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Comparative effectiveness of multiple interventions for Alzheimer's disease on ABC syndromes and QoL: A Bayesian network meta-analysis.

Overview

Authors: Fujia Gao1, Guangxin Luo2, Bokuan Liu1, Xu Qiu3, Jing Bai4, Ruimin Wang1,4
  1. Key Laboratory of Dementia and Cognitive Dysfunction, School of Public Health, North China University of Science and Technology, International Science and Technology Cooperation Base of Geriatric Medicine, Hebei Province Key Laboratory of Occupational Health and Safety for Coal Industry, North China University of Science and Technology, Tangshan, Hebei 063210, China
  2. Department of Nutrition and Food Hygiene, Hubei Key Laboratory of Food Nutrition and Safety, MOE Key Laboratory of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
  3. Department of Medical Literature Retrieval, Library, North China University of Science and Technology, Tangshan, Hebei 063210, China
  4. School of Basic Medical Sciences, North China University of Science and Technology, Tangshan, Hebei 063210, China
Institutions: North China University of Science and Technology (China); Huazhong University of Science and Technology (China)
Journal: iScience, volume 29, issue 9, article 117250
Dates: received 25 September 2025; accepted 3 August 2026; published online 24 August 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1016/j.isci.2026.117250 · PMID 42668611 · PMCID PMC13524775 · OpenAlex W7204148079
Open access: gold, a free copy (OpenAlex)
Status: code on request
Categories: human (organism), Alzheimer's / dementia (population), clinical / translational (subfield)
Methods: Statistics
Keywords: Alzheimer’s disease, ABC symptoms, quality of life, Bayesian network meta-analysis, randomized controlled trial
Topic: Laser Applications in Dentistry and Medicine (Radiology, Nuclear Medicine and Imaging, Medicine), according to OpenAlex
Funding: Tangshan Science and Technology Bureau (21130236C); National Natural Science Foundation of China (82471467); North China University of Technology (20237591); North China University of Science and Technology; Technology Development (246Z7717G)
Citations: not cited yet (Europe PMC); 81 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Code

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Data

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Code and data availability statement

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Read it in the paper: doi.org/10.1016/j.isci.2026.117250.

Versions

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 6 authors, 5 keywords, 5 funders, 80 references.

Cite

This paper

Gao, F., Luo, G., Liu, B., Qiu, X., Bai, J., & Wang, R. (2026). Comparative effectiveness of multiple interventions for Alzheimer's disease on ABC syndromes and QoL: A Bayesian network meta-analysis. iScience, 29(9), 117250. https://doi.org/10.1016/j.isci.2026.117250

BibTeX

@article{gao2026comparative,
author = {Gao, Fujia and Luo, Guangxin and Liu, Bokuan and Qiu, Xu and Bai, Jing and Wang, Ruimin},
title = {{Comparative effectiveness of multiple interventions for Alzheimer's disease on ABC syndromes and QoL: A Bayesian network meta-analysis}},
journal = {iScience},
year = {2026},
month = aug,
volume = {29},
number = {9},
pages = {117250},
publisher = {Elsevier},
issn = {2589-0042},
doi = {10.1016/j.isci.2026.117250},
url = {https://doi.org/10.1016/j.isci.2026.117250},
pmid = {42668611},
pmcid = {PMC13524775}
}

RIS

TY - JOUR
AU - Gao, Fujia
AU - Luo, Guangxin
AU - Liu, Bokuan
AU - Qiu, Xu
AU - Bai, Jing
AU - Wang, Ruimin
TI - Comparative effectiveness of multiple interventions for Alzheimer's disease on ABC syndromes and QoL: A Bayesian network meta-analysis
T2 - iScience
J2 - iScience
PY - 2026
DA - 2026/08/24
VL - 29
IS - 9
SP - 117250
SN - 2589-0042
PB - Elsevier
DO - 10.1016/j.isci.2026.117250
UR - https://doi.org/10.1016/j.isci.2026.117250
LA - en
ER -

CSL-JSON

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"language": "en",
"issued": {
"date-parts": [
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}

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