Hedychium coronarium
J. Koenig (Zingiberaceae) is a medicinal plant with diverse phytochemicals and biological activities. This study characterized the phytochemical profile of the methanolic extract of
H. coronarium
(MEHC) and evaluated its antibacterial, antifungal, and anticancer activities. Gas chromatography–mass spectrometry (GC–MS) analysis tentatively identified 15 compounds, dominated by (
Z
)-2-methoxycinnamaldehyde (69.18%), along with sesquiterpenoids such as
δ-
cadinene,
α-
copaene, and
α-
muurolene. MEHC exhibited high total phenolic content (125.17 ± 2.36 mg GAE/g extract) and moderate total flavonoid content (78.43 ± 3.25 mg QE/g extract). The extract showed dose-dependent cytotoxicity against MCF-7 and HepG2 cells, with IC
50
values of 112.65 ± 1.68 and 98.34 ± 0.58 µg/mL, respectively. Gene expression analysis suggested the involvement of apoptotic pathways, as evidenced by the upregulation of caspase-3, -8, -9, and Bax and the downregulation of Bcl-2 and Bcl-xL. MEHC also demonstrated notable antibacterial activity, particularly against Gram-negative bacteria (MIC: 6.25–25 µg/mL), and antifungal activity against
Candida
species, with the strongest effect against
Candida albicans
(MIC: 12.5 µg/mL). Overall, MEHC exhibited antimicrobial and cytotoxic activities and modulated the expression of apoptosis-related genes. However, these findings are limited to in vitro observations, and further studies involving bioactive compound isolation, mechanistic validation, and in vivo evaluation are required to confirm therapeutic applicability.
Ibrahim M. Aziz, M. Farrag, Noorah A. Alkubaisi et al.· Scientific Reports· 0 citations
The escalating threat of viral pandemics, dramatically illustrated by the COVID-19 crisis, has exposed the critical shortcomings of conventional reactive virology in addressing rapidly evolving pathogens. This review introduces predictive virology (PV) as an artificial intelligence (AI)-driven discipline within broader epidemic intelligence and public health surveillance that uses advanced computational tools to forecast viral threats and accelerate countermeasure design. The current review systematically examines how AI-driven approaches (e.g., machine learning and deep learning) are reshaping virology by integrating vast genomic datasets, multimodal surveillance signals, and advanced computational models to anticipate viral emergence and evolution before widespread transmission occurs. Core pillars of PV discussed include zero-shot mutational fitness and antigenic escape prediction using large protein language models; multimodal early-warning systems that fuse wastewater monitoring, digital epidemiology, mobility data, and social media; neural differential equation-based transmission modeling; generative AI for de novo design of broad-spectrum antivirals and vaccines; and ecological risk assessment of zoonotic spillovers. In retrospective benchmarks against deep mutational scanning experiments and real-world epidemiological outcomes (SARS-CoV-2 variants, influenza, and other outbreaks), several AI-powered tools have demonstrated performance comparable to or exceeding traditional methods, although prospective validation at scale remains limited. Despite remarkable progress, significant challenges persist, including data bias, overfitting to historical patterns, lack of prospective validation, and limited generalizability across settings. In addition, there are concerns about mechanistic interpretability, equitable global data integration, and responsible deployment. This review also critically addresses the ethical, governance, and equity implications of deploying predictive capabilities at a global scale. By consolidating cutting-edge AI methodologies with virological insights and acknowledging current limitations, this work provides a comprehensive framework for transitioning virology from a reactive to a truly predictive discipline, ultimately strengthening global health security and pandemic preparedness.
M. Farrag· Vector Borne and Zoonotic Di...· 0 citations
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