Category: Nevin Manimala

PLoS One. 2021 May 21;16(5):e0252019. doi: 10.1371/journal.pone.0252019. eCollection 2021.

ABSTRACT

Against the current COVID-19 pandemic, governments worldwide have devised a variety of non-pharmaceutical interventions to mitigate it. However, it is generally difficult to estimate the joint impact of different control strategies. In this paper, we tackle this question with an extended epidemic SEIR model, informed by a socio-political classification of different interventions. First, we inquire the conceptual effect of mitigation parameters on the infection curve. Then, we illustrate the potential of our model to reproduce and explain empirical data from a number of countries, to perform cross-country comparisons. This gives information on the best synergies of interventions to control epidemic outbreaks while minimising impact on socio-economic needs. For instance, our results suggest that, while rapid and strong lockdown is an effective pandemic mitigation measure, a combination of social distancing and early contact tracing can achieve similar mitigation synergistically, while keeping lower isolation rates. This quantitative understanding can support the establishment of mid- and long-term interventions, to prepare containment strategies against further outbreaks. This paper also provides an online tool that allows researchers and decision makers to interactively simulate diverse scenarios with our model.

PMID:34019589 | DOI:10.1371/journal.pone.0252019

PLoS One. 2021 May 21;16(5):e0251843. doi: 10.1371/journal.pone.0251843. eCollection 2021.

ABSTRACT

Studies on physiotherapists are generally focused on clinical professionalism, with very few examining job performance from a management standpoint. To address this gap, this study sought to investigate the relationship between impression management and organizational citizenship behavior and job performance. This study targeted medical institutions offering rehabilitation and physiotherapy services and conducted a questionnaire survey based on scales developed by domestic and foreign scholars. A total of 600 questionnaires were distributed and 523 valid ones collected. The data was tested and verified using regression analysis and hierarchical linear modeling (HLM). In the survey, the Impression Management Scale, Organizational Citizenship Behavior Scale, and Job Performance Scale indicated that at the individual level, the impression management of physiotherapists is significantly related to their organizational citizenship behaviors and job performance. The organizational citizenship behaviors were also found to have a mediating effect between impression management and job performance. At the group level, impression management had a conditioning effect on organizational citizenship behaviors and job performance. In terms of statistical methods, group-level variables act as moderators, which affects the power of individual-level explanatory variables on outcome variables, i.e., the influence of the slope. The job behaviors of physiotherapists entail direct service and their performance is closely related to organizational development. Impression management gives people certain purposes and behaviors while organizational citizenship behaviors are a type of non-self-seeking, selfless dedication behaviors. Therefore, the motivation of physiotherapists who demonstrate organizational citizenship behaviors should be further explored.

PMID:34019557 | DOI:10.1371/journal.pone.0251843

PLoS One. 2021 May 21;16(5):e0250824. doi: 10.1371/journal.pone.0250824. eCollection 2021.

ABSTRACT

The aim of our study was to quantify sex-specific patterns of smoking prevalence and initiation in 10-year birth cohorts from 1910 to 1989 in Australia. We combined individual data of 385,810 participants from 33 cross-sectional surveys conducted between 1962 and 2018. We found that age-specific smoking prevalence varied considerably between men and women within birth cohorts born before 1960. The largest difference was observed in the earliest cohort (1910-1919), with up to 37.7% point greater proportion of current smokers in men than in women. In subsequent cohorts, the proportion decreased among men, but increased among women, until there was no more than 7.4% point difference in the 1960-69 birth cohort. In the 1970-79 and 1980-89 cohorts, smoking among men marginally increased, but the proportion was at most ~11.0% points higher than women. Our analysis of initiation indicated that many women born before the 1930s who smoked commenced smoking after age 25 years (e.g., ~27% born in 1910-19); compared to at most 8% of men in any birth cohort. The earliest birth cohort (1910-1919) had the greatest difference in age at initiation between sexes; 26.6 years in women versus 19.0 in men. In later cohorts, male and female smokers initiated increasingly earlier, converging in the 1960-69 cohort (17.6 and 17.8 years, respectively). While 22.9% of men and 8.4% of women initiated smoking aged < = 15 in the 1910-1919 cohort, in the latest cohort (1980-89) the reverse was true (21.4% and 28.8% for men and women, respectively). Marked differences in smoking prevalence and age at initiation existed between birth cohorts of Australian men and women born before 1960; after this, sex-specific trends in prevalence and initiation were similar. Understanding these patterns may inform the evaluation of tobacco control policies and the targeting of potential interventions for exposed populations such as lung cancer screening.

PMID:34019558 | DOI:10.1371/journal.pone.0250824

PLoS One. 2021 May 21;16(5):e0251849. doi: 10.1371/journal.pone.0251849. eCollection 2021.

ABSTRACT

We investigated the feasibility aspects of two choice reaction time tasks designed to assess implicit sequence learning and dual task ability in individuals with mild to moderate Parkinson’s disease in comparison to healthy individuals. Twelve individuals with mild to moderate Parkinson’s disease and 12 healthy individuals, all ≥ 60 years of age, were included. A serial reaction time task was used as a measure of implicit sequence learning and a similar task but with the addition of a simple counting task, was used as a measure of dual task ability. We have present thorough descriptive statistics of the data but we have refrained from any inferential statistics due to the small sample size. All participants understood the task instructions and the difficulty level of both tasks was deemed acceptable. There were indications of task fatigue that demand careful choices for how best to analyse the data from such tasks in future trials. Ceiling effects were present in several accuracy outcomes, but not in the reaction time outcomes. Overall, we found both tasks to be feasible to use in samples of individuals with mild to moderate Parkinson’s disease and healthy older individuals.

PMID:34019565 | DOI:10.1371/journal.pone.0251849

PLoS One. 2021 May 21;16(5):e0248156. doi: 10.1371/journal.pone.0248156. eCollection 2021.

ABSTRACT

BACKGROUND: Malnutrition among under-five children is a common public health problem and it is one of the main cause for the mortality of under-five children in developing countries, including Ethiopia. Therefore, lack of evidence about geographic heterogeneity and predictors of under-nutrition hinders for evidence-based decision-making process for the prevention and control programs of under-nutrition in Ethiopia. Thus, this study aimed to address this gap.

METHODS: The data were obtained from the Ethiopian Demographic and Health Survey (EDHS) 2016. A total of 9,384 under-five children nested in 645 clusters were included with a stratified two-stage cluster sampling. ArcGIS version 10.5 software was used for global, local and ordinary least square analysis and mapping. The spatial autocorrelation (Global Moran’s I) statistic was held in order to assess the pattern of wasting, stunting, and underweight whether it was dispersed, clustered, or randomly distributed. In addition, a Bernoulli model was used to analyze the purely spatial cluster detection of under-nutrition indicators through SaTScan version 9.6 software. Geographically weighted regression (GWR) version 4.0 software was used to model spatial relationships in the GWR analysis. Finally, a statistical decision was made at p-value<0.05 with 95%CI for ordinary least square analysis and geographically weighted regression.

MAIN FINDINGS: Childhood under-nutrition showed geographical variations at zonal levels in Ethiopia. Accordingly, Somali region (Afder, Gode, Korahe, Warder Zones), Afar region (Zone 2), Tigray region (Southern Zone), and Amhara region (Waghmira Zones) for wasting, Amhara region (West Gojam, Awi, South Gondar, and Waghmira Zones) for stunting and Amhara region (South Wollo, North Wollo, Awi, South Gondar, and Waghmira zones), Afar region (Zone 2), Tigray region (Eastern Zone, North Western Zone, Central Zone, Southern Zone, and Mekele Special Zones), and Benshangul region (Metekel and Assosa Zones) for underweight were detected as hot spot (high risk) regions. In GWR analysis, had unimproved toilet facility for stunting, wasting and underweight, father had primary education for stunting and wasting, father had secondary education for stunting and underweight, mothers age 35-49 years for wasting and underweight, having female children for stunting, having children eight and above for wasting, and mother had primary education for underweight were significant predictors at (p<0.001).

CONCLUSIONS: Our study showed that the spatial distribution of under-nutrition was clustered and high-risk areas were identified in all forms of under-nutrition indicators. Predictors of under-nutrition were identified in all forms of under-nutrition indicators. Thus, geographic-based nutritional interventions mainly mobilizing additional resources could be held to reduce the burden of childhood under-nutrition in hot spot areas. In addition, improving sanitation and hygiene practice, improving the life style of the community, and promotion of parent education in the identified hot spot zones for under-nutrition should be more emphasized.

PMID:34019545 | DOI:10.1371/journal.pone.0248156

Sci Rep. 2021 May 20;11(1):10650. doi: 10.1038/s41598-021-89763-7.

ABSTRACT

3D free-hand ultrasound (3DFUS) is becoming increasingly popular to assist clinical gait analysis because it is cost- and time-efficient and does not expose participants to radiation. The aim of this study was to evaluate its reliability in localizing the anterior superior iliac spine (ASIS) at the pelvis and the hip joint centers (HJC). Additionally, we evaluated its accuracy to get a rough estimation of the potential to use of 3DFUS to segment bony surface. This could offer potential to register medical images to motion capture data in future. To evaluate reliability, a test-retest study was conducted in 16 lean and 19 obese individuals. The locations of the ASIS were determined by manual marker placement (MMP), an instrumented pointer technique (IPT), and with 3DFUS. The HJC location was also determined with 3DFUS. To quantify reliability, intraclass correlation coefficients (ICCs), the standard error of measurement (SEm), among other statistical parameters, were calculated for the identified locations between the test and retest. To assess accuracy, the surface of a human plastic pelvic phantom was segmented with 3DFUS in a distilled water bath in 27 trials and compared to a 3D laser scan of the pelvis. Regarding reliability, the MMP, but especially the IPT showed high reliability in lean (SEm: 2-3 mm) and reduced reliability in obese individuals (SEm: 6-15 mm). Compared to MMP and IPT, 3DFUS presented lower reliability in the lean group (SEm: 2-4 mm vs. 2-8 mm, respectively) but slightly better values in the obese group (SEm: 7-11 mm vs. 6-16 mm, respectively). Correlations between test-retest reliability and torso body fat mass (% of body mass) indicated a moderate to strong relationship for MMP and IPT but only a weak correlation for the 3DFUS approach. The water-bath experiments indicated an acceptable level of 3.5 (1.7) mm of accuracy for 3DFUS in segmenting bone surface. Despite some difficulties with single trials, our data give further rise to the idea that 3DFUS could serve as a promising tool in future to inform marker placement and hip joint center location, especially in groups with higher amount of body fat.

PMID:34017023 | DOI:10.1038/s41598-021-89763-7

Sci Rep. 2021 May 20;11(1):10641. doi: 10.1038/s41598-021-90189-4.

ABSTRACT

COVID 19 is known to cause immune dysregulation and vitamin D is a known immunomodulator. This study aims to objectively investigate the impact of Pulse D therapy in reducing the inflammatory markers of COVID-19. Consented COVID-19 patients with hypovitaminosis D were evaluated for inflammatory markers (N/L ratio, CRP, LDH, IL6, Ferritin) along with vitamin D on 0th day and 9th/11th day as per their respective BMI category. Subjects were randomised into VD and NVD groups. VD group received Pulse D therapy (targeted daily supplementation of 60,000 IUs of vitamin D for 8 or 10 days depending upon their BMI) in addition to the standard treatment. NVD group received standard treatment alone. Differences in the variables between the two groups were analysed for statistical significance. Eighty seven out of one hundred and thirty subjects have completed the study (VD:44, NVD:43). Vitamin D level has increased from 16 ± 6 ng/ml to 89 ± 32 ng/ml after Pulse D therapy in VD group and highly significant (p < 0.01) reduction of all the measured inflammatory markers was noted. Reduction of markers in NVD group was insignificant (p > 0.05). The difference in the reduction of markers between the groups (NVD vs VD) was highly significant (p < 0.01). Therapeutic improvement in vitamin D to 80-100 ng/ml has significantly reduced the inflammatory markers associated with COVID-19 without any side effects. Hence, adjunctive Pulse D therapy can be added safely to the existing treatment protocols of COVID-19 for improved outcomes.

PMID:34017029 | DOI:10.1038/s41598-021-90189-4

Sci Rep. 2021 May 20;11(1):10643. doi: 10.1038/s41598-021-89588-4.

ABSTRACT

Wideband Absorbance Immittance (WAI) has been available for more than a decade, however its clinical use still faces the challenges of limited understanding and poor interpretation of WAI results. This study aimed to develop Machine Learning (ML) tools to identify the WAI absorbance characteristics across different frequency-pressure regions in the normal middle ear and ears with otitis media with effusion (OME) to enable diagnosis of middle ear conditions automatically. Data analysis included pre-processing of the WAI data, statistical analysis and classification model development, and key regions extraction from the 2D frequency-pressure WAI images. The experimental results show that ML tools appear to hold great potential for the automated diagnosis of middle ear diseases from WAI data. The identified key regions in the WAI provide guidance to practitioners to better understand and interpret WAI data and offer the prospect of quick and accurate diagnostic decisions.

PMID:34017019 | DOI:10.1038/s41598-021-89588-4

Nat Commun. 2021 May 20;12(1):2985. doi: 10.1038/s41467-021-22616-z.

ABSTRACT

Simulating nature and in particular processes in particle physics require expensive computations and sometimes would take much longer than scientists can afford. Here, we explore ways to a solution for this problem by investigating recent advances in generative modeling and present a study for the generation of events from a physical process with deep generative models. The simulation of physical processes requires not only the production of physical events, but to also ensure that these events occur with the correct frequencies. We investigate the feasibility of learning the event generation and the frequency of occurrence with several generative machine learning models to produce events like Monte Carlo generators. We study three processes: a simple two-body decay, the processes e⁺e^– → Z → l⁺l^– and [Formula: see text] including the decay of the top quarks and a simulation of the detector response. By buffering density information of encoded Monte Carlo events given the encoder of a Variational Autoencoder we are able to construct a prior for the sampling of new events from the decoder that yields distributions that are in very good agreement with real Monte Carlo events and are generated several orders of magnitude faster. Applications of this work include generic density estimation and sampling, targeted event generation via a principal component analysis of encoded ground truth data, anomaly detection and more efficient importance sampling, e.g., for the phase space integration of matrix elements in quantum field theories.

PMID:34016982 | DOI:10.1038/s41467-021-22616-z

Nat Commun. 2021 May 20;12(1):2991. doi: 10.1038/s41467-021-23234-5.

ABSTRACT

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.

PMID:34016992 | DOI:10.1038/s41467-021-23234-5