Background: Few studies have assessed built environment correlates of active commuting in low-and-middle-income countries, but the different context could yield distinct findings. This cross-sectional study investigated associations between home neighbourhood environment characteristics and active commuting in Chennai, India. Methods: Adults (N = 370, 47.2% female, mean age = 37.9 years) were recruited from 155 wards in the metropolitan area of Chennai in southern India between January and June 2015. Participants self-reported their usual mode of commute to work, with responses recoded into three categories: (1) multi-modal or active commuting (walking and bicycling); (2) public transit; and (3) private transport. Environmental attributes around participants’ homes were assessed using the Neighborhood Environment Walkability Scale-India (NEWS-India). Associations between environmental characteristics and likelihood of active commuting and public transit use were modelled using logistic regression with private transport (driving alone or carpool) as the reference category, adjusting for age, gender, and household car ownership. Results: Consistent with other international studies, participants living in neighbourhoods with a mix of land-uses and a transit stop within a 10-minute walk from home were more likely to use active commuting (both p < 0.01). Land-use mix was significantly associated with the use of public transit compared to private transport (aOR = 5.2, p = 0.002). Contrary to findings in high-income countries, the odds of active commuting were reduced with improved safety from crime (aOR = 0.2, p = 0.003), aesthetics (aOR = 0.2, p = 0.05) and street connectivity (aOR = 0.2, p = 0.003). Conclusions: Different environmental attributes were associated with active commuting, suggesting that these relationships are complex and may distinctly differ from those in high-income countries. Unexpected inverse associations of perceived safety from crime and aesthetics with active commuting emphasize the need for high quality epidemiologic studies with greater context-specificity in the study of physical activity in LMICs. Findings have public health implications for India and suggest that caution should be taken when translating evidence across countries.

Chronic hepatitis B (CHB) infection and the Hepatitis B virus X protein (HBx) are major risk factors associated with hepatocellular carcinoma (HCC). In CHB infection, HBx induces mitochondrial dysfunction, exhaustion and impaired function in hepatocytes. Restoring hepatocyte health along with reduction in virus replication could be an ideal treatment for CHB. Thiourea derivatives are well known for their antiviral property though their effect on mitochondrial and/ or hepatocyte health remains obscure. This study focus on the repurposing of thiourea derivatives (DSA-00, DSA-02, and DSA-09) on hepatocyte replenishment. HepG2.2.15 cells were treated with thiourea derivatives, alongside Entecavir (ETV). The proteomics analysis showed both DSA-00 and ETV were enriched with proteins associated with antiviral responses. In addition, DSA-00 additionally showed increase in proteins linked to mitochondrial response. Whereas DSA-02 exhibited association with innate immune system and citric acid cycle and DSA-09 displayed pathways similar to DSA-00 and ETV. Treated groups exhibited enhanced bio-energetic and antiviral response as compared to the untreated group. FACS analysis revealed the restoration of exhausted hepatocytes by thiourea derivatives through targeting mitochondria. Our findings suggest that thiourea derivatives hold potential as a novel therapeutic agent that seems to restore mitochondrial health along with anti-viral response in CHB.

Objective: To summarize the prevalence of PASC/Long COVID symptoms among the general population reported in published articles. This narrative review examined 21 PASC symptoms. Methods: A PubMed/manual search returned 114 articles on general PASC/Long COVID symptoms. Manuscripts were excluded if they were not research studies, didn’t report symptom prevalence, or used a pediatric population. Ninety-eight studies were selected for review and 59 met criteria for inclusion. Risk of bias was assessed with the Hoy critical appraisal tool. Results: After excluding studies with high risk of bias, meta-analysis of prevalence for 21 symptom categories ranged from 2.6-28.7% in studies based on surveys to 0.3%-7.1% in studies based on EHR data. Conclusions: PASC symptom studies are limited by variability in study design and representation of the general population. Further research is needed to effectively cluster symptoms in meaningful ways that enable focused treatment.

Viroids are smallest pathogen that consist of non-capsidated, single-stranded non-coding RNA replicons and exploits host factors for their replication and propagation. The severe stunting disease caused by Citrus bark cracking viroid (CBCVd) is a serious threat, which spread rapidly within hop gardens. In this study, we employed comprehensive transcriptome analyses to dissect host-viroid interactions and identify gene expression changes associated with disease development in hop. Our analysis revealed that CBCVd-infection resulted in the massive modulation of activity of over 2000 genes. Expression of genes associated with plant immune responses (protein kinase and mitogen-activated protein kinase), hypersensitive responses, phytohormone signaling pathways, photosynthesis, pigment metabolism, protein metabolism, sugar metabolism and modification and others were altered, which could be attributed to systemic symptom development upon CBCVd-infection in hop. In addition, genes encoding RNA-dependent RNA polymerase, pathogenesis-related protein, chitinase as well as those related to basal defense responses were up-regulated. The expression levels of several genes identified from RNA sequencing analysis were confirmed by qRT-PCR. Our systematic comprehensive CBCVd-responsive transcriptome analysis provides a better understanding and insights into complex viroid-hop plant interaction. This information will assist further in the development of future measures for the prevention of CBCVd spread in hop fields.

Maize (Zea mays L.) stands as one of the most extensively cultivated crops globally, offering significant contributions to food, animal feed, and biofuel production. Maize yield is significantly impacted by diverse climate and soil variables, yet its productivity faces hindrances due to abiotic stresses, including drought, salinity, extreme temperatures, and cold conditions. In confronting these hurdles, the field of crop breeding has undergone a transformation thanks to high-throughput sequencing technologies (HSTs). These advancements have streamlined the identification of beneficial quantitative trait loci (QTL), associations between markers and traits (MTAs), as well as genes and alleles that contribute to crop improvement. Presently, well-established omics techniques such as genomics, transcriptomics, proteomics, and metabolomics are being amalgamated into maize breeding studies. These approaches have unveiled new biological markers that are being employed to bolster maize's capacity to withstand an array of challenges. In this chapter, we explore the current knowledge regarding the morpho-physiological and molecular mechanisms that underlie the resistance and tolerance of maize to biotic and abiotic stresses. The focus is on utilizing omics techniques to augment maize's ability to withstand these challenges. Moreover, it emphasizes the significant potential of integrating various omics techniques to tackle the challenges presented by biotic and abiotic stress in maize productivity, contrasting with singular approaches.

Type 2 diabetes mellitus (T2DM) is a polygenic metabolic disease described by hyperglycemia, which is caused by insulin resistance or reduced insulin secretion. Interaction between various genetic variants and environmental factors triggers T2DM. The main aim of this study was to find the risk associated with genetic variant (rs5210) of KCNJ11gene in the development of T2D in Indian Population. A total number of 300 cases of T2D and 100 control samples were studied to find the polymorphism in KCNJ11 through PCR-RFLP. The genotype and allele frequencies in T2DM cases were significantly different from the control population. We found a significant association of KCNJ11 (rs5210) gene polymorphism with T2DM in North Indian patients indicating the role of this variant in developing risk for T2DM.

The present study was aimed to evaluate the levels of oxidative stress markers in breast diseases by measuring the 8-hydoxy-2-deoxyguanosine (8-OHdG), vitamin A, vitamin C, vitamin E and total antioxidant status (TAS) alterations in relation to cell proliferation activity and disease progression. Significant increases in the level of oxidative damage marker 8-OHdG and cell proliferation activity were observed in breast carcinoma patients in comparison to benign and normal controls, which were accompanied by significant decrease in non enzymatic antioxidants and TAS concentrations. 8-OHdG and cell proliferation level were negatively correlated with non enzymatic antioxidants viz., Vitamin A, Vitamin C, vitamin E level and total antioxidant activity. Altered levels of biomarkers of oxidative stress and cell proliferation activity amongst the malignant, benign and controls suggest a correlation of increased oxidative stress and cell proliferation activity in the progression of disease in breast carcinoma patients. Among the oxidative stress markers and cell proliferation index, decreased level of vitamin A, vitamin C, vitamin E, TAS and increased level of 8-OHdG, cell proliferation index emerged as best predicted biomarkers for subjects with malignancy and benign breast disease.

In the intersection of the Internet of Things (IoT) and Machine Learning (ML), the choice between high-level and low-level programming libraries presents a significant dilemma for developers, impacting not only the efficiency and effectiveness of ML models but also their environmental footprint. We have proposed a comprehensive framework to aid in this decision-making process, underpinned by a detailed comparative analysis of both types of libraries on one of the key IoT ML task; image classification. We have introduced a novel algorithm designed to calculate the green footprint of ML model training, factoring in execution time, memory utilization, power consumption, and CPU temperature, addressing the urgent need for sustainable ML practices. Through an empirical evaluation of popular libraries such as PyTorch for high-level and Libtorch for low-level development, we have assessed their performance, development efficiency, and hardware compatibility. The culmination of our research is a decision support system that synthesizes the experimental findings to guide developers toward choices that harmonize model performance with environmental sustainability.