Acyclovir is the first-line treatment for neonatal herpes simplex virus infection; however, safety evidence in neonates remains limited. This study aimed to evaluate the safety profile of acyclovir in neonates using real-world pharmacovigilance data from the Food and Drug Administration Adverse Event Reporting System. This retrospective observational study analyzed adverse event (AE) reports in neonates (≤28 days) in which acyclovir was identified as the primary suspected drug in the Food and Drug Administration Adverse Event Reporting System from 2004 to Q3 2024. Disproportionality analyses were performed using the reporting odds ratio (ROR), proportional reporting ratio (PRR), Bayesian confidence propagation neural network, and empirical Bayesian geometric mean (EBGM). Positive risk signals were defined as those meeting the statistical significance criteria across all 4 methods. A total of 130 reports comprising 409 AEs were identified. Four system organ classes showed positive risk signals: skin and subcutaneous tissue disorders (ROR = 6.22, PRR = 5.88, lower limit of 95% confidence interval [CI] of the information component [IC025] = 1.98, lower limit of 95% CI of EBGM [EBGM05] = 4.16), general disorders and administration site conditions (ROR = 4.27, PRR = 3.47, IC025 = 1.47, EBGM05 = 2.85), hepatobiliary disorders (ROR = 3.85, PRR = 3.74, IC025 = 1.19, EBGM05 = 2.43), and renal and urinary disorders (ROR = 3.84, PRR = 3.70, IC025 = 1.24, EBGM05 = 2.51). At the preferred term level, 12 positive signals were detected, with the strongest signals observed for infusion site necrosis/edema (ROR = 939.92, 95% CI: 97.56–9055.25), arthritis (ROR = 179.47, 95% CI: 52.33–615.44), skin exfoliation/necrosis (ROR = 131.18, 95% CI: 46.00–374.06), and Kawasaki disease (ROR = 114.20, 95% CI: 36.21–360.15). Notably, Kawasaki disease, rectal hemorrhage, and necrotizing colitis were not described in existing product labeling. Acyclovir use in neonates may be associated with immune, gastrointestinal, hepatic, and renal AEs, underscoring the need for careful clinical monitoring. Further prospective studies are warranted to validate these associations.
Long-Bing He, Bo Wang, Yang Wang et al.· Medicine· 0 citations
Multimodal artificial intelligence (AI) agents are emerging in healthcare as systems that integrate heterogeneous clinical data, foundation models (FMs), tools, and agentic workflows, but their applications and translational readiness remain unclear. We conducted a scoping review of 37 peer-reviewed studies published between January 2022 and June 2025. Included studies covered clinical decision support (
N
= 17), clinical documentation and report generation (
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= 3), clinical monitoring and health management (
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= 13), and medical education and training (
N
= 4). We synthesized modality combinations and fusion strategies, FM utilization and agent architectures, tool integration, agent capabilities, and evaluation practices. Current systems were predominantly text-centric, frequently used closed-source FMs, and remained concentrated in prototype or early technical evaluation stages. Safety, fairness, prospective outcome-based validation, and real-world deployment evidence were limited. These findings suggest that multimodal AI agents are best interpreted as emerging augmentative systems requiring stronger evaluation before clinical translation.
Kai Yu, Shuang Zhou, Yu Hou et al.· npj Digital Medicine· 1 citation
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