Evidence map›Paper›PMID 41993707›Full record

ArticlePharmaceutical science advances2026

NAD

Lizhi Shi, Xuezhu Huang, Xiao Han, Chenchen Ning, Xinyi Zhao, Haohao Li, Zhe Yu, Qiuju Han

Abstract read
In one paragraph

Article in Pharmaceutical science advances, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

8 authors.

Lizhi ShiState Key Laboratory of Discovery and Utilization of Functional Components in Traditional Chinese Medicine, School of Pharmaceutical Sciences, Shandong University, Jinan, Shandong, China.
Xuezhu HuangState Key Laboratory of Discovery and Utilization of Functional Components in Traditional Chinese Medicine, School of Pharmaceutical Sciences, Shandong University, Jinan, Shandong, China.
Xiao HanDepartment of Hematology, Qilu Hospital of Shandong University, Jinan, Shandong, 250012, China.
Chenchen NingDepartment of Hematology, Qilu Hospital of Shandong University, Jinan, Shandong, 250012, China.
Xinyi ZhaoDepartment of Hematology, Qilu Hospital of Shandong University, Jinan, Shandong, 250012, China.
Haohao LiState Key Laboratory of Discovery and Utilization of Functional Components in Traditional Chinese Medicine, School of Pharmaceutical Sciences, Shandong University, Jinan, Shandong, China.
Zhe YuDepartment of Hematology, Shandong Provincial Third Hospital, Jinan, Shandong, China.
Qiuju HanState Key Laboratory of Discovery and Utilization of Functional Components in Traditional Chinese Medicine, School of Pharmaceutical Sciences, Shandong University, Jinan, Shandong, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Natural killer (NK) cells play a key role in the standard treatment of diffuse large B-cell lymphoma (DLBCL). However, NK cells in DLBCL patients frequently display an exhausted phenotype, which is associated with poor clinical outcomes. The metabolic mechanisms contributing to this functional impairment remain poorly understood. We assessed degranulation (CD107a), cytokine secretion (IFN-γ, TNF-α), mitochondrial activity, and lipid metabolism in NK cells from DLBCL patients and healthy donors. Dysregulated lipid species were identified by GC-MS lipidomics and validated in NK-92MI and primary NK cells. The functional involvement of CD36 was assessed using the specific inhibitor, with subsequent examination of its correlation with cytotoxic activity. NAD

Indexed as

CD36DLBCLLipid metabolismNAD+NK cell

Identifiers

PMID41993707
PMCPMC13080478

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.