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Inferring Tripartite Associations of Vector-Borne Plant Pathogens Using a Next-Generation Sequencing Approach.
Pathogens
January 2025
Illinois Natural History Survey, Prairie Research Institute, University of Illinois at Urbana-Champaign, Champaign, IL 61820, USA.
Phytoplasmas are a group of plant-pathogenic, cell-wall-less bacteria vectored primarily by leafhoppers (Hemiptera Cicadellidae), one of the most diverse families of insects. Despite the importance of documenting associations between phytoplasmas, their insect vectors, and plant hosts to prevent disease outbreaks, such knowledge is currently highly incomplete and largely neglects the diversity of the system in natural areas. Here, we used anchored hybrid enrichment (AHE) to recover the DNA of five plant genes (, , , , and ) in 58 phloem-feeding leafhoppers from around the world that had previously tested positive for phytoplasma infection.
View Article and Find Full Text PDFAndrogens induce renal synthesis of urinary lipocalin-family protein, a potential inter-sexual transmitter in viviparous rockfish.
Biochim Biophys Acta Gen Subj
January 2025
Division of Marine Life Science, Faculty of Fisheries Sciences, Hokkaido University, 3-1-1 Minato, Hakodate, Hokkaido 041-8611, Japan. Electronic address:
In viviparous black rockfish (Sebastes schlegelii), the kidney of reproductive-phase males actively produces lipocalin-type prostaglandin D synthase homolog (LPGDSh) protein, which is presumably involved in inter-sexual communication when emitted in the urine. The present study was undertaken to discover whether androgens and their nuclear receptors (Ars) are engaged in regulation of renal LPGDSh protein synthesis in black rockfish. Quantitative real-time polymerase chain reaction, in conjunction with immunohistochemistry and highly sensitive enzyme-linked immunosorbent assay, revealed that intra-abdominal administration of a synthetic androgen, 17α-methyltestosterone (MT), to juvenile black rockfish induced their renal expression of LPGDSh transcript and protein.
View Article and Find Full Text PDFProduction of Hyaluronidase by .
J Fungi (Basel)
December 2024
Division of Pharmacognosy and Toxicology, Faculty of Pharmaceutical Sciences, Khon Kaen University, Khon Kaen 40002, Thailand.
Hyaluronidases have been a subject of great interest in medical and cosmeceutical applications. Previously, our group demonstrated that the venom glands of contain hyaluronidase enzymes (VesT2s), and heterologous expression of the corresponding gene () in systems results in inclusion bodies, necessitating functional folding using urea. Here, we report the successful heterologous expression of VesT2a in the expression system, with gene construction achieved using Golden.
View Article and Find Full Text PDFTip-Induced 3D Printing on the Nanoscale with Field Emission Scanning Probes.
Small
December 2024
Institute of Micro- and Nanotechnologies MacroNano, Microsystems Technology Group, Technische Universität Ilmenau, Max-Planck-Ring 12, 98693, Ilmenau, Germany.
3D printing down to the nanoscale remains a significant challenge. In this paper, the study explores the use of scanning probes that emit low-energy electrons (<100 eV) coupled with the localized injection and electron-induced decomposition of precursor molecules, for the precise localized deposition of 3D nanostructures. The experiments are performed inside the chamber of a scanning electron microscope (SEM), enabling the use of the in-built gas injector system (GIS) with gaseous naphthalene precursor for carbon deposition, as well as immediate inspection of the deposits by SEM.
View Article and Find Full Text PDFFocused review on artificial intelligence for disease detection in infants.
Front Digit Health
November 2024
Division of Phoniatrics, Medical University of Graz, Graz, Austria.
Article Synopsis
- Studies have shown a rise in using artificial intelligence (AI) to detect and predict diseases, especially in vulnerable groups such as infants, highlighting the potential of large language models (LLMs) like ChatGPT.
- Researchers systematically reviewed 154 out of 927 articles published between 2018 and 2022 to summarize developments in automated disease detection and prediction for infants in their first year of life.
- A growing trend in research activity was noted, with a particular focus on diverse medical conditions, the use of clinical and laboratory data, and a preference for deep neural networks for prediction tasks, though traditional methods still have relevance.
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