Defect Induced Semiconductor Probes for Ultrasensitive Detection of Metastatic Cancer
Bibliographic record
Abstract
Metastasis, a secondary tumor, behaves distinctly different from its state-of-origin. Aggressive metastasis comprises a diverse heterogeneous population; demonstrates varying molecular mechanisms. Hence, critical to investigate the heterogeneity of a metastatic cancer cell at a single-cell level. So far, available detection methods depend on bulk tumor samples lose the heterogeneity inherited in metastatic cancers, making them incapable of detection. Consequently, there exists no technique to detect metastatic cancers. Surface-enhanced Raman scattering (SERS), a label-free bioanalytical technique, can obtain the spectral fingerprints of biomolecules from cells. However, conventional SERS focuses on plasmonic metal structures depending on hot spots requires an expensive methodology and difficult procedures that result in low reproducibility and stability. As a new alternative, semiconductors recently evolved with good stability, reproducibility, and biocompatibility for SERS applications. However, limited due to low detection and enhancement efficiency. This thesis introduced a new phenomenon of defect-induced functionalization in semiconductor materials to transform non-SERS materials to SERS active quantum/nano-sized probes. The unique functionalization activates the probes by tuning the concentration of surface and subsurface defects for ultrasensitive molecule detection. Different types of defects such as oxygen vacancy, interstitial defects, and dopants impart plasmonic resonance in semiconductor probes in addition to the charge-transfer mechanism. Thus, we first explored a collective behavior of intrinsic defects in probes as a sensing platform for molecular-level detection. Incorporating defects gives semiconductors other distinct properties such as multiple wavelength activity, non-degradable SERS activity, and detection of low-cross section cancer biomolecules. In addition, surface defects impart anionic property, increase biocompatible nature for safe adherence and penetration, permitting intracellular readout of biomarker signals. The diagnostic transformation signals accurately pointed to metastatic cancer and differentiated them from other cancer and normal cells. This study then focused on the primary cause of the development of metastatic cancer called cancer stem cells. Very scarce in number, we then focused on developing the sensing platform to capture these populations and magnify the trace cues present. This study presents underlying key targets that lead to the development of most cancer-related deaths and enhances the chance to increase the cancer prognosis improving clinical outcomes.
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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 | 0.001 | 0.001 |
| 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".