Tag: nevin manimala

PLoS One. 2021 Aug 18;16(8):e0255445. doi: 10.1371/journal.pone.0255445. eCollection 2021.

ABSTRACT

Pharmaceuticals and their packaging have a significant negative impact on the environment providing a very strong argument for action on the part of pharmacists and pharmacy technicians to engage with pro-environmental behaviours (PEBs) in their workplaces. The aims of this research were therefore to investigate in hospital pharmacists and pharmacy technicians, 1) factors affecting engagement with workplace PEBs, and 2) determine if legislated carbon reduction targets in the UK influenced workplace PEBs in the UK compared with Australia which does not have legislated carbon reduction targets. The environmentally responsible disposal of pharmaceutical waste was the PEB of interest in this study. A mixed methods research design was utilised and a conceptual model (key variables: environmental attitude, concern, and knowledge, and organisational factors) was developed to identify factors influencing workplace PEBs. Participants were from five hospitals in Queensland, Australia and five NHS hospitals in England, UK. There was no statistically significant difference in environmental attitude or concern between the two groups-most had a mid-environmental attitude score and low levels of environmental concern. Participants lacked knowledge of the issue and the link between the environment and public health. Both Australian and UK participants reported recycling packaging waste was not a priority in the hospital pharmacy workplace (even in hospitals with recycling capability) as hospitals focused on compliance with clinical (contaminated) and confidential waste streams. Environmental attitude, knowledge, and concern therefore appeared to be weak influences on intention to perform workplace PEBs with workplace social norms (compliance due to audits) appearing to be a significant mediator of action. The key difference between the cohorts was that UK pharmacists felt waste was not in the scope of their role, and therefore not their responsibility. This study identified that legislated carbon reduction targets did not influence hospital pharmacy workplace PEBs-neither cohort reported engaging significantly in workplace PEBs. UK Government and NHS sustainability policy did not appear to have disseminated to pharmacy department level of UK public hospitals to any great extent.

PMID:34407108 | DOI:10.1371/journal.pone.0255445

PLoS One. 2021 Aug 18;16(8):e0255237. doi: 10.1371/journal.pone.0255237. eCollection 2021.

ABSTRACT

BACKGROUND: Telomere length (TL) in peripheral blood mononuclear cells (PBMC) from fresh venous blood is increasingly used to estimate molecular impacts of accumulated social adversity on population health. Sometimes, TL extracted from saliva or dried blood spots (DBS) are substituted as less invasive and more scalable specimen collection methods; yet, are they interchangeable with fresh blood? Studies find TL is correlated across tissues, but have not addressed the critical question for social epidemiological applications: Do different specimen types show the same association between TL and social constructs?

METHODS: We integrate expertise in social epidemiology, molecular biology, and the statistical impact of measurement error on parameter estimates. Recruiting a diverse sample of 132 Metro-Detroit women, we measure TL for each woman from fresh blood PBMC, DBS, and saliva. Using regression methods, we estimate associations between social characteristics and TL, comparing estimates across specimen types for each woman.

RESULTS: Associations between TL and social characteristics vary by specimen type collected from the same woman, sometimes qualitatively altering estimates of the magnitude or direction of a theorized relationship. Being Black is associated with shorter TL in PBMC, but longer TL in saliva or DBS. Education is positively associated with TL in fresh blood, but negatively associated with TL using DBS.

CONCLUSION: Findings raise concerns about the use of TL measures derived from different tissues in social epidemiological research. Investigators need to consider the possibility that associations between social variables and TL may be systematically related to specimen type, rather than be valid indicators of socially-patterned biopsychosocial processes.

PMID:34407110 | DOI:10.1371/journal.pone.0255237

PLoS Comput Biol. 2021 Aug 18;17(8):e1009280. doi: 10.1371/journal.pcbi.1009280. Online ahead of print.

ABSTRACT

Ketamine is an NMDA receptor antagonist commonly used to maintain general anesthesia. At anesthetic doses, ketamine causes high power gamma (25-50 Hz) oscillations alternating with slow-delta (0.1-4 Hz) oscillations. These dynamics are readily observed in local field potentials (LFPs) of non-human primates (NHPs) and electroencephalogram (EEG) recordings from human subjects. However, a detailed statistical analysis of these dynamics has not been reported. We characterize ketamine’s neural dynamics using a hidden Markov model (HMM). The HMM observations are sequences of spectral power in seven canonical frequency bands between 0 to 50 Hz, where power is averaged within each band and scaled between 0 and 1. We model the observations as realizations of multivariate beta probability distributions that depend on a discrete-valued latent state process whose state transitions obey Markov dynamics. Using an expectation-maximization algorithm, we fit this beta-HMM to LFP recordings from 2 NHPs, and separately, to EEG recordings from 9 human subjects who received anesthetic doses of ketamine. Our beta-HMM framework provides a useful tool for experimental data analysis. Together, the estimated beta-HMM parameters and optimal state trajectory revealed an alternating pattern of states characterized primarily by gamma and slow-delta activities. The mean duration of the gamma activity was 2.2s([1.7,2.8]s) and 1.2s([0.9,1.5]s) for the two NHPs, and 2.5s([1.7,3.6]s) for the human subjects. The mean duration of the slow-delta activity was 1.6s([1.2,2.0]s) and 1.0s([0.8,1.2]s) for the two NHPs, and 1.8s([1.3,2.4]s) for the human subjects. Our characterizations of the alternating gamma slow-delta activities revealed five sub-states that show regular sequential transitions. These quantitative insights can inform the development of rhythm-generating neuronal circuit models that give mechanistic insights into this phenomenon and how ketamine produces altered states of arousal.

PMID:34407069 | DOI:10.1371/journal.pcbi.1009280

PLoS One. 2021 Aug 18;16(8):e0253843. doi: 10.1371/journal.pone.0253843. eCollection 2021.

ABSTRACT

BACKGROUND: Knowing the true infected and symptomatic case fatality ratios (IFR and CFR) for COVID-19 is of high importance for epidemiological model projections. Early in the pandemic many locations had limited testing and reporting, so that standard methods for determining IFR and CFR required large adjustments for missed cases. We present an alternate approach, based on results from the countries at the time that had a high test to positive case ratio to estimate symptomatic CFR.

METHODS: We calculated age specific (0-69, 70-79, 80+ years old) time corrected crude symptomatic CFR values from 7 countries using two independent time to fatality correction methods. Data was obtained through May 7, 2020. We applied linear regression to determine whether the mean of these coefficients had converged to the true symptomatic CFR values. We then tested these coefficients against values derived in later studies as well as a large random serological study in NYC at that time.

RESULTS: The age dependent symptomatic CFR values accurately predicted the percentage of the population infected as reported by two random testing studies in NYC. They also were in good agreement with later studies that estimated age specific IFR and CFR values from serological studies and more extensive data sets available later in the pandemic.

CONCLUSIONS: We found that for regions with extensive testing it is possible to get early accurate symptomatic CFR coefficients. These values, in combination with an estimate of the age dependence of infection, allows symptomatic CFR values and percentage of the population that is infected to be determined in similar regions with limited testing.

PMID:34407073 | DOI:10.1371/journal.pone.0253843

PLoS One. 2021 Aug 18;16(8):e0255256. doi: 10.1371/journal.pone.0255256. eCollection 2021.

ABSTRACT

Sampling rare and clustered populations is challenging because of the effort required to find rare units. Heuristically, a practitioner would prefer to discontinue sampling in areas where rare units of interest are apparently extremely sparse or absent. We take advantage of the characteristics of inverse sampling to adaptively inform practitioners when it is efficient to move on to sample new areas. We introduce Adaptive Two-stage Inverse Sampling (ATIS), which is designed to leave a selected area after observation of an a priori number of only non-rare units and to continue sampling in the area when rare units are observed. ATIS is efficient in many cases and yields more rare units than conventional sampling for a rare and clustered population. We derive unbiased estimators of population total and variance. We also introduce an easy-to-compute estimator, which is nearly as efficient as the unbiased estimator. A simulation study on a rare plant population of buttercups (Ranunculus) shows that ATIS even with the easy-to-compute estimator is more efficient than its conventional sampling counterparts and is more efficient than Two-stage Adaptive Cluster Sampling (TACS) for small and moderate final sample sizes. Additional simulations reveal that ATIS is efficient for binary data (e.g., presence or absence) whereas TACS is inefficient for binary data. The overall results indicate that ATIS is consistently efficient compared to conventional sampling and to adaptive cluster sampling in some important cases.

PMID:34407106 | DOI:10.1371/journal.pone.0255256

Nat Commun. 2021 Aug 17;12(1):4969. doi: 10.1038/s41467-021-25212-3.

ABSTRACT

Multimeric cytoskeletal protein complexes orchestrate normal cellular function. However, protein-complex distributions in stressed, heterogeneous cell populations remain unknown. Cell staining and proximity-based methods have limited selectivity and/or sensitivity for endogenous multimeric protein-complex quantification from single cells. We introduce micro-arrayed, differential detergent fractionation to simultaneously detect protein complexes in hundreds of individual cells. Fractionation occurs by 60 s size-exclusion electrophoresis with protein complex-stabilizing buffer that minimizes depolymerization. Proteins are measured with a ~5-hour immunoassay. Co-detection of cytoskeletal protein complexes in U2OS cells treated with filamentous actin (F-actin) destabilizing Latrunculin A detects a unique subpopulation (~2%) exhibiting downregulated F-actin, but upregulated microtubules. Thus, some cells may upregulate other cytoskeletal complexes to counteract the stress of Latrunculin A treatment. We also sought to understand the effect of non-chemical stress on cellular heterogeneity of F-actin. We find heat shock may dysregulate filamentous and globular actin correlation. In this work, our assay overcomes selectivity limitations to biochemically quantify single-cell protein complexes perturbed with diverse stimuli.

PMID:34404787 | DOI:10.1038/s41467-021-25212-3

Nat Commun. 2021 Aug 17;12(1):4992. doi: 10.1038/s41467-021-25210-5.

ABSTRACT

Liquid chromatography-mass spectrometry-based metabolomics studies are increasingly applied to large population cohorts, which run for several weeks or even years in data acquisition. This inevitably introduces unwanted intra- and inter-batch variations over time that can overshadow true biological signals and thus hinder potential biological discoveries. To date, normalisation approaches have struggled to mitigate the variability introduced by technical factors whilst preserving biological variance, especially for protracted acquisitions. Here, we propose a study design framework with an arrangement for embedding biological sample replicates to quantify variance within and between batches and a workflow that uses these replicates to remove unwanted variation in a hierarchical manner (hRUV). We use this design to produce a dataset of more than 1000 human plasma samples run over an extended period of time. We demonstrate significant improvement of hRUV over existing methods in preserving biological signals whilst removing unwanted variation for large scale metabolomics studies. Our tools not only provide a strategy for large scale data normalisation, but also provides guidance on the design strategy for large omics studies.

PMID:34404777 | DOI:10.1038/s41467-021-25210-5

Nat Commun. 2021 Aug 17;12(1):4988. doi: 10.1038/s41467-021-25183-5.

ABSTRACT

Glycans are fundamental cellular building blocks, involved in many organismal functions. Advances in glycomics are elucidating the essential roles of glycans. Still, it remains challenging to properly analyze large glycomics datasets, since the abundance of each glycan is dependent on many other glycans that share many intermediate biosynthetic steps. Furthermore, the overlap of measured glycans can be low across samples. We address these challenges with GlyCompare, a glycomic data analysis approach that accounts for shared biosynthetic steps for all measured glycans to correct for sparsity and non-independence in glycomics, which enables direct comparison of different glycoprofiles and increases statistical power. Using GlyCompare, we study diverse N-glycan profiles from glycoengineered erythropoietin. We obtain biologically meaningful clustering of mutant cell glycoprofiles and identify knockout-specific effects of fucosyltransferase mutants on tetra-antennary structures. We further analyze human milk oligosaccharide profiles and find mother’s fucosyltransferase-dependent secretor-status indirectly impact the sialylation. Finally, we apply our method on mucin-type O-glycans, gangliosides, and site-specific compositional glycosylation data to reveal tissues and disease-specific glycan presentations. Our substructure-oriented approach will enable researchers to take full advantage of the growing power and size of glycomics data.

PMID:34404781 | DOI:10.1038/s41467-021-25183-5

BMJ Open. 2021 Aug 17;11(8):e048436. doi: 10.1136/bmjopen-2020-048436.

ABSTRACT

INTRODUCTION: This study was conducted to investigate the incidence and time trend of lower limb amputation (LLA) among people with and without diabetes.

RESEARCH DESIGN AND METHODS: This retrospective population-based cohort study was based on the national claims data in Japan, comprising a total population of 150 million. Data of all individuals who had LLA from April 2013 to March 2018 were obtained. We analysed the sex-adjusted and age-adjusted annual LLA rate (every fiscal year) in people with and without diabetes for major and minor amputation. To test for time trend, Poisson regression models were fitted.

RESULTS: In the 5-year period, 30 187 major and 29 299 minor LLAs were performed in Japan. The sex-adjusted and age-adjusted incidence of major and minor LLAs was 9.5 (people with diabetes, 21.8 vs people without diabetes, 2.3, per 100 000 person-years) and 14.9 (people with diabetes, 28.4 vs people without diabetes, 1.9, per 100 000 person-years) times higher, respectively, in people with diabetes compared with those without. A significant decline in the annual major amputation rate was observed (p<0.05) and the annual minor amputation rate remained stable (p=0.63) when sex, age and people with and without diabetes were included as dependent variables.

CONCLUSIONS: This is the first report of the national statistics of LLAs in Japan. The incidence of major and minor LLAs was 10 and 15 times higher, respectively, in people with diabetes compared with those without. A significant decline in the major amputation rate was observed, and the annual minor amputation rate remained stable during the observation period. This information can help to create an effective national healthcare strategy for preventing limb amputations, which affect the quality of life of patients with diabetes and add to the national healthcare expenditure.

PMID:34404707 | DOI:10.1136/bmjopen-2020-048436

Hosp Pediatr. 2021 Aug 17:hpeds.2021-006011. doi: 10.1542/hpeds.2021-006011. Online ahead of print.

ABSTRACT

BACKGROUND AND OBJECTIVES: Pediatric sepsis quality improvement in emergency departments has been well described and associated with improved survival. Acute care (non-ICU inpatient) units differ in important ways, and optimal approaches to improving sepsis processes and outcomes in this setting are not yet known. Our objective was to increase the proportion of acute care sepsis cases in our health system with initial antibiotic order-to-administration time ≤60 minutes by 20% from a baseline of 43% to 52% by December 2020.

METHODS: Employing the Model for Improvement with broad stakeholder engagement, we developed and implemented interventions aimed at effective intervention for sepsis cases on acute care units. We analyzed process and outcome metrics over time using statistical process control charts. We used descriptive statistics to explore differences in antibiotic order-to-administration time and inform ongoing improvement.

RESULTS: We cared for 187 patients with sepsis over the course of our initiative. The proportion within our goal antibiotic order-to-administration time rose from 43% to 64% with evidence of special cause variation after our interventions. Of all patients, 66% experienced ICU transfer and 4% died.

CONCLUSIONS: We successfully decreased antibiotic order-to-administration time. We also introduced a novel model for sepsis response systems that integrates interventions designed for the complexities of acute care settings. We demonstrated impactful local improvements in the acute care setting where quality improvement reports and success have previously been limited.

PMID:34404744 | DOI:10.1542/hpeds.2021-006011