Publications

Complementary split ring resonators (CSRRs) have been extensively studied as planar sensors in the last two decades. However, their practical use remains limited to controlled environments and classification problems. Their performance relies on high-end vector network analyzers (VNAs), highly repeatable laboratory conditions, and special sample holders or microfluidic circuits hinders its regular use in chemistry laboratories as an analytical tool. Temperature drifts and humidity variations during measuring, uncertainties in the electromagnetic properties of the sample containers, and careless sample handling introduce significant uncertainties in measurements, leading to unreliable results. Therefore, the prediction of target compounds concentration in samples has been out of the research focus up to now. Machine learning (ML) algorithms can help to mitigate these uncertainties and open the applicability of CSRR sensors to quantification problems, where it is necessary to determine the amount of a substance in a liquid (or solid) sample. This work presents a novel approach that tackles this issue, combining a CSRR sensor with well-stabilized ML algorithms that enhance its quantification performance. For illustration purposes, a low-cost, benchtop CSRR-based system is proposed to predict ethanol concentration in water solutions. Ethanol samples from 10% to 96% concentration were prepared in commercial vials, generating 450 randomized measurements. Principal component analysis (PCA) was employed for data exploration, while a partial least squares (PLS) regression model, tuned with leave-one-group-out cross validation (LOGO-CV), was trained for ethanol concentration prediction. No feature extraction technique or noise reduction strategy was applied. Although this straightforward workflow is well known in the chemical sensing field, it has not been applied to data acquired with CSRR sensors. The trained model achieved a root mean square error in prediction (RMSEP) of 3.7% . Compared with 23.4% RMSEP when using univariate calibration at optimized frequencies, it presents a prediction performance reduced by a factor of 6. No evidence of underfitting or overfitting was observed during the test of the trained model. The low RMSEP achieved by the presented setup demonstrates the potential of CSRR-based sensors when combined with ML techniques for concentration prediction working in realistic, uncontrolled conditions. This pushes forward the applicability of CSRR sensors in the chemical analysis field, which might lead to benchtop, low-cost, and reliable analysis devices for many laboratories.

JTD Keywords: Chemical analysis, Chemical sensors, Complementary, Complementary split ring resonator (csrr) sensors, Concentration prediction, Design, Electronic mail, Ethanol, Feature extraction, Filters, Machine learning (ml), Metamaterials, Principal component analysis, Resonators, Rf sensors, Sensor phenomena and characterization, Sensors, Split-ring resonators, Temperature measurement, Transmission, Transmission line measurements, Uncertainty, Variable selection

Visual impairments are a critical medical hurdle to be addressed in modern society. Müller glia (MG) have regenerative potential in the retina in lower vertebrates, but not in mammals. However, in mice, in vivo cell fusion between MG and adult stem cells forms hybrids that can partially regenerate ablated neurons.We used organotypic cultures of human retina and preparations of dissociated cells to test the hypothesis that cell fusion between human MG and adult stem cells can induce neuronal regeneration in human systems. Moreover, we established a microinjection system for transplanting human retinal organoids to demonstrate hybrid differentiation.We first found that cell fusion occurs between MG and adult stem cells, in organotypic cultures of human retina as well as in cell cultures. Next, we showed that the resulting hybrids can differentiate and acquire a proto-neural electrophysiology profile when the Wnt/beta-catenin pathway is activated in the adult stem cells prior fusion. Finally, we demonstrated the engraftment and differentiation of these hybrids into human retinal organoids.We show fusion between human MG and adult stem cells, and demonstrate that the resulting hybrid cells can differentiate towards neural fate in human model systems. Our results suggest that cell fusion-mediated therapy is a potential regenerative approach for treating human retinal dystrophies.This work was supported by La Caixa Health (HR17-00231), Velux Stiftung (976a) and the Ministerio de Ciencia e Innovación, (BFU2017-86760-P) (AEI/FEDER, UE), AGAUR (2017 SGR 689, 2017 SGR 926).Published by Elsevier B.V.

JTD Keywords: cell fusion, expression, fusion, ganglion-cells, in-vitro, mouse, müller glia, neural differentiation, organoids, regeneration, retina regeneration, stem cells, stromal cells, transplantation, 4',6 diamidino 2 phenylindole, 5' nucleotidase, Agarose, Alcohol, Arpe-19 cell line, Article, Beta catenin, Beta tubulin, Bone-marrow-cells, Bromophenol blue, Buffer, Calcium cell level, Calcium phosphate, Calretinin, Canonical wnt signaling, Cd34 antigen, Cell culture, Cell fusion, Cell viability, Coculture, Complementary dna, Confocal microscopy, Cornea transplantation, Cryopreservation, Cryoprotection, Crystal structure, Current clamp technique, Dimethyl sulfoxide, Dodecyl sulfate sodium, Edetic acid, Electrophysiology, Endoglin, Fetal bovine serum, Fibroblast growth factor 2, Flow cytometry, Fluorescence activated cell sorting, Fluorescence intensity, Glyceraldehyde 3 phosphate dehydrogenase, Glycerol, Glycine, Hoe 33342, Immunofluorescence, Immunohistochemistry, Incubation time, Interleukin 1beta, Lentivirus vector, Matrigel, Mercaptoethanol, Microinjection, Mueller cell, Müller glia, N methyl dextro aspartic acid, Nerve cell differentiation, Neural differentiation, Nitrogen, Nonhuman, Organoids, Paraffin, Paraffin embedding, Paraformaldehyde, Patch clamp technique, Penicillin derivative, Phenolsulfonphthalein, Phenotype, Phosphate buffered saline, Phosphoprotein phosphatase inhibitor, Polyacrylamide gel electrophoresis, Potassium chloride, Povidone iodine, Promoter region, Proteinase inhibitor, Real time polymerase chain reaction, Receptor type tyrosine protein phosphatase c, Restriction endonuclease, Retina, Retina dystrophy, Retina regeneration, Retinol, Rhodopsin, Rna extraction, Stem cell, Stem cells, Subcutaneous fat, Tunel assay, Visual impairment, Western blotting

In the present work, we have studied the QRS loop in the Vectorcardiogram (VCG) of 95 chronic chagasic patients classified in different groups (I, II and III) according to their degree of myocardial damage. For comparison, the VCGs of 11 healthy subjects used as control group (Group O) were also examined. The QRS loop was obtained for each patient from the XYZ orthogonal leads of their High-Resolution Electrocardiogram (HRECG) records. In order to analyze the variations of QRS loop in each detected beat, it has been proposed in this study the following vectorcardiographic parameters a) Maximum magnitude of the cardiac depolarization vector, b) Volume, c) Area of QRS loop, d) Ratio between the Area and Perimeter, e) Ratio between the major and minor axes of the QRS loop and f) QRS loop Energy. It has been found that one or more indexes exhibited statistical differences (p<0.05) between groups 0-II, O-III, I-II, I-III and II-III. We concluded that the proposed method could be use as complementary diagnosis technique to evaluate the degree of myocardial damage in chronic chagasic patients.

JTD Keywords: Practical, Experimental/ bioelectric phenomena, Diseases, Electrocardiography, Medical signal, Processing/ QRS loop, Vectorcardiogram, Cardiac depolarization vector, Myocardial damage, Chagas disease, Complementary diagnosis technique, High-resolution electrocardiogram