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Abstract WP151: Rapamycin Improves Post-Recanalization Blood Flow After Acute Experimental Stroke in Rats
Background and Purpose: In the era of thrombectomy, there is evidence suggesting that successful recanalization is not always accompanied by complete reperfusion, the so called “no-reflow phenomenon”. Given the importance of reperfusion as a predictor of stroke outcome, this represents a potential target for stroke therapy. Rapamycin, a clinically approved inhibitor of Mammalian Target of Rapamycin (mTOR) has been shown to improve cerebral blood flow (CBF) in Alzheimer’s disease. However, there has been little investigation into the effect of rapamycin on post-stroke microvascular perfusion. The aim of this study was to investigate the effects of rapamycin treatment on post-recanalisation CBF and stroke outcome in an experimental animal model of stroke. Methods: Male Wistar rats (300-350g) were subjected to 90min of transient middle cerebral artery occlusion (tMCAo) followed by randomized administration of 250μg/kg intravenous rapamycin (n=8) or vehicle (n=6), 30min after the onset of MCAo. Laser Doppler flowmetry was used to continuously measure changes in MCA perfusion during MCAo and for min after recanalisation. Neurobehavioral tests were performed 24hrs after MCAo before tissue was collected for infarct volume measurement. Results: MCAo was confirmed by a 70% reduction in MCA perfusion. Rapamycin treatment significantly improved post-recanalization CBF at 55min after recanalization (p<0.01). Rapamycin significantly increased average CBF during the 60min post-recanalization period (p<0.01). Rapamycin showed a trend towards reduced final infarct volume. Rapamycin significantly improved neuroscores (p<0.05). Post-recanalization CBF was significantly inversely correlated with infarct volume (R 2 =0.4994, p<0.05). Conclusion: Rapamycin significantly improved post-recanalization CBF and behavioural outcomes after MCAo. These results suggest that rapamycin may be an effective acute intervention to improve post-recanalisation blood flow to improve stroke outcome. However, further studies are need to determine the mechanism of improved CBF and if improvements in post-stroke CBF and neurological outcome are sustained long-term post-stroke.
Population-Specific Glucose Prediction in Diabetes Care With Transformer-Based Deep Learning on the Edge.
Leveraging continuous glucose monitoring (CGM) systems, real-time blood glucose (BG) forecasting is essential for proactive interventions, playing a crucial role in enhancing the management of type 1 diabetes (T1D) and type 2 diabetes (T2D). However, developing a model generalized to a population and subsequently embedding it within a microchip of a wearable device presents significant technical challenges. Furthermore, the domain of BG prediction in T2D remains under-explored in the literature. In light of this, we propose a population-specific BG prediction model, leveraging the capabilities of the temporal fusion Transformer (TFT) to adjust predictions based on personal demographic data. Then the trained model is embedded within a system-on-chip, integral to our low-power and low-cost customized wearable device. This device seamlessly communicates with CGM systems through Bluetooth and provides timely BG predictions using edge computing. When evaluated on two publicly available clinical datasets with a total of 124 participants with T1D or T2D, the embedded TFT model consistently demonstrated superior performance, achieving the lowest prediction errors when compared with a range of machine learning baseline methods. Executing the TFT model on our wearable device requires minimal memory and power consumption, enabling continuous decision support for more than 51 days on a single Li-Poly battery charge. These findings demonstrate the significant potential of the proposed TFT model and wearable device in enhancing the quality of life for people with diabetes and effectively addressing real-world challenges.
GTAC enables parallel genotyping of multiple genomic loci with chromatin accessibility profiling in single cells.
Understanding clonal evolution and cancer development requires experimental approaches for characterizing the consequences of somatic mutations on gene regulation. However, no methods currently exist that efficiently link high-content chromatin accessibility with high-confidence genotyping in single cells. To address this, we developed Genotyping with the Assay for Transposase-Accessible Chromatin (GTAC), enabling accurate mutation detection at multiple amplified loci, coupled with robust chromatin accessibility readout. We applied GTAC to primary acute myeloid leukemia, obtaining high-quality chromatin accessibility profiles and clonal identities for multiple mutations in 88% of cells. We traced chromatin variation throughout clonal evolution, showing the restriction of different clones to distinct differentiation stages. Furthermore, we identified switches in transcription factor motif accessibility associated with a specific combination of driver mutations, which biased transformed progenitors toward a leukemia stem cell-like chromatin state. GTAC is a powerful tool to study clonal heterogeneity across a wide spectrum of pre-malignant and neoplastic conditions.
CATCH-UP: A High-Throughput Upstream-Pipeline for Bulk ATAC-Seq and ChIP-Seq Data.
Assay for transposase-accessible chromatin (ATAC) and chromatin immunoprecipitation (ChIP), coupled with next-generation sequencing (NGS), have revolutionized the study of gene regulation. A lack of standardization in the analysis of the highly dimensional datasets generated by these techniques has made reproducibility difficult to achieve, leading to discrepancies in the published, processed data. Part of this problem is due to the diverse range of bioinformatic tools available for the analysis of these types of data. Secondly, a number of different bioinformatic tools are required sequentially to convert raw data into a fully processed and interpretable output, and these tools require varying levels of computational skills. Furthermore, there are many options for quality control that are not uniformly employed during data processing. We address these issues with a complete assay for transposase-accessible chromatin sequencing (ATAC-seq) and chromatin immunoprecipitation sequencing (ChIP-seq) upstream pipeline (CATCH-UP), an easy-to-use, Python-based pipeline for the analysis of bulk ChIP-seq and ATAC-seq datasets from raw fastq files to visualizable bigwig tracks and peaks calls. This pipeline is simple to install and run, requiring minimal computational knowledge. The pipeline is modular, scalable, and parallelizable on various computing infrastructures, allowing for easy reporting of methodology to enable reproducible analysis of novel or published datasets.
MLL-AF4 cooperates with PAF1 and FACT to drive high-density enhancer interactions in leukemia.
Aberrant enhancer activation is a key mechanism driving oncogene expression in many cancers. While much is known about the regulation of larger chromosome domains in eukaryotes, the details of enhancer-promoter interactions remain poorly understood. Recent work suggests co-activators like BRD4 and Mediator have little impact on enhancer-promoter interactions. In leukemias controlled by the MLL-AF4 fusion protein, we use the ultra-high resolution technique Micro-Capture-C (MCC) to show that MLL-AF4 binding promotes broad, high-density regions of enhancer-promoter interactions at a subset of key targets. These enhancers are enriched for transcription elongation factors like PAF1C and FACT, and the loss of these factors abolishes enhancer-promoter contact. This work not only provides an additional model for how MLL-AF4 is able to drive high levels of transcription at key genes in leukemia but also suggests a more general model linking enhancer-promoter crosstalk and transcription elongation.
MAGNETO: Cell type marker panel generator from single-cell transcriptomic data.
Single-cell RNA sequencing experiments produce data useful to identify different cell types, including uncharacterized and rare ones. This enables us to study the specific functional roles of these cells in different microenvironments and contexts. After identifying a (novel) cell type of interest, it is essential to build succinct marker panels, composed of a few genes referring to cell surface proteins and clusters of differentiation molecules, able to discriminate the desired cells from the other cell populations. In this work, we propose a fully-automatic framework called MAGNETO, which can help construct optimal marker panels starting from a single-cell gene expression matrix and a cell type identity for each cell. MAGNETO builds effective marker panels solving a tailored bi-objective optimization problem, where the first objective regards the identification of the genes able to isolate a specific cell type, while the second conflicting objective concerns the minimization of the total number of genes included in the panel. Our results on three public datasets show that MAGNETO can identify marker panels that identify the cell populations of interest better than state-of-the-art approaches. Finally, by fine-tuning MAGNETO, our results demonstrate that it is possible to obtain marker panels with different specificity levels.
Consensus Clustering Strategy for Cell Type Assignments of scRNA-seq Data
Cell type annotation is a crucial step for analyzing single-cell RNA sequencing data. Among others, single-cell Automatic Labeling of cell POpulations (scALPO) is a computational pipeline developed to automatically assign the cell types to the identified clusters in scRNA-seq data. Different from most of the approaches, scALPO relies only on the information on marker genes from published literature. Specifically, after the definition of the dataset obtained from gene information retrieved from online databases, the Leiden clustering algorithm is executed to partition cells that are finally annotated. Since the Leiden algorithm might struggle to obtain a reliable outcome under certain circumstances, in this work, we include several clustering algorithms in scALPO, and we propose a pseudo-voting consensus approach that combines the outcome of a set of clustering algorithms. The results obtained on three different datasets show that the consensus approach can improve the cell type annotation without selecting a specific clustering algorithm that best suits the data under investigation.
Copeptin and the syndrome of inappropriate antidiuresis (SIAD) after pituitary transsphenoidal surgery
Objective: This study evaluates the predictive value of copeptin for syndrome of inappropriate antidiuresis (SIAD) postpituitary transsphenoidal surgery (TSS). Design: Data from 133 consecutive patients undergoing TSS (November 2017–October 2022) at Oxford University Hospitals NHS trust are presented in this retrospective study. Methods: Logistic regression (LR) and receiver operating characteristic (ROC) curves were performed to evaluate the diagnostic utility of copeptin. The Mann–Whitney U test was used to compare copeptin levels between the SIAD and no SIAD groups. Results: Fourteen patients (10.8%) developed SIAD. Copeptin was available in 121, 53 and 87 patients for Days 1, 241 and 8 post-TSS, respectively. LR for Day 1 copeptin to predict SIAD gave an odds ratio (OR) of 1.0 (95%CI 42 0.84–1.20, p =.99), area under-ROC curve (AUC) was 0.49; Day 2 copeptin OR was 0.65 (95%CI 0.39–1.19, 43 p =.77), AUC was 0.57 LR for Day 1 sodium to predict SIAD gave an odds ratio (OR) of 1.0 (95%CI 0.85–1.21, p =.99), AUC was 0.50. Conclusions: In conclusion, our data provide no evidence for copeptin as a predictive marker for post-TSS SIAD.
Why is early-onset atrial fibrillation uncommon in patients with Duchenne Muscular Dystrophy? Insights from the mdx mouse.
BACKGROUND: A reduction in both dystrophin and neuronal nitric oxide synthase (NOS1) secondary to microRNA-31 (miR-31) upregulation contributes to the atrial electrical remodelling that underpins human and experimental atrial fibrillation (AF). By contrast, patients with Duchenne Muscular Dystrophy (DMD), who lack dystrophin and NOS1 and, at least in the skeletal muscle, have raised miR-31 expression, do not have increase susceptibility to AF in the absence of left ventricular (LV) dysfunction. Here we investigated whether dystrophin-deficiency is also associated with atrial upregulation of miR-31, loss of NOS1 protein, and increased AF susceptibility in young mdx mice. METHODS AND RESULTS: Echocardiography showed normal cardiac structure and function in 12- 13 weeks mdx mice, with no indication by assay of hydroxyproline that atrial fibrosis had developed. Absence of dystrophin in mdx mice was accompanied by an overall reduction in syntrophin and a lower NOS1 protein content in the skeletal muscle and in the left atrial and ventricular myocardium, with the latter occurring alongside reduced Nos1 transcript levels (exons 1-2 by qPCR) and an increase in NOS1-polyubiquitination (assessed using tandem polyubiquitination pulldowns; P<0.05 vs. WT). Neither the upregulation of miR-31 nor the substantial reduction in NOS activity observed in the skeletal muscle was present in the atrial tissue of mdx mice. At difference with the skeletal muscle, the mdx atrial myocardium showed a reduction in the constitutive NOS inhibitor, caveolin-1, coupled with an increase in NOS3 serine1177 phosphorylation, in the absence of differences in the protein content of other NOS isoforms or in the relative expression NOS1 splice variants. In line with these findings, transoesophageal atrial burst pacing revealed no difference in AF susceptibility between mdx mice and their wild type littermates. CONCLUSIONS: Dystrophin depletion is not associated with atrial miR-31 upregulation, reduced NOS activity or increased AF susceptibility in the mdx mouse. Compared with the skeletal muscle, the milder atrial biochemical phenotype may explain why patients with DMD do not exhibit a higher prevalence of atrial arrhythmias despite a reduction in NOS1 content.
Guidelines for the diagnosis and management of adult aplastic anaemia: A British Society for Haematology Guideline.
Pancytopenia with hypocellular bone marrow is the hallmark of aplastic anaemia (AA) and the diagnosis is confirmed after careful evaluation, following exclusion of alternate diagnosis including hypoplastic myelodysplastic syndromes. Emerging use of molecular cyto-genomics is helpful in delineating immune mediated AA from inherited bone marrow failures (IBMF). Camitta criteria is used to assess disease severity, which along with age and availability of human leucocyte antigen compatible donor are determinants for therapeutic decisions. Supportive care with blood and platelet transfusion support, along with anti-microbial prophylaxis and prompt management of opportunistic infections remain key throughout the disease course. The standard first-line treatment for newly diagnosed acquired severe/very severe AA patients is horse anti-thymocyte globulin and ciclosporin-based immunosuppressive therapy (IST) with eltrombopag or allogeneic haemopoietic stem cell transplant (HSCT) from a matched sibling donor. Unrelated donor HSCT in adults should be considered after lack of response to IST, and up front for young adults with severe infections and a readily available matched unrelated donor. Management of IBMF, AA in pregnancy and in elderly require special attention. In view of the rarity of AA and complexity of management, appropriate discussion in multidisciplinary meetings and involvement of expert centres is strongly recommended to improve patient outcomes.
The impact of Alzheimer's disease risk factors on the pupillary light response.
Alzheimer's disease (AD) is the leading cause of dementia, and its prevalence is increasing and is expected to continue to increase over the next few decades. Because of this, there is an urgent requirement to determine a way to diagnose the disease, and to target interventions to delay and ideally stop the onset of symptoms, specifically those impacting cognition and daily livelihood. The pupillary light response (PLR) is controlled by the sympathetic and parasympathetic branches of the autonomic nervous system, and impairments to the pupillary light response (PLR) have been related to AD. However, most of these studies that assess the PLR occur in patients who have already been diagnosed with AD, rather than those who are at a higher risk for the disease but without a diagnosis. Determining whether the PLR is similarly impaired in subjects before an AD diagnosis is made and before cognitive symptoms of the disease begin, is an important step before using the PLR as a diagnostic tool. Specifically, identifying whether the PLR is impaired in specific at-risk groups, considering both genetic and non-genetic risk factors, is imperative. It is possible that the PLR may be impaired in association with some risk factors but not others, potentially indicating different pathways to neurodegeneration that could be distinguished using PLR. In this work, we review the most common genetic and lifestyle-based risk factors for AD and identify established relationships between these risk factors and the PLR. The evidence here shows that many AD risk factors, including traumatic brain injury, ocular and intracranial hypertension, alcohol consumption, depression, and diabetes, are directly related to changes in the PLR. Other risk factors currently lack sufficient literature to make any conclusions relating directly to the PLR but have shown links to impairments in the parasympathetic nervous system; further research should be conducted in these risk factors and their relation to the PLR.
Modelling spatiotemporal dynamics of cerebral blood flow using multiple-timepoint arterial spin labelling MRI.
Introduction: Cerebral blood flow (CBF) is an important physiological parameter that can be quantified non-invasively using arterial spin labelling (ASL) imaging. Although most ASL studies are based on single-timepoint strategies, multi-timepoint approaches (multiple-PLD) in combination with appropriate model fitting strategies may be beneficial not only to improve CBF quantification but also to retrieve other physiological information of interest. Methods: In this work, we tested several kinetic models for the fitting of multiple-PLD pCASL data in a group of 10 healthy subjects. In particular, we extended the standard kinetic model by incorporating dispersion effects and the macrovascular contribution and assessed their individual and combined effect on CBF quantification. These assessments were performed using two pseudo-continuous ASL (pCASL) datasets acquired in the same subjects but during two conditions mimicking different CBF dynamics: normocapnia and hypercapnia (achieved through a CO2 stimulus). Results: All kinetic models quantified and highlighted the different CBF spatiotemporal dynamics between the two conditions. Hypercapnia led to an increase in CBF whilst decreasing arterial transit time (ATT) and arterial blood volume (aBV). When comparing the different kinetic models, the incorporation of dispersion effects yielded a significant decrease in CBF (∼10-22%) and ATT (∼17-26%), whilst aBV (∼44-74%) increased, and this was observed in both conditions. The extended model that includes dispersion effects and the macrovascular component has been shown to provide the best fit to both datasets. Conclusion: Our results support the use of extended models that include the macrovascular component and dispersion effects when modelling multiple-PLD pCASL data.
Vascular smooth muscle cell dysfunction in neurodegeneration.
Vascular smooth muscle cells (VSMCs) are the key moderators of cerebrovascular dynamics in response to the brain's oxygen and nutrient demands. Crucially, VSMCs may provide a sensitive biomarker for neurodegenerative pathologies where vasculature is compromised. An increasing body of research suggests that VSMCs have remarkable plasticity and their pathophysiology may play a key role in the complex process of neurodegeneration. Furthermore, extrinsic risk factors, including environmental conditions and traumatic events can impact vascular function through changes in VSMC morphology. VSMC dysfunction can be characterised at the molecular level both preclinically, and clinically ex vivo. However the identification of VSMC dysfunction in living individuals is important to understand changes in vascular function at the onset and progression of neurological disorders such as dementia, Alzheimer's disease, and Parkinson's disease. A promising technique to identify changes in the state of cerebral smooth muscle is cerebrovascular reactivity (CVR) which reflects the intrinsic dynamic response of blood vessels in the brain to vasoactive stimuli in order to modulate regional cerebral blood flow (CBF). In this work, we review the role of VSMCs in the most common neurodegenerative disorders and identify physiological systems that may contribute to VSMC dysfunction. The evidence collected here identifies VSMC dysfunction as a strong candidate for novel therapeutics to combat the development and progression of neurodegeneration, and highlights the need for more research on the role of VSMCs and cerebrovascular dynamics in healthy and diseased states.
Cerebrovascular Reactivity Mapping Without Gas Challenges: A Methodological Guide.
Cerebrovascular reactivity (CVR) is defined as the ability of vessels to alter their caliber in response to vasoactive factors, by means of dilating or constricting, in order to increase or decrease regional cerebral blood flow (CBF). Importantly, CVR may provide a sensitive biomarker for pathologies where vasculature is compromised. Furthermore, the spatiotemporal dynamics of CVR observed in healthy subjects, reflecting regional differences in cerebral vascular tone and response, may also be important in functional MRI studies based on neurovascular coupling mechanisms. Assessment of CVR is usually based on the use of a vasoactive stimulus combined with a CBF measurement technique. Although transcranial Doppler ultrasound has been frequently used to obtain global flow velocity measurements, MRI techniques are being increasingly employed for obtaining CBF maps. For the vasoactive stimulus, vasodilatory hypercapnia is usually induced through the manipulation of respiratory gases, including the inhalation of increased concentrations of carbon dioxide. However, most of these methods require an additional apparatus and complex setups, which not only may not be well-tolerated by some populations but are also not widely available. For these reasons, strategies based on voluntary breathing fluctuations without the need for external gas challenges have been proposed. These include the task-based methodologies of breath holding and paced deep breathing, as well as a new generation of methods based on spontaneous breathing fluctuations during resting-state. Despite the multitude of alternatives to gas challenges, existing literature lacks definitive conclusions regarding the best practices for the vasoactive modulation and associated analysis protocols. In this work, we perform an extensive review of CVR mapping techniques based on MRI and CO2 variations without gas challenges, focusing on the methodological aspects of the breathing protocols and corresponding data analysis. Finally, we outline a set of practical guidelines based on generally accepted practices and available data, extending previous reports and encouraging the wider application of CVR mapping methodologies in both clinical and academic MRI settings.
PyPlr: A versatile, integrated system of hardware and software for researching the human pupillary light reflex.
We introduce PyPlr-a versatile, integrated system of hardware and software to support a broad spectrum of research applications concerning the human pupillary light reflex (PLR). PyPlr is a custom Python library for integrating a research-grade video-based eye-tracker system with a light source and streamlining stimulus design, optimisation and delivery, device synchronisation, and extraction, cleaning, and analysis of pupil data. We additionally describe how full-field, homogenous stimulation of the retina can be realised with a low-cost integrating sphere that serves as an alternative to a more complex Maxwellian view setup. Users can integrate their own light source, but we provide full native software support for a high-end, commercial research-grade 10-primary light engine that offers advanced control over the temporal and spectral properties of light stimuli as well as spectral calibration utilities. Here, we describe the hardware and software in detail and demonstrate its capabilities with two example applications: (1) pupillometer-style measurement and parametrisation of the PLR to flashes of white light, and (2) comparing the post-illumination pupil response (PIPR) to flashes of long and short-wavelength light. The system holds promise for researchers who would favour a flexible approach to studying the PLR and the ability to employ a wide range of temporally and spectrally varying stimuli, including simple narrowband stimuli.