Evaluating the efficacy of Tigecycline to target multiple cancer-types: A Review
Bibliographic record
Abstract
Tigecycline (TIG) is a Food and Drug Administration (FDA)-approved antibiotic that has recently demonstrated its anti-cancer properties in diverse tumour types. This review will discuss current research findings and future directions pertaining to the use of TIG in mitigating acute myeloid leukemia (AML), non-small cell lung cancer (NSCLC), gastric cancer, hepatocellular carcinoma (HCC), breast cancer, melanoma, cervical squamous cell carcinoma (CSCC), and glioblastoma (GM). TIG exerts its therapeutic effects via inhibition of mitochondrial functionality, interference of various signal transduction pathways, and acting synergistically with pre-existing chemotherapy drugs, all of which contribute to cell death. In comparison to conventional treatments such as chemotherapy, TIG may result in less severe and reduced side effects; this may be attributed to its selectivity and non-invasiveness. Upon evaluation of TIG’s efficacy in targeting multiple cancer-types, future efforts should aim to validate findings through human trials, broadening the scope of cancers targeted, establishing novel TIG derivatives, and assessing its performance when used in combination with other treatments.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".