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Background Maternal and child health among tribal populations in Odisha, India, is significantly influenced by socio-economic, cultural, and infrastructural factors. Cultural practices, reliance on traditional medicine, and limited awareness of modern healthcare benefits shape health-seeking behavior. This scoping review synthesises evidence on health-seeking behaviour, healthcare utilisation, awareness of healthcare services, government policies, and barriers in tribal communities in Odisha, India. Methods This scoping review was conducted following the Joanna Briggs Institute guidelines. We followed the Arksey and O’Malley methodological framework and applied the PAGER framework (Patterns, Advances, Gaps, Evidence for Practice, Research Recommendations) for quality of reporting. Studies were identified through systematic searches of international and Indian databases, Indian journal websites, organisational websites, repositories, and registries, focusing on health-seeking behaviour and healthcare utilisation among Odisha’s tribal communities. Only English-language articles published between January 2011 and July 2024 were included. The methodological quality of the selected studies was independently assessed by two reviewers using the JBI Quality Assessment Checklist. Results A total of 39 studies, encompassing 36,613 participants, were included in the review. The findings highlight significant barriers to healthcare access among tribal communities, including poverty, illiteracy, cultural practices, geographic isolation, distance to healthcare centres, transportation availability and mistrust of government services. While some tribes have shown progress in adopting modern healthcare services, many continue to rely on traditional medicine and indigenous practices. Socio-cultural factors, such as patriarchal norms and religious rituals, further influence healthcare-seeking behavior. Government initiatives like the National Rural Health Mission and the Integrated Child Development Services have had some success in improving healthcare utilisation among tribal populations. However, strengthening community support, conducting village-level awareness campaigns, and implementing targeted educational interventions can play a transformative role in enhancing healthcare access and overall well-being. Conclusion Improving maternal and child health in Odisha’s tribal populations requires culturally sensitive approaches integrated with modern healthcare strategies. Enhancing awareness, infrastructure, and community health workers’ roles can bridge access gaps while respecting tribal traditions.

Data imputation of missing values is one of the critical issues for data engineering, such as air quality modeling. It is challenging to handle missing pollutant values because they are collected at irregular and different times. Accurate estimation of those missing values is critical for the air pollution prediction task. Effective forecasting is a significant part of air quality modeling for a robust early warning system. This study developed a neural network model, a Temporal Convolutional Network (TCN) with an imputation block (TCN-I), to simultaneously perform data imputation and forecasting tasks. As pollution sensor data suffer from different types of missing values whose causes are varied, TCN is attempted to impute those missing values in this study and perform prediction tasks in a single model. The results prove that the TCN-I model outperforms the baseline models.

Air pollution exposure during daily transportation is becoming a critical issue worldwide due to its adverse effect on human health. Predicting the least air polluted healthier path is the best alternative way to mitigate personal air pollution exposure risk. Computing the least polluted path for the current time might not be helpful for real-time applications. Therefore, we develop a routing algorithm based on a neural network-based CNN-LSTM-EBK (CLE), a temporal-spatial interpolation model. The proposed model predicts pollution levels at high temporal granularity. This paper introduces a weight function to compute air pollution concentration at the road network. It also predicts the least air polluted path among all possible paths from a source to a destination at different time granularity. The results show that the predicted path may be longer than the shortest route but minimize pollution exposure risk all the time, which proves its effectiveness during daily transportation.

Air quality level is closely associated with our day-to-day life due to its serious negative impact on human health. Air pollution monitoring is one of the major steps of air pollution control and prevention. However, limited air pollution monitoring sites make it difficult to measure each corner of a region's pollution level. This research work proposes a methodology framework incorporating a deep learning network, namely CNN-BIGRU-ANN and geostatistical Ordinary Kriging Interpolation model, to address this research gap. The proposed CNN-BIGRU-ANN time series prediction model predicts the$P{M_{10}}$pollutant level for existing monitoring sites. Each monitoring site's predicted output is transferred as input to the geostatistical Ordinary Kriging interpolation layer to generate the entire region's spatial-temporal interpolation prediction map. The experimental results show the effectiveness of the proposed method in regional control of air pollution.

The harmful impact of air pollution has drawn raising concerns from ordinary citizens, researchers, policy makers, and smart city users. It is of great importance to identify air pollution levels at the spatial resolution on time so that its negative impact on human health and environment can be minimized. This paper proposed the CNN-BILSTM-IDW model, which aims to predict and spatially analyze the pollutant levelin the study area in advance using past observations. The neural network-based Convolutional Bidirectional Long short-term memory (CNN-BILSTM) network is employed to perform time series prediction over the next four weeks. Inverse Distance Weighting (IDW) is utilized to perform spatial prediction. The proposed CNN-BILSTM-IDW model provides almost 16% better prediction performance than the ordinary IDW method, which fails to predict spatial prediction at a high temporal period. The results of the presented comparative analysis signify the efficiency of the proposed model.

Caspases are proteolytic enzymes that belong to the cysteine protease family and play a crucial role in homeostasis and programmed cell death. Caspases have been broadly classified by their known roles in apoptosis (caspase-3, caspase-6, caspase-7, caspase-8, and caspase-9 in mammals) and in inflammation (caspase-1, caspase-4, caspase-5, and caspase-12 in humans, and caspase-1, caspase-11, and caspase-12 in mice). Caspases involved in apoptosis have been subclassified by their mechanism of action as either initiator caspases (caspase-8 and caspase-9) or executioner caspases (caspase-3, caspase-6, and caspase-7). Caspases that participate in apoptosis are inhibited by proteins known as inhibitors of apoptosis (IAPs). In addition to apoptosis, caspases play a role in necroptosis, pyroptosis, and autophagy, which are non-apoptotic cell death processes. Dysregulation of caspases features prominently in many human diseases, including cancer, autoimmunity, and neurodegenerative disorders, and increasing evidence shows that altering caspase activity can confer therapeutic benefits. This review covers the different types of caspases, their functions, and their physiological and biological activities and roles in different organisms.

Arsenic-contaminated drinking water can induce various disorders by disrupting lipid and glucose metabolism in adipose tissue, leading to insulin resistance. It inhibits adipocyte development and exacerbates insulin resistance, though the precise impact on lipid synthesis and lipolysis remains unclear. This review aims to explore the processes and pathways involved in adipogenesis and lipolysis within adipose tissue concerning arsenic-induced diabetes. Although arsenic exposure is linked to type 2 diabetes, the specific role of adipose tissue in its pathogenesis remains uncertain. The review delves into arsenic’s effects on adipose tissue and related signaling pathways, such as SIRT3-FOXO3a, Ras-MAP-AP-1, PI(3)-K-Akt, endoplasmic reticulum stress proteins, CHOP10, and GPCR pathways, emphasizing the role of adipokines. This analysis relies on existing literature, striving to offer a comprehensive understanding of different adipokine categories contributing to arsenic-induced diabetes. The findings reveal that arsenic detrimentally impacts white adipose tissue (WAT) by reducing adipogenesis and promoting lipolysis. Epidemiological studies have hinted at a potential link between arsenic exposure and obesity development, with limited research suggesting a connection to lipodystrophy. Further investigations are needed to elucidate the mechanistic association between arsenic exposure and impaired adipose tissue function, ultimately leading to insulin resistance.

Silver nanoparticles (AgNPs) are widely used in various industries, including textiles, electronics, and biomedical fields, due to their unique optical, electronic, and antimicrobial properties. However, the extensive use of AgNPs has raised concerns about their potential ecotoxicity and adverse effects on the environment. AgNPs can enter the environment through different pathways, such as wastewater, surface runoff, and soil application and can interact with living organisms through adsorption, ingestion, and accumulation, causing toxicity and harm. The small size, high surface area-to-volume ratio, and ability to generate reactive oxygen species (ROS) make AgNPs particularly toxic. Various bioremediation strategies, such as phytoremediation, have been proposed to mitigate the toxic effects of AgNPs and minimize their impact on the environment. Further research is needed to improve these strategies and ensure their safety and efficacy in different environmental settings.

Background Cardiac myxoma with glandular differentiation is a rare finding. It accounts for approximately ≤ 3% of all cardiac myxomas. The presence of cellular atypia resembling adenocarcinoma is an extremely rare finding in cardiac myxoma. Case presentation Here, we present a case of villiform cardiac myxoma with atypical glandular differentiation in a 16-year-old Indo-Aryan girl, which was initially mistaken as metastatic adenocarcinoma on routine histology. Conclusion A thorough histopathological examination, supported by immunohistochemical and radiological correlation, assists in accurate diagnosis and concurrently evades the misdiagnosis of metastatic adenocarcinoma.