Category: Statistics

Commun Biol. 2022 Apr 1;5(1):295. doi: 10.1038/s42003-022-03196-0.

ABSTRACT

The brain’s functional connectivity fluctuates over time instead of remaining steady in a stationary mode even during the resting state. This fluctuation establishes the dynamical functional connectivity that transitions in a non-random order between multiple modes. Yet it remains unexplored how the transition facilitates the entire brain network as a dynamical system and what utility this mechanism for dynamic reconfiguration can bring over the widely used graph theoretical measurements. To address these questions, we propose to conduct an energetic analysis of functional brain networks using resting-state fMRI and behavioral measurements from the Human Connectome Project. Through comparing the state transition energy under distinct adjacent matrices, we justify that dynamic functional connectivity leads to 60% less energy cost to support the resting state dynamics than static connectivity when driving the transition through default mode network. Moreover, we demonstrate that combining graph theoretical measurements and our energy-based control measurements as the feature vector can provide complementary prediction power for the behavioral scores (Combination vs. Control: t = 9.41, p = 1.64e-13; Combination vs. Graph: t = 4.92, p = 3.81e-6). Our approach integrates statistical inference and dynamical system inspection towards understanding brain networks.

PMID:35365757 | DOI:10.1038/s42003-022-03196-0

Sci Rep. 2022 Apr 1;12(1):5501. doi: 10.1038/s41598-022-09511-3.

ABSTRACT

Epidemiology of stunting in < 5 years old is well characterized; however, its prevalence in adolescence is inconsistent in different geographical locations. We estimated the prevalence of stunting in schoolgirls of Punjab, Pakistan, to standardize local references according to international and national references. In this population-wide cross-sectional study, 10,050 schoolgirls aged 8-16 years from 12 different districts of northern, central, and southern Punjab were analyzed. The prevalence of stunting was calculated by applying Centres for Disease Control and Prevention (CDC) and World Health Organisation (WHO) height-for-age references and the local reference for the study population. We used Cohen’s kappa statistics to analyze the agreement of our data with reference values, and chi-square test was used as the test of trend. Marked overestimation of the prevalence of stunting was observed (22.72% and 17.49% according to CDC and WHO, respectively) in comparison to local reference (4.94%). According to CDC and WHO references, there was an increasing trend of prevalence of stunting with higher age; however, data was comparable across all the age groups when local references were applied. We recommend that the prevalence of stunting in school-age girls should be determined by applying local height references rather than international ones to plan health strategies and treatments in the local population.

PMID:35365745 | DOI:10.1038/s41598-022-09511-3

Sci Rep. 2022 Apr 1;12(1):5534. doi: 10.1038/s41598-022-09564-4.

ABSTRACT

The 2018-2020 Ebola virus disease epidemic in Democratic Republic of the Congo (DRC) resulted in 3481 cases (probable and confirmed) and 2299 deaths. In this paper, we use a novel statistical method to analyze the individual-level incidence and hospitalization data on DRC Ebola victims. Our analysis suggests that an increase in the rate of quarantine and isolation that has shortened the infectiousness period by approximately one day during the epidemic’s third and final wave was likely responsible for the eventual containment of the outbreak. The analysis further reveals that the total effective population size or the average number of individuals at risk for the disease exposure in three epidemic waves over the period of 24 months was around 16,000-a much smaller number than previously estimated and likely an evidence of at least partial protection of the population at risk through ring vaccination and contact tracing as well as adherence to strict quarantine and isolation policies.

PMID:35365724 | DOI:10.1038/s41598-022-09564-4

Sci Rep. 2022 Apr 1;12(1):5511. doi: 10.1038/s41598-022-09529-7.

ABSTRACT

Glutamine provides carbon and nitrogen for macromolecular synthesis and participates in adenosine triphosphate (ATP) generation, anabolic metabolism, redox homeostasis, cell signaling, and cancer stem cell (CSC) metabolism. New treatment strategies targeting glutamine metabolism in cancer have emerged recently. We previously reported the magnetic resonance imaging (MRI) assessment of glutamine uptake by tumors and activated glutamine metabolism in CSC. In the present study, using MRI, we determined the correlation between glutamine uptake and the distribution of glutamine transporters, namely ASCT2 and SLC38A2 (SNAT2), glutaminase (GLS), and CSC markers, such as CD44 and CD166, in a mouse xenograft model of HT29 human colorectal cancer cells. MRI data revealed an obvious change in intensity following glutamine administration. Immunohistochemistry (IHC) results indicated that ASCT2 staining was stronger in regions that exhibited high glutamine uptake (p = 0.0079). Significant differences were found in the IHC staining intensities of SNAT2, GLS, and CSC markers in the areas of high and low glutamine uptake (p = 0.0079, p = 0.0159 and p = 0.0079, respectively). We also investigated the effect of an ASCT2 inhibitor on the uptake of glutamine using MRI. A statistically significant difference in the initial glutamine uptake was found after ASCT2 inhibitor administration. To conclude, glutamine uptake is positively correlated with the distribution of ASCT2 and certain CSC markers.

PMID:35365739 | DOI:10.1038/s41598-022-09529-7

Sci Rep. 2022 Apr 1;12(1):5554. doi: 10.1038/s41598-022-09567-1.

ABSTRACT

Caesarean delivery (C-section) has been increasing worldwide; however, many women from developing countries in Sub-Saharan Africa are deprived of these lifesaving services. This study aimed to explore the impact of certain socioeconomic factors, including respondent’s education, husband’s education, place of residence, and wealth index, on C-section delivery for women in Sub-Saharan Africa. We used pooled data from 36 demographic and health surveys (DHS) in Sub-Saharan Africa. Married women aged 15-49 years who have at least one child in the last five years were considered in this survey. After inclusion and excluding criteria, 234,660 participants were eligible for final analysis. Binary logistic regression was executed to determine the effects of selected socioeconomic factors. The countries were assembled into four sub-regions (Southern Africa, West Africa, East Africa, and Central Africa), and a meta-analysis was conducted. We performed random-effects model estimation for meta-analysis to assess the overall effects and consistency between covariates and utilization of C-section delivery as substantial heterogeneity was identified (I² > 50%). Furthermore, the meta-regression was carried out to explain the additional amount of heterogeneity by country levels. We performed a sensitivity analysis to examine the effects of outliers in this study. Findings suggest that less than 15% of women in many Sub-Saharan African countries had C-section delivery. Maternal education (OR 4.12; CI 3.75, 4.51), wealth index (OR 2.05; CI 1.94, 2.17), paternal education (OR 1.71; CI 1.57, 1.86), and place of residence (OR 1.51; CI 1.44, 1.58) were significantly associated with the utilization of C-section delivery. These results were also consistent in sub-regional meta-analyses. The meta-regression suggests that the total percentage of births attended by skilled health staff (TPBASHS) has a significant inverse association with C-section utilization regarding educational attainment (respondent & husband), place of residence, and wealth index. The data structure was restricted to define the distinction between elective and emergency c-sections. It is essential to provide an appropriate lifesaving mechanism, such as C-section delivery opportunities, through proper facilities for rural, uneducated, impoverished Sub-Saharan African women to minimize both maternal and infant mortality.

PMID:35365718 | DOI:10.1038/s41598-022-09567-1

Sci Rep. 2022 Apr 1;12(1):5547. doi: 10.1038/s41598-022-09290-x.

ABSTRACT

The mechanisms underlying liver disease in patients with COVID-19 are not entirely known. The aim is to investigate, by means of novel statistical techniques, the changes over time in the relationship between inflammation markers and liver damage markers in relation to survival in COVID-19. The study included 221 consecutive patients admitted to the hospital during the first COVID-19 wave in Spain. Generalized additive mixed models were used to investigate the influence of time and inflammation markers on liver damage markers in relation to survival. Joint modeling regression was used to evaluate the temporal correlations between inflammation markers (serum C-reactive protein [CRP], interleukin-6, plasma D-dimer, and blood lymphocyte count) and liver damage markers, after adjusting for age, sex, and therapy. The patients who died showed a significant elevation in serum aspartate transaminase (AST) and alkaline phosphatase levels over time. Conversely, a decrease in serum AST levels was observed in the survivors, who showed a negative correlation between inflammation markers and liver damage markers (CRP with serum AST, alanine transaminase [ALT], and gamma-glutamyl transferase [GGT]; and D-dimer with AST and ALT) after a week of hospitalization. Conversely, most correlations were positive in the patients who died, except lymphocyte count, which was negatively correlated with AST, GGT, and alkaline phosphatase. These correlations were attenuated with age. The patients who died during COVID-19 infection displayed a significant elevation of liver damage markers, which is correlated with inflammation markers over time. These results are consistent with the role of systemic inflammation in liver damage during COVID-19.

PMID:35365705 | DOI:10.1038/s41598-022-09290-x

Sci Rep. 2022 Apr 1;12(1):5516. doi: 10.1038/s41598-022-09396-2.

ABSTRACT

Depression and cardiovascular disease (CVD) are main contributors to the global disease burden and are linked. Pathophysiological pathways through increased blood pressure (BP) are a common focus in studies aiming to explain the relationship. However, studies to date have not differentiated between the predictive effect of depression on the course of BP versus hypertension diagnosis. Hence, we aimed to elucidate this relationship by incorporating these novel aspects in the context of a cohort study. We included initially normotensive participants (n = 3214) from the second (2001-2003), third (2009-2011), and fourth (2016-2018) waves of the Swiss Cohort Study on Air Pollution and Lung and Heart Diseases in Adults (SAPALDIA). We defined depression based on physician diagnosis, depression treatment and/or SF-36 Mental Health score < 50. The prospective association between depression and BP change was quantified using multivariable censored regression models, and logistic regression for the association between depression and incident hypertension diagnosis. All models used clustered robust standard errors to account for repeat measurements. The age-related increase in systolic BP was slightly lower among people with depression at baseline (β = – 2.08 mmHg/10 years, 95% CI – 4.09 to – 0.07) compared to non-depressed. A similar trend was observed with diastolic BP (β = – 0.88 mmHg/10 years, 95% CI – 2.15 to 0.39), albeit weaker and not statistically significant. Depression predicted the incidence of hypertension diagnosis (OR 1.86, 95% CI 1.33 to 2.60). Our findings do not support the hypothesis that depression leads to CVD by increasing BP. Future research on the role of depression in the pathway to hypertension and CVD is warranted in larger cohorts, taking into account healthcare utilization as well as medication for depression and hypertension.

PMID:35365701 | DOI:10.1038/s41598-022-09396-2

Sci Rep. 2022 Apr 1;12(1):5508. doi: 10.1038/s41598-022-09421-4.

ABSTRACT

Mood disorders, including generalized anxiety disorder, are associated with disruptions in circadian rhythms and are linked to polymorphisms in circadian clock genes. Molecular mechanisms underlying these connections may be direct-via transcriptional activity of clock genes on downstream mood pathways in the brain, or indirect-via clock gene influences on the phase and amplitude of circadian rhythms which, in turn, modulate physiological processes influencing mood. Employing machine learning combined with statistical approaches, we explored clock genotype combinations that predict risk for anxiety symptoms in a deeply phenotyped population. We identified multiple novel circadian genotypes predictive of anxiety, with the PER3(rs17031614)-AG/CRY1(rs2287161)-CG genotype being the strongest predictor of anxiety risk, particularly in males. Molecular chronotyping, using clock gene expression oscillations, revealed that advanced circadian phase and robust circadian amplitudes are associated with high levels of anxiety symptoms. Further analyses revealed that individuals with advanced phases and pronounced circadian misalignment were at higher risk for severe anxiety symptoms. Our results support both direct and indirect influences of clock gene variants on mood: while sex-specific clock genotype combinations predictive of anxiety symptoms suggest direct effects on mood pathways, the mediation of PER3 effects on anxiety via diurnal preference measures and the association of circadian phase with anxiety symptoms provide evidence for indirect effects of the molecular clockwork on mood. Unraveling the complex molecular mechanisms underlying the links between circadian physiology and mood is essential to identifying the core clock genes to target in future functional studies, thereby advancing the development of non-invasive treatments for anxiety-related disorders.

PMID:35365695 | DOI:10.1038/s41598-022-09421-4

Sci Data. 2022 Apr 1;9(1):145. doi: 10.1038/s41597-022-01175-y.

ABSTRACT

During the second half of 2020, many European governments responded to the resurging transmission of SARS-CoV-2 with wide-ranging non-pharmaceutical interventions (NPIs). These efforts were often highly targeted at the regional level and included fine-grained NPIs. This paper describes a new dataset designed for the accurate recording of NPIs in Europe’s second wave to allow precise modelling of NPI effectiveness. The dataset includes interventions from 114 regions in 7 European countries during the period from the 1st August 2020 to the 9th January 2021. The paper includes NPI definitions tailored to the second wave following an exploratory data collection. Each entry has been extensively validated by semi-independent double entry, comparison with existing datasets, and, when necessary, discussion with local epidemiologists. The dataset has considerable potential for use in disentangling the effectiveness of NPIs and comparing the impact of interventions across different phases of the pandemic.

PMID:35365668 | DOI:10.1038/s41597-022-01175-y

Sci Data. 2022 Apr 1;9(1):148. doi: 10.1038/s41597-022-01261-1.

ABSTRACT

Antibiotic resistance is a major threat to public health. The development of chemo-informatic tools to guide medicinal chemistry campaigns in the efficint design of antibacterial libraries is urgently needed. We present AB-DB, an open database of all-atom force-field parameters, molecular dynamics trajectories, quantum-mechanical properties, and curated physico-chemical descriptors of antimicrobial compounds. We considered more than 300 molecules belonging to 25 families that include the most relevant antibiotic classes in clinical use, such as β-lactams and (fluoro)quinolones, as well as inhibitors of key bacterial proteins. We provide traditional descriptors together with properties obtained with Density Functional Theory calculations. Noteworthy, AB-DB contains less conventional descriptors extracted from μs-long molecular dynamics simulations in explicit solvent. In addition, for each compound we make available force-field parameters for the major micro-species at physiological pH. With the rise of multi-drug-resistant pathogens and the consequent need for novel antibiotics, inhibitors, and drug re-purposing strategies, curated databases containing reliable and not straightforward properties facilitate the integration of data mining and statistics into the discovery of new antimicrobials.

PMID:35365662 | DOI:10.1038/s41597-022-01261-1