Evidence map›Paper›PMID 42424286›Full record

ArticleRevista peruana de medicina experimental y salud publica2026

Development of a voltammetric analytical method for the quantification of mercury in urine.

Manuel Chavez-Ruiz, Ruddy Mesa-Landeo, Lenin Rueda-Torres, Betsabe Ruffner-Camargo, Sabir Khan

Abstract read
In one paragraph

Article in Revista peruana de medicina experimental y salud publica, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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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.

Manuel Chavez-RuizNational Center for Occupational Health and Environmental Protection for Health (CENSOPAS), Instituto Nacional de Salud, Lima, Peru.
Ruddy Mesa-LandeoNational Center for Occupational Health and Environmental Protection for Health (CENSOPAS), Instituto Nacional de Salud, Lima, Peru.
Lenin Rueda-TorresNational Center for Occupational Health and Environmental Protection for Health (CENSOPAS), Instituto Nacional de Salud, Lima, Peru.
Betsabe Ruffner-CamargoNational Center for Occupational Health and Environmental Protection for Health (CENSOPAS), Instituto Nacional de Salud, Lima, Peru.
Sabir KhanUniversidade Federal Rural do Semi-Arido, Engineering Center, Rio Grande do Norte, Brazil.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Exposure to mercury represents a critical challenge for public health, necessitating precise and portable analytical methods for biomonitoring. This experimental analytical study develops an analytical method and evaluates the performance of square wave anodic stripping voltammetry (SWASV) for the quantification of mercury in urine. For this purpose, the operating conditions of the SWASV system were optimized through tests with standard mercury solutions. Commercial urine (BIO-RAD®) pretreated with nitric acid was used. Linearity, precision, trueness, limit of detection (LOD), and limit of quantification (LOQ) were evaluated. The results showed high precision, with a relative standard deviation (%RSD) of 6.21. Furthermore, the trueness analysis revealed no significant differences between SWASV and the cold vapor atomic absorption (CVAA) method, p = 0.2601. The LOD and LOQ were 0.68 and 2.2 μg L-1, respectively. These findings suggest that SWASV is a precise method comparable to CVAA for the detection of mercury in urine under controlled laboratory conditions. Additional studies are required to evaluate its applicability in real population biomonitoring scenarios.

Indexed as

Electrochemical TechniquesMercuryHumansMercuryElectrochemistryExposure to Chemical CompoundsMercuryUrine

Identifiers

PMID42424286
PMCPMC13245983

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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.