Using The Rat Grimace Scale to Detect Orofacial Pain in Mechanically-induced Temporomandibular Joint Pain in Rats

Introduction: Orofacial pain in animal models of TMJ disorders is typically evaluated by measuring evoked reflexive responses. Since the rat grimace scale (RGS) was adopted recently to assess spontaneous pain in other pathologies, this study evaluated its effectiveness for TMJ pain in the rat. RGS was evaluated using a well-defined pain model of TMJ loading. Material and Methods: Female Holtzman rats were assigned to separate groups: loading (n=10); sham (n=4); loading with naproxen (n=4) or vehicle (n=3) on days 4 and 5 after pain developed. Jaw loading was imposed for 7 consecutive days under anesthesia by repeated mouth-opening for 1hr. Sham had no mouth-opening. Naproxen or vehicle (1mg/kg) was given intravenously. Rats were videotaped for 30mins daily after loading, and for 7 days after loading was stopped. Images were randomized and quantitatively scored using 4 action units: orbital tightening, nose/cheek flattening, ear change, whisker change. The RGS score was compared between groups using a repeated-measures ANOVA and Tukey\u27s post-hoc test. Results: Loading induced significantly higher (p\u3c0.001) RGS scores than sham on days 1 and 5. After loading was stopped, RGS scores returned to sham levels for the remainder of test days. Naproxen injection significantly lowered (p\u3c0.001) RGS scores from loading alone on day 7. Conclusion: Orofacial pain can be detected by the RGS, which may provide a useful new method to evaluate TMJ pain

The Human Phenotype Ontology in 2024: phenotypes around the world.

The Human Phenotype Ontology (HPO) is a widely used resource that comprehensively organizes and defines the phenotypic features of human disease, enabling computational inference and supporting genomic and phenotypic analyses through semantic similarity and machine learning algorithms. The HPO has widespread applications in clinical diagnostics and translational research, including genomic diagnostics, gene-disease discovery, and cohort analytics. In recent years, groups around the world have developed translations of the HPO from English to other languages, and the HPO browser has been internationalized, allowing users to view HPO term labels and in many cases synonyms and definitions in ten languages in addition to English. Since our last report, a total of 2239 new HPO terms and 49235 new HPO annotations were developed, many in collaboration with external groups in the fields of psychiatry, arthrogryposis, immunology and cardiology. The Medical Action Ontology (MAxO) is a new effort to model treatments and other measures taken for clinical management. Finally, the HPO consortium is contributing to efforts to integrate the HPO and the GA4GH Phenopacket Schema into electronic health records (EHRs) with the goal of more standardized and computable integration of rare disease data in EHRs

An Investigation Into the Role of Groundwater As a Point Source of Emerging Contaminants to Smallmouth Bass in the Susquehanna River Basin

Since 2005, high young-of-year natural mortality rates and declines in adult indices of abundance have been observed in some smallmouth bass populations in the Chesapeake Bay Watershed, and specifically in the Susquehanna River Basin. Endocrine disruptin

Target-agnostic drug prediction integrated with medical record analysis uncovers differential associations of statins with increased survival in COVID-19 patients.

Drug repurposing requires distinguishing established drug class targets from novel molecule-specific mechanisms and rapidly derisking their therapeutic potential in a time-critical manner, particularly in a pandemic scenario. In response to the challenge to rapidly identify treatment options for COVID-19, several studies reported that statins, as a drug class, reduce mortality in these patients. However, it is unknown if different statins exhibit consistent function or may have varying therapeutic benefit. A Bayesian network tool was used to predict drugs that shift the host transcriptomic response to SARS-CoV-2 infection towards a healthy state. Drugs were predicted using 14 RNA-sequencing datasets from 72 autopsy tissues and 465 COVID-19 patient samples or from cultured human cells and organoids infected with SARS-CoV-2. Top drug predictions included statins, which were then assessed using electronic medical records containing over 4,000 COVID-19 patients on statins to determine mortality risk in patients prescribed specific statins versus untreated matched controls. The same drugs were tested in Vero E6 cells infected with SARS-CoV-2 and human endothelial cells infected with a related OC43 coronavirus. Simvastatin was among the most highly predicted compounds (14/14 datasets) and five other statins, including atorvastatin, were predicted to be active in > 50% of analyses. Analysis of the clinical database revealed that reduced mortality risk was only observed in COVID-19 patients prescribed a subset of statins, including simvastatin and atorvastatin. In vitro testing of SARS-CoV-2 infected cells revealed simvastatin to be a potent direct inhibitor whereas most other statins were less effective. Simvastatin also inhibited OC43 infection and reduced cytokine production in endothelial cells. Statins may differ in their ability to sustain the lives of COVID-19 patients despite having a shared drug target and lipid-modifying mechanism of action. These findings highlight the value of target-agnostic drug prediction coupled with patient databases to identify and clinically evaluate non-obvious mechanisms and derisk and accelerate drug repurposing opportunities