Tilted Fiber Bragg Grating Active Heater for Controlled and Localised Hyperthermia
Bibliographic record
Abstract
We present data from localized heat inducement studies at both cellular and tissue levels along with a computational model built to predict the temperature increase and damage extent in tissues receiving hyperthermia treatment by a fiber-based active heater.This novel fiber-based active heater serves as a heat source and a temperature sensor.Five important insights are highlighted from this thesis work.First, heat-induced controlled cell deaths were observed experimentally in the three cell lines with MCF-10A being more susceptible to heat compared to HEK 293 and MCF7 cells.Second, comparison between the phantom tissue and ex vivo experimental and computational results shows a lesion size of 5×12 mm and 4.87×11.6mm in the phantom tissue and 7×15 mm and 8.8×14.3mm in the ex vivo studies at pumping power of 1.8 W for 10 minutes respectively.Thus, this computational model is able to provide information about the heat transfer characteristics caused by the active heater in living biological tissue.Third, under similar conditions of pumping power and heating time to that used in the ex vivo experiment, we found that the blood perfusion has a profound effect on the amount of induced heat at the active heater surface (or at the heat source).Because of the small dimension of lethal volume, heat dissipation by blood with a volumetric perfusion rate of 6.4×10 -3 Kg/m 3 s in the liver tissues is very small.Forth, in all the experimental and computational studies, hyperthermia position and damage extent can be controlled by the active heater through managing the temperature increase and the power supply during heating, thereby avoiding the transient effect of heat outside of the target volume.Thus, this hybrid simulation/active heater approach I am indebted first to God for giving me all the people whom support me during my PhD studies.I would like to express my gratitude to my supervisors, Prof. Jacques Albert and Prof. Christopher W. Smelser.I appreciate the freedom you have given me in conducting my experiments and provided me with the guidance and support whenever I needed help.Dr. Jacques, you have always made yourself available to me and to my research.He has provided me with a lot of fruitful comments and questions.His scientific vision and his way in reading reports along with his vast knowledge are very impressive, yet scary!I can literally say that I have always taken something from him each time we talked.Dr. Christopher, you were the first person I met in the group.Chris is someone you will instantly like; he is very kind and lively.I was very lucky that when I started my PhD degree in Fall 2014, you were teaching the adv.topics in electromagnetics course.It was very rich with the necessary information about guiding and coupling waves in general and particularly about the FBGs devices giving me a fast and easy start to my PhD research.He is always available to write letters for my sponsorship, to fix my writing, to discuss my experiments providing me with insightful comments.Each of you are very big-hearted and have given me a lot from your expertise and time over the last four years, so thank you so much.
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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".