A Review on Antibacterial Biomaterials in Biomedical Applications: From Materials Perspective to Bioinks Design
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
In tissue engineering, three-dimensional (3D) printing is an emerging approach to producing functioning tissue constructs to repair wounds and repair or replace sick tissue/organs. It allows for precise control of materials and other components in the tissue constructs in an automated way, potentially permitting great throughput production. An ink made using one or multiple biomaterials can be 3D printed into tissue constructs by the printing process; though promising in tissue engineering, the printed constructs have also been reported to have the ability to lead to the emergence of unforeseen illnesses and failure due to biomaterial-related infections. Numerous approaches and/or strategies have been developed to combat biomaterial-related infections, and among them, natural biomaterials, surface treatment of biomaterials, and incorporating inorganic agents have been widely employed for the construct fabrication by 3D printing. Despite various attempts to synthesize and/or optimize the inks for 3D printing, the incidence of infection in the implanted tissue constructs remains one of the most significant issues. For the first time, here we present an overview of inks with antibacterial properties for 3D printing, focusing on the principles and strategies to accomplish biomaterials with anti-infective properties, and the synthesis of metallic ion-containing ink, chitosan-containing inks, and other antibacterial inks. Related discussions regarding the mechanics of biofilm formation and antibacterial performance are also presented, along with future perspectives of the importance of developing printable inks.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 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 it