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Biology subjects

Ameri, M.

Publications and source records attributed to Ameri, M..

2 recordsLinked to original sources

Altered Crosstalk of Bacterial Lipopolysaccharide with Immune Cells in Colorectal Cancer Compared to Paired Adjacent Intestinal Tissue

Commensal bacteria play a crucial role in modulating human immune responses in the intestine. Under homeostatic conditions, gut microbiota are tightly regulated by interactions with the mucosal immune system. However, colorectal cancer (CRC) is characterized by an imbalance in bacterial composition and bacterial translocation across the intestinal barrier. The spatial distribution of bacteria and their interactions with immune cells in CRC tumors are poorly understood. By applying 3D light-sheet imaging, spatial transcriptomics, and imaging mass cytometry to patient-derived CRC and adjacent tissue, bacterial lipopolysaccharide (LPS) is visualized alongside immune cells and vessels. The results show regional bacterial LPS accumulation and colocalization with distinct immune cell subsets. In CRC-adjacent tissue, bacterial LPS is mainly associated with CD11c+ dendritic cells, CD15+ neutrophils, and CD163+ macrophages. In matched CRC tissue, the number and LPS colocalization of CD163+ macrophages and CD11c+ dendritic cells decreased, while CD15+ neutrophils and their colocalization with LPS increased. Notably, immune cell composition and immune cell-bacteria interactions differ between tumor and adjacent tissue, offering insights into host-microbiota dynamics and mechanistic interactions.

cancer biology↗

BioTrouble: A Multi-Agent Workflow for Troubleshooting Molecular Biology Techniques

Troubleshooting is a critical yet often underdocumented aspect of molecular biology experiments across laboratories. Failures in core techniques such as PCR, qPCR, molecular cloning, and related assays can lead to experimental failure, wasted resources, and delays in research progress. Here, we present BioTrouble, a multi-agent AI workflow designed to assist researchers in troubleshooting a wide range of molecular biology experiments. It leverages a custom-designed troubleshooting knowledge base through a retrieval-augmented generation (RAG) framework. BioTrouble employs small language models to generate the troubleshooting plan and utilizes a smart model routing system to manage cost per request. User interactions and feedback are stored as structured cases, enabling BioTrouble to expand its troubleshooting knowledge base and improve response generation over time. Compared with single-model SOTA LLM, BioTrouble generated comparable troubleshooting recommendations using small language models.

bioinformatics↗