Background: Amniotic fluid provides a protective milieu for the growing fetus in pregnancy and labour. A decrease in the amniotic fluid volume has been associated with increased maternal morbidity and fetal morbidity and mortality. The purpose was to compare the effect of labour induction on the fetomaternal outcome in women with oligohydramnios, borderline liquor and normal liquor at term.Methods: A retrospective study of all the labour induction in women with oligohydramnios, borderline liquor and normal liquor volume at 37-42 weeks gestation in a tertiary care teaching hospital. The demographic characteristics, maternal outcomes like mode of delivery, indication for operative delivery, meconium stained liquor and perinatal outcomes were compared in between the three groups. Parametric data was compared by chi-square test and non-parametric data by students’-test. A p-value less than 0.05 was taken as significant.Results: Among the 2338 deliveries during the study period, labour was induced in 266 women (11.3%). Out of which, 109 cases (40.9%) in oligohydramnios group, 111cases (41.7%) in borderline liquor group and 46 cases in normal liquor group. The incidence of meconium stained liquor, the number of operative deliveries and fetal distress was significantly higher and significantly lower birth weight (<2.5 kg) in the group with oligohydramnios and borderline liquor (p <0.05). Low Apgar score and admission to neonatal intensive care unit was higher in the oligohydramnios group (p<0.05).Conclusions: Induction of labour on detecting borderline liquor at term may help in reduction of maternal and fetal morbidity and mortality.
Deep learning models are widely being used to provide relevant recommendations in hybrid recommender systems. These hybrid systems combine the advantages of both content based and collaborative filtering approaches.However, these learning systems hamper the user privacy and disclose sensitive information. This paper proposes a privacy preserving deep learning based hybrid recommender system. In hybrid deep neural network, user's side information such as age, location, occupation, zip code along with user rating is embedded and provided as input. These embedding's pose a severe threat to individual privacy. In order to eliminate this breach of privacy, we have proposed a private embedding scheme that protects user privacy while ensuring that the nonlinear latent factors are also learnt. In this paper, we address the privacy in hybrid system using differential privacy, a rigorous mathematical privacy mechanism in statistical and machine learning systems. In the reduced feature set, the proposed adaptive perturbation mechanism is used to achieve higher accuracy. The performance is evaluated using three datasets with root mean square error (RMSE), mean absolute error (MAE), mean squared error (MSE), R squared, precision and recall. These evaluation metrics are compared with varying values of privacy parameter ϵ . The experimental results show that the proposed solution provides high user privacy with reasonable accuracy than the existing system. As the engine is generic, it can be used on any recommendation framework.
No abstract
Melatonin is an important hormone that is clinically significant due to its physiological role, making its detection in real samples crucial for monitoring body function. Compared to other traditional approaches, electrochemical sensors have advantages such as high sensitivity, point-of-care analysis, rapid response time, ease of use, and cost-effectiveness. Recently, natural polymer-based biocomposites such as chitosan, gum acacia, xanthan gum, and chitinhave been employed due to their low cost, biocompatibility, and high surface area for biosensing applications. We have investigated Tungsten oxide (WO3) nanospheres decorated with functionalized chitosan (FCH) for the detection of melatonin. The functionalization of chitosan introduced plentiful amine groups and inter-hydrogen bonding provided necessary stability for the formation of WO3/FCH biocomposite. Further, the electroanalysis confirmed the excellent electrocatalytic performance of the biocomposite towards melatonin in with a limit of detection of 4.9 nM. The proposed nanocomposite exhibited excellent selectivity, reproducibility, stability, and its practical reliability was evaluated real samples. Herein, functionalization of introduced large density of amine groups that offered efficient binding affinity with WO3.and improved the conductivity of the nanocomposite for a highly sensitive melatonin detection. The proposed modified electrode will be a suitable platform for futuristic clinical biosensing applications.
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