What's a brain: neuroanatomy and neurochemistry of anxiety disorders in dogs

This review deals with the neurocircuitry of fear and anxiety disorders, with the focus on neuroanatomy and neurochemistry. This knowledge is required to correctly diagnose and treat dogs with anxiety-related behavioral disorders. Research to date has shown the involvement of the frontal cortex, the amygdala, the thalamus and the hippocampus as core regions in regulating fear. Imbalances (hyper- or hypoactivation) in this fear circuitry can trigger inappropriate fear responses, i.e. anxiety disorders. Serotonin, dopamine and norepinephrine are the main neurotransmitters of emotion in the brain, but gamma-aminobutyric acid (GABA), glutamate, and the hypothalamic-pituitary-adrenal (HPA) axis producing glucocorticoids are also important in the neurochemistry of anxiety

Critical evaluation of the environment in Belgian dog breeding kennels during the puppies' socialization period

Different authors emphasize the role of an appropriate early environment during the juvenile period in the prevention of behavioral problems in puppies. In the present study, the authors investigate whether the conditions in which Belgian breeders raise and sell puppies meet the recommendations posed in the scientific literature. A questionnaire consisting of 20 questions was returned by 48 breeders. From the results it could be concluded that in all the breeding kennels both major and minor deviations from the conditions recommended in the literature were found. In a high percentage of the kennels that were examined, weaning occurs when the puppies are too young, the remaining puppies are kept solitary after the others have been sold, and not enough unfamiliar visual, olfactory and acoustic stimuli or toys are provided. A significant percentage of the puppies never leave the kennels and have no regular contact (or no contact at all) with unfamiliar humans or other non-canine animals. This leads to the conclusion that in a significant percentage of the breeding kennels the environment may not provide a solid basis for proper socialization. Consequently, efforts made by the new owners to achieve socialization are crucial. Depending on the number of breeding bitches in the kennel, the nature and degree of the deficiency will vary. In larger kennels the conditions seem to be less suitable than in smaller kennels (up to 19 breeding bitches). However, a larger scale study is needed to confirm the tendencies that were found in this preliminary investigation. If these tendencies are confirmed, then amendments should be made in the Belgian legislation concerning the recognition of dog breeding kennels

Functional brain imaging : a brief overview of imaging techniques and their use in human and canine anxiety research

When used in combination with specific radioactive markers, functional imaging modalities such as Single Photon Emission Computed Tomography (SPECT) and Positron Emission Tomography (PET) enable the visualization of several neurotransmitter receptors and transporters, as well as of the perfusion and metabolism of the brain. This paper gives an overview of the functional imaging techniques, as well as of the studies that have been performed on humans and canines with anxiety disorders. Thus far, most of the research in this field has been focused on brain perfusion and the serotonergic and dopaminergic neurotransmitters, and less on gamma-aminobutyric acid (GABA), glutamate, norepinephrine and the hypothalamic-pituitary-adrenal (HPA) axis

Effect of angiotensin-converting enzyme inhibitor and angiotensin receptor blocker initiation on organ support-free days in patients hospitalized with COVID-19

IMPORTANCE Overactivation of the renin-angiotensin system (RAS) may contribute to poor clinical outcomes in patients with COVID-19. Objective To determine whether angiotensin-converting enzyme (ACE) inhibitor or angiotensin receptor blocker (ARB) initiation improves outcomes in patients hospitalized for COVID-19. DESIGN, SETTING, AND PARTICIPANTS In an ongoing, adaptive platform randomized clinical trial, 721 critically ill and 58 non–critically ill hospitalized adults were randomized to receive an RAS inhibitor or control between March 16, 2021, and February 25, 2022, at 69 sites in 7 countries (final follow-up on June 1, 2022). INTERVENTIONS Patients were randomized to receive open-label initiation of an ACE inhibitor (n = 257), ARB (n = 248), ARB in combination with DMX-200 (a chemokine receptor-2 inhibitor; n = 10), or no RAS inhibitor (control; n = 264) for up to 10 days. MAIN OUTCOMES AND MEASURES The primary outcome was organ support–free days, a composite of hospital survival and days alive without cardiovascular or respiratory organ support through 21 days. The primary analysis was a bayesian cumulative logistic model. Odds ratios (ORs) greater than 1 represent improved outcomes. RESULTS On February 25, 2022, enrollment was discontinued due to safety concerns. Among 679 critically ill patients with available primary outcome data, the median age was 56 years and 239 participants (35.2%) were women. Median (IQR) organ support–free days among critically ill patients was 10 (–1 to 16) in the ACE inhibitor group (n = 231), 8 (–1 to 17) in the ARB group (n = 217), and 12 (0 to 17) in the control group (n = 231) (median adjusted odds ratios of 0.77 [95% bayesian credible interval, 0.58-1.06] for improvement for ACE inhibitor and 0.76 [95% credible interval, 0.56-1.05] for ARB compared with control). The posterior probabilities that ACE inhibitors and ARBs worsened organ support–free days compared with control were 94.9% and 95.4%, respectively. Hospital survival occurred in 166 of 231 critically ill participants (71.9%) in the ACE inhibitor group, 152 of 217 (70.0%) in the ARB group, and 182 of 231 (78.8%) in the control group (posterior probabilities that ACE inhibitor and ARB worsened hospital survival compared with control were 95.3% and 98.1%, respectively). CONCLUSIONS AND RELEVANCE In this trial, among critically ill adults with COVID-19, initiation of an ACE inhibitor or ARB did not improve, and likely worsened, clinical outcomes. TRIAL REGISTRATION ClinicalTrials.gov Identifier: NCT0273570

A study of the transferability of influenza case detection systems between two large healthcare systems

<div><p>Objectives</p><p>This study evaluates the accuracy and transferability of Bayesian case detection systems (BCD) that use clinical notes from emergency department (ED) to detect influenza cases.</p><p>Methods</p><p>A BCD uses natural language processing (NLP) to infer the presence or absence of clinical findings from ED notes, which are fed into a Bayesain network classifier (BN) to infer patients’ diagnoses. We developed BCDs at the University of Pittsburgh Medical Center (BCD_UPMC) and Intermountain Healthcare in Utah (BCD_IH). At each site, we manually built a rule-based NLP and trained a Bayesain network classifier from over 40,000 ED encounters between Jan. 2008 and May. 2010 using feature selection, machine learning, and expert debiasing approach. Transferability of a BCD in this study may be impacted by seven factors: development (source) institution, development parser, application (target) institution, application parser, NLP transfer, BN transfer, and classification task. We employed an ANOVA analysis to study their impacts on BCD performance.</p><p>Results</p><p>Both BCDs discriminated well between <i>influenza</i> and <i>non-influenza</i> on local test cases (AUCs > 0.92). When tested for transferability using the other institution’s cases, BCD_UPMC discriminations declined minimally (AUC decreased from 0.95 to 0.94, p<0.01), and BCD_IH discriminations declined more (from 0.93 to 0.87, p<0.0001). We attributed the BCD_IH decline to the lower recall of the IH parser on UPMC notes. The ANOVA analysis showed five significant factors: development parser, application institution, application parser, BN transfer, and classification task.</p><p>Conclusion</p><p>We demonstrated high influenza case detection performance in two large healthcare systems in two geographically separated regions, providing evidentiary support for the use of automated case detection from routinely collected electronic clinical notes in national influenza surveillance. The transferability could be improved by training Bayesian network classifier locally and increasing the accuracy of the NLP parser.</p></div