Crimean-Congo hemorrhagic fever virus(CCHFV)is a highly pathogenic tick-borne virus that causes severe hemorrhagic fever with high mortality rates in humans.No licensed vaccines or efficacious antiviral therapies are ...Crimean-Congo hemorrhagic fever virus(CCHFV)is a highly pathogenic tick-borne virus that causes severe hemorrhagic fever with high mortality rates in humans.No licensed vaccines or efficacious antiviral therapies are currently available.Here,we identified seven heavy chain antibodies targeting CCHFV Gc,which consist of heavy-chain variable domain(VHH)fused to human IgG1 Fc region(VHHFc).These VHH-Fc antibodies exhibited neutralizing activity against both recombinant vesicular stomatitis virus(VSV)-vectored CCHFV pseudoviruses and CCHFV transcriptionand entry-competent virus-like particles(tecVLPs).Among these,N025 achieved the most potent pseudovirus neutralization,while N013 showed remarkable efficacy in tecVLP systems,with IC_(50) values of 22.7 ng/mL and 33.0 ng/mL,respectively.AlphaFold3 structural predictions revealed that all characterized VHH-Fc antibodies target epitopes within Domain Ⅱ of the Gc protein,with partial or complete overlap with the fusion loop region.Alanine scanning mutagenesis confirmed the functional significance of these epitopes,with N013 showing the highest binding energy change(ΔΔG=25.36 kcal/mol)and moderate competition with a known fusion loop-targeting antibody.Sequence conservation analysis across representative CCHFV strains from different genetic lineages demonstrated complete conservation of the N013 and N025 epitopes,suggesting potential for broad-spectrum neutralizing activity.Together,our findings provide a novel strategy for developing CCHFV therapeutics and identify promising antibody candidates that could inform future broad-spectrum antiviral development efforts.展开更多
In multimodal learning, Vision-Language Models (VLMs) have become a critical research focus, enabling the integration of textual and visual data. These models have shown significant promise across various natural lang...In multimodal learning, Vision-Language Models (VLMs) have become a critical research focus, enabling the integration of textual and visual data. These models have shown significant promise across various natural language processing tasks, such as visual question answering and computer vision applications, including image captioning and image-text retrieval, highlighting their adaptability for complex, multimodal datasets. In this work, we review the landscape of Bootstrapping Language-Image Pre-training (BLIP) and other VLM techniques. A comparative analysis is conducted to assess VLMs’ strengths, limitations, and applicability across tasks while examining challenges such as scalability, data quality, and fine-tuning complexities. The work concludes by outlining potential future directions in VLM research, focusing on enhancing model interpretability, addressing ethical implications, and advancing multimodal integration in real-world applications.展开更多
基金supported by the National Natural Science Foundation of China(grant no.82522044).
文摘Crimean-Congo hemorrhagic fever virus(CCHFV)is a highly pathogenic tick-borne virus that causes severe hemorrhagic fever with high mortality rates in humans.No licensed vaccines or efficacious antiviral therapies are currently available.Here,we identified seven heavy chain antibodies targeting CCHFV Gc,which consist of heavy-chain variable domain(VHH)fused to human IgG1 Fc region(VHHFc).These VHH-Fc antibodies exhibited neutralizing activity against both recombinant vesicular stomatitis virus(VSV)-vectored CCHFV pseudoviruses and CCHFV transcriptionand entry-competent virus-like particles(tecVLPs).Among these,N025 achieved the most potent pseudovirus neutralization,while N013 showed remarkable efficacy in tecVLP systems,with IC_(50) values of 22.7 ng/mL and 33.0 ng/mL,respectively.AlphaFold3 structural predictions revealed that all characterized VHH-Fc antibodies target epitopes within Domain Ⅱ of the Gc protein,with partial or complete overlap with the fusion loop region.Alanine scanning mutagenesis confirmed the functional significance of these epitopes,with N013 showing the highest binding energy change(ΔΔG=25.36 kcal/mol)and moderate competition with a known fusion loop-targeting antibody.Sequence conservation analysis across representative CCHFV strains from different genetic lineages demonstrated complete conservation of the N013 and N025 epitopes,suggesting potential for broad-spectrum neutralizing activity.Together,our findings provide a novel strategy for developing CCHFV therapeutics and identify promising antibody candidates that could inform future broad-spectrum antiviral development efforts.
文摘In multimodal learning, Vision-Language Models (VLMs) have become a critical research focus, enabling the integration of textual and visual data. These models have shown significant promise across various natural language processing tasks, such as visual question answering and computer vision applications, including image captioning and image-text retrieval, highlighting their adaptability for complex, multimodal datasets. In this work, we review the landscape of Bootstrapping Language-Image Pre-training (BLIP) and other VLM techniques. A comparative analysis is conducted to assess VLMs’ strengths, limitations, and applicability across tasks while examining challenges such as scalability, data quality, and fine-tuning complexities. The work concludes by outlining potential future directions in VLM research, focusing on enhancing model interpretability, addressing ethical implications, and advancing multimodal integration in real-world applications.
