Nipah virus (NiV) is an animal-borne RNA virus of the genus Henipavirus that poses a significant global health threat. This threat is driven by the virus’s high mortality rate, its capacity to cause epidemics, and the lack of licensed therapeutic interventions or vaccines. Since its initial identification during the 1998–1999 outbreak in Malaysia and Singapore, recurrent episodes have occurred primarily in Bangladesh and India, with mortality rates frequently exceeding 70%. Fruit bats of the genus Pteropus serve as the biological host for the virus. Transmission to humans occurs via contact with infected wildlife, consumption of contaminated products, such as freshly harvested date palm sap, or direct person-to-person exposure. Other modes of transmission, such as transplacentally or via breast milk, are still under investigation. The clinical presentation of NiV infection varies widely, from mild flu-like symptoms to life-threatening respiratory disease and acute encephalitis. It frequently attacks the nervous system, which can lead to coma, permanent neurological damage, or relapsing encephalitis. The virus enters host cells via ephrin-B2/B3 receptors, enabling systemic dissemination and infiltration of the central nervous system. Diagnosis relies primarily on RT-PCR and serological assays, and virus isolation requires high-containment laboratories. Management remains largely supportive, as no approved antiviral therapy exists. Experimental agents, such as remdesivir, favipiravir, and monoclonal antibodies such as m102.4, have shown promise in preclinical studies. Multiple vaccine platforms—including subunit, viral vector, mRNA, and nanoparticle-based approaches—are under development, though none is yet licensed for human use. Strengthened surveillance, infection control measures, and continued research are essential to mitigate the threat posed by this emerging pathogen. This review summarizes current knowledge on NiV, including its virology, epidemiology, pathogenesis, transmission, and recent progress in therapeutic and vaccine development.

Background: Breastfeeding is the recommended source of infant nutrition, yet mothers may experience concerns and difficulties that require timely access to reliable information and professional support. As the use of digital health tools increases, AI-powered chatbots such as ChatGPT and Gemini are increasingly accessible sources of breastfeeding information and support. This study aimed to descriptively compare responses generated by ChatGPT and Gemini with established breastfeeding information and multidisciplinary clinical support provided in real-world practice, using both standardized breastfeeding questions and clinic-derived scenarios. Materials and Methods: A descriptive comparative content-analysis study was conducted at Aretaieio University Hospital. Ten breastfeeding-related questions were included: five standardized questions derived from educational materials of the Institute of Child Health in Greece and five clinic-derived scenarios originating from individualized consultations through the hospital’s breastfeeding-support helpline. Identical prompts were submitted independently to ChatGPT and Gemini as single-turn interactions without supplementary information or follow-up. AI-generated responses to the five standardized questions were descriptively compared with the corresponding information provided by the Institute of Child Health, whereas responses to five clinic-derived scenarios were compared with the clinical guidance and management provided in real-world practice. Results: Both AI platforms provided relevant, detailed, and supportive responses, particularly to standardized questions. However, the clinic-derived scenarios highlighted dimensions of individualized lactation care that extended beyond the AI-generated responses, particularly direct clinical assessment, access to relevant clinical history, interpretation of emotional and contextual cues, continuity of care, and coordination of multidisciplinary care and practical support. Conclusions: As AI becomes increasingly integrated into everyday health information-seeking, its role in breastfeeding support needs to be clearly defined. AI platforms may complement professional care by providing accessible preliminary information and support, while individualized breastfeeding care remains rooted in direct assessment, continuity of professional care, and trusted human relationships.