Corticosteroids but not Anti-TNF Are Associated With Increased COVID-19 Complications in Patients With Inflammatory Bowel Disease

BACKGROUND AND AIMS: Immunosuppressed individuals are at higher risk for COVID-19 complications, yet data in patients with inflammatory bowel disease (IBD) are limited. We evaluated the risk of COVID-19- severe sequelae by medication utilization in a large cohort of patients with IBD. METHODS: We conducted a retrospective cohort study utilizing insurance claims data between August 31, 2019, and August 31, 2021.We included IBD patients identified by diagnosis and treatment codes. Use of IBD medications was defined in the 90 days prior to cohort entry. Study outcomes included COVID-19 hospitalization, mechanical ventilation, and inpatient death. Patients were followed until the outcome of interest, outpatient death, disenrollment, or end of study period. Due to the aggregate nature of available data, we were unable to perform multivariate analyses. RESULTS: We included 102 986 patients (48 728 CD, 47 592 UC) with a mean age of 53 years; 55% were female. Overall, 412 (0.4%) patients were hospitalized with COVID-19. The incidence of hospitalization was higher in those on corticosteroids (0.6% vs 0.3%; P < .0001; 13.6 per 1000 person-years; 95% confidence interval [CI], 10.8-16.9) and lower in those receiving anti-tumor necrosis factor α therapy (0.2% vs 0.5%; P < .0001; 3.9 per 1000 person-years; 95% CI, 2.7-5.4). Older age was associated with increased hospitalization with COVID-19. Overall, 71 (0.07%) patients required mechanical ventilation and 52 (0.05%) died at the hospital with COVID-19. The proportion requiring mechanical ventilation (1.9% vs 0.05%; P < .0001; 3.9 per 1000 person-years; 95% CI, 2.5-5.9) was higher among users of corticosteroids. CONCLUSIONS: Among patients with IBD, those on corticosteroids had more hospitalizations and mechanical ventilation with COVID-19. Anti-tumor necrosis factor α therapy was associated with a decreased risk of hospitalization. These findings reinforce previous guidance to taper and/or discontinue corticosteroids in IBD

The adhesive properties of pyridine-terminated self-assembled monolayers

The atomic force microscopy (AFM) adhesion force behaviour and contact angle titration behaviour of self-assembled monolayers (SAMs) presenting surface pyridine and substituted pyridine moieties has been investigated as a function of pH and electrolyte concentration. The pK(a)s of the pyridine moieties were modified through the incorporation of fluorine, chlorine and bromine substituents in the pyridyl ring. Contact angle titration and AFM adhesion force measurements were performed using aqueous phosphate buffered saline solutions over the pH range 3-9, and at concentrations of 150 mM and 0.1 mM. AFM adhesion force measurements were performed using a clean Si3N4 pyramidal-tipped AFM cantilever. (C) 2009 Elsevier B.V. All rights reserved

4D Flow cardiovascular magnetic resonance consensus statement: 2023 update

Hemodynamic assessment is an integral part of the diagnosis and management of cardiovascular disease. Four-dimensional cardiovascular magnetic resonance flow imaging (4D Flow CMR) allows comprehensive and accurate assessment of flow in a single acquisition. This consensus paper is an update from the 2015 '4D Flow CMR Consensus Statement'. We elaborate on 4D Flow CMR sequence options and imaging considerations. The document aims to assist centers starting out with 4D Flow CMR of the heart and great vessels with advice on acquisition parameters, post-processing workflows and integration into clinical practice. Furthermore, we define minimum quality assurance and validation standards for clinical centers. We also address the challenges faced in quality assurance and validation in the research setting. We also include a checklist for recommended publication standards, specifically for 4D Flow CMR. Finally, we discuss the current limitations and the future of 4D Flow CMR. This updated consensus paper will further facilitate widespread adoption of 4D Flow CMR in the clinical workflow across the globe and aid consistently high-quality publication standards

A research agenda to support the development and implementation of genomics-based clinical informatics tools and resources.

OBJECTIVE: The Genomic Medicine Working Group of the National Advisory Council for Human Genome Research virtually hosted its 13th genomic medicine meeting titled Developing a Clinical Genomic Informatics Research Agenda . The meeting\u27s goal was to articulate a research strategy to develop Genomics-based Clinical Informatics Tools and Resources (GCIT) to improve the detection, treatment, and reporting of genetic disorders in clinical settings. MATERIALS AND METHODS: Experts from government agencies, the private sector, and academia in genomic medicine and clinical informatics were invited to address the meeting\u27s goals. Invitees were also asked to complete a survey to assess important considerations needed to develop a genomic-based clinical informatics research strategy. RESULTS: Outcomes from the meeting included identifying short-term research needs, such as designing and implementing standards-based interfaces between laboratory information systems and electronic health records, as well as long-term projects, such as identifying and addressing barriers related to the establishment and implementation of genomic data exchange systems that, in turn, the research community could help address. DISCUSSION: Discussions centered on identifying gaps and barriers that impede the use of GCIT in genomic medicine. Emergent themes from the meeting included developing an implementation science framework, defining a value proposition for all stakeholders, fostering engagement with patients and partners to develop applications under patient control, promoting the use of relevant clinical workflows in research, and lowering related barriers to regulatory processes. Another key theme was recognizing pervasive biases in data and information systems, algorithms, access, value, and knowledge repositories and identifying ways to resolve them

Randomised, open-label, phase II study of Gemcitabine with and without IMM-101 for advanced pancreatic cancer

Background: Immune Modulation and Gemcitabine Evaluation-1, a randomised, open-label, phase II, first-line, proof of concept study (NCT01303172), explored safety and tolerability of IMM-101 (heat-killed Mycobacterium obuense; NCTC 13365) with gemcitabine (GEM) in advanced pancreatic ductal adenocarcinoma. Methods: Patients were randomised (2 : 1) to IMM-101 (10 mg ml−l intradermally)+GEM (1000 mg m−2 intravenously; n=75), or GEM alone (n=35). Safety was assessed on frequency and incidence of adverse events (AEs). Overall survival (OS), progression-free survival (PFS) and overall response rate (ORR) were collected. Results: IMM-101 was well tolerated with a similar rate of AE and serious adverse event reporting in both groups after allowance for exposure. Median OS in the intent-to-treat population was 6.7 months for IMM-101+GEM v 5.6 months for GEM; while not significant, the hazard ratio (HR) numerically favoured IMM-101+GEM (HR, 0.68 (95% CI, 0.44–1.04, P=0.074). In a pre-defined metastatic subgroup (84%), OS was significantly improved from 4.4 to 7.0 months in favour of IMM-101+GEM (HR, 0.54, 95% CI 0.33–0.87, P=0.01). Conclusions: IMM-101 with GEM was as safe and well tolerated as GEM alone, and there was a suggestion of a beneficial effect on survival in patients with metastatic disease. This warrants further evaluation in an adequately powered confirmatory study

Supervised Machine Learning Applications to Winter Road Impacts

Winter weather causes major disruptions to road travel, increasing delays and accident rates. Significant investments are made towards maintaining the condition and safety of the road network. Weather forecasts provide the state of the art prognosis for winter weather phenomena, but are not designed to explicitly predict road surface conditions. This thesis implements supervised machine learning techniques, most successfully random forests, using weather forecast data to directly predict the Winter Driving Index of roadways as defined by the Indiana Department of Transportation (INDOT). Attempts are made to also predict the amount of traffic reductions due to winter weather using random forests and artificial neural networks. Different case studies are presented to demonstrate successes and pitfalls of the models, and possibilities for future development are discussed

Automated Estimation of Winter Driving Conditions

There is great potential for improvements in traveler safety and satisfaction as new sources of information are incorporated into advanced analytics and prediction systems. It is critical for transportation agencies to be able to monitor weather conditions in real-time as well as over the long-term for purposes of maintenance, planning, and performance evaluation. Travelers also use this information to plan their routes, make decisions on timing, and improve their awareness of potential hazards. A machine learning model was developed in this project to automatically estimate winter driving conditions in real-time using weather information as input. This system can provide rapid updates of changing conditions and help reduce the effort required to report driving conditions during intense winter storms. The system performed well in an experimental evaluation during the 2017-18 season. Seasonal analyses of winter precipitation that incorporated crowd-sourced observations were also generated and displayed significant differences from previous research