Our stories: Women speak out against HIV and AIDS—An interactive communication package for rural low-literate women

The National AIDS Control Organisation (NACO) estimates that there are over 5.1 million people living with HIV and AIDS in India. Among new infections reported in 2006, 88 percent were reported in the reproductive age group (15–49 years). The virus is spreading rapidly among women, including married and monogamous women, and adolescent girls. Existing literature shows that women\u27s vulnerability is compounded due to their gendered disadvantage in information access, literacy, and decision-making. This disadvantage is particularly acute in the case of married women in rural India. NACO has indicated the need to develop audience-appropriate strategies for communicating HIV-related information. To ensure that rural low- and neo-literate women are equipped with correct information about HIV and AIDS it is necessary to design communication strategies that are evidence-based, participatory, and take women\u27s visual perceptions into account. As detailed in this brief, the Population Council and partners undertook a participatory communications project to develop visually appropriate communication materials on HIV and AIDS for married rural women

Assessing the impact of body mass index on insulin resistance and metabolic risk factors in pre-diabetic individuals: A comprehensive cross-sectional study

Background: Insulin resistance plays a crucial role in the onset of type 2 diabetes, with body mass index (BMI) being a significant determinant. Aims and Objectives: This study examines the link between BMI and insulin resistance in pre-diabetic individuals to inform strategies for early diabetes intervention. Materials and Methods: This cross-sectional study involved 100 pre-diabetic participants. Data on demographic characteristics, BMI, insulin resistance (measured by the Homeostatic Model Assessment for Insulin Resistance, HOMA-IR), lipid profiles, and blood pressure (BP) were collected. Participants were categorized into normal weight, overweight, and obese groups to explore the relationship between BMI and insulin resistance and its impact on metabolic and cardiovascular health. Results: The average participant age was 45.8 years (SD=12.3), with a slight majority being female (52%) and an average BMI of 28.4 kg/m² (SD=4.5). A significant positive correlation (r=0.64, P<0.001) between BMI and the HOMA-IR index highlighted the association between increased BMI and insulin resistance. Obese individuals had a notably higher HOMA-IR index (3.5±1.3) compared to those overweight (2.5±1.0) and of normal weight (1.9±0.8). In addition, the study found worsening lipid profiles and increased BP with higher BMI categories. Gender did not significantly affect insulin resistance, whereas a slight increase in HOMA-IR with age was noted (r=0.23, P=0.02). Conclusion: The findings highlight the strong correlation between higher BMI and increased insulin resistance in pre-diabetics. They emphasize the importance of managing body weight to mitigate the risk of diabetes and cardiovascular diseases

Global burden and strength of evidence for 88 risk factors in 204 countries and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

Background: Understanding the health consequences associated with exposure to risk factors is necessary to inform public health policy and practice. To systematically quantify the contributions of risk factor exposures to specific health outcomes, the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 aims to provide comprehensive estimates of exposure levels, relative health risks, and attributable burden of disease for 88 risk factors in 204 countries and territories and 811 subnational locations, from 1990 to 2021. Methods: The GBD 2021 risk factor analysis used data from 54 561 total distinct sources to produce epidemiological estimates for 88 risk factors and their associated health outcomes for a total of 631 risk–outcome pairs. Pairs were included on the basis of data-driven determination of a risk–outcome association. Age-sex-location-year-specific estimates were generated at global, regional, and national levels. Our approach followed the comparative risk assessment framework predicated on a causal web of hierarchically organised, potentially combinative, modifiable risks. Relative risks (RRs) of a given outcome occurring as a function of risk factor exposure were estimated separately for each risk–outcome pair, and summary exposure values (SEVs), representing risk-weighted exposure prevalence, and theoretical minimum risk exposure levels (TMRELs) were estimated for each risk factor. These estimates were used to calculate the population attributable fraction (PAF; ie, the proportional change in health risk that would occur if exposure to a risk factor were reduced to the TMREL). The product of PAFs and disease burden associated with a given outcome, measured in disability-adjusted life-years (DALYs), yielded measures of attributable burden (ie, the proportion of total disease burden attributable to a particular risk factor or combination of risk factors). Adjustments for mediation were applied to account for relationships involving risk factors that act indirectly on outcomes via intermediate risks. Attributable burden estimates were stratified by Socio-demographic Index (SDI) quintile and presented as counts, age-standardised rates, and rankings. To complement estimates of RR and attributable burden, newly developed burden of proof risk function (BPRF) methods were applied to yield supplementary, conservative interpretations of risk–outcome associations based on the consistency of underlying evidence, accounting for unexplained heterogeneity between input data from different studies. Estimates reported represent the mean value across 500 draws from the estimate's distribution, with 95% uncertainty intervals (UIs) calculated as the 2·5th and 97·5th percentile values across the draws. Findings: Among the specific risk factors analysed for this study, particulate matter air pollution was the leading contributor to the global disease burden in 2021, contributing 8·0% (95% UI 6·7–9·4) of total DALYs, followed by high systolic blood pressure (SBP; 7·8% [6·4–9·2]), smoking (5·7% [4·7–6·8]), low birthweight and short gestation (5·6% [4·8–6·3]), and high fasting plasma glucose (FPG; 5·4% [4·8–6·0]). For younger demographics (ie, those aged 0–4 years and 5–14 years), risks such as low birthweight and short gestation and unsafe water, sanitation, and handwashing (WaSH) were among the leading risk factors, while for older age groups, metabolic risks such as high SBP, high body-mass index (BMI), high FPG, and high LDL cholesterol had a greater impact. From 2000 to 2021, there was an observable shift in global health challenges, marked by a decline in the number of all-age DALYs broadly attributable to behavioural risks (decrease of 20·7% [13·9–27·7]) and environmental and occupational risks (decrease of 22·0% [15·5–28·8]), coupled with a 49·4% (42·3–56·9) increase in DALYs attributable to metabolic risks, all reflecting ageing populations and changing lifestyles on a global scale. Age-standardised global DALY rates attributable to high BMI and high FPG rose considerably (15·7% [9·9–21·7] for high BMI and 7·9% [3·3–12·9] for high FPG) over this period, with exposure to these risks increasing annually at rates of 1·8% (1·6–1·9) for high BMI and 1·3% (1·1–1·5) for high FPG. By contrast, the global risk-attributable burden and exposure to many other risk factors declined, notably for risks such as child growth failure and unsafe water source, with age-standardised attributable DALYs decreasing by 71·5% (64·4–78·8) for child growth failure and 66·3% (60·2–72·0) for unsafe water source. We separated risk factors into three groups according to trajectory over time: those with a decreasing attributable burden, due largely to declining risk exposure (eg, diet high in trans-fat and household air pollution) but also to proportionally smaller child and youth populations (eg, child and maternal malnutrition); those for which the burden increased moderately in spite of declining risk exposure, due largely to population ageing (eg, smoking); and those for which the burden increased considerably due to both increasing risk exposure and population ageing (eg, ambient particulate matter air pollution, high BMI, high FPG, and high SBP). Interpretation: Substantial progress has been made in reducing the global disease burden attributable to a range of risk factors, particularly those related to maternal and child health, WaSH, and household air pollution. Maintaining efforts to minimise the impact of these risk factors, especially in low SDI locations, is necessary to sustain progress. Successes in moderating the smoking-related burden by reducing risk exposure highlight the need to advance policies that reduce exposure to other leading risk factors such as ambient particulate matter air pollution and high SBP. Troubling increases in high FPG, high BMI, and other risk factors related to obesity and metabolic syndrome indicate an urgent need to identify and implement interventions

Discovering Multi-Level Association Rules using Fuzzy Hierarchies

ABSTRACT: In this paper, Fuzzy concept hierarchies are used for multi-level association rule mining from large datasets via Attribute-Oriented Induction approach [1]. In this the process of fuzzy hierarchical induction approach is used and extends it with two new characteristics which improve applicability of the original approach in data mining. The proposed drilling-down approach of fuzzy induction model allows user to retrieve estimated explanations of the generated abstract concept. An application to discovery of multi-level association rules from environmental data stored in a Toxic Release Inventory is presented. Keywords: Fuzzy Hierarchies, Propogation, Association Rules I. INTRODUCTION The need for generalization mechanisms has been proven to be a critical factor for many data mining tasks. To efficiently analyze voluminous data sets, which are currently stored in databases of almost all companies, it is often necessary to start from pruning and reducing of the size of these repositories. Decision makers are usually not interested in time consuming extraction of technical details (i.e. serial numbers, time of transactions with precision in seconds, detailed GPS locations, etc.) stored originally in large databases. Instead, they want to obtain knowledge at a certain level of abstraction, as dictated by their professional goals or by the character of the analysed dataset. The data conceptually represent information at multiple levels . For instance, an item, represented by a bar code: 040101000 is a Milky Way Bar, which is a Chocolate Bar, pertaining to a Snack, or even a Food group, etc. Attribute-Oriented Induction (AOI) [1] allows compression of the original set of data into a generalized relation to provide data analysts with concise and summarize information about the original, massive set of task-relevant data. The AOI process employs background knowledge represented in the form of concept hierarchies, separately declared for each of the attributes in the analyzed database table. Induction of the original relation is performed one step at a time on an attribute-by-attribute basis. The method has rather straightforward character [1]: (1) Gather task-relevant data into the initial relation (2) Generalize the data by removal or generalization of derived attributes. An attribute can be removed if it has a large set of distinct values and no generalization hierarchy for the attribute is available or if the abstract concepts are reflected by other attributes in the initial relation. Attribute values can be generalized if a concept hierarchy for them has been defined. (3) Aggregate the data by merging identical tuples and accumulating their respective counts. New attribute COUNT has to be added to the relation, to keep track of original records, which are gradually merged during the transformation of attribute values from one level of abstraction to another. Value stored in the COUNT column reflects the number of original records merged together into a generalized tuple. (4) Present generated (generalized) table to the user. Additional data mining algorithms can be further applied on this relation. The hierarchical character of AOI provides analysts with ability to view the original information at multiple levels of abstraction, allowing them to progressively discover interesting data concentration points. In contrast to the flat summarization a gradual process of attribute-oriented induction through concept hierarchies allows detailed tracking o

Study on plasticized Poly (vinylidene chloride- co- acrylonitrile) polymer electrolytes for battery applications

Plasticized Poly (vinylidene chloride- co- acrylonitrile) [P(VdC-co-AN)] polymer electrolytes comprising of Lithium Perchloride (LiClO _4 ) as complexing salt and plasticizers such as Propylene carbonate (PC) and β -butyrolactone ( β bl) is prepared by solution casting technique. Polymer electrolytes were prepared in the ratio [(x)PVdC-co-PAN+(100-x-z) Plasticizer+(z) LiClO _4 ] and were subjected to various characterizations. X-ray Diffraction (XRD), and Fourier transform infrared spectroscopy (FTIR) were carried out to study the structural and functional groups present in the material. Impedance spectroscopy to find the ionic conductivity of the material. The maximum ionic conductivity at room temperature was exhibited by the samples containing 68% PC (9.237 × 10 ^−4 S cm ^−1 ) and 66% of β bl (2.284 × 10 ^−4 S cm ^−1 ). Samples exhibiting higher ionic conductivity (68% PC and 66% of β bl) are subjected to Linear sweep voltammetry and transference number measurements. The electrochemical stability is 4.5 v for the both films, whereas transference number is 0.955 and 0.94, respectively. Thermogravimetry/Differential Thermal analysis (TG/DTA) shows the prepared films doesn’t not undergo any weight loss till 220 °C (thermally stable). The surface morphology of the polymer membrane was explored through Atomic force microscopy (AFM)

Liver oxidative stress of the grey mullet Mugil cephalus presents seasonal variations in Ennore estuary

The objective of this study was to determine the liver oxidative stress status of grey mullets living in heavy-metal-rich polluted Ennore estuary compared with unpolluted Kovalam estuary. Fish were collected from both estuaries during the monsoon and summer seasons from October 2004 to September 2006. Fish liver homogenate (N = 20 per group) was prepared for evaluating oxidative stress parameters. Fish living in the polluted estuary had significantly higher lipid oxidation products, conjugated dienes (0.346 ± 0.017 vs 0.141 ± 0.012 DA233/mg protein), lipid hydroperoxides (0.752 ± 0.032 vs 0.443 ± 0.03 nmol/mg protein), and lipid peroxides (3.447 ± 0.14vs 1.456 ± 0.096 nmol MDA/mg protein) than those of the unpolluted estuary during the summer. In contrast, significantly lower levels of superoxide dismutase (20.39 ± 1.14 vs 53.63 ± 1.48 units/mg protein) and catalase (116 ± 6.87vs 153 ± 8.92 units/mg protein) were detected in the liver of fish from the polluted estuary (Ennore) compared to fish from the unpolluted estuary (Kovalam) during the summer. Variations in most of the oxidative stress parameters were observed between the summer and monsoon seasons, indicating the importance of seasonal variation for estuaries and their inhabitants