Diastereo- and Enantioselective Construction of Stereochemical Arrays Exploiting Non-Classical Hydrogen Bonding in Enolborates.

We report a copper-catalyzed reductive aldol addition to aldehydes and ketones, with pinacolborane as stoichiometric reductant, that results in the generation of stereodefined syn-aldol products.  Cyclic, acyclic, fused and spirocyclic aldols bearing contiguous stereocenters are obtained with excellent yields and diastereoselectivities.  Moreover, enantioselective reactions could be carried out with cycloalkenones to deliver aldols bearing three contiguous stereocenters and with up to 98% ee.  Computations reveal that the enolborate intermediate undergoes the syn-aldol reaction via a twist-boat transition state that is stabilized by non-classical hydrogen bonding interactions.

Real-World Performance Benchmarks in the Reading Ability of Prescription Drug Labels by Community-Dwelling Older Adults.

BACKGROUND: Despite known recommendations regarding standards for print size and the intuitive importance of vision in reading prescription labels, the predictive nature of vision and prescription label readability remains largely undefined. Furthermore, while the importance of vision is recognized, various demographic factors associated with the ability to read prescription labels have not been fully elucidated. OBJECTIVE: Describe relationships between visual acuity, point size, and readability of prescription labels and provide insight into demographic factors associated with prescription label readability. METHODS: Cross-sectional examination of prescription label readability by older, community-dwelling adults. Subjects were evaluated as to demographics, visual acuity, and ability to read test instruments consisting of unaltered prescription label features of five medications dispensed by community pharmacies and two drug samples. Descriptive statistics in conjunction with a logit predictive model were employed for data analysis. RESULTS: Instructions for medication use were most recognizable, identified and correctly read by 95.60% of the study cohort while directions for the use of drug samples were lowest (34.91%). Among prescription label features, auxiliary labels consistently demonstrated poor readability. Level of visual acuity was statistically related to the ability to read prescription labels while identifying prescription label components increased proportionally with point size. Race, gender, and history of a recent eye examination were statistically significant predictors of prescription label reading ability. Visual acuity alone was found to explain approximately 26% of the variablity in ability to read Rx labels. CONCLUSION: Visual acuity is predictive of the ability to access Rx label information and should be considered a modifiable variable for improving prescription label reading ability amenable by appropriate eye care and spectacle correction.

Fulminant sepsis secondary to severe lung contusion from the Lund University Cardiopulmonary Assist System device.

INTRODUCTION: Portable mechanical chest compression devices have been developed to improve upon many problems of manual compression, increase patient survival, and improve neurologic outcomes. However, the use of these devices is not without risk of harm to the patient. CASE REPORT: We describe a patient who received chest compressions from a mechanical compression device after cardiac arrest and subsequently developed fulminant sepsis secondary to lung contusions and a necrotizing pulmonary infection. DISCUSSION: Although injuries from the LUCAS have been reported, we believe this is the first reported fatal complication related to direct pulmonary injury from a mechanical compression device. CONCLUSION: More investigation is needed to determine the safety and efficacy of the LUCAS especially in obese patients.

[ 11 C]-PBR28 positron emission tomography signal as an imaging marker of joint inflammation in knee osteoarthritis.

ABSTRACT: Although inflammation is known to play a role in knee osteoarthritis (KOA), inflammation-specific imaging is not routinely performed. In this article, we evaluate the role of joint inflammation, measured using [ 11 C]-PBR28, a radioligand for the inflammatory marker 18-kDa translocator protein (TSPO), in KOA. Twenty-one KOA patients and 11 healthy controls (HC) underwent positron emission tomography/magnetic resonance imaging (PET/MRI) knee imaging with the TSPO ligand [ 11 C]-PBR28. Standardized uptake values were extracted from regions-of-interest (ROIs) semiautomatically segmented from MRI data, and compared across groups (HC, KOA) and subgroups (unilateral/bilateral KOA symptoms), across knees (most vs least painful), and against clinical variables (eg, pain and Kellgren-Lawrence [KL] grades). Overall, KOA patients demonstrated elevated [ 11 C]-PBR28 binding across all knee ROIs, compared with HC (all P 's < 0.005). Specifically, PET signal was significantly elevated in both knees in patients with bilateral KOA symptoms (both P 's < 0.01), and in the symptomatic knee ( P < 0.05), but not the asymptomatic knee ( P = 0.95) of patients with unilateral KOA symptoms. Positron emission tomography signal was higher in the most vs least painful knee ( P < 0.001), and the difference in pain ratings across knees was proportional to the difference in PET signal ( r = 0.74, P < 0.001). Kellgren-Lawrence grades neither correlated with PET signal (left knee r = 0.32, P = 0.19; right knee r = 0.18, P = 0.45) nor pain ( r = 0.39, P = 0.07). The current results support further exploration of [ 11 C]-PBR28 PET signal as an imaging marker candidate for KOA and a link between joint inflammation and osteoarthritis-related pain severity.

Polysulfide regulation by defect-modulated Ta3N5-x electrocatalyst toward superior room-temperature sodium-sulfur batteries.

Resolving low sulfur reaction activity and severe polysulfide dissolution remains challenging in metal-sulfur batteries. Motivated by a theoretical prediction, herein, we strategically propose nitrogen-vacancy tantalum nitride (Ta3N5-x) impregnated inside the interconnected nanopores of nitrogen-decorated carbon matrix as a new electrocatalyst for regulating sulfur redox reactions in room-temperature sodium-sulfur batteries. Through a pore-constriction mechanism, the nitrogen vacancies are controllably constructed during the nucleation of Ta3N5-x. The defect manipulation on the local environment enables well-regulated Ta 5d-orbital energy level, not only modulating band structure toward enhanced intrinsic conductivity of Ta-based materials, but also promoting polysulfide stabilization and achieving bifunctional catalytic capability toward completely reversible polysulfide conversion. Moreover, the interconnected continuous Ta3N5-x-in-pore structure facilitates electron and sodium-ion transport and accommodates volume expansion of sulfur species while suppressing their shuttle behavior. Due to these attributes, the as-developed Ta3N5-x-based electrode achieves superior rate capability of 730 mAh g-1 at 3.35 A g-1, long-term cycling stability over 2000 cycles, and high areal capacity over 6 mAh cm-2 under high sulfur loading of 6.2 mg cm-2. This work not only presents a new sulfur electrocatalyst candidate for metal-sulfur batteries, but also sheds light on the controllable material design of defect structure in hopes of inspiring new ideas and directions for future research.

A Convolutional Neural Network for Beamforming and Image Reconstruction in Passive Cavitation Imaging.

Convolutional neural networks (CNNs), initially developed for image processing applications, have recently received significant attention within the field of medical ultrasound imaging. In this study, passive cavitation imaging/mapping (PCI/PAM), which is used to map cavitation sources based on the correlation of signals across an array of receivers, is evaluated. Traditional reconstruction techniques in PCI, such as delay-and-sum, yield high spatial resolution at the cost of a substantial computational time. This results from the resource-intensive process of determining sensor weights for individual pixels in these methodologies. Consequently, the use of conventional algorithms for image reconstruction does not meet the speed requirements that are essential for real-time monitoring. Here, we show that a three-dimensional (3D) convolutional network can learn the image reconstruction algorithm for a 16×16 element matrix probe with a receive frequency ranging from 256 kHz up to 1.0 MHz. The network was trained and evaluated using simulated data representing point sources, resulting in the successful reconstruction of volumetric images with high sensitivity, especially for single isolated sources (100% in the test set). As the number of simultaneous sources increased, the network's ability to detect weaker intensity sources diminished, although it always correctly identified the main lobe. Notably, however, network inference was remarkably fast, completing the task in approximately 178 s for a dataset comprising 650 frames of 413 volume images with signal duration of 20µs. This processing speed is roughly thirty times faster than a parallelized implementation of the traditional time exposure acoustics algorithm on the same GPU device. This would open a new door for PCI application in the real-time monitoring of ultrasound ablation.

Species-Selective Detection of Volatile Organic Compounds by Ionic Liquid-Based Electrolyte Using Electrochemical Methods.

The detection of volatile organic compounds (VOCs) is an important topic for environmental safety and public health. However, the current commercial VOC detectors suffer from cross-sensitivity and low reproducibility. In this work, we present species-selective detection for VOCs using an electrochemical cell based on ionic liquid (IL) electrolytes with features of high selectivity and reliability. The voltammograms measured with the IL-based electrolyte absorbing different VOCs exhibited species-selective features that were extracted and classified by linear discriminant analysis (LDA). The detection system could identify as many as four types of VOCs, including methanol, ethanol, acetone, formaldehyde, and additional water. A mixture of methanol and formaldehyde was detected as well. The sample required for the VOCs classification system was 50 µL, or 1.164 mmol, on average. The response time for each VOC measurement is as fast as 24 s. The volume of VOCs such as formaldehyde in solution could also be quantified by LDA and electrochemical impedance spectroscopy techniques, respectively. The system showed a tunable detection range for 1.6 and 16% (w/v) CH2O solution by adjusting the composition of the electrolyte. The limit of detection was as low as 1 µL. For the 1.6% CH2O solution, the linearity calibration range was determined to be from 5.30 to 53.00 µmol with a limit of detection at 0.53 µmol. The mechanisms for VOCs determination and quantification are also thoroughly discussed. It is expected that this work could provide a new insight into the concept of electrochemical detection of VOCs with machine learning analysis and be applied to both VOCs gas monitoring and fluid detection.

Triarylmethyl cation redox mediators enhance Li-O2 battery discharge capacities.

A major impediment to Li-O2 battery commercialization is the low discharge capacities resulting from electronically insulating Li2O2 film growth on carbon electrodes. Redox mediation offers an effective strategy to drive oxygen chemistry into solution, avoiding surface-mediated Li2O2 film growth and extending discharge lifetimes. As such, the exploration of diverse redox mediator classes can aid the development of molecular design criteria. Here we report a class of triarylmethyl cations that are effective at enhancing discharge capacities up to 35-fold. Surprisingly, we observe that redox mediators with more positive reduction potentials lead to larger discharge capacities because of their improved ability to suppress the surface-mediated reduction pathway. This result provides important structure-property relationships for future improvements in redox-mediated O2/Li2O2 discharge capacities. Furthermore, we applied a chronopotentiometry model to investigate the zones of redox mediator standard reduction potentials and the concentrations needed to achieve efficient redox mediation at a given current density. We expect this analysis to guide future redox mediator exploration.

Appropriate Reliance on Artificial Intelligence in Radiology Education.

Users of artificial intelligence (AI) can become overreliant on AI, negatively affecting the performance of human-AI teams. For a future in which radiologists use interpretive AI tools routinely in clinical practice, radiology education will need to evolve to provide radiologists with the skills to use AI appropriately and wisely. In this work, we examine how overreliance on AI may develop in radiology trainees and explore how this problem can be mitigated, including through the use of AI-augmented education. Radiology trainees will still need to develop the perceptual skills and mastery of knowledge fundamental to radiology to use AI safely. We propose a framework for radiology trainees to use AI tools with appropriate reliance, drawing on lessons from human-AI interactions research.

Caring for two in the ICU: Pharmacologic management of pregnancy-related complications.

Maternal mortality continues to be an issue globally despite advances in technology and pharmacotherapy. Pregnancy can lead to complications that necessitate immediate action to prevent severe morbidity and mortality. Patients may need escalation to the ICU setting for close monitoring and administration of advanced therapies not available elsewhere. Obstetric emergencies are rare but high-stakes events that require clinicians to have prompt identification and management. The purpose of this review is to describe complications of pregnancy and provide a focused resource of pharmacotherapy considerations that clinicians may encounter. For each disease state, the epidemiology, pathophysiology, and management are summarized. Brief descriptions of non-pharmacological (e.g., cesarean or vaginal delivery of the baby) interventions are provided. Mainstays of pharmacotherapy highlighted include oxytocin for obstetric hemorrhage, methotrexate for ectopic pregnancy, magnesium and antihypertensive agents for preeclampsia and eclampsia, eculizumab for atypical hemolytic uremic syndrome, corticosteroids, and immunosuppressive agents for thrombotic thrombocytopenic purpura, diuretics, metoprolol, and anticoagulation for peripartum cardiomyopathy, and pulmonary vasodilators for amniotic fluid embolism.

Transformer versus traditional natural language processing: how much data is enough for automated radiology report classification?

OBJECTIVES: Current state-of-the-art natural language processing (NLP) techniques use transformer deep-learning architectures, which depend on large training datasets. We hypothesized that traditional NLP techniques may outperform transformers for smaller radiology report datasets. METHODS: We compared the performance of BioBERT, a deep-learning-based transformer model pre-trained on biomedical text, and three traditional machine-learning models (gradient boosted tree, random forest, and logistic regression) on seven classification tasks given free-text radiology reports. Tasks included detection of appendicitis, diverticulitis, bowel obstruction, and enteritis/colitis on abdomen/pelvis CT reports, ischemic infarct on brain CT/MRI reports, and medial and lateral meniscus tears on knee MRI reports (7,204 total annotated reports). The performance of NLP models on held-out test sets was compared after training using the full training set, and 2.5%, 10%, 25%, 50%, and 75% random subsets of the training data. RESULTS: In all tested classification tasks, BioBERT performed poorly at smaller training sample sizes compared to non-deep-learning NLP models. Specifically, BioBERT required training on approximately 1,000 reports to perform similarly or better than non-deep-learning models. At around 1,250 to 1,500 training samples, the testing performance for all models began to plateau, where additional training data yielded minimal performance gain. CONCLUSIONS: With larger sample sizes, transformer NLP models achieved superior performance in radiology report binary classification tasks. However, with smaller sizes (<1000) and more imbalanced training data, traditional NLP techniques performed better. ADVANCES IN KNOWLEDGE: Our benchmarks can help guide clinical NLP researchers in selecting machine-learning models according to their dataset characteristics.

Caring for two in the ICU: Pharmacotherapy in the critically ill pregnant patient.

Safe and thoughtful medication management of pregnant patients requiring intensive care unit (ICU) level of care is key to optimizing outcomes for both mother and fetus. Pregnancy induces physiologic alterations that closely mirror the changes expected in a critically ill patient. These changes can be predictable depending on the gestational age and trimester and will directly impact the pharmacokinetic profile of medications commonly used in the ICU; examples include decreased gastric emptying, increased blood and plasma volume, increased glomerular filtration, and increased cardiac output. When pregnant patients require ICU care, the resulting impact on drug absorption, distribution, metabolism, and elimination can be difficult to predict. In addition, there are many nuances of medication metabolism and interface with the placental barrier that should be considered when selecting pharmacotherapy for the pregnant patient. Critical care clinicians need to be aware of medication interactions with the placenta and weigh the risk versus benefit profile of medication use in this patient population. Obstetric critical care admissions have increased over the years, especially during the coronavirus waves. Therefore, understanding the interplay between pregnancy and critical illness to optimize pharmacotherapy selection is crucial to improving health outcomes of mother and fetus. This review highlights pharmacotherapy considerations in the pregnant ICU patient for the following topics: physiologic alterations, categorizing medication risk, supportive care, sepsis, cardiogenic shock, acute respiratory distress syndrome, and venous thromboembolism.

A Prospective Observational Study on Short and Long-Term Outcomes of COVID-19 Patients with Acute Hypoxic Respiratory Failure Treated with High-Flow Nasal Cannula.

(1) The use of high-flow nasal cannula (HFNC) combined with frequent respiratory monitoring in patients with acute hypoxic respiratory failure due to COVID-19 has been shown to reduce intubation and mechanical ventilation. (2) This prospective, single-center, observational study included consecutive adult patients with COVID-19 pneumonia treated with a high-flow nasal cannula. Hemodynamic parameters, respiratory rate, inspiratory fraction of oxygen (FiO2), saturation of oxygen (SpO2), and the ratio of oxygen saturation to respiratory rate (ROX) were recorded prior to treatment initiation and every 2 h for 24 h. A 6-month follow-up questionnaire was also conducted. (3) Over the study period, 153 of 187 patients were eligible for HFNC. Of these patients, 80% required intubation and 37% of the intubated patients died in hospital. Male sex (OR = 4.65; 95% CI [1.28; 20.6], p = 0.03) and higher BMI (OR = 2.63; 95% CI [1.14; 6.76], p = 0.03) were associated with an increased risk for new limitations at 6-months after hospital discharge. (4) 20% of patients who received HFNC did not require intubation and were discharged alive from the hospital. Male sex and higher BMI were associated with poor long-term functional outcomes.

Use of Intravenous Vitamin C in Critically Ill Patients With COVID-19 Infection.

INTRODUCTION: The pathophysiology for Coronavirus Disease 2019 (COVID-19) infection is characterized by cytokine oxidative stress and endothelial dysfunction. Intravenous (IV) vitamin C has been utilized as adjuvant therapy in critically ill patients with sepsis for its protective effects against reactive oxygen species and immunomodulatory effects. The primary objective of this study was to evaluate the effects of IV vitamin C in critically ill patients with COVID-19 infection. METHODS: Retrospective observational cohort study with propensity score matching of intensive care unit (ICU) patients who received 1.5 grams IV vitamin C every 6 hours for up to 4 days for COVID-19 infection. The primary study outcome was in-hospital mortality. Secondary outcomes included vasopressor requirements in norepinephrine equivalents, ICU length of stay, and change in Sequential Organ Failure Assessment (SOFA) score. RESULTS: Eight patients received IV vitamin C and were matched to 24 patients. Patients in the IV vitamin C group had higher rates of hospital mortality [7 (88%) vs. 19 (79%), P = 0.049]. There was no difference in the daily vasopressor requirement in the treatment group or between the 2 groups. The mean SOFA scores post-treatment was higher in the IV vitamin C group (12.4 ± 2.8 vs. 8.1 ± 3.5, P < 0.005). There was no difference in ICU length of stay between the treatment and control groups. CONCLUSION: Adjunctive IV vitamin C for the management of COVID-19 infection in critically ill patients may not decrease the incidence of mortality, vasopressor requirements, SOFA scores, or ventilator settings.

Assessing the Severity of COVID-19 Lung Injury in Rheumatic Diseases Versus the General Population Using Deep Learning-Derived Chest Radiograph Scores.

OBJECTIVE: COVID-19 patients with rheumatic disease have a higher risk of mechanical ventilation than the general population. The present study was undertaken to assess lung involvement using a validated deep learning algorithm that extracts a quantitative measure of radiographic lung disease severity. METHODS: We performed a comparative cohort study of rheumatic disease patients with COVID-19 and ≥1 chest radiograph within ±2 weeks of COVID-19 diagnosis and matched comparators. We used unadjusted and adjusted (for age, Charlson comorbidity index, and interstitial lung disease) quantile regression to compare the maximum pulmonary x-ray severity (PXS) score at the 10th to 90th percentiles between groups. We evaluated the association of severe PXS score (>9) with mechanical ventilation and death using Cox regression. RESULTS: We identified 70 patients with rheumatic disease and 463 general population comparators. Maximum PXS scores were similar in the rheumatic disease patients and comparators at the 10th to 60th percentiles but significantly higher among rheumatic disease patients at the 70th to 90th percentiles (90th percentile score of 10.2 versus 9.2; adjusted P = 0.03). Rheumatic disease patients were more likely to have a PXS score of >9 (20% versus 11%; P = 0.02), indicating severe pulmonary disease. Rheumatic disease patients with PXS scores >9 versus ≤9 had higher risk of mechanical ventilation (hazard ratio [HR] 24.1 [95% confidence interval (95% CI) 6.7, 86.9]) and death (HR 8.2 [95% CI 0.7, 90.4]). CONCLUSION: Rheumatic disease patients with COVID-19 had more severe radiographic lung involvement than comparators. Higher PXS scores were associated with mechanical ventilation and will be important for future studies leveraging big data to assess COVID-19 outcomes in rheumatic disease patients.

COVID-19 and China commodity price jump behavior: An information spillover and wavelet coherency analysis.

Jumps in commodity prices can make asset risk management challenging. This study explores the influence feature of the COVID-19 epidemic on China's commodity price jumps, using 5-min intraday high-frequency futures data of three China's commodity markets (energy, chemical, and metal) from January 23, 2020 to June 10, 2022. We find that firstly the information spillover from the COVID-19 spread situation to China's energy price jumps is relatively weak, and the COVID-19 epidemic shows the most substantial jump information spillover pattern to China's chemical price. The information spillover pattern is time-varying across the COVID-19 spread situation phase. Secondly, there are co-movement patterns between China's commodity price and China/global COVID-19 confirmed cases. This co-movement feature mainly occurs at the medium- or long-run time scales, and varies across commodities. Thirdly, the demand elasticity for China's commodities and its dependence on imports and exports are the main factors influencing the sensitivity of its price jumps to the COVID-19 outbreak.