Ensemble classifier fostered detection of arrhythmia using ECG data.

Electrocardiogram (ECG) is a non-invasive medical tool that divulges the rhythm and function of the human heart. This is broadly employed in heart disease detection including arrhythmia. Arrhythmia is a general term for abnormal heart rhythms that can be identified and classified into many categories. Automatic ECG analysis is provided by arrhythmia categorization in cardiac patient monitoring systems. It aids cardiologists to diagnose the ECG signal. In this work, an Ensemble classifier is proposed for accurate arrhythmia detection using ECG Signal. Input data are taken from the MIT-BIH arrhythmia dataset. Then the input data was pre-processed using Python in Jupyter Notebook which run the code in an isolated manner and was able to keep code, formula, comments, and images. Then, Residual Exemplars Local Binary Pattern is applied for extracting statistical features. The extracted features are given to ensemble classifiers, like Support vector machines (SVM), Naive Bayes (NB), and random forest (RF) for classifying the arrhythmia as normal (N), supraventricular ectopic beat (S), ventricular ectopic beat (V), fusion beat (F), and unknown beat (Q). The proposed AD-Ensemble SVM-NB-RF method is implemented in Python. The proposed AD-Ensemble SVM-NB-RF method is 44.57%, 52.41%, and 29.49% higher accuracy; 2.01%, 3.33%, and 3.19% higher area under the curve (AUC); and 21.52%, 23.05%, and 12.68% better F-Measure compared with existing models, like multi-model depending on the ensemble of deep learning for ECG heartbeats arrhythmia categorization (AD-Ensemble CNN-LSTM-RRHOS), ECG signal categorization utilizing VGGNet: a neural network based classification method (AD-Ensemble CNN-LSTM) and higher performance arrhythmic heartbeat categorization utilizing ensemble learning along PSD based feature extraction method (AD-Ensemble MLP-NB-RF).

The successful use of botulinum toxin for the treatment of nasal hyperhidrosis.

BACKGROUND: Patients with facial hyperhidrosis often experience occupational, social, and psychological problems. Botulinum toxin type A, which has been shown to be useful in the treatment of hyperhidrosis, can cause the paralysis of facial muscles if used in the skin overlying the facial muscles. It can, however, be used in the skin of the nose without significantly affecting the muscles of facial expression. METHODS: Two patients with severe nasal hyperhidrosis were treated with botulinum toxin. RESULTS: Two patients with severe nasal hyperhidrosis responded to a single session of treatment with intradermal injections of botulinum toxin. CONCLUSIONS: This treatment method for nasal hyperhidrosis is simple, well tolerated, and effective, and significantly improves the quality of life of the affected individuals.

Ceric ammonium nitrate on silica gel for efficient and selective removal of trityl and silyl groups

Silica gel-supported ceric ammonium nitrate (CAN-SiO2) was found effective for rapid and selective cleavage of trityl (Tr), monomethoxytrityl (MMTr), and dimethoxytrityl (DMTr) groups from protected nucleosides and nucleotides under mild conditions. Efficiency of deprotections depended upon the stability of the resultant carbocationic species: DMTr+ > MMTr+ > Tr+. Use of a catalytic amount of this solid-supported reagent can also efficiently and selectively remove the tert-butyldimethylsilyl or the triisopropylsilyl group from a primary hydroxyl functionality in di- or trisilyl ethers of ribonucleosides. A comparative study of deprotection reactions by utilization of CAN alone or CAN-SiO2 indicates a remarkable increase in the rate of the reactions involving a solid support. The mechanism of electron-transfer processes is proposed for the use of CAN-SiO2 in the removal of these protective groups from organic molecules.