热疗对肿瘤免疫微环境中免疫细胞及免疫相关细胞因子的影响

随着对肿瘤热疗和肿瘤免疫微环境(TIME)的深入研究,近年来热疗对TIME的作用越来越受到学者们的重视。本文就目前国内外研究进展,对热疗与TIME中几类主要免疫细胞和免疫相关细胞因子的影响及作用机制作一综述。全面而透彻的了解热疗对TIME的调控作用,有助于为肿瘤治疗提供新的思路和方法。     

中国县级及以上医疗机构青光眼诊疗服务 现状分析

目的：统计分析中国县级及以上医疗机构青光眼亚专科建设、诊疗设备配备和服务提供情况，为进 一步推动我国青光眼防治工作提供理论依据。方法：调查研究。2015年对全国提供眼科服务的县级 及以上医疗机构通过网上填报的方式进行普查，采用描述性统计方法，对我国县级及以上医疗机构 青光眼亚专科建设、诊疗设备配备和服务提供情况进行系统整理和统计分析。结果：本次调查覆盖 全国6 341家县级及以上医疗机构，其中设立青光眼亚专科的医疗机构有672家（10.60%）。在青光眼 检查和治疗相关设备中，视力表配置率最高（99.30%），平均每家医疗机构配置3.28台；超声生物显 微镜配置率最低（11.50%），平均每家医疗机构配置0.13台。眼压测量、白内障手术和小梁切开术是 青光眼主要的诊疗服务类型。结论：我国县级及以上医疗机构青光眼亚专科建设亟待加强，诊疗设 备配备不全，诊疗服务能力需要全面提升。     

非小细胞肺癌新辅助靶向及免疫治疗研究进展和展望

肺癌是全球发病率和死亡率最高的恶性肿瘤。近年来,随着新型药物的出现和治疗模式的优化,肺癌患者的预后已有一定改善。新辅助治疗是指对潜在可接受手术切除的患者,先给予术前抗肿瘤治疗后再行手术治疗。新辅助治疗通过术前治疗可以缩小肿瘤体积,降低肿瘤分期;并且可以杀灭患者机体中循环肿瘤细胞及微转移病灶,令患者远期生存获益。靶向治疗及免疫治疗已被应用于晚期非小细胞肺癌(non-small cell lung cancer,NSCLC)的一线治疗。因此,有临床试验尝试将以上两种治疗手段应用于早期可切除NSCLC患者的新辅助治疗。本文针对新辅助靶向及免疫治疗对早期可切除NSCLC患者的疗效、治疗中的潜在风险予以综述,并探讨新辅助治疗的未来发展方向。     

Sequential Therapy Based on Evolvement of Patterns: A New Model for Treatment of Alzheimer’s Disease

In order to solve the problem of long-term (>9 months) efficacy in the treatment of Alzheimer''s disease (AD) by conventional therapy (CT), a staged and multiply-targeted sequential therapy based on the evolvement of patterns (STEP) was developed. Its main innovations include: (1) the time order of evolution of patterns defined by Chinese medicine (CM) in AD was found, that is, "the orderly pattern evolution starting from Shen (Kidney) deficiency, progressing to phlegm, stasis and fire, and worsening to severe toxin as well as functional collapse"; (2) the cascade hypothesis of Shen deficiency in AD and its sequential therapy based on Shen-reinforcing was proposed, that is, "reinforcing Shen in the early stage and throughout the whole process, resolving phlegm, activating blood and purging fire in the middle stage, detoxifying and replenishing vitality to stop the collapse in the advanced stage", and through meta-analysis, clinical drug use was optimized, thus the leap from "inferential selection" to "evidence-based selection" was realized; (3) the STEP regimen combined with CT maintained cognitive and behavioral stability in AD patients for at least 12 months, with cognitive enhancement and behavioral synergy after 9 months, and cognitive benefit was superior to CT at 9, 12, 15, 18, 21, and 24 months, respectively. The 2-year cognitive improvement rate was increased by 25.64% (P=0.020) and the cognitive deterioration rate was decreased by 48.71% (P=0.000). Among them, the cognitive and functional benefits of Shen-reinforcing therapy for very early AD (350 cases) for 1 year were better than the placebo (P<0.001), and the dementia conversion rate was reduced by 8.85% (P=0.002). The behavioral symptomatic relief of patients with vascular dementia received fire-purging therapy (540 cases) was superior to those received CT (P=0.016). These data suggested that the STEP regimen has synergistic effects on CTs at least in terms of cognitive benefit, and the earlier the use, the greater the benefit will have. Therefore, the STEP regimen should be considered as one of the clinical options, particularly for the dearth of effective pharmaceutical or immunological interventions that are currently available for AD.     

A Multi-Scale DNN Algorithm for Nonlinear Elliptic Equations with Multiple Scales

Algorithms based on deep neural networks (DNNs) have attracted increasing attention from the scientific computing community. DNN based algorithms are easy to implement, natural for nonlinear problems, and have shown great potential to overcome the curse of dimensionality. In this work, we utilize the multi-scale DNN-based algorithm (MscaleDNN) proposed by Liu, Cai and Xu (2020) to solve multi-scale elliptic problems with possible nonlinearity, for example, the p-Laplacian problem. We improve the MscaleDNN algorithm by a smooth and localized activation function. Several numerical examples of multi-scale elliptic problems with separable or non-separable scales in low-dimensional and high-dimensional Euclidean spaces are used to demonstrate the effectiveness and accuracy of the MscaleDNN numerical scheme.