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The coronavirus 2019 pandemic led to rapid expansion of outpatient telemedicine. We sought to characterize patient factors influencing outpatient teleneurology utilization at an urban safety-net hospital. We reviewed all neurology televisits scheduled between June 15, 2020 to April 15, 2021. We used the chi-squared test and multivariate logistic regression to characterize patient demographic factors associated with televisit completion and video use. Of 8875 scheduled televisit encounters, 7530 were completed successfully, 44% via video. Non-English speaking patients, Black patients, Latinx patients, and those with a zip code-linked annual income less than $50,000 were less likely to successfully complete a scheduled televisit. The same demographic groups other than Latinx ethnicity were also less likely to use the video option. Our study found unequal telehealth utilization based on patients' demographic factors. Currently declining telemedicine reimbursement rates asymmetrically affect audio-only visits, which may limit telehealth access for vulnerable patient populations.

Introduction. The Versius Surgical System is a next generation soft-tissue robot with instrument and endoscope arms split into individual modules. Despite its similarities with previous systems, the basic changes in the design raise concerns relating to the feasibility of the set-up for the different approaches in robotic head and neck surgery procedures. Materials and Methods. We used a complete unit with a surgeon´s console and four modules on a training mannequin to depict the different configurations in the operating room. We tested transoral robotic surgery and the four basic configurations for the remote access to the neck: transoral/transvestibular, retroauricular, axillary and bilateral axillo-breast approaches. Results. We obtained a high quality simulation for transoral robotic surgery, as well as for the usual remote access approaches, except for the axillary approach. We were able to obtain an optimal distribution of the modules around the surgical table and an adequate configuration of the joints allowing the instruments to reach their targets. Conclusion. The Versius Surgical System might be an alternative device for robotic procedures in Head and Neck Surgery, although this needs to be proved in a clinical setting.

Introduction Cyst adrenal lesions are uncommon with a reported incidence in autopsies of 0. 064% to 0.18%. Functional adrenal cysts are rarer, to our knowledge with 3 cases reported in the literature, 2 producing catecholamines and 1 producing aldosterone. Clinical case: A 39-year-old male patient with arterial hypertension since the age of 38 without target organ involvement or hydroelectrolytic alterations was taken to studies of secondary hypertension, as a positive finding we found aldosterone in 10.5 ng/dl (n 1 mU/L) for an aldosterone-renin ratio of 354 (n Conclusion This patient is the 4th case published in the literature of a biochemically confirmed functional adrenal cyst, which is important in order not to rule out the presence of adrenal cysts as a possible etiology of hormone overproduction. Presentation: No date and time listed

BI therapy is recommended in people with T2D uncontrolled on oral antidiabetic agents. However, due to delay in BI initiation and titration, many people with T2D fail to achieve recommended glycemic targets. MDC, an FDA-approved digital app, helps people with T2D to adjust their BI doses based on their fasting blood glucose (FBG) readings and hypoglycemic event data. We evaluated real-world outcomes from people with T2D on BI therapy, who registered on MDC app from 08/01/2018 to 09/30/2021 and received insulin glargine 100 U/mL or 300 U/mL. FBG target achievement (≥3 consecutive measurements within the target range) , change in FBG, and hypoglycemia events were assessed. A total of 5MDC users from Colombia with mean (SD) age 56.6 (15.1) years (55.8% female) were included (Table) . Overall, 4 (80.9%) users achieved individualized FBG target, 44 (8.7%) were still titrating and 52 (10.3%) stopped using MDC. The mean (SD) time to reach FBG target was 10.3 (15.8) days. Mean (SD) FBG at baseline was 141.7 (55.0) mg/dL, which decreased to 117.3 (31.7) mg/dL and mean (SD) BI dose increased from 22.2 (11.3) U to 24.2 (12.4) U. Hypoglycemia events were reported in 72 (14%) participants overall. These results show that people with T2D from Colombia using MDC were able to successfully titrate their BI doses and achieve FBG target with low risk of hypoglycemia. Disclosure A.Gomez: Advisory Panel; AstraZeneca, Research Support; Abbott Diabetes, Speaker's Bureau; Eli Lilly and Company, Medtronic, Novo Nordisk, Sanofi. A.Rincon: Advisory Panel; AstraZeneca, Medtronic, Sanofi, Consultant; Abbott, Medtronic, Novo Nordisk, Novo Nordisk, Sanofi, Other Relationship; Boehringer Ingelheim International GmbH, Novo Nordisk, Sanofi. K.Feriz: Other Relationship; Abbott, Boehringer Ingelheim International GmbH, Novo Nordisk, Sanofi, Speaker's Bureau; AstraZeneca, Lilly Diabetes, Medtronic, Merck Sharp & Dohme Corp. C.F.Salamanca: Employee; Sanofi. L.P.Silva: Employee; Merck KGaA, Sanofi. Funding Sanofi

OBJECTIVES RNASEH1 gene has recently been associated with type 1 diabetes (T1D) in Colombia. The purpose of this study was to fine mapping the putative functional variant in RNASEH1 and testing its interaction with HLA tagSNPs. METHODS Two-hundred nuclear families with T1D were included in this study. Probands were tested for GAD65 and IA-2 autoantibodies. Genotyping was performed using 20 coding tagSNPs uncovered through Sanger sequencing (N = 96), in addition to 23 tagSNPs chosen from 1000genomes to cover the extent of the gene region. Also, 45 tagSNPs for classic HLA alleles associated with T1D were also genotyped. The transmission disequilibrium test (TDT) was used to test for association and a multiple testing correction was made using permutation. Interaction between RNASEH1 variants and HLA was evaluated by means of the M-TDT test. RESULTS We identified 20 variants (15 were novel) in the 96 patients sequenced. None of these variants were in linkage disequilibrium. In total, 43 RNASEH1 variants were genotyped in the 200 families. Association between T1D and rs7607888 was identified (P = .002). Haplotype analysis involving rs7607888 variant revealed even stronger association with T1D (most significative P = .0003). HLA tagSNPs displayed stronger associations (OR = 6.39, 95% CI = 4.33-9.44, P-value = 9.74E-28). Finally, we found several statistically significant interactions of HLA variants with rs7607888 (P-value ranged from 8.77E-04 to 5.33E-12). CONCLUSION Our results verify the association of rs7607888 in RNASEH1 gene with T1D. It is also shown in the interaction between RNASEH1 and HLA for conveying risk to T1D in Northwest Colombia. Work is underway aiming to identify the actual classic HLA alleles associated with the tagSNPs tested here.

Object extraction is an important tool in many applications within the image processing and computer vision communities. You Only Look Once version 3 (YOLOv3) has been extensively applied to many fields as a state-of-the-art technique for object semantic detection. Despite its numerous characteristics, YOLOv3 has to be combined with appropriate image segmentation technologies to achieve effective 2D object extraction in real-time monitoring, robot navigation, and target search. In this article, the K-means algorithm is applied to the segmentation of depth images. Considering the inherent sensitivity to the randomness of the initial cluster center and the uncertainty of cluster number K in the initialization phase of the K-means algorithm, this article proposes a new method that combines the semantic image information with the image depth information. Specifically, this method proposed to pre-classify the center depth of the object to determine the appropriate value of K required in the K-means algorithm. At the same time, the proposed algorithm improves the selection of the initial center via the maximin method. This article introduces a multi-parameter extraction method to enable to correctly identify the object of interest after image segmentation. The technique considers three parameters to achieve this: i) the elements of size, ii) the connected domain, and iii) the diagonal detection. Experiments using open-source datasets demonstrate that the average processing time and the segmentation accuracy of the improved K-means algorithm are 20.36% faster and 3.12% higher than the conventional K-means algorithm, respectively. The extraction accuracy of the proposed method is 6.69% higher than that of the SuperCut extraction method.

Background we present a case of hypophysitis with gonadotropic and somatotropic axis compromise and adrenalitis secondary to the use of nivolumab and ipilimumab. Clinical Case A 74-year-old woman with a history of metastatic renal cell carcinoma treated with ipilimumab and nivolumab for 10 months, suspended treatment due to pneumonitis. Three months later, she consulted the emergency department due to limitations in daily activities, asthenia, and drowsiness. physical examination was unremarkable except for hypotension. Laboratory studies showed profound hyponatremia (106 mEq/l, n: 135 a 145 mEq/l), low serum osmolality (236 mmol/kg n: 275 - 295 mmol/kg) and a basal cortisol (5 μg/dL n: 3.7 - 19.4 μg/dL). Hypophysitis was suspected and a pituitary profile revealed low somatomedin C (25 ng/ml n: 54 204 ng/ml), FSH (20.6 UI/L n: 26.7 -133.4 UI/L), and strikingly elevated ACTH (91.9 pg/mL n: 4.7 - 48.5 pg/mL). Pituitary MRI had no structural anomaly. A diagnosis of hypophysitis and primary adrenal insufficiency secondary to adrenalitis due to the use of immunotherapy was made. The compromise of the gonadotropic and somatotropic axis with a normal image can occur in up to 23% of patients with this clinical entity. The patient had significant clinical improvement with glucocorticoid and mineralocorticoid replacement. Conclusions To our knowledge this is the first reported case of hypophysitis and adrenalitis secondary to immunotherapy. Hypophysitis has a prevalence up to 8% with the combination of ipilimumab plus nivolumab. It should be considered despite a normal pituitary MRI. However, adrenalitis associated with the use of these drugs has only been reported in 2.6% and the coexistence of both entities is even rarer Presentation: No date and time listed

Background Lymphocytic (LH) or autoimmune hypophysitis (AH) is a rare inflammatory disorder of the hypophyseal gland, often miss-diagnosed. LH is predominant in females, occasionally related with other rheumatic disorders (1-20%). The disease can lead to pituitary dysfunction and symptoms of hypopituitarism, the most affected axes are the ACTH and TSH. Definitive diagnosis of AH can be based only on pathological examination of a pituitary biopsy sample but such invasive procedure is seldom needed. Different imaging modalities, especially MRI, can be useful but up to 30% can be unremarkable, thus clinical manifestations and biochemical evaluation is of great importance in guiding the diagnosis. Hormonal replacement therapy is the main treatment strategy. Immunosuppressive drugs are indicated by the severity of the symptoms and the underlying autoimmune disease. Clinical Case A 36 year old patient with active lupus and Sjogren disease presented to the emergency department with syncope and hypotension preceded by a 6 month history of intense fatigue. She did not take any steroids in the 6 months prior. Initial tests were consistent with secondary adrenal insufficiency; low plasma cortisol ( Presentation: No date and time listed

[Purpose] To determine whether physical activity (PA), primarily the recommended 60 minutes of moderate-to-vigorous PA, is associated with gut bacterial microbiota in 10-year-old children.[Methods] The Block Physical Activity Screener, which provides minutes/day PA variables, was used to determine whether the child met the PA recommendations. 16S rRNA sequencing was performed on stool samples from the children to profile the composition of their gut bacterial microbiota. Differences in alpha diversity metrics (richness, Pielou’s evenness, and Faith’s phylogenetic diversity) by PA were determined using linear regression, whereas beta diversity (unweighted and weighted UniFrac) relationships were assessed using PERMANOVA. Taxon relative abundance differentials were determined using DESeq2.[Results] The analytic sample included 321 children with both PA and 16S rRNA sequencing data (mean age [SD] =10.2 [0.8] years; 54.2% male; 62.9% African American), where 189 (58.9%) met the PA recommendations. After adjusting for covariates, meeting the PA recommendations as well as minutes/day PA variables were not significantly associated with gut richness, evenness, or diversity (p ≥ 0.19). However, meeting the PA recommendations (weighted UniFrac R2 = 0.014, p = 0.001) was significantly associated with distinct gut bacterial composition. These compositional differences were partly characterized by increased abundance of Megamonas and Anaerovorax as well as specific Christensenellaceae_R-7_group taxa in children with higher PA.[Conclusion] Children who met the recommendations of PA had altered gut microbiota compositions. Whether this translates to a reduced risk of obesity or associated metabolic diseases is still unclear.

Visual Simultaneous Localization and Mapping (SLAM) technologies based on point features achieve high positioning accuracy and complete map construction. However, despite their time efficiency and accuracy, such SLAM systems are prone to instability and even failure in poor texture environments. In this paper, line features are integrated with point features to enhance the robustness and reliability of stereo SLAM systems in poor texture environments. Firstly, method Edge Drawing lines (EDlines) is applied to reduce the line feature detection time. Meanwhile, the proposed method improves the reliability of features by eliminating outliers of line features based on the entropy scale and geometric constraints. Furthermore, this paper proposes a novel Bags of Word (BoW) model combining the point and line features to improve the accuracy and robustness of loop detection used in SLAM. The proposed PL-BoW technique achieves this by taking into account the co-occurrence information and spatial proximity of visual words. Experiments using the KITTI and EuRoC datasets demonstrate that the proposed stereo Point and EDlines SLAM (PEL-SLAM) achieves high accuracy consistently, including in challenging environments difficult to sense accurately. The processing time of the proposed method is reduced by 9.9% and 4.5% when compared to the Point and Line SLAM (PL-SLAM) and Point and stereo Point and Line based Visual Odometry (sPLVO) methods, respectively.

Background: This work evaluates the accuracy and agreement between the FreeStyle Libre sensor (FSL) and an off-label converted real-time continuous glucose monitor (c-rtCGM) device consisting of the MiaoMiao transmitter and the xDrip+ application which can be coupled to the FSL. Methods: Four weeks of glucose data were collected from 21 participants with type 1 diabetes using the c-rtCGM and FSL: two weeks with a single initial calibration (uncalibrated) and two weeks with a daily calibration (calibrated). Accuracy and agreement evaluation included mean absolute relative difference (MARD), the %20/20 rule, Bland-Altman plots, and the Consensus Error Grid analysis. Results: Values reported by the c-rtCGM system compared with the FSL resulted in an overall MARD of 12.06% and 84.71% of the results falling within Consensus Error Grid Zone A when the device is calibrated. For uncalibrated devices, an overall MARD of 17.49% was obtained. Decreased accuracy was shown in the hypoglycemic range and for rates of change greater than 2 mg/dL/min. The between-device bias also incremented with increasing glucose values. Conclusion: Measurements recorded by the c-rtCGM were found to be accurate when compared with FSL data only when performing daily c-rtCGM device calibrations. High drops in accuracy and agreement between devices occurred when the c-rtCGM was not calibrated.

BACKGROUND The scientific community is just beginning to uncover the potential long-term effects of COVID-19, and one way to start gathering information is by examining the present discourse on the topic. The conversation about long COVID-19 on Twitter provides insight into related public perception and personal experiences. OBJECTIVE The aim of this study was to investigate the #longCOVID and #longhaulers conversations on Twitter by examining the combined effects of topic discussion and social network analysis for discovery on long COVID-19. METHODS A multipronged approach was used to analyze data (N=2500 records from Twitter) about long COVID-19 and from people experiencing long COVID-19. A text analysis was performed by both human coders and Netlytic, a cloud-based text and social networks analyzer. The social network analysis generated Name and Chain networks that showed connections and interactions between Twitter users. RESULTS Among the 2010 tweets about long COVID-19 and 490 tweets by COVID-19 long haulers, 30,923 and 7817 unique words were found, respectively. For both conversation types, “#longcovid” and “covid” were the most frequently mentioned words; however, through visually inspecting the data, words relevant to having long COVID-19 (ie, symptoms, fatigue, pain) were more prominent in tweets by COVID-19 long haulers. When discussing long COVID-19, the most prominent frames were “support” (1090/1931, 56.45%) and “research” (435/1931, 22.53%). In COVID-19 long haulers conversations, “symptoms” (297/483, 61.5%) and “building a community” (152/483, 31.5%) were the most prominent frames. The social network analysis revealed that for both tweets about long COVID-19 and tweets by COVID-19 long haulers, networks are highly decentralized, fragmented, and loosely connected. CONCLUSIONS This study provides a glimpse into the ways long COVID-19 is framed by social network users. Understanding these perspectives may help generate future patient-centered research questions.

Hybrid aircraft are systems capable of flying as either fixed-wing aircraft or rotorcraft, as well as transition between these modes. Nowadays, hybrid aircraft have many potential applications as t...

Video sharing (e.g., YouTube, Vimeo, Facebook, TikTok) accounts for the majority of internet traffic, and video processing is also foundational to several other key workloads (video conferencing, virtual/augmented reality, cloud gaming, video in Internet-of-Things devices, etc.). The importance of these workloads motivates larger video processing infrastructures and – with the slowing of Moore’s law – specialized hardware accelerators to deliver more computing at higher efficiencies. This paper describes the design and deployment, at scale, of a new accelerator targeted at warehouse-scale video transcoding. We present our hardware design including a new accelerator building block – the video coding unit (VCU) – and discuss key design trade-offs for balanced systems at data center scale and co-designing accelerators with large-scale distributed software systems. We evaluate these accelerators “in the wild" serving live data center jobs, demonstrating 20-33x improved efficiency over our prior well-tuned non-accelerated baseline. Our design also enables effective adaptation to changing bottlenecks and improved failure management, and new workload capabilities not otherwise possible with prior systems. To the best of our knowledge, this is the first work to discuss video acceleration at scale in large warehouse-scale environments.

At present, most of the current Simultaneous Localization and Mapping (SLAM) algorithms are limited to work in static environments. However, the world is not static and dynamic objects are typically present in the environments, leading to the failure of the general SLAM. In this paper, a dynamic object removal method combining semantic detection with depth image segmentation is proposed and applied in the real-time SLAM library for cameras ORB-SLAM2 system to achieve robust RGB-D SLAM for dynamic environments. In the proposed method, the potential dynamic regions are captured via YOLOv4 and K-means image segmentation. Different from the general purposes of processing dynamic regions, the potential dynamic regions are redetected by dynamic outliers rejection, which improves the reliability of dynamic object removal. Experiments using the TUM RGB-D dataset demonstrate that the proposed method performs with increased robustness and accuracy when compared to the original ORB-SLAM2 without dynamic object removal in dynamic environments.

Healthcare is considered as one of the most promising application areas for artificial intelligence and analytics (AIA) just after the emergence of the latter. AI combined to analytics technologies is increasingly changing medical practice and healthcare in an impressive way using efficient algorithms from various branches of information technology (IT). Indeed, numerous works are published every year in several universities and innovation centers worldwide, but there are concerns about progress in their effective success. There are growing examples of AIA being implemented in healthcare with promising results. This review paper summarizes the past 5 years of healthcare applications of AIA, across different techniques and medical specialties, and discusses the current issues and challenges, related to this revolutionary technology. A total of 24,782 articles were identified. The aim of this paper is to provide the research community with the necessary background to push this field even further and propose a framework that will help integrate diverse AIA technologies around patient needs in various healthcare contexts, especially for chronic care patients, who present the most complex comorbidities and care needs.

Digital holographic microscopy is well suited to the search for microbial species due in part to its intrinsic stability, volumetric imaging capability, and sensitivity to very dilute samples. This is all done with a system having no moving parts, making it additionally attractive for flight instrumentation. Our devices have been field tested on several occasions, and have demonstrated robustness to extreme conditions. When bacteria are alive and moving, they are easy to detect. However, some measurements are subtler. Can we distinguish between a live yet nonmotile species and a mineral, for instance? To provide another method of discrimination, we have added the ability to measure object polarization. This will allow us to characterize the sample by polarization state without any sacrifice in the spatial or temporal resolution (

The Collaborative Learning of Usability Experiences (CLUE) training program1 is an NSERC CREATE grant that trains Canada's leaders in HCI. We aim to improve our trainees' capabilities across the disciplinary boundaries (Information Technology, Psychology, Computer Science, and Design), through collaborative professional skills development, experiential learning, and technical skills. Within human computer interaction (HCI), usability professionals employ research-based methods and principles to understand users’ conceptual models of tasks and design interfaces and experiences accordingly. There is an increased demand for skills in usability experience (UX) design and testing, yet we identify a lack of training in these skills in current graduate programs across Canada. Even in the context of multidisciplinary HCI programs, graduates often face a usability knowledge gap, which may be due to a lack of grounding in real-world contexts, without business constraints.