Bibliographic record
Abstract
Tau protein is critical for normal brain function but then it undergoes modification it is prone to aggregation into cytotoxic species which lead to diseases [1]. Hence, tau is a druggable target, as well as the biomarker of neurodegenerative disease. We reported on the detection of tau modification, specifically phosphorylation, and its inhibition by antibodies by using electrochemical impedance spectroscopy (EIS) [2]. In addition, the EIS was utilized to monitor binding interactions between large molecules, such as heparin, ferritin and transferrin to tau protein [3]. Tau protein biomarker detection was also achieved by using the same method. Currently, we are exploring tau protein self-assembly towards developing electrochemical assay for detection of early onset of tau pathology. Electrochemical assay for tau protein would allow for early detection and diagnosis of diseases, as well as drug inhibitor screening against neurodegenerations. References: 1. Lasagna-Reeves, C.A., Castillo-Carranza, D.L., Sengupta, U., Sarmiento, J., Troncoso,J., Jackson, G. R., Kayed, R. Identification of oligomers at early stages of tau aggregation in Alzheimer’s disease. FASEB J., 2011, 26, 1946-1959. 2. Esteves, J.O.V., Martic-Milne, S. Electrochemical detection of anti-tau antibodies binding to tau protein and inhibition of GSK-3-β-catalyzed phosphorylation, Anal. Biochem. 2016, 496, 55-62. 3. Jahshan, A., Esteves, J.O.V., Martic-Milne, S. Evaluation of ferritin and transferrin binding to tau protein, J. Inorg. Biochem. 2016, 162, 127-134.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".