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ObjectiveTo comprehensively analyze the ADRs associated with Denosumab (Prolia) in the treatment of osteoporosis using data from the FAERS database, and gain a better understanding of the potential risks and side effects of Denosumab (Prolia) therapy.MethodsData of Denosumab (Prolia) were collected from the FAERS database covering the period from first quarter of 2010 to the third quarter of 2023. Disproportionality analysis was performed by calculating the reporting odds ratios (ROR), proportional reporting ratio (PRR), and Bayesian analysis confidence propagation neural network (BCPNN) to detect positive signals.ResultsTotally, 17,985,365 reports were collected from the FAERS database, 1,97,807 reports of Denosumab (Prolia) were identified as the “primary suspected (PS)” ADRs. Denosumab (Prolia) induced ADRs occurred in 27 organ systems. 38 significant disproportionality PTs satisfying with the three algorithms were retained at the same time. Unexpected significant ADRs such as bone density abnormal and immobile also occur. The majority of the ADRs occurred within the first 30 days after Denosumab (Prolia) initiation.ConclusionBased on the American FAERS database, the high frequency ADRs of Denosumab (Prolia) were hypocalcaemia, bone density abnormal, eczema, rebound effect, spinal deformity, etc. Clinical use of this drug should focus on this part of ADRs. Attention should also be paid to newly discovered ADRs, such as immobile, menopausal symptoms, etc., to avoid more serious consequences. Cohort studies, more detailed and comprehensive case information, and long-term clinical investigations are needed to confirm these results and to further understand the safety profile of Denosumab (Prolia).

ObjectivesThis study aimed to develop a deep learning radiomic model using multimodal imaging to differentiate benign and malignant breast tumours.MethodsMultimodality imaging data, including ultrasonography (US), mammography (MG), and magnetic resonance imaging (MRI), from 322 patients (112 with benign breast tumours and 210 with malignant breast tumours) with histopathologically confirmed breast tumours were retrospectively collected between December 2018 and May 2023. Based on multimodal imaging, the experiment was divided into three parts: traditional radiomics, deep learning radiomics, and feature fusion. We tested the performance of seven classifiers, namely, SVM, KNN, random forest, extra trees, XGBoost, LightGBM, and LR, on different feature models. Through feature fusion using ensemble and stacking strategies, we obtained the optimal classification model for benign and malignant breast tumours.ResultsIn terms of traditional radiomics, the ensemble fusion strategy achieved the highest accuracy, AUC, and specificity, with values of 0.892, 0.942 [0.886–0.996], and 0.956 [0.873–1.000], respectively. The early fusion strategy with US, MG, and MRI achieved the highest sensitivity of 0.952 [0.887–1.000]. In terms of deep learning radiomics, the stacking fusion strategy achieved the highest accuracy, AUC, and sensitivity, with values of 0.937, 0.947 [0.887–1.000], and 1.000 [0.999–1.000], respectively. The early fusion strategies of US+MRI and US+MG achieved the highest specificity of 0.954 [0.867–1.000]. In terms of feature fusion, the ensemble and stacking approaches of the late fusion strategy achieved the highest accuracy of 0.968. In addition, stacking achieved the highest AUC and specificity, which were 0.997 [0.990–1.000] and 1.000 [0.999–1.000], respectively. The traditional radiomic and depth features of US+MG + MR achieved the highest sensitivity of 1.000 [0.999–1.000] under the early fusion strategy.ConclusionThis study demonstrated the potential of integrating deep learning and radiomic features with multimodal images. As a single modality, MRI based on radiomic features achieved greater accuracy than US or MG. The US and MG models achieved higher accuracy with transfer learning than the single-mode or radiomic models. The traditional radiomic and depth features of US+MG + MR achieved the highest sensitivity under the early fusion strategy, showed higher diagnostic performance, and provided more valuable information for differentiation between benign and malignant breast tumours.

Atherosclerosis, a highly prevalent cardiovascular disease, has significantly contributed to morbidity and mortality worldwide. Despite the availability of various treatment options in clinical practice, complete reversal of the atherosclerotic pathological process remains challenging. Traditional anti-arteriosclerosis drugs often suffer from poor solubility, limited targeting ability and obvious adverse effects. Additionally, novel therapeutics such as genetic and protein-based drugs inherently suffer from instability issues, thereby compromising their actual curative efficacy and impeding further clinical application. Recently, nanomedicine-based drug delivery strategies have emerged as promising approaches for treating atherosclerosis due to their ability to specifically transport various drugs to target cells, microstructures and specific molecules within lesion sites. These advanced features enhance drug properties and bioavailability while minimizing off-target effects. Moreover, numerous studies have demonstrated that nanomedicine not only inhibits plaque progression but also reduces lesional areas and eventually reverses atherosclerosis. The utilization of nanomedicine is considered an important therapeutic tactic for further mitigating residual risks associated with atherosclerosis. This review provides a comprehensive overview of emerging nanomedicine-based strategies for targeted delivery of anti-atherosclerosis drugs, elucidating their distinct mechanisms and highlighting their remarkable efficacy in combating atherosclerosis. Additionally, critical challenges and future perspectives associated with the development and application of anti-arteriosclerosis nanomedicine are thoroughly discussed.

Equine herpesvirus type 8 (EHV-8) causes abortion and respiratory disease in horses and donkeys, leading to serious economic losses in the global equine industry. Currently, there is no effective vaccine or drug against EHV-8 infection, underscoring the need for a novel antiviral drug to prevent EHV-8-induced latent infection and decrease the pathogenicity of this virus. The present study demonstrated that hyperoside can exert antiviral effects against EHV-8 infection in RK-13 (rabbit kidney cells), MDBK (Madin-Darby bovine kidney), and NBL-6 cells (E. Derm cells). Mechanistic investigations revealed that hyperoside induces heme oxygenase-1 expression by activating the c-Jun N-terminal kinase/nuclear factor erythroid-2-related factor 2/Kelch-like ECH-associated protein 1 axis, alleviating oxidative stress and triggering a downstream antiviral interferon response. Accordingly, hyperoside inhibits EHV-8 infection. Meanwhile, hyperoside can also mitigate EHV-8-induced injury in the lungs of infected mice. These results indicate that hyperoside may serve as a novel antiviral agent against EHV-8 infection. IMPORTANCE Hyperoside has been reported to suppress viral infections, including herpesvirus, hepatitis B virus, infectious bronchitis virus, and severe acute respiratory syndrome coronavirus 2 infection. However, its mechanism of action against equine herpesvirus type 8 (EHV-8) is currently unknown. Here, we demonstrated that hyperoside significantly inhibits EHV-8 adsorption and internalization in susceptible cells. This process induces HO-1 expression via c-Jun N-terminal kinase/nuclear factor erythroid-2-related factor 2/Kelch-like ECH-associated protein 1 axis activation, alleviating oxidative stress and triggering an antiviral interferon response. These findings indicate that hyperoside could be very effective as a drug against EHV-8. [ABSTRACT FROM AUTHOR]