Mesoporous Silica‐Based Materials with Bactericidal Properties
Bibliographic record
Abstract
Bacterial infections are the main cause of chronic infections and even mortality. In fact, due to extensive use of antibiotics and, then, emergence of antibiotic resistance, treatment of such infections by conventional antibiotics has become a major concern worldwide. One of the promising strategies to treat infection diseases is the use of nanomaterials. Among them, mesoporous silica materials (MSMs) have attracted burgeoning attention due to high surface area, tunable pore/particle size, and easy surface functionalization. This review discusses how one can exploit capacities of MSMs to design and fabricate multifunctional/controllable drug delivery systems (DDSs) to combat bacterial infections. At first, the emergency of bacterial and biofilm resistance toward conventional antimicrobials is described and then how nanoparticles exert their toxic effects upon pathogenic cells is discussed. Next, the main aspects of MSMs (e.g., physicochemical properties, multifunctionality, and biosafety) which one should consider in the design of MSM-based DDSs against bacterial infections are introduced. Finally, a comprehensive analysis of all the papers published dealing with the use of MSMs for delivery of antibacterial chemicals (antimicrobial agents functionalized/adsorbed on mesoporous silica (MS), MS-loaded with antimicrobial agents, gated MS-loaded with antimicrobial agents, MS with metal-based nanoparticles, and MS-loaded with metal ions) is provided.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".