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Food fortification with micronutrients is one of the cost-effective and sustainable methods to prevent micronutrient deficiencies at community level. The rice fortified with iron, folic acid, and vitamin B12 is being supplied through various social welfare schemes in India in a phased manner and planned to cover the entire country by March 2024. We have conducted a situational analysis to assess the rollout of fortified rice supplied through the Public Distribution System (PDS) and to evaluate the accessibility, availability, acceptability, and utilization of fortified rice by the beneficiaries of the PDS. This was a mixed-method, sequential exploratory study conducted in six districts from six different states of India that had begun distribution of fortified rice through PDS in pilot mode during 2020-2021. In each district, the district supply officer of the PDS, Food Corporation of India (FCI) or State Food Corporation (SFC) warehouse supervisor, and four Fair Price Shop (FPS) dealers were interviewed. Under each FPS, a minimum of seven beneficiary households were randomly selected and interviewed using a structured questionnaire. The in-depth interviews were conducted with different stakeholders using theme guides. All the interviewed stakeholders were aware about their roles and responsibilities and purpose to distribute fortified rice. There was a continuous supply of fortified rice (across all visited districts) to beneficiaries through a well-established system. Acceptability and compliance to intake of fortified rice was good with no reported gastrointestinal adverse outcomes following fortified rice intake. There was an efficient roll-out of fortified rice though PDS with a good compliance to intake of fortified rice. It is feasible to design and conduct a study to assess the impact of fortified rice on anemia and iron storage at the community level.

Background: Cyclophosphamide is widely prescribed as an anti-cancer drug and used as an immunosuppressant. Hemorrhagic cystitis is one of the common complications of cyclophosphamide intake. We hypothesized that endoplasmic reticulum stress-related proteins could be altered in urothelium treated with cyclophosphamide. Objectives: We checked the effect of cyclophosphamide on the expression of various endoplasmic reticulum stress-related proteins in Vero cells. Methods: We treated Vero cells with varying doses of cyclophosphamide and observed its viability in flow cytometry using propidium iodide staining. We looked for changes in the expression of endoplasmic reticulum stress-related proteins in Vero cells treated with cyclophosphamide by western blot technique. Results: Cyclophosphamide at higher doses caused more death in Vero cells that could be attributed to an increase in apoptosis as evidenced by the changes in the morphology of cells and increased expression of endoplasmic reticulum specific caspase-12 proteins. Growth arrest/DNA damage 153 (GADD 153), one of the key transcription factors involved in the mediation of endoplasmic reticulum stress and apoptosis, was upregulated in Vero cells treated with cyclophosphamide. The protective effect of glucose-regulated protein GRP 78 against apoptosis was lost in Vero cells treated with a higher dose of cyclophosphamide, which is corroborated by decreased expression of GRP 78 in Vero cells treated with higher doses compared to Vero cells treated with lower doses of cyclophosphamide. Expression of disulfide isomerase protein, which guides misfolded proteins to fold properly, was downregulated in Vero cells treated with cyclophosphamide. Conclusion: To summarize, our study showed an alteration in the expression of key endoplasmic reticulum stress-related proteins in Vero cells treated with cyclophosphamide.

BackgroundChronic respiratory diseases (CRDs) are major causes of mortality and morbidity worldwide with a substantial burden of the disease being borne by the low and middle income countries (LMICs). Interventions to change health behaviour which aim to improve the quality of life and reduce disease burden due to CRD require knowledge of the problem and factors influencing such behaviour. Our study sought to appreciate the lived experiences of people with CRD, their understanding of the disease and its risk factors, and usual practice of health behaviour in a rural low-literate community in southern India.MethodsQualitative data were collected between September and December 2018 through eight focus group discussions (FGDs), five in-depth interviews and four key-informant interviews from patients and community members. Community engagement was undertaken prior to the study and all interviews and discussions were recorded with permission. Inductive coding was used to thematically analyse the results.ResultsMajor themes included understanding of chronic lung disease, health behaviours, lived experiences with the disease and social norms, attitudes and other factors influencing health behaviour.DiscussionPoor understanding of CRDs and their risk factors affect health seeking behaviour and/or health practices. Stigma associated with the disease and related health behaviours (e.g. inhaler use) creates emotional challenges and mental health problems, besides influencing health behaviour. However barriers can be circumvented by increasing community awareness; communication and connection with the community through community based health care providers can turn challenges into opportunities for better health care.

Background: Epithelial integrity is important for maintenance of periodontal health. It is not fully known if non-surgical periodontal therapy is capable of recreating the epithelial barrier in its functional state. Patients and Methods: Sixty-five patients (31 males and 34 females) were included in the study. They were divided into group A (healthy gingiva 16 patients), group B (gingivitis 17 patients), group C (periodontitis 17 patients), and group D (post-treatment 15 patients). Gingival samples were collected and immunohistochemical study was done using E-cadherin and CD1a antibody. Statistical analysis was done using analysis of variance (ANOVA), followed by Tukey-Kramer multiple comparison test for CD1a and Tukey′s highly significant difference (HSD) test for E-cadherin. Result: There was a statistically significant difference (P < 0.001) in the expression of E-cadherin between healthy (1.846±0.555), gingivitis (1.100±0.994), and periodontitis group (0.700±0.483). Similarly, there was a statistically significant difference (P < 0.001) in the expression of CD1a between healthy (75.70±3.09), gingivitis (42.53±3.09), and periodontitis group (29.07±3.08). However, the expression of E-cadherin (1.242±0.653) and CD1a in post-treatment samples (52.18±2.90) was lower with no statistically significant difference when compared to health. Discussion: The significant reduction in E-cadherin and CD1a levels in periodontal disease when compared to health could possibly be a result of invasion by the periodontopathogens and its subsequent sequel. Although, the post-treatment samples showed significant improvement when compared to disease, the reduction in E-cadherin and CD1a levels when compared to gingival health suggests that the epithelial barrier was not yet fully established in its functional state.

Cleidocranial dysplasias is an autosomal dominant disorder that presents with skeletal dysplasia. The dental manifestations are mainly delayed exfoliation of primary teeth and delayed eruption of permanent teeth, with multiple impacted supernumeraries. This report addresses the complex nature of the treatment modalities. In our patient, surgical exposure of unerupted teeth was done with orthodontic traction. Post-surgical follow-up was uneventful.

In the current scenario, the words Artificial Intelligence and Virtual Intelligence are the familiar ones among students of all ages. This study analyzes the usage of virtual intelligence during the pandemic period. The students of higher education mostly adopted the present way of teaching by using technology. A Positive approach to virtual Intelligence is exhibited in this study. 142 samples were collected by using a convenient sampling method through the well-framed questionnaire for the data analysis.

In the current scenario, Smart Phones are one of the Artificial Intelligence devices regularly used by the students. The study aimed to know the utility of the SmartPhones in their day to day life, most useful applications and the valid security features of the Smart Phones and its impact among the Students at Higher Education level. Primary data collected from the College Students in and around Chennai City during September 2019 and December 2019 was used for the present study. The collected data were analyzed by using Statistical tools of Percentage, Mean Score, and Mean Rank.

Blockchain is an innovative technology for storing data in decentralized, distributed, and secure chain blocks. Consortium blockchains are commonly used in transactions where transactions between organizations are also achieved by the blockchain. In the classic consortium blockchain system, entire nodes are added to each other in the process of transaction consensus. This leads to lower confidentiality in protecting transaction data within the organizations in the consortium. The throughput of the existing consortium blockchain system is still low. To solve the above problems, the paper proposes a two‐tier consortium blockchain with transaction privacy based on sharding technology. First, a trust value assessment is carried out to select the nodes of the blockchain. The duo‐head observation strategy uses these trust values to identify the nonmalicious node. Finally, the consensus separation approach based on the guarantee mechanism strategy with the shard nodes is presented. This approach is used to select reliable nodes for the blocks to be stored. The proposed consortium blockchain approach evaluation is done in terms of latency, throughput, and transactions per second metrics. As a result of the evaluations, the proposed model with 32 shards possesses 143,891 tx/s$$ tx/s $$ of throughput and 1.11 s of latency. Moreover, by the proposed two‐tier consortium model, time consumption is also decreased when uploading data. For a data set of 50,000, the suggested model has a time consumption of 96 s. The proposed research results in higher throughput and less latency in transactions. Also, the research enhances the scalability and reliability by overcoming the poor node issues. [ABSTRACT FROM AUTHOR]

Early Alzheimer's disease detection is essential for facilitating prompt intervention and enhancing the quality of care provided to patients. This research presents a novel strategy for the diagnosis of Alzheimer's disease that makes use of sophisticated sampling methods in conjunction with a hybrid model of deep learning. We use stratified sampling, ADASYN (Adaptive Synthetic Sampling), and Cluster- Centroids approaches to ensure a balanced representation of Alzheimer's and non-Alzheimer's cases during model training in order to meet the issues posed by imbalanced data distributions in clinical datasets. This allows us to solve the challenges posed by imbalanced data distributions in clinical datasets. A strong hybrid architecture is constructed by combining a Residual Neural Network (ResNet) with Residual Neural Network (ResNet) units. This architecture makes the most of both the feature extraction capabilities of ResNet and the capacity of LSTM to capture temporal dependencies. The findings demonstrate that the model is superior to traditional approaches to machine learning and single-model architectures in terms of accuracy, sensitivity, and specificity. The hybrid deep learning model demonstrates exceptional capabilities in identifying early indicators of Alzheimer's disease with a high degree of accuracy, which paves the way for early diagnosis and treatment. In addition, an interpretability study is carried out in order to provide light on the decision-making process underlying the model. This helps to contribute to a better understanding of the characteristics and biomarkers that play a role in the identification of Alzheimer's disease. In general, the strategy that was provided provides a promising foundation for accurate and reliable Alzheimer's disease identification. It does this by harnessing the capabilities of hybrid deep learning models and sophisticated sampling approaches to improve clinical decision support and, as a result, eventually improve patient outcomes. [ABSTRACT FROM AUTHOR]

Detecting defects and classifying them is a primary requirement for textile industries. Manual methods of defect identification and classification happens in most cases for which the accuracy could not exceed more than 70%. A real time, fast and automated system for defect detection and classification is required for textile industries. This paper addresses this challenge and explores the use of different machine-learning models for the global features such as GLCM and Tamura extracted from checked pattern fabrics. In the training phase, machine-learning models produce an accuracy of > 90% and testing with new fabric images provides a maximum accuracy of 65%. To improve the accuracy of the classification system, an artificial neural network-based machine-learning model with features fusing has been proposed. Using the proposed approach, a testing accuracy of 80% is observed, and an accuracy of 96.1% is achieved in the training phase. [ABSTRACT FROM AUTHOR]