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Muhammad Ayaz

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Open access Jul 2026

Integrated metagenomic and metabolomic insights into microbial metabolic reprogramming in the rhizosphere of the invasive plant Praxelis clematidea under low-temperature stress

A primary factor preventing the spread of the invasive plant Praxelis clematidea to higher latitudes and altitudes is the low-temperature stress induced by global climate change. The present study investigated the impact of low-temperature stress on the rhizosphere soil micro-ecosystem of P. clematidea, with the aim of examining its adaptive micro-ecological mechanisms via a comprehensive multi-omics approach. The rhizosphere soils of plants were compared under low-temperature (LT, 5 °C) or normal-temperature (HT, 25 °C) treatments. Using soil physicochemical analysis, enzyme activity assay, metagenomics, and non-targeted metabolomics, we observed that LT stress did not significantly alter microbial alpha diversity but strongly shifted the community structure. This change enriched cold-tolerant bacterial taxa, including Nocardiopsis, Sphingobium and Azoarcus. The LT stress was associated with altered carbon and nitrogen cycling, as indicated by increased soil urease activity but decreased alkaline phosphatase and catalase activities. The nitrate-N and ammonium-N levels increased, but total nitrogen, total organic carbon, and organic matter were reduced. Additionally, metagenomic study revealed overexpression of major microbial carbon metabolism genes (e.g., TCA cycle and glycolysis) and downregulation of nitrogen assimilation genes (e.g., glnA and NasA). Furthermore, metabolomics indicated a rise in carbohydrates and vitamins, along with a notable accumulation of stress-resistant secondary metabolites such as phenolic acids, flavonoids, and terpenes in the rhizosphere soils under LT stress. Correlation analysis indicated strong positive associations between the enriched cold-tolerant genera and these stress-resistant metabolites (e.g., costunolide and choline sulfate). Functional enrichment analysis suggested a metabolic reprogramming signature coupled with low-temperature treatment. Finally, this integrated multi-omics study reveals that P. clematidea is associated with an altered rhizosphere microbiome, differential functional gene abundance, and reorganized metabolic networks under low-temperature conditions. These findings offer a vital micro-ecological elucidation for P. clematidea effective colonization and propagation in novel, colder habitats.

Xiaowen Liu, Wen-Zhi Cheng, Chunhong Li et al. · 0 citations
Review Jul 2026

Medical question answering: A comprehensive multimodal and LLM-driven survey.

Medical Question Answering (MQA) has emerged as a critical artificial intelligence (AI) capability for supporting clinicians, researchers, and the general public with timely and evidence-based responses to medical queries. Recent advances in natural language processing (NLP), computer vision, and large language models (LLMs) have expanded MQA from text-only systems to multimodal frameworks. This survey aims to provide a comprehensive and structured review of MQA systems, covering both text and image-based approaches. We present a systematic review of MQA literature, including applications, datasets, and modeling paradigms. We introduce a unified taxonomy categorizing MQA systems into scientific, clinical, consumer, and examination-oriented tasks. We also analyze representative datasets for text-based and vision-based question answering, focusing on data sources, annotation strategies, task formulations, and evaluation protocols. Furthermore, we review methodological developments ranging from classical and transformer-based models to multimodal vision-language systems and LLM-driven approaches. The analysis highlights a rapid evolution of MQA systems toward multimodal and LLM-based frameworks, particularly in medical visual question answering. Existing datasets and models demonstrate strong progress but also reveal limitations in generalization, reasoning, and real-world clinical applicability. Key challenges remain, including reliability, hallucination, explainability, fairness, and clinical safety. This survey identifies open research directions such as improved data quality, knowledge-grounded reasoning, trustworthy evaluation, and real-world deployment. The study provides a comprehensive reference and roadmap for developing reliable and clinically applicable MQA systems.

Eya Mhedhbi, Xiang Zhu, Muhammad Ayaz et al. · 0 citations