Dual-Signal Readout Nanospheres for Rapid Point-of-Care Detection of Ebola Virus Glycoprotein
Bibliographic record
Abstract
Rapid detection of highly contagious pathogens is the key to increasing the probability of survival and reducing infection rates. We developed a sensitive and quantitative lateral flow assay for detection of Ebola virus (EBOV) glycoprotein with a novel multifunctional nanosphere (RNs@Au) as a reporter. Each RNs@Au contains hundreds of quantum dots and dozens of Au nanoparticles and can achieve enhanced dual-signal readout (fluorescence signal for quantitative detection and colorimetric signal for visual detection). Antibody (Ab) and streptavidin (SA) were simultaneously modified onto the RNs@Au to label the target and act as signal enhancer. After the target was labeled by the Ab-RNs@Au-SA and captured on the test line, biotin-modified RNs@Au was used to amplify the dual signal by the reaction of SA with biotin. The assay enables naked-eye detection of 2 ng/mL glycoprotein within 20 min, and the quantitative detection limit is 0.18 ng/mL. Additionally, the assay has been successfully tested in field work for detecting EBOV in spiked urine, plasma, and tap water samples and is thus a promising candidate for early diagnosis of suspect infections in EBOV-stricken areas.
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) | 0.000 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".