The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Discrimination between ricin and sulphur mustard toxicity in vitro using Raman spectroscopy
Open accessAbstract
A Raman spectroscopy cell-based biosensor has been proposed for rapid detection of toxic agents, identification of the type of toxin and prediction of the concentration used. This technology allows the monitoring of the biochemical properties of living cells over long periods of time by measuring the Raman spectra of the cells non-invasively, rapidly and without use of labels (Notingher et al.2004 doi:10.1016/j.bios.2004.04.008). Here we show that this technology can be used to distinguish between changes induced in A549 lung cells by the toxin ricin and the chemical warfare agent sulphur mustard. A multivariate model based on principal component analysis (PCA) and linear discriminant analysis (LDA) was used for the analysis of the Raman spectra of the cells. The leave-one-out cross-validation of the PCA-LDA model showed that the damaged cells can be detected with high sensitivity (98.9%) and high specificity (87.7%). High accuracy in identifying the toxic agent was also found: 88.6% for sulphur mustard and 71.4% for ricin. The prediction errors were observed mostly for the ricin treated cells and the cells exposed to the lower concentration of sulphur mustard, as they induced similar biochemical changes, as indicated by cytotoxicity assays. The concentrations of sulphur mustard used were also identified with high accuracy: 93% for 200 μM and 500 μM, and 100% for 1000 μM. Thus, biological Raman microspectroscopy and PCA-LDA analysis not only distinguishes between viable and damaged cells, but can also discriminate between toxic challenges based on the cellular biochemical and structural changes induced by these agents and the eventual mode of cell death.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Technological advances in high-throughput screening
Fernandes, P. B. (1998) · Current Opinion in Chemical Biology · doi:10.1016/s1367-5931(98)80089-6 · Cited by 170
Cited by
Raman Spectroscopy of Biological Tissues
Movasaghi, Z., Rehman, S., & Rehman, I. U. (2007) · Applied Spectroscopy Reviews · doi:10.1080/05704920701551530 · Cited by 2,518
Raman spectroscopy: techniques and applications in the life sciences
Shipp, D. W., Sinjab, F., & Notingher, I. (2017) · Advances in Optics and Photonics · doi:10.1364/aop.9.000315 · Cited by 396
Self-citationCell death stages in single apoptotic and necrotic cells monitored by Raman microspectroscopy
Brauchle, E., Thude, S., Brucker, S. Y., & Schenke-Layland, K. (2014) · Scientific Reports · doi:10.1038/srep04698 · Cited by 310
Full range characterization of the Raman spectra of organs in a murine model
Huang, N., Short, M., Zhao, J., Wang, H., Lui, H., Korbelik, M., & Zeng, H. (2011) · Optics Express · doi:10.1364/oe.19.022892 · Cited by 131
Hinderer, S., Brauchle, E., & Schenke‐Layland, K. (2015) · Advanced Healthcare Materials · doi:10.1002/adhm.201400762 · Cited by 68
Metadata
- Authors
- Notingher, I., Green, C., Dyer, C., Perkins, E., Hopkins, N., Lindsay, C., & Hench, L. L.
- Venue
- Journal of The Royal Society Interface
- Publisher
- The Royal Society
- Published
- 2004-11-22
- Volume
- 1
- Issue
- 1
- Pages
- 79-90
- ISSN
- 1742-5662 (ISSN-L)
- 1742-5689
- OpenAlex
- W2154743597
- Cited by
- 291
- References
- 1
- FWCI
- 1.81
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Discrimination between ricin and sulphur mustard toxicity in vitro using Raman spectroscopy. https://app.cochat.ai/papers/10.1098/rsif.2004.0008
- The paper
- Notingher, I., Green, C., Dyer, C., Perkins, E., Hopkins, N., Lindsay, C., & Hench, L. L. (2004). Discrimination between ricin and sulphur mustard toxicity in vitro using Raman spectroscopy. Journal of The Royal Society Interface. https://doi.org/10.1098/rsif.2004.0008
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.