ABSTRACT
Understanding large-scale cooperation among related individuals has been one of the largest challenges. Since humans are in multiple social networks, the theoretical framework of multilayer networks is perfectly suited for studying this fascinating aspect of our biology. To that effect, we here study the cooperation in evolutionary game on interdependent networks. Importantly, a part of players are set to adopt Discrepant Accumulations Strategy. Players employing this mechanism not only use their payoffs in the multilayer network as the basis for the updating process as in previous experiments, but also take into account the similarities and differences in strategies across different layers. Monte Carlo simulations demonstrate that considering the similarities and differences in strategies across layers when calculating fitness can significantly enhance the cooperation level in the system. By examining the behavior of different pairing modes within cooperators and defectors, the equilibrium state can be attributed to the evolution of correlated pairing modes between interdependent networks. Our results provide a theoretical analysis of the group cooperation induced by the Discrepant Accumulations Strategy. And we also discuss potential implications of these findings for future human experiments concerning the cooperation on multilayer networks.
ABSTRACT
Controllable contraception in male animals was demonstrated through the utilization of gold nanorods' photothermal effect to accomplish mild testicular hyperthermia. However, the challenges arising from testicular administration and the non-biodegradability of nanoparticles hinder further clinical implementation. Therefore, a straightforward, non-invasive, and enhanced contraception approach is required. This study explores the utilization of human heavy chain ferritin (HFn) nanocarriers loaded with aggregation-induced emission luminogens (AIEgens) for noninvasive, controllable male contraception guided by Near-Infrared-II (NIR-II) fluorescence imaging. The HFn-caged AIEgens (HFn@BBT) are delivered via intravenous injection and activated by near-infrared irradiation. Lower hyperthermia treatment induces partial damage to the testes and seminiferous tubules, reducing fertility indices by approximately 100% on the 7th day, which gradually recovers to 80% on the 60th day. Conversely, implementation of elevated hyperthermia therapy causes total destruction of both testes and seminiferous tubules, leading to a complete loss of fertility on the 60th day. Additionally, the use of AIEgens in NIR-II imaging offers improved fluorescence efficiency and penetration depth. The findings of this study hold significant promise for the advancement of safe and effective male contraceptive methods, addressing the need for noninvasive and controllable approaches to reproductive health and population control.
ABSTRACT
BACKGROUND: The reaserch of artificial intelligence (AI) model for predicting spinal refracture is limited to bone mineral density, X-ray and some conventional laboratory indicators, which has its own limitations. Besides, it lacks specific indicators related to osteoporosis and imaging factors that can better reflect bone quality, such as computed tomography (CT). OBJECTIVE: To construct a novel predicting model based on bone turn-over markers and CT to identify patients who were more inclined to suffer spine refracture. METHODS: CT images and clinical information of 383 patients (training set = 240 cases of osteoporotic vertebral compression fractures (OVCF), validation set = 63, test set = 80) were retrospectively collected from January 2015 to October 2022 at three medical centers. The U-net model was adopted to automatically segment ROI. Three-dimensional (3D) cropping of all spine regions was used to achieve the final ROI regions including 3D_Full and 3D_RoiOnly. We used the Densenet 121-3D model to model the cropped region and simultaneously build a T-NIPT prediction model. Diagnostics of deep learning models were assessed by constructing ROC curves. We generated calibration curves to assess the calibration performance. Additionally, decision curve analysis (DCA) was used to assess the clinical utility of the predictive models. RESULTS: The performance of the test model is comparable to its performance on the training set (dice coefficients of 0.798, an mIOU of 0.755, an SA of 0.767, and an OS of 0.017). Univariable and multivariable analysis indicate that T_P1NT was an independent risk factor for refracture. The performance of predicting refractures in different ROI regions showed that 3D_Full model exhibits the highest calibration performance, with a Hosmer-Lemeshow goodness-of-fit (HL) test statistic exceeding 0.05. The analysis of the training and test sets showed that the 3D_Full model, which integrates clinical and deep learning results, demonstrated superior performance with significant improvement (p-value < 0.05) compared to using clinical features independently or using only 3D_RoiOnly. CONCLUSION: T_P1NT was an independent risk factor of refracture. Our 3D-FULL model showed better performance in predicting high-risk population of spine refracture than other models and junior doctors do. This model can be applicable to real-world translation due to its automatic segmentation and detection.