Low-melt Viscosity Polyimide Resins for Resin Transfer Molding (RTM) II

A series of polyimide resins with low-melt viscosities in the range of 10-30 poise and high glass transition temperatures (Tg s) of 330-370 C were developed for resin transfer molding (RTM) applications. These polyimide resins were formulated from 2,3,3 ,4 -biphenyltetracarboxylic dianhydride (a-BPDA) with 4-phenylethynylphthalic anhydride endcaps along with either 3,4 - oxyaniline (3,4 -ODA), 3,4 -methylenedianiline, (3,4 -MDA) or 3,3 -methylenedianiline (3,3 -MDA). These polyimides had pot lives of 30-60 minutes at 260-280 C, enabling the successful fabrication of T650-35 carbon fiber reinforced composites via RTM process. The viscosity profiles of the polyimide resins and the mechanical properties of the polyimide carbon fiber composites will be discussed

Low Viscosity Imides Based on Asymmetric Oxydiphthalic Anhydride

A series of low-melt viscosity imide resins were prepared from asymmetric oxydiphthalic dianhydride (a-ODPA) and 4-phenylethynylphthalic anhydride as the endcap, along with 3,4' - oxydianiline (3,4' -ODA), 3,4' -methylenedianiline (3,4' -MDA), 3,3' -methylenedianiline (3,3' - MDA) and 3,3'-diaminobenzophenone (3,3'-DABP), using a solvent-free melt process. These imide oligomers displays low-melt viscosities (2-15 poise) at 260-280 C, which made them amenable to low-cost resin transfer molding (RTM) process. The a-ODPA based RTM resins exhibits glass transition temperatures (Tg's) in the range of 265-330 C after postcure at 343 C. The mechanical properties of these polyimide/carbon fiber composites fabricated by RTM will be discussed

AMG 837: A Novel GPR40/FFA1 Agonist that Enhances Insulin Secretion and Lowers Glucose Levels in Rodents

Agonists of GPR40 (FFA1) have been proposed as a means to treat type 2 diabetes. Through lead optimization of a high throughput screening hit, we have identified a novel GPR40 agonist called AMG 837. The objective of these studies was to understand the preclinical pharmacological properties of AMG 837. The activity of AMG 837 on GPR40 was characterized through GTPγS binding, inositol phosphate accumulation and Ca2+ flux assays. Activity of AMG 837 on insulin release was assessed on isolated primary mouse islets. To determine the anti-diabetic activity of AMG 837 in vivo, we tested AMG 837 using a glucose tolerance test in normal Sprague-Dawley rats and obese Zucker fatty rats. AMG 837 was a potent partial agonist in the calcium flux assay on the GPR40 receptor and potentiated glucose stimulated insulin secretion in vitro and in vivo. Acute administration of AMG 837 lowered glucose excursions and increased glucose stimulated insulin secretion during glucose tolerance tests in both normal and Zucker fatty rats. The improvement in glucose excursions persisted following daily dosing of AMG 837 for 21-days in Zucker fatty rats. Preclinical studies demonstrated that AMG 837 was a potent GPR40 partial agonist which lowered post-prandial glucose levels. These studies support the potential utility of AMG 837 for the treatment of type 2 diabetes

Multidrug resistant pulmonary tuberculosis treatment regimens and patient outcomes: an individual patient data meta-analysis of 9,153 patients.

Treatment of multidrug resistant tuberculosis (MDR-TB) is lengthy, toxic, expensive, and has generally poor outcomes. We undertook an individual patient data meta-analysis to assess the impact on outcomes of the type, number, and duration of drugs used to treat MDR-TB

Evaluation of pragmatic oxygenation measurement as a proxy for Covid-19 severity

Choosing optimal outcome measures maximizes statistical power, accelerates discovery and improves reliability in early-phase trials. We devised and evaluated a modification to a pragmatic measure of oxygenation function, the [Formula: see text] ratio. Because of the ceiling effect in oxyhaemoglobin saturation, [Formula: see text] ratio ceases to reflect pulmonary oxygenation function at high [Formula: see text] values. We found that the correlation of [Formula: see text] with the reference standard ([Formula: see text]/[Formula: see text] ratio) improves substantially when excluding [Formula: see text] and refer to this measure as [Formula: see text]. Using observational data from 39,765 hospitalised COVID-19 patients, we demonstrate that [Formula: see text] is predictive of mortality, and compare the sample sizes required for trials using four different outcome measures. We show that a significant difference in outcome could be detected with the smallest sample size using [Formula: see text]. We demonstrate that [Formula: see text] is an effective intermediate outcome measure in COVID-19. It is a non-invasive measurement, representative of disease severity and provides greater statistical power

Evaluation of pragmatic oxygenation measurement as a proxy for Covid-19 severity

Choosing optimal outcome measures maximizes statistical power, accelerates discovery and improves reliability in early-phase trials. We devised and evaluated a modification to a pragmatic measure of oxygenation function, the [Formula: see text] ratio. Because of the ceiling effect in oxyhaemoglobin saturation, [Formula: see text] ratio ceases to reflect pulmonary oxygenation function at high [Formula: see text] values. We found that the correlation of [Formula: see text] with the reference standard ([Formula: see text]/[Formula: see text] ratio) improves substantially when excluding [Formula: see text] and refer to this measure as [Formula: see text]. Using observational data from 39,765 hospitalised COVID-19 patients, we demonstrate that [Formula: see text] is predictive of mortality, and compare the sample sizes required for trials using four different outcome measures. We show that a significant difference in outcome could be detected with the smallest sample size using [Formula: see text]. We demonstrate that [Formula: see text] is an effective intermediate outcome measure in COVID-19. It is a non-invasive measurement, representative of disease severity and provides greater statistical power

True versus Apparent Malaria Infection Prevalence: The Contribution of a Bayesian Approach

AIMS: To present a new approach for estimating the "true prevalence" of malaria and apply it to datasets from Peru, Vietnam, and Cambodia. METHODS: Bayesian models were developed for estimating both the malaria prevalence using different diagnostic tests (microscopy, PCR & ELISA), without the need of a gold standard, and the tests' characteristics. Several sources of information, i.e. data, expert opinions and other sources of knowledge can be integrated into the model. This approach resulting in an optimal and harmonized estimate of malaria infection prevalence, with no conflict between the different sources of information, was tested on data from Peru, Vietnam and Cambodia. RESULTS: Malaria sero-prevalence was relatively low in all sites, with ELISA showing the highest estimates. The sensitivity of microscopy and ELISA were statistically lower in Vietnam than in the other sites. Similarly, the specificities of microscopy, ELISA and PCR were significantly lower in Vietnam than in the other sites. In Vietnam and Peru, microscopy was closer to the "true" estimate than the other 2 tests while as expected ELISA, with its lower specificity, usually overestimated the prevalence. CONCLUSIONS: Bayesian methods are useful for analyzing prevalence results when no gold standard diagnostic test is available. Though some results are expected, e.g. PCR more sensitive than microscopy, a standardized and context-independent quantification of the diagnostic tests' characteristics (sensitivity and specificity) and the underlying malaria prevalence may be useful for comparing different sites. Indeed, the use of a single diagnostic technique could strongly bias the prevalence estimation. This limitation can be circumvented by using a Bayesian framework taking into account the imperfect characteristics of the currently available diagnostic tests. As discussed in the paper, this approach may further support global malaria burden estimation initiatives

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment

Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts

Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas

Although theMYConcogene has been implicated incancer, a systematic assessment of alterations ofMYC, related transcription factors, and co-regulatoryproteins, forming the proximal MYC network (PMN),across human cancers is lacking. Using computa-tional approaches, we define genomic and proteo-mic features associated with MYC and the PMNacross the 33 cancers of The Cancer Genome Atlas.Pan-cancer, 28% of all samples had at least one ofthe MYC paralogs amplified. In contrast, the MYCantagonists MGA and MNT were the most frequentlymutated or deleted members, proposing a roleas tumor suppressors.MYCalterations were mutu-ally exclusive withPIK3CA,PTEN,APC,orBRAFalterations, suggesting that MYC is a distinct onco-genic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such asimmune response and growth factor signaling; chro-matin, translation, and DNA replication/repair wereconserved pan-cancer. This analysis reveals insightsinto MYC biology and is a reference for biomarkersand therapeutics for cancers with alterations ofMYC or the PMN