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Fractures are frequently encountered diseases troubling the senior population, and the research on fracture repair and the exploration of effective treatment methods are of great significance. This study aimed to clarify the effect of human umbilical cord mesenchymal stromal cell-derived extracellular vesicles (hUMSC-EVs) on the proliferation and osteogenic differentiation of autologous bone marrow stem cells (ABMSCs). The two kinds of cells were co-cultured firstly, 5-Ethynyl-2'- deoxyuridine (EDU) staining and alizarin red staining were used to detect the proliferation and osteogenic differentiation of ABMSCs. The exosomes of hUMSCs were subsequently extracted to process ABMSCs to further test the effect on the cells. The EDU positive rate of ABMSCs and Collagen II expression were elevated, whereas the TdT-mediated dUTP nick end labeling (TUNEL) positive rate and Matrix Metallopeptidase 13 (MMP13) were markedly decreased after the co-culture of hUMSCs and ABMSCs using Transwell chamber assays. The results indicated that hUMSCs could increase the proliferation of ABMSCs, reduce apoptosis, and promote matrix metabolism. The hUMSCs exosomes were separated and added to ABMSCs. As the exosomes content increased, the proliferation of ABMSCs increased simultaneously, and ABMSCs apoptosis decreased. Meanwhile, ABMSCs that migrated to the submembrane increased compared with untreated ABMSCs. Western blot, qPCR and immunofluorescence results revealed that increased exosomes contents promoted the expression of ABMSCs anabolic-related indicators gradually, while decreased the expression of catabolism-related indicators gradually. The previously described results indicated that hUMSCs promoted the proliferation and osteogenic differentiation of ABMSCs by secreting exosomes.

Artificial intelligence (AI) techniques have been widely applied to infectious disease outbreak detection and early warning, trend prediction, and public health response modeling and assessment. Such public health surveillance and response tasks of major importance pose unique technical challenges such as data sparsity, lack of positive training samples, difficulty in developing baselines and quantifying the control measures, and interwoven dependencies between spatiotemporal elements and finer-grained risk analyses through contact and social networks. Traditional public health surveillance relies heavily on statistical techniques. Recent years have seen tremendous growth of AI-enabled methods, including but not limited to deep learning–based models, complementing statistical approaches. This chapter aims to provide a systematic review of these recent advances applying AI techniques to address public health surveillance and response challenges.

To investigate the drug release and anti-adhesion effects of a TET (tetrandrine)-loaded PDLLA (poly-dl-lactide) film. Detection of TET release in vitro was carried out by high-performance liquid chromatography (HPLC) every 2 days following immersion of the tetrandrine-loaded PDLLA film in simulated body fluid until the TET content of the eluate could not be detected. For the in vivo test, TET-loaded PDLLA films were implanted into animal laminectomy models and positive and blank control groups were also set up. Postoperative serum tests, and macroscopic and histological analyses at 1, 4, 8, and 12 weeks, were used to assess the effects of the film. Statistical analyses were performed by one-way ANOVA. The drug release of the tetrandrine-loaded PDLLA film in vitro showed two phases with a second release peak. Ultimately, the duration of continuous delivery was up to 66 days and the cumulative delivery rate was up to 93.18%. Scores for the proliferation of epidural scars or adhesion of the dura mater in the test group were much lower than those for the two control groups. Histological analysis revealed the test group had fewer inflammatory cells and fibroblasts, as well as fewer extracellular collagen fibers, and a lower histology score than those of the two control groups at all time points. Tetrandrine-loaded PDLLA film is a novel controlled drug release and anti-adhesion material in vitro and in vivo.

The topology structure of social contacts network has a big impact on dynamic patterns of epidemic spreading and effectiveness of non-pharmaceutical interventions. Corresponding to individuals’ behavioral or functional units, people are commonly organized in small communities, meaning that most of social contacts networks tend to display community structure property. Through empirical investigation and Monte-Carlo simulation on a big H1N1 outbreak in a Chinese university campus, this paper explores the impact of community structure property of social contacts network on epidemic spreading and effectiveness of interventions. A stochastic model based on social contacts networks among students is constructed to simulate this outbreak, revealing that epidemic outbreaks commonly occur in local community. Moreover, effectiveness of three quarantine-based interventions is quantitatively studied by our proposed model, finding that community structure of social networks determines the effects these measures.

Based on the spatial units of community, village and town in Beijing, the relationship betweent HFMD morbidity and the potential risk factors has been examined. According to the 6 selected risk factors (namely population density, disposable income of urban residents, the number of medical and health institutions, the number of hospital beds, average annual temperature and average annual relative humidity) significantly related to HFMD morbidity, the prediction performance of Classical Linear Regression Model(CLRM) and Spatial Lag Model(SLM) has been compared. The results showed that SLM achieved better effect and R square reached 0.82. It was showed that spatial effect played the crucial role in the HFMD morbidity prediction and its contribution attained 88%. However, CLRM showed low prediction accuracy and bias estimation. It was demonstrated that including spatial effect item into CLRM could greatly improve the performance of HFMD morbidity prediciton model.