The development of high-throughput mass spectrometric methods for the qualitative and quantitative analysis of diquaternary ammonium gemini surfactants

For over a decade, diquaternary ammonium gemini surfactants have shown promise as non-viral gene delivery agents in both in vitro and in vivo systems. Their continued development, however, requires an understanding of their biological fate. The absence of identification and quantification methods that can achieve that goal is what drove the development of simple and rapid mass spectrometry (MS)-based methods; the focus of my Ph.D. dissertation. Prior to the development of these MS-based methods, an understanding of the gas phase behavior of diquaternary ammonium gemini surfactants is required. The development of a universal fragmentation pathway for gemini surfactants was achieved using low resolution and high resolution MS instruments. Single stage (MS), tandem stage (MS/MS and quasi-multi-stage (quasi MS3) mass spectrometry analysis allowed for the confirmation of the molecular composition and structure of each gemini surfactant through the identification of common and unique mass to charge values. Understanding the fragmentation behavior allowed for the specific identification and/or quantification of gemini surfactants by MS-based methods; including liquid chromatography low resolution tandem mass spectrometry (LC-LR-MS/MS), fast chromatography low resolution tandem mass spectrometry, fast chromatography high resolution mass spectrometry, desorption electrospray ionization low resolution mass spectrometry and matrix assisted laser desorption ionization high resolution mass spectrometry. We hypothesized that a LC-LR-MS/MS method would be the most effective quantitative method for the quantification of N,N-bis(dimethylhexadecyl)-1,3-propane-diammonium dibromide (G16-3) within PAM212 cellular lysate; achieving the lowest lower limit of quantification (LLOQ). Although the LC-LR-MS/MS method achieved a LLOQ suitable for analysis of G16-3 within PAM212 cell lysate, its limitations made it an inefficient method. In comparison, the four alternative mass spectrometry methods were faster, more efficient and less expensive than a conventional LC-LR-MS/MS method for the post transfection quantification of G16-3 within PAM212 cell lysate to be determined; 1.45 ± 0.06 μM. Future application of the universal fragmentation pathway and each MS-based quantification method will be beneficial for the future development of diquaternary ammonium gemini surfactants to further understand their post transfection fate

Antimicrobial resistance among migrants in Europe: a systematic review and meta-analysis

BACKGROUND: Rates of antimicrobial resistance (AMR) are rising globally and there is concern that increased migration is contributing to the burden of antibiotic resistance in Europe. However, the effect of migration on the burden of AMR in Europe has not yet been comprehensively examined. Therefore, we did a systematic review and meta-analysis to identify and synthesise data for AMR carriage or infection in migrants to Europe to examine differences in patterns of AMR across migrant groups and in different settings. METHODS: For this systematic review and meta-analysis, we searched MEDLINE, Embase, PubMed, and Scopus with no language restrictions from Jan 1, 2000, to Jan 18, 2017, for primary data from observational studies reporting antibacterial resistance in common bacterial pathogens among migrants to 21 European Union-15 and European Economic Area countries. To be eligible for inclusion, studies had to report data on carriage or infection with laboratory-confirmed antibiotic-resistant organisms in migrant populations. We extracted data from eligible studies and assessed quality using piloted, standardised forms. We did not examine drug resistance in tuberculosis and excluded articles solely reporting on this parameter. We also excluded articles in which migrant status was determined by ethnicity, country of birth of participants' parents, or was not defined, and articles in which data were not disaggregated by migrant status. Outcomes were carriage of or infection with antibiotic-resistant organisms. We used random-effects models to calculate the pooled prevalence of each outcome. The study protocol is registered with PROSPERO, number CRD42016043681. FINDINGS: We identified 2274 articles, of which 23 observational studies reporting on antibiotic resistance in 2319 migrants were included. The pooled prevalence of any AMR carriage or AMR infection in migrants was 25·4% (95% CI 19·1-31·8; I2 =98%), including meticillin-resistant Staphylococcus aureus (7·8%, 4·8-10·7; I2 =92%) and antibiotic-resistant Gram-negative bacteria (27·2%, 17·6-36·8; I2 =94%). The pooled prevalence of any AMR carriage or infection was higher in refugees and asylum seekers (33·0%, 18·3-47·6; I2 =98%) than in other migrant groups (6·6%, 1·8-11·3; I2 =92%). The pooled prevalence of antibiotic-resistant organisms was slightly higher in high-migrant community settings (33·1%, 11·1-55·1; I2 =96%) than in migrants in hospitals (24·3%, 16·1-32·6; I2 =98%). We did not find evidence of high rates of transmission of AMR from migrant to host populations. INTERPRETATION: Migrants are exposed to conditions favouring the emergence of drug resistance during transit and in host countries in Europe. Increased antibiotic resistance among refugees and asylum seekers and in high-migrant community settings (such as refugee camps and detention facilities) highlights the need for improved living conditions, access to health care, and initiatives to facilitate detection of and appropriate high-quality treatment for antibiotic-resistant infections during transit and in host countries. Protocols for the prevention and control of infection and for antibiotic surveillance need to be integrated in all aspects of health care, which should be accessible for all migrant groups, and should target determinants of AMR before, during, and after migration. FUNDING: UK National Institute for Health Research Imperial Biomedical Research Centre, Imperial College Healthcare Charity, the Wellcome Trust, and UK National Institute for Health Research Health Protection Research Unit in Healthcare-associated Infections and Antimictobial Resistance at Imperial College London

Understanding and conceptualising the adoption, use and diffusion of mobile banking in older adults: A research agenda and conceptual framework

Mobile banking has become increasingly important to society; however, not all members of society adopt and/or use it as much as others: older adults, the disabled and lower-income families remain behind in their use and adoption of this service. This finding helped us recognise a research gap and led us to form our primary aim: to understand and explain the factors that influence the adoption, use and diffusion of mobile banking among one of those groups in particular, older adults, in the UK. To form a theoretical understanding, this paper presents a comprehensive review of the surrounding literature in the area and proposes a conceptual framework that can be used for future research. The implications of this research for academia and businesses are also provided in this paper

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages

Surgical site infection after gastrointestinal surgery in high-income, middle-income, and low-income countries: a prospective, international, multicentre cohort study

Background: Surgical site infection (SSI) is one of the most common infections associated with health care, but its importance as a global health priority is not fully understood. We quantified the burden of SSI after gastrointestinal surgery in countries in all parts of the world. Methods: This international, prospective, multicentre cohort study included consecutive patients undergoing elective or emergency gastrointestinal resection within 2-week time periods at any health-care facility in any country. Countries with participating centres were stratified into high-income, middle-income, and low-income groups according to the UN's Human Development Index (HDI). Data variables from the GlobalSurg 1 study and other studies that have been found to affect the likelihood of SSI were entered into risk adjustment models. The primary outcome measure was the 30-day SSI incidence (defined by US Centers for Disease Control and Prevention criteria for superficial and deep incisional SSI). Relationships with explanatory variables were examined using Bayesian multilevel logistic regression models. This trial is registered with ClinicalTrials.gov, number NCT02662231. Findings: Between Jan 4, 2016, and July 31, 2016, 13 265 records were submitted for analysis. 12 539 patients from 343 hospitals in 66 countries were included. 7339 (58·5%) patient were from high-HDI countries (193 hospitals in 30 countries), 3918 (31·2%) patients were from middle-HDI countries (82 hospitals in 18 countries), and 1282 (10·2%) patients were from low-HDI countries (68 hospitals in 18 countries). In total, 1538 (12·3%) patients had SSI within 30 days of surgery. The incidence of SSI varied between countries with high (691 [9·4%] of 7339 patients), middle (549 [14·0%] of 3918 patients), and low (298 [23·2%] of 1282) HDI (p < 0·001). The highest SSI incidence in each HDI group was after dirty surgery (102 [17·8%] of 574 patients in high-HDI countries; 74 [31·4%] of 236 patients in middle-HDI countries; 72 [39·8%] of 181 patients in low-HDI countries). Following risk factor adjustment, patients in low-HDI countries were at greatest risk of SSI (adjusted odds ratio 1·60, 95% credible interval 1·05–2·37; p=0·030). 132 (21·6%) of 610 patients with an SSI and a microbiology culture result had an infection that was resistant to the prophylactic antibiotic used. Resistant infections were detected in 49 (16·6%) of 295 patients in high-HDI countries, in 37 (19·8%) of 187 patients in middle-HDI countries, and in 46 (35·9%) of 128 patients in low-HDI countries (p < 0·001). Interpretation: Countries with a low HDI carry a disproportionately greater burden of SSI than countries with a middle or high HDI and might have higher rates of antibiotic resistance. In view of WHO recommendations on SSI prevention that highlight the absence of high-quality interventional research, urgent, pragmatic, randomised trials based in LMICs are needed to assess measures aiming to reduce this preventable complication

The development and assessment of high-throughput mass spectrometry-based methods for the quantification of a nanoparticle drug delivery agent in cellular lysate

The safe use of lipid-based drug delivery agents requires fast and sensitive qualitative and quantitative assessment of their cellular interactions. Many mass spectrometry (MS) based analytical platforms can achieve such task with varying capabilities. Therefore, four novel high-throughput MS-based quantitative methods were evaluated for the analysis of a small organic gene delivery agent: N,N-bis(dimethylhexadecyl)-1,3-propane-diammonium dibromide (G16-3). Analysis utilized MS instruments that detect analytes using low-resolution tandem MS (MS/MS) analysis (i.e. QTRAP or linear ion trap in this work) or high-resolution MS analysis (i.e. time of flight (ToF) or Orbitrap). Our results indicate that the validated fast chromatography (FC)-QTRAP-MS/MS, FC- LTQ-Orbitrap-MS, desorption electrospray ionization-collision-induced dissociation (CID)-MS/MS and matrix assisted laser desorption ionization-ToF/ToF-MS MS methods were superior in the area of method development and sample analysis time to a previously developed liquid chromatography (LC)-CID-MS/MS. To our knowledge, this is the first evaluation of the abilities of five MS-based quantitative methods that target a single pharmaceutical analyte. Our findings indicate that, in comparison to conventional LC-CID-MS/MS, the new MS-based methods resulted in a (1) substantial reduction in the analysis time, (2) reduction in the time required for method development and (3) production of either superior or comparable quantitative data. The four new high-throughput MS methods, therefore, were faster, more efficient and less expensive than a conventional LC-CID-MS/MS for the quantification of the G16-3 analyte within tissue culture. When applied to cellular lysate, no significant change in the concentration of G16-3 gemini surfactant within PAM212 cells was observed between 5 and 53 h, suggesting the absence of any metabolism/excretion from PAM212 cells.Peer reviewed: YesNRC publication: Ye

Mass Spec Studio for Integrative Structural Biology

The integration of biophysical data from multiple sources is critical for developing accurate structural models of large multiprotein systems and their regulators. Mass spectrometry (MS) can be used to measure the insertion location for a wide range of topographically sensitive chemical probes, and such insertion data provide a rich, but disparate set of modeling restraints. We have developed a software platform that integrates the analysis of label-based MS and tandem MS (MS 2) data with protein modeling activities (Mass Spec Studio). Analysis packages can mine any labeling data from any mass spectrometer in a proteomics-grade manner, and link labeling methods with data-directed protein interaction modeling using HADDOCK. Support is provided for hydrogen/deuterium exchange (HX) and covalent labeling chemistries, including novel acquisition strategies such as targeted HX-MS 2 and data-independent HX-MS 2. The latter permits the modeling of highly complex systems, which we demonstrate by the analysis of microtubule interactions