Higher body mass index is strongly linked to poor outcomes in adult COVID-19 hospitalizations: A National Inpatient Sample Study.

Aims: The coronavirus disease 2019 (COVID-19) pandemic has resulted in more than 6 million deaths worldwide. Studies on the impact of obesity on patients hospitalized with COVID-19 pneumonia have been conflicting, with some studies describing worse outcomes in patients with obesity, while other studies reporting no difference in outcomes. Previous studies on obesity and critical illness have described improved outcomes in patients with obesity, termed the "obesity paradox." The study assessed the impact of obesity on the outcomes of COVID-19 hospitalizations, using a nationally representative database. Materials and Methods: ICD-10 code U071 was used to identify all hospitalizations with the principal diagnosis of COVID-19 infection in the National Inpatient Database 2020. ICD-10 codes were used to identify outcomes and comorbidities. Hospitalizations were grouped based on body mass index (BMI). Multivariable logistic regression was used to adjust for demographic characteristics and comorbidities. Results: A total of 56,033 hospitalizations were identified. 48% were male, 49% were white and 22% were black. Patients hospitalized with COVID-19 pneumonia in the setting of obesity and clinically severe obesity were often younger. Adjusted for differences in comorbidities, there was a significant increase in mortality, incidence of mechanical ventilation, shock, and sepsis with increased BMI. The mortality was highest among hospitalizations with BMI ≥60, with an adjusted odds ratio of 2.66 (95% Confidence interval 2.18-3.24) compared to hospitalizations with normal BMI. There were increased odds of mechanical ventilation across all BMI groups above normal, with the odds of mechanical ventilation increasing with increasing BMI. Conclusion: The results show that obesity is independently associated with worse patient outcomes in COVID-19 hospitalizations and is associated with higher in-patient mortality and higher rates of mechanical ventilation. The underlying mechanism of this is unclear, and further studies are needed to investigate the cause of this.

Vegetation as an ecological indicator in assessing environmental restoration in mining areas.

As global demand for natural resources escalates, the environmental impact stemming from resource extraction has risen to the forefront of contemporary discussions. This paper probed the potential of using vegetation cover as an ecological barometer to gauge the level of environmental damage and restoration in mining areas: a decline in vegetation cover may signify detrimental impacts from intense mining activities, while an increase may indicate effective local environmental stewardship. Therefore, this paper undertook an assessment and discussion of mining damage and environmental management at China's Ta'ershan Mining Area since 2007, calculating and visualizing FVC (Fractional Vegetation Cover) of the Ta'ershan Mining Area to track changes in vegetation cover between 2007 and 2021. Changes in vegetation cover in the Ta'ershan Mining Area could act as a reflection of both mining-induced damage and subsequent successful environmental management by local authorities, providing a practical way to evaluate ecological effects in resource development.

Reclaiming abandoned mine tailings ponds for agricultural use: Opportunities and challenges.

Tailings ponds, large man-made structures conceived during the mining process for waste storage, often become deserted post-mining, leaving behind a stark, contaminated landscape. This paper posits that these forsaken tailings ponds can be rejuvenated into fertile agricultural land through adept reclamation efforts. Serving as a discussion paper, it engages in a stimulating exploration of the environmental and health risks linked to tailings ponds. It sheds light on the potential and impediments in the transformation of these ponds into agricultural land. The discussion concludes that despite the substantial hurdles in repurposing tailings ponds for agriculture, there are encouraging prospects with the application of multifaceted efforts.

Infectious Complications After Tocilizumab in Patients with COVID: A Real-World Experience.

Introduction: Controversies remain regarding the safety of tocilizumab in the treatment of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. In this study, we seek to describe the infectious complications after tocilizumab in COVID patients and determine the related risk factors. Methods: A single-center retrospective observational study was conducted among adult patients with SARS-CoV-2 infection admitted between 06/01/2020 and 12/31/2021 who received tocilizumab at our institution. Baseline demographics and laboratory values are obtained through reviewing electronic medical records. Risk factors of infectious complications after tocilizumab are identified through regression analysis. Statistics are performed using SPSS. P-value <0.05 is considered statistically significant. Results: Out of the 52 patients identified, infectious complications after tocilizumab were documented in 30 patients (57.7%). The most common infections include pneumonia, urinary tract infections, and bacteremia of unknown sources. Overall mortality was 42.3%. Through multivariate regression analysis, age more than 65, hyperglycemia on admission, and tocilizumab administration more than 2 days after hospital admission are independent risk factors associated with developing infections. Conclusions: In real-world experience, infectious complications are not uncommon in COVID patients who receive tocilizumab. Early use of tocilizumab may be of benefit. More rigorous patient selection and monitoring should be explored in future studies.

PM2.5 forecasting for an urban area based on deep learning and decomposition method.

Rapid growth in industrialization and urbanization have resulted in high concentration of air pollutants in the environment and thus causing severe air pollution. Excessive emission of particulate matter to ambient air has negatively impacted the health and well-being of human society. Therefore, accurate forecasting of air pollutant concentration is crucial to mitigate the associated health risk. This study aims to predict the hourly PM2.5 concentration for an urban area in Malaysia using a hybrid deep learning model. Ensemble empirical mode decomposition (EEMD) was employed to decompose the original sequence data of particulate matter into several subseries. Long short-term memory (LSTM) was used to individually forecast the decomposed subseries considering the influence of air pollutant parameters for 1-h ahead forecasting. Then, the outputs of each forecast were aggregated to obtain the final forecasting of PM2.5 concentration. This study utilized two air quality datasets from two monitoring stations to validate the performance of proposed hybrid EEMD-LSTM model based on various data distributions. The spatial and temporal correlation for the proposed dataset were analysed to determine the significant input parameters for the forecasting model. The LSTM architecture consists of two LSTM layers and the data decomposition method is added in the data pre-processing stage to improve the forecasting accuracy. Finally, a comparison analysis was conducted to compare the performance of the proposed model with other deep learning models. The results illustrated that EEMD-LSTM yielded the highest accuracy results among other deep learning models, and the hybrid forecasting model was proved to have superior performance as compared to individual models.

A Case Report of Pulmonary Embolism Caused by Substernal Goiter.

Substernal goiter is overall an uncommon disease. Obstructive symptoms can occasionally develop in older patients with a longstanding history of goiter. Here, we describe a rare case of pulmonary embolism presenting as a complication of benign substernal goiter in a young patient without preceding recognized thyroid disease. After three separate biopsies, surgical resection was eventually performed, with pathology confirming the diagnosis of multinodular thyroid with cystic changes. Four months after the surgery, a CT angiogram of the chest was performed, which showed resolution of bilateral pulmonary emboli.

Artificial neural network model with different backpropagation algorithms and meteorological data for solar radiation prediction.

Solar energy serves as a great alternative to fossil fuels as they are clean and renewable energy. Accurate solar radiation (SR) prediction can substantially lower down the impact cost pertaining to the development of solar energy. Lately, many SR forecasting system has been developed such as support vector machine, autoregressive moving average and artificial neural network (ANN). This paper presents a comprehensive study on the meteorological data and types of backpropagation (BP) algorithms used to train and develop the best SR predicting ANN model. The meteorological data, which includes temperature, relative humidity and wind speed are collected from a meteorological station from Kuala Terrenganu, Malaysia. Three different BP algorithms are employed into training the model i.e., Levenberg-Marquardt, Scaled Conjugate Gradient and Bayesian Regularization (BR). This paper presents a comparison study to select the best combination of meteorological data and BP algorithm which can develop the ANN model with the best predictive ability. The findings from this study shows that temperature and relative humidity both have high correlation with SR whereas wind temperature has little influence over SR. The results also showed that BR algorithm trained ANN models with maximum R of 0.8113 and minimum RMSE of 0.2581, outperform other algorithm trained models, as indicated by the performance score of the respective models.

Delineation of urban expansion influences urban heat islands and natural environment using remote sensing and GIS-based in industrial area.

Land transformation monitoring is essential for controlling the anthropogenic activities that could cause the degradation of natural environment. This study investigated the urban heat island (UHI) effect at the Asansol and Kulti blocks of Paschim Bardhaman district, India. The increasing land surface temperature (LST) can cause the UHI effect and affect the environmental conditions in the urban area. The vulnerability of the UHI effect was measured quantitatively and qualitatively by using the urban thermal field variation index (UTFVI). The land use and land cover (LULC) dynamics are identified by utilizing the remote sensing and maximum likelihood supervised classification techniques for the years 1990, 2000, 2010, and 2020, respectively. The results indicated a decrease around 19.05 km2, 15.47 km2, and 9.86 km2 for vegetation, agricultural land, and grassland, respectively. Meanwhile, there is an increase of 35.69 km2 of the built-up area from the year 1990 to 2020. The highest LST has increased by 11.55 °C, while the lowest LST increased by 8.35 °C from 1990 to 2020. The correlation analyses showed negative relationship between LST and vegetation index, while positive correlation was observed for built-up index. Hotspot maps have identified the spatio-temporal thermal variations in Mohanpur, Lohat, Ramnagar, Madhabpur, and Hansdiha where these cities are mostly affected by the urban expansion and industrialization developments. This study will be helpful to urban planners, stakeholders, and administrators for monitoring the anthropological activities and thus ensuring a sustainable urban development.

Machine learning algorithm as a sustainable tool for dissolved oxygen prediction: a case study of Feitsui Reservoir, Taiwan.

Water quality status in terms of one crucial parameter such as dissolved oxygen (D.O.) has been an important concern in the Fei-Tsui reservoir for decades since it's the primary water source for Taipei City. Therefore, this study aims to develop a reliable prediction model to predict D.O. in the Fei-Tsui reservoir for better water quality monitoring. The proposed model is an artificial neural network (ANN) with one hidden layer. Twenty-nine years of water quality data have been used to validate the accuracy of the proposed model. A different number of neurons have been investigated to optimize the model's accuracy. Statistical indices have been used to examine the reliability of the model. In addition to that, sensitivity analysis has been carried out to investigate the model's sensitivity to the input parameters. The results revealed the proposed model capable of capturing the dissolved oxygen's nonlinearity with an acceptable level of accuracy where the R-squared value was equal to 0.98. The optimum number of neurons was found to be equal to 15-neuron. Sensitivity analysis shows that the model can predict D.O. where four input parameters have been included as input where the d-factor value was equal to 0.010. This main achievement and finding will significantly impact the water quality status in reservoirs. Having such a simple and accurate model embedded in IoT devices to monitor and predict water quality parameters in real-time would ease the decision-makers and managers to control the pollution risk and support their decisions to improve water quality in reservoirs.

Partial Anomalous Pulmonary Venous Return Presenting in Adults: A Case Series With Review of Literature.

Partial anomalous pulmonary venous return (PAPVR) is a congenital anomaly in which some of the pulmonary veins drain erroneously into the superior vena cava (SVC) or directly into the right atrium (RA). We present four cases of PAPVR presenting in adults. We discussed various presentations, diagnostic approaches and challenges in the management of these patients. Our first case had anomalous drainage from the right upper lobe of lung to SVC and was managed medically with riociguat and ambrisentan. The second patient had an unsuccessful attempt at repair of the anomalous vein. Our other two patients had right upper lobe veins draining into SVC. One of them had a successful surgical repair whereas the other patient declined surgery and is being monitored. In PAPVR patients, the decision for surgical repair depends on symptoms, shunt fraction, recurrent pulmonary infections, and concurrent indication for cardiac surgery.

Suspended sediment load prediction using artificial neural network and ant lion optimization algorithm.

Suspended sediment load (SSL) estimation is a required exercise in water resource management. This article proposes the use of hybrid artificial neural network (ANN) models, for the prediction of SSL, based on previous SSL values. Different input scenarios of daily SSL were used to evaluate the capacity of the ANN-ant lion optimization (ALO), ANN-bat algorithm (BA) and ANN-particle swarm optimization (PSO). The Goorganrood basin in Iran was selected for this study. First, the lagged SSL data were used as the inputs to the models. Next, the rainfall and temperature data were used. Optimization algorithms were used to fine-tune the parameters of the ANN model. Three statistical indexes were used to evaluate the accuracy of the models: the root-mean-square error (RMSE), mean absolute error (MAE) and Nash-Sutcliffe efficiency (NSE). An uncertainty analysis of the predicting models was performed to evaluate the capability of the hybrid ANN models. A comparison of models indicated that the ANN-ALO improved the RMSE accuracy of the ANN-BA and ANN-PSO models by 18% and 26%, respectively. Based on the uncertainty analysis, it can be surmised that the ANN-ALO has an acceptable degree of uncertainty in predicting daily SSL. Generally, the results indicate that the ANN-ALO is applicable for a variety of water resource management operations.

Correction to: Suspended sediment load prediction using artificial neural network and ant lion optimization algorithm.

Following the publication of the article it has come to the authors' attention that the first panel of Fig. 11 has been repeated with the second panel of Fig. 11.

Advanced machine learning model for better prediction accuracy of soil temperature at different depths.

Soil temperature has a vital importance in biological, physical and chemical processes of terrestrial ecosystem and its modeling at different depths is very important for land-atmosphere interactions. The study compares four machine learning techniques, extreme learning machine (ELM), artificial neural networks (ANN), classification and regression trees (CART) and group method of data handling (GMDH) in estimating monthly soil temperatures at four different depths. Various combinations of climatic variables are utilized as input to the developed models. The models' outcomes are also compared with multi-linear regression based on Nash-Sutcliffe efficiency, root mean square error, and coefficient of determination statistics. ELM is found to be generally performs better than the other four alternatives in estimating soil temperatures. A decrease in performance of the models is observed by an increase in soil depth. It is found that soil temperatures at three depths (5, 10 and 50 cm) could be mapped utilizing only air temperature data as input while solar radiation and wind speed information are also required for estimating soil temperature at the depth of 100 cm.

Feedforward Artificial Neural Network-Based Model for Predicting the Removal of Phenolic Compounds from Water by Using Deep Eutectic Solvent-Functionalized CNTs.

In the recent decade, deep eutectic solvents (DESs) have occupied a strategic place in green chemistry research. This paper discusses the application of DESs as functionalization agents for multi-walled carbon nanotubes (CNTs) to produce novel adsorbents for the removal of 2,4-dichlorophenol (2,4-DCP) from aqueous solution. Also, it focuses on the application of the feedforward backpropagation neural network (FBPNN) technique to predict the adsorption capacity of DES-functionalized CNTs. The optimum adsorption conditions that are required for the maximum removal of 2,4-DCP were determined by studying the impact of the operational parameters (i.e., the solution pH, adsorbent dosage, and contact time) on the adsorption capacity of the produced adsorbents. Two kinetic models were applied to describe the adsorption rate and mechanism. Based on the correlation coefficient (R2) value, the adsorption kinetic data were well defined by the pseudo second-order model. The precision and efficiency of the FBPNN model was approved by calculating four statistical indicators, with the smallest value of the mean square error being 5.01 × 10-5. Moreover, further accuracy checking was implemented through the sensitivity study of the experimental parameters. The competence of the model for prediction of 2,4-DCP removal was confirmed with an R2 of 0.99.

Artificial Neural Network Approach for Modelling of Mercury Ions Removal from Water Using Functionalized CNTs with Deep Eutectic Solvent.

Multi-walled carbon nanotubes (CNTs) functionalized with a deep eutectic solvent (DES) were utilized to remove mercury ions from water. An artificial neural network (ANN) technique was used for modelling the functionalized CNTs adsorption capacity. The amount of adsorbent dosage, contact time, mercury ions concentration and pH were varied, and the effect of parameters on the functionalized CNT adsorption capacity is observed. The (NARX) network, (FFNN) network and layer recurrent (LR) neural network were used. The model performance was compared using different indicators, including the root mean square error (RMSE), relative root mean square error (RRMSE), mean absolute percentage error (MAPE), mean square error (MSE), correlation coefficient (R2) and relative error (RE). Three kinetic models were applied to the experimental and predicted data; the pseudo second-order model was the best at describing the data. The maximum RE, R2 and MSE were 9.79%, 0.9701 and 1.15 × 10-3, respectively, for the NARX model; 15.02%, 0.9304 and 2.2 × 10-3 for the LR model; and 16.4%, 0.9313 and 2.27 × 10-3 for the FFNN model. The NARX model accurately predicted the adsorption capacity with better performance than the FFNN and LR models.

Integrated support vector regression and an improved particle swarm optimization-based model for solar radiation prediction.

Solar energy is a major type of renewable energy, and its estimation is important for decision-makers. This study introduces a new prediction model for solar radiation based on support vector regression (SVR) and the improved particle swarm optimization (IPSO) algorithm. The new version of algorithm attempts to enhance the global search ability for the PSO. In practice, the SVR method has a few parameters that should be determined through a trial-and-error procedure while developing the prediction model. This procedure usually leads to non-optimal choices for these parameters and, hence, poor prediction accuracy. Therefore, there is a need to integrate the SVR model with an optimization algorithm to achieve optimal choices for these parameters. Thus, the IPSO algorithm, as an optimizer is integrated with SVR to obtain optimal values for the SVR parameters. To examine the proposed model, two solar radiation stations, Adana, Antakya and Konya, in Turkey, are considered for this study. In addition, different models have been tested for this prediction, namely, the M5 tree model (M5T), genetic programming (GP), SVR integrated with four different optimization algorithms SVR-PSO, SVR-IPSO, Genetic Algorithm (SVR-GA), FireFly Algorithm (SVR-FFA) and the multivariate adaptive regression (MARS) model. The sensitivity analysis is performed to achieve the highest accuracy level of the prediction by choosing different input parameters. Several performance measuring indices have been considered to examine the efficiency of all the prediction methods. The results show that SVR-IPSO outperformed M5T and MARS.

Multiple myocardial abscesses secondary to late stent infection.

A 53-year-old woman presented to our hospital with dizziness and low-grade fever. She underwent percutaneous coronary intervention to the obtuse marginal artery with a drug-eluting stent 20 months prior to this presentation. Physical examination was remarkable for bradycardia. Electrocardiogram showed a junctional rhythm with heart rate of 35 bpm. Blood and urine cultures were negative. Despite successful urgent pacemaker placement, she had cardiac arrest the following day with unsuccessful cardiopulmonary resuscitation attempt. Cardiac autopsy report revealed multiple abscesses involving the obtuse marginal and left anterior descending arteries as well as the adjacent myocardial regions.

Identification and Management of Pump Thrombus in the HeartWare Left Ventricular Assist Device System: A Novel Approach Using Log File Analysis.

OBJECTIVES: The study sought to characterize patterns in the HeartWare (HeartWare Inc., Framingham, Massachusetts) ventricular assist device (HVAD) log files associated with successful medical treatment of device thrombosis. BACKGROUND: Device thrombosis is a serious adverse event for mechanical circulatory support devices and is often preceded by increased power consumption. Log files of the pump power are easily accessible on the bedside monitor of HVAD patients and may allow early diagnosis of device thrombosis. Furthermore, analysis of the log files may be able to predict the success rate of thrombolysis or the need for pump exchange. METHODS: The log files of 15 ADVANCE trial patients (algorithm derivation cohort) with 16 pump thrombus events treated with tissue plasminogen activator (tPA) were assessed for changes in the absolute and rate of increase in power consumption. Successful thrombolysis was defined as a clinical resolution of pump thrombus including normalization of power consumption and improvement in biochemical markers of hemolysis. Significant differences in log file patterns between successful and unsuccessful thrombolysis treatments were verified in 43 patients with 53 pump thrombus events implanted outside of clinical trials (validation cohort). RESULTS: The overall success rate of tPA therapy was 57%. Successful treatments had significantly lower measures of percent of expected power (130.9% vs. 196.1%, p = 0.016) and rate of increase in power (0.61 vs. 2.87, p < 0.0001). Medical therapy was successful in 77.7% of the algorithm development cohort and 81.3% of the validation cohort when the rate of power increase and percent of expected power values were <1.25% and 200%, respectively. CONCLUSIONS: Log file parameters can potentially predict the likelihood of successful tPA treatments and if validated prospectively, could substantially alter the approach to thrombus management.