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Microinfarcts and microhemorrhages are characteristic lesions of cerebrovascular disease. Although multiple studies have been published, there is no one universal standard criteria for the neuropathological assessment of cerebrovascular disease. In this study, we propose a novel application of machine learning in the automated screening of microinfarcts and microhemorrhages. Utilizing whole slide images (WSIs) from postmortem human brain samples, we adapted a patch-based pipeline with convolutional neural networks. Our cohort consisted of 22 cases from the University of California Davis Alzheimer's Disease Research Center brain bank with hematoxylin and eosin-stained formalin-fixed, paraffin-embedded sections across 3 anatomical areas: frontal, parietal, and occipital lobes (40 WSIs with microinfarcts and/or microhemorrhages, 26 without). We propose a multiple field-of-view prediction step to mitigate false positives. We report screening performance (ie, the ability to distinguish microinfarct/microhemorrhage-positive from microinfarct/microhemorrhage-negative WSIs), and detection performance (ie, the ability to localize the affected regions within a WSI). Our proposed approach improved detection precision and screening accuracy by reducing false positives thereby achieving 100% screening accuracy. Although this sample size is small, this pipeline provides a proof-of-concept for high efficacy in screening for characteristic brain changes of cerebrovascular disease to aid in screening of microinfarcts/microhemorrhages at the WSI level.

This paper reviews particle packing models and explores their application in geotechnical engineering, specifically for sand-silt mixtures. The review covers key models, including limiting case, linear, and non-linear packing models, focusing on their mathematical structures, physical principles, assumptions, and limitations through the concept of excess free volume. The application of particle packing models in geotechnical engineering is explored in characterizing the properties of sand-silt mixtures, offering insights into maximum, minimum, and critical void ratios and inter-granular void ratio, and the prediction of mechanical properties.

Transmembrane-4 L-six family member-1 (TM4SF1) is a small cell surface glycoprotein that is highly and selectively expressed on endothelial cell and mesenchymal stem cell surfaces. TM4SF1 regulates cellular functions by forming protein complexes called TMED (TM4SF1-enriched microdomains) that either recruit growth-factor activated proteins and internalize them via microtubules to distribute the recruited molecules intracellularly or support the formation of nanopodia for intercellular interactions extracellularly. Through a genetically manipulated mouse model for global Tm4sf1 gene knockout, we demonstrate here that TM4SF1 is essential for blood vessel development. Tm4sf1-null embryos fail to develop blood vessels and experience lethality at E9.5. Tm4SF1-heterozygous embryos are smaller in body size during early embryonic development, and almost half die in utero due to intracranial hemorrhage in the intraventricular and subarachnoid space, which becomes apparent by E17.5. Surviving Tm4SF1-heterozygotes do not display overt phenotypic differences relative to wild type littermates postnatally. Together, these studies demonstrate that TM4SF1, through its molecular facilitator and nanopodia formation roles in TMED, intimately regulates blood vessel formation during embryonic development.

This paper reviews particle packing models and explores their application in geotechnical engineering, specifically for sand-silt mixtures. The review covers key models, including limiting case, linear, and non-linear packing models, focusing on their mathematical structures, physical principles, assumptions, and limitations through the concept of excess free volume. The application of particle packing models in geotechnical engineering is explored in characterizing the properties of sand-silt mixtures, offering insights into maximum, minimum, and critical void ratios and inter-granular void ratio, and the prediction of mechanical properties. [ABSTRACT FROM AUTHOR]

Abstract The genus Flavivirus contains many important pathogens, including dengue virus (DENV), Zika virus (ZIKV), and Japanese encephalitis virus (JEV). AR-12 is a celecoxib-derived anticancer agent that possesses antiviral activity against a broad range of viruses. We pharmacologically exploited this unique activity to develop additional antiviral agents, resulting in the production of the AR-12 derivatives P12-23 and P12-34. At nanomolar concentrations, these compounds were effective in suppressing DENV, ZIKV and JEV replication, exhibiting 10-fold improvements in the efficacy and selectivity indices as compared to AR-12. Regarding the mode of antiviral action, P12-23 and P12-34 inhibited viral RNA replication but had no effect on viral binding, entry or translation. Moreover, these AR-12 derivatives co-localized with mitochondrial markers, and their antiviral activity was lost in mitochondria-depleted cells. Interestingly, exogenous uridine or orotate, the latter being a metabolite of the mitochondrial enzyme dihydroorotate dehydrogenase (DHODH), abolished the antiviral activity of AR-12 and its derivatives. As DHODH is a key enzyme in the de novo pyrimidine biosynthesis pathway, these AR-12 derivatives may act by targeting pyrimidine biosynthesis in host cells to inhibit viral replication. Importantly, treatment with P12-34 significantly improved the survival of mice that were subcutaneously challenged with DENV. Thus, P12-34 may warrant further evaluation as a therapeutic to control flaviviral outbreaks.