Digital health technology to support care and improve outcomes of chronic kidney disease patients: as a case illustration, the Withings toolkit health sensing tools.

Cardiovascular disease (CVD) is a major burden in dialysis-dependent chronic kidney disease (CKD5D) patients. Several factors contribute to this vulnerability including traditional risk factors such as age, gender, life style and comorbidities, and non-traditional ones as part of dialysis-induced systemic stress. In this context, it appears of utmost importance to bring a closer attention to CVD monitoring in caring for CKD5D patients to ensure early and appropriate intervention for improving their outcomes. Interestingly, new home-used, self-operated, connected medical devices offer convenient and new tools for monitoring in a fully automated and ambulatory mode CKD5D patients during the interdialytic period. Sensoring devices are installed with WiFi or Bluetooth. Some devices are also available in a cellular version such as the Withings Remote Patient Monitoring (RPM) solution. These devices analyze the data and upload the results to Withings HDS (Hybrid data security) platform servers. Data visualization can be viewed by the patient using the Withings Health Mate application on a smartphone, or with a web interface. Health Care Professionals (HCP) can also visualize patient data via the Withings web-based RPM interface. In this narrative essay, we analyze the clinical potential of pervasive wearable sensors for monitoring ambulatory dialysis patients and provide an assessment of such toolkit digital medical health devices currently available on the market. These devices offer a fully automated, unobtrusive and remote monitoring of main vital functions in ambulatory subjects. These unique features provide a multidimensional assessment of ambulatory CKD5D patients covering most physiologic functionalities, detecting unexpected disorders (i.e., volume overload, arrhythmias, sleep disorders) and allowing physicians to judge patient's response to treatment and recommendations. In the future, the wider availability of such pervasive health sensing and digital technology to monitor patients at an affordable cost price will improve the personalized management of CKD5D patients, so potentially resulting in improvements in patient quality of life and survival.

Polyvalent immunization elicits a synergistic broadly neutralizing immune response to hypervariable region 1 variants of hepatitis C virus.

A hepatitis C virus (HCV) vaccine is urgently needed. Vaccine development has been hindered by HCV's genetic diversity, particularly within the immunodominant hypervariable region 1 (HVR1). Here, we developed a strategy to elicit broadly neutralizing antibodies to HVR1, which had previously been considered infeasible. We first applied a unique information theory-based measure of genetic distance to evaluate phenotypic relatedness between HVR1 variants. These distances were used to model the structure of HVR1's sequence space, which was found to have five major clusters. Variants from each cluster were used to immunize mice individually, and as a pentavalent mixture. Sera obtained following immunization neutralized every variant in a diverse HCVpp panel (n = 10), including those resistant to monovalent immunization, and at higher mean titers (1/ID50 = 435) than a glycoprotein E2 (1/ID50 = 205) vaccine. This synergistic immune response offers a unique approach to overcoming antigenic variability and may be applicable to other highly mutable viruses.

A Novel Information-Theory-Based Genetic Distance That Approximates Phenotypic Differences.

Application of genetic distances to measure phenotypic relatedness is a challenging task, reflecting the complex relationship between genotype and phenotype. Accurate assessment of proximity among sequences with different phenotypic traits depends on how strongly the chosen distance is associated with structural and functional properties. In this study, we present a new distance measure Mutual Information and Entropy H (MIH) for categorical data such as nucleotide or amino acid sequences. MIH applies an information matrix (IM), which is calculated from the data and captures heterogeneity of individual positions as measured by Shannon entropy and coordinated substitutions among positions as measured by mutual information. In general, MIH assigns low weights to differences occurring at high entropy positions or at dependent positions. MIH distance was compared with other common distances on two experimental and two simulated data sets. MIH showed the best ability to distinguish cross-immunoreactive sequence pairs from non-cross-immunoreactive pairs of variants of the hepatitis C virus hypervariable region 1 (26,883 pairwise comparisons), and Major Histocompatibility Complex (MHC) binding peptides (n = 181) from non-binding peptides (n = 129). Analysis of 74 simulated RNA secondary structures also showed that the ratio between MIH distance of sequences from the same RNA structure and MIH of sequences from different structures is three orders of magnitude greater than for Hamming distances. These findings indicate that lower MIH between two sequences is associated with greater probability of the sequences to belong to the same phenotype. Examination of rule-based phenotypes generated in silico showed that (1) MIH is strongly associated with phenotypic differences, (2) IM of sequences under selection is very different from IM generated under random scenarios, and (3) IM is robust to sampling. In conclusion, MIH strongly approximates structural/functional distances and should have important applications to a wide range of biological problems, including evolution, artificial selection of biological functions and structures, and measuring phenotypic similarity.

Efecto de la edad relativa en la natación española / Effect of relative age on Spanish swimming

El efecto de la edad relativa se refiere a las diferencias cronológicas de sujetos pertenecientes a un mismo grupo de edad, que puede provocar que los nacidos en los primeros meses del año parezcan más talentosos. El objetivo de este estudio fue analizar la posible existencia del efecto de la edad relativa en la natación española. Para llevarlo a cabo se consideraron los 100 mejores resultados en piscina de 50 metros de las últimas seis temporadas (2015-2021), para todas las edades de las categorías con campeonatos nacionales, en ambos sexos y en los cuatro estilos. El análisis se realizó sobre 28.373 resultados obtenidos de la base de datos de la Real Federación Española de Natación, tomando como referencia las fechas de nacimiento de los nadadores. Para identificar si existe el efecto de la edad relativa en la natación en España se aplicó el odds ratio y la prueba estadística chi cuadrado. Así, para calcular las posibles diferencias entre los rendimientos medios entre los trimestres de nacimiento de los nadadores, se realizó́ un análisis de varianza. Los resultados mostraron una distribución desigual de las fechas de nacimiento por trimestres y unos rendimientos medios diferentes para casi todos los grupos de edad, en ambos géneros. En conclusión, se puede decir que la sobrerrepresentación de deportistas en los primeros trimestres del año, y la diferencia en cuanto al éxito deportivo alcanzado, son dos efectos de la edad relativa a considerar. (AU)

The relative age effect refers to chronological differences in subjects belonging to the same age group, which may cause those born in the first months of the year to appear more talented. The aim of this research is to analyse the effect of relative age in Spanish swimming. The 100 best results in pool of 50 meters of the last six seasons (2015-2021) were analysed for all ages of the categories with national championships, in both genders and in the four styles. The sample included 28.373 results obtained from the database of the Royal Spanish Swimming Federation, taking as reference the dates of birth of the swimmers. To analyse the effect of relative age in Spanish swimming, the Chi-squared statistical est was used. The possible differences between the average yields between the birth quarters of the swimmers was calculated by an analysis of variance. The results showed an unequal distribution of birth dates by trimesters and different average yields for almost all age groups, in both genders. In conclusion, it can be said that the over-representation of athletes in the first quarters of the year, and the difference in terms of sports success achieved, are two effects of the relative age to consider. (AU)

Polymorphic Molecular Signatures in Variable Regions of the Plasmodium falciparum var2csa DBL3x Domain Are Associated with Virulence in Placental Malaria.

The Plasmodium falciparum protein VAR2CSA allows infected erythrocytes to accumulate within the placenta, inducing pathology and poor birth outcomes. Multiple exposures to placental malaria (PM) induce partial immunity against VAR2CSA, making it a promising vaccine candidate. However, the extent to which VAR2CSA genetic diversity contributes to immune evasion and virulence remains poorly understood. The deep sequencing of the var2csa DBL3X domain in placental blood from forty-nine primigravid and multigravid women living in malaria-endemic western Kenya revealed numerous unique sequences within individuals in association with chronic PM but not gravidity. Additional analysis unveiled four distinct sequence types that were variably present in mixed proportions amongst the study population. An analysis of the abundance of each of these sequence types revealed that one was inversely related to infant gestational age, another was inversely related to placental parasitemia, and a third was associated with chronic PM. The categorization of women according to the type to which their dominant sequence belonged resulted in the segregation of types as a function of gravidity: two types predominated in multigravidae whereas the other two predominated in primigravidae. The univariate logistic regression analysis of sequence type dominance further revealed that gravidity, maternal age, placental parasitemia, and hemozoin burden (within maternal leukocytes), reported a lack of antimalarial drug use, and infant gestational age and birth weight influenced the odds of membership in one or more of these sequence predominance groups. Cumulatively, these results show that unique var2csa sequences differentially appear in women with different PM exposure histories and segregate to types independently associated with maternal factors, infection parameters, and birth outcomes. The association of some var2csa sequence types with indicators of pathogenesis should motivate vaccine efforts to further identify and target VAR2CSA epitopes associated with maternal morbidity and poor birth outcomes.

Atrial Fibrillation Detection With an Analog Smartwatch: Prospective Clinical Study and Algorithm Validation.

BACKGROUND: Atrial fibrillation affects approximately 4% of the world's population and is one of the major causes of stroke, heart failure, sudden death, and cardiovascular morbidity. It can be difficult to diagnose when asymptomatic or in the paroxysmal stage, and its natural history is not well understood. New wearables and connected devices offer an opportunity to improve on this situation. OBJECTIVE: We aimed to validate an algorithm for the automatic detection of atrial fibrillation from a single-lead electrocardiogram taken with a smartwatch. METHODS: Eligible patients were recruited from 4 sites in Paris, France. Electrocardiograms (12-lead reference and single lead) were captured simultaneously. The electrocardiograms were reviewed by independent, blinded board-certified cardiologists. The sensitivity and specificity of the algorithm to detect atrial fibrillation and normal sinus rhythm were calculated. The quality of single-lead electrocardiograms (visibility and polarity of waves, interval durations, heart rate) was assessed in comparison with the gold standard (12-lead electrocardiogram). RESULTS: A total of 262 patients (atrial fibrillation: n=100, age: mean 74.3 years, SD 12.3; normal sinus rhythm: n=113, age: 61.8 years, SD 14.3; other arrhythmia: n=45, 66.9 years, SD 15.2; unreadable electrocardiograms: n=4) were included in the final analysis; 6.9% (18/262) were classified as Noise by the algorithm. Excluding other arrhythmias and Noise, the sensitivity for atrial fibrillation detection was 0.963 (95% CI lower bound 0.894), and the specificity was 1.000 (95% CI lower bound 0.967). Visibility and polarity accuracies were similar (1-lead electrocardiogram: P waves: 96.9%, QRS complexes: 99.2%, T waves: 91.2%; 12-lead electrocardiogram: P waves: 100%, QRS complexes: 98.8%, T waves: 99.5%). P-wave visibility accuracy was 99% (99/100) for patients with atrial fibrillation and 95.7% (155/162) for patients with normal sinus rhythm, other arrhythmias, and unreadable electrocardiograms. The absolute values of the mean differences in PR duration and QRS width were <3 ms, and more than 97% were <40 ms. The mean difference between the heart rates from the 1-lead electrocardiogram calculated by the algorithm and those calculated by cardiologists was 0.55 bpm. CONCLUSIONS: The algorithm demonstrated great diagnostic performance for atrial fibrillation detection. The smartwatch's single-lead electrocardiogram also demonstrated good quality for physician use in daily routine care. TRIAL REGISTRATION: ClinicalTrials.gov NCT04351386; http://clinicaltrials.gov/ct2/show/NCT04351386.

Primary case inference in viral outbreaks through analysis of intra-host variant population.

BACKGROUND: Investigation of outbreaks to identify the primary case is crucial for the interruption and prevention of transmission of infectious diseases. These individuals may have a higher risk of participating in near future transmission events when compared to the other patients in the outbreak, so directing more transmission prevention resources towards these individuals is a priority. Although the genetic characterization of intra-host viral populations can aid the identification of transmission clusters, it is not trivial to determine the directionality of transmissions during outbreaks, owing to complexity of viral evolution. Here, we present a new computational framework, PYCIVO: primary case inference in viral outbreaks. This framework expands upon our earlier work in development of QUENTIN, which builds a probabilistic disease transmission tree based on simulation of evolution of intra-host hepatitis C virus (HCV) variants between cases involved in direct transmission during an outbreak. PYCIVO improves upon QUENTIN by also adding a custom heterogeneity index and identifying the scenario when the primary case may have not been sampled. RESULTS: These approaches were validated using a set of 105 sequence samples from 11 distinct HCV transmission clusters identified during outbreak investigations, in which the primary case was epidemiologically verified. Both models can detect the correct primary case in 9 out of 11 transmission clusters (81.8%). However, while QUENTIN issues erroneous predictions on the remaining 2 transmission clusters, PYCIVO issues a null output for these clusters, giving it an effective prediction accuracy of 100%. To further evaluate accuracy of the inference, we created 10 modified transmission clusters in which the primary case had been removed. In this scenario, PYCIVO was able to correctly identify that there was no primary case in 8/10 (80%) of these modified clusters. This model was validated with HCV; however, this approach may be applicable to other microbial pathogens. CONCLUSIONS: PYCIVO improves upon QUENTIN by also implementing a custom heterogeneity index which empowers PYCIVO to make the important 'No primary case' prediction. One or more samples, possibly including the primary case, may have not been sampled, and this designation is meant to account for these scenarios.

Associations of Physical Activity Level and Variability With 6-Month Weight Change Among 26,935 Users of Connected Devices: Observational Real-Life Study.

BACKGROUND: Physical activity (PA) is a modifiable lifestyle factor that can be targeted to increase energy expenditure and promote weight loss. However, the amount of PA required for weight loss remains inconsistent. Wearable activity trackers constitute a valuable opportunity to obtain objective measurements of PA and study large populations in real-life settings. OBJECTIVE: We aim to study the associations of initial device-assessed PA characteristics (average step counts and step count variability) and their evolution with 6-month weight change. METHODS: We analyzed data from 26,935 Withings-connected device users (wearable activity trackers and digital scales). To assess the initial PA characteristics and their 6-month changes, we used data recorded during the first and sixth 30-day periods of activity tracker use. For each of these periods, we used the monthly mean of daily step values as a proxy for PA level and derived the monthly coefficient of variation (CV) of daily step values to estimate PA level variability. Associations between initial PA characteristics and 6-month weight change were assessed using multivariable linear regression analyses controlled for age, sex, blood pressure, heart rate, and the predominant season. Restricted cubic spline regression was performed to better characterize the continuous shape of the associations between PA characteristics and weight change. Secondary analyses were performed by analyzing the 6-month evolution of PA characteristics in relation to weight change. RESULTS: Our results revealed that both a greater PA level and lower PA level variability were associated with weight loss. Compared with individuals who were initially in the sedentary category (<5000 steps/day), individuals who were low active (5000-7499 steps/day), somewhat active (7500-9999 steps/day), and active (≥10,000 steps/day) had a 0.21-kg, a 0.52-kg, and a 1.17-kg greater decrease in weight, respectively (95% CI -0.36 to -0.06, -0.70 to -0.33, and -1.42 to -0.93, respectively). Compared with users whose PA level CV was >63%, users whose PA level CV ranged from 51% to 63%, 40% to 51%, and was ≤40%, had a 0.19-kg, a 0.23-kg, and a 0.33-kg greater decrease in weight, respectively (95% CI -0.38 to -0.01, -0.41 to -0.04, and -0.53 to -0.13, respectively). We also observed that each 1000 steps/day increase in PA level over the 6-month follow-up was associated with a 0.26-kg (95% CI -0.29 to -0.23) decrease in weight. No association was found between the 6-month changes in PA level variability and weight change. CONCLUSIONS: Our results add to the current body of knowledge that health benefits can be observed below the 10,000 steps/day threshold and suggest that not only increased mean PA level but also greater regularity of the PA level may play important roles in short-term weight loss.

Validation of the Withings Sleep Analyzer, an under-the-mattress device for the detection of moderate-severe sleep apnea syndrome.

STUDY OBJECTIVES: To assess the diagnostic performance of a nonintrusive device placed under the mattress to detect sleep apnea syndrome. METHODS: One hundred eighteen patients suspected to have obstructive sleep apnea syndrome completed a night at a sleep clinic with a simultaneous polysomnography (PSG) and recording with the Withings Sleep Analyzers. PSG nights were scored twice: first as simple polygraphy, then as PSG. RESULTS: Average (standard deviation) apnea-hypopnea index from PSG was 31.2 events/h (25.0) and 32.8 events/h (29.9) according to the Withings Sleep Analyzers. The mean absolute error was 9.5 events/h. The sensitivity, specificity, and area under the receiver operating characteristic curve at thresholds of apnea-hypopnea index ≥ 15 events/h were, respectively, sensitivity (Se)15 = 88.0%, specificity (Sp)15 = 88.6%, and area under the receiver operating characteristic curve (AUROC) 15 = 0.926. At the threshold of apnea-hypopnea index ≥ 30 events/h, results included Se30 = 86.0%, Sp30 = 91.2%, AUROC30 = 0.954. The average total sleep time from PSG and the Withings Sleep Analyzers was 366.6 (61.2) and 392.4 (67.2) minutes, sleep efficiency was 82.5% (11.6) and 82.6% (11.6), and wake after sleep onset was 62.7 (48.0) and 45.2 (37.3) minutes, respectively. CONCLUSIONS: Withings Sleep Analyzers accurately detect moderate-severe sleep apnea syndrome in patients suspected of sleep apnea syndrome. This simple and automated approach could be of great clinical value given the high prevalence of sleep apnea syndrome in the general population. CLINICAL TRIAL REGISTRATION: Registry: ClinicalTrials.gov; Name: Validation of Withings Sleep for the Detection of Sleep Apnea Syndrome; URL: https://clinicaltrials.gov/ct2/show/NCT04234828; Identifier: NCT04234828.

Convex hulls in hamming space enable efficient search for similarity and clustering of genomic sequences.

BACKGROUND: In molecular epidemiology, comparison of intra-host viral variants among infected persons is frequently used for tracing transmissions in human population and detecting viral infection outbreaks. Application of Ultra-Deep Sequencing (UDS) immensely increases the sensitivity of transmission detection but brings considerable computational challenges when comparing all pairs of sequences. We developed a new population comparison method based on convex hulls in hamming space. We applied this method to a large set of UDS samples obtained from unrelated cases infected with hepatitis C virus (HCV) and compared its performance with three previously published methods. RESULTS: The convex hull in hamming space is a data structure that provides information on: (1) average hamming distance within the set, (2) average hamming distance between two sets; (3) closeness centrality of each sequence; and (4) lower and upper bound of all the pairwise distances among the members of two sets. This filtering strategy rapidly and correctly removes 96.2% of all pairwise HCV sample comparisons, outperforming all previous methods. The convex hull distance (CHD) algorithm showed variable performance depending on sequence heterogeneity of the studied populations in real and simulated datasets, suggesting the possibility of using clustering methods to improve the performance. To address this issue, we developed a new clustering algorithm, k-hulls, that reduces heterogeneity of the convex hull. This efficient algorithm is an extension of the k-means algorithm and can be used with any type of categorical data. It is 6.8-times more accurate than k-mode, a previously developed clustering algorithm for categorical data. CONCLUSIONS: CHD is a fast and efficient filtering strategy for massively reducing the computational burden of pairwise comparison among large samples of sequences, and thus, aiding the calculation of transmission links among infected individuals using threshold-based methods. In addition, the convex hull efficiently obtains important summary metrics for intra-host viral populations.

Accurate spatiotemporal mapping of drug overdose deaths by machine learning of drug-related web-searches.

Persons who inject drugs (PWID) are at increased risk for overdose death (ODD), infections with HIV, hepatitis B (HBV) and hepatitis C virus (HCV), and noninfectious health conditions. Spatiotemporal identification of PWID communities is essential for developing efficient and cost-effective public health interventions for reducing morbidity and mortality associated with injection-drug use (IDU). Reported ODDs are a strong indicator of the extent of IDU in different geographic regions. However, ODD quantification can take time, with delays in ODD reporting occurring due to a range of factors including death investigation and drug testing. This delayed ODD reporting may affect efficient early interventions for infectious diseases. We present a novel model, Dynamic Overdose Vulnerability Estimator (DOVE), for assessment and spatiotemporal mapping of ODDs in different U.S. jurisdictions. Using Google® Web-search volumes (i.e., the fraction of all searches that include certain words), we identified a strong association between the reported ODD rates and drug-related search terms for 2004-2017. A machine learning model (Extremely Random Forest) was developed to produce yearly ODD estimates at state and county levels, as well as monthly estimates at state level. Regarding the total number of ODDs per year, DOVE's error was only 3.52% (Median Absolute Error, MAE) in the United States for 2005-2017. DOVE estimated 66,463 ODDs out of the reported 70,237 (94.48%) during 2017. For that year, the MAE of the individual ODD rates was 4.43%, 7.34%, and 12.75% among yearly estimates for states, yearly estimates for counties, and monthly estimates for states, respectively. These results indicate suitability of the DOVE ODD estimates for dynamic IDU assessment in most states, which may alert for possible increased morbidity and mortality associated with IDU. ODD estimates produced by DOVE offer an opportunity for a spatiotemporal ODD mapping. Timely identification of potential mortality trends among PWID might assist in developing efficient ODD prevention and HBV, HCV, and HIV infection elimination programs by targeting public health interventions to the most vulnerable PWID communities.

Entropy of mitochondrial DNA circulating in blood is associated with hepatocellular carcinoma.

BACKGROUND: Ultra-Deep Sequencing (UDS) enabled identification of specific changes in human genome occurring in malignant tumors, with current approaches calling for the detection of specific mutations associated with certain cancers. However, such associations are frequently idiosyncratic and cannot be generalized for diagnostics. Mitochondrial DNA (mtDNA) has been shown to be functionally associated with several cancer types. Here, we study the association of intra-host mtDNA diversity with Hepatocellular Carcinoma (HCC). RESULTS: UDS mtDNA exome data from blood of patients with HCC (n = 293) and non-cancer controls (NC, n = 391) were used to: (i) measure the genetic heterogeneity of nucleotide sites from the entire population of intra-host mtDNA variants rather than to detect specific mutations, and (ii) apply machine learning algorithms to develop a classifier for HCC detection. Average total entropy of HCC mtDNA is 1.24-times lower than of NC mtDNA (p = 2.84E-47). Among all polymorphic sites, 2.09% had a significantly different mean entropy between HCC and NC, with 0.32% of the HCC mtDNA sites having greater (p < 0.05) and 1.77% of the sites having lower mean entropy (p < 0.05) as compared to NC. The entropy profile of each sample was used to further explore the association between mtDNA heterogeneity and HCC by means of a Random Forest (RF) classifier The RF-classifier separated 232 HCC and 232 NC patients with accuracy of up to 99.78% and average accuracy of 92.23% in the 10-fold cross-validation. The classifier accurately separated 93.08% of HCC (n = 61) and NC (n = 159) patients in a validation dataset that was not used for the RF parameter optimization. CONCLUSIONS: Polymorphic sites contributing most to the mtDNA association with HCC are scattered along the mitochondrial genome, affecting all mitochondrial genes. The findings suggest that application of heterogeneity profiles of intra-host mtDNA variants from blood may help overcome barriers associated with the complex association of specific mutations with cancer, enabling the development of accurate, rapid, inexpensive and minimally invasive diagnostic detection of cancer.

Impact of a multicomponent physical activity intervention on cognitive performance: The MOVI-KIDS study.

INTRODUCTION: This study examined the impact of a multicomponent physical activity (PA) intervention (MOVI-KIDS) on improving cognition in schoolchildren. This paper also analyzed the mediator role of motor fitness between MOVI-KIDS and cognition. METHODS: Propensity score analysis of data from a cluster randomized controlled trial (MOVI-KIDS study). This analysis including 240 5-7 years old children from nine schools in the provinces of Cuenca and Ciudad Real, Spain. MOVI-KIDS program consisted of: (a) three weekly after-school sessions of recreational non-competitive PA lasting 60 minutes during one academic year, (b) educational materials for parents and teachers, and (c) school playground modifications. Changes in cognition (logical reasoning, verbal factor, numerical factor, spatial factor, and general intelligence) were measured. A propensity score cross-cluster matching procedure and mediation analysis (Hayes's PROCESS macro) were conducted. RESULTS: All cognitive variables pre-post mean changes were significantly higher (P ≤ 0.05) in children from intervention schools than those from control schools (effect size ranged from 0.33 to 1.48). The effect of the intervention on the spatial factor and general intelligence was partially mediated by motor fitness (indirect effect = 0.92, 95% CI: 0.36; 1.65; and indirect effect = 1.21, 95% CI: 0.06; 2.62, respectively). CONCLUSIONS: This study shows that a one-school-year multicomponent intervention consisting of a recreational non-competitive PA program, educational materials for parents and teachers, and school playground modifications improved the cognition of first-grade children. Further, our results suggest that the effect of the intervention on cognition was mediated by changes in motor fitness.

Motivos de práctica deportiva, bienestar psicológico e inmigración en practicantes de Street Workout / Motives for sports practice, psychological well-being and immigration in Street Workout practitioners / Motivos da prática esportiva, bem-estar psicológico e imigração em praticantes de Street Workout

El objetivo de la investigación es desarrollar un perfil psicosocial de los practicantes de SW y contribuir al conocimiento de esta actividad. Utilizamos un diseño de tipo transversal descriptivo-correlacional, que contó con 216 participantes de entre 18 y 36 años. El 92.6% de la muestra se conformó por hombres y un 21.3% por inmigrantes. Evaluamos los motivos de práctica, bienestar psicológico, las diferencias en función de la situación migratoria y de la práctica de deportes adicionales. Obtuvimos datos sociodemográficos (sexo, edad, situación sociolaboral y país de origen) y relacionados con la actividad (antigüedad en la práctica, tiempo de entrenamiento y modo de aproximación). Los resultados mostraron que el SW se practica principalmente por jóvenes (X= 18.01), estudiantes (77.8%). Los principales modos de aproximación son los conocidos y las redes sociales. El 57.4% practica exclusivamente SW. Adicionalmente, los motivos de práctica más relevantes fueron el disfrute, la competencia y el fitness; también, los participantes obtuvieron puntuaciones altas en el bienestar psicológico. Estos datos nos hacen pensar en el SW como una herramienta útil para la promoción de la actividad física y valores de integración, especialmente destacable por su bajo coste económico y accesibilidad a la población

The objective of this research is to develop a psychosocial profile of the SW practitioners and contribute to the knowledge of this activity. A descriptive-correlational single-measure design was used, which included 216 participants aged 18-36 years old. 92.6% of the sample was formed by men and 21.3% by immigrants. We evaluate practice motivation, psychological well-being, and differences depending on the migratory situation and the practice of additional sports. We obtained sociodemographic data (sex, age, socio-labor situation and country of origin) and data related to the activity (seniority in practice, training time and approach mode). The results showed that SW is practiced mainly by the young male (X= 18.01), students (77.8%). The main modes of approach by acquaintances and social networks. The 57.4% of the participants practice SW exclusively. The most relevant reasons for practice were enjoyment, competence and fitness; also, the participants obtained high scores in psychological well-being. These data make us think of the SW as a useful tool for the promotion of physical activity and values of integration, especially remarkable for its low economic cost and accessibility to the population

O objetivo da investigação é desenvolver um perfil psicossocial dos participantes de SW e contribuir ao conhecimento da atividade. Usamos um desenho de tipo transversal descritivo - correlacionado, que contava com 216 participantes entre 18 y 36 años de idade. 92.6% da mostra foi composta por homens e um 21.3% por imigrantes. Avaliamos os motivos da prática, bem-estar psicológico, as diferenças dependendo da situação migratória e a prática de esportes adicionais. Obtivemos dados sócios-demográficos (sexo, idade, situação sócio-trabalhista e o país de origem) e relacionado com a atividade (antiguidade na prática, o tempo de treinamento e modo de aproximação). Os resultados mostraram que o SW é praticado principalmente por jovens (x = 18.01), estudantes (77.8%). As principais maneiras de abordagem são os conhecidos e as redes sociais. 57.4% práticas exclusivamente SW., Os motivos da prática mais importantes foram o prazer, a competição e o fitness; da mesma forma, os participantes obtiveram altas pontuações no bem-estar psicológico. Esses dados nos fazem pensar sobre o SW como uma ferramenta útil para a promoção da atividade física e valores de integração, especialmente notável por seu baixo custo econômico e acessibilidade à população

Fast estimation of genetic relatedness between members of heterogeneous populations of closely related genomic variants.

BACKGROUND: Many biological analysis tasks require extraction of families of genetically similar sequences from large datasets produced by Next-generation Sequencing (NGS). Such tasks include detection of viral transmissions by analysis of all genetically close pairs of sequences from viral datasets sampled from infected individuals or studying of evolution of viruses or immune repertoires by analysis of network of intra-host viral variants or antibody clonotypes formed by genetically close sequences. The most obvious naïeve algorithms to extract such sequence families are impractical in light of the massive size of modern NGS datasets. RESULTS: In this paper, we present fast and scalable k-mer-based framework to perform such sequence similarity queries efficiently, which specifically targets data produced by deep sequencing of heterogeneous populations such as viruses. It shows better filtering quality and time performance when comparing to other tools. The tool is freely available for download at https://github.com/vyacheslav-tsivina/signature-sj CONCLUSION: The proposed tool allows for efficient detection of genetic relatedness between genomic samples produced by deep sequencing of heterogeneous populations. It should be especially useful for analysis of relatedness of genomes of viruses with unevenly distributed variable genomic regions, such as HIV and HCV. For the future we envision, that besides applications in molecular epidemiology the tool can also be adapted to immunosequencing and metagenomics data.

Automated quality control for a molecular surveillance system.

BACKGROUND: Molecular surveillance and outbreak investigation are important for elimination of hepatitis C virus (HCV) infection in the United States. A web-based system, Global Hepatitis Outbreak and Surveillance Technology (GHOST), has been developed using Illumina MiSeq-based amplicon sequence data derived from the HCV E1/E2-junction genomic region to enable public health institutions to conduct cost-effective and accurate molecular surveillance, outbreak detection and strain characterization. However, as there are many factors that could impact input data quality to which the GHOST system is not completely immune, accuracy of epidemiological inferences generated by GHOST may be affected. Here, we analyze the data submitted to the GHOST system during its pilot phase to assess the nature of the data and to identify common quality concerns that can be detected and corrected automatically. RESULTS: The GHOST quality control filters were individually examined, and quality failure rates were measured for all samples, including negative controls. New filters were developed and introduced to detect primer dimers, loss of specimen-specific product, or short products. The genotyping tool was adjusted to improve the accuracy of subtype calls. The identification of "chordless" cycles in a transmission network from data generated with known laboratory-based quality concerns allowed for further improvement of transmission detection by GHOST in surveillance settings. Parameters derived to detect actionable common quality control anomalies were incorporated into the automatic quality control module that rejects data depending on the magnitude of a quality problem, and warns and guides users in performing correctional actions. The guiding responses generated by the system are tailored to the GHOST laboratory protocol. CONCLUSIONS: Several new quality control problems were identified in MiSeq data submitted to GHOST and used to improve protection of the system from erroneous data and users from erroneous inferences. The GHOST system was upgraded to include identification of causes of erroneous data and recommendation of corrective actions to laboratory users.

Intelligent Network DisRuption Analysis (INDRA): A targeted strategy for efficient interruption of hepatitis C transmissions.

Hepatitis C virus (HCV) infection is a global public health problem. The implementation of public health interventions (PHI) to control HCV infection could effectively interrupt HCV transmission. PHI targeting high-risk populations, e.g., people who inject drugs (PWID), are most efficient but there is a lack of tools for prioritizing individuals within a high-risk community. Here, we present Intelligent Network DisRuption Analysis (INDRA), a targeted strategy for efficient interruption of hepatitis C transmissions.Using a large HCV transmission network among PWID in Indiana as an example, we compare effectiveness of random and targeted strategies in reducing the rate of HCV transmission in two settings: (1) long-established and (2) rapidly spreading infections (outbreak). Identification of high centrality for the network nodes co-infected with HIV or > 1 HCV subtype indicates that the network structure properly represents the underlying contacts among PWID relevant to the transmission of these infections. Changes in the network's global efficiency (GE) were used as a measure of the PHI effects. In setting 1, simulation experiments showed that a 50% GE reduction can be achieved by removing 11.2 times less nodes using targeted vs random strategies. A greater effect of targeted strategies on GE was consistently observed when networks were simulated: (1) with a varying degree of errors in node sampling and link assignment, and (2) at different levels of transmission reduction at affected nodes. In simulations considering a 10% removal of infected nodes, targeted strategies were ~2.8 times more effective than random in reducing incidence. Peer-education intervention (PEI) was modeled as a probabilistic distribution of actionable knowledge of safe injection practices from the affected node to adjacent nodes in the network. Addition of PEI to the models resulted in a 2-3 times greater reduction in incidence than from direct PHI alone. In setting 2, however, random direct PHI were ~3.2 times more effective in reducing incidence at the simulated conditions. Nevertheless, addition of PEI resulted in a ~1.7-fold greater efficiency of targeted PHI. In conclusion, targeted PHI facilitated by INDRA outperforms random strategies in decreasing circulation of long-established infections. Network-based PEI may amplify effects of PHI on incidence reduction in both settings.

QUENTIN: reconstruction of disease transmissions from viral quasispecies genomic data.

Motivation: Genomic analysis has become one of the major tools for disease outbreak investigations. However, existing computational frameworks for inference of transmission history from viral genomic data often do not consider intra-host diversity of pathogens and heavily rely on additional epidemiological data, such as sampling times and exposure intervals. This impedes genomic analysis of outbreaks of highly mutable viruses associated with chronic infections, such as human immunodeficiency virus and hepatitis C virus, whose transmissions are often carried out through minor intra-host variants, while the additional epidemiological information often is either unavailable or has a limited use. Results: The proposed framework QUasispecies Evolution, Network-based Transmission INference (QUENTIN) addresses the above challenges by evolutionary analysis of intra-host viral populations sampled by deep sequencing and Bayesian inference using general properties of social networks relevant to infection dissemination. This method allows inference of transmission direction even without the supporting case-specific epidemiological information, identify transmission clusters and reconstruct transmission history. QUENTIN was validated on experimental and simulated data, and applied to investigate HCV transmission within a community of hosts with high-risk behavior. It is available at https://github.com/skumsp/QUENTIN. Contact: pskums@gsu.edu or alexz@cs.gsu.edu or rahul@sfsu.edu or yek0@cdc.gov. Supplementary information: Supplementary data are available at Bioinformatics online.

Transmissibility of intra-host hepatitis C virus variants.

BACKGROUND: Intra-host hepatitis C virus (HCV) populations are genetically heterogeneous and organized in subpopulations. With the exception of blood transfusions, transmission of HCV occurs via a small number of genetic variants, the effect of which is frequently described as a bottleneck. Stochasticity of transmission associated with the bottleneck is usually used to explain genetic differences among HCV populations identified in the source and recipient cases, which may be further exacerbated by intra-host HCV evolution and differential biological capacity of HCV variants to successfully establish a population in a new host. RESULTS: Transmissibility was formulated as a property that can be measured from experimental Ultra-Deep Sequencing (UDS) data. The UDS data were obtained from one large hepatitis C outbreak involving an epidemiologically defined source and 18 recipient cases. k-Step networks of HCV variants were constructed and used to identify a potential association between transmissibility and network centrality of individual HCV variants from the source. An additional dataset obtained from nine other HCV outbreaks with known directionality of transmission was used for validation. Transmissibility was not found to be dependent on high frequency of variants in the source, supporting the earlier observations of transmission of minority variants. Among all tested measures of centrality, the highest correlation of transmissibility was found with Hamming centrality (r = 0.720; p = 1.57 E-71). Correlation between genetic distances and differences in transmissibility among HCV variants from the source was found to be 0.3276 (Mantel Test, p = 9.99 E-5), indicating association between genetic proximity and transmissibility. A strong correlation ranging from 0.565-0.947 was observed between Hamming centrality and transmissibility in 7 of the 9 additional transmission clusters (p < 0.05). CONCLUSIONS: Transmission is not an exclusively stochastic process. Transmissibility, as formally measured in this study, is associated with certain biological properties that also define location of variants in the genetic space occupied by the HCV strain from the source. The measure may also be applicable to other highly heterogeneous viruses. Besides improving accuracy of outbreak investigations, this finding helps with the understanding of molecular mechanisms contributing to establishment of chronic HCV infection.