Multiplex Photoluminescent Silicon Nanoprobe for Diagnostic Bioimaging and Intracellular Analysis
Bibliographic record
Abstract
Herein, a label-free multiplex photoluminescent silicon nanoprobe (PLSN-probe) is introduced as a potential substitute for quantum dots (QDs) in bioimaging. An inherently non-photoluminescent silicon substrate is altered to create the PLSN-probe, to overcome the major drawbacks of presently available QDs. Additionally, crystallinity alterations of the multiplane crystalline PLSN-probes lead to broad absorption and multiplex fluorescence emissions, which are attributed to the simultaneous existence of multiple crystal planes. The PLSN-probe not only demonstrates unique optical properties that can be exploited for bioimaging but also exhibits cell-selective uptake that allows the differentiation and diagnosis of HeLa and fibroblast cells. Moreover, multiplex emissions of the PLSN-probe illuminate different organelles such as the nucleus, nucleolemma, and cytoskeleton, depending on size-based preferential uptake by the cell organs. This in vitro study reveals that cancerous HeLa cells have a higher propensity for taking up the PLSN-probe compared to fibroblast cells, allowing the diagnosis of cancerous HeLa cells. Additionally, the fluorescence intensity per unit area of the cell is found to be a reliable means for distinguishing between dead and healthy cells. It is anticipated that the multifunctionality of the PLSN-probes will lead to better insight into the use of such probes for bioimaging and diagnosis applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".