79 research outputs found

    Q&A: Expansion microscopy

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    Expansion microscopy (ExM) is a recently invented technology that uses swellable charged polymers, synthesized densely and with appropriate topology throughout a preserved biological specimen, to physically magnify the specimen 100-fold in volume, or more, in an isotropic fashion. ExM enables nanoscale resolution imaging of preserved samples on inexpensive, fast, conventional microscopes. How does ExM work? How good is its performance? How do you get going on using it? In this Q & A, we provide the answers to these and other questions about this new and rapidly spreading toolbox

    Ultrasound Image Segmentation of Thyroid Nodule via Latent Semantic Feature Co-Registration

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    Segmentation of nodules in thyroid ultrasound imaging plays a crucial role in the detection and treatment of thyroid cancer. However, owing to the diversity of scanner vendors and imaging protocols in different hospitals, the automatic segmentation model, which has already demonstrated expert-level accuracy in the field of medical image segmentation, finds its accuracy reduced as the result of its weak generalization performance when being applied in clinically realistic environments. To address this issue, the present paper proposes ASTN, a framework for thyroid nodule segmentation achieved through a new type co-registration network. By extracting latent semantic information from the atlas and target images and utilizing in-depth features to accomplish the co-registration of nodules in thyroid ultrasound images, this framework can ensure the integrity of anatomical structure and reduce the impact on segmentation as the result of overall differences in image caused by different devices. In addition, this paper also provides an atlas selection algorithm to mitigate the difficulty of co-registration. As shown by the evaluation results collected from the datasets of different devices, thanks to the method we proposed, the model generalization has been greatly improved while maintaining a high level of segmentation accuracy

    The Activity of Small Urea‐γ‐AApeptides Toward Gram‐Positive Bacteria

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    Host Defense Peptides (HDPs) have gained considerable interest due to the omnipresent threat of bacterial infection as a serious public health concern. However, development of HDPs is impeded by several drawbacks, such as poor selectivity, susceptibility to proteolytic degradation, low‐to‐moderate activity and requiring complex syntheses. Herein we report a class of lipo‐linear α/urea‐γ‐AApeptides with a hybrid backbone and low molecular weight. The heterogeneous backbone not only enhances chemodiversity, but also shows effective antimicrobial activity against Gram‐positive bacteria and is capable of disrupting bacterial membranes and killing bacteria rapidly. Given their low molecular weight and ease of access via facile synthesis, they could be practical antibiotic agents.Double‐AA peptides: We investigated a new class of small linear molecules as potential antibiotic agents against Gram‐positive bacteria. Our studies suggest that these compounds can disrupt bacterial membranes and kill bacteria rapidly. Given their low molecular weight and ease of accessibility through a facile synthesis approach, they are good candidates for development into antibiotic agents.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/152544/1/cmdc201900520-sup-0001-misc_information.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/152544/2/cmdc201900520.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/152544/3/cmdc201900520_am.pd

    Intracellular Recordings of Action Potentials by an Extracellular Nanoscale Field-Effect Transistor

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    The ability to make electrical measurements inside cells has led to many important advances in electrophysiology. The patch clamp technique, in which a glass micropipette filled with electrolyte is inserted into a cell, offers both high signal-to-noise ratio and temporal resolution. Ideally, the micropipette should be as small as possible to increase the spatial resolution and reduce the invasiveness of the measurement, but the overall performance of the technique depends on the impedance of the interface between the micropipette and the cell interior, which limits how small the micropipette can be. Techniques that involve inserting metal or carbon microelectrodes into cells are subject to similar constraints. Field-effect transistors (FETs) can also record electric potentials inside cells, and because their performance does not depend on impedance, they can be made much smaller than micropipettes and microelectrodes. Moreover, FET arrays are better suited for multiplexed measurements. Previously, we have demonstrated FET-based intracellular recording with kinked nanowire structures, but the kink configuration and device design places limits on the probe size and the potential for multiplexing. Here, we report a new approach in which a SiO2SiO_2 nanotube is synthetically integrated on top of a nanoscale FET. This nanotube penetrates the cell membrane, bringing the cell cytosol into contact with the FET, which is then able to record the intracellular transmembrane potential. Simulations show that the bandwidth of this branched intracellular nanotube FET (BIT-FET) is high enough for it to record fast action potentials even when the nanotube diameter is decreased to 3 nm, a length scale well below that accessible with other methods. Studies of cardiomyocyte cells demonstrate that when phospholipid-modified BIT-FETs are brought close to cells, the nanotubes can spontaneously penetrate the cell membrane to allow the full-amplitude intracellular action potential to be recorded, thus showing that a stable and tight seal forms between the nanotube and cell membrane. We also show that multiple BIT-FETs can record multiplexed intracellular signals from both single cells and networks of cells.Chemistry and Chemical BiologyEngineering and Applied SciencesPhysic

    Ad-Syn-Net: Systematic Identification of alzheimer\u27s Disease-Associated Mutation and Co-Mutation Vulnerabilities Via Deep Learning

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    Alzheimer\u27s disease (AD) is one of the most challenging neurodegenerative diseases because of its complicated and progressive mechanisms, and multiple risk factors. Increasing research evidence demonstrates that genetics may be a key factor responsible for the occurrence of the disease. Although previous reports identified quite a few AD-associated genes, they were mostly limited owing to patient sample size and selection bias. There is a lack of comprehensive research aimed to identify AD-associated risk mutations systematically. to address this challenge, we hereby construct a large-scale AD mutation and co-mutation framework (\u27AD-Syn-Net\u27), and propose deep learning models named Deep-SMCI and Deep-CMCI configured with fully connected layers that are capable of predicting cognitive impairment of subjects effectively based on genetic mutation and co-mutation profiles. Next, we apply the customized frameworks to data sets to evaluate the importance scores of the mutations and identified mutation effectors and co-mutation combination vulnerabilities contributing to cognitive impairment. Furthermore, we evaluate the influence of mutation pairs on the network architecture to dissect the genetic organization of AD and identify novel co-mutations that could be responsible for dementia, laying a solid foundation for proposing future targeted therapy for AD precision medicine. Our deep learning model codes are available open access here: https://github.com/Pan-Bio/AD-mutation-effectors
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