Reactive capture and electrochemical conversion of CO2 with ionic liquids and deep eutectic solvents.

Ionic liquids (ILs) and deep eutectic solvents (DESs) have tremendous potential for reactive capture and conversion (RCC) of CO2 due to their wide electrochemical stability window, low volatility, and high CO2 solubility. There is environmental and economic interest in the direct utilization of the captured CO2 using electrified and modular processes that forgo the thermal- or pressure-swing regeneration steps to concentrate CO2, eliminating the need to compress, transport, or store the gas. The conventional electrochemical conversion of CO2 with aqueous electrolytes presents limited CO2 solubility and high energy requirement to achieve industrially relevant products. Additionally, aqueous systems have competitive hydrogen evolution. In the past decade, there has been significant progress toward the design of ILs and DESs, and their composites to separate CO2 from dilute streams. In parallel, but not necessarily in synergy, there have been studies focused on a few select ILs and DESs for electrochemical reduction of CO2, often diluting them with aqueous or non-aqueous solvents. The resulting electrode-electrolyte interfaces present a complex speciation for RCC. In this review, we describe how the ILs and DESs are tuned for RCC and specifically address the CO2 chemisorption and electroreduction mechanisms. Critical bulk and interfacial properties of ILs and DESs are discussed in the context of RCC, and the potential of these electrolytes are presented through a techno-economic evaluation.

A US perspective on closing the carbon cycle to defossilize difficult-to-electrify segments of our economy.

Electrification to reduce or eliminate greenhouse gas emissions is essential to mitigate climate change. However, a substantial portion of our manufacturing and transportation infrastructure will be difficult to electrify and/or will continue to use carbon as a key component, including areas in aviation, heavy-duty and marine transportation, and the chemical industry. In this Roadmap, we explore how multidisciplinary approaches will enable us to close the carbon cycle and create a circular economy by defossilizing these difficult-to-electrify areas and those that will continue to need carbon. We discuss two approaches for this: developing carbon alternatives and improving our ability to reuse carbon, enabled by separations. Furthermore, we posit that co-design and use-driven fundamental science are essential to reach aggressive greenhouse gas reduction targets.

A scalable federated learning solution for secondary care using low-cost microcomputing: privacy-preserving development and evaluation of a COVID-19 screening test in UK hospitals.

BACKGROUND: Multicentre training could reduce biases in medical artificial intelligence (AI); however, ethical, legal, and technical considerations can constrain the ability of hospitals to share data. Federated learning enables institutions to participate in algorithm development while retaining custody of their data but uptake in hospitals has been limited, possibly as deployment requires specialist software and technical expertise at each site. We previously developed an artificial intelligence-driven screening test for COVID-19 in emergency departments, known as CURIAL-Lab, which uses vital signs and blood tests that are routinely available within 1 h of a patient's arrival. Here we aimed to federate our COVID-19 screening test by developing an easy-to-use embedded system-which we introduce as full-stack federated learning-to train and evaluate machine learning models across four UK hospital groups without centralising patient data. METHODS: We supplied a Raspberry Pi 4 Model B preloaded with our federated learning software pipeline to four National Health Service (NHS) hospital groups in the UK: Oxford University Hospitals NHS Foundation Trust (OUH; through the locally linked research University, University of Oxford), University Hospitals Birmingham NHS Foundation Trust (UHB), Bedfordshire Hospitals NHS Foundation Trust (BH), and Portsmouth Hospitals University NHS Trust (PUH). OUH, PUH, and UHB participated in federated training, training a deep neural network and logistic regressor over 150 rounds to form and calibrate a global model to predict COVID-19 status, using clinical data from patients admitted before the pandemic (COVID-19-negative) and testing positive for COVID-19 during the first wave of the pandemic. We conducted a federated evaluation of the global model for admissions during the second wave of the pandemic at OUH, PUH, and externally at BH. For OUH and PUH, we additionally performed local fine-tuning of the global model using the sites' individual training data, forming a site-tuned model, and evaluated the resultant model for admissions during the second wave of the pandemic. This study included data collected between Dec 1, 2018, and March 1, 2021; the exact date ranges used varied by site. The primary outcome was overall model performance, measured as the area under the receiver operating characteristic curve (AUROC). Removable micro secure digital (microSD) storage was destroyed on study completion. FINDINGS: Clinical data from 130 941 patients (1772 COVID-19-positive), routinely collected across three hospital groups (OUH, PUH, and UHB), were included in federated training. The evaluation step included data from 32 986 patients (3549 COVID-19-positive) attending OUH, PUH, or BH during the second wave of the pandemic. Federated training of a global deep neural network classifier improved upon performance of models trained locally in terms of AUROC by a mean of 27·6% (SD 2·2): AUROC increased from 0·574 (95% CI 0·560-0·589) at OUH and 0·622 (0·608-0·637) at PUH using the locally trained models to 0·872 (0·862-0·882) at OUH and 0·876 (0·865-0·886) at PUH using the federated global model. Performance improvement was smaller for a logistic regression model, with a mean increase in AUROC of 13·9% (0·5%). During federated external evaluation at BH, AUROC for the global deep neural network model was 0·917 (0·893-0·942), with 89·7% sensitivity (83·6-93·6) and 76·6% specificity (73·9-79·1). Site-specific tuning of the global model did not significantly improve performance (change in AUROC <0·01). INTERPRETATION: We developed an embedded system for federated learning, using microcomputing to optimise for ease of deployment. We deployed full-stack federated learning across four UK hospital groups to develop a COVID-19 screening test without centralising patient data. Federation improved model performance, and the resultant global models were generalisable. Full-stack federated learning could enable hospitals to contribute to AI development at low cost and without specialist technical expertise at each site. FUNDING: The Wellcome Trust, University of Oxford Medical and Life Sciences Translational Fund.

Digital Health and Machine Learning Technologies for Blood Glucose Monitoring and Management of Gestational Diabetes.

Innovations in digital health and machine learning are changing the path of clinical health and care. People from different geographical locations and cultural backgrounds can benefit from the mobility of wearable devices and smartphones to monitor their health ubiquitously. This paper focuses on reviewing the digital health and machine learning technologies used in gestational diabetes - a subtype of diabetes that occurs during pregnancy. This paper reviews sensor technologies used in blood glucose monitoring devices, digital health innovations and machine learning models for gestational diabetes monitoring and management, in clinical and commercial settings, and discusses future directions. Despite one in six mothers having gestational diabetes, digital health applications were underdeveloped, especially the techniques that can be deployed in clinical practice. There is an urgent need to (1) develop clinically interpretable machine learning methods for patients with gestational diabetes, assisting health professionals with treatment, monitoring, and risk stratification before, during and after their pregnancies; (2) adapt and develop clinically-proven devices for patient self-management of health and well-being at home settings ("virtual ward" and virtual consultation), thereby improving clinical outcomes by facilitating timely intervention; and (3) ensure innovations are affordable and sustainable for all women with different socioeconomic backgrounds and clinical resources.

Assessment of risk factors associated with outpatient parenteral antimicrobial therapy (OPAT) complications: A retrospective cohort study.

Objective: To characterize factors associated with increased risk of outpatient parenteral antimicrobial therapy (OPAT) complication. Design: Retrospective cohort study. Setting: Four hospitals within NYU Langone Health (NYULH). Patients: All patients aged ≥18 years with OPAT episodes who were admitted to an acute-care facility at NYULH between January 1, 2017, and December 31, 2020, who had an infectious diseases consultation during admission. Results: Overall, 8.45% of OPAT patients suffered a vascular complication and 6.04% suffered an antimicrobial complication. Among these patients, 19.95% had a 30-day readmission and 3.35% had OPAT-related readmission. Also, 1.58% of patients developed a catheter-related bloodstream infection (CRBSI). After adjusting for key confounders, we found that patients discharged to a subacute rehabilitation center (SARC) were more likely to develop a CRBSI (odds ratio [OR], 4.75; P = .005) and to be readmitted for OPAT complications (OR, 2.89; P = .002). Loss to follow-up with the infectious diseases service was associated with increased risks of CRBSI (OR, 3.78; P = .007) and 30-day readmission (OR, 2.59; P < .001). Conclusions: Discharge to an SARC is strongly associated with increased risks of readmission for OPAT-related complications and CRBSI. Loss to follow-up with the infectious diseases service is strongly associated with increased risk of readmission and CRBSI. CRBSI prevention during SARC admission is a critically needed public health intervention. Further work must be done for patients undergoing OPAT to improve their follow-up retention with the infectious diseases service.

Standardising the assessment of caesarean birth using an oxford caesarean prediction score for mothers with gestational diabetes.

Mothers with gestational diabetes are at increased risk of giving birth by caesarean section. A standardised assessment method may help to guide in recommendations in planning caesarean birth. We analysed 203 women with gestational diabetes managed in a single centre and developed an aggregate heuristic risk score. Among 155 women who had not had a previous caesarean birth, five risk factors (previous birth, weight gain during pregnancy, mother's height, and glycated haemoglobin and fasting blood glucose results at the beginning of pregnancy) were found associated with primary caesarean birth. Risk of primary caesarean birth in low-risk women (score 0-1) was 13.8%, medium-risk (score 2-3) 24.5% and high risk (score ≥ 4) 66.7%. The area under the receiver operating characteristic (AUROC) for primary caesarean birth prediction is 0.726 ± 0.003. Machine learning models were then deployed on 97 patients to explore the role of temporal blood glucose in predicting caesarean birth, achieving an AUROC of 0.857 ± 0.008. In conclusion, Oxford caesarean prediction score could help clinicians counselling women with gestational diabetes about their individual risk of primary caesarean birth. Temporal blood glucose measurements may improve the prediction subject to further validation.

The Geographic Availability of Certified Cleft Care in the United States: A National Geospatial Analysis of 1-Hour Access to Care.

PURPOSE: Children with cleft lip and/or palate (CLP) require longitudinal multidisciplinary care. Travel distance to comprehensive cleft centers may be a barrier for some families. This study evaluated the geospatial availability of certified cleft teams across the United States. MATERIALS AND METHODS: A geographic catchment area within a 1-hour travel radius of each American Cleft Palate-Craniofacial Association-certified cleft center was mapped using TravelTime distance matrix programming. The proportion of children located within each catchment area was calculated using county-level data from the National Kids Count Data Center, with aggregate estimates of patients with CLP based on state-level data from the Centers for Disease Control and Prevention. One-hour access was compared across regions and based on urbanization data collected from the US Census. RESULTS: There were 182 American Cleft Palate-Craniofacial Association-certified centers identified. As per study estimates, 28,331 (27.3%) children with CLP did not live within 1-hour travel distance to any center. One-hour access was highest in the Northeast (84.2% of children, P < .001) and lowest in the South (65.7%) and higher in states with the greatest urbanization in comparison with more rural states (85.1 vs 37.4%, P < .001). Similar patterns were seen for access to 2 or more cleft centers. The number of CLP children-per-center was highest in the West (775) and lowest in the Northeast (452). CONCLUSIONS: Travel distances of more than 1 hour may affect more than 25,000 (1 of 4) CLP children in the US, with significant variation across geographic regions. Future studies should seek to understand the impact of and provide strategies for overcoming geographic barriers.

Racial Disparities in Complications, Length of Stay, and Costs Among Patients Receiving Orthognathic Surgery in the United States.

INTRODUCTION: Black and Hispanic/Latino patients in the United States often experience poorer health outcomes in comparison to White patients. We aimed to assess the impact of race on complications, length of stay, and costs after orthognathic surgery. METHODS: Pediatric and young adult orthognathic surgeries (age <21) were isolated from the Kids Inpatient Database from 2000-2012. Procedures were grouped into cohorts based on the preoperative diagnosis: apnea, malocclusion, or congenital anomaly. T tests and χ2 analyses were employed to compare complications, length of stay (LOS), and costs among Black, Hispanic, Asian/Pacific Islander, and other patients in comparison to White patients. Multivariable regression was performed to identify associations between sociodemographic variables and the primary outcomes. Post-hoc χ2 analyses were performed to compare proportions of patients of a given race/ethnicity across the 3 surgical cohorts. RESULTS: There were 8,809 patients identified in the KID database (mean age of 16.3 years). Compared to White patients, complication rates were increased among Hispanic patients (2.1 vs 1.3%, P = .037) and other patients treated for apnea (8.7 vs 0.83%, P = .002). Hospital LOS was increased in both Black (3.3 vs 2.1 days, P < .001) and Hispanic (2.9 days, P < .001) patients. Costs were higher than Whites ($35,633.47) among Hispanic ($48,029.15, P < .001), Black ($47,034.41, P < .001), and Asian/Pacific-Islander ($44,192.49, P < .001) patients. White patients comprised a larger proportion of the malocclusion group (77.8%) than apnea (66.9%, P < .001) or congenital anomaly (59.1%, P < .001), while the opposite was true for Black, Hispanic, and Asian/Pacific-Islander patients. CONCLUSION: There are significant differences in complications, LOS, and costs after orthognathic surgery among patients of different race/ethnicity. Further studies are needed to better understand the causes of disparity and their clinical manifestations.

Structural Aspects and Prediction of Calmodulin-Binding Proteins.

Calmodulin (CaM) is an important intracellular protein that binds Ca2+ and functions as a critical second messenger involved in numerous biological activities through extensive interactions with proteins and peptides. CaM's ability to adapt to binding targets with different structures is related to the flexible central helix separating the N- and C-terminal lobes, which allows for conformational changes between extended and collapsed forms of the protein. CaM-binding targets are most often identified using prediction algorithms that utilize sequence and structural data to predict regions of peptides and proteins that can interact with CaM. In this review, we provide an overview of different CaM-binding proteins, the motifs through which they interact with CaM, and shared properties that make them good binding partners for CaM. Additionally, we discuss the historical and current methods for predicting CaM binding, and the similarities and differences between these methods and their relative success at prediction. As new CaM-binding proteins are identified and classified, we will gain a broader understanding of the biological processes regulated through changes in Ca2+ concentration through interactions with CaM.

Medical students in gynaecology clinics.

OBJECTIVE: This study aimed to investigate factors that influence a patient's acceptance of a medical student's involvement in their consultation when attending a public hospital gynaecology clinic. Factors that influence a patient's acceptance of a medical student's involvement in a consultation METHODS: This was an observational study of women attending gynaecology clinics at Royal Prince Alfred Hospital (RPAH) from January to December 2011. The questionnaire sought demographic information and asked women about their knowledge of medical student attendance at the clinics, if they would allow a student to be present during their consultation and whether they would allow a student to examine them. It also sought reasons for their responses. RESULTS: Of the 460 questionnaires distributed, 97 per cent (446) were completed. Overall, 85.6 per cent (382) of patients expressed an acceptance of medical students being present in their consultation, and 63.9 per cent (285) said they would allow students to examine them. Factors significantly associated with an increased acceptance of examination by medical students included being aware that a student may be present (p=0.003), and being married or in a committed relationship (p=0.023). Age and education level were not significantly associated with acceptance of being examined by a student, and ethnicity was too diverse to assess any possible bias. All groups maintained a preference for female students. CONCLUSION: This study has found that being aware that medical students may be present in gynaecology clinics may increase patient acceptance of being examined by a student. This demonstrates a role for information to be distributed to patients prior to their appointment to facilitate medical training.