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Pre-clinical immunogenicity and safety evaluation of H2 strain Hepatitis A Inactivated Vaccine in rhesus macaques.

BACKGROUND: Hepatitis A is a viral infection of the liver that can cause mild to severe illness. Currently, two types of HAV vaccines are used worldwide, inactivated hepatitis A vaccines, which are used in most countries, and live attenuated vaccines (H2 and L-A-1 strain), which are mainly used in China. The major disadvantage of live attenuated virus to cause secondary infections among contacts and mutation shifts of the live vaccine strain. The H2 strain was selected for the development of an inactivated hepatitis A vaccine to further reduce biosafety risks. Rhesus macaques high genomic homology with humans and the incubation period after hepatitis A vaccination and human natural infections are similar. We use rhesus macaques to assess immunogenicity and safety of the H2 strain Hepatitis A Inactivated Vaccine. METHODS: The vaccine was assessed in rhesus macaques, divided into four groups (n = 10 per group): the control group (adjuvant buffer; aluminum content 0.35 mg/mL; 2 mL per dose), the low-dose group (320EU, 0.5 mL of 640EU/mL with aluminum content 0.35 mg/mL), the medium-dose group (640EU, 1 mL of 640EU/mL with aluminum content 0.35 mg/mL), and the high-dose group (1280EU, 2 mL of 640EU/mL with aluminum content 0.35 mg/mL). Animals were injected intramuscularly at multiple sites in the hind limbs and received four inoculations at 4-week intervals. Test items including Clinical indicators, immunogenicity indicators and Histopathological examination. RESULTS: No abnormalities were observed in any group in terms of general clinical condition throughout the study period except for slight decreases in body temperature after immunization. Hematological parameters, serum biochemistry indices, and histopathological findings showed fluctuated to different degrees of fluctuation after immunization across all groups. Immunogenicity assessments showed that the inactivated hepatitis A vaccine (H2) induced both humoral and cellular immune responses effectively, and the levels of antibodies increased with certain dose- and time-response trends. CONCLUSION: The inactivated hepatitis A vaccine (H2 strain, human diploid cell) was safe and immunogenic in non-human primates. The results provide strong preclinical support for the further clinical development of this vaccine candidate.

Animals

Artificial intelligence (AI) uses in stereotactic radiosurgery (SRS): diagnosis with brain metastasis (BM) - A systematic review.

BACKGROUND: Brain metastases (BM) are the most common intracranial tumors in adults, and stereotactic radiosurgery (SRS) has become a mainstay of management. However, several diagnostic challenges persist in the SRS pathway, particularly the differentiation of radiation necrosis (RN) from true tumor progression, which conventional MRI and even advanced imaging techniques often cannot reliably resolve. Recent advances in artificial intelligence (AI) offer the potential to address these diagnostic limitations. This systematic review synthesizes current literature on AI applications for MRI-based diagnostic decision support in BM patients undergoing SRS, with a focus on radiomics and deep learning tools for distinguishing RN from progression, classifying molecular and histologic subtypes, and predicting treatment response. METHODS: A systematic review was performed in accordance with PRISMA guidelines. PubMed, Web of Science, and Scopus were searched using a targeted query combining terms related to AI, brain metastasis, diagnosis or imaging, and SRS. After screening 483 records and applying strict inclusion and exclusion criteria, 18 studies published between 2015 and 2025 were included. Data were extracted on study design, cohort characteristics, imaging modality, AI methodology, validation strategy, and reported diagnostic performance. RESULTS: Among the 18 included studies, AI models demonstrated strong performance across diagnostic tasks in the BM-SRS pathway. The differentiation of RN from true tumor progression was the most extensively studied application, addressed by 14 of 18 studies, with reported AUCs ranging from 0.71 to 0.94. Support vector machines, random-forest ensembles, convolutional neural networks, and transformer-based multimodal architectures were widely used. The literature evolved from single-sequence radiomic classifiers in 2018 to multimodal deep learning frameworks fusing imaging with clinical and genomic data in 2025. Contrast-enhanced T1-weighted MRI was the dominant imaging input, and texture-based radiomic features (GLCM, GLSZM, GLDM, and wavelet-derived features) were the most consistently predictive. The highest-performing models reached AUCs of 0.85-0.91 through multimodal integration of imaging with clinical and genomic features, and consistently outperformed expert neuroradiologist read on matched cases. Remaining studies addressed longitudinal segmentation-based detection of local failure and adverse radiation effects, BRAF mutation status in melanoma BM, early Gamma Knife treatment response, and primary tumor histology classification, with more variable performance. CONCLUSION: AI models, particularly those integrating MRI-derived radiomic features with clinical and genomic data, show high accuracy in supporting diagnostic decisions for BM patients treated with SRS. The post-SRS differentiation of radiation necrosis from true tumor progression has reached the greatest level of maturity and is closest to clinical translation, with potential to reduce unnecessary biopsies, personalize surveillance intervals, and rationalize treatment-pathway decisions. Other diagnostic applications, including molecular subtyping and primary tumor histology classification, remain exploratory and require further multicenter validation. Integration of AI tools into multidisciplinary tumor-board workflows, combined with prospective validation and standardized reporting, will be essential to realize the full clinical benefits of AI in SRS for brain metastases.

Humans