Inequalities in cardiovascular disease among elderly Indians: A gender perspective analysis using LASI wave-I (2017-18).

BACKGROUND: While Cardiovascular disease (CVD) affects both men and women, emerging evidence suggests notable gender differentials in disease prevalence. This study aims to explore and analyse the gender differentials in CVD disease prevalence in India. METHODS: The present study utilizes data from first wave of the nationally representative survey "Longitudinal Ageing Study in India" (LASI, WAVE-I, 2017-18) with the eligible sample size of 31,464 individuals aged 60 years and above. Logistic regression analysis was used to understand risk of CVD by demographic characteristics. Factors contribution to gender differences in CVD prevalence was examined using a non-linear Fairlie decomposition. RESULTS: The prevalence of CVD was lower in men (31.06%) compared to women (38.85%). Women have a 33% higher likelihood of CVD compared to men (OR: 1.33; 95% CI: 1.25-1.42). Lack of education also confers a lower risk, more pronounced in women with no schooling (OR: 0.81; 95% CI: 0.7-0.94) compared to men (OR: 0.52; 95% CI: 0.47-0.58). Morbidity influences CVD presence more among women than men, with individuals suffering from three or more diseases having markedly increased odds (Men: OR: 3.89; 95% CI: 3.54-4.3, Women: OR: 6.97; 95% CI: 6.48-10.11). Smoking accounted increase in (20.52%) the gender gap while years of schooling dramatically lessened the gender gap (-46.30%). CONCLUSION: Result show gender differential in CVD prevalence and underlying risk factors, underscoring the need for gender-specific preventive strategies and interventions. Our findings highlight the importance of refined approach to cardiovascular health that considers the complex interplay of biological, social, and environmental determinants.

Prediction of bitcoin stock price using feature subset optimization.

In light of recent cryptocurrency value fluctuations, Bitcoin is gradually gaining recognition as an investment vehicle. Given the market's inherent volatility, accurate forecasting becomes crucial for making informed investment decisions. Notably, previous research has utilized machine learning methods to enhance the accuracy of Bitcoin price predictions. However, few studies have explored the potential of employing diverse modeling methods for sampling with varying data formats and dimensional characteristics. This study aims to identify the internal feature subset that yields the highest returns in forecasting Bitcoin's price. Specifically, Bitcoin's internal features were categorized into four groups: currency data, block details, mining information, and network difficulty. Subsequently, a long short-term memory (LSTM) artificial neural network was employed to predict the next day's Bitcoin closing price, utilizing various categorizations of feature subsets. The model underwent training using two and a half years of historical data for each feature. The findings revealed a mean absolute error rate of 6.38% when modeling with the block details category features. This enhanced performance primarily stemmed from the positive relationship between Bitcoin price and this data subset's low ambiguity. Experimental results underscored that, compared to other investigated feature subsets, the categorization of block detail features provided the most accurate Bitcoin price predictions, laying the foundation for future research in this domain.

Inequalities in the prevalence of double burden of malnutrition among mother-child dyads in India.

In the midst of rapid urbanization and economic shifts, the global landscape witnesses a surge in overweight and obese individuals, even as child malnutrition persists as a formidable public health challenge in low- and middle-income countries (LMICs). This study seeks to unravel the prevalence of the double burden of malnutrition (DBM) within the context of India and delve into the associated disparities rooted in wealth. This study leverages data from the fifth wave of the National Family and Health Survey (NFHS-5), a nationally representative survey conducted in the year 2019-21 in India. This study focuses on mother-child dyads with children under the age of 3 years. Descriptive, bivariate and logistic regression analysis is used to decipher the intricate web of DBM's prevalence and risk factors, as underscored by socio-demographic attributes. Wagstaff decomposition analysis is applied to quantify the contribution of each inequality in the social determinants on the observed income-related inequality in the DBM. Result from bivariate and logistic regression indicated a heightened risk of DBM within households marked by C-section births, affluence, ongoing breastfeeding practices, advanced maternal age, and larger household sizes. Additionally, households harbouring women with abdominal obesity emerge as hotspots for elevated DBM risk. Notably, the interplay of abdominal obesity and geographical disparities looms large as drivers of substantial inequality in DBM prevalence, whereas other factors exert a comparably milder influence. As India grapples with the burgeoning burden of DBM, a conspicuous imbalance in its prevalence pervades, albeit inadequately addressed. This juncture warrants the formulation of dual-purpose strategies, and a slew of innovative actions to deftly navigate the complex challenges poised by the dual burden of malnutrition. Amidst these exigencies, the imperative to forge a holistic approach that encompasses both sides of the malnutrition spectrum remains a beacon guiding the quest for equitable health and nutrition outcomes.

Learning from intermittent water supply schedules: Visualizing equality, equity, and hydraulic capacity in Bengaluru and Delhi, India.

Intermittent distribution affects one in five piped water users, threatens water quality, and magnifies inequity. Research and regulations to improve intermittent systems are hindered by system complexity and missing data. We created four new methods to visually harness insights from intermittent supply schedules and demonstrate these methods in two of the world's most complicated intermittent systems. First, we created a new way to visualize the varieties of supply continuities (hours/week of supply) and supply frequencies (days between supplies) within complicated intermittent systems. We demonstrated using Delhi and Bengaluru, where 3278 water schedules vary from continuous to only 30 minutes/week. Second, we quantified equality based on how uniformly supply continuity and frequency were divided between neighbourhoods and cities. Delhi provides 45 % more supply continuity than Bengaluru, but with similar inequality. Bengaluru's infrequent schedules require consumers to store four times more water (for four times longer) than in Delhi, but Bengaluru's storage burden is more equally shared. Third, we considered supply inequitable where affluent neighbourhoods (using census data) received better service. Neighbourhood wealth was inequitably correlated with the percent of households with piped connections. In Bengaluru, supply continuity and required storage were also inequitably divided. Finally, we inferred hydraulic capacity from the coincidence of supply schedules. Delhi's highly coincident schedules result in city-wide peak flows 3.8 times their average - sufficient for continuous supply. Bengaluru's inconvenient nocturnal schedules may indicate upstream hydraulic limitations. Towards improved equity and quality, we provided four new methods to harness key insights from intermittent water supply schedules.

A simple and low-cost setup for part per billion level frequency stabilization and characterization of red He-Ne laser.

This work describes the frequency stabilization of a dual longitudinal mode, red (632.8 nm) He-Ne laser, implemented using an open source low-cost microcontroller (Arduino Uno) and its performance characterization using a simple interferometric method. Our studies demonstrate that frequency stability up to 0.42 MHz (3σ, 17 h) can be achieved using this setup. This simple and low-cost system can serve as an excellent part per billion level frequency reference for high-resolution spectroscopy based applications.

Assessment of removal rate coefficient in vertical flow constructed wetland employing machine learning for low organic loaded systems.

Secondary datasets of 42 low organic loading Vertical flow constructed wetlands (LOLVFCWs) were assessed to optimize their area requirements for N and P (nutrients) removal. Significant variations in removal rate coefficients (k20) (0.002-0.464 md-1) indicated scope for optimization. Data classification based on nitrogen loading rate, temperature and depth could reduce the relative standard deviations of the k20 values only in some cases. As an alternative method of deriving k20 values, the effluent concentrations of the targeted pollutants were predicted using two machine learning approaches, MLR and SVR. The latter was found to perform better (R2 = 0.87-0.9; RMSE = 0.08-3.64) as validated using primary data of a lab-scale VFCW. The generated model equations for predicting effluent parameters and computing corresponding k20 values can assist in a customized design for nutrient removal employing minimal surface area for such systems for attaining the desired standards.

Cost Analysis on Total Cost Incurred (Including Out-of-pocket Expenditure and Social Cost) During Palliative Care in Cases of Head-and-Neck Cancer at a Government Regional Cancer Centre in North India.

Objectives: Palliative care involves providing symptomatic relief from the pain and stress of a severe illness to markedly improve the quality of life for both the patients and their families. It imposes high indirect costs on the patients. The study was conducted at SGPGIMS, which caters to 500 head-and-neck cancer patients annually. Out of these, 30-40% of cases require dedicated palliative care. Unfortunately, often, when patients reach the stage of palliative care, they have exhausted their all financial reserves. Therefore, a cost analysis of total cost incurred (including out-of-pocket expenditure and social cost) during palliative care in cases of head-and-neck cancer at a Government Regional Cancer Centre was undertaken. Material and Methods: The study is a descriptive study and the study sample consisted of (a) patients who had undergone surgery, chemotherapy, or radiotherapy and had recurred/relapsed and were now candidates for palliative care and (b) patients who presented de novo to the Regional Cancer Centre, SGPGIMS with advanced-stage disease, where the cure was not possible. The expenditure incurred was obtained retrospectively and prospectively from the study samples. Results: The out-of-pocket expenditure per patient per day was INR 2044.21. The social cost per patient per day was INR 518.21. Out of the total expenditure of INR 2562.42/patient/day, 80% of the cost was out-of-pocket expenditure and the remaining 20% was social cost borne by the patient. Conclusion: The study thus added to perspective on the average expenditure on out-of-pocket expenses and social costs being incurred as of date, while getting palliative care for head-and-neck cancer at a Regional Cancer Centre.

Recent updates in biohydrogen production strategies and life-cycle assessment for sustainable future.

Biohydrogen (bio-H2) is regarded as a clean, non-toxic, energy carrier and has enormous potential for transforming fossil fuel-based economy. The development of a continuous high-rate H2 production with low-cost economics following an environmentally friendly approach should be admired for technology demonstration. Thus, the current review discusses the biotechnological and thermochemical pathways for H2 production. Thermochemical conversion involves pyrolysis and gasification routes, while biotechnological involves light-dependent processes (e.g., direct and indirect photolysis, photo/ dark fermentation strategies). Moreover, environmentally friendly technologies can be created while utilizing renewable energy sources including lignocellulosic, wastewater, sludge, microalgae, and others, which are still being developed. Lifecycle assessment (LCA) evaluates and integrates the economic, environmental, and social performance of H2 production from biomass, microalgae, and biochar. Moreover, system boundaries evaluation, i.e., global warming potential, acidification, eutrophication, and sensitivity analysis could lead in development of sustainable bioenergy transition with high economic and environmental benefits.

Impact of Healthcare on Stock Market Volatility and Its Predictive Solution Using Improved Neural Network.

The unprecedented Corona Virus Disease (COVID-19) pandemic has put the world in peril and shifted global landscape in unanticipated ways. The SARSCoV2 virus, which caused the COVID-19 outbreak, first appeared in Wuhan, Hubei Province, China, in December 2019 and quickly spread around the world. This pandemic is not only a global health crisis, but it has caused the major global economic depression. As soon as the virus spread, stock market prices plummeted and volatility increased. Predicting the market during this outbreak has been of substantial importance and is the primary motivation to carry out this work. Given the nonlinearity and dynamic nature of stock data, the prediction of stock market is a challenging task. The machine learning models have proven to be a good choice for the development of effective and efficient prediction systems. In recent years, the application of hyperparameter optimization techniques for the development of highly accurate models has increased significantly. In this study, a customized neural network model is proposed and the power of hyperparameter optimization in modelling stock index prices is explored. A novel dataset is generated using nine standard technical indicators and COVID-19 data. In addition, the primary focus is on the importance of selection of optimal features and their preprocessing. The utilization of multiple feature ranking techniques combined with extensive hyperparameter optimization procedures is comprehensive for the prediction of stock index prices. Moreover, the model is evaluated by comparing it with other models, and results indicate that the proposed model outperforms other models. Given the detailed design methodology, preprocessing, exploratory feature analysis, and hyperparameter optimization procedures, this work gives a significant contribution to stock analysis research community during this pandemic.

AutoProstate: Towards Automated Reporting of Prostate MRI for Prostate Cancer Assessment Using Deep Learning.

Multiparametric magnetic resonance imaging (mpMRI) of the prostate is used by radiologists to identify, score, and stage abnormalities that may correspond to clinically significant prostate cancer (CSPCa). Automatic assessment of prostate mpMRI using artificial intelligence algorithms may facilitate a reduction in missed cancers and unnecessary biopsies, an increase in inter-observer agreement between radiologists, and an improvement in reporting quality. In this work, we introduce AutoProstate, a deep learning-powered framework for automatic MRI-based prostate cancer assessment. AutoProstate comprises of three modules: Zone-Segmenter, CSPCa-Segmenter, and Report-Generator. Zone-Segmenter segments the prostatic zones on T2-weighted imaging, CSPCa-Segmenter detects and segments CSPCa lesions using biparametric MRI, and Report-Generator generates an automatic web-based report containing four sections: Patient Details, Prostate Size and PSA Density, Clinically Significant Lesion Candidates, and Findings Summary. In our experiment, AutoProstate was trained using the publicly available PROSTATEx dataset, and externally validated using the PICTURE dataset. Moreover, the performance of AutoProstate was compared to the performance of an experienced radiologist who prospectively read PICTURE dataset cases. In comparison to the radiologist, AutoProstate showed statistically significant improvements in prostate volume and prostate-specific antigen density estimation. Furthermore, AutoProstate matched the CSPCa lesion detection sensitivity of the radiologist, which is paramount, but produced more false positive detections.

Phytomicrobiome for promoting sustainable agriculture and food security: Opportunities, challenges, and solutions.

Ensuring food security in an environmentally sustainable way is a global challenge. To achieve this agriculture productivity requires increasing by 70 % under increasingly harsh climatic conditions without further damaging the environmental quality (e.g. reduced use of agrochemicals). Most governmental and inter-governmental agencies have highlighted the need for alternative approaches that harness natural resource to address this. Use of beneficial phytomicrobiome, (i.e. microbes intimately associated with plant tissues) is considered as one of the viable solutions to meet the twin challenges of food security and environmental sustainability. A diverse number of important microbes are found in various parts of the plant, i.e. root, shoot, leaf, seed, and flower, which play significant roles in plant health, development and productivity, and could contribute directly to improving the quality and quantity of food production. The phytomicrobiome can also increase productivity via increased resource use efficiency and resilience to biotic and abiotic stresses. In this article, we explore the role of phytomicrobiome in plant health and how functional properties of microbiome can be harnessed to increase agricultural productivity in environmental-friendly approaches. However, significant technical and translation challenges remain such as inconsistency in efficacy of microbial products in field conditions and a lack of tools to manipulate microbiome in situ. We propose pathways that require a system-based approach to realize the potential to phytomicrobiome in contributing towards food security. We suggest if these technical and translation constraints could be systematically addressed, phytomicrobiome can significantly contribute towards the sustainable increase in agriculture productivity and food security.

A Survey on Applications of Artificial Intelligence for Pre-Parametric Project Cost and Soil Shear-Strength Estimation in Construction and Geotechnical Engineering.

Ensuring soil strength, as well as preliminary construction cost and duration prediction, is a very crucial and preliminary aspect of any construction project. Similarly, building strong structures is very important in geotechnical engineering to ensure the bearing capability of structures against external forces. Hence, in this first-of-its-kind state-of-the-art review, the capability of various artificial intelligence (AI)-based models toward accurate prediction and estimation of preliminary construction cost, duration, and shear strength is explored. Initially, background regarding the revolutionary AI technology along with its different models suited for geotechnical and construction engineering is presented. Various existing works in the literature on the usage of AI-based models for the abovementioned applications of construction and maintenance are presented along with their advantages, limitations, and future work. Through analysis, various crucial input parameters with great impact on the estimation of preliminary construction cost, duration, and soil shear strength are enumerated and presented. Lastly, various challenges in using AI-based models for accurate predictions in these applications, as well as factors contributing to the cost-overrun issues, are presented. This study can, thus, greatly assist civil engineers in efficiently using the capabilities of AI for solving complex and risk-sensitive tasks, and it can also be used in Internet of things (IoT) environments for automated applications such as smart structural health-monitoring systems.

Impact of various solid carriers and spray drying on pre/post compression properties of solid SNEDDS loaded with glimepiride: in vitro-ex vivo evaluation and cytotoxicity assessment.

Development of self-nanoemulsifying drug delivery systems (SNEDDS) of glimepiride is reported with the aim to achieve its oral delivery. Lauroglycol FCC, Tween-80, and ethanol were used as oil, surfactant, and co-surfactant, respectively as independent variables. The optimized composition of SNEDDS formulation (F1) was 10% v/v Lauroglycol FCC, 45% v/v Tween 80, 45% v/v ethanol, and 0.005% w/v glimepiride. Further, the optimized liquid SNEDDS were solidified through spray drying using various hydrophilic and hydrophobic carriers. Among the various carriers, Aerosil 200 was found to provide desirable flow, compression, dissolution, and diffusion. Both, liquid and solid-SNEDDS have shown release of more than 90% within 10 min. Results of permeation studies performed on Caco-2 cell showed that optimized SNEDDS exhibited 1.54 times higher drug permeation amount and 0.57 times lower drug excretion amount than that of market tablets at 4 hours (p < .01). Further, the cytotoxicity study performed on Caco-2 cell revealed that the cell viability was lower in SNEDDS (92.22% ± 4.18%) compared with the market tablets (95.54% ± 3.22%; p > .05, i.e. 0.74). The formulation was found stable with temperature variation and freeze thaw cycles in terms of droplet size, zeta potential, drug precipitation and phase separation. Crystalline glimepiride was observed in amorphous state in solid SNEDDS when characterized through DSC, PXRD, and FT-IR studies. The study revealed successful formulation of SNEDDS for glimepiride.

Source and Assessment of Metal Pollution at Khetri Copper Mine Tailings and Neighboring Soils, Rajasthan, India.

We present here the results of the study on metal pollution by identifying source, abundance and distribution in soil and tailings of Khetri copper complex (KCC) mines, Rajasthan India. The region is highly contaminated by copper (Cu) with higher values in the soil near overburden material (1224 mg/kg) and tailings (111 mg/kg). The average Cu (231 mg/kg) concentration of soil is ~9, 5 and 32 times higher than upper crust, world average shale (WAS) and local background soil (LS), respectively. However this reaches to ~82, 46 and 280 times higher in case of tailing when compared. The correlation and principal component analysis for soil reveals that the source of Cu, Zn, Co, Ni, Mn and Fe is mining and Pb and Cd could be result of weathering of parent rocks and other anthropogenic activities. The source for Cr in soil is both mining activities and weathering of parent rocks. The values of index of geo-accumulation (Igeo) and pollution load index for soil using LS as background are higher compared to values calculated using WAS. The metal rich sulphide bearing overburden material as well as tailings present in the open environment at KCC mines region warrants a proper management to minimize their impact on the environment.

Role of Halide Ions in the Nature of the Magnetic Anisotropy in Tetrahedral CoII Complexes.

A series of mononuclear tetrahedral CoII complexes with a general molecular formula [CoL2 X2 ] [L=thiourea and X=Cl (1), Br (2) and I (3)] were synthesized and their structures were characterized by single-crystal X-ray diffraction. Direct-current (dc) magnetic susceptibility [χM T(T) and M(H)] and its slow relaxation of magnetization were measured for all three complexes. The experimental dc magnetic data are excellently reproduced by fitting both χM T(T) and M(H) simultaneously with the parameters D=+10.8 cm-1 , g1 =2.2, g2 =2.2, and g3 =2.4 for 1; D=-18.7 cm-1 , giso =2.21 for 2; and D=-19.3 cm-1 , giso =2.3 for 3. The replacement of chloride in 1 by bromide or iodide (in 2 and 3, respectively) was accompanied by a change in both sign and magnitude of the magnetic anisotropy D. Field-induced out-of-phase susceptibility signals observed in 10 % diluted samples of 1-3 imply slow relaxation of magnetization of molecular origin. To better understand the magnetization relaxation dynamics of complexes 1-3, detailed ab initio CASSCF/NEVPT2 calculations were performed. The computed spin Hamiltonian parameters are in good agreement with experimental data. In particular, the calculations unveil the role of halide ions in switching the sign of D on moving from Cl- to I- . The large spin-orbit coupling constant associated with the heavier halide ion and weaker π donation reduces the ground state-excited state gap, which leads to a larger contribution to negative D for complex 3 compared to complex 1. Further magnetostructural D correlations were developed to understand the role of structural distortion in the sign and magnitude of D values in this family of complexes.

Vitamin E pretreatment prevents the immunotoxicity of dithiocarbamate pesticide mancozeb in vitro: A comparative age-related assessment in mice and chick.

Pesticides used for crop protection cause life-threatening diseases affecting the immune system of non-target organisms including birds and mammals. Functionality of immune system is age-dependent; early- as well as old-life stages are more susceptible to toxic exposures because of less competent immune system. Vitamins are so far known to reduce toxic effect of several pesticides and/or xenobiotics. The present in vitro study elucidated immunotoxicity of fungicide mancozeb through comparable stages of immune system maturation in mice (1, 3, and 12months) and chicks (4, 8, and 11weeks). In vitro splenocytes viability on exposure to mancozeb was quantitatively assessed by MTT assay and qualitatively by acridine orange and ethidium bromide (AO/EB) double fluorescence staining. Mancozeb exposure dose dependently (250, 500, 1000, 2500, 5000 and 10,000ng/ml) decreased the splenocytes viability. The in vitro preventive effect of Vitamin E has also been explored on toxicity induced by mancozeb. The increased susceptibility observed both in early and aged groups was due to less/decline competence of the immune system.