Neutrophil recruitment in endotoxin-induced murine mastitis is strictly dependent on mammary alveolar macrophages

Mastitis, inflammation of the mammary tissue, is a common disease in dairy animals and mammary pathogenic Escherichia coli (MPEC) is a leading cause of the disease. Lipopolysaccharide (LPS) is an important virulence factor of MPEC and inoculation of the mammary glands with bacterial LPS is sufficient to induce an inflammatory response. We previously showed using adoptive transfer of normal macrophages into the mammary gland of TLR4-deficient C3H/HeJ mice that LPS/TLR4 signaling on mammary alveolar macrophages is sufficient to elicit neutrophil recruitment into the alveolar space. Here we show that TLR4-normal C3H/HeN mice, depleted of alveolar macrophages, were completely refractory to LPS intramammary challenge. These results indicate that alveolar macrophages are both sufficient and essential for neutrophil recruitment elicited by LPS/TLR4 signaling in the mammary gland. Using TNFα gene-knockout mice and adoptive transfer of wild-type macrophages, we show here that TNFα produced by mammary alveolar macrophages in response to LPS/TLR4 signaling is an essential mediator eliciting blood neutrophil recruitment into the milk spaces. Furthermore, using the IL8 receptor or IL1 receptor gene-knockout mice we observed abrogated recruitment of neutrophils into the mammary gland and their entrapment on the basal side of the alveolar epithelium in response to intramammary LPS challenge. Adoptive transfer of wild-type neutrophils to IL1 receptor knockout mice, just before LPS challenge, restored normal neutrophil recruitment into the milk spaces. We conclude that neutrophil recruitment to the milk spaces is: (i) mediated through TNFα, which is produced by alveolar macrophages in response to LPS/TLR4 signaling and (ii) is dependent on IL8 and IL1β signaling and regulated by iNOS-derived NO

Explainable automated recognition of emotional states from canine facial expressions: the case of positive anticipation and frustration.

In animal research, automation of affective states recognition has so far mainly addressed pain in a few species. Emotional states remain uncharted territories, especially in dogs, due to the complexity of their facial morphology and expressions. This study contributes to fill this gap in two aspects. First, it is the first to address dog emotional states using a dataset obtained in a controlled experimental setting, including videos from (n = 29) Labrador Retrievers assumed to be in two experimentally induced emotional states: negative (frustration) and positive (anticipation). The dogs' facial expressions were measured using the Dogs Facial Action Coding System (DogFACS). Two different approaches are compared in relation to our aim: (1) a DogFACS-based approach with a two-step pipeline consisting of (i) a DogFACS variable detector and (ii) a positive/negative state Decision Tree classifier; (2) An approach using deep learning techniques with no intermediate representation. The approaches reach accuracy of above 71% and 89%, respectively, with the deep learning approach performing better. Secondly, this study is also the first to study explainability of AI models in the context of emotion in animals. The DogFACS-based approach provides decision trees, that is a mathematical representation which reflects previous findings by human experts in relation to certain facial expressions (DogFACS variables) being correlates of specific emotional states. The deep learning approach offers a different, visual form of explainability in the form of heatmaps reflecting regions of focus of the network's attention, which in some cases show focus clearly related to the nature of particular DogFACS variables. These heatmaps may hold the key to novel insights on the sensitivity of the network to nuanced pixel patterns reflecting information invisible to the human eye

ZYG11A Is Expressed in Epithelial Ovarian Cancer and Correlates With Low Grade Disease

The insulin-like growth factors (IGF) are important players in the development of gynecological malignancies, including epithelial ovarian cancer (EOC). The identification of biomarkers that can help in the diagnosis and scoring of EOC patients is of fundamental importance in clinical oncology. We have recently identified the ZYG11A gene as a new candidate target of IGF1 action. The aim of the present study was to evaluate the expression of ZYG11A in EOC patients and to correlate its pattern of expression with histological grade and pathological stage. Furthermore, and in view of previous analyses showing an interplay between ZYG11A, p53 and the IGF1 receptor (IGF1R), we assessed a potential coordinated expression of these proteins in EOC. In addition, zyg11a expression was assessed in ovaries and uteri of growth hormone receptor (GHR) knock-out mice. Tissue microarray analysis was conducted on 36 patients with EOC and expression of ZYG11A, IGF1R and p53 was assessed by immunohistochemistry. Expression levels were correlated with clinical parameters. qPCR was employed to assess zyg11a mRNA levels in mice tissues. Our analyses provide evidence of reduced ZYG11A expression in high grade tumors, consistent with a putative tumor suppressor role. In addition, an inverse correlation between ZYG11A and p53 levels in individual tumors was noticed. Taken together, our data justify further exploration of the role of ZYG11A as a novel biomarker in EOC

Neural traces of stress: cortisol related sustained enhancement of amygdala-hippocampal functional connectivity

Stressful experiences modulate neuro-circuitry function, and the temporal trajectory of these alterations, elapsing from early disturbances to late recovery, heavily influences resilience and vulnerability to stress. Such effects of stress may depend on processes that are engaged during resting-state, through active recollection of past experiences and anticipation of future events, all known to involve the default mode network (DMN). By inducing social stress and acquiring resting-state functional magnetic resonance imaging (fMRI) before stress, immediately following it, and 2 h later, we expanded the time-window for examining the trajectory of the stress response. Throughout the study repeated cortisol samplings and self-reports of stress levels were obtained from 51 healthy young males. Post-stress alterations were investigated by whole brain resting-state functional connectivity (rsFC) of two central hubs of the DMN: the posterior cingulate cortex (PCC) and hippocampus. Results indicate a ’recovery’ pattern of DMN connectivity, in which all alterations, ascribed to the intervening stress, returned to pre-stress levels. The only exception to this pattern was a stress-induced rise in amygdala-hippocampal connectivity, which was sustained for as long as 2 h following stress induction. Furthermore, this sustained enhancement of limbic connectivity was inversely correlated to individual stress-induced cortisol responsiveness (AUCi) and characterized only the group lacking such increased cortisol (i.e., non-responders). Our observations provide evidence of a prolonged post-stress response profile, characterized by both the comprehensive balance of most DMN functional connections and the distinct time and cortisol dependent ascent of intra-limbic connectivity. These novel insights into neuro-endocrine relations are another milestone in the ongoing search for individual markers in stress-related psychopathologies

Coronavirus disease 2019-associated invasive fungal infection

Coronavirus disease 2019 (COVID-19) can become complicated by secondary invasive fungal infections (IFIs), stemming primarily from severe lung damage and immunologic deficits associated with the virus or immunomodulatory therapy. Other risk factors include poorly controlled diabetes, structural lung disease and/or other comorbidities, and fungal colonization. Opportunistic IFI following severe respiratory viral illness has been increasingly recognized, most notably with severe influenza. There have been many reports of fungal infections associated with COVID-19, initially predominated by pulmonary aspergillosis, but with recent emergence of mucormycosis, candidiasis, and endemic mycoses. These infections can be challenging to diagnose and are associated with poor outcomes. The reported incidence of IFI has varied, often related to heterogeneity in patient populations, surveillance protocols, and definitions used for classification of fungal infections. Herein, we review IFI complicating COVID-19 and address knowledge gaps related to epidemiology, diagnosis, and management of COVID-19-associated fungal infections

Explainable automated recognition of emotional states from canine facial expressions: the case of positive anticipation and frustration

In animal research, automation of affective states recognition has so far mainly addressed pain in a few species. Emotional states remain uncharted territories, especially in dogs, due to the complexity of their facial morphology and expressions. This study contributes to fill this gap in two aspects. First, it is the first to address dog emotional states using a dataset obtained in a controlled experimental setting, including videos from (n = 29) Labrador Retrievers assumed to be in two experimentally induced emotional states: negative (frustration) and positive (anticipation). The dogs’ facial expressions were measured using the Dogs Facial Action Coding System (DogFACS). Two different approaches are compared in relation to our aim: (1) a DogFACS-based approach with a two-step pipeline consisting of (i) a DogFACS variable detector and (ii) a positive/negative state Decision Tree classifier; (2) An approach using deep learning techniques with no intermediate representation. The approaches reach accuracy of above 71% and 89%, respectively, with the deep learning approach performing better. Secondly, this study is also the first to study explainability of AI models in the context of emotion in animals. The DogFACS-based approach provides decision trees, that is a mathematical representation which reflects previous findings by human experts in relation to certain facial expressions (DogFACS variables) being correlates of specific emotional states. The deep learning approach offers a different, visual form of explainability in the form of heatmaps reflecting regions of focus of the network’s attention, which in some cases show focus clearly related to the nature of particular DogFACS variables. These heatmaps may hold the key to novel insights on the sensitivity of the network to nuanced pixel patterns reflecting information invisible to the human eye

Clinical phenotypes of infantile onset CACNA1A-related disorder

BACKGROUND: CACNA1A-related disorders present with persistent progressive and non-progressive cerebellar ataxia and paroxysmal events: epileptic seizures and non-epileptic attacks. These phenotypes overlap and co-exist in the majority of patients. OBJECTIVE: To describe phenotypes in infantile onset CACNA1A-related disorder and to explore intra-familial variations and genotype-phenotype correlations. MATERIAL AND METHODS: This study was a multicenter international collaboration. A retrospective chart review of CACNA1A patients was performed. Clinical, radiological, and genetic data were collected and analyzed in 47 patients with infantile-onset disorder. RESULTS: Paroxysmal non-epileptic events (PNEE) were observed in 68% of infants, with paroxysmal tonic upward gaze (PTU) noticed in 47% of infants. Congenital cerebellar ataxia (CCA) was diagnosed in 51% of patients including four patients with developmental delay and only one neurological sign. PNEEs were found in 63% of patients at follow-up, with episodic ataxia (EA) in 40% of the sample. Cerebellar ataxia was found in 58% of the patients at follow-up. Four patients had epilepsy in infancy and nine in childhood. Seven infants had febrile convulsions, three of which developed epilepsy later; all three patients had CCA. Cognitive difficulties were demonstrated in 70% of the children. Cerebellar atrophy was found in only one infant but was depicted in 64% of MRIs after age two. CONCLUSIONS: Nearly all of the infants had CCA, PNEE or both. Cognitive difficulties were frequent and appeared to be associated with CCA. Epilepsy was more frequent after age two. Febrile convulsions in association with CCA may indicate risk of epilepsy in later childhood. Brain MRI was normal in infancy. There were no genotype-phenotype correlations found

Evolutionary Sequence Modeling for Discovery of Peptide Hormones

There are currently a large number of “orphan” G-protein-coupled receptors (GPCRs) whose endogenous ligands (peptide hormones) are unknown. Identification of these peptide hormones is a difficult and important problem. We describe a computational framework that models spatial structure along the genomic sequence simultaneously with the temporal evolutionary path structure across species and show how such models can be used to discover new functional molecules, in particular peptide hormones, via cross-genomic sequence comparisons. The computational framework incorporates a priori high-level knowledge of structural and evolutionary constraints into a hierarchical grammar of evolutionary probabilistic models. This computational method was used for identifying novel prohormones and the processed peptide sites by producing sequence alignments across many species at the functional-element level. Experimental results with an initial implementation of the algorithm were used to identify potential prohormones by comparing the human and non-human proteins in the Swiss-Prot database of known annotated proteins. In this proof of concept, we identified 45 out of 54 prohormones with only 44 false positives. The comparison of known and hypothetical human and mouse proteins resulted in the identification of a novel putative prohormone with at least four potential neuropeptides. Finally, in order to validate the computational methodology, we present the basic molecular biological characterization of the novel putative peptide hormone, including its identification and regional localization in the brain. This species comparison, HMM-based computational approach succeeded in identifying a previously undiscovered neuropeptide from whole genome protein sequences. This novel putative peptide hormone is found in discreet brain regions as well as other organs. The success of this approach will have a great impact on our understanding of GPCRs and associated pathways and help to identify new targets for drug development

Physiological Correlates of Volunteering

We review research on physiological correlates of volunteering, a neglected but promising research field. Some of these correlates seem to be causal factors influencing volunteering. Volunteers tend to have better physical health, both self-reported and expert-assessed, better mental health, and perform better on cognitive tasks. Research thus far has rarely examined neurological, neurochemical, hormonal, and genetic correlates of volunteering to any significant extent, especially controlling for other factors as potential confounds. Evolutionary theory and behavioral genetic research suggest the importance of such physiological factors in humans. Basically, many aspects of social relationships and social activities have effects on health (e.g., Newman and Roberts 2013; Uchino 2004), as the widely used biopsychosocial (BPS) model suggests (Institute of Medicine 2001). Studies of formal volunteering (FV), charitable giving, and altruistic behavior suggest that physiological characteristics are related to volunteering, including specific genes (such as oxytocin receptor [OXTR] genes, Arginine vasopressin receptor [AVPR] genes, dopamine D4 receptor [DRD4] genes, and 5-HTTLPR). We recommend that future research on physiological factors be extended to non-Western populations, focusing specifically on volunteering, and differentiating between different forms and types of volunteering and civic participation