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Background: To evaluate the diagnostic value of 68Ga-PSMA-11 PET-CT with multiparametric magnetic resonance imaging (mpMRI) for lymph node (LN) staging in patients with intermediate- to high-risk prostate cancer (PCa) undergoing radical prostatectomy (RP) with pelvic lymph node dissection (PLND).Methods: We retrospectively identified 42 consecutive patients with intermediate- to high-risk PCa according to D'Amico and without concomitant cancer. Preoperative 68Ga-PSMA-11 PET-CT, pelvic mpMRI and subsequent robot assisted laparoscopic RP with PLND were performed in all patients.Results: Among 42 patients assessed, the preoperative PSA value, Gleason score, pT stage and intraprostatic PCa volume of patients with LN metastases were all significantly higher than those without metastases (P = 0.029, 0.028, 0.004, respectively). The average maximum standardized uptake value (SUV) of 68Ga-PSMA-11 PET-CT positive PCa of patients with or without LN metastases were 13.10 (range 6.12-51.75) and 7.22 (range 5.4-11.2), respectively (P < 0.001). 68Ga-PSMA-11 PET-CT and pelvic mpMRI had the ability of succeed on preoperative definite accurate diagnosis and accurate localization of primary PCa in all 42 patients. Fifteen patients (35.71%) had a pN1 stage. 51 positive LN were found. Both 68Ga-PSMA-11 PET-CT and pelvic mpMRI displayed brillient patient-based and region-based sensitivity, specificity, negative predictive value and positive predictive value. There was no statistical difference for the detection of LNMs according to the diameter of the LNMs between 68Ga-PSMA-11 PET-CT and mpMRI in this study.Conclusions: Both 68Ga-PSMA-11 PET-CT and mpMRI performed great value for LN staging in patients with intermediate- to high-risk PCa undergoing RP with PLND. However, despite excellent performance of 68Ga-PSMA-11 PET-CT, it cannot replace mpMRI that remains excellent for lymph node staging. [ABSTRACT FROM AUTHOR]

Machine learning is becoming a preferred method for the virtual screening of organic materials due to its cost-effectiveness over traditional computationally demanding techniques. However, the scarcity of labeled data for organic materials poses a significant challenge for training advanced machine learning models. This study showcases the potential of utilizing databases of drug-like small molecules and chemical reactions to pretrain the BERT model, enhancing its performance in the virtual screening of organic materials. By fine-tuning the BERT models with data from five virtual screening tasks, the version pretrained with the USPTO-SMILES dataset achieved R 2 scores exceeding 0.94 for three tasks and over 0.81 for two others. This performance surpasses that of models pretrained on the small molecule or organic materials databases and outperforms three traditional machine learning models trained directly on virtual screening data. The success of the USPTO-SMILES pretrained BERT model can be attributed to the diverse array of organic building blocks in the USPTO database, offering a broader exploration of the chemical space. The study further suggests that accessing a reaction database with a wider range of reactions than the USPTO could further enhance model performance. Overall, this research validates the feasibility of applying transfer learning across different chemical domains for the efficient virtual screening of organic materials.Scientific contributionThis study verifies the feasibility of applying transfer learning to large language models in different chemical fields to help organic materials perform virtual screening. Through the comparison of transfer learning from different chemical fields to a variety of organic material molecules, the high precision virtual screening of organic materials is realized., (© 2024. The Author(s).)

Background: To evaluate the diagnostic value of 68 Ga-PSMA-11 PET-CT with multiparametric magnetic resonance imaging (mpMRI) for lymph node (LN) staging in patients with intermediate- to high-risk prostate cancer (PCa) undergoing radical prostatectomy (RP) with pelvic lymph node dissection (PLND)., Methods: We retrospectively identified 42 consecutive patients with intermediate- to high-risk PCa according to D'Amico and without concomitant cancer. Preoperative 68 Ga-PSMA-11 PET-CT, pelvic mpMRI and subsequent robot assisted laparoscopic RP with PLND were performed in all patients., Results: Among 42 patients assessed, the preoperative PSA value, Gleason score, pT stage and intraprostatic PCa volume of patients with LN metastases were all significantly higher than those without metastases (P = 0.029, 0.028, 0.004, respectively). The average maximum standardized uptake value (SUV) of 68 Ga-PSMA-11 PET-CT positive PCa of patients with or without LN metastases were 13.10 (range 6.12-51.75) and 7.22 (range 5.4-11.2), respectively (P < 0.001). 68 Ga-PSMA-11 PET-CT and pelvic mpMRI had the ability of succeed on preoperative definite accurate diagnosis and accurate localization of primary PCa in all 42 patients. Fifteen patients (35.71%) had a pN1 stage. 51 positive LN were found. Both 68 Ga-PSMA-11 PET-CT and pelvic mpMRI displayed brillient patient-based and region-based sensitivity, specificity, negative predictive value and positive predictive value. There was no statistical difference for the detection of LNMs according to the diameter of the LNMs between 68 Ga-PSMA-11 PET-CT and mpMRI in this study., Conclusions: Both 68 Ga-PSMA-11 PET-CT and mpMRI performed great value for LN staging in patients with intermediate- to high-risk PCa undergoing RP with PLND. However, despite excellent performance of 68 Ga-PSMA-11 PET-CT, it cannot replace mpMRI that remains excellent for lymph node staging.

Background: Biological environment is uncertain and its dynamic is similar to the multiagent environment, thus the research results of the multiagent system area can provide valuable insights to the understanding of biology and are of great significance for the study of biology. Learning in a multiagent environment is highly dynamic since the environment is not stationary anymore and each agent's behavior changes adaptively in response to other coexisting learners, and vice versa. The dynamics becomes more unpredictable when we move from fixed-agent interaction environments to multiagent social learning framework. Analytical understanding of the underlying dynamics is important and challenging., Results: In this work, we present a social learning framework with homogeneous learners (e.g., Policy Hill Climbing (PHC) learners), and model the behavior of players in the social learning framework as a hybrid dynamical system. By analyzing the dynamical system, we obtain some conditions about convergence or non-convergence. We experimentally verify the predictive power of our model using a number of representative games. Experimental results confirm the theoretical analysis., Conclusion: Under multiagent social learning framework, we modeled the behavior of agent in biologic environment, and theoretically analyzed the dynamics of the model. We present some sufficient conditions about convergence or non-convergence and prove them theoretically. It can be used to predict the convergence of the system.