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Abstract Numerous under-researched edible plants are present in the desert regions of the world. These plants could be potential candidates to ensure food security and provide valuable bioactive compounds through diet. In general, the bioactives present in food manifest synergistic, additive, or antagonistic interactions. The current study investigates such interactions between food combinations traditionally consumed in (semi) arid regions. Five edible plants (representing three food categories) were selected: Prosopis cineraria and Acacia senegal (legume), Capparis decidua and Cordia dichotoma (non-legume), and Mangifera indica (fruit), in which the first four are largely underutilized. The antioxidant capacities of individual plant extracts and their binary mixtures were analyzed by DPPH free radical scavenging and FRAP assays. The total phenolic content (TPC) and total flavonoid content (TFC) were also determined. The highest antioxidant activity was obtained for Prosopis cineraria extract (EC50—1.24 ± 0.02 mg/ml, FRAP value—380.58 ± 11.17 μM/g), while Mangifera indica exhibited the lowest antioxidant activity (EC50—2.54 ± 0.05 mg/ml, FRAP value—48.91 ± 4.34 μM/g). Binary mixture of Prosopis cineraria (legume) and Mangifera indica (fruit) manifested maximum synergy (experimental EC50—0.89 ± 0.01 mg/ml, theoretical EC50—3.79 ± 0.05 mg/ml). Correlation studies [Pearson’s correlation coefficient (r) and Principal component analysis (PCA)] showed a high correlation of TFC with DPPH and TPC with FRAP values. LC–MS analysis of methanolic plant extracts detected 43 phenolic compounds (including phenolic acids, flavonoids, and isoflavonoids), possibly responsible for the observed food synergy. For edible plants of the (semi) arid zones, this study is a first-of-its-kind and provides scientific validation to the traditional wisdom of consuming these foods together. Such indigenous food combinations derived from desert flora could offer valuable insights into development of sustainable functional foods and nutraceuticals. Graphical Abstract

The use of enzymes to accelerate chemical reactions for the synthesis of industrially important products is rapidly gaining popularity. Biocatalysis is an environment-friendly approach as it not only uses non-toxic, biodegradable, and renewable raw materials but also helps to reduce waste generation. In this context, enzymes from organisms living in extreme conditions (extremozymes) have been studied extensively and used in industries (food and pharmaceutical), agriculture, and molecular biology, as they are adapted to catalyze reactions withstanding harsh environmental conditions. Enzyme engineering plays a key role in integrating the structure-function insights from reference enzymes and their utilization for developing improvised catalysts. It helps to transform the enzymes to enhance their activity, stability, substrates-specificity, and substrate-versatility by suitably modifying enzyme structure, thereby creating new variants of the enzyme with improved physical and chemical properties. Here, we have illustrated the relatively less-tapped potentials of plant enzymes in general and their sub-class of extremozymes for industrial applications. Plants are exposed to a wide range of abiotic and biotic stresses due to their sessile nature, for which they have developed various mechanisms, including the production of stress-response enzymes. While extremozymes from microorganisms have been extensively studied, there are clear indications that plants and algae also produce extremophilic enzymes as their survival strategy, which may find industrial applications. Typical plant enzymes, such as ascorbate peroxidase, papain, carbonic anhydrase, glycoside hydrolases and others have been examined in this review with respect to their stress-tolerant features and further improvement via enzyme engineering. Some rare instances of plant-derived enzymes that point to greater exploration for industrial use have also been presented here. The overall implication is to utilize biochemical clues from the plant-based enzymes for robust, efficient, and substrate/reaction conditions-versatile scaffolds or reference leads for enzyme engineering.

IntroductionPhenolic phytochemicals are known for antioxidant-mediated pharmacological effects in various diseases (diabetes, cancer, CVDs, obesity, inflammatory and neurodegenerative disorders). However, individual compounds may not exert the same biological potency as in combination with other phytochemicals. Cyamopsis tetragonoloba (Guar), an underutilized semi-arid legume which has been used as a traditional food in Rajasthan (India), is also a source of the important industrial product guar gum. However, studies on its biological activity, like antioxidant, are limited.MethodsWe tested the effect of C. tetragonoloba seed extract to enhance the antioxidant activity of well-known dietary flavonoids (quercetin, kaempferol, luteolin, myricetin, and catechin) and non-flavonoid phenolics (caffeic acid, ellagic acid, taxifolin, epigallocatechin gallate (EGCG), and chlorogenic acid) using DPPH radical scavenging assay. The most synergistic combination was further validated for its cytoprotective and anti-lipid peroxidative effects in in vitro cell culture system, at different concentrations of the extract. LC-MS analysis of purified guar extract was also performed.Results and discussionIn most cases, we observed synergy at lower concentrations of the seed extract (0.5-1 mg/ml). The extract concentration of 0.5 mg/ml enhanced the antioxidant activity of Epigallocatechin gallate (20 µg/ml) by 2.07-folds, implicating its potential to act as an antioxidant activity enhancer. This synergistic seed extract-EGCG combination diminished the oxidative stress nearly by double-fold when compared with individual phytochemical treatments in in vitro cell culture. LC-MS analysis of the purified guar extract revealed some previously unreported metabolites, including catechin hydrate, myricetin-3-galactoside, gossypetin-8-glucoside, and puerarin (daidzein-8-C-glucoside) which possibly explains its antioxidant enhancer effect. The outcomes of this study could be used for development of effective nutraceutical/dietary supplements.

Retinoblastoma (RB) is a primary childhood eye cancer. HMGA2 shows promise as a molecule for targeted therapy. The involvement of miRNAs in genome-level molecular dys-regulation in HMGA2 -silenced RB cells is poorly understood. Through miRNA expression microarray profiling, and an integrated array analysis of the HMGA2 -silenced RB cells, the dysregulated miRNAs and the miRNA-target relationships were modelled. Loop network analysis revealed a regulatory association between the transcription factor ( SOX5 ) and the deregulated miRNAs imiR-29a, miR-9*, miR-9-3 ). Silencing of HMGA2 deregulated the vital oncomirs ( miR-7, miR-331, miR-26a, miR-221, miR-17∼92 and miR-106b∼25 ) in RB cells. From this list, the role of the miR-106b∼25 cluster was examined further for its expression in primary RB tumor tissues (n = 20). The regulatory targets of miR-106b∼25 cluster namely p21 (cyclin-dependent kinase inhibitor) and BIM (pro-apoptotic gene) were elevated, and apoptotic cell death was observed, in RB tumor cells treated with the specific antagomirs of the miR-106b∼25 cluster. Thus, suppression of miR-106b∼25 cluster controls RB tumor growth. Taken together, HMGA2 mediated anti-tumor effect present in RB is, in part, mediated through the miR-106b∼25 cluster.

In the present study, the three main process parameters in the Fenton process for the removal of pharmaceutical compound Mefenamic acid from an aqueous solution were optimized using response surface methodology (RSM). Central composite design (CCD) was used for process optimization. The primary and secondary interaction effects of the selected parameters such as H2O2, Fe2+ and pH on the removal of mefenamic acid were examined. A mathematical model for the removal process based on the selected variables was developed. The interaction effect between the chosen parameters shows that the removal of mefenamic acid was enhanced in the acidic pH range at a high concentration of H2O2 and in a medium concentration level of the catalyst Fe2+. The removal efficiency of 81.24% was obtained for mefenamic acid at the optimized condition of variables such as 9.36 mM H2O2, 0.058 mM Fe2+and at a pH value of 2.1.

Mayiladuthurai is situated on the banks of river Cauveri. This research work aims to determine the quality of water which were collected from twenty groundwater sources. From the identified twenty locations, water samples were collected. The sampling stations include three taluks in the Mayiladuthurai district. The samples were analysed for various physicochemical qualities of water and compared with BIS & WHO standards. Frequency histogram and statistical analysis were applied to analyse the obtained data. Results of hydrochemistry revealed the dominance of hardness, magnesium and fluoride ions. The present investigation of hydrochemistry concludes that the drinking nature of the analyzed samples was very weak quality-wise and could not be used for drinking as such. Besides, fluoride ion related health problems were raised in some packets of the study area. The analytical report reveals that the quality of water has deteriorated and this may have a severe impact on human beings and other organisms in the study area.

Introduction: There was a low level of pandemic preparedness in South Asia, but the region has done well in mounting an appropriate response to the coronavirus disease 2019 (COVID-19) pandemic. The rate and proportion of deaths attributed to COVID-19 are lower despite case surges similar to the rest of the world. Results: The COVID-19 pandemic has revealed the glaring vulnerabilities of the health system. In addition, the high burden of non-communicable diseases in South Asia multiplies the complexities in combating present and future health crises. The advantage offered by the younger population demographics in South Asia may not be sustained with the rising burden of non-communicable diseases and lack of priority setting for improving health systems. Conclusion: The COVID-19 pandemic has provided a window for introspection, scaling up preparedness for future pandemics, and improving the health of the population overall.

The Water Footprint (WFP) is an estimate of freshwater utilization in food production and its impacts on water resources by individuals, communities and industries. It is partitioned into green component that denotes rain water use, blue, ground and surface water, and grey, water used to assimilate the pollutant. In this study we try to review the current understanding of WFP concept from a volumetric and impact-oriented perspective. A meta-analysis was done from published peer reviewed literature related to the agricultural WFP of crops from google scholar database for the time frame 2006 to 2020. The results from the volumetric review shows the goal of nearly 60% of the studies is on the water consumption of crops, 62% of the studies focused on the three components of WFP and 80% of them used the WFP assessment methodology for its accounting. The progress of WFP research illustrates water scarcity or depletion is well explored compared to water degradation. However, after the emergence of ISO 14046, a stand-alone Life Cycle Assessment (LCA) method, scientific community started focusing on sustainability/impact assessment from a water degradation (eutrophication, ecotoxicity footprints) outlook. From the impact-oriented review on freshwater ecotoxicity, it is understood that majority of the studies use compartment models such us USEtox and ReCiPe methods for impact assessments using an egalitarian time frame. This review suggests the potential factors influencing the water impact indicators and guidance for comprehensive WFP assessment. Besides, future WFP assessments may consider the drivers of water, food and energy security for a complete understanding for water resource and environmental management decisions

Significant variabilities in planting and harvesting dates of crops have been observed throughout Florida in recent decades, indicating a change in their phenology. This study innovatively uses an agroecosystem indicator, growing degree days (GDD), to understand the change in cotton crop phenology throughout the region and develop adaptation strategies using the Driver‐Pressure‐State‐Impact‐Response (DPSIR) framework. GDD is the amount of heat absorbed by the growing stages of cotton. It is computed from temperature simulations obtained from the 21 models participating in the Coupled Model Inter-comparison Project Phase 5 (CMIP5) for the historical (1950–2005) and future scenarios (Representative concentration pathway (RCP) 8.5, 2006–2100) at a spatial resolution of 0.125°x0.125°. The future projections from the 21 models show an increase in surface temperature ranging from 3.5 °C to 5.5 °C. Additionally, the variability in dates for the different phenological stages shows an early occurrence of the simulation’s growth stages. Historically, the minimum and maximum ranges of trend shift towards the funnel’s negative side in the RCP 8.5 scenarios. The trends are estimated for two time-periods during historical (1950–1975 and 1976–2005) and future (2006–2050 and 2015–2100) periods of time. They ranged from −3.5 to 3.4 days per decade and −3.6 to 0 (no change) days per decade, respectively, among the six stages namely: emergence stage, the appearance of the first square, the appearance of the first flower, peak blooming, first open boll, and defoliation. Warming accelerated plant growth and shortened the growing period, which is translated to develop adaptation strategies for a climate-resilient crop production system, using casual chain/loops and the DPSIR framework. Identifying the multiple adaptation strategies for levels of adaptation and degree of climate change and variability can be used by different stakeholders and policymakers as a guide for making decisions to adapt cotton to climate change better. Although this methodology is applied to the cotton crop in Florida, it can be used for other crops and regions of the world.

Introduction: Rickettsial disease in children has shown an increasing trend in several parts of India over recent years. The clinical features and seroprevalence vary in different geographic areas. Aim: To determine the seroprevalence and clinical features of Scrub typhus in paediatric patients. Materials and Methods: The prospective study was conducted for a period of six months included all paediatric patients

The present study was carried out to evaluate the effects of microwave (MW) radiation on viability of wood decaying fungi. The white rot (Trametes versicolor) and brown rot (Rhodonia placenta) fungi were grown on bamboo culm-samples. The mycelium growths were observed in controlled as well as microwave treated samples. The results showed that the viability of fungi decreased according to the applied MW time. This study proved the ability of the microwaves and exposure time MW3 (180 seconds) to kill the fungal colonies and do not allow for the growth of fungal spores, means the rate of growth of fungal colonies is inversely proportional to time of microwave exposure.

In any communication system, the emphasis is on estimating the channel response so as to retrieve the transmitted input signal accurately at the receiver‟s end. Channel Equalization at the transmitter refers to pre-distorting the input signal so that the effect of the channel is nullified during transmission. In this article, we study the performance of Multiple Input Multiple Output (MIMO) wavelet packet multicarrier modulation (WPMCM) with a low complex pre-distortive type channel equalization technique at the transmitter which adapts to the channel variations. WPM offers much lower side lobes in transmitted signal, which reduce its sensitivity to ICI and narrowband interference. Due to time overlapping, WPMCM systems don‟t require cyclic prefix (CP) or guard interval (GI) thus enhancing the bandwidth efficiency compared to conventional OFDM systems. This approach works out for slow fading channels where the channel response remains almost constant for a considerable amount of time (coherence time). [ABSTRACT FROM AUTHOR]

Nowadays, Photovoltaic (PV) system’s contribution to the power grid is increasing immensely. The PV system is linked with power grid through one or more inverters and a power conditioning unit to ensure smooth power delivery while also maintaining system voltage and frequency stability. However, due to grid load expansion, weak power grids have emerged as the dominant one today. As a result, the voltage and frequency of the grid got distorted. It may cause serious damage to the entire system in some cases. A single phase ZSI with the proposed Fractional order SMC technique is used to address these issues. The single phase ZSI consists of only two power switches and perpetuates the voltage transfer ratio like full bridge inverter. It also corroborates the sharing of power, regulatesactivepower,voltage, frequency, reactive powerand alleviates the harmonics in current. The input voltage of inverter is boostedby using theshoot through state and it also improvesthe reliability of inverter. The execution of the single phaseZSI withproposed FOSMCtechnique is assessed in MATLAB/Simulink to bear out the magnificent features of the proposed FOSMC in weak grid.

Internet of Things (IoT) edge devices are becoming extremely popular because of their ability to process data locally, conserve bandwidth, and reduce latency. However, with the developing count of IoT devices, threat detection, and security are becoming major concerns. IoT edge devices must avoid cyber threats and protect user data. These devices frequently take limited resources and can run on lightweight operating systems, which makes them vulnerable to security attacks. Intrusion detection systems (IDS) can be run on edge devices to recognize suspicious actions and possible risks. These systems monitor traffic patterns, and behavior, and identify attack signatures to detect and report on possible attacks. This study presents a design for an inverse chi square-based flamingo search optimization algorithm with machine learning (ICSFSO-ML) as a security solution for Internet of Things edge devices. The goal of the ICSFSO-ML technique is to apply ML and metaheuristics for threat recognition in IoT edge devices. To reduce the high dimensionality problem, the ICSFSO-ML technique uses the ICSFSO algorithm for feature selection purposes. Further, the ICSFSO-ML technique exploits the stacked bidirectional long short-term memory (SBiLSTM) model for the threat detection process. To enhance the efficacy of the SBiLSTM model, an arithmetic optimization algorithm (AOA) is applied for the hyperparameter selection process. The simulation performance of the ICSFSO-ML technique can be tested on a benchmark threat database. The performance analysis showed the benefits of the ICSFSO-ML methodology compared to existing methodologies with a maximum accuracy of 98.22%.

IntroductionIndia has a high prevalence of Vitamin D insufficiency among women of childbearing age. In this study, we aimed to evaluate the potential relationship between Vitamin D deficiency and gestational diabetes mellitus (GDM) and low birth weight (LBW) of newborns in the “Maternal antecedents of adiposity and studying the transgenerational role of hyperglycaemia and insulin” (MAASTHI) birth cohort.MethodsA prospective cohort study involving 230 participants was conducted in public hospitals located in urban Bengaluru, India. Healthy pregnant women who visited these hospitals for antenatal care (ANC) and who were between 14 and 36 weeks of gestational age were recruited after obtaining their informed consent. An oral glucose tolerance test (OGTT) was administered between 24 and 36 weeks of pregnancy and blood samples were preserved at −80°C for Vitamin D analysis. Follow-up at birth included recording the child's birth weight.ResultsWe found that 178 (77.4%) of the study participants were vitamin D deficient, 44 (19.1%) were diagnosed with GDM, and 64 (27.8%) gave birth to LBW babies. Women in the lowest quartile of serum Vitamin D levels had three times higher odds of developing GDM than women in the higher quartiles [OR = 3.22 (95% CI: 1.03, 10.07), p = 0.04] after adjusting for age, parity, socioeconomic status, season, and adiposity. For every one-unit increase in Vitamin D levels, Homeostatic Model Assessment for Insulin Resistance (HOMA-IR) decreased by nearly 18%. Furthermore, causal mediation analysis showed that a decrease in one unit of Vitamin D is associated with a decrease of 0.015 units of fasting blood sugar (FBS) and 0.019 units of postprandial blood sugar (PPBS) as it flows through the mediator variable insulin resistance. Vitamin D-deficient women were twice at risk of giving birth to LBW babies (OR 2.04, 95% CI 0.99, 4.19, p = 0.05).DiscussionsLow levels of Vitamin D during pregnancy are associated with a greater risk of pregnant women developing GDM and giving birth to LBW babies in urban Bengaluru.

This Review explores the transformative impact of artificial intelligence (AI) on the early detection of skin cancer, with a specific focus on melanoma, a potentially lethal form of the disease. Beginning with an overview of traditional diagnostic methods and their limitations, this paper delves into the evolution of AI within dermatology, emphasizing its application in image analysis and pattern recognition. A comprehensive examination of AI algorithms for melanoma detection, including machine learning and deep learning models, is provided. This Review critically assesses the performance metrics, training datasets, and comparative analyses with traditional methods. Addressing challenges such as data quality, interpretability, and ethical considerations, this paper outlines future directions, emphasizing ongoing research, algorithm improvements, and integration with clinical practices. Case studies and success stories highlight the real-world impact of AI in dermatology. This Review concludes by summarizing key findings and underlining the pivotal role of AI in revolutionizing early melanoma detection, with implications for personalized medicine and enhanced patient outcomes.

This article focuses on the early prediction of cardiovascular disease (CVD) through the application of machine learning to health records. This study systematically reviews existing literature and employs advanced machine learning algorithms to discern predictive factors within electronic health data. Key findings highlight the significance of genetic predispositions, lifestyle choices, and clinical markers as influential contributors to CVD development. The integration of these factors into machine learning models demonstrates notable accuracy in preemptive risk assessment. The implications of this research are profound, offering potential advancements in preventive healthcare strategies, personalized interventions, and resource allocation for populations at heightened cardiovascular risk.

The main focus of this work was the effect of chemical alkaline treatment on Himalayan nettle fibre extraction and the characterization analysis of surface-modified nettle fibre. Nettle fibre is an eco-friendly material naturally grown in the Himalayan hills of India, and it is replacing man-made fibres. The fibres are primarily bound to each other and, in turn, to the core of the plant with pectin, lignin, and gums, which begin to break down through fungal, bacterial, enzymes and chemical treatment action. The stem from the nettle plant is fibrous and has a high-quality fibre to develop nettle yarn, which is utilized to make clothes and handicrafts, mostly aimed at generating livelihood opportunities for the rural tribe’s people. This method of extraction is an effective chemical treatment for enhancing interfacial adhesion between nettle fibres and the epoxy, which is one of the significant challenges to their usage in textiles. In this paper, nettle fibres treated with chemicals such as 1% sodium hydroxide (NaOH), 0.5% sodium sulphite (Na2SO3), 0.05% ethylenediaminetetraacetic acid (EDTA), and 2% acetic acid (CH3COOH). The impact of bacterial and chemical treatments on nettle fibre and untreated nettle fibre was characterized by Fourier transform infrared spectroscopy (FTIR) analysis, which is used to study the functional elements, Scanning electron microscopy (SEM) images revealed that there is a fibre breaking mechanism and cross-section of yarn twist formation, physical and mechanical characteristics were then determined for fibre tensile strength, fibre length, Young’s modulus, elongation break, fineness, and moisture content.

The realm of healthcare is undergoing a revolutionary transformation through the integration of artificial intelligence (AI) for early cancer detection, ushering in a new era of enhanced well-being. This review paper delves into the paradigm shift brought about by AI, highlighting its potential to identify diseases in their nascent stages, thereby revolutionizing preventive healthcare. Harnessing the computational prowess of AI, this paradigm empowers us to analyze intricate medical data, including internal imagery and health records, facilitating the identification of subtle disease markers imperceptible to the human eye. The key focus lies not only in early detection but also in ensuring diagnostic accuracy. A comprehensive analysis of various studies underscores AI’s superiority in comparison to traditional methods, manifesting in quicker and more precise identification of anomalies. This transformation translates to expedited medical interventions and improved patient outcomes. The crux of this evolution lies in AI’s capacity to redefine healthcare, transforming it into a proactive endeavor that identifies and addresses health concerns while they remain manageable.

This paper explores the synergy between early diabetes detection and artificial intelligence (AI)-based diabetic retinopathy screening. It emphasizes the crucial role of integration for enhancing patient care. Diabetes and its complications, such as diabetic retinopathy, can be better managed with early intervention. AI techniques are revolutionizing medical diagnostics, offering potential for accurate prediction of diabetes risk and automated analysis of retinal images for retinopathy detection. By combining these aspects, we uncover a comprehensive strategy that not only identifies diabetes in its early stages but also ensures timely management of its ocular complications. This integrated approach shows promise for personalized care, improved outcomes, and efficient resource utilization. This paper underscores the need for collaboration between medical and AI experts to maximize the potential of this dual-focused approach for transforming diabetes management.

Unexpected event modelling is a affluent area of study in fuzzy matrix (FM) modelling. Every fuzzy matrix may be shown as a multidimensional cocept, but standard matrices cannot achieve this without the proper scale. To solve this issue, a certain kind of classical fuzzy matrix is required. In this study, the idea of an inverse of a k-regular fuzzy matrix is introduced, and some of its key characteristics are listed. As a result, the same regularity indicator is used to describe a matrix. Investigation is also done on the relationship between the regular, k-regular, and consistency of fuzzy matrices powers.

Introduction: Human Immunodeficiency Virus (HIV) and syphilis co-infection is common and affects similar age groups. The presence of syphilis infection increases the chances of transmission of HIV. Syphilis has a negative impact on HIV infection, resulting in increasing viral loads and decreasing Cluster of Differentiation 4 (CD4) cell counts during syphilis infection. Aim: To determine the seroprevalence of syphilis, quantify HIV-Syphilis co-infection in patients attending an Integrated Counseling and Testing Centre (ICTC) centre. Materials and Methods: This cross-sectional study was conducted at the Department of Microbiology, Government Villupuram Medical College, Tamil Nadu, India, for a period of four months from March 2021 to June 2021. All consecutive adult patients attending ICTC during the study period for voluntary testing or referred from antenatal clinic or Sexually Transmitted Disease (STD) clinic were included. Patients who were on treatment or follow-up of syphilis were excluded. Pretest counselling session was given and consent was taken for HIV testing. The HIV antibody tests were performed and interpreted according to the National AIDS Control Programme (NACO) guidelines. Rapid Plasma Reagin test (RPR) screening was done. Positive cases were subjected to Treponema pallidum Haemagglutination Assay (TPHA) and Syphilis Rapid Immunochromatographic Test (RICT). The results were analysed with the data systematically entered in Microsoft Excel format and p-value was calculated by Fisher’s- exact test using epi infoTM software. Results: Among the 400 patients attending the ICTC who were included in the study, six patients had a reactive HIV test. The overall HIV positivity was 1.5% (6/400). The RPR test was positive in 7/400 patients (1.75%). Among the seven patients with RPR positivity, five patients were positive by TPHA and Syphilis RICT. Biological False Positivity (BFP) rate in RPR against TPHA was 28%. Thus the frequency of syphilis among the study population by the confirmatory test TPHA was 1.25%. Among the five confirmed cases of syphilis, four patients were males (80%) and one patient was female gender (20%). Four of the five patients were in the 28-37 year old age group (80%) and one patient was in the 38-47 year age group (20%). The frequency of syphilis was 16.67% among HIV positives (17%) and 1.02% among HIV negatives biological false positivity by RPR was observed in 29% of the cases. Conclusion: HIV and Syphilis co-infection is common and Specific Treponemal tests could contribute to reducing errors due to false positivity by non specific tests like RPR which can be used as a screening test in microbiology laboratories.

Purpose The Maternal Antecedents of Adiposity and Studying the transgenerational role of Hyperglycaemia and Insulin cohort in Bengaluru, South India, aims to understand the transgenerational role of increased circulating glucose levels or hyperglycaemia and other nutrients and psychosocial environment, on the risk of childhood obesity, as an early marker of chronic diseases.Participants Through this paper, we describe the baseline characteristics of the cohort participants and their children, along with plans and challenges. A total of 5694 pregnant women were screened, with 4862 (85.4%) eligible pregnant women recruited at baseline. We assessed anthropometry, Haemoglobin status, Oral Glucose Tolerance Test (OGTT), dietary practices, depressive symptoms using the Edinburgh Postnatal Depression Scale and social support in all women. Follow-up visits involved assessing anthropometry and the health profile of mothers and children.Findings to date Among 4862 eligible participants recruited, 3260 (67%) underwent OGTT, while 2962 participants completed OGTT (90.9%). During the pregnancy, 9.7% of women were obese (>90th percentile of skinfold thickness), and 14.3% had gestational diabetesmellitus. Moreover, 6.2% and 16.8% of women had symptoms suggestive of depression during pregnancy and the immediate postnatal period, respectively. We found that 3.3% of children were small for gestational age, 10.8% were large for gestational age and 9.7% of children were obese at birth.Future plans We have completed recruitment and baseline data collection in 2019, and are conducting annual follow-ups until age 4 of the participant’s children. For delineating causal pathways of childhood obesity, blood aliquots are stored in the biorepository. The study will inform policy formulation and community awareness in the prevention and control of non-communicable diseases and health promotion.

Vitamins B12 and B6 and folate are known to have implications for pregnancy outcomes. We aimed to describe B6, B12, and folate status in pregnancy and investigate their associations with low birth weight and preterm delivery in mothers recruited from public hospitals in urban Bengaluru. Pregnant women between 18 and 45 years were included in the MAASTHI prospective cohort study. Each participant’s age, socioeconomic status, and anthropometry were recorded during baseline and followed up after delivery. Blood samples were collected between the 24th and 32nd weeks of gestation and stored at −80° for analysis. B6, B12, folate, homocysteine, and methylmalonic acid (MMA) levels were analyzed in the stored samples. We found low plasma vitamin B12, folate, and B6 levels in 48.5%, 42.0%, and 10.4% of the women (n = 230), respectively. Elevated MMA and homocysteine were observed among 73.6% and 6.1% of the women, respectively. We found B6 levels were significantly associated with birth weight (β(SE) −0.002(0.0), p = 0.001) after adjusting for age, parity, adiposity, gestational diabetes, and socioeconomic status of the mother. Those with impaired folate deficiency were twice at risk (AOR 1.95 (1.29, 3.07), p = 0.002) of low birth weight. Vitamin B6 levels and impaired folate status were associated with low birth weight in the MAASTHI birth cohort.

A social media is rapidly expanding, and its anonymity feature completely supports free speech. Hate speech directed at anyone or any group because of their ethnicity, clan, religion, national or cultural their heritage, sex, disability, gender orientation, or other characteristics is a violation of their authority. Seriously encourages violence or hate crimes and causes social unrest by undermining peace, trustworthiness, and human rights, among other things. Identifying toxic remarks in social media conversation is a critical but difficult job. There are several difficulties in detecting toxic text remarks using a suitable and particular social media dataset and its high-performance, selected classifier. People nowadays share messages not only in person, but also in online settings such as social networking sites and online groups. As a result, all social media sites and apps, as well as all current communities in the digital world, require an identification and prevention system. Finding toxic social media remarks has proven critical for content screening. The identifying blocker in such a system would need to notice any bad online behavior and alert the prophylactic blocker to take appropriate action. The purpose of this research was to assess each text and find various kinds of toxicities such as profanity, threats, name-calling, and identity-based hatred. Jigsaw's designed Wikipedia remark collection is used for this.

Wind energy is a rapidly growing field, and the ability to accurately predict wind turbine performance is essential for optimizing wind energy production. Machine learning technology has been successfully applied to predict wind turbine performance using various models such as neural networks, decision trees, and support vector machines. However, traditional machine learning models such as neural networks require a significant amount of time to train and optimize, and their performance can be affected by overfitting and underfitting. To address these challenges, a proposed backpropagation algorithm is introduced to predict wind turbine performance using a neural network model. The proposed methodology can be used in real-world scenarios to predict wind turbine performance and optimize wind energy production, contributing to the transition towards sustainable and clean energy sources.

Abstract Background Few studies have explored the relationship between food habits and the risk of gestational diabetes mellitus (GDM) in women from India. We aimed to investigate the associations of food habits and the risk of GDM. Methods As part of the MAASTHI prospective cohort study in urban Bengaluru, India, pregnant women between 18 and 45 years, less than 36 weeks of gestation were included. During baseline, the participant’s age, education, physical activity levels, and food habits were recorded. Screening of GDM was done by the World Health Organization diagnostic criteria using a 2-h 75-g oral glucose tolerance test between the 24th–36th weeks of gestation. Results We included 1777 pregnant women in the study. We show that 17.6% of the women had GDM, of which 76.7% consume red meat. Red meat consumption was associated with an increased risk of GDM (aRR = 2.1, 95% CI 1.5, 2.9) after adjusting for age, family history of diabetes and socioeconomic status. Conclusion The high intake of red meat consumption in pregnancy needs further examination. Also, future evaluations should consider evaluating the risk of red meat consumption against the combined effect of inadequate consumption of vegetables, fruits, and dairy products in pregnant women. Interventions to educate women in lower socioeconomic status on inexpensive, seasonal, and healthy food might be helpful.

Combining ability analysis was carried out in cowpea for yield and its component traits through Line × Tester design involving 24 hybrids by crossing 10 parents (6 lines and 4 testers). Analysis of variance revealed that non-additive gene action was prevalent for all the traits studied since the magnitude of SCA variances were higher than GCA variances and less magnitude of GCA:SCA ratio further confirmed it. Among the lines, VBN 1 had significant gca values for seven traits viz., days to 50% flowering, plant height, number of pods per plant, pod length, hundred seed weight, protein content and single plant yield and VBN 2 was the best general combiner for five traits like number of pods per plant, number of seeds per pod, hundred seed weight, protein content and single plant yield. The tester VCP-09-019 was regarded as the best general combiner among the testers for the traits such as plant height, pod length and single plant yield. The cross VBN 1 × VCP-09-013 which showed significant sca for six traits viz., days to 50% flowering, plant height, number of pods per plant, number of seeds per pod, protein content and single plant yield followed by the hybrid CO 6 × VCP-09-024 was showing best specific combining ability for five traits viz., plant height, number of pods per plant, pod length, hundred seed weight and single plant yield.

Background: Annually, more than a million low birthweight (LBW) is born in India, often afflicting disadvantaged families. Several studies have undertaken the association of poverty, nutritional status, and obstetric factors with LBW. Through our study, we aimed to examine the possibility of any relation between the Edinburgh Postnatal Depression Scale (EPDS) score measured during pregnancy with the incidence of babies born Small for Gestational Age (SGA). Methods: Pregnant women attending the antenatal clinic at a public hospital between 14 to 32 weeks were recruited from April 2016 to Oct 2017. The EPDS was administered to assess depression through face-to-face interviews. Newborn anthropometry was performed post-delivery. For analysis, birth weight 11) was 16.5% (n=108/654) in antenatal mothers. These women delivered a higher proportion of SGA babies (21.3 v/s 15.8) compared to women with no symptoms. The odds of women giving birth to a child with SGA were twice as high for women with EPDS scores >11 (adjusted OR = 2.03; 95% CI = 1.12 – 3.70) compared to the women with EPDS scores of ≤11, The EPDS 12 (Adjusted OR = 1.96; 95% CI = 1.04 – 3.69) and EPDS 13 (Adjusted OR = 2.42; 95% CI = 1.24 – 4.70) cut-off categories also proved to be a risk factor for SGA with significant p-value (0.0006 and 0.0003) and the individuals with more than 13 EPDS score is found to have the highest odds of SGA. Conclusions: We found a strong association of antenatal depressive symptoms during pregnancy with SGA measured by EPDS. Thus, we recommend the implementation of timely and effective screening, diagnostic services, and evidence-based antenatal mental health services to combat SGA and further associated-metabolic syndromes.