Transcriptomic responses in human intestinal cells (Caco-2, HIEC-6) exposed to dietary nanoparticles
Bibliographic record
Abstract
Use of nanoparticles (NPs) in the food industry raises health concerns particularly in the gastrointestinal system. Assessing dietary NPs remains challenged due the vast number of products and the resource-intensive nature of traditional toxicity testing. Recent advancements in high-throughput transcriptomics, coupled with benchmark dose (BMD) analysis are poised to modernize chemical safety assessments. The objective of this study was to derive transcriptomic point of departure (tPOD) values for common dietary NPs through dose-response analysis of 3’ RNA-sequencing data. Two intestinal cell lines (Caco-2, HIEC-6) were exposed to 9 forms of Ag, SiO2, and TiO2 (including Food Grade and Non-Food Grade particles, as well as Nanoparticles and Microparticles), and expression of L1000 landmark genes was characterized using the QIAseq UPX 3’ targeted RNA panel kit catalog number 333041 / CSHS-10608Z-990. Each transcript in each well of the RT plate was labeled with a unique molecular index (UMI) for each cDNA molecule, and samples in a given microplate well were labeled with a cell ID. Library quality control assays were performed with the seven prepared libraries and subsequently sequenced on an Illumina NextSeq500. This dataset provides the gene expression values for this study after they were demultiplexed according to the various indexes used (i.e., cell ID and UMI) by the GeneGlobe analysis hub (https://geneglobe.qiagen.com/) to yield UMI count matrices. These RNAseq data are in one spreadsheet. The data were next analyzed for transcriptomics points of departure using www.FastBMD.ca, and these data are provided in a separate zip file (1 file per chemical tested)
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".