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Cannabis is commercially cultivated for both therapeutic and recreational purposes in a growing number of jurisdictions. The main cannabinoids of interest are cannabidiol (CBD) and delta-9 tetrahydrocannabidiol (THC), which have applications in different therapeutic treatments. The rapid, nondestructive determination of cannabinoid levels has been achieved using near-infrared (NIR) spectroscopy coupled to high-quality compound reference data provided by liquid chromatography. However, most of the literature describes prediction models for the decarboxylated cannabinoids, e.g., THC and CBD, rather than naturally occurring analogues, tetrahydrocannabidiolic acid (THCA) and cannabidiolic acid (CBDA). The accurate prediction of these acidic cannabinoids has important implications for quality control for cultivators, manufacturers and regulatory bodies. Using high-quality liquid chromatography–mass spectroscopy (LCMS) data and NIR spectra data, we developed statistical models including principal component analysis (PCA) for data quality control, partial least squares regression (PLS-R) models to predict cannabinoid concentrations for 14 different cannabinoids and partial least squares discriminant analysis (PLS-DA) models to characterise cannabis samples into high-CBDA, high-THCA and even-ratio classes. This analysis employed two spectrometers, a scientific grade benchtop instrument (Bruker MPA II–Multi-Purpose FT-NIR Analyzer) and a handheld instrument (VIAVI MicroNIR Onsite-W). While the models from the benchtop instrument were generally more robust (99.4–100% accuracy prediction), the handheld device also performed well (83.1–100% accuracy prediction) with the added benefits of portability and speed. In addition, two cannabis inflorescence preparation methods were evaluated: finely ground and coarsely ground. The models generated from coarsely ground cannabis provided comparable predictions to that of the finely ground but represent significant timesaving in terms of sample preparation. This study demonstrates that a portable NIR handheld device paired with LCMS quantitative data can provide accurate cannabinoid predictions and potentially be of use for the rapid, high-throughput, nondestructive screening of cannabis material.

The transition to electric vehicles is driving a fundamental shift in the automobile design process. Changes in constraints afforded by the absence of a combustion engine create new opportunities for modifying vehicle geometries. Current approaches to optimizing vehicle aerodynamics require a vast amount of computational studies and physical experiments, which are expensive when performing parameter sweeps over conceivable geometric configurations, suggesting the need for more efficient surrogate models to assist analysis. Here we analyze a dataset of industry-quality automobile geometries with their associated aerodynamic performance obtained from experimentally validated, high-fidelity large-eddy simulations. We show that a relationship between these geometries and their respective aerodynamics can be extracted in a low-dimensional manner by leveraging a nonlinear autoencoder which is simultaneously trained to estimate the drag coefficient from the latent variables. We perform aerodynamic design optimization of vehicle designs by making use of the learned aerodynamic relationship in the low-order space obtained by the model. We demonstrate that the aerodynamic trends for the geometries produced from the optimization process show agreement with validation simulations. The findings of this work demonstrate the application of data-driven approaches to the analysis and design of vehicles in a production environment. Jonathan Tran and colleagues use aerodynamics-guided machine learning for the shape optimization of electric cars. Their approach saves computational time for high complexity engineering tasks, e.g., computational fluid dynamics-based design optimization.

Cannabis is cultivated for therapeutic and recreational purposes where delta-9 tetrahydrocannabinol (THC) is a main target for its therapeutic effects. As the global cannabis industry and research into cannabinoids expands, more efficient and cost-effective analysis methods for determining cannabinoid concentrations will be beneficial to increase efficiencies and maximize productivity. The utilization of machine learning tools to develop near-infrared (NIR) spectroscopy-based prediction models, which have been validated from accurate and sensitive chemical analysis, such as gas chromatography (GC) or liquid chromatography mass spectroscopy (LCMS), is essential. Previous research on cannabinoid prediction models targeted decarboxylated cannabinoids, such as THC, rather than the naturally occurring precursor, tetrahydrocannabinolic acid (THCA), and utilize finely ground cannabis inflorescence. The current study focuses on building prediction models for THCA concentrations in whole cannabis inflorescences prior to harvest, by employing non-destructive screening techniques so cultivators may rapidly characterize high-performing cultivars for chemotype in real time, thus facilitating targeted optimization of crossbreeding efforts. Using NIR spectroscopy and LCMS to create prediction models we can differentiate between high-THCA and even ratio classes with 100% prediction accuracy. We have also developed prediction models for THCA concentration with a R2 = 0.78 with a prediction error average of 13%. This study demonstrates the viability of a portable handheld NIR device to predict THCA concentrations on whole cannabis samples before harvest, allowing the evaluation of cannabinoid profiles to be made earlier, therefore increasing high-throughput and rapid capabilities.

The high-throughput quantitation of cannabinoids is important for the cannabis industry. As medicinal products increase, and research into compounds that have pharmacological benefits increase, and the need to quantitate more than just the main cannabinoids becomes more important. This study aims to provide a rapid, high-throughput method for cannabinoid quantitation using a liquid chromatography triple-quadrupole mass spectrometer (LC-QQQ-MS) with an ultraviolet diode array detector (UV-DAD) for 16 cannabinoids: CBDVA, CBDV, CBDA, CBGA, CBG, CBD, THCV, THCVA, CBN, CBNA, THC, Δ8-THC, CBL, CBC, THCA-A and CBCA. Linearity, limit of detection (LOD), limit of quantitation (LOQ), accuracy, precision, recovery and matrix effect were all evaluated. The validated method was used to determine the cannabinoid concentration of four different Cannabis sativa strains and a low THC strain, all of which have different cannabinoid profiles. All cannabinoids eluted within five minutes with a total analysis time of eight minutes, including column re-equilibration. This was twice as fast as published LC-QQQ-MS methods mentioned in the literature, whilst also covering a wide range of cannabinoid compounds.

An 84-year-old man presented with a two-month history of bilateral breast enlargement. Left nipple discharge began 2 1/2 weeks before presentation. He had no discharge from his right nipple. The milky, beige discharge occurred daily, most notably in the early morning and evening, and sometimes contained bright red blood. It was nonpurulent. The patient reported intermittent tenderness in his breasts but had no other symptoms, including fever, chills, night sweats, unexpected weight loss, fatigue, or change in libido or sexual function. He did not have a history of similar symptoms, steroid use, or hormone therapy. The patient's medical history included atrial fibrillation, anticoagulation, hypertension, chronic heart failure, and arthritis treated with long-term opiate use.

Sitravatinib (MGCD516) is an orally available, small molecule, tyrosine kinase inhibitor that has been evaluated in patients with advanced solid tumors. Concentration‐corrected QT interval (QTc; C‐QTc) modeling was undertaken, using 767 matched concentration‐ECG observations from 187 patients across two clinical studies in patients with advanced solid malignancies, across a dose range of 10–200 mg, via a linear mixed‐effects (LME) model. The effect on heart rate (HR)‐corrected QT interval via Fridericia's correction method (QTcF) at the steady‐state maximum concentration (Cmax,ss) for the sitravatinib proposed therapeutic dosing regimen (100 mg malate once daily [q.d.]) without and with relevant intrinsic and extrinsic factors were predicted. No significant changes in HR from baseline were observed. Hysteresis between sitravatinib plasma concentration and change in QTcF from baseline (ΔQTcF) was not observed. There was no significant relationship between sitravatinib plasma concentration and ΔQTcF. The final C‐QTc model predicted a mean (90% confidence interval [CI]) ΔQTcF of 3.92 (1.95–5.89) ms and 2.94 (0.23–6.10) ms at the proposed therapeutic dosing regimen in patients with normal organ function (best case scenario) and patients with hepatic impairment (worst‐case scenario), respectively. The upper bounds of the 90% CIs were below the regulatory threshold of concern of 10 ms. The results of the described C‐QTc analysis, along with corroborating results from nonclinical safety pharmacology studies, indicate that sitravatinib has a low risk of QTc interval prolongation at the proposed therapeutic dose of 100 mg malate q.d.

Near the end of his life, Michel Foucault turned his attention to the early church Fathers.He did so not for anything like a return to God but rather because he found in those sources alternatives for re-imaging the self.

Identifying the sequence polymorphisms underlying complex trait variation is a key goal of genetics research, since knowing the precise causative molecular events allows insight into the pathways governing trait variation. Genetic analysis of complex traits in model systems regularly starts by constructing QTL maps, but generally fails to identify causative sequence polymorphisms. Previously we mapped a series of QTL contributing to resistance to nicotine in a Drosophila melanogaster multiparental mapping resource and here use a battery of functional tests to resolve QTL to the molecular level. One large-effect QTL resided over a cluster of UDP-glucuronosyltransferases, and quantitative complementation tests using deficiencies eliminating subsets of these detoxification genes revealed allelic variation impacting resistance. RNAseq showed that Ugt86Dd had significantly higher expression in genotypes that are more resistant to nicotine, and anterior midgut-specific RNA interference (RNAi) of this gene reduced resistance. We discovered a segregating 22-bp frameshift deletion in Ugt86Dd, and accounting for the InDel during mapping largely eliminates the QTL, implying the event explains the bulk of the effect of the mapped locus. CRISPR/Cas9 editing of a relatively resistant genotype to generate lesions in Ugt86Dd that recapitulate the naturally occurring putative loss-of-function allele, leads to a large reduction in resistance. Despite this major effect of the deletion, the allele appears to be very rare in wild-caught populations and likely explains only a small fraction of the natural variation for the trait. Nonetheless, this putatively causative coding InDel can be a launchpad for future mechanistic exploration of xenobiotic detoxification.

Ozanimod, approved by regulatory agencies in multiple countries for the treatment of adults with relapsing multiple sclerosis, is a sphingosine 1‐phosphate (S1P) receptor modulator, which binds with high affinity selectively to S1P receptor subtypes 1 and 5. The relationships between plasma concentrations of ozanimod and its major active metabolites, CC112273 and CC1084037, and the QTc interval (C‐QTc) from a phase I multiple‐dose study in healthy subjects were analyzed using nonlinear mixed effects modeling. QTc was modeled linearly as the sum of a sex‐related fixed effect, baseline, and concentration‐related random effects that incorporated interindividual and residual variability. Common linear, power, and maximum effect (Emax) functions were assessed for characterizing the relationship of QTc with concentrations. Model goodness‐of‐fit and performance were evaluated by standard diagnostic tools, including a visual predictive check. The placebo‐corrected change from baseline in QTc (ΔΔQTc) was estimated based on the developed C‐QTc model using a nonparametric bootstrapping approach. QTc was better derived using a study‐specific population formula (QTcP). Among the investigated functions, an Emax function most adequately described the relationship of QTcP with concentrations. Separate models for individual analytes characterized the C‐QTcP relationship better than combined analytes models. Attributing QT prolongation independently to CC1084037 or CC112273, the upper bound of the 95% confidence interval of the predicted ΔΔQTcP was ~ 4 msec at the plateau of the Emax curves. Therefore, ΔΔQTcP is predicted to remain below 10 msec at the supratherapeutic concentrations of the major active metabolites.

Background and Objectives Daclizumab high-yield process (HYP) is a humanized IgG1 monoclonal antibody that binds to the α-subunit (CD25) of the interleukin-2 receptor. The present work characterized the population pharmacokinetics of daclizumab HYP in healthy volunteers (HVs) and subjects with relapsing–remitting multiple sclerosis (RRMS) and evaluated the effects of covariates on daclizumab HYP exposure. Methods Measurable serum concentrations ( n  = 17,139) from 1670 subjects in seven clinical studies (three phase I, one immunogenicity, one phase II with extension, and one phase III) were included in this pharmacokinetic analysis using non-linear mixed–effects modeling. The three phase I studies evaluated single or multiple doses that ranged from 50 to 400 mg with either intravenous or subcutaneous (SC) administration in HVs ( n  = 71). The phase II with extension studies evaluated doses of 150 or 300 mg SC every 4 weeks ( n  = 567), and the immunogenicity ( n  = 113) and the phase III ( n  = 919) studies evaluated 150 mg SC every 4 weeks, all in RRMS patients. Results A two-compartment model with first-order absorption and elimination adequately described daclizumab HYP pharmacokinetics. Clearance (CL) was 0.212 L/day and the central volume of distribution was 3.92 L, scaled by [body weight (kg)/68] with exponents of 0.87 and 1.12, respectively. The peripheral volume of distribution was 2.42 L. Absorption lag time, mean absorption time, and absolute bioavailability (100–300 mg SC) were 1.61 h, 7.2 days, and 88 %, respectively. The daclizumab HYP terminal half-life was 21 days. Baseline CD25, age, and sex did not influence daclizumab HYP pharmacokinetics. Body weight explained 37 and 27 % of the inter-individual variability for CL and central volume of distribution, respectively. Neutralizing antibody (NAb)-positive status (included as a time-varying covariate) increased daclizumab HYP CL by 19 %. Conclusions Consistent with previous findings in HVs, this analysis including extensive data from RRMS patients demonstrates that daclizumab HYP is characterized by slow CL, linear pharmacokinetics at doses above 100 mg, and high SC bioavailability. The pharmacokinetics of daclizumab HYP were not influenced by age (range 18–66 years), the sex of adult subjects, or the baseline CD4+CD25+ T cells (target level). The impact of covariates (body weight, NAb) on daclizumab HYP pharmacokinetics is unlikely to be clinically relevant.