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Objective Risk-guided atrial fibrillation (AF) screening may be an opportunity to prevent adverse events in addition to stroke. We compared events rates for new diagnoses of cardio-renal-metabolic diseases and death in individuals identified at higher versus lower-predicted AF risk.Methods From the UK Clinical Practice Research Datalink-GOLD dataset, 2 January 1998–30 November 2018, we identified individuals aged ≥30 years without known AF. The risk of AF was estimated using the FIND-AF (Future Innovations in Novel Detection of Atrial Fibrillation) risk score. We calculated cumulative incidence rates and fit Fine and Gray’s models at 1, 5 and 10 years for nine diseases and death adjusting for competing risks.Results Of 416 228 individuals in the cohort, 82 942 were identified as higher risk for AF. Higher-predicted risk, compared with lower-predicted risk, was associated with incident chronic kidney disease (cumulative incidence per 1000 persons at 10 years 245.2; HR 6.85, 95% CI 6.70 to 7.00; median time to event 5.44 years), heart failure (124.7; 12.54, 12.08 to 13.01; 4.06), diabetes mellitus (123.3; 2.05, 2.00 to 2.10; 3.45), stroke/transient ischaemic attack (118.9; 8.07, 7.80 to 8.34; 4.27), myocardial infarction (69.6; 5.02, 4.82 to 5.22; 4.32), peripheral vascular disease (44.6; 6.62, 6.28 to 6.98; 4.28), valvular heart disease (37.8; 6.49, 6.14 to 6.85; 4.54), aortic stenosis (18.7; 9.98, 9.16 to 10.87; 4.41) and death from any cause (273.9; 10.45, 10.23 to 10.68; 4.75). The higher-risk group constituted 74% of deaths from cardiovascular or cerebrovascular causes (8582 of 11 676).Conclusions Individuals identified for risk-guided AF screening are at risk of new diseases across the cardio-renal-metabolic spectrum and death, and may benefit from interventions beyond ECG monitoring.

Introduction Atrial fibrillation (AF) is associated with a fivefold increased risk of stroke. Oral anticoagulation reduces the risk of stroke, but AF is elusive. A machine learning algorithm (Future Innovations in Novel Detection of Atrial Fibrillation (FIND-AF)) developed to predict incident AF within 6 months using data in primary care electronic health records (EHRs) could be used to guide AF screening. The objectives of the FIND-AF pilot study are to determine yields of AF during ECG monitoring across AF risk estimates and establish rates of recruitment and protocol adherence in a remote AF screening pathway.Methods and analysis The FIND-AF Pilot is an interventional, non-randomised, single-arm, open-label study that will recruit 1955 participants aged 30 years or older, without a history of AF and eligible for oral anticoagulation, identified as higher risk and lower risk by the FIND-AF risk score from their primary care EHRs, to a period of remote ECG monitoring with a Zenicor-ECG device. The primary outcome is AF diagnosis during ECG monitoring, and secondary outcomes include recruitment rates, withdrawal rates, adherence to ECG monitoring and prescription of oral anticoagulation to participants diagnosed with AF during ECG monitoring.Ethics and dissemination The study has ethical approval (the North West—Greater Manchester South Research Ethics Committee reference 23/NW/0180). Findings will be announced at relevant conferences and published in peer-reviewed journals in line with the Funder’s open access policy.Trial registration number NCT05898165.

Teachers are vital to the success of any education system. However, concern continues to be raised about the quality of teachers’ preparation in the teachers’ colleges and about the quality of teachers in schools in Tanzania (Global Partnership for Educational Support in Tanzania Mainland, 2013; Mgaiwa, 2018; Makoro, 2020). In line with such concerns, this study explored Tanzania’s pre-service teachers’ demonstration of knowledge and skills of teaching during teaching practice. The study was conducted in three secondary schools located in Moshi rural District in Kilimanjaro region in Tanzania. It involved five pre-service teachers from one of the Diploma Teachers Colleges, five supervisors from the same Teachers’ College and mentor teachers from the three home schools where pre-service teachers were placed for teaching practice. The study is descriptive, employing qualitative methods of data collection. Interviews, focus group discussions and observations were used to collect data. Drawing on Shulman’s (1986) categories of knowledge, data were analyzed thematically. The findings revealed that the pre-service teachers had limited skill in lesson preparation and classroom management, which negatively impacted their learning during teaching practice. Based on these findings, the study recommends that teacher education colleges should take steps to better prepare pre-service teachers to effectively handle lesson planning, teaching, and classroom management.

In the United States, one to two children are born with hearing loss, per 1,000 births (Centers for Disease Control and Prevention, 2018), of which ~90% will be born to [...], Purpose: This study sought to (a) characterize the demographic, audiological, and intervention variability in a population of Deaf and Hard of Hearing (DHH) children receiving state services for hearing loss; (b) identify predictors of vocabulary delays; and (c) evaluate factors influencing the success and timing of early identification and intervention efforts at a state level. Method: One hundred DHH infants and toddlers (aged 4-36 months) enrolled in early intervention completed the MacArthur-Bates Communicative Development Inventories, and detailed information about their audiological and clinical history was collected. We examined the influence of demographic, clinical, and audiological factors on vocabulary outcomes and early intervention efforts. Results: We found that this sample showed spoken language vocabulary delays (production) relative to hearing peers and showed room for improvement in rates of early diagnosis and intervention. These delays in vocabulary and early support services were predicted by an overlapping subset of hearing-, health-, and home-related variables. Conclusions: In a diverse sample of DHH children receiving early intervention, we identify variables that predict delays in vocabulary and early support services, which reflected both dimensions that are immutable, and those that clinicians and caretakers can potentially alter. We provide a discussion on the implications for clinical practice. Supplemental Material: https://doi.org/10.23641/asha.19449839

Aims: Elective orthopaedic surgery was cancelled early in the COVID-19 pandemic and is currently running at significantly reduced capacity in most institutions. This has resulted in a significant backlog to treatment, with some hospitals projecting that waiting times for arthroplasty is three times the pre-COVID-19 duration. There is concern that the patient group requiring arthroplasty are often older and have more medical comorbidities—the same group of patients advised they are at higher risk of mortality from catching COVID-19. The aim of this study is to investigate the morbidity and mortality in elective patients operated on during the COVID-19 pandemic and compare this to a pre-pandemic cohort. Primary outcome was 30-day mortality. Secondary outcomes were perioperative complications, including nosocomial COVID-19 infection. These operations were performed in a district general hospital, with COVID-19 acute admissions in the same building. Methods: Our institution reinstated elective operations using a “Blue stream” pathway, which involves isolation before and after surgery, COVID-19 testing pre-admission, and separation of ward and theatre pathways for “blue” patients. A register of all arthroplasties was taken, and their clinical course and investigations recorded. Results: During a seven-month period, 340 elective arthroplasties were performed. There was zero mortality. One patient had a positive swab for COVID-19 while an inpatient, but remained asymptomatic. There were two readmissions within a 12-week period for hip dislocation. Patients had a mean age of 68 years (28 to 90), mean BMI of 30 kg/m2 (19.0 to 45.6), and mean American Society of Anesthesiologists grade of 2 (1 to 3). Conclusion: Results show no increased morbidity or mortality in this cohort of patients compared to the same hospital’s morbidity and mortality pre-COVID-19. The screened pathway for elective patients is effective in ensuring that patients can be safely operated on electively in an acute hospital. This study should reassure clinicians and patients that arthroplasties can be carried out safely when the appropriate precautions are in place. Cite this article: Bone Jt Open 2021;2(11):940–944.

BackgroundMany artificial intelligence (AI) studies have focused on development of AI models, novel techniques, and reporting guidelines. However, little is understood about clinicians' perspectives of AI applications in medical fields including ophthalmology, particularly in light of recent regulatory guidelines. The aim for this study was to evaluate the perspectives of ophthalmologists regarding AI in 4 major eye conditions: diabetic retinopathy (DR), glaucoma, age-related macular degeneration (AMD) and cataract.MethodsThis was a multi-national survey of ophthalmologists between March 1st, 2020 to February 29th, 2021 disseminated via the major global ophthalmology societies. The survey was designed based on microsystem, mesosystem and macrosystem questions, and the software as a medical device (SaMD) regulatory framework chaired by the Food and Drug Administration (FDA). Factors associated with AI adoption for ophthalmology analyzed with multivariable logistic regression random forest machine learning.ResultsOne thousand one hundred seventy-six ophthalmologists from 70 countries participated with a response rate ranging from 78.8 to 85.8% per question. Ophthalmologists were more willing to use AI as clinical assistive tools (88.1%, n = 890/1,010) especially those with over 20 years' experience (OR 3.70, 95% CI: 1.10–12.5, p = 0.035), as compared to clinical decision support tools (78.8%, n = 796/1,010) or diagnostic tools (64.5%, n = 651). A majority of Ophthalmologists felt that AI is most relevant to DR (78.2%), followed by glaucoma (70.7%), AMD (66.8%), and cataract (51.4%) detection. Many participants were confident their roles will not be replaced (68.2%, n = 632/927), and felt COVID-19 catalyzed willingness to adopt AI (80.9%, n = 750/927). Common barriers to implementation include medical liability from errors (72.5%, n = 672/927) whereas enablers include improving access (94.5%, n = 876/927). Machine learning modeling predicted acceptance from participant demographics with moderate to high accuracy, and area under the receiver operating curves of 0.63–0.83.ConclusionOphthalmologists are receptive to adopting AI as assistive tools for DR, glaucoma, and AMD. Furthermore, ML is a useful method that can be applied to evaluate predictive factors on clinical qualitative questionnaires. This study outlines actionable insights for future research and facilitation interventions to drive adoption and operationalization of AI tools for Ophthalmology.

Aim: Patients with metastatic breast cancer (MBC) may be vulnerable to changes in healthcare management, safety standards and protocols that occurred during the COVID-19 pandemic. Materials & methods: The REthink Access to Care & Treatment (REACT) survey assessed USA-based patient perspectives on COVID-19-related impacts to their MBC treatment experience between 27 April 2021 and 17 August 2021. Results: Participants (n = 341; 98.5% females, mean age 50.8 years) reported that overall oncology treatment quality was maintained during the pandemic. Delayed/canceled diagnostic imaging was reported by 44.9% of participants while telemedicine uptake was high among participants (80%). Conclusion: Overall, MBC care was minimally affected by the pandemic, possibly due to the expanded use of telemedicine, informing MBC management for future public health emergencies. Plain Language Summary The COVID-19 pandemic has forced healthcare providers to change the way that healthcare is delivered. These changes could particularly affect people with metastatic breast cancer (MBC), an advanced stage of cancer that has spread to other parts of the body. The authors of this study used a web-based survey to ask 341 volunteers with MBC how the pandemic has affected their cancer treatment. The authors found that people with MBC thought that the quality of their care stayed the same during the pandemic. Most people (80%) surveyed were able to use telemedicine, the remote delivery of care by phone or computer, to replace in-person visits to their doctor. However, almost half of people surveyed reported delays or cancellation of their diagnostic imaging appointments. Overall, this study shows that the COVID-19 pandemic did not affect peoples' opinions of their MBC care. Increased use of telemedicine may have contributed to the lack of disruption in care. These findings will help guide MBC care during future public health emergencies. Tweetable abstract A survey of patient perspectives on their care for metastatic breast cancer during the COVID-19 pandemic has shown consistent quality of care. Care delivery adaptations such as telemedicine may be key to maintaining cancer care quality during future public health emergencies. Substantial changes in healthcare management, safety standards and protocols occurred during the COVID-19 pandemic. Patients with metastatic breast cancer (MBC) are particularly at risk of pandemic-related negative consequences due to the need for clinical visits, multimodal therapies, potential immunosuppression and compromised support services. This cross-sectional study used a two-part REthink Access to Care & Treatment (REACT) survey to assess the perspective of patients with MBC on the effects of COVID-19 with regards to their MBC treatment experience. Data from 341 validated participants (98.5% females; mean age 50.8 years) revealed that patient-reported overall quality of oncology treatment remained about the same during the pandemic. Telemedicine uptake was high among participants (80%); replacement of in-person visits with telemedicine was the most reported change to regular clinical checkups and mental health visits due to the pandemic. However, 44.9% of participants reported that diagnostic imaging appointments were either delayed or canceled. During the pandemic, 48.1% of participants reported disease progression. Overall, participants perceived that the management and treatment of their MBC were minimally disrupted by the pandemic, likely due in part to the utilization of telemedicine. While these findings can inform MBC management decisions during future public health crises, future studies should solicit perceptions of care from more diverse patient populations. [ABSTRACT FROM AUTHOR]

Multi-omics (genomics, transcriptomics, epigenomics, proteomics, metabolomics, etc.) research approaches are vital for understanding the hierarchical complexity of human biology and have proven to be extremely valuable in cancer research and precision medicine. Emerging scientific advances in recent years have made high-throughput genome-wide sequencing a central focus in molecular research by allowing for the collective analysis of various kinds of molecular biological data from different types of specimens in a single tissue or even at the level of a single cell. Additionally, with the help of improved computational resources and data mining, researchers are able to integrate data from different multi-omics regimes to identify new prognostic, diagnostic, or predictive biomarkers, uncover novel therapeutic targets, and develop more personalized treatment protocols for patients. For the research community to parse the scientifically and clinically meaningful information out of all the biological data being generated each day more efficiently with less wasted resources, being familiar with and comfortable using advanced analytical tools, such as Google Cloud Platform becomes imperative. This project is an interdisciplinary, cross-organizational effort to provide a guided learning module for integrating transcriptomics and epigenetics data analysis protocols into a comprehensive analysis pipeline for users to implement in their own work, utilizing the cloud computing infrastructure on Google Cloud. The learning module consists of three submodules that guide the user through tutorial examples that illustrate the analysis of RNA-sequence and Reduced-Representation Bisulfite Sequencing data. The examples are in the form of breast cancer case studies, and the data sets were procured from the public repository Gene Expression Omnibus. The first submodule is devoted to transcriptomics analysis with the RNA sequencing data, the second submodule focuses on epigenetics analysis using the DNA methylation data, and the third submodule integrates the two methods for a deeper biological understanding. The modules begin with data collection and preprocessing, with further downstream analysis performed in a Vertex AI Jupyter notebook instance with an R kernel. Analysis results are returned to Google Cloud buckets for storage and visualization, removing the computational strain from local resources. The final product is a start-to-finish tutorial for the researchers with limited experience in multi-omics to integrate transcriptomics and epigenetics data analysis into a comprehensive pipeline to perform their own biological research. This manuscript describes the development of a resource module that is part of a learning platform named ''NIGMS Sandbox for Cloud-based Learning'' https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox [ 16 ] at the beginning of this Supplement. This module delivers learning materials on the analysis of bulk and single-cell ATAC-seq data in an interactive format that uses appropriate cloud resources for data access and analyses. Highlights DNA methylation and gene expression are essential for unraveling the biological mechanisms underlying development, disease, and cellular function. Module will bridge a critical need in development tools for the next generation of bioinformaticians using cloud computing with modern multi-omics techniques. The three submodules (transcriptomics, epigenomics, and integration) provide deeper insights into the complexity of genetics and epigenetic regulation. [ABSTRACT FROM AUTHOR]

Background: Essential non-healthcare workers experienced higher rates of SARS-CoV-2 infection compared to non-essential workers. Objective: Identify characteristics associated with SARS-CoV-2 testing, infection and vaccine uptake among essential non-healthcare workers in Montréal, Québec. Methods: Secondary, cross-sectional analysis of data collected from participants prospectively recruited in two observational studies (first study, Onsite Testing Study, January-March 2021; second study, Self-Testing Study, July-October 2021) of essential non-healthcare workers in 2021. Logistic regression with generalized linear mixed models was used to explore characteristics associated with our outcomes (previous SARS-CoV-2 testing, exposure and vaccination). Results: Overall, 2,755 participants were included (first study, Onsite Testing Study, n=2,128; and second study, Self-Testing Study, n=627). A higher proportion of participants identified as male (n=1,601; 58%), non-White (n=1,527; 55%) and worked in the manufacturing/supplier sector (n=1,706; 62%). Relative to the first study, Onsite Testing Study, participants in the second study, Self-Testing Study, had higher odds (78% vs. 46%; aOR 4.1, 95% CI: 3.2-5.2) of previous SARS-CoV-2 testing and of testing positive prior to study enrolment (6.2% vs. 4.3%; aOR 1.7, 95% CI: 1.1-2.6). Individuals reporting recent SARS-CoV-2 exposure had higher odds of previous SARS-CoV-2 testing (aOR 4.0, 95% CI: 3.0-5.4), while older age (aOR 0.98, 95% CI: 0.98-0.99 per one-year increase) and being male (aOR 0.6, 95% CI: 0.5-0.7) were associated with lower odds of previous testing. Results were similar in stratified analyses. Participants from businesses with more than 50 employees had higher odds of having received a SARS-CoV-2 vaccine (91% vs. 80%; aOR 2.6, 95% CI: 1.4-4.8). Conclusion: Consideration of individual and business characteristics associated with testing and vaccination programs for SARS-CoV-2 could improve equity, uptake and impact. [ABSTRACT FROM AUTHOR]

Based on research-to-policy narratives provided by UC Cooperative Extension (UCCE) academics, we argue that current, effective Cooperative Extension (Extension) practices support a broader, more convincing account of Extension's public value than its leaders often articulate. This proposed account incorporates the familiar Extension narrative in which technical expertise and objectivity are emphasized. It also incorporates the insight, derived from our data, that Extension can achieve its greatest relevance in policy circles when it weaves together its ability to provide trustworthy technical knowledge with its capacity to influence policy dialogue, debate and practice across multiple settings and over the long term. In a policy world often marked by short-term thinking and polarization, Extension's ability to foster deliberative, context-sensitive and future-oriented policymaking is a critical contribution to society. Interview data reveals three approaches to effective policy-oriented relationship building: community-government partnership building; stakeholder-oriented experimental research; and community empowerment. Understanding these approaches can help reframe the story that we in the Extension system tell ourselves and the public about the public value we create.

Abstract. Same-day delivery for online purchases is a recent trend in online retail. We introduce a multi-vehicle dynamic pickup and delivery problem with time constraints that incorporates key features associated [...]

Several lines of evidence support a role for the brain in both the development and maintenance of polycystic ovary syndrome (PCOS), the most common cause of anovulatory infertility worldwide. Persistently elevated luteinizing hormone secretion and impaired gonadal steroid hormone feedback in PCOS patients suggest impairments within the neuronal networks that regulate the reproductive axis. Evidence from preclinical models has linked androgen excess during prenatal life with altered structure and function of the developing female brain that might underpin syndrome development in adulthood. Studies investigating the mechanisms by which excess androgens program changes in the female brain have highlighted an important role for microglia. This review discusses how these non-neuronal cells shape the developing female brain in response to excess androgens and focuses on how microglia may be involved in the development of the neuroendocrine dysfunctions associated with PCOS. [ABSTRACT FROM AUTHOR]

From Freud through to modern times researchers have aimed to develop a clearer understanding of therapeutic processes and outcomes. Despite this continued interest in the field, the representation of psychotherapy processes and the applicability of research findings and recommendations to the therapeutic field continue to prove difficult. Quantitative and qualitative studies each purport to provide answers, however, they differ greatly in their research methods and underlying ontological and epistemological views. Efficacy and effectiveness studies and the measures of statistical significance and clinical significance are explored with their inherent strengths and weaknesses highlighted. This paper presents the view that research into psychotherapy should enhance the experiences of both the therapist and client. For this to be achieved it is recommended that quantitative and qualitative data, or objective and subjective experiences, should collude rather than collide.

Introduction Thrombocytopenia (low platelet count) is observed in numerous diseases and can be life threatening due to bleeding complications. Bortezomib, a reversible chemotherapeutic inhibitor used to treat patients with relapsed [...], The proteasome inhibiter bortezomib has been successfully used to treat patients with relapsed multiple myeloma; however, many of these patients become thrombocytopenic, and it is not clear how the proteasome influences platelet production. Here we determined that pharmacologic inhibition of proteasome activity blocks proplatelet formation in human and mouse megakaryocytes. We also found that megakaryocytes isolated from mice deficient for PSMC1, an essential subunit of the 26S proteasome, fail to produce proplatelets. Consistent with decreased proplatelet formation, mice lacking PSMC1 in platelets ([Psmc1.sup.fl/fl] Pf4-Cre mice) exhibited severe thrombocytopenia and died shortly after birth. The failure to produce proplatelets in proteasome-inhibited megakaryocytes was due to upregulation and hyperactivation of the small GTPase, RhoA, rather than NF-κB, as has been previously suggested. Inhibition of RhoA or its downstream target, Rho-associated protein kinase (ROCK), restored megakaryocyte proplatelet formation in the setting of proteasome inhibition in vitro. Similarly, fasudil, a ROCK inhibitor used clinically to treat cerebral vasospasm, restored platelet counts in adult mice that were made thrombocytopenic by tamoxifen-induced suppression of proteasome activity in megakaryocytes and platelets ([Psmc1.sup.fl/fl] PdgfCre-ER mice). These results indicate that proteasome function is critical for thrombopoiesis, and suggest inhibition of RhoA signaling as a potential strategy to treat thrombocytopenia in bortezomib-treated multiple myeloma patients.

The Internet of Things (IoT) boom has revolutionized almost every corner of people’s daily lives: healthcare, environment, transportation, manufacturing, supply chain, and so on. With the recent development of sensor and communication technology, IoT artifacts, including smart wearables, cameras, smartwatches, and autonomous systems can accurately measure and perceive their surrounding environment. Continuous sensing generates massive amounts of data and presents challenges for machine learning. Deep learning models (e.g., convolution neural networks and recurrent neural networks) have been extensively employed in solving IoT tasks by learning patterns from multi-modal sensory data. Graph neural networks (GNNs), an emerging and fast-growing family of neural network models, can capture complex interactions within sensor topology and have been demonstrated to achieve state-of-the-art results in numerous IoT learning tasks. In this survey, we present a comprehensive review of recent advances in the application of GNNs to the IoT field, including a deep dive analysis of GNN design in various IoT sensing environments, an overarching list of public data and source codes from the collected publications, and future research directions. To keep track of newly published works, we collect representative papers and their open-source implementations and create a Github repository at GNN4IoT. [ABSTRACT FROM AUTHOR]