Bibliographic record
Abstract
Haem is the major iron source for bacteria that develop in higher organisms. In these hosts, bacteria have to cope with nutritional immunity imposed by the host, since haem and iron are tightly bound to carrier and storage proteins. Siderophores were the first recognized fighters in the battle for iron between bacteria and host. They are non-proteinaceus organic molecules having an extremely high affinity for Fe(3+) and able to extract it from host proteins. Haemophores, that display functional analogy with siderophores, were more recently discovered. They are a class of secreted proteins with a high affinity for haem; they are able to extract haem from host haemoproteins and deliver it to specific receptors that internalize haem. In the past few years, a wealth of data has accumulated on haem acquisition systems that are dependent on surface exposed/secreted bacterial proteins. They promote haem transfer from its initial source (in most cases, a eukaryotic haem binding protein) to the transporter that carries out the membrane crossing step. Here we review recent discoveries in this field, with particular emphasis on similar and dissimilar mechanisms in haemophores and siderophores, from the initial host source to the binding protein/receptor at the cell surface.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".