Reviving Dormant Immunity: Millimeter Waves Reprogram the Immunosuppressive Microenvironment to Potentiate Immunotherapy without Obvious Side Effects
Bibliographic record
Abstract
Addressing the variability in cancer immunotherapeutic outcomes among patients and the challenge of devising safe strategies to overcome immune evasion in solid tumors are crucial in advancing cancer therapy. This study investigated the anti-tumor effect of millimeter waves (MMWs) alone and in combination with the anti-programmed cell death-ligand 1 (α-PD-L1) antibody in a 4T1 "cold tumor" model. The results show that MMWs not only inhibit tumor growth but also improve tumor metabolism and the immune microenvironment and enhance anti-tumor immune responses by inducing conformational changes of key immune proteins. Further experiments conducted on cellular and animal models demonstrated that the anti-tumor efficacy of MMWs, which plays a pivotal role, was substantially enhanced with the aid of α-PD-L1. This collaboration resulted in a synergistic effect that not only inhibited tumor progression but also promoted a sustained immune response and prevented recurrence. The additional CT26 "cold tumor" model validates the applicability of this strategy across other "cold tumor" types, particularly in reprogramming the immunosuppressed state of "cold tumor". These findings underscore the unique potential of MMWs as a nonionizing, nonthermal therapeutic tool that complements cancer immunotherapy, offering a novel approach for the precision treatment of solid tumors.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".