Publications

Diamine oxydase and peroxidase have been co-immobilized onto layered double hydroxide (LDH) thin films for the development of real-time histamine biosensors. The chosen LDH materials are Mg2AlCO3, Mg4FeCl and Ca2AlCl. Prepared bi-enzymatic hybrid nanomaterials are capable of detecting histamine through the electrochemical oxidation of H(2)O(2)and are used as the sensitive membrane for potentiometric microelectrode. Histamine biosensors developed in this work have fast response of less than 20 s, are sensitive and selective, with a large dynamic range of 10(-8)-10(-3) M and a limit of detection of less than 10(-8) M. The detection limit of the developed bi-enzymatic biosensors is relatively higher than those corresponding with gas and liquid chromatography, which are still considered as the reference methods. Finally, the reproducibility, the specificity and the storage stability of the biosensors were studied.

JTD Keywords: Biogenic-amines, Biosensor, Diamine oxidase, Film, Fish, Histamine, Hybrid nanomaterial, Immobilization, Layer double hydroxide, Potentiometric biosensor, Specificity

© 2021 Elsevier B.V. Malaria is a parasitic disease caused by protists of the genus Plasmodium, which are transmitted to humans through the bite of infected female Anopheles mosquitoes. Analytical methodologies and efficient drugs exist for the early detection and treatment of malaria, and yet this disease continues infecting millions of people and claiming several hundred thousand lives each year. One of the reasons behind this failure to control the disease is that the standard method for malaria diagnosis, microscopy, is time-consuming and requires trained personnel. Alternatively, rapid diagnostic tests, which have become common for point-of-care testing thanks to their simplicity of use, tend to be insufficiently sensitive and reliable, and PCR, which is sensitive, is too complex and expensive for massive population screening. In this work, we report a sensitive simplified ELISA for the quantitation of Plasmodium falciparum lactate dehydrogenase (Pf-LDH), which is capable of detecting malaria in 45–60 min. Assay development was founded in the selection of high-performance antibodies, implementation of a poly-horseradish peroxidase (polyHRP) signal amplifier, and optimization of whole-blood sample pre-treatment. The simplified ELISA achieved limits of detection (LOD) and quantification (LOQ) of 0.11 ng mL−1 and 0.37 ng mL−1, respectively, in lysed whole blood, and an LOD comparable to that of PCR in Plasmodium in vitro cultures (0.67 and 1.33 parasites μL−1 for ELISA and PCR, respectively). Accordingly, the developed immunoassay represents a simple and effective diagnostic tool for P. falciparum malaria, with a time-to-result of <60 min and sensitivity similar to the reference PCR, but easier to implement in low-resource settings.

JTD Keywords: malaria quantitative diagnosis, plasmodium culture, plasmodium ldh, polyhrp signal amplifier, simplified elisa, Animals, Enzyme-linked immunosorbent assay, Female, Humans, Malaria, Malaria quantitative diagnosis, Malaria, falciparum, Plasmodium culture, Plasmodium falciparum, Plasmodium ldh, Polyhrp signal amplifier, Sensitivity and specificity, Simplified elisa

Classification algorithms with unbalanced datasets tend to produce high predictive accuracy over the majority class, but poor predictive accuracy over the minority class. This problem is very common in biomedical data mining. This paper introduces a Support Vector Machine (SVM)-based optimized feature selection method, to select the most relevant features and maintain an accurate and well-balanced sensitivity–specificity result between unbalanced groups. A new metric called the balance index (B) is defined to implement this optimization. The balance index measures the difference between the misclassified data within each class. The proposed optimized feature selection is applied to the classification of patients' weaning trials from mechanical ventilation: patients with successful trials who were able to maintain spontaneous breathing after 48 h and patients who failed to maintain spontaneous breathing and were reconnected to mechanical ventilation after 30 min. Patients are characterized through cardiac and respiratory signals, applying joint symbolic dynamic (JSD) analysis to cardiac interbeat and breath durations. First, the most suitable parameters (C+,C−,σ) are selected to define the appropriate SVM. Then, the feature selection process is carried out with this SVM, to maintain B lower than 40%. The best result is obtained using 6 features with an accuracy of 80%, a B of 18.64%, a sensitivity of 74.36% and a specificity of 82.42%.

JTD Keywords: Support vector machines, Balance index, Sensitivity-specificity balance, Cardiorespiratory interaction, Joint symbolic dynamics, Feature selection, Weaning procedure

In this paper we compare three different spectral estimation techniques for the classification of mental tasks. These techniques are the standard periodogram, the Welch periodogram and the Burg method, applied to electroencephalographic (EEG) signals. For each one of these methods we compute two parameters: the mean power and the root mean square (RMS), in various frequency bands. The classification of the mental tasks was conducted with a linear discriminate analysis. The Welch periodogram and the Burg method performed better than the standard periodogram. The use of the RMS allows better classification accuracy than the obtained with the power of EEG signals.

JTD Keywords: Adult, Algorithms, Artificial Intelligence, Cognition, Electroencephalography, Female, Humans, Male, Pattern Recognition, Automated, Reproducibility of Results, Sensitivity and Specificity, Task Performance and Analysis, User-Computer Interface