Laser Photobiomodulation as a Potential Multi-Target Anticancer Therapy-Review
Bibliographic record
Abstract
Background: Anticancer drugs directed at single specific molecular targets tend to induce transitory responses, followed by relapses. Laser photobiomodulation may offer the possibility of targeting multiple hallmarks of cancer by using fit-for-purpose electromagnetic (EM) energy aiming to restore homeostasis-homeokinesis . Methods/Principal Findings: A literature search of English-language articles in five databases (Medline, ISI Web of Knowledge, Cochrane, Google Scholar, Scirus) was conducted using search terms relating to cancer (neoplasm, advanced cancer, palliative) in combination with photobiomodulation and/or low-level laser therapy (LLLT) in the period from January 1990 to January 2013. Controlled clinical trials with at least 1 year of follow up and minimum compliance of 90% were included. Clinical studies evaluating lymphedema, mucositis or pain were also included to illustrate post-LLLT responses to adverse effects of chemo-radiotherapy. In vitro and in vivo studies were considered as preliminary data for clinical trials. Retrieved articles suggest that photobiomodulation can modulate anti-tumor effects and reduce the adverse effects of chemo-radiotherapy. Results are discussed giving particular attention to two mechanistic proposals with potential anticancer applications, photo-infrared pulsed biomodulation (PIPBM) and water oscillator (WO). Conclusions/Significance: Translational research with laser photobiomodulation as a multi-target (multi-hallmark) therapy in cancer and other complex diseases appears warranted.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".