Biological tissues experience various stretch gradients which act as mechanical signaling from the extracellular environment to cells. These mechanical stimuli are sensed by cells, triggering essential signaling cascades regulating cell migration, differentiation, and tissue remodeling. In most previous studies, a simple, uniform stretch to 2D elastic substrates has been applied to analyze the response of living cells. However, induction of nonuniform strains in controlled gradients, particularly in biomimetic 3D hydrogels, has proven challenging. In this study, 3D fibrin hydrogels of manipulated geometry are stretched by a silicone carrier to impose programmable strain gradients along different chosen axes. The resulting strain gradients are analyzed and compared to finite element simulations. Experimentally, the programmed strain gradients result in similar gradient patterns in fiber alignment within the gels. Additionally, temporal changes in the orientation of fibroblast cells embedded in the stretched fibrin gels correlate to the strain and fiber alignment gradients. The experimental and simulation data demonstrate the ability to custom-design mechanical gradients in 3D biological hydrogels and to control cell alignment patterns. It provides a new technology for mechanobiology and tissue engineering studies.
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http://dx.doi.org/10.1002/smtd.202201070 | DOI Listing |
ACS Nano
January 2025
Instituto de Física de São Carlos, Universidade de São Paulo, São Carlos 13566-590, Brazil.
Monolayers of transition-metal dichalcogenides, such as MoS, have attracted significant attention for their exceptional electronic and optical properties, positioning them as ideal candidates for advanced optoelectronic applications. Despite their strong excitonic effects, the atomic-scale thickness of these materials limits their light absorption efficiency, necessitating innovative strategies to enhance light-matter interactions. Plasmonic nanostructures offer a promising solution to overcome those challenges by amplifying the electromagnetic field and also introducing other mechanisms, such as hot electron injection.
View Article and Find Full Text PDFSci Rep
January 2025
Microbiology Division, ICMR-Regional Medical Research Centre, Chandrasekharpur, Bhubaneswar, 751023, Odisha, India.
This research delves into the evolving dynamics of antibiogram trends, the diversity of antibiotic resistance genes and antibiotic efficacy against Vibrio cholerae strains that triggered the cholera outbreak 2022 in Odisha, India. The study will provide valuable insights managing antimicrobial resistance during cholera outbreaks. Eighty V.
View Article and Find Full Text PDFFEMS Microbiol Lett
January 2025
Rochester Institute of Technology, Thomas H. Gosnell School of Life Sciences, Rochester, NY, USA.
Commensal Neisseria are members of a healthy human oropharyngeal microbiome; however, they also serve as a reservoir of antimicrobial resistance for their pathogenic relatives. Despite their known importance as sources of novel genetic variation for pathogens, we still do not understand the full suite of resistance mutations commensal species can harbor. Here, we use in vitro selection to assess the mutations that emerge in response to ciprofloxacin selection in commensal Neisseria by passaging 4 replicates of 4 different species in the presence of a selective antibiotic gradient for 20 days; then categorized derived mutations with whole genome sequencing.
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January 2025
Department of Microbiology, Nicolaus Copernicus University, The Ludwik Rydygiers Collegium Medicum, Bydgoszcz, Poland.
Klebsiella pneumoniae complex (KPc) is a group of opportunistic pathogens that pose a serious threat to public health. Multidrug resistance is increasing, and limiting therapeutic options. Aztreonam-avibactam (AZA) is a combination of an established β-lactam with a new β-lactamase inhibitor.
View Article and Find Full Text PDFJ Clin Med
December 2024
Department of Biomedical Sciences, School of Medicine, Nazarbayev University, Astana 010000, Kazakhstan.
: The rapid onset of COVID-19 placed immense strain on many already overstretched healthcare systems. The unique physiological changes in pregnancy, amplified by the complex effects of COVID-19 in pregnant women, rendered prioritization of infected expectant mothers more challenging. This work aims to use state-of-the-art machine learning techniques to predict whether a COVID-19-infected pregnant woman will be admitted to ICU (Intensive Care Unit).
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