ReviewMediators of inflammation2016
Inflammation Thread Runs across Medical Laboratory Specialities.
Review in Mediators of inflammation, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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
5 citing papers in PubMed, 10 citations in OpenAlex.
- Big Data in Laboratory Medicine-FAIR Quality for AI?Diagnostics (Basel, Switzerland) · 2022Review
- Autoimmune diseases - New insights into a troublesome field.Journal of translational autoimmunity · 2021Article
- A consideration of convalescent plasma and plasma derivatives in the care of Severely-ill patients with COVID-19.Transfusion and apheresis science : official journal of the World Apheresis Association : official journal of the European Society for Haemapheresis · 2020Review
- The complement system in liver diseases: Evidence-based approach and therapeutic options.Journal of translational autoimmunity · 2019Review
- A New Model Based on 25-Hydroxyvitamin D3 for Predicting Active Crohn's Disease in Chinese Patients.Mediators of inflammation · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We work on the assumption that four major specialities or sectors of medical laboratory assays, comprising clinical chemistry, haematology, immunology, and microbiology, embraced by genome sequencing techniques, are routinely in use. Medical laboratory markers for inflammation serve as model: they are allotted to most fields of medical lab assays including genomics. Incessant coding of assays aligns each of them in the long lists of big data. As exemplified with the complement gene family, containing C2, C3, C8A, C8B, CFH, CFI, and ITGB2, heritability patterns/risk factors associated with diseases with genetic glitch of complement components are unfolding. The C4 component serum levels depend on sufficient vitamin D whilst low vitamin D is inversely related to IgG1, IgA, and C3 linking vitamin sufficiency to innate immunity. Whole genome sequencing of microbial organisms may distinguish virulent from nonvirulent and antibiotic resistant from nonresistant varieties of the same species and thus can be listed in personal big data banks including microbiological pathology; the big data warehouse continues to grow.
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.