Abstract 1901: Applications of silicon quantum dots as biomarkers for cancer detection and treatment, emphasizing biocompatibility studies
Bibliographic record
Abstract
Abstract Silicon quantum dots (SiQDs) are emerging as advanced nanomaterials for cancer treatment and detection. SiQDs are an attractive alternative to organic dyes for biological labeling, offering tunable photoluminescence response and high resistance to photobleaching. In our hands, SiQDs possess tailorable surface chemistry, as well as stable size and surface chemistry dependent luminescent response spanning visible and near-infrared spectral regions with high photoluminescence quantum efficiencies (approaching 85%). SiQDs are an enticing heavy-metal-free, non-toxic and biocompatible alternative to status quo QDs. Applied Quantum Materials Inc. (AQM) possesses world leading expertise to produce high-purity narrow size distribution SiQDs with custom surface modifications, including biomolecules (8 kDa to 150 kDa) for biology applications. We have performed a biocompatibility study that shows AQM SiQDs have achieved over 90% cell viability, even at concentrations up to 500 µg/mL after 24 h of incubation. In addition, we modified SiQDs to improve cancer cell uptake to enhance photodynamic therapy (PDT) efficacy under low-level laser therapy (LLLT). Using NIR laser irradiation, AQM SiQDs significantly reduced cancer cell viability, enhancing reactive oxygen species (ROS) generation and establishing SiQDs as effective photosensitizers for cancer therapy. SiQDs exhibited excellent photostability and minimal toxicity, outperforming organic dyes. Bioconjugation of SiQDs with biomolecules are proving to be powerful tools for cancer cell detection. This opens new possibilities for oncologists to select the most suitable therapy based on a comprehensive understanding of the cancer type and its progression. We are currently developing a liquid biopsy system for precision cancer management. Powered by SiQDs, this advanced diagnostic tool will enable automated blood analysis to identify and quantify rare circulating tumor cells (CTCs) with high sensitivity in blood patients. Working with our academic collaborators, we are addressing the complexities of brain tumor resection in glioblastoma. We synthesized a novel nanoscale material combining SiQDs with anti-cancer DNA aptamers for fluorescence-guided surgery. These SiQDs labeled aptamers will be validated on resected brain tumor samples and compared with pathological margin assessments. Additionally, the imaging potential of AptaSiQDs for primary and metastatic brain tumors will be evaluated in preclinical in vivo models. As we progress to in vivo studies, we will assess their cytotoxicity and biocompatibility in mice, aiming to expand their use in targeted drug delivery, imaging, and theranostics for personalized medicine. SiQDs are at the forefront of life sciences, offering diverse benefits, making them ideal for cancer diagnosis. Citation Format: Dorra Gargouri, Artjima Ounkaew, Xuyang Liu, David Antoniuk, Jonathan Veinot. Applications of silicon quantum dots as biomarkers for cancer detection and treatment, emphasizing biocompatibility studies [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 1901.
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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) | 0.003 | 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".