ReviewHuman cell2026
Advancing diagnostic biomarkers in Alzheimer's disease: interdisciplinary innovations and technological frontiers.
Review in Human cell, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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.
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.
Who cites it
3 citing papers in PubMed.
- Special Issue "Molecular Advances in Biomarkers of Central Nervous System Diseases and Disorders".International journal of molecular sciences · 2026Article
- Emerging diagnostic biomarkers and therapeutic targets in Alzheimer's disease.Inflammopharmacology · 2026Review
- Ferroptosis-neuroinflammation interplay: mechanistic pathways and therapeutic opportunities in central nervous system disorders.Frontiers in aging neuroscience · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Developing diagnostic biomarkers for Alzheimer's disease (AD) is at the cutting edge of interdisciplinary research and technical advancement. This comprehensive analysis investigates potential options for improving diagnostic accuracy and early detection of AD. Identifying biomarkers other than Aβ and tau proteins, such as synaptic dysfunction markers and metabolic indicators, is a novel technique. Integrating multi-omics data provides a comprehensive picture of AD pathophysiology, assisting in the discovery of biomarkers and treatment targets. Advances in technology, notably nanotechnology and biosensors, show promise for highly sensitive and specific platforms capable of identifying AD-related biomarkers in physiological fluids. AI and machine learning algorithms are critical in analyzing large datasets, improving pattern identification, and increasing diagnostic accuracy. Predictive models based on various biomarkers and clinical data open the way for personalized medicine methods in the treatment of AD. More advancements in PET and MRI tracers are required for targeted and sensitive imaging of specific AD-related clinical alterations. Wearing gadgets and seeing digital health signs have helped us to find diseases early and track them over time. They even allow monitoring from afar and all the time. This comprehensive review brings together new developments and teamwork across different fields. In this way, it guides to enhance how to identify AD. By mixing these new methods, we aim to change the diagnosis of AD early and accurately. This allows us to focus on treatments and push forward new cures for AD.
Indexed as
Identifiers
41925947What Socratic holds
Registered trials
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.