Multitask prediction of organ dysfunction in the intensive care unit using sequential subnetwork routing.

OBJECTIVE: Multitask learning (MTL) using electronic health records allows concurrent prediction of multiple endpoints. MTL has shown promise in improving model performance and training efficiency; however, it often suffers from negative transfer - impaired learning if tasks are not appropriately selected. We introduce a sequential subnetwork routing (SeqSNR) architecture that uses soft parameter sharing to find related tasks and encourage cross-learning between them. MATERIALS AND METHODS: Using the MIMIC-III (Medical Information Mart for Intensive Care-III) dataset, we train deep neural network models to predict the onset of 6 endpoints including specific organ dysfunctions and general clinical outcomes: acute kidney injury, continuous renal replacement therapy, mechanical ventilation, vasoactive medications, mortality, and length of stay. We compare single-task (ST) models with naive multitask and SeqSNR in terms of discriminative performance and label efficiency. RESULTS: SeqSNR showed a modest yet statistically significant performance boost across 4 of 6 tasks compared with ST and naive multitasking. When the size of the training dataset was reduced for a given task (label efficiency), SeqSNR outperformed ST for all cases showing an average area under the precision-recall curve boost of 2.1%, 2.9%, and 2.1% for tasks using 1%, 5%, and 10% of labels, respectively. CONCLUSIONS: The SeqSNR architecture shows superior label efficiency compared with ST and naive multitasking, suggesting utility in scenarios in which endpoint labels are difficult to ascertain

Use of deep learning to develop continuous-risk models for adverse event prediction from electronic health records

Early prediction of patient outcomes is important for targeting preventive care. This protocol describes a practical workflow for developing deep-learning risk models that can predict various clinical and operational outcomes from structured electronic health record (EHR) data. The protocol comprises five main stages: formal problem definition, data pre-processing, architecture selection, calibration and uncertainty, and generalizability evaluation. We have applied the workflow to four endpoints (acute kidney injury, mortality, length of stay and 30-day hospital readmission). The workflow can enable continuous (e.g., triggered every 6 h) and static (e.g., triggered at 24 h after admission) predictions. We also provide an open-source codebase that illustrates some key principles in EHR modeling. This protocol can be used by interdisciplinary teams with programming and clinical expertise to build deep-learning prediction models with alternate data sources and prediction tasks

The 999th Swift gamma-ray burst: Some like it thermal: A multiwavelength study of GRB 151027A

We present a multiwavelength study of GRB 151027A. This is the 999th GRB detected by the Swift satellite and it has a densely sampled emission in the X-ray and optical band and has been observed and detected in the radio up to 140 days after the prompt. The multiwavelength light curve from 500 s to 140 days can be modelled through a standard forward shock afterglow but requires an additional component to reproduce the early X-ray and optical emission. We present TNG and LBT optical observations performed 19.6, 33.9 and 92.3 days after the trigger which show a bump with respect to a standard afterglow flux decay and are possibly interpreted as due to the underlying SN and host galaxy (of 0.4 uJy in the R band). Radio observations, performed with SRT, Medicina, EVN and VLBA between day 4 and 140, suggest that the burst exploded in an environment characterised by a density profile scaling with the distance from the source (wind profile). A remarkable feature of the prompt emission is the presence of a bright flare 100 s after the trigger, lasting 70 seconds in the soft X-ray band, which was simultaneously detected from the optical band up to the MeV energy range. By combining Swift-BAT/XRT and Fermi-GBM data, the broadband (0.3-1000 keV) time resolved spectral analysis of the flare reveals the coexistence of a non-thermal (power law) and thermal blackbody components. The BB component contributes up to 35% of the luminosity in the 0.3-1000 keV band. The gamma-ray emission observed in Swift-BAT and Fermi-GBM anticipates and lasts less than the soft X-ray emission as observed by Swift-XRT, arguing against a Comptonization origin. The BB component could either be produced by an outflow becoming transparent or by the collision of a fast shell with a slow, heavy and optically thick fireball ejected during the quiescent time interval between the initial and later flares of the burst

Monocyte induction of e-selectin-mediated endothelial activation releases VE-Cadherin junctions to promote tumor cell extravasation in the metastasis cascade

Tumor cells interact with blood constituents and these interactions promote metastasis. Selectins are vascular receptors facilitating interactions of tumor cells with platelets, leukocytes, and endothelium, but the role of endothelial E-selectin remains unclear. Here we show that E-selectin is a major receptor for monocyte recruitment to tumor cell-activated endothelium. Experimental and spontaneous lung metastasis using murine tumor cells, without E-selectin ligands, were attenuated in E-selectin-deficient mice. Tumor cell-derived CCL2 promoted endothelial activation, resulting in enhanced endothelial E-selectin expression. The recruitment of inflammatory monocytes to metastasizing tumor cells was dependent on the local endothelial activation and the presence of E-selectin. Monocytes promoted transendothelial migration of tumor cells through the induction of E-selectin-dependent endothelial retractions and a subsequent modulation of tight junctions through dephosphorylation of VE-cadherin. Thus, endothelial E-selectin shapes the tumor microenvironment through the recruitment, adhesion, and activation of monocytes that facilitate tumor cell extravasation and thereby metastasis. These findings provide evidence that endothelial E-selectin is a novel factor contributing to endothelial retraction required for efficient lung metastasis. Cancer Res; 76(18); 5302-12. ©2016 AACR

CCL2 is a vascular permeability factor inducing CCR2-dependent endothelial retraction during lung metastasis.

Increased levels of the chemokine CCL2 in cancer patients are associated with poor prognosis. Experimental evidence suggests that CCL2 correlates with inflammatory monocyte recruitment and induction of vascular activation, but the functionality remains open. Here, we show that endothelial Ccr2 facilitates pulmonary metastasis using an endothelial-specific Ccr2-deficient mouse model (Ccr2 ec KO). Similar levels of circulating monocytes and equal leukocyte recruitment to metastatic lesions of Ccr2 ec KO and Ccr2 fl / fl littermates were observed. The absence of endothelial Ccr2 strongly reduced pulmonary metastasis, while the primary tumor growth was unaffected. Despite a comparable cytokine milieu in Ccr2 ec KO and Ccr2 fl / fl littermates the absence of vascular permeability induction was observed only in Ccr2 ec KO mice. CCL2 stimulation of pulmonary endothelial cells resulted in increased phosphorylation of MLC2, endothelial cell retraction, and vascular leakiness that was blocked by an addition of a CCR2 inhibitor. These data demonstrate that endothelial CCR2 expression is required for tumor cell extravasation and pulmonary metastasis. Implications: The findings provide mechanistic insight into how CCL2–CCR2 signaling in endothelial cells promotes their activation through myosin light chain phosphorylation, resulting in endothelial retraction and enhanced tumor cell migration and metastasis