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Optimizing Diagnostic Tools & Outcomes Evaluation for t (9;22)-Positive Leukemias in Pediatric Low Middle-Income Country (LMIC) Patients.
Cancer Invest
January 2025
Department of Pathology & Laboratory Medicine, Aga Khan University, Karachi, Pakistan.
Accurate and timely diagnosis of t(9;22)-positive leukemias is vital to improving survival in pediatric patients. In low-resource settings, where healthcare disparities are exacerbated by limited resources, cost-effective and efficient diagnostic methods are essential for bridging these gaps and ensuring better outcomes. Among the diagnostic tools evaluated among 23 patients sample, RT-PCR demonstrated superior sensitivity (100%) and the shortest turnaround time (7 days), significantly outperforming FISH and karyotyping in both accuracy and timeliness.
View Article and Find Full Text PDFWastewater Monitoring During the COVID-19 Pandemic in the Veneto Region, Italy: Longitudinal Observational Study.
JMIR Public Health Surveill
January 2025
Unit of Biostatistics, Epidemiology and Public Health, Department of Cardio-Thoraco-Vascular Sciences and Public Health, University of Padova, Via Loredan 18, Padova, Italy, 39 049 8275384.
Background: As the COVID-19 pandemic has affected populations around the world, there has been substantial interest in wastewater-based epidemiology (WBE) as a tool to monitor the spread of SARS-CoV-2. This study investigates the use of WBE to anticipate COVID-19 trends by analyzing the correlation between viral RNA concentrations in wastewater and reported COVID-19 cases in the Veneto region of Italy.
Objective: We aimed to evaluate the effectiveness of the cumulative sum (CUSUM) control chart method in detecting changes in SARS-CoV-2 concentrations in wastewater and its potential as an early warning system for COVID-19 outbreaks.
Complex hierarchical structures analysis in single-cell data with Poincaré deep manifold transformation.
Brief Bioinform
November 2024
School of Engineering, Westlake University, No. 600 Dunyu Road, 310030 Zhejiang, P.R. China.
Single-cell RNA sequencing (scRNA-seq) offers remarkable insights into cellular development and differentiation by capturing the gene expression profiles of individual cells. The role of dimensionality reduction and visualization in the interpretation of scRNA-seq data has gained widely acceptance. However, current methods face several challenges, including incomplete structure-preserving strategies and high distortion in embeddings, which fail to effectively model complex cell trajectories with multiple branches.
View Article and Find Full Text PDFDeep learning-based design and experimental validation of a medicine-like human antibody library.
Brief Bioinform
November 2024
Biotherapeutics Molecule Discovery, Boehringer Ingelheim Pharmaceutical Inc., 900 Ridgebury Road, Ridgefield, CT 06877, United States.
Antibody generation requires the use of one or more time-consuming methods, namely animal immunization, and in vitro display technologies. However, the recent availability of large amounts of antibody sequence and structural data in the public domain along with the advent of generative deep learning algorithms raises the possibility of computationally generating novel antibody sequences with desirable developability attributes. Here, we describe a deep learning model for computationally generating libraries of highly human antibody variable regions whose intrinsic physicochemical properties resemble those of the variable regions of the marketed antibody-based biotherapeutics (medicine-likeness).
View Article and Find Full Text PDFscMMAE: masked cross-attention network for single-cell multimodal omics fusion to enhance unimodal omics.
Brief Bioinform
November 2024
Guangdong Provincial Key Laboratory of Mathematical and Neural Dynamical Systems, Great Bay University, No. 16 Daxue Rd, Songshanhu District, Dongguan, Guangdong, 523000, China.
Multimodal omics provide deeper insight into the biological processes and cellular functions, especially transcriptomics and proteomics. Computational methods have been proposed for the integration of single-cell multimodal omics of transcriptomics and proteomics. However, existing methods primarily concentrate on the alignment of different omics, overlooking the unique information inherent in each omics type.
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