European Journal of Molecular & Clinical Medicine

Purpose: The purpose of our study was to compare EEG changes of AUSTISM patients with normally developing children, by comparing alpha wave frequency in different EEG electrodes.
Methods: The present study was conducted at SPMCHI, J. K. Lone Hospital attached to S. M. S. Medical College, Jaipur after obtaining the desired clearance from Institutional Research Review Board (IRRB) and Ethics Committee of the Institution. An informed written consent and assent was taken from every subject’s parent before commencing any procedure. The study population consisted of 25 Autistic Spectrum Disorder cases of below 18 years of age. An equal number of healthy control subjects were recruited as controls.
Results: The mean value of alpha wave frequency in F3 electrode was 10.77±2.94 in cases while in the control group it was 9.62±1.23. Although the p value was not significant here (0.078). Mean value of alpha wave for P3 electrode. Here for cases, mean value it was 12.42±5.73while for controls it was 11.49±1.57. Again, P value was not significant here (0.439. Comparison of alpha wave profile among cases showed that cases with severe ASD have significant difference from cases with mild &moderate autism. The mean value of alpha wave relative power in F8 electrode in mild cases was 11.28±3.29, in moderate cases was 8.01±2.21 & in severe cases was 7.52±1.92 and here the comparison among them shows that differences are significant (P=0.014). Similarly, we found statistically significant difference in F7 electrode. Although there was no difference in rest of frontal region electrodes and in any of parietal &temporal region electrode.
Conclusion: We conclude that currently EEG cannot be used as a digital biomarker for early diagnosis of ASD till further larger studies

Background:In adults, a secundum form atrial septal defect (ASD) is a popular form of
congenital heart defects.An elevation in cardiopulmonary exercise potential was discovered six to
twelve months after the ASD was closed.BNP (B-type natriuretic peptide) is a prohormone
produced by the myocardium and a rise in ventricular wall tension is the trigger for its release.
Both markers have been shown to be extremely susceptible for myocardial stress in a variety of
cardiovascular disorders in many studies.Aim of work:To identify the predictors of left
ventricular outcome after percutaneous closure of atrial septal defect in adults. Subjects and
methods:This study is a clinical trial (pre-post single interventional study) carried out in the
cardiology department at Zagazig university hospital. The study included 80 cases admitted
during one year. All patients were subjected to complete history taking. General examination for
all body systems and local cardiological examination were done. 12 lead ECG, transesophageal
and transthoracic echocardiography were done. Blood samples were drawn after a fasting period
of 12 hours then we measured BNP in a blood sample before, immediately, after and 3-6 months
after procedure. Trans catheter-closure of ASD, with full haemodynamic study was also
done.Results:there was asignificant decrease in the area of the RA and the RV with increased
LVFP with LV wall thickening, impaired LV diastolic function. And atunivariate and
multivariate analysis the results showed that age, ASD size, RV area, RA area, Peak TRPG,
mitral E/e’, TAPSE appeared to be independent predictors of left ventricular
dysfunction.Conclusion:percutaneous closure of an ASD leads to immediate and sustained
changes in cardiac anatomy and functions involving both sides of the heart. The NT-pro BNP
level begins to increase within 24 hours and continue for 30 days after the procedure in relation
to an increase in LV dimension and volume. increased LVFP with LV wall thickening, impaired
LV diastolic function.LVFP, however, decreased to the normal range by 6 months after closure
in most patients. These findings suggest that after transcatheter closure of an ASD with the
removal of a left-to-right shunt, the LV may be subjected to hemodynamic stress, depending on
volume overload

Autism spectrum disorder (ASD) is a psychiatric disorder which leads to
neurological anddevelopmental growth of a person which starts in early age and gets
carried throughout their life.It is a condition associated with significant healthcare costs
and early diagnosis can reduce these.Unfortunately, waiting time is lengthy for an ASD
diagnosis and it is cost effective. Due to theincrease in economy for autism prediction and
the increase in the number of ASD cases across theworld is in need of easily implemented
and effective screening methods by GUI results. Toovercome the time complexity for
identifying the disorder advanced technologies can be used suchas machine learning
algorithms to improve precision, accuracy and quality of the diagnosisprocess. Machine
learning helps us by providing intelligent techniques to discover the affectedpatient, which
can be utilized in prediction and to improve decision making. And hence, wepropose the
data set features related to autism screening of adult and child to be used for
furtheranalysis and to improve the classification of ASD cases.