Abstract 2507 Finding an RNA Aptamer Against SOD-1 to be Used as a Therapeutic for ALS Patients
Bibliographic record
Abstract
The potential impact of early and efficient detection of biological threats in safeguarding public health and national security highlights the importance of reliable bio-threat detectors.Such detectors could provide a critical line of defence against potential bioterrorism, emerging infectious diseases, and environmental hazards.Performing the initial stages of developing sensors to recognize infectious viruses is challenging when high-safety level environments are required due to the toxicity of the viral targets.Although virus-like particles can be used safely, they are not available for all viral biothreats and can be difficult to produce.Therefore, we are developing a structural imitation of a virus that can be readily produced in any chemistry or molecular biology laboratory and be used to test the efficiency of biosensors during the early assembly and testing stages with no associated health hazards.The design involves attaching a target protein to magnetic or other beads of dimensions similar to the target viruses.The average amount of protein per bead can be quantified using the same detection molecule (e.g.antibodies, aptamers) to be integrated in the sensor.For our development of aptasensors, we are quantifying with a 32P-labeled aptamer using a filter capture assay.Validation of the "proxy" viruses also involves testing them in the aptasensor, which is performed under BSL1 conditions and maintains personnel safety.Financial support was provided by the Department of Homeland Security (DHS) (Cooperative Agreement: 20CWDARI00033).
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".