Membrane Microviscosity Modulates Μ-Opioid Receptor Conformational Transitions and Agonist Efficacy

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/66282/1/j.1471-4159.1999.0730289.x.pd

Capital structure and its determinants in the United Kingdom – a decompositional analysis

Prior research on capital structure by Rajan and Zingales (1995) suggests that the level of gearing in UK companies is positively related to size and tangibility, and negatively correlated with profitability and the level of growth opportunities. However, as argued by Harris and Raviv (1991), 'The interpretation of results must be tempered by an awareness of the difficulties involved in measuring both leverage and the explanatory variables of interest'. In this study the focus is on the difficulties of measuring gearing, and the sensitivity of Rajan and Zingales' results to variations in gearing measures are tested. Based on an analysis of the capital structure of 822 UK companies, Rajan and Zingales' results are found to be highly definitional-dependent. The determinants of gearing appear to vary significantly, depending upon which component of debt is being analysed. In particular, significant differences are found in the determinants of long- and short-term forms of debt. Given that trade credit and equivalent, on average, accounts for more than 62% of total debt, the results are particularly sensitive to whether such debt is included in the gearing measure. It is argued, therefore, that analysis of capital structure is incomplete without a detailed examination of all forms of corporate debt

Resolution of Biphasic Binding of the Opioid Antagonist Naltrexone in Brain Membranes

In synaptosomal membranes from rat brain cortex, in the presence of 150 m M NaC1, the opioid antagonist [ 3 H] naltrexone bound to two populations of receptor sites with affinities of 0.27 and 4.3 n M , respectively. Guanosine-5′-(3-thiotriphosphate) had little modulating effect and did not alter the biphasic nature of ligand binding. On the other hand, receptor-selective opioids differentially inhibited the two binding components of [ 3 H] naltrexone. As shown by nonlinear least-squares analysis, the Μ opioids Tyr-D-Ala-Gly-(Me)Phe-Gly-ol or sufentanil abolished high-affinity [ 3 H] naltrexone binding, whereas the Δ-selective ligands [D- Pen 2 , D-Pen 5 ] enkephalin, ICI 174, 864, and oxymorphindole inhibited and eventually eliminated the low-affinity component in a concentration-dependent manner. These results indicate that, in contrast to the guanine nucleotide-sensitive biphasic binding of opioid-alkaloid agonists, the heterogeneity of naltrexone binding in brain membranes reflects ligand interaction with different opioid-receptor types.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/66340/1/j.1471-4159.1991.tb08288.x.pd

Generalized Interpolation Material Point Approach to High Melting Explosive with Cavities Under Shock

Criterion for contacting is critically important for the Generalized Interpolation Material Point(GIMP) method. We present an improved criterion by adding a switching function. With the method dynamical response of high melting explosive(HMX) with cavities under shock is investigated. The physical model used in the present work is an elastic-to-plastic and thermal-dynamical model with Mie-Gr\"uneissen equation of state. We mainly concern the influence of various parameters, including the impacting velocity $v$, cavity size $R$, etc, to the dynamical and thermodynamical behaviors of the material. For the colliding of two bodies with a cavity in each, a secondary impacting is observed. Correspondingly, the separation distance $D$ of the two bodies has a maximum value $D_{\max}$ in between the initial and second impacts. When the initial impacting velocity $v$ is not large enough, the cavity collapses in a nearly symmetric fashion, the maximum separation distance $D_{\max}$ increases with $v$. When the initial shock wave is strong enough to collapse the cavity asymmetrically along the shock direction, the variation of $D_{\max}$ with $v$ does not show monotonic behavior. Our numerical results show clear indication that the existence of cavities in explosive helps the creation of ``hot spots''.Comment: Figs.2,4,7,11 in JPG format; Accepted for publication in J. Phys. D: Applied Physic

Evaluation of diurnal responses of Tetradesmus obliquus under nitrogen limitation

Tetradesmus obliquus is an oleaginous microalga with high potential for triacylglycerol production. We characterized the biochemical composition and the transcriptional landscape of T. obliquus wild-type and the starchless mutant (slm1), adapted to 16:8 h light dark (LD) cycles under nitrogen limitation. In comparison to the nitrogen replete conditions, the diurnal RNA samples from both strains also displayed a cyclic pattern, but with much less variation which could be related to a reduced transcription activity in at least the usually highly active processes. During nitrogen limitation, the wild-type continued to use starch as the preferred storage compound to store energy and carbon. Starch was accumulated to an average content of 0.25 g·gDW−1, which is higher than the maximum observed under nitrogen replete conditions. Small oscillations were observed, indicating that starch was being used as a diurnal energy storage compound, but to a lesser extent than under nitrogen replete conditions. For the slm1 mutant, TAG content was higher than for the wild-type (average steady state value was 0.26 g·gDW−1 for slm1 compared to 0.06 g·gDW−1 for the wild-type). Despite the higher TAG content in the slm1, the conversion efficiency of photons into biomass components for the slm1 was only half of the one obtained for the wild-type. This is related to the observed decrease in biomass productivity (from 1.29 gDW·L−1·day−1 for the wild-type to 0.52 gDW·L−1·day−1 for the slm1). While the transcriptome of slm1 displayed clear signs of energy generation by degrading TAG and amino-acids during the dark period, no significant variation of these metabolites could be measured. When looking through the diurnal cycle, the photosynthetic efficiency was lower for the slm1 mutant compared to the wild-type especially during the second half of the light period, where starch accumulation occurred in the wild-type.publishedVersionPaid Open Acces

Comparison of Magnetic Resonance Imaging-Based Risk Calculators to Predict Prostate Cancer Risk

Importance: Magnetic resonance imaging (MRI)-based risk calculators can replace or augment traditional prostate cancer (PCa) risk prediction tools. However, few data are available comparing performance of different MRI-based risk calculators in external cohorts across different countries or screening paradigms. Objective: To externally validate and compare MRI-based PCa risk calculators (Prospective Loyola University Multiparametric MRI [PLUM], UCLA [University of California, Los Angeles]-Cornell, Van Leeuwen, and Rotterdam Prostate Cancer Risk Calculator-MRI [RPCRC-MRI]) in cohorts from Europe and North America. Design, Setting, and Participants: This multi-institutional, external validation diagnostic study of 3 unique cohorts was performed from January 1, 2015, to December 31, 2022. Two cohorts from Europe and North America used MRI before biopsy, while a third cohort used an advanced serum biomarker, the Prostate Health Index (PHI), before MRI or biopsy. Participants included adult men without a PCa diagnosis receiving MRI before prostate biopsy. Interventions: Prostate MRI followed by prostate biopsy. Main Outcomes and Measures: The primary outcome was diagnosis of clinically significant PCa (grade group ≥2). Receiver operating characteristics for area under the curve (AUC) estimates, calibration plots, and decision curve analysis were evaluated. Results: A total of 2181 patients across the 3 cohorts were included, with a median age of 65 (IQR, 58-70) years and a median prostate-specific antigen level of 5.92 (IQR, 4.32-8.94) ng/mL. All models had good diagnostic discrimination in the European cohort, with AUCs of 0.90 for the PLUM (95% CI, 0.86-0.93), UCLA-Cornell (95% CI, 0.86-0.93), Van Leeuwen (95% CI, 0.87-0.93), and RPCRC-MRI (95% CI, 0.86-0.93) models. All models had good discrimination in the North American cohort, with an AUC of 0.85 (95% CI, 0.80-0.89) for PLUM and AUCs of 0.83 for the UCLA-Cornell (95% CI, 0.80-0.88), Van Leeuwen (95% CI, 0.79-0.88), and RPCRC-MRI (95% CI, 0.78-0.87) models, with somewhat better calibration for the RPCRC-MRI and PLUM models. In the PHI cohort, all models were prone to underestimate clinically significant PCa risk, with best calibration and discrimination for the UCLA-Cornell (AUC, 0.83 [95% CI, 0.81-0.85]) model, followed by the PLUM model (AUC, 0.82 [95% CI, 0.80-0.84]). The Van Leeuwen model was poorly calibrated in all 3 cohorts. On decision curve analysis, all models provided similar net benefit in the European cohort, with higher benefit for the PLUM and RPCRC-MRI models at a threshold greater than 22% in the North American cohort. The UCLA-Cornell model demonstrated highest net benefit in the PHI cohort. Conclusions and Relevance: In this external validation study of patients receiving MRI and prostate biopsy, the results support the use of the PLUM or RPCRC-MRI models in MRI-based screening pathways regardless of European or North American setting. However, tools specific to screening pathways incorporating advanced biomarkers as reflex tests are needed due to underprediction.</p

Presymptomatic risk assessment for chronic non-communicable diseases

The prevalence of common chronic non-communicable diseases (CNCDs) far overshadows the prevalence of both monogenic and infectious diseases combined. All CNCDs, also called complex genetic diseases, have a heritable genetic component that can be used for pre-symptomatic risk assessment. Common single nucleotide polymorphisms (SNPs) that tag risk haplotypes across the genome currently account for a non-trivial portion of the germ-line genetic risk and we will likely continue to identify the remaining missing heritability in the form of rare variants, copy number variants and epigenetic modifications. Here, we describe a novel measure for calculating the lifetime risk of a disease, called the genetic composite index (GCI), and demonstrate its predictive value as a clinical classifier. The GCI only considers summary statistics of the effects of genetic variation and hence does not require the results of large-scale studies simultaneously assessing multiple risk factors. Combining GCI scores with environmental risk information provides an additional tool for clinical decision-making. The GCI can be populated with heritable risk information of any type, and thus represents a framework for CNCD pre-symptomatic risk assessment that can be populated as additional risk information is identified through next-generation technologies.Comment: Plos ONE paper. Previous version was withdrawn to be updated by the journal's pdf versio

Patient-Derived Xenografts and Organoids Recapitulate Castration-Resistant Prostate Cancer with Sustained Androgen Receptor Signaling

Castration-resistant prostate cancer (CRPC) remains an incurable and lethal malignancy. The development of new CRPC treatment strategies is strongly impeded by the scarcity of representative, scalable and transferable preclinical models of advanced, androgen receptor (AR)-driven CRPC. Here, we present contemporary patient-derived xenografts (PDXs) and matching PDX-derived organoids (PDXOs) from CRPC patients who had undergone multiple lines of treatment. These models were comprehensively profiled at the morphologic, genomic ( n = 8) and transcriptomic levels ( n = 81). All are high-grade adenocarcinomas that exhibit copy number alterations and transcriptomic features representative of CRPC patient cohorts. We identified losses of PTEN and RB1, MYC amplifications, as well as genomic alterations in TP53 and in members of clinically actionable pathways such as AR, PI3K and DNA repair pathways. Importantly, the clinically observed continued reliance of CRPC tumors on AR signaling is preserved across the entire set of models, with AR amplification identified in four PDXs. We demonstrate that PDXs and PDXOs faithfully reflect donor tumors and mimic matching patient drug responses. In particular, our models predicted patient responses to subsequent treatments and captured sensitivities to previously received therapies. Collectively, these PDX-PDXO pairs constitute a reliable new resource for in-depth studies of treatment-induced, AR-driven resistance mechanisms. Moreover, PDXOs can be leveraged for large-scale tumor-specific drug response profiling critical for accelerating therapeutic advances in CRPC. </p