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Background Women in India’s informal economy face significant occupational health risks that remain largely undocumented and unaddressed. With limited labour protections and inadequate access to health services, informal women workers (IWWs) experience overlapping vulnerabilities related to gender, work conditions, and environmental exposures. This study explored the multi-level determinants of health among IWWs in Ahmedabad, India, to inform gender-responsive integration of occupational health within primary health care systems. Methods A qualitative study was conducted using focus group discussions (FGDs) guided by the Socioecological Model (SEM). Five FGDs were held with 41 women representing key occupational groups—agricultural workers, construction workers, street vendors, home-based workers, and waste recyclers. Discussions were recorded, transcribed, translated, and thematically analysed. Themes were organized across SEM domains: intrapersonal, interpersonal, organizational, community, and policy levels. Findings Participants reported multiple, intersecting health risks such as musculoskeletal disorders, respiratory problems, skin irritation, and heat-related illnesses. Psychological stress, economic insecurity, and work-family conflict were pervasive, compounded by gendered expectations and absence of social protection. Poor workplace infrastructure, including lack of sanitation and shade exacerbated illness and fatigue. Many women avoided drinking water due to lack of toilets, leading to dehydration and urinary problems. Health-seeking behaviour was shaped by trust and convenience; private clinics were preferred over public facilities despite higher costs. Awareness of government schemes such as Ayushman Bharat and e-Shram was limited. Participants expressed demand for pensions, maternity protection, and home-based livelihood support. Conclusion Findings underscore the urgent need for gender-responsive occupational health integration into primary health care system. The study informed a national policy roundtable that convened key stakeholders to co-develop actionable recommendations to improve occupational health coverage for women in India’s informal economy.

Here, we explore the application of Raman spectroscopy for the assessment of plant biodiversity. Raman spectra from 11 vascular plant species commonly found in forest ecosystems, specifically angiosperms (both monocots and eudicots) and pteridophytes (ferns), were acquired in vivo and in situ using a Raman leaf-clip. We achieved an overall accuracy of 91% for correct classification of a species within a plant group and identified lignin Raman spectral features as a useful discriminator for classification. The results demonstrate the potential of Raman spectroscopy in contributing to plant biodiversity assessment.

Plant beneficial rhizobacteria (PBR) is a group of naturally occurring rhizospheric microbes that enhance nutrient availability and induce biotic and abiotic stress tolerance through a wide array of mechanisms to enhance agricultural sustainability. Application of PBR has the potential to reduce worldwide requirement of agricultural chemicals and improve agro-ecological sustainability. The PBR exert their beneficial effects in three major ways; (1) fix atmospheric nitrogen and synthesize specific compounds to promote plant growth, (2) solubilize essential mineral nutrients in soils for plant uptake, and (3) produce antimicrobial substances and induce systemic resistance in host plants to protect them from biotic and abiotic stresses. Application of PBR as suitable inoculants appears to be a viable alternative technology to synthetic fertilizers and pesticides. Furthermore, PBR enhance nutrient and water use efficiency, influence dynamics of mineral recycling, and tolerance of plants to other environmental stresses by improving health of soils. This report provides comprehensive reviews and discusses beneficial effects of PBR on plant and soil health. Considering their multitude of functions to improve plant and soil health, we propose to call the plant growth-promoting bacteria (PGPR) as PBR.

The recent news that 80 Christiar families of Chhindia village in... Gujarat's Vyara taluka have been forcibly driven out of the village by VHP activists--who have warned them that they will be allowed to return only if they embrace...

Here, we used Raman spectroscopy to characterize the effects of chitin treatment and fungal inoculations on Arabidopsis thaliana and Brassica vegetables. Chitin, a recognized fungal pathogen-associated molecular pattern (PAMP), elicited a dose dependent positive Elicitor Response Index (ERI) in wild-type Arabidopsis. Mutant plants lacking chitin receptors ( cerk1 and lyk4/5 ) displayed minimal ERI, whereas fls2 mutant deficient in the bacterial-specific flg22 receptor was hyper-responsive. These results confirm critical role of chitin receptors in activating downstream pathways and highlighting distinct responses in two separate pattern-triggered immunity (PTI) systems. Inoculations of Colletotrichum higginsianum and Alternaria brassicicola induced significant changes in Infection Response Index (IRI) values, with the former giving positive IRI at 12–48 hours post-inoculation whereas the latter exhibited a transient negative IRI before transitioning to positive values. Notably, Raman shifts could predict fungal infection before the appearance of visible symptoms, establishing Raman shifts as a potential early diagnostic marker. Comparative analyses of infected Brassica vegetables revealed varied sensitivity to fungal pathogens and a correlation between symptom severity and IRI values. Furthermore, randomized controlled trials validated the reliability of Raman technology for early, pre-symptomatic detection of fungal infections, achieving an accuracy rate of 76.2% in Arabidopsis and 72.5% in Pak-Choy ( Brassica rapa chinensis ). Principal component analysis differentiated Raman spectral features associated with fungal and bacterial infections, emphasizing their unique profiles and reinforcing the utility of Raman spectroscopy for early detection of pathogen-related plant stress. Our work supports the application of non-invasive diagnostic techniques in agricultural practices, enabling timely intervention against crop diseases.

Importance: Exact etiopathogenesis of chronic spontaneous urticaria (CSU) remains elusive. Infections, pseudoallergens, autoimmunity, and contact sensitization are various postulated factors. Few studies are available measuring cytokine levels in CSU. Objectives: The aim was to study various etiological factors of CSU and levels of IL-6 and IFN-ϒ in cases and controls, and correlation between various etiologies with the levels of the abovementioned interleukins in the cases. Design: Case-control study performed over 2 years with no follow-up of the participants. Setting: It was a referral-center-based study. Participants: Sixty patients of CSU and equal age and sex-matched healthy controls were recruited on the basis of convenience sampling. Exposures: Biochemical and hematological investigations with hepatitis serology, thyroid function tests, anti-thyroid antibodies, and levels IL-6 and IFN-ϒ were performed in all cases and controls. All cases were subjected to ASST. Cases with all above negative tests were patch-tested with Indian standard series. Urticaria activity score (UAS7) was calculated for all the cases and repeated in patients with positive etiological factor after 3 weeks (improvement after allergen or drug avoidance, treatment of infection). Outcomes: To study the various etiological factors (food, infection, autoimmunity, autoreactivity, and contact sensitization) and the levels of IL-6 and IFN- γ in patients of chronic spontaneous urticaria. Results: Etiology was ascertained in 75% of patients (autoimmunity: 50%, contact sensitization: 21.67%, food and drug allergy: 1.67% each). Mean values of the interleukins and anti-thyroid antibodies were significantly higher in cases versus controls. Levels of IFN-ϒ were significantly elevated in patients with higher UAS7 scores. Conclusion: Antithyroid antibodies, ASST, and patch testing are important tools and should be considered in patients of CSU after a thorough history and history-based workup. Elevated levels of IL-6 and IFN- ϒ in cases suggest that both Th1 and Th2 type of immune responses are implicated in pathogenesis of CSU.

This study examined factors influencing students' intention to use ChatGPT using UTAUT2 model. The cross-sectional study is based on responses collected from 578 students selected through convenience sampling at a university in Nepal through a structured questionnaire. Using PLS-SEM, the study found that habit (β = 0.315, p < 0.001), learning value (β = 0.254, p < 0.001), and social influence (β = 0.127, p < 0.001) were the significant factors positively affecting students' intention to use ChatGPT. In contrary, effort expectancy, hedonic motivation, facilitating conditions, and performance expectancy did not significantly influence intention to use. The study extended the UTAUT2 model by incorporating information accuracy as a moderating variable. However, information accuracy of ChatGPT did not moderate the hypothesized relationships. This study provides valuable insights for intention to use ChatGPT in education, with a unique focus on the moderating role of information accuracy. The findings offer practical implications for enhancing the adoption and effectiveness of AI tools in education, contributing significantly to the literature on educational technology.

This paper employs an analytical approach to achieve a precise solution and physical application for the unsteady one-dimensional adiabatic flow of weak shock waves with generalized geometries in a non-viscous perfect fluid under the influence of a weak gravitational field. In the disturbed region, the density is considered to have a functional relationship with distance, meaning that a relative change in distance from the source of disturbance causes a corresponding change in density. Finally, the problem’s solution comes in the shape of distance and time power. The current technique handles this scenario in a natural way, and the approximations produce results that are reasonably accurate.

Novel methods and materials are used in healthcare applications for finding cancer in various parts of the human system. To select the most suitable therapy plan for individuals with domestically progressed cervical cancer, robustness metrics are required to estimate their early phase. The goal of the research is to increase the effectiveness of cervical cancer patients' detection by using deep learning-based radiomics assessment of magnetic resonance imaging (MRI). From March 2016 to November 2019, 125 patients with early-stage cervical cancer provided 980 dynamic X1 contrast-enhanced (X1DCE) and 850 X2 weighted imaging (X2WI) MRI images for training and testing. A convolutional neural network model was used to estimate cervical cancer state based on the specified characteristics. The X1DCE exhibited high discriminative outcomes than X2WI MRI in terms of prediction ability, as calculated by the confusion matrix assessment and receiver operating characteristic (ROC) curve approach. The mean maximum region under the curve of 0.95 was found using an attentive ensemble learning method that included both MRI sequencing (Sensitivity = 0.94, Specificity = 0.94, and accuracy = 0.96). Whenever compared with conventional radiomic approaches, the results show that a variety of radiomics based on deep learning might be created to help radiologists anticipate vascular invasion in patients with cervical cancer before surgery. Based on radiomics technique, it has proven to be an effective tool for estimating cervical cancer in its early stages. It would help people choose the best therapy method for them and make medical judgments.