Normal Body Mass Index and Heart Rate Variability

The autonomic nervous system (ANS) works in synergy with the Endocrine system that affects the body-mass and height and in turn the body mass index (BMI). The ANS activities are often assessed with one index that is heart rate variability (HRV). Reduced HRV has been reported in underweight (low BMI) and overweight (high BMI) individuals, but there is scarce information available on the relationship between normal BMI and HRV. Further, as per WHO expert consultation report, the Asian population has higher percentage of body fat than their European counterparts, therefore Asian people may have greater risk factors for type 2 diabetes and cardiovascular disease even below the existing upper edge of normal BMI. Thus it was recommended by WHO to consider the intermediate cutoff points within the normal BMI range as 18.5 Kg/m2, 20 Kg/m2, 23 Kg/m2, and 25 Kg/m2 for the Asian population. Therefore, the present study was aimed to investigate ANS activity among intermediate cutoff points of normal BMI using HRV. Seventy young individuals participated in the non-invasive and benign study. Subjects were divided into three groups based on their BMI as per the recommendation of the WHO report; NB1 (18.5<BMI≤20), NB2 (20<BMI<23) and NB3 (23<BMI<25). For all the subjects, 10 min of electrocardiogram was recorded and short term HRV analysis was carried out. Student t test was carried out to find the significance of study parameters in BMI groups. The BMI was correlated with HRV measures using Spearman’s correlation method. Statistically significant negative correlation was found between BMI and various HRV parameters. The sympathovagal balance was comparable in NB1 and NB2 group whereas it shifted towards sympathetic dominance in NB3 group. Higher sympathetic activity for BMI greater than 23 in Indian youth may lead to predictability of risks associated with overweight and obesit

A Study On The Clinical Outcome In Ischemic Stroke Patient With Hyperglycemia In A Tertiary Care Hospital Of Southern Bihar

Background: Previous research has been able to establish an association between diabetes/ increased sugar levels and increased mortality, length of hospital stay, readmission rates, and poorer functional and rehabilitation outcomes after stroke. The present study was done to assess the link of the blood sugar levels with acute stroke and thereby evaluate the severity and prognosis of stroke with reference to hyperglycemia.Materials and method: The blood sugar and HbA1C levels at the time of admission were recorded. The severity of stroke for each patient is calculated based on NIH stroke scale, which takes the clinical findings in to account and each criteria awarded specific points. The Student's t-test was applied for the comparing the mean values, Chi-square test for the comparison of frequencies and Spearman’s correlation test for correlation between the 2 variables.Results: Maximum number of the subjects in the present study belonged to the age group of 51-60 years with predominance among the males. High sugar levels as assessed by Random blood sugar and HbA1C levels was associated with increased severity of stroke as per NIH stroke class severity and poor functional outcome as well as the mortality. Conclusion: There is a linear correlation between admission day hyperglycemia and the severity of ischemic stroke. There is a good correlation between glucose level at the time of admission and the ischemic stroke outcome.   Keywords: Diabetes mellitus, HbA1C level, Blood sugar level, Ischemic Strok

Role of Predictive Modeling in Healthcare Research: A Scoping Review

The huge preponderance of inferences drawn in empirical medical research follows from model-based relations (e.g. regression). Here, we described the role of predictive modeling as a complement to this approach. Predictive models are usually probabilistic model which gives a good quality fit to our data. In medical research, it’s very common to use regression models for predictive purposes. Here in this article, we described the types of predictive modeling (Linear and Non-linear) used in medical research and how effectively the researchers take decisions based on predictive modeling, and what precautions, we have to take while building a predictive model. Finally, we consider a working example to illustrate the effectiveness of the predictive model in healthcare

Overweight and Obesity among Women by Economic Stratum in Urban India

Using data of the third round of the National Family Health Survey (NFHS) 2005-2006, this study examined the prevalence of overweight and obesity among women from different economic strata in urban India. The study used a separate wealth index for urban India constructed using principal components analysis (PCA). The result shows that prevalence of overweight and obesity is very high in urban areas, more noticeably among the non-poor households. Furthermore, overweight and obesity increase with age, education, and parity of women. The results of multinomial logistic regression show that non-poor women are about 2 and 3 times more at risk of being overweight and obese respectively. Marital status and media exposure are the other covariates associated positively with overweight and obesity. Thus, the growing demand which now appears before the Government or urban health planners is to address this rising urban epidemic with equal importance as given to other issues in the past

Management of foot and mouth disease in a dairy farm: By ethnoveterinary practice

Gruel feeding remitted in rapid recovery of foot and mouth disease (FMD) affected dairy cows. The gruel was prepared by cooking equal proportion of whole rice, wheat flour and finger millet flour in adequate quantity of water, jaggery (10%) and mineral mixture. Four organized dairy cattle farms, affected with FMD were selected, where animals at first and second dairy farms were fed gruel @ 2 kg/day for 20 days, at the third dairy farm 2 kg/ day/animal for 10 days and in the fourth farm no gruel was given. Wounds were sprayed with 1% KMnO4 solution and then applied with paste of honey (50%, v/v) and finger millet flour. Topical application of honey- finger millet flour paste, remitted in observation of pain relief in cows having tongue lesions and healing of the tongue/mouth wounds in 3 days thereby enabling the cows to resume eating. The per cent drop in milk yield in FMD affected cows in the first, second, third and fourth dairy farms was 85, 67, 45 and 81 respectively, regain by 80– 100 % in the treatment group after 16 to 20 days post infection, while in untreated animals, only 50% milk yield could be achieved at day 30–35 post infection. Therefore, gruel being low cost, locally available and easy to apply at farm level for rapid relief to the affected cows and faster improvement in daily milk yield helps in improving economic status of small, marginal farmers or livestock holders

Comparison of Some Prediction Models and their Relevance in the Clinical Research

In healthcare research, predictive modeling is commonly utilized to forecast risk variables and enhance treatment procedures for improved patient outcomes. Enormous quantities of data are being created as a result of recent advances in research, clinical trials, next-generation genomic sequencing, biomarkers, and transcriptional and translational studies. Understanding how to handle and comprehend scientific data to offer better treatment for patients is critical. Currently, multiple prediction models are being utilized to investigate patient outcomes. However, it is critical to recognize the limitations of these models in the research design and their unique benefits and drawbacks. In this overview, we will look at linear regression, logistic regression, decision trees, and artificial neural network prediction models, as well as their advantages and disadvantages. The two most perilous requirements for building any predictive healthcare model are feature selection and model validation. Typically, feature selection is done by a review of the literature and expert opinion on that subject. Model validation is also an essential component of every prediction model. It characteristically relates to the predictive model's performance and accuracy. It is strongly recommended that all clinical parameters should be thoroughly examined before using any prediction model

Profiling of bovine toll like receptors (TLRs) in foot and mouth disease vaccinated cattle

Foot and mouth disease virus (FMDV) elicits acute humoral antibody response in both infected and vaccinated animals. Toll like receptors (TLRs) are type 1 transmembrane proteins expressed in almost all cell types and activate the innate immune system. The current study was performed to evaluate expression profiling of bovine TLRs like TLR 2, TLR 3, TLR 7, TLR 8 and TLR 10, in response to FMD inactivated vaccine using quantitative real-time RT-PCR technique. Blood samples were collected from control, test group 1 and test group 2, at 0, 14th and 21st days post-vaccination (dpv). The mRNA abundance of these target genes was calibrated with a housekeeping gene (18 S) and expressed as fold over expression of the TLRs genes in bovine over the 0th dpv as control. On 0 day, expression of all TLRs did not vary significantly. The expression of TLR2 and TLR3 genes significantly increased in both test group 1 and 2 after 14th day and 21st DPV but expression of other TLRs increase in test groups 1 and 2 did not differ significantly. Expression of TLR2 and TLR3 genes considerably increased in test group 1 and 2 but expression of these genes were more in test group 1 as compared to test group 2. From preliminary findings, if there is inclusion of TLR2 and TLR 3 agonist in vaccine, it may enhance the innate immunity of animals and helps in clearing of virus and may prevent establishment of infection

Strategies for identifying stable lentil cultivars (Lens culinaris Medik) for combating hidden hunger, malnourishment, and climate variability

Iron and zinc malnutrition is a global humanitarian concern that mostly affects newborns, children, and women in low- and middle-income countries where plant-based diets are regularly consumed. This kind of malnutrition has the potential to result in a number of immediate and long-term implications, including stunted growth, an elevated risk of infectious diseases, and poor development, all of which may ultimately cause children to not develop to the fullest extent possible. A determination of the contributions from genotype, environment, and genotype by environment interactions is necessary for the production of nutrient-dense lentil varieties that offer greater availability of iron and zinc with a high level of trait stability. Understanding the genotype and environmental parameters that affect G x E (Genotype x Environment) interactions is essential for plant breeding. We used GGE(Genotype, Genotype x Environment interactions) and AMMI (Additive Main effects and Multiplicative Interaction) models to study genetic stability and GE(Genotype x Environment interactions) for grain Fe, Zn, Al, and anti-nutritional factors like phytic acid content in sixteen commercially produced lentil cultivars over several different six geographical locations across India. Significant genetic variability was evident in the Fe and Zn levels of different genotypes of lentils. The amounts of grain iron, zinc, and phytic acid varied from 114.10 to 49.90 mg/kg, 74.62 to 21.90 mg/kg, and 0.76 to 2.84 g/100g (dw) respectively. The environment and GE (Genotype x Environment interactions) had an impact on the concentration of grain Fe, Zn, and phytic acid (PA). Heritability estimations ranged from low to high (53.18% to 99.48%). The study indicated strong correlation between the contents of Fe and Zn, a strategy for simultaneously increasing Fe and Zn in lentils may be recommended. In addition, our research revealed that the stable and ideal lentil varieties L4076 (Pusa Shivalik) for Fe concentration and L4717 (Pusa Ageti) for Zn content, which have lower phytic acid contents, will not only play an essential role as stable donors in the lentil bio-fortification but will also enable the expansion of the growing area of bio-fortified crops for the security of health and nutrition

Forward-central two-particle correlations in p-Pb collisions at root s(NN)=5.02 TeV

Two-particle angular correlations between trigger particles in the forward pseudorapidity range (2.5 2GeV/c. (C) 2015 CERN for the benefit of the ALICE Collaboration. Published by Elsevier B. V.Peer reviewe

Azimuthal anisotropy of charged jet production in root s(NN)=2.76 TeV Pb-Pb collisions

We present measurements of the azimuthal dependence of charged jet production in central and semi-central root s(NN) = 2.76 TeV Pb-Pb collisions with respect to the second harmonic event plane, quantified as nu(ch)(2) (jet). Jet finding is performed employing the anti-k(T) algorithm with a resolution parameter R = 0.2 using charged tracks from the ALICE tracking system. The contribution of the azimuthal anisotropy of the underlying event is taken into account event-by-event. The remaining (statistical) region-to-region fluctuations are removed on an ensemble basis by unfolding the jet spectra for different event plane orientations independently. Significant non-zero nu(ch)(2) (jet) is observed in semi-central collisions (30-50% centrality) for 20 <p(T)(ch) (jet) <90 GeV/c. The azimuthal dependence of the charged jet production is similar to the dependence observed for jets comprising both charged and neutral fragments, and compatible with measurements of the nu(2) of single charged particles at high p(T). Good agreement between the data and predictions from JEWEL, an event generator simulating parton shower evolution in the presence of a dense QCD medium, is found in semi-central collisions. (C) 2015 CERN for the benefit of the ALICE Collaboration. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).Peer reviewe