ArticleMethods in molecular biology (Clifton, N.J.)2025
Computational Tools and Methods for the Study of Systemic Amyloidosis at the Clinical and Molecular Level.
Article in Methods in molecular biology (Clifton, N.J.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- A journey through the molecular networks of endomyocardial biopsies to explore ATTR and ALiScience · 2026Article
- Dipeptidyl peptidase 3 sets the threshold for immune activation and survival during experimental bacterial infection.Nature communications · 2026Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Amyloidosis diseases are characterized by protein misfolding, which forms insoluble beta-sheet fibrils progressively deposited in tissues. Deposition in the form of amyloid aggregates can occur in various organs, damaging their structure and function. The hallmark of amyloidosis is aberrant interactions leading to protein aggregation and proteotoxicity. Accordingly, amyloidosis-related samples represent a valuable source of information to generate new knowledge useful for diagnostic, prognostic, and therapeutic purposes. In this scenario, we outline the path to apply computational methods and strategies based on the combination of proteomics and systems biology approaches. In addition to algorithms useful for subtyping amyloid deposits or assessing proteome recovery after drug treatment, our chapter provides workflows based on protein-protein interaction and protein co-expression network models. In particular, the main steps to reconstruct and analyze them at both functional and topological levels are described. Our chapter aims to provide tools and instructions to identify and monitor prognostic, diagnostic, and therapeutic markers and to shed light on the processes, pathways, and functions affected by amyloidogenic proteins.
Indexed as
Identifiers
39716014What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.