Host–Pathogen Interactions of Marine Gram-Positive Bacteria
Bibliographic record
Abstract
spp. cause economic losses in marine fish aquaculture worldwide. Comprehensive information on these pathogens and their dynamic interactions with their respective fish-host systems are critical to developing effective prophylactic measures and treatments. While much is known about bacterial virulence and fish immune response, it is necessary to synthesize the knowledge in terms of host-pathogen interactions as a centerpiece to establish a crucial connection between the intricate details of marine Gram-positive pathogens and their fish hosts. Therefore, this review provides a holistic view and discusses the different stages of the host-pathogen interactions of marine Gram-positive pathogens. Gram-positive pathogens can invade fish tissues, evade the fish defenses, proliferate in the host system, and modulate the fish immune response. Marine Gram-positive pathogens have a unique set of virulence factors that facilitate adhesion (e.g., adhesins, hemagglutination activity, sortase, and capsules), invasion (e.g., toxins, hemolysins/cytolysins, the type VII secretion system, and immune-suppressive proteins), evasion (e.g., free radical quenching, actin-based motility, and the inhibition of phagolysosomal fusion), and proliferation and survival (e.g., heme utilization and siderophore-mediated iron acquisition systems) in the fish host. After infection, the fish host initiates specific innate and adaptive immune responses according to the extracellular or intracellular mechanism of infection. Although efforts have continued to be made in understanding the complex interplay at the host-pathogen interface, integrated omics-based investigations targeting host-pathogen-marine environment interactions hold promise for future research.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.001 |
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 teacher head, 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".