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Dra. Mennickent-Barros, Daniela
Nombre de publicación
Dra. Mennickent-Barros, Daniela
Nombre completo
Mennickent Barros, Daniela Francisca
Facultad
Email
dmennickent@ucsc.cl
ORCID
2 results
Research Outputs
Now showing 1 - 2 of 2
- PublicationSimple and fast prediction of gestational diabetes mellitus based on machine learning and near-infrared spectra of serum: A proof of concept study at different stages of pregnancy(MDPI, 2024)
; ;Romero-Albornoz, Lucas ;Gutiérrez-Vega, Sebastián ;Aguayo, Claudio ;Marini, Federico ;Guzmán-Gutiérrez, EnriqueAraya, JuanGestational 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. - PublicationSex and disease severity-based analysis of steroid hormones in ME/CFS(Springer Nature, 2024)
; ;Pipper, Cornelia ;Bliem, Linda ;León, Luis ;Bodner, Claudia ;Guzmán‑Gutiérrez, Enrique ;Stingl, Michael ;Untersmayr, Eva ;Wagner, Bernhard ;Bertinat, Romina ;Sepúlveda, NunoWestermeier, FranciscoPurpose: 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.