Invasive fungal infections caused by yeasts of the genus Candida carry high morbidity and cause systemic infections with high mortality rate in both immunocompetent and immunosuppressed patients. Resistance rates against antifungal drugs vary among Candida species, the most concerning specie being Candida auris, which exhibits resistance to all major classes of available antifungal drugs. The presently available identification methods for Candida species face a severe trade-off between testing speed and accuracy. Here, we propose and validate a machine-learning approach adapted to Raman spectroscopy as a rapid, precise, and labor-efficient method of clinical microbiology for C. auris identification and drug efficacy assessments. This paper demonstrates that the combination of Raman spectroscopy and machine learning analyses can provide an insightful and flexible mycology diagnostic tool, easily applicable on-site in the clinical environment.

The transplantation of engineered three-dimensional (3D) bone graft substitutes is a viable approach to the regeneration of severe bone defects. For large bone defects, an appropriate 3D scaffold may be necessary to support and stimulate bone regeneration, even when a sufficient number of cells and cell cytokines are available. In this study, we evaluated the in vivo performance of a nanogel tectonic 3D scaffold specifically developed for bone tissue engineering, referred to as nanogel cross-linked porous-freeze-dry (NanoCliP-FD) gel. Samples were characterized by a combination of micro-computed tomography scanning, Raman spectroscopy, histological analyses, and synchrotron radiation–based Fourier transform infrared spectroscopy. NanoCliP-FD gel is a modified version of a previously developed nanogel cross-linked porous (NanoCliP) gel and was designed to achieve highly improved functionality in bone mineralization. Spectroscopic imaging of the bone tissue grown in vivo upon application of NanoCliP-FD gel enables an evaluation of bone quality and can be employed to judge the feasibility of NanoCliP-FD gel scaffolding as a therapeutic modality for bone diseases associated with large bone defects.

Oral candidiasis, a common opportunistic infection of the oral cavity, is mainly caused by the following four Candida species (in decreasing incidence rate): Candida albicans, Candida glabrata, Candida tropicalis, and Candida krusei. This study offers in-depth Raman spectroscopy analyses of these species and proposes procedures for an accurate and rapid identification of oral yeast species. We first obtained average spectra for different Candida species and systematically analyzed them in order to decode structural differences among species at the molecular scale. Then, we searched for a statistical validation through a chemometric method based on principal component analysis (PCA). This method was found only partially capable to mechanistically distinguish among Candida species. We thus proposed a new Raman barcoding approach based on an algorithm that converts spectrally deconvoluted Raman sub-bands into barcodes. Barcode-assisted Raman analyses could enable on-site identification in nearly real-time, thus implementing preventive oral control, enabling prompt selection of the most effective drug, and increasing the probability to interrupt disease transmission.

Tooth loss impairs mastication, deglutition and esthetics and affects systemic health through nutritional deficiency, weight loss, muscle weakness, delayed wound healing, and bone fragility. Approximately 90% of tooth loss is due to dental caries and periodontal disease. Accordingly, early treatment of dental caries is essential to maintaining quality of life. To date, the clinical diagnosis of dental caries has been based on each dentist’s subjective assessment, but this visual method lacks objectivity. To improve diagnostic ability, highly sensitive quantitative methods have been developed for the diagnosis and prevention of dental caries and are gradually becoming a mandatory item in modern dentistry. High-resolution Raman spectroscopy is a suitable tool for recognizing the subtle structural changes that occur in dental enamel in already developed or, more importantly, incipient dental caries. Raman analysis could soon emerge as a breakthrough in dentistry because of its high diagnostic sensitivity. In this study, we build upon our previous findings in a new analysis of dental caries using Raman spectroscopy imaging and discuss the possibility of using Raman photonic imaging in support of objective diagnostics in dentistry. Our findings support the Raman method of caries detection in comparison with other conventional or new approaches.

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