Probing the expression and adhesion of glycans involved in Helicobacter pylori infection
Bibliographic record
Abstract
Helicobacter pylori infects approximately half the human population and has an unusual infective niche of the human stomach. Helicobacter pylori is a major cause of gastritis and has been classified as a group 1 carcinogen by the WHO. Treatment involves triple or quadruple antibiotic therapy, but antibiotic resistance is becoming increasingly prevalent. Helicobacter pylori expresses certain blood group related antigens (Lewis system) as a part of its lipopolysaccharide (LPS), which is thought to assist in immune evasion. Additionally, H. pylori LPS participates in adhesion to host cells alongside several adhesion proteins. This study profiled the carbohydrates of H. pylori reference strains (SS1 and 26695) using monoclonal antibodies (mAbs) and lectins, identifying interactions between two carbohydrate-targeting mAbs and multiple lectins. Atomic force microscopy (AFM) scans were used to probe lectin and antibody interactions with the bacterial surfaces. The selected mAb and lectins displayed an increased adhesive force over the surface of the curved H. pylori rods. Furthermore, this study demonstrates the ability of anti-carbohydrate antibodies to reduce the adhesion of H. pylori 26695 to human gastric adenocarcinoma cells via AFM. Targeting bacterial carbohydrates to disrupt crucial adhesion and immune evasion mechanisms represents a promising strategy for combating H. pylori infection.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".