Sputum and salivary protein biomarkers and point-of-care biosensors for the management of COPD
Bibliographic record
Abstract
Chronic obstructive pulmonary disease (COPD) has become one of the most fatal diseases of the century considering mortality and morbidity levels worldwide. This disease is an inflammatory response to environmental stress and tobacco smoking. Although spirometry is the gold-standard diagnostic test administrated in primary and secondary care, it often exhibits low accuracy in cases of predicting disease worsening and possible bias due to the operator, patient, and conditions. Recent developments in proteomics research suggest that the presence of protein biomarkers can aid in the accurate diagnosis and prediction of disease outcomes. This review presents the cutting-edge research progress in the area of protein biomarkers towards the management of COPD. The literature review was confined to protein biomarkers in saliva and sputum because testing these bodily fluids shows great promise for point-of-care (POC) testing due to its practicality, non-invasiveness and inexpensive handling and sampling. Although it is conclusive that more studies on sputum and saliva are needed, this review studies the promising clinical value of interleukin (IL)-6 and IL-8, matrix metalloproteinase (MMP)-8 and MMP-9, C-reactive protein (CRP), tumor necrosis factor-alpha (TNF-α), and neutrophil elastase (NE). Following the critical analysis of salivary and sputum biomarkers, the recent development of POC biosensors for the multiplexed detection of biomarkers is also reported. Overall, the review aims to explore the possibility for the future development of POC sensors for chronic lung disease management utilizing clinically relevant biomarkers in saliva and sputum.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".