Browsing by Author "Rodriguez A."

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    Machine learning-based models for gestational diabetes mellitus prediction before 24–28 weeks of pregnancy: A review
    (Elsevier B.V., 2022) Mennickent D.; Guzman-Gutierrez E.; Araya J.; Rodriguez A.; Farias-Jofre M.
    © 2022Gestational Diabetes Mellitus (GDM) is a hyperglycemia state that impairs maternal and offspring health, short and long-term. It is usually diagnosed at 24–28 weeks of pregnancy (WP), but at that time the fetal phenotype is already altered. Machine learning (ML)-based models have emerged as an auspicious alternative to predict this pathology earlier, however, they must be validated in different populations before their implementation in routine clinical practice. This review aims to give an overview of the ML-based models that have been proposed to predict GDM before 24–28 WP, with special emphasis on their current validation state and predictive performance. Articles were searched in PubMed. Manuscripts written in English and published before January 1, 2022, were considered. 109 original research studies were selected, and categorized according to the type of variables that their models involved: medical, i.e. clinical and/or biochemical parameters; alternative, i.e. metabolites, peptides or proteins, micro-ribonucleic acid molecules, microbiota genera, or other variables that did not fit into the first category; or mixed, i.e. both medical and alternative data. Only 8.3 % of the reviewed models have had validation in independent studies, with low or moderate performance for GDM prediction. In contrast, several models that lack of independent validation have shown a very high predictive power. The evaluation of these promising models in future independent validation studies would allow to assess their performance on different populations, and continue their way towards clinical implementation. Once settled, ML-based models would help to predict GDM earlier, initiate its treatment timely and prevent its negative consequences on maternal and offspring health.
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    Multivariable optimization of ultrasound-assisted extraction for the determination of phenolic and antioxidants compounds from arrayan (Luma apiculata (DC.) Burret) leaves by microplate-based methods and mass spectrometry
    (Elsevier GmbH, 2022) C.-Sandoval J.; Rodriguez A.; H.-Aedo K.; Falco I.; Sanchez G.; Fabra M.J.; L.-Rubio A.; Aranda M.
    © 2022 Elsevier GmbHThe present study reports a multivariable optimization of ultrasound-assisted extraction of phenolic compounds from arrayan leaves. The parameters ethanol percentage in extraction solvent, temperature and extraction time, and mass to solvent ratio were optimized applying full factorial and central composite designs. According to the models, the optimal extraction conditions were: 42% of ethanol, extraction time of 27 min, extraction temperature of 50ºC and a mass to solvent ratio of 1:33.4 g mL−1. Under these conditions, a total phenolic content (TPC) of 128.16 ± 1.18 and 593.64 ± 6.49 mg of gallic acid equivalent per g of dry weight (DW) were determined in raw extract (RE) and polyphenols-rich extract (PRE), which was obtained by semi-preparative chromatography on XAD-7 column. Antioxidant capacity determined via FRAP analysis showed values of 1349.53 ± 28.99 and 6175.47 ± 127.64 µmol Trolox equivalent per g of DW for RE and PRE, and DPPH IC50 values of 831.40 ± 0.80 and 132.21 ± 2.51 µg mL−1, respectively. Polyphenols profile analyzed by liquid chromatography-mass spectrometry showed the presence of quercetin 3-ß-D-glucoside, myricetin, quercetin, kaempferol 3-glucoside and gallic acid; the latter compounds are reported for the first time in arrayan leaves. Due to the presence of polyphenols, antiviral activity was assayed over norovirus and hepatitis A showing a significant dose-dependent effect.