Central Indian Region Research Articles

Comprehensive Neonatal Screening for Genetic Disorders in Tribal Populations of Central India

ABSTRACT Background: Genetic disorders, including sickle cell disease (SCD), thalassemia, and glucose-6-phosphate dehydrogenase (G6PD) deficiency, pose significant health challenges in central India, especially among tribal populations. Comprehensive neonatal screening is crucial for early diagnosis and management. Aim and Objective: This study aims to evaluate the prevalence of SCD, thalassemia, and G6PD deficiency in newborns from tribal regions of central India and assess the impact of comprehensive neonatal screening programs. Materials and Methods: A total of 382 newborns were screened for SCD and thalassemia using high-performance liquid chromatography (HPLC) and for G6PD deficiency using a colorimetric assay. Demographic data were collected, and statistical analyses were performed. Results: The screening identified 28 cases of Sickle cell disease (SCD) (7.3%), 35 cases of thalassemia (9.1%), and 34 cases of G6PD deficiency (8.9%). The study found a significant relationship between maternal education and the prevalence of genetic disorders. Conclusion: The findings highlight the importance of implementing comprehensive neonatal screening programs in tribal populations to improve early detection and management of genetic disorders.

Hemoglobin F% and Complete Blood Count Patterns in Two Independent Sickle Cell Anemia Cohorts from India & the US

Sickle cell anemia (SCA) is among the most common monogenetic diseases affecting approximately 8 million people and 300,000 newborns per year worldwide. In certain regions of India, the hemoglobin (Hb) S mutation is observed in up to 40% of the population with 2% being homozygous (Hb SS). Despite the high prevalence of SCA in India, the characteristics of SCA in this population and the impact of Hb F% on the SCA phenotype are not well understood. We conducted a prospective, observational study to better understand the Hb F% patterns and relationship to complete blood count (CBC) parameters in a tribal region of central India. 575 patients with SCA were recruited between 11/2021 and 4/2024 and blood was collected to determine Hb F%, Hb concentrations, and white blood cell (WBC) counts. We compared our findings to a separate cohort of 240 SCA patients enrolled in a registry from a large urban academic center in the US. The Hb F% from the US cohort were obtained either before initiating hydroxyurea therapy or at the time of recruitment if not on hydroxyurea therapy. The median age of the SCA cohort from India was 16 (interquartile range [IQR], 9 - 25) years old and 284 (49%) were female. The median Hb F% was 26.8% (IQR, 22.3% - 31.2%) and the median Hb concentration was 8.5 g/dL (IQR, 7.3 - 9.6 g/dL). Females had significantly higher Hb F% versus males (25.7% vs. 23.4%, respectively; P = 0.001) while older age was associated with a trend for lower Hb F% (β -0.032 ± 0.027; P = 0.1). A higher Hb F% was associated with improved Hb concentrations (β 0.083 ± 0.012; P < 0.0001) and lower WBC counts (β -0.129 ± 0.035; P < 0.0001). In the US cohort, the median age was 31 (IQR, 23 - 42) years old and 143 (59%) were female. The median Hb F% was 4.4% (IQR, 2.4 - 7.9%) and significantly lower compared to the Hb F% observed in SCA patients from India (age-adjusted P < 0.0001). The median Hb concentration was 8.6 g/dL (IQR, 7.7 - 9.4 g/dL), similar to the Hb concentrations observed in SCA patients from India (P = 0.6). Consistent with the Indian cohort, females had significantly higher Hb F% versus males (5.0% vs. 3.4%, respectively; P = 0.002) while there was no association between age and Hb F% (P = 0.7) in the US SCA cohort. Also similar to the Indian cohort, a higher Hb F% was associated with improved Hb concentrations (β 0.084 ± 0.019; P < 0.0001) and lower WBC counts (β -0.125 ± 0.047; P = 0.008). In conclusion, we demonstrate laboratory phenotyping of a large cohort of patients with SCA from a tribal region in central India. Hb F% were significantly higher in this cohort compared to a cohort of SCA patients from the US. We observed parallel findings between the two cohorts for higher Hb F% in women versus men as well as the protective effects of Hb F% on improved hemoglobin concentrations and reduced WBC counts, a biomarker that predicts vaso-occlusive episodes. Interestingly, although Hb F% were higher in SCA patients from the cohort in India vs. US, the Hb concentrations were similar. This suggests other factors, such as nutritional deficiencies, may be contributing to the anemia in SCA patients from India. Continued research is needed to better understand the phenotype of SCA in India as well as the impact of therapies that augment Hb F%, including gene therapy, in this patient population.

Implications of CMIP6 Models‐Based Climate Biases and Runoff Sensitivity on Runoff Projection Uncertainties Over Central India

ABSTRACTAccurate runoff projections are vital for developing climate adaptation strategies, yet significant uncertainties persist. The commonly employed approaches to constrain these uncertainties rely on the stationarity of climate biases and runoff sensitivity, which may not hold for climate‐sensitive regions (e.g., semi‐arid regions). This study investigates the validity of the stationarity assumption across 29 CMIP6 models, encompassing diverse climate biases (Dry Warm, Wet Warm, Dry Cold, and Wet Cold), utilising a semi‐arid region in central India as a testbed. The implications of this assumption on runoff projection uncertainties were comprehensively assessed across the runoff modelling chain for three time periods (the 2030s, 2060s and 2090s) based on the Soil and Water Assessment Tool (SWAT) simulations. The results highlight the non‐stationary nature of climate biases and runoff sensitivity under future scenarios, challenging the widespread applicability of common uncertainty‐constraining approaches. Moreover, the impact of non‐stationarity on runoff projection uncertainty was found to be strongly influenced by the choice of GCMs, preprocessing methods and climate change scenarios. In the 2030s, GCMs dominate runoff uncertainty, with dry models exhibiting ~10%–15% higher uncertainty compared to warm models, which is further amplified when interacting with warm biases. However, from the mid‐century onwards, the bias‐adjustment approaches and climate change scenarios significantly shape runoff projection uncertainties under non‐stationary conditions. These findings emphasise the potential of climate bias and runoff sensitivity‐based GCM selection for reducing runoff uncertainty in near‐future assessment (2030s). For mid‐term and long‐term runoff projections, addressing diverse climate biases through bias‐adjustment approaches is more viable. This study offers critical insights to prioritise the development of a non‐stationarity‐based approach for reliable runoff projections in climate‐sensitive regions.

Direct Seeding of wheat Using Happy Seeder in Paddy Residue Conditions in Madhya Pradesh, India

Trials were conducted on direct sowing of wheat variety GW-322 through happy seeder in combine harvested rice fields in district Narsinghpur, Madhya Pradesh, during two successive year’s viz. 2015-16 and 2016-17. The control treatment was conventional field preparation after combine harvesting of rice followed by sowing of wheat by seed cum fertilizer drill. District Narsinghpur lies in Central Narmada valley region of India. The average annual rainfall of the district is 1136 mm [1] (Yadav et.al. 2021). Soils of the district come under the vertisol category. Results showed that crop yield and net return under the direct sowing of wheat through happy seeder was higher than that obtained under the conventional method. Direct sowing through happy seeder fetched an average crop yield of 47.45 q/ha whereas under the conventional method it was 43.10 q/ha. The net return under direct sowing through happy seeder was Rs. 59025/- per ha whereas it was only Rs. 48850/- per ha under the conventional method. Happy seeder is an innovative technology and is being widely adopted to reduce stubble problem and conserve soil and environment. The results showed that the direct sowing of wheat through happy seeder performed better than conventional field preparation followed by sowing through conventional seed cum fertilizer drill in combine harvested rice fields.

Climate Change Effects on Spatiotemporal Distribution of Precipitation over West Central India: A Statistical Downscaling Approach

The long-term shifts in temperatures and weather patterns are referred to as climate change. Climate change not only leads to long-term shifts in average temperatures but also changes in the spatial and temporal distribution of rainfall over continents. This study aims to predict likely changes in the rainfall pattern induced by climate change over the West Central (WC) region of India. The approach uses a statistical downscaling technique that converts coarse-scale outputs of the global climate model (GCM) to high-resolution future precipitation projections giving refined distribution. The decision support tool that uses a robust statistical downscaling technique viz. statistical downscaling model (SDSM) is used for assessing local climate change impacts. The research includes the study of likely regional climate variability, by integrating historical observational data and empirical relationships between large-scale climate variables and local weather patterns. Historical data and the SDSM, version 4.2, are employed to forecast future rainfall trends. Rainfall data from the India Meteorological Department and the National Centre for Environmental Prediction (NCEP) from 1961 to 2001 are used along with outputs from general circulation models (GCMs), viz. Hadley Centre coupled model, version 3 (HadCM3), and coupled global climate model, version 3 (CGCM3), for the period 1961–2099. Rainfall scenarios are presented for three future time periods (2011–2040, 2041–2070, and 2071–2099). The study indicates a significant increase in the mean annual precipitation across the West Central India region, particularly in the 2050s and 2080s. Mean annual rainfall is projected to rise by 10–19.4% under HadCM3 A2 and B2 scenarios. The HadCM3 indicates the month of September as the month of the highest precipitation in later time periods, whereas it is the month of August, according to CGCM3 simulations. When comparing the results of the two models, HadCM3 gives better results, as indicated by better R2 value in validation. Thus, the analysis gives climate change-induced likely changes in the spatiotemporal distribution of precipitation over the West Central India region. The insight given by the work will be useful for decision making in many sectors like agriculture, water management, disaster risk reduction, and infrastructure planning.

Gond painting, and their women artiest: journey from tradition to modernity

ABSTRACT The central region of India, home to several tribal groups, is rich in cultural heritage, with the Gond tribe being one of the largest and most renowned for its artistic traditions. Gond painting, in particular, has evolved from wall art to canvas, gaining global recognition. This study examines the transformation of Gond painting, focusing on female Gond artists from Patangarh Mal village in Madhya Pradesh. Through 87 in-depth interviews, 3 case studies, and long-term observations, this research explores the evolution of Gond art, the challenges women face in adapting to technology, navigating markets, and interacting with media, and their current socio-economic status. The findings reveal that while the content of Gond painting has shifted to address contemporary issues, women artists struggle with patriarchal norms, low literacy, and limited mobility. These challenges hinder their professional growth and market engagement, resulting in noticeable disparities compared to male artists.

The maternal genetic history of tribal populations of Chhattisgarh, India

The central region of India boasts a rich tribal heritage and the highest number of tribal populations in the country. Analysing the genetic history of this population can offer valuable insights into various demographic processes that shaped the gene pool of present-day settlers of this region. In this study, we utilize a recently validated Next-generation sequencing (NGS) technique to sequence 24 tribal mitogenomes from the Chhattisgarh population for genetic ancestry and forensic analysis. The identified ancient haplogroups in this population can be traced back to the pre-Last Glacial Maximum (LGM) period. Our Bayesian analysis provides evidence for maternal ancestral expansion following the earliest Out-of-Africa migration, followed by a prolonged steady phase. We identified three basal founding haplogroups, M2, R5, and U2 in the Chhattisgarh region that diversified during the Neolithic period. Indistinct distribution pattern of these haplogroups among tribes and castes suggests that the maternal ancestry of Chhattisgarh population predates any kind of social stratification that exists today in the Indian subcontinent. Furthermore, our analysis suggests that this region remained unaffected by the Last Glacial Maximum. The forensic analysis of the mitogenomes demonstrates a high power of discrimination (0.9256) within the Chhattisgarh population, thus supporting the applicability of mitogenome NGS technology in forensic contexts.

In vitro toxicity assessment of bioavailable iron in coal varieties of Central India

Introduction Information on bioavailable Iron (BAI) content in respirable coal dust (RCD) is crucial to address occupational health and safety, especially in preventing coal workers’ pneumoconiosis (CWP). Materials and methods In the present study, we determined BAI concentrations in seventy-seven coal samples collected from ten coal mining regions of Central India. The cytotoxic potential of BAI-RCD was established invitro by using alveolar epithelial (A549) and macrophage (U937) cell lines. The oxidative/antioxidant status, inflammations, and genotoxicity attributed to BAI-RCD exposure were evaluated and correlated with CWP pathophysiology. Results The mean BAI concentrations in the coal samples (n = 77) range from (275 to 9065 mg kg-1) and showed wide variability. Both cell lines were exposed to low (275 mg kg-1), moderate (4650 mg kg-1), and high (9065 mg kg-1) BAI-RCD samples showed significant (p < 0.001) cytotoxicity in a dose-dependent manner (low < moderate < high) compared to the control. After BAI-RCD treatment, both cell lines showed a decrease in antioxidant stress measures (SOD, CAT, and GSH) and a significant (p < 0.001) increase in oxidative stress parameters (NADPH, MPO, LPO, and PC). Furthermore, these cell line models demonstrated a statistically significant (p < 0.001) dose-dependent increase in cytokines (TGF-β1, IL-1β, TNF-α, MCP-1, and IL-6 cytokines) and oxidative DNA damage marker (8-OH-dG). Conclusion Results indicated that the central India coals (even at low BAI content) may be accountable for inflammatory responses and cytotoxicity. Hence, BAI can be important characteristic to establish safety standards for coal dust exposure before active mining.

P.115: Human leukocyte antigen distribution among kidney transplant donors and recipients: Data from central Indian region.

P.115: Human leukocyte antigen distribution among kidney transplant donors and recipients: Data from central Indian region.

Impact of projected climate variability on agricultural water demand of key crops in drought-prone central India region

ABSTRACT Water scarcity predominantly affects agriculture production. It is mandatory to save future water to prevent its shortage due to variations in climate. Variation in climate is a significant factor responsible for frequent droughts over the Bundelkhand region in central India. It is necessary to obtain early warning studies in this region to manage and arrange future agricultural water. Out of best Regional Climate Models (RCMs) under the Coordinated Regional Downscaling Experiment (CORDEX) and their driving Coupled Model Intercomparison Project Phase 5 (CMIP5), General Circulation Models (GCMs) for India, the most effective model has been found based on the observed multiscale data for the region. The daily projected data (2021–2100) of best GCM, Earth System Model, ESM-2M have been used to evaluate the future water requirements for major rabi and hot weather crops grown in the region. The climate change effect is seen in the majority, especially in the upper Bundelkhand part. Most of the crops will need more water than the present condition, especially from 2071 to 2100. The findings will also help researchers and policymakers to utilize the future projected data to propose suitable water management strategies for agriculture.

Applicability of calibrated diffuse reflectance spectroscopy models across spatial and temporal boundaries

Diffuse reflectance spectroscopy (DRS) is an emerging soil testing approach. Although several studies have validated the DRS approach, limited efforts are made to assess the applicability of calibrated DRS models on new samples collected at different locations and/or time. To test such spatio-temporal applicability of calibrated DRS models, we collected surface soil samples from 1,112 smallholder farms during 2018 (T2018) and 607 farms during 2021 (T2021) covering seven districts of the Bundelkhand region of central India. The T2018 samples covered 7 development blocks; the T2021 samples were also collected from these blocks but from different sampling locations. Additionally, a new sampling site (Jhansi-Bamour block) was added during 2021 to create an independent test dataset. Collected samples were analysed for 17 soil parameters (basic soil properties, macronutrients, and micronutrients) and spectral reflectance over the visible to near-infrared region. Corresponding soil test crop response (STCR) ratings were also estimated. The Cubist model was calibrated in the T2018 dataset and tested against the T2021 dataset using the coefficient of determination (R2), root-mean-squared error (RMSE), and percentage relative error deviation (PRED) at 30% error threshold as performance statistics. Model applicability was assessed at each block level (site-specific), by dividing the study site into their two geology-specific regions, and by treating the entire dataset as a regional-scale spectral library. Results showed that DRS models calibrated on a finer scale (site-specific) are less efficient in estimating soil parameters in broader scale (geology-specific and regional-scale) test T2021 samples although their STCR ratings may safely be estimated at local scales. When site-specific data were aggregated to broader scales and T2018 dataset was spiked with 20% samples from the T2021 dataset, model performance improved for critical soil parameters such as soil organic carbon (SOC) contents and several plant nutrients and their ratings; application of such large-scale models also improved the estimation accuracy when applied to site-specific datasets. Exchangeable Ca and Mg, clay and SOC contents were frequently well-estimated with R2 values ranging from 0.54 to 0.93. Fine sand was the next best estimated soil property with R2 values in the range of 0.40–0.75. The STCR ratings estimated in the DRS approach matched the wet chemistry-based STCR ratings to the tune of 43 to 100%. Overall, as many as 60% of all new samples could be estimated with more than 70% accuracy for 8 out of 17 parameters. With the DRS approach tested on both spatially- and temporally-independent test datasets and, specifically, with high estimation accuracy of STCR ratings, our results suggest that the DRS approach may safely be used as a viable alternative to conventional soil testing in smallholder farms.

Assessing forest fire dynamics and risk zones in Central Indian forests: a comparative study of the Khandwa and North Betul forest divisions of Madhya Pradesh.

Forest fires pose significant environmental and socioeconomic threats, particularly in regions such as Central India, where forest ecosystems are vital for biodiversity and local livelihoods. Understanding forest fire dynamics and identifying fire risk zones are crucial for effective mitigation. The current study explores the spatiotemporal dynamics of forest fires in the Khandwa and North Betul forest divisions in the Central Indian region over 22years using Mann-Kendall and Sen's slope tests on MODIS (Moderate Resolution Imaging Spectroradiometer) fire point data. We found a nonsignificant increase in forest fires in both divisions. Khandwa showed a nonsignificant slope rise of more than three events per year, while North Betul revealed an increase of around one event per year. The lack of statistical significance suggests that upward trends of forest fire events may result from random fluctuations rather than consistent patterns. Spatial autocorrelation analysis revealed significant clustering of fire incidents in both regions. Khandwa confirmed moderate clustering (Moran's I = 0.043), whereas North Betul showed robust clustering (Moran's I = 0.096). Kernel density estimation further identified high-risk clusters in both divisions, necessitating zonal-wise targeted fire management strategies. Fire risk zonation was developed using the analytic hierarchy process (AHP), combining 10 environmental and socioeconomic factors. The AHP model, validated using MODIS fire data, showed reliable accuracy. The results revealed many of both divisions in the high- to very high-risk categories. Approximately, 45% of the area of the Khandwa and nearly 50% of the area of North Betul fall under high to very high fire risk zones. Khandwa's high-risk areas mainly lie in the northern and southeastern parts, while North Betul lies in the northwestern and north-eastern regions. The identified fire-prone areas indicate the pressing need for local or region-specific fire prevention and mitigation strategies. Thus, the findings of this study provide valuable insights into forest fire risk management and contribute to more focused research and methodological developments.

Evaluation of Organic Manure Amendments on Soil Carbon Saturation Deficit in a Rainfed Agriculture System of Himalaya, India

ABSTRACT Organic manure amendments into the rainfed agricultural soil could change the carbon saturation deficit. This study was conducted in the central Himalayan region of India to assess the impact of varying quantities of farmyard manure (FYM) and vermicompost (VC) on changes of soil carbon saturation deficit in the rainfed agricultural system. The experimental plots with three replications and three different amendment rates, such as 15, 30, and 60 Mg ha−1 of FYM and VC were used on the traditional paddy-wheat-finger millet-fallow cropping system. The soil amended with FYM and VC showed significant (p < .05) variations in the soil carbon content, nitrogen content, total microbial activity, β-glucosidase activity, and acid phosphatase activity with an increasing rate of both the manures in each cropping season. The mean soil carbon saturation deficit values varied from 25 to 36% and 19 to 28% in the first year cropping and second year cropping seasons, respectively. Our results demonstrate that the VC amendment was better than FYM for the accumulation of more carbon content in the rainfed agricultural soils of Himalaya, whereas the organic manure amendment rates must be considered for climate change impact mitigation action policy in the future.

Feasibility and Growth Dynamics of Mustard Farming in Uttar Pradesh and Bundelkhand Region of U.P. India

Bundelkhand is a region of central India lying in Uttar Pradesh and Madhya Pradesh. It is one of the least socio-economically developed regions of the country mainly due to regular incidence of drought and erratic rainfall. Mustard is one of the most important oilseed crop of rabi season for Bundelkhand region on the basis of area under the crop. With the limited resources, there has been a noticeable change in the area, production and productivity of mustard. Apart from edible oil, it also has industrial use in producing items like varnishes, soaps, paints, hydrogenated oil, perfumery, lubricants, etc. The study is an attempt to examine the pattern of growth, instability, cost and return of production of mustard crops at state (Uttar Pradesh) and regional (Bundelkhand region of Uttar Pradesh) level. Study is based on secondary data obtained from various published sources. Compound Annual Growth Rate, coefficient of variation, Commission for Agricultural Costs &amp; Prices (CACP) cost concept and returns were used as analytical tools to assess the financial suitability and performance of the crop in both the Uttar Pradesh and Bundelkhand region. It was observed that the growth in area (2.94%) and production (4.36%) of mustard crop in Bundelkhand region was increasing at a higher rate as that of Uttar Pradesh. Higher net return was earned by the farmer growing mustard in Uttar Pradesh than the farmer of Bundelkhand region.

PERFORMANCE OF RAPID POLYMYXIN NP TEST AMONG TWO SPECIES OF FAMILY ENTEROBACTERIACEAE

Objectives: Performing polymyxin susceptibility testing is a challenge in resource limited settings with broth microdilution as a standard method. Nordmann and Poirel introduced Rapid Polymyxin NP Test, qualitative calorimetric test for interpreting the susceptibility or resistance to colistin for Enterobacteriaceae. Due to limited data on the diagnostic accuracy of the test in central region of India, the current study was conducted to assess the diagnostic accuracy with ease of performance of Rapid polymyin NP test in comparison to the reference method, broth microdilution for Carbapenam resistant Escherichia coli and Klebsiella pneumoniae, the two prevalent species isolated across samples. Methods: Single center tertiary care hospital based cross sectional study for a period of eight months. The Rapid polymyxin NP test and broth microdilution test was performed for 13 isolates of carbapenam resistant E.coli and 10 isolates of Klebsiella pneumoniae sub species pneumoniae, including only one isolate per patient. Results: Rapid Polymyxin NP test showed 86.95% sensitivity and 100% specificity when compared to BMD test. Time to positivity by rapid polymyxin NP ST was 3 to 4 hrs when compared to BMD ST which was as almost 10-12hrs. Conclusions: The specificity of 100% for the Polymyxin NP test indicates that it may be utilized as a confirmatory test. With the sensitivity as 86.95% Polymyxin NP test can be used as a very good screening test. The NPV was 40% hence whenever the test result is resistant by Polymyxin NP test one can only be 40% sure that it is a true resistant.

Changes in Soil Physical Properties and Soybean Productivity under Preparatory Tillage Systems in Virgin Vertisols of Central India

Preparatory tillage, undoubtedly, is one of the most crucial practices that alter soil physical attributes and also affect crop growth and yield. In this sense, this experiment was designed to evaluate the effect of various preparatory tillage systems on soil physical properties and soybean productivity thereby find out a suitable tillage combination for vertisol of Vidarbha region of central India. The experiment was laid out in a randomized block design with eight tillage treatments and three replicates. Experimental results revealed that soil physical properties and productivity of soybean differed significantly under different preparatory tillage system and zero tillage. Further data revealed that conventional tillage treatment T8 (1ploughing + 2 tyne harrow + blade harrow) and T7 (1 Ploughing + 1 Tyne Harrow + 1 Rotavator) significantly improved the soil physical properties (infiltration rate, bulk density, porosity, soil moisture content) and grain yield (2378 and 2268 kg ha-1, respectively). Moreover zero tillage treatment T1 and shallow tillage treatments (1Blade Harrow + 1Rotavator and 1Tyne Harrow + 1 Rotavator) did not show any significant improvement in soil physical properties and grain yield of soybean.

Vaccine Adverse Effects Following Covishield among Health-care Workers in Central India

Abstract Introduction: This study aims to analyze common adverse effects following immunization that are temporally associated after receiving the above 2 doses of ChAdOx1 nCoV-19 Vaccine (Covishield) COVID-19 vaccines among health-care worker from January 2021 to April 2021, in the central region of India in a tertiary care hospital. Materials and Methods: A surveillance-based study was conducted between January 2021 and April 2021 among the health-care worker of the tertiary care hospital in the central part of India. The study was designed to identify the adverse effects reported after receiving a ChAdOx1 nCoV-19 Vaccine (Covishield) COVID-19 vaccination and demographic details were collected. Results: Within 48 h of the initial immunization dose, 651 mild adverse effects in all participants were documented. Headache (n = 168, 16%), fever (n = 105, 10%), fatigue (n = 115, 11%), and muscular discomfort (n = 115, 10%) were among the frequent side effects. One hundred and five minor adverse effects were reported between 3 and 7 days. No major adverse effects were observed after the first and second doses of vaccine. Conclusion: The first 48 h were the most common observational period for the short-term adverse effects of both dosages. After 15 days of both doses, the incidence declined for consecutive weeks until it was completely absent. The symptoms were transient and of a minor in nature. There were no documented severe vaccine-related side effects. Our research demonstrated that the vaccination had a decreased adverse event profile, was safe, and was well-tolerated.

Prevalence of Osteoporosis and Its Effect on Residual Ridge Resorption in Postmenopausal Females of Rural Central Indian Region.

To determine the correlation between body mass index (BMI), bone mineral density (BMD), and residual ridge resorption (RRR) in postmenopausal females and the effect of osteoporosis on RRR. A study was conducted with 60 postmenopausal female individuals. BMI was calculated using the weight and height of the patient using a formula. BMD was assessed and graded using a T-score. RRR was determined using the Tallgren method. Most individuals showed a higher BMI (63.33%), which is in the overweight or obese category. BMD was lower in approximately 68.33% of patients, and RRR was significantly higher in about 60% of total patients. The higher the BMI values, the lesser the BMDand the higher the RRR.

Assessment of personality factors amongst alcoholics and non-alcoholics in rural part of Central India: A cross-sectional study

Background Consuming alcohol has a significant negative impact on an individual’s mental, physical, and overall health in India. Recent data from the World Health Organization reveals that alcohol consumption is a leading cause of numerous fatalities. Some individuals may turn to alcohol as a way to cope with grief and anxiety; however, it diminishes the brain’s serotonin levels, potentially leading to suicidal thoughts and changes in personality. Consequently, there is an immediate need for health education and awareness campaigns addressing the consequences of alcohol use. This research aims to assess and compare the influence of personality factors on individuals who are alcohol-dependent and those who abstain from alcohol, exploring their role in the development of alcohol-related disorders in rural areas. Methods This in-hospital cross-sectional study is conducted among adults aged 18 to 65 in the rural region of central India. Employing a non-probability sampling method, specifically the Daniel Formula, a sample size of 180 was determined from the accessible population. The research has received approval from the ethics committee. In both the inpatient and outpatient departments of the Psychiatry unit at the hospital, individuals diagnosed with alcoholism and their non-alcoholic counterparts will be approached. Prior to providing socio-demographic information and completing questionnaires on the Alcohol Use Disorders Identification Test (AUDIT), CAGE Questionnaire, and Sixteen Personality Factor Questionnaire (16 PF), informed consent will be obtained from the participants. Expected outcome Upon obtaining responses to the aforementioned questionnaires, we will be able to analyze and determine the correlation of personality factors between individuals who are alcohol-dependent and those who abstain from alcohol.

Predicting global horizontal irradiance of north central region of India via machine learning regressor algorithms

The ability to predict global horizontal irradiance (GHI) in the wake of changing weather conditions is becoming increasingly important in driving economic growth in the renewable energy industry. The choice of relevant meteorological variables for predicting GHI is crucial because it influences the prediction accuracy due to various geographical conditions. To fulfill this aim, this proposed study identifies a subset of the relevant input variables for the prediction of GHI by applying two methods Feature Combination (FC) and Feature Selection (FS). The results reveal that the most significant input variables for predicting GHI are Solar Zenith Angle, Dew Point, Diffuse Horizontal irradiance, Direct Normal Irradiance, and Wind speed obtained by the FS method. The predictive performance of the selected features is evaluated by feeding them into six different types of Machine Learning (ML) regressor algorithms such as Multiple Linear regressor (MLR), Decision Tree (DT), Random Forest (RF), Gradient Boost (GB), Light Gradient Boost Machine (LGBM) and Extra Tree (ET). The performances of models are evaluated by using statistical measures such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). ET regressor gives the best prediction performance among all the six models. It is observed that the percentage reduction in error for the FS method as compared to the FC method in terms of the statistical indicators MAE, RMSE, and MAPE are 8.5%, 7.72%, and 25.84% respectively. It shows that the FS method gives better predictive performance than the FC method for the estimation of GHI.