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Background: Previously, we demonstrated that changes in circulating tumor DNA (ctDNA) are promising biomarkers for early response prediction (ERP) to immune checkpoint inhibitors (ICI) in metastatic urothelial cancer (mUC). In this study, we investigated the value of whole blood immunotranscriptomics for ERP-ICI and integrated both biomarkers into a multimodal model to boost accuracy.

Methods: Blood samples of 93 patients were collected at baseline and after 2-6 weeks of ICI for ctDNA (N=88) and immunotranscriptome (N=79) analyses.

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Background: We aimed to characterize factors associated with the under-studied complication of cognitive decline in aging people with long-duration type 1 diabetes (T1D).

Methods: Joslin "Medalists" (n = 222; T1D ≥ 50 years) underwent cognitive testing. Medalists (n = 52) and age-matched non-diabetic controls (n = 20) underwent neuro- and retinal imaging.

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Background: Randomised controlled trials (RCTs) evaluating new systemic treatments for atopic dermatitis (AD) have increased dramatically over the last decade. These trials often incorporate topical therapies either as permitted concomitant or rescue treatments. Differential use of these topicals post-randomisation introduces potential bias as they may nullify or exaggerate treatment responses.

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Imaging-based spatial transcriptomics (iST), such as MERFISH, CosMx SMI, and Xenium, quantify gene expression level across cells in space, but more importantly, they directly reveal the subcellular distribution of RNA transcripts at the single-molecule resolution. The subcellular localization of RNA molecules plays a crucial role in the compartmentalization-dependent regulation of genes within individual cells. Understanding the intracellular spatial distribution of RNA for a particular cell type thus not only improves the characterization of cell identity but also is of paramount importance in elucidating unique subcellular regulatory mechanisms specific to the cell type.

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A comprehensive benchmarking for evaluating TCR embeddings in modeling TCR-epitope interactions.

Brief Bioinform

November 2024

Department of Computer Science, City University of Hong Kong, 83 Tat Chee Avenue, Kowloon Tong, Hong Kong, 999077, China.

The complexity of T cell receptor (TCR) sequences, particularly within the complementarity-determining region 3 (CDR3), requires efficient embedding methods for applying machine learning to immunology. While various TCR CDR3 embedding strategies have been proposed, the absence of their systematic evaluations created perplexity in the community. Here, we extracted CDR3 embedding models from 19 existing methods and benchmarked these models with four curated datasets by accessing their impact on the performance of TCR downstream tasks, including TCR-epitope binding affinity prediction, epitope-specific TCR identification, TCR clustering, and visualization analysis.

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