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Background: Accurate diagnosis and early treatment are essential in the fight against lymphatic cancer. The application of artificial intelligence (AI) in the field of medical imaging shows great potential, but the diagnostic accuracy of lymphoma is unclear. This study was done to systematically review and meta-analyse researches concerning the diagnostic performance of AI in detecting lymphoma using medical imaging for the first time. Methods: Searches were conducted in Medline, Embase, IEEE and Cochrane up to December 2023. Data extraction and assessment of the included study quality were independently conducted by two investigators. Studies that reported the diagnostic performance of an AI model/s for the early detection of lymphoma using medical imaging were included in the systemic review. We extracted the binary diagnostic accuracy data to obtain the outcomes of interest: sensitivity (SE), specificity (SP), and Area Under the Curve (AUC). The study was registered with the PROSPERO, CRD42022383386. Results: Thirty studies were included in the systematic review, sixteen of which were meta-analyzed with a pooled sensitivity of 87% (95%CI 83–91%), specificity of 94% (92–96%), and AUC of 97% (95–98%). Satisfactory diagnostic performance was observed in subgroup analyses based on algorithms types (machine learning versus deep learning, and whether transfer learning was applied), sample size (≤ 200 or > 200), clinicians versus AI models and geographical distribution of institutions (Asia versus non-Asia). Conclusions: Even if possible overestimation and further studies with a better standards for application of AI algorithms in lymphoma detection are needed, we suggest the AI may be useful in lymphoma diagnosis. [ABSTRACT FROM AUTHOR]

Takifugu rubripes is a highly valued cultured fish in Asia, while pathogen infections can result in severe diseases and lead to substantial economic losses. Toll-like receptors (TLRs), as pattern recognition receptors, play a crucial role on recognition pathogens and initiation innate immune response. However, the immunological properties of teleost-specific TLR23 remain largely unknown. In this study, we investigated the biological functions of TLR23 (TrTLR23) from T. rubripes , found that TrTLR23 existed in various organs. Following bacterial pathogen challenge, the expression levels of TrTLR23 were significantly increased in immune related organs. TrTLR23 located on the cellular membrane and specifically recognized pathogenic microorganism. Co-immunoprecipitation and antibody blocking analysis revealed that TrTLR23 recruited myeloid differentiation primary response protein (MyD88), thereby mediating the activation of the ERK signaling pathway. Furthermore, in vivo showed that, when TrTLR23 is overexpressed in T. rubripes , bacterial replication in fish tissues is significantly inhibited. Consistently, when TrTLR23 expression in T. rubripes is knocked down, bacterial replication is significantly enhanced. In conclusion, these findings suggested that TrTLR23 played a critical role on mediation TLR23-MyD88-ERK axis against bacterial infection. This study revealed that TLR23 involved in the innate immune mechanism, and provided the foundation for development disease control strategies in teleost. • TrTLR23 is phylogenetically distantly related to teleost-specific TLR23. • Bacterial infection enhanced TrTLR23 expression in liver, spleen, and kidney. • TrTLR23 located on the cellular membrane and specifically recognized pathogen. • TrTLR23 recruited myeloid MyD88 and activated the ERK signaling pathway. • TrTLR23 is required for optimal defense against bacterial infection. [ABSTRACT FROM AUTHOR]