The Modified Exenatide Microspheres: PLGA-PEG-PLGA Gel and Zinc-Exenatide Complex Synergistically Reduce Burst Release and Shorten Platform Stage.

The existing exenatide microspheres have the problem of burst release in the early stage, and minimal release in the middle stage which makes it difficult to achieve effective blood drug concentration (platform period). In this study, the modified exenatide microspheres were constructed to address the aforementioned issues. Poly(D,L-lactic-co-glycolic acid) (PLGA) and triblock copolymer with sol-gel conversion characteristics (PLGA-PEG-PLGA gel) were introduced as carriers to prepare microspheres. The hot gel characteristics and hydrophilicity of PLGA-PEG-PLGA gel were utilized to decline the burst release and shorten the platform period. Simultaneously, zinc acetate and exenatide were combined to generate an insoluble complex to further reduce the burst release. Herein, we prepared three types of exenatide microspheres using the solvent evaporation method and investigated their characterization as well as in vitro and in vivo release. According to the experimental findings, the modified exenatide microspheres, i.e., PLGA-PEG-PLGA gel and PLGA co-loaded zinc-exenatide insoluble complex microspheres (Zn-EXT-Gel-MS), had smooth and rounded surfaces, with a particle size of 24.7 µm, and the encapsulation rate reached 89.43%. And it was released for 40 days in vitro, behaving better than the other two microspheres in terms of release behavior. When this product was administered subcutaneously to rats, it produced a comparatively constant plasma exenatide concentration that lasted for 24 days and superior bioavailability than the exenatide microspheres (EXT-MS). The creation of modified exenatide microspheres may serve as a heuristic method for other long-acting medications. Schematic diagram of the synthesis process and release curves of three types of exenatide microspheres in vitro and in vivo.

MPS-NeRF: Generalizable 3D Human Rendering From Multiview Images.

There has been rapid progress recently on 3D human rendering, including novel view synthesis and pose animation, based on the advances of neural radiance fields (NeRF). However, most existing methods focus on person-specific training and their training typically requires multi-view videos. This paper deals with a new challenging task - rendering novel views and novel poses for a person unseen in training, using only multiview still images as input without videos. For this task, we propose a simple yet surprisingly effective method to train a generalizable NeRF with multiview images as conditional input. The key ingredient is a dedicated representation combining a canonical NeRF and a volume deformation scheme. Using a canonical space enables our method to learn shared properties of human and easily generalize to different people. Volume deformation is used to connect the canonical space with input and target images and query image features for radiance and density prediction. We leverage the parametric 3D human model fitted on the input images to derive the deformation, which works quite well in practice when combined with our canonical NeRF. The experiments on both real and synthetic data with the novel view synthesis and pose animation tasks collectively demonstrate the efficacy of our method.

Prognostic risk factors for T1 thoracic esophageal cancer: a retrospective cohort study.

Background: Esophageal cancer has a poor overall prognosis and a high incidence of post-treatment complications. This study aimed to analyze the common surgical methods for treating T1 thoracic esophageal cancer and explore its prognostic risk factors to provide a basis for appropriate treatment selection. Methods: In this population-based retrospective cohort study, data of patients diagnosed with T1 thoracic esophageal cancer from 2010 to 2016 were obtained from the Surveillance, Epidemiology, and End Results (SEER) database. Patients were divided according to those who had surgery and those who had radiotherapy. Survival curves were generated using the Kaplan-Meier method and validated by the log-rank test. Cox's regression model was used to analyze the independent prognostic risk factors. Results: Overall, 2,027 eligible patients, including 824 and 1,203 patients in the surgical and non-surgical groups, respectively, were analyzed. There was no significant difference in survival between the surgical and non-surgical groups (P=0.79). In subgroup analysis, the Cox regression analysis showed that radiotherapy was a significant prognostic factor (P=0.00059). Conclusions: The impact of surgery on patients with T1 thoracic esophageal cancer was insignificant; however, radiotherapy was an independent prognostic risk factor. These results provide a reliable basis for clinical treatment of patients with T1 thoracic esophageal cancer.