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. However, this process is technically challenging, requiring the vessel to be properly positioned over rigid boney surfaces and applying sufficient pressure to maintain proper occlusion. As a first step toward automating this life-saving intervention, we demonstrate an artificial intelligence algorithm that classifies a vessel as patent or occluded, which can guide a user to apply the appropriate pressure required to stop flow. Neural network models were trained using images captured from a custom tissue-mimicking phantom and an ex vivo swine model of the inguinal region, as pressure was applied using an ultrasound probe with and without color Doppler overlays. Using these images, we developed an image classification algorithm suitable for the determination of patency or occlusion in an ultrasound image containing color Doppler overlay. Separate AI models for both test platforms were able to accurately detect occlusion status in test-image sets to more than 93% accuracy. In conclusion, this methodology can be utilized for guiding and monitoring proper vessel occlusion, which, when combined with automated actuation and other AI models, can allow for automated junctional tourniquet application.

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

Introduction: 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. Here, we have developed a tissue phantom modeling the ultrasound views imaged during the enhanced focused assessment with sonography in trauma exam (eFAST). Methods: The tissue phantom utilized synthetic clear ballistic gel with carveouts in the abdomen and rib cage corresponding to the various eFAST scan points. Various approaches were taken to simulate proper physiology without injuries present or to mimic pneumothorax, hemothorax, or abdominal hemorrhage at multiple locations in the torso. Multiple ultrasound imaging systems were used to acquire ultrasound scans with or without injury present and were used to train deep learning image classification predictive models. Results: Performance of the artificial intelligent (AI) models trained in this study achieved over 97% accuracy for each eFAST scan site. We used a previously trained AI model for pneumothorax which achieved 74% accuracy in blind predictions for images collected with the novel eFAST tissue phantom. Grad-CAM heat map overlays for the predictions identified that the AI models were tracking the area of interest for each scan point in the tissue phantom. Discussion: Overall, the eFAST tissue phantom ultrasound scans resembled human images and were successful in training AI models. Tissue phantoms are critical first steps in troubleshooting and developing medical imaging automation technologies for this application that can accelerate the widespread use of ultrasound imaging for emergency triage.

Benzidine Derivatives as Electroactive Materials for Aqueous Organic Redox Flow Batteries.

This paper presents a theoretical and experimental evaluation of benzidine derivatives as electroactive molecules for organic redox flow batteries. These redox indicators are novel electroactive materials that can perform multielectron transfers in aqueous media. We performed the synthesis, electrochemical characterization, and theoretical study of the dimer of sodium 4-diphenylamine sulfonate, a benzidine derivative with high water solubility properties. The Pourbaix diagram of the dimer shows a bielectronic process at highly acidic pH values (≤ 0.9) and two single-electron transfers in a pH range from 0 to 9. The dimer was prepared in situ and tested on a neutral electrochemical flow cell as a stability diagnostic. To improve cell performance, we calculate and calibrate, with experimental data, the Pourbaix diagrams of benzidine derivatives using different substitution patterns and functional groups. A screening process allowed the selection of those derivatives with a bielectronic process in the entire pH window or at acidic/neutral pH values. Given the redox potential difference, they can be potential catholytes or anolytes in a flow cell. The couples formed with the final candidates can generate a theoretical cell voltage of 0.60 V at pH 0 and up to 0.68 V at pH 7. These candidate molecules could be viable as electroactive materials for a full-organic redox flow battery.

An Explainable Machine-Learning Model for Compensatory Reserve Measurement: Methods for Feature Selection and the Effects of Subject Variability.

Tracking vital signs accurately is critical for triaging a patient and ensuring timely therapeutic intervention. The patient's status is often clouded by compensatory mechanisms that can mask injury severity. The compensatory reserve measurement (CRM) is a triaging tool derived from an arterial waveform that has been shown to allow for earlier detection of hemorrhagic shock. However, the deep-learning artificial neural networks developed for its estimation do not explain how specific arterial waveform elements lead to predicting CRM due to the large number of parameters needed to tune these models. Alternatively, we investigate how classical machine-learning models driven by specific features extracted from the arterial waveform can be used to estimate CRM. More than 50 features were extracted from human arterial blood pressure data sets collected during simulated hypovolemic shock resulting from exposure to progressive levels of lower body negative pressure. A bagged decision tree design using the ten most significant features was selected as optimal for CRM estimation. This resulted in an average root mean squared error in all test data of 0.171, similar to the error for a deep-learning CRM algorithm at 0.159. By separating the dataset into sub-groups based on the severity of simulated hypovolemic shock withstood, large subject variability was observed, and the key features identified for these sub-groups differed. This methodology could allow for the identification of unique features and machine-learning models to differentiate individuals with good compensatory mechanisms against hypovolemia from those that might be poor compensators, leading to improved triage of trauma patients and ultimately enhancing military and emergency medicine.

Military Standard Testing of Commercially Available Supraglottic Airway Devices for Use in a Military Combat Setting.

INTRODUCTION: Airway obstruction is the second leading cause of death on the battlefield. The harsh conditions of the military combat setting require that devices be able to withstand extreme circumstances. Military standards (MIL-STD) testing is necessary before devices are fielded. We sought to determine the ability of supraglottic airway (SGA) devices to withstand MIL-STD testing. METHODS: We tested 10 SGA models according to nine MIL-STD-810H test methods. We selected these tests by polling five military and civilian emergency-medicine subject matter experts (SMEs), who weighed the relevance of each test. We performed tests on three devices for each model, with operational and visual examinations, to assign a score (1 to 10) for each device after each test. We calculated the final score of each SGA model by averaging the score of each device and multiplying that by the weight for each test, for a possible final score of 2.6 to 26.3. RESULTS: The scores for the SGA models were LMA Classic Airway, 25.9; AuraGain Disposable Laryngeal Mask, 25.5; i-gel Supraglottic Airway, 25.2; Solus Laryngeal Mask Airway, 24.4; LMA Fastrach Airway, 24.4; AuraStraight Disposable Laryngeal Mask, 24.1; King LTS-D Disposable Laryngeal Tube, 22.1; LMA Supreme Airway, 21.0; air-Q Disposable Intubating Laryngeal Airway, 20.1; and Baska Mask Supraglottic Airway, 18.1. The limited (one to three) samples available for testing provide adequate preliminary information but restrict the range of failures that could be discovered. CONCLUSIONS: Lower scoring SGA models may not be optimal for military field use. Models scoring sufficiently close to the top performers (LMA Classic, AuraGain, i-gel, Solus, LMA Fastrach, AuraStraight) may be viable for use in the military setting. The findings of our testing should help guide device procurement appropriate for different battlefield conditions.

Expert Consensus Panel Recommendations for Selection of the Optimal Supraglottic Airway Device for Inclusion to the Medic's Aid Bag.

INTRODUCTION: Airway obstruction is the second leading cause of potentially survivable death on the battlefield. The Committee on Tactical Combat Casualty Care (CoTCCC) has evolving recommendations for the optimal supraglottic airway (SGA) device for inclusion to the medics' aid bag. METHODS: We convened an expert consensus panel consisting of a mix of 8 prehospital specialists, emergency medicine experts, and experienced combat medics, with the intent to offer recommendations for optimal SGA selection. Prior to meeting, we independently reviewed previously published studies conducted by our study team, conducted a virtual meeting, and summarized the findings to the panel. The studies included an analysis of end-user after action reviews, a market analysis, engineering testing, and prospective feedback from combat medics. The panel members then made recommendations regarding their top 3 choices of devices including the options of military custom design. Simple descriptive statistics were used to analyze panel recommendations. RESULTS: The preponderance (7/8, 88%) of panel members recommended the gel-cuffed SGA, followed by the self-inflating-cuff SGA (5/8, 62%) and laryngeal tube SGA (5/8, 62%). Panel members expressed concerns primarily related to the (1) devices' tolerance for the military environment, and (2) ability to effectively secure the gel-cuffed SGA and the self-inflating-cuff SGA during transport. CONCLUSIONS: A preponderance of panel members selected the gel-cuff SGA with substantial feedback highlighting the need for military-specific customizations to support the combat environment needs.

Spectral Reflectance as a Unique Tissue Identifier in Healthy Humans and Inhalation Injury Subjects.

Tracheal intubation is the preferred method of airway management, a common emergency trauma medicine problem. Currently, methods for confirming tracheal tube placement are lacking, and we propose a novel technology, spectral reflectance, which may be incorporated into the tracheal tube for verification of placement. Previous work demonstrated a unique spectral profile in the trachea, which allowed differentiation from esophageal tissue in ex vivo swine, in vivo swine, and human cadavers. The goal of this study is to determine if spectral reflectance can differentiate between trachea and other airway tissues in living humans and whether the unique tracheal spectral profile persists in the presence of an inhalation injury. Reflectance spectra were captured using a custom fiber-optic probe from the buccal mucosa, posterior oropharynx, and trachea of healthy humans intubated for third molar extraction and from the trachea of patients admitted to a burn intensive care unit with and without inhalation injury. Using ratio comparisons, we found that the tracheal spectral profile was significantly different from buccal mucosa or posterior oropharynx, but the area under the curve values are not high enough to be used clinically. In addition, inhalation injury did not significantly alter the spectral reflectance of the trachea. Further studies are needed to determine the utility of this technology in a clinical setting and to develop an algorithm for tissue differentiation.

Review of Commercially Available Supraglottic Airway Devices for Prehospital Combat Casualty Care.

BACKGROUND: Airway obstruction is the second leading cause of potentially survivable death on the battlefield. The Committee on Tactical Combat Casualty Care lists airway optimization among the top 5 battlefield research and development priorities; however, studies show that combat medics lack access to the recommended supraglottic airway (SGA) devices. SGA devices are an alternative airway management technique to endotracheal tube intubation. Reports have shown SGA devices are easier to use and take fewer attempts to provide patent airflow to the patient when compared to endotracheal tube intubation. Military settings require a higher degree of skill to perform airway management on patients due to the environment, limited availability of equipment, and potential chaos of the battlefield. Finding the optimal SGA device for the military setting is an unmet need. The International Organization for Standardization describes basic functional requirements for SGA devices, as well as patient configurations and size limitations. Beyond that, no SGA device manufacturer states that their devices are intended for military settings. MATERIALS AND METHODS: We conducted a market review of 25 SGA devices that may meet inclusion into the medics' aid bag. The company's official "Instructions for Use" document, Google Scholar, and FDA reports were reviewed to obtain information for each SGA device. RESULTS: Twenty-five commercially available SGA devices are explored from manufacturer online sources. A commercially available device list is shown later in this paper, which provides the device's features, indications, and contraindications based on the manufacturer's product information documentation. CONCLUSIONS: There are a variety of devices that require further testing to determine whether they should be included in sets, kits, and outfits.

Charge-transfer electronic states in organic solar cells: a TDDFT study.

The prediction of new organic photovoltaic materials in organic solar cells (OSCs) must include a precise description of charge-transfer states because they are involved in electron-transfer processes such as charge separation and charge recombination which govern the device efficiency. Also, as the experimental performance of an optoelectronic device is measured for nonequilibrium nanostructures, computational approaches need models that can incorporate morphology effects. Usually, this aspect is treated by molecular dynamics simulation (MDS) methodologies; however, methodologies and formalisms to calculate the electron-transfer processes are still controversial and sometimes do not connect their information with the phase morphologies. In this work we propose a simple and fast characterization of electron-transfer processes to find the rate constants by analysing the distribution of vertical excitation energies of both local excitation (LE) and charge-transfer (CT) states using TD-DFT calculations in the donor-acceptor pair structures which were extracted from MDS. This proposal assumes that conformational changes are prevented and equilibria are not achieved while the electron-transfer events take effect, and thus the only pathway that connects the LE and CT states is their surface crossing point where an ideal distribution might exist. Different density functionals and dialectric models were tested. The results indicate a close relationship between the proposal and experimental data for electron-transfer events, suggesting the application of this method in the rational design of new photovoltaic materials.

Electronic structure data at ground and excited state of the structural and opto-electronic properties of organic photovoltaic materials.

This work presents data coming from electronic structure calculations at the Density Functional Theory level, performed in a series of organic photovoltaic materials. The data represents the Cartesian coordinates of such molecular systems at the lowest energy geometry and at the first excited state. Data evidencing the nature of the photo-isomerization in the OPV systems was also obtained. Additionally, the highest probabilities of the molecular electronic transitions giving rise to the absorption spectra observed in excited state were also computed. These data may aid to estimate photovoltaic parameters, and to tailor materials intended to be implemented in solar cell devices. They may also be used as input to design a training set for machine learning analysis and artificial intelligence.

Saliva is a reliable and accessible source for the detection of SARS-CoV-2.

OBJECTIVES: The aim of this study was to investigate the feasibility of saliva sampling as a non-invasive and safer tool to detect severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and to compare its reproducibility and sensitivity with nasopharyngeal swab samples (NPS). The use of sample pools was also investigated. METHODS: A total of 2107 paired samples were collected from asymptomatic healthcare and office workers in Mexico City. Sixty of these samples were also analyzed in two other independent laboratories for concordance analysis. Sample processing and analysis of virus genetic material were performed according to standard protocols described elsewhere. A pooling analysis was performed by analyzing the saliva pool and the individual pool components. RESULTS: The concordance between NPS and saliva results was 95.2% (kappa 0.727, p = 0.0001) and 97.9% without considering inconclusive results (kappa 0.852, p = 0.0001). Saliva had a lower number of inconclusive results than NPS (0.9% vs 1.9%). Furthermore, saliva showed a significantly higher concentration of both total RNA and viral copies than NPS. Comparison of our results with those of the other two laboratories showed 100% and 97% concordance. Saliva samples are stable without the use of any preservative, and a positive SARS-CoV-2 sample can be detected 5, 10, and 15 days after collection when the sample is stored at 4 °C. CONCLUSIONS: The study results indicate that saliva is as effective as NPS for the identification of SARS-CoV-2-infected asymptomatic patients. Sample pooling facilitates the analysis of a larger number of samples, with the benefit of cost reduction.

Atomistic simulations of bulk heterojunctions to evaluate the structural and packing properties of new predicted donors in OPVs.

Organic photovoltaic materials (OPVs), with low cost and structure flexibility, are of great interest and importance for their application in solar cell device development. However, the optimization of new OPV structures and the study of the structure arrangements and packing morphologies when materials are blended takes time and consumes raw materials, thus theoretical models could be of considerable value. In this work, we performed molecular dynamics simulations of present OPVs to understand the morphological packing of the donor-acceptor (DA) phases and DA heterojunction during evaporation and annealing processes, following inter and intramolecular properties like frontier orbitals, π-π stacking, coordination, distances, angles, and aggregation. Our considered donor molecules were selected from already proved experimental studies and also from predicted optimal compounds, designed through high throughput studies. The acceptor molecule employed in all our studied systems was PCBM ([6,6]-phenyl-C61-butyric acid methyl ester). Furthermore, we also analyze the influence of including different lateral aliphatic chains on the structural properties of the resulting DA packing morphologies. Our results can guide the design of new OPVs and subsequent studies applying charge transport and charge separation models.

Theoretical exploration of 2,2'-bipyridines as electro-active compounds in flow batteries.

Compounds from the 2,2'-bipyridine molecular family were investigated for use as redox-active materials in organic flow batteries. For 156 2,2'-bipyridine derivatives reported in the academic literature, we calculated the redox potential, the pKa for the second deprotonation reaction, and the solubility in aqueous solutions. Using experimental data on a small subset of derivatives, we were able to calibrate our calculations. We find that functionalization with electron-withdrawing groups leads to an increase of the redox potential and to an increase of the molecular acidity (as expressed in a reduction of the pKa value for the second deprotonation step). Furthermore, calculations of solubility in water indicate that some of the studied derivatives have adequate solubility for flow battery applications. Based on an analysis of the phyisco-chemical properties of the 156 studied compounds, we down-select five molecules with carbonyl- and nitro-based functional groups, whose parameters are especially promising for potential applications as negative redox-active materials in organic flow batteries.

Parameterization of prototype organic small molecules suitable for OPVs and molecular dynamics simulations: the BTT and BPT cases.

Organic photovoltaics (OPV) have been theoretically studied within the usual parameters: open circuit voltage (Voc), short circuit current (Jsc), and fill factor (FF). The first two refer mostly to electronic properties, whereas the last contains all other possible contributions to cell efficiency, importantly including molecular geometrical and topological structures, both within a single molecule as amongst a system of molecules. In order to study the effects of molecular morphology of the heterostructures used in OPVs, molecular dynamic simulations (MDS) are imperative, and therefore parameterization of force fields to account for the description of planarity between aromatic rings, both intra- and inter-molecules, is of key importance. In this work, we present quantum mechanical analysis of geometry for the ground, and singlet and triplet excited states, of two simple prototypical molecules used to parametrize the corresponding force-fields. Central for this parameterization is the assignment of local charges within molecules; we studied and compared six different methods to assign atomic charges. With the parameters obtained, we performed molecular dynamics simulations of nanosystems of these molecules. Planarity effects and comparison between different methods for deriving atomic charges are investigated. These results can be applied in future MDS to interpret and characterize charge-transfer models in molecules suitable for OPV design. Graphical Abstract Depiction of a replicated system of BTT and BPT molecules after simulation.

Reassessment of the Four-Point Approach to the Electron-Transfer Marcus-Hush Theory.

The Marcus-Hush theory has been successfully applied to describe and predict the activation barriers and hence the electron-transfer (ET) rates in several physicochemical and biological systems. This theory assumes that in the ET reaction, the geometry of the free Gibbs energy landscape is parabolic, with equal curvature near the local minimum for both reactants and products. In spite of its achievements, more realistic models have included the assumption of the two parabolas having not the same curvature. This situation is analyzed by the Nelsen's four-point method. As a benchmark to compare the Marcus-Hush approximation to a precise calculation of the excitation energy, we studied the non-ET process of the electronic excitation of the aluminum dimer that has two local minima (3∑g - and 3∏u electronic states) and allows to obtain analytically the Marcus-Hush nonsymmetric parameters. We appraise the ability of the Marcus-Hush formula to approximate the analytical results by using several averages of the two reorganization energies associated with the forward and backward transitions and analyze the error. It is observed that the geometric average minimizes the relative error and that the analytical case is recovered. The main results of this paper are obtained by the application of the Nelsen's four-point method to compute the reorganization energies of a large set of potential π-conjugated molecules proposed for organic photovoltaic devices using the above-mentioned averages for the Marcus-Hush formula. The activation energies obtained with the geometric average are significantly larger for some donor-acceptor pairs in comparison with the previously employed arithmetic average, their differences being suitable for experimental testing.

Accelerating resolution-of-the-identity second-order Møller-Plesset quantum chemistry calculations with graphical processing units.

The modification of a general purpose code for quantum mechanical calculations of molecular properties (Q-Chem) to use a graphical processing unit (GPU) is reported. A 4.3x speedup of the resolution-of-the-identity second-order Møller-Plesset perturbation theory (RI-MP2) execution time is observed in single point energy calculations of linear alkanes. The code modification is accomplished using the compute unified basic linear algebra subprograms (CUBLAS) library for an NVIDIA Quadro FX 5600 graphics card. Furthermore, speedups of other matrix algebra based electronic structure calculations are anticipated as a result of using a similar approach.

Reagents for electrophilic amination: a quantum Monte Carlo study.

Electrophilic amination is an appealing synthetic strategy to construct carbon-nitrogen bonds. The authors explore the use of the quantum Monte Carlo method and a proposed variant of the electron pair localization function--the electron pair localization function density--as a measure of the nucleophilicity of nitrogen lone pairs as a possible screening procedure for electrophilic reagents.