Fibroblast-like synoviocyte targeting antibodies are associated with failure to reach early and sustained remission or low disease activity after first-line therapy in rheumatoid arthritis.

Objective: To discover antibody biomarkers that can predict a lack of response to first-line therapy in rheumatoid arthritis (RA) patients.

Methods: Two RA cDNA phage display libraries were screened for novel antibodies in baseline RA sera from the Care in early RA (CareRA) trial, differentiating between patients who did or did not reach remission after first-line therapy (n=20 each). Antibody reactivity to identified University Hasselt (UH)-RA antigens was validated in baseline samples from 136 additional CareRA participants.

Outcome Reporting in Studies Investigating Treatment for Caesarean Scar Ectopic Pregnancy: A Systematic Review.

Background: Caesarean scar ectopic pregnancy (CSEP) is associated with significant maternal and foetal morbidity. However, the optimal treatment remains unknown.

Objectives: The aim of this study was to review outcomes reported in studies on CSEP treatment and outcome reporting quality.

CPred: Charge State Prediction for Modified and Unmodified Peptides in Electrospray Ionization.

The mass-to-charge ratio serves as a critical parameter in peptide identification via mass spectrometry, enabling the precise determination of peptide masses and facilitating their differentiation based on unique charge characteristics, especially when peptides are ionized by tools like electrospray ionization, which produces multiply charged ions. We developed a neural network called CPred, which can accurately predict the charge state distribution from +1 to +7 for the modified and unmodified peptides. CPred was trained on the large-scale synthetic training data, consisting of tryptic and non-tryptic peptides, and various fragmentation methods.

The influence of resolution on the predictive power of spatial heterogeneity measures as biomarkers of liver fibrosis.

Spatial heterogeneity of cells in liver biopsies can be used as biomarker for disease severity of patients. This heterogeneity can be quantified by non-parametric statistics of point pattern data, which make use of an aggregation of the point locations. The method and scale of aggregation are usually chosen ad hoc, despite values of the aforementioned statistics being heavily dependent on them.

A compositional data model to predict the isotope distribution for average peptides using a compositional spline model.

We propose an updated approach for approximating the isotope distribution of average peptides given their monoisotopic mass. Our methodology involves in-silico cleavage of the entire UNIPROT database of human-reviewed proteins using Trypsin, generating a theoretical peptide dataset. The isotope distribution is computed using BRAIN.