Surveillance cues do not enhance altruistic behavior among anonymous strangers in the field

The degree of altruistic behavior among strangers is an evolutionary puzzle. A prominent explanation is the evolutionary legacy hypothesis according to which an evolved reciprocity-based psychology affects behavior even when reciprocity is impossible, i.e., altruistic behavior in such instances is maladaptive. Empirical support for this explanation comes from laboratory experiments showing that surveillance cues, e.g., photographs of watching eyes, increase altruistic behavior. A competing interpretation for this evidence, however, is that the cues signal the experimenter’s expectations and participants, aware of being monitored, intentionally behave more altruistically to boost their reputation. Here we report the first results from a field experiment on the topic in which participants are unaware they are being monitored and reciprocity is precluded. The experiment investigates the impact of surveillance cues on a textbook example of altruistic behavior—hand hygiene prior to treating a ‘patient’. We find no evidence surveillance cues affect hand hygiene, despite using different measures of hand-hygiene quality and cues that have been previously shown to be effective. We argue that surveillance cues may have an effect only when participants have reasons to believe they are actually monitored. Thus they cannot support claims altruistic behavior between strangers is maladaptive

Epidemiology of severe pediatric adenovirus lower respiratory tract infections in Manitoba, Canada, 1991-2005

<p>Abstract</p> <p>Background</p> <p>Most pediatric adenovirus respiratory infections are mild and indistinguishable from other viral causes. However, in a few children, the disease can be severe and result in substantial morbidity. We describe the epidemiologic, clinical, radiologic features and outcome of adenovirus lower respiratory tract infections (LRTI) in Aboriginal and Non-Aboriginal children in Manitoba, Canada during the years 1991 and 2005.</p> <p>Methods</p> <p>This was a retrospective study of 193 children who presented to the department of pediatrics at Winnipeg Children's Hospital, Manitoba, Canada with LRTI and had a positive respiratory culture for adenovirus. Patients' demographics, clinical and radiologic features and outcomes were collected. Adenovirus serotype distributions and temporal associations were described. Approximate incidence comparisons (detection rates) of adenovirus LRTI among Aboriginal and Non-Aboriginal children were estimated with 95% confidence intervals.</p> <p>Results</p> <p>Adenovirus infections occurred throughout the year with clusters in the fall and winter. Serotypes 1 to 3 were the predominant isolates (two thirds of the cases). The infection was more frequent among Canadian Aboriginals, as illustrated in 2004, where its incidence in children 0-4 years old was 5.6 fold higher in Aboriginals (13.51 vs. 2.39 per 10,000, <it>p </it>< 0.000). There were no significant differences in length of hospitalization and use of ventilator assistance between the two groups (<it>p </it>> 0.185 and <it>p </it>> 0.624, respectively) nor across serotypes (<it>p </it>> 0.10 and <it>p </it>> 0.05, respectively). The disease primarily affected infants (median age, 9.5 months). Most children presented with bronchiolitis or pneumonia, with multi-lobar consolidations on the chest x-ray. Chronic (residual) changes were documented in 16 patients, with eight patients showing bronchiectasis on the chest computerized tomography scan.</p> <p>Conclusions</p> <p>Adenovirus infection is associated with significant respiratory morbidities, especially in young infants. The infection appears to be more frequent in Aboriginal children. These results justify a careful follow-up for children with adenovirus LRTI.</p

Global Perspectives on Immunization During Pregnancy and Priorities for Future Research and Development: An International Consensus Statement.

Immunization during pregnancy has been recommended in an increasing number of countries. The aim of this strategy is to protect pregnant women and infants from severe infectious disease, morbidity and mortality and is currently limited to tetanus, inactivated influenza, and pertussis-containing vaccines. There have been recent advancements in the development of vaccines designed primarily for use in pregnant women (respiratory syncytial virus and group B Streptococcus vaccines). Although there is increasing evidence to support vaccination in pregnancy, important gaps in knowledge still exist and need to be addressed by future studies. This collaborative consensus paper provides a review of the current literature on immunization during pregnancy and highlights the gaps in knowledge and a consensus of priorities for future research initiatives, in order to optimize protection for both the mother and the infant

Utilizing machine learning for survival analysis to identify risk factors for COVID-19 intensive care unit admission: A retrospective cohort study from the United Arab Emirates.

BackgroundThe current situation of the unprecedented COVID-19 pandemic leverages Artificial Intelligence (AI) as an innovative tool for addressing the evolving clinical challenges. An example is utilizing Machine Learning (ML) models-a subfield of AI that take advantage of observational data/Electronic Health Records (EHRs) to support clinical decision-making for COVID-19 cases. This study aimed to evaluate the clinical characteristics and risk factors for COVID-19 patients in the United Arab Emirates utilizing EHRs and ML for survival analysis models.MethodsWe tested various ML models for survival analysis in this work we trained those models using a different subset of features extracted by several feature selection methods. Finally, the best model was evaluated and interpreted using goodness-of-fit based on calibration curves,Partial Dependence Plots and concordance index.ResultsThe risk of severe disease increases with elevated levels of C-reactive protein, ferritin, lactate dehydrogenase, Modified Early Warning Score, respiratory rate and troponin. The risk also increases with hypokalemia, oxygen desaturation and lower estimated glomerular filtration rate and hypocalcemia and lymphopenia.ConclusionAnalyzing clinical data using AI models can provide vital information for clinician to measure the risk of morbidity and mortality of COVID-19 patients. Further validation is crucial to implement the model in real clinical settings

Exacerbation of Influenza A Virus Disease Severity by Respiratory Syncytial Virus Co-Infection in a Mouse Model

Influenza A virus (IAV) and respiratory syncytial virus (RSV) are leading causes of childhood infections. RSV and influenza are competitive in vitro. In this study, the in vivo effects of RSV and IAV co-infection were investigated. Mice were intranasally inoculated with RSV, with IAV, or with both viruses (RSV+IAV and IAV+RSV) administered sequentially, 24 h apart. On days 3 and 7 post-infection, lung tissues were processed for viral loads and immune cell populations. Lung functions were also evaluated. Mortality was observed only in the IAV+RSV group (50% of mice did not survive beyond 7 days). On day 3, the viral loads in single-infected and co-infected mice were not significantly different. However, on day 7, the IAV titer was much higher in the IAV+RSV group, and the RSV viral load was reduced. CD4 T cells were reduced in all groups on day 7 except in single-infected mice. CD8 T cells were higher in all experimental groups except the RSV-alone group. Increased airway resistance and reduced thoracic compliance were demonstrated in both co-infected groups. This model indicates that, among all the infection types we studied, infection with IAV followed by RSV is associated with the highest IAV viral loads and the most morbidity and mortality

Baseline characteristics statistical analysis: Baseline characteristics of patients stratified by severity measure by accessing the ICU, mean (SD) or N (%).

Baseline characteristics statistical analysis: Baseline characteristics of patients stratified by severity measure by accessing the ICU, mean (SD) or N (%).</p

Missing data patterns in multivariate data.

Explore patterns of missingness between levels of included variables. The pairs plots show relationships between missing values (gray) and observed values (Blue) for all the features. The distributions are used to visualize the continuous features, and the proportions are shown for categorical variables (continue). (PDF)</p

Missing data patterns in multivariate data.

Explore patterns of missingness between levels of included variables. The pairs plots show relationships between missing values (gray) and observed values (Blue) for all the features. The distributions are used to visualize the continuous features, and the proportions are shown for categorical variables (continue). (PDF)</p