During a 25-year period (1972-1996), 585 patients with esophageal atresia with or without tracheoesophageal fistula were treated at the Department of Pediatric Surgery, SMS Medical College, Jaipur, India. Increasing awareness of the anomaly has led to early detection and referral with fewer pulmonary complications. For purposes of analysis the period has been divided into five phases, with a steady decline in overall mortality observed from 95.4% in phase 1 to 41% in phase V. Although postoperative complications have also shown a declining trend, delay in diagnosis, prematurity, low birth weight, delayed arrival at the surgical centers, sepsis, pulmonary complications including pneumonitis, and inadequate nursing care all continue to contribute substantially to lower the survival in developing countries such as ours.

A feature extraction method for epilepsy prediction and localization of the brain region responsible for the seizure is proposed in this paper. The epileptic seizure can be predicted in advance by using power analysis in beta band of the EEG signals. The brain waves are divided into five different frequency bands (alpha, beta, delta, gamma and theta). The changes in the pre-ictal period are essential for seizure to start. Increase in power of the beta band of EEG signals in pre-ictal period of epileptic patient can be the indication of the occurrence of the seizure. In this work, the topographical map of the brain region divided into five different zones and the power in beta band is calculated from these five different zones. So, the particular region of the brain in which the highest change in power or the region which is responsible for the epileptic seizure is also localized.

INTRODUCTIONOne third of outpatient"s visits to the gynaecologists are for abnormal uterine bleeding (AUB), and it accounts for more than 70% of all gynaecologic consults in the perimenopausal and postmenopausal years. The International Federation of Gynaecology and Obstetrics (FIGO) has proposed a classification system for 9 major causes of AUB (PALMCOEIN).2 and the likely cause of abnormal uterine bleeding is usually related to age. Abnormal bleeding caused by hormone imbalance is more common in teenagers or in women who are approaching menopause. Obesity, PCOS, anorexia or crash diets, stress and extreme exercise account can all disrupt normal ovulatory function and may be considered as preventable risk factors for AUB.3 There has been a move to describe AUB in terms of its impact on a woman"s physical, emotional, social and material quality of life (QOL).Though few studies have been done to assess the burden of AUB on society however, the knowledge of women regarding various aspects of AUB and its association with their socio demographic status had never been assessed. Hence, the study was undertaken to assess the knowledge and attitude of women with AUB in relation to their education level and socio economic status. METHODSThis was a cross sectional descriptive study carried out among women with AUB. Two hundred women with ABSTRACT Background: Abnormal uterine bleeding (AUB) is a common but overlooked condition. The lack of information and poor attitude towards health is often the reason. The knowledge of women regarding AUB and the preventable factors and its association with their education and socioeconomic status has never been assessed. Hence, the study was undertaken to assess the knowledge and attitude of women with AUB regarding the condition, its etiology, risk factors and treatment modalities in relation to their education and socio economic status. Methods:The cross sectional descriptive study was carried out among women with AUB. Structured interviewer administered questionnaire was used to collect the required information. Knowledge of AUB and its risk factors from women with AUB were given scores 1-8 and 1-6 respectively. Information on education and socioeconomic status was also noted. Statistical analysis of data was done. Results: Knowledge score for AUB ≤4 and its risk factors ≤3 was found in 63% and 72% of respondents respectively. Poor reading ability and low socio economic status was significantly associated with poor knowledge scores. Conclusions:Intensive enlightenment of population using the mass media by trained personnel and strengthening of primary health care services is recommended.

Background: To evaluate quality of life of females after mastectomy and factors affecting the same, in various domains of life, and to assess whether a policy of mastectomy is practical or pragmatic in the scenario of low socioeconomic status prevalent in our region.Methods: Two hundred and forty post-operative mastectomized patients were clinically examined and subjected to a questionnaire designed by WHOQOL-BREF along with an ethically cleared questionnaire prepared according to the local prevailing conditions and Quality of Life was evaluated.Results: 52% patients reported no change in body image, only 2% patients reported depression. The total score of the quality of life was good for 40% of the patients with score of 96-130, followed by 55% with moderate 61-95 and only 5% patients with poor with score of <60.Conclusions: In developing countries like India where, there are no proper facilities for advanced haematological and radiological investigations, there is a severe lack of compliance between doctor and patient and there are no facilities for adjuvant and neo-adjuvant treatment, surgeons are forced to choose mastectomy as the surgery of choice for malignant breast lesions.

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Pancytopenia as a manifested complication of plasmodium vivax malaria is extremely rare and mainly reported with plasmodium falciparum. We report a 29-year old Indian pregnant female who lives in rural area of Haryana (northern India) and presented with a two-week long history of intermittent fever, chills and rigor. She was found to have spleenomegaly, pancytopenia, hyperferrtinemia with positive peripheral blood smear for plasmodium vivax. not responding to oral chloroquine. The patient had a full recovery from pancytopenia with artesunate therapy.

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Electric activity of brain gets disturbed prior to epileptic seizure onset. Early prediction of an upcoming seizure can help to increase effectiveness of antiepileptic drugs. The scalp electroencephalogram signals contain information about the dynamics of brain and have been used to predict an upcoming seizure and localise its zone. The objective of this paper is to localise the epileptogenic region and predict an upcoming seizure at the earliest. To localise epileptogenic region, Electroencephalogram signals are categorised into four regions of brain (Frontal, Temporal, Parietal and Central). For each signal seventy-two (72) parameters in frequency domain have been extracted by using ten minute non overlapping window. Four prominent ratio parameters, γ1/γ5, γ3/γ1, θ/γ2 and γ4/θ have been identified as best parameters based on relative fisher score. Zone 2 shows the highest change in all the parameters as compared to the other zones. So, temporal region is identified as the epileptogenic region in this work. For prediction of the epileptic seizure machine learning algorithm artificial neural network (ANN) is proposed. The proposed machine learning algorithm has an accuracy of 92.3%, sensitivity of 100% and specificity of 83.3%.