Reliability of a Battery of cognitive tests in Young, healthy people / Fiabilidad de una batería de pruebas cognitivas en personas jóvenes y sanas

Cognitive performance is an essential aspect of sports. Still, few studies provide reliable cognitive tests for training and evaluating cognitive aspects related to sports, particularly in a healthy and/or youthful population. This study assessed the intraday and interday reliability of a cognitive test battery related to sports performance. In the research, seventeen healthy volunteers participated. DynavisionTMD2, Bassin Anticipation Timer, Go/No-Go, Eriksen Flanker, and the Trail Making Test (TMT) were analyzed. TMT-A and TMT-B (intraday), as well as the following variables in the interday analysis, exhibited significant differences: "Physical RT - Fastest" (Test 1), "Visual RT - Fastest" and "Physical RT - Fastest" (Test 2), "Visual RT - Fastest" (Test 4) and TMT-A. Thirteen of the thirty-one variables analyzed on the intraday exam had moderate Intraclass Correlation Coefficients (ICC), six were good, and one was exceptional. In the interday analysis, 15 variables with moderate ICC and 5 variables with excellent ICC were identified. Reliable tests include "Reaction Time" (Tests 5 and 6) and "Red and Green Lights" of the DynavisionTMD2, the 15 mph speed of the Bassin Anticipation Timer, and the Go/No-Go, Eriksen Flanker, and TMT tests.(AU)

Trusting in God: Religious and Spiritual Support in Mental Health Treatment Expectations in Mexico.

Religious and spiritual practices have been identified as a main source of mental health support for Latinxs to improve overall health and well-being. This qualitative secondary data analysis sought to elucidate how Mexican patients and family members engaged in religious and spiritual practices to help alleviate patients' experiences of mental illness. Three main findings are discussed: (1) positive religious coping such as entrusting God with one's suffering, consejos (i.e., emotional support and advice giving), and positive social supports through religious communities; (2) negative religious coping such as harmful views of God as punishing; and (3) indigenous healing practices such as engagement with curanderos (medicine doctor) and limpias (i.e., herb-based cleanses). The authors discuss these findings in the context of tensions between culturally sanctioned healing and the perception of psychotherapeutic effectiveness reported by Mexican patients and their family members. The authors also provide future directions for incorporating patients' religious and spiritual practices into multiculturally competent treatment.

Intervention in the Cohesion of Narrative Discourse in Pupils with Developmental Language Disorder / La intervención en la cohesión del discurso narrativo en los alumnos que tienen un trastorno evolutivo del lenguaje

The main objective of this study was to verify the effectiveness of an intervention program on cohesion in pupils with typical development and with developmental language disorder. A total of 99 5-year-old pupils from schools in the island of Tenerife (Canary Islands, Spain) participated. For the narrative analysis, a story retelling task was used, studying cohesive resources such as ellipsis, anaphora, possessives, and connectors. The intervention program was organized at different levels of practice and involved teachers and speech language therapists. The results indicated that the pupils diagnosed with developmental language disorder initially presented worse performance in cohesion than their peers with typical development. Finally, the two groups of children who received the intervention program showed significantly higher gains than the two groups without treatment, with medium or small effect sizes.

El objetivo principal del presente estudio ha sido comprobar la efectividad de un programa de intervención en la cohesión de alumnado con desarrollo típico y con trastorno del desarrollo del lenguaje. Participaron 99 alumnos de 5 años de colegios de la isla de Tenerife (Islas Canarias, España). Para el análisis narrativo se utilizó el recontado de un cuento, estudiándose recursos cohesivos como las elipsis, las anáforas, los posesivos y los conectores. El programa de intervención se organizó en diferentes niveles de práctica y contó con la colaboración entre el profesorado y las logopedas. Los resultados indicaron que el alumnado diagnosticado con trastorno del desarrollo del lenguaje presentaba inicialmente un peor rendimiento en cohesión que sus compañeros con desarrollo normal. Finalmente, los dos grupos de niños que recibieron el programa de intervención mostraron ganancias significativamente más altas que los dos grupos sin tratamiento, con tamaños de efecto medianos o pequeños.

A method for variant agnostic detection of SARS-CoV-2, rapid monitoring of circulating variants, detection of mutations of biological significance, and early detection of emergent variants such as Omicron

The rapid emergence of new SARS-CoV-2 variants raises a number of public health questions including the capability of diagnostic tests to detect new strains, the efficacy of vaccines, and how to map the geographical distribution of variants to better understand patterns of transmission and possible load on healthcare resources. Next-Generation Sequencing (NGS) is the primary method for detecting and tracing the emergence of new variants, but it is expensive, and it can take weeks before sequence data is available in public repositories. Here, we describe a Polymerase Chain Reaction (PCR)-based genotyping approach that is significantly less expensive, accelerates reporting on SARS-CoV-2 variants, and can be implemented in any testing lab performing PCR. Specific Single Nucleotide Polymorphisms (SNPs) and indels are identified that have high positive percent agreement (PPA) and negative percent agreement (NPA) compared to NGS for the major genotypes that circulated in 2021. Using a 48-marker panel, testing on 1,128 retrospective samples yielded a PPA and NPA in the 96.3 to 100% and 99.2 to 100% range, respectively, for the top 10 most prevalent lineages. The effect on PPA and NPA of reducing the number of panel markers was also explored. In addition, with the emergence of Omicron, we also developed an Omicron genotyping panel that distinguishes the Delta and Omicron variants using four (4) highly specific SNPs. Data from testing demonstrates the capability to use the panel to rapidly track the growing prevalence of the Omicron variant in the United States in December 2021.

Rapa Nui (Easter Island) Rano Raraku crater lake basin: Geochemical characterization and implications for the Ahu-Moai Period.

Rano Raraku, the crater lake constrained by basaltic tuff that served as the primary quarry used to construct the moai statues on Rapa Nui (Easter Island), has experienced fluctuations in lake level over the past centuries. As one of the only freshwater sources on the island, understanding the present and past geochemical characteristics of the lake water is critical to understand if the lake could have been a viable freshwater source for Rapa Nui. At the time of sampling in September 2017, the maximum lake depth was ~1 m. The lake level has substantially declined in the subsequent years, with the lake drying almost completely in January 2018. The lake is currently characterized by highly anoxic conditions, with a predominance of ammonium ions on nitrates, a high concentration of organic carbon in the water-sediment interface and reducing conditions of the lake, as evidenced by Mn/Fe and Cr/V ratios. Our estimates of past salinity inferred from the chloride mass balance indicates that it was unlikely that Rano Raraku provided a viable freshwater source for early Rapa Nui people. The installation of an outlet pipe around 1950 that was active until the late 1970s, as well as grazing of horses on the lake margins appear to have significantly impacted the geochemical conditions of Rano Raraku sediments and lake water in recent decades. Such impacts are distinct from natural environmental changes and highlight the need to consider the sensitivity of the lake geochemistry to human activities.

National Population-Level Disparities in COVID-19 Mortality Across the Intersection of Race/Ethnicity and Sex in the United States

Males and certain racial/ethnic minority groups have borne a disproportionate burden of COVID-19 mortality in the United States, and substantial scientific research has sought to quantify and characterize population-level disparities in COVID-19 mortality outcomes by sex and across categories of race/ethnicity. However, there has not yet been a national population-level study to quantify disparities in COVID-19 mortality outcomes across the intersection of these demographic dimensions. Here, we analyze a publicly available dataset from the National Center for Health Statistics comprising COVID-19 death counts stratified by race/ethnicity, sex, and age for the year 2020, calculating mortality rates for each race/ethnicity-sex-age stratum and age-adjusted mortality rates for each race/ethnicity-sex stratum, quantifying disparities in terms of mortality rate ratios and rate differences. Our results reveal persistently higher COVID-19 age-adjusted mortality rates for males compared to females within every racial/ethnic group, with notable variation in the magnitudes of the sex disparity by race/ethnicity. However, non-Hispanic Black, Hispanic, and non-Hispanic American Indian or Alaska Native females have higher age-adjusted mortality rates than non-Hispanic White and non-Hispanic Asian/Pacific Islander males. Moreover, persistent racial/ethnic disparities are observed among both males and females, with higher COVID-19 age-adjusted mortality rates observed for non-Hispanic Blacks, Hispanics, and non-Hispanic American Indian or Alaska Natives relative to non-Hispanic Whites.

Rapid displacement of SARS-CoV-2 variant B.1.1.7 by B.1.617.2 and P.1 in the United States

This study reports on the displacement of Alpha (B.1.1.7) by Delta (B.1.617.2 and its substrains AY.1, AY.2, and AY.3) in the United States. By analyzing RT-qPCR testing results and viral sequencing results of samples collected across the United States, we show that the percentage of SARS-CoV-2 positive cases caused by Alpha dropped from 67% in May 2021 to less than 3.0% in just 10 weeks. We also show that the Delta variant has outcompeted the Iota (B.1.526) variant of interest and Gamma (P.1) variant of concern. An analysis of the mean quantification cycles (Cq) values in positive tests over time also reveal that Delta infections lead to a higher viral load on average compared to Alpha infections, but this increase is only 2 to 3x on average for our study design. Our results are consistent with the hypothesis that the Delta variant is more transmissible than the Alpha variant, and that this could be due to the Delta variants ability to establish a higher viral load earlier in the infection compared to the Alpha variant.

Male-Female Disparities in Years of Potential Life Lost Attributable to COVID-19 in the United States: A State-by-State Analysis

Males are at higher risk relative to females of severe outcomes following COVID-19 infection. Focusing on COVID-19-attributable mortality in the United States (U.S.), we quantify and contrast years of potential life lost (YPLL) attributable to COVID-19 by sex based on data from the U.S. National Center for Health Statistics as of 31 March 2021, specifically by contrasting male and female percentages of total YPLL with their respective percent population shares and calculating age-adjusted male-to-female YPLL rate ratios both nationally and for each of the 50 states and the District of Columbia. Using YPLL before age 75 to anchor comparisons between males and females and a novel Monte Carlo simulation procedure to perform estimation and uncertainty quantification, our results reveal a near-universal pattern across states of higher COVID-19-attributable YPLL among males compared to females. Furthermore, the disproportionately high COVID-19 mortality burden among males is generally more pronounced when measuring mortality in terms of YPLL compared to age-irrespective death counts, reflecting dual phenomena of males dying from COVID-19 at higher rates and at systematically younger ages relative to females. The U.S. COVID-19 epidemic also offers lessons underscoring the importance of a public health environment that recognizes sex-specific needs as well as different patterns in risk factors, health behaviors, and responses to interventions between men and women. Public health strategies incorporating focused efforts to increase COVID-19 vaccinations among men are particularly urged.

Genomic epidemiology identifies emergence and rapid transmission of SARS-CoV-2 B.1.1.7 in the United States

As of January of 2021, the highly transmissible B.1.1.7 variant of SARS-CoV-2, which was first identified in the United Kingdom (U.K.), has gained a strong foothold across the world. Because of the sudden and rapid rise of B.1.1.7, we investigated the prevalence and growth dynamics of this variant in the United States (U.S.), tracking it back to its early emergence and onward local transmission. We found that the RT-qPCR testing anomaly of S gene target failure (SGTF), first observed in the U.K., was a reliable proxy for B.1.1.7 detection. We sequenced 212 B.1.1.7 SARS-CoV-2 genomes collected from testing facilities in the U.S. from December 2020 to January 2021. We found that while the fraction of B.1.1.7 among SGTF samples varied by state, detection of the variant increased at a logistic rate similar to those observed elsewhere, with a doubling rate of a little over a week and an increased transmission rate of 35-45%. By performing time-aware Bayesian phylodynamic analyses, we revealed several independent introductions of B.1.1.7 into the U.S. as early as late November 2020, with onward community transmission enabling the variant to spread to at least 30 states as of January 2021. Our study shows that the U.S. is on a similar trajectory as other countries where B.1.1.7 rapidly became the dominant SARS-CoV-2 variant, requiring immediate and decisive action to minimize COVID-19 morbidity and mortality.

SARS-CoV-2 Worldwide Replication Drives Rapid Rise and Selection of Mutations across the Viral Genome: A Time-Course StudyPotential Challenge for Vaccines and Therapies

Scientists and the public were alarmed at the first large viral variant of SARS-CoV2 reported in December 2020. We have followed the time course of emerging viral mutants and variants during the SARS-CoV-2 pandemic in ten countries on four continents. We examined complete SARS-CoV-2 nucleotide sequences in GISAID, (Global Initiative of Sharing All Influenza Data) with sampling dates extending until January 20, 2021. These sequences originated from ten different countries: United Kingdom, South Africa, Brazil, USA, India, Russia, France, Spain, Germany, and China. Among the novel mutations, some previously reported mutations waned and some of them increased in prevalence over time. VUI2012/01 (B.1.1.7) and 501Y.V2 (B.1.351), the so-called UK and South Africa variants, respectively, and two variants from Brazil, 484K.V2, now called P.1 and P.2, increased in prevalence. Despite lockdowns, worldwide active replication in genetically and socio-economically diverse populations facilitated selection of new mutations. The data on mutant and variant SARS-CoV-2 strains provided here comprise a global resource for easy access to the myriad mutations and variants detected to date globally. Rapidly evolving new variant and mutant strains might give rise to escape variants, capable of limiting the efficacy of vaccines, therapies, and diagnostic tests. Significance and New Aspects of Study - SynopsisO_LIWe examine the time course of emerging mutations in the SARS-CoV-2 genome that have rapidly been selected in the worlds populations through the beginning of 2021. A study of the prevalence of viral mutations in the GISAID database in ten different countries - United Kingdom, South Africa, Brazil, US, India, Russia, France, Spain, Germany, and China - revealed widespread mutations along the genome. C_LIO_LIWe previously identified about 10 hotspot mutations in the SARS-CoV-2 genome that became prevalent in many of the countries studied1. Since the beginning of February, many new mutations arose in the ten countries (and worldwide). The preponderance of variants and mutations correlated with the increased spread of Covid-19. C_LIO_LIThere was a temporal progression from about 10 predominant mutants shared by several countries up to the end of May 2020, followed by a consistent and rapid increase in the number of new mutations between June and December along with the emergence of variants of concern, first reported in December 2020. C_LIO_LIWe examine the relative frequencies of mutations, along with variants of interest, in 10 countries up until January 20, 2021. Investigations on the pathogenic properties of individual SARS-CoV-2 mutations will be urgently needed to understand the kaleidoscopic patterns of worldwide Covid-19 outbreaks and symptoms. Monitoring the frequency and speed of mutant selection have direct relevance to diagnostic testing, vaccines and therapeutics. C_LIO_LIAs an explanation for efficient viral mutagenesis, we hypothesize that the viral spike protein - as documented - facilitates viral entry via the cells ACE receptor2. This in turn interacts with the APOBEC polypeptide, an m-RNA editing function. The actually observed frequent C to U (T) transitions and other base exchanges are thus effected. Hence, as one of the earliest steps upon viral entry, active mutagenesis commences, since SARS-CoV-2 exploits one of the cells defenses against viral infections. C_LI

Racial and Ethnic Disparities in Years of Potential Life Lost Attributable to COVID-19 in the United States: An Analysis of 45 States and the District of Columbia

The coronavirus disease 2019 (COVID-19) epidemic in the United States has disproportionately impacted communities of color across the country. Focusing on COVID-19-attributable mortality, we expand upon a national comparative analysis of years of potential life lost (YPLL) attributable to COVID-19 by race/ethnicity (Bassett et al., 2020), estimating percentages of total YPLL for non-Hispanic Whites, non-Hispanic Blacks, Hispanics, non-Hispanic Asians, and non-Hispanic American Indian or Alaska Natives, contrasting them with their respective percent population shares, as well as age-adjusted YPLL rate ratios - anchoring comparisons to non-Hispanic Whites - in each of 45 states and the District of Columbia using data from the National Center for Health Statistics as of December 30, 2020. Using a novel Monte Carlo simulation procedure to quantify estimation uncertainty, our results reveal substantial racial/ethnic disparities in COVID-19-attributable YPLL across states, with a prevailing pattern of non-Hispanic Blacks and Hispanics experiencing disproportionately high and non-Hispanic Whites experiencing disproportionately low COVID-19-attributable YPLL. Furthermore, observed disparities are generally more pronounced when measuring mortality in terms of YPLL compared to death counts, reflecting the greater intensity of the disparities at younger ages. We also find substantial state-to-state variability in the magnitudes of the estimated racial/ethnic disparities, suggesting that they are driven in large part by social determinants of health whose degree of association with race/ethnicity varies by state.

Using symptom-based case predictions to identify host genetic factors that contribute to COVID-19 susceptibility

Epidemiological and genetic studies on COVID-19 are currently hindered by inconsistent and limited testing policies to confirm SARS-CoV-2 infection. Recently, it was shown that it is possible to predict potential COVID-19 cases using cross-sectional self-reported disease-related symptoms. Using a previously reported COVID-19 prediction model, we show that it is possible to conduct a GWAS on predicted COVID-19, and this GWAS benefits from the larger sample size to provide new insights into the genetic susceptibility of the disease. Furthermore, we find suggestive evidence that genetic variants for other viral infectious diseases do not overlap with COVID-19 susceptibility and that severity of COVID-19 may have a different genetic architecture compared to COVID-19 susceptibility. Our findings demonstrate the added value of using self-reported symptom assessments to quickly monitor novel endemic viral outbreaks in a scenario of limited testing. Should there be another outbreak of a novel infectious disease, we recommend repeatedly collecting data of disease-related symptoms.

Mask-associated de novo headache in healthcare workers during the Covid-19 pandemic.

IntroductionThe pandemic caused by the new coronavirus (COVID-19) has led to changes in the development of health care activities by health professionals. We analysed whether there is an association between the appearance of "de novo" headache according to the type of mask used, the related factors, as well as the impact of the headache on health professionals. Methodcross-sectional study in a tertiary hospital in Extremadura, Spain. We administered an online questionnaire to healthcare workers during the period of maximum incidence of COVID-19 in our setting. Resultsn=306, 244 women (79.7%), with an average age of 43 years (range 23-65). Of the total, 129 (42.2%) were physicians, 112 (36.6%) nurses and 65 (21.2%) other health workers. 208 (79.7%) used surgical masks and 53 (20.3%) used filtering masks. Of all those surveyed, 158 (51.6%) presented "de novo" headache. The occurrence of headache was independently associated with the use of a filtering mask, OR 2.14 (IC95% 1.07-4.32), being a nurse OR 2.09 (IC95% 1.18-3.72) or another health worker OR 6.94 (IC95% 3.01-16.04) or having a history of asthma OR 0.29 (IC95% 0.09-0.89). Depending on the type of mask used there were differences in headache intensity. And the impact of headache in the subjects who used a filtering mask was worse in the all aspects evaluated. ConclusionsThe appearance of "de novo" headache is associated with the use of filtering masks and is more frequent in certain health care workers, causing a greater occupational, family, personal and social impact.

Longitudinal COVID-19 Surveillance and Characterization in the Workplace with Public Health and Diagnostic Endpoints

BackgroundThe rapid spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and the associated coronavirus disease 2019 (COVID-19) have precipitated a global pandemic heavily challenging our social behavior, economy, and healthcare infrastructure. Public health practices currently represent the primary interventions for managing the spread of the pandemic. We hypothesized that frequent, longitudinal workplace disease surveillance would represent an effective approach to controlling SARS-CoV-2 transmission among employees and their household members, reducing potential economic consequences and loss of productivity of standard isolation methods, while providing new insights into viral-host dynamics. Methodology and FindingsOn March 23, 2020 a clinical study (OCIS-05) was initiated at a small Southern California organization. Results from the first 3 months of the ongoing study are presented here. Study participants (27 employees and 27 household members) consented to provide frequent nasal or oral swab samples that were analyzed by RT-qPCR for SARS-CoV-2 RNA using CDC protocols. Only participants testing negative were allowed to enter the "safe zone" workplace facility. Optional blood samples were collected at baseline and throughout the 3-month study. Serum virus-specific antibody concentrations (IgG, IgM, and IgA) were measured using a selective, sensitive, and quantitative ELISA assay developed in house. A COVID-19 infection model, based on traditional SEIR compartmental models combined with Bayesian non-linear mixed models and modern machine learning, was used to predict the number of employees and household members who would have become infected in the absence of workplace surveillance. Two study participants were found to be infected by SARS-CoV-2 during the study. One subject, a household member, tested positive clinically by RT-qPCR prior to enrollment and experienced typical COVID-19 symptoms that did not require hospitalization. While on study, the participant was SARS-CoV-2 RNA positive for at least 71 days and had elevated virus-specific antibody concentrations (medians: IgM, 9.83 {micro}g mL-1; IgG, 11.5 {micro}g mL-1; IgA, 1.29 {micro}g mL-1) in serum samples collected at three timepoints. A single, unrelated employee became positive for SARS-CoV-2 RNA over the course of the study, but remained asymptomatic with low associated viral RNA copy numbers. The participant did not have detectable serum IgM and IgG concentrations, and IgA concentrations decayed rapidly (half-life: 1.3 d). The employee was not allowed entry to the safe zone workplace until testing negative three consecutive times over 7 d. No other employees or household members contracted COVID-19 over the course of the study. Our model predicted that under the current prevalence in Los Angeles County without surveillance intervention, up to 7 employees (95% CI = 3-10) would have become infected with at most 1 of them requiring hospitalizations and 0 deaths. ConclusionsOur clinical study met its primary objectives by using intense longitudinal testing to provide a safe work environment during the COVID-19 pandemic, and elucidating SARS-CoV-2 dynamics in recovering and asymptomatic participants. The surveillance plan outlined here is scalable and transferrable. The study represents a powerful example on how an innovative public health initiative can be dovetailed with scientific discovery.

Pseudo-Likelihood Based Logistic Regression for Estimating COVID-19 Infection and Case Fatality Rates by Gender, Race, and Age in California

In emerging epidemics, early estimates of key epidemiological characteristics of the disease are critical for guiding public policy. In particular, identifying high risk population subgroups aids policymakers and health officials in combatting the epidemic. This has been challenging during the coronavirus disease 2019 (COVID-19) pandemic, because governmental agencies typically release aggregate COVID-19 data as marginal summary statistics of patient demographics. These data may identify disparities in COVID-19 outcomes between broad population subgroups, but do not provide comparisons between more granular population subgroups defined by combinations of multiple demographics. We introduce a method that overcomes the limitations of aggregated summary statistics and yields estimates of COVID-19 infection and case fatality rates -- key quantities for guiding public policy related to the control and prevention of COVID-19 -- for population subgroups across combinations of demographic characteristics. Our approach uses pseudo-likelihood based logistic regression to combine aggregate COVID-19 case and fatality data with population-level demographic survey data to estimate infection and case fatality rates for population subgroups across combinations of demographic characteristics. We illustrate our method on California COVID-19 data to estimate test-based infection and case fatality rates for population subgroups defined by gender, age, and race and ethnicity. Our analysis indicates that in California, males have higher test-based infection rates and test-based case fatality rates across age and race/ethnicity groups, with the gender gap widening with increasing age. Although elderly infected with COVID-19 are at an elevated risk of mortality, the test-based infection rates do not increase monotonically with age. LatinX and African Americans have higher test-based infection rates than other race/ethnicity groups. The subgroups with the highest 5 test-based case fatality rates are African American male, Multi-race male, Asian male, African American female, and American Indian or Alaska Native male, indicating that African Americans are an especially vulnerable California subpopulation.

Fusing a Bayesian case velocity model with random forest for predicting COVID-19 in the U.S.

Predictions of COVID-19 case growth and mortality are critical to the decisions of political leaders, businesses, and individuals grappling with the pandemic. This predictive task is challenging due to the novelty of the virus, limited data, and dynamic political and societal responses. We embed a Bayesian nonlinear mixed model and a random forest algorithm within an epidemiological compartmental model for empirically grounded COVID-19 predictions. The Bayesian case model fits a location-specific curve to the velocity (first derivative) of the transformed cumulative case count, borrowing strength across geographic locations and incorporating prior information to obtain a posterior distribution for case trajectory. The compartmental model uses this distribution and predicts deaths using a random forest algorithm trained on COVID-19 data and population-level characteristics, yielding daily projections and interval estimates for infections and deaths in U.S. states. We evaluate forecasting accuracy on a two-week holdout set, finding that the model predicts COVID-19 cases and deaths well, with a mean absolute scaled error of 0.40 for cases and 0.32 for deaths throughout the two-week evaluation period. The substantial variation in predicted trajectories and associated uncertainty between states is illustrated by comparing three unique locations: New York, Ohio, and Mississippi. The sophistication and accuracy of this COVID-19 model offer reliable predictions and uncertainty estimates for the current trajectory of the pandemic in the U.S. and provide a platform for future predictions as shifting political and societal responses alter its course. Author summaryCOVID-19 models can be roughly classified as mathematical models that simulate disease within a population, including epidemiological compartmental models, or statistical curve-fitting models that fit a function to observed data and extrapolate forward into the future. Bridging this divide, we combine the strengths of curve-fitting statistical models and the structure of epidemiological models, by embedding a Bayesian nonlinear mixed model for case velocity and a machine learning algorithm (random forest) into the framework of a compartmental model. Fusing these models together exploits the particular strengths of each to glean as much information as possible from the currently available data. We also identify the velocity of log cumulative cases as an excellent target for modeling and extrapolating COVID-19 case trajectories. We empirically evaluate the predictive performance of the model and provide predicted trajectories with credible intervals for cumulative confirmed case count, active confirmed infections and COVID-19 deaths for each of the 50 U.S. states. Combining sophisticated data analytic methods with proven epidemiological models offers an empirically grounded strategy for making realistic predictions and quantifying their uncertainty. These predictions indicate substantial variation in the COVID-19 trajectories of U.S. states.

Awareness, attitudes and preferences of patients 40 to 59 years of age on advance care planning in a private hospital

@#<p style="text-align: justify;"><strong>BACKGROUND:</strong> Decisions during medical crisis made by the more-abled family member. More often than not, it is the adult son or daughter who makes it. As such, it creates stress and anxiety in the family who will be left behind.With Advance Care Planning (ACP), symptoms of stress, anxiety and depression are reduced for both the patients and their families.<br /><strong>OBJECTIVE:</strong> To describe and examine the awareness, preferences and attitudes of in-patients aged 40-59 years old in Rivera Medical Center, Inc. towards advance care planning through the Advance Care Planning Questions (ACPQ).<br /><strong>METHODS:</strong> Descriptive,correlational, cross-sectional study design was used. Necessary permissions were obtained. The modified questionnaire underwent validity prior to the actual application. Coding was done in Microsoft Excel while the statistical analyses were made using SPSS.<br /><strong>RESULTS:</strong> The ACPQ Cebuano version has good reliability (Cronbach's a=0.71-0.92). Participants were 49 years old (±5.5), mostly female (62%), married (74%), Cebuano/Visayan (82%), at high school level (48%), Christian of Catholic (98%), regular employees (42%), earning less than Php 10,000 a month (36%), living with their spouse and children (66%), having hypertension (44%) or diabetes mellitus (22%), and in "good" health despite the current hospitalization (60%). Majority have limited awareness of ACP (14-22%) but were willing to discuss and learn about ACP (70%). Awareness, attitudes and preferences towards ACP can be greatly influenced by the doctors.<br /><strong>CONCLUSION:</strong> Limited public understanding and awareness cause the negative reception and slow progress of ACP in the Philippines. The unconscious fear of death accounts for the unease and hesitance whenever the topic on death surfaces, impeding acceptance of ACP. The doctor, being the preferred decision-maker, should therefore be competent enough to help the family understand and cause a positive attitude towards ACP.</p>

New records of ectoparasitic Acari (Arachnida) and Streblidae (Diptera) from bats in Jalisco, Mexico.

Ectoparasites of bats in the Neotropics are diverse and play numerous ecological roles as vectors of microbial pathogens and endoparasites and as food sources for other cave fauna living both on their hosts and in bat roosts. The ectoparasites of bats in Jalisco State of western Mexico have not been as well described as those of other states with recent checklists that have focused primarily on the Yucatan Peninsula. We captured bats from 2011-2015 on the south coast and Sierra de Amula, Jalisco using mist nets, and we removed ectoparasites by hand. We identified 24 species of streblid bat flies and six ectoparasitic mites from bats caught in mist nets. There were an additional eight possibly undescribed species of Streblidae. Our collections extend the known range of species into Jalisco.