Your browser doesn't support javascript.
loading
Show: 20 | 50 | 100
Results 1 - 4 de 4
Filter
1.
J Diabetes Res ; 2023: 9931010, 2023.
Article in English | MEDLINE | ID: mdl-37794995

ABSTRACT

Aim: Analyse the diabetes mellitus (DM) of a person through the facial skin region using vision diabetology. Diabetes mellitus is caused by persistent high blood glucose levels and related complications, which show variation in facial skin regions due to reduced blood flow in the facial arteries. Materials and Method. In this study, 200 facial images of diabetes patients with skin conditions such as Bell's palsy, rubeosis faciei, scleroderma, and vitiligo were collected from existing face videos. Moreover, face images are collected from diabetic persons in India. Viola Jones' face-detecting algorithm extracts face skin regions from a diabetic person's face image in video frames. The affected skin area on the diabetic person's face is detected using HSV colour model segmentation. The proposed multiwavelet transform convolutional neural network (MWTCNN) extracts the features for diabetic measurement from up- and downfacial scaled images of diabetic persons. Results: The existing deep learning models are compared with the proposed MWTCNN model, which provides the highest accuracy of 98.3%. Conclusion: The facial skin region-based diabetic measurement avoids pricking of the serum and is used for continuous glucose monitoring.


Subject(s)
Diabetes Mellitus, Type 2 , Humans , Diabetes Mellitus, Type 2/complications , Diabetes Mellitus, Type 2/diagnosis , Blood Glucose Self-Monitoring , Blood Glucose , Skin , Face
2.
J Med Syst ; 43(7): 215, 2019 Jun 03.
Article in English | MEDLINE | ID: mdl-31161372

ABSTRACT

In this paper, early detection of schizophrenia types such as hallucination and delusion propose through the high Q-factor of RADWT in EEG signal acquired during the cognitive task of the patient. The earlier diagnose obtains from the energy distribution of the EEG signal in the high resolution via optimum tuning in dilation factor,which influences the Q-factor, redundancy and ringing in the EEG signal. The early detection of type of schizophrenia prevents the illness progression and lifelong disease. In existing clinical trial, the psych clinician diagnose only the schizophrenia disease through the standard DSM screening question and Prodromal signs checklist according to the standard of American Psychiatric Association. Furthermore, clinician tries to diagnose the disease through brain imaging and EEG signal. However, procedure in the diagnosis of Schizophrenia possible only in the acute stage, minimum after 2 years of illness progression and still sub classification of the type of schizophrenia is a challenging task. In the proposed system, we acquire EEG signal during the three conditions such as reverse counting of the number, screening questions (DSM), and eye rest state with a distance of 1-m part of the clinician and patient to analyse cognitive behaviour. From the result of 25 patients EEG, signal during cognitive task show the different sub band energy pattern in RADWT to distinguish hallucination and delusion patient exactly for 21 patients and provide 84% of accuracy in sub-classification of type schizophrenia.


Subject(s)
Delusions/diagnosis , Hallucinations/diagnosis , Schizophrenia , Thyroid Gland/diagnostic imaging , Thyroid Gland/physiopathology , Ultrasonography/methods , Algorithms , Humans , Pattern Recognition, Automated
3.
Microbes Infect ; 11(1): 2-11, 2009 Jan.
Article in English | MEDLINE | ID: mdl-18983930

ABSTRACT

Most pandemic influenza virus strains undergo adaptation or reassortment before they acquire the ability to cause fatal infections in a new host species. The pathologic changes and tissue tropism during virus adaptation are not fully understood. Here we investigated pathologic changes and tissue tropism by serial lung-to-lung passaging of human influenza virus strain A/Aichi/2/68 (H3N2) in a BALB/c mouse model. Enhanced pulmonary lesions and systemic virus infection were observed during adaptation. Late passage 10 (P10) virus caused extra-pulmonary spread with necrotic and inflammatory lesions in the brain, heart, spleen and intestine of infected animals, in contrast to infection with earlier passage viruses which were restricted to lungs. Non-conservative mutations in the hemagglutinin (Gly218Glu) and non-structural 1 (Asp125Gly) proteins were identified in P10 virus which exhibited high virulence. Virus growth kinetics showed enhanced replication ability of P10 virus in different cell lines. P10 virus also exhibited the ability to bind to erythrocytes of different host species. These results demonstrate extra-pulmonary spread of influenza virus during adaptation with enhanced replication ability in a new host. This mouse adaptation model may provide a basis for understanding cross-species adaptability corresponding to increased virulence of the influenza A virus, a phenomenon of relevance to the emergence of future highly pathogenic strains.


Subject(s)
Adaptation, Physiological , Disease Models, Animal , Host-Pathogen Interactions , Influenza A Virus, H3N2 Subtype , Influenza, Human , Pneumonia , Adaptation, Physiological/genetics , Amino Acid Sequence , Animals , CHO Cells , Cricetinae , Cricetulus , Dogs , Female , Hemagglutinin Glycoproteins, Influenza Virus/chemistry , Hemagglutinin Glycoproteins, Influenza Virus/genetics , Humans , Influenza A Virus, H3N2 Subtype/genetics , Influenza A Virus, H3N2 Subtype/pathogenicity , Influenza A Virus, H3N2 Subtype/physiology , Influenza, Human/pathology , Influenza, Human/virology , Lung/pathology , Lung/virology , Mice , Mice, Inbred BALB C , Molecular Sequence Data , Mutation , Organ Specificity , Pneumonia/pathology , Pneumonia/virology , Viral Nonstructural Proteins/chemistry , Viral Nonstructural Proteins/genetics , Virulence
SELECTION OF CITATIONS
SEARCH DETAIL
...