Deep humoral profiling coupled to interpretable machine learning unveils diagnostic markers and pathophysiology of schistosomiasis.

Schistosomiasis, a highly prevalent parasitic disease, affects more than 200 million people worldwide. Current diagnostics based on parasite egg detection in stool detect infection only at a late stage, and current antibody-based tests cannot distinguish past from current infection. Here, we developed and used a multiplexed antibody profiling platform to obtain a comprehensive repertoire of antihelminth humoral profiles including isotype, subclass, Fc receptor (FcR) binding, and glycosylation profiles of antigen-specific antibodies.

Sliding Window INteraction Grammar (SWING): a generalized interaction language model for peptide and protein interactions.

The explosion of sequence data has allowed the rapid growth of protein language models (pLMs). pLMs have now been employed in many frameworks including variant-effect and peptide-specificity prediction. Traditionally, for protein-protein or peptide-protein interactions (PPIs), corresponding sequences are either co-embedded followed by post-hoc integration or the sequences are concatenated prior to embedding.

A supervised take on dimensionality reduction via hybrid subset selection.

Amouzgar et al. present HSS-LDA, a supervised dimensionality reduction approach for single-cell data that outperforms existing unsupervised techniques. They couple hybrid subset selection to linear discriminant analysis and identify interpretable linear combinations of predictors that best separate predefined biological groups.

DataRemix: a universal data transformation for optimal inference from gene expression datasets.

Motivation: RNA-seq technology provides unprecedented power in the assessment of the transcription abundance and can be used to perform a variety of downstream tasks such as inference of gene-correlation network and eQTL discovery. However, raw gene expression values have to be normalized for nuisance biological variation and technical covariates, and different normalization strategies can lead to dramatically different results in the downstream study.

Results: We describe a generalization of singular value decomposition-based reconstruction for which the common techniques of whitening, rank-k approximation and removing the top k principal components are special cases.

Predictors of time to first cannulation for arteriovenous fistula in pediatric hemodialysis patients: Midwest Pediatric Nephrology Consortium study.

Background: Permanent vascular access (PVA) is preferred for long-term hemodialysis. Arteriovenous fistulae (AVF) have the best patency and the lowest complication rates compared to arteriovenous grafts (AVG) and tunneled cuffed catheters (TCC). However, AVF need time to mature.

Correction to: Predictors of patency for arteriovenous fistulae and grafts in pediatric hemodialysis patients.

The original version of this article unfortunately contained a mistake. The name of Vimal Chadha was presented incorrectly. The corrected author list is given above.

Predictors of patency for arteriovenous fistulae and grafts in pediatric hemodialysis patients.

Background: Hemodialysis (HD) guidelines recommend permanent vascular access (PVA) in children unlikely to receive kidney transplant within 1 year of starting HD. We aimed to determine predictors of primary and secondary patency of PVA in pediatric HD patients.

Methods: Retrospective chart reviews were performed for first PVAs in 20 participating centers.