PDX1+ cell budding morphogenesis in a stem cell-derived islet spheroid system.

Remarkable advances in protocol development have been achieved to manufacture insulin-secreting islets from human pluripotent stem cells (hPSCs). Distinct from current approaches, we devised a tunable strategy to generate islet spheroids enriched for major islet cell types by incorporating PDX1+ cell budding morphogenesis into staged differentiation. In this process that appears to mimic normal islet morphogenesis, the differentiating islet spheroids organize with endocrine cells that are intermingled or arranged in a core-mantle architecture, accompanied with functional heterogeneity. Through in vitro modelling of human pancreas development, we illustrate the importance of PDX1 and the requirement for EphB3/4 signaling in eliciting cell budding morphogenesis. Using this new approach, we model Mitchell-Riley syndrome with RFX6 knockout hPSCs illustrating unexpected morphogenesis defects in the differentiation towards islet cells. The tunable differentiation system and stem cell-derived islet models described in this work may facilitate addressing fundamental questions in islet biology and probing human pancreas diseases.

An alternative broad-specificity pathway for glycan breakdown in bacteria.

The vast majority of glycosidases characterized to date follow one of the variations of the 'Koshland' mechanisms1 to hydrolyse glycosidic bonds through substitution reactions. Here we describe a large-scale screen of a human gut microbiome metagenomic library using an assay that selectively identifies non-Koshland glycosidase activities2. Using this, we identify a cluster of enzymes with extremely broad substrate specificities and thoroughly characterize these, mechanistically and structurally. These enzymes not only break glycosidic linkages of both α and ß stereochemistry and multiple connectivities, but also cleave substrates that are not hydrolysed by standard glycosidases. These include thioglycosides, such as the glucosinolates from plants, and pseudoglycosidic bonds of pharmaceuticals such as acarbose. This is achieved through a distinct mechanism of hydrolysis that involves oxidation/reduction and elimination/hydration steps, each catalysed by enzyme modules that are in many cases interchangeable between organisms and substrate classes. Homologues of these enzymes occur in both Gram-positive and Gram-negative bacteria associated with the gut microbiome and other body parts, as well as other environments, such as soil and sea. Such alternative step-wise mechanisms appear to constitute largely unrecognized but abundant pathways for glycan degradation as part of the metabolism of carbohydrates in bacteria.

Equitable resource allocation in health emergencies: addressing racial disparities and ethical dilemmas.

This paper explores resource allocation complexities during health emergencies, focusing on pervasive racial disparities, notably affecting black communities. It aims to investigate alternatives to the Most Lives Saved approach, particularly its potential to exacerbate disparities. To analyse resource allocation strategies, the essay reviews the Dual-Principled System proposed by Bruce and Tallman (B+T) in 2021. B+T's proposal critiques previous methods like the Area Deprivation Index and First Come First Serve while seeking to balance equity and utility by adjusting triage scores based on diseases displaying racial disparities. However, the study identifies inherent challenges in subjectivity, complexity and fairness, necessitating a careful examination and potential innovative solutions. The examination of the Dual-Principled System uncovers challenges, leading to the identification of three main issues and potential solutions. Furthermore, to address subjectivity concerns, it is necessary to adopt objective disease selection criteria through data analysis. Moreover, proposed solutions for complexity include real-time data updates, adaptability and regional considerations. Fairness concerns can be mitigated through educational campaigns and a lottery system integrated with triage score adjustments. The study emphasises nuanced resource allocation with objective disease selection, adaptable strategies and educational initiatives, including a lottery system, aligning with fairness, equity and practicality. As healthcare evolves, resource allocation must align with justice, fostering inclusivity and responsiveness for all.

Detecting schizophrenia with 3D structural brain MRI using deep learning.

Schizophrenia is a chronic neuropsychiatric disorder that causes distinct structural alterations within the brain. We hypothesize that deep learning applied to a structural neuroimaging dataset could detect disease-related alteration and improve classification and diagnostic accuracy. We tested this hypothesis using a single, widely available, and conventional T1-weighted MRI scan, from which we extracted the 3D whole-brain structure using standard post-processing methods. A deep learning model was then developed, optimized, and evaluated on three open datasets with T1-weighted MRI scans of patients with schizophrenia. Our proposed model outperformed the benchmark model, which was also trained with structural MR images using a 3D CNN architecture. Our model is capable of almost perfectly (area under the ROC curve = 0.987) distinguishing schizophrenia patients from healthy controls on unseen structural MRI scans. Regional analysis localized subcortical regions and ventricles as the most predictive brain regions. Subcortical structures serve a pivotal role in cognitive, affective, and social functions in humans, and structural abnormalities of these regions have been associated with schizophrenia. Our finding corroborates that schizophrenia is associated with widespread alterations in subcortical brain structure and the subcortical structural information provides prominent features in diagnostic classification. Together, these results further demonstrate the potential of deep learning to improve schizophrenia diagnosis and identify its structural neuroimaging signatures from a single, standard T1-weighted brain MRI.

Activity-based photoacoustic probe for biopsy-free assessment of copper in murine models of Wilson's disease and liver metastasis.

The development of high-performance photoacoustic (PA) probes that can monitor disease biomarkers in deep tissue has the potential to replace invasive medical procedures such as a biopsy. However, such probes must be optimized for in vivo performance and exhibit an exceptional safety profile. In this study, we have developed PACu-1, a PA probe designed for biopsy-free assessment (BFA) of hepatic Cu via photoacoustic imaging. PACu-1 features a Cu(I)-responsive trigger appended to an aza-BODIPY dye platform that has been optimized for ratiometric sensing. Owing to its excellent performance, we were able to detect basal levels of Cu in healthy wild-type mice as well as elevated Cu in a Wilson's disease model and in a liver metastasis model. To showcase the potential impact of PACu-1 for BFA, we conducted two blind studies in which we were able to successfully identify Wilson's disease animals from healthy control mice in each instance.