Long-Distance Signals Are Required for Morphogenesis of the Regenerating Xenopus Tadpole Tail, as Shown by Femtosecond-Laser Ablation

tadpoles has recently emerged as an important model for these studies; we explored the role of the spinal cord during tadpole tail regeneration.Using ultrafast lasers to ablate cells, and Geometric Morphometrics to quantitatively analyze regenerate morphology, we explored the influence of different cell populations. For at least twenty-four hours after amputation (hpa), laser-induced damage to the dorsal midline affected the morphology of the regenerated tail; damage induced 48 hpa or later did not. Targeting different positions along the anterior-posterior (AP) axis caused different shape changes in the regenerate. Interestingly, damaging two positions affected regenerate morphology in a qualitatively different way than did damaging either position alone. Quantitative comparison of regenerate shapes provided strong evidence against a gradient and for the existence of position-specific morphogenetic information along the entire AP axis.We infer that there is a conduit of morphology-influencing information that requires a continuous dorsal midline, particularly an undamaged spinal cord. Contrary to expectation, this information is not in a gradient and it is not localized to the regeneration bud. We present a model of morphogenetic information flow from tissue undamaged by amputation and conclude that studies of information coming from far outside the amputation plane and regeneration bud will be critical for understanding regeneration and for translating fundamental understanding into biomedical approaches

Wellbeing for older adults in Aotearoa/New Zealand Vision for Ageing in Aotearoa.

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Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Recent studies have shown that an automated, lifespan-inclusive, transdiagnostic, and clinically based, individualized risk calculator provides a powerful system for supporting the early detection of individuals at-risk of psychosis at a large scale, by leveraging electronic health records (EHRs). This risk calculator has been externally validated twice and is undergoing feasibility testing for clinical implementation. Integration of this risk calculator in clinical routine should be facilitated by prospective feasibility studies, which are required to address pragmatic challenges, such as missing data, and the usability of this risk calculator in a real-world and routine clinical setting. Here, we present an approach for a prospective implementation of a real-time psychosis risk detection and alerting service in a real-world EHR system. This method leverages the CogStack platform, which is an open-source, lightweight, and distributed information retrieval and text extraction system. The CogStack platform incorporates a set of services that allow for full-text search of clinical data, lifespan-inclusive, real-time calculation of psychosis risk, early risk-alerting to clinicians, and the visual monitoring of patients over time. Our method includes: 1) ingestion and synchronization of data from multiple sources into the CogStack platform, 2) implementation of a risk calculator, whose algorithm was previously developed and validated, for timely computation of a patient's risk of psychosis, 3) creation of interactive visualizations and dashboards to monitor patients' health status over time, and 4) building automated alerting systems to ensure that clinicians are notified of patients at-risk, so that appropriate actions can be pursued. This is the first ever study that has developed and implemented a similar detection and alerting system in clinical routine for early detection of psychosis

Investigating light sensitivity in bipolar disorder (HELIOS-BD)

Copyright: \ua9 2024 Roguski A et al.Many people with bipolar disorder have disrupted circadian rhythms. This means that the timing of sleep and wake activities becomes out-of-sync with the standard 24-hour cycle. Circadian rhythms are strongly influenced by light levels and previous research suggests that people with bipolar disorder might have a heightened sensitivity to light, causing more circadian rhythm disruption, increasing the potential for triggering a mood switch into mania or depression. Lithium has been in clinical use for over 70 years and is acknowledged to be the most effective long-term treatment for bipolar disorder. Lithium has many reported actions in the body but the precise mechanism of action in bipolar disorder remains an active area of research. Central to this project is recent evidence that lithium may work by stabilising circadian rhythms of mood, cognition and rest/activity. Our primary hypothesis is that people with bipolar disorder have some pathophysiological change at the level of the retina which makes them hypersensitive to the visual and non-visual effects of light, and therefore more susceptible to circadian rhythm dysfunction. We additionally hypothesise that the mood-stabilising medication lithium is effective in bipolar disorder because it reduces this hypersensitivity, making individuals less vulnerable to light-induced circadian disruption. We will recruit 180 participants into the HELIOS-BD study. Over an 18-month period, we will assess visual and non-visual responses to light, as well as retinal microstructure, in people with bipolar disorder compared to healthy controls. Further, we will assess whether individuals with bipolar disorder who are being treated with lithium have less pronounced light responses and attenuated retinal changes compared to individuals with bipolar disorder not being treated with lithium. This study represents a comprehensive investigation of visual and non-visual light responses in a large bipolar disorder population, with great translational potential for patient stratification and treatment innovation

Multi-domain clinical natural language processing with MedCAT:The Medical Concept Annotation Toolkit

Electronic health records (EHR) contain large volumes of unstructured text, requiring the application of Information Extraction (IE) technologies to enable clinical analysis. We present the open-source Medical Concept Annotation Toolkit (MedCAT) that provides: a) a novel self-supervised machine learning algorithm for extracting concepts using any concept vocabulary including UMLS/SNOMED-CT; b) a feature-rich annotation interface for customising and training IE models; and c) integrations to the broader CogStack ecosystem for vendor-agnostic health system deployment. We show improved performance in extracting UMLS concepts from open datasets (F1:0.448-0.738 vs 0.429-0.650). Further real-world validation demonstrates SNOMED-CT extraction at 3 large London hospitals with self-supervised training over ~8.8B words from ~17M clinical records and further fine-tuning with ~6K clinician annotated examples. We show strong transferability (F1 > 0.94) between hospitals, datasets, and concept types indicating cross-domain EHR-agnostic utility for accelerated clinical and research use cases.Comment: Preprint: 27 Pages, 3 Figure