Health Risk Assessment of Different Heavy Metals Dissolved in Drinking Water.

Water pollution is a major threat to public health worldwide. The health risks of ingesting trace elements in drinking water were assessed in the provinces of Punjab and Khyber Pakhtunkhwa, Pakistan. Eight trace elements were measured in drinking water, using Inductively Coupled Plasma Mass Spectrometry (ICP-MS), and compared with permissible limits established by the World Health Organization (WHO) and the Pakistan Environmental Protection Agency (Pak EPA). In addition, health risk indicators such as the chronic daily intake (CDI) and the health risk index (HRI) were calculated. Our results showed that the concentrations of chromium (Cr), nickel (Ni), and manganese (Mn) were 2593, 1306, and 695 ng/g, respectively, in Lahore and Jhang, while the concentrations of arsenic (As) in Lahore, Vehari, Multan, and Jhang were 51, 50.4, 24, and 22 ng/g, respectively, which were higher than the permissible limits suggested by the WHO. The values of CDI were found to be in the order of Cr > Ni > Mn > Cu > As > Pb > Co > Cd. Similarly, the health risk index (HRI) values exceeded the safe limits (>1) in many cities (eg, Cr and Ni in Lahore and As in Vehari, Jhang, Lahore, and Multan). The aforementioned analysis shows that consumption of trace element-contaminated water poses an emerging health danger to the populations of these localities. Furthermore, inter-metal correlation and principal component analysis (PCA) showed that both anthropogenic and geologic activities were primary sources of drinking water contamination in the investigated areas.

Deep Deterministic Learning for Pattern Recognition of Different Cardiac Diseases through the Internet of Medical Things.

Electrocardiography (ECG) sensors play a vital role in the Internet of Medical Things, and these sensors help in monitoring the electrical activity of the heart. ECG signal analysis can improve human life in many ways, from diagnosing diseases among cardiac patients to managing the lifestyles of diabetic patients. Abnormalities in heart activities lead to different cardiac diseases and arrhythmia. However, some cardiac diseases, such as myocardial infarction (MI) and atrial fibrillation (Af), require special attention due to their direct impact on human life. The classification of flattened T wave cases of MI in ECG signals and how much of these cases are similar to ST-T changes in MI remain an open issue for researchers. This article presents a novel contribution to classify MI and Af. To this end, we propose a new approach called deep deterministic learning (DDL), which works by combining predefined heart activities with fused datasets. In this research, we used two datasets. The first dataset, Massachusetts Institute of Technology-Beth Israel Hospital, is publicly available, and we exclusively obtained the second dataset from the University of Malaya Medical Center, Kuala Lumpur Malaysia. We first initiated predefined activities on each individual dataset to recognize patterns between the ST-T change and flattened T wave cases and then used the data fusion approach to merge both datasets in a manner that delivers the most accurate pattern recognition results. The proposed DDL approach is a systematic stage-wise methodology that relies on accurate detection of R peaks in ECG signals, time domain features of ECG signals, and fine tune-up of artificial neural networks. The empirical evaluation shows high accuracy (i.e., ≤99.97%) in pattern matching ST-T changes and flattened T waves using the proposed DDL approach. The proposed pattern recognition approach is a significant contribution to the diagnosis of special cases of MI.

Quantification of Climate Warming and Crop Management Impacts on Cotton Phenology.

Understanding the impact of the warming trend on phenological stages and phases of cotton (Gossypium hirsutum L.) in central and lower Punjab, Pakistan, may assist in optimizing crop management practices to enhance production. This study determined the influence of the thermal trend on cotton phenology from 1980-2015 in 15 selected locations. The results demonstrated that observed phenological stages including sowing (S), emergence (E), anthesis (A) and physiological maturity (M) occurred earlier by, on average, 5.35, 5.08, 2.87 and 1.12 days decade-1, respectively. Phenological phases, sowing anthesis (S-A), anthesis to maturity (A-M) and sowing to maturity (S-M) were reduced by, on average, 2.45, 1.76 and 4.23 days decade-1, respectively. Observed sowing, emergence, anthesis and maturity were negatively correlated with air temperature by, on average, -2.03, -1.93, -1.09 and -0.42 days °C-1, respectively. Observed sowing-anthesis, anthesis to maturity and sowing-maturity were also negatively correlated with temperature by, on average, -0.94, -0.67 and -1.61 days °C-1, respectively. Applying the cropping system model CSM-CROPGRO-Cotton model using a standard variety in all locations indicated that the model-predicted phenology accelerated more due to warming trends than field-observed phenology. However, 30.21% of the harmful influence of the thermal trend was compensated as a result of introducing new cotton cultivars with higher growing degree day (thermal time) requirements. Therefore, new cotton cultivars which have higher thermal times and are high temperature tolerant should be evolved.

Mining personal data using smartphones and wearable devices: a survey.

The staggering growth in smartphone and wearable device use has led to a massive scale generation of personal (user-specific) data. To explore, analyze, and extract useful information and knowledge from the deluge of personal data, one has to leverage these devices as the data-mining platforms in ubiquitous, pervasive, and big data environments. This study presents the personal ecosystem where all computational resources, communication facilities, storage and knowledge management systems are available in user proximity. An extensive review on recent literature has been conducted and a detailed taxonomy is presented. The performance evaluation metrics and their empirical evidences are sorted out in this paper. Finally, we have highlighted some future research directions and potentially emerging application areas for personal data mining using smartphones and wearable devices.

Ethanol induced hepatotoxicity in albino rats.

Twelve male albino rats of 6-8 weeks old, weighing 150-200 gm each were divided into two groups of 6 rats each. Group A was used as control while Group B was given ethanol at a dose of 0.6 ml (0.5 gm)/100 gm/day for 8 weeks. Serum enzymes and liver histology was determined in both groups. Statistically significant increase in the mean enzyme levels, liver weight and volume were observed in the ethanol treated group compared to the controls. Histologically, hepatocytes contained large number of cytoplasmic vacuoles, pyknotic nuclei, and lymphocytic infiltration in treated animals. Ethanol appeared to be hepatotoxic in albino rats.

Affect of silymarin on serum levels of ALT and GGT in ethanol induced hepatotoxicity in albino rats.

BACKGROUND: Alcoholic liver disease is a worldwide health problem. At least 80% of heavy drinkers have been reported to develop steatosis, 10-35% alcoholic hepatitis, and approximately 10% liver cirrhosis. The objective of this study was to determine the affect of silymarin on the levels of serum ALT and GGT in ethanol induced hepatotoxicity in albino rats. This study was an experimental Randomised Control Trial (RCT), and was conducted at the experimental research laboratory of University of Health Sciences, Lahore, from January 2007 to December 2007. METHODS: Eighteen male albino rats of 6-8 weeks age, weighing 150-200 gm each were divided into 3 groups of 6 rats each. Group A served as control, Group B was given ethanol at a dose of 0.6 ml (0.5 gm)/100 gm/day and group C was given ethanol and silymarin at a dose of 0.5 gm/100 gm/day, and 20 mg/100 gm/day respectively for 8 weeks. At the end of the experiment, each animal was euthenised with chloroform. Blood was drawn from each animal by cardiac puncture for liver function tests (ALT and GGT). After taking blood sample, each euthenised animal was sacrificed and then its liver was removed for gross and histological examination. RESULTS: The mean values of serum alanine-aminotransferase (ALT) in groups A, B and C were 28.16 +/- 7.13, 82.33 +/- 10.89 and 49.66 +/- 6.12 U/L respectively, whereas, the mean values of gamma-glutamyl transferase (GGT) in groups A, B and C were 27.33 +/- 3.05, 79.33 +/- 4.37 and 45.66 +/- 1.85 U/L respectively. ANOVA showed significant (p < 0.05) difference in mean value of these serum enzymes among groups. Post Hoc test, using the Tukey honestly significant difference (HSD) showed that there was significant (p < 0.05) increase in mean value of ALT and GGT in group B as compared to group A and C. This test also showed that there was significant (p < 0.05) decrease in mean value of these enzymes in-group C as compared to group B. CONCLUSION: Silymarin tends to normalise liver function test in alcoholic liver disease.