Bibliographic record
Abstract
Tau is a neuronal protein and one of the biomarkers of neurodegenerative diseases, such as Alzheimer’s disease. Tau protein undergoes post-translational modifications, aggregation, and is a viable drug target. In addition, this protein is a vital biomarker in biological fluids towards early detection of neurodegenerative diseases. We reported on using electrochemical impedance spectroscopy and cyclic voltammetry for detection of protein-protein interactions (1), protein-ligand interactions (2), enzymatic catalysis and enzyme inhibition (3) while focusing on tau protein and its biochemistry. For example, tau-tau, tau-ferritin, tau-transferrin protein interactions were monitored, and protein kinase-catalyzed phosphorylation of tau protein was detected. The phosphorylation inhibitors, such as antibodies, were screened for their efficacy. Bioelectrochemical methodologies were used to gain insight into various facets of protein biochemistry, and represent promising tools in neuroscience research. References A) Carlin, N., Martic-Milne, S. (2018). Anti-tau antibodies based electrochemical sensor for detection of tau protein. J. Electrochem. Soc.165: G3018-G3025. B) Jahshan, A., Esteves, J.O.V., Martic-Milne, S. (2016). Evaluation of ferritin and transferrin binding to tau protein. J. Inorg. Biochem. 162: 127-134. C) Esteves, J.O.V., Trzeciakiewicz, H., Loeffler, D.A., Martic, S. (2015). Effects of tau domain-specific antibodies and intravenous immunoglobulin on tau aggregation and aggregate degradation. Biochemistry. 54: 15-18. Trzeciakiewicz, H., Esteves, J.O.V., Carlin, N., Martic, S. (2015). Electrochemistry of heparin binding to tau protein on Au surfaces. Electrochim. Acta. 162: 24-30, Esteves, J.O.V., Martic-Milne, S. (2016). Electrochemical detection of anti-tau antibodies binding to tau protein and inhibition of GSK-3-β-catalyzed phosphorylation. Anal. Biochem. 496: 55-62
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.381 | 0.177 |
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".