Glycodendrimers as Anti-Adhesion Drugs Against Type 1 Fimbriated E. coli Uropathogenic Infections
Bibliographic record
Abstract
Bacterial drug resistance against antimicrobial agents is a prevalent and central worldwide impasse. Infections with resistant organisms lead to adverse clinical outcomes, increased mortality, and are costly to healthcare systems. Several infectious diseases are initiated by the binding of pathogenic lectins to host cells glycoconjugates. The molecular understanding of these adhesion phenomena is crucial and presents promising new alternatives compared to traditional antibiotic therapies. Glycans or glycan mimetics could be used to inhibit the initial recognition events leading to adhesion and colonization of host tissues by pathogens. The bladder and urothelial lining are widely covered by cell surface glycoproteins bearing the required carbohydrate ligands responsible for the adhesion phenomena. However, when these interactions are measured on a per saccharide basis, they are generally too weak (mM) for the design of beneficial inhibition therapies. The interactions between microbial pathogens and host cells are often governed by polyvalent and overall strong avidities. To overcome this drawback, glycobiologists have design a new family of well-defined small macromolecules, called glycodendrimers that can successfully address this issue. This review will provide a brief introduction on glycodendrimers and detailed descriptions of design and applications of mannosylated inhibitors against fimbriated type 1 E. coli.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".