Diastereoselective Synthesis of the HIV Protease Inhibitor Darunavir and Related Derivatives via a Titanium Tetrachloride-Mediated Asymmetric Glycolate Aldol Addition Reaction.

Darunavir is a potent HIV protease inhibitor that has been established as an effective tool in the fight against the progression of HIV/AIDS in the global community. The successful application of this drug has spurred the development of derivatives wherein strategic regions (e.g., P1, P1', P2, and P2') of the darunavir framework have been structurally modified. An alternate route for the synthesis of darunavir and three related P1 and P1' derivatives has been developed. This synthetic pathway involves the use of a Crimmins titanium tetrachloride-mediated oxazolidine-2-thione-guided asymmetric glycolate aldol addition reaction. The resultant aldol adduct introduces the P1 fragment of darunavir via an aldehyde. Transamidation with a selected amine (isobutylamine or 2-ethyl-1-butylamine) to cleave the auxiliary yields an amide wherein the P1' component is introduced. From this stage, the amide is reduced to the corresponding ß-amino alcohol and the substrate is then bis-nosylated to introduce the requisite p-nitrobenzenesulfonamide component and activate the secondary alcohol for nucleophilic substitution. Treatment with sodium azide yielded the desired azides, and the deprotection of the p-methoxyphenoxy group is achieved with the use of ceric ammonium nitrate. Finally, hydrogenation to reduce both the aniline and azide functionalities with concurrent acylation yields darunavir and its derivatives.

Where the rubber meets the road: Emerging environmental impacts of tire wear particles and their chemical cocktails.

About 3 billion new tires are produced each year and about 800 million tires become waste annually. Global dependence upon tires produced from natural rubber and petroleum-based compounds represents a persistent and complex environmental problem with only partial and often-times, ineffective solutions. Tire emissions may be in the form of whole tires, tire particles, and chemical compounds, each of which is transported through various atmospheric, terrestrial, and aquatic routes in the natural and built environments. Production and use of tires generates multiple heavy metals, plastics, PAH's, and other compounds that can be toxic alone or as chemical cocktails. Used tires require storage space, are energy intensive to recycle, and generally have few post-wear uses that are not also potential sources of pollutants (e.g., crumb rubber, pavements, burning). Tire particles emitted during use are a major component of microplastics in urban runoff and a source of unique and highly potent toxic substances. Thus, tires represent a ubiquitous and complex pollutant that requires a comprehensive examination to develop effective management and remediation. We approach the issue of tire pollution holistically by examining the life cycle of tires across production, emissions, recycling, and disposal. In this paper, we synthesize recent research and data about the environmental and human health risks associated with the production, use, and disposal of tires and discuss gaps in our knowledge about fate and transport, as well as the toxicology of tire particles and chemical leachates. We examine potential management and remediation approaches for addressing exposure risks across the life cycle of tires. We consider tires as pollutants across three levels: tires in their whole state, as particulates, and as a mixture of chemical cocktails. Finally, we discuss information gaps in our understanding of tires as a pollutant and outline key questions to improve our knowledge and ability to manage and remediate tire pollution.

SARS-CoV-2 variant transition dynamics are associated with vaccination rates, number of co-circulating variants, and convalescent immunity.

BACKGROUND: Throughout the COVID-19 pandemic, the SARS-CoV-2 virus has continued to evolve, with new variants outcompeting existing variants and often leading to different dynamics of disease spread. METHODS: In this paper, we performed a retrospective analysis using longitudinal sequencing data to characterize differences in the speed, calendar timing, and magnitude of 16 SARS-CoV-2 variant waves/transitions for 230 countries and sub-country regions, between October 2020 and January 2023. We then clustered geographic locations in terms of their variant behavior across several Omicron variants, allowing us to identify groups of locations exhibiting similar variant transitions. Finally, we explored relationships between heterogeneity in these variant waves and time-varying factors, including vaccination status of the population, governmental policy, and the number of variants in simultaneous competition. FINDINGS: This work demonstrates associations between the behavior of an emerging variant and the number of co-circulating variants as well as the demographic context of the population. We also observed an association between high vaccination rates and variant transition dynamics prior to the Mu and Delta variant transitions. INTERPRETATION: These results suggest the behavior of an emergent variant may be sensitive to the immunologic and demographic context of its location. Additionally, this work represents the most comprehensive characterization of variant transitions globally to date. FUNDING: Laboratory Directed Research and Development (LDRD), Los Alamos National Laboratory.

Fast increased fidelity samplers for approximate Bayesian Gaussian process regression.

Gaussian processes (GPs) are common components in Bayesian non-parametric models having a rich methodological literature and strong theoretical grounding. The use of exact GPs in Bayesian models is limited to problems containing several thousand observations due to their prohibitive computational demands. We develop a posterior sampling algorithm using H -matrix approximations that scales at O ( n log 2 n ) . We show that this approximation's Kullback-Leibler divergence to the true posterior can be made arbitrarily small. Though multidimensional GPs could be used with our algorithm, d-dimensional surfaces are modeled as tensor products of univariate GPs to minimize the cost of matrix construction and maximize computational efficiency. We illustrate the performance of this fast increased fidelity approximate GP, FIFA-GP, using both simulated and non-synthetic data sets.

Bayesian hierarchical factor regression models to infer cause of death from verbal autopsy data.

In low-resource settings where vital registration of death is not routine it is often of critical interest to determine and study the cause of death (COD) for individuals and the cause-specific mortality fraction (CSMF) for populations. Post-mortem autopsies, considered the gold standard for COD assignment, are often difficult or impossible to implement due to deaths occurring outside the hospital, expense, and/or cultural norms. For this reason, Verbal Autopsies (VAs) are commonly conducted, consisting of a questionnaire administered to next of kin recording demographic information, known medical conditions, symptoms, and other factors for the decedent. This article proposes a novel class of hierarchical factor regression models that avoid restrictive assumptions of standard methods, allow both the mean and covariance to vary with COD category, and can include covariate information on the decedent, region, or events surrounding death. Taking a Bayesian approach to inference, this work develops an MCMC algorithm and validates the FActor Regression for Verbal Autopsy (FARVA) model in simulation experiments. An application of FARVA to real VA data shows improved goodness-of-fit and better predictive performance in inferring COD and CSMF over competing methods. Code and a user manual are made available at https://github.com/kelrenmor/farva.

Student Stress Management and Wellness Programs among Colleges of Pharmacy.

OBJECTIVE: To describe the programmatic stress-related interventions that colleges of pharmacy are providing for their students. METHODS: A paper-based questionnaire was distributed to 80 college teams who attended two consecutive offerings of the American Association of Colleges of Pharmacy institute focused on promoting student well-being. The five-part questionnaire consisted of: 1) tracking and assessment of perceived student stress levels, 2) the types and formats of stress-coping interventions that are offered, 3) the measured impacts of any stress-coping interventions, 4) the level of faculty/staff training and involvement in student stress remediation, and 5) institutional demographics. RESULTS: Of the 40 college teams responding to the survey there were similar numbers of private (44%) and public (56%) institutions. More than half (57.5%) reported measuring student stress levels. The most common interventions offered were counseling (95%), academic advising (82%), physical exercise support (77%), and relationship building activities (70%). Topics offered in the curriculum were most often related to handling substance abuse (50%), time-management (45%), and finances (40%). A majority (79.5%) of schools reported they do not offer formal training on student stress and mental health to faculty and staff and do not formally assess the impact of stress and coping interventions. CONCLUSION: Colleges of pharmacy are addressing student stress and well-being, yet variability exists in terms of assessment, interventions, and didactic offerings. Multiple barriers to improvement remain and mediating barriers and determining assessments for coping and interventions may be next steps for Colleges of Pharmacy.

Multiscale influenza forecasting.

Influenza forecasting in the United States (US) is complex and challenging due to spatial and temporal variability, nested geographic scales of interest, and heterogeneous surveillance participation. Here we present Dante, a multiscale influenza forecasting model that learns rather than prescribes spatial, temporal, and surveillance data structure and generates coherent forecasts across state, regional, and national scales. We retrospectively compare Dante's short-term and seasonal forecasts for previous flu seasons to the Dynamic Bayesian Model (DBM), a leading competitor. Dante outperformed DBM for nearly all spatial units, flu seasons, geographic scales, and forecasting targets. Dante's sharper and more accurate forecasts also suggest greater public health utility. Dante placed 1st in the Centers for Disease Control and Prevention's prospective 2018/19 FluSight challenge in both the national and regional competition and the state competition. The methodology underpinning Dante can be used in other seasonal disease forecasting contexts having nested geographic scales of interest.

Improving probabilistic infectious disease forecasting through coherence.

With an estimated $10.4 billion in medical costs and 31.4 million outpatient visits each year, influenza poses a serious burden of disease in the United States. To provide insights and advance warning into the spread of influenza, the U.S. Centers for Disease Control and Prevention (CDC) runs a challenge for forecasting weighted influenza-like illness (wILI) at the national and regional level. Many models produce independent forecasts for each geographical unit, ignoring the constraint that the national wILI is a weighted sum of regional wILI, where the weights correspond to the population size of the region. We propose a novel algorithm that transforms a set of independent forecast distributions to obey this constraint, which we refer to as probabilistically coherent. Enforcing probabilistic coherence led to an increase in forecast skill for 79% of the models we tested over multiple flu seasons, highlighting the importance of respecting the forecasting system's geographical hierarchy.

BAYESIAN JOINT MODELING OF CHEMICAL STRUCTURE AND DOSE RESPONSE CURVES.

Today there are approximately 85,000 chemicals regulated under the Toxic Substances Control Act, with around 2,000 new chemicals introduced each year. It is impossible to screen all of these chemicals for potential toxic effects, either via full organism in vivo studies or in vitro high-throughput screening (HTS) programs. Toxicologists face the challenge of choosing which chemicals to screen, and predicting the toxicity of as yet unscreened chemicals. Our goal is to describe how variation in chemical structure relates to variation in toxicological response to enable in silico toxicity characterization designed to meet both of these challenges. With our Bayesian partially Supervised Sparse and Smooth Factor Analysis (BS3FA) model, we learn a distance between chemicals targeted to toxicity, rather than one based on molecular structure alone. Our model also enables the prediction of chemical dose-response profiles based on chemical structure (i.e., without in vivo or in vitro testing) by taking advantage of a large database of chemicals that have already been tested for toxicity in HTS programs. We show superior simulation performance in distance learning and modest to large gains in predictive ability compared to existing methods. Results from the high-throughput screening data application elucidate the relationship between chemical structure and a toxicity-relevant high-throughput assay. An R package for BS3FA is available online at https://github.com/kelrenmor/bs3fa.

Water Quality Impairments Due to Aquatic Life Pesticide Toxicity: Prevention and Mitigation in California, USA.

The management of pesticides to protect water quality remains a significant global challenge. Historically, despite regulatory frameworks intended to prevent, minimize, and manage off-site movement of pesticides, multiple generations of pesticide active ingredients have created a seemingly unending cycle of pesticide water pollution in both agricultural and urban watersheds. In California, the most populous and most agricultural US state, pesticide and water quality regulators realized in the 1990s that working independently of each other was not an effective approach to address pesticide water pollution. Over the years, these California agencies have developed a joint vision and have continued to develop a unified approach that has the potential to minimize pesticide risks to aquatic life through a combination of prevention, monitoring, and management actions, while maintaining pesticide availability for effective pest control. Key elements of the current California pesticide/water quality effort include: 1) pesticide and toxicity monitoring, coupled with watershed modeling, to maximize information obtained from monitoring; 2) predictive fate and exposure modeling to identify potential risks to aquatic life for new pesticide products when used as allowed by the label or to identify effective mitigation measures; and 3) management approaches tailored to the different pesticide uses, discharge sources, physical environments, and regulatory environments that exist for agricultural runoff, urban runoff, and municipal wastewater. Lessons from this effort may inform pesticide management elsewhere in the world as well as other chemical regulatory programs, such as the recently reformed US Toxic Substances Control Act and California's Safer Consumer Products regulatory program. Environ Toxicol Chem 2020;39:953-966. © 2020 SETAC.

Measuring Global Disease with Wikipedia: Success, Failure, and a Research Agenda.

Effective disease monitoring provides a foundation for effective public health systems. This has historically been accomplished with patient contact and bureaucratic aggregation, which tends to be slow and expensive. Recent internet-based approaches promise to be real-time and cheap, with few parameters. However, the question of when and how these approaches work remains open. We addressed this question using Wikipedia access logs and category links. Our experiments, replicable and extensible using our open source code and data, test the effect of semantic article filtering, amount of training data, forecast horizon, and model staleness by comparing across 6 diseases and 4 countries using thousands of individual models. We found that our minimal-configuration, language-agnostic article selection process based on semantic relatedness is effective for improving predictions, and that our approach is relatively insensitive to the amount and age of training data. We also found, in contrast to prior work, very little forecasting value, and we argue that this is consistent with theoretical considerations about the nature of forecasting. These mixed results lead us to propose that the currently observational field of internet-based disease surveillance must pivot to include theoretical models of information flow as well as controlled experiments based on simulations of disease.

Advancing Alternative Analysis: Integration of Decision Science.

BACKGROUND: Decision analysis-a systematic approach to solving complex problems-offers tools and frameworks to support decision making that are increasingly being applied to environmental challenges. Alternatives analysis is a method used in regulation and product design to identify, compare, and evaluate the safety and viability of potential substitutes for hazardous chemicals. OBJECTIVES: We assessed whether decision science may assist the alternatives analysis decision maker in comparing alternatives across a range of metrics. METHODS: A workshop was convened that included representatives from government, academia, business, and civil society and included experts in toxicology, decision science, alternatives assessment, engineering, and law and policy. Participants were divided into two groups and were prompted with targeted questions. Throughout the workshop, the groups periodically came together in plenary sessions to reflect on other groups' findings. RESULTS: We concluded that the further incorporation of decision science into alternatives analysis would advance the ability of companies and regulators to select alternatives to harmful ingredients and would also advance the science of decision analysis. CONCLUSIONS: We advance four recommendations: a) engaging the systematic development and evaluation of decision approaches and tools; b) using case studies to advance the integration of decision analysis into alternatives analysis; c) supporting transdisciplinary research; and d) supporting education and outreach efforts. https://doi.org/10.1289/EHP483.

Advancing alternatives analysis: The role of predictive toxicology in selecting safer chemical products and processes.

Alternatives analysis (AA) is a method used in regulation and product design to identify, assess, and evaluate the safety and viability of potential substitutes for hazardous chemicals. It requires toxicological data for the existing chemical and potential alternatives. Predictive toxicology uses in silico and in vitro approaches, computational models, and other tools to expedite toxicological data generation in a more cost-effective manner than traditional approaches. The present article briefly reviews the challenges associated with using predictive toxicology in regulatory AA, then presents 4 recommendations for its advancement. It recommends using case studies to advance the integration of predictive toxicology into AA, adopting a stepwise process to employing predictive toxicology in AA beginning with prioritization of chemicals of concern, leveraging existing resources to advance the integration of predictive toxicology into the practice of AA, and supporting transdisciplinary efforts. The further incorporation of predictive toxicology into AA would advance the ability of companies and regulators to select alternatives to harmful ingredients, and potentially increase the use of predictive toxicology in regulation more broadly. Integr Environ Assess Manag 2017;13:915-925. © 2017 SETAC.

Passage of fiproles and imidacloprid from urban pest control uses through wastewater treatment plants in northern California, USA.

Urban pest control insecticides-specifically fipronil and its 4 major degradates (fipronil sulfone, sulfide, desulfinyl, and amide), as well as imidacloprid-were monitored during drought conditions in 8 San Francisco Bay (San Francisco, CA, USA) wastewater treatment plants (WWTPs). In influent and effluent, ubiquitous detections were obtained in units of ng/L for fipronil (13-88 ng/L), fipronil sulfone (1-28 ng/L), fipronil sulfide (1-5 ng/L), and imidacloprid (58-306 ng/L). Partitioning was also investigated; in influent, 100% of imidacloprid and 62 ± 9% of total fiproles (fipronil and degradates) were present in the dissolved state, with the balance being bound to filter-removable particulates. Targeted insecticides persisted during wastewater treatment, regardless of treatment technology utilized (imidacloprid: 93 ± 17%; total fiproles: 65 ± 11% remaining), with partitioning into sludge (3.7-151.1 µg/kg dry wt as fipronil) accounting for minor losses of total fiproles entering WWTPs. The load of total fiproles was fairly consistent across the facilities but fiprole speciation varied. This first regional study on fiprole and imidacloprid occurrences in raw and treated California sewage revealed ubiquity and marked persistence to conventional treatment of both phenylpyrazole and neonicotinoid compounds. Flea and tick control agents for pets are identified as potential sources of pesticides in sewage meriting further investigation and inclusion in chemical-specific risk assessments. Environ Toxicol Chem 2017;36:1473-1482. © 2016 SETAC.

A Meta-Analysis of the Association between Gender and Protective Behaviors in Response to Respiratory Epidemics and Pandemics.

Respiratory infectious disease epidemics and pandemics are recurring events that levy a high cost on individuals and society. The health-protective behavioral response of the public plays an important role in limiting respiratory infectious disease spread. Health-protective behaviors take several forms. Behaviors can be categorized as pharmaceutical (e.g., vaccination uptake, antiviral use) or non-pharmaceutical (e.g., hand washing, face mask use, avoidance of public transport). Due to the limitations of pharmaceutical interventions during respiratory epidemics and pandemics, public health campaigns aimed at limiting disease spread often emphasize both non-pharmaceutical and pharmaceutical behavioral interventions. Understanding the determinants of the public's behavioral response is crucial for devising public health campaigns, providing information to parametrize mathematical models, and ultimately limiting disease spread. While other reviews have qualitatively analyzed the body of work on demographic determinants of health-protective behavior, this meta-analysis quantitatively combines the results from 85 publications to determine the global relationship between gender and health-protective behavioral response. The results show that women in the general population are about 50% more likely than men to adopt/practice non-pharmaceutical behaviors. Conversely, men in the general population are marginally (about 12%) more likely than women to adopt/practice pharmaceutical behaviors. It is possible that factors other than pharmaceutical/non-pharmaceutical status not included in this analysis act as moderators of this relationship. These results suggest an inherent difference in how men and women respond to epidemic and pandemic respiratory infectious diseases. This information can be used to target specific groups when developing non-pharmaceutical public health campaigns and to parameterize epidemic models incorporating demographic information.

Generation of Zerovalent Metal Core Nanoparticles Using n-(2-aminoethyl)-3-aminosilanetriol.

In this work, a facile one-pot reaction for the formation of metal nanoparticles in a water solution through the use of n-(2-aminoethyl)-3-aminosilanetriol is presented. This compound can be used to effectively reduce and complex metal salts into metal core nanoparticles coated with the compound. By controlling the concentrations of salt and silane one is able to control reaction rates, particle size, and nanoparticle coating. The effects of these changes were characterized through transmission electron microscopy (TEM), UV-Vis spectrometry (UV-Vis), Nuclear Magnetic Resonance spectroscopy (NMR) and Fourier Transform Infrared spectroscopy (FTIR). A unique aspect to this reaction is that usually silanes hydrolyze and cross-link in water; however, in this system the silane is water-soluble and stable. It is known that silicon and amino moieties can form complexes with metal salts. The silicon is known to extend its coordination sphere to form penta- or hexa-coordinated species. Furthermore, the silanol group can undergo hydrolysis to form a Si-O-Si silica network, thereby transforming the metal nanoparticles into a functionalized nanocomposites.

Epidemic Forecasting is Messier Than Weather Forecasting: The Role of Human Behavior and Internet Data Streams in Epidemic Forecast.

Mathematical models, such as those that forecast the spread of epidemics or predict the weather, must overcome the challenges of integrating incomplete and inaccurate data in computer simulations, estimating the probability of multiple possible scenarios, incorporating changes in human behavior and/or the pathogen, and environmental factors. In the past 3 decades, the weather forecasting community has made significant advances in data collection, assimilating heterogeneous data steams into models and communicating the uncertainty of their predictions to the general public. Epidemic modelers are struggling with these same issues in forecasting the spread of emerging diseases, such as Zika virus infection and Ebola virus disease. While weather models rely on physical systems, data from satellites, and weather stations, epidemic models rely on human interactions, multiple data sources such as clinical surveillance and Internet data, and environmental or biological factors that can change the pathogen dynamics. We describe some of similarities and differences between these 2 fields and how the epidemic modeling community is rising to the challenges posed by forecasting to help anticipate and guide the mitigation of epidemics. We conclude that some of the fundamental differences between these 2 fields, such as human behavior, make disease forecasting more challenging than weather forecasting.

Chiral cationic polyamines for chiral microcapsules and siRNA delivery.

Reported herein is the use of chiral cationic polyamines for two intriguing applications: fabrication of chiral covalently-linked microcapsules, and enantiospecific delivery of siRNA to Huh 7 cells. The microcapsules are easily fabricated from homochiral polymers, and the resulting architectures can be used for supramolecular chiral catalysis and many other potential applications. Enantiospecific delivery of siRNA to Huh 7 cells is seen by one 'enantiomer' of the polymers delivering siRNA with significantly improved transfection efficiency and reduced toxicity compared to the 'enantiomeric' polymer and commercially available transfection reagents. Taken together, the use of these easily accessible polyamine structures for diverse applications is highlighted in this Letter herein and can lead to numerous future research efforts.

An initiative by midlevel providers to conduct tertiary surveys at a level I trauma center.

INTRODUCTION: Increased patient volume and residents' work hour restrictions have escalated the workload at trauma centers. Because tertiary surveys (TSs) are integral to care, midlevel providers (MLPs) can help streamline this time-consuming process. In this study, we implemented a care plan in which MLPs conduct all TSs, initiate appropriate consultations, and offload residents' work hours. METHODS: From January 2007 to December 2008, we conducted a prospective evaluation of an initiative in which MLPs performed all TSs within 48 hours of admission. A TS consisted of a complete history and physical examination, follow-up of radiologic interpretations, and appropriate consultations. Data included patient demographics, incidence of additional diagnoses noted during TSs and reduction in residents' work hours. Data are presented as mean +/- standard error. RESULTS: During the 2-year period, there were 5,143 patients admitted to the trauma service. The mean age was 36 years +/- 4.8 years, and mean Injury Severity Score (ISS) was 14.2 +/- 4.2. Overall mortality was 5%. Blunt mechanisms accounted for 85%, and penetrating mechanisms resulted in 14% of injuries. MLPs conducted TSs in 56% of patients during the first year and 76% in the second year. In 80 patients (mean age of 44 years +/- 7.1 years, mean Injury Severity Score 21.7 +/- 2.8; p < 0.05 vs. entire cohort), TSs revealed additional injuries, for an incidence of 1.5%. The majority of these diagnoses were of "minor" fractures, half requiring consultations, and 9% necessitating operative intervention. Residents' workload was reduced by 1,802 hours. CONCLUSIONS: Implementation of a MLP initiative to conduct TSs in trauma patients can achieve a consistent and comprehensive workup while offsetting residents' workload and helping to ensure compliance with the 80-hour resident work policy.