154 research outputs found
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
Π‘ΡΠ°ΡΡΡ ΠΏΠΎΡΠ²ΡΡΠ΅Π½Π° ΠΎΡΠΈΠ³ΠΈΠ½Π°Π»ΡΠ½ΠΎΠΉ ΠΏΡΠΈΡ
ΠΎΠ°Π½Π°Π»ΠΈΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΠΈΠ½ΡΠ΅ΡΠΏΡΠ΅ΡΠ°ΡΠΈΠΈ ΡΡΠ°Π½ΠΎΠ²Π»Π΅Π½ΠΈΡ ΡΠΎΡΡΠΈΠΉΡΠΊΠΎΠΉ Π³ΠΎΡΡΠ΄Π°ΡΡΡΠ²Π΅Π½Π½ΠΎΡΡΠΈ ΠΈ ΡΠ°ΠΌΠΎΠ΄Π΅ΡΠΆΠ°Π²ΠΈΡ, ΠΊΠΎΡΠΎΡΠ°Ρ ΡΠ°Π½Π΅Π΅ Π½Π΅ ΠΏΡΠΎΠ²ΠΎΠ΄ΠΈΠ»Π°ΡΡ Π² ΠΌΠ°ΡΠ΅ΡΠΈΠ°Π»Π΅ ΡΡΡΡΠΊΠΎΠ³ΠΎ Π‘ΡΠ΅Π΄Π½Π΅Π²Π΅ΠΊΠΎΠ²ΡΡ. ΠΠ²ΡΠΎΡ Π²ΠΏΠ΅ΡΠ²ΡΠ΅ ΠΎΠ±ΡΠ°ΡΠ°Π΅ΡΡΡ ΠΊ ΠΏΡΠΈΡ
ΠΎΠΈΡΡΠΎΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΠΊΠΎΠ½ΡΠ΅ΠΏΡΠΈΠΈ Π³Π΅Π½Π΅Π·ΠΈΡΠ° ΡΠ°ΠΌΠΎΠ΄Π΅ΡΠΆΠ°Π²ΠΈΡ: ΠΎΡ ΡΠΎΡΠΌΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΠΎΡΠΊΠΎΠ²ΡΠΊΠΎΠ³ΠΎ ΡΠ΅Π½ΡΡΠ°Π»ΠΈΠ·ΠΎΠ²Π°Π½Π½ΠΎΠ³ΠΎ Π³ΠΎΡΡΠ΄Π°ΡΡΡΠ²Π° Π΄ΠΎ Π½Π°ΡΠΈΡ
Π΄Π½Π΅ΠΉ. ΠΡΠΎΡ ΠΏΠΎΠ΄Ρ
ΠΎΠ΄ ΠΎΠΏΠΈΡΠ°Π΅ΡΡΡ Π½Π° Π·Π°ΡΡΠ±Π΅ΠΆΠ½ΡΠ΅ ΠΏΡΠΈΡ
ΠΎΠΈΡΡΠΎΡΠΈΡΠ΅ΡΠΊΠΈΠ΅ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ, Π½Π°ΡΠ°ΡΡΠ΅ Π. ΠΠ΅ ΠΠΎΠ·ΠΎΠΌ, ΠΈ ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»ΡΠ΅Ρ Π½ΠΎΠ²ΡΡ ΠΈ Π°ΠΊΡΡΠ°Π»ΡΠ½ΡΡ ΡΡΡΠ°ΡΠ΅Π³ΠΈΡ ΡΠΎΡΠΈΠΎΠΊΡΠ»ΡΡΡΡΠ½ΡΡ
ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠΉ. Π Π΅Π·ΡΠ»ΡΡΠ°ΡΡ. ΠΡΡΠΎΡΠΈΡ ΡΡΡΠ°Π½Ρ ΠΏΡΠ΅Π΄ΡΡΠ°Π΅Ρ ΠΊΠ°ΠΊ Ρ
ΡΠΎΠ½ΠΎΠ»ΠΎΠ³ΠΈΡ Π±Π΅ΡΡΠΎΠ·Π½Π°ΡΠ΅Π»ΡΠ½ΠΎΠΉ ΠΊΠΎΠ½ΠΊΡΡΠ΅Π½ΡΠΈΠΈ Π²Π΅Π΄ΡΡΠΈΡ
ΠΏΡΠΈΡ
ΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΊΠΎΠΌΠΏΠ»Π΅ΠΊΡΠΎΠ² ΡΠΎΡΡΠΈΠΉΡΠΊΠΎΠΉ ΠΌΠ΅Π½ΡΠ°Π»ΡΠ½ΠΎΡΡΠΈ. ΠΠΎ-ΠΏΠ΅ΡΠ²ΡΡ
, ΡΡΠΎ Β«Π½ΠΎΠ²Π³ΠΎΡΠΎΠ΄ΡΠΊΠΈΠΉ ΠΊΠΎΠΌΠΏΠ»Π΅ΠΊΡ Π½Π΅ΠΏΠΎΠ»Π½ΠΎΡΠ΅Π½Π½ΠΎΡΡΠΈ ΠΌΠΎΡΠΊΠΎΠ²ΡΠΊΠΎΠΉ Π°ΡΠΈΡΡΠΎΠΊΡΠ°ΡΠΈΠΈΒ» - ΠΈΡΠΎΠ³ Π°ΠΊΡΠΈΠ²Π½ΠΎΠ³ΠΎ ΠΏΡΠΎΡΠΈΠ²ΠΎΡΡΠΎΡΠ½ΠΈΡ ΠΠΎΡΠΊΠ²Ρ ΠΈ ΠΠΎΠ²Π³ΠΎΡΠΎΠ΄Π°, Π²ΠΏΠ»ΠΎΡΡ Π΄ΠΎ ΠΏΠ°Π΄Π΅Π½ΠΈΡ Π²Π΅ΡΠ΅Π²ΠΎΠΉ ΡΠ΅ΡΠΏΡΠ±Π»ΠΈΠΊΠΈ. ΠΡΠΎΡΡΠΌ ΡΡΠ΅Π½Π°ΡΠΈΠ΅ΠΌ, Π²ΡΡΠ΅ΡΠ½ΡΡΡΠΈΠΌ Β«Π½ΠΎΠ²Π³ΠΎΡΠΎΠ΄ΡΠΊΠΈΠΉ ΠΊΠΎΠΌΠΏΠ»Π΅ΠΊΡΒ», ΡΡΠ°Π» Β«Π·Π°ΠΏΠ°Π΄Π½ΡΠΉ ΠΊΠΎΠΌΠΏΠ»Π΅ΠΊΡ ΠΏΡΠ΅Π²ΠΎΡΡ
ΠΎΠ΄ΡΡΠ²Π°Β». ΠΠ½ Π²ΠΎΡΠΏΡΠΎΠΈΠ·Π²ΠΎΠ΄ΠΈΠ» ΠΊΠΎΠ»ΠΎΠ½ΠΈΠ°Π»ΡΠ½ΠΎΠ΅ ΠΎΡΠ½ΠΎΡΠ΅Π½ΠΈΠ΅ ΠΈΠΌΠΏΠ΅ΡΠΈΠΈ ΠΊ ΡΠ²ΠΎΠ΅ΠΉ ΡΠ΅ΡΡΠΈΡΠΎΡΠΈΠΈ. Π’ΡΠ΅ΡΡΡ Π³ΡΠ°Π½Ρ ΠΏΡΠΈΡ
ΠΎΠΈΡΡΠΎΡΠΈΠΈ ΡΠ°ΠΌΠΎΠ΄Π΅ΡΠΆΠ°Π²ΠΈΡ ΡΠ²ΡΠ·Π°Π½Π° Ρ ΠΌΠ½ΠΎΠ³ΠΎΠ²Π΅ΠΊΠΎΠ²ΡΠΌ ΠΏΡΠΎΡΠΈΠ²ΠΎΡΡΠΎΡΠ½ΠΈΠ΅ΠΌ ΡΠ°ΡΠ°ΡΠΎ-ΠΌΠΎΠ½Π³ΠΎΠ»ΡΡΠΊΠΎΠΌΡ Π½Π°ΡΠ΅ΡΡΠ²ΠΈΡ. Π‘ΠΎΡΠΈΠ°Π»ΡΠ½ΠΎ-ΠΏΠΎΠ»ΠΈΡΠΈΡΠ΅ΡΠΊΠΈΠΉ ΡΠΊΠ»Π°Π΄ ΡΠ°ΡΠ°Ρ ΠΈ Π΄ΡΡΠ³ΠΈΡ
ΡΡΠ΅ΠΏΠ½ΡΠΊΠΎΠ² ΠΎΡΠ΅Π½Ρ ΡΠΈΠ»ΡΠ½ΠΎ ΠΏΠΎΠ²Π»ΠΈΡΠ» Π½Π° ΠΎΡΠ΅ΡΠ΅ΡΡΠ²Π΅Π½Π½ΡΡ ΠΊΡΠ»ΡΡΡΡΡ, ΡΠ·ΡΠΊ ΠΈ ΠΏΠΎΠ»ΠΈΡΠΈΡΠ΅ΡΠΊΡΡ ΡΡΠ°Π΄ΠΈΡΠΈΡ. ΠΠ½ ΠΏΡΠΎΡΠ²ΠΈΠ»ΡΡ Π² Π°Π±ΡΠΎΠ»ΡΡΠ½ΠΎ Π±Π΅ΡΡΠΎΠ·Π½Π°ΡΠ΅Π»ΡΠ½ΠΎΠΌ ΠΊΠΎΠΌΠΏΠ»Π΅ΠΊΡΠ΅ Β«Π·Π°Π²ΠΈΡΡΠ½ΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΡΡΠ°Π²Π½Π΅Π½ΠΈΡΒ», ΠΎΡΠΊΡΡΡΠΎΠΌ Π’. ΠΠ΅Π±Π»Π΅Π½ΠΎΠΌ. ΠΠΎΡΠΊΠΎΠ²ΡΠΊΠ°Ρ Π°ΡΠΈΡΡΠΎΠΊΡΠ°ΡΠΈΡ Π±Π΅Π· ΡΠ΅Π½ΡΡΠ°Π»ΠΈΠ·ΠΎΠ²Π°Π½Π½ΠΎΠ³ΠΎ Π³ΠΎΡΡΠ΄Π°ΡΡΡΠ²Π° Π²ΡΠ½ΡΠΆΠ΄Π΅Π½Π° ΡΡΠ±Π»ΠΈΠΌΠΈΡΠΎΠ²Π°ΡΡ ΠΏΡΠΈΡ
ΠΎΠΈΡΡΠΎΡΠΈΡΠ΅ΡΠΊΠΈΠΉ ΠΊΠΎΠ½ΡΠ»ΠΈΠΊΡ Π² ΡΠΎΡΠΌΠ΅ ΡΠ°ΡΠΈΠΎΠ½Π°Π»ΠΈΠ·ΠΈΡΠΎΠ²Π°Π½Π½ΠΎΠΉ, ΠΏΡΠ°Π²ΠΎΡΠ»Π°Π²Π½ΠΎ- Π°Π΄Π°ΠΏΡΠΈΡΠΎΠ²Π°Π½Π½ΠΎΠΉ ΠΈΠ΄Π΅ΠΎΠ»ΠΎΠ³ΠΈΠΈ. Π Π½Π΅ΠΉ Π±ΡΠ»Π° ΡΡΠ±Π»ΠΈΠΌΠΈΡΠΎΠ²Π°Π½Π° ΠΈ ΠΊΠΎΡΠ΅Π²Π°Ρ ΠΌΠΎΠ΄Π΅Π»Ρ ΡΠΏΡΠ°Π²Π»Π΅Π½ΠΈΡ Π³Π΅ΠΎΠΏΠΎΠ»ΠΈΡΠΈΡΠ΅ΡΠΊΠΈΠΌΠΈ ΠΏΡΠΎΡΡΡΠ°Π½ΡΡΠ²Π°ΠΌΠΈ, ΠΏΡΠΈΠ½ΡΠ΄ΠΈΡΠ΅Π»ΡΠ½ΠΎ Π½Π°Π»ΠΎΠΆΠ΅Π½Π½Π°Ρ Π½Π° ΡΡΠ°Π΄ΠΈΡΠΈΠΎΠ½Π½ΡΠΉ Π·Π΅ΠΌΠ»Π΅Π΄Π΅Π»ΡΡΠ΅ΡΠΊΠΈΠΉ ΠΎΠ±ΡΠΈΠ½Π½ΠΎ- ΡΠΎΠ΄ΠΎΠ²ΠΎΠΉ ΡΠΊΠ»Π°Π΄ ΠΡΠ΅Π²Π½Π΅ΠΉ ΠΈ Π‘ΡΠ΅Π΄Π½Π΅Π²Π΅ΠΊΠΎΠ²ΠΎΠΉ Π ΡΡΠΈ. ΠΠΎΡΠ»Π΅Π΄ΡΡΡΠ°Ρ ΠΈΡΡΠΎΡΠΈΡ XVβXVI Π²Π². Π»ΠΈΡΡ ΠΏΠΎΠ΄ΡΠ²Π΅ΡΠΆΠ΄Π°Π΅Ρ ΡΠ°ΠΊΡ ΡΡΠ±Π»ΠΈΠΌΠ°ΡΠΈΠΈ ΠΊΠΎΠΌΠΏΠ»Π΅ΠΊΡΠΎΠ² Π½Π΅ΠΏΠΎΠ»Π½ΠΎΡΠ΅Π½Π½ΠΎΡΡΠΈ ΠΈ Π·Π°Π²ΠΈΡΡΠ½ΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΡΡΠ°Π²Π½Π΅Π½ΠΈΡ ΠΏΠΎ ΠΎΡΠ½ΠΎΡΠ΅Π½ΠΈΡ ΠΊ ΡΠ°ΡΠ°ΡΠ°ΠΌ.Horde on Moscow Rus predetermined the strategy of Β«enviousΒ» sublimation of the Β«steppe complexΒ» by Moscow elite, and final abandonment of sociocultural lessons and historical perspective of the Β«Novgorod complexΒ»
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 complexβspecific 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 complexβmediated 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
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