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Portable, low-field strength MRI systems have the potential to transform neuroimaging – provided that their low spatial resolution and low signal-to-noise (SNR) ratio can be overcome. Researchers at Harvard Medical School are harnessing artificial intelligence (AI) to achieve this goal. They have developed a machine learning super-resolution algorithm that generates synthetic images with high spatial resolution from lower resolution brain MRI scans.

The convolutional neural network (CNN) algorithm, known as LF-SynthSR, converts low-field strength (0.064 T) T1-and T2-weighted brain MRI sequences into isotropic images with 1 mm spatial resolution and the appearance of a T1-weighted magnetization-prepared rapid gradient-echo (MP-RAGE) acquisition. Describing their proof-of-concept study in Radiology, the researchers report that the synthetic images exhibited high correlation with images acquired by 1.5 T and 3.0 T MRI scanners.

Morphometry, the quantitative size and shape analysis of structures in an image, is central to many neuroimaging studies. Unfortunately, most MRI analysis tools are designed for near-isotropic, high-resolution acquisitions and typically require T1-weighted images such as MP-RAGE. Their performance often drops rapidly as voxel size and anisotropy increase. As the vast majority of existing clinical MRI scans are highly anisotropic, they cannot be reliably analysed with existing tools.

The anti-plagiarism platform is rolling out a feature that can tell if a student has cheated with ChatGPT, but experts say detection will get tougher.

A recent study of US adults over 50 found that those who owned a pet for more than five years scored better on cognitive memory tests than those living witho…

Just published from my son.

Automatic hippocampus imaging, with about 20 minutes of cloud computing per scan.

Like neocortical structures, the archicortical hippocampus differs in its folding patterns across individuals. Here, we present an automated and robust BIDS-App, HippUnfold, for defining and indexing individual-specific hippocampal folding in MRI, analogous to popular tools used in neocortical reconstruction. Such tailoring is critical for inter-individual alignment, with topology serving as the basis for homology. This topological framework enables qualitatively new analyses of morphological and laminar structure in the hippocampus or its subfields. It is critical for refining current neuroimaging analyses at a meso-as well as micro-scale. HippUnfold uses state-of-the-art deep learning combined with previously developed topological constraints to generate uniquely folded surfaces to fit a given subject’s hippocampal conformation. It is designed to work with commonly employed sub-millimetric MRI acquisitions, with possible extension to microscopic resolution. In this paper, we describe the power of HippUnfold in feature extraction, and highlight its unique value compared to several extant hippocampal subfield analysis methods.

Continue reading “Automated hippocampal unfolding for morphometry and subfield segmentation with HippUnfold” »

Tumor cells traverse many different types of fluids as they travel through the body. Christoph Burgstedt/Science Photo Library via Getty Images.

This article was originally featured on The Conversation.

Continue reading “A look at how cancer cells move and metastatize could help doctors stop them from spreading” »

Even though the clinical efficacy of antibody-based therapeutics has been established, no methods that involve the de novo design of antibodies with wet lab validation are available.

About the study

A recent study, posted in the bioRxiv* preprint server, used generative AI models to develop de novo design antibodies against three distinct targets in a zero-shot fashion. A zero-shot designing method involves designing an antibody to bind to an antigen without follow-up optimization. The newly designed process has been termed de novo, meaning proteins (antibodies) were designed from first principles or from scratch.

In this interview, News Medical speaks to Assistant Professor Ryan Jackson about his latest work, published in tandem Nature papers, detailing the discovery of a new CRISPR immune system.

Please can you introduce yourself and tell us about your professional background?

I am an Assistant Professor at Utah State University (USU). I use biochemical and structural techniques to understand how the molecules that perform the reactions of life function. I’ve been working in the CRISPR field since 2011. I started as a postdoc in Blake Wiedenheft’s lab at Montana State University, and in 2016 I started my own research lab at USU. I earned both of my degrees (a B.S. in Biology and a Ph.D. in Biochemistry) from USU, so joining the faculty was like coming home. My research lab specializes in determining the structure and function of newly discovered and obscure CRISPR systems.

The FAA said normal operations were “resuming gradually” after ordering a nationwide pause on all domestic departures until 9 a.m. on Wednesday morning following a computer failure that has impacted flights around the country.

“The ground stop has been lifted,” officials said at about 8:50 a.m. ET. “We continue to look into the cause of the initial problem”

Departures were resuming at about 8:15 a.m. ET at two of the nation’s busiest hubs — Newark and Atlanta — FAA officials said on Twitter, adding, “We expect departures to resume at other airports at 9 a.m. ET.”

The difference between predictions and observations of the magnetic properties of muons suggests a mystery for the Standard Model.