Rivaroxaban for stroke patients with antiphospholipid syndrome (RISAPS): protocol for a randomized controlled, phase IIb proof-of-principle trial.

Background: Optimal secondary prevention antithrombotic therapy for patients with antiphospholipid syndrome (APS)-associated ischemic stroke, transient ischemic attack, or other ischemic brain injury is undefined. The standard of care, warfarin or other vitamin K antagonists at standard or high intensity (international normalized ratio (INR) target range 2.0-3.0/3.0-4.0, respectively), has well-recognized limitations. Direct oral anticoagulants have several advantages over warfarin, and the potential role of high-dose direct oral anticoagulants vs high-intensity warfarin in this setting merits investigation. Objectives: The Rivaroxaban for Stroke patients with APS trial (RISAPS) seeks to determine whether high-dose rivaroxaban could represent a safe and effective alternative to high-intensity warfarin in adult patients with APS and previous ischemic stroke, transient ischemic attack, or other ischemic brain manifestations. Methods: This phase IIb prospective, randomized, controlled, noninferiority, open-label, proof-of-principle trial compares rivaroxaban 15 mg twice daily vs warfarin, target INR range 3.0-4.0. The sample size target is 40 participants. Triple antiphospholipid antibody-positive patients are excluded. The primary efficacy outcome is the rate of change in brain white matter hyperintensity volume on magnetic resonance imaging, a surrogate marker of presumed ischemic damage, between baseline and 24 months follow-up. Secondary outcomes include additional neuroradiological and clinical measures of efficacy and safety. Exploratory outcomes include high-dose rivaroxaban pharmacokinetic modeling. Conclusion: Should RISAPS demonstrate noninferior efficacy and safety of high-dose rivaroxaban in this APS subgroup, it could justify larger prospective randomized controlled trials.

Realistic morphology-preserving generative modelling of the brain.

Medical imaging research is often limited by data scarcity and availability. Governance, privacy concerns and the cost of acquisition all restrict access to medical imaging data, which, compounded by the data-hungry nature of deep learning algorithms, limits progress in the field of healthcare AI. Generative models have recently been used to synthesize photorealistic natural images, presenting a potential solution to the data scarcity problem. But are current generative models synthesizing morphologically correct samples? In this work we present a three-dimensional generative model of the human brain that is trained at the necessary scale to generate diverse, realistic-looking, high-resolution and morphologically preserving samples and conditioned on patient characteristics (for example, age and pathology). We show that the synthetic samples generated by the model preserve biological and disease phenotypes and are realistic enough to permit use downstream in well-established image analysis tools. While the proposed model has broad future applicability, such as anomaly detection and learning under limited data, its generative capabilities can be used to directly mitigate data scarcity, limited data availability and algorithmic fairness.

Generating multi-pathological and multi-modal images and labels for brain MRI.

The last few years have seen a boom in using generative models to augment real datasets, as synthetic data can effectively model real data distributions and provide privacy-preserving, shareable datasets that can be used to train deep learning models. However, most of these methods are 2D and provide synthetic datasets that come, at most, with categorical annotations. The generation of paired images and segmentation samples that can be used in downstream, supervised segmentation tasks remains fairly uncharted territory. This work proposes a two-stage generative model capable of producing 2D and 3D semantic label maps and corresponding multi-modal images. We use a latent diffusion model for label synthesis and a VAE-GAN for semantic image synthesis. Synthetic datasets provided by this model are shown to work in a wide variety of segmentation tasks, supporting small, real datasets or fully replacing them while maintaining good performance. We also demonstrate its ability to improve downstream performance on out-of-distribution data.

Multi-detector fusion and Bayesian smoothing for tracking viral and chromatin structures.

Automatic tracking of viral and intracellular structures displayed as spots with varying sizes in fluorescence microscopy images is an important task to quantify cellular processes. We propose a novel probabilistic tracking approach for multiple particle tracking based on multi-detector and multi-scale data fusion as well as Bayesian smoothing. The approach integrates results from multiple detectors using a novel intensity-based covariance intersection method which takes into account information about the image intensities, positions, and uncertainties. The method ensures a consistent estimate of multiple fused particle detections and does not require an optimization step. Our probabilistic tracking approach performs data fusion of detections from classical and deep learning methods as well as exploits single-scale and multi-scale detections. In addition, we use Bayesian smoothing to fuse information of predictions from both past and future time points. We evaluated our approach using image data of the Particle Tracking Challenge and achieved state-of-the-art results or outperformed previous methods. Our method was also assessed on challenging live cell fluorescence microscopy image data of viral and cellular proteins expressed in hepatitis C virus-infected cells and chromatin structures in non-infected cells, acquired at different spatial-temporal resolutions. We found that the proposed approach outperforms existing methods.

Are sleep disorders associated with traumatic dental injuries in school children? A cross-sectional study.

BACKGROUND: To evaluate whether the prevalence of traumatic dental injuries (TDIs) in permanent anterior teeth among school children is associated with sleep behaviours and disorders. METHODS: A cross-sectional study was carried out with a representative sample of schoolchildren aged 8 to 10 years (n = 1402) from Florianopolis, Brazil. Clinical examinations for TDIs were performed according to the classification proposed by Andreasen. Parents/caregivers completed a questionnaire addressing sociodemographic characteristics and sleep behaviours/disorders (sleep duration, insomnia, sleep rhythmic movement, snoring, and signs of sleep apnoea). Descriptive analysis and Poisson regression were performed. RESULTS: The prevalence of TDIs was 10.9%. Insomnia was observed in 3.0% of the children, snoring in 42.8%, sleep rhythmic movement in 27.9%, and signs of obstructive sleep apnoea in 33.6% of the schoolchildren. Most children (75.2%) slept less than eight hours a day. The prevalence of TDIs was higher among schoolchildren with an increased overjet (PR: 1.65; 95% CI: 1.15-2.35; P < 0.01), after adjusting for monthly family income, caregiver's schooling, and sleep behaviours. The prevalence of TDIs was not associated with sleep behaviours/disorders. CONCLUSIONS: Parent-reported sleep disorders such as insomnia, sleep rhythmic movement, snoring and signs of sleep apnoea were not associated with the prevalence of TDIs in schoolchildren. © 2024 Australian Dental Association.

DNA choreography: correlating mobility and organization of DNA across different resolutions from loops to chromosomes.

The dynamics of DNA in the cell nucleus plays a role in cellular processes and fates but the interplay of DNA mobility with the hierarchical levels of DNA organization is still underexplored. Here, we made use of DNA replication to directly label genomic DNA in an unbiased genome-wide manner. This was followed by live-cell time-lapse microscopy of the labeled DNA combining imaging at different resolutions levels simultaneously and allowing one to trace DNA motion across organization levels within the same cells. Quantification of the labeled DNA segments at different microscopic resolution levels revealed sizes comparable to the ones reported for DNA loops using 3D super-resolution microscopy, topologically associated domains (TAD) using 3D widefield microscopy, and also entire chromosomes. By employing advanced chromatin tracking and image registration, we discovered that DNA exhibited higher mobility at the individual loop level compared to the TAD level and even less at the chromosome level. Additionally, our findings indicate that chromatin movement, regardless of the resolution, slowed down during the S phase of the cell cycle compared to the G1/G2 phases. Furthermore, we found that a fraction of DNA loops and TADs exhibited directed movement with the majority depicting constrained movement. Our data also indicated spatial mobility differences with DNA loops and TADs at the nuclear periphery and the nuclear interior exhibiting lower velocity and radius of gyration than the intermediate locations. On the basis of these insights, we propose that there is a link between DNA mobility and its organizational structure including spatial distribution, which impacts cellular processes.

MONAI Label: A framework for AI-assisted interactive labeling of 3D medical images.

The lack of annotated datasets is a major bottleneck for training new task-specific supervised machine learning models, considering that manual annotation is extremely expensive and time-consuming. To address this problem, we present MONAI Label, a free and open-source framework that facilitates the development of applications based on artificial intelligence (AI) models that aim at reducing the time required to annotate radiology datasets. Through MONAI Label, researchers can develop AI annotation applications focusing on their domain of expertise. It allows researchers to readily deploy their apps as services, which can be made available to clinicians via their preferred user interface. Currently, MONAI Label readily supports locally installed (3D Slicer) and web-based (OHIF) frontends and offers two active learning strategies to facilitate and speed up the training of segmentation algorithms. MONAI Label allows researchers to make incremental improvements to their AI-based annotation application by making them available to other researchers and clinicians alike. Additionally, MONAI Label provides sample AI-based interactive and non-interactive labeling applications, that can be used directly off the shelf, as plug-and-play to any given dataset. Significant reduced annotation times using the interactive model can be observed on two public datasets.

Evolution of innate immunity: lessons from mammalian models shaping our current view of insect immunity.

The innate immune system, a cornerstone for organismal resilience against environmental and microbial insults, is highly conserved across the evolutionary spectrum, underpinning its pivotal role in maintaining homeostasis and ensuring survival. This review explores the evolutionary parallels between mammalian and insect innate immune systems, illuminating how investigations into these disparate immune landscapes have been reciprocally enlightening. We further delve into how advancements in mammalian immunology have enriched our understanding of insect immune responses, highlighting the intertwined evolutionary narratives and the shared molecular lexicon of immunity across these organisms. Therefore, this review posits a holistic understanding of innate immune mechanisms, including immunometabolism, autophagy and cell death. The examination of how emerging insights into mammalian and vertebrate immunity inform our understanding of insect immune responses and their implications for vector-borne disease transmission showcases the imperative for a nuanced comprehension of innate immunity's evolutionary tale. This understanding is quintessential for harnessing innate immune mechanisms' potential in devising innovative disease mitigation strategies and promoting organismal health across the animal kingdom.

Developmental Changes in Genome Replication Progression in Pluripotent versus Differentiated Human Cells.

DNA replication is a fundamental process ensuring the maintenance of the genome each time cells divide. This is particularly relevant early in development when cells divide profusely, later giving rise to entire organs. Here, we analyze and compare the genome replication progression in human embryonic stem cells, induced pluripotent stem cells, and differentiated cells. Using single-cell microscopic approaches, we map the spatio-temporal genome replication as a function of chromatin marks/compaction level. Furthermore, we mapped the replication timing of subchromosomal tandem repeat regions and interspersed repeat sequence elements. Albeit the majority of these genomic repeats did not change their replication timing from pluripotent to differentiated cells, we found developmental changes in the replication timing of rDNA repeats. Comparing single-cell super-resolution microscopic data with data from genome-wide sequencing approaches showed comparable numbers of replicons and large overlap in origins numbers and genomic location among developmental states with a generally higher origin variability in pluripotent cells. Using ratiometric analysis of incorporated nucleotides normalized per replisome in single cells, we uncovered differences in fork speed throughout the S phase in pluripotent cells but not in somatic cells. Altogether, our data define similarities and differences on the replication program and characteristics in human cells at different developmental states.

Timeless-Tipin interactions with MCM and RPA mediate DNA replication stress response.

The accuracy of replication is one of the most important mechanisms ensuring the stability of the genome. The fork protection complex prevents premature replisome stalling and/or premature disassembly upon stress. Here, we characterize the Timeless-Tipin complex, a component of the fork protection complex. We used microscopy approaches, including colocalization analysis and proximity ligation assay, to investigate the spatial localization of the complex during ongoing replication in human cells. Taking advantage of the replication stress induction and the ensuing polymerase-helicase uncoupling, we characterized the Timeless-Tipin localization within the replisome. Replication stress was induced using hydroxyurea (HU) and aphidicolin (APH). While HU depletes the substrate for DNA synthesis, APH binds directly inside the catalytic pocket of DNA polymerase and inhibits its activity. Our data revealed that the Timeless-Tipin complex, independent of the stress, remains bound on chromatin upon stress induction and progresses together with the replicative helicase. This is accompanied by the spatial dissociation of the complex from the blocked replication machinery. Additionally, after stress induction, Timeless interaction with RPA, which continuously accumulates on ssDNA, was increased. Taken together, the Timeless-Tipin complex acts as a universal guardian of the mammalian replisome in an unperturbed S-phase progression as well as during replication stress.

Metrics reloaded: recommendations for image analysis validation.

Increasing evidence shows that flaws in machine learning (ML) algorithm validation are an underestimated global problem. In biomedical image analysis, chosen performance metrics often do not reflect the domain interest, and thus fail to adequately measure scientific progress and hinder translation of ML techniques into practice. To overcome this, we created Metrics Reloaded, a comprehensive framework guiding researchers in the problem-aware selection of metrics. Developed by a large international consortium in a multistage Delphi process, it is based on the novel concept of a problem fingerprint-a structured representation of the given problem that captures all aspects that are relevant for metric selection, from the domain interest to the properties of the target structure(s), dataset and algorithm output. On the basis of the problem fingerprint, users are guided through the process of choosing and applying appropriate validation metrics while being made aware of potential pitfalls. Metrics Reloaded targets image analysis problems that can be interpreted as classification tasks at image, object or pixel level, namely image-level classification, object detection, semantic segmentation and instance segmentation tasks. To improve the user experience, we implemented the framework in the Metrics Reloaded online tool. Following the convergence of ML methodology across application domains, Metrics Reloaded fosters the convergence of validation methodology. Its applicability is demonstrated for various biomedical use cases.

Understanding metric-related pitfalls in image analysis validation.

Validation metrics are key for tracking scientific progress and bridging the current chasm between artificial intelligence research and its translation into practice. However, increasing evidence shows that, particularly in image analysis, metrics are often chosen inadequately. Although taking into account the individual strengths, weaknesses and limitations of validation metrics is a critical prerequisite to making educated choices, the relevant knowledge is currently scattered and poorly accessible to individual researchers. Based on a multistage Delphi process conducted by a multidisciplinary expert consortium as well as extensive community feedback, the present work provides a reliable and comprehensive common point of access to information on pitfalls related to validation metrics in image analysis. Although focused on biomedical image analysis, the addressed pitfalls generalize across application domains and are categorized according to a newly created, domain-agnostic taxonomy. The work serves to enhance global comprehension of a key topic in image analysis validation.

The Proteomic Composition and Organization of Constitutive Heterochromatin in Mouse Tissues.

Pericentric heterochromatin (PCH) forms spatio-temporarily distinct compartments and affects chromosome organization and stability. Albeit some of its components are known, an elucidation of its proteome and how it differs between tissues in vivo is lacking. Here, we find that PCH compartments are dynamically organized in a tissue-specific manner, possibly reflecting compositional differences. As the mouse brain and liver exhibit very different PCH architecture, we isolated native PCH fractions from these tissues, analyzed their protein compositions using quantitative mass spectrometry, and compared them to identify common and tissue-specific PCH proteins. In addition to heterochromatin-enriched proteins, the PCH proteome includes RNA/transcription and membrane-related proteins, which showed lower abundance than PCH-enriched proteins. Thus, we applied a cut-off of PCH-unspecific candidates based on their abundance and validated PCH-enriched proteins. Amongst the hits, MeCP2 was classified into brain PCH-enriched proteins, while linker histone H1 was not. We found that H1 and MeCP2 compete to bind to PCH and regulate PCH organization in opposite ways. Altogether, our workflow of unbiased PCH isolation, quantitative mass spectrometry, and validation-based analysis allowed the identification of proteins that are common and tissue-specifically enriched at PCH. Further investigation of selected hits revealed their opposing role in heterochromatin higher-order architecture in vivo.

Understanding metric-related pitfalls in image analysis validation.

Validation metrics are key for the reliable tracking of scientific progress and for bridging the current chasm between artificial intelligence (AI) research and its translation into practice. However, increasing evidence shows that particularly in image analysis, metrics are often chosen inadequately in relation to the underlying research problem. This could be attributed to a lack of accessibility of metric-related knowledge: While taking into account the individual strengths, weaknesses, and limitations of validation metrics is a critical prerequisite to making educated choices, the relevant knowledge is currently scattered and poorly accessible to individual researchers. Based on a multi-stage Delphi process conducted by a multidisciplinary expert consortium as well as extensive community feedback, the present work provides the first reliable and comprehensive common point of access to information on pitfalls related to validation metrics in image analysis. Focusing on biomedical image analysis but with the potential of transfer to other fields, the addressed pitfalls generalize across application domains and are categorized according to a newly created, domain-agnostic taxonomy. To facilitate comprehension, illustrations and specific examples accompany each pitfall. As a structured body of information accessible to researchers of all levels of expertise, this work enhances global comprehension of a key topic in image analysis validation.

Acquisition-invariant brain MRI segmentation with informative uncertainties.

Combining multi-site data can strengthen and uncover trends, but is a task that is marred by the influence of site-specific covariates that can bias the data and, therefore, any downstream analyses. Post-hoc multi-site correction methods exist but have strong assumptions that often do not hold in real-world scenarios. Algorithms should be designed in a way that can account for site-specific effects, such as those that arise from sequence parameter choices, and in instances where generalisation fails, should be able to identify such a failure by means of explicit uncertainty modelling. This body of work showcases such an algorithm that can become robust to the physics of acquisition in the context of segmentation tasks while simultaneously modelling uncertainty. We demonstrate that our method not only generalises to complete holdout datasets, preserving segmentation quality but does so while also accounting for site-specific sequence choices, which also allows it to perform as a harmonisation tool.

Federated learning for medical imaging radiology.

Federated learning (FL) is gaining wide acceptance across the medical AI domains. FL promises to provide a fairly acceptable clinical-grade accuracy, privacy, and generalisability of machine learning models across multiple institutions. However, the research on FL for medical imaging AI is still in its early stages. This paper presents a review of recent research to outline the difference between state-of-the-art [SOTA] (published literature) and state-of-the-practice [SOTP] (applied research in realistic clinical environments). Furthermore, the review outlines the future research directions considering various factors such as data, learning models, system design, governance, and human-in-loop to translate the SOTA into SOTP and effectively collaborate across multiple institutions.

Replisome loading reduces chromatin motion independent of DNA synthesis.

Chromatin has been shown to undergo diffusional motion, which is affected during gene transcription by RNA polymerase activity. However, the relationship between chromatin mobility and other genomic processes remains unclear. Hence, we set out to label the DNA directly in a sequence unbiased manner and followed labeled chromatin dynamics in interphase human cells expressing GFP-tagged proliferating cell nuclear antigen (PCNA), a cell cycle marker and core component of the DNA replication machinery. We detected decreased chromatin mobility during the S-phase compared to G1 and G2 phases in tumor as well as normal diploid cells using automated particle tracking. To gain insight into the dynamical organization of the genome during DNA replication, we determined labeled chromatin domain sizes and analyzed their motion in replicating cells. By correlating chromatin mobility proximal to the active sites of DNA synthesis, we showed that chromatin motion was locally constrained at the sites of DNA replication. Furthermore, inhibiting DNA synthesis led to increased loading of DNA polymerases. This was accompanied by accumulation of the single-stranded DNA binding protein on the chromatin and activation of DNA helicases further restricting local chromatin motion. We, therefore, propose that it is the loading of replisomes but not their catalytic activity that reduces the dynamics of replicating chromatin segments in the S-phase as well as their accessibility and probability of interactions with other genomic regions.

Sertraline associated with gold nanoparticles reduce cellular toxicity and induce sex-specific responses in behavior and neuroinflammation biomarkers in a mouse model of anxiety.

This study aimed to evaluate the effects of sertraline associated with gold nanoparticles (AuNPs) in vitro cell viability and in vivo behavior and inflammatory biomarkers in a mouse model of anxiety. Sertraline associated with AuNPs were synthesized and characterized. For the in vitro study, NIH3T3 and HT-22 cells were treated with different doses of sertraline, AuNPs, and sertraline + AuNPs and their viability was evaluated using the MTT assay. For the in vivo study, pregnant Swiss mice were administered a single dose of lipopolysaccharide (LPS) on the ninth day of gestation. The female and male offspring were divided into five treatment groups on PND 60 and administered chronic treatment for 28 days. The animals were subjected to behavioral testing and were subsequently euthanized. Their brains were collected and analyzed for inflammatory biomarkers. Sertraline associated with AuNPs exhibited significant changes in surface characteristics and increased diameters. Different doses of sertraline + AuNPs showed higher cell viability in NIH3T3 and HT-22 cells compared with sertraline alone. The offspring of LPS-treated dams exhibited anxiety-like behavior and neuroinflammatory biomarker changes during adulthood, which were ameliorated via sertraline + AuNPs treatment. The treatment response was sex-dependent and brain region-specific. These results suggest that AuNPs, which demonstrate potential to bind to other molecules, low toxicity, and reduced inflammation, can be synergistically used with sertraline to improve drug efficacy and safety by decreasing neuroinflammation and sertraline toxicity.

Latent Transformer Models for out-of-distribution detection.

Any clinically-deployed image-processing pipeline must be robust to the full range of inputs it may be presented with. One popular approach to this challenge is to develop predictive models that can provide a measure of their uncertainty. Another approach is to use generative modelling to quantify the likelihood of inputs. Inputs with a low enough likelihood are deemed to be out-of-distribution and are not presented to the downstream predictive model. In this work, we evaluate several approaches to segmentation with uncertainty for the task of segmenting bleeds in 3D CT of the head. We show that these models can fail catastrophically when operating in the far out-of-distribution domain, often providing predictions that are both highly confident and wrong. We propose to instead perform out-of-distribution detection using the Latent Transformer Model: a VQ-GAN is used to provide a highly compressed latent representation of the input volume, and a transformer is then used to estimate the likelihood of this compressed representation of the input. We demonstrate this approach can identify images that are both far- and near- out-of-distribution, as well as provide spatial maps that highlight the regions considered to be out-of-distribution. Furthermore, we find a strong relationship between an image's likelihood and the quality of a model's segmentation on it, demonstrating that this approach is viable for filtering out unsuitable images.