Biology subjects

Nguyen, D. V.

Publications and source records attributed to Nguyen, D. V..

5 recordsLinked to original sources

bioRxiv2026

A rapid, field-deployable paper-based biosensor for the detection of African swine fever virus in whole blood

African swine fever virus (ASFV) poses a major transboundary threat to global swine production, underscoring the need for rapid and field-deployable diagnostic tools. Although quantitative polymerase chain reaction (qPCR)-based assays are the standard molecular assay for ASFV detection, their reliance on centralized laboratory infrastructure, multi-step sample preparation, and trained personnel limit their utility for timely decision-making at the point of need (PON). Here, we report a portable molecular diagnostic platform that enables colorimetric quantitative loop-mediated isothermal amplification (qLAMP) directly from diluted whole blood on microfluidic paper-based analytical devices ({micro}PADs). The assay targets the conserved ASFV viral protein 72 (VP72) and topoisomerase II (TOPII) genes and incorporates objective image-based colorimetric signal analysis to reduce user-dependent interpretation. Using plasmid DNA spiked into whole blood diluted to 5% (v/v) in 5% D-mannitol, the {micro}PAD-LAMP assay achieved a limit of detection (LOD) of 25 copies per reaction (67 copies/{micro}L of whole blood sample) for VP72 targets with no observed cross-reactivity against nine common swine pathogens, demonstrating 100% analytical sensitivity and specificity during in-house testing and 90% and 92% analytical sensitivity and specificity respectively in an external laboratory evaluation. The complete assay was performed within 60 minutes using a portable heating and imaging platform. Together, these results demonstrate a simple, DNA extraction-free molecular diagnostic approach that enables rapid and reliable ASFV detection from whole blood applicable to field-relevant conditions.

bioengineering↗

bioRxiv2026

Myrmecocystus honeypot ants have species specific resident gut microbiome

Myrmecocystus honeypot ants rely on specialized workers, repletes, to store dissolved carbohydrates in their crops long term. The repletes store this liquid, which does not spoil in their crops, for many months at a time. When resources are scarce, repletes redistribute the stored nutrients to their colony members via trophallaxis. While we suspect that the gut microbiome of honeypot ants may aid in spoilage prevention, before we can investigate this, we must first characterize it. Here, we used 16S rRNA gene sequencing to determine the microbial community composition across six Myrmecocystus honeypot ant species, sampling multiple colonies, castes, and organs. We found that microbiome community composition was strongly shaped by species, with variation between colonies in M. arenarius, M. depilis, and M. mexicanus. Organ level differences were observed in the crop and midgut in M. mexicanus. Caste differences were observed in M. flaviceps and M. mexicanus. Replete crops of M. mexicanus and M. depilis were enriched in Fructilactobacillus, other lactic acid bacteria, and acetic acid bacteria, whereas halophiles were more prominent in the gut of species such as M. flaviceps and M. wheeleri. In this study we demonstrate that Myrmecocystus ants host species-specific gut microbiomes and identify an association between lactic acid bacteria, acetic acid bacteria, and halophiles within replete crops. While much work remains in understanding the roles of the microbes in the symbiosis with their host ants, the dominance of these particular taxonomic groups suggests an association with a high sugar environment and a potential microbial role in preventing spoilage of the crop contents.

microbiology↗

bioRxiv2024

Tensor Kernel Learning for Classification of Alzheimer's Conditions using Multimodal Data

Early, timely, and accurate assessment of Alzheimers disease (AD), particularly at its earlier stage - mild cognitive impairment (MCI) -, is central to detecting, managing, and potentially treating the disease. The biological underpinnings of AD, however, is multifaceted, from genetic variations, abnormal protein accumulation, to irregular brain functions and structure. A joint analysis of these data, therefore, may offer potentially new insights about AD-related biomarkers and AD prediction. But such explorations must confront the complexity of these data: heterogeneity, multimodality and high-dimensionality. Here, to address these challenges, we propose a new machine-learning method, namely the tensor kernel learning (TKL), leveraging tensor methods and kernel learning, to enhance AD assessment by enhancing multi-modal data integration. More specifically, TKL first uses CP/PARAFAC decomposition and graph diffusion to fuse multiple kernels learned from four complementary data modalities (MRI, PET, CSF, and SNP data). We then used a supervised kernel for a kernel SVM classifier to identify potential patients. To evaluate the effectiveness of TKL, we apply it to data from the Alzheimers Disease Neuroimaging Initiative (ADNI; the sample size n = 331 subjects), including cognitively normal individuals (CN), MCI subjects, and AD patients. TKL improves AD classification performance in both linear and nonlinear combinations, achieving accuracies of 91.31% for CN vs. AD, 81.45% for CN vs. MCI, and 78.27% for AD vs. MCI, compared to 85.48%, 70.89%, and 73.51% using the best single modality. Additionally, TKL reveals clearer, more structured patterns in the data, enhancing interpretation and understanding of the relationships among different modalities. Detailed implementation details of our method can be found at: https://github.com/thanhvd18/Tensor-Kernel-Learning-matlab.

neuroscience↗

bioRxiv2020

Imaging voltage in complete neuronal networks within patterned microislands reveals preferential wiring of excitatory hippocampal neurons

Voltage imaging with fluorescent dyes affords the opportunity to map neuronal activity in both time and space. One limitation to imaging is the inability to image complete neuronal networks: some fraction of cells remains outside of the observation window. Here, we combine voltage imaging, post hoc immunocytochemistry, and patterned microisland hippocampal culture to provide imaging of complete neuronal networks. The patterned microislands completely fill the field of view of our high-speed (500 Hz) camera, enabling reconstruction of the spiking patterns of every single neuron in the network. Cultures raised on microislands develop similarly to neurons grown on coverslips and display similar composition of inhibitory and excitatory cell types. The principal excitatory cell types (CA1, CA3, and dentate granule cells, or DGC) are also present in similar proportions in both preparations. We calculate the likelihood that action potential firing in one neuron to trigger action potential firing in a downstream neuron in a spontaneously active network to construct a functional connection map of these neuronal ensembles. Importantly, this functional map indicates preferential connectivity between DGC and CA3 neurons and between CA3 and CA1 neurons, mimicking the neuronal circuitry of the intact hippocampus. We envision that patterned microislands, in combination with voltage imaging and methods to classify cell types, will be a powerful method for exploring neuronal function in both healthy and disease states. Additionally, because the entire neuronal network is sampled simultaneously, this strategy has the power to go further, revealing all functional connections between all cell types. Significance StatementIn vitro model systems provide unsurpassed control and access for exploring the molecular and cellular details of neurobiology. We developed a patterned microisland system for culturing rat hippocampal neurons that recapitulates the features of bulk hippocampal cultures, but with the added benefit of allowing access to high-speed imaging of entire neuronal ensembles using voltage imaging. By using far-red voltage-sensitive fluorophores, we map the functional connections across all cells in the neuronal ensemble, revealing that several important functional synapses present in the intact hippocampus are recapitulated in this microisland system. We envision the methods described here will be a powerful complement to ongoing research into basic neurobiological mechanisms and the search for therapies to treat diseases arising from their dysfunction.

neuroscience↗

bioRxiv2020

Copper nanoparticle application enhances plant growth and grain yield in maize under drought stress conditions

Abiotic stresses, including drought, detrimentally affect the growth and productivity of many economically important crop plants, leading to significant yield losses, which can result in food shortages and threaten the sustainability of agriculture. Balancing between plant growth and stress responses is one of the most important characters for agricultural application to maximize plant production. In this study, we initially report that copper nanoparticle priming positively regulates drought stress responses in maize. The copper nanoparticle priming plants displayed enhanced drought tolerance indicated by their higher leaf water content and plant biomass under drought as compared with water-treated plants. Moreover, our data showed that the treatment of copper nanoparticle on plants increased anthocyanin, chlorophyll and carotenoid contents compared to water-treated plants under drought stress conditions. Additionally, histochemical analyses with nitro blue tetrazolium and 3,3-diaminobenzidine revealed that reactive oxygen species accumulation of priming plants was decreased as a result of enhancement of reactive oxygen species scavenging enzyme activities under drought. Furthermore, our comparative yield analysis data indicated applying copper nanoparticle to plant increased total seed number and grain yield under drought stress conditions. Our data provided the evidences that copper nanoparticle regulates plant protective mechanisms associated with drought tolerance, which is a promising approach for the production of drought tolerant crop plants.

plant biology↗