基于全反式维甲酸修饰白蛋白的紫杉醇纳米给药系统的构建及体外抗肿瘤效果

目的 以全反式维甲酸(RA)修饰白蛋白为载体构建紫杉醇白蛋白纳米给药系统，探讨其对肝癌HepG2细胞的体外增殖抑制效果。方法 将RA与人血清白蛋白(HSA)共价结合，构建载体材料HSA-RA,利用傅立叶变换红外(FT-IR)光谱对其结构进行确证，紫外—可见吸收光谱测定RA偶联度；自组装法制备负载抗肿瘤药物紫杉醇(PTX)的载药纳米粒PTX@HSA-RA,纳米粒度仪检测其粒径分布和表面Zeta电位，透射电镜观察其形貌；HPLC法研究其药物负载和释放行为；以HepG2细胞为模型，MTT法考察PTX@HSA-RA的体外细胞毒性，利用流式细胞仪和共聚焦显微镜研究其体外细胞摄取情况；利用HepG2细胞构建3D细胞模型(MTCSs),考察PTX@HSA-RA对其体外生长抑制作用。结果 PTX@HSA-RA呈规整的球形，具有较高的包封率和载药量。HSA与RA投料摩尔比为1∶20时制备得到的PTX@HSA-RA平均粒径为(155.2±2.9)nm,表面Zeta电位为(-20.9±1.0)mV,具有良好的稳定性和pH值敏感性药物缓释效果。PTX@HSA-RA可被HepG2细胞快速、持续摄取，且表现出较游...     

Nd:YAG激光手术治疗白内障术后囊袋收缩综合征疗效观察

目的 研究Nd:YAG激光前囊松解术治疗白内障术后囊袋收缩综合征(CCS)患者的疗效及安全性.方法 选择2019年1月至2020年12月宝鸡市人民医院收治的100例(150眼)白内障术后CCS患者为研究对象.所有患者均行Nd:YAG激光前囊松解术治疗,治疗后,比较患者治疗前后的屈光度、视力效果及并发症发生情况.结果 治疗后,屈光度在-0.75～+0.50 D、-2.00～-1.00 D内的患者比例分别为70.67％和13.33％,明显高于治疗前的48.00％和3.33％,而测不出、+2.00～+3.00 D、+0.75～+1.750 D内的患者比例分别为0、3.33％、12.67％,明显低于治疗前的6.67％、19.33％、22.67％,差异均有统计学意义(P<0.05);治疗后,视力在0.6～1.0范围内的患者比例为62.67％,明显高于治疗前的28.67％,而视力在<0.1、0.1～0.25、0.3～0.5内的患者比例分别为3.33％、3.33％、30.67％,明显低于治疗前的10.00％、19.33％、42.00％,差异均有统计学意义(P<0.05).结论 Nd:YAG激光手术治疗CCS可明显改善患者的屈光度,减少严重并发症,视觉恢复效果优良,值得临床推广应用.     

动脉三级淋巴组织与动脉粥样硬化关系的研究进展

学者们对动脉血管的研究常聚焦在血管内膜动脉粥样硬化(As)斑块的形成,而很少关注血管外膜的免疫反应及其对疾病病程的影响。前期研究发现,老龄的ApoE－/－小鼠动脉外膜出现免疫细胞的有序集聚,形成类似淋巴结的异位淋巴组织,称之为动脉三级淋巴组织(ATLO),其形成对内膜As具有明显的调控作用。ATLO的发现及研究为As的研究指明了新的方向,并为其他疾病状况下三级淋巴组织(TLO)的研究提供了很好的范例。因此,阐明TLO的特点及形成机制对As的防治具有重要的意义,并为其在其他疾病中的临床运用提供坚实的基础。     

Time-varying association of second primary malignancy and long-term survival outcomes in patients with head and neck cancer

A high risk of developing second primary malignancy (SPM) has been reported among head and neck cancer patients. Here, we aimed to statistically quantify the impact of SPM development on the survival of head and neck cancer patients. Our study was conducted using the Surveillance, Epidemiology and End Results database to collect the data of 48 316 patients who received curative surgical resection for initial primary head and neck squamous cell carcinoma (IP-HNSCC) in 1975 to 2019. SPM diagnosis was treated as a time-varying covariate and multivariable Cox regression analysis was conducted to estimate the association between SPM development and survival, overall or by the subsite of IP-HNSCC. Of the included patients, 11 238 patients (23.3%) developed SPM during the follow-up period. A significant reduction in survival was observed among patients with SPM (hazard ratio [HR] for overall survival, 3.30; 95% confidence interval [CI]: 3.20-3.41). The impact of SPM development on reduced survival was more significant in patients with localized IP-HNSCC vs regional IP-HNSCC (HR_OS, 3.41; 95% CI: 3.24-3.6 vs HR_OS, 3.18; 95% CI: 3.05-3.31; P for interaction <.001). The survival impact of SPM development was more evident in younger patients than in older patients. SPM in lung and bronchus was associated with the most pronounced reduction in survival, overall and across all subsites of HNSCC. Our results indicated that SPM development led to a significant reduction in survival. A greater survival benefit may be achieved through intensive surveillance for SPM in lung and bronchus targeting younger patients and those with localized HNSCC.     

Frame Invariance and Scalability of Neural Operators for Partial Differential Equations

Partial differential equations (PDEs) play a dominant role in the mathematical modeling of many complex dynamical processes. Solving these PDEs often requires prohibitively high computational costs, especially when multiple evaluations must be made for different parameters or conditions. After training, neural operators can provide PDEs solutions significantly faster than traditional PDE solvers. In this work, invariance properties and computational complexity of two neural operators are examined for transport PDE of a scalar quantity. Neural operator based on graph kernel network (GKN) operates on graph-structured data to incorporate nonlocal dependencies. Here we propose a modified formulation of GKN to achieve frame invariance. Vector cloud neural network (VCNN) is an alternate neural operator with embedded frame invariance which operates on point cloud data. GKN-based neural operator demonstrates slightly better predictive performance compared to VCNN. However, GKN requires an excessively high computational cost that increases quadratically with the increasing number of discretized objects as compared to a linear increase for VCNN.