Evidence map›Paper›PMID 39529880›Full record

ArticleFrontiers in pharmacology2024

Characterization and comparation of toxicity between natural realgar and artificially optimized realgar.

Lu Luo, Xueying Xin, Qiaochu Wang, Mengjia Wei, Nanxi Huang, Shuangrong Gao, Xuezhu Gu, Raorao Li

Abstract read
In one paragraph

Article in Frontiers in pharmacology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
  2. Article
  3. Review
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

8 authors.

Lu LuoInstitute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, China.
Xueying XinDepartment of Pharmacy, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Qiaochu WangDepartments of Oncology, Georgetown University, Washington, DC, United States.
Mengjia WeiInstitute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, China.
Nanxi HuangDepartments of Oncology, Georgetown University, Washington, DC, United States.
Shuangrong GaoInstitute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, China.
Xuezhu GuInstitute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, China.
Raorao LiInstitute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: Realgar possesses important medical properties. This article aims to evaluate realgar and emerging artificially optimized realgar to ensure safe clinical use. Methods: Multiple techniques were employed to test natural realgar and artificially optimized realgar. Soluble arsenic content in representative samples were measured. Natural realgar and artificially optimized realgar were administered to KM mice via gavage for 28 days, and the extent of liver and kidney tissue damage, arsenic accumulation and form of arsenic were measured. Results: Natural realgar and artificially optimized realgar can be distinguished by their physical properties or spectral signatures. ICP-MS and EPMA identified different contents of elements between two groups. In simulated gastric and intestinal fluids, only As (III) and As (V) were detected. Toxicity experiments Conclusion: The differences between natural realgar and artificially optimized realgar were successfully distinguished through several methods.

Indexed as

arsenic valenceartificially optimized realgarcomparison of toxicityidentification characterizationnatural realgar

Identifiers

PMID39529880
PMCPMC11550961

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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.