Plasma Vitamin D levels in correlation with circulatory proteins could be a potential biomarker tool for pulmonary tuberculosis and treatment monitoring

Background: Tuberculosis (TB), a life-threatening immune challenging disease to the global human community has to be diagnosed earlier and eliminated in the upcoming era. Vitamin D, a fat-soluble micronutrient, mainly from epidermal cells of the skin and a few dietary sources, is associated with the immune system in various disease management. Therefore, a better understanding of vitamin D metabolism and immune function in tuberculosis should be studied for the consideration of biomarkers. Methods: The study consist of Pulmonary Tuberculosis (PTB) patients (n = 32) at two-time points: Baseline (PTB BL) and after 6 months of anti-TB treatment (ATT) (PTB PT), latently Mtb infected (IFNγ + ) group (n = 32) and a non-LTB healthy control (IFNγ-) group (n = 32). Vitamin D levels were measured using High-performance liquid chromatography (HPLC). The cytokine data from the same participants assayed by ELISA from our earlier in�vestigations were used to correlate it with serum Vitamin D levels. Results: The assayed serum Vitamin D levels between the groups showed significantly lowered levels in PTB BL when compared with IFNγ + and IFNγ- groups. And, the Vitamin D levels in the PTB group after ATT were significantly lower than the baseline levels. The Vitamin D data were compared with pro- and anti-inflammatory cytokines and adipokines levels by performing a principal component regression analysis. Based on the PC scores, the study group showed distinct clusters for the TB group and control group. And, the correlation analysis be�tween the study group and immunological indices showed significant correlations. Vitamin D significantly correlated with IFNγ, TNFα, IL17A, IL-4 and Resistin in the TB group, whereas IL-6 and G-CSF in the control group. Conclusion: The baseline measurement of Vitamin D levels was significantly decreased in the PTB group when compared with IFNγ + and IFNγ- groups showing the importance of Vitamin D as a preventive factor against the TB disease progression. The six-month post-treatment of TB showed a further decrease in Vitamin D levels in PTB. The significantly correlated immunological indices with Vitamin D levels are the biomarker profile that could predict TB

Scalable and accurate deep learning for electronic health records

Predictive modeling with electronic health record (EHR) data is anticipated to drive personalized medicine and improve healthcare quality. Constructing predictive statistical models typically requires extraction of curated predictor variables from normalized EHR data, a labor-intensive process that discards the vast majority of information in each patient's record. We propose a representation of patients' entire, raw EHR records based on the Fast Healthcare Interoperability Resources (FHIR) format. We demonstrate that deep learning methods using this representation are capable of accurately predicting multiple medical events from multiple centers without site-specific data harmonization. We validated our approach using de-identified EHR data from two U.S. academic medical centers with 216,221 adult patients hospitalized for at least 24 hours. In the sequential format we propose, this volume of EHR data unrolled into a total of 46,864,534,945 data points, including clinical notes. Deep learning models achieved high accuracy for tasks such as predicting in-hospital mortality (AUROC across sites 0.93-0.94), 30-day unplanned readmission (AUROC 0.75-0.76), prolonged length of stay (AUROC 0.85-0.86), and all of a patient's final discharge diagnoses (frequency-weighted AUROC 0.90). These models outperformed state-of-the-art traditional predictive models in all cases. We also present a case-study of a neural-network attribution system, which illustrates how clinicians can gain some transparency into the predictions. We believe that this approach can be used to create accurate and scalable predictions for a variety of clinical scenarios, complete with explanations that directly highlight evidence in the patient's chart.Comment: Published version from https://www.nature.com/articles/s41746-018-0029-

β-Arrestin-dependent, G protein-independent ERK1/2 activation by the β2 adrenergic receptor

International audiencePhysiological effects of β adrenergic receptor (β2AR) stimulation have been classically shown to result from Gs-dependent adenylyl cyclase activation. Here we demonstrate a novel signaling mechanism wherein β-arrestins mediate β2AR signaling to extracellular-signal regulated kinases 1/2 (ERK 1/2) independent of G protein activation. Activation of ERK1/2 by the β2AR expressed in HEK-293 cells was resolved into two components dependent, respectively, on Gs-Gi/protein kinase A (PKA) or β-arrestins. G protein-dependent activity was rapid, peaking within 2-5 min, was quite transient, was blocked by pertussis toxin (Gi inhibitor) and H-89 (PKA inhibitor), and was insensitive to depletion of endogenous β-arrestins by siRNA. β-Arrestin-dependent activation was slower in onset (peak 5-10 min), less robust, but more sustained and showed little decrement over 30 min. It was insensitive to pertussis toxin and H-89 and sensitive to depletion of either β-arrestin1 or -2 by small interfering RNA. In Gs knock-out mouse embryonic fibroblasts, wild-type β2AR recruited β-arrestin2-green fluorescent protein and activated pertussis toxin-insensitive ERK1/2. Furthermore, a novel β2AR mutant (β2ART68F,Y132G,Y219A or β2ARTYY), rationally designed based on Evolutionary Trace analysis, was incapable of G protein activation but could recruit β-arrestins, undergo β-arrestin-dependent internalization, and activate β-arrestin-dependent ERK. Interestingly, overexpression of GRK5 or -6 increased mutant receptor phosphorylation and β-arrestin recruitment, led to the formation of stable receptor-β-arrestin complexes on endosomes, and increased agonist-stimulated phospho-ERK1/2. In contrast, GRK2, membrane translocation of which requires Gβγ release upon G protein activation, was ineffective unless it was constitutively targeted to the plasma membrane by a prenylation signal (CAAX). These findings demonstrate that the β2AR can signal to ERK via a GRK5/6-β-arrestin-dependent pathway, which is independent of G protein coupling

An Angiotensin II type 1 receptor activation switch patch revealed through Evolutionary Trace analysis

Seven transmembrane (7TM) or G protein-coupled receptors constitute a large superfamily of cell surface receptors sharing a structural motif of seven transmembrane spanning alpha helices. Their activation mechanism most likely involves concerted movements of the transmembrane helices, but remains to be completely resolved. Evolutionary Trace (ET) analysis is a computational method, which identifies clusters of functionally important residues by integrating information on evolutionary important residue variations with receptor structure. Combined with known mutational data, ET predicted a patch of residues in the cytoplasmic parts of TM2, TM3, and TM6 to form an activation switch that is common to all family A 7TM receptors. We tested this hypothesis in the rat Angiotensin II (Ang II) type 1 (AT1) receptor. The receptor has important roles in the cardiovascular system, but has also frequently been applied as a model for 7TM receptor activation and signaling. Six mutations: F66A, L67R, L70R, L119R, D125A, and I245F were targeted to the putative switch and assayed for changes in activation state by their ligand binding, signaling, and trafficking properties. All but one receptor mutant (that was not expressed well) displayed phenotypes associated with changed activation state, such as increased agonist affinity or basal activity, promiscuous activation, or constitutive internalization highlighting the importance of testing different signaling pathways. We conclude that this evolutionary important patch mediates interactions important for maintaining the inactive state. More broadly, these observations in the AT1 receptor are consistent with computational predictions of a generic role for this patch in 7TM receptor activation

Scalable and accurate deep learning with electronic health records.

Predictive modeling with electronic health record (EHR) data is anticipated to drive personalized medicine and improve healthcare quality. Constructing predictive statistical models typically requires extraction of curated predictor variables from normalized EHR data, a labor-intensive process that discards the vast majority of information in each patient's record. We propose a representation of patients' entire raw EHR records based on the Fast Healthcare Interoperability Resources (FHIR) format. We demonstrate that deep learning methods using this representation are capable of accurately predicting multiple medical events from multiple centers without site-specific data harmonization. We validated our approach using de-identified EHR data from two US academic medical centers with 216,221 adult patients hospitalized for at least 24 h. In the sequential format we propose, this volume of EHR data unrolled into a total of 46,864,534,945 data points, including clinical notes. Deep learning models achieved high accuracy for tasks such as predicting: in-hospital mortality (area under the receiver operator curve [AUROC] across sites 0.93-0.94), 30-day unplanned readmission (AUROC 0.75-0.76), prolonged length of stay (AUROC 0.85-0.86), and all of a patient's final discharge diagnoses (frequency-weighted AUROC 0.90). These models outperformed traditional, clinically-used predictive models in all cases. We believe that this approach can be used to create accurate and scalable predictions for a variety of clinical scenarios. In a case study of a particular prediction, we demonstrate that neural networks can be used to identify relevant information from the patient's chart