The ongoing progress in deep learning contributes to handle coronavirus infection and plays an effective role to develop appropriate solutions. It is expected that this paper would be a great help for the researchers who would like to contribute to the development of remedies for this current pandemic in this area.The SMS phishing is another method where the phisher operates the SMS as a medium to communicate with the victims and this method is identified as smishing (SMS + phishing). Researchers promoted several anti-phishing methods where the correlation algorithm is applied to explore the relevancy of the features since there are numerous features in the features corpus. The correlation algorithm assesses the rank of the features that is the highest rank leads to the more relevant to the appropriate assignment. Therefore, this paper analyses four rank correlation algorithms particularly Pearson rank correlation, Spearman’s rank correlation, Kendall rank correlation, and Point biserial rank correlation with a machine-learning algorithm to determine the best features set for detecting Smishing messages. The result of the investigation reveals that the AdaBoost classifier offered better accuracy. Further analysis shows that the classifier with the ranking algorithm that is Kendall rank correlation appeared superior accuracy than the other correlation algorithms. The inferred of this experiment confirms that the ranking algorithm was able to reduce the dimension of features with 61.53% and presented an accuracy of 98.40%.Pneumonia, an acute respiratory infection, causes serious breathing hindrance by damaging lung/s. Recovery of pneumonia patients depends on the early diagnosis of the disease and proper treatment. This paper proposes an ensemble method-based pneumonia diagnosis from Chest X-ray images. The deep Convolutional Neural Networks (CNNs)-CheXNet and VGG-19 are trained and used to extract features from given X-ray images. These features are then ensembled for classification. To overcome data irregularity problem, Random Under Sampler (RUS), Random Over Sampler (ROS) and Synthetic Minority Oversampling Technique (SMOTE) are applied on the ensembled feature vector. The ensembled feature vector is then classified using several Machine Learning (ML) classification techniques (Random Forest, Adaptive Boosting, K-Nearest Neighbors). Among these methods, Random Forest got better performance metrics than others on the available standard dataset. Comparison with existing methods shows that the proposed method attains improved classification accuracy, AUC values and outperforms all other models providing 98.93% accurate prediction. The model also exhibits potential generalization capacity when tested on different dataset. Outcomes of this study can be great to use for pneumonia diagnosis from chest X-ray images.Heteroatom-doped porous carbon membranes (HPCMMs) with a tailor-made pore architecture, chemical composition, atomic structural order, and surface state represent an exciting family of porous carbon materials for diverse potential applications in catalysis, water treatment, biofiltration, energy conversion/storage, and so forth. Conventional porous carbon membranes possess intrinsic structural integrity, interconnectivity, and chemical purity across the atomic-to-macro world and have been popularly incorporated into devices as separators or chemically inert conductive supports, circumventing otherwise the inevitable complicated processing and structure weakness of their fine powderous counterpart. Motivated by the distinguished heteroatom-doping effect that revolutionizes the chemical and physical nature of carbon materials, the HPCMM research surges very recently, and focuses not only on the eminent conductive supports or separators but also on electro(co)catalysts in energy devices. Synergy of the porous naings of HPCMMs, particularly the advancements in how to tailor structures and properties of HPCMMs by rational structure design of porous polymer membranes as sacrificial template built up especially from heteroatom-rich poly(ionic liquid)s (PILs). We will also stress the carbonization craft and the state-of-the-art electrochemical applications for HPCMMs. Key factors and thoughts in heteroatom doping and porous systems in HPCMMs are discussed. A future perspective of the challenges and promising potential of HPCMMs is cast on the basis of these achievements.

Nonsteroidal anti-inflammatory drugs (NSAIDS) are increasingly important alternatives to opioids for analgesia during hospitalization as health systems implement opioid-minimization initiatives. Increasing NSAID use may increase AKI rates, particularly in patients with predisposing risk factors. Inconclusive data in outpatient populations suggests that NSAID nephrotoxicity is magnified by renin-angiotensin system inhibitors (RAS-I). No studies have tested this in hospitalized patients.

Retrospective, active-comparator cohort study of patients admitted to four hospitals in Philadelphia, Pennsylvania. To minimize confounding by indication, NSAIDs were compared to oxycodone, and RAS-I were compared to amlodipine. We tested synergistic NSAID+RAS-I nephrotoxicity by comparing the difference in AKI rate between NSAID versus oxycodone in patients treated with RAS-I to the difference in AKI rate between NSAID versus oxycodone in patients treated with amlodipine. In a secondary analysis, we restricted the cohort t choose opioids in lieu of NSAIDs in this population. find more Synergistic nephrotoxicity cannot be ruled out in patients treated with diuretics.

Synergistic nephrotoxicity was not observed with short-term NSAID+RAS-I treatment in the absence of concomitant diuretics, suggesting that RAS-I treatment may not be a reason to choose opioids in lieu of NSAIDs in this population. Synergistic nephrotoxicity cannot be ruled out in patients treated with diuretics.

Prolonged use of dexmedetomidine has become increasingly common due to its favorable sedative and anxiolytic properties. Hypersympathetic withdrawal symptoms have been reported with abrupt discontinuation of prolonged dexmedetomidine infusions. Clonidine has been used to transition patients off dexmedetomidine infusions for ICU sedation. The objective of this study was to compare the occurrence of dexmedetomidine withdrawal symptoms in ICU patients transitioning to a clonidine taper versus those weaned off dexmedetomidine alone after prolonged dexmedetomidine infusion.

This was a single-center, prospective, double cohort observational study conducted from November 2017 to December 2018.

Medical-surgical, cardiothoracic, and neurosurgical ICUs in a tertiary care hospital.

We included adult ICU patients being weaned off dexmedetomidine after receiving continuous infusions for at least 3 days.

Patients were either weaned off dexmedetomidine alone or with a clonidine taper at the discretion of the providers.