Calcium mobilisation controls tyrosine protein phosphorylation independently of the activation of protein kinase C in human platelets

AbstractWe have investigated the regulation of tyrosine proteins phosphorylation by intracellular Ca2+ level ([Ca2+]i) and protein kinase C (PKC) during platelet stimulation. We found that chelation of extracellular calcium completely prevented phosphorylation of tyrosine proteins induced by thapsigargin and phorbol 12-myristate 13-acetate (PMA), whereas, when induced by thrombin, it prevented a subset of tyrosine proteins. The selective inhibition of PKC by OF 109203X did not abolish tyrosine protein phosphorylation when induced by thrombin and thapsigargin. The results suggest that in human platelets tyrosine protein phosphorylation is dependent on [Ca2+]i, although direct PKC activation can also induce phosphorylation of tyrosine proteins

Psycho-historical rivalry of complexes in mentality of the Russian autocracy

Статья посвящена оригинальной психоаналитической интерпретации становления российской государственности и самодержавия, которая ранее не проводилась в материале русского Средневековья. Автор впервые обращается к психоисторической концепции генезиса самодержавия: от формирования Московского централизованного государства до наших дней. Этот подход опирается на зарубежные психоисторические исследования, начатые Л. Де Мозом, и представляет новую и актуальную стратегию социокультурных исследований. Результаты. История страны предстает как хронология бессознательной конкуренции ведущих психических комплексов российской ментальности. Во-первых, это «новгородский комплекс неполноценности московской аристократии» - итог активного противостояния Москвы и Новгорода, вплоть до падения вечевой республики. Вторым сценарием, вытесняющим «новгородский комплекс», стал «западный комплекс превосходства». Он воспроизводил колониальное отношение империи к своей территории. Третья грань психоистории самодержавия связана с многовековым противостоянием татаро-монгольскому нашествию. Социально-политический уклад татар и других степняков очень сильно повлиял на отечественную культуру, язык и политическую традицию. Он проявился в абсолютно бессознательном комплексе «завистнического сравнения», открытом Т. Вебленом. Московская аристократия без централизованного государства вынуждена сублимировать психоисторический конфликт в форме рационализированной, православно- адаптированной идеологии. В ней была сублимирована и кочевая модель управления геополитическими пространствами, принудительно наложенная на традиционный земледельческий общинно- родовой уклад Древней и Средневековой Руси. Последующая история XV−XVI вв. лишь подтверждает факт сублимации комплексов неполноценности и завистнического сравнения по отношению к татарам.Horde on Moscow Rus predetermined the strategy of «envious» sublimation of the «steppe complex» by Moscow elite, and final abandonment of sociocultural lessons and historical perspective of the «Novgorod complex»

Improving Image-Based Precision Medicine with Uncertainty-Aware Causal Models

Image-based precision medicine aims to personalize treatment decisions based on an individual's unique imaging features so as to improve their clinical outcome. Machine learning frameworks that integrate uncertainty estimation as part of their treatment recommendations would be safer and more reliable. However, little work has been done in adapting uncertainty estimation techniques and validation metrics for precision medicine. In this paper, we use Bayesian deep learning for estimating the posterior distribution over factual and counterfactual outcomes on several treatments. This allows for estimating the uncertainty for each treatment option and for the individual treatment effects (ITE) between any two treatments. We train and evaluate this model to predict future new and enlarging T2 lesion counts on a large, multi-center dataset of MR brain images of patients with multiple sclerosis, exposed to several treatments during randomized controlled trials. We evaluate the correlation of the uncertainty estimate with the factual error, and, given the lack of ground truth counterfactual outcomes, demonstrate how uncertainty for the ITE prediction relates to bounds on the ITE error. Lastly, we demonstrate how knowledge of uncertainty could modify clinical decision-making to improve individual patient and clinical trial outcomes

Personalized Prediction of Future Lesion Activity and Treatment Effect in Multiple Sclerosis from Baseline MRI

Precision medicine for chronic diseases such as multiple sclerosis (MS) involves choosing a treatment which best balances efficacy and side effects/preferences for individual patients. Making this choice as early as possible is important, as delays in finding an effective therapy can lead to irreversible disability accrual. To this end, we present the first deep neural network model for individualized treatment decisions from baseline magnetic resonance imaging (MRI) (with clinical information if available) for MS patients. Our model (a) predicts future new and enlarging T2 weighted (NE-T2) lesion counts on follow-up MRI on multiple treatments and (b) estimates the conditional average treatment effect (CATE), as defined by the predicted future suppression of NE-T2 lesions, between different treatment options relative to placebo. Our model is validated on a proprietary federated dataset of 1817 multi-sequence MRIs acquired from MS patients during four multi-centre randomized clinical trials. Our framework achieves high average precision in the binarized regression of future NE-T2 lesions on five different treatments, identifies heterogeneous treatment effects, and provides a personalized treatment recommendation that accounts for treatment-associated risk (e.g. side effects, patient preference, administration difficulties).Comment: Accepted to MIDL 202

Debiasing Counterfactuals In the Presence of Spurious Correlations

Deep learning models can perform well in complex medical imaging classification tasks, even when basing their conclusions on spurious correlations (i.e. confounders), should they be prevalent in the training dataset, rather than on the causal image markers of interest. This would thereby limit their ability to generalize across the population. Explainability based on counterfactual image generation can be used to expose the confounders but does not provide a strategy to mitigate the bias. In this work, we introduce the first end-to-end training framework that integrates both (i) popular debiasing classifiers (e.g. distributionally robust optimization (DRO)) to avoid latching onto the spurious correlations and (ii) counterfactual image generation to unveil generalizable imaging markers of relevance to the task. Additionally, we propose a novel metric, Spurious Correlation Latching Score (SCLS), to quantify the extent of the classifier reliance on the spurious correlation as exposed by the counterfactual images. Through comprehensive experiments on two public datasets (with the simulated and real visual artifacts), we demonstrate that the debiasing method: (i) learns generalizable markers across the population, and (ii) successfully ignores spurious correlations and focuses on the underlying disease pathology.Comment: Accepted to the FAIMI (Fairness of AI in Medical Imaging) workshop at MICCAI 202

Filamin A, the Arp2/3 complex, and the morphology and function of cortical actin filaments in human melanoma cells

The Arp2/3 complex and filamin A (FLNa) branch actin filaments. To define the role of these actin-binding proteins in cellular actin architecture, we compared the morphology of FLNa-deficient human melanoma (M2) cells and three stable derivatives of these cells expressing normal FLNa concentrations. All the cell lines contain similar amounts of the Arp2/3 complex. Serum addition causes serum-starved M2 cells to extend flat protrusions transiently; thereafter, the protrusions turn into spherical blebs and the cells do not crawl. The short-lived lamellae of M2 cells contain a dense mat of long actin filaments in contrast to a more three-dimensional orthogonal network of shorter actin filaments in lamellae of identically treated FLNa-expressing cells capable of translational locomotion. FLNa-specific antibodies localize throughout the leading lamellae of these cells at junctions between orthogonally intersecting actin filaments. Arp2/3 complex–specific antibodies stain diffusely and label a few, although not the same, actin filament overlap sites as FLNa antibody. We conclude that FLNa is essential in cells that express it for stabilizing orthogonal actin networks suitable for locomotion. Contrary to some proposals, Arp2/3 complex–mediated branching of actin alone is insufficient for establishing an orthogonal actin organization or maintaining mechanical stability at the leading edge

Counterfactual Image Synthesis for Discovery of Personalized Predictive Image Markers

The discovery of patient-specific imaging markers that are predictive of future disease outcomes can help us better understand individual-level heterogeneity of disease evolution. In fact, deep learning models that can provide data-driven personalized markers are much more likely to be adopted in medical practice. In this work, we demonstrate that data-driven biomarker discovery can be achieved through a counterfactual synthesis process. We show how a deep conditional generative model can be used to perturb local imaging features in baseline images that are pertinent to subject-specific future disease evolution and result in a counterfactual image that is expected to have a different future outcome. Candidate biomarkers, therefore, result from examining the set of features that are perturbed in this process. Through several experiments on a large-scale, multi-scanner, multi-center multiple sclerosis (MS) clinical trial magnetic resonance imaging (MRI) dataset of relapsing-remitting (RRMS) patients, we demonstrate that our model produces counterfactuals with changes in imaging features that reflect established clinical markers predictive of future MRI lesional activity at the population level. Additional qualitative results illustrate that our model has the potential to discover novel and subject-specific predictive markers of future activity.Comment: Accepted to the MIABID workshop at MICCAI 202

Rethinking Generalization: The Impact of Annotation Style on Medical Image Segmentation

Generalization is an important attribute of machine learning models, particularly for those that are to be deployed in a medical context, where unreliable predictions can have real world consequences. While the failure of models to generalize across datasets is typically attributed to a mismatch in the data distributions, performance gaps are often a consequence of biases in the 'ground-truth' label annotations. This is particularly important in the context of medical image segmentation of pathological structures (e.g. lesions), where the annotation process is much more subjective, and affected by a number underlying factors, including the annotation protocol, rater education/experience, and clinical aims, among others. In this paper, we show that modeling annotation biases, rather than ignoring them, poses a promising way of accounting for differences in annotation style across datasets. To this end, we propose a generalized conditioning framework to (1) learn and account for different annotation styles across multiple datasets using a single model, (2) identify similar annotation styles across different datasets in order to permit their effective aggregation, and (3) fine-tune a fully trained model to a new annotation style with just a few samples. Next, we present an image-conditioning approach to model annotation styles that correlate with specific image features, potentially enabling detection biases to be more easily identified.Comment: Accepted for publication at the Journal of Machine Learning for Biomedical Imaging (MELBA) https://www.melba-journal.org/papers/2022:029.htm