Heterogeneity of autoreactive T cell clones specific for the E2 component of the pyruvate dehydrogenase complex in primary biliary cirrhosis.

The extraordinary specificity of bile duct destruction in primary biliary cirrhosis (PBC) and the presence of T cell infiltrates in the portal tracts have suggested that biliary epithelial cells are the targets of an autoimmune response. The immunodominant antimitochondrial response in patients with PBC is directed against the E2 component of pyruvate dehydrogenase (PDC-E2). Hitherto, there have only been limited reports on the characterization and V beta usage of PDC-E2-specific cloned T cell lines. In this study, we examined peripheral blood mononuclear cells (PBMC) for their reactivity to the entire PDC complex as well as to the E1- and E2-specific components. We also examined the phenotype, lymphokine profile, and V beta usage of PDC-specific T cell clones isolated from cellular infiltrates from the livers of PBC patients. We report that PBMC from 16/19 patients with PBC, but not 12 control patients, respond to the PDC-E2 subunit. Interestingly, this response was directed to the inner and/or the outer lipoyl domains, despite the serologic observation that the autoantibody response is directed predominantly to the inner lipoyl domain. Additionally, lymphokine analysis of interleukin (IL) 2/IL-4/interferon gamma production from individual liver-derived autoantigen-specific T cell clones suggests that both T helper cell Th1- and Th2-like clones are present in the liver. Moreover, there was considerable heterogeneity in the T cell receptor for antigen (TCR) V beta usage of these antigen-specific autoreactive T cell clones. This is in contrast to murine studies in which animals are induced to develop autoimmunity by specific immunization and have an extremely limited T cell V beta repertoire. Thus, our data suggest that in human organ-specific autoimmune diseases, such as PBC, the TCR V beta repertoire is heterogenous

Lymphapheresis in organ transplantation: preliminary report.

Reduction of lymphoid tissue by splenectomy and/or thymectomy has been used as a part of immunosuppression in organ transplantation (4). More recently Walker (7), Johnson (2), Franksson (1) and Starzl (5,6) and their associates have shown that chronic depletion of lymphocytes by thoracic duct drainage decreases the incidence of rejection and hence increases renal graft survival. Mechanical removal of lymphocytes from circulation peripheral blood should theoretically achieve the same or similar effect on the immunity as thoracic duct drainage. Since September, 1979, five organ transplant recipients have received multiple lymphocytapheresis by IBM 2997 Blood Cell Separator as a mechanical pretransplant immunosuppression. The changes in cellular and humoral immunity and the clinical outcome are presented in this report

Eating Disorders: An Evolutionary Psychoneuroimmunological Approach

Eating disorders are evolutionarily novel conditions. They lead to some of the highest mortality rates of all psychiatric disorders. Several evolutionary hypotheses have been proposed for eating disorders, but only the intrasexual competition hypothesis is extensively supported by evidence. We present the mismatch hypothesis as a necessary extension to the current theoretical framework of eating disorders. This hypothesis explains the evolutionarily novel adaptive metaproblem that has arisen when mating motives conflict with the large-scale and easy availability of hyper-rewarding but obesogenic foods. This situation is exacerbated particularly in those contemporary environments that are characterized by sedentary lifestyles, ever-present junk foods, caloric surplus and the ubiquity of social comparisons that take place via social media. Our psychoneuroimmunological model connects ultimate-level causation with proximate mechanisms by showing how the adaptive metaproblem between mating motives and food rewards leads to chronic stress and, further, to disordered eating. Chronic stress causes neuroinflammation, which increases susceptibility to OCD-like behaviors that typically co-occur with eating disorders. Chronic stress upregulates the serotonergic system and causes dysphoric mood in anorexia nervosa patients. Dieting, however, reduces serotonin levels and dysphoric mood, leading to a vicious serotonergic-homeostatic stress/starvation cycle whereby cortisol and neuroinflammation increase through stringent dieting. Our psychoneuroimmunological model indicates that between-individual and within-individual variation in eating disorders partially arises from (co)variation in gut microbiota and stress responsivity, which influence neuroinflammation and the serotonergic system. We review the advances that have been made in recent years in understanding how to best treat eating disorders, outlining directions for future clinical research. Current evidence indicates that eating disorder treatments should aim to reduce the chronic stress, neuroinflammation, stress responsivity and gut dysbiosis that fuel the disorders. Connecting ultimate causes with proximate mechanisms and treating biopsychosocial causes rather than manifest symptoms is expected to bring more effective and sophisticated long-term interventions for the millions of people who suffer from eating disorders

A physics-based machine learning technique rapidly reconstructs the wall-shear stress and pressure fields in coronary arteries

With the global rise of cardiovascular disease including atherosclerosis, there is a high demand or accurate diagnostic tools that can be used during a short consultation. In view of pathology, abnormal blood flow patterns have been demonstrated to be strong predictors of atherosclerotic lesion incidence, location, progression, and rupture. Prediction of patient-specific blood flow patterns can hence enable fast clinical diagnosis. However, the current state of art for the technique is by employing 3D-imaging-based Computational Fluid Dynamics (CFD). The high computational cost renders these methods impractical. In this work, we present a novel method to expedite the reconstruction of 3D pressure and shear stress fields using a combination of a reduced-order CFD modelling technique together with non-linear regression tools from the Machine Learning (ML) paradigm. Specifically, we develop a proof-of-concept automated pipeline that uses randomised perturbations of an atherosclerotic pig coronary artery to produce a large dataset of unique mesh geometries with variable blood flow. A total of 1407 geometries were generated from seven reference arteries and were used to simulate blood flow using the CFD solver Abaqus. This CFD dataset was then post-processed using the mesh-domain common-base Proper Orthogonal Decomposition (cPOD) method to obtain Eigen functions and principal coefficients, the latter of which is a product of the individual mesh flow solutions with the POD Eigenvectors. Being a data-reduction method, the POD enables the data to be represented using only the ten most significant modes, which captures cumulatively greater than 95% of variance of flow features due to mesh variations. Next, the node coordinate data of the meshes were embedded in a two-dimensional coordinate system using the t-distributed Stochastic Neighbor Embedding (t-SNE) algorithm. The reduced dataset for t-SNE coordinates and corresponding vector of POD coefficients were then used to train a Random Forest Regressor (RFR) model. The same methodology was applied to both the volumetric pressure solution and the wall shear stress. The predicted pattern of blood pressure, and shear stress in unseen arterial geometries were compared with the ground truth CFD solutions on 'unseen' meshes. The new method was able to reliably reproduce the 3D coronary artery haemodynamics in less than 10 seconds

A physics-based machine learning technique rapidly reconstructs the wall-shear stress and pressure fields in coronary arteries

With the global rise of cardiovascular disease including atherosclerosis, there is a high demand for accurate diagnostic tools that can be used during a short consultation. In view of pathology, abnormal blood flow patterns have been demonstrated to be strong predictors of atherosclerotic lesion incidence, location, progression, and rupture. Prediction of patient-specific blood flow patterns can hence enable fast clinical diagnosis. However, the current state of art for the technique is by employing 3D-imaging-based Computational Fluid Dynamics (CFD). The high computational cost renders these methods impractical. In this work, we present a novel method to expedite the reconstruction of 3D pressure and shear stress fields using a combination of a reduced-order CFD modelling technique together with non-linear regression tools from the Machine Learning (ML) paradigm. Specifically, we develop a proof-of-concept automated pipeline that uses randomised perturbations of an atherosclerotic pig coronary artery to produce a large dataset of unique mesh geometries with variable blood flow. A total of 1,407 geometries were generated from seven reference arteries and were used to simulate blood flow using the CFD solver Abaqus. This CFD dataset was then post-processed using the mesh-domain common-base Proper Orthogonal Decomposition (cPOD) method to obtain Eigen functions and principal coefficients, the latter of which is a product of the individual mesh flow solutions with the POD Eigenvectors. Being a data-reduction method, the POD enables the data to be represented using only the ten most significant modes, which captures cumulatively greater than 95% of variance of flow features due to mesh variations. Next, the node coordinate data of the meshes were embedded in a two-dimensional coordinate system using the t-distributed Stochastic Neighbor Embedding ((Formula presented.) -SNE) algorithm. The reduced dataset for (Formula presented.) -SNE coordinates and corresponding vector of POD coefficients were then used to train a Random Forest Regressor (RFR) model. The same methodology was applied to both the volumetric pressure solution and the wall shear stress. The predicted pattern of blood pressure, and shear stress in unseen arterial geometries were compared with the ground truth CFD solutions on “unseen” meshes. The new method was able to reliably reproduce the 3D coronary artery haemodynamics in less than 10 s

Ecological Stoichiometry: A Link Between Developmental Speed and Physiological Stress in an Omnivorous Insect

The elemental composition of organisms belongs to a suite of functional traits that may adaptively respond to fluctuating selection pressures. Life history theory predicts that predation risk and resource limitations impose selection pressures on organisms' developmental time and are further associated with variability in energetic and behavioral traits. Individual differences in developmental speed, behaviors and physiology have been explained using the pace-of-life syndrome (POLS) hypothesis. However, how an organism's developmental speed is linked with elemental body composition, metabolism and behavior is not well understood. We compared elemental body composition, latency to resume activity and resting metabolic rate (RMR) of western stutter-trilling crickets (Gryllus integer) in three selection lines that differ in developmental speed. We found that slowly developing crickets had significantly higher body carbon, lower body nitrogen and higher carbon-to-nitrogen ratio than rapidly developing crickets. Slowly developing crickets had significantly higher RMR than rapidly developing crickets. Male crickets had higher RMR than females. Slowly developing crickets resumed activity faster in an unfamiliar relative to a familiar environment. The rapidly developing crickets did the opposite. The results highlight the tight association between life history, physiology and behavior. This study indicates that traditional methods used in POLS research should be complemented by those used in ecological stoichiometry, resulting in a synthetic approach that potentially advances the whole field of behavioral and physiological ecology

Microbiome symbionts and diet diversity incur costs on the immune system of insect larvae.

Communities of symbiotic microorganisms that colonize the gastrointestinal tract play an important role in food digestion and protection against opportunistic microbes. Diet diversity increases the number of symbionts in the intestines, a benefit that is considered to impose no cost for the host organism. However, less is known about the possible immunological investments that hosts have to make in order to control the infections caused by symbiont populations that increase because of diet diversity. Using taxonomical composition analysis of the 16S rRNAV3 region, we show that enterococci are the dominating group of bacteria in the midgut of the larvae of the greater wax moth (Galleria mellonella). We found that the number of colony-forming units of enterococci and expressions of certain immunity-related antimicrobial peptide (AMP) genes such as Gallerimycin, Gloverin, 6-tox, Cecropin-D and Galiomicin increased in response to a more diverse diet, which in turn decreased the encapsulation response of the larvae. Treatment with antibiotics significantly lowered the expression of all AMP genes. Diet and antibiotic treatment interaction did not affect the expression of Gloverin and Galiomicin AMP genes, but significantly influenced the expression of Gallerimycin, 6-tox and Cecropin-D. Taken together, our results suggest that diet diversity influences microbiome diversity and AMP gene expression, ultimately affecting an organism's capacity to mount an immune response. Elevated basal levels of immunity-related genes (Gloverin and Galiomicin) might act as a prophylactic against opportunistic infections and as a mechanism that controls the gut symbionts. This would indicate that a diverse diet imposes higher immunity costs on organisms

Dependence of Intramyocardial Pressure and Coronary Flow on Ventricular Loading and Contractility: A Model Study

The phasic coronary arterial inflow during the normal cardiac cycle has been explained with simple (waterfall, intramyocardial pump) models, emphasizing the role of ventricular pressure. To explain changes in isovolumic and low afterload beats, these models were extended with the effect of three-dimensional wall stress, nonlinear characteristics of the coronary bed, and extravascular fluid exchange. With the associated increase in the number of model parameters, a detailed parameter sensitivity analysis has become difficult. Therefore we investigated the primary relations between ventricular pressure and volume, wall stress, intramyocardial pressure and coronary blood flow, with a mathematical model with a limited number of parameters. The model replicates several experimental observations: the phasic character of coronary inflow is virtually independent of maximum ventricular pressure, the amplitude of the coronary flow signal varies about proportionally with cardiac contractility, and intramyocardial pressure in the ventricular wall may exceed ventricular pressure. A parameter sensitivity analysis shows that the normalized amplitude of coronary inflow is mainly determined by contractility, reflected in ventricular pressure and, at low ventricular volumes, radial wall stress. Normalized flow amplitude is less sensitive to myocardial coronary compliance and resistance, and to the relation between active fiber stress, time, and sarcomere shortening velocity