Nevin Manimala

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

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):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

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

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

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

BMJ Open. 2021 Aug 17;11(8):e047576. doi: 10.1136/bmjopen-2020-047576.

ABSTRACT

OBJECTIVE: To describe the discrimination and calibration of clinical prediction models, identify characteristics that contribute to better predictions and investigate predictors that are associated with unplanned hospital readmissions.

DESIGN: Systematic review and meta-analysis.

DATA SOURCE: Medline, EMBASE, ICTPR (for study protocols) and Web of Science (for conference proceedings) were searched up to 25 August 2020.

ELIGIBILITY CRITERIA FOR SELECTING STUDIES: Studies were eligible if they reported on (1) hospitalised adult patients with acute heart disease; (2) a clinical presentation of prediction models with c-statistic; (3) unplanned hospital readmission within 6 months.

PRIMARY AND SECONDARY OUTCOME MEASURES: Model discrimination for unplanned hospital readmission within 6 months measured using concordance (c) statistics and model calibration. Meta-regression and subgroup analyses were performed to investigate predefined sources of heterogeneity. Outcome measures from models reported in multiple independent cohorts and similarly defined risk predictors were pooled.

RESULTS: Sixty studies describing 81 models were included: 43 models were newly developed, and 38 were externally validated. Included populations were mainly patients with heart failure (HF) (n=29). The average age ranged between 56.5 and 84 years. The incidence of readmission ranged from 3% to 43%. Risk of bias (RoB) was high in almost all studies. The c-statistic was <0.7 in 72 models, between 0.7 and 0.8 in 16 models and >0.8 in 5 models. The study population, data source and number of predictors were significant moderators for the discrimination. Calibration was reported for 27 models. Only the GRACE (Global Registration of Acute Coronary Events) score had adequate discrimination in independent cohorts (0.78, 95% CI 0.63 to 0.86). Eighteen predictors were pooled.

CONCLUSION: Some promising models require updating and validation before use in clinical practice. The lack of independent validation studies, high RoB and low consistency in measured predictors limit their applicability.

PROSPERO REGISTRATION NUMBER: CRD42020159839.

PMID:34404703 | DOI:10.1136/bmjopen-2020-047576

BMJ Open. 2021 Aug 17;11(8):e046630. doi: 10.1136/bmjopen-2020-046630.

ABSTRACT

OBJECTIVES: A collaborative service initiative involving community pharmacists and a specialist mental health pharmacist was developed to provide pharmacist reviews for care home residents with intellectual disabilities (IDs). This study aimed to characterise the medicines and lifestyle risk outcomes of the service and determine how these align with national priority issues in ID.

DESIGN: Descriptive statistical analysis of routinely collected service delivery data.

SETTING: Residential care homes in the Wirral, England for people with ID.

PARTICIPANTS: 160 residents.

INTERVENTIONS: Pharmacist review of residents’ medicines and lifestyle risk factors between November 2019 and May 2020.

PRIMARY AND SECONDARY OUTCOME MEASURES: Numbers of medicines prescribed, the nature of pharmacists’ interventions/recommendations and general practitioner (GP)/psychiatrist acceptance.

RESULTS: The 160 residents were prescribed 1207 medicines, 74% were prescribed ≥5 medicines and 507 interventions/recommendations were made, averaging 3.3 per resident. The highest proportion (30.4%) were lifestyle risk related, while changing and stopping medicines accounted for 17.9% and 12.8%, respectively. Of the recommendations discussed with GPs/psychiatrists, 86% were accepted. Medicines with anticholinergic properties were prescribed for 115 (72%) residents, of whom 43 (37%) had a high anticholinergic burden score. Pharmacists recommended anticholinergic discontinuation or dose reduction for 28 (24%) residents. The pharmacists made interventions/recommendations about constipation management for 10% of residents and about respiratory medicines for 17 (81%) of the 21 residents with respiratory diagnoses.

CONCLUSIONS: The findings indicate considerable polypharmacy among the residents and a high level of pharmacists’ interventions/recommendations about medicines and lifestyle risk, most of which were accepted by GPs/psychiatrists. This included anticholinergic burden reduction and improving respiratory disease and constipation management, which are national priority issues. Wider adoption of collaborative pharmacist review models could have similar benefits for residential populations with ID and potentially reduce pressure on other health services.

PMID:34404698 | DOI:10.1136/bmjopen-2020-046630

BMJ Open. 2021 Aug 17;11(8):e045544. doi: 10.1136/bmjopen-2020-045544.

ABSTRACT

OBJECTIVES: Anaemia is a global public health problem with major health and socioeconomic consequences. Though childhood anaemia is a major public health problem in Ethiopia, there is limited evidence on the spatio-temporal variability of childhood anaemia over time in the country. Therefore, this study aimed to assess the spatio-temporal distribution and associated factors of childhood anaemia using the Ethiopian Demographic and Health Survey (EDHS) data from 2005 to 2016.

DESIGN: Survey-based cross-sectional study design was employed for the EDHS.

SETTING: Data were collected in all nine regions and two city administrations of Ethiopia in 2005, 2011 and 2016.

PARTICIPANTS: The source population for this study was all children in Ethiopia aged 6-59 months. A total of 21 302 children aged 6-59 months were included in this study.

OUTCOME MEASURE: The outcome variable was child anaemia status.

RESULTS: The prevalence of anaemia declined from 53.9% in 2005 to 44.6% in 2011, but it showed an increase in 2016 to 57.6%. The spatial analysis revealed that the spatial distribution of anaemia varied across the regions. The spatial scan statistics analysis indicated a total of 22 clusters (relative risk (RR)=1.5, p<0.01) in 2005, 180 clusters (RR=1.4, p<0.01) in 2011 and 219 clusters (RR=1.4, p<0. 0.01) in 2016, significant primary clusters were identified. The child’s age, mother’s age, maternal anaemia status, wealth index, birth order, fever, stunting, wasting status and region were significant predictors of childhood anaemia.

CONCLUSIONS: In this study, childhood anaemia remains a public health problem. The spatial distribution of childhood anaemia varied significantly across the country. Individual-level and community-level factors were associated with childhood anaemia. Therefore, in regions with a high risk of childhood anaemia, individual-level and community-level factors should be intensified by allocating additional resources and providing appropriate and tailored strategies.

PMID:34404697 | DOI:10.1136/bmjopen-2020-045544

Respir Care. 2021 Aug 17:respcare.09053. doi: 10.4187/respcare.09053. Online ahead of print.

ABSTRACT

BACKGROUND: Several markers of oxygenation are used as prognostic markers in acute hypoxemic respiratory failure. Real-world use is limited by the need for invasive measurements and unreliable availability in the electronic health record. A pragmatic, reliable, and accurate marker of acute hypoxemic respiratory failure is needed to facilitate epidemiologic studies, clinical trials, and shared decision-making with patients. F_IO2 is easily obtained at the bedside and from the electronic health record. The F_IO2 trajectory may be a valuable marker of recovery in patients with acute hypoxemic respiratory failure.

METHODS: This was a historical cohort study of adult subjects admitted to an ICU with acute hypoxemic respiratory failure secondary to community-acquired pneumonia and/or ARDS.

RESULTS: Our study included 2,670 subjects. F_IO2 and S_pO2 were consistently more available than was P_aO2 in the electronic health record: (F_IO2 vs S_pO2 vs P_aO2 : 100 vs 100 vs 72.8% on day 1, and 100 vs 99 vs 21% on day 5). A worsening F_IO2 trajectory was associated with reduced ventilator-free days. From days 2 to 5, every increase in F_IO2 by 10% from the previous day was associated with fewer ventilator-free days (on day 2: adjusted mean -1.25 [95% CI -1.45 to -1.05] d, P < .001). The S_pO2 /F_IO2 trajectory also provided prognostic information. On days 3 – 5, an increase in S_pO2 /F_IO2 from the previous day was associated with increased ventilator-free days (on day 3: adjusted mean 2.09 (95% CI 1.44-2.74) d; P < .001). S_pO2 /F_IO2 models did not add predictive information compared with models with F_IO2 alone (on day 2: adjusted F_IO2 vs S_pO2 /F_IO2 R² 0.122 vs 0.119; and on day 3: 0.153 vs 0.163).

CONCLUSIONS: F_IO2 and S_pO2 /F_IO2 are pragmatic and readily available intermediate prognostic markers in acute hypoxic respiratory failure. The F_IO2 trajectory in the first 5 d of ICU admission provided important prognostic information (ventilator-free days). Although the S_pO2 /F_IO2 trajectory was also associated with ventilator-free days, it did not provide more information than the F_IO2 trajectory alone.

PMID:34404689 | DOI:10.4187/respcare.09053