Evidence map›Paper›PMID 35327412›Full record

ArticleBiomedicines2022

Neural Network-Based Calculator for Rat Glomerular Filtration Rate.

Óscar J Pellicer-Valero, Giampiero A Massaro, Alfredo G Casanova, María Paniagua-Sancho, Isabel Fuentes-Calvo, Mykola Harvat, José D Martín-Guerrero, Carlos Martínez-Salgado, Francisco J López-Hernández

Open access · goldAbstract read
In one paragraph

Article in Biomedicines, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
2.0field-weighted citation impact, top 14% of its field
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed, 15 citations in OpenAlex.

  1. Article
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  5. Ameliorative Effect ofInternational journal of nephrology · 2023
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors at 2 institutions in 1 country.

Óscar J Pellicer-ValeroIntelligent Data Analysis Laboratory (IDAL), Department Electronic Engineering, School of Engineering (ETSE-UV), Universitat de València, 46100 Valencia, Spain.ORCID 0000-0002-0104-1029
Giampiero A MassaroInstitute of Biomedical Research of Salamanca, 37007 Salamanca, Spain.ORCID 0000-0002-4739-919X
Alfredo G CasanovaInstitute of Biomedical Research of Salamanca, 37007 Salamanca, Spain.ORCID 0000-0002-0725-6637
María Paniagua-SanchoInstitute of Biomedical Research of Salamanca, 37007 Salamanca, Spain.
Isabel Fuentes-CalvoInstitute of Biomedical Research of Salamanca, 37007 Salamanca, Spain.
Mykola HarvatIntelligent Data Analysis Laboratory (IDAL), Department Electronic Engineering, School of Engineering (ETSE-UV), Universitat de València, 46100 Valencia, Spain.
José D Martín-GuerreroIntelligent Data Analysis Laboratory (IDAL), Department Electronic Engineering, School of Engineering (ETSE-UV), Universitat de València, 46100 Valencia, Spain.ORCID 0000-0001-9378-0285
Carlos Martínez-SalgadoInstitute of Biomedical Research of Salamanca, 37007 Salamanca, Spain.ORCID 0000-0003-4641-6717
Francisco J López-HernándezInstitute of Biomedical Research of Salamanca, 37007 Salamanca, Spain.ORCID 0000-0001-7778-7495
Universidad de Salamanca · ESUniversitat de València · ES

Funding

European Commission FEDERInstituto de Salud Carlos III PI18/00996, PI21/01226, and Retic RD021/0005/0004Junta de Castilla y León IES160P20
6 · The paper itself

Abstract

Glomerular filtration is a pivotal process of renal physiology, and its alterations are a central pathological event in acute kidney injury and chronic kidney disease. Creatinine clearance (ClCr), a standard method for glomerular filtration rate (GFR) measurement, requires a long and tedious procedure of timed (usually 24 h) urine collection. We have developed a neural network (NN)-based calculator of rat ClCr from plasma creatinine (pCr) and body weight. For this purpose, matched pCr, weight, and ClCr trios from our historical records on male Wistar rats were used. When evaluated on the training (1165 trios), validation (389), and test sets (660), the model committed an average prediction error of 0.196, 0.178, and 0.203 mL/min and had a correlation coefficient of 0.863, 0.902, and 0.856, respectively. More importantly, for all datasets, the NN seemed especially effective at comparing ClCr among groups within individual experiments, providing results that were often more congruent than those measured experimentally. ACLARA, a friendly interface for this calculator, has been made publicly available to ease and expedite experimental procedures and to enhance animal welfare in alignment with the 3Rs principles by avoiding unnecessary stressing metabolic caging for individual urine collection.

Indexed as

calculatorcreatinine clearancemachine learningneural networkrat glomerular filtration rate

Identifiers

PMID35327412
PMCPMC8945373
OpenAlexW4221037187

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.