Multifunctional Nanomedicine Platforms for Imaging and Treatment of Cancer and Other Diseases
Bibliographic record
Abstract
The Taratula laboratory focuses on the development of nanomaterial-based platforms for imaging and targeted eradication of endometriosis and cancer.Thermal therapy (hyperthermia), a clinical intervention to ablate cancerous tissue by increasing the temperature of the tumors, is one of our major research directions.Different strategies can be used to elevate intratumoral temperatures for cancer treatment.For example, nanoparticle-mediated magnetic hyperthermia is a form of thermal therapy where nanoparticles delivered to disease sites generate heat after exposure to an external alternating magnetic field.Many studies have validated the significant potential of magnetic hyperthermia to either kill cancer cells directly or enhance their susceptibility to radiation and chemotherapy.Despite its promising potential, magnetic hyperthermia is currently limited to the treatment of localized and accessible tumors because the required therapeutic temperatures (>42 0 C) can only be achieved by direct intratumoral injection of conventional magnetic nanoparticles.To overcome this, we have developed novel nanoparticles that efficiently accumulate at tumor sites following intravenous injection and generate desirable intratumoral temperatures (>42 0 C).A significant portion of our research is also focused on nanomedicine-based image-guided photothermal therapy.We develop nanoparticles that efficiently delineate cancer lesions with fluorescence signals following systemic injection and eliminate them with heat upon exposure to targeted near-infrared light.Our research team also demonstrated that some fundamental principles of cancer nanomedicine can be used for the development of novel nanoparticle-based strategies for the treatment of endometriosis.Endometriosis is a devastating disease characterized by the presence of endometrium-like tissues outside of the uterus, and there is no cure for this disorder.To tackle this issue, we validated that the aforementioned nanomedicine strategies are also effective for the diagnosis and eradication of endometriotic lesions.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".