CLIRPath-AI – Linking Spectroscopy, Pathology and AI

Raman spectroscopy supported by machine learning reveals changes in balance of macromolecules in diabetic rat serum - Analytical and Bioanalytical Chemistry

In type 2 diabetes mellitus (T2DM), changes in glucose and lipid levels in serum are the first signals of disease progression. In this article, Raman spectroscopy supported with machine learning (ML) was used both to determine the level of imbalance between major metabolites caused by the disease and to investigate which metabolites were impacted the most. The principal component analysis separated the T2DM and control rats by PC1 and PC3. The bands that enabled that separation were assigned to all four main metabolite groups—amino acids (745–763 cm−1, 985–1027 cm−1), amides (1199–1258 cm−1, 1574–1600 cm−1, 1653–1712 cm−1), polysaccharides (1104–1192 cm−1), and lipids (1442–1472 cm−1, 2831–3041 cm−1). Moreover, diabetes induction caused major correlation changes between both proteins (amide) and the general amino acid band, with the organic matter band (CH band), which mainly arises due to lipid presence. Moreover, four different machine algorithms were employed to detect changes between the groups. AdaBoost showed the best performance in classifying the serum samples from diabetic and control rats (F1 = 0.84), which was further refined when only the relevant bands were used for learning and classification (F1 = 0.94). The bands responsible for the separation suggest that T2DM induces alterations in the protein profile, including changes in the levels of aromatic amino acids, as well as in both the quantity and composition of lipids, which is further confirmed by the ML classification.

Application of Raman and ATR-MIR Spectroscopy in Colorectal Cancer Diagnosis Combined with Chemometrics Techniques: A Review

Colorectal Cancer (CC) is recognized as the third most prevalent cancer worldwide and constitutes a major cause of cancer-related fatalities among both genders. Current diagnostic approaches for CC...

Emerging Trends in Pharmaceutical and Biopharmaceutical Analysis

RamanSPy: An Open-Source Python Package for Integrative Raman Spectroscopy Data Analysis

Raman spectroscopy is a nondestructive and label-free chemical analysis technique, which plays a key role in the analysis and discovery cycle of various branches of science. Nonetheless, progress in Raman spectroscopic analysis is still impeded by the lack of software, methodological and data standardization, and the ensuing fragmentation and lack of reproducibility of analysis workflows thereof. To address these issues, we introduce RamanSPy, an open-source Python package for Raman spectroscopic research and analysis. RamanSPy provides a comprehensive library of tools for spectroscopic analysis that supports day-to-day tasks, integrative analyses, the development of methods and protocols, and the integration of advanced data analytics. RamanSPy is modular and open source, not tied to a particular technology or data format, and can be readily interfaced with the burgeoning ecosystem for data science, statistical analysis, and machine learning in Python. RamanSPy is hosted at https://github.com/barahona-research-group/RamanSPy, supplemented with extended online documentation, available at https://ramanspy.readthedocs.io, that includes tutorials, example applications, and details about the real-world research applications presented in this paper.