Intact primate brain tissue identification using a completely fibered coherent Raman spectroscopy system
Why this work is in the frame
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Bibliographic record
Abstract
Coherent Raman fiber probes have not yet found their way into the clinic despite their immense potential for label-free sensing and imaging. This is mainly due to the traditional bulky laser systems required to create the high peak power laser pulses needed for coherent Raman, as well as the complications that arise from the propagation of this type of energy through silica. Specifically, a coherent anti-Stokes Raman scattering (CARS) probe that could select its integration volume at high resolution, away from the tip of the fiber, is particularly interesting in the case of electrode implantation neurosurgeries, wherein it is possible to place optical fibers on-board the chronic electrode and provide optical guidance during its implantation, through the semi-transparent tip. To this clinical end, we have created an all fiber CARS system, consisting of small, rapidly tunable, turn-key fiber-lasers, capable of creating high wavenumber CARS spectra on the order of tens-of-milliseconds. The use of traditional silica fibers is made possible by the use of the laser’s long pulse-widths (25 ps). The probe itself has an outer diameter of 250 μm allowing it to fit within commercially available metal tubes that can replace deep brain stimulation (DBS) stylets. Using this system, we identified brain tissue types in intact nonhuman primates’ brains and showed the ability to delineate white and gray matters with high resolution. Its advantages over spontaneous Raman stem from the orders of magnitude improvement in spatial resolution, its inherent translatability to three-dimensional (3-D) imaging, as well as the theoretical ability to remove parasitic Raman signal from probe encasements, such as a DBS electrode. The system is planned to have clinical implications in neurosurgical guidance as well as diseased tissue detection.
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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 | 0.000 |
| 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) | 0.000 | 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 it