Combination Therapy Using Inhalable GapmeR and Recombinant ACE2 for COVID-19
Bibliographic record
Abstract
Here we report our perspective on applying GapmeR technology in combination with recombinant angiotensin-converting enzyme 2 (ACE2) in the treatment of COVID-19 patients. GapmeR is a cell-permeating antisense single-stranded DNA molecule that can be designed to specifically target intracellular severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Once internalized into host cells, such as lung alveolar cells, GapmeR molecules can bind to the viral RNA. This RNA/DNA hybrid will then be degraded by the RNase H enzyme abundantly present in the host cells. GapmeRs can be delivered to COVID-19 patients through inhalation or via nebulization. SARS-CoV-2-targeted GapmeR can also be given to frontline healthcare workers as a prophylactic protection. The recombinant ACE2 protein, the efficacy of which is being evaluated in clinical trials, will bind to the spike (S) glycoprotein of extracellular SARS-CoV-2 and potentially block viral infectivity. We propose that combining inhalable SARS-CoV-2-targeted GapmeRs with recombinant ACE2 could provide a viable and rapidly implementable more effective therapeutic approach for eradicating SARS-CoV-2 and save millions of lives.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".