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We present a case of a 45-year-old female with advanced heart failure due to nonischemic cardiomyopathy, an ejection fraction of 15% complicated by ventricular arrhythmias degenerating into an electrical storm. The treatment approach involved antiarrhythmic medications, inotrope support, diuretics, and the placement of an Impella 5.5 device for mechanical circulatory support. Due to persistent polymorphic ventricular tachycardia, a bilateral stellate ganglion blockade was performed along with intensified antiarrhythmic therapy and deep sedation. This case highlights the role of axillary mechanical circulatory support with the Impella 5.5 in providing unfettered hemodynamic stability and the potential added benefit of stellate ganglion blockade in managing malignant ventricular arrhythmias.

BackgroundThe COVID-19 pandemic intensified the use of scarce resources, including extracorporeal membrane oxygenation (ECMO) and mechanical ventilation (MV). The combinatorial features of the immune system may be considered to estimate such needs and facilitate continuous open-ended knowledge discovery.Materials and methodsComputer-generated distinct data patterns derived from 283 white blood cell counts collected within five days after hospitalization from 97 COVID-19 patients were used to predict patient’s use of hospital resources.ResultsAlone, data on separate cell types—such as neutrophils—did not identify patients that required MV/ECMO. However, when structured as multicellular indicators, distinct data patterns displayed by such markers separated patients later needing or not needing MV/ECMO. Patients that eventually required MV/ECMO also revealed increased percentages of neutrophils and decreased percentages of lymphocytes on admission.Discussion/conclusionFuture use of limited hospital resources may be predicted when combinations of available blood leukocyte-related data are analyzed. New methods could also identify, upon admission, a subset of COVID-19 patients that reveal inflammation. Presented by individuals not previously exposed to MV/ECMO, this inflammation differs from the well-described inflammation induced after exposure to such resources. If shown to be reproducible in other clinical syndromes and populations, it is suggested that the analysis of immunological combinations may inform more and/or uncover novel information even in the absence of pre-established questions.

The paper analyses the effect of electronic-trading (e-trading) on the wholesale prices of select commodities in agricultural marketplaces to aid agrarian income levels. Post a literature review, the researcher performed a field survey to get the opinions of 371 farmers and traders. The researcher also conducted the descriptive statistical analysis, the difference in differences analysis, and the t-test analysis to show the wholesale price improvement after the e-trading platform implementation. The field survey findings and the statistical analysis suggest an increase in average wholesale prices of select agri-commodities in select markets of India due to the National Agriculture Market (eNAM) e-trading platform effect. The suggestions to practitioners and social policymakers highlight an improvement in the functioning of e-trading in terms of better prices for farmers, lowering transaction costs, increased transaction speed, reduced administrative hurdles, improved infrastructure and eventually higher income for a farmer. The knowledge base developed in this paper will help researchers reduce the knowledge gap in agricultural pricing.

BackgroundThe Surviving Sepsis Campaign suggested preferential resuscitation with balanced crystalloids, such as Lactated Ringer’s (LR), although the level of recommendation was weak, and the quality of evidence was low. Past studies reported an association of unbalanced solutions, such as normal saline (NS), with increased AKI risks, metabolic acidosis, and prolonged ICU stay, although some of the findings are conflicting. We have compared the outcomes with the preferential use of normal saline vs. ringer’s lactate in a cohort of sepsis patients.MethodWe performed a retrospective cohort analysis of patients visiting the ED of 19 different Mayo Clinic sites between August 2018 to November 2020 with sepsis and receiving at least 30 mL/kg fluid in the first 6 h. Patients were divided into two cohorts based on the type of resuscitation fluid (LR vs. NS) and propensity-matching was done based on clinical characteristics as well as fluid amount (with 5 ml/kg). Single variable logistic regression (categorical outcomes) and Cox proportional hazards regression models were used to compare the primary and secondary outcomes between the 2 groups.ResultsOut of 2022 patients meeting our inclusion criteria; 1,428 (70.6%) received NS, and 594 (29.4%) received LR as the predominant fluid (>30 mL/kg). Patients receiving predominantly NS were more likely to be male and older in age. The LR cohort had a higher BMI, lactate level and incidence of septic shock. Propensity-matched analysis did not show a difference in 30-day and in-hospital mortality rate, mechanical ventilation, oxygen therapy, or CRRT requirement. We did observe longer hospital LOS in the LR group (median 5 vs. 4 days, p = 0.047 and higher requirement for ICU post-admission (OR: 0.70; 95% CI: 0.51–0.96; p = 0.026) in the NS group. However, these did not remain statistically significant after adjustment for multiple testing.ConclusionIn our matched cohort, we did not show any statistically significant difference in mortality rates, hospital LOS, ICU admission after diagnosis, mechanical ventilation, oxygen therapy and RRT between sepsis patients receiving lactated ringers and normal saline as predominant resuscitation fluid. Further large-scale prospective studies are needed to solidify the current guidelines on the use of balanced crystalloids.

Coronavirus disease 2019 (COVID-19), a novel etiology of end-stage lung disease, has resulted in major disruptions to the process of health care delivery worldwide. These disruptions have led to team-based innovations globally, resulting in a broad range of new processes in cardiopulmonary perioperative management. A key intersection of multidisciplinary teamwork and COVID-19 is found in lung transplantation, in which diverse teams collaborate throughout the perioperative period to achieve optimal outcomes. In this article, we describe the multidisciplinary approach taken by Mayo clinic in Florida to manage patients with COVID-19 presenting for lung transplantation.

Learning effective segment depictions and resemblance measures are fundamental to the recuperation execution of a substance based picture recuperation (CBIR) structure. Regardless of wide research tries for a significant long time, it stays one of the most testing open gives that broadly impedes the achievements of real-world CBIR structures. The key test has been credited to the extraordinary "semantic hole" subject that happens between low-level photo pixels got by technologies and raised close semantic thoughts saw by a human. Among various techniques, AI has been successfully analyzed as a possible course to interface the semantic gap in the whole deal. Impelled by late triumphs of significant learning techniques for PC vision and various applications, in this paper, we try to address an open issue: if significant learning is a longing for spreading over the semantic gap in CBIR and how much updates in CBIR endeavors can be cultivated by exploring the front line significant learning methodology for learning feature depictions and likeness measures. Specifically, we explore a structure of significant learning with application to CBIR assignments with a wide game plan of definite examinations by investigating front line significant learning methodologies for CBIR endeavors under moved settings. From our exploratory examinations, we find some encouraging results and compress some huge bits of information for upcoming research.

Rapid integration of renewable energy into the electric grid has ramifications for grid management and planning. Therefore, system operators have formulated grid code requirements to ensure that the grid continues to operate in a secure, safe, and cost-effective manner. The current state of grid code in Ethiopia, as well as the need for it, is discussed in this article. It lays out the technological grid integration requirements, with a focus on small and microgrids, which are especially important for the integration of renewable. The barriers to grid code normalization and renewable energy grid compatibility testing are identified, and suggestions for continued grid code development in Ethiopia based on Danish observations are provided. Further, a detailed comparative analysis of the Ethiopian grid code with the IEEE 1547-2003 and IEEE 1547-2018 standards is presented.

Orbital cellulitis is a potentially life-threatening and an uncommon emergency condition in children. There are little published data from incidence and prevalence in the developing country. This study was done to identify incidence, etiology and management of orbital cellulitis in children from two service hospitals in east and west coast of India. Methods : The two multispecialty service hospitals cater tertiary level care to dependant population with 80700 children between ages 1 month to 12 years. This study was a 2 years retrospective observational study using hospital based electronic information system and electronic case records from Jan 2017 to Dec 2018 between ages of 1 month to 12 years. Results : A total of 15 children with orbital cellulitis were included in the study giving incidence of 9.29 per 100000 in children

A modular multilevel converter’s (MMC’s) submodule (SM)-capacitor voltage will increase under unbalanced grid conditions. Depending on the imbalance level, the voltage ripple can be considerably high, and it can exceed the pre-defined safe limits. If this occurs, the converter will trip, which can lead to serious stability problems for the grid. This paper first proposes an analytical solution for deriving the three-phase imbalanced SM ripple of an MMC under an unbalanced grid. With this analytical tool, the imbalance mechanism of the SM voltage ripple can be easily understood. What is more, the symmetrical component method is first applied to analyze the three-phase SM capacitor ripple, and the positive-/negative-/zero-sequence components of the three-phase SM voltage ripple are easily identified by the proposed analytical method. Then, based on this powerful analytical tool, the proper circulating-current profile to be injected can be obtained, allowing for the right compensation of the voltage ripple. Based on this approach, two new voltage ripple compensation methods are proposed in this paper. Simulations were carried out to validate the analytical description of the submodule-capacitor voltage ripple proposed in this paper. Moreover, simulation and experimental results are provided to validate the new compensation techniques introduced in this paper.

Service can be delivered anywhere and anytime in cloud computing using virtualization. The main issue to handle virtualized resources is to balance ongoing workloads. The migration of virtual machines has two major techniques: (i) reducing dirty pages using CPU scheduling and (ii) compressing memory pages. The available techniques for live migration are not able to predict dirty pages in advance. In the proposed framework, time series based prediction techniques are developed using historical analysis of past data. The time series is generated with transferring of memory pages iteratively. Here, two different regression based models of time series are proposed. The first model is developed using statistical probability based regression model and it is based on ARIMA (autoregressive integrated moving average) model. The second one is developed using statistical learning based regression model and it uses SVR (support vector regression) model. These models are tested on real data set of Xen to compute downtime, total number of pages transferred, and total migration time. The ARIMA model is able to predict dirty pages with 91.74% accuracy and the SVR model is able to predict dirty pages with 94.61% accuracy that is higher than ARIMA.