's side effects further worsen the prognosis. PubMed and Google Scholar articles were curated in a Google document with shared access. Discussion and development of the paper were achieved over Zoom meetings. This narrative review aims to analyze and summarize various pathogens responsible for infections in children receiving treatment for ALL and their treatment regimen and prophylaxis. The incidence of viral infection is higher in ALL patients, followed by bacterial and fungal infections. Prevention via prophylaxis and timely initiation of treatment is essential for positive outcomes.

Cancer Classification Utilizing Voting Classifier with Ensemble Feature Selection Method and Transcriptomic Data.

Biomarker-based cancer identification and classification tools are widely used in bioinformatics and machine learning fields. However, the high dimensionality of microarray gene expression data poses a challenge for identifying important genes in cancer diagnosis. Many feature selection algorithms optimize cancer diagnosis by selecting optimal features. This article proposes an ensemble rank-based feature selection method (EFSM) and an ensemble weighted average voting classifier (VT) to overcome this challenge. The EFSM uses a ranking method that aggregates features from individual selection methods to efficiently discover the most relevant and useful features. The VT combines support vector machine, k-nearest neighbor, and decision tree algorithms to create an ensemble model. The proposed method was tested on three benchmark datasets and compared to existing built-in ensemble models. The results show that our model achieved higher accuracy, with 100% for leukaemia, 94.74% for colon cancer, and 94.34% for the 11-tumor dataset. This study concludes by identifying a subset of the most important cancer-causing genes and demonstrating their significance compared to the original data. The proposed approach surpasses existing strategies in accuracy and stability, significantly impacting the development of ML-based gene analysis. It detects vital genes with higher precision and stability than other existing methods.