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CRISPR‐based gene therapies are making remarkable strides toward the clinic. But the large size of most widely used Cas endonucleases including Cas9 and Cas12a restricts their efficient delivery by the adeno‐associated virus (AAV) for in vivo gene editing. Being exceptionally small, the recently engineered type V‐F CRISPR‐Cas12f1 systems can overcome the cargo packaging bottleneck and present as strong candidates for therapeutic applications. In this study, the pairwise editing efficiencies of different engineered Cas12f1/sgRNA scaffold combinations are systemically screened and optimized, and the CasMINI_v3.1/ge4.1 system is identified as being able to significantly boost the gene editing activity. Moreover, packaged into single AAV vectors and delivered via subretinal injection, CasMINI_v3.1/ge4.1 achieves remarkably high in vivo editing efficiencies, over 70% in transduced retinal cells. Further, the efficacy of this Cas12f1 system‐based gene therapy to treat retinitis pigmentosa in RhoP23H mice is demonstrated by the therapeutic benefits achieved including rescued visual function and structural preservation. And minimal bystander editing activity is detected. This work advances and expands the therapeutic potential of the miniature Cas12f1 system to support efficient and accurate in vivo gene therapy. [ABSTRACT FROM AUTHOR]

The article presents a comprehensive summary of Intelligent Ship Marine Object Detection (ISMOD) based on the RGB Camera. Marine object detection plays a pivotal role in enabling intelligent ships to acquire crucial data and security assurances for autonomous navigation. Among the various detection sensors, the RGB Camera is an informative and cost‐effective tool with a wide range of civil applications. In the beginning, the ISMOD metrics based on the RGB camera is analyzed from three significant aspects, namely accuracy, speed, and robustness. Subsequently, the latest research status and comparative overview are presented, encompassing three mainstream detection methods: traditional detection, deep learning detection, and sensor fusion detection. Finally, the existing challenges of ISMOD are discussed and future development trends are recommended. The results demonstrate that forthcoming development will predominantly concentrate on deep learning approaches, complemented by other techniques. It is imperative to advance detection performance in domains such as deep fusion, multi‐feature extraction, multi‐fusion technology, and lightweight detection architecture. [ABSTRACT FROM AUTHOR]