Recent Advances of Polyaniline-Based Biomaterials for Phototherapeutic Treatments of Tumors and Bacterial Infections
Bibliographic record
Abstract
Conventional treatments fail to completely eradicate tumor or bacterial infections due to their inherent shortcomings. In recent years, photothermal therapy (PTT) has emerged as an attractive treatment modality that relies on the absorption of photothermal agents (PTAs) at a specific wavelength, thereby transforming the excitation light energy into heat. The advantages of PTT are its high efficacy, specificity, and minimal damage to normal tissues. To this end, various inorganic nanomaterials such as gold nanostructures, carbon nanostructures, and transition metal dichalcogenides have been extensively explored for PTT applications. Subsequently, the focus has shifted to the development of polymeric PTAs, owing to their unique properties such as biodegradability, biocompatibility, non-immunogenicity, and low toxicity when compared to inorganic PTAs. Among various organic PTAs, polyaniline (PANI) is one of the best-known and earliest-reported organic PTAs. Hence, in this review, we cover the recent advances and progress of PANI-based biomaterials for PTT application in tumors and bacterial infections. The future prospects in this exciting area are also addressed.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 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) | 0.002 | 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".