The novel coronavirus (COVID-19) is an emergent disease that initially had no historical data to guide scientists on predicting/ forecasting its global or national impact over time. The ability to predict the progress of this pandemic has been crucial for decision making aimed at fighting this pandemic and controlling its spread. In this work we considered four different statistical/time series models that are readily available from the 'forecast’ package in R. We performed novel applications with these models, forecasting the number of infected cases (confirmed cases and similarly the number of deaths and recovery) along with the corresponding 90% prediction interval to estimate uncertainty around pointwise forecasts. Since the future may not repeat the past for this pandemic, no prediction model is certain. However, any prediction tool with acceptable prediction performance (or prediction error) could still be very useful for public-health planning to handle spread of the pandemic, and could policy decision-making and facilitate transition to normality. These four models were applied to publicly available data of the COVID-19 pandemic for both the USA and Italy. We observed that all models reasonably predicted the future numbers of confirmed cases, deaths, and recoveries of COVID-19. However, for the majority of the analyses, the time series model with autoregressive integrated moving average (ARIMA) and cubic smoothing spline models both had smaller prediction errors and narrower prediction intervals, compared to the Holt and Trigonometric Exponential smoothing state space model with Box-Cox transformation (TBATS) models. Therefore, the former two models were preferable to the latter models. Given similarities in performance of the models in the USA and Italy, the corresponding prediction tools can be applied to other countries grappling with the COVID-19 pandemic, and to any pandemics that can occur in future.Finding medications or vaccines that may decrease the infectious period of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) could potentially reduce transmission in the broader population. We developed a computational model of the U.S. simulating the spread of SARS-CoV-2 and the potential clinical and economic impact of reducing the infectious period duration. Simulation experiments found that reducing the average infectious period duration could avert a median of 442,852 [treating 25% of symptomatic cases, reducing by 0.5 days, reproductive number (R0) 3.5, and starting treatment when 15% of the population has been exposed] to 44.4 million SARS-CoV-2 cases (treating 75% of all infected cases, reducing by 3.5 days, R0 2.0). With R0 2.5, reducing the average infectious period duration by 0.5 days for 25% of symptomatic cases averted 1.4 million cases and 99,398 hospitalizations; increasing to 75% of symptomatic cases averted 2.8 million cases. At $500/person, treating 25% of symptomatic cases saved $209.5 billion (societal perspective). Further reducing the average infectious period duration by 3.5 days averted 7.4 million cases (treating 25% of symptomatic cases). Expanding treatment to 75% of all infected cases, including asymptomatic infections (R0 2.5), averted 35.9 million cases and 4 million hospitalizations, saving $48.8 billion (societal perspective and starting treatment after 5% of the population has been exposed). Our study quantifies the potential effects of reducing the SARS-CoV-2 infectious period duration.With the rapid development of big data and artificial intelligence technology, computer-aided pulmonary nodule detection based on deep learning has achieved some successes. However, the sizes of pulmonary nodules vary greatly, and the pulmonary nodules have visual similarity with structures such as blood vessels and shadows around pulmonary nodules, which make the quick and accurate detection of pulmonary nodules in CT image still a challenging task. In this paper, we propose two kinds of 3D multi-scale deep convolution neural networks for nodule candidate detection and false positive reduction respectively. Among them, the nodule candidate detection network consists of two parts 1) the backbone network part Res2SENet, which is used to extract multi-scale feature information of pulmonary nodules, it is composed of the multi-scale Res2Net modules of multiple available receptive fields at a granular level and the squeeze-and-excitation units; 2) the detection part, which uses a region proposal network structure to determine region candidates, and introduces context enhancement module and spatial attention module to improve detection performance. The false positive reduction network, also composed of the multi-scale Res2Net modules and the squeeze-and-excitation units, can further classify the nodule candidates generated by the nodule candidate detection network and screen out the ground truth positive nodules. Finally, the prediction probability generated by the nodule candidate detection network is weighted average with the prediction probability generated by the false positive reduction network to obtain the final results. The experimental results on the publicly available LUNA16 dataset showed that the proposed method has a superior ability to detect pulmonary nodules in CT images.

A multicenter study was organized to explore sources of variation (SVs) of reference values (RVs) for 22 major immunochemistry analytes and to determine reference intervals (RIs) for the Russian population.

According to IFCC Committee on Reference Intervals and Decision Limits (C-RIDL) protocol, 758 healthy volunteers were recruited in St. Petersburg, Moscow, and Yekaterinburg. Serum samples were tested for five tumor markers, 17 hormones and related tests by Beckman Coulter’s UniCel DxI 800 immunochemistry analyzer. SVs were explored using multiple regression analysis and ANOVA. Standard deviation ratio (SDR) of 0.4 was used as primary guide for partitioning RIs by gender and age.

SDR for between-city difference was <0.4 for all analytes. Secondary exclusion of individuals was done under the following conditions for female sex-hormones, those with contraceptives (8%); for CA19-9, those supposed to have negative Lewis blood-group (10.5% males and 11.3% females); for insulin, those with BMI≥28 kg/m2 (tion were derived based on the up-to-date internationally harmonized protocol by careful consideration of analyte-specific SVs.West Nile virus is a widely spread arthropod-born virus, which has mosquitoes as vectors and birds as reservoirs. Humans, as dead-end hosts of the virus, may suffer West Nile Fever (WNF), which sometimes leads to death. In Europe, the first large-scale epidemic of WNF occurred in 1996 in Romania. Since then, human cases have increased in the continent, where the highest number of cases occurred in 2018. Using the location of WNF cases in 2017 and favorability models, we developed two risk models, one environmental and the other spatio-environmental, and tested their capacity to predict in 2018 1) the location of WNF; 2) the intensity of the outbreaks (i.e. the number of confirmed human cases); and 3) the imminence of the cases (i.e. the Julian week in which the first case occurred). We found that climatic variables (the maximum temperature of the warmest month and the annual temperature range), human-related variables (rain-fed agriculture, the density of poultry and horses), and topo-hydrographic variables (the presence of rivers and altitude) were the best environmental predictors of WNF outbreaks in Europe. The spatio-environmental model was the most useful in predicting the location of WNF outbreaks, which suggests that a spatial structure, probably related to bird migration routes, has a role in the geographical pattern of WNF in Europe. Both the intensity of cases and their imminence were best predicted using the environmental model, suggesting that these features of the disease are linked to the environmental characteristics of the areas. We highlight the relevance of river basins in the propagation dynamics of the disease, as outbreaks started in the lower parts of the river basins, from where WNF spread towards the upper parts. Therefore, river basins should be considered as operational geographic units for the public health management of the disease.[This corrects the article DOI 10.1371/journal.pone.0234214.].Policymakers need clear, fast assessment of the real spread of the COVID-19 epidemic in each of their respective countries. Standard measures of the situation provided by the governments include reported positive cases and total deaths. While total deaths indicate immediately that countries like Italy and Spain had the worst situation as of mid-April, 2020, reported cases alone do not provide a complete picture of the situation. Different countries diagnose differently and present very distinctive reported case fatality ratios. Similar levels of reported incidence and mortality might hide a very different underlying pictures. Here we present a straightforward and robust estimation of the diagnostic rate in each European country. From that estimation we obtain a uniform, unbiased incidence of the epidemic. The method to obtain the diagnostic rate is transparent and empirical. The key assumption of the method is that the infection fatality ratio of COVID-19 in Europe is not strongly country-dependent. We show that this number is not expected to be biased due to demography nor to the way total deaths are reported. The estimation protocol is dynamic, and it has been yielding converging numbers for diagnostic rates in all European countries as from mid-April, 2020. Using this diagnostic rate, policy makers can obtain Effective Potential Growth updated every day, providing an unbiased assessment of the countries at greater risk of experiencing an uncontrolled situation. The method developed has been and will be used to track possible improvements in the diagnostic rate in European countries as the epidemic evolves.Antibiotic resistance is a rapidly increasing medical problem that severely limits the success of antibiotic treatments, and the identification of resistance determinants is key for surveillance and control of resistance dissemination. Horizontal transfer is the dominant mechanism for spread of resistance genes between bacteria but little is known about the original emergence of resistance genes. Here, we examined experimentally if random sequences can generate novel antibiotic resistance determinants de novo. By utilizing highly diverse expression libraries encoding random sequences to select for open reading frames that confer resistance to the last-resort antibiotic colistin in Escherichia coli, six de novo colistin resistance conferring peptides (Dcr) were identified. The peptides act via direct interactions with the sensor kinase PmrB (also termed BasS in E. coli), causing an activation of the PmrAB two-component system (TCS), modification of the lipid A domain of lipopolysaccharide and subsequent colistin resistance. This kinase-activation was extended to other TCS by generation of chimeric sensor kinases. Our results demonstrate that peptides with novel activities mediated via specific peptide-protein interactions in the transmembrane domain of a sensory transducer can be selected de novo, suggesting that the origination of such peptides from non-coding regions is conceivable. In addition, we identified a novel class of resistance determinants for a key antibiotic that is used as a last resort treatment for several significant pathogens. The high-level resistance provided at low expression levels, absence of significant growth defects and the functionality of Dcr peptides across different genera suggest that this class of peptides could potentially evolve as bona fide resistance determinants in natura.Coronavirus Disease 2019 (COVID-19), a disease caused by the betacoronavirus Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), has only recently emerged, while Mycobacterium leprae, the etiological agent of leprosy, has endured for more than 2,000 years. As soon as the initial reports of COVID-19 became public, several entities, including the Brazilian Leprosy Society, warned about the possible impact of COVID-19 on leprosy patients. It has been verified that COVID-19 carriers can be either asymptomatic or present varying degrees of severe respiratory failure in association with cytokine storm and death, among other diseases. Severe COVID-19 patients show increased numbers of neutrophils and serum neutrophil extracellular trap (NET) markers, in addition to alterations in the neutrophil-to-lymphocyte ratio (NLR). The absence of antiviral drugs and the speed of COVID-19 transmission have had a major impact on public health systems worldwide, leading to the almost total collapse of many national and local healthcare services. Leprosy, an infectious neurological and dermatological illness, is widely considered to be the most frequent cause of physical disabilities globally. The chronic clinical course of the disease may be interrupted by acute inflammatory episodes, named leprosy reactions. These serious immunological complications, characterized by cytokine storms, are responsible for amplifying peripheral nerve damage. From 30% to 40% of all multibacillary leprosy (MB) patients experience erythema nodosum leprosum (ENL), a neutrophilic immune-mediated condition. ENL patients often present these same COVID-19-like symptoms, including high levels of serum NET markers, altered NLR, and neutrophilia. Moreover, the consequences of a M. leprae-SARS-CoV-2 coinfection have yet to be fully investigated. The goal of the present viewpoint is to describe some of the similarities that may be found between COVID-19 and leprosy disease in the context of neutrophilic biology.State-of-the-art rare variant association testing methods aggregate the contribution of rare variants in biologically relevant genomic regions to boost statistical power. However, testing single genes separately does not consider the complex interaction landscape of genes, nor the downstream effects of non-synonymous variants on protein structure and function. Here we present the NETwork Propagation-based Assessment of Genetic Events (NETPAGE), an integrative approach aimed at investigating the biological pathways through which rare variation results in complex disease phenotypes. We applied NETPAGE to sporadic, late-onset Alzheimer’s disease (AD), using whole-genome sequencing from the AD Neuroimaging Initiative (ADNI) cohort, as well as whole-exome sequencing from the AD Sequencing Project (ADSP). NETPAGE is based on network propagation, a framework that models information flow on a graph and simulates the percolation of genetic variation through tissue-specific gene interaction networks. The result of network propagation is a set of smoothed gene scores that can be tested for association with disease status through sparse regression. The application of NETPAGE to AD enabled the identification of a set of connected genes whose smoothed variation profile was robustly associated to case-control status, based on gene interactions in the hippocampus. Additionally, smoothed scores significantly correlated with risk of conversion to AD in Mild Cognitive Impairment (MCI) subjects. Lastly, we investigated tissue-specific transcriptional dysregulation of the core genes in two independent RNA-seq datasets, as well as significant enrichments in terms of gene sets with known connections to AD. We present a framework that enables enhanced genetic association testing for a wide range of traits, diseases, and sample sizes.The Strength Use and Deficit Correction (SUDCO) Questionnaire has been shown to be a reliable instrument for the measurement of its four dimensions perceived organizational support for strengths use, perceived organizational support for deficit correction, strengths use behavior, and deficit correction behavior in the context of organizations. This paper aims to adapt and validate the SUDCO for the German-speaking population (SUDCO-G). Three studies were conducted. Confirmatory factor analyses and correlations with other psychological constructs on the data of three German samples (N1 = 302; N2 = 243, N3 = 295) were performed. The twenty-four item SUDCO-G exhibits the anticipated factorial structure with four factors and an acceptable model fit in all three studies (CFI = .920-.937, TLI = .911-.929, RMSEA = .063-.079, SRMR = 0.52-.075). The associations of the four dimensions to other constructs concur with previous findings (study 2) and the subscales of the SUDCO-G also show positive relations with general strengths use, meaning of work and Psychological Capital (study 3). We conclude that the SUDCO-G is a reliable and valid instrument for the use in the German-speaking population.

Studies assessing personality dimensions by the „Temperament and Character Inventory” (TCI) have previously found an association between Parkinson’s disease (PD) and lower Novelty Seeking and higher Harm Avoidance scores. Here, we aimed to describe personality dimensions of PD patients with motor fluctuations and compare them to a normative population and other PD populations.

All PD patients awaiting Deep Brain Stimulation (DBS) answered the TCI before neurosurgery. Their results were compared to those of historical cohorts (a French normative population, a de novo PD population, and a PD population with motor fluctuations).

Most personality dimensions of our 333 included PD patients with motor fluctuations who are candidates for DBS were different from those of the normative population and some were also different from those of the De Novo PD population, whereas they were similar to those of another population of PD patients with motor fluctuations.

During the course of PD, personality dimensions can change in parallel with the development of motor fluctuations, either due to the evolution of the disease and/or dopaminergic treatments.

During the course of PD, personality dimensions can change in parallel with the development of motor fluctuations, either due to the evolution of the disease and/or dopaminergic treatments.Lassa fever is an haemorrhagic fever caused by Lassa virus (LASV). There is no vaccine approved against LASV and the only recommended antiviral treatment relies on ribavirin, despite limited evidence of efficacy. Recently, the nucleotide analogue favipiravir showed a high antiviral efficacy, with 100% survival obtained in an otherwise fully lethal non-human primate (NHP) model of Lassa fever. However the mechanism of action of the drug is not known and the absence of pharmacokinetic data limits the translation of these results to the human setting. Here we aimed to better understand the antiviral effect of favipiravir by developping the first mathematical model recapitulating Lassa viral dynamics and treatment. We analyzed the viral dynamics in 24 NHPs left untreated or treated with ribavirin or favipiravir, and we put the results in perspective with those obtained with the same drugs in the context of Ebola infection. Our model estimates favipiravir EC50 in vivo to 2.89 μg.mL-1, which is much lower than what was found against Ebola virus. The main mechanism of action of favipiravir was to decrease virus infectivity, with an efficacy of 91% at the highest dose. Based on our knowledge acquired on the drug pharmacokinetics in humans, our model predicts that favipiravir doses larger than 1200 mg twice a day should have the capability to strongly reduce the production infectious virus and provide a milestone towards a future use in humans.Pomalidomide (Pom) is an immunomodulatory drug that has efficacy against Kaposi’s sarcoma, a tumor caused by Kaposi’s sarcoma-associated herpesvirus (KSHV). Pom also induces direct cytotoxicity in primary effusion lymphoma (PEL), a B-cell malignancy caused by KSHV, in part through downregulation of IRF4, cMyc, and CK1α as a result of its interaction with cereblon, a cellular E3 ubiquitin ligase. Additionally, Pom can reverse KSHV-induced downregulation of MHCI and co-stimulatory immune surface molecules ICAM-1 and B7-2 on PELs. Here, we show for the first time that Pom-induced increases in ICAM-1 and B7-2 on PEL cells lead to an increase in both T-cell activation and NK-mediated cytotoxicity against PEL. The increase in T-cell activation can be prevented by blocking ICAM-1 and/or B7-2 on the PEL cell surface, suggesting that both ICAM-1 and B7-2 are important for T-cell co-stimulation by PELs. To gain mechanistic insights into Pom’s effects on surface markers, we generated Pom-resistant (PomR) PEL cells, which showed about 90% reduction in cereblon protein level and only minimal changes in IRF4 and cMyc upon Pom treatment. Pom no longer upregulated ICAM-1 and B7-2 on the surface of PomR cells, nor did it increase T-cell and NK-cell activation. Cereblon-knockout cells behaved similarly to the pomR cells upon Pom-treatment, suggesting that Pom’s interaction with cereblon is necessary for these effects. Further mechanistic studies revealed PI3K signaling pathway as being important for Pom-induced increases in these molecules. These observations provide a rationale for the study of Pom as therapy in treating PEL and other KSHV-associated tumors.Liver stiffness is a reliable non-invasive predictor of Hepatic Venous Pressure Gradient (HVPG) above 10 mm Hg. However, it failed to predict higher thresholds of HVPG. Our aim was to investigate whether liver stiffness and selected previously published non-invasive blood biomarkers could predict higher HVPG thresholds in liver transplant candidates without ongoing alcohol use. One hundred and nine liver transplant candidates with liver cirrhosis of various aetiologies underwent direct HVPG measurement, liver stiffness measurement by 2D shear-wave elastography (Aixplorer Multiwave, Supersonic Imagine, France) and assessment of blood HVPG biomarkers (osteopontin, VCAM-1, IL-6, TNF-α, IL-1ra/IL-1F3 and ELF score). The correlation between liver stiffness and HVPG was linear up to 30 mm Hg of HVPG (r = 0.765, p 0.05) and the correlation in patients with HVPG less then 16 mm Hg (r = 0.456, p = 0.01) was similar to patients with HVPG ≥ 16 mm Hg (r = 0.499, p less then 0.0001). The correlation was similar in the subgroup patients with alcoholic (r = 0.718, p less then 0.0001), NASH (r = 0.740, p = 0.008), cryptogenic (r = 0.648, p = 0,0377), cholestatic and autoimmune (r = 0.706, p less then 0.0001) and viral cirrhosis (r = 0.756, p less then 0.0001). Liver stiffness distinguished patients with HVPG above 16, and 20 mm Hg with AUROCs 0.90243, and 0.86824, sensitivity 0.7656, and 0.7027, and specificity 0.9333, and 0.8750. All studied blood biomarkers correlated better with liver stiffness than with HVPG and their AUROCs did not exceed 0.8 at both HVPG thresholds. Therefore, a composite predictor superior to liver stiffness could not be established. We conclude that liver stiffness is a clinically reliable predictor of higher HVPG thresholds in non-drinking subjects with advanced liver cirrhosis.[This corrects the article DOI 10.1371/journal.pmed.1002220.].We introduce a hybrid two-dimensional multiscale model of angiogenesis, the process by which endothelial cells (ECs) migrate from a pre-existing vascular bed in response to local environmental cues and cell-cell interactions, to create a new vascular network. Recent experimental studies have highlighted a central role of cell rearrangements in the formation of angiogenic networks. Our model accounts for this phenomenon via the heterogeneous response of ECs to their microenvironment. These cell rearrangements, in turn, dynamically remodel the local environment. The model reproduces characteristic features of angiogenic sprouting that include branching, chemotactic sensitivity, the brush border effect, and cell mixing. These properties, rather than being hardwired into the model, emerge naturally from the gene expression patterns of individual cells. After calibrating and validating our model against experimental data, we use it to predict how the structure of the vascular network changes as the baseline gene emaking it a potential biomarker for pathological angiogenesis.Cocoyam (Xanthosoma sagittifolium (L.) Schott) is an exotic species from tropical America that is widely cultivated in Ethiopia for its edible cormels and leaves. There is a dearth of information on the genetic diversity of Ethiopian cocoyam. In order to evaluate and select cocoyam germplasm for breeding and conservation, genetic diversity of 100 Ethiopian cocoyam accessions (65 green- and 35 purple- cocoyam) were analyzed using 29 morphological traits (16 qualitative and 13 quantitative) and 12 SSR loci. Two classes of qualitative traits were observed. ANOVA revealed significant variation in 11 (84.6%) of the 13 studied quantitative traits. The SSR marker analysis showed high genetic diversity. A total of 36 alleles were detected with a range of 2 to 5 (average of 3.273) alleles per locus. The average observed heterozygosity (Ho) and expected heterozygosity (He) values across populations were 0.503 and 0.443, respectively. The analysis of molecular variance showed that the variation among populations, among individuals within populations, and within individuals explained 14%, 18%, and 68% of the total variation, respectively. Cluster analysis grouped the accessions irrespective of the collection sites. A dendrogram based on Nei’s standard genetic distance grouped the green cocoyam accessions together while the purple cocoyam accessions occupied a separate position within the dendrogram. Significant variation in quantitative traits and the high level of genetic diversity revealed by the SSR markers suggest that diverse cocoyam accessions, probably with multiple lineage, were introduced multiple times, through multiple routes and probably by multiple agents, an hypothesis that needs futher testing and analyis. The crop, therefore, needs more research efforts commensurate with its economic and social values than it has been accorded thus far. Further study is recommended to clarify the taxonomic status of Ethiopian cocoyam accesions and to trace their evolutionary relationships with Xanthosoma species elsewhere.Vertebrate behavior is strongly influenced by light. Light receptors, encoded by functional opsin proteins, are present inside the vertebrate brain and peripheral tissues. This expression feature is present from fishes to human and appears to be particularly prominent in diurnal vertebrates. Despite their conserved widespread occurrence, the nonvisual functions of opsins are still largely enigmatic. This is even more apparent when considering the high number of opsins. Teleosts possess around 40 opsin genes, present from young developmental stages to adulthood. Many of these opsins have been shown to function as light receptors. This raises the question of whether this large number might mainly reflect functional redundancy or rather maximally enables teleosts to optimally use the complex light information present under water. We focus on tmt-opsin1b and tmt-opsin2, c-opsins with ancestral-type sequence features, conserved across several vertebrate phyla, expressed with partly similar expression in non-rod, n organism for behavioral fine-tuning. This work also provides a stepping stone to unravel how vertebrate species with conserved opsins, but living in different ecological niches, respond to similar light cues and how human-generated artificial light might impact on behavioral processes in natural environments.Dual-reinforcement learning theory proposes behaviour is under the tutelage of a retrospective, value-caching, model-free (MF) system and a prospective-planning, model-based (MB), system. This architecture raises a question as to the degree to which, when devising a plan, a MB controller takes account of influences from its MF counterpart. We present evidence that such a sophisticated self-reflective MB planner incorporates an anticipation of the influences its own MF-proclivities exerts on the execution of its planned future actions. Using a novel bandit task, wherein subjects were periodically allowed to design their environment, we show that reward-assignments were constructed in a manner consistent with a MB system taking account of its MF propensities. Thus, in the task participants assigned higher rewards to bandits that were momentarily associated with stronger MF tendencies. Our findings have implications for a range of decision making domains that includes drug abuse, pre-commitment, and the tension between short and long-term decision horizons in economics.Fostering students’ classroom engagement is a research hotspot in classroom teaching management. Enhancing classroom engagement requires consideration of the interactive effects of physical and interpersonal environments. Considering the characteristics of physical space, the teacher gives feedback on student engagement in terms of different seating positions. Further, near-seated peer group engagement has an impact, though previous research has found this to be inconsistent. The teacher and near-seated peer groups have different paths of influence on classroom engagement, and there is interplay between them. However, based on realistic classroom scenarios, it is difficult for traditional research methods to reveal how spatially heterogeneous and non-linear micro-interactions among teachers, students, and near-seated peer groups evolve into dynamic changes in macro-classroom engagement. Hence, this study utilized agent-based simulation to explore the non-linear dynamic mechanism underlying how teacher-student.

Digital devices and wearables allow for the measurement of a wide range of health-related parameters in a non-invasive manner, which may be particularly valuable in pediatrics. Incorporation of such parameters in clinical trials or care as digital endpoint could reduce the burden for children and their parents but requires clinical validation in the target population. This study aims to determine the tolerability, repeatability, and reference values of novel digital endpoints in healthy children.

Apparently healthy children (n = 175, 46% male) aged 2-16 were included. Subjects were monitored for 21 days using a home-monitoring platform with several devices (smartwatch, spirometer, thermometer, blood pressure monitor, scales). Endpoints were analyzed with a mixed effects model, assessing variables that explained within- and between-subject variability. Endpoints based on physical activity, heart rate, and sleep-related parameters were included in the analysis. For physical-activity-related endpoints, a sample size needed to detect a 15% increase was calculated.

Median compliance was 94%. Variability in each physical activity-related candidate endpoint was explained by age, sex, watch wear time, rain duration per day, average ambient temperature, and population density of the city of residence. Estimated sample sizes for candidate endpoints ranged from 33-110 per group. Daytime heart rate, nocturnal heart rate and sleep duration decreased as a function of age and were comparable to reference values published in the literature.

Wearable- and portable devices are tolerable for pediatric subjects. The raw data, models and reference values presented here can be used to guide further validation and, in the future, clinical trial designs involving the included measures.

Wearable- and portable devices are tolerable for pediatric subjects. The raw data, models and reference values presented here can be used to guide further validation and, in the future, clinical trial designs involving the included measures.[This corrects the article DOI 10.1371/journal.pone.0228254.].The value learning process has been investigated using decision-making tasks with a correct answer specified by the external environment (externally guided decision-making, EDM). In EDM, people are required to adjust their choices based on feedback, and the learning process is generally explained by the reinforcement learning (RL) model. In addition to EDM, value is learned through internally guided decision-making (IDM), in which no correct answer defined by external circumstances is available, such as preference judgment. In IDM, it has been believed that the value of the chosen item is increased and that of the rejected item is decreased (choice-induced preference change; CIPC). An RL-based model called the choice-based learning (CBL) model had been proposed to describe CIPC, in which the values of chosen and/or rejected items are updated as if own choice were the correct answer. However, the validity of the CBL model has not been confirmed by fitting the model to IDM behavioral data. The present study aims to examine the CBL model in IDM. We conducted simulations, a preference judgment task for novel contour shapes, and applied computational model analyses to the behavioral data. The results showed that the CBL model with both the chosen and rejected value’s updated were a good fit for the IDM behavioral data compared to the other candidate models. Although previous studies using subjective preference ratings had repeatedly reported changes only in one of the values of either the chosen or rejected items, we demonstrated for the first time both items’ value changes were based solely on IDM choice behavioral data with computational model analyses.Characterizing the gut microbiota in terms of their capacity to interfere with drug metabolism is necessary to achieve drug efficacy and safety. Although examples of drug-microbiome interactions are well-documented, little has been reported about a computational pipeline for systematically identifying and characterizing bacterial enzymes that process particular classes of drugs. The goal of our study is to develop a computational approach that compiles drugs whose metabolism may be influenced by a particular class of microbial enzymes and that quantifies the variability in the collective level of those enzymes among individuals. The present paper describes this approach, with microbial β-glucuronidases as an example, which break down drug-glucuronide conjugates and reactivate the drugs or their metabolites. We identified 100 medications that may be metabolized by β-glucuronidases from the gut microbiome. These medications included morphine, estrogen, ibuprofen, midazolam, and their structural analogues. The analysis of metagenomic data available through the Sequence Read Archive (SRA) showed that the level of β-glucuronidase in the gut metagenomes was higher in males than in females, which provides a potential explanation for the sex-based differences in efficacy and toxicity for several drugs, reported in previous studies. Our analysis also showed that infant gut metagenomes at birth and 12 months of age have higher levels of β-glucuronidase than the metagenomes of their mothers and the implication of this observed variability was discussed in the context of breastfeeding as well as infant hyperbilirubinemia. Overall, despite important limitations discussed in this paper, our analysis provided useful insights on the role of the human gut metagenome in the variability in drug response among individuals. Importantly, this approach exploits drug and metagenome data available in public databases as well as open-source cheminformatics and bioinformatics tools to predict drug-metagenome interactions.

Histopathologic factors predictive of nintedanib efficacy in idiopathic pulmonary fibrosis have not been studied. We aimed to describe the characteristics, focusing on histopathology, of idiopathic pulmonary fibrosis patients who did and did not respond to nintedanib.

This study retrospectively examined the clinicoradiopathologic features of 40 consecutive patients with surgical lung biopsy-confirmed idiopathic pulmonary fibrosis treated with nintedanib. Additionally, we compared the histopathologic scoring of 21 microscopic features between patients with functional or radiological progression and those with non-progression during 12 months of treatment.

The histopathologic evaluation showed edematous changes in the interlobular septum as the only histologic finding observed more frequently in patients with both functional and radiological progression than in those without (58% vs. 14%, P = 0.007 and 50% vs. 0%, P = 0.003, respectively). Regarding per-year change, patients with edematous changes in the interlobular septum showed greater progression in median changes in spared area (-12%, interquartile range [-25%–5%], vs. -3% [-7%-0%], P = 0.004) and reticular shadow (7% [3%-13%], vs. 0% [0%-5%], P = 0.041) on computed tomography. Functional and radiological progression-free survival were shorter in patients with edematous changes in the interlobular septum than in those without (6.6 months, 95% confidence interval [5.9-25.3], vs. event <50%, [12.1-Not available], P = 0.0009, and 6.1 months, [5.2-6.6] vs. 14.5 months [7.8-not available], P<0.0001).

Edematous changes in the interlobular septum may indicate poor nintedanib efficacy in idiopathic pulmonary fibrosis. Further studies are needed to validate these findings and address the mechanism behind ECIS.

Edematous changes in the interlobular septum may indicate poor nintedanib efficacy in idiopathic pulmonary fibrosis. Further studies are needed to validate these findings and address the mechanism behind ECIS.

Reducing potentially preventable hospitalisations (PPH) is a priority for health services. This paper describes the factors that clinicians perceived contributed to preventable admissions for angina, diabetes, congestive heart failure (CHF) and chronic obstructive pulmonary disease (COPD), and what they considered might have been done in the three months leading up to an admission to prevent it.

The study was conducted in a rural and a metropolitan health district in NSW, Australia. Expert Panels reviewed detailed case reports to assess preventability. For those admissions identified as preventable, comments from clinicians indicating what they perceived could have made a difference and/or been done differently to prevent each of the preventable admissions were analysed qualitatively.

148 (46%) of 323 admissions were assessed as preventable. Across the two districts, the most commonly identified groups of contributing factors to preventable admissions were 'Systems issues Community based services missinor comprehensiveness may explain why many programs seeking to reduce PPH have been unsuccessful.

These findings suggest preventability of individual admissions is complex and context specific. There is no single, simple solution likely to reduce PPH. Rather, an approach addressing multiple factors is required. This need for comprehensiveness may explain why many programs seeking to reduce PPH have been unsuccessful.Facial expressions are complex and subtle signals, central for communication and emotion in social mammals. Traditionally, facial expressions have been classified as a whole, disregarding small but relevant differences in displays. Even with the same morphological configuration different information can be conveyed depending on the species. Due to a hardwired processing of faces in the human brain, humans are quick to attribute emotion, but have difficulty in registering facial movement units. The well-known human FACS (Facial Action Coding System) is the gold standard for objectively measuring facial expressions, and can be adapted through anatomical investigation and functional homologies for cross-species systematic comparisons. Here we aimed at developing a FACS for Japanese macaques, following established FACS methodology first, we considered the species’ muscular facial plan; second, we ascertained functional homologies with other primate species; and finally, we categorised each independent facial moves, particularly in captivity and laboratory settings.

Within HIV/HBV infected patients, an increase in HDV infection has been observed; there is inadequate information on HDV prevalence as well as virologic profile in Ghana. This study sought to determine the presence of HDV in HIV/HBV co-infected patients in Ghana.

This was a longitudinal purposive study which enrolled 113 HIV/HBV co-infected patients attending clinic at Korle-Bu Teaching Hospital (KBTH) in Accra, Ghana. After consenting, 5 mL whole blood was collected at two-time points (baseline and 4-6 months afterwards). The sera obtained were tested to confirm the presence of HIV, HBV antibodies and/or antigens, and HBV DNA. Antibodies and viral RNA were also determined for HDV. Amplified HBV DNA and HDV RNA were sequenced and phylogenetic analysis carried out with reference sequences from the GenBank to establish the genotypes.

Of the 113 samples tested 63 (55.7%) were females and 50 (44.25%) were males with a median age of 45 years. A total of 100 (88.5%) samples had detectable HBV surface antigen esence of HDV and occult HBV in HIV/HBV co-infected patients presenting with potential risk of liver cancers and HBV transmission through haemodialysis and blood transfusions.We have previously shown that the microfilarial (mf) stage of Brugia malayi can inhibit the mammalian target of rapamycin (mTOR; a conserved serine/threonine kinase critical for immune regulation and cellular growth) in human dendritic cells (DC) and we have proposed that this mTOR inhibition is associated with the DC dysfunction seen in filarial infections. Extracellular vesicles (EVs) contain many proteins and nucleic acids including microRNAs (miRNAs) that might affect a variety of intracellular pathways. Thus, EVs secreted from mf may elucidate the mechanism by which the parasite is able to modulate the host immune response during infection. EVs, purified from mf of Brugia malayi and confirmed by size through nanoparticle tracking analysis, were assessed by miRNA microarrays (accession number GSE157226) and shown to be enriched (>2-fold, p-value less then 0.05, FDR = 0.05) for miR100, miR71, miR34, and miR7. The microarray analysis compared mf-derived EVs and mf supernatant. After confirming their presence in EVs using qPCR for these miRNA targets, web-based target predictions (using MIRPathv3, TarBAse and MicroT-CD) predicted that miR100 targeted mTOR and its downstream regulatory protein 4E-BP1. Our previous data with live parasites demonstrated that mf downregulate the phosphorylation of mTOR and its downstream effectors. Additionally, our proteomic analysis of the mf-derived EVs revealed the presence of proteins commonly found in these vesicles (data are available via ProteomeXchange with identifier PXD021844). We confirmed internalization of mf-derived EVs by human DCs and monocytes using confocal microscopy and flow cytometry, and further demonstrated through flow cytometry, that mf-derived EVs downregulate the phosphorylation of mTOR in human monocytes (THP-1 cells) to the same degree that rapamycin (a known mTOR inhibitor) does. Our data collectively suggest that mf release EVs that interact with host cells, such as DC, to modulate host responses.The purposes of this study were to (i) develop a field-goal shooting performance analysis template and (ii) explore the impact of each identified variable upon the likely outcome of a field-goal attempt using binary logistic regression modelling in elite men’s wheelchair basketball. First, a field-goal shooting performance analysis template was developed that included 71 Action Variables (AV) grouped within 22 Categorical Predictor Variables (CPV) representing offensive, defensive and game context variables. Second, footage of all 5,105 field-goal attempts from 12 teams during the men’s 2016 Rio De Janeiro Paralympic Games wheelchair basketball competition were analysed using the template. Pearson’s chi-square analyses found that 18 of the CPV were significantly associated with field-goal attempt outcome (p less then 0.05), with seven of them reaching moderate association (Cramer’s V 0.1-0.3). Third, using 70% of the dataset (3,574 field-goal attempts), binary logistic regression analyses identified that five offensive variables (classification category of the player, the action leading up to the field-goal attempt, the time left on the clock, the location of the shot, and the movement of the player), two defensive variables (the pressure being exerted by the defence, and the number of defenders within a 1-meter radius) and 1 context variable (the finishing position of the team in the competition) affected the probability of a successful field-goal attempt. The quality of the developed model was determined acceptable (greater than 65%), producing an area under the curve value of 68.5% when the model was run against the remaining 30% of the dataset (1,531 field-goal attempts). The development of the model from such a large sample of objective data is unique. As such it offers robust empirical evidence to enable coaches, performance analysts and players to move beyond anecdote, in order to appreciate the potential effect of various and varying offensive, defensive and contextual variables on field-goal success.

A plethora of studies has investigated and compared social cognition in autism and schizophrenia ever since both conditions were first described in conjunction more than a century ago. Recent computational theories have proposed similar mechanistic explanations for various symptoms beyond social cognition. They are grounded in the idea of a general misestimation of uncertainty but so far, almost no studies have directly compared both conditions regarding uncertainty processing. The current study aimed to do so with a particular focus on estimation of volatility, i.e. the probability for the environment to change.

A probabilistic decision-making task and a visual working (meta-)memory task were administered to a sample of 86 participants (19 with a diagnosis of high-functioning autism, 21 with a diagnosis of schizophrenia, and 46 neurotypically developing individuals).

While persons with schizophrenia showed lower visual working memory accuracy than neurotypical individuals, no significant group differences were found for metamemory or any of the probabilistic decision-making task variables. Nevertheless, exploratory analyses suggest that there may be an overestimation of volatility in subgroups of participants with autism and schizophrenia. Correlations revealed relationships between different variables reflecting (mis)estimation of uncertainty, visual working memory accuracy and metamemory.

Limitations include the comparably small sample sizes of the autism and the schizophrenia group as well as the lack of cognitive ability and clinical symptom measures.

The results of the current study provide partial support for the notion of a general uncertainty misestimation account of autism and schizophrenia.

The results of the current study provide partial support for the notion of a general uncertainty misestimation account of autism and schizophrenia.

Breast cancer is the most common cancer diagnosed in women globally, and 5-year net survival probabilities in high-income countries are generally >80%. A cancer diagnosis and treatment are often traumatic events, and many women struggle to cope during this period. Less is known, however, about the long-term mental health impact of the disease, despite many women living several years beyond their breast cancer and mental health being a major source of disability in modern societies. The objective of this study was to quantify the risk of several adverse mental health-related outcomes in women with a history of breast cancer followed in primary care in the United Kingdom National Health Service, compared to similar women who never had cancer.

We conducted a matched cohort study using data routinely collected in primary care across the UK to quantify associations between breast cancer history and depression, anxiety, and other mental health-related outcomes. All women with incident breast cancer in the Clicy makers are likely to be important to mitigate the impacts of these raised risks.

Quality standards are important for improving health care by providing compelling evidence for best practice. High quality person-centered health care requires information on patients’ experience of disease and of functioning in daily life.

To analyze and compare the content of five Swedish National Quality Registries (NQRs) and two standard sets of the International Consortium of Health Outcomes Measurement (ICHOM) related to cardiovascular diseases.

An analysis of 2588 variables (= data items) of five NQRs-the Swedish Registry of Congenital Heart Disease, Swedish Cardiac Arrest Registry, Swedish Catheter Ablation Registry, Swedish Heart Failure Registry, SWEDEHEART (including four sub-registries) and two ICHOM standard sets-the Heart Failure Standard Set and the Coronary Artery Disease Standard Set. According to the name and definition of each variable, the variables were mapped to Donabedian’s quality criteria, whereby identifying whether they capture health care processes or structures or patients’ ommon person-centered quality indicator set.

Quality standards in the cardiovascular field focus predominately on processes (e.g. treatment) and on body functions-related outcomes. Less attention is given to patients’ lived experience of disease and their daily activities and participation. The results can serve as a starting-point for harmonizing data and developing a common person-centered quality indicator set.

The first cases of coronavirus disease (COVID-19) in Brazil were diagnosed in February 2020. Our Emergency Department (ED) was designated as a COVID-19 exclusive service. We report our first 500 confirmed COVID-19 pneumonia patients.

From 14 March to 16 May 2020, we enrolled all patients admitted to our ED that had a diagnosis of COVID-19 pneumonia. Infection was confirmed via nasopharyngeal swabs or tracheal aspirate PCR. The outcomes included hospital discharge, invasive mechanical ventilation, and in-hospital death, among others.

From 2219 patients received in the ED, we included 506 with confirmed COVID-19 pneumonia. We found that 333 patients were discharged home (65.9%), 153 died (30.2%), and 20 (3.9%) remained in the hospital. A total of 300 patients (59.3%) required ICU admission, and 227 (44.9%) needed invasive ventilation. The multivariate analysis found age, number of comorbidities, extension of ground glass opacities on chest CT and troponin with a direct relationship with all-cause mortality, whereas dysgeusia, use of angiotensin converting enzyme inhibitor or angiotensin-ii receptor blocker and number of lymphocytes with an inverse relationship with all-cause mortality.

This was a sample of severe patients with COVID-19, with 59.2% admitted to the ICU and 41.5% requiring mechanical ventilator support. We were able to ascertain the outcome in majority (96%) of patients. While the overall mortality was 30.2%, mortality for intubated patients was 55.9%. Multivariate analysis agreed with data found in other studies although the use of angiotensin converting enzyme inhibitor or angiotensin-ii receptor blocker as a protective factor could be promising but would need further studies.

The study was registered in the Brazilian registry of clinical trials RBR-5d4dj5.

The study was registered in the Brazilian registry of clinical trials RBR-5d4dj5.