Raman spectroscopic studies on screening of myopathies.

Myopathies are among the major causes of mortality in the world. There is no complete cure for this heterogeneous group of diseases, but a sensitive, specific, and fast diagnostic tool may improve therapy effectiveness. In this study, Raman spectroscopy is applied to discriminate between muscle mutants in Drosophila on the basis of associated changes at the molecular level. Raman spectra were collected from indirect flight muscles of mutants, upheld(1) (up(1)), heldup(2) (hdp(2)), myosin heavy chain(7) (Mhc(7)), actin88F(KM88) (Act88F(KM88)), upheld(101) (up(101)), and Canton-S (CS) control group, for both 2 and 12 days old flies. Difference spectra (mutant minus control) of all the mutants showed an increase in nucleic acid and ß-sheet and/or random coil protein content along with a decrease in α-helix protein. Interestingly, the 12th day samples of up(1) and Act88F(KM88) showed significantly higher levels of glycogen and carotenoids than CS. A principal components based linear discriminant analysis classification model was developed based on multidimensional Raman spectra, which classified the mutants according to their pathophysiology and yielded an overall accuracy of 97% and 93% for 2 and 12 days old flies, respectively. The up(1) and Act88F(KM88) (nemaline-myopathy) mutants form a group that is clearly separated in a linear discriminant plane from up(101) and hdp(2) (cardiomyopathy) mutants. Notably, Raman spectra from a human sample with nemaline-myopathy formed a cluster with the corresponding Drosophila mutant (up(1)). In conclusion, this is the first demonstration in which myopathies, despite their heterogeneity, were screened on the basis of biochemical differences using Raman spectroscopy.

Feature Selection and Rapid Characterization of Bloodstains on Different Substrates.

Establishing the precise timeline of a crime can be challenging as current analytical techniques used suffer from many limitations and are destructive to the body fluids encountered at crime scenes. Raman spectroscopy has demonstrated excellent potential in forensic science as it provides direct information about the structural and molecular changes without the need for processing or extracting samples. However, its current applicability is limited to pure body fluids, as signals from the substrate underlying these fluids greatly influence the current models used for age estimation. In this study, we utilized Raman spectroscopy to identify selective spectral markers that delineate the bloodstain age in the presence of interfering signals from the substrate. The pure bloodstains and the bloodstains on the substrate were aged for two weeks at 21 ± 2 ℃ in the dark. Least absolute shrinkage and selection operator (LASSO) regression was employed to guide the feature selection in the presence of interference from substrates to accurately predict the bloodstain age. Substrate-specific regression models guided by an automated feature selection algorithm yielded low values of predictive root mean square error (0.207, 0.204, 0.222 h in logarithmic scale) and high R2 (0.924, 0.926, 0.913) on test data consisting of blood spectra on floor tile, facial tissue, and linoleum-polymer substrates, respectively. This framework for an automated feature selection algorithm relies entirely on pure bloodstain spectra to train substrate-specific models for estimating the age of composite (blood on substrate) spectra. The model can thus be easily applied to any new composite spectra and is highly scalable to new environments. This study demonstrates that Raman spectroscopy coupled with LASSO could serve as a reliable and nondestructive technique to determine the age of bloodstains on any surface while aiding forensic investigations in real-world scenarios.