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Effects of glucosinolate-enriched red radish (Raphanus sativus) on in vitro models of intestinal microbiota and metabolic syndrome-related functionalities
No full textThe gut microbiotaprofile is determined by diet composition, andtherefore this interaction is crucial for promoting specific bacterialgrowth and enhancing the health status. Red radish (Raphanus sativus L.) contains severalsecondary plant metabolites that can exert a protective effect onhuman health. Recent studies have shown that radish leaves have ahigher content of major nutrients, minerals, and fiber than roots,and they have garnered attention as a healthy food or supplement.Therefore, the consumption of the whole plant should be considered,as its nutritional value may be of greater interest. The aim of thiswork is to evaluate the effects of glucosinolate (GSL)-enriched radishwith elicitors on the intestinal microbiota and metabolic syndrome-relatedfunctionalities by using an in vitro dynamic gastrointestinalsystem and several cellular models developed to study the GSL impacton different health indicators such as blood pressure, cholesterolmetabolism, insulin resistance, adipogenesis, and reactive oxygenspecies (ROS). The treatment with red radish had an influence on short-chainfatty acids (SCFA) production, especially on acetic and propionicacid and many butyrate-producing bacteria, suggesting that consumptionof the entire red radish plant (leaves and roots) could modify thehuman gut microbiota profile toward a healthier one. The evaluationof the metabolic syndrome-related functionalities showed a significantdecrease in the gene expression of endothelin, interleukin IL-6, andcholesterol transporter-associated biomarkers (ABCA1 and ABCG5), suggestingan improvement of three risk factors associated with metabolic syndrome.The results support the idea that the use of elicitors on red radishcrops and its further consumption (the entire plant) may contributeto improving the general health status and gut microbiota profile
Effect of particle size on grain growth of Nd-Fe-B powders produced by gas atomization
Get PDFGas atomized Nd-Fe-B powders of several compositions were separated in different size fractions by sieving. These fractions were annealed between 1100 degrees C and 1150 degrees C for 24 and 96 h. The oxygen content of the powders was measured before and after annealing for the different size fractions. The oxygen concentration of the powders depends strongly on the particle size and increases significantly during annealing, particularly in the case of small particle sizes. The effect of particle size on the microstructural changes was analyzed in detail, particularly on grain growth, using high resolution scanning electron microscopy and transmission electron microscopy. Electron back scattering diffraction was used to measure grain size. When the particle size rises, the degree of sintering decreases and the higher solid/vapor surface area reduces the mobility of grain boundaries. Oxidation also reduces grain growth rate and its effect is more evident for particles sizes below 45-63 mu m and high Nd concentrations. Nb addition leads to the formation of intra- and intergranular precipitates. The size of these Nb-Fe-containing precipitates increases with the particle size for equivalent annealing conditions. At 1150 degrees C, Nb loses its effect as an inhibitor of grain growth in the particle size fractions larger than 45-63 mu m
Interpretable precision medicine for acute myeloid leukemia
Get PDFPrecision medicine (PM) is a branch of medicine that defines a disease at a higher
resolution using genetic and other technologies to enable more specific targeting of its
subgroups. Because of its uses in clinical treatment and diagnostics, this field exemplifies
the modern era of medicine. PM looks for not just the right drug, but also the right dosage
and treatment regimen. PM encounters a variety of challenges, which will be explored in
this dissertation.
Large-scale sensitivity screens and whole-exome sequencing experiments (WES) have
fostered a new wave of targeted treatments based on finding associations between drug
sensitivity and response biomarkers. These experiments with the aid of state-of-the-art
artificial intelligence (AI) algorithms are opening new therapeutic opportunities for diseases
with unmet clinical needs. It has been proved that AI is capable of predicting novel
personalized treatments based on complex genotypic and phenotypic patterns in tumors.
The scientific community should make an effort to make these algorithms to be interpretable
to humans so that the results could be easily approved by the medical regulators. The
purpose of this thesis is to apply AI algorithms for precision oncology that are highly
accurate, while guaranteeing that the predictions are interpretable by humans.
This work is divided in three main sections. The first section comprises a new methodology
to increase the predictive power of the discovery of novel treatments in large-scale
screenings by exploiting that some biomarkers tend to appear in many treatments. This fact
is called hub effect in gene essentiality (HUGE). Content of this section was published in
[1]. The second section contains a novel interpretable AI method -called multi-dimensional
module optimization (MOM)- that associates drug screening with genetic events and
proposes a treatment guideline. Content of this section was published in [2]. Finally, the
third section includes a detailed comparison of different recently published algorithms that
attempt to overcome the barriers proposed by today's precision medicine. This study also
includes two novel algorithms specifically designed to solve the challenges of applicability
to clinical practice: Optimal Decision Tree (ODT) and Multinomial Lasso.
The characterization of Interpretable Artificial Intelligence as approach with strong potential
for use in clinical practice is one of the study's most significant achievements. We presen tunique methods for PM that are highly interpretable, and we summarize the needs that
could be considered for constructing interpretable AI. We are confident that this method will
transform the way PM is addressed, bridging the gap between AI and clinical practice
Experimental designs for controlling the correlation of estimators in two-parameter models
No full textThe state of the art related to parameter correlation in two-parameter models
has been reviewed in this paper. The apparent contradictions between the different authors regarding the ability of D-optimality to simultaneously reduce
the correlation and the area of the confidence ellipse in two-parameter models
were analyzed. Two main approaches were found: (1) those who consider that
the optimality criteria simultaneously control the precision and correlation of
the parameter estimators and (2) those that consider a combination of criteria
to achieve the same objective. An analytical criterion combining in its structure both the optimality of the precision of the estimators of the parameters
and the reduction of the correlation between their estimators is provided. The
criterion was tested both in a simple linear regression model, considering all
possible design spaces, and in a nonlinear model with strong correlation of the
estimators of the parameters (Michaelis–Menten) to show its performance.
This criterion showed a superior behavior to all the strategies and criteria to
control at the same time the precision and the correlation
The diagnostic accuracy of transvaginal ultrasound for detection of ureteral involvement in deep infiltrating endometriosis
No full textObjective—The aim of this study is to determine the accuracy of transvaginal
ultrasound (TVU) for the diagnosis of ureteral involvement in women with deep
infiltrating endometriosis (DIE).
Methods—The meta-analysis included primary studies comparing the use of
TVU for diagnosing endometriotic involvement of the ureter, using laparoscopic surgery and histological diagnosis as the reference standard. Search
was performed in several databases (Scopus, Web of Science, and PubMed/
MEDLINE). The studies’ quality and bias risk were assessed using the
Quality Assessment of Diagnostic Accuracy Study-2 (QUADAS-2). Diagnostic performance was estimated by assessing pooled sensitivity and
specificity.
Results—A total of 496 citations were found. Six articles were ultimately selected
for this systematic review and meta-analysis after the inclusion and exclusion
criteria were applied. Pooled sensitivity and specificity were 0.81 (95% CI: 0.42–
0.96), 1.00 (95% CI: 0.93–1.00). The heterogeneity observed was high for both
sensitivity and specificity. Overall risk of bias was low.
Conclusion—TVU is a valuable tool for the pre-operative identification of ureteral involvement by DIE
Ulnar distribution pattern may be predominant in upper extremity lymphatic malformations
No full textThe locoregional distribution patterns of lymphatic malformations (LM) in the upper extremity have not been described in the scientific literature. Twelve patients were diagnosed with a LM in their upper extremities between 1998 and 2021 at our center. In all cases, these were isolated malformations. Nine patients (75%) presented an ulnar distribution pattern of the LM, two (16.7%) presented a radial distribution pattern, and one (8.3%) presented involvement of both territories. We found no statistically significant differences in any sociodemographic or clinical variable between patients with ulnar or radial LM distribution patterns. In this work, we found that the ulnar distribution pattern was more frequent than the radial distribution pattern in upper extremity LM. Furthermore, we observed that LM in the radial territory appeared as lesions limited to the distal segment of the upper limb (thumb and distal radius). This is a small retrospective case series and therefore, these findings should be interpreted with caution. Larger sample size studies are necessary to validate and characterize this finding