CT analysis of subcutaneous and visceral adipose tissue in normal BMI subjects: association with level of physical activity and hypertension

Background: The distribution of adipose tissue, complex factors affecting it and its pathological consequences are among the hot topics in medical research nowadays. Most of the studies reported in the literature however describe the association of factors affecting the fat distribution in overweight and obese individuals. This particular study was however planned to find out the same in subjects having normal basal metabolic index (BMI). The objectives of the study were to analyze total adipose tissue (TAT), subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT) in the abdomen volumetrically using CT, to establish the association of these to the levels of physical activity, presence or absence of hypertension and to compare these associations in both the genders.Methods: A prospective study was carried out on seventy five, normal BMI subjects aged between 20–50 years. CT imaging was used for volumetric measurement of TAT, SAT and VAT. Pearson’s correlation of these were then found out with age. Kruskal Wallis test was also performed to compare these in hypertensive and non-hypertensive subjects and in those with different physical activity levels (PAL).Results: Women showed significantly higher volumes of TAT and SAT. Men showed statistically significant correlations of TAT and VAT with age. SAT volumes had significant negative association with the PAL in both genders. Men showed higher responsiveness of fat deposition in all compartments to the presence of hypertension.Conclusion: In conclusion, factors such as gender, age, level of physical activity and hypertension affect the site specific deposition of fat even in those individuals who aren’t over-weight or obese.Keywords: Total Adipose Tissue (TAT); Subcutaneous Adipose Tissue (SAT); Visceral Adipose Tissue (VAT

Survival, growth, behavior, hematology and serum biochemistry of mice under different concentrations of orally administered amorphous silica nanoparticle

Silica nanoparticles (SiNPs) are used extensively in consumer products and biomedical research basically due to ease of production and low cost. However, insufficient literature is reported regarding the toxicity and biocompatibility of SiNPs. The present study aimed to investigate the potential role of amorphous SiNPs on survival, growth, behavioral alterations, hematology and serum biochemistry of mice at four concentrations (control, 50, 100 and 150 mg/kg/day) of an oral supplementation for a period of 3 months. Signs of toxicity (lethargy, nausea, coma, tremors, vomiting and diarrhea, etc.) were noted at 9:00 am and 9:00 pm (twice a day) and the body weight of each of these mice was measured every week. The data were subjected to mean, standard deviation (S.D). Moreover, One-Way Analysis of Variance (ANOVA) and Dunnett’s test were applied for analysis of statistical significance between groups by using SPSS software, version 20. All the mice survived with minor alterations in behavior and no significant weight changes were observed during the stipulated time period. Complete blood count (CBC) analysis indicated non-significant (P ≥ 0.05) systemic dysfunctions of organ systems. However, there was elevation in the level of AST and ALT in the analysis of serum biochemistry, while the values of all other examined parameters were not-significant (P ≥ 0.05). The study concluded that orally administered large silica nanoparticles up to the dose level of 150 mg/kg/day are nontoxic for the in vivo use in mice

LSTM Neural Network Based Forecasting Model for Wheat Production in Pakistan

Pakistan’s economy is largely driven by agriculture, and wheat, mostly, stands out as its second most produced crop every year. On the other hand, the average consumption of wheat is steadily increasing as well, due to which its exports are not proportionally growing, thereby, threatening the country’s economy in the years to come. This work focuses on developing an accurate wheat production forecasting model using the Long Short Term Memory (LSTM) neural networks, which are considered to be highly accurate for time series prediction. A data pre-processing smoothing mechanism, in conjunction with the LSTM based model, is used to further improve the prediction accuracy. A comparison of the proposed mechanism with a few existing models in literature is also given. The results verify that the proposed model achieves better performance in terms of forecasting, and reveal that while the wheat production will gradually increase in the next ten years, the production to consumption ratio will continue to fall and pose threats to the overall economy. Our proposed framework, therefore, may be used as guidelines for wheat production in particular, and is amenable to other crops as well, leading to sustainable agriculture development in general