Colorimetric and Electrochemical Screening for Early Detection of Diabetes Mellitus and Diabetic Retinopathy-Application of Sensor Arrays and Machine Learning.

In this review, a selection of works on the sensing of biomarkers related to diabetes mellitus (DM) and diabetic retinopathy (DR) are presented, with the scope of helping and encouraging researchers to design sensor-array machine-learning (ML)-supported devices for robust, fast, and cost-effective early detection of these devastating diseases. First, we highlight the social relevance of developing systematic screening programs for such diseases and how sensor-arrays and ML approaches could ease their early diagnosis. Then, we present diverse works related to the colorimetric and electrochemical sensing of biomarkers related to DM and DR with non-invasive sampling (e.

Sulfur-substituted and alpha-methylated fatty acids as peroxisome proliferator-activated receptor activators.

FA with varying chain lengths and an alpha-methyl group and/or a sulfur in the beta-position were tested as peroxisome proliferator-activated receptor (PPAR)alpha, -delta(beta), and -gamma ligands by transient transfection in COS-1 cells using chimeric receptor expression plasmids, containing cDNAs encoding the ligand-binding domain of PPARalpha, -delta, and -gamma. For PPARalpha, an increasing activation was found with increasing chain length of the sulfur-substituted FA up to C14-S acetic acid (tetradecylthioacetic acid = TTA). The derivatives were poor, and nonsignificant, activators of PPARdelta.