Dra. Mennickent-Barros, Daniela

Berberis microphylla G Forst (Calafate) have been used in traditional medicine from prehispanic times in Patagonia. In the last decade the consumption of the fruit has been increased due to their antioxidant capacity, and because several studies demonstrated health benefits associated with the protection against atherosclerosis and other metabolic diseases. Nevertheless, the bioactivity properties of the leaves, a by-product of agronomic management, have been poorly studied. Recently, 108 compounds mainly hydroxycinnamic acids, flavonols, and berberine were identified in a methanolic extract of the leaves, demonstrating great potential for the development of new functional beverages. Based on these, for first time a comprehensive chemical characterization and bioactivity was evaluated for a Calafate leaves infusion prepared in hot water. For this, chemical characterization of the infusion was performed by UHPLC-Q-TOF and TXRF. Bioactivity was assayed by antioxidant capacity, cell cytotoxicity, and cell oxidative stress assays. Inhibition of both Aβ aggregation for Alzheimer's disease and gastrointestinal enzymes for metabolic syndromes were evaluated. The results show that the infusion is rich in hydroxycinnamic acids and other bioactive compounds. The infusion does not contain toxic metals or cytotoxicity activity. The infusion can reduce intracellular reactive oxygen species in HUVEC cells and showed a reduction in the Aβ aggregation being a potential beverage for Alzheimer's prevention. Finally, the infusion had in-vitro hypoglycemic and hypolipidemic effects. These results support the usage of Berberis microphylla G Forst leaves as a new functional beverage.

Gestational diabetes mellitus (GDM) is a hyperglycemic state that is typically diagnosed by an oral glucose tolerance test (OGTT), which is unpleasant, time-consuming, has low reproducibility, and results are tardy. The machine learning (ML) predictive models that have been proposed to improve GDM diagnosis are usually based on instrumental methods that take hours to produce a result. Near-infrared (NIR) spectroscopy is a simple, fast, and low-cost analytical technique that has never been assessed for the prediction of GDM. This study aims to develop ML predictive models for GDM based on NIR spectroscopy, and to evaluate their potential as early detection or alternative screening tools according to their predictive power and duration of analysis. Serum samples from the first trimester (before GDM diagnosis) and the second trimester (at the time of GDM diagnosis) of pregnancy were analyzed by NIR spectroscopy. Four spectral ranges were considered, and 80 mathematical pretreatments were tested for each. NIR data-based models were built with single- and multi-block ML techniques. Every model was subjected to double cross-validation. The best models for first and second trimester achieved areas under the receiver operating characteristic curve of 0.5768 ± 0.0635 and 0.8836 ± 0.0259, respectively. This is the first study reporting NIR-spectroscopy-based methods for the prediction of GDM. The developed methods allow for prediction of GDM from 10 µL of serum in only 32 min. They are simple, fast, and have a great potential for application in clinical practice, especially as alternative screening tools to the OGTT for GDM diagnosis.

Purpose: Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a debilitating disease characterized by persistent fatigue and decreased daily activity following physical and/or cognitive exertion. While ME/CFS afects both sexes, there is a higher prevalence in women. However, studies evaluating this sex-related bias are limited. Methods: Circulating steroid hormones, including mineralocorticoids (aldosterone), glucocorticoids (cortisol, corticosterone, 11-deoxycortisol, cortisone), androgens (androstenedione, testosterone), and progestins (progesterone, 17α-hydroxyprogesterone), were measured in plasma samples using ultra-high performance liquid chromatography–tandem mass spectrometry (UHPLC–MS/MS). Samples were obtained from mild/moderate (ME/CFSmm; females, n=20; males, n=8), severely afected patients (ME/CFSsa; females, n=24; males, n=6), and healthy controls (HC, females, n=12; males, n=17). Results: After correction for multiple testing, we observed that circulating levels of 11-deoxycortisol, 17α-hydroxyprogesterone in females, and progesterone in males were signifcantly diferent between HC, ME/CFSmm, and ME/CFSsa. Comparing two independent groups, we found that female ME/CFSsa had higher levels of 11-deoxycortisol (vs. HC and ME/CFSmm) and 17α-hydroxyprogesterone (vs. HC). In addition, female ME/CFSmm showed a signifcant increase in progesterone levels compared to HC. In contrast, our study found that male ME/CFSmm had lower circulating levels of cortisol and corticosterone, while progesterone levels were elevated compared to HC. In addition to these univariate analyses, our correlational and multivariate approaches identifed diferential associations between our study groups. Also, using two-component partial least squares discriminant analysis (PLS-DA), we were able to discriminate ME/CFS from HC with an accuracy of 0.712 and 0.846 for females and males, respectively. Conclusion: Our fndings suggest the potential value of including steroid hormones in future studies aimed at improving stratifcation in ME/CFS. Additionally, our results provide new perspectives to explore the clinical relevance of these diferences within specifc patient subgroups.

Maternal thyroid alterations have been widely associated with the risk of gestational diabetes mellitus (GDM). This study aims to 1) test the first and the second trimester full maternal thyroid profile on the prediction of GDM, both alone and combined with non-thyroid data; and 2) make that prediction independent of the diagnostic criteria, by evaluating the effectiveness of the different maternal variables on the prediction of oral glucose tolerance test (OGTT) post load glycemia. Pregnant women were recruited in Concepción, Chile. GDM diagnosis was performed at 24–28 weeks of pregnancy by an OGTT (n = 54 for normal glucose tolerance, n = 12 for GDM). 75 maternal thyroid and non-thyroid parameters were recorded in the first and the second trimester of pregnancy. Various combinations of variables were assessed for GDM and post load glycemia prediction through different classification and regression machine learning techniques. The best predictive models were simplified by variable selection. Every model was subjected to leave-one-out cross-validation. Our results indicate that thyroid markers are useful for the prediction of GDM and post load glycemia, especially at the second trimester of pregnancy. Thus, they could be used as an alternative screening tool for GDM, independently of the diagnostic criteria used. The final classification models predict GDM with cross-validation areas under the receiver operating characteristic curve of 0.867 (p<0.001) and 0.920 (p<0.001) in the first and the second trimester of pregnancy, respectively. The final regression models predict post load glycemia with cross-validation Spearman r correlation coefficients of 0.259 (p = 0.036) and 0.457 (p<0.001) in the first and the second trimester of pregnancy, respectively. This investigation constitutes the first attempt to test the performance of the whole maternal thyroid profile on GDM and OGTT post load glycemia prediction. Future external validation studies are needed to confirm these findings in larger cohorts and different populations.

Polyphenols are bioactive substances that participate in the prevention of chronic illnesses. High content has been described in Berberis microphylla G. Forst (calafate), a wild berry extensively distributed in Chilean–Argentine Patagonia. We evaluated its beneficial effect through the study of mouse plasma metabolome changes after chronic consumption of this fruit. Characterized calafate extract was administered in water, for four months, to a group of mice fed with a high-fat diet and compared with a control diet. Metabolome changes were studied using UHPLC-DAD-QTOF-based untargeted metabolomics. The study was complemented by the analysis of protein biomarkers determined using Luminex technology, and quantification of OH radicals by electron paramagnetic resonance spectroscopy. Thirteen features were identified with a maximum annotation level-A, revealing an increase in succinic acid, activation of tricarboxylic acid and reduction of carnitine accumulation. Changes in plasma biomarkers were related to inflammation and cardiovascular disease, with changes in thrombomodulin (−24%), adiponectin (+68%), sE-selectin (−34%), sICAM-1 (−24%) and proMMP-9 (−31%) levels. The production of OH radicals in plasma was reduced after calafate intake (−17%), especially for the group fed with a high-fat diet. These changes could be associated with protection against atherosclerosis due to calafate consumption, which is discussed from a holistic and integrative point of view.

Introduction: Machine learning (ML) corresponds to a wide variety of methods that use mathematics, statistics and computational science to learn from multiple variables simultaneously. By means of pattern recognition, ML methods are able to find hidden correlations and accomplish accurate predictions regarding different conditions. ML has been successfully used to solve varied problems in different areas of science, such as psychology, economics, biology and chemistry. Therefore, we wondered how far it has penetrated into the field of obstetrics and gynecology. Aim: To describe the state of art regarding the use of ML in the context of pregnancy diseases and complications. Methodology: Publications were searched in PubMed, Web of Science and Google Scholar. Seven subjects of interest were considered: gestational diabetes mellitus, preeclampsia, perinatal death, spontaneous abortion, preterm birth, cesarean section, and fetal malformations. Current state: ML has been widely applied in all the included subjects. Its uses are varied, the most common being the prediction of perinatal disorders. Other ML applications include (but are not restricted to) biomarker discovery, risk estimation, correlation assessment, pharmacological treatment prediction, drug screening. data acquisition and data extraction. Most of the reviewed articles were published in the last five years. The most employed ML methods in the field are non-linear. Except for logistic regression, linear methods are rarely used. Future challenges: To improve data recording, storage and update in medical and research settings from different realities. To develop more accurate and understandable ML models using data from cutting-edge instruments. To carry out validation and impact analysis studies of currently existing high-accuracy ML models. Conclusion: The use of ML in pregnancy diseases and complications is quite recent, and has increased over the last few years. The applications are varied and point not only to the diagnosis, but also to the management, treatment, and pathophysiological understanding of perinatal alterations. Facing the challenges that come with working with different types of data, the handling of increasingly large amounts of information, the development of emerging technologies, and the need of translational studies, it is expected that the use of ML continue growing in the field of obstetrics and gynecology.

Gestational 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.

Gestational Diabetes Mellitus (GDM) is a highly prevalent maternal pathology characterized by maternal glucose intolerance during pregnancy that is, associated with severe complications for both mother and offspring. Several risk factors have been related to GDM; one of the most important among them is genetic predisposition. Numerous single nucleotide polymorphisms (SNPs) in genes that act at different levels on various tissues, could cause changes in the expression levels and activity of proteins, which result in glucose and insulin metabolism dysfunction. In this review, we describe various SNPs; which according to literature, increase the risk of developing GDM. These SNPs include: (1) those associated with transcription factors that regulate insulin production and excretion, such as rs7903146 (TCF7L2) and rs5015480 (HHEX); (2) others that cause a decrease in protective hormones against insulin resistance such as rs2241766 (ADIPOQ) and rs6257 (SHBG); (3) SNPs that cause modifications in membrane proteins, generating dysfunction in insulin signaling or cell transport in the case of rs5443 (GNB3) and rs2237892 (KCNQ1); (4) those associated with enzymes such as rs225014 (DIO2) and rs9939609 (FTO) which cause an impaired metabolism, resulting in an insulin resistance state; and (5) other polymorphisms, those are associated with growth factors such as rs2146323 (VEGFA) and rs755622 (MIF) which could cause changes in the expression levels of these proteins, producing endothelial dysfunction and an increase of pro-inflammatory cytokines, characteristic on GDM. While the pathophysiological mechanism is unclear, this review describes various potential effects of these polymorphisms on the predisposition to develop GDM.

The high sensitivity Compton and Rayleigh X-ray scattering signals can be used to gain valuable information on the chemical composition of various matrices, by exploiting the ratio of those signals as a function of the effective atomic number (Zeff). Neither total reflection X-ray fluorescence (TXRF) nor the effect of the experimental setup, including sample preparation, X-ray excitation source selection, and band deconvolution procedure, has been assessed in this kind of approach. Here, a Compton/Rayleigh ratio and Zeff-based TXRF method was set up and tested as an analytical tool for milk samples differentiation. The method was developed using a 90° scattering angle and assessed using different X-ray excitation sources: a molybdenum tube (Mo Kα 17.5 KeV) and a tungsten tube (W Lα 8.5 KeV and W-Brems 35 KeV). The evaluation of independent Compton and Rayleigh signals was performed by non-Gaussian and Gaussian curve resolution methods, and both height and area-based calculations were evaluated. Different sample preparation conditions were assessed. By using 11 standard materials, a calibration curve for Compton/Rayleigh ratio versus Zeff was established. The method was tested to determine the Zeff of milk samples, which enabled its use as a parameter to differentiate them. Good precisions were obtained with the Mo excitation source and the area-based calculations, which allowed to differentiate undiluted milk samples by species, treatment, and fat content according to their Compton/Rayleigh ratio. This simple and rapid method has the potential to be used for the differentiation of various types of samples, including liquids, solids, and aerosols.

There is evidence about a possible relationship between thyroid abnormalities and gestational diabetes mellitus (GDM). However, there is still no conclusive data on this dependence, since no strong correlation has been proved. In this work, we used machine learning to determine whether there is a correlation between maternal thyroid profile in first and second trimester of pregnancy and GDM. Using principal component analysis, it was possible to find an evident correlation between both, which could be used as a complement for a more sensitive GDM diagnosis.