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

Novel agents for chronic lymphocytic leukemia (CLL) target specific pathways and mechanisms to overcome drug resistance and improve treatment outcomes. Here’s how they work:

Radiomics has emerged as a promising non-invasive imaging approach in pediatric neuro-oncology, offering the ability to extract high-dimensional quantitative features from routine MRI to support diagnosis, risk stratification, molecular characterization, and outcome prediction. Pediatric brain tumors, which differ significantly from adult tumors in biology and imaging appearance, present unique diagnostic and prognostic challenges. By integrating radiomics with machine learning algorithms, studies have demonstrated strong performance in classifying tumor types such as medulloblastoma, ependymoma, and gliomas, and predicting molecular subgroups and mutations such as H3K27M and BRAF. Recent studies combining radiomics with machine learning algorithms — including support vector machines, random forests, and deep learning CNNs — have demonstrated promising performance, with AUCs ranging from 0.75 to 0.98 for tumor classification and 0.77 to 0.88 for molecular subgroup prediction, across cohorts from 50 to over 450 patients, with internal cross-validation and external validation in some cases. In resource-limited settings or regions with limited radiologist manpower, radiomics-based tools could help augment diagnostic accuracy and consistency, serving as decision support to prioritize patients for further evaluation or biopsy. Emerging applications such as radio-immunomics and radio-pathomics may further enhance understanding of tumor biology but remain investigational. Despite its potential, clinical translation faces notable barriers, including limited pediatric-specific datasets, variable imaging protocols, and the lack of standardized, reproducible workflows. Multi-institutional collaboration, harmonized pipelines, and prospective validation are essential next steps. Radiomics should be viewed as a supplementary tool that complements existing clinical and pathological frameworks, supporting more informed and equitable care in pediatric brain tumor management.

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.

The CRISPR-Cas9 technology innovation has revolutionized the practice of genetic engineering, particularly in crop improvement, by allowing unprecedented precision and efficiency. This review provides a thorough overview of CRISPR-Cas9 discovery and utilization in crop characteristic enhancement, with particular focus on rice genetics. We begin with a concise overview of its discovery and technical development before describing how CRISPR-Cas9 has been used to improve major agricultural traits such as disease resistance, abiotic stress tolerance, yield enhancement, and nutritional improvement. We also discuss its potential to propel the development of drought tolerance, pest resistance, and nutrient enrichment of rice and demonstrate its revolutionizing potential in modern agriculture. Along with its benefits, this review also addresses major challenges of CRISPR-Cas9, including off-target mutations, regulatory challenges, and public opinion. In addition, we present future directions of CRISPR-Cas9, including multi-trait genome editing, improved delivery mechanisms, and cross-technology integration with other genomic tools. By synthesizing current work and pushing forward innovations, this review presents the tremendous potential of CRISPR-Cas9 to revolutionize plant breeding and contribute to sustainable agriculture solutions to world food security.

In this work, we reported the synthesis of honey bee (Apis mellifera) venom-derived nanoparticles via a hydrothermal method. This method not only ensures the preservation of the bee venom’s bioactive components but also enhances their potential stability, thus broadening the scope for their applications in the biomedicinal field. The synthesis method started with the homogenization suspension of bee venom, followed by its hydrothermal process to synthesize bee venom nanoparticles (BVNPs). The successful synthesis of BVNPs was characterized using various characteristic techniques such as Ultraviolet–visible (UV–Vis) spectroscopy, Fourier Transforms Infrared (FTIR) Spectroscopy, Zeta Potential (ZP), Liquid Chromatography-Mass Spectrometry (LCMS), and Transmission Electron Microscopy (TEM). The synthesis of BVNPs through biosynthesis is shown by the visible violet-brown color development at 347 nm by UV–Vis spectroscopy. FTIR analysis revealed the presence of several functional groups in the BVNPs, including alcohols (–OH), phenols (C6H5–), carboxylic acids (–COOH), amines (–NH2, –NH–), aldehydes (–CHO), ketones (–CO–), nitriles (–CN), amides (–CO–N–), imines (–CNH–), esters (–COO–), and polysaccharides. These functional groups, as confirmed by their specific stretching and bending vibrational modes, contribute to the diverse biological activities of BVNPs, including cytotoxicity against MCF-7 breast cancer cells. The ZP of the BVNPs indicated good colloidal stability at − 45 mV. LCMS analysis confirmed the presence of major bioactive molecules, including melittin & apamin and TEM analysis shows the BVNPs exhibited a quasi-spherical shape with good dispersion, the average size was approximately 25 nm, with some being smaller (quantum dots) and interplanar spacing of 0.236 nm indicated a highly ordered crystalline structure. Moreover, the anticancer efficacy of the BVNPs was ascertained through in vitro assays against MCF-7 breast cancer cells, showing a dose-dependent cytotoxic effect. The findings of this study underscore the viability of hydrothermal synthesis in producing biologically active and structurally stable BVNPs, with a significant potential for anticancer activities. Graphical Abstract

Aggressive behaviours towards other dogs is a common behavioural issue in pet dogs and frequently occurs during on-lead walks. These behaviour are of signi

Clean energy transitions are not just about technology. They are also about people, equity, and justice. Women play a pivotal role in advancing sustainable energy solutions, yet sociocultural, financial, and institutional barriers continue to limit their participation in decision-making and access to clean energy. This research combines BERTopic modeling, SDG mapping, and case study analysis to bridge quantitative insights with real-world narratives, offering a comprehensive examination of the gender‒energy nexus. Grounded in energy justice, gender empowerment, and SDG frameworks, the study applies Kabeer’s and Friedmann’s empowerment models to link agency, resources, and achievements with distributional, procedural, and recognitional justice in energy transitions. The study covered 616 publications identified through an extensive Scopus database search, spanning literature from 2015—coinciding with the adoption of SDGs—to 2024, specifically mapped to SDG 5 (gender) and SDG 7 (energy). Addressing the main energy justice dimensions and relevant SDGs, the findings of this systematic review reveal that clean energy adoption reduces unpaid domestic work (SDG 5.4), enhances women’s leadership (SDG 5.5), and strengthens economic opportunities (SDG 7.1, SDG 7.2) but remains constrained by gendered power dynamics, technology adaptation barriers, and financial accessibility issues. The study highlights how women’s participation in energy transitions leads to greater community engagement, increased household energy efficiency, and a shift toward sustainable energy behaviors. However, moderating factors of gender empowerment interventions show that intrahousehold bargaining, a lack of financial incentives, and limited representation in governance structures continue to restrict equitable energy access. Additionally, the findings emphasize that policies designed without a gender lens risk reinforcing existing inequalities rather than alleviating them. By embedding SDG goals in the analysis, this study ensures alignment with global sustainability goals and reinforces the urgency of justice-oriented energy policies. Advocating for inclusive, community-driven approaches, this research underscores the need for intersectional frameworks that integrate energy justice and gender empowerment, ensuring that energy transitions are not only technologically sound but also socially equitable and accessible to all.

The coronavirus disease 2019 (COVID-19) pandemic has presented humanity with enormous challenges in terms of both medically managing the acute waves of infection and dealing with the sociopolitical consequences of the important measures taken to combat the viral threat. The division of societies into opposing factions—those who support the measures and those who doubt the viral causality of the observed disease progression—has had a profound impact on our social and political systems. Furthermore, the prevalence of postviral syndrome (long COVID-19) and postvaccination syndrome is increasing. Numerous viral mechanisms that have facilitated the exponential spread of infection are known from the study of other viral pathogens. For example, the cleavage of the spike glycoprotein (SGP) into S1 and S2 by transmembrane serine protease 2 (TMPSSR2) markedly increases the affinity of the virus for the angiotensin-converting enzyme 2 (ACE2) receptor, thereby significantly increasing infectivity. Similarly, the potential blockade of α7n-acetylcholine receptors (α7nARs) can significantly impair the body's primary anti-inflammatory mechanism, the cholinergic anti-inflammatory pathway. The various mechanistic pathways of SARS-CoV-2 infection, such as SGP cleavage to accelerate viral entry and nonintrinsic high-affinity binding to acetylcholine receptors, are likely common to other viruses. Given the inevitability of the next viral pandemic, in addition to the continued advancement of antiviral vaccines, it is imperative to incorporate strategies to prevent future viral threats that target these mechanisms. This narrative review aims to search the literature for antiviral strategies that, in addition to the development of specific vaccines, may help to contain the rapid spread of infection chains. The proposed measures, such as the use of transmembrane serine protease 2 (TMPSSR2) inhibitors, antiviral mouthwashes or high-affinity cholinergic ligands, have the potential not only to counteract the development of viral resistance to vaccines, but also to prevent and treat post-acute infection syndromes in the event of future viral pandemics. Graphical Abstract

We explore how artificial intelligence (AI) and machine learning (ML) can enhance Network-on-Chip (NoC) application mapping. I discuss the challenges posed by the end of Moore's Law and highlight ML's potential to optimize performance, efficiency, and scalability in complex computing systems.

The integration of artificial intelligence (AI) into augmentative and alternative communication (AAC) systems has revolutionized the way non-verbal individuals interact with their environment. AI-powered symbolic text prediction offers innovative solutions to enhance expressive and receptive communication, promoting autonomy and social inclusion. This article examines the role of AI-driven predictive text within the frameworks of metaliteracy and Universal Design for Learning (UDL), emphasizing its potential to create adaptive and ethical digital communication environments. Recent advancements in machine learning models and natural language processing (NLP) have contributed to more sophisticated AAC tools; however, challenges such as bias in predictive algorithms, accessibility limitations, and ethical considerations persist. This study critically evaluates the benefits, challenges, and future directions of AI-powered symbolic text prediction, particularly in educational, therapeutic, and social settings. The findings highlight the need for equitable AI design that accounts for diverse linguistic and cognitive needs, reinforcing AI’s role in fostering digital inclusivity and empowering non-verbal individuals to become autonomous communicators. Clinical Trial Number: Not applicable.

As ancient Chinese historical records, Hua Tuo, a doctor, had used anger to cure patients with depression 2000 years ago. Traditional Chinese medicine theory believes that human emotions influence each other, and one emotion can suppress another emotion and it is applied to clinical practice.