Proteoforms and their expanding role in laboratory medicine
Bibliographic record
Abstract
The term “proteoforms” describes the range of different structures of a protein product of a single gene, including variations in amino acid sequence and post-translational modifications. This diversity in protein structure contributes to the biological complexity observed in living organisms. As the concentration of a particular proteoform may increase or decrease in abnormal physiological states, proteoforms have long been used in medicine as biomarkers of health and disease. Notably, the analytical approaches used to analyze proteoforms have evolved considerably over the years. While ligand binding methods continue to play a large role in proteoform measurement in the clinical laboratory, unanticipated or unknown post-translational modifications and sequence variants can upend even extensively tested and vetted assays that have successfully made it through the medical regulatory process. As an alternate approach, mass spectrometry—with its high molecular selectivity—has become an essential tool in detection, characterization, and quantification of proteoforms in biological fluids and tissues. This review explores the analytical techniques used for proteoform detection and quantification, with an emphasis on mass spectrometry and its various applications in clinical research and patient care including, revealing new biomarker targets, helping improve the design of contemporary ligand binding in vitro diagnostics, and as mass spectrometric laboratory developed tests used in routine patient care.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".