Method for Electrochemical Detection of Brain Derived Neurotrophic Factor (BDNF) in Plasma
Bibliographic record
Abstract
Currently, a blood test for the diagnosis of endometriosis, a common estrogen-dependent gynecological disease, does not exist. Recent studies suggest that circulating concentrations of brain-derived neurotrophic factor (BDNF) have potential for the diagnosis of endometriosis. However, at present, BDNF can only be measured by ELISA, which requires a clinic visit, a routine blood sample, and laboratory testing. Therefore, we developed a point-of-care device (EndoChip) for use with small blood volumes that can be collected through a finger prick. Specifically, the presented device is a polymer-based chip with a nanoporous, wrinkled gold film acting as the electrode/sensing layer, allowing for the electrochemical detection of BDNF in plasma. Increasing concentrations of BDNF (0.1–2.0 ng/mL) induced significant differences in redox current. The biosensor produces a signal readout in a matter of seconds, and is ideal for realizing multiplexing. Blood samples were collected from women ( n = 20) with chronic pelvic pain undergoing a diagnostic laparoscopy. Plasma BDNF concentrations measured by commercial ELISA were positively correlated ( r 2 = 0.8033; p < 0.001) with results from the EndoChip. Our results demonstrate a quick and reliable method for point-of-care quantification of circulating concentrations of BDNF and a promising diagnostic tool for endometriosis.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| 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".