Treatment of Hypertensive Crisis Using Beta Blockers Vs Diuretics: Review

Current review aiming to evaluate and the advantage and disadvantage and also to discuss the differences in use and combination therapy of beta blockers VS diuretics in the treatment of hypertensive crisis.  Literature were search on topic concerning the treatment of hypertensive crisis, using biomedical databases; PubMed, and Embase, up to August, 2017.  Patients with hypertensive crises could call for immediate reduction in raised high blood pressure to stop and also detain modern end-organ damage. The best scientific setup in which to attain this blood pressure control remains in the intensive care unit, with making use of titratable intravenous hypotensive agents. Beta-blocker- based therapy, numerous possible randomized trials have recorded that diuretic-based treatment is efficient in reducing morbidity and also mortality in hypertensive patients. The advantages of diuretic therapy have actually been shown to be more significant in the senior compared to in younger patients. The result of diuretics is especially articulated when it comes to decrease of the risk of stroke and also somewhat less excellent with regard to the reduction of the danger of coronary heart disease. Keywords: Hypertension, Diuretics Therapy, Beta Blocker, Hypertensive Crisis

Burnout among surgeons before and during the SARS-CoV-2 pandemic: an international survey

Background: SARS-CoV-2 pandemic has had many significant impacts within the surgical realm, and surgeons have been obligated to reconsider almost every aspect of daily clinical practice. Methods: This is a cross-sectional study reported in compliance with the CHERRIES guidelines and conducted through an online platform from June 14th to July 15th, 2020. The primary outcome was the burden of burnout during the pandemic indicated by the validated Shirom-Melamed Burnout Measure. Results: Nine hundred fifty-four surgeons completed the survey. The median length of practice was 10 years; 78.2% included were male with a median age of 37 years old, 39.5% were consultants, 68.9% were general surgeons, and 55.7% were affiliated with an academic institution. Overall, there was a significant increase in the mean burnout score during the pandemic; longer years of practice and older age were significantly associated with less burnout. There were significant reductions in the median number of outpatient visits, operated cases, on-call hours, emergency visits, and research work, so, 48.2% of respondents felt that the training resources were insufficient. The majority (81.3%) of respondents reported that their hospitals were included in the management of COVID-19, 66.5% felt their roles had been minimized; 41% were asked to assist in non-surgical medical practices, and 37.6% of respondents were included in COVID-19 management. Conclusions: There was a significant burnout among trainees. Almost all aspects of clinical and research activities were affected with a significant reduction in the volume of research, outpatient clinic visits, surgical procedures, on-call hours, and emergency cases hindering the training. Trial registration: The study was registered on clicaltrials.gov "NCT04433286" on 16/06/2020

Outcomes from elective colorectal cancer surgery during the SARS-CoV-2 pandemic

This study aimed to describe the change in surgical practice and the impact of SARS-CoV-2 on mortality after surgical resection of colorectal cancer during the initial phases of the SARS-CoV-2 pandemic

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Improving the performance of a commercial absorption cooling system by using ejector: A theoretical study

Absorption cooling systems (ACS) have lower coefficients of performance (COP) compared to direct expansion (DX) cooling systems. Nevertheless, ACS offers a green alternative to typical DX systems. In this study, a numerical model was developed for the commercial low-capacity Robur® absorption cooling system (RACS). The model was developed based on mass, concentration, and energy balance equations, in addition to heat transfer equations. The model results were validated against experimental data available in the literature for the same cooling unit yielding a good agreement. Hence, to improve the COP of the RACS, a vapor ejector was introduced between the generator and the condenser. An improvement of 70.6% in the COP was obtained at the design condition. A parametric analysis was implemented to study the significance of the key parameters in the RACS performance. It was found that the increase in the ambient temperature not only increased the activation temperature, but it also decreased the COP and increased the circulation ratio (CR). Consequently, in hot environments, lowering the evaporator temperature is recommended to avoid the need for higher CR. Optimizing the nozzle throat and the mixing tube diameter improves the ejector performance, and hence the RACS performance, as long as the ejector operates under critical conditions. Finally, the absorber coil was found to have the most significance on the RACS performance in comparison with the rectifier coil and the refrigerant heat exchanger

Chaotic Equilibrium Optimizer-Based Green Communication With Deep Learning Enabled Load Prediction in Internet of Things Environment

Currently, there is an emerging requirement for applications related to the Internet of Things (IoT). Though the capability of IoT applications is huge, there are frequent limitations namely energy optimization, heterogeneity of devices, memory, security, privacy, and load balancing (LB) that should be solved. Such constraints must be optimised to enhance the network’s efficiency. Hence, the core objective of this study was to formulate the intelligent-related cluster head (CH) selection method to establish green communication in IoT. Therefore, this study develops a chaotic equilibrium optimizer-based green communication with deep learning-enabled load prediction (CEOGC-DLLP) in the IoT environment. The study recognizes the emerging need for IoT applications and acknowledges the critical challenges, such as energy optimization, device heterogeneity, memory constraints, security, privacy, and load balancing, which are essential to enhancing the efficiency of IoT networks. The presented CEOGC-DLLP technique mainly accomplishes green communication via clustering and future load prediction processes. To do so, the presented CEOGC-DLLP model derives the CEOGC technique with a fitness function encompassing multiple parameters. In addition, the presented CEOGC-DLLP technique follows the deep belief network (DBN) model for the load prediction process, which helps to balance the load among the IoT devices for effective green communication. The experimental assessment of the CEOGC-DLLP technique is performed and the outcomes are investigated under different aspects. The comparison study represents the supremacy of the CEOGC-DLLP method compared to existing techniques with a maximum throughput of 64662 packets and minimum MSE of 0.2956

Bone Cancer Detection and Classification Using Owl Search Algorithm With Deep Learning on X-Ray Images

Bone cancer is treated as a severe health problem, and, in many cases, it causes patient death. Early detection of bone cancer is efficient in reducing the spread of malignant cells and decreasing mortality. Since the manual detection process is a laborious task, it is needed to design an automated system to classify and identify the cancerous bone and the healthy bone. Therefore, this article develops an Owl Search Algorithm with a Deep Learning-Driven Bone Cancer Detection and Classification (OSADL-BCDC) technique. The OSADL-BCDC algorithm follows the principle of transfer learning with a hyperparameter tuning strategy for bone cancer detection. The OSADL-BCDC model employs Inception v3 as a pretrained model for the feature extraction process which does not necessitate a manual segmentation of X-ray images. Besides, the OSA is applied as a hyperparameter optimizer for enhancing the efficacy of the Inception v3 method. Finally, the long short-term memory (LSTM) approach is used for identifying the presence of bone cancer. The proposed OSADL-BCDC technique reduces diagnosis time and achieves faster convergence. The experimental analysis of the OSADL-BCDC algorithm is tested using a set of medical images and the outcomes were measured under different aspects. The comparison study highlighted the improved performance of the OSADL-BCDC model over existing algorithms

A novel automated Parkinson’s disease identification approach using deep learning and EEG

The neurological ailment known as Parkinson’s disease (PD) affects people throughout the globe. The neurodegenerative PD-related disorder primarily affects people in middle to late life. Motor symptoms such as tremors, muscle rigidity, and sluggish, clumsy movement are common in patients with this disorder. Genetic and environmental variables play significant roles in the development of PD. Despite much investigation, the root cause of this neurodegenerative disease is still unidentified. Clinical diagnostics rely heavily on promptly detecting such irregularities to slow or stop the progression of illnesses successfully. Because of its direct correlation with brain activity, electroencephalography (EEG) is an essential PD diagnostic technique. Electroencephalography, or EEG, data are biomarkers of brain activity changes. However, these signals are non-linear, non-stationary, and complicated, making analysis difficult. One must often resort to a lengthy human labor process to accomplish results using traditional machine-learning approaches. The breakdown, feature extraction, and classification processes are typical examples of these stages. To overcome these obstacles, we present a novel deep-learning model for the automated identification of Parkinson’s disease (PD). The Gabor transform, a standard method in EEG signal processing, was used to turn the raw data from the EEG recordings into spectrograms. In this research, we propose densely linked bidirectional long short-term memory (DLBLSTM), which first represents each layer as the sum of its hidden state plus the hidden states of all layers above it, then recursively transmits that representation to all layers below it. This study’s suggested deep learning model was trained using these spectrograms as input data. Using a robust sixfold cross-validation method, the proposed model showed excellent accuracy with a classification accuracy of 99.6%. The results indicate that the suggested algorithm can automatically identify PD

Land Use and Land Cover Classification Using River Formation Dynamics Algorithm With Deep Learning on Remote Sensing Images

Currently, remote sensing images (RSIs) are often exploited in the explanation of urban and rural areas, change recognition, and other domains. As the majority of RSI is high-resolution and contains wide and varied data, proper interpretation of RSIs is most important. Land use and land cover (LULC) classification utilizing deep learning (DL) is a common and efficient manner in remote sensing and geospatial study. It is very important in land planning, environmental monitoring, mapping, and land management. But, one of the recent approaches is problems like vulnerability to noise interference, low classification accuracy, and worse generalization ability. DL approaches, mostly Convolutional Neural Networks (CNNs) revealed impressive performance in image recognition tasks, making them appropriate for LULC classification in RSIs. Therefore, this study introduces a novel Land Use and Land Cover Classification employing the River Formation Dynamics Algorithm with Deep Learning (LULCC-RFDADL) technique on RSIs. The main objective of the LULCC-RFDADL methodology is to recognize the diverse types of LC on RSIs. In the presented LULCC-RFDADL technique, the dense EfficientNet approach is applied for feature extraction. Furthermore, the hyperparameter tuning of the Dense EfficientNet method was implemented using the RFDA technique. For the classification process, the LULCC-RFDADL technique uses the Multi-Scale Convolutional Autoencoder (MSCAE) model. At last, the seeker optimization algorithm (SOA) has been exploited for the parameter choice of the MSCAE system. The achieved outcomes of the LULCC-RFDADL algorithm were examined on benchmark databases. The simulation values show the better result of the LULCC-RFDADL methods with other approaches in terms of different metrics

The acute surgical unit: improving emergency care

Background:  Acute care surgical teams are a new concept in the provision of emergency general surgery. Juggling emergency patients around the surgeons\u27 and staffs\u27 elective commitments resulted in semi-emergency procedures routinely being delayed. In an era of increasing financial pressure and the recent introduction of \u27safe work hours\u27 practices, the need for a new system which optimized available resources became apparent. Methods:  At Fremantle Hospital we developed a new system in a concerted effort to minimize the waiting time for general surgical referrals in the Emergency Department, as well as to move semi-urgent operating from the afterhours to the daytime. To analyse the impact of the ASU, data were collected during February, March, and April 2009 and compared with data from the same period in 2008. Results:  Although most referrals were received afterhours, over 85% of operations were performed during working hours compared with 72% in the 2008 period. The time from referral to review decreased from an average of 3.2 h in 2008 to 2.1 h. The mean duration of stay in 2009 was 3 days, which was a reduction from 4.2 days in 2008. An increase in weekend discharge rates was seen after the introduction of the ASU. Conclusion:  Despite an increased workload, more referrals were seen and more operations performed during working hours and the time from referral to review was reduced. Higher discharge rates and reduced length of stays increased the availability of beds. We have demonstrated a successful new model which continues to evolve