Effect of telehealth-based versus in-person nutritional and exercise intervention on type II diabetes mellitus improvement and efficiency of human resources utilization in patients with obesity

Aims: Telehealth became a patient necessity during the COVID pandemic and evolved into a patient preference in the post-COVID era. This study compared the % total body weight loss (%TBWL), HbA1c reduction, and resource utilization among patients with obesity and diabetes who participated in lifestyle interventions with or without telehealth. Method(s): A total of 150 patients with obesity and diabetes who were followed every 4-6 weeks either in-person (n = 83) or via telehealth (n = 67), were included. All patients were provided with an individualized nutritional plan that included a weight-based daily protein intake from protein supplements and food, an activity/sleep schedule-based meal times, and an aerobic exercise goal of a 2000-calorie burn/week, customized to patient's preferences, physical abilities, and comorbidities. The goal was to lose 10%TBWL. Telehealth-based follow-up required transmission via texting of weekly body composition measurements and any blood glucose levels below 100 mg/dl for medication adjustments. Weight, BMI, %TBWL, HbA1c (%), and medication effect score (MES) were compared. Patient no-show rates, number of visits, program duration, and drop-out rate were used to assess resource utilization based on cumulative staff and provider time spent (CSPTS), provider lost time (PLT) and patient spent time (PST). Result(s): Mean age was 47.2 +/- 10.6 years and 74.6% were women. Mean Body Mass Index (BMI) decreased from 44.1 +/- 7.7-39.7 +/- 6.7 kg/m2 (p < 0.0001). Mean program duration was 189.4 +/- 169.3 days. An HbA1c% unit decline of 1.3 +/- 1.5 was achieved with a 10.1 +/- 5.1%TBWL. Diabetes was cured in 16% (24/150) of patients. %TBWL was similar in regards to telehealth or in-person appointments (10.6% +/- 5.1 vs. 9.6% +/- 4.9, p = 0.14). Age, initial BMI, MES, %TBWL, and baseline HbA1c had a significant independent effect on HbA1c reduction (p < 0.0001). Program duration was longer for in-person follow-up (213.8 +/- 194 vs. 159.3 +/- 127, p = 0.019). The mean annual telehealth and in-person no-show rates were 2.7% and 11.2%, respectively (p < 0.0001). Mean number of visits (5.7 +/- 3.0 vs. 8.6 +/- 5.1) and drop-out rates (16.49% vs. 25.83%) were lower in telehealth group (p < 0.0001). The CSPTS (440.4 +/- 267.5 min vs. 200.6 +/- 110.8 min), PLT (28.9 +/- 17.5 min vs. 3.1 +/- 1.6 min), and PST (1033 +/- 628 min vs. 113.7 +/- 61.4 min) were significantly longer (p < 0.0001) for the in-person group. Conclusion(s): Telehealth offered comparable %TBWL and HbA1c decline as in-person follow-up, but with a shorter follow-up, fewer appointments, and no-shows. If improved resource utilization is validated by other studies, telehealth should become the standard of care for the management of obesity and diabetes.Copyright © 2023 The Authors. Obesity Science & Practice published by World Obesity and The Obesity Society and John Wiley & Sons Ltd.

COVID-19 feature detection with deep neural networks trained on simulated lung ultrasound B-mode images

Deep learning has been implemented to detect COVID-19 features in lung ultrasound B-mode images. However, previous work primarily relied on in vivo images as the training data, which suffers from limited access to required manual labeling of thousands of training image examples. To avoid this manual labeling, which is tedious and time consuming, we propose the detection of in vivo COVID-19 features (i.e., A-line, B-line, consolidation) with deep neural networks (DNNs) trained on simulated B-mode images. The simulation-trained DNNs were tested on in vivo B-mode images from healthy subjects and COVID-19 patients. With data augmentation included during the training process, Dice similarity coefficients (DSCs) between ground truth and DNN predictions were maximized, producing mean ± standard deviatio values as high as 0.48 ± 0.29, 0.45 ± 0.25, and 0.46 ± 0.35 when segmenting in vivo A-line, B-line, and consolidation features, respectively. Results demonstrate that simulation-trained DNNs are a promising alternative to training with real patient data when segmenting in vivo COVID-19 features. © 2022 IEEE.

Applications of Ultrasound Image Formation in the Deep Learning Age

Historically, there are many options to improve image quality that are each derived from the same raw ultrasound sensor data. However, none of these historical options combine multiple contributions in a single image formation step. This invited contribution discusses novel alternatives to beamforming raw ultrasound sensor data to improve image quality, delivery speed, and feature detection after learning from the physics of sound wave propagation. Applications include cyst detection, coherence-based beamforming, and COVID-19 feature detection. A new resource for the entire community to standardize and accelerate research at the intersection of ultrasound beamforming and deep learning is summarized (https://cubdl.jhu.edu). The connection to optics with the integration of ultrasound hardware and software is also discussed from the perspective of photoacoustic source detection, reflection artifact removal, and resolution i mprovements. These innovations demonstrate outstanding potential to combine multiple outputs and benefits in a single signal processing step with the assistance of deep learning. © 2022 SPIE.

LOGISTICS AND SUPPLY CHAIN AROUND THE WORLD

The coronavirus pandemic led to supply chain disruptions resulting in adverse economic impacts on global supply chains. Nationwide lockdowns in countries that play key roles in global manufacturing restricted freight movements through air, ocean, and land routes resulting in delivery delays, higher freight rates and congestion. At the same time, the pandemic has accelerated the growth of the e-commerce sector. Concern around infections has led to a surge in first-time online consumers for categories such as health and pharmaceuticals and fast-moving consumer goods. Companies have had to rethink their approaches to optimising warehouse locations and inventory to meet customer demand. From a freight perspective, the focus has shifted from a single-mode model towards multi-modal logistics to reduce costs and dependence on any one mode. This chapter will review recent developments, long term impacts and opportunities for growth in the context of this important sector and illustrate some of the key impacts of the pandemic using the example of the emerging economy in India. It concludes by synthesising key takeaways and reflecting on the future of the sector. © 2022 by Emerald Publishing Limited.

COVID-19 VACCINE HESITANCY REMAINS HIGH IN INFLAMMATORY BOWEL DISEASE PATIENTS

Introduction Despite the global impact of the COVID-19 pandemic, vaccine hesitancy remains common in the general public. Adults who were on immunosuppressive medications were among the earlier groups recommended by the Centers for Disease Control and Prevention to receive the COVID-19 vaccine. It is unclear whether similar vaccine hesitancy is seen in patients with inflammatory bowel disease (IBD), especially those who are on immunosuppressive medications. We sought to examine rate of vaccine hesitancy in patients with IBD as well as associated demographic and socioeconomic risk factors. Methods We performed a retrospective chart review in November 2021 of 1383 patients with IBD seen at University of Maryland Medical Center, a tertiary referral medical center, between November 2020 and October 2021. Data obtained from patients' charts included demographics;disease characteristics including disease phenotype, number of years since diagnosis, number of IBD-related surgeries;and IBD therapy including biologics, thiopurines or methotrexate, corticosteroids, and mesalamine. Information on COVID vaccination and routinely recommended vaccines were also collected which included annual influenza vaccine, Prevnar/ Pneumovax, and Shingrix. Those with no recorded COVID-19 vaccine were contacted by nurses for updated vaccine status. Results 72% (990/1383) of patients in this cohort were on a biologic, 17% (232/1383) were on corticosteroids, and 16% (224/1383) were on thiopurine or methotrexate, indicating a cohort of patients with moderate to severe disease phenotype. Fifty-seven percent (792/1383) of patients received either the Pfizer, Moderna, or Johnson & Johnson vaccine. In a multivariate regression analysis, COVID vaccination was found to be positively associated with a number of factors including older age (p-value= 4.92e-4), female sex (p=1.61e-3), Asian and Caucasian races (p=9.13e-3, 6.47e-06), number of years since diagnosis (p=2.73e-2), number of clinic visits in the past 12 months (p= 2.66e-10), and biologic use (p=4.41e-4). This remained the case while controlling for IBD disease type;marital status;insurance (Commercial vs Medicaid vs Medicare);and tobacco, alcohol, and substance use history. Patients who received other routinely recommended vaccines (influenza, Prevnar/Pneumovax, Shingrix) were not more likely to receive COVID- 19 vaccine. Discussion Although majority of patients in this cohort were on an immunosuppressive medication, COVID-19 vaccination rate is only recorded to be at 57%. Number of clinic visits, presumably more education and conversation with healthcare providers, had a positive impact on COVID-19 vaccination. In this cohort, younger adults, males, and African Americans were less likely to receive COVID-19 vaccine. Healthcare providers need to recognize these potential risk factors for COVID-19 vaccine hesitancy.

Multi-stage investigation of deep neural networks for COVID-19 B-line feature detection in simulated and in vivo ultrasound images

COVID-19 is a highly infectious disease with high morbidity and mortality, requiring tools to support rapid triage and risk stratification. In response, deep learning has demonstrated great potential to quicklyand autonomously detect COVID-19 features in lung ultrasound B-mode images. However, no previous work considers the application of these deep learning models to signal processing stages that occur prior to traditional ultrasound B-mode image formation. Considering the multiple signal processing stages required to achieve ultrasound B-mode images, our research objective is to investigate the most appropriate stage for our deep learning approach to COVID-19 B-line feature detection, starting with raw channel data received by an ultrasound transducer. Results demonstrate that for our given training and testing configuration, the maximum Dice similarity coefficient (DSC) was produced by B-mode images (DSC = 0.996) when compared with three alternative image formation stages that can serve as network inputs: (1) raw in-phase and quadrature (IQ) data before beamforming, (2) beamformed IQ data, (3) envelope detected IQ data. The best-performing simulation-trained network was tested on in vivo B-mode images of COVID-19 patients, ultimately achieving 76% accuracy to detect the same (82% of cases) or more (18% of cases) B-line features when compared to B-line feature detection by human observers interpreting B-mode images. Results are promising to proceed with future COVID-19 B-line feature detection using ultrasound B-mode images as the input to deep learning models. © 2022 SPIE.

Bending bad - Testing caramel wafer bars (#TestATunnocks)

During the coronavirus pandemic, there have been significant challenges in the remote teaching and demonstration of experiments, especially those that require laboratory testing equipment. With a desire to give students a feel for our materials laboratory on open days and allow them to gain a deeper understanding of what materials science and engineering is about, we have designed an experiment focused on composite materials that can be performed remotely and without specialist equipment. This enabled students to experience a bend test sensorily through seeing, hearing and feeling it, creating a strong link to then being able to relate it to the pre-prepared experimental data taken in the laboratory. This fun, easy-to-run and engaging experiment allowed a shared experience and encouraged a discussion about students' observations, differences in results and implications of the bend strength of sandwich composites. We have found it not only works well universally by all ages but can be used with younger children to think about words such as 'stronger', 'stiffer' and 'flexible' and how materials can be different in different directions. © 2021 The Author(s). Published by IOP Publishing Ltd.

Severe COVID-19 in children and young adults in the Washington DC metropolitan region

Background: Children and young adults were initially reported as largely spared from severe complications of SARS-CoV-2 infection, but the impact to this population has been significant. Methods: This observational retrospective cohort study includes 420 symptomatic children and young adults with lab confirmed SARS-CoV-2 infection treated between March 15 and June 16, 2020 at Children's National Hospital in Washington DC. We identified and compared cohorts of non-hospitalized (N=324) and hospitalized (N=96) patients, including non-critically ill (N=64) and critically ill hospitalized (N=32) patients. Clinical and demographic data were extracted from medical records Results: Of 420 SARS-CoV-2-infected symptomatic patients, 23% required hospitalization, of which 67% were non-critically ill and 33% were critically ill. All age groups were represented in the symptomatic cohort, with a median age of 8.6 years. Patients > 15 years of age represented 44% of critical care admissions. Males and females were equally represented in all cohorts. Underlying medical conditions were present in 36%, but more common in hospitalized (59 %) and critically ill (66 %) patients. The most frequent underlying diagnosis overall was asthma (16 %), but also included neurologic (6 %), diabetes (3 %), obesity (3 %), cardiac (3 %), hematologic (3 %) and oncologic (1 %) conditions. The majority (66 %) of SARSCoV- 2 infected patients presented with respiratory symptoms with or without fever. Other symptoms were also present, including diarrhea/vomiting (21 %), myalgia (11 %), chest pain (8 %) and loss of sense of smell or taste (7%). Hospitalized patients required varying levels of respiratory support, including mechanical ventilation, BiPAP, RAM cannula and HFNC. Additional presentations included diabetic hyperglycemia, sickle cell vaso-occlusive crisis, vascular complications, and multisystem inflammation. Treatment included remdesivir, convalescent plasma, tocilizumab and other therapies. Conclusion: Although children/young adults have been less affected than elderly adults, the impact of SARS-CoV2 on this population has been significant in Washington DC and informs other regions anticipating their surge.

SARS CoV-2-associated multisystem inflammatory syndrome of children (MIS-C) in the Washington DC metropolitan region

Background: Background: Multi-system Inflammatory Syndrome of Children (MIS-C) has recently emerged internationally as a serious inflammatory complication of SARS-CoV-2 infection with significant morbidity for the pediatric population. Methods: This observational retrospective cohort study includes 33 children meeting CDC criteria for MIS-C treated between March 15 and June 17, 2020 at Children's National Hospital in Washington DC. Clinical and demographic data were extracted from medical records and are summarized. Results: Of 33 hospitalized MIS-C patients, 42% were critically ill, and 58% were non-critically ill. The median age was 8.9 years (0.7-18.7 years). More males (58 %) than females (43 %) were represented in the MIS-C cohort. The majority (75%) of children had no underlying medical condition. Criteria for incomplete or complete Kawasaki Disease (KD) were present in 39% of patients, while an additional 9% had some features of KD. However the remaining 52% of MIS-C patients presented with other sub-phenotypes including prominent severe abdominal pain and/or nonspecific multiorgan dysfunction. 30% presented with shock requiring volume and/or inotropic support. SARS-CoV-2 antibodies were present in 61% of patients. Virus was detectable by PCR in 36% of patients. At the time of initial evaluation, 39% (13/33) of children had identified cardiac abnormalities including myocardial dysfunction (5/33;15%), coronary ectasia (4/33;12%), coronary aneurysm (3/33;9%), or pericardial effusion 5/33;15%) either alone or in combination. Cytokine profiling identified elevation of several cytokines in this cohort, including IL-6. Treatment has included intravenous immunoglobulin, aspirin, anakinra and other immunomodulatory therapies, with overall rapid response to therapy. No deaths have occurred. Conclusion: The emergence of MIS-C late in the surge of SARS-CoV-2 circulation in the Washington DC metropolitan region has added to the already significant burden of hospitalized and critically ill children in our region. A significant percentage of these children present with cardiac dysfunction and abnormalities, whether or not with KD features at presentation. Detailed characterization of immune responses and long term outcome of these patients is a priority.

COVID-19 in Correctional and Detention Facilities — United States, February–April 2020

This report describes confirmed coronavirus disease 2019 cases among incarcerated or detained persons or staff members across 420 correctional and detention facilities in the United States.