Jose Luis Izquierdo-Garcia

Nuclear magnetic resonance (NMR)-based metabolomics is a valuable tool for identifying biomarkers and understanding the underlying metabolic changes associated with various diseases. However, the translation of metabolomics analysis to clinical practice has been limited by the high cost and large size of traditional high-resolution NMR spectrometers. Benchtop NMR, a compact and low-cost alternative, offers the potential to overcome these limitations and facilitate the wider use of NMR-based metabolomics in clinical settings. This review summarizes the current state of benchtop NMR for clinical applications where benchtop NMR has demonstrated the ability to reproducibly detect changes in metabolite levels associated with diseases such as type 2 diabetes and tuberculosis. Benchtop NMR has been used to identify metabolic biomarkers in a range of biofluids, including urine, blood plasma and saliva. However, further research is needed to optimize the use of benchtop NMR for clinical applications and to identify additional biomarkers that can be used to monitor and manage a range of diseases. Overall, benchtop NMR has the potential to revolutionize the way metabolomics is used in clinical practice, providing a more accessible and cost-effective way to study metabolism and identify biomarkers for disease diagnosis, prognosis, and treatment.

Magnetic resonance imaging (MRI) is a valuable diagnostic tool that provides detailed information about the structure and function of tissues in the human body. In particular, measuring relaxation times, such as T1 and T2, can provide important insights into the composition and properties of different tissues. Accurate relaxation time mapping is therefore critical for clinical diagnosis and treatment planning, as it can help to identify and characterize pathological conditions, monitor disease progression, and guide interventions. However, the computation of relaxation time maps in MRI is a complex and challenging task that requires sophisticated mathematical algorithms. Thus, there is a need for robust and accurate algorithms that can reliably extract the desired information from MRI data. This article compares the performance of the Reduced Dimension Nonlinear Least Squares (RD-NLS) algorithm versus several widely used algorithms to compute relaxation times in MRI, such as Levenberg-Marquardt and Nelder-Mead. RD-NLS simplifies the search space for the optimum fit by leveraging the partial linear relationship between signal intensity and model parameters. The comparison was performed on several datasets and signal models, resulting in T1 and T2 maps. The algorithms were evaluated based on their fit error, with the RD-NLS algorithm showing a lower error than other fit-ting algorithms. The improvement was particularly notable in T1 maps, with less of a difference in T2 maps. Additionally, the average T1 values computed with different algorithms differed by up to 14 ms, indicating the importance of algorithm selection. These results suggest that the RD-NLS algorithm outperforms other commonly used algorithms for computing relaxation times in MRI.