ASOBIOTICS 2024: an interdisciplinary symposium on antisense-based programmable RNA antibiotics
Bibliographic record
Abstract
The international symposium ASOBIOTICS 2024 brought together scientists across disciplines to discuss the challenges of advancing antibacterial antisense oligomers (ASOs) from basic research to clinical application. Hosted by the Helmholtz Institute for RNA-based Infection Research (HIRI) in Würzburg, Germany, on September 12-13, 2024, the event featured presentations covering major milestones and current challenges of this antimicrobial technology and its applications against pathogens, commensals, and bacterial viruses. General design principles and modification of ASOs based on peptide nucleic acid (PNA) or phosphorodiamidate-morpholino-oligomer (PMO) chemistry, promising cellular RNA targets, new delivery technologies, as well as putative resistance mechanisms, were discussed. A panel discussion noted the challenge of nomenclature: antibacterial ASOs lack a single, universally used name. To address this, the term "asobiotics" was proposed to unite a community of like-minded scientists that are committed to advancing ASOs as antimicrobials. A consistent name will simplify literature searches and help scientists and funders appreciate the potential of programmable RNA antibiotics to combat antimicrobial resistance and enable precise microbiome editing.
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 | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".