SHMAD: A Smart Health Care System to Monitor Alzheimer's Disease Patients.

BACKGROUND: In the digital era monitoring the patient's health status is more effective and consistent with smart healthcare systems. Smart health care facilitates secure and reliable maintenance of patient data. Sensors, machine learning algorithms, Internet of things, and wireless technology has led to the development of Artificial Intelligence-driven Internet of Things models. OBJECTIVE: This research study proposes an Artificial Intelligence driven Internet of Things model to monitor Alzheimer's disease patient condition. The proposed Smart health care system to monitor and alert caregivers of Alzheimer's disease patients includes different modules to monitor the health parameters of the patients. This study implements the detection of fall episodes using an artificial intelligence model in Python. METHODS: The fall detection model is implemented with data acquired from the IMU open dataset. The ensemble machine learning algorithm AdaBoost performs classification of the fall episode and daily life activity using the feature set of each data sample. The common machine learning classification algorithms are compared for their performance on the IMU fall dataset. RESULTS: AdaBoost ensemble classifier exhibits high performance compared to the other machine learning algorithms. The AdaBoost classifier shows 100% accuracy for the IMU dataset. This high accuracy is achieved as multiple weak learners in the ensemble model classify the data samples in the test data accurately. CONCLUSIONS: This study proposes a smart healthcare system for monitoring Alzheimer's disease patients. The proposed model can alert the caregiver in case of fall detection via mobile applications installed in smart devices.

Artificial Intelligence Model for Alzheimer's Disease Detection with Convolution Neural Network for Magnetic Resonance Images.

BACKGROUND: Alzheimer's disease (AD) is a neurodegenerative disease that drastically affects brain cells. Early detection of this disease can reduce the brain cell damage rate and improve the prognosis of the patient to a great extent. The patients affected with AD tend to depend on their children and relatives for their daily chores. OBJECTIVE: This research study utilizes the latest technologies of artificial intelligence and computation power to aid the medical industry. The study aims at early detection of AD to enable doctors to treat patients with the appropriate medication in the early stages of the disease condition. METHODS: In this research study, convolutional neural networks, an advanced deep learning technique, are adopted to classify AD patients with their MRI images. Deep learning models with customized architecture are precise in the early detection of diseases with images retrieved by neuroimaging techniques. RESULTS: The convolution neural network model classifies the patients as diagnosed with AD or cognitively normal. Standard metrics evaluate the model performance to compare with the state-of-the-art methodologies. The experimental study of the proposed model shows promising results with an accuracy of 97%, precision of 94%, recall rate of 94%, and f1-score of 94%. CONCLUSION: This study leverages powerful technologies like deep learning to aid medical practitioners in diagnosing AD. It is crucial to detect AD early to control and slow down the rate at which the disease progresses.

Second-derivative synchronous spectrofluorimetric determination of nebivolol hydrochloride and amlodipine besylate in their combined dosage form.

A rapid, simple, accurate and highly sensitive spectrofluorimetric method was developed for the simultaneous analysis of nebivolol hydrochloride (NEB) and amlodipine besylate (AML). The method was based on measuring the synchronous fluorescence intensity of the drugs at Δλ = 40 nm in methanol. Various experimental parameters affecting the synchronous fluorescence of the studied drugs were carefully studied and optimized. The calibration plots were rectilinear over concentration ranges of 0.05-1.5 µg/mL and 0.5-10 µg/mL for NEB and AML with limits of detection (LOD) of 0.010 and 0.051 µg/mL and limits of quantitation (LOQ) of 0.031 and 0.156, respectively. The peak amplitudes ((2) D) of the second derivative synchronous fluorimetry (SDSF) were estimated at 282 nm for NEB and at 393 nm for AML. Good linearity was obtained over the concentration ranges. The proposed method was successfully applied to the determination of the studied compounds in laboratory-prepared mixtures, commercial single and laboratory-prepared tablets. The results were in good agreement with those obtained using the comparison method. The mean percent recoveries were found to be 100.12 ± 0.77 and 99.91 ± 0.77 for NEB and AML, respectively.

The reaction of N-nitroso compounds with 2,4-bis (4-methoxyphenyl)-1,3,2,4-dithiadiphosphetane-2,4-disulfide. New aspects of the destruction of the N-nitroso function.

In connection with work on 2,4-bis(4-methoxyphenyl)-1,3,2,4-dithiadiphosphetane-2,4-disulfide (I), used for thiation of carbonyl compounds, different kinds of compounds with semipolar bondings have been reacted with I, especially C- and N-nitroso compounds. C-Nitroso compounds give azoxy and azo compounds. N-Nitroso compounds give new phosphorus ring systems, depending on the starting material, and the N-nitroso function is destroyed. An elimination of HNO has been observed where there was resonance stabilization in the imine formed.

Extraction of protactinium from mineral acid-alcohol media.

The extraction of protactinium with organic solvents has been investigated in the presence of water-miscible alcohols and acetone. These additives were found to increase considerably the extraction of protactinium in the cases of trilaurylamine, tributyl phosphate and isobutyl methyl ketone. The influence was less in the case of thenoyltrifluoroacetone. In mixtures of an acid with various alcohols, the influence depended on the alcohol concentration, the acidity and on the chain lengths and dielectric constants of the alcohol introduced into the extraction system.

Neutron activation analysis by standard addition and solvent extraction Determination of impurities in thorium and iron.

The neutron activation analysis of trace impurities in thorium nitrate and in iron is facilitated by solvent extraction procedures prior to counting.

Neutron-activation analysis by standard addition and solvent extraction Determination of traces of antimony.

The application of neutron activation analysis by standard addition and solvent extraction to the determination of traces of antimony in aluminium and rocks is reported. Three simple extraction procedures, using isopropyl ether, hexone, and tributyl phosphate, are described for the selective separation of radioantimony from interfering radionuclides. Antimony concentration is measured by counting the activities of the (122)Sb and (124)Sb photopeaks at 0.564 and 0.603 MeV.