The use of vibrational spectroscopy techniques, such as Fourier-transform infrared (FTIR) and Raman spectroscopy, has been a successful method to study the interaction of light with biological materials and facilitate novel cell biology analysis. Disease screening and diagnosis, microbiological studies, forensic and environmental investigations make use of spectrochemical analysis very attractive due to its low cost, minimal sample preparation, non-destructive nature and substantially accurate results. However, there is now an urgent need for multivariate classification protocols allowing one to analyse biological-derived spectrochemical data in order to obtain accurate and reliable results. This is stimulated by the fact that applications of deep-learning algorithms of complex datasets are being increasingly recognized as critical towards extracting important information and visualizing it in a readily interpretable form. Hereby, we have constructed a protocol for multivariate classification analysis of vibrational spectroscopy data [FTIR, Raman and near-infrared (NIR)] highlighting a series of critical steps, such as pre-processing, data selection, feature extraction, classification and model validation. This is an essential aspect towards the construction of a practical spectrochemical analysis model for biological analysis in real-world applications, where fast, accurate and reliable classification models are fundamental.

There is an urgent need for ultrarapid testing regimens to detect the severe acute respiratory syndrome coronavirus 2 [SARS-CoV-2] infections in real-time within seconds to stop its spread. Current testing approaches for this RNA virus focus primarily on diagnosis by RT-qPCR, which is time-consuming, costly, often inaccurate, and impractical for general population rollout due to the need for laboratory processing. The latency until the test result arrives with the patient has led to further virus spread. Furthermore, latest antigen rapid tests still require 15–30 min processing time and are challenging to handle. Despite increased polymerase chain reaction (PCR)-test and antigen-test efforts, the pandemic continues to evolve worldwide. Herein, we developed a superfast, reagent-free, and nondestructive approach of attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy with subsequent chemometric analysis toward the prescreening of virus-infected samples. Contrived saliva samples spiked with inactivated γ-irradiated COVID-19 virus particles at levels down to 1582 copies/mL generated infrared (IR) spectra with a good signal-to-noise ratio. Predominant virus spectral peaks are tentatively associated with nucleic acid bands, including RNA. At low copy numbers, the presence of a virus particle was found to be capable of modifying the IR spectral signature of saliva, again with discriminating wavenumbers primarily associated with RNA. Discrimination was also achievable following ATR-FTIR spectral analysis of swabs immersed in saliva variously spiked with virus. Next, we nested our test system in a clinical setting wherein participants were recruited to provide demographic details, symptoms, parallel RT-qPCR testing, and the acquisition of pharyngeal swabs for ATR-FTIR spectral analysis. Initial categorization of swab samples into negative versus positive COVID-19 infection was based on symptoms and PCR results ( n = 111 negatives and 70 positives). Following training and validation (using n = 61 negatives and 20 positives) of a genetic algorithm-linear discriminant analysis (GA-LDA) algorithm, a blind sensitivity of 95% and specificity of 89% was achieved. This prompt approach generates results within 2 min and is applicable in areas with increased people traffic that require sudden test results such as airports, events, or gate controls.

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The synucleins are a small, soluble, highly conserved group of neuronal proteins that have been implicated in both neurodegenerative diseases and cancer. The synuclein family consists of alpha-, beta-, and gamma-synucleins (gamma-syn). They are a natively unfolded group of proteins that share sequence homologies and structural properties. So far, the biological functions of the synucleins are still unclear, but their involvement in neurodegenerative diseases and cancer may provide insights into the pathological processes that result from these two groups of debilitating diseases, and present the possibility to use them as potential targets for early diagnosis and treatment. Recently, elevated levels of gamma-syn proteins have been detected in various types of cancer, especially in advanced stages of the disease. Furthermore, studies to date indicate that overexpression of gamma-syn compromises normal mitotic checkpoint controls, resulting in multinucleation as well as faster cell growth. Gamma-syn has also been shown to promote invasion and metastasis in in vitro assays as well as in animal models. Overexpression of gamma-syn also interferes with drug-induced apoptotic responses. These observations raise questions about the involvement of gamma-syn in the process of tumorigenesis and metastasis, and efforts have already been made to use gamma-syn as a marker for assessing breast cancer progression. This review will discuss the involvement of gamma-syn in cancer progression, metastasis and its potential as a marker.

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