No abstract

In biospectroscopy, suitably annotated and statistically independent samples (e.g. patients, batches, etc.) for classifier training and testing are scarce and costly. Learning curves show the model performance as function of the training sample size and can help to determine the sample size needed to train good classifiers. However, building a good model is actually not enough: the performance must also be proven. We discuss learning curves for typical small sample size situations with 5-25 independent samples per class. Although the classification models achieve acceptable performance, the learning curve can be completely masked by the random testing uncertainty due to the equally limited test sample size. In consequence, we determine test sample sizes necessary to achieve reasonable precision in the validation and find that 75-100 samples will usually be needed to test a good but not perfect classifier. Such a data set will then allow refined sample size planning on the basis of the achieved performance. We also demonstrate how to calculate necessary sample sizes in order to show the superiority of one classifier over another: this often requires hundreds of statistically independent test samples or is even theoretically impossible. We demonstrate our findings with a data set of ca. 2550 Raman spectra of single cells (five classes: erythrocytes, leukocytes and three tumour cell lines BT-20, MCF-7 and OCI-AML3) as well as by an extensive simulation that allows precise determination of the actual performance of the models in question.

Raman spectroscopy has been recognized to be a powerful tool to study cells and tissues because the method provides molecular information without external markers such as stains or radioactive labels. To overcome the disadvantage of low signal intensities from most biomolecules, enhancement effects are utilized. A non-linear variant of Raman spectroscopy called coherent anti-Stokes Raman spectroscopy (CARS) belongs to the most promising techniques because it combines signal enhancement due to the coherent nature of the process with further advantages such as directional emission, narrow spectral bandwidth and no disturbing interference with autofluorescence. This review describes briefly the principles of the methods and summarizes applications to cells and tissues that are expected to gain significance in the future such as the combination with imaging approaches, microscopy, optical traps and fiber-optic probes.

Experimental studies in a rat model of fecal peritonitis conducted by Michael Bauer and colleagues show that in this model, changes in liver function occur early in the development of sepsis, with potential implications for prognosis and development of new therapeutic approaches.

Raman spectroscopy is an emerging technique in bioanalysis and imaging of biomaterials owing to its unique capability of generating spectroscopic fingerprints. Imaging cells and tissues by Raman microspectroscopy represents a nondestructive and label-free approach. All components of cells or tissues contribute to the Raman signals, giving rise to complex spectral signatures. Resonance Raman scattering and surface-enhanced Raman scattering can be used to enhance the signals and reduce the spectral complexity. Raman-active labels can be introduced to increase specificity and multimodality. In addition, nonlinear coherent Raman scattering methods offer higher sensitivities, which enable the rapid imaging of larger sampling areas. Finally, fiber-based imaging techniques pave the way towards in vivo applications of Raman spectroscopy. This Review summarizes the basic principles behind medical Raman imaging and its progress since 2012.