Cancer does not have a universally agreed-upon description, but it is characterized by the unregulated proliferation of cells, which may result in the infiltration of the cell basement or the spread to other organs in the body.

In this study, benzene extraction from benzene-hexane mixture in room temperature [Emim][NTf2] ionic liquid (IL) has been studied by employing molecular dynamics simulation. We observed that [Emim][NTf2] IL can extract benzene from benzene-hexane mixture efficiently and effectively even if it is present at < 10 wt%. The extraction of benzene from the benzene-hexane mixture is characterized from calculation of distribution coefficient (β) and selectivity (S). An important rational behind the benzene extraction by IL is observed that benzene-cation interactions are stronger than benzene-anion interactions, which is also supported by the DFT calculations. Graphical Abstract

Background and aims The COVID-19 pandemic significantly impacted high-performance athletes (HPAs) by affecting their training and participation in competitions due to cancellations. The aim of this study is to analyze HPAs’ physical activity, sedentary behavior, and mental health during and after the COVID-19 lockdown while considering sex and type of sport. Materials and methods A repeated-measures observational study with a census sampling approach (N = 556) HPAs from a public university in Mexico. Informed consent was obtained, and a survey was administered in person and online. Physical activity and sedentary behavior were measured using the International Physical Activity Questionnaire (IPAQ), and mental health was assessed using the Profile of Mood States (POMS). Percentages, means, standard deviations, Student’s t-test, and Kruskal–Wallis tests were analyzed. Results Participants were an average age of 19.5 years; 50.2% were male, 55.9% practiced ball sports, 28.5% engaged in athletic sports, and 15.6% participated in combat sports. During confinement, 63.1% reported a high level of physical activity, which increased to 89.4% after confinement (X2 = 20.37, p < 0.0001). Moreover, 86% exhibited sedentary behavior during confinement, which decreased to 57.9% afterward (p < 0.00001). No significant differences were found considering sex or type of sport. Regarding mental health, significant improvements were observed in all dimensions by sex and type of sport (p < 0.05), except for the vigor dimension. Conclusions The lockdown impacted HPAs’ physical activity levels and mental health. After confinement, athletes increased their levels of vigorous physical activity and reduced sedentary behavior, reflecting adaptation to the new circumstances. Mental health indicators improved post-lockdown, although differences by sex and type of sport persisted in certain aspects.

Objective This study systematically reviews the integration of artificial intelligence (AI) with investment funds in the asset management industry and emphasizes its transformative impact. Aiming to bridge knowledge gaps, this study explores AI's position within the industry, analyzes its variety, and assesses the transformational implications of existing practices. Methodology Adhering to systematic review methodology, a comprehensive search was conducted across the Web of Science and Scopus databases, identifying 27 high-quality studies published from 2020 to 2024. The study was then analyzed thematically. Findings The first theme of the review classifies AI applications into front-end and back-end roles, illustrating the transition from traditional processes. On the front-end, AI assists simple activities by analyzing an investor's profile to determine a suitable fund, similar to a human financial consultant. The back-end sees AI performing autonomous trading and managing pooled fund investments, resembling a human fund manager. As a secondary theme, this review analyse AI deployment include using robo-advisors and chatbots for front-end tasks, screening analysis, predictive analytics, automated algorithmic trading, and automated trading technical analysis for back-end tasks. This study also includes a deductive discussion on the implications and transofrmation of AI deployment. Contribution/implications This review serves as an analytical framework to highlight AI in investment funds in asset management and serves as a springboard for future research and industrial applications.

Seed-associated microorganisms, including bacteria, fungi, archaea, and viruses, play diverse roles in seed physiology, influencing germination, seedling vigor, and long-term plant resilience. With increasing pressure to reduce synthetic inputs in agriculture, understanding the dynamics of seed microbiomes is critical for developing eco-friendly seed treatments. Recent advances in microbial engineering, bio-priming, and seed coating technologies have created new opportunities for harnessing beneficial microbes to improve germination and crop establishment under both optimal and stressful conditions. This review synthesizes current knowledge on the composition, transmission, and functional mechanisms of seed microbiomes of major crops. It highlights that germination outcomes are shaped by nutrient mobilization, phytohormone regulation, and stress buffering by beneficial microbes, as well as by pathogenic taxa that reduce viability through toxin secretion, decay, and competitive exclusion. Crucially, these effects are stage-specific, genotype-dependent, and mediated by microbe–microbe interactions. Furthermore, we examine the challenges in detection, strain compatibility, and large-scale application, and discuss emerging technologies such as synthetic microbial communities, nano delivery systems, and CRISPR-based strain optimization. We also explore potential biosafety concerns, such as horizontal gene transfer. This review presents a conceptual framework for integrating microbial-based solutions into seed systems, aiming to enhance agricultural sustainability in a changing climate.

Recent advancements in artificial intelligence (AI) technologies especially generative AI (GenAI) are promising to support the digital transformation of ed

This study explores the relationship between climatic, economic, and social factors and poverty across districts in India, focusing on the role of climate shocks in exacerbating poverty. Using a logistic regression model, the research investigates how variables such as climatic factors (temperature variability, precipitation, and flood-affected areas), economic structure (tertiary sector share in GSDP, primary sector dependence, and drought-prone areas), and social factors (Scheduled Tribe population) influence poverty rates. The findings suggest that climate shocks, especially variability in maximum temperature and flood impacts are significantly associated with higher poverty rates. Conversely, a higher share of the tertiary sector in district-level GSDP is linked to reduced poverty, emphasizing the role of economic diversification and human capital development in building climate resilience. Furthermore, districts with greater dependence on the primary sector, particularly in drought-prone regions, exhibit stronger correlations with poverty. Additionally, spatial analysis reveals that some agriculturally dependent regions experience lower poverty levels, likely due to stronger institutional arrangements such as better infrastructure and access to markets. This highlights the importance of regional context in shaping poverty outcomes. The study underscores the need for integrated and locally tailored policy interventions that promote economic transformation, climate adaptation, and institutional strengthening. It contributes to the existing literature on climate-induced poverty and aligns with the objectives of SDG 1 (No Poverty), SDG 2 (Zero Hunger), and SDG 13 (Climate Action), offering practical insights for targeted policy responses in climate-vulnerable regions.

This study explores the complex interplay between cognitive factors and STEM achievement, examining how various cognitive constructs influence studentsR

Imagine a world where diagnosing viral infections like common cold, influenza, Covid-19, measles or chickenpox is fast and affordable?

Migraine is a prevalent and disabling condition affecting more than 12% of the global population that remains enigmatic due to its multifaceted nature. Despite its high global burden, the underlying psychosocial and environmental dynamics contributing to episodic migraine (EM) remain underexplored, particularly within structured theoretical frameworks. Guided by the Four P model, this qualitative study, one of the first to apply the Four P model specifically to EM, investigates how psychosocial and environmental factors operate across predisposing, precipitating, perpetuating, and protective domains in EM. Twenty participants with EM took part in semi-structured interviews. Thematic analysis revealed strong inter-coder reliability and generated a comprehensive map of factors using network analysis of qualitative data, a novel method that visualizes the interconnectivity of themes. Predisposing factors included hormonal influence, long-term stressors, childhood trauma, personality traits, and genetic vulnerability. Academic pressure, interpersonal stress, health changes, diet-related factors, and life transitions emerged as key factors responsible for the onset of migraine. Perpetuating factors included environmental and routine instability, occupational strain, hormones, and personality rigidity sustained migraine vulnerability. Conversely, protective factors involved life management, self-awareness, absence of stress stimuli, and social support. The findings underscore the psychological and relational dimensions of migraine, offering implications for culturally sensitive prevention and intervention strategies. This study contributes to an integrative understanding of EM through a psychosocial and environmental lens. It highlights the need for further research into the role of social relationships in the experience and management of migraine.

AI has a significant impact on the evolution of web development and technologies, digital applications, and other applications. This study explores the integration of AI into web technologies, highlighting its contribution to interactivity, accessibility, personalization, and security. The use of AI in speech recognition, natural language processing, image processing, and reasoning can enhance user engagement through tools like voice navigation, chatbots, visual search tools, and intelligent recognition systems. Additionally, initiatives like Microsoft Airband, which deploys AI-powered network planning to expand broadband access, showcase how integrating AI in web applications and other services can boost digital equity, market growth, access to users, and broaden opportunities for innovation. However, these advancements also raise significant concerns around ethical standards, regulatory concerns, biases, accountability ty and user privacy, which must be addressed effectively. To address these challenges, the study evaluates the significance of the framework NIST AI RMF to emphasize management, risk mapping, and governance to meet all regulatory guidelines and safeguard trustworthiness. By tracing the evolution of the web from Web 1.0 to 4.0, the review identifies AI as central to the future web trends when humans and machines co-exist and create in an intelligent and immersive ecosystem. The findings in this study underscore the dual impacts of AI as a driver for web development innovation and a potential source of risk. Therefore, calling for balanced polices and an approach to implementation.

Insect’s harbour diverse microorganisms in their guts, which play crucial roles in nutrition, development, and pathogen defence. By studying these sy

Drought poses significant challenges to agricultural systems, particularly in arid and semi-arid regions, emphasizing the need to understand resilience strategies and their determinants to inform climate policies. This study assessed farmers' resilience to climatic drought using a relational framework connecting farmers’ perceptions and adaptation strategies, aiming to support climate-resilient pistachio orchards (POs) in Rafsanjan, Iran. The research was conducted in three stages: (i) Regional Drought Assessment—drought status was evaluated using indices and individual farmer characteristics; (ii) Perception Analysis—farmers’ awareness of drought and environmental impacts was examined; and (iii) Adaptation Strategy Identification—binary logit regression identified farmers’ adaptation strategies. Data were collected through structured questionnaires from 130 pistachio growers and regional climate datasets. Findings indicated declining regional drought indices (0.4 to − 1.11), reflecting increasing severity. Farmers reported major socio-economic impacts, including social conflict, migration, and poverty, as well as environmental effects such as groundwater depletion and poor water quality. Adaptation varied by age and experience, with key strategies including adoption of advanced irrigation technologies, off-farm employment, and securing land and water resources outside the region. These strategies enhanced resilience and reduced vulnerability to drought. To ensure the sustainability of pistachio orchards, the study highlights the importance of strengthening farmers’ capabilities and adaptive capacity. These insights serve as a foundation for developing targeted policies and advancing transformative climate governance for agricultural sustainability in drought-prone areas.

This article explores the historical roots and significant contributions of the Science, Technology, Society (STS) movement and its evolutionary offshoots Science, Technology, Society, Environment (STSE) and Socio-Scientific Issues (SSIs) within the context of science education. It critically assesses traditional STEM education, noting its alignment with global competition, national security, and labor market demands while frequently overlooking socio-political and ethical dimensions. The paper emphasizes the necessity of revisiting STS principles and Critical Education practices to enhance STEM education, advocating for an integrated framework that aligns with broader sociocultural and political contexts. It proposes adopting a critical and anti-oppressive perspective within integrated STEM education, focusing on the incorporation of ethical and democratic values. This approach seeks to align STEM education with students' needs, embracing a humanistic perspective and promoting equity and social justice. By fostering an integrated STEM education that prepares students to address the global challenges of the twenty-first century, science education can significantly contribute to contemporary democratic societies by providing students with the knowledge and skills to responsibly and ethically address critical social issues. Understanding and making informed decisions on scientific and technological issues with social, ethical, political, economic, and environmental dimensions are essential for the well-being and prosperity of the society as a whole.

Breast cancer is a pervasive global health concern, impacting millions of women worldwide. Timely detection and precise diagnosis are pivotal factors in improving patient outcomes. This review presents a comprehensive analysis of machine learning (ML), deep learning (DL), and transfer learning (TL) models applied to breast cancer detection. It encompasses the classification of different types of breast cancer, prognosis, diagnosis, prediction, and clinical decision support. The present study examines a wide range of articles to recognize the frequently used architectures, datasets, activation functions, and evaluation metrics. Furthermore, the review scrutinizes the effectiveness of various AI techniques in predicting and diagnosing breast cancer, elucidating various evaluation metrics and their utilization. The WDBC and BreakHis databases are image datasets commonly used for breast cancer prediction. The performance of machine learning, deep learning, and transfer learning algorithms varies significantly in terms of precision, recall, F1 score, and accuracy. CNN model is the most commonly used deep learning technique, with the study indicating that it is used by about 60% of researchers. In terms of network architecture, ResNet is used by about 57% of researchers. Conspicuously, Softmax occurs as the most frequently used activation function i.e., 89%, and accuracy and precision are the foremost metrics for performance evaluation i.e., 60%. According to the study, deep learning and transfer learning methods achieve the highest accuracy, reaching 99.54% in breast cancer detection which raises concerns about dataset bias, overfitting, and lack of external validation. In terms of machine learning based breast cancer detection, the random forest algorithm demonstrates remarkable success, achieving the highest accuracy rate of 99%. This review serves as a comprehensive exploration of the current state of AI applications in breast cancer, highlighting their potential to reshape the landscape of breast cancer healthcare.

Background Addiction is a chronic medical condition shaped by complex neurological, genetic, environmental, and experiential factors. Behavioral addictions, including gambling, internet use, and video gaming, often emerge during adolescence and can impair psychological functioning if untreated. In Nigeria, the prevalence and impact of behavioral addictions, particularly among higher institution students, remain underexplored. This study investigates the pathogenic influence of addictive behaviors on mental well-being among students in Kwara State, Nigeria. Methods A descriptive cross-sectional survey was conducted across six higher institutions, recruiting 629 participants through a multistage sampling technique. A structured and revalidated questionnaire assessed internet use, drug consumption, gambling, sexual behavior, and mental well-being. The instrument achieved a Cronbach’s alpha of 0.8. Data were analyzed using STATA 18, employing chi-square and multiple linear regression. Ethical approval was obtained from the Ethics Research Committee of the Kwara State Ministry of Health. Results The findings revealed that sexual habits (β = − 0.148, p < 0.001) and internet usage (β = − 0.101, p = 0.015) are both linked to poor mental well-being, with sexual habits having a slightly stronger negative impact. Positive mental well-being was reported by 73.7% of students. Significant associations were identified between age, religion, and social media habits (p < 0.05). Gender showed a strong association with drug use, with males exhibiting higher moderate usage (15.9%) compared to females (5.4%). Conclusion Behavioral addictions were found but relatively low among higher institution students in Kwara State, with notable socio-demographic variations. These findings highlight the need for targeted interventions to mitigate addiction risks and promote mental well-being, especially among university students.

Climate change particularly prevalent in semi-arid areas affects crop productivity negatively. The objective of this systematic review was to explore the r