Inhibition of TRPM7 with carvacrol suppresses glioblastoma functions in vivo
Bibliographic record
Abstract
Glioblastoma (GBM) is the most prevalent and aggressive type of primary human brain tumours originating in the central nervous system. Despite the fact that current treatments involve surgery, chemotherapy (Temozolomide), and radiation therapy, the prognosis for patients diagnosed with GBM remains extremely poor. The standard treatment is not only unable to completely eradicate the tumour cells, but also tumour recurrence after surgical resection presents a major challenge. Furthermore, adjuvant therapies including radiation and chemotherapy have high cytotoxicity which causes extensive damage to surrounding healthy tissues and treatment is usually halted before GBM is fully eradicated. Finally, most GBM cases demonstrate temozolomide resistance, a common reason for GBM treatment failure. Therefore, there is an urgent need to develop a suitable alternative therapy that targets GBM specifically and has low cytotoxicity for healthy cells. We previously reported that transient receptor potential melastatin 7 (TRPM7) channels are aberrantly upregulated in GBM, and inhibition of TRPM7 reduced GBM cellular functions including proliferation, migration, and invasion. This suggests TRPM7 is a potential therapeutic target for GBM treatment. In this study, we investigated the effects of the TRPM7 inhibitor, carvacrol, on human GBM cell lines U87 and U251 in vivo. With the use of a flank xenograft GBM mouse model, we demonstrated that carvacrol significantly reduced the tumour size in both mice injected with U87 and U251 cells, decreased p-Akt protein level and increased p-GSK3β protein levels. Therefore, these results suggest that carvacrol may have therapeutic potential for GBM treatment.
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.
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".