As more infectious viruses emerge that result in respiratory illness, there is a significant need to standardize airway harvests and maximize data acquisition. Animal models of respiratory viral infections have been outlined to allow for the analysis of the host immune response and viral pathogenesis kinetics. This chapter outlines two separate tissue harvest protocols following the intranasal infection of mice to investigate both the host immune response and viral pathogenesis. These protocols combine standard laboratory techniques for the analysis of the samples, making it easily integrable for labs without the need for specialized training. In offering two separate yet parallel tissue collection techniques, investigators can ultimately decide which technique will yield the best data for their particular research questions and can maximize data from each animal study.
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http://dx.doi.org/10.1016/bs.mcb.2021.12.021 | DOI Listing |
Sci Rep
January 2025
Department of Pharmacy, Nhan Dan Gia Dinh Hospital, Ho Chi Minh City, Vietnam.
Evidence of antihypertensive drug-related problems (aDRP) is limited in Asian ambulatory care. To better detect aDRP without causing alert fatigue, we investigated whether adding more antihypertensive agents was associated with increasing aDRP risk and factors associated with physician acceptance of aDRP correction. We conducted a cross-sectional study targeting ambulatory prescriptions of Vietnamese patients with hypertension who either received standard therapy (using two or fewer medications, SdT) or standard plus add-on therapy (using more than two medications, SdT + add-on).
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January 2025
School of Mathematics and Statistics, Shaoguan University, Shaoguan, 512005, China.
Recently, deep latent variable models have made significant progress in dealing with missing data problems, benefiting from their ability to capture intricate and non-linear relationships within the data. In this work, we further investigate the potential of Variational Autoencoders (VAEs) in addressing the uncertainty associated with missing data via a multiple importance sampling strategy. We propose a Missing data Multiple Importance Sampling Variational Auto-Encoder (MMISVAE) method to effectively model incomplete data.
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January 2025
Computational Learning Theory Team, RIKEN-Advanced Intelligence Project, Fukuoka, 819-0395, Japan.
Providing continuous wireless connectivity for high-speed trains (HSTs) is challenging due to their high speeds, making installing numerous ground base stations (BSs) along the HST route an expensive solution, particularly in rural and wilderness areas. This paper proposes using multiple unmanned aerial vehicles (UAVs) to deliver high data rate wireless connectivity for HSTs, taking advantage of their ability to fly, hover, and maneuver at low altitudes. However, autonomously selecting the optimal UAV by the HST is challenging.
View Article and Find Full Text PDFCell Death Discov
January 2025
Institute of Biopharmaceutical Sciences, College of Pharmaceutical Sciences, National Yang Ming Chiao Tung University, Taipei, Taiwan.
TP53 mutations are recognized to correlate with a worse prognosis in individuals with non-small cell lung cancer (NSCLC). There exists an immediate necessity to pinpoint selective treatment for patients carrying TP53 mutations. Potential drugs were identified by comparing drug sensitivity differences, represented by the half-maximal inhibitory concentration (IC50), between TP53 mutant and wild-type NSCLC cell lines using database analysis.
View Article and Find Full Text PDFAcad Radiol
January 2025
Medical Image Processing Group, 602 Goddard building, 3710 Hamilton Walk, Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104 (M.L., M.A., J.K.U., Y.T., C.W., N.P., S.M., D.A.T.). Electronic address:
Rationale And Objectives: Cardiovascular toxicity is a well-known complication of thoracic radiation therapy (RT), leading to increased morbidity and mortality, but existing techniques to predict cardiovascular toxicity have limitations. Predictive biomarkers of cardiovascular toxicity may help to maximize patient outcomes.
Methods: The machine learning optimal biomarker (OBM) method was employed to predict development of cardiotoxicity (based on serial echocardiographic measurements of left ventricular ejection fraction and longitudinal strain) from computed tomography (CT) images in patients with thoracic malignancy undergoing RT.
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