Explore the vast range of titles available.

Standard episodic patient monitoring of vital signs on the medical-surgical wards can potentially miss changes in health status and delay recognition of risk. To reduce these delays, we develop a clinical wearable-based deep learning model, using 9 inputs, comprised of continuous vital signs and demographics, to identify the onset of deterioration early and accurately. Using validated data from 888 adult non-intensive care unit inpatient visits with 135 outcomes, from two different clinical grade wearables, we train a recurrent neural network to predict clinical alerts and adverse clinical outcomes in the subsequent 24 hours. Our continuous clinical alert model is able to predict both clinical alerts (Area under both the Receiver Operator Characteristic curve 0.89 + /− 0.3, Precision Recall curve 0.58 + /− 0.14) and adverse clinical outcomes (accuracy: 81.8% on 11 events) up to 17 hours in advance. Our wearable based continuous clinical alert system outperforms episodic clinical support tools in detecting deterioration, retains its performance when tested on data from a different clinical wearable than the one it was trained on and can produce alerts ahead of a broad class of adverse clinical outcomes, enabling timely interventions that can avert preventable deteriorations and reduce hospital costs. Standard monitoring of patients on surgical wards can lead to delays in recognition of changes in health status and subsequent risk. Here, the authors develop and validate a multi-modal machine learning-based framework using vitals monitored continuously with wearables to predict clinical inpatient deterioration alerts.

The increased prevalence of obesity has mandated extensive research focused on mechanisms responsible for associated clinical complications. Emerging from the focus on adipose tissue biology as a vitally important adipokine is adiponectin which is now believed to mediate anti-diabetic, anti-atherosclerotic, anti-inflammatory, cardioprotective and cancer modifying actions. Adiponectin mediates these primarily beneficial effects via direct signaling effects and via enhancing insulin sensitivity via crosstalk with insulin signaling pathways. Reduced adiponectin action is detrimental and occurs in obesity via decreased circulating levels of adiponectin action or development of adiponectin resistance. This review will focus on cellular mechanisms of adiponectin action, their crosstalk with insulin signaling and the resultant role of adiponectin in cardiovascular disease, diabetes and cancer and reviews data from in vitro cell based studies through animal models to clinical observations.

Key Points Since its discovery 10 years ago, studies have shown that PKB, also known as AKT, is an important enzyme in mediating the pro-survival signals of PI3K. Activation of PKB/AKT is complex and tightly controlled by the activities of upstream protein kinases such as PDK1, which targets the T-loop in the protein. A hypothesized kinase, called PDK2, has also been implicated in PKB/AKT activation. Candidates for this kinase include MAPK-activated protein kinase 2 (MAPKAP-K2), Rsk (MAPKAP-K1) and integrin-linked kinase (ILK). Identifying PKB/AKT substrates has been fundamental to understanding how the kinase impacts on insulin signalling, cell growth and apoptosis. PKB/AKT has been found to interact with factors, including the Forkhead family of transcription factors and GSK3, which indicates PKB/AKT might have several inputs into the control of the cell cycle. PKB/AKT activation cooperates with other genes in promoting cancer and inflammation. PTEN is a tumour suppressor that acts to inactivate PKB/AKT, as well as other PI3K-activated targets. Small molecule drugs that inhibit PKB/AKT may have therapeutic potential in cancer treatment. A relative of PKB/AKT, termed SGK/CISK, might cooperate with PKB in overlapping or distinct physiological functions. The difficulty will be to resolve the true functions of PKB/AKT and those of SGK/CISK, as it could be that SGK/CISK is responsible for some of the functions originally attributed to PKB/AKT. Since its discovery 10 years ago, the potential functions of protein kinase B (PKB)/AKT have been catalogued with increasing efficiency. The physiological relevance of some of the proposed mechanisms by which PKB/AKT mediates many of its effects has been questioned, and recent work using new reagents and approaches has revealed some cracks in our understanding of this important molecule, and also hinted that these effects may involve other players.

The phosphatidylinositol 3-kinase (PI3K)-signaling pathway has emerged as an important component of cytokine-mediated survival of hemopoietic cells. Recently, the protein kinase PKB/akt (referred to here as PKB) has been identified as a downstream target of PI3K necessary for survival. PKB has also been implicated in the phosphorylation of Bad, potentially linking the survival effects of cytokines with the Bcl-2 family. We have shown that granulocyte/macrophage colonystimulating factor (GM-CSF) maintains survival in the absence of PI3K activity, and we now show that when PKB activation is also completely blocked, GM-CSF is still able to stimulate phosphorylation of Bad. Interleukin 3 (IL-3), on the other hand, requires PI3K for survival, and blocking PI3K partially inhibited Bad phosphorylation. IL-4, unique among the cytokines in that it lacks the ability to activate the p21ras--mitogen-activated protein kinase (MAPK) cascade, was found to activate PKB and promote cell survival, but it did not stimulate Bad phosphorylation. Finally, although our data suggest that the MAPK pathway is not required for inhibition of apoptosis, we provide evidence that phosphorylation of Bad may be occurring via a MAPK/ERK kinase (MEK)-dependent pathway. Together, these results demonstrate that although PI3K may contribute to phosphorylation of Bad in some instances, there is at least one other PI3K-independent pathway involved, possibly via activation of MEK. Our data also suggest that although phosphorylation of Bad may be one means by which cytokines can inhibit apoptosis, it may be neither sufficient nor necessary for the survival effect.

Understanding the neural basis of human movement in naturalistic scenarios is critical for expanding neuroscience research beyond constrained laboratory paradigms. Here, we describe our Annotated Joints in Long-term Electrocorticography for 12 human participants (AJILE12) dataset, the largest human neurobehavioral dataset that is publicly available; the dataset was recorded opportunistically during passive clinical epilepsy monitoring. AJILE12 includes synchronized intracranial neural recordings and upper body pose trajectories across 55 semi-continuous days of naturalistic movements, along with relevant metadata, including thousands of wrist movement events and annotated behavioral states. Neural recordings are available at 500 Hz from at least 64 electrodes per participant, for a total of 1280 hours. Pose trajectories at 9 upper-body keypoints were estimated from 118 million video frames. To facilitate data exploration and reuse, we have shared AJILE12 on The DANDI Archive in the Neurodata Without Borders (NWB) data standard and developed a browser-based dashboard.Measurement(s)Brain activity measurement • Body Position • Behavior labels • Brain electrode locationsTechnology Type(s)Electrocorticography • Video RecordingSample Characteristic - OrganismHomo sapiensSample Characteristic - EnvironmenthospitalSample Characteristic - LocationHarborview Medical Center

Background The Rho-associated coiled-coil kinase-2 (ROCK2) is an important signaling transducer in the transmission of extracellular signals effecting organization of the actin cytoskeleton. ROCK2 has been implicated in numerous pathologies and the current focus is on understanding the molecular events that couple ROCK2 activity to biological function. To aid in the search for new ROCK2 substrates, we have developed an analog-sensitive (AS) ROCK2 protein that allows the use of selective ATP analogs that are not efficiently utilized by other protein kinases. Results The analog sensitive protein, M160A ROCK2, was highly active and could phosphorylate proteins from a cellular homogenate with γ 32 P-N 6 (benzyl)ATP. We show the utility of this approach by identifying a putative ROCK2 substrate, elongation initiation factor-1-α1. We further show that the major site of ROCK2 phosphorylation of EIF1α1 is Thr 432 . Conclusions Our work demonstrates that AS-ROCK2 could be useful in a systematic proteomic approach for identifying novel ROCK2 substrates.

Cortical high gamma activity (HGA) is used in many scientific investigations, yet its biophysical source is a matter of debate. Two leading hypotheses are that HGA predominantly represents summed postsynaptic potentials or-more commonly- predominantly represents summed local spikes. If the latter were true, the nearest neurons to an electrode should contribute most to HGA recorded on that electrode. We trained subjects to decouple spiking from HGA on a single electrode using a brain-machine interface. Their ability to decouple them indicated that HGA is not primarily generated by summed local spiking. Instead, HGA correlated with neuronal population co-firing of neurons that were widely distributed across millimeters. The neuronal spikes that contributed more to this co-firing also contributed more to, and preceded, spike-triggered HGA. These results suggest that HGA arises predominantly from summed postsynaptic potentials triggered by synchronous co-firing of widely distributed neurons.

ObjectiveTo assess the utility of machine learning algorithms for automatically estimating prognosis in patients with repaired tetralogy of Fallot (ToF) using cardiac magnetic resonance (CMR).MethodsWe included 372 patients with ToF who had undergone CMR imaging as part of a nationwide prospective study. Cine loops were retrieved and subjected to automatic deep learning (DL)-based image analysis, trained on independent, local CMR data, to derive measures of cardiac dimensions and function. This information was combined with established clinical parameters and ECG markers of prognosis.ResultsOver a median follow-up period of 10 years, 23 patients experienced an endpoint of death/aborted cardiac arrest or documented ventricular tachycardia (defined as >3 documented consecutive ventricular beats). On univariate Cox analysis, various DL parameters, including right atrial median area (HR 1.11/cm², p=0.003) and right ventricular long-axis strain (HR 0.80/%, p=0.009) emerged as significant predictors of outcome. DL parameters were related to adverse outcome independently of left and right ventricular ejection fraction and peak oxygen uptake (p<0.05 for all). A composite score of enlarged right atrial area and depressed right ventricular longitudinal function identified a ToF subgroup at significantly increased risk of adverse outcome (HR 2.1/unit, p=0.007).ConclusionsWe present data on the utility of machine learning algorithms trained on external imaging datasets to automatically estimate prognosis in patients with ToF. Due to the automated analysis process these two-dimensional-based algorithms may serve as surrogates for labour-intensive manually attained imaging parameters in patients with ToF.

Suppression of apoptosis is now recognized as a key contributory element to tumorigenesis in animal models and human cancer. The phosphatidylinositol 3' kinase pathway plays a seminal role in cell death suppression or \"survival signaling.\" Over the past 5 years, the molecular mechanisms by which this pathway exerts its death suppressive effects have slowly been revealed. This review summarizes the players involved, their importance in human cancer and their specific involvement in breast cancer.

Restoring movement to individuals with paralysis is the primary goal of motor brain-machine interfaces (BMIs). BMIs use movement intention signals typically recorded from the primary motor cortex (M1) to drive assistive devices. One of the major limitations of BMIs however, is the duration and stability of the signals recorded from M1. Currently signals can only be recorded for months to a few years, which falls far short of the decades required for successful clinical translation. Two particularly promising signals to overcome this limitation are multi-unit spikes (MSPs) and local field potentials (LFPs), i.e. spatially summed extracellular potentials, recorded from the motor cortex, which have been shown to reliably decode arm movement in order to intuitively control a computer cursor. However, to date there has not been a direct comparison of these two signal sources. In Chapter 1, monkeys used either their hand or fixed decoders of LFP and MSP activity to control a computer cursor over the course of several years and 200 days respectively. We compared the stability of these signals with respect to movement and found they are both very stable over the course of 6 months to several years during brain and hand control. Looking closer at LFPs, one of the most movement-informative components of LFPs is the gamma frequency band (30-300 Hz). There is, however, a gap in knowledge about the mechanisms that generate the gamma band in motor cortex. This limits our ability to understand motor cortical physiology and also limits the optimal design of a motor BMI utilizing the gamma LFP band. There are two ongoing debates about 1) the relationship between spiking activity and high gamma rhythms and 2) whether the gamma band contains numerous distinct sub-bands or is one monolithic broadband. These features of the gamma band have been largely unexplored in M1. In Chapter 2, I show that the high gamma rhythm is distinct from spiking activity in motor cortex, and in Chapter 3, I show that the gamma band contains two distinct sub-bands. In Chapter 2 and 3 monkeys used a BMI to independently control M1 spiking activity and high gamma rhythms or two M1 gamma sub-bands (high and low gamma), to determine whether spiking is distinct from high gamma rhythms or whether multiple gamma bands exist. The data I show in Chapter 2 and 3 suggests that subjects can differentially modulate spiking activity from high gamma power as well as the power in various motor cortical gamma sub-bands. Spiking activity can be modulated independently from the high gamma 2 band (200-300 Hz) and the high gamma band (130-200 Hz) power can be modulated independently of low gamma band power (30-50 Hz).