Spatiotemporally dynamic electric fields for brain cancer treatment: an <i>in vitro</i> investigation
Bibliographic record
Abstract
Abstract Objective . The treatment of glioblastoma (GBM) using low intensity electric fields (∼1 V cm −1 ) is being investigated using multiple implanted bioelectrodes, which was termed intratumoral modulation therapy (IMT). Previous IMT studies theoretically optimized treatment parameters to maximize coverage with rotating fields, which required experimental investigation. In this study, we employed computer simulations to generate spatiotemporally dynamic electric fields, designed and purpose-built an IMT device for in vitro experiments, and evaluated the human GBM cellular responses to these fields. Approach . After measuring the electrical conductivity of the in vitro culturing medium, we designed experiments to evaluate the efficacy of various spatiotemporally dynamic fields: (a) different rotating field magnitudes, (b) rotating versus non-rotating fields, (c) 200 kHz versus 10 kHz stimulation, and (d) constructive versus destructive interference. A custom printed circuit board (PCB) was fabricated to enable four-electrode IMT in a 24-well plate. Patient derived GBM cells were treated and analyzed for viability using bioluminescence imaging. Main results . The optimal PCB design had electrodes placed 6.3 mm from the center. Spatiotemporally dynamic IMT fields at magnitudes of 1, 1.5, and 2 V cm −1 reduced GBM cell viability to 58%, 37% and 2% of sham controls respectively. Rotating versus non-rotating, and 200 kHz versus 10 kHz fields showed no statistical difference. The rotating configuration yielded a significant reduction ( p < 0.01) in cell viability (47 ± 4%) compared to the voltage matched (99 ± 2%) and power matched (66 ± 3%) destructive interference cases. Significance . We found the most important factors in GBM cell susceptibility to IMT are electric field strength and homogeneity. Spatiotemporally dynamic electric fields have been evaluated in this study, where improvements to electric field coverage with lower power consumption and minimal field cancellations has been demonstrated. The impact of this optimized paradigm on cell susceptibility justifies its future use in preclinical and clinical trial investigations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".