Emergency Care Interventions for Victims of Explosive Ordnance Reduce Mortality: A Modeling Study.

Background: Modern conflicts are characterized by wide-spread use of conventional explosive ordnance (EO), improvised explosive devices (IEDs), and other air-launched explosives. In contrast to advances in military medicine and high-income civilian trauma systems since the United States-led wars in Afghanistan and Iraq, the mortality rate among civilian EO casualties has not decreased in decades. Although humanitarian mine action (HMA) stakeholders have extensive presence and medical capabilities in EO-affected settings, coordination between HMA and health actors has not been leveraged systematically.

Aspartame exposures in the US population: Demonstration of a novel approach for exposure estimates to food additives using NHANES data.

Background: Exposure to food additives is widespread but up-to-date and accurate intake estimates are rarely available. The safety of the food additive aspartame is the subject of recent controversy and intake estimates for this nonnutritive sweetener are typically derived from surrogates such as diet soda consumption.

Objective: We describe an approach for developing nationally representative dietary exposure estimates for food additives that combines intake from dietary recalls and grocery purchasing information.

Estimated Childhood Lead Exposure From Drinking Water in Chicago.

Importance: There is no level of lead in drinking water considered to be safe, yet lead service lines are still commonly used in water systems across the US.

Objective: To identify the extent of lead-contaminated drinking water in Chicago, Illinois, and model its impact on children younger than 6 years.

Design, Setting, And Participants: For this cross-sectional study, a retrospective assessment was performed of lead exposure based on household tests collected from January 2016 to September 2023.

Immunohistochemistry for acetylcholinesterase and butyrylcholinesterase in the dorsal motor nucleus of the vagus (DMNV) of formalin-fixed, paraffin-embedded tissue: comparison with reported literature.

The majority of research regarding the expression of acetylcholinesterase (AChE) and butyrylcholinesterase (BuChE) in the brain has been conducted using histochemistry to identify enzymatic activity in frozen fixed tissue. However, retrospective human neurochemistry studies are generally restricted to formalin-fixed, paraffin-embedded (FFPE) tissues that are not suitable for histochemical procedures. The availability of commercially available antibody formulations provides the means to study such tissues by immunohistochemistry (IHC).

Public health impacts of an imminent Red Sea oil spill.

The possibility of a massive oil spill in the Red Sea is increasingly likely. The , a deteriorating oil tanker containing 1.1 million barrels of oil, has been deserted near the coast of Yemen since 2015 and threatens environmental catastrophe to a country presently in a humanitarian crisis.

Comparison of Locus Coeruleus Pathology with Nigral and Forebrain Pathology in Parkinson's Disease.

Background: Pathology in the noradrenergic A6 locus coeruleus has not been compared with more rostral dopaminergic A9 substantia nigra and A10 ventral tegmental area, and cholinergic Ch4 basal nucleus and Ch1/2 septal regions in the same cases of Parkinson's disease (PD).

Objective: To determine whether there is a gradient of caudal to rostral cell loss in PD.

Methods: Postmortem brains were collected from longitudinally followed donors with PD (n = 14) and aged-matched healthy donors (n = 13), six with restricted brainstem Lewy pathology (RLP), fixed in formalin and serial tissue slabs processed for cell and pathological quantitation.

Routine asymptomatic testing strategies for airline travel during the COVID-19 pandemic: a simulation study.

Background: Routine viral testing strategies for SARS-CoV-2 infection might facilitate safe airline travel during the COVID-19 pandemic and mitigate global spread of the virus. However, the effectiveness of these test-and-travel strategies to reduce passenger risk of SARS-CoV-2 infection and population-level transmission remains unknown.

Methods: In this simulation study, we developed a microsimulation of SARS-CoV-2 transmission in a cohort of 100 000 US domestic airline travellers using publicly available data on COVID-19 clinical cases and published natural history parameters to assign individuals one of five health states of susceptible to infection, latent period, early infection, late infection, or recovered.

Frequency of Routine Testing for Coronavirus Disease 2019 (COVID-19) in High-risk Healthcare Environments to Reduce Outbreaks.

Routine asymptomatic testing strategies for COVID-19 have been proposed to prevent outbreaks in high-risk healthcare environments. We used simulation modeling to evaluate the optimal frequency of viral testing. We found that routine testing substantially reduces risk of outbreaks, but may need to be as frequent as twice weekly.

Routine asymptomatic testing strategies for airline travel during the COVID-19 pandemic: a simulation analysis.

Background: Airline travel has been significantly reduced during the COVID-19 pandemic due to concern for individual risk of SARS-CoV-2 infection and population-level transmission risk from importation. Routine viral testing strategies for COVID-19 may facilitate safe airline travel through reduction of individual and/or population-level risk, although the effectiveness and optimal design of these "test-and-travel" strategies remain unclear.

Methods: We developed a microsimulation of SARS-CoV-2 transmission in a cohort of airline travelers to evaluate the effectiveness of various testing strategies to reduce individual risk of infection and population-level risk of transmission.

Projected geographic disparities in healthcare worker absenteeism from COVID-19 school closures and the economic feasibility of child care subsidies: a simulation study.

Background: School closures have been enacted as a measure of mitigation during the ongoing coronavirus disease 2019 (COVID-19) pandemic. It has been shown that school closures could cause absenteeism among healthcare workers with dependent children, but there remains a need for spatially granular analyses of the relationship between school closures and healthcare worker absenteeism to inform local community preparedness.

Methods: We provide national- and county-level simulations of school closures and unmet child care needs across the USA.

Frequency of routine testing for COVID-19 in high-risk healthcare environments to reduce outbreaks.

Routine asymptomatic testing strategies for COVID-19 have been proposed to prevent outbreaks in high-risk healthcare environments. We used simulation modeling to evaluate the optimal frequency of viral testing. We found that routine testing substantially reduces risk of outbreaks, but may need to be as frequent as twice weekly.

Projected geographic disparities in healthcare worker absenteeism from COVID-19 school closures and the economic feasibility of child care subsidies: a simulation study.

Background: School closures have been enacted as a measure of mitigation during the ongoing COVID-19 pandemic. It has been shown that school closures could cause absenteeism amongst healthcare workers with dependent children, but there remains a need for spatially granular analyses of the relationship between school closures and healthcare worker absenteeism to inform local community preparedness.

Methods: We provide national- and county-level simulations of school closures and unmet child care needs across the United States.

Forecasting Internally Displaced Population Migration Patterns in Syria and Yemen.

Objectives: Armed conflict has contributed to an unprecedented number of internally displaced persons (IDPs), individuals who are forced out of their homes but remain within their country. IDPs often urgently require shelter, food, and healthcare, yet prediction of when IDPs will migrate to an area remains a major challenge for aid delivery organizations. We sought to develop an IDP migration forecasting framework that could empower humanitarian aid groups to more effectively allocate resources during conflicts.

Breast lesion classification based on dynamic contrast-enhanced magnetic resonance images sequences with long short-term memory networks.

We present a breast lesion classification methodology, based on four-dimensional (4-D) dynamic contrast-enhanced magnetic resonance images (DCE-MRI), using recurrent neural networks in combination with a pretrained convolutional neural network (CNN). The method enables to capture not only the two-dimensional image features but also the temporal enhancement patterns presented in DCE-MRI. We train a long short-term memory (LSTM) network on temporal sequences of feature vectors extracted from the dynamic MRI sequences.

Deep learning in breast cancer risk assessment: evaluation of convolutional neural networks on a clinical dataset of full-field digital mammograms.

To evaluate deep learning in the assessment of breast cancer risk in which convolutional neural networks (CNNs) with transfer learning are used to extract parenchymal characteristics directly from full-field digital mammographic (FFDM) images instead of using computerized radiographic texture analysis (RTA), 456 clinical FFDM cases were included: a "high-risk" BRCA1/2 gene-mutation carriers dataset (53 cases), a "high-risk" unilateral cancer patients dataset (75 cases), and a "low-risk dataset" (328 cases). Deep learning was compared to the use of features from RTA, as well as to a combination of both in the task of distinguishing between high- and low-risk subjects. Similar classification performances were obtained using CNN [area under the curve [Formula: see text]; standard error [Formula: see text]] and RTA ([Formula: see text]; [Formula: see text]) in distinguishing BRCA1/2 carriers and low-risk women.

A deep feature fusion methodology for breast cancer diagnosis demonstrated on three imaging modality datasets.

Background: Deep learning methods for radiomics/computer-aided diagnosis (CADx) are often prohibited by small datasets, long computation time, and the need for extensive image preprocessing.

Aims: We aim to develop a breast CADx methodology that addresses the aforementioned issues by exploiting the efficiency of pre-trained convolutional neural networks (CNNs) and using pre-existing handcrafted CADx features.

Materials & Methods: We present a methodology that extracts and pools low- to mid-level features using a pretrained CNN and fuses them with handcrafted radiomic features computed using conventional CADx methods.

Digital mammographic tumor classification using transfer learning from deep convolutional neural networks.

Convolutional neural networks (CNNs) show potential for computer-aided diagnosis (CADx) by learning features directly from the image data instead of using analytically extracted features. However, CNNs are difficult to train from scratch for medical images due to small sample sizes and variations in tumor presentations. Instead, transfer learning can be used to extract tumor information from medical images via CNNs originally pretrained for nonmedical tasks, alleviating the need for large datasets.