Category: Nevin Manimala

PLoS One. 2021 Jul 13;16(7):e0254665. doi: 10.1371/journal.pone.0254665. eCollection 2021.

ABSTRACT

BACKGROUND: Changes in plasma volume, a marker of plasma volume expansion and contraction, are gaining attention in the field of cardiovascular disease because of its role in the prevention and management of heart failure. However, it remains unknown whether a 1-year change in plasma volume is a risk factor for all-cause, cardiovascular, and non-cardiovascular mortality in the general population.

METHODS AND RESULTS: We used a nationwide database of 134,291 subjects (age 40-75 years) who participated in the annual “Specific Health Check and Guidance in Japan” check-up for 2 consecutive years between 2008 and 2011. A 1-year change in plasm volume was calculated using the Strauss-Davis-Rosenbaum formula. There were 220 cardiovascular deaths, 1,001 non-cardiovascular deaths including 718 cancer deaths, and 1,221 all-cause deaths during the follow-up period of 3.9 years. All subjects were divided into quintiles based on the 1-year change in plasma volume. Kaplan-Meier analysis demonstrated that the highest 5th quintile had the greatest risk among the five groups. Multivariate Cox proportional hazard regression analysis demonstrated that a 1-year change in plasma volume was an independent risk factor for all-cause, cardiovascular, non-cardiovascular, and cancer deaths. The addition of a 1-year change in plasma volume to cardiovascular risk factors significantly improved the C-statistic, net reclassification, and integrated discrimination indexes.

CONCLUSIONS: Here, we have demonstrated for the first time that a 1-year change in plasma volume could be an additional risk factor for all-cause, cardiovascular, and non-cardiovascular (mainly cancer) mortality in the general population.

PMID:34255808 | DOI:10.1371/journal.pone.0254665

Bioinformatics. 2021 Jul 13:btab508. doi: 10.1093/bioinformatics/btab508. Online ahead of print.

ABSTRACT

MOTIVATION: Probabilistic Identification of bacterial essential genes using TraDIS data based on Tn5 libraries has received relatively little attention in the literature; most methods are designed for mariner transposon insertions. Analysis of Tn5 transposon-based genomic data is challenging due to the high insertion density and genomic resolution. We present a novel probabilistic Bayesian approach for classifying bacterial essential genes using transposon insertion density derived from transposon insertion sequencing data. We implement a Markov chain Monte Carlo sampling procedure to estimate the posterior probability that any given gene is essential. We implement a Bayesian decision theory approach to selecting essential genes. We assess the effectiveness of our approach via analysis of both simulated data and three previously published Escherichia coli, Salmonella Typhimurium and Staphylococcus aureus datasets. These three bacteria have relatively well characterised essential genes which allows us to test our classification procedure using receiver operating characteristic curves and area under the curves. We compare the classification performance with that of Bio-Tradis, a standard tool for bacterial gene classification.

RESULTS: Our method is able to classify genes in the three datasets with areas under the curves between 0.967 and 0.983. Our simulated synthetic datasets show that both the number of insertions and the extent to which insertions are tolerated in the distal regions of essential genes are both important in determining classification accuracy. Importantly our method gives the user the option of classifying essential genes based on the user-supplied costs of false discovery and false non-discovery.

AVAILABILITY: An R package that implements the method presented in this paper is available for download from https://github.com/Kevin-walters/insdens.

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PMID:34255819 | DOI:10.1093/bioinformatics/btab508

PLoS One. 2021 Jul 13;16(7):e0254638. doi: 10.1371/journal.pone.0254638. eCollection 2021.

ABSTRACT

The Chilean health authorities have implemented a sanitary strategy known as dynamic quarantine or strategic quarantine to cope with the COVID-19 pandemic. Under this system, lockdowns were established, lifted, or prolonged according to the weekly health authorities’ assessment of municipalities’ epidemiological situation. The public announcements about the confinement situation of municipalities country-wide are made typically on Tuesdays or Wednesdays before noon, have received extensive media coverage, and generated sharp stock market fluctuations. Municipalities are the smallest administrative division in Chile, with each city broken down typically into several municipalities. We analyze social media behavior in response to the confinement situation of the population at the municipal level. The dynamic quarantine scheme offers a unique opportunity for our analysis, given that municipalities display a high degree of heterogeneity, both in size and in the socioeconomic status of their population. We exploit the variability over time in municipalities’ confinement situations, resulting from the dynamic quarantine strategy, and the cross-sectional variability in their socioeconomic characteristics to evaluate the impact of these characteristics on social sentiment. Using event study and panel data methods, we find that proxies for social sentiment based on Twitter queries are negatively related (more pessimistic) to increases in the number of confined people, but with a statistically significant effect concentrated on people from the wealthiest cohorts of the population. For indicators of social sentiment based on Google Trends, we found that search intensity during the periods surrounding government announcements is positively related to increases in the total number of confined people. Still, this effect does not seem to be dependent on the segments of the population affected by the quarantine. Furthermore, we show that the observed heterogeneity in sentiment mirrors heterogeneity in stock market reactions to government announcements. We provide evidence that the observed stock market behavior around quarantine announcements can be explained by the number of people from the wealthiest segments of the population entering or exiting lockdown.

PMID:34255804 | DOI:10.1371/journal.pone.0254638

PLoS One. 2021 Jul 13;16(7):e0254419. doi: 10.1371/journal.pone.0254419. eCollection 2021.

ABSTRACT

BACKGROUND: Pulmonary rehabilitation (PR) is not very often used by physicians in Lebanon despite evidence on its positive effects on health-related quality of life.

AIM: This study assesses the knowledge, attitudes and practices of PR among physicians in Lebanon. In addition, the study identifies the main barriers to access to PR according to chest physicians. Insight into these issues will help to increase awareness about the need for PR programs and can contribute to designing such programs in the country.

METHODS: A survey was conducted during the regional conference of the Lebanese Pulmonary Society. One week after the initial survey, the survey questionnaire was sent by email to all chest physicians who were registered with the Lebanese Pulmonary Society but did not attend the conference. A 25-item questionnaire was used to collect information on PR.

RESULTS: Responses were analyzed using descriptive statistics. The response rate was 40%. Results show that only one-third of Lebanese chest physicians have good knowledge about the nature and multidisciplinary content of PR. Physicians generally support the current “Pulmonary Rehabilitation Program” in Beirut. Key barriers found are the lack of referral, lack of motivation by patients due to their health, cost of care and lack of qualified health care specialists in Lebanon.

CONCLUSION: Absence of awareness and education about PR among healthcare providers plays an important role in increasing access to the “Pulmonary Rehabilitation Program”. Awareness campaigns and education for physicians, health care professionals and patients should be considered to increase PR in the country.

PMID:34255790 | DOI:10.1371/journal.pone.0254419

PLoS One. 2021 Jul 13;16(7):e0254550. doi: 10.1371/journal.pone.0254550. eCollection 2021.

ABSTRACT

BACKGROUND: COVID-19 pandemic has rapidly required a high demand of hospitalization and an increased number of intensive care units (ICUs) admission. Therefore, it became mandatory to develop prognostic models to evaluate critical COVID-19 patients.

MATERIALS AND METHODS: We retrospectively evaluate a cohort of consecutive COVID-19 critically ill patients admitted to ICU with a confirmed diagnosis of SARS-CoV-2 pneumonia. A multivariable Cox regression model including demographic, clinical and laboratory findings was developed to assess the predictive value of these variables. Internal validation was performed using the bootstrap resampling technique. The model’s discriminatory ability was assessed with Harrell’s C-statistic and the goodness-of-fit was evaluated with calibration plot.

RESULTS: 242 patients were included [median age, 64 years (56-71 IQR), 196 (81%) males]. Hypertension was the most common comorbidity (46.7%), followed by diabetes (15.3%) and heart disease (14.5%). Eighty-five patients (35.1%) died within 28 days after ICU admission and the median time from ICU admission to death was 11 days (IQR 6-18). In multivariable model after internal validation, age, obesity, procaltitonin, SOFA score and PaO2/FiO2 resulted as independent predictors of 28-day mortality. The C-statistic of the model showed a very good discriminatory capacity (0.82).

CONCLUSIONS: We present the results of a multivariable prediction model for mortality of critically ill COVID-19 patients admitted to ICU. After adjustment for other factors, age, obesity, procalcitonin, SOFA and PaO2/FiO2 were independently associated with 28-day mortality in critically ill COVID-19 patients. The calibration plot revealed good agreements between the observed and expected probability of death.

PMID:34255793 | DOI:10.1371/journal.pone.0254550

PLoS One. 2021 Jul 13;16(7):e0252928. doi: 10.1371/journal.pone.0252928. eCollection 2021.

ABSTRACT

PURPOSE: Volumetric liver fat fraction (VLFF) measurements were made using the HepaFat-Scan® technique at 1.5T and 3T to determine the agreement between the measurements obtained at the two fields.

METHODS: Sixty patients with type 2 diabetes (67% male, mean age 50.92 ± 6.56yrs) and thirty healthy volunteers (50% male, mean age 48.63 ± 6.32yrs) were scanned on 1.5T Aera and 3T Skyra (Siemens, Erlangen, Germany) MRI scanners on the same day using the HepaFat-Scan® gradient echo protocol with modification of echo times for 3T (TEs 2.38, 4.76, 7.14 ms at 1.5T and 1.2, 2.4, 3.6 ms at 3T). The 3T analyses were performed independently of the 1.5T analyses by a different analyst, blinded from the 1.5T results. Data were analysed for agreement and bias using Bland-Altman methods and intraclass correlation coefficients (ICC). A second cohort of 17 participants underwent interstudy repeatability assessment of VLFF measured by HepaFat-Scan® at 3T.

RESULTS: A small, but statistically significant mean bias of 0.48% was observed between 3T and 1.5T with 95% limits of agreement -2.2% to 3.2% VLFF. The ICC for agreement between field strengths was 0.983 (95% CI 0.972-0.989). In the repeatability cohort studied at 3T the repeatability coefficient was 4.2%. The ICC for agreement was 0.971 (95% CI 0.921-0.989).

CONCLUSION: There is minimal bias and excellent agreement between the measures of VLFF using the HepaFat-Scan® at 1.5 and 3T. The test retest repeatability coefficient at 3T is comparable to the 95% limits of agreement between 1.5T and 3T suggesting that measurements can be made interchangeably between field strengths.

PMID:34255778 | DOI:10.1371/journal.pone.0252928

PLoS One. 2021 Jul 13;16(7):e0254491. doi: 10.1371/journal.pone.0254491. eCollection 2021.

ABSTRACT

The treatment of complex diseases often relies on combinatorial therapy, a strategy where drugs are used to target multiple genes simultaneously. Promising candidate genes for combinatorial perturbation often constitute epistatic genes, i.e., genes which contribute to a phenotype in a non-linear fashion. Experimental identification of the full landscape of genetic interactions by perturbing all gene combinations is prohibitive due to the exponential growth of testable hypotheses. Here we present a model for the inference of pairwise epistatic, including synthetic lethal, gene interactions from siRNA-based perturbation screens. The model exploits the combinatorial nature of siRNA-based screens resulting from the high numbers of sequence-dependent off-target effects, where each siRNA apart from its intended target knocks down hundreds of additional genes. We show that conditional and marginal epistasis can be estimated as interaction coefficients of regression models on perturbation data. We compare two methods, namely glinternet and xyz, for selecting non-zero effects in high dimensions as components of the model, and make recommendations for the appropriate use of each. For data simulated from real RNAi screening libraries, we show that glinternet successfully identifies epistatic gene pairs with high accuracy across a wide range of relevant parameters for the signal-to-noise ratio of observed phenotypes, the effect size of epistasis and the number of observations per double knockdown. xyz is also able to identify interactions from lower dimensional data sets (fewer genes), but is less accurate for many dimensions. Higher accuracy of glinternet, however, comes at the cost of longer running time compared to xyz. The general model is widely applicable and allows mining the wealth of publicly available RNAi screening data for the estimation of epistatic interactions between genes. As a proof of concept, we apply the model to search for interactions, and potential targets for treatment, among previously published sets of siRNA perturbation screens on various pathogens. The identified interactions include both known epistatic interactions as well as novel findings.

PMID:34255784 | DOI:10.1371/journal.pone.0254491

PLoS One. 2021 Jul 13;16(7):e0254405. doi: 10.1371/journal.pone.0254405. eCollection 2021.

ABSTRACT

BACKGROUND: Mycoplasma pneumoniae is one of the main causes of community-acquired pneumonia. Due to the imperfect immune system of children, this also causes Mycoplasma pneumoniae pneumonia (MPP) to be more common in children. Globally, the incidence of MPP in children is gradually increasing. This study was the first to systematically review the clinical efficacy and safety of Shuanghuanglian (SHL) oral preparations combined with azithromycin in the treatment of MPP in children.

METHODS: This study fully retrieved 3 Chinese databases and 5 English databases to search the randomized controlled trials (RCTs) of SHL oral preparations combined with azithromycin in the treatment of children with MPP. The search time is from the inception to September 2020. Data extraction and risk bias evaluation were performed independently by two researchers. We conducted a Meta-analysis of all the outcome indicators. Besides, Meta-regression, subgroup analysis, and heterogeneity analysis were used for the primary outcomes to find the possible potential confounding factors.

RESULTS: Finally, we included 27 RCTs involving 2884 patients. SHL oral preparations combined with azithromycin were better than azithromycin alone in response rate (RR = 1.14, 95% CI[1.11, 1.18]; low certainty evidence), disappearance time of fever(MD = -1.72, 95% CI[-2.47, -0.97]; low certainty evidence), disappearance time of cough (MD = -2.95, 95% CI[-3.55, -2.34]; low certainty evidence), and disappearance time of pulmonary rales (MD = -2.13, 95% CI[-2.88, -1.38]; low certainty evidence). The Meta-regression results showed that the course of disease, age, and method of administration may be the source of heterogeneity. Subgroup analysis and sensitivity analysis have found that the results were stable. For other related clinical symptoms, T lymphocytes, and Serum inflammatory factors, SHL oral preparations combined with azithromycin was better than azithromycin alone, and the difference was statistically significant. For adverse events with low certainty evidence, safety needs further verification.

CONCLUSION: Based on the results of meta-analysis with low certainty evidence, we believed that SHL oral preparations combined with azithromycin likely be effectively improved clinical symptoms compared with azithromycin alone. Low certainty evidence showed that SHL may safety with no serious adverse events. Due to these limitations, the safety needs further verification. More high-quality, multicenter, and large-sample RCTs should be tested and verified in the future.

PMID:34255785 | DOI:10.1371/journal.pone.0254405

JCI Insight. 2021 Jul 13:147096. doi: 10.1172/jci.insight.147096. Online ahead of print.

ABSTRACT

BACKGROUND: The aberrant activation of the PI3K/mTOR signaling circuitry is one of the most frequently dysregulated signaling events in head and neck squamous cell carcinoma (HNSCC). Here, we conducted a single-arm, open label phase IIa clinical trial (NCT02581137) in individuals with oral premalignant lesions (OPL) to explore the potential of metformin to target PI3K/mTOR signaling for HNSCC prevention.

METHODS: Subjects with OPL, otherwise healthy and without diabetes, underwent pre- and post-treatment clinical exam and biopsy. Participants received metformin for 12 weeks (week 1, 500 mg; week 2, 1,000 mg; week 3-12, 2,000 mg daily). Pre- and post-treatment biopsies, saliva, and blood were obtained for biomarker analysis, including immunohistochemical (IHC) assessment of mTOR signaling and exome sequencing.

RESULTS: Twenty-three participants were evaluable for response. The clinical response rate (defined as ≥50% reduction in lesion size) was 17%. While lower than the proposed threshold for favorable clinical response, the histologic response rate (improvement in histologic grade) was 60%, including 17% complete responses and 43% partial responses. Logistic regression analysis revealed that when compared to never smokers, current and former smokers had statistically significantly increased histologic responses (p=0.016). Remarkably, a significant correlation existed between decreased mTOR activity (pS6 IHC staining) in the basal epithelial layer of OPL and the histological (p=0.04) and clinical (p=0.01) responses.

CONCLUSIONS: This is the first phase II trial of metformin in individuals with OPL, providing evidence that metformin administration results in encouraging histological responses and mTOR pathway modulation, thus supporting its further investigation as a chemopreventive agent.

PMID:34255745 | DOI:10.1172/jci.insight.147096

PLoS Med. 2021 Jul 13;18(7):e1003693. doi: 10.1371/journal.pmed.1003693. eCollection 2021 Jul.

ABSTRACT

BACKGROUND: With the availability of multiple Coronavirus Disease 2019 (COVID-19) vaccines and the predicted shortages in supply for the near future, it is necessary to allocate vaccines in a manner that minimizes severe outcomes, particularly deaths. To date, vaccination strategies in the United States have focused on individual characteristics such as age and occupation. Here, we assess the utility of population-level health and socioeconomic indicators as additional criteria for geographical allocation of vaccines.

METHODS AND FINDINGS: County-level estimates of 14 indicators associated with COVID-19 mortality were extracted from public data sources. Effect estimates of the individual indicators were calculated with univariate models. Presence of spatial autocorrelation was established using Moran’s I statistic. Spatial simultaneous autoregressive (SAR) models that account for spatial autocorrelation in response and predictors were used to assess (i) the proportion of variance in county-level COVID-19 mortality that can explained by identified health/socioeconomic indicators (R2); and (ii) effect estimates of each predictor. Adjusting for case rates, the selected indicators individually explain 24%-29% of the variability in mortality. Prevalence of chronic kidney disease and proportion of population residing in nursing homes have the highest R2. Mortality is estimated to increase by 43 per thousand residents (95% CI: 37-49; p < 0.001) with a 1% increase in the prevalence of chronic kidney disease and by 39 deaths per thousand (95% CI: 34-44; p < 0.001) with 1% increase in population living in nursing homes. SAR models using multiple health/socioeconomic indicators explain 43% of the variability in COVID-19 mortality in US counties, adjusting for case rates. R2 was found to be not sensitive to the choice of SAR model form. Study limitations include the use of mortality rates that are not age standardized, a spatial adjacency matrix that does not capture human flows among counties, and insufficient accounting for interaction among predictors.

CONCLUSIONS: Significant spatial autocorrelation exists in COVID-19 mortality in the US, and population health/socioeconomic indicators account for a considerable variability in county-level mortality. In the context of vaccine rollout in the US and globally, national and subnational estimates of burden of disease could inform optimal geographical allocation of vaccines.

PMID:34255766 | DOI:10.1371/journal.pmed.1003693