Two Different Therapeutic Approaches for SARS-CoV-2 in hiPSCs-Derived Lung Organoids
Bibliographic record
Abstract
The global health emergency for SARS-CoV-2 (COVID-19) created an urgent need to develop new treatments and therapeutic drugs. In this study, we tested, for the first time on human cells, a new tetravalent neutralizing antibody (15033-7) targeting Spike protein and a synthetic peptide homologous to dipeptidyl peptidase-4 (DPP4) receptor on host cells. Both could represent powerful immunotherapeutic candidates for COVID-19 treatment. The infection begins in the proximal airways, namely the alveolar type 2 (AT2) cells of the distal lung, which express both ACE2 and DPP4 receptors. Thus, to evaluate the efficacy of both approaches, we developed three-dimensional (3D) complex lung organoid structures (hLORGs) derived from human-induced pluripotent stem cells (iPSCs) and resembling the in vivo organ. Afterward, hLORGs were infected by different SARS-CoV-2 S pseudovirus variants and treated by the Ab15033-7 or DPP4 peptide. Using both approaches, we observed a significant reduction of viral entry and a modulation of the expression of genes implicated in innate immunity and inflammatory response. These data demonstrate the efficacy of such approaches in strongly reducing the infection efficiency in vitro and, importantly, provide proof-of-principle evidence that hiPSC-derived hLORGs represent an ideal in vitro system for testing both therapeutic and preventive modalities against COVID-19.
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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 | 0.000 | 0.001 |
| 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".