Information management hits and misses in the COVID19 emergency in Brazil
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ABSTRACT:
SUBMITTER: Luciano E
PROVIDER: S-EPMC7956992 | biostudies-literature | 2020 Jul
REPOSITORIES: biostudies-literature
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Project description:Mania has long been recognized as aberrant behaviour indicative of mental illness. Manic states include a variety of complex and multifaceted symptoms that challenge clear clinical distinctions. Symptoms include over-activity, hypersexuality, irritability and reduced need for sleep, with cognitive deficits recently linked to functional outcome. Current treatments have arisen through serendipity or from other disorders. Hence, treatments are not efficacious for all patients, and there is an urgent need to develop targeted therapeutics. Part of the drug discovery process is the assessment of therapeutics in animal models. Here we review pharmacological, environmental and genetic manipulations developed to test the efficacy of therapeutics in animal models of mania. The merits of these models are discussed in terms of the manipulation used and the facet of mania measured. Moreover, the predictive validity of these models is discussed in the context of differentiating drugs that succeed or fail to meet criteria as approved mania treatments. The multifaceted symptomatology of mania has not been reflected in the majority of animal models, where locomotor activity remains the primary measure. This approach has resulted in numerous false positives for putative treatments. Recent work highlights the need to utilize multivariate strategies to enable comprehensive assessment of affective and cognitive dysfunction. Advances in therapeutic treatment may depend on novel models developed with an integrated approach that includes: (i) a comprehensive battery of tests for different aspects of mania, (ii) utilization of genetic information to establish aetiological validity and (iii) objective quantification of patient behaviour with translational cross-species paradigms.
| S-EPMC3229761 | biostudies-literature
Project description:The recent upsurge in the usage of social media and the multimedia data generated therein has attracted many researchers for analyzing and decoding the information to automate decision-making in several fields. This work focuses on one such application: disaster management in times of crises and calamities. The existing research on disaster damage analysis has primarily taken only unimodal information in the form of text or image into account. These unimodal systems, although useful, fail to model the relationship between the various modalities. Different modalities often present supporting facts about the task, and therefore, learning them together can enhance performance. We present MEMIS, a system that can be used in emergencies like disasters to identify and analyze the damage indicated by user-generated multimodal social media posts, thereby helping the disaster management groups in making informed decisions. Our leave-one-disaster-out experiments on a multimodal dataset suggest that not only does fusing information in different media forms improves performance, but that our system can also generalize well to new disaster categories. Further qualitative analysis reveals that the system is responsive and computationally efficient.
| S-EPMC7148216 | biostudies-literature
Project description:BackgroundThe benefits of treatment for many conditions are time dependent. The burden of these emergency care sensitive conditions (ECSCs) is especially high in low- and middle-income countries. Our objective was to analyze geospatial trends in ECSCs and characterize regional disparities in access to emergency care in Brazil.MethodsFrom publicly available datasets, we extracted data on patients assigned an ECSC-related ICD-10 code and on the country's emergency facilities from 2015-2019. Using ArcGIS, OpenStreetMap, and WorldPop, we created catchment areas corresponding to 180 minutes of driving distance from each hospital. We then used ArcGIS to characterize space-time trends in ECSC admissions and to complete an Origin-Destination analysis to determine the path from household to closest hospital.FindingsThere were 1362 municipalities flagged as "hot spots," areas with a high volume of ECSCs. Of those, 69.7% were more than 180 minutes (171 km) from the closest emergency facility. These municipalities were primarily located in the states of Minas Gerais, Bahia, Espiríto Santo, Tocantins, and Amapá. In the North region, only 69.1% of the population resided within 180 minutes of an emergency hospital.InterpretationsSignificant geographical barriers to accessing emergency care exist in certain areas of Brazil, especially in peri-urban areas and the North region. One limitation of this approach is that geolocation was not possible in some areas and thus we are likely underestimating the burden of inadequate access. Subsequent work should evaluate ECSC mortality data.FundingThis study was funded by the Duke Global Health Institute Artificial Intelligence Pilot Project.
| S-EPMC9903578 | biostudies-literature
Project description:IntroductionEmergency medicine (EM) is a high-risk specialty for burnout. COVID-19 has had and will continue to have important consequences on wellness and burnout for EM physicians in Canada. Baseline data are crucial to monitor the health of EM physicians in Canada, and evaluate any interventions designed to help during and after COVID-19.ObjectivesTo describe the rates of burnout, depression, and suicidality in practicing EM physicians in Canada, just before the COVID-19 pandemic.MethodsA modified snowball method was used for survey distribution. Participants completed the Maslach Burnout Inventory - Health Services Tool (MBI-HSS), a screening measure for depression (PHQ-9), and a question regarding if the physician had ever or in the past 12 months contemplated suicide.ResultsA total of 384 respondent surveys were included in the final analysis: 86.1% (329/382) met at least one of the criteria for burnout, 58% (217/374) scored minimal to none on the PHQ-9 screening tool for depression, 14.3% (53/371) had contemplated suicide during their staff career in EM, and of those, 5.9% (22/371) had actively considered suicide in the past year.ConclusionCanadian EM physicians just before the COVID-19 pandemic had an alarming number of respondents meet the threshold for burnout, confirming EM as a high-risk specialty. This important baseline information can be used to monitor the physical and mental risks to EM physicians during and after COVID-19, and evaluate support for mental health and wellness, which is urgently needed now and post pandemic.
| S-EPMC7369344 | biostudies-literature
Project description:The control of spreading of COVID-19 in emergency situation the entire world is a challenge, and therefore, the aim of this study was to propose a spherical intelligent fuzzy decision model for control and diagnosis of COVID-19. The emergency event is known to have aspects of short time and data, harmfulness, and ambiguity, and policy makers are often rationally bounded under uncertainty and threat. There are some classic approaches for representing and explaining the complexity and vagueness of the information. The effective tool to describe and reduce the uncertainty in data information is fuzzy set and their extension. Therefore, we used fuzzy logic to develop fuzzy mathematical model for control of transmission and spreading of COVID19. The fuzzy control of early transmission and spreading of coronavirus by fuzzy mathematical model will be very effective. The proposed research work is on fuzzy mathematical model of intelligent decision systems under the spherical fuzzy information. In the proposed work, we will develop a newly and generalized technique for COVID19 based on the technique for order of preference by similarity to ideal solution (TOPSIS) and complex proportional assessment (COPRAS) methods under spherical fuzzy environment. Finally, an illustrative the emergency situation of COVID-19 is given for demonstrating the effectiveness of the suggested method, along with a sensitivity analysis and comparative analysis, showing the feasibility and reliability of its results.
| S-EPMC7529095 | biostudies-literature
Project description: Not available
| S-BSST1192 | biostudies-other
Project description:BackgroundCOVID-19 has been linked to over 40 million infections and 1.1 million deaths in 210 countries as of October 19, 2020. This highly contagious communicable disease has put not only infected individuals but other patients and frontline workers like nurses at risk in hospitals, especially in Intensive Care units (ICUs). There is a need for minimizing patient contact, improving hand hygiene practices, and optimizing healthcare provider time, especially nurses. Globally it is estimated that nearly a million health care providers have been infected with COVID-19 as of the end of October 2020.MethodsThis retrospective service evaluation documents the experience of health care providers in a COVID-19 ICU in India that was used to implement new protocols for secretion management and oral hygiene. Patient chart information and staff feedback were utilized.InterventionThis pilot study captures the practical benefits of using VAPCare, an automated, closed-loop system for oral secretion removal.ResultsSix patients were included in this small-scale study; three patients following the current standard of care for suctioning and oral hygiene and three receiving the new VAPCare and Lumen device protocol. With the new device protocol, the number of infected secretion interactions by a nurse was 50% lower, and nursing time spent on oral hygiene and secretion management 70% less than seen with the current standard of care. The number of disposable gloves used with VAPCare and Lumen was reduced by over 50%. All 10 nurses and six doctors gave positive feedback on device usage. The department recommended updating protocols to prioritize the use of the new secretion management system for patients with COVID19 and other highly contagious conditions.ConclusionThe findings are an early indication that using VAPCare for patients could help protect infected patients, other ICU patients, and health care workers.
| S-EPMC8013287 | biostudies-literature
Project description:In this work, we develop a method for generating targeted hit compounds by applying deep reinforcement learning and attention mechanisms to predict binding affinity against a biological target while considering stereochemical information. The novelty of this work is a deep model Predictor that can establish the relationship between chemical structures and their corresponding [Formula: see text] values. We thoroughly study the effect of different molecular descriptors such as ECFP4, ECFP6, SMILES and RDKFingerprint. Also, we demonstrated the importance of attention mechanisms to capture long-range dependencies in molecular sequences. Due to the importance of stereochemical information for the binding mechanism, this information was employed both in the prediction and generation processes. To identify the most promising hits, we apply the self-adaptive multi-objective optimization strategy. Moreover, to ensure the existence of stereochemical information, we consider all the possible enumerated stereoisomers to provide the most appropriate 3D structures. We evaluated this approach against the Ubiquitin-Specific Protease 7 (USP7) by generating putative inhibitors for this target. The predictor with SMILES notations as descriptor plus bidirectional recurrent neural network using attention mechanism has the best performance. Additionally, our methodology identify the regions of the generated molecules that are important for the interaction with the receptor's active site. Also, the obtained results demonstrate that it is possible to discover synthesizable molecules with high biological affinity for the target, containing the indication of their optimal stereochemical conformation.
| S-EPMC10618333 | biostudies-literature
Project description:Very high mortality rates of coronavirus pandemic (COVID-19) are observed around the world due to lack of medical equipment. The increased need for medical devices and personal protective equipment (PPE) has kept several healthcare professionals at risk. Fortunately, 3D printing technology allows to overcome the lack of medical supplies. This study highlights the impact of 3D printing on the combat against COVID19, and its importance in the medical product supply chain. Indeed, the existing medical equipment fabricated by 3D printing technology and its role in the management of Covid19 pandemic is presented. Moreover, the last works are examined to know whether the models of the medical equipment are free of use and whether useful informations are presented (eg, available design data and setup guidelines).
| S-EPMC7361600 | biostudies-literature
Project description:BackgroundOver the course of the COVID19 pandemic, global healthcare delivery has declined. Surgery is one of the most resource-intensive area of medicine; loss of surgical care has had untold health and economic consequences. Herein, we evaluate resource utilization, outcomes, and healthcare costs associated with unplanned surgery admissions during the height of the pandemic in 2020 versus the same period in 2019.MethodsRetrospective analysis on patients ≥18 years admitted from the emergency department to General & Digestive and Gastrointestinal Surgery Services between February and May 2019 and 2020 at our center; clinical outcomes and unadjusted and adjusted per-person healthcare costs were analyzed.ResultsConsults and admissions to surgery declined between February and May 2020 by 37% and 19%, respectively, relative to the same period in 2019, with even greater relative decline during late March and early April. Time between onset of symptoms to diagnosis increased from 2±3 days 2019 to 5±22 days 2020 (P = 0.01). Overall hospital stay was two days less in 2020 (P = 0.19). Complications (Comprehensive Complication Index 10.3±23.7 2019 vs. 13.9±25.5 2020, P = 0.10) and mortality rates (3% vs. 4%, respectively, P = 0.58) did not vary. Mean unadjusted per-person costs for patients in the 2019 and 2020 cohorts were 5,886.72€±12,576.33€ and 5,287.62±7,220.16€, respectively (P = 0.43). Following multivariate analysis, costs remained similar (4,656.89€±390.53€ 2019 vs. 4,938.54±406.55€ 2020, P = 0.28).ConclusionsHealthcare delivery and spending for unplanned general surgery admissions declined considerably due to COVID19. These results provide a small yet relevant illustration of clinical and economic ramifications of this healthcare crisis.
| S-EPMC8213049 | biostudies-literature