Effect of three modalities on emergence agitation among post-traumatic stress disorder patients undergoing laparoscopy: a randomized controlled study.

BACKGROUND AND AIM: Emergence agitation (EA) after general anesthesia is common in patients with post-traumatic stress disorder (PTSD). Due to the recent worldwide events such as the Covid-19 pandemic and wars, PTSD is not rare. Accordingly, a reliable, cost-effective anesthetic protocol to lower the incidence of EA is crucial. Therefore, we aimed to compare three different interventions for avoiding EA in PTSD patients undergoing gynecological laparoscopic surgery. Participants were divided into four groups: 1: performing pre-operative relaxation techniques (deep breathing exercise and progressive muscle relaxation [PMR]); 2: administrating intra-operative Ketamine; 3: applying both previously mentioned strategies and 4 as controls. METHODS: This study was carried out on 144 adult women scheduled for gynecological laparoscopy, randomly allocated into four groups: three intervention groups and a control group (36 each). Women aged 18-45 years old, with a diagnosis of PTSD were included in the study. Patients with a positive history of major neurological, cardiovascular, metabolic, respiratory, or renal disease were excluded. Any patient who reported the use of psychiatric drugs were also excluded from the study. Data was analyzed using IBM SPSS Statistics software version 26. Kolmogorov- Smirnov was used to verify the normality of the distribution of variables. Odds ratio was calculated to clarify the strength and direction of the association between intervention groups and control. Data was deemed significant at a p-value ≤0.05. RESULTS: Heart rate (HR) and Mean Arterial Blood Pressure (MABP) intra-operative and post-operative till 24 hours were significantly lower in groups 1, 2, and 3 compared to group 4 (p<0.001). There was a significant statistical difference in the intraoperative HR percentage decrease. MABP percentage decrease post-operative was higher in all the intervention groups with no statistically significant difference, except for group 1 compared to group 4, which was statistically significant (12.28 ± 11.77 and 6.10 ± 7.24, p=0.025). Visual Analogue Scale measurements were significantly less in the intervention groups 1, 2, and 3 compared to group 4. On Riker sedation-agitation scores, group 1 was 85 times more likely to be non-agitated (85 (15.938 - 453.307), p<0.001), group 2 was 175 times more likely to be non-agitated (175 (19.932-1536.448), p<0.001) and group 3 was protected against agitation. CONCLUSION: Pre-operative relaxation techniques (breathing exercises and PMR) significantly lowered HR, MABP, VAS score, and EA than controls. These effects were not significantly different from intra-operative ketamine injection or the combination of both (relaxation techniques and ketamine). We recommend routine pre-operative screening for PTSD and the application of relaxation techniques (breathing exercises and PMR) in the pre-operative preparation protocol of PTSD-positive cases as well as routine practical application of preoperative relaxation techniques. Further studies on using pre-operative relaxation techniques in general could be cost-effective.

Artificial intelligence models for methylene blue removal using functionalized carbon nanotubes.

This study aims to assess the practicality of utilizing artificial intelligence (AI) to replicate the adsorption capability of functionalized carbon nanotubes (CNTs) in the context of methylene blue (MB) removal. The process of generating the carbon nanotubes involved the pyrolysis of acetylene under conditions that were determined to be optimal. These conditions included a reaction temperature of 550 °C, a reaction time of 37.3 min, and a gas ratio (H2/C2H2) of 1.0. The experimental data pertaining to MB adsorption on CNTs was found to be extremely well-suited to the Pseudo-second-order model, as evidenced by an R2 value of 0.998, an X2 value of 5.75, a qe value of 163.93 (mg/g), and a K2 value of 6.34 × 10-4 (g/mg min).The MB adsorption system exhibited the best agreement with the Langmuir model, yielding an R2 of 0.989, RL value of 0.031, qm value of 250.0 mg/g. The results of AI modelling demonstrated a remarkable performance using a recurrent neural network, achieving with the highest correlation coefficient of R2 = 0.9471. Additionally, the feed-forward neural network yielded a correlation coefficient of R2 = 0.9658. The modeling results hold promise for accurately predicting the adsorption capacity of CNTs, which can potentially enhance their efficiency in removing methylene blue from wastewater.

Analysis of urban heat islands with landsat satellite images and GIS in Kuala Lumpur Metropolitan City.

Cities are growing geographically in response to the enormous increase in urban population; consequently, comprehending growth and environmental changes is critical for long-term planning. Urbanization transforms naturally permeable surfaces into impermeable surfaces, causing an increase in urban land surface temperature, leading to the phenomenon known as urban heat islands. The urban heat islands are noticeable across Malaysia's rural communities and villages, particularly in Kuala Lumpur. These effects must be addressed to slow, if not halt, climate change and meet the Paris Agreement's 2030 goal. The study posits an application of thermal remote sensing utilizing a space-borne satellite-based technique to demonstrate urban evolution for urban heat island analysis and its relationship to land surface temperature. The urban heat island (UHI) was analyzed by converting infrared radiation into visible thermal images utilizing thermal imaging from remote sensing techniques. The heat island is validated by reference to the characteristics of the normalized difference vegetation index (NDVI), which define the land surface temperature (LST) of distinct locations. Based on the digital information from the satellite, the highest temperature difference between urban and rural regions for a few chosen cities in 2013 varied from 10.8 to 25.5 °C, while in 2021, it ranged from 16.1 to 26.73 °C, highlighting crucial temperature changes. The results from ANOVA test has substantially strengthened the credibility of the significant temperature changes. Some notable reveals are as follows: The Sungai Batu area, due to its rapid development and industry growth, was more vulnerable to elevated urban heat due to reduced vegetation cover; therefore, higher relative vulnerability. Contrary, the Bukit Ketumbar area, which region lies in the woodland region, experienced the lowest, with urban heat islands reading from 2013 at -0.3044 and 0.0154 in 2021. It shows that despite having urban heat islands increase two-fold from 2013 to 2021, increasing the amount of vegetation coverage is a simple and effective way of reducing the urban heat island effect, as evidenced by the low urban heat islands in the Bukit Ketumbar woodland region. The study findings are critical for advising municipal officials and urban planners to decrease urban heat islands by investing in open green spaces.

Exploring machine learning algorithms for accurate water level forecasting in Muda river, Malaysia.

Accurate water level prediction for both lake and river is essential for flood warning and freshwater resource management. In this study, three machine learning algorithms: multi-layer perceptron neural network (MLP-NN), long short-term memory neural network (LSTM) and extreme gradient boosting XGBoost were applied to develop water level forecasting models in Muda River, Malaysia. The models were developed using limited amount of daily water level and meteorological data from 2016 to 2018. Different input scenarios were tested to investigate the performance of the models. The results of the evaluation showed that the MLP model outperformed both the LSTM and the XGBoost models in predicting water levels, with an overall accuracy score of 0.871 compared to 0.865 for LSTM and 0.831 for XGBoost. No noticeable improvement has been achieved after incorporating meteorological data into the models. Even though the lowest reported performance was reported by the XGBoost, it is the faster of the three algorithms due to its advanced parallel processing capabilities and distributed computing architecture. In terms of different time horizons, the LSTM model was found to be more accurate than the MLP and XGBoost model when predicting 7 days ahead, demonstrating its superiority in capturing long-term dependencies. Therefore, it can be concluded that each ML model has its own merits and weaknesses, and the performance of different ML models differs on each case because these models depends largely on the quantity and quality of data available for the model training.

Rainfall-runoff modelling based on global climate model and tropical rainfall measuring mission (GCM -TRMM): A case study in Hulu Terengganu catchment, Malaysia.

The hydropower Plant in Terengganu is one of the major hydroelectric dams currently operated in Malaysia. For better operating and scheduling, accurate modelling of natural inflow is vital for a hydroelectric dam. The rainfall-runoff model is among the most reliable models in predicting the inflow based on the rainfall events. Such a model's reliability depends entirely on the reliability and consistency of the rainfall events assessed. However, due to the hydropower plant's remote location, the cost associated with maintaining the installed rainfall stations became a burden. Therefore, the study aims to create a continuous set of rainfall data before, during, and after the construction of a hydropower plant and simulate a rainfall-runoff model for the area. It also examines the reliability of alternative methods by combining rainfall data from two sources: the general circulation model and tropical rainfall measuring mission. Rainfall data from ground stations and generated data using inverse distance weighted method will be compared. The statistical downscaling model will obtain regional rainfall from the general circulation model. The data will be divided into three stages to evaluate the accuracy of the models in capturing inflow changes. The results revealed that rainfall data from TRMM is more correlated to ground station data with R2 = 0.606, while SDSM data has R2 = 0.592. The proposed inflow model based on GCM-TRMM data showed higher precision compared to the model using ground station data. The proposed model consistently predicted inflow during three stages with R2 values ranging from 0.75 to 0.93.

Developing a mortality risk prediction model using data of 3663 hospitalized COVID-19 patients: a retrospective cohort study in an Egyptian University Hospital.

PURPOSE: Since the declaration of COVID-19 as a pandemic, a wide between-country variation was observed regarding in-hospital mortality and its predictors. Given the scarcity of local research and the need to prioritize the provision of care, this study was conducted aiming to measure the incidence of in-hospital COVID-19 mortality and to develop a simple and clinically applicable model for its prediction. METHODS: COVID-19-confirmed patients admitted to the designated isolation areas of Ain-Shams University Hospitals (April 2020-February 2021) were included in this retrospective cohort study (n = 3663). Data were retrieved from patients' records. Kaplan-Meier survival and Cox proportional hazard regression were used. Binary logistic regression was used for creating mortality prediction models. RESULTS: Patients were 53.6% males, 4.6% current smokers, and their median age was 58 (IQR 41-68) years. Admission to intensive care units was 41.1% and mortality was 26.5% (972/3663, 95% CI 25.1-28.0%). Independent mortality predictors-with rapid mortality onset-were age ≥ 75 years, patients' admission in critical condition, and being symptomatic. Current smoking and presence of comorbidities particularly, obesity, malignancy, and chronic haematological disorders predicted mortality too. Some biomarkers were also recognized. Two prediction models exhibited the best performance: a basic model including age, presence/absence of comorbidities, and the severity level of the condition on admission (Area Under Receiver Operating Characteristic Curve (AUC) = 0.832, 95% CI 0.816-0.847) and another model with added International Normalized Ratio (INR) value (AUC = 0.842, 95% CI 0.812-0.873). CONCLUSION: Patients with the identified mortality risk factors are to be prioritized for preventive and rapid treatment measures. With the provided prediction models, clinicians can calculate mortality probability for their patients. Presenting multiple and very generic models can enable clinicians to choose the one containing the parameters available in their specific clinical setting, and also to test the applicability of such models in a non-COVID-19 respiratory infection.

Evaluation of protein C and S levels in patients with COVID-19 infection and their relation to disease severity.

Background: The COVID-19 pandemic has been associated with millions of deaths around the world. One of the important causes of death associated with COVID-19 was pulmonary thromboembolism. The risk for venous thromboembolism was markedly increased in patients with COVID-19 especially those admitted to the intensive care unit. The aims of our study were to measure the protein C and S levels in COVID-19-infected patients in comparison with the normal population and to assess the correlation of protein C and S levels in the plasma to the severity of infection. Methods: This was a case-control study measuring the protein C and S levels in patients infected with COVID-19 at the time of diagnosis compared to the normal population. The study included one hundred participants, sixty of them are patients with COVID-19, and forty of them are normal healthy adults. The patient group was subclassified into three subgroups according to disease severity: mild, moderate, and severe COVID-19 infections. Results: The activity of protein C in the patient group serum was significantly lower than that in the control group serum (79.35 ± 26.017 vs 97.43 ± 15.007; p < 0.001). Protein S is also significantly decreased in patients' serum when compared to the control group (70.233 ± 22.476 vs 91 ± 14.498; p < 0.001). There was a statistically significant decrease in the levels of protein C and S associated with the increase in disease severity (p < 0.05). However, protein S showed no statistically significant difference between the moderate and severe disease subgroups. Conclusion: The study concluded that the levels of protein C and S activities were both decreased in patients with COVID-19 when compared to the healthy population. It also concluded that the decrease in their levels is statistically significant in relation to the disease severity.

Earthquake multi-classification detection based velocity and displacement data filtering using machine learning algorithms.

Earthquake is one of the natural disasters that have a big impact on society. Currently, there are many studies on earthquake detection. However, the vibrations that were detected by sensors were not only vibrations caused by the earthquake, but also other vibrations. Therefore, this study proposed an earthquake multi-classification detection with machine learning algorithms that can distinguish earthquake and non-earthquake, and vandalism vibration using acceleration seismic waves. In addition, velocity and displacement as integration products of acceleration have been considered additional features to improve the performances of machine learning algorithms. Several machine learning algorithms such as Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), and Artificial Neural Network (ANN) have been used to develop the best algorithm for earthquake multi-classification detection. The results of this study indicate that the ANN algorithm is the best algorithm to distinguish between earthquake and non-earthquake, and vandalism vibrations. Moreover, it's also more resistant to various input features. Furthermore, using velocity and displacement as additional features has been proven to increase the performance of every model.

Acute Intermittent Porphyria as a Rare Challenging Neuro-Metabolic Disease; a Case Report.

Porphyria is a challenging metabolic disease due to its heterogeneous presentation symptoms and its difficult diagnosis. Many affected individuals can complain of recurrent neuro-visceral attacks per year, some of which may be persistent and life-threatening, which is confusing if there is no established diagnosis. Although the motor manifestations, autonomic changes and seizure are highly suggestive, the diagnosis is often overlooked and needs confirmatory genetic testing. To the best of our knowledge, the acute intermittent porphyria (AIP) reported in this case, involving severe electrolyte disturbances and rapid severe weakness is a challenging neuro-metabolic case and is extremely rare worldwide. Here, we reported a case of AIP in a young girl who presented to the emergency department of Al-Araby international Hospital, Monufia, Egypt with severe abdominal pain, constipation, and headache which had started 10 days ago. It seems that the diagnosis of porphyria should be considered particularly in those patients with abdominal complaints associated with electrolyte disturbances, seizures, and severe progressive neuropathy.

Ivermectin role in COVID-19 treatment (IRICT): single-center, adaptive, randomized, double-blind, placebo-controlled, clinical trial.

BACKGROUND: To investigate the efficacy and safety of ivermectin compared to hydroxychloroquine and placebo in hospitalized moderate to severe COVID-19 patients. RESEARCH DESIGN AND METHODS: The study was an adaptive, randomized, double-blinded, controlled, single-center trial. The study was a series of 3-arm comparisons between two different investigational therapeutic agents (ivermectin and hydroxychloroquine) and a placebo. There was interim monitoring to allow early stopping for futility, efficacy, or safety. RESULTS: Ivermectin decreased survival time from 29 to 18.3 days (HR, 9.8, 95%CI, 3.7-26.2), while it did not shorten the recovery time (HR, 1.02, 95%CI, 0.69-1.5). Subgroup analysis showed an association between ivermectin-related mortality and baseline oxygen saturation level. Moreover, stratified groups showed higher risk among patients on high flow O2. Hydroxychloroquine delayed recovery from 10.1 to 12.5 days (HR, 0.62, 95%CI, 0.4-0.95) and non-significantly decreased survival time from 29 to 26.8 days (HR, 1.47, 95%CI, 0.73-2.9). However, 3 months mortality rates were increased with hydroxychloroquine (RR, 2.05, 95%CI, 1.33-3.16). Neither ivermectin nor hydroxychloroquine increased adverse events and demonstrated safety profile compared to placebo. CONCLUSIONS: The study recommends against using either ivermectin or hydroxychloroquine for treatment of COVID-19 in hospitalized patients with any degree of severity. Clinical trial registration: www.clinicaltrials.gov identifier is: NCT04746365.

Left Atrial Appendage Closure Versus Oral Anticoagulation in Non-Valvular Atrial Fibrillation: A Systematic Review and Meta-Analysis.

BACKGROUND: Left atrial appendage closure (LAAC) devices are an alternative therapy in non-valvular atrial fibrillation (NVAF) patients with contraindications to oral anticoagulation (OAC). However, there are limited data about the clinical outcomes of LAAC devices compared to medical treatment. METHODS: A comprehensive research for studies comparing LAAC devices and OAC for patients with NVAF was performed from inception to January 1, 2021. A meta-analysis was performed using a random effect model to calculate odds ratios (OR) with 95% confidence intervals (CIs). RESULTS: Five studies were eligible that included a total of 4778 patients with a median-weighted follow-up period was 2.6 years. Compared to OAC, the LAAC device arm was associated with a lower risk of the composite of stroke, systemic embolism, and cardiovascular death (OR 0.71; 95% CI 0.51-1.00; p = 0.05). LAAC device arm was also associated with a lower risk of all-cause mortality (OR of 0.60, 95% CI 0.46-0.77; p < 0.0001), cardiovascular mortality (OR of 0.57, 95% CI 0.46-0.70; p < 0.00001), hemorrhagic stroke (OR of 0.19, 95% CI 0.07-0.50; p= 0.0006), all major bleeding (OR of 0.61, 95% CI 0.43-0.88; p = 0.007) and non-procedural major bleeding (OR of 0.46, 95% CI 0.32-0.65; p < 0.0001). There was no significant difference in all stroke, ischemic stroke, and systemic embolization between the two groups. CONCLUSIONS: Our meta-analysis showed lower all-cause mortality, cardiovascular mortality, hemorrhagic stroke, major bleeding, non-procedural major bleeding and the composite of stroke, systemic embolism, and cardiovascular death in the LAAC device arm when compared to OAC. However, the risk of all stroke, ischemic stroke, and systemic embolism were similar between the two arms.

Intranasal versus sublingual route of dexmedetomidine sedation in paediatric dentistry: A randomized controlled clinical trial.

BACKGROUND: Many children experience dental anxiety during dental treatment. Conscious sedation is used to alleviate anxiety and enhance a child's cooperation. AIM: This study aimed to compare the efficacy of intranasal versus sublingual dexmedetomidine. DESIGN: Forty-two healthy, uncooperative children participated in the study. They were divided randomly into two groups: In the first visit, Group I received intranasal dexmedetomidine and group II received sublingual dexmedetomidine, whereas at the second visit, the alternate route was implemented in a crossover design. The child's acceptance of drug administration method was assessed using a 4-point rating scale. Time until optimum sedation was measured. Anxiety during local anesthesia administration was scored using Venham's rating scale. Postoperative response was recorded through Vernon et al's questionnaire. RESULTS: The sublingual dexmedetomidine route was better accepted than the intranasal route (P=0.01), while the latter acted faster (P>0.001). No significant difference in anxiety scores was found between groups at baseline (P=0.84) or during local anesthetic administration (P=0.44). No negative effect was recorded by the parents who answered the Modified Vernon et al questionnaire 24 hour after the dental visit compared to before the dental visit (P=1.00). CONCLUSIONS: Both routes prevented the increase in anxiety scores equally during local anesthesia and do not have negative effect on postoperative behavior of children. However, the sublingual route showed better acceptance with longer onset time of action than the intranasal route.

Predicting evaporation with optimized artificial neural network using multi-objective salp swarm algorithm.

Evaporation is a crucial component to be established in agriculture management and water engineering. Evaporation prediction is thus an essential issue for modeling researchers. In this study, the multilayer perceptron (MLP) was used for predicting daily evaporation. MLP model is as one of the famous ANN models with multilayers for predicting different target variables. A new strategy was used to enhance the accuracy of the MLP model. Three multi-objective algorithms, namely, the multi-objective salp swarm algorithm (MOSSA), the multi-objective crow algorithm (MOCA), and the multi-objective particle swarm optimization (MOPSO), were respectively and separately coupled to the MLP model for determining the model parameters, the best input combination, and the best activation function. In this study, three stations in Malaysia, namely, the Muadzam Shah (MS), the Kuala Terengganu (KT), and the Kuantan (KU), were selected for the prediction of the respective daily evaporation. The spacing (SP) and maximum spread (MS) indices were used to evaluate the quality of generated Pareto front (PF) by the algorithms. The lower SP and higher MS showed better PF for the models. It was observed that the MOSSA had higher MS and lower SP than the other algorithms, at all stations. The root means square error (RMSE), mean absolute error (MAE), percent bias (PBIAS), and Nash Sutcliffe efficiency (NSE) quantifiers were used to compare the ability of the models with each other. The MLP-MOSSA had reduced RMSE compared to the MLP-MOCA, MLP-MOPSO, and MLP models by 18%, 25%, and 35%, respectively, at the MS station. The MAE of the MLP-MOSSA was 2.7%, 4.1%, and 26%, respectively lower than those of the MLP-MOCA, MLP-MOPSO, and MLP models at the KU station. The MLP-MOSSA showed lower MAE than the MLP-MOCA, MLP-MOPSO, and MLP models by 16%, 18%, and 19%, respectively, at the KT station. An uncertainty analysis was performed based on the input and parameter uncertainty. The results indicated that the MLP-MOSSA had the lowest uncertainty among the models. Also, the input uncertainty was lower than the parameter uncertainty. The general results indicated that the MLP-MOSSA had the high efficiency for predicting evaporation.

Direct-acting antiviral regimens in Egyptian patients with chronic hepatitis C virus infection: A real-world single-center experience.

BACKGROUND AND STUDY AIMS: Chronic hepatitis C virus (HCV) infection has always been identified as a major health threat and a potential cause of liver cirrhosis, portal hypertension, and other associated problems. The introduction of direct-acting antiviral agents (DAAs) has represented a paradigm shift in HCV management. In this study, we aim to observe the rate of sustained virologic response (SVR12) in a large scale of patients at a single center as well as record the post-treatment changes in the hematologic, hepatic, and renal biochemical profiles. PATIENTS AND METHODS: In total, 1933 chronic HCV genotype 4 mono-infected non-HCC patients who completed the treatment with six different DAA regimens in the Faculty of Medicine, Ain Shams University Research Institute (MASRI), were retrospectively enrolled in this study. The rate of sustained virologic response after 12 weeks off-therapy (SVR12) was assessed. The baseline characteristics to predict the SVR12 were then analyzed. The post-treatment changes in many profiles were recorded and analyzed. RESULTS: The overall SVR12 rate was 96.2% (after excluding 84 cases who were lost to follow-up). It was achieved in 346/375 patients (92.3%), 466/477 patients (97.7%), 60/62 patients (96.8%), 11/11 patients (100%), 532/545 patients (97.6%), and 445/463 patients (96.1%) who received sofosbuvir/daclatasvir (SOF/DCV), sofosbuvir/daclatasvir/ribavirin (SOF/DCV/RBV), sofosbuvir/ledipasvir (SOF/LDV), sofosbuvir/ledipasvir/ribavirin (SOF/LDV/RBV), sofosbuvir/simeprevir (SOF/SMV), and ombitasvir/paritaprevir/ritonavir/ribavirin (OBV/PTV/r + RBV), respectively. In total, 73 patients (3.8%) failed to achieve SVR12. The baseline aspartate aminotransferase (AST), cirrhotic status, and treatment regimen were determined to have a significant impact on SVR12. In the overall treated population, the levels of serum AST, alanine aminotransferase, albumin, creatinine, bilirubin, and hemoglobin and platelet count improved significantly after treatment. Furthermore, sustained virologic response was strongly related to cirrhosis and its degree. CONCLUSION: The interferon-free DAA regimens offered high SVR12 rates in Egyptian patients with chronic HCV infection. They were associated with a significant improvement in the hematologic, hepatic, and renal biochemical profiles. The baseline AST, liver cirrhosis, and treatment regimen might have an impact on achieving SVR.

Suspended sediment load prediction using long short-term memory neural network.

Rivers carry suspended sediments along with their flow. These sediments deposit at different places depending on the discharge and course of the river. However, the deposition of these sediments impacts environmental health, agricultural activities, and portable water sources. Deposition of suspended sediments reduces the flow area, thus affecting the movement of aquatic lives and ultimately leading to the change of river course. Thus, the data of suspended sediments and their variation is crucial information for various authorities. Various authorities require the forecasted data of suspended sediments in the river to operate various hydraulic structures properly. Usually, the prediction of suspended sediment concentration (SSC) is challenging due to various factors, including site-related data, site-related modelling, lack of multiple observed factors used for prediction, and pattern complexity.Therefore, to address previous problems, this study proposes a Long Short Term Memory model to predict suspended sediments in Malaysia's Johor River utilizing only one observed factor, including discharge data. The data was collected for the period of 1988-1998. Four different models were tested, in this study, for the prediction of suspended sediments, which are: ElasticNet Linear Regression (L.R.), Multi-Layer Perceptron (MLP) neural network, Extreme Gradient Boosting, and Long Short-Term Memory. Predictions were analysed based on four different scenarios such as daily, weekly, 10-daily, and monthly. Performance evaluation stated that Long Short-Term Memory outperformed other models with the regression values of 92.01%, 96.56%, 96.71%, and 99.45% daily, weekly, 10-days, and monthly scenarios, respectively.

Rare Cardiac Papillary Fibroelastoma: Right Atrial, Non-Valvular, Large, Symptomatic With Pulmonary Embolism.

INTRODUCTION: Primary cardiac tumors are rarely seen in the general population and only a subset are classified as cardiac papillary fibroelastoma. CASE PRESENTATION: A 59-year-old female that presented for unresponsiveness and cardiac arrest required 4 rounds of cardiopulmonary resuscitation and intubation. Laboratory investigations showed uncompensated respiratory acidosis, hyperkalemia, and elevated troponins. A chest computed tomography angiogram illustrated an acute right pulmonary embolism and a right atrial filling defect. Furthermore, an echocardiogram demonstrated a normal ejection fraction and a large, pedunculated, mobile, and non-valvular echodensity that was attached to the right atrium endocardium. Therefore, the patient was started on a heparin infusion and catheter-directed thrombolysis; however, the mass persisted. A surgical excision was performed, and a 40 mm was removed. The patient was diagnosed with a papillary fibroelastoma based on the clinical symptoms, imaging, and histological findings. CONCLUSION: This patient's papillary fibroelastoma had multiple rare features including right atrial origin, large size, non-valvular location, and developed symptoms. Although this disease can be initially fatal, the patients typically have a favorable prognosis after a successful excision.

Hydroxychloroquine for treatment of nonsevere COVID-19 patients: Systematic review and meta-analysis of controlled clinical trials.

Being a pandemic and having a high global case fatality rate directed us to assess the evidence strength of hydroxychloroquine efficacy in treating coronavirus disease-2019 (COVID-19) arising from clinical trials and to update the practice with the most reliable clinical evidence. A comprehensive search was started in June up to 18 July, 2020 in many databases, including PubMed, Embase, and others. Of 432 studies found, only six studies fulfilled the inclusion criteria, which includes: clinical trials, age more than 12 years with nonsevere COVID-19, polymerase chain reaction-confirmed COVID-19, hydroxychloroquine is the intervention beyond the usual care. Data extraction and bias risk assessment were done by two independent authors. Both fixed-effect and random-effect models were utilized for pooling data using risk difference as a summary measure. The primary outcomes are clinical and radiological COVID-19 progression, severe acute respiratory syndrome coronavirus-2 clearance in the pharyngeal swab, and mortality. The secondary outcomes are the adverse effects of hydroxychloroquine. Among 609 COVID-19 confirmed patients obtained from pooling six studies, 294 patients received hydroxychloroquine and 315 patients served as a control. Hydroxychloroquine significantly prevents early radiological progression relative to control with risk difference and 95% confidence interval of -0.2 (-0.36 to -0.03). On the other hand, hydroxychloroquine did not prevent clinical COVID-19 progression, reduce 5-day mortality, or enhance viral clearance on days 5, 6, and 7. Moreover, many adverse effects were reported with hydroxychloroquine therapy. Failure of hydroxychloroquine to show viral clearance or clinical benefits with additional adverse effects outweigh its protective effect from radiological progression in nonsevere COVID-19 patients. Benefit-risk balance should determine the hydroxychloroquine use in COVID-19.

Efficacy and safety of remdesivir in hospitalized Covid-19 patients: Systematic review and meta-analysis including network meta-analysis.

Remdesivir is an antiviral agent that has shown broad-spectrum activity, including against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Clinical trials investigating the role of remdesivir in coronavirus disease 2019 (Covid-19) reported conflicting results. This study aimed to systematically review the best available evidence and synthesize the results. Several electronic databases were searched for candidate studies up to 12 October 2020. Studies eligible for meta-analysis were selected based on the inclusion criteria. Primary outcomes are the recovery and mortality rates, while secondary outcomes are the safety profile of remdesivir. The main effective measures are the rate ratio (RR) and rate difference (RD). Four clinical trials and one observational study were included. Remdesivir treatment for 10 days increased the recovery rate on day 14 by 50% among severe Covid-19 patients (RR = 1.5, 95%CI = 1.33-1.7), while on day 28 it was increased by 14% among moderate and severe Covid-19 patients (RR = 1.14, 95%CI = 1.06-1.22). Additionally, remdesivir decreased the mortality rate on day 14 by 36% among all patients (RR = 0.64, 95%CI = 0.45-0.92) but not on day 28 (RR = 1.05, 95%CI = 0.56-1.97). Nonmechanically ventilated Covid-19 patients showed better response to remdesivir in the recovery (RR = 0.3, 95%CI = 0.13-0.7) and mortality (RR = 2.33, 95%CI = 1.24-4.4) rates on day 14. Remdesivir reduced serious adverse effects by absolute 6% and no significant Grade 3 or 4 adverse effects were reported. At this early stage of the pandemic, there is evidence that remdesivir can be safely administered for hospitalized Covid-19 patients. It improves the recovery rate in both moderate and severe patients but, the optimal effect is achieved for those who are severely affected but not mechanically ventilated.

The rare presentation of the de Winter's pattern: Case report and literature review.

Although not classified as a ST elevated myocardial infarction (STEMI), the patterns known as equivalents also require prompt recognition and treatment. A 50-year-old male with no pertinent history presented to the emergency department for chest pain that radiated to his left shoulder. An electrocardiogram (EKG) revealed findings consistent with the de Winter's pattern, which were greater than 1 mm upsloping ST depressions at the J point in leads V3-V6 (maximally in leads V3-V5), tall, peaked T waves in leads II, III, and V3-V5, ST elevations in lead aVR, and 1 mm ST elevation in V1 and V2. The physical exam, troponins, and other laboratory investigations were unrevealing. Urgent, diagnostic coronary angiography revealed complete occlusion of the proximal left anterior descending (LAD) artery, which was successfully treated with percutaneous coronary intervention (PCI) and two drug-eluting stents. After the stent placement, arterial blood flow was re-established and the ECG normalized. The patient was started on guideline based treatment and discharged home once medically stable. The de Winter's pattern on electrocardiogram indicates a significant coronary artery disease. This pattern requires urgent intervention, typically percutaneous stent placement.