Antibodies to combat melioidosis: bridging immune mechanisms with diagnostics and therapeutic potential
Bibliographic record
Abstract
PURPOSE OF REVIEW: Melioidosis, caused by the Gram-negative soil bacterium Burkholderia pseudomallei , is a frequently fatal (sub)tropical infection with a substantial global burden. This review summarizes current insights into antibody-mediated immunity in melioidosis and its clinical potential. RECENT FINDINGS: Antibodies against B. pseudomallei polysaccharides and protein antigens promote bacterial clearance through opsonophagocytosis and complement activation, reducing intracellular spread. People who survive melioidosis develop broader and more durable antibody repertoires than nonsurvivors. Protective antibody responses vary by antigen class and are influenced by host factors, notably diabetes, a main risk factor for melioidosis. Diagnostic serology has advanced from low-performing indirect hemagglutination to single-antigen tests and multiplex platforms. Performance, however, is specimen-dependent, limited by cross-reactivity with related Burkholderia spp., and by cost and resource constraints in endemic settings. In preclinical models of melioidosis, monoclonal antibodies against B. pseudomallei confer passive protection and are being developed for prophylaxis, adjunctive therapy, and as platforms for antibody-antibiotic conjugates or vaccine design. SUMMARY: Antibody responses to key virulence factors correlate with disease course and are increasingly studied for diagnostic and therapeutic applications. This review aids in informing novel diagnostics and guide antibody-based therapies offering new directions for clinical management and prevention of this high-burden but underrecognized 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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".