The mechanisms of Salmonella serovar-host specificity are not well defined. Pig ileal loops were used to compare phenotypic differences in early cellular invasion between non-host-adapted Salmonella serovar Typhimurium (SsT) and host-adapted Salmonella serovar Choleraesuis (SsC). By 10 minutes postinoculation, both serovars invaded a small number of M cells, enterocytes, and goblet cells. Multiple SsC organisms (up to 6 per cell) simultaneously invaded M cells, whereas SsT often invaded as one to two organisms per M cell. Internalization of both serovars resulted in vacuoles containing a single bacterium. The follicle-associated epithelium (FAE) of SsC-inoculated loops responded with more filopodia and lamellipodia although exhibiting less cell swelling than SsT. Additionally, SsT showed an enhanced affinity for sites of cell extrusion compared with SsC at 60 minutes. These results suggest: 1) both SsC and SsT exhibit non-cell-specific invasion as early as 10 minutes postinoculation, 2) Salmonella serovars exhibit differences in early invasion of FAE and M cells, and 3) cells undergoing extrusion may provide a site for preferential adherence by SsT and SsC.
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http://dx.doi.org/10.1354/vp.40-4-371 | DOI Listing |
Biomed Phys Eng Express
January 2025
Radiation Oncology, Emory University, Emory Midtown Hospital, Atlanta, Georgia, 30322, UNITED STATES.
Although radiotherapy techniques are the primary treatment for head and neck cancer (HNC), they are still associated with substantial toxicity, and side effect. Machine learning (ML) based radiomics models for predicting toxicity mostly rely on features extracted from pre-treatment imaging data. This study aims to compare different models in predicting radiation-induced xerostomia and sticky saliva in both early and late stage of HNC patients using CT and MRI image features along with demographics and dosimetric information.
View Article and Find Full Text PDFAppl Neuropsychol Adult
January 2025
Faculty Xavier Institute of Engineering, Mahim, India.
In the fields of engineering, science, technology, and medicine, artificial intelligence (AI) has made significant advancements. In particular, the application of AI techniques in medicine, such as machine learning (ML) and deep learning (DL), is rapidly growing and offers great potential for aiding physicians in the early diagnosis of illnesses. Depression, one of the most prevalent and debilitating mental illnesses, is projected to become the leading cause of disability worldwide by 2040.
View Article and Find Full Text PDFBrain
January 2025
Translational Neuroimaging Laboratory, Montreal Neurological Institute, H3A 2B4, Montreal, Canada.
Plasma phosphorylated tau biomarkers open unprecedented opportunities for identifying carriers of Alzheimer's disease pathophysiology in early disease stages using minimally invasive techniques. Plasma p-tau biomarkers are believed to reflect tau phosphorylation and secretion. However, it remains unclear to what extent the magnitude of plasma p-tau abnormalities reflects neuronal network disturbance in the form of cognitive impairment.
View Article and Find Full Text PDFJ Med Internet Res
January 2025
Cancer Screening, American Cancer Society, Atlanta, GA, United States.
Background: The online nature of decision aids (DAs) and related e-tools supporting women's decision-making regarding breast cancer screening (BCS) through mammography may facilitate broader access, making them a valuable addition to BCS programs.
Objective: This systematic review and meta-analysis aims to evaluate the scientific evidence on the impacts of these e-tools and to provide a comprehensive assessment of the factors associated with their increased utility and efficacy.
Methods: We followed the 2020 PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines and conducted a search of MEDLINE, PsycINFO, Embase, CINAHL, and Web of Science databases from August 2010 to April 2023.
Traffic Inj Prev
January 2025
School of Civil and Hydraulic Engineering, NingXia University, YinChuan, China.
Objective: This study aims to address the issue of driving safety on highways in the desert region of Northwest China during extreme weather conditions such as sandstorms, with the goal of reducing driver risk. It explores driver behavior under extreme conditions of sandstorms and sand accumulation, proposing safety speed recommendations and warning models for different environments to calculate the optimal warning distance in windy and sandy conditions.
Methods: Natural driving simulation experiments were conducted in windy and sandy environments, collecting driving behavior data from 45 drivers under varying visibility and road conditions with or without sand accumulation.
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