Over the past decades, increasing evidence has indicated that mechanical loads can regulate the morphogenesis, proliferation, migration, and apoptosis of living cells. Investigations of how cells sense mechanical stimuli or the mechanotransduction mechanism is an active field of biomaterials and biophysics. Gaining a further understanding of mechanical regulation and depicting the mechanotransduction network inside cells require advanced experimental techniques and new theories. In this review, the fundamental principles of various experimental approaches that have been developed to characterize various types and magnitudes of forces experienced at the cellular and subcellular levels are summarized. The broad applications of these techniques are introduced with an emphasis on the difficulties in implementing these techniques in special biological systems. The advantages and disadvantages of each technique are discussed, which can guide readers to choose the most suitable technique for their questions. A perspective on future directions in this field is also provided. It is anticipated that technical advancement can be a driving force for the development of mechanobiology.
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http://dx.doi.org/10.1002/advs.202105254 | DOI Listing |
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Department of Biophysics, Medical College of Wisconsin, Milwaukee, WI, 53226, USA.
The NMR signals from protein sidechains are rich in information about intra- and inter-molecular interactions, but their detection can be complicated due to spectral overlap as well as conformational and hydrogen exchange. In this work, we demonstrate a protocol for multi-dimensional solid-state NMR spectral editing of signals from basic sidechains based on Hadamard matrix encoding. The Hadamard method acquires multi-dimensional experiments in such a way that both the backbone and under-sampled sidechain signals can be decoded for unambiguous editing in the N spectral frequency dimension.
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Department of Chemistry and Biotechnology, School of Science, Computing and Engineering Technologies, Swinburne University of Technology, Melbourne, Australia.
In the evolving landscape of nanotechnology and pharmaceuticals, lipid nanostructures have emerged as pivotal areas of research due to their unique ability to mimic biological membranes and encapsulate active molecules. These nanostructures offer promising avenues for drug delivery, vaccine development, and diagnostic applications. This comprehensive review explores the complex mechanisms underlying the formation and stability of various lipid nanostructures, including lipid liquid crystalline nanoparticles and solid lipid nanoparticles.
View Article and Find Full Text PDFUltrason Sonochem
January 2025
Department of Computer Engineering, Faculty of Engineering and Natural Sciences, Istanbul Health and Technology University 34421 Istanbul, Türkiye.
In this study, the effects of ultrasound treatment on bioactive components and functional properties of hawthorn vinegar (Crataegus tanacetifolia) were investigated. Parameters such as total phenolic compound (TPC), total flavonoid content (TFC), ascorbic acid (AA), DPPH radical scavenging activity and CUPRAC reducing capacity were optimised by surface response method (RSM) and 14 min duration and 61.40 % amplitude were determined as the most suitable treatment conditions.
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January 2025
Department of Physiology, University of California-San Francisco, San Francisco, CA 94143, USA.
Subconscious breathing is generated by a network of brainstem nodes with varying purposes, like pacing breathing or patterning a certain breath phase. Decades of anatomy, pharmacology, and physiology studies have identified and characterized the system's fundamental properties that produce robust breathing, and we now have well-conceived computational models of breathing that are based on the detailed descriptions of neuronal connectivity, biophysical properties, and functions in breathing. In total, we have a considerable understanding of the brainstem breathing control circuit.
View Article and Find Full Text PDFmSystems
January 2025
Malopolska Centre of Biotechnology, Jagiellonian University, Kraków, Poland.
Average nucleotide identity (ANI) is a widely used metric to estimate genetic relatedness, especially in microbial species delineation. While ANI calculation has been well optimized for bacteria and closely related viral genomes, accurate estimation of ANI below 80%, particularly in large reference data sets, has been challenging due to a lack of accurate and scalable methods. To bridge this gap, we introduce MANIAC, an efficient computational pipeline optimized for estimating ANI and alignment fraction (AF) in viral genomes with divergence around ANI of 70%.
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