Photocatalytic Hydroaminoalkylation of Styrenes with Unprotected Primary Alkylamines.

Catalytic, intermolecular hydroaminoalkylation (HAA) of styrenes provides a powerful disconnection for pharmacologically relevant γ-arylamines, but current methods cannot utilize unprotected primary alkylamines as feedstocks. Metal-catalyzed HAA protocols are also highly sensitive to α-substitution on the amine partner, and no catalytic solutions exist for α-tertiary γ-arylamine synthesis via this approach. We report a solution to these problems using organophotoredox catalysis, enabling a direct, modular, and sustainable preparation of α-(di)substituted γ-arylamines, including challenging electron-neutral and moderately electron-rich aryl groups. A broad range of functionalities are tolerated, and the reactions can be run on multigram scale in continuous flow. The method is applied to a concise, protecting-group-free synthesis of the blockbuster drug Fingolimod, as well as a phosphonate mimic of its in vivo active form (by iterative α-C-H functionalization of ethanolamine). The reaction can also be sequenced with an intramolecular N-arylation to provide a general and modular access to valuable (spirocyclic) 1,2,3,4-tetrahydroquinolines and 1,2,3,4-tetrahydronaphthyridines. Mechanistic and kinetic studies support an irreversible hydrogen atom transfer activation of the alkylamine by the azidyl radical and some contribution from a radical chain. The reaction is photon-limited and exhibits a zero-order dependence on amine, azide, and photocatalyst, with a first-order dependence on styrene.

Chan-Lam Amination of Secondary and Tertiary Benzylic Boronic Esters.

We report a Chan-Lam coupling reaction of benzylic and allylic boronic esters with primary and secondary anilines to form valuable alkyl amine products. Both secondary and tertiary boronic esters can be used as coupling partners, with mono-alkylation of the aniline occurring selectively. This is a rare example of a transition-metal-mediated transformation of a tertiary alkylboron reagent. Initial investigation into the reaction mechanism suggests that transmetalation from B to Cu occurs through a single-electron, rather than a two-electron process.

Effect of viscosity on photodegradation rates in complex secondary organic aerosol materials.

This work explores the effect of environmental conditions on the photodegradation rates of atmospherically relevant, photolabile, organic molecules embedded in a film of secondary organic material (SOM). Three types of SOM were studied: α-pinene/O3 SOM (PSOM), limonene/O3 SOM (LSOM), and aged limonene/O3 obtained by exposure of LSOM to ammonia (brown LSOM). PSOM and LSOM were impregnated with 2,4-dinitrophenol (2,4-DNP), an atmospherically relevant molecule that photodegrades faster than either PSOM or LSOM alone, to serve as a probe of SOM matrix effects on photochemistry. Brown LSOM contains an unidentified chromophore that absorbs strongly at 510 nm and photobleaches upon irradiation. This chromophore served as a probe molecule for the brown LSOM experiments. In all experiments, either the temperature or relative humidity (RH) surrounding the SOM films was varied. The extent of photochemical reaction in the samples was monitored using UV-vis absorption spectroscopy. For all three model systems examined, the observed photodegradation rates were slower at lower temperatures and lower RH, conditions that make SOM more viscous. Additionally, the activation energies for photodegradation of each system were positively correlated with the viscosity of the SOM matrix as measured in poke-flow experiments. These activation energies were calculated to be 50, 24, and 17 kJ mol(-1) for 2,4-DNP in PSOM, 2,4-DNP in LSOM, and the chromophore in brown LSOM, respectively, and PSOM was found to be the most viscous of the three. These results suggest that the increased viscosity is hindering the motion of the molecules in SOM and is slowing down their respective photochemical reactions.

Viscosity of α-pinene secondary organic material and implications for particle growth and reactivity.

Particles composed of secondary organic material (SOM) are abundant in the lower troposphere. The viscosity of these particles is a fundamental property that is presently poorly quantified yet required for accurate modeling of their formation, growth, evaporation, and environmental impacts. Using two unique techniques, namely a "bead-mobility" technique and a "poke-flow" technique, in conjunction with simulations of fluid flow, the viscosity of the water-soluble component of SOM produced by α-pinene ozonolysis is quantified for 20- to 50-µm particles at 293-295 K. The viscosity is comparable to that of honey at 90% relative humidity (RH), similar to that of peanut butter at 70% RH, and at least as viscous as bitumen at ≤30% RH, implying that the studied SOM ranges from liquid to semisolid or solid across the range of atmospheric RH. These data combined with simple calculations or previous modeling studies are used to show the following: (i) the growth of SOM by the exchange of organic molecules between gas and particle may be confined to the surface region of the particles for RH ≤ 30%; (ii) at ≤30% RH, the particle-mass concentrations of semivolatile and low-volatility organic compounds may be overpredicted by an order of magnitude if instantaneous equilibrium partitioning is assumed in the bulk of SOM particles; and (iii) the diffusivity of semireactive atmospheric oxidants such as ozone may decrease by two to five orders of magnitude for a drop in RH from 90% to 30%. These findings have possible consequences for predictions of air quality, visibility, and climate.

Obesity-Associated Differentially Methylated Regions in Colon Cancer.

Obesity with adiposity is a common disorder in modern days, influenced by environmental factors such as eating and lifestyle habits and affecting the epigenetics of adipose-based gene regulations and metabolic pathways in colorectal cancer (CRC). We compared epigenetic changes of differentially methylated regions (DMR) of genes in colon tissues of 225 colon cancer cases (154 non-obese and 71 obese) and 15 healthy non-obese controls by accessing The Cancer Genome Atlas (TCGA) data. We applied machine-learning-based analytics including generalized regression (GR) as a confirmatory validation model to identify the factors that could contribute to DMRs impacting colon cancer to enhance prediction accuracy. We found that age was a significant predictor in obese cancer patients, both alone (p = 0.003) and interacting with hypomethylated DMRs of ZBTB46, a tumor suppressor gene (p = 0.008). DMRs of three additional genes: HIST1H3I (p = 0.001), an oncogene with a hypomethylated DMR in the promoter region; SRGAP2C (p = 0.006), a tumor suppressor gene with a hypermethylated DMR in the promoter region; and NFATC4 (p = 0.006), an adipocyte differentiating oncogene with a hypermethylated DMR in an intron region, are also significant predictors of cancer in obese patients, independent of age. The genes affected by these DMR could be potential novel biomarkers of colon cancer in obese patients for cancer prevention and progression.

Reliable and Scalable SARS-CoV-2 qPCR Testing at a High Sample Throughput: Lessons Learned from the Belgian Initiative.

We present our approach to rapidly establishing a standardized, multi-site, nation-wide COVID-19 screening program in Belgium. Under auspices of a federal government Task Force responsible for upscaling the country's testing capacity, we were able to set up a national testing initiative with readily available resources, putting in place a robust, validated, high-throughput, and decentralized qPCR molecular testing platform with embedded proficiency testing. We demonstrate how during an acute scarcity of equipment, kits, reagents, personnel, protective equipment, and sterile plastic supplies, we introduced an approach to rapidly build a reliable, validated, high-volume, high-confidence workflow based on heterogeneous instrumentation and diverse assays, assay components, and protocols. The workflow was set up with continuous quality control monitoring, tied together through a clinical-grade information management platform for automated data analysis, real-time result reporting across different participating sites, qc monitoring, and making result data available to the requesting physician and the patient. In this overview, we address challenges in optimizing high-throughput cross-laboratory workflows with minimal manual intervention through software, instrument and assay validation and standardization, and a process for harmonized result reporting and nation-level infection statistics monitoring across the disparate testing methodologies and workflows, necessitated by a rapid scale-up as a response to the pandemic.

Amyloid binding and beyond: a new approach for Alzheimer's disease drug discovery targeting Aßo-PrPC binding and downstream pathways.

Amyloid ß oligomers (Aßo) are the main toxic species in Alzheimer's disease, which have been targeted for single drug treatment with very little success. In this work we report a new approach for identifying functional Aßo binding compounds. A tailored library of 971 fluorine containing compounds was selected by a computational method, developed to generate molecular diversity. These compounds were screened for Aßo binding by a combined 19F and STD NMR technique. Six hits were evaluated in three parallel biochemical and functional assays. Two compounds disrupted Aßo binding to its receptor PrPC in HEK293 cells. They reduced the pFyn levels triggered by Aßo treatment in neuroprogenitor cells derived from human induced pluripotent stem cells (hiPSC). Inhibitory effects on pTau production in cortical neurons derived from hiPSC were also observed. These drug-like compounds connect three of the pillars in Alzheimer's disease pathology, i.e. prion, Aß and Tau, affecting three different pathways through specific binding to Aßo and are, indeed, promising candidates for further development.

Gene-Metabolite Interaction in the One Carbon Metabolism Pathway: Predictors of Colorectal Cancer in Multi-Ethnic Families.

For personalized healthcare, the purpose of this study was to examine the key genes and metabolites in the one-carbon metabolism (OCM) pathway and their interactions as predictors of colorectal cancer (CRC) in multi-ethnic families. In this proof-of-concept study, we included a total of 30 participants, 15 CRC cases and 15 matched family/friends representing major ethnic groups in southern California. Analytics based on supervised machine learning were applied, with the target variable being specified as cancer, including the ensemble method and generalized regression (GR) prediction. Elastic Net with Akaike's Information Criterion with correction (AICc) and Leave-One-Out cross validation GR methods were used to validate the results for enhanced optimality, prediction, and reproducibility. The results revealed that despite some family members sharing genetic heritage, the CRC group had greater combined gene polymorphism-mutations than the family controls (p < 0.1) for five genes including MTHFR C677T, MTHFR A1298C, MTR A2756G, MTRR A66G, and DHFR 19bp. Blood metabolites including homocysteine (7 µmol/L), methyl-folate (40 nmol/L) with total gene mutations (≥4); age (51 years) and vegetable intake (2 cups), and interactions of gene mutations and methylmalonic acid (MMA) (400 nmol/L) were significant predictors (all p < 0.0001) using the AICc. The results were validated by a 3% misclassification rate, AICc of 26, and >99% area under the receiver operating characteristic curve. These results point to the important roles of blood metabolites as potential markers in the prevention of CRC. Future intervention studies can be designed to target the ways to mitigate the enzyme-metabolite deficiencies in the OCM pathway to prevent cancer.

Gene Environment Interactions and Predictors of Colorectal Cancer in Family-Based, Multi-Ethnic Groups.

For the personalization of polygenic/omics-based health care, the purpose of this study was to examine the gene-environment interactions and predictors of colorectal cancer (CRC) by including five key genes in the one-carbon metabolism pathways. In this proof-of-concept study, we included a total of 54 families and 108 participants, 54 CRC cases and 54 matched family friends representing four major racial ethnic groups in southern California (White, Asian, Hispanics, and Black). We used three phases of data analytics, including exploratory, family-based analyses adjusting for the dependence within the family for sharing genetic heritage, the ensemble method, and generalized regression models for predictive modeling with a machine learning validation procedure to validate the results for enhanced prediction and reproducibility. The results revealed that despite the family members sharing genetic heritage, the CRC group had greater combined gene polymorphism rates than the family controls (p < 0.05), on MTHFR C677T, MTR A2756G, MTRR A66G, and DHFR 19 bp except MTHFR A1298C. Four racial groups presented different polymorphism rates for four genes (all p < 0.05) except MTHFR A1298C. Following the ensemble method, the most influential factors were identified, and the best predictive models were generated by using the generalized regression models, with Akaike's information criterion and leave-one-out cross validation methods. Body mass index (BMI) and gender were consistent predictors of CRC for both models when individual genes versus total polymorphism counts were used, and alcohol use was interactive with BMI status. Body mass index status was also interactive with both gender and MTHFR C677T gene polymorphism, and the exposure to environmental pollutants was an additional predictor. These results point to the important roles of environmental and modifiable factors in relation to gene-environment interactions in the prevention of CRC.

Personalized Nutrition-Genes, Diet, and Related Interactive Parameters as Predictors of Cancer in Multiethnic Colorectal Cancer Families.

To personalize nutrition, the purpose of this study was to examine five key genes in the folate metabolism pathway, and dietary parameters and related interactive parameters as predictors of colorectal cancer (CRC) by measuring the healthy eating index (HEI) in multiethnic families. The five genes included methylenetetrahydrofolate reductase (MTHFR) 677 and 1298, methionine synthase (MTR) 2756, methionine synthase reductase (MTRR 66), and dihydrofolate reductase (DHFR) 19bp, and they were used to compute a total gene mutation score. We included 53 families, 53 CRC patients and 53 paired family friend members of diverse population groups in Southern California. We measured multidimensional data using the ensemble bootstrap forest method to identify variables of importance within domains of genetic, demographic, and dietary parameters to achieve dimension reduction. We then constructed predictive generalized regression (GR) modeling with a supervised machine learning validation procedure with the target variable (cancer status) being specified to validate the results to allow enhanced prediction and reproducibility. The results showed that the CRC group had increased total gene mutation scores compared to the family members (p < 0.05). Using the Akaike's information criterion and Leave-One-Out cross validation GR methods, the HEI was interactive with thiamine (vitamin B1), which is a new finding for the literature. The natural food sources for thiamine include whole grains, legumes, and some meats and fish which HEI scoring included as part of healthy portions (versus limiting portions on salt, saturated fat and empty calories). Additional predictors included age, as well as gender and the interaction of MTHFR 677 with overweight status (measured by body mass index) in predicting CRC, with the cancer group having more men and overweight cases. The HEI score was significant when split at the median score of 77 into greater or less scores, confirmed through the machine-learning recursive tree method and predictive modeling, although an HEI score of greater than 80 is the US national standard set value for a good diet. The HEI and healthy eating are modifiable factors for healthy living in relation to dietary parameters and cancer prevention, and they can be used for personalized nutrition in the precision-based healthcare era.

Predictors of the Healthy Eating Index and Glycemic Index in Multi-Ethnic Colorectal Cancer Families.

For personalized nutrition in preparation for precision healthcare, we examined the predictors of healthy eating, using the healthy eating index (HEI) and glycemic index (GI), in family-based multi-ethnic colorectal cancer (CRC) families. A total of 106 participants, 53 CRC cases and 53 family members from multi-ethnic families participated in the study. Machine learning validation procedures, including the ensemble method and generalized regression prediction, Elastic Net with Akaike's Information Criterion with correction and Leave-One-Out cross validation methods, were applied to validate the results for enhanced prediction and reproducibility. Models were compared based on HEI scales for the scores of 77 versus 80 as the status of healthy eating, predicted from individual dietary parameters and health outcomes. Gender and CRC status were interactive as additional predictors of HEI based on the HEI score of 77. Predictors of HEI 80 as the criterion score of a good diet included five significant dietary parameters (with intake amount): whole fruit (1 cup), milk or milk alternative such as soy drinks (6 oz), whole grain (1 oz), saturated fat (15 g), and oil and nuts (1 oz). Compared to the GI models, HEI models presented more accurate and fitted models. Milk or a milk alternative such as soy drink (6 oz) is the common significant parameter across HEI and GI predictive models. These results point to the importance of healthy eating, with the appropriate amount of healthy foods, as modifiable factors for cancer prevention.

Gene-environment interactions and predictors of breast cancer in family-based multi-ethnic groups.

Breast cancer (BC) is the most common cancer in women worldwide and second leading cause of cancer-related death. Understanding gene-environment interactions could play a critical role for next stage of BC prevention efforts. Hence, the purpose of this study was to examine the key gene-environmental factors affecting the risks of BC in a diverse sample. Five genes in one-carbon metabolism pathway including MTHFR 677, MTHFR 1298, MTR 2756, MTRR 66, and DHFR 19bp together with demographics, lifestyle, and dietary intake factors were examined in association with BC risks. A total of 80 participants (40 BC cases and 40 family/friend controls) in southern California were interviewed and provided salivary samples for genotyping. We presented the first study utilizing both conventional and new analytics including ensemble method and predictive modeling based on smallest errors to predict BC risks. Predictive modeling of Generalized Regression Elastic Net Leave-One-Out demonstrated alcohol use (p = 0.0126) and age (p < 0.0001) as significant predictors; and significant interactions were noted between body mass index (BMI) and alcohol use (p = 0.0027), and between BMI and MTR 2756 polymorphisms (p = 0.0090). Our findings identified the modifiable lifestyle factors in gene-environment interactions that are valuable for BC prevention.

Imaging Practice Patterns: Referral Network Analysis of a Single State of Origination.

PURPOSE: The aim of this study was to examine the referral pattern of imaging studies requested in a single state compared with the potential location of interpretation. METHODS: Analysis of Medicare patients in a DocGraph data set was performed to identify sequential different physician services claims for the same patient for which the second claim was for services provided by a radiologist. RESULTS: In the 2011 Medicare population, radiology referrals from physicians practicing in Georgia resulted in 76.5% of radiology interpretations by radiologists inside the state of Georgia. The states bordering Georgia accounted for 11.6% of interpretations in the Georgia market. The remaining interpretations were distributed throughout the remainder of the country. CONCLUSIONS: A significant proportion of routine imaging interpretation occurs outside the state in which an examination is performed. Additional studies are needed to identify complex drivers of imaging referral patterns, such as patient geographic location and demographics, radiologist workforce distribution, contractual obligations, and social relationships.

Water diffusion in atmospherically relevant α-pinene secondary organic material.

Secondary organic material (SOM) constitutes a large mass fraction of atmospheric aerosol particles. Understanding its impact on climate and air quality relies on accurate models of interactions with water vapour. Recent research shows that SOM can be highly viscous and can even behave mechanically like a solid, leading to suggestions that particles exist out of equilibrium with water vapour in the atmosphere. In order to quantify any kinetic limitation we need to know water diffusion coefficients for SOM, but this quantity has, until now, only been estimated and has not yet been measured. We have directly measured water diffusion coefficients in the water soluble fraction of α-pinene SOM between 240 and 280 K. Here we show that, although this material can behave mechanically like a solid, at 280 K water diffusion is not kinetically limited on timescales of 1 s for atmospheric-sized particles. However, diffusion slows as temperature decreases. We use our measured data to constrain a Vignes-type parameterisation, which we extend to lower temperatures to show that SOM can take hours to equilibrate with water vapour under very cold conditions. Our modelling for 100 nm particles predicts that under mid- to upper-tropospheric conditions radial inhomogeneities in water content produce a low viscosity surface region and more solid interior, with implications for heterogeneous chemistry and ice nucleation.

The Monte Carlo method improves physician practice valuation.

This article demonstrates the use of the Monte Carlo simulation method in physician practice valuation. The Monte Carlo method allows the valuator to incorporate probability ranges into the discounted cash flow model and obtain an output indicating the probability for specified ranges of practice valuation. Given the high level of uncertainty in projected cash flows associated with physician practices, the value of this kind of information in a practice valuation decision would quite obviously be superior to any single point estimate generated by a traditional discounted cash flow model. It is postulated that virtually all hospitals support an information system that can easily accommodate a Monte Carlo simulation.