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The P2 purinergic receptor family implicated in many physiological processes, including neurotransmission, mechanical adaptation and inflammation, consists of ATP-gated non-specific cation channels P2XRs and G-protein coupled receptors P2YRs. Different cells, including bone forming osteoblasts, express multiple P2 receptors; however, how P2X and P2Y receptors interact in generating cellular responses to various doses of [ATP] remains poorly understood. Using primary bone marrow and compact bone derived osteoblasts and BMP2-expressing C2C12 osteoblastic cells, we demonstrated conserved features in the P2-mediated Ca 2+ responses to ATP, including a transition of Ca 2+ response signatures from transient at low [ATP] to oscillatory at moderate [ATP], and back to transient at high [ATP], and a non-monotonic changes in the response magnitudes which exhibited two troughs at 10 −4 and 10 −2 M [ATP]. We identified P2Y2 and P2X7 receptors as predominantly contributing to these responses and constructed a mathematical model of P2Y2R-induced inositol trisphosphate (IP 3 ) mediated Ca 2+ release coupled to a Markov model of P2X7R dynamics to study this system. Model predictions were validated using parental and CRISPR/Cas9-generated P2Y2 and P2Y7 knockouts in osteoblastic C2C12-BMP cells. Activation of P2Y2 by progressively increasing [ATP] induced a transition from transient to oscillatory to transient Ca 2+ responses due to the biphasic nature of IP 3 Rs and the interaction of SERCA pumps with IP 3 Rs. At high [ATP], activation of P2X7R modulated the response magnitudes through an interplay between the biphasic nature of IP 3 Rs and the desensitization kinetics of P2X7Rs. Moreover, we found that P2Y2 activity may alter the kinetics of P2X7 towards favouring naïve state activation. Finally, we demonstrated the functional consequences of lacking P2Y2 or P2X7 in osteoblast mechanotransduction. This study thus provides important insights into the biophysical mechanisms underlying ATP-dependent Ca 2+ response signatures, which are important in mediating bone mechanoadaptation.

Lung ultrasound, the most precise diagnostic tool for pleural effusions, is underutilized due to healthcare providers' limited proficiency. To address this, deep learning models can be trained to recognize pleural effusions. However, current models lack the ability to diagnose effusions in diverse clinical contexts, which presents significant challenges. To develop and validate a deep learning model for detecting pleural effusions in lung ultrasound images, with adaptable performance characteristics tailored to specific clinical scenarios. A retrospective study was conducted at two Canadian tertiary hospitals to evaluate the detection of pleural effusions of varying sizes and complexities using lung ultrasound. A deep learning model incorporating a frame-level convolutional neural network and a clip-level prediction algorithm was developed and validated against expert annotations. The model was evaluated using a holdout dataset of 103 lung ultrasound clips from 46 patients with pleural effusion and 136 clips from 83 patients without effusion. The general model achieved a sensitivity of 0.90 for small-to-large effusions, with a specificity of 0.89. The large effusion model demonstrated a sensitivity of 0.97 for large effusions while maintaining a specificity of 0.90. The trauma model showed high sensitivity to all effusions, including trace (0.91) and small (0.97) effusions. Our research highlights the development of a deep learning model that effectively detects pleural effusions of varying sizes and complexities on lung ultrasound in different clinical settings. This tool has the potential to enhance emergency physicians' ability to quickly and accurately diagnose effusions, particularly in time-sensitive situations.

The opioid epidemic persists in the U.S., with over 80,000 deaths annually since 2021, primarily driven by synthetic opioids. Responding to this evolving epidemic requires reliable and timely information. One source of data is social media platforms. We assessed the utility of Reddit data for surveillance, covering heroin, prescription, and synthetic drugs. We built a natural language processing pipeline to identify opioid-related content and created a cohort of 1,689,039 Reddit users, each assigned to a state based on their previous Reddit activity. We measured their opioid-related posts over time and compared rates against CDC overdose and NFLIS report rates. To simulate the real-world prediction of synthetic opioid overdose rates, we added near real-time Reddit data to a model relying on CDC mortality data with a typical 6-month reporting lag. Reddit data significantly improved the prediction accuracy of overdose rates. This work suggests that social media can help monitor drug epidemics.

When creating any new anti-parasitic interventions, it is important to evaluate their effects across all life stages. This study had three objectives, which were to evaluate the effect of feeding cranberry vine pellet (CVP) on (1) ewes’ body weights and BCS during late gestation and lactation; (2) ewes’ milk quality during lactation; and (3) lambs’ body weight and growth parameters from birth to 65 days of age. Across two years, 41 Dorset ewes were fed either a 50% CVP or a matching control pellet (CON) from 104 ± 1.60 days of gestation for 62.8 ± 0.68 days of lactation. Measurements were collected from ewes (BW, BCS, and milk) and lambs (BW and body size). Milk from CVP ewes exhibited reduced milk fat and solids (p < 0.01) and increased concentrations of milk urea nitrogen (p = 0.02) when evaluated for the treatment–time. There was no significant difference in the BCS, protein, lamb BW, or growth measurements for treatment–time (p ≥ 0.05). Additional research that targets blood biochemistry and metabolic assessments is needed to fully determine the impact of this pellet on ewes and lambs.

Social media can provide real-time insight into trends in substance use, addiction, and recovery. Prior studies have used platforms such as Reddit and X (formerly Twitter), but evolving policies around data access have threatened these platforms’ usability in research. We evaluate the potential of a broad set of platforms to detect emerging trends in the opioid use disorder and overdose epidemic. From these, we identified 11 high-potential platforms, for which we documented policies regulating drug-related discussion, data accessibility, geolocatability, and prior use in opioid-related studies. We quantified their volume of opioid discussion, including in informal language by including slang generated using a large language model. Beyond the most commonly used Reddit and X/Twitter, the platforms with high potential for use in opioid-related surveillance are TikTok, YouTube, and Facebook. Leveraging a variety of social platforms, instead of merely one, yields broader subpopulation representation and safeguards against reduced data access in any single platform.

The renin-angiotensin system (RAS) plays a pivotal role in the maintenance of volume homeostasis and blood pressure. In addition to the well-studied systemic RAS, local RAS have been documented in various tissues, including the kidney. Given the role of the intrarenal RAS in the pathogenesis of hypertension, a role established via various pharmacologic and genetic studies, substantial efforts have been made to unravel the processes that govern intrarenal RAS activity. In particular, several mechanisms have been proposed to explain the rise in intrarenal angiotensin II (Ang II) that accompanies Ang II infusion, including increased angiotensin type 1 receptor (AT1R)-mediated uptake of Ang II and enhanced intrarenal Ang II production. However, experimentally isolating their contribution to the intrarenal accumulation of Ang II in Ang II–induced hypertension is challenging, given that they are fundamentally connected. Computational modelling is advantageous because the feedback underlying each mechanism can be removed and the effect on intrarenal Ang II can be studied. In this work, the mechanisms governing the intrarenal accumulation of Ang II during Ang II infusion experiments are delineated and the role of the intrarenal RAS in Ang II-induced hypertension is studied. To accomplish this, a compartmental ODE model of the systemic and intrarenal RAS is developed and Ang II infusion experiments are simulated. Simulations indicate that AT1R-mediated uptake of Ang II is the primary mechanism by which Ang II accumulates in the kidney during Ang II infusion. Enhanced local Ang II production is unnecessary. The results demonstrate the role of the intrarenal RAS in the pathogenesis of Ang II-induced hypertension and consequently, clinical hypertension associated with an overactive RAS.

Pharmacogenetics, the study of how interindividual genetic differences affect drug response, does not explain all observed heritable variance in drug response. Epigenetic mechanisms, such as DNA methylation, and histone acetylation may account for some of the unexplained variances. Epigenetic mechanisms modulate gene expression and can be suitable drug targets and can impact the action of nonepigenetic drugs. Pharmacoepigenetics is the field that studies the relationship between epigenetic variability and drug response. Much of this research focuses on compounds targeting epigenetic mechanisms, called epigenetic drugs, which are used to treat cancers, immune disorders, and other diseases. Several studies also suggest an epigenetic role in classical drug response; however, we know little about this area. The amount of information correlating epigenetic biomarkers to molecular datasets has recently expanded due to technological advances, and novel computational approaches have emerged to better identify and predict epigenetic interactions. We propose that the relationship between epigenetics and classical drug response may be examined using data already available by (1) finding regions of epigenetic variance, (2) pinpointing key epigenetic biomarkers within these regions, and (3) mapping these biomarkers to a drug-response phenotype. This approach expands on existing knowledge to generate putative pharmacoepigenetic relationships, which can be tested experimentally. Epigenetic modifications are involved in disease and drug response. Therefore, understanding how epigenetic drivers impact the response to classical drugs is important for improving drug design and administration to better treat disease.

Deep learning (DL) models for medical image classification frequently struggle to generalize to data from outside institutions. Additional clinical data are also rarely collected to comprehensively assess and understand model performance amongst subgroups. Following the development of a single-center model to identify the lung sliding artifact on lung ultrasound (LUS), we pursued a validation strategy using external LUS data. As annotated LUS data are relatively scarce—compared to other medical imaging data—we adopted a novel technique to optimize the use of limited external data to improve model generalizability. Externally acquired LUS data from three tertiary care centers, totaling 641 clips from 238 patients, were used to assess the baseline generalizability of our lung sliding model. We then employed our novel Threshold-Aware Accumulative Fine-Tuning (TAAFT) method to fine-tune the baseline model and determine the minimum amount of data required to achieve predefined performance goals. A subgroup analysis was also performed and Grad-CAM++ explanations were examined. The final model was fine-tuned on one-third of the external dataset to achieve 0.917 sensitivity, 0.817 specificity, and 0.920 area under the receiver operator characteristic curve (AUC) on the external validation dataset, exceeding our predefined performance goals. Subgroup analyses identified LUS characteristics that most greatly challenged the model’s performance. Grad-CAM++ saliency maps highlighted clinically relevant regions on M-mode images. We report a multicenter study that exploits limited available external data to improve the generalizability and performance of our lung sliding model while identifying poorly performing subgroups to inform future iterative improvements. This approach may contribute to efficiencies for DL researchers working with smaller quantities of external validation data.

Background: Annotating large medical imaging datasets is an arduous and expensive task, especially when the datasets in question are not organized according to deep learning goals. Here, we propose a method that exploits the hierarchical organization of annotating tasks to optimize efficiency. Methods: We trained a machine learning model to accurately distinguish between one of two classes of lung ultrasound (LUS) views using 2908 clips from a larger dataset. Partitioning the remaining dataset by view would reduce downstream labelling efforts by enabling annotators to focus on annotating pathological features specific to each view. Results: In a sample view-specific annotation task, we found that automatically partitioning a 780-clip dataset by view saved 42 min of manual annotation time and resulted in 55±6 additional relevant labels per hour. Conclusions: Automatic partitioning of a LUS dataset by view significantly increases annotator efficiency, resulting in higher throughput relevant to the annotating task at hand. The strategy described in this work can be applied to other hierarchical annotation schemes.

Gastrointestinal nematodes (GIN) are a major health concern globally, particularly for small ruminant producers. The most deadly of these GIN is an anemia-inducing parasite called Haemonchus contortus. Infections of H.contortus are most severe during what's known as the periparturient period (PPP), which is the last 6 weeks of gestation and first 8 weeks of lactation in maternal animals. Widespread genetic resistance of H.contortus to the traditionally used chemical dewormers have led producers and researchers alike to turn to the plant kingdom for answers. Plant based compounds have been used as bioactive compounds for centuries to cure a wide range of metabolic, physiological, and physical ailments. Recently, plant secondary compounds, specifically polyphenols such as proanthocyanidins (PAC) and tannins (CT), have demonstrated success in suppressing GIN infections. Although the mechanisms of these reactions are not well understood, antiparasitic efficacy has been demonstrated both in vivo and in vitro. Work from our lab group has shown both in vivo anthelmintic activity of cranberry vine PAC aqueous extracts (CV-PACaq) and in vitro suppression of fecal egg counts (FEC) and packed cell volumes (PCV), both of which are clear indicators of H.contortus infection. In this thesis we studied the effect of a 50% cranberry vine based pellet (CVP) on periparturient ewes and their offspring. By examining the milk, body weight (BW), FEC and PCV of the ewes, the BW, a variety of growth factors, and proteomics of the lambs, as well as a number of various circulating factors and metabolomics analysis, we hope to develop a more clear understand of the effects of the CVP on the overall health and well-being of these animals. Overall, the results from the variety of trials conducted throughout this thesis showed that the CVP had no effect on ewe BW or any lamb BW or growth parameters. There were observed effects of the CVP on a variety of milk components, circulating factors, proteins, and metabolites. All observed effects led to similar conclusions, that the CT and PAC levels in the CVP had many antioxidant effects on the animals metabolic stress levels but the high copper content of the pellet may be impairing the liver functionality and impacting the ewes milk quality. More research is needed to determine the exact bioactive compounds within the cranberry vine and understand the metabolic compounds that the lambs could be receiving through the milk. In the future, it might be more beneficial to conduct this research in a less copper sensitive species, such as goats.