Alzheimer’s And Parkinson’s Disease Classification Using Deep Learning Based On MRI: A Review

Neurodegenerative disorders present a current challenge for accurate diagnosis and for providing precise prognostic information. Alzheimer’s disease (AD) and Parkinson's disease (PD), may take several years to obtain a definitive diagnosis. Due to the increased aging population in developed countries, neurodegenerative diseases such as AD and PD have become more prevalent and thus new technologies and more accurate tests are needed to improve and accelerate the diagnostic procedure in the early stages of these diseases. Deep learning has shown significant promise in computer-assisted AD and PD diagnosis based on MRI with the widespread use of artificial intelligence in the medical domain. This article analyses and evaluates the effectiveness of existing Deep learning (DL)-based approaches to identify neurological illnesses using MRI data obtained using various modalities, including functional and structural MRI. Several current research issues are identified toward the conclusion, along with several potential future study directions

Effect of hydrochloric acid treated neetle fibre on oil absorbency

Hydrochloric acid (HCl) treatment on nettle fibres has been performed to improve the oil absorbency. Box-Behnken experimental design is used to study the influence of parameters, such as treatment time, treatment temperature and concentration on oil absorbency. It is observed that the maximum oil absorbency of 15.39 g/g of nettle fibres is achieved at 2 % of HCl concentration, 75°C of treatment temperature and 60 min of treatment time. Scanning electron microscopic study reveals that the raw nettle fibre surface exhibits waxy and protruding parts, and on HCl treatment the surface becomes rougher. The influence of other parameters such as stirring speed, environmental temperature and reusability on oil absorbency is also studied. Oil sorbed nettle fibres are also subjected to soil burial tests and burning tests. In conclusion, the raw nettle fibres only show an oil absorbency of 9.25 (g/g), whereas HCl treated nettle fibres show a maximum oil absorbency of 15.39 g/g.

Quantitative differences in intestinal Faecalibacterium prausnitzii in obese Indian children

Gut bacteria contribute to energy conservation in man through their ability to ferment unabsorbed carbohydrate. The present study examined the composition of predominant faecal microbiota in obese and non-obese children. The participants (n 28) aged 11-14 years provided fresh faecal samples and completed a dietary survey consisting of 24 h diet recall and a FFQ of commonly used foods taken over the previous 3 months. Faecal bacteria were quantitated by real-time PCR using primers targeted at 16S rDNA. Of the participants, fifteen (seven female) were obese, with median BMI-for-age at the 99th percentile (range 97 to<99) while thirteen participants (seven female) were normal weight, with median BMI-for age being at the 50th percentile (range 1-85). Consumption of energy, carbohydrates, fat and protein was not significantly different between the obese and non-obese participants. There was no significant difference between the two groups in faecal levels of Bacteroides-Prevotella, Bifidobacterium species, Lactobacillus acidophilus group or Eubacterium rectale. Levels of Faecalibacterium prausnitzii were significantly higher in obese children than in non-obese participants (P = 0.0253). We concluded that the finding of increased numbers of F. prausnitzii in the faeces of obese children in south India adds to the growing information on alterations in faecal microbiota in obesity

Recurrence Quantification Analysis of EEG signals for Children with ASD

438-448The present study aims at identifying the brain response for auditory/visual stimuli in typically developing (TD) and children with autism through electroencephalography (EEG). Early diagnoses do help in customized training and progressing the children in regular stream. To reveal the underlying brain dynamics, non-linear analysis was employed. In the current study, Recurrent Quantification Analysis (RQA) with varying parameters was analyzed. For better information retrieval, cosine distance metric is additionally considered for analysis and compared with other distance metrics in RQA. Each computational combination of RQA is measured and the responding channels were analyzed and discussed. It was observed that the FAN neighborhood with cosine distance parameters was able to discriminate between ASD and TD prominently