Evidence map›Paper›PMID 39598936›Full record

ArticleSensors (Basel, Switzerland)2024

Advanced Low-Cost Technology for Assessing Metal Accumulation in the Body of a Metropolitan Resident Based on a Neural Network Model.

Yulia Tunakova, Svetlana Novikova, Vsevolod Valiev, Maxim Danilaev, Rashat Faizullin

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

5 authors.

Yulia TunakovaDepartment of General Chemistry and Ecology, Kazan National Research Technical University Named after A. N. Tupolev-KAI, Kazan 420111, Russia.
Svetlana NovikovaDepartment of Applied Mathematics and Computer Science, Kazan National Research Technical University Named after A. N. Tupolev-KAI, 10 K. Marx St., Kazan 420111, Russia.ORCID 0000-0001-8207-1010
Vsevolod ValievResearch Institute for Problems of Ecology and Mineral Wealth Use of Tatarstan Academy of Sciences, 28 Daurskaya St., Kazan 420087, Russia.ORCID 0000-0002-8848-5326
Maxim DanilaevDepartment of Electronic and Quantum Information Transmission Systems, Kazan National Research Technical University Named after A. N. Tupolev-KAI, 10 K. Marx St., Kazan 420111, Russia.ORCID 0000-0002-7733-9200
Rashat FaizullinInstitute of Fundamental Medicine and Biology, Kazan Federal University, Kazan 420008, Russia.

Funding

Ministry of Education and Science of Russia as part of the fulfillment of obligations under Agreement No. 075-03-2024-067 dated 17.01.2024 (topic number FZSU-2023-0005).
6 · The paper itself

Abstract

This study is devoted to creating a neural network technology for assessing metal accumulation in the body of a metropolis resident with short-term and long-term intake from anthropogenic sources. Direct assessment of metal retention in the human body is virtually impossible due to the many internal mechanisms that ensure the kinetics of metals and the wide variety of organs, tissues, cellular structures, and secretions that ensure their functional redistribution, transport, and cumulation. We have developed an intelligent multi-neural network model capable of calculating the content of metals in the human body based on data on their environmental content. The model is two interconnected neural networks trained on actual measurement data. Since metals enter the body from the environment, the predictors of the model are metal content in drinking water and soil. In this case, water characterizes the short-term impact on the organism, and drinking water, combined with metal contents in soil, is a depository medium that accumulates metals from anthropogenic sources-the long-term impact. In addition, human physiological characteristics are taken into account in the calculations. Each period of exposure is taken into account by its neural network. Two variants of the model are proposed: open loop, where the calculation is performed by each neural network separately, and closed loop, where neural networks work together. The model built in this way was trained and tested on the data of real laboratory studies of 242 people living in different districts of Kazan. As a result, the accuracy of the neural network block for calculating long-term impact was 90% and higher, and the accuracy of the block for calculating short-term impact was 92% and higher. The closed double-loop model showed an accuracy of at least 96%. Conclusions: Our proposed method of assessing and quantifying metal accumulation in the body has high accuracy and reliability. It does not require expensive laboratory tests and allows quantifying the body's metal accumulation content based on readily available information. The calculation results can be used as a tool for clinical diagnostics and operational and planned management to reduce the levels of polymetallic contamination in urban areas.

Indexed as

MetalsNeural Networks, ComputerAdultDrinking WaterEnvironmental MonitoringFemaleHumansMaleSoilDrinking WaterMetalsSoilclosed multi-neural network systemdouble-loop neural network systementry into the bodyhealth monitoringlong-term effects of metals on the bodymetalsmetropolismulti-neural network modelopen multi-neural network systemretention of metalsshort-term effects of metals on the body

Identifiers

PMID39598936
PMCPMC11598805

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LicenceCC BY
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Registered trials

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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.