A Shrewd Artificial Neural Network-Based Hybrid Model for Pervasive Stress Detection of Students Using Galvanic Skin Response and Electrocardiogram Signals.

Mental illness issues are a very common health issue in youths and adults across the world. The usage of real-time data analytics in health care has a great potential to improve and enhance the quality of health care services, including diagnosis and medical prescription. Stress is one of the major health issues these days, which leads to many acute and sometimes incurable diseases to the students of very young age. Stress affects physiological parameters of the human body; due to these, human emotions may also change. This research paper proposes a hybrid model for pervasive stress detection, which deals with imbalance class problems using real-time data analytics and Internet of Things and it also presents a new stress analysis system to detect stressful conditions of the student, and to diagnose whether they are stressed or relaxed by using a designed set of experimental tasks. Regular monitoring of students'/professionals' health, including measurement of Galvanic Skin Response (GSR) and Electrocardiogram (ECG) data, provides a good understanding of their stress level. Data are acquired by using GSR and ECG sensors for 34 participants while undertaking five different tasks discussed in the proposed experiment. The graphical relationship between heart rate, blood pressure, and skin conductance across various experimental activities highlights the fact as to how physiological parameters of the human body get affected along with the mental status of the students. This article performs accuracy computation by using different machine-learning models such as Logistic Regression (LR), Support Vector Machine (SVM), K-Nearest Neighbours (KNN), Bagging Classifiers (BAG), Random Forest (RF), Gradient Boosting (GB), and Artificial Neural Network (ANN) followed by tuning with the best set of hyper parameters for each model. The proposed hybrid classification model deals with the class imbalance problem by using the Synthetic Minority Oversampling Technique. The shrewd ANN-based hybrid model achieves 99.4% accuracy on the self-generated dataset for the mental state classification of the students, which is best among other classifiers such as LR, SVM, KNN, BAG, RF, GB, and ANN. The prediction result of all 34 participants of the experiment is also classified into four categories: relaxed, stressed, partially stressed, and happy.

Supplementation of antioxidant micronutrients reduces stress and improves immune function/response in periparturient dairy cows and their calves.

BACKGROUND: Periparturient period induces stress in cows which fluctuates hormonal and metabolic function and causes immune suppression. Apart from impairing the health, production, and reproduction of cows, it also influences the well-being of newborn calves by decreasing the colostrum quality. Micronutrients are known for optimal health and production and their effects on parturition stress, immune response in both cow and its calf need to be explored. AIM: The aim of this study was to see the effect of oral supplementation of micronutrients during the prepartum period on the health status of crossbred dairy cows and subsequently on their newborn calves. METHODS: A total of 42 healthy multiparous cows were selected and randomly divided into five groups with seven cows in each group, i.e. control (Basal Diet, BD), VA group (BD + vitamin A, 105 IU), Zn group (BD + zinc sulphate, 60 ppm), VE group (BD + vitamin E, 2500 IU), and combined supplementation (CS) group (BD + combination of VA, Zn, and VE). The supplements were offered in compounded concentrate DM (100 g) to individual cows once daily before the morning feeding and the remaining portion was incorporated in the TMR. Feeding was started one month before the expected days of calving till calving. Blood samples were collected from cows at days -15, -7, -3, 0, +3, +7, and +15 relative to the day of calving. Blood samples from newborn calves and milk samples of cows were collected at days 0, +3, +7, and +15. Milk somatic cell counts (SCC) were estimated using a cell counter. Cortisol was estimated by ELISA kit in blood and milk plasma of cows and in the blood plasma of their calves. Total immunoglobulins (Ig) were estimated in milk of cows and serum of calves using zinc sulphate turbidity method. Blood neutrophils from cows and calves were studied for phagocytic activity (PA) using nitro blue tetrazolium (NBT) assay.Data were analysed by repeated-measures two-way ANOVA using the mixed procedure of SAS, and the pairwise comparison was performed using a multiple comparison test (Tukey). RESULTS: Combined supplementation of micronutrients decreased (P < 0.05) maternal blood plasma (control vs. CS group, 5.98 ±â€¯0.20 vs. 3.86 ±â€¯0.23 ng/mL) and milk plasma (3.96 ±â€¯0.13 vs. 2.71 ±â€¯0.10 ng/mL) cortisol, milk SCC (3.05 ±â€¯0.11 vs. 2.12 ±â€¯0.10 × 105 cells/mL) and increased (P < 0.05) total milk Ig concentration (18.80 ±â€¯0.11 vs. 23.04 ±â€¯0.57 mg/mL) and the PA of blood neutrophils (0.84 ±â€¯0.03 vs. 1.07 ±â€¯0.03). Similarly, lower blood cortisol concentration (9.69 ±â€¯0.35 vs. 6.02 ±â€¯0.18 ng/mL) and higher (P < 0.05) total Ig (23.26 ±â€¯0.11 vs. 30.34 ±â€¯0.70 mg/mL) and PA of blood neutrophils (0.37 ±â€¯0.02 vs. 0.52 ±â€¯0.02) were observed in the calves born to CS group of cows as compared to the control. Highest (P < 0.05) positive effects (lower stress levels and higher immune response) of treatment were noticed in CS group followed by VE group and then Zn group. However, VA group didn't differ from the control group. CONCLUSION: Our results indicate that micronutrient interventions during the prepartum period can improve the health status of dairy calves and subsequently the well-being of their calves.

Zinc oxide nanorods functionalized paper for protein preconcentration in biodiagnostics.

Distinguishing a specific biomarker from a biofluid sample containing a large variety of proteins often requires the selective preconcentration of that particular biomarker to a detectable level for analysis. Low-cost, paper-based device is an emerging opportunity in diagnostics. In the present study, we report a novel Zinc oxide nanorods functionalized paper platform for the preconcentration of Myoglobin, a cardiac biomarker. Zinc oxide nanorods were grown on a Whatman filter paper no. 1 via the standard hydrothermal route. The growth of Zinc oxide nanorods on paper was confirmed by a combination of techniques consisting of X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS,) scanning electron microscopy (SEM), and energy dispersive spectroscopy (EDX) analysis. The Zinc oxide nanorods modified Whatman filter paper (ZnO-NRs/WFP) was further tested for use as a protein preconcentrator. Paper-based ELISA was performed for determination of pre-concentration of cardiac marker protein Myoglobin using the new ZnO-NRs/WFP platform. The ZnO-NRs/WFP could efficiently capture the biomarker even from a very dilute solution (Myoglobin < 50 nM). Our ELISA results show a threefold enhancement in protein capture with ZnO-NRs/WFP compared to unmodified Whatman filter paper, allowing accurate protein analysis and showing the diagnostic concept.