Erythrocytes of the common carp are immune sentinels that sense pathogen molecular patterns, engulf particles and secrete pro-inflammatory cytokines against bacterial infection.

Introduction: Red blood cells (RBCs), also known as erythrocytes, are underestimated in their role in the immune system. In mammals, erythrocytes undergo maturation that involves the loss of nuclei, resulting in limited transcription and protein synthesis capabilities. However, the nucleated nature of non-mammalian RBCs is challenging this conventional understanding of RBCs. Notably, in bony fishes, research indicates that RBCs are not only susceptible to pathogen attacks but express immune receptors and effector molecules. However, given the abundance of RBCs and their interaction with every physiological system, we postulate that they act in surveillance as sentinels, rapid responders, and messengers. Methods: We performed a series of in vitro experiments with Cyprinus carpio RBCs exposed to Aeromonas hydrophila, as well as in vivo laboratory infections using different concentrations of bacteria. Results: qPCR revealed that RBCs express genes of several inflammatory cytokines. Using cyprinid-specific antibodies, we confirmed that RBCs secreted tumor necrosis factor alpha (TNFα) and interferon gamma (IFNγ). In contrast to these indirect immune mechanisms, we observed that RBCs produce reactive oxygen species and, through transmission electron and confocal microscopy, that RBCs can engulf particles. Finally, RBCs expressed and upregulated several putative toll-like receptors, including tlr4 and tlr9, in response to A. hydrophila infection in vivo. Discussion: Overall, the RBC repertoire of pattern recognition receptors, their secretion of effector molecules, and their swift response make them immune sentinels capable of rapidly detecting and signaling the presence of foreign pathogens. By studying the interaction between a bacterium and erythrocytes, we provide novel insights into how the latter may contribute to overall innate and adaptive immune responses of teleost fishes.

Dermatologist-like explainable AI enhances trust and confidence in diagnosing melanoma.

Artificial intelligence (AI) systems have been shown to help dermatologists diagnose melanoma more accurately, however they lack transparency, hindering user acceptance. Explainable AI (XAI) methods can help to increase transparency, yet often lack precise, domain-specific explanations. Moreover, the impact of XAI methods on dermatologists' decisions has not yet been evaluated. Building upon previous research, we introduce an XAI system that provides precise and domain-specific explanations alongside its differential diagnoses of melanomas and nevi. Through a three-phase study, we assess its impact on dermatologists' diagnostic accuracy, diagnostic confidence, and trust in the XAI-support. Our results show strong alignment between XAI and dermatologist explanations. We also show that dermatologists' confidence in their diagnoses, and their trust in the support system significantly increase with XAI compared to conventional AI. This study highlights dermatologists' willingness to adopt such XAI systems, promoting future use in the clinic.