Overview of Wearable Healthcare Devices for Clinical Decision Support in the Prehospital Setting.

Prehospital medical care is a major challenge for both civilian and military situations as resources are limited, yet critical triage and treatment decisions must be rapidly made. Prehospital medicine is further complicated during mass casualty situations or remote applications that require more extensive medical treatments to be monitored. It is anticipated on the future battlefield where air superiority will be contested that prolonged field care will extend to as much 72 h in a prehospital environment.

In Vivo Evaluation of Two Hemorrhagic Shock Resuscitation Controllers with Non-Invasive, Intermittent Sensors.

Hemorrhage is a leading cause of preventable death in military and civilian trauma medicine. Fluid resuscitation is the primary treatment option, which can be difficult to manage when multiple patients are involved. Traditional vital signs needed to drive resuscitation therapy being unavailable without invasive catheter placement is a challenge.

Evaluation of a Semi-Automated Ultrasound Guidance System for Central Vascular Access.

Hemorrhage remains a leading cause of death in both military and civilian trauma settings. Oftentimes, the control and treatment of hemorrhage requires central vascular access and well-trained medical personnel. Automated technology is being developed that can lower the skill threshold for life-saving interventions.

Predicting blood loss volume in a canine model of hemorrhagic shock using arterial waveform machine learning analysis.

Objective: To determine if the compensatory reserve algorithm validated in humans can be applied to canines. Our secondary objective was to determine if a simpler waveform analysis could predict the percentage of blood loss volume.

Methods: 6 purpose-bred, anesthetized dogs underwent 5 rounds of controlled hemorrhage and resuscitation while continuously recording invasive arterial blood pressure waveforms in this prospective, experimental study.

Machine Learning Models for Tracking Blood Loss and Resuscitation in a Hemorrhagic Shock Swine Injury Model.

Hemorrhage leading to life-threatening shock is a common and critical problem in both civilian and military medicine. Due to complex physiological compensatory mechanisms, traditional vital signs may fail to detect patients' impending hemorrhagic shock in a timely manner when life-saving interventions are still viable. To address this shortcoming of traditional vital signs in detecting hemorrhagic shock, we have attempted to identify metrics that can predict blood loss.

evaluation of an adaptive resuscitation controller using whole blood and crystalloid infusates for hemorrhagic shock.

Introduction: Hemorrhage remains the leading cause of preventable death on the battlefield. The most effective means to increase survivability is early hemorrhage control and fluid resuscitation. Unfortunately, fluid resuscitation requires constant adjustments to ensure casualty is properly managed, which is often not feasible in the pre-hospital setting.

Refinement of machine learning arterial waveform models for predicting blood loss in canines.

Introduction: Hemorrhage remains a leading cause of death in civilian and military trauma. Hemorrhages also extend to military working dogs, who can experience injuries similar to those of the humans they work alongside. Unfortunately, current physiological monitoring is often inadequate for early detection of hemorrhage.

First Simultaneous Measurement of Differential Muon-Neutrino Charged-Current Cross Sections on Argon for Final States with and without Protons Using MicroBooNE Data.

We report the first double-differential neutrino-argon cross section measurement made simultaneously for final states with and without protons for the inclusive muon neutrino charged-current interaction channel. The proton kinematics of this channel are further explored with a differential cross section measurement as a function of the leading proton's kinetic energy that extends across the detection threshold. These measurements use data collected with the MicroBooNE detector from 6.

Retraction: Synthesis of Yttrium Superhydride Superconductor with a Transition Temperature up to 262 K by Catalytic Hydrogenation at High Pressures [Phys. Rev. Lett. 126, 117003 (2021)].

Retraction of DOI: 10.1103/PhysRevLett.126.

First Search for Dark-Trident Processes Using the MicroBooNE Detector.

We present a first search for dark-trident scattering in a neutrino beam using a dataset corresponding to 7.2×10^{20} protons on target taken with the MicroBooNE detector at Fermilab. Proton interactions in the neutrino target at the main injector produce π^{0} and η mesons, which could decay into dark-matter (DM) particles mediated via a dark photon A^{'}.

Deep learning models for interpretation of point of care ultrasound in military working dogs.

Introduction: Military working dogs (MWDs) are essential for military operations in a wide range of missions. With this pivotal role, MWDs can become casualties requiring specialized veterinary care that may not always be available far forward on the battlefield. Some injuries such as pneumothorax, hemothorax, or abdominal hemorrhage can be diagnosed using point of care ultrasound (POCUS) such as the Global FAST® exam.

First Measurement of η Meson Production in Neutrino Interactions on Argon with MicroBooNE.

We present a measurement of η production from neutrino interactions on argon with the MicroBooNE detector. The modeling of resonant neutrino interactions on argon is a critical aspect of the neutrino oscillation physics program being carried out by the DUNE and Short Baseline Neutrino programs. η production in neutrino interactions provides a powerful new probe of resonant interactions, complementary to pion channels, and is particularly suited to the study of higher-order resonances beyond the Δ(1232).

Evaluation of Deep Learning Model Architectures for Point-of-Care Ultrasound Diagnostics.

Point-of-care ultrasound imaging is a critical tool for patient triage during trauma for diagnosing injuries and prioritizing limited medical evacuation resources. Specifically, an eFAST exam evaluates if there are free fluids in the chest or abdomen but this is only possible if ultrasound scans can be accurately interpreted, a challenge in the pre-hospital setting. In this effort, we evaluated the use of artificial intelligent eFAST image interpretation models.

Design and testing of ultrasound probe adapters for a robotic imaging platform.

Medical imaging-based triage is a critical tool for emergency medicine in both civilian and military settings. Ultrasound imaging can be used to rapidly identify free fluid in abdominal and thoracic cavities which could necessitate immediate surgical intervention. However, proper ultrasound image capture requires a skilled ultrasonography technician who is likely unavailable at the point of injury where resources are limited.

Using AI Segmentation Models to Improve Foreign Body Detection and Triage from Ultrasound Images.

Medical imaging can be a critical tool for triaging casualties in trauma situations. In remote or military medicine scenarios, triage is essential for identifying how to use limited resources or prioritize evacuation for the most serious cases. Ultrasound imaging, while portable and often available near the point of injury, can only be used for triage if images are properly acquired, interpreted, and objectively triage scored.

Toward Smart, Automated Junctional Tourniquets-AI Models to Interpret Vessel Occlusion at Physiological Pressure Points.

Hemorrhage is the leading cause of preventable death in both civilian and military medicine. Junctional hemorrhages are especially difficult to manage since traditional tourniquet placement is often not possible. Ultrasound can be used to visualize and guide the caretaker to apply pressure at physiological pressure points to stop hemorrhage.

Search for Heavy Neutral Leptons in Electron-Positron and Neutral-Pion Final States with the MicroBooNE Detector.

We present the first search for heavy neutral leptons (HNLs) decaying into νe^{+}e^{-} or νπ^{0} final states in a liquid-argon time projection chamber using data collected with the MicroBooNE detector. The data were recorded synchronously with the NuMI neutrino beam from Fermilab's main injector corresponding to a total exposure of 7.01×10^{20} protons on target.

Retraction: Carbon content drives high temperature superconductivity in a carbonaceous sulfur hydride below 100 GPa.

Retraction of 'Carbon content drives high temperature superconductivity in a carbonaceous sulfur hydride below 100 GPa' by G. Alexander Smith , , 2022, , 9064-9067, https://doi.org/10.

Expression of Concern: Synthesis of Yttrium Superhydride Superconductor with a Transition Temperature up to 262 K by Catalytic Hydrogenation at High Pressures [Phys. Rev. Lett. 126, 117003 (2021)].

This corrects the article DOI: 10.1103/PhysRevLett.126.

An extended focused assessment with sonography in trauma ultrasound tissue-mimicking phantom for developing automated diagnostic technologies.

Medical imaging-based triage is critical for ensuring medical treatment is timely and prioritized. However, without proper image collection and interpretation, triage decisions can be hard to make. While automation approaches can enhance these triage applications, tissue phantoms must be developed to train and mature these novel technologies.

Verification and Validation of Lower Body Negative Pressure as a Non-Invasive Bioengineering Tool for Testing Technologies for Monitoring Human Hemorrhage.

Since hemorrhage is a leading cause of preventable death in both civilian and military settings, the development of advanced decision support monitoring capabilities is necessary to promote improved clinical outcomes. The emergence of lower body negative pressure (LBNP) has provided a bioengineering technology for inducing progressive reductions in central blood volume shown to be accurate as a model for the study of the early compensatory stages of hemorrhage. In this context, the specific aim of this study was to provide for the first time a systematic technical evaluation to meet a commonly accepted engineering standard based on the FDA-recognized Standard for Assessing Credibility of Modeling through Verification and Validation (V&V) for Medical Devices (ASME standard V&V 40) specifically highlighting LBNP as a valuable resource for the safe study of hemorrhage physiology in humans.

First Double-Differential Measurement of Kinematic Imbalance in Neutrino Interactions with the MicroBooNE Detector.

We report the first measurement of flux-integrated double-differential quasielasticlike neutrino-argon cross sections, which have been made using the Booster Neutrino Beam and the MicroBooNE detector at Fermi National Accelerator Laboratory. The data are presented as a function of kinematic imbalance variables which are sensitive to nuclear ground-state distributions and hadronic reinteraction processes. We find that the measured cross sections in different phase-space regions are sensitive to different nuclear effects.

The alternative sigma factor SigN of is intrinsically toxic.

Sigma factors bind and direct the RNA polymerase core to specific promoter sequences, and alternative sigma factors direct transcription of different regulons of genes. Here, we study the pBS32 plasmid-encoded sigma factor SigN of to determine how it contributes to DNA damage-induced cell death. We find that SigN causes cell death when expressed at high levels and does so in the absence of its regulon suggesting it is intrinsically toxic.

Retraction: Colossal Density-Driven Resistance Response in the Negative Charge Transfer Insulator MnS_{2} [Phys. Rev. Lett. 127, 016401 (2021)].

Retraction of DOI: 10.1103/PhysRevLett.127.