Primary Sites and Clinicopathological Features of Corneal Melanoma: A SEER Population-Based Study of 29 Cases.

Introduction: Corneal Melanoma (CM) is a rare malignancy that develops from melanocytes within the cornea, constituting a minority of all ocular tumors. In this study, we sought to investigate the clinicopathological characteristics correlated with the prognosis of CM patients.

Methods: We collected patients with CM between 1983 and 2018 from the Surveillance, Epidemiology, and End Results (SEER) database.

Mental health and wellbeing outcomes of youth participation: A scoping review protocol.

There is growing recognition that young people should be given opportunities to participate in the decisions that affect their lives, such as advisory groups, representative councils, advocacy or activism. Positive youth development theory and sociopolitical development theory propose pathways through which youth participation can influence mental health and wellbeing outcomes. However, there is limited empirical research synthesising the impact of participation on youth mental health and/or wellbeing, or the characteristics of activities that are associated with better or worse mental health and/or wellbeing outcomes.

A hidden pandemic? An umbrella review of global evidence on mental health in the time of COVID-19.

Background: The mental health impacts of the COVID-19 pandemic remain a public health concern. High quality synthesis of extensive global literature is needed to quantify this impact and identify factors associated with adverse outcomes.

Methods: We conducted a rigorous umbrella review with meta-review and present (a) pooled prevalence of probable depression, anxiety, stress, psychological distress, and post-traumatic stress, (b) standardised mean difference in probable depression and anxiety pre-versus-during the pandemic period, and (c) comprehensive narrative synthesis of factors associated with poorer outcomes.

Predation thresholds for reintroduction of native avifauna following suppression of invasive Brown Treesnakes on Guam.

The brown treesnake (BTS) (Boiga irregularis) invasion on Guåhan (in English, Guam) led to the extirpation of nearly all native forest birds. In recent years, methods have been developed to reduce BTS abundance on a landscape scale. To help assess the prospects for the successful reintroduction of native birds to Guåhan following BTS suppression, we modeled bird population persistence based on their life history characteristics and relative sensitivity to BTS predation.

Alterations in Gut Microbiome Composition and Function in Irritable Bowel Syndrome and Increased Probiotic Abundance with Daily Supplementation.

Irritable bowel syndrome (IBS) is characterized by abdominal discomfort and irregular bowel movements and stool consistency. As such, the gut microbiome has been posited as being influential for the syndrome. However, identifying microbial features associated with IBS symptom heterogeneity is difficult without large cohorts.

Estimating the cumulative incidence of COVID-19 in the United States using influenza surveillance, virologic testing, and mortality data: Four complementary approaches.

Effectively designing and evaluating public health responses to the ongoing COVID-19 pandemic requires accurate estimation of the prevalence of COVID-19 across the United States (US). Equipment shortages and varying testing capabilities have however hindered the usefulness of the official reported positive COVID-19 case counts. We introduce four complementary approaches to estimate the cumulative incidence of symptomatic COVID-19 in each state in the US as well as Puerto Rico and the District of Columbia, using a combination of excess influenza-like illness reports, COVID-19 test statistics, COVID-19 mortality reports, and a spatially structured epidemic model.

Toward the use of neural networks for influenza prediction at multiple spatial resolutions.

Mitigating the effects of disease outbreaks with timely and effective interventions requires accurate real-time surveillance and forecasting of disease activity, but traditional health care-based surveillance systems are limited by inherent reporting delays. Machine learning methods have the potential to fill this temporal "data gap," but work to date in this area has focused on relatively simple methods and coarse geographic resolutions (state level and above). We evaluate the predictive performance of a gated recurrent unit neural network approach in comparison with baseline machine learning methods for estimating influenza activity in the United States at the state and city levels and experiment with the inclusion of real-time Internet search data.

An early warning approach to monitor COVID-19 activity with multiple digital traces in near real time.

Given still-high levels of coronavirus disease 2019 (COVID-19) susceptibility and inconsistent transmission-containing strategies, outbreaks have continued to emerge across the United States. Until effective vaccines are widely deployed, curbing COVID-19 will require carefully timed nonpharmaceutical interventions (NPIs). A COVID-19 early warning system is vital for this.

Real-time estimation of disease activity in emerging outbreaks using internet search information.

Understanding the behavior of emerging disease outbreaks in, or ahead of, real-time could help healthcare officials better design interventions to mitigate impacts on affected populations. Most healthcare-based disease surveillance systems, however, have significant inherent reporting delays due to data collection, aggregation, and distribution processes. Recent work has shown that machine learning methods leveraging a combination of traditionally collected epidemiological information and novel Internet-based data sources, such as disease-related Internet search activity, can produce meaningful "nowcasts" of disease incidence ahead of healthcare-based estimates, with most successful case studies focusing on endemic and seasonal diseases such as influenza and dengue.

An Early Warning Approach to Monitor COVID-19 Activity with Multiple Digital Traces in Near Real-Time.

Non-pharmaceutical interventions (NPIs) have been crucial in curbing COVID-19 in the United States (US). Consequently, relaxing NPIs through a phased re-opening of the US amid still-high levels of COVID-19 susceptibility could lead to new epidemic waves. This calls for a COVID-19 early warning system.

Estimating the Cumulative Incidence of COVID-19 in the United States Using Four Complementary Approaches.

Effectively designing and evaluating public health responses to the ongoing COVID-19 pandemic requires accurate estimation of the prevalence of COVID-19 across the United States (US). Equipment shortages and varying testing capabilities have however hindered the useful-ness of the official reported positive COVID-19 case counts. We introduce four complementary approaches to estimate the cumulative incidence of symptomatic COVID-19 in each state in the US as well as Puerto Rico and the District of Columbia, using a combination of excess influenza-like illness reports, COVID-19 test statistics, COVID-19 mortality reports, and a spatially structured epidemic model.

Combining Participatory Influenza Surveillance with Modeling and Forecasting: Three Alternative Approaches.

Background: Influenza outbreaks affect millions of people every year and its surveillance is usually carried out in developed countries through a network of sentinel doctors who report the weekly number of Influenza-like Illness cases observed among the visited patients. Monitoring and forecasting the evolution of these outbreaks supports decision makers in designing effective interventions and allocating resources to mitigate their impact.

Objective: Describe the existing participatory surveillance approaches that have been used for modeling and forecasting of the seasonal influenza epidemic, and how they can help strengthen real-time epidemic science and provide a more rigorous understanding of epidemic conditions.

Combining Search, Social Media, and Traditional Data Sources to Improve Influenza Surveillance.

We present a machine learning-based methodology capable of providing real-time ("nowcast") and forecast estimates of influenza activity in the US by leveraging data from multiple data sources including: Google searches, Twitter microblogs, nearly real-time hospital visit records, and data from a participatory surveillance system. Our main contribution consists of combining multiple influenza-like illnesses (ILI) activity estimates, generated independently with each data source, into a single prediction of ILI utilizing machine learning ensemble approaches. Our methodology exploits the information in each data source and produces accurate weekly ILI predictions for up to four weeks ahead of the release of CDC's ILI reports.

Flu Near You: Crowdsourced Symptom Reporting Spanning 2 Influenza Seasons.

Objectives: We summarized Flu Near You (FNY) data from the 2012-2013 and 2013-2014 influenza seasons in the United States.

Methods: FNY collects limited demographic characteristic information upon registration, and prompts users each Monday to report symptoms of influenza-like illness (ILI) experienced during the previous week. We calculated the descriptive statistics and rates of ILI for the 2012-2013 and 2013-2014 seasons.

Multiple genetic pathways to similar fitness limits during viral adaptation to a new host.

The gain in fitness during adaptation depends on the supply of beneficial mutations. Despite a good theoretical understanding of how evolution proceeds for a defined set of mutations, there is little understanding of constraints on net fitness-whether fitness will reach a limit despite ongoing selection and mutation, and if there is a limit, what determines it. Here, the dsDNA bacteriophage SP6, a virus of Salmonella, was adapted to Escherichia coli K-12.

Thromboembolic prophylaxis in blunt traumatic intracranial hemorrhage: a retrospective review.

There are few data in the literature on venous thromboembolic (VTE) prophylaxis for the traumatic population with intracranial hemorrhage (ICH). We reviewed our institutional experience and compared the incidence of deep vein thrombosis and pulmonary embolism in patients with ICH receiving either early prophylaxis (< 72 hours from admission) or late prophylaxis (> 72 hours from admission), and the respective incidences in progression of intracranial hemorrhage. We identified 124 patients for this study.

Laparoscopic resection of gastric stromal tumor: a tailored approach.

Many different laparoscopic approaches to resection of gastric stromal tumor have been described in the literature. We reviewed our experience of laparoscopic approaches to surgical resection of gastric stromal tumors seven in six consecutive patients. The tumor locations were the gastric cardia (n = 2), gastroesophageal junction (n = 1), gastric fundus (n = 2), and gastric antrum (n = 2).