Lipid Droplet-Specific Fluorescent Probe for <i>In Vivo</i> Visualization of Polarity in Fatty Liver, Inflammation, and Cancer Models
Bibliographic record
Abstract
Elucidating the intrinsic relationship between diseases and lipid droplet (LD) polarity remains a great challenge owing to the lack of the research on multiple disease models. Until now, the visualization of abnormal LD polarity in models of inflammation and clinical cancer patient samples has not been achieved. To meet the urgent challenge, we facilely synthesized a robust LD-specific and polarity-sensitive fluorescent probe ( LD - TTP ), which consists of a triphenylamine segment as an electron-donor group (D) and a pyridinium as an electron-acceptor moiety (A), forming a typical D−π–A molecular configuration. Owing to the unique intramolecular charge transfer effect, LD-TTP exhibits high sensitivity to polarity change in the linear range from Δ f = 0.258 to 0.312, with over 278-fold fluorescence enhancement. Moreover, we revealed that LD-TTP possessed satisfactory ability for sensitively monitoring LD-polarity changes in living cells. Using LD-TTP, we first demonstrated the detection of LD-polarity changes in fatty liver tissues and inflammatory living mice via confocal laser scanning fluorescence imaging. Surprisingly, the visualization of LD polarity has been achieved not only at the cellular levels and living organs but also in surgical specimens from cancer patients, thus holding great potential in the clinical diagnosis of human cancer. All these features render LD-TTP an effective tool for medical diagnosis of LD polarity-related diseases.
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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".