Terahertz dielectric spectroscopy and solid immersion microscopy of ex vivo glioma model 101.8: brain tissue heterogeneity
Bibliographic record
Abstract
Terahertz (THz) technology holds strong potential for the intraoperative label-free diagnosis of brain gliomas, aimed at ensuring their gross-total resection. Nevertheless, it is still far from clinical applications due to the limited knowledge about the THz-wave–brain tissue interactions. In this work, rat glioma model 101.8 was studied ex vivo using both the THz pulsed spectroscopy and the 0.15λ-resolution THz solid immersion microscopy (λ is a free-space wavelength). The considered homograft model mimics glioblastoma, possesses heterogeneous character, unclear margins, and microvascularity. Using the THz spectroscopy, effective THz optical properties of brain tissues were studied, as averaged within the diffraction-limited beam spot. Thus measured THz optical properties revealed a persistent difference between intact tissues and a tumor, along with fluctuations of the tissue response over the rat brain. The observed THz microscopic images showed heterogeneous character of brain tissues at the scale posed by the THz wavelengths, which is due to the distinct response of white and gray matters, the presence of different neurovascular structures, as well as due to the necrotic debris and hemorrhage in a tumor. Such heterogeneities might significantly complicate delineation of tumor margins during the intraoperative THz neurodiagnosis. The presented results for the first time pose the problem of studying the inhomogeneity of brain tissues that causes scattering of THz waves, as well as the urgent need to use the radiation transfer theory for describing the THz-wave — tissue interactions.
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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 | 0.000 |
| 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".