EcAMSat: Small Satellite to Examine E. coli's Response in Microgravity to the Antibiotic Gentamicin

We have successfully flown the EcAMSat (Escherichia coli Antimicrobial Satellite) free-flyer mission. This was a 6U small satellite that autonomously conducted an experiment in low Earth orbit to explore the impact of the space environment on antibiotic resistance in uropathogenic E. coli (UPEC) and the role a particular sigma factor plays in the response. After being held in stasis during transport to orbit, two strains a wildtype UPEC and an isogenic mutant with a deleted gene that encodes a sigma factor were grown to stationary phase in a fluidic card inside EcAMSat's payload, then incubated with three concentrations of the antibiotic gentamicin. The payload then administered alamarBlue, a redox indicator, into all wells of the fluidic card. The cells were then incubated for 144 hours and metabolic activity was measured optically using the payloads' LED and detector system. Data were then telemetered to the ground and compared to a control experiment conducted in an identical satellite in a lab. The results of this experiment will help us better understand important therapeutic targets for treating bacterial infections on Earth and in space. Such targets are particularly relevant to deep-space and long-duration missions where crew may be more susceptible to infection and treatments for them may work differently

EcAMSat: A Small Satellite Flown to Explore the Role a Sigma Factor Plays in E. coli's Response to the Antibiotic Gentamicin

We have successfully flown the EcAMSat (Escherichia coli Antimicrobial Satellite) free-flyer mission. This was a 6U (six unit - CubeSat) small satellite that autonomously conducted an experiment in low Earth orbit to explore the impact of the space environment on antibiotic resistance in uropathogenic E. coli (UPEC) and the role a particular sigma factor plays in the response. After being held in stasis during transport to orbit, two strains - a wildtype UPEC and an isogenic mutant with a deleted gene that encodes a sigma factor - were grown to stationary phase in a fluidic card inside EcAMSat's payload, then incubated with three concentrations of the antibiotic gentamicin. The payload then administered alamarBlue (registered trademark), a redox indicator, into all wells of the fluidic card. The cells were then incubated for 144 hours and metabolic activity was measured optically using the payloads' LED (Light-Emitting Diode) and detector system. Data were then telemetered to the ground and compared to a control experiment conducted in an identical satellite in a lab. The results of this experiment will help us better understand important therapeutic targets for treating bacterial infections on Earth and in space. Such targets are particularly relevant to deep-space and long-duration missions where crew may be more susceptible to infection and treatments for them may work differently

Identification of an autoantibody panel to separate lung cancer from smokers and nonsmokers

<p>Abstract</p> <p>Background</p> <p>Sera from lung cancer patients contain autoantibodies that react with tumor associated antigens (TAAs) that reflect genetic over-expression, mutation, or other anomalies of cell cycle, growth, signaling, and metabolism pathways.</p> <p>Methods</p> <p>We performed immunoassays to detect autoantibodies to ten tumor associated antigens (TAAs) selected on the basis of previous studies showing that they had preferential specificity for certain cancers. Sera examined were from lung cancer patients (22); smokers with ground-glass opacities (GGOs) (46), benign solid nodules (55), or normal CTs (35); and normal non-smokers (36). Logistic regression models based on the antibody biomarker levels among the high risk and lung cancer groups were developed to identify the combinations of biomarkers that predict lung cancer in these cohorts.</p> <p>Results</p> <p>Statistically significant differences in the distributions of each of the biomarkers were identified among all five groups. Using Receiver Operating Characteristic (ROC) curves based on age, c-myc, Cyclin A, Cyclin B1, Cyclin D1, CDK2, and survivin, we obtained a sensitivity = 81% and specificity = 97% for the classification of cancer vs smokers(no nodules, solid nodules, or GGO) and correctly predicted 31/36 healthy controls as noncancer.</p> <p>Conclusion</p> <p>A pattern of autoantibody reactivity to TAAs may distinguish patients with lung cancer versus smokers with normal CTs, stable solid nodules, ground glass opacities, or normal healthy never smokers.</p

Serum microrna biomarkers for detection of non-small cell lung cancer

Non small cell lung cancer (NSCLC) is the leading cause of cancer-related mortality world-wide and the majority of cases are diagnosed at late stages of disease. There is currently no cost-effective screening test for NSCLC, and the development of such a test is a public health imperative. Recent studies have suggested that chest computed tomography screening of patients at high risk of lung cancer can increase survival from disease, however, the cost effectiveness of such screening has not been established. In this Phase I/II biomarker study we examined the feasibility of using serum miRNA as biomarkers of NSCLC using RT-qPCR to examine the expression of 180 miRNAs in sera from 30 treatment naive NSCLC patients and 20 healthy controls. Receiver operating characteristic curves (ROC) and area under the curve were used to identify differentially expressed miRNA pairs that could distinguish NSCLC from healthy controls. Selected miRNA candidates were further validated in sera from an additional 55 NSCLC patients and 75 healthy controls. Examination of miRNA expression levels in serum from a multi-institutional cohort of 50 subjects (30 NSCLC patients and 20 healthy controls) identified differentially expressed miRNAs. A combination of two differentially expressed miRNAs miR-15b and miR-27b, was able to discriminate NSCLC from healthy controls with sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of 100% in the training set. Upon further testing on additional 130 subjects (55 NSCLC and 75 healthy controls), this miRNA pair predicted NSCLC with a specificity of 84% (95% CI 0.73-0.91), sensitivity of 100% (95% CI; 0.93-1.0), NPV of 100%, and PPV of 82%. These data provide evidence that serum miRNAs have the potential to be sensitive, cost-effective biomarkers for the early detection of NSCLC. Further testing in a Phase III biomarker study in is necessary for validation of these results. © 2012 Hennessey et al

Emphysema phenotypes and lung cancer risk.

BackgroundTo assess the relationship between lung cancer and emphysema subtypes.ObjectiveAirflow obstruction and emphysema predispose to lung cancer. Little is known, however, about the lung cancer risk associated with different emphysema phenotypes. We assessed the risk of lung cancer based on the presence, type and severity of emphysema, using visual assessment.MethodsSeventy-two consecutive lung cancer cases were selected from a prospective cohort of 3,477 participants enrolled in the Clínica Universidad de Navarra's lung cancer screening program. Each case was matched to three control subjects using age, sex, smoking history and body mass index as key variables. Visual assessment of emphysema and spirometry were performed. Logistic regression and interaction model analysis were used in order to investigate associations between lung cancer and emphysema subtypes.ResultsAirflow obstruction and visual emphysema were significantly associated with lung cancer (OR = 2.8, 95%CI: 1.6 to 5.2; OR = 5.9, 95%CI: 2.9 to 12.2; respectively). Emphysema severity and centrilobular subtype were associated with greater risk when adjusted for confounders (OR = 12.6, 95%CI: 1.6 to 99.9; OR = 34.3, 95%CI: 25.5 to 99.3, respectively). The risk of lung cancer decreases with the added presence of paraseptal emphysema (OR = 4.0, 95%CI: 3.6 to 34.9), losing this increased risk of lung cancer when it occurs alone (OR = 0.7, 95%CI: 0.5 to 2.6).ConclusionsVisual scoring of emphysema predicts lung cancer risk. The centrilobular phenotype is associated with the greatest risk