Physico-Chemical Properties of CdTe/Glutathione Quantum Dots Obtained by Microwave Irradiation for Use in Monoclonal Antibody and Biomarker Testing

In this report, we present the results on the physicochemical characterization of cadmium telluride quantum dots (QDs) stabilized with glutathione and prepared by optimizing the synthesis conditions. An excellent control of emissions and the composition of the nanocrystal surface for its potential application in monoclonal antibody and biomarker testing was achieved. Two samples (QDYellow, QDOrange, corresponding to their emission colors) were analyzed by dynamic light scattering (DLS), and their hydrodynamic sizes were 6.7 nm and 19.4 nm, respectively. Optical characterization by UV-vis absorbance spectroscopy showed excitonic peaks at 517 nm and 554 nm. Photoluminescence spectroscopy indicated that the samples have a maximum intensity emission at 570 and 606 nm, respectively, within the visible range from yellow to orange. Infrared spectroscopy showed vibrational modes corresponding to the functional groups OH-C-H, C-N, C=C, C-O, C-OH, and COOH, which allows for the formation of functionalized QDs for the manufacture of biomarkers. In addition, the hydrodynamic radius, zeta potential, and approximate molecular weight were determined by dynamic light scattering (DLS), electrophoretic light scattering (ELS), and static light scattering (SLS) techniques. Size dispersion and the structure of nanoparticles was obtained by Transmission Electron Microscopy (TEM) and by X-ray diffraction. In the same way, we calculated the concentration of Cd2+ ions expressed in mg/L by using the Inductively Coupled Plasma Atomic Emission Spectrometry (ICP-OES). In addition to the characterization of the nanoparticles, the labeling of murine myeloid cells was carried out with both samples of quantum dots, where it was demonstrated that quantum dots can diffuse into these cells and connect mostly with the cell nucleus.

Species Discrimination within the Metarhizium PARB Clade: Ribosomal Intergenic Spacer (rIGS)-Based Diagnostic PCR and Single Marker Taxonomy

(1) Background: The entomopathogenic fungus Metarhizium anisopliae sensu lato forms a species complex, comprising a tight cluster made up of four species, namely M. anisopliae sensu stricto, M. pinghaense, M. robertsii and M. brunneum. Unambiguous species delineation within this "PARB clade" that enables both the taxonomic assignment of new isolates and the identification of potentially new species is highly solicited. (2) Methods: Species-discriminating primer pairs targeting the ribosomal intergenic spacer (rIGS) sequence were designed and a diagnostic PCR protocol established. A partial rIGS sequence, referred to as rIGS-ID800, was introduced as a molecular taxonomic marker for PARB species delineation. (3) Results: PARB species from a validation strain set not implied in primer design were clearly discriminated using the diagnostic PCR protocol developed. Using rIGS-ID800 as a single sequence taxonomic marker gave rise to a higher resolution and statistically better supported delineation of PARB clade species. (4) Conclusions: Reliable species discrimination within the Metarhizium PARB clade is possible through both sequencing-independent diagnostic PCR and sequencing-dependent single marker comparison, both based on the rIGS marker.

Stable Sparse Classifiers predict cognitive impairment from gait patterns

Background

Although gait patterns disturbances are known to be related to cognitive decline, there is no consensus on the possibility of predicting one from the other. It is necessary to find the optimal gait features, experimental protocols, and computational algorithms to achieve this purpose.

Purposes

To assess the efficacy of the Stable Sparse Classifiers procedure (SSC) for discriminating young and healthy older adults (YA vs. HE), as well as healthy and cognitively impaired elderly groups (HE vs. MCI-E) from their gait patterns. To identify the walking tasks or combinations of tasks and specific spatio-temporal gait features (STGF) that allow the best prediction with SSC.

Methods

A sample of 125 participants (40 young- and 85 older-adults) was studied. They underwent assessment with five neuropsychological tests that explore different cognitive domains. A summarized cognitive index (MDCog), based on the Mahalanobis distance from normative data, was calculated. The sample was divided into three groups (young adults, healthy and cognitively impaired elderly adults) using k-means clustering of MDCog in addition to Age. The participants executed four walking tasks (normal, fast, easy- and hard-dual tasks) and their gait patterns, measured with a body-fixed Inertial Measurement Unit, were used to calculate 16 STGF and dual-task costs. SSC was then employed to predict which group the participants belonged to. The classification's performance was assessed using the area under the receiver operating curves (AUC) and the stable biomarkers were identified.

Results

The discrimination HE vs. MCI-E revealed that the combination of the easy dual-task and the fast walking task had the best prediction performance (AUC = 0.86, sensitivity: 90.1%, specificity: 96.9%, accuracy: 95.8%). The features related to gait variability and to the amplitude of vertical acceleration had the largest predictive power. SSC prediction accuracy was better than the accuracies obtained with linear discriminant analysis and support vector machine classifiers.

Conclusions

The study corroborated that the changes in gait patterns can be used to discriminate between young and healthy older adults and more importantly between healthy and cognitively impaired adults. A subset of gait tasks and STGF optimal for achieving this goal with SSC were identified, with the latter method superior to other classification techniques.