ArticleFASEB journal : official publication of the Federation of American Societies for Experimental Biology2019
Interactive alkaptonuria database: investigating clinical data to improve patient care in a rare disease.
Article in FASEB journal : official publication of the Federation of American Societies for Experimental Biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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
9 citing papers in PubMed, 22 citations in OpenAlex.
- HGA-Induced Oxidative Stress Impairs Autophagy via Lysosomal Dysfunction in Alkaptonuria.Antioxidants (Basel, Switzerland) · 2026Article
- Review
- Alkaptonuria.Nature reviews. Disease primers · 2024Review
- Article
- Computational Approaches Integrated in a Digital Ecosystem Platform for a Rare Disease.Frontiers in molecular medicine · 2022Review
- HGDiscovery: An online tool providing functional and phenotypic information on novel variants of homogentisate 1,2- dioxigenase.Current research in structural biology · 2022Article
- A molecular spectroscopy approach for the investigation of early phase ochronotic pigment development in Alkaptonuria.Scientific reports · 2021Article
- Towards a Precision Medicine Approach Based on Machine Learning for Tailoring Medical Treatment in Alkaptonuria.International journal of molecular sciences · 2021Article
- Machine learning application for development of a data-driven predictive model able to investigate quality of life scores in a rare disease.Orphanet journal of rare diseases · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Alkaptonuria (AKU) is an ultrarare autosomal recessive disorder (MIM 203500) that is caused byby a complex set of mutations in homogentisate 1,2-dioxygenasegene and consequent accumulation of homogentisic acid (HGA), causing a significant protein oxidation. A secondary form of amyloidosis was identified in AKU and related to high circulating serum amyloid A (SAA) levels, which are linked with inflammation and oxidative stress and might contribute to disease progression and patients' poor quality of life. Recently, we reported that inflammatory markers (SAA and chitotriosidase) and oxidative stress markers (protein thiolation index) might be disease activity markers in AKU. Thanks to an international network, we collected genotypic, phenotypic, and clinical data from more than 200 patients with AKU. These data are currently stored in our AKU database, named ApreciseKUre. In this work, we developed an algorithm able to make predictions about the oxidative status trend of each patient with AKU based on 55 predictors, namely circulating HGA, body mass index, total cholesterol, SAA, and chitotriosidase. Our general aim is to integrate the data of apparently heterogeneous patients with AKUAKU by using specific bioinformatics tools, in order to identify pivotal mechanisms involved in AKU for a preventive, predictive, and personalized medicine approach to AKU.-Cicaloni, V., Spiga, O., Dimitri, G. M., Maiocchi, R., Millucci, L., Giustarini, D., Bernardini, G., Bernini, A., Marzocchi, B., Braconi, D., Santucci, A. Interactive alkaptonuria database: investigating clinical data to improve patient care in a rare disease.
Indexed as
Identifiers
What 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.