Evidence map›Paper›PMID 42345883›Full record

ReviewBiosensors2026

Advancements in Nanomaterial-Based Biosensors for Neuropsychiatric and Neurodegenerative Diagnostics: From Biomarker Discovery to Clinical Translation.

Xinyue Li, Xiaopeng Han, Qing Han, Xuan He, Yixin Huang, Aimei Liu

Abstract readReview
In one paragraph

Review in Biosensors, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

6 authors.

Xinyue LiSchool of Life Sciences and Health, University of Health and Rehabilitation Sciences, Qingdao 266113, China.ORCID 0009-0003-4345-5910
Xiaopeng HanSchool of Life Sciences and Health, University of Health and Rehabilitation Sciences, Qingdao 266113, China.ORCID 0009-0009-2218-0712
Qing HanSchool of Life Sciences and Health, University of Health and Rehabilitation Sciences, Qingdao 266113, China.ORCID 0009-0007-6564-4922
Xuan HeSchool of Life Sciences and Health, University of Health and Rehabilitation Sciences, Qingdao 266113, China.ORCID 0009-0007-8141-5273
Yixin HuangSchool of Life Sciences and Health, University of Health and Rehabilitation Sciences, Qingdao 266113, China.ORCID 0009-0000-6658-9880
Aimei LiuSchool of Life Sciences and Health, University of Health and Rehabilitation Sciences, Qingdao 266113, China.ORCID 0009-0005-7771-1796

Funding

National Natural Science Foundation of China 82401622Natural Science Foundation of Shandong Province ZR2023QH131
6 · The paper itself

Abstract

Nanobiosensors, with their unique physicochemical properties, are transformative tools for diagnosing and monitoring neurodegenerative diseases and mental disorders. This article systematically reviews the latest progress of nanomaterial systems and integrated sensing modalities in neurological disease diagnosis. First, we clarify the multiple functional roles of nanomaterials in biosensors, including signal amplification, interface optimization, and spatial positioning, and compare the applicable scenarios of various sensing principles based on different nanomaterials. Second, we evaluate the design and integration strategies of molecular recognition elements (antibodies, nucleic acid aptamers, molecularly imprinted polymers, and CRISPR-Cas systems) and discuss their synergistic integration mechanisms for improving detection performance. In terms of detection targets, we focus on three applications: high-sensitivity quantification of established protein biomarkers, real-time monitoring of dynamic neurochemicals (dopamine, serotonin, glutamate), and emerging liquid biopsy targets such as exosomal cargo and circulating microRNAs. Finally, to address the core challenges of biofouling, sensitivity-selectivity trade-offs, and multiplex detection in complex matrices, we propose three breakthrough directions for next-generation diagnostics: deep integration of multimodal and multiplexing platforms, closed-loop chemical brain-computer interfaces (cBCIs), and AI-driven predictive diagnostic models, collectively enabling a transition from passive detection to active sensing and intervention for precise, rapid, and non-invasive neurological disease management.

Indexed as

Biosensing TechniquesMental DisordersNanostructuresNeurodegenerative DiseasesBiomarkersHumansBiomarkersclinical translationnanobiosensorsneurodegenerative disordersneuropsychiatric disorders

Identifiers

PMID42345883
PMCPMC13296684

What Socratic holds

Textmetadata
LicenceCC BY
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 Socratic graph.