Accelerating Computational Materials Discovery with Machine Learning and Cloud High-Performance Computing: from Large-Scale Screening to Experimental Validation.

High-throughput computational materials discovery has promised significant acceleration of the design and discovery of new materials for many years. Despite a surge in interest and activity, the constraints imposed by large-scale computational resources present a significant bottleneck. Furthermore, examples of very large-scale computational discovery carried out through experimental validation remain scarce, especially for materials with product applicability. Here, we demonstrate how this vision became reality by combining state-of-the-art machine learning (ML) models and traditional physics-based models on cloud high-performance computing (HPC) resources to quickly navigate through more than 32 million candidates and predict around half a million potentially stable materials. By focusing on solid-state electrolytes for battery applications, our discovery pipeline further identified 18 promising candidates with new compositions and rediscovered a decade's worth of collective knowledge in the field as a byproduct. We then synthesized and experimentally characterized the structures and conductivities of our top candidates, the NaxLi3-xYCl6 (0≤ x≤ 3) series, demonstrating the potential of these compounds to serve as solid electrolytes. Additional candidate materials that are currently under experimental investigation could offer more examples of the computational discovery of new phases of Li- and Na-conducting solid electrolytes. The showcased screening of millions of materials candidates highlights the transformative potential of advanced ML and HPC methodologies, propelling materials discovery into a new era of efficiency and innovation.

Association of Patient Reported Outcomes With Caregiver Burden in Older Patients With Advanced Heart Failure: Insights From the SUSTAIN-IT Study.

BACKGROUND: Caregivers of patients with advanced heart failure may experience burden in providing care, but whether changes in patient health status are associated with caregiver burden is unknown. METHODS: This observational study included older patients (60-80 years old) receiving advanced surgical heart failure therapies and their caregivers at 13 US sites. Patient health status was assessed using the 12-item Kansas City Cardiomyopathy Questionnaire (range, 0-100; higher scores are better). Caregiver burden was assessed using the Oberst Caregiving Burden Scale, which measures time on task (OCBS-time) and task difficulty (OCBS-difficulty; range, 1-5; lower scores are better). Measurements occurred before surgery and 12 months after in 3 advanced heart failure cohorts: patients receiving long-term left ventricular assist device support; heart transplantation with pretransplant left ventricular assist device support; and heart transplantation without pretransplant left ventricular assist device support. Multivariable linear regression was used to identify predictors of change in OCBS-time and OCBS-difficulty at 12 months. RESULTS: Of 162 caregivers, the mean age was 61.0±9.4 years, 139 (86%) were female, and 140 (86%) were the patient's spouse. At 12 months, 99 (61.1%) caregivers experienced improved OCBS-time, and 61 (37.7%) experienced improved OCBS-difficulty (versus no change or worse OCBS). A 10-point higher baseline 12-item Kansas City Cardiomyopathy Questionnaire predicted lower 12-month OCBS-time (ß=-0.09 [95% CI, -0.14 to -0.03]; P<0.001) and OCBS-difficulty (ß=-0.08 [95% CI, -0.12 to -0.05]; P<0.001). Each 10-point improvement in the 12-item Kansas City Cardiomyopathy Questionnaire predicted lower 12-month OCBS-time (ß=-0.07 [95% CI, -0.12 to -0.03]; P=0.002) and OCBS-difficulty (ß=-0.09 [95% CI, -0.12 to -0.06]; P<0.001). CONCLUSIONS: Among survivors at 12 months, baseline and change in patient health status were associated with subsequent caregiver time on task and task difficulty in dyads receiving advanced heart failure surgical therapies, highlighting the potential for serial 12-item Kansas City Cardiomyopathy Questionnaire assessments to identify caregivers at risk of increased burden. REGISTRATION: URL: https://www.clinicaltrials.gov; unique identifier: NCT02568930.

The triple combination of Remdesivir (GS-441524), Molnupiravir and Ribavirin is highly efficient in inhibiting coronavirus replication in human nasal airway epithelial cell cultures and in a hamster infection model.

The use of fixed dose-combinations of antivirals with different mechanisms of action has proven a key in the successful treatment of infections with HIV and HCV. For the treatment of infections with SARS-CoV-2 and possible future epi-/pandemic coronaviruses, it will be important to explore the efficacy of combinations of different drugs, in particular to avoid resistance development, such as in patients with immunodeficiencies. As a first effort, we studied the antiviral potency of combinations of antivirals. To that end, we made use of primary human airway epithelial cell (HAEC) cultures grown at the air-liquid interface that were infected with the beta coronavirus OC43. We found that the triple combination of GS-441524 (parent nucleoside of remdesivir), molnupiravir, and ribavirin resulted in a more pronounced antiviral efficacy than what could be expected from a purely additive antiviral effect. The potency of this triple combination was next tested in SARS-CoV-2 infected hamsters. To that end, for each of the drugs, intentionally suboptimal or even ineffective doses were selected. Yet, in the lungs of all hamsters that received triple prophylactic therapy with suboptimal/inactive doses of GS-441524, molnupiravir, and ribavirin, no infectious virus was detectable. Our finding indicate that co-administration of approved drugs for the treatment of coronavirus infections should be further explored but also against other families of viruses with epidemic and pandemic potential for which no effective antiviral treatment is available.

Multimodal radiotherapy dose prediction using a multi-task deep learning model.

BACKGROUND: In radiation therapy (RT), accelerated partial breast irradiation (APBI) has emerged as an increasingly preferred treatment modality over conventional whole breast irradiation due to its targeted dose delivery and shorter course of treatment. APBI can be delivered through various modalities including Cobalt-60-based systems and linear accelerators with C-arm, O-ring, or robotic arm design. Each modality possesses distinct features, such as beam energy or the degrees of freedom in treatment planning, which influence their respective dose distributions. These modality-specific considerations emphasize the need for a quantitative approach in determining the optimal dose delivery modality on a patient-specific basis. However, manually generating treatment plans for each modality across every patient is time-consuming and clinically impractical. PURPOSE: We aim to develop an efficient and personalized approach for determining the optimal RT modality for APBI by training predictive models using two different deep learning-based convolutional neural networks. The baseline network performs a single-task (ST), predicting dose for a single modality. Our proposed multi-task (MT) network, which is capable of leveraging shared information among different tasks, can concurrently predict dose distributions for various RT modalities. Utilizing patient-specific input data, such as a patient's computed tomography (CT) scan and treatment protocol dosimetric goals, the MT model predicts patient-specific dose distributions across all trained modalities. These dose distributions provide patients and clinicians quantitative insights, facilitating informed and personalized modality comparison prior to treatment planning. METHODS: The dataset, comprising 28 APBI patients and their 92 treatment plans, was partitioned into training, validation, and test subsets. Eight patients were dedicated to the test subset, leaving 68 treatment plans across 20 patients to divide between the training and validation subsets. ST models were trained for each modality, and one MT model was trained to predict doses for all modalities simultaneously. Model performance was evaluated across the test dataset in terms of Mean Absolute Percent Error (MAPE). We conducted statistical analysis of model performance using the two-tailed Wilcoxon signed-rank test. RESULTS: Training times for five ST models ranged from 255 to 430 min per modality, totaling 1925 min, while the MT model required 2384 min. MT model prediction required an average of 1.82 s per patient, compared to ST model predictions at 0.93 s per modality. The MT model yielded MAPE of 1.1033 ± 0.3627% as opposed to the collective MAPE of 1.2386 ± 0.3872% from ST models, and the differences were statistically significant (p = 0.0003, 95% confidence interval = [-0.0865, -0.0712]). CONCLUSION: Our study highlights the potential benefits of a MT learning framework in predicting RT dose distributions across various modalities without notable compromises. This MT architecture approach offers several advantages, such as flexibility, scalability, and streamlined model management, making it an appealing solution for clinical deployment. With such a MT model, patients can make more informed treatment decisions, physicians gain more quantitative insight for pre-treatment decision-making, and clinics can better optimize resource allocation. With our proposed goal array and MT framework, we aim to expand this work to a site-agnostic dose prediction model, enhancing its generalizability and applicability.

Low HBV knowledge is associated with low HBV vaccination uptake in general adult population despite incentivization of HBV vaccination.

BACKGROUND: Hepatitis B virus (HBV) vaccination in Vietnamese adults remains low and unequally distributed. We conducted a study on HBV-naïve adults living in Ho Chi Minh City, Viet Nam, to determine barriers associated with HBV vaccination uptake after removing the financial barrier by providing free coupons for HBV vaccination. METHODS: After being screened for HBsAg, anti-HBs, and anti-HBc, 284 HBV-naïve study participants aged 18 and over (i.e., negative for HBsAg, anti-HBs, and anti-HBc total) were provided free 3-dose HBV vaccine coupons. Next, study participants' receipt of 1st, 2nd, and 3rd doses of HBV vaccine was documented at a pre-specified study healthcare facility, where HBV vaccines were distributed at no cost to the participants. Upon study entry, participants answered questionnaires on sociodemographics, knowledge of HBV and HBV vaccination, and related social and behavioral factors. The proportions of three doses of HBV vaccine uptake and their confidence intervals were analyzed. Associations of HBV vaccine initiation with exposures at study entry were evaluated using modified Poisson regression. RESULTS: 98.9% (281 of 284) of study participants had complete data and were included in the analysis. The proportion of participants obtaining the 1st, 2nd, and 3rd doses of HBV vaccine was 11.7% (95% Confidence Interval [95% CI] 8.0-15.5%), 10.7% (95%CI 7.1-14.3%), and 8.9% (95%CI 5.6-12.2%), respectively. On the other hand, participants were more likely to initiate the 1st dose if they had adequate knowledge of transmission (adjusted relative risk [aRR] = 2.58, 95% CI 1.12-5.92), adequate knowledge of severity (aRR = 6.75, 95%CI 3.38-13.48), and annual health-checking seeking behavior (aRR = 2.04, 95%CI 1.07-3.87). CONCLUSION: We documented a low HBV vaccination uptake despite incentivization. However, increased vaccine initiation was associated with better HBV knowledge and annual health check-up adherence. When considering expanding HBV vaccination to the general adult population, we should appreciate that HBV knowledge is an independent predictor of vaccine uptake.

Deep learning based automatic segmentation of the Internal Pudendal Artery in definitive radiotherapy treatment planning of localized prostate cancer.

Background and purpose: Radiation-induced erectile dysfunction (RiED) commonly affects prostate cancer patients, prompting clinical trials across institutions to explore dose-sparing to internal-pudendal-arteries (IPA) for preserving sexual potency. IPA, challenging to segment, isn't conventionally considered an organ-at-risk (OAR). This study proposes a deep learning (DL) auto-segmentation model for IPA, using Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) or CT alone to accommodate varied clinical practices. Materials and methods: A total of 86 patients with CT and MRI images and noisy IPA labels were recruited in this study. We split the data into 42/14/30 for model training, testing, and a clinical observer study, respectively. There were three major innovations in this model: 1) we designed an architecture with squeeze-and-excite blocks and modality attention for effective feature extraction and production of accurate segmentation, 2) a novel loss function was used for training the model effectively with noisy labels, and 3) modality dropout strategy was used for making the model capable of segmentation in the absence of MRI. Results: Test dataset metrics were DSC 61.71 ± 7.7 %, ASD 2.5 ± .87 mm, and HD95 7.0 ± 2.3 mm. AI segmented contours showed dosimetric similarity to expert physician's contours. Observer study indicated higher scores for AI contours (mean = 3.7) compared to inexperienced physicians' contours (mean = 3.1). Inexperienced physicians improved scores to 3.7 when starting with AI contours. Conclusion: The proposed model achieved good quality IPA contours to improve uniformity of segmentation and to facilitate introduction of standardized IPA segmentation into clinical trials and practice.

American Society of Pain and Neuroscience Best Practice (ASPN) Guideline for the Treatment of Sacroiliac Disorders.

Clinical management of sacroiliac disease has proven challenging from both diagnostic and therapeutic perspectives. Although it is widely regarded as a common source of low back pain, little consensus exists on the appropriate clinical management of sacroiliac joint pain and dysfunction. Understanding the biomechanics, innervation, and function of this complex load bearing joint is critical to formulating appropriate treatment algorithms for SI joint disorders. ASPN has developed this comprehensive practice guideline to serve as a foundational reference on the appropriate management of SI joint disorders utilizing the best available evidence and serve as a foundational guide for the treatment of adult patients in the United States and globally.

Axonal endoplasmic reticulum tubules control local translation via P180/RRBP1-mediated ribosome interactions.

Local mRNA translation in axons is critical for the spatiotemporal regulation of the axonal proteome. A wide variety of mRNAs are localized and translated in axons; however, how protein synthesis is regulated at specific subcellular sites in axons remains unclear. Here, we establish that the axonal endoplasmic reticulum (ER) supports axonal translation in developing rat hippocampal cultured neurons. Axonal ER tubule disruption impairs local translation and ribosome distribution. Using nanoscale resolution imaging, we find that ribosomes make frequent contacts with axonal ER tubules in a translation-dependent manner and are influenced by specific extrinsic cues. We identify P180/RRBP1 as an axonally distributed ribosome receptor that regulates local translation and binds to mRNAs enriched for axonal membrane proteins. Importantly, the impairment of axonal ER-ribosome interactions causes defects in axon morphology. Our results establish a role for the axonal ER in dynamically localizing mRNA translation, which is important for proper neuron development.

From Bulk to Interface: Solvent Exchange Dynamics and Their Role in Ion Transport and the Interfacial Model of Rechargeable Magnesium Batteries.

Multivalent battery chemistries have been explored in response to the increasing demand for high-energy rechargeable batteries utilizing sustainable resources. Solvation structures of working cations have been recognized as a key component in the design of electrolytes; however, most structure-property correlations of metal ions in organic electrolytes usually build upon favorable static solvation structures, often overlooking solvent exchange dynamics. We here report the ion solvation structures and solvent exchange rates of magnesium electrolytes in various solvents by using multimodal nuclear magnetic resonance (NMR) analysis and molecular dynamics/density functional theory (MD/DFT) calculations. These magnesium solvation structures and solvent exchange dynamics are correlated to the combined effects of several physicochemical properties of the solvents. Moreover, Mg2+ transport and interfacial charge transfer efficiency are found to be closely correlated to the solvent exchange rate in the binary electrolytes where the solvent exchange is tunable by the fraction of diluent solvents. Our primary findings are (1) most battery-related solvents undergo ultraslow solvent exchange coordinating to Mg2+ (with time scales ranging from 0.5 µs to 5 ms), (2) the cation transport mechanism is a mixture of vehicular and structural diffusion even at the ultraslow exchange limit (with faster solvent exchange leading to faster cation transport), and (3) an interfacial model wherein organic-rich regions facilitate desolvation and inorganic regions promote Mg2+ transport is consistent with our NMR, electrochemistry, and cryogenic X-ray photoelectron spectroscopy (cryo-XPS) results. This observed ultraslow solvent exchange and its importance for ion transport and interfacial properties necessitate the judicious selection of solvents and informed design of electrolyte blends for multivalent electrolytes.

Endoplasmic reticulum - condensate interactions in protein synthesis and secretion.

In the past decade, a growing amount of evidence has demonstrated that organelles do not act autonomously and independently but rather communicate with each other to coordinate different processes for proper cellular function. With a highly extended network throughout the cell, the endoplasmic reticulum (ER) plays a central role in interorganelle communication through membrane contact sites. Here, we highlight recent evidence indicating that the ER also forms contacts with membrane-less organelles. These interactions contribute to the dynamic assembly and disassembly of condensates and controlled protein secretion. Additionally, emerging evidence suggests their involvement in mRNA localization and localized translation. We further explore exciting future directions of this emerging theme in the organelle contact site field.

Sample-to-answer lateral flow assay with integrated plasma separation and NT-proBNP detection.

Through enabling whole blood detection in point-of-care testing (POCT), sedimentation-based plasma separation promises to enhance the functionality and extend the application range of lateral flow assays (LFAs). To streamline the entire process from the introduction of the blood sample to the generation of quantitative immune-fluorescence results, we combined a simple plasma separation technique, an immunoreaction, and a micropump-driven external suction control system in a polymer channel-based LFA. Our primary objective was to eliminate the reliance on sample-absorbing separation membranes, the use of active separation forces commonly found in POCT, and ultimately allowing finger prick testing. Combining the principle of agglutination of red blood cells with an on-device sedimentation-based separation, our device allows for the efficient and fast separation of plasma from a 25-µL blood volume within a mere 10 min and overcomes limitations such as clogging, analyte adsorption, and blood pre-dilution. To simplify this process, we stored the agglutination agent in a dried state on the test and incorporated a filter trench to initiate sedimentation-based separation. The separated plasma was then moved to the integrated mixing area, initiating the immunoreaction by rehydration of probe-specific fluorophore-conjugated antibodies. The biotinylated immune complex was subsequently trapped in the streptavidin-rich detection zone and quantitatively analyzed using a fluorescence microscope. Normalized to the centrifugation-based separation, our device demonstrated high separation efficiency of 96% and a yield of 7.23 µL (= 72%). Furthermore, we elaborate on its user-friendly nature and demonstrate its proof-of-concept through an all-dried ready-to-go NT-proBNP lateral flow immunoassay with clinical blood samples.

Membrane-Free Lateral Flow Assay with the Active Control of Fluid Transport for Ultrasensitive Cardiac Biomarker Detection.

Membrane-based lateral flow immunoassays (LFAs) have been employed as early point-of-care (POC) testing tools in clinical settings. However, the varying membrane properties, uncontrollable sample transport in LFAs, visual readout, and required large sample volumes have been major limiting factors in realizing needed sensitivity and desirable precise quantification. Addressing these challenges, we designed a membrane-free system in which the desirable three-dimensional (3D) structure of the detection zone is imitated and used a small pump for fluid flow and fluorescence as readout, all the while maintaining a one-step assay protocol. A hydrogel-like protein-polyelectrolyte complex (PPC) within a polyelectrolyte multilayer (PEM) was developed as the test line by complexing polystreptavidin (pSA) with poly(diallyldimethylammonium chloride) (PDDA), which in turn was layered with poly(acrylic acid) (PAA) resulting in a superior 3D streptavidin-rich test line. Since the remainder of the microchannel remains material-free, good flow control is achieved, and with the total volume of 20 µL, 7.5-fold smaller sample volumes can be used in comparison to conventional LFAs. High sensitivity with desirable reproducibility and a 20 min total assay time were achieved for the detection of NT-proBNP in plasma with a dynamic range of 60-9000 pg·mL-1 and a limit of detection of 56 pg·mL-1 using probe antibody-modified fluorescence nanoparticles. While instrument-free visual detection is no longer possible, the developed lateral flow channel platform has the potential to dramatically expand the LFA applicability, as it overcomes the limitations of membrane-based immunoassays, ultimately improving the accuracy and reducing the sample volume so that finger-prick analyses can easily be done in a one-step assay for analytes present at very low concentrations.

NT-proBNP detection with a one-step magnetic lateral flow channel assay.

Point-of-care sensors targeting blood marker analysis must be designed to function with very small volumes since acquiring a blood sample through a simple, mostly pain-free finger prick dramatically limits the sample size and comforts the patient. Therefore, we explored the potential of converting a conventional lateral flow assay (LFA) for a significant biomarker into a self-contained and compact polymer channel-based LFA to minimize the sample volume while maintaining the analytical merits. Our primary objective was to eliminate the use of sample-absorbing fleece and membrane materials commonly present in LFAs. Simultaneously, we concentrated on developing a ready-to-deploy one-step LFA format, characterized by dried reagents, facilitating automation and precise sample transport through a pump control system. We targeted the detection of the heart failure biomarker NT-proBNP in only 15 µL human whole blood and therefore implemented strategies that ensure highly sensitive detection. The biosensor combines streptavidin-functionalized magnetic beads (MNPs) as a 3D detection zone and fluorescence nanoparticles as signal labels in a sandwich-based immunoassay. Compared to the currently commercialized LFA, our biosensor demonstrates comparable analytical performance with only a tenth of the sample volume. With a detection limit of 43.1 pg∙mL-1 and a mean error of 18% (n ≥ 3), the biosensor offers high sensitivity and accuracy. The integration of all-dried long-term stable reagents further enhances the convenience and stability of the biosensor. This lateral flow channel platform represents a promising advancement in point-of-care diagnostics for heart failure biomarkers, offering a user-friendly and sensitive platform for rapid and reliable testing with low finger-prick blood sample volumes.

Segmentation of liver and liver lesions using deep learning.

Segmentation of organs and lesions could be employed for the express purpose of dosimetry in nuclear medicine, assisted image interpretations, and mass image processing studies. Deep leaning created liver and liver lesion segmentation on clinical 3D MRI data has not been fully addressed in previous experiments. To this end, the required data were collected from 128 patients, including their T1w and T2w MRI images, and ground truth labels of the liver and liver lesions were generated. The collection of 110 T1w-T2w MRI image sets was divided, with 94 designated for training and 16 for validation. Furthermore, 18 more datasets were separately allocated for use as hold-out test datasets. The T1w and T2w MRI images were preprocessed into a two-channel format so that they were used as inputs to the deep learning model based on the Isensee 2017 network. To calculate the final Dice coefficient of the network performance on test datasets, the binary average of T1w and T2w predicted images was used. The deep learning model could segment all 18 test cases, with an average Dice coefficient of 88% for the liver and 53% for the liver tumor. Liver segmentation was carried out with rather a high accuracy; this could be achieved for liver dosimetry during systemic or selective radiation therapies as well as for attenuation correction in PET/MRI scanners. Nevertheless, the delineation of liver lesions was not optimal; therefore, tumor detection was not practical by the proposed method on clinical data.

One Pot Photomediated Formation of Electrically Conductive Hydrogels.

Electrically conductive hydrogels represent an innovative platform for the development of bioelectronic devices. While photolithography technologies have enabled the fabrication of complex architectures with high resolution, photoprinting conductive hydrogels is still a challenging task because the conductive polymer absorbs light which can outcompete photopolymerization of the insulating scaffold. In this study, we introduce an approach to synthesizing conductive hydrogels in one step. Our approach combines the simultaneous photo-cross-linking of a polymeric scaffold and the polymerization of 3,4-ethylene dioxythiophene (EDOT), without additional photocatalysts. This process involves the copolymerization of photo-cross-linkable coumarin-containing monomers with sodium styrenesulfonate to produce a water-soluble poly(styrenesulfonate-co-coumarin acrylate) (P(SS-co-CoumAc)) copolymer. Our findings reveal that optimizing the [SS]:[CoumAc] ratio at 100:5 results in hydrogels with the strain at break up to 16%. This mechanical resilience is coupled with an electronic conductivity of 9.2 S m-1 suitable for wearable electronics. Furthermore, the conductive hydrogels can be photopatterned to achieve micrometer-sized structures with high resolution. The photo-cross-linked hydrogels are used as electrodes to record stable and reliable surface electromyography (sEMG) signals. These novel photo-cross-linkable polymers combined with one-pot PEDOT (poly-EDOT) polymerization open possibilities for rapidly prototyping complex bioelectronic devices and creating custom-designed interfaces between electronics and biological systems.

Simulation and pre-planning omitted radiotherapy (SPORT): a feasibility study for prostate cancer.

This study explored the feasibility of on-couch intensity modulated radiotherapy (IMRT) planning for prostate cancer (PCa) on a cone-beam CT (CBCT)-based online adaptive RT platform without an individualized pre-treatment plan and contours. Ten patients with PCa previously treated with image-guided IMRT (60 Gy/20 fractions) were selected. In contrast to the routine online adaptive RT workflow, a novel approach was employed in which the same preplan that was optimized on one reference patient was adapted to generate individual on-couch/initial plans for the other nine test patients using Ethos emulator. Simulation CTs of the test patients were used as simulated online CBCT (sCBCT) for emulation. Quality assessments were conducted on synthetic CTs (sCT). Dosimetric comparisons were performed between on-couch plans, on-couch plans recomputed on the sCBCT and individually optimized plans for test patients. The median value of mean absolute difference between sCT and sCBCT was 74.7 HU (range 69.5-91.5 HU). The average CTV/PTV coverage by prescription dose was 100.0%/94.7%, and normal tissue constraints were met for the nine test patients in on-couch plans on sCT. Recalculating on-couch plans on the sCBCT showed about 0.7% reduction of PTV coverage and a 0.6% increasing of hotspot, and the dose difference of the OARs was negligible (<0.5 Gy). Hence, initial IMRT plans for new patients can be generated by adapting a reference patient's preplan with online contours, which had similar qualities to the conventional approach of individually optimized plan on the simulation CT. Further study is needed to identify selection criteria for patient anatomy most amenable to this workflow.

Time to bystander cardiopulmonary resuscitation by patient sex for out-of-hospital cardiac arrest.

BACKGROUND: Delays in bystander cardiopulmonary resuscitation (CPR) are associated with worse out-of-hospital cardiac arrest (OHCA) outcomes. Whether disparities exist in time to CPR between women and men is unknown. METHODS: We included witnessed OHCAs treated with bystander CPR from the Cardiac Arrest Registry Enhancing Survival between 2013-2021. The primary outcome was time to first bystander CPR, and secondary outcomes were survival to hospital discharge and favorable neurological survival. Hierarchical ordinal regression was used to model time to first CPR, which estimates the odds of having a 2-minute longer delay (from 0 to ≥10 minutes) in receiving bystander CPR. The model included sex, age, race, location of arrest, cardiac arrest etiology, day of week, and season as fixed effects and EMS agency as a random effect to account for clustering of patients within an agency. RESULTS: Of 78,043 patients with a witnessed OHCA that received bystander CPR, 25,197 (32.3%) were women. The median [IQR] time to first bystander CPR was 2 [1,5] minutes for both women and men. In adjusted analysis, time to bystander CPR was similar in men and women (p = 0.26). Moreover, there was a statistically significantly graded inverse association between time to bystander CPR and survival. CONCLUSION: For patients with witnessed OHCA that received bystander CPR, women and men had similar times to CPR, although 5-minute or greater delays in initiating CPR was not uncommon. Delays in bystander CPR in OHCA were associated with worse survival outcomes.

Home Therapies to Neutralize Button Battery Injury in a Porcine Esophageal Model.

STUDY OBJECTIVE: Button battery ingestion can cause alkaline esophageal injury. There is interest in first-aid household products to neutralize the injury. The objective was to investigate which household products are effective at reducing button battery injury. METHODS: Two cadaveric porcine experiments were performed. Experiment 1 utilized esophageal mucosal segments. A button battery (3VCR2032) was placed onto the mucosa, and substances (saline control, honey, jam, orange juice, yogurt, milk, and cola) were applied every 10 minutes for 6 applications. Tissue pH was measured every 10 minutes, and macroscopic ulceration size was assessed at 120 minutes. Experiment 2 used an intact esophageal model with a battery inserted into the lumen and jam, honey, and saline irrigation as per experiment 1. Tissue pH, macroscopic and histopathology changes were evaluated at 60, 90 and 120 minutes. RESULTS: In experiment 1, only honey and jam had a lower mean tissue pH at 120 minutes (8.0 [standard deviation [SD] 0.9, n=12] and 7.1 [SD 1.7, n=12], respectively) compared to saline solution 11.9 (SD 0.6, n=6, P<.0001). Both honey (0.24 cm2, SD 0.17) and jam (0.37 cm2, SD 0.40) had smaller mean areas of ulceration compared to saline solution (3.90 cm2, SD 1.03, P<.0001). In experiment 2, honey and jam had significantly lower mean tissue pH at all timepoints compared to saline solution. Histologic changes were evident at 60 minutes in the saline group, whereas honey and jam exhibited no or minimal changes until 120 minutes. CONCLUSIONS: Honey and jam were able to neutralize injury caused by a button battery resulting in a smaller area of ulceration. Jam should be further explored as a possible first-aid option as an alternative to honey in suspected button battery ingestion prior to definitive management.