Schizochytrium , a marine eukaryotic microorganism, has emerged as a candidate for the commercial production of PUFAs. EPA is an omega-3 PUFA with preventive and therapeutic effects against cardiovascular diseases, schizophrenia, and other disorders.

Introduction: Accurate and rapid diagnosis is crucial for the effective treatment of parasitosis. Traditional etiological methods, especially microscopic examination, are time-consuming, labor-intensive, and prone to false or missed detections. In response to these challenges, this study explores the use of artificial intelligence (AI) for the detection and classification of human parasite eggs through the YOLOv4 deep learning object detection algorithm.Methods: Eggs from species such as Ascaris lumbricoides (A. lumbricoides), Trichuris trichiura (T. trichiura), Enterobius vermicularis (E. vermicularis), Ancylostoma duodenale (A. duodenale), Schistosoma japonicum (S. japonicum), Paragonimus westermani (P. westermani), Fasciolopsis buski (F. buski), Clonorchis sinensis (C. sinensis), and Taenia spp. (T. spp.) were collected and prepared as both single species and mixed egg smears. These samples were photographed under a light microscope and analyzed using the YOLO (You Only Look Once) v4 model.Results: The model demonstrated high recognition accuracy, achieving 100% for Clonorchis sinensis and Schistosoma japonicum, with slightly lower accuracies for other species such as E. vermicularis (89.31%), F. buski (88.00%), and T. trichiura (84.85%). For mixed helminth eggs, the recognition accuracy rates arrived at Group 1 (98.10, 95.61%), Group 2 (94.86, 93.28 and 91.43%), and Group 3 (93.34 and 75.00%), indicating the platform’s robustness but also highlighting areas for improvement in complex diagnostic scenarios.Discussion: The results show that this AI-assisted platform significantly reduces reliance on professional expertise while maintaining real-time efficiency and high accuracy, offering a powerful tool for the diagnosis and treatment of parasitosis. With further optimization, such as expanding training datasets and refining recognition algorithms, this AI system could become a key resource in both clinical and public health efforts to combat parasitic infections.

Severe environmental conditions enhance the resilience of biomining microorganisms to elevated metal ion concentrations. The mechanism of how biomining microorganisms resist metal ions is poorly understood. We identified a novel reactive sulfur species (RSS)-sensitive MarR family transcription factor (SscRAc) in Acidithiobacillus caldus by persulfidation proteomics and observed increase in RSS and protein persulfidation levels under 250 mM Cu2+ stress. The deletion of sscRAc gene via CRISPR–Cas9 and conjugative transfer technology enhanced copper sensitivity in A. caldus. ChIP-seq/qRT-PCR revealed that SscRAc regulates copper detoxification by blocking efflux pumps and stimulating RSS metabolism. LC-MS/MS analysis revealed that both Cys74 and Cys78 in SscRAc interact with RSS and undergo persulfidation, resulting in the dissociation of the protein from the promoter–DNA of target genes. Upstream signaling analysis indicated that copper-sensitive repressor CsoRAc, regulated by SscRAc, inversely regulates SscRAc, thereby jointly enabling copper–RSS signal transduction. In conclusion, we identify SscRAc as the first RSS-dependent transcriptional switch directly linking copper toxicity with the persulfidation signaling pathway in extremophiles.