Evidence map›Paper›PMID 41255516›Full record

ArticleBiomaterials research2025

Epigallocatechin Gallate Attenuates CaOx Crystal-Induced Renal Tubular Injury to Inhibit CaOx Nephrolithiasis via GRP94/PI3K/AKT Signaling.

Jian Wu, Minghui Liu, Meng Gao, Yongchao Li, Youjie Zhang, Liang Tang, Hao Yu, Zhangcheng Liao, Yu Cui, Feng Zeng and 2 more

Abstract read
In one paragraph

Article in Biomaterials research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. M2 Macrophages Attenuate AQP2Research (Washington, D.C.) · 2026
    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

12 authors.

Jian WuDepartment of Urology, Xiangya Hospital, Central South University, Changsha, Hunan, China.
Minghui LiuDepartment of Urology, Xiangya Hospital, Central South University, Changsha, Hunan, China.
Meng GaoDepartment of Urology, Xiangya Hospital, Central South University, Changsha, Hunan, China.
Yongchao LiDepartment of Urology, Xiangya Hospital, Central South University, Changsha, Hunan, China.
Youjie ZhangDepartment of Urology, Xiangya Hospital, Central South University, Changsha, Hunan, China.
Liang TangDepartment of Urology, Xiangya Hospital, Central South University, Changsha, Hunan, China.
Hao YuDepartment of Urology, Xiangya Hospital, Central South University, Changsha, Hunan, China.
Zhangcheng LiaoDepartment of Urology, Xiangya Hospital, Central South University, Changsha, Hunan, China.
Yu CuiDepartment of Urology, Xiangya Hospital, Central South University, Changsha, Hunan, China.
Feng ZengDepartment of Urology, Xiangya Hospital, Central South University, Changsha, Hunan, China.
Hequn ChenDepartment of Urology, Xiangya Hospital, Central South University, Changsha, Hunan, China.
Zewu ZhuDepartment of Urology, Xiangya Hospital, Central South University, Changsha, Hunan, China.ORCID https://orcid.org/0000-0001-5320-9391

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Although tea consumption has been suggested to affect kidney stone formation, epidemiological evidence remains inconsistent, and the underlying molecular mechanisms are unclear. To assess the association between tea intake and kidney stone risk, we initially conducted a prospective cohort analysis of 481,393 participants from the UK Biobank and a 2-sample Mendelian randomization (MR) analysis. Our findings revealed that heavy tea drinkers (>5 cups/day) had a significantly reduced risk of kidney stones (hazard ratio: 0.79, 95% confidence interval [CI]: 0.72 to 0.86,

Identifiers

PMID41255516
PMCPMC12620625

What OpenQuestion holds

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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.