BackgroundSepsis is one of the leading causes of mortality in hospitalized patients. Despite this fact, a reliable means of predicting sepsis onset remains elusive. Early and accurate sepsis onset predictions could allow more aggressive and targeted therapy while maintaining antimicrobial stewardship. Existing detection methods suffer from low performance and often require time-consuming laboratory test results.ObjectiveTo study and validate a sepsis prediction method, InSight, for the new Sepsis-3 definitions in retrospective data, make predictions using a minimal set of variables from within the electronic health record data, compare the performance of this approach with existing scoring systems, and investigate the effects of data sparsity on InSight performance.MethodsWe apply InSight, a machine learning classification system that uses multivariable combinations of easily obtained patient data (vitals, peripheral capillary oxygen saturation, Glasgow Coma Score, and age), to predict sepsis using the retrospective Multiparameter Intelligent Monitoring in Intensive Care (MIMIC)-III dataset, restricted to intensive care unit (ICU) patients aged 15 years or more. Following the Sepsis-3 definitions of the sepsis syndrome, we compare the classification performance of InSight versus quick sequential organ failure assessment (qSOFA), modified early warning score (MEWS), systemic inflammatory response syndrome (SIRS), simplified acute physiology score (SAPS) II, and sequential organ failure assessment (SOFA) to determine whether or not patients will become septic at a fixed period of time before onset. We also test the robustness of the InSight system to random deletion of individual input observations.ResultsIn a test dataset with 11.3% sepsis prevalence, InSight produced superior classification performance compared with the alternative scores as measured by area under the receiver operating characteristic curves (AUROC) and area under precision-recall curves (APR). In detection of sepsis onset, InSight attains AUROC = 0.880 (SD 0.006) at onset time and APR = 0.595 (SD 0.016), both of which are superior to the performance attained by SIRS (AUROC: 0.609; APR: 0.160), qSOFA (AUROC: 0.772; APR: 0.277), and MEWS (AUROC: 0.803; APR: 0.327) computed concurrently, as well as SAPS II (AUROC: 0.700; APR: 0.225) and SOFA (AUROC: 0.725; APR: 0.284) computed at admission (P<.001 for all comparisons). Similar results are observed for 1-4 hours preceding sepsis onset. In experiments where approximately 60% of input data are deleted at random, InSight attains an AUROC of 0.781 (SD 0.013) and APR of 0.401 (SD 0.015) at sepsis onset time. Even with 60% of data missing, InSight remains superior to the corresponding SIRS scores (AUROC and APR, P<.001), qSOFA scores (P=.0095; P<.001) and superior to SOFA and SAPS II computed at admission (AUROC and APR, P<.001), where all of these comparison scores (except InSight) are computed without data deletion.ConclusionsDespite using little more than vitals, InSight is an effective tool for predic...

Background COVID-19 is an ongoing global pandemic. It is a systemic infection with a significant impact on the hematopoietic and the immune system. In this study we aimed to evaluate the different inflammatory markers and indexes of systemic inflammatory response in predicting the mortality in patients with COVID 19. Methods In this cross sectional study, various inflammatory markers like D-dimer, CRP, serum ferritin, LDH and CBC derived indexes of inflammation were analyzed in predicting mortality in COVID 19 infection. Results We enrolled 302 COVID 19 patients who had a mean age of 54.51 yrs with 210 (69.5%) males. Among them 21% were asymptomatic and fever was the commonest among symptomatic patients. Majority of patients (66.7%) had no comorbidities and 20% had multiple comorbidities. On analyzing different hematological variables, survivors had statistically significant higher hemoglobin count, lymphocytes, monocytes, eosinophil and platelet count and lower leukocyte, neutrophil count. Inflammatory markers D-dimer, serum ferritin and LDH were significantly elevated among non survivors. Among the indexes of inflammation, only NLR showed significant higher values among non survivors. All the inflammatory markers were able to predict mortality among the COVID 19 infected cases with a sensitivity and specificity of 85% and 65% for d dimer levels, 85% and 72% for serum ferritin, 85% and 72% for LDH, 85% and 51% for CRP levels respectively. Among the indexes of inflammation, validity of NLR was best in predicting mortality with 85% sensitivity and 51% specificity. Conclusion Abnormalities in peripheral blood parameters and increase in inflammatory markers are common findings in COVID 19 infection. NLR was best at predicting mortality followed by D-dimer and serum ferritin levels

Yersinia pestis, the causative agent of plague, is known to develop strategies to overcome the host immune mechanisms and survive in the host. The molecular changes induced by Y. pestis in the host are not well delineated. Here, we examined the early events triggered after the intracellular infection of Y. pestis in human monocytes and lymphocytes by analyzing the host transcriptional profiles using cDNA arrays. We found that sets of genes that, especially at early time periods, were highly upregulated in monocytes alone when compared with a mixed culture of lymphocytes and monocytes. Gene expression responses revealed genes coding for cytokines, chemokines, transcription factors, inflammatory and apoptosis-related genes. Protein levels were measured, and real-time polymerase chain reaction was used to validate the microarray results. Our data suggest that intracellular infection of human monocytes with Y. pestis results in a strong inflammatory response at early time periods and a downregulation of genes such as thromobomodulin, which may play a role in coagulation, resulting in disseminated intravascular coagulation, a primary cause of death in plague infected hosts. We provide evidence that genomic analysis can provide a solid foundation to mechanistic insights to explain some of the symptoms induced by Y. pestis.

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The acquisition, maintenance and modulation of dendritic architecture are critical to neuronal form, plasticity and function. Morphologically, dendritic shape impacts functional connectivity and is largely mediated by organization and dynamics of cytoskeletal fibers that provide the underlying scaffold and tracks for intracellular trafficking. Identifying molecular factors that regulate dendritic cytoskeletal architecture is therefore important in understanding mechanistic links between cytoskeletal organization and neuronal function. In a neurogenomic-driven genetic screen of cytoskeletal regulatory molecules, we identified Formin3 (Form3) as a critical regulator of cytoskeletal architecture in Drosophila nociceptive sensory neurons. Form3 is a member of the conserved Formin family of multi-functional cytoskeletal regulators and time course analyses reveal Form3 is cell-autonomously required for maintenance of complex dendritic arbors. Cytoskeletal imaging demonstrates form3 mutants exhibit a specific destabilization of the dendritic microtubule (MT) cytoskeleton, together with defective dendritic trafficking of mitochondria, satellite Golgi and the TRPA channel Painless. Biochemical studies reveal Form3 directly interacts with MTs via FH1-FH2 domains and promotes MT stabilization via acetylation. Neurologically, mutations in human Inverted Formin 2 (INF2; ortholog of form3) have been causally linked to Charcot-Marie-Tooth (CMT) disease. CMT sensory neuropathies lead to impaired peripheral sensitivity. Defects in form3 function in nociceptive neurons results in a severe impairment in noxious heat evoked behaviors. Expression of the INF2 FH1-FH2 domains rescues form3 defects in MT stabilization and nocifensive behavior revealing conserved functions in regulating the cytoskeleton and sensory behavior thereby providing novel mechanistic insights into potential etiologies of CMT sensory neuropathies.Significance StatementMechanisms governing cytoskeletal architecture are critical in regulating neural function as aberrations are linked to a broad spectrum of neurological and neurocognitive disorders. Formins are important cytoskeletal regulators however their mechanistic roles in neuronal architecture are poorly understood. We demonstrate mutations in Drosophila formin3 lead to progressive destabilization of the dendritic microtubule cytoskeleton resulting in severely reduced arborization coupled to impaired organelle and ion channel trafficking, as well as nociceptive sensitivity. INF2 mutations are implicated in CMT sensory neuropathies, and INF2 expression can rescue microtubule and nociceptive behavioral defects in form3 mutants. While CMT sensory neuropathies have been linked to defects in axonal development and myelination, our studies connect dendritic cytoskeletal defects with peripheral insensitivity suggesting possible alternative etiological bases.

Aim: To develop a simple and sensitive liquid chromatography-tandem mass spectrometry (LC-MS/MS) method validation for the quantifi cation of mevalonate (MVA), an endogenous compound responsible for synthesis of cholesterol in human plasma. Settings and Design: The method was validated and impended for application on human to study the concentration of MVA in plasma level after administration of atorvastatin (ATVS) individually and with combination to olmesartan (OLM). Materials and Methods:The assay procedure involved the isolation of MVA from plasma samples using solid-phase extraction, preconditioned cartridge, washed with methanol followed by 0.1 N HCI. The analytes were eluted with 3 × 0.5 ml of methanol and evaporated to dryness Nitrogen stream. The residue was reconstitute for LCMS/MS analysis, were chromatographic separation was carried on a HyPurity advance, 50 × 4.6 mm column with a mobile phase 10 mM ammonium formate (pH = 8) and Acetonitrile. The fl ow rate was 0.8 ml/min throughout the process. The LC, Agilent 1290 coupled to electrospray ion mass spectrometer. Results: The developed method was validated for specifi city, accuracy, precision, stability, linearity, sensitivity, and recovery. The method was linear and found to be acceptable over the range of 50-1000 ng/ml. The method was successfully applied for the drug interaction study of ATVS + OLM by quantifying changes in levels of MVA in hypertensive patients. Conclusion: The study revealed concentration levels of MVA in ATVS + OLM compared to ATVS as single treated condition are nearly equal. This conclude that, MVA synthesize equal level of blood cholesterol on both the stage, but failed to reduce BP synergistically, that associate with vascular plague formation.

The SARS-CoV-2 is a positive stranded RNA virus with a genome size of ~29.9 kilobase pairs which spans 29 open reading frames. Studies have revealed that the genome encodes about 16 non-structural proteins (nsp), four structural proteins, and six or seven accessory proteins. Based on prevalent knowledge on SARS-CoV and other coronaviruses, functions have been assigned for majority of the proteins. While, researchers across the globe are engrossed in identifying a potential pharmacological intervention to control the viral outbreak, none of the work has come up with new antiviral drugs or vaccines yet. One possible approach that has shown some positive results is by treating infected patients with the plasma collected from convalescent COVID-19 patients. Several vaccines around the world have entered their final trial phase in humans and we expect that these will in time be available for application to worldwide population to combat the disease. In this work we analyse the effect of prevalent mutations in the major pathogenesis related proteins of SARS-COV2 and attempt to pinpoint the effects of those mutations on the structural stability of the proteins. Our observations and analysis direct us to identify that all the major mutations have a negative impact in context of stability of the viral proteins under study and the mutant proteins suffer both structural and functional alterations as a result of the mutations. Our binary scoring scheme identifies L84S mutation in ORF8 as the most disruptive of the mutations under study. We believe that, the virus is under the influence of an evolutionary phenomenon similar to Muller s ratchet where the continuous accumulation of these mutations is making the virus less virulent which may also explain the reduction in fatality rates worldwide. Keywords: SARS-COV2, Covid19, Mutations, Structural Analysis