Evidence map›Paper›PMID 38454653›Full record

ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2024

Deep Learning-Driven Exploration of Pyrroloquinoline Quinone Neuroprotective Activity in Alzheimer's Disease.

Xinuo Li, Yuan Sun, Zheng Zhou, Jinran Li, Sai Liu, Long Chen, Yiting Shi, Min Wang, Zheying Zhu, Guangji Wang and 1 more

Abstract read
In one paragraph

Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

12 citing papers in PubMed.

  1. Trial
  2. Review
  3. Pyrroloquinoline Quinone and NADPharmaceuticals (Basel, Switzerland) · 2026
    Review
  4. Article
  5. Article
  6. Review
  7. Article
  8. Review
  9. Article
  10. Article
  11. Review
  12. 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

11 authors.

Xinuo LiJiangsu Provincial Key Laboratory of Drug Metabolism and Pharmacokinetics, State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing, 211166, China.
Yuan SunJiangsu Provincial Key Laboratory of Drug Metabolism and Pharmacokinetics, State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing, 211166, China.
Zheng ZhouDepartment of Computer Science, RWTH Aachen University, 52074, Aachen, Germany.
Jinran LiJiangsu Provincial Key Laboratory of Drug Metabolism and Pharmacokinetics, State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing, 211166, China.
Sai LiuJiangsu Provincial Key Laboratory of Drug Metabolism and Pharmacokinetics, State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing, 211166, China.
Long ChenJiangsu Provincial Key Laboratory of Drug Metabolism and Pharmacokinetics, State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing, 211166, China.
Yiting ShiJiangsu Provincial Key Laboratory of Drug Metabolism and Pharmacokinetics, State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing, 211166, China.
Min WangAffiliated Brain Hospital of Nanjing Medical University, Nanjing, 210029, China.
Zheying ZhuSchool of Pharmacy, The University of Nottingham, Nottingham, NG7 2RD, UK.
Guangji WangJiangsu Provincial Key Laboratory of Drug Metabolism and Pharmacokinetics, State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing, 211166, China.
Qiulun LuJiangsu Provincial Key Laboratory of Drug Metabolism and Pharmacokinetics, State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing, 211166, China.ORCID 0009-0002-5431-9765

Funding

CAMS Innovation Fund for Medical Sciences 2021-I2M-5-011Hebei Laboratory of Cell Ecosystem Innovation Fund 22HHXBSS00005Leading Technology Foundation Research Project of Jiangsu Province BK20192005National Natural Science Foundation of China 81970414National Natural Science Foundation of China 82073928National Natural Science Foundation of China 82270272Nature Science Foundation of Jiangsu Province BK20221051Postdoctoral Excellence Program of Jiangsu Province 2022ZB297
6 · The paper itself

Abstract

Alzheimer's disease (AD) is a pressing concern in neurodegenerative research. To address the challenges in AD drug development, especially those targeting Aβ, this study uses deep learning and a pharmacological approach to elucidate the potential of pyrroloquinoline quinone (PQQ) as a neuroprotective agent for AD. Using deep learning for a comprehensive molecular dataset, blood-brain barrier (BBB) permeability is predicted and the anti-inflammatory and antioxidative properties of compounds are evaluated. PQQ, identified in the Mediterranean-DASH intervention for a diet that delays neurodegeneration, shows notable BBB permeability and low toxicity. In vivo tests conducted on an Aβ₁₋₄₂-induced AD mouse model verify the effectiveness of PQQ in reducing cognitive deficits. PQQ modulates genes vital for synapse and anti-neuronal death, reduces reactive oxygen species production, and influences the SIRT1 and CREB pathways, suggesting key molecular mechanisms underlying its neuroprotective effects. This study can serve as a basis for future studies on integrating deep learning with pharmacological research and drug discovery.

Indexed as

Alzheimer DiseaseDeep LearningDisease Models, AnimalNeuroprotective AgentsAnimalsBlood-Brain BarrierMaleMicePQQ CofactorNeuroprotective AgentsPQQ CofactorAlzheimer's diseasedeep learningneuroprotective activitiespyrroloquinoline quinones

Identifiers

PMID38454653
PMCPMC11095145

What OpenQuestion holds

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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.