Optimal Planar Flow Network Designs for Tissue Engineered Constructs with Built-in Vasculature

Convective delivery of nutrients is important to enhance mass transport within tissue engineered (TE) products. Depending on the target tissue, an ideal TE product will have an integrated microvasculature that will eliminate mass transport limitations that can occur during product growth in vitro and integration in vivo. A synthetic approach to develop microvasculature involves development of network designs with efficient mass transfer characteristics. In this paper, utilizing a planar bifurcating network as a basis, we develop an approach to design optimal flow networks that have maximum mass transport efficiency for a given pressure drop. We formulated the optimization problem for a TE skin product, incorporating two types of duct flow, rectangular and square, and solved using a generalized reduced gradient algorithm. Under the conditions of this study, we found that rectangular ducts have superior mass transport characteristics than square ducts. Microvascular area per volume values obtained in this work are significantly greater than those reported in the literature. We discuss the effect of network variables such as porosity and generations on the optimal designs. This research forms the engineering basis for the rational development of TE products with built-in microvasculature and will pave the way to design complex flow networks with optimal mass transfer characteristics.     

The antivascular action of physiotherapy ultrasound on a murine tumor: role of a microbubble contrast agent

This study investigated whether a microbubble-containing ultrasound contrast agent had a role in the antivascular action of physiotherapy ultrasound on tumor neovasculature. Ultrasound images (B-mode and contrast-enhanced power Doppler [0.02 mL Definity]) were made of 22 murine melanomas (K1735²²). The tumor was insonated (I_SATA = 1.7 W cm^–2, 1 MHz, continuous output) for 3 min and the power Doppler observations of the pre- and postinsonation tumor vascularities were analyzed. Significant reductions (p = 0.005 for analyses of color-weighted fractional area) in vascularity occurred when a contrast-enhanced power Doppler study occurred before insonation. Vascularity was unchanged in tumors without a pretherapy Doppler study. Histologic studies revealed tissue structural changes that correlated with the ultrasound findings. The underlying etiology of the interaction between the physiotherapy ultrasound beam, the microbubble-containing contrast agent and the tumor neovasculature is unknown. It was concluded that contrast agents play an important role in the antivascular effects induced by physiotherapy ultrasound. (E-mail: sehgalc@uphs.upenn.edu)     

机器学习算法在肝细胞癌微血管侵犯术前预测中的应用价值

目的探讨机器学习算法在肝细胞癌微血管侵犯(MVI)术前预测中的应用价值。方法采用回顾性描述性研究方法。收集2015年5月至2018年12月福建医科大学孟超肝胆医院收治的277例肝细胞癌患者的临床病理资料;男235例,女42例;年龄为(56±10)岁,年龄范围为33~80岁。患者术前均行磁共振成像检查。227例肝细胞癌患者通过计算机产生随机数方法以7∶3比例分为训练集193例和验证集84例。应用逻辑回归列线图,支持向量机(SVM)、随机森林(RF)、人工神经网络(ANN)和轻量级梯度提升机(LightGBM)机器学习算法构建MVI术前预测模型。观察指标:(1)训练集及验证集患者临床病理资料分析。(2)影响训练集患者肿瘤MVI危险因素分析。(3)机器学习算法预测模型构建及其术前预测肿瘤MVI准确性比较。正态分布的计量资料以±s表示,组间比较采用配对t检验。计数资料以绝对数表示,组间比较采用χ2检验。单因素和多因素分析采用Logistic回归模型。结果(1)训练集及验证集患者临床病理资料分析:训练集和验证集患者性别(男,女)分别为157、36例和78、6例,两组比较,差异有统计学意义(χ2=6.028,P<0.05)。(2)影响训练集患者肿瘤MVI危险因素分析:训练集193例患者中,MVI阳性108例,MVI阴性85例。单因素分析结果显示:年龄、肿瘤数目、肿瘤直径、卫星病灶、肿瘤边界、甲胎蛋白(AFP)、碱性磷酸酶(ALP)和纤维蛋白原水平是影响肿瘤MVI的相关因素(比值比=0.971,2.449,1.368,4.050,2.956,4.083,2.532,1.996,95%可信区间为0.943~1.000,1.169~5.130,1.180~1.585,1.316~12.465,1.310~6.670,2.214~7.532,1.016~6.311,1.323~3.012,P<0.05)。多因素分析结果显示:AFP>20μg/L、肿瘤多发、肿瘤直径越大、肿瘤边界不光滑是影响肿瘤MVI的独立危险因素(比值比=3.680,3.100,1.438,3.628,95%可信区间为1.842~7.351,1.334~7.203,1.201~1.721,1.438~9.150,P<0.05),而年龄越大,MVI发生风险越低(比值比=0.958,95%可信区间为0.923~0.994,P<0.05)。(3)机器学习算法预测模型构建及其术前预测肿瘤MVI准确性比较:①应用多因素分析结果筛选指标,包括年龄、AFP、肿瘤数目、肿瘤直径、肿瘤边界,构建逻辑回归列线图,SVM、RF、ANN及LightGBM机器学习算法预测模型,一致性分析结果显示逻辑回归列线图预测模型稳定性较好。逻辑回归列线图、SVM、RF、ANN、LightGBM机器学习算法预测模型训练集和验证集曲线下面积(AUC)分别为0.812、0.794、0.807、0.814、0.810和0.784、0.793、0.783、0.803、0.815,SVM、RF、ANN、LightGBM机器学习算法AUC分别与逻辑回归列线图AUC比较,差异均无统计学意义[(95%可信区间为0.731~0.849,0.744~0.860,0.752~0.867,0.747~0.862,Z=0.995,0.245,0.130,0.102,P>0.05)和(95%可信区间为0.690~0.873,0.679~0.865,0.702~0.882,0.715~0.891,Z=0.325,0.026,0.744,0.803,P>0.05)]。②应用RF、LightGBM机器学习算法自行筛选临床病理因素指标构建预测模型。根据指标对预测模型重要度排序,选择重要度>0.01的指标,包括年龄、肿瘤直径、AFP、白细胞(WBC)、血小板、总胆红素、天冬氨酸氨基转移酶、γ-谷氨酰转移酶、ALP和纤维蛋白原,构建RF机器学习算法预测模型;挑选重要度>5.0的指标,包括年龄、肿瘤直径、AFP、WBC、ALP和纤维蛋白原,构建LightGBM机器学习算法预测模型;由于ANN及SVM机器学习算法不具备筛选指标能力,应用单因素分析结果筛选指标,包括年龄、肿瘤数目、肿瘤直径、卫星病灶、肿瘤边界、AFP、ALP和纤维蛋白原水平,构建SVM、ANN机器学习算法预测模型。SVM、RF、ANN、LightGBM机器学习算法预测模型训练集和验证集AUC分别为0.803、0.838、0.793、0.847和0.810、0.802、0.802、0.836,分别与逻辑回归列线图AUC比较,差异均无统计学意义[(95%可信区间为0.740~0.857,0.779~0.887,0.729~0.848,0.789~0.895,Z=0.421,0.119,0.689,1.517,P>0.05)和(95%可信区间为0.710~0.888,0.700~0.881,0.701~0.881,0.740~0.908,Z=0.856,0.458,0.532,1.306,P>0.05)]。结论机器学习算法可用于术前预测肝细胞癌MVI,但其应用价值尚需多中心大样本数据进一步验证。     

Relationship between diabetic retinopathy microalbuminuria and other modifiable risk factors

BackgroundDiabetic Retinopathy (DR) is an important microvascular complication of diabetes that can lead to irreversible blindness. Microalbuminuria is strongly associated with diabetic retinopathy and can be used as a reliable marker of diabetic retinopathy.AimTo assess the association between DR, microalbuminuria, and other modifiable risk factors in patients with type 2 diabetes.Methodology3090 patients with T2DM visiting North Delhi Diabetes Centre, New Delhi between July 2016 to October 2019 were evaluated for the clinical and biochemical parameters that included urinary albumin, HbA1C, lipid profiles, serum creatinine estimation and underwent biothesiometry.Results3090 patients (1350 females and 1740 males), with mean age of 52.7 ± 9.2 years and diabetes duration ranging from 1 to 19 years (mean 9.4 ± 6), duration of less than 5 years, 6–10 years and more than 10 years in 52%, 26% and in 22%, respectively. Duration of diabetes was strong predictor of retinopathy (p = 0.001). The HbA1c and BMI in patients with DR was significantly higher than in those without DR. 18.2% patients were diagnosed to have retinopathy. Peripheral neuropathy was observed in 24.2% and was positively associated with DR (p = 0.002). 33.9% and 4.5% patients had microalbuminuria macroalbuminuria, respectively and 9.7% patients had creatinine >1.3 mg/dL. There was significant positive relationship between different grades of retinopathy and albuminuria.ConclusionsOur study is a large real-world study that demonstrates that HbA1c, BMI, duration of diabetes, microalbuminuria and peripheral neuropathy are relatively, yet cohesively contributing factors towards varying grade of retinopathy.     

寰前节间动脉

寰前节间动脉是成人较少见的一种永久性颈动脉一椎基底动脉吻合，常与脑血管病的发生、临床表现及治疗相关。文章对已报道的寰前节间动脉病例做了综述。     

微血管性心绞痛冠状动脉血流速度与内皮损伤的研究

目的　通过冠状动脉血流多普勒检测 ,探讨微血管病变患者的冠状动脉血流速度及其与血管内皮损伤、局部微血栓的关系。方法　对有胸痛而冠状动脉造影正常的 16例患者的 43支血管 (右冠状动脉 14支 ,左前降支 15支 ,左回旋支 14支 )行冠状动脉内多普勒超声血流检查 ,记录基础血流参数和充血相血流参数 ,同时记录冠状动脉血流速率储备 (CFVR)。以CFVR 2 5为标准 ,分为正常组 (A组 )和微血管病变组 (B组 ) ,比较二组的冠状动脉血流速度参数及血浆血管性假血友病因子(vWF)的差异。结果　A组包括 7例患者的 19支血管 ,B组包括 9例患者的 2 4支血管。A组的基础平均峰值流速 (bAPV)显著小于B组 [(17 7± 4 8)vs (2 0 9± 5 4)cm s ,P <0 0 0 1];而充血相平均峰值流速 (hAPV)A组显著大于B组 [(5 1 0± 13 3)vs (4 2 5± 11 3)cm s ,P <0 0 5 ];A组的CFVR显著大于B组 [(2 9± 0 5 )vs (2 0± 0 3) ,P <0 0 0 1];A组的血浆vWF显著小于B组 [(112 5± 2 7 5 ) %vs(173 2± 40 8) % ,P <0 0 5 ]。结论　微血管病变患者的基础平均峰血流速度显著增大 ,而充血相平均峰血流速度显著减小 ,可能与冠状动脉内皮损伤及局部微血栓形成有关。     

Peripheral microvascular dysfunction predicts residual risk in coronary artery disease patients on statin therapy

Objective

Although lowering of low-density lipoprotein cholesterol (LDL-C) by statins is essential in treatment of coronary artery disease (CAD) patients, there is considerable residual risk of secondary coronary artery events (CAE). We examined whether microvascular dysfunction (MiD), measured by peripheral artery tonometry (PAT), can predict prognosis of CAD patients previously treated with statins.

Methods

We measured log-transformed reactive hyperemia index (L_RHI) in 213 CAD patients who had already achieved LDL-C <100 by statin therapy. Patients were followed-up for secondary CAE for a median of 2.7 years. Patients were divided into two groups: L_RHI ≥ 0.54 (n = 99) and L_RHI < 0.54 (n = 114).

Results

During follow-up, CAE occurred in 4 (4.0%) patients in the L_RHI ≥ 0.54 group and 18 (15.8%) patients in the L_RHI < 0.54 group (P = 0.006). Cox regression analysis indicated that L_RHI was an independent predictor for CAE even after adjustment by Framingham traditional risk factors (FRF; age, T-C/HDL-C ratio, systolic blood pressure, diabetes, current smoker, and gender) and estimated glomerular filtration rate (eGFR) for secondary CAE (HR 0.79, 95% CI: 0.66–0.95). ROC analysis for CAE prediction showed that the AUC for models including FRF only, FRF + eGFR, and FRF + eGFR + L_RHI were 0.60, 0.71, and 0.77, respectively. Moreover, adding eGFR to FRF only (0.63, P = 0.003) and adding L_RHI to the FRF + eGFR model were associated with significant improvement of net reclassification improvement (0.79, P = 0.007).

Conclusion

MiD measured by non-invasive PAT adds incremental predictive ability to traditional risk factors for prognosis of CAD patients successfully treated with statins.