Understanding the Pathological Basis of Neurological Diseases Through Diagnostic Platforms Based on Innovations in Biomedical Engineering: New Concepts and Theranostics Perspectives
Bibliographic record
Abstract
The pace of advancement of genomics and proteomics together with the recent understanding of the molecular basis behind rare diseases could lead in the near future to significant advances in the diagnosing and treating of many pathological conditions. Innovative diagnostic platforms based on biomedical engineering (microdialysis and proteomics, biochip analysis, non-invasive impedance spectroscopy, etc.) are introduced at a rapid speed in clinical practice: this article primarily aims to highlight how such platforms will advance our understanding of the pathological basis of neurological diseases. An overview of the clinical challenges and regulatory hurdles facing the introduction of such platforms in clinical practice, as well as their potential impact on patient management, will complement the discussion on foreseeable theranostic perspectives. Indeed, the techniques outlined in this article are revolutionizing how we (1) identify biomarkers that better define the diagnostic criteria of any given disease, (2) develop research models, and (3) exploit the externalities coming from innovative pharmacological protocols (i.e., those based on monoclonal antibodies, nanodrugs, etc.) meant to tackle the molecular cascade so far identified.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".