Immunomodulatory biomaterials against bacterial infections: Progress, challenges, and future perspectives
Bibliographic record
Abstract
Bacterial infectious diseases are one of the leading causes of death worldwide. Even with the use of multiple antibiotic treatment strategies, 4.95 million people died from drug-resistant bacterial infections in 2019. By 2050, the number of deaths will reach 10 million annually. The increasing mortality may be partly due to bacterial heterogeneity in the infection microenvironment, such as drug-resistant bacteria, biofilms, persister cells, intracellular bacteria, and small colony variants. In addition, the complexity of the immune microenvironment at different stages of infection makes biomaterials with direct antimicrobial activity unsatisfactory for the long-term treatment of chronic bacterial infections. The increasing mortality may be partly attributed to the biomaterials failing to modulate the active antimicrobial action of immune cells. Therefore, there is an urgent need for effective alternatives to treat bacterial infections. Accordingly, the development of immunomodulatory antimicrobial biomaterials has recently received considerable interest; however, a comprehensive review of their research progress is lacking. In this review, we focus mainly on the research progress and future perspectives of immunomodulatory antimicrobial biomaterials used at different stages of infection. First, we describe the characteristics of the immune microenvironment in the acute and chronic phases of bacterial infections. Then, we highlight the immunomodulatory strategies for antimicrobial biomaterials at different stages of infection and their corresponding advantages and disadvantages. Moreover, we discuss biomaterial-mediated bacterial vaccines' potential applications and challenges for activating innate and adaptive immune memory. This review will serve as a reference for future studies to develop next-generation immunomodulatory biomaterials and accelerate their translation into clinical practice.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".