Continuous renal replacement therapy in COVID-19-associated AKI: adding heparin to citrate to extend filter life-a retrospective cohort study.

BACKGROUND: Coronavirus disease 2019 (COVID-19) may predispose patients to thrombotic events. The best anticoagulation strategy for continuous renal replacement therapy (CRRT) in such patients is still under debate. The purpose of this study was to evaluate the impact that different anticoagulation protocols have on filter clotting risk. METHODS: This was a retrospective observational study comparing two different anticoagulation strategies (citrate only and citrate plus intravenous infusion of unfractionated heparin) in patients with acute kidney injury (AKI), associated or not with COVID-19 (COV + AKI and COV - AKI, respectively), who were submitted to CRRT. Filter clotting risks were compared among groups. RESULTS: Between January 2019 and July 2020, 238 patients were evaluated: 188 in the COV + AKI group and 50 in the COV - AKI group. Filter clotting during the first filter use occurred in 111 patients (46.6%). Heparin use conferred protection against filter clotting (HR = 0.37, 95% CI 0.25-0.55), resulting in longer filter survival. Bleeding events and the need for blood transfusion were similar between the citrate only and citrate plus unfractionated heparin strategies. In-hospital mortality was higher among the COV + AKI patients than among the COV - AKI patients, although it was similar between the COV + AKI patients who received heparin and those who did not. Filter clotting was more common in patients with D-dimer levels above the median (5990 ng/ml). In the multivariate analysis, heparin was associated with a lower risk of filter clotting (HR = 0.28, 95% CI 0.18-0.43), whereas an elevated D-dimer level and high hemoglobin were found to be risk factors for circuit clotting. A diagnosis of COVID-19 was marginally associated with an increased risk of circuit clotting (HR = 2.15, 95% CI 0.99-4.68). CONCLUSIONS: In COV + AKI patients, adding systemic heparin to standard regional citrate anticoagulation may prolong CRRT filter patency by reducing clotting risk with a low risk of complications.

Chlamydia trachomatis Infection, when Treated during Pregnancy, Is Not Associated with Preterm Birth in an Urban Safety-Net Hospital.

Preterm birth is a major public health problem, occurring in more than half a million births per year in the United States. A number of maternal conditions have been recognized as risk factors for preterm birth, but for the majority of cases, the etiology is not completely understood. Chlamydia trachomatis is one of the most prevalent sexually transmitted infections in the world. However, its role in adverse pregnancy outcome in women is still debated. In order to determine if genitourinary tract infection with C. trachomatis during pregnancy was associated with preterm birth, we conducted a case-control study on women who delivered at Boston Medical Center, an urban "safety-net" hospital that serves a socioeconomically disadvantaged and racially diverse population. Women with known risk factors for preterm birth or immune suppression were excluded. Variables collected on enrolled subjects included demographics; diagnosis of C. trachomatis during or prior to pregnancy; tobacco, alcohol, and illicit substance use; gestational age; and birthweight and gender of the newborn. We also collected urine for chlamydia testing at the time of delivery and placental biopsies for nucleic acid amplification and histological studies. A total of 305 subjects were enrolled: 100 who delivered preterm and 205 who delivered full term. Among those subjects, we identified 19 cases of pregnancy-associated C. trachomatis infection: 6/100 preterm and 13/205 full term, a difference which was not statistically significant. Only two cases of untreated chlamydia infection were identified postpartum, and both occurred in women who delivered at term. We conclude that genitourinary tract infection with C. trachomatis during pregnancy, when appropriately treated, is not associated with preterm birth.

Disease networks and their contribution to disease understanding: A review of their evolution, techniques and data sources.

Over a decade ago, a new discipline called network medicine emerged as an approach to understand human diseases from a network theory point-of-view. Disease networks proved to be an intuitive and powerful way to reveal hidden connections among apparently unconnected biomedical entities such as diseases, physiological processes, signaling pathways, and genes. One of the fields that has benefited most from this improvement is the identification of new opportunities for the use of old drugs, known as drug repurposing. The importance of drug repurposing lies in the high costs and the prolonged time from target selection to regulatory approval of traditional drug development. In this document we analyze the evolution of disease network concept during the last decade and apply a data science pipeline approach to evaluate their functional units. As a result of this analysis, we obtain a list of the most commonly used functional units and the challenges that remain to be solved. This information can be very valuable for the generation of new prediction models based on disease networks.

Metal-assisted exfoliation (MAE): green, roll-to-roll compatible method for transferring graphene to flexible substrates.

Graphene is expected to play a significant role in future technologies that span a range from consumer electronics, to devices for the conversion and storage of energy, to conformable biomedical devices for healthcare. To realize these applications, however, a low-cost method of synthesizing large areas of high-quality graphene is required. Currently, the only method to generate large-area single-layer graphene that is compatible with roll-to-roll manufacturing destroys approximately 300 kg of copper foil (thickness = 25 µm) for every 1 g of graphene produced. This paper describes a new environmentally benign and scalable process of transferring graphene to flexible substrates. The process is based on the preferential adhesion of certain thin metallic films to graphene; separation of the graphene from the catalytic copper foil is followed by lamination to a flexible target substrate in a process that is compatible with roll-to-roll manufacturing. The copper substrate is indefinitely reusable and the method is substantially greener than the current process that uses relatively large amounts of corrosive etchants to remove the copper. The sheet resistance of the graphene produced by this new process is unoptimized but should be comparable in principle to that produced by the standard method, given the defects observable by Raman spectroscopy and the presence of process-induced cracks. With further improvements, this green, inexpensive synthesis of single-layer graphene could enable applications in flexible, stretchable, and disposable electronics, low-profile and lightweight barrier materials, and in large-area displays and photovoltaic modules.

[Rituximab-induced interstitial lung disease]. / Enfermedad pulmonar intersticial asociada a rituximab.

The introduction of the anti-CD20 antibody rituximab into clinical practice has improved substantially the prognosis of a variety of haematological and autoimmune diseases. The interstitial lung disease is one of most serious and potentially fatal complications of rituximab therapy. This diagnosis should be considered in patients who have received the drug and present with dyspnea, fever and cough without clear evidence of infection. We report a case of rituximab-induced interstitial lung disease.

Effectiveness of an Enhanced Community Doula Intervention in a Safety Net Setting: A Randomized Controlled Trial.

Background: Racial inequities in maternal health outcomes, the result of systemic racism and social determinants of health, require maternity care systems to implement interventions that reduce disparities. One such approach may be support from a community doula, a health worker who provides emotional support, peer education, navigation, and advocacy for pregnant, birthing, and postpartum people who share similar racial identities, cultural backgrounds, and/or lived experiences. While community support during birth has a long tradition within communities of Black Indigenous and People of Color (BIPOC), the reframing of community doula support as a social intervention that reduces disparities in clinical outcomes is recent. Methods: We conducted a pragmatic randomized trial at an urban safety net hospital, comparing standard maternity care with standard care plus enhanced community doula support. We tested the effectiveness of a community doula program embedded in a safety net hospital in improving birth outcomes and explored the association between community doula support and health equity. Participants were nulliparous, insured by publicly funded health plans, and had lower risk pregnancies. The primary outcome was cesarean birth. Secondary outcomes included preterm birth and breastfeeding outcomes. Exploratory subgroup analysis was conducted by race-ethnicity. Results: Three hundred sixty-seven participants were included in the primary analysis. In the intent-to-treat analysis, outcomes were similar between groups. There was a trend toward increased breastfeeding initiation (p=0.08). There was a statistically nonsignificant 12% absolute reduction in cesarean birth and 11.5% increase in exclusive breastfeeding during delivery hospitalization among Black non-Hispanic participants. Discussion: While outcomes for the study sample were similar between randomization groups, health outcomes were improved for Black birthing people in cesarean and breastfeeding rates. Conclusion: This study demonstrates the need for larger studies of community doula support for Black birthing people. Clinicaltrials.gov ID: NCT02550730.

A survey on deep learning for skin lesion segmentation.

Skin cancer is a major public health problem that could benefit from computer-aided diagnosis to reduce the burden of this common disease. Skin lesion segmentation from images is an important step toward achieving this goal. However, the presence of natural and artificial artifacts (e.g., hair and air bubbles), intrinsic factors (e.g., lesion shape and contrast), and variations in image acquisition conditions make skin lesion segmentation a challenging task. Recently, various researchers have explored the applicability of deep learning models to skin lesion segmentation. In this survey, we cross-examine 177 research papers that deal with deep learning-based segmentation of skin lesions. We analyze these works along several dimensions, including input data (datasets, preprocessing, and synthetic data generation), model design (architecture, modules, and losses), and evaluation aspects (data annotation requirements and segmentation performance). We discuss these dimensions both from the viewpoint of select seminal works, and from a systematic viewpoint, examining how those choices have influenced current trends, and how their limitations should be addressed. To facilitate comparisons, we summarize all examined works in a comprehensive table as well as an interactive table available online3.

A clinical model to predict successful renal replacement therapy (RRT) discontinuation in patients with Acute Kidney Injury (AKI).

INTRODUCTION: Ideal timing of Renal Replacement Therapy (RRT) discontinuation in Acute Kidney Injury (AKI) is still unknown. We aimed to study the role of creatinine-related variables in predicting RRT successful discontinuation and to propose a clinical predictive score. METHODS: In this single-centre retrospective study, we evaluated all AKI patients in whom RRT was interrupted for at least 48 hours. Patients who were still RRT-independent 7 days after initial RRT cessation were included in the "Success" group and opposed to the "Failure" group. We evaluated baseline characteristics and variables collected at the time of RRT interruption, as well as the Kinetic estimated Glomerular Filtration Rate (KeGFR), the simple variation in serum Creatinine (ΔsCr), and the incremental creatinine ratio on the first three days after RRT interruption. Multivariable analysis was performed to evaluate prediction of success. Internal validation using a simple binomial generalized regression model with Lasso estimation and 5-fold cross validation method was performed. RESULTS: We included 124 patients, 49 in the "Failure" group and 75 in the "Success" group. All creatinine-related variables predicted success in simple and multiple logistic regression models. The best model generated a clinical score based on the odds ratio obtained for each variable and included urine output, non-renal SOFA score, fluid balance, serum urea, serum potassium, blood pH, and the variation in sCr values after RRT discontinuation. The score presented an area under the ROC of 0.86 (95% CI 0.76‒1.00). CONCLUSION: Creatinine variation between the first 2 consecutive days after RRT discontinuation might predict success in RRT discontinuation. The developed clinical score based on these variables might be a useful clinical decision tool to guide hemodialysis catheter safe removal.

First-Row Transition Metal Antimonates for the Oxygen Reduction Reaction.

The development of inexpensive and abundant catalysts with high activity, selectivity, and stability for the oxygen reduction reaction (ORR) is imperative for the widespread implementation of fuel cell devices. Herein, we present a combined theoretical-experimental approach to discover and design first-row transition metal antimonates as excellent electrocatalytic materials for the ORR. Theoretically, we identify first-row transition metal antimonates─MSb2O6, where M = Mn, Fe, Co, and Ni─as nonprecious metal catalysts with good oxygen binding energetics, conductivity, thermodynamic phase stability, and aqueous stability. Among the considered antimonates, MnSb2O6 shows the highest theoretical ORR activity based on the 4e- ORR kinetic volcano. Experimentally, nanoparticulate transition metal antimonate catalysts are found to have a minimum of a 2.5-fold enhancement in intrinsic mass activity (on transition metal mass basis) relative to the corresponding transition metal oxide at 0.7 V vs RHE in 0.1 M KOH. MnSb2O6 is the most active catalyst under these conditions, with a 3.5-fold enhancement on a per Mn mass activity basis and 25-fold enhancement on a surface area basis over its antimony-free counterpart. Electrocatalytic and material stability are demonstrated over a 5 h chronopotentiometry experiment in the stability window identified by theoretical Pourbaix analysis. This study further highlights the stable and electrically conductive antimonate structure as a framework to tune the activity and selectivity of nonprecious metal oxide active sites for ORR catalysis.

Classifying diseases by using biological features to identify potential nosological models.

Established nosological models have provided physicians an adequate enough classification of diseases so far. Such systems are important to correctly identify diseases and treat them successfully. However, these taxonomies tend to be based on phenotypical observations, lacking a molecular or biological foundation. Therefore, there is an urgent need to modernize them in order to include the heterogeneous information that is produced in the present, as could be genomic, proteomic, transcriptomic and metabolic data, leading this way to more comprehensive and robust structures. For that purpose, we have developed an extensive methodology to analyse the possibilities when it comes to generate new nosological models from biological features. Different datasets of diseases have been considered, and distinct features related to diseases, namely genes, proteins, metabolic pathways and genetical variants, have been represented as binary and numerical vectors. From those vectors, diseases distances have been computed on the basis of several metrics. Clustering algorithms have been implemented to group diseases, generating different models, each of them corresponding to the distinct combinations of the previous parameters. They have been evaluated by means of intrinsic metrics, proving that some of them are highly suitable to cover new nosologies. One of the clustering configurations has been deeply analysed, demonstrating its quality and validity in the research context, and further biological interpretations have been made. Such model was particularly generated by OPTICS clustering algorithm, by studying the distance between diseases based on gene sharedness and following cosine index metric. 729 clusters were formed in this model, which obtained a Silhouette coefficient of 0.43.

Leveraging network analysis to evaluate biomedical named entity recognition tools.

The ever-growing availability of biomedical text sources has resulted in a boost in clinical studies based on their exploitation. Biomedical named-entity recognition (bio-NER) techniques have evolved remarkably in recent years and their application in research is increasingly successful. Still, the disparity of tools and the limited available validation resources are barriers preventing a wider diffusion, especially within clinical practice. We here propose the use of omics data and network analysis as an alternative for the assessment of bio-NER tools. Specifically, our method introduces quality criteria based on edge overlap and community detection. The application of these criteria to four bio-NER solutions yielded comparable results to strategies based on annotated corpora, without suffering from their limitations. Our approach can constitute a guide both for the selection of the best bio-NER tool given a specific task, and for the creation and validation of novel approaches.

DisMaNET: A network-based tool to cross map disease vocabularies.

BACKGROUND AND OBJECTIVES: The growing integration of healthcare sources is improving our understanding of diseases. Cross-mapping resources such as UMLS play a very important role in this area, but their coverage is still incomplete. With the aim to facilitate the integration and interoperability of biological, clinical and literary sources in studies of diseases, we built DisMaNET, a system to cross-map terms from disease vocabularies by leveraging the power and interpretability of network analysis. METHODS: First, we collected and normalized data from 8 disease vocabularies and mapping sources to generate our datasets. Next, we built DisMaNET by integrating the generated datasets into a Neo4j graph database. Then we exploited the query mechanisms of Neo4j to cross-map disease terms of different vocabularies with a relevance score metric and contrasted the results with some state-of-the-art solutions. Finally, we made our system publicly available for its exploitation and evaluation both through a graphical user interface and REST APIs. RESULTS: DisMaNET contains almost half a million nodes and near nine hundred thousand edges, including hierarchical and mapping relationships. Its query capabilities enabled the detection of connections between disease vocabularies that are not present in major mapping sources such as UMLS and the Disease Ontology, even for rare diseases. Furthermore, DisMaNET was capable of obtaining more than 80% of the mappings with UMLS reported in MonDO and DisGeNET, and it was successfully exploited to resolve the missing mappings in the DISNET project. CONCLUSIONS: DisMaNET is a powerful, intuitive and publicly available system to cross-map terms from different disease vocabularies. Our study proves that it is a competitive alternative to existing mapping systems, incorporating the potential of network analysis and the interpretability of the results through a visual interface as its main advantages. Expansion with new sources, versioning and the improvement of the search and scoring algorithms are envisioned as future lines of work.

DISNET: a framework for extracting phenotypic disease information from public sources.

BACKGROUND: Within the global endeavour of improving population health, one major challenge is the identification and integration of medical knowledge spread through several information sources. The creation of a comprehensive dataset of diseases and their clinical manifestations based on information from public sources is an interesting approach that allows one not only to complement and merge medical knowledge but also to increase it and thereby to interconnect existing data and analyse and relate diseases to each other. In this paper, we present DISNET (http://disnet.ctb.upm.es/), a web-based system designed to periodically extract the knowledge from signs and symptoms retrieved from medical databases, and to enable the creation of customisable disease networks. METHODS: We here present the main features of the DISNET system. We describe how information on diseases and their phenotypic manifestations is extracted from Wikipedia and PubMed websites; specifically, texts from these sources are processed through a combination of text mining and natural language processing techniques. RESULTS: We further present the validation of our system on Wikipedia and PubMed texts, obtaining the relevant accuracy. The final output includes the creation of a comprehensive symptoms-disease dataset, shared (free access) through the system's API. We finally describe, with some simple use cases, how a user can interact with it and extract information that could be used for subsequent analyses. DISCUSSION: DISNET allows retrieving knowledge about the signs, symptoms and diagnostic tests associated with a disease. It is not limited to a specific category (all the categories that the selected sources of information offer us) and clinical diagnosis terms. It further allows to track the evolution of those terms through time, being thus an opportunity to analyse and observe the progress of human knowledge on diseases. We further discussed the validation of the system, suggesting that it is good enough to be used to extract diseases and diagnostically-relevant terms. At the same time, the evaluation also revealed that improvements could be introduced to enhance the system's reliability.

Cov-hep study: heparin in standard anticoagulation based on citrate for continuous veno-venous hemodialysis in patients with COVID-19: a structured summary of a study protocol for a randomized controlled trial.

OBJECTIVES: The primary objective is to test if heparin added to a standard regional anticoagulation protocol based on citrate is able to reduce dialysis circuit losses by clotting without increasing the risk of thrombocytopenia or bleeding, in patients with COVID-19 with acute kidney injury requiring dialysis. TRIAL DESIGN: Randomized, parallel-group, open-label trial, with two arms (ratio 1:1) comparing different continuous renal replacement therapy anticoagulation strategies. PARTICIPANTS: Eligibility conditions: All ICU patients of University of Sao Paulo General Hospital (Hospital das Clínicas), Brazil will be screened for eligibility conditions. Adults (> 18 years old) with confirmed COVID-19 and acute kidney injury requiring dialysis with agreement between ICU and nephrology teams for the introduction of renal continuous replacement therapy in daily ICU rounds. Continuous renal replacement therapy will be prescribed by consulting nephrologists based on standard clinical guidelines, including acute kidney injury with hemodynamic instability plus hyperkalemia, severe acidosis, volume overload, respiratory distress, multiorgan failure or some combination of these factors. DATA COLLECTION: Patients demographics and associated clinical data and comorbidities will be recorded at ICU entry. Demographic information will include the patient's age, sex, and admission dates. Clinical data comprise comorbidities, APACHE 2, SAPS 3, need for mechanical ventilation, and use of vasopressor drugs. Physiological data collected by the day of CRRT start will be vital signs, the arterial oxygen tension/fraction of inspired oxygen (PaO2/FiO2) index, and serum creatinine, blood urea nitrogen, bilirubin, hemoglobin, hematocrit, platelets, white blood cell count levels and Peak D-dimer levels. Patients will be analyzed for the first 72h of CRRT, and they will be evaluated regarding clinical variables, filter patency and any adverse events that could be related to the anticoagulation choice, as bleeding (mild or major) or low platelets counts (<100.000 ui/uL) during treatment period. Mild and major bleeding will be defined by hemorrhagic event without clinical impact or hemoglobin (Hb) fall lesser than 1g/dL and hemorrhagic event with clinical impact or Hb fall higher than 1g/dL, respectively. EXCLUSION CRITERIA: Hypersensitivity to any of the substances going to be used in the study (Citric acid dextrosol 2.2% and unfractionated heparin); Previous diagnosis of coagulopathy or thrombophilia; Contraindication to the use of unfractionated heparin; Risk of citrate poisoning - (Lactate> 30 mg/dL, international normalized ratio > 2.5, Total bilirubin> 15 mg/dL); Pregnancy; Patients unlikely to survive for more than 24 hours. The trial is being undertaken at the University of Sao Paulo General Hospital (Hospital das Clinicas), Brazil. INTERVENTION AND COMPARATOR: Group A (control) - Patients on continuous renal replacement therapy (blood flow 150 ml/min, dose of 30 mL/Kg/h) receiving anticoagulation with sodium citrate at 4 mmol/L Group B (experiment): Patients on continuous hemodialysis (blood flow 150 mL/min, dose of 30 mL/Kg/h) receiving anticoagulation with sodium citrate at 4 mmol/L associated with unfractionated heparin at 10 U/Kg/h. MAIN OUTCOMES: The percentage of clotted dialyzers within 72 hours in each of the studied groups (Primary outcome) Secondary outcomes: Number of dialyzers used in the first 72 hours of dialysis protocol, Mortality in the first 72 h of dialysis protocol, Bleeding events (Major or minor) in the first 72 h of dialysis protocol, Thrombocytopenia (less than 50.000 platelets) proportion in the first 72 h of dialysis protocol, Dialysis efficiency (Urea sieving) - variation in urea sieving between the first, second and third days of dialysis protocol, Continuous renal replacement therapy pressures (Arterial, Venous, dialysate and pre-filter pressure) in the first 72 h of dialysis protocol, in-hospital mortality. RANDOMIZATION: RedCap→ randomization - 2 blocks randomization by D-dimer level (5000ng/dL cut-off) and catheter site (Right Internal Jugular versus other sites) with 1:1 allocation ratio. BLINDING (MASKING): No blinding - Open label format NUMBERS TO BE RANDOMIZED (SAMPLE SIZE): Total number of patients 90 (45 per group) TRIAL STATUS: Trial version 2.0 - ongoing recruitment. First recruitment: June 29, 2020 Estimated date for last recruitment: December 31, 2020 TRIAL REGISTRATION: Responsible Party: University of Sao Paulo General Hospital (Hospital das Clinicas) ClinicalTrials.gov Identifier: NCT04487990 , registered July 27, 2020, ReBec www.ensaiosclinicos.gov.br/rg/RBR-45kf9p/ Other Study ID Numbers: U1111-1252-0194 FULL PROTOCOL: The full protocol is attached as an additional file, accessible from the Trials website (Additional file 1) In the interest in expediting dissemination of this material, the familiar formatting has been eliminated; this Letter serves as a summary of the key elements of the full protocol.

A data-driven approach to referable diabetic retinopathy detection.

Prior art on automated screening of diabetic retinopathy and direct referral decision shows promising performance; yet most methods build upon complex hand-crafted features whose performance often fails to generalize. OBJECTIVE: We investigate data-driven approaches that extract powerful abstract representations directly from retinal images to provide a reliable referable diabetic retinopathy detector. METHODS: We gradually build the solution based on convolutional neural networks, adding data augmentation, multi-resolution training, robust feature-extraction augmentation, and a patient-basis analysis, testing the effectiveness of each improvement. RESULTS: The proposed method achieved an area under the ROC curve of 98.2% (95% CI: 97.4-98.9%) under a strict cross-dataset protocol designed to test the ability to generalize - training on the Kaggle competition dataset and testing using the Messidor-2 dataset. With a 5 × 2-fold cross-validation protocol, similar results are achieved for Messidor-2 and DR2 datasets, reducing the classification error by over 44% when compared to most published studies in existing literature. CONCLUSION: Additional boost strategies can improve performance substantially, but it is important to evaluate whether the additional (computation- and implementation-) complexity of each improvement is worth its benefits. We also corroborate that novel families of data-driven methods are the state of the art for diabetic retinopathy screening. SIGNIFICANCE: By learning powerful discriminative patterns directly from available training retinal images, it is possible to perform referral diagnostics without detecting individual lesions.

Naltrexone Treatment for Pregnant Women With Opioid Use Disorder Compared With Matched Buprenorphine Control Subjects.

PURPOSE: The use of the opioid antagonist naltrexone (NTX) for pregnant women with opioid use disorder (OUD) remains understudied. The purpose of this pilot study was to examine pregnancy and neonatal outcomes in a cohort of NTX-treated women. METHODS: This single-center, retrospective cohort study included 6 mother-infant dyads taking NTX compared with 13 taking buprenorphine (BUP) between 2017 and 2019. Maternal demographic characteristics, any unprescribed or illicit opioid use per urine toxicology or provider report during the pregnancy or 6 months' postdelivery, delivery outcomes, gestational age, birth weight, Apgar scores, neonatal intensive care unit admission, and neonatal abstinence syndrome (NAS) outcomes (NAS diagnosis, pharmacologic treatment, and total hospital length of stay) were compared. FINDINGS: Maternal and infant demographic characteristics were similar between the 2 groups, with the exception of cigarette smoking in the BUP group being more common (92% vs 33%; P = 0.02). None of the women on NTX versus 23% of the women on BUP had documented opioid misuse (P = 0.52). No infants in the NTX group had a NAS diagnosis versus 92% in the BUP group (P < 0.001). Forty-six percent of the BUP-exposed infants were treated for NAS versus 0% in the NTX group (P < 0.001). NTX-exposed infants had a shorter length of stay (mean [SD], 3.2 [1.6] vs 10.9 [8.2] days; P = 0.008). IMPLICATIONS: Maintaining women on NTX during pregnancy was associated with favorable outcomes. These results support the need for larger multicenter studies sufficiently powered to detect possible differences between the medications on long-term maternal and child safety and efficacy outcomes.

A non-precious metal hydrogen catalyst in a commercial polymer electrolyte membrane electrolyser.

We demonstrate the translation of a low-cost, non-precious metal cobalt phosphide (CoP) catalyst from 1 cm2 lab-scale experiments to a commercial-scale 86 cm2 polymer electrolyte membrane (PEM) electrolyser. A two-step bulk synthesis was adopted to produce CoP on a high-surface-area carbon support that was readily integrated into an industrial PEM electrolyser fabrication process. The performance of the CoP was compared head to head with a platinum-based PEM under the same operating conditions (400 psi, 50 °C). CoP was found to be active and stable, operating at 1.86 A cm-2 for >1,700 h of continuous hydrogen production while providing substantial material cost savings relative to platinum. This work illustrates a potential pathway for non-precious hydrogen evolution catalysts developed in past decades to translate to commercial applications.

DREAM: Empowering Preclinical Medical Students With Labor Support.

Introduction: Delivery Resources, Experiences, and Advocacy for Moms (DREAM) is an interprofessional service-learning program that empowers preclinical medical students by training them to provide labor support. Boston Medical Center is a safety-net hospital featuring an in-house doula service with limited coverage capacity. Consequently, many patients do not receive continuous labor support, although evidence shows that continuous labor support improves outcomes and may help reduce birth-outcome health disparities. We present a pragmatic approach to integrating preclinical students as labor-support staff and outline the methods and content of the training process as well as the evaluations used to assess program effectiveness. Methods: Students were trained by doulas (Birth Sisters) and midwives to provide prenatal, labor, and postpartum support. Students completed an orientation and training workshop and then partnered with a Birth Sister for one prenatal visit, labor, and postpartum visit prior to working independently. Student leaders provided structure, mentoring, and support for preclinical students. Pre- and postsurveys assessed student confidence and obstetric knowledge acquisition. Budget, logistics, and program evaluation process are reviewed. Results: Students demonstrated increased knowledge, as well as confidence in communication, advocacy, and support. Although balancing DREAM with academics was stressful, students continued to meet academic standards and felt their participation was gratifying and worthwhile. Student reflections and patient statements on their experience show the program was mutually beneficial. Discussion: Preclinical students need gratifying clinical opportunities to develop confidence in communication and advocacy skills. Partnering them with underserved women to provide labor support is a pragmatic and clinically valuable intervention.

A clinical model to predict successful renal replacement therapy (RRT) discontinuation in patients with Acute Kidney Injury (AKI)

Abstract Introduction Ideal timing of Renal Replacement Therapy (RRT) discontinuation in Acute Kidney Injury (AKI) is still unknown. We aimed to study the role of creatinine-related variables in predicting RRT successful discontinuation and to propose a clinical predictive score. Methods In this single-centre retrospective study, we evaluated all AKI patients in whom RRT was interrupted for at least 48 hours. Patients who were still RRT-independent 7 days after initial RRT cessation were included in the "Success" group and opposed to the "Failure" group. We evaluated baseline characteristics and variables collected at the time of RRT interruption, as well as the Kinetic estimated Glomerular Filtration Rate (KeGFR), the simple variation in serum Creatinine (ΔsCr), and the incremental creatinine ratio on the first three days after RRT interruption. Multivariable analysis was performed to evaluate prediction of success. Internal validation using a simple binomial generalized regression model with Lasso estimation and 5-fold cross validation method was performed. Results We included 124 patients, 49 in the "Failure" group and 75 in the "Success" group. All creatinine-related variables predicted success in simple and multiple logistic regression models. The best model generated a clinical score based on the odds ratio obtained for each variable and included urine output, non-renal SOFA score, fluid balance, serum urea, serum potassium, blood pH, and the variation in sCr values after RRT discontinuation. The score presented an area under the ROC of 0.86 (95% CI 0.76‒1.00). Conclusion Creatinine variation between the first 2 consecutive days after RRT discontinuation might predict success in RRT discontinuation. The developed clinical score based on these variables might be a useful clinical decision tool to guide hemodialysis catheter safe removal.

Beyond Lesion-Based Diabetic Retinopathy: A Direct Approach for Referral.

Diabetic retinopathy (DR) is the leading cause of blindness in adults, but can be managed if detected early. Automated DR screening helps by indicating which patients should be referred to the doctor. However, current techniques of automated screening still depend too much on the detection of individual lesions. In this study, we bypass lesion detection, and directly train a classifier for DR referral. Additional novelties are the use of state-of-the-art mid-level features for the retinal images: BossaNova and Fisher Vector. Those features extend the classical Bags of Visual Words and greatly improve the accuracy of complex classification tasks. The proposed technique for direct referral is promising, achieving an area under the curve of 96.4%, thus, reducing the classification error by almost 40% over the current state of the art, held by lesion-based techniques.