地鳖纤溶蛋白口服时间/pH依赖结肠靶向微囊的开发及评价＊

地鳖中的纤溶活性蛋白是从地鳖中提取的具有抗栓及抗肿瘤作用的有效成分，其口服易被上消化道酶分解从而限制了应用。采用恒流泵滴制法开发地鳖纤溶活性蛋白时间/pH依赖口服结肠靶向微囊（EnpolypHaga fibrinolytic protein oral colon targeting microcapsules， CTM-EFP）。采用单因素实验和正交实验相结合的方法寻找到包封率为60.17 % ± 2.72 %、载药量为15.50 % ± 0.44 % 的最佳配方。扫描电子显微镜（SEM）显示微囊呈球形、表面光滑，在人工肠液中24 h的累积释放度为99.53 % ± 0.69 %，在人工胃液中24 h累积释放度为7.43 ± 1.04 %，通过时间/pH依赖达到结肠靶向作用。CTM-EFP在人工肠液中的体外释放曲线符合Korsmeyer方程，提示地鳖纤溶活性蛋白（EnpolypHaga fibrinolytic protein， EFP）是通过扩散和侵蚀机制结合释放的。CTM-EFP为EFP的口服给药提供了一种新的剂型，为EFP应用于临床提供参考。     

黄芩素调控 NLRP3 / Caspase-1 通路对牙周炎大鼠牙槽骨吸收的影响

目的 探索黄芩素调控核苷酸结合寡聚化结构域样受体蛋白 3 ( nucleotide-binding oligomerization domain-like receptor protein 3, NLRP3) / 半胱氨酸天冬氨酸蛋白酶 1 ( cysteine aspartate protease 1, Caspase1) 通路对牙周炎大鼠牙槽骨吸收的影响。 方法 将 40 只牙周炎大鼠随机分为模型组、 黄芩素组、 激活剂 组、 黄芩素 + 激活剂组, 另取 10 只正常作为对照组。 检测大鼠釉牙骨质界到牙槽嵴顶 (CEJ-AC) 的距离、 血清中白细胞介素-6 (IL-6)、 转化生长因子-β (TGF-β) 含量以及牙周组织病理变化、 IL-6、 TGF-β 阳性 表达和 NLRP3、 Caspase-1 蛋白表达。 结果 模型组大鼠 CEJ-AC、 NLRP3、 Caspase-1、 IL-6、 TGF-β 水平及 阳性表达水平以及蛋白表达水平均升高 (P< 0. 05); 经黄芩素干预后, 各项指标均降低 (P< 0. 05); 引入 激活剂明显削弱了黄芩素对牙周炎大鼠的抗炎作用。 结论 黄芩素通过抑制 NLRP3 / Caspase-1 通路减轻炎性反应, 控制牙槽骨吸收。     

Global colorectal cancer research, 2007-2021: Outputs and funding

The purpose of this study was to provide an evidence base for colorectal cancer research activity that might influence policy, mainly at the national level. Improvements in healthcare delivery have lengthened life expectancy, but within a situation of increased cancer incidence. The disease burden of CRC has risen significantly, particularly in Africa, Asia and Latin America. Research is key to its control and reduction, but few studies have delineated the volume and funding of global research on CRC. We identified research papers in the Web of Science (WoS) from 2007 to 2021, and determined the contributions of the leading countries, the research domains studied, and their sources of funding. We identified 62 716 papers, representing 5.7% of all cancer papers. This percentage was somewhat disproportionate to the disease burden (7.7% in 2015), especially in Eastern Europe. International collaboration increased over the time period in almost all countries except in China. Genetics, surgery and prognosis were the leading research domains. However, research on palliative care and quality-of-life in CRC was lacking. In Western Europe, the main funding source was the charity sector, particularly in the UK, but in most other countries government played the leading role, especially in China and the USA. There was little support from industry. Several Asian countries provided minimal contestable funding, which may have reduced the impact of their CRC research. Certain countries must perform more CRC research overall, especially in domains such as screening, palliative care and quality-of-life. The private-non-profit sector should be an alternative source of support.     

What Should We Focus on in Sepsis Fluid Resuscitation?--A Research Based on Scientometrics and Visual Analysis

Sepsis is a life-threatening condition and a global disease burden.Heterogeneous syndrome is defined as severe organ dysfunction caused by a dysregulated host response to infection,with renewed emphasis on the immune pathophysiology.Researchers worldwide constantly update the diagnostic criteria of sepsis and have introduced concepts such as“sepsis-3”“Surviving Sepsis Campaign(SSC)”“Early Goal-Directed Therapy(EGDT)”,the 3-h and 6-h bundles to an hour-1 bundle[1],“limited ventilation”“the best PEEP[2]”,and“Lung Protective-Ventilation”.Despite all efforts of experimental and clinical research during the last three decades,the ability to positively influence the course and outcome of the syndrome remains limited.     

护士"互联网+护理服务"参与行为意愿量表的编制及信效度检验

目的编制护士"互联网+护理服务"参与行为意愿量表,并进行信效度检验。方法以计划行为理论为框架,采用文献回顾、半结构式访谈法形成量表的条目池;通过专家咨询、项目分析对量表条目进行筛选,初步形成护士"互联网+护理服务"参与行为意愿量表。采用方便抽样法,于2020年10—12月选取江苏省6所医院(2所三级医院、2所二级医院、2所一级医院)的825名护士进行正式调查,检验问卷的信效度。结果护士"互联网+护理服务"参与行为意愿量表包括3个分量表(参与行为态度、主观行为规范、感知行为控制)、8个维度、17个条目。总量表的Cronbach'sα系数为0.927、重测信度系数为0.805。护士"互联网+护理服务"参与行为意愿与参与行为的相关系数为0.743(P<0.01)。因子分析提取出8个公因子,累积方差贡献率为72.33%。结论护士"互联网+护理服务"参与行为意愿量表具有较好的信效度,可用于护士"互联网+护理服务"参与行为意愿的评估。     

定量分析对常规超声联合超声造影定性误诊甲状腺结节的诊断价值

摘要：目的：探讨超声造影（CEUS）定量分析对常规超声联合CEUS定性诊断误诊为恶性的甲状腺结节与甲状腺乳头状癌的鉴别诊断价值。方法：选取2018年7月至2021年11月在我院超声科行甲状腺常规超声及CEUS检查，术前联合超声诊断为恶性（C-TIRADS≥4b 或 CEUS 表现为低增强和快速消退任一征像）而病理为良性的35位患者共35个结节为研究组；选择同期联合超声诊断与病理一致的甲状腺乳头状癌患者47例做为对照组。分析常规超声及CEUS特征，并将结节整体、中央部、边缘部及邻近正常甲状腺实质作为感兴趣区分别绘制时间-强度曲线（TIC）进行定量分析，比较两组结节各定量参数之间的差异。结果：研究组整体相对上升时间（△RT）以0.45为截断值，诊断恶性结节的敏感度为97.9%，特异度为85.7%，受试者工作（ROC）曲线下面积为0.96；研究组中央区△RT以0.55为截断值，诊断恶性结节的敏感度为95.7%，特异度为82.9%，ROC曲线下面积为0.87；研究组边缘区△RT以0.25为截断值，诊断恶性结节的敏感度为89.4%，特异度为77.1%，ROC曲线下面积为0.89。结论：CEUS定量分析在常规超声联合CEUS定性误诊甲状腺结节的诊断中具有一定的鉴别诊断价值。     

Arterial spin labeling MR image denoising and reconstruction using unsupervised deep learning

Arterial spin labeling (ASL) imaging is a powerful magnetic resonance imaging technique that allows to quantitatively measure blood perfusion non-invasively, which has great potential for assessing tissue viability in various clinical settings. However, the clinical applications of ASL are currently limited by its low signal-to-noise ratio (SNR), limited spatial resolution, and long imaging time. In this work, we propose an unsupervised deep learning-based image denoising and reconstruction framework to improve the SNR and accelerate the imaging speed of high resolution ASL imaging. The unique feature of the proposed framework is that it does not require any prior training pairs but only the subject's own anatomical prior, such as T1-weighted images, as network input. The neural network was trained from scratch in the denoising or reconstruction process, with noisy images or sparely sampled k-space data as training labels. Performance of the proposed method was evaluated using in vivo experiment data obtained from 3 healthy subjects on a 3T MR scanner, using ASL images acquired with 44-min acquisition time as the ground truth. Both qualitative and quantitative analyses demonstrate the superior performance of the proposed txtc framework over the reference methods. In summary, our proposed unsupervised deep learning-based denoising and reconstruction framework can improve the image quality and accelerate the imaging speed of ASL imaging.