Immunomodulatory biomaterials against bacterial infections: Progress, challenges, and future perspectives
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.000 |
| 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 | 0.001 | 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 it