Evidence map›Paper›PMID 41364321›Full record

ArticleBiological trace element research2026

Analysis of Heavy Metal Concentrations in Cosmetics Derived from Dead Sea Mud and Comparative Products.

Raghad Massadeh, Idrees Al-Momani, Dima Albals

Abstract readComparative Study
PubMed Publisher
In one paragraph

Article in Biological trace element research, 2026. 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
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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

3 authors.

Raghad MassadehDepartment of Chemistry, Yarmouk University, Faculty of Science, Irbid, 21163, Jordan. raghadmassadeh@gmail.com.
Idrees Al-MomaniDepartment of Chemistry, Yarmouk University, Faculty of Science, Irbid, 21163, Jordan.
Dima AlbalsDepartment of Medicinal Chemistry and Pharmacognosy, Faculty of Pharmacy, Yarmouk University, Irbid, 21163, Jordan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study assessed heavy-metal content in Dead Sea mud-based cosmetics and comparison products to inform safety evaluation. Flame atomic absorption spectroscopy quantified Ni, Pb, Cd, Cr, Cu, Fe, Co, Mn, and Zn in 95 samples (raw Dead Sea mud and commercial body, face, hair, and hand products) after acid digestion. Raw mud was sampled from the Jordanian shoreline, and cosmetics were purchased from the local market. Dead Sea mud in cosmetics generally contained lower concentrations of most metals than raw mud, except Zn, which was frequently elevated because ZnO is added for skin protection; comparison with non-Dead Sea mud products showed distinct heavy-metal profiles. Representative values for Zn in Dead Sea mud-based products were ~ 174% of crude-mud levels for face products, ~ 105% for body products, and ~ 42% for hair and hand products, and enrichment-factor analysis indicated minor enrichment for Cd (EF ≈ 4), moderate enrichment for Zn (EF ≈ 10), and EF < 3 for other elements. International cosmetic guidelines showed variation between jurisdictions; using Canadian and German benchmark values, maximum Ni, Cr, and Co concentrations in our dataset were below reference limits, whereas Pb and Cd occasionally exceeded stricter "technically avoidable" thresholds. Overall, Dead Sea mud-based cosmetics exhibited low-to-moderate metal levels that were generally consistent with current European and North American guidance, with elevated Zn reflecting formulation choices and underscoring the importance of routine quality control of raw materials and finished products.

Indexed as

CosmeticsMetals, HeavyHumansSpectrophotometry, AtomicCosmeticsMetals, Heavy

Identifiers

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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.