Quantification of Low-Picomolar Concentrations of TNF-α in Serum Using the Dual-Network Microfluidic ELISA Platform
Bibliographic record
Abstract
For both research and diagnostic purposes, the ability to detect low levels of proteins in a cost- and time-effective manner is essential. In this study, the cytokine TNF-alpha (tumor necrosis factor-alpha), a widely used protein indicator of inflammatory response, was chosen to demonstrate the ability of the dual-network microfluidic ELISA (enzyme-linked immunosorbent assay) platform developed by the authors to rapidly quantify low concentrations of this biomarker in serum. Through the optimization of several experimental parameters, the system was shown to meet the requirements for fundamental and applied studies, while also being relevant for challenging clinical applications such as the diagnosis of septic patients. A sensitivity of 45 pg/mL (2.6 pM) in both culture medium and serum, with inter- and intravariations of less than 15%, was attained for the quantification of human TNF-alpha to a concentration of up to 500 pg/mL. The overall time for completion of the assay in eight parallel reactions was less than 1 h.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".