Routinely collected physiological data improves the assessment of acute brain injury severity
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Different types of cell death and their interactions in myocardial ischemia–reperfusion injury
Myocardial ischemia–reperfusion (I/R) injury is a multifaceted process observed in patients with coronary artery disease when blood flow is restored to the heart tissue following ischemia-induced damage. Cardiomyocyte cell death, particularly through apoptosis, necroptosis, autophagy, pyroptosis, and ferroptosis, is pivotal in myocardial I/R injury. Preventing cell death during the process of I/R is vital for improving ischemic cardiomyopathy. These multiple forms of cell death can occur simultaneously, interact with each other, and contribute to the complexity of myocardial I/R injury. In this review, we aim to provide a comprehensive summary of the key molecular mechanisms and regulatory patterns involved in these five types of cell death in myocardial I/R injury. We will also discuss the crosstalk and intricate interactions among these mechanisms, highlighting the interplay between different types of cell death. Furthermore, we will explore specific molecules or targets that participate in different cell death pathways and elucidate their mechanisms of action. It is important to note that manipulating the molecules or targets involved in distinct cell death processes may have a significant impact on reducing myocardial I/R injury. By enhancing researchers’ understanding of the mechanisms and interactions among different types of cell death in myocardial I/R injury, this review aims to pave the way for the development of novel interventions for cardio-protection in patients affected by myocardial I/R injury.
Calibrated confidence learning for large-scale real-time crash and severity prediction
Real-time crash and severity prediction is a complex task, and there is no existing framework to predict crash likelihood and severity together. Creating such a framework poses numerous challenges, particularly not independent and identically distributed (non-IID) data, large model sizes with high computational costs, missing data, sensitivity vs. false alarm rate (FAR) trade-offs, and real-world deployment strategies. This study introduces a novel modeling technique to address these challenges and develops a deployable real-world framework. We used extensive real-time traffic and weather data to develop a crash likelihood prediction modeling prototype, leveraging our preliminary work of spatial ensemble modeling. Next, we equipped this spatial ensemble model with local model regularization to calibrate model confidence training. The investigated regularizations include weight decay, label smoothing and knowledge distillation. Furthermore, post-calibration on model outputs was conducted to improve severity rating identification. We tested the framework to predict crashes and severity in real-time, categorizing crashes into four levels. Results were compared with benchmark models, real-world deployment mechanisms were explained, traffic safety improvement potential and sustainability aspects of the study were discussed. Modeling results demonstrated excellent performance, and fatal, severe, minor and PDO crash severities were predicted with 91.7%, 83.3%, 85.6%, and 87.7% sensitivity, respectively, and with very low FAR. Similarly, the viability of our model to predict different severity levels for specific crash types, i.e., all-crash types, rear-end crashes, and sideswipe/angle crashes, were examined, and it showed excellent performance. Our modeling technique showed great potential for reducing model size, lowering computational costs, improving sensitivity, and, most importantly, reducing FAR. Finally, the deployment strategy for the proposed crash and severity prediction technique is discussed, and its potential to predict crashes with severity levels in real-time will make a substantial contribution to tailoring specific strategies to prevent crashes.
Nasal anti-CD3 monoclonal antibody ameliorates traumatic brain injury, enhances microglial phagocytosis and reduces neuroinflammation via IL-10-dependent Treg–microglia crosstalk
Neuroinflammation plays a crucial role in traumatic brain injury (TBI), contributing to both damage and recovery, yet no effective therapy exists to mitigate central nervous system (CNS) injury and promote recovery after TBI. In the present study, we found that nasal administration of an anti-CD3 monoclonal antibody ameliorated CNS damage and behavioral deficits in a mouse model of contusional TBI. Nasal anti-CD3 induced a population of interleukin (IL)-10-producing regulatory T cells (Treg cells) that migrated to the brain and closely contacted microglia. Treg cells directly reduced chronic microglia inflammation and regulated their phagocytic function in an IL-10-dependent manner. Blocking the IL-10 receptor globally or specifically on microglia in vivo abrogated the beneficial effects of nasal anti-CD3. However, the adoptive transfer of IL-10-producing Treg cells to TBI-injured mice restored these beneficial effects by enhancing microglial phagocytic capacity and reducing microglia-induced neuroinflammation. These findings suggest that nasal anti-CD3 represents a promising new therapeutic approach for treating TBI and potentially other forms of acute brain injury.
Utilising an in silico model to predict outcomes in senescence-driven acute liver injury
Currently liver transplantation is the only treatment option for liver disease, but organ availability cannot meet patient demand. Alternative regenerative therapies, including cell transplantation, aim to modulate the injured microenvironment from inflammation and scarring towards regeneration. The complexity of the liver injury response makes it challenging to identify suitable therapeutic targets when relying on experimental approaches alone. Therefore, we adopted a combined in vivo–in silico approach and developed an ordinary differential equation model of acute liver disease able to predict the host response to injury and potential interventions. The Mdm2fl/fl mouse model of senescence-driven liver injury was used to generate a quantitative dynamic characterisation of the key cellular players (macrophages, endothelial cells, myofibroblasts) and extra cellular matrix involved in liver injury. This was qualitatively captured by the mathematical model. The mathematical model was then used to predict injury outcomes in response to milder and more severe levels of senescence-induced liver injury and validated with experimental in vivo data. In silico experiments using the validated model were then performed to interrogate potential approaches to enhance regeneration. These predicted that increasing the rate of macrophage phenotypic switch or increasing the number of pro-regenerative macrophages in the system will accelerate the rate of senescent cell clearance and resolution. These results showcase the potential benefits of mechanistic mathematical modelling for capturing the dynamics of complex biological systems and identifying therapeutic interventions that may enhance our understanding of injury-repair mechanisms and reduce translational bottlenecks.
Stroke-induced neuroplasticity in spiny mice in the absence of tissue regeneration
Stroke is a major cause of disability for adults over 40 years of age. While research into animal models has prioritized treatments aimed at diminishing post-stroke damage, no studies have investigated the response to a severe stroke injury in a highly regenerative adult mammal. Here we investigate the effects of transient ischemia on adult spiny mice, Acomys cahirinus, due to their ability to regenerate multiple tissues without scarring. Transient middle cerebral artery occlusion was performed and Acomys showed rapid behavioral recovery post-stroke yet failed to regenerate impacted brain regions. An Acomys brain atlas in combination with functional (f)MRI demonstrated recovery coincides with neuroplasticity. The strength and quality of the global connectome are preserved post-injury with distinct contralateral and ipsilateral brain regions compensating for lost tissue. Thus, we propose Acomys recovers functionally from an ischemic stroke injury not by tissue regeneration but by altering its brain connectome.
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