Tumour Cell Membrane Poration and Ablation by Pulsed Low-Intensity Electric Field with Carbon Nanotubes

Electroporation is a physical method to increase permeabilization of cell membrane by electrical pulses. Carbon nanotubes (CNTs) can potentially act like “lighting rods” or exhibit direct physical force on cell membrane under alternating electromagnetic fields thus reducing the required field strength. A cell poration/ablation system was built for exploring these effects of CNTs in which two-electrode sets were constructed and two perpendicular electric fields could be generated sequentially. By applying this system to breast cancer cells in the presence of multi-walled CNTs (MWCNTs), the effective pulse amplitude was reduced to 50 V/cm (main field)/15 V/cm (alignment field) at the optimized pulse frequency (5 Hz) of 500 pulses. Under these conditions instant cell membrane permeabilization was increased to 38.62%, 2.77-fold higher than that without CNTs. Moreover, we also observed irreversible electroporation occurred under these conditions, such that only 39.23% of the cells were viable 24 h post treatment, in contrast to 87.01% cell viability without presence of CNTs. These results indicate that CNT-enhanced electroporation has the potential for tumour cell ablation by significantly lower electric fields than that in conventional electroporation therapy thus avoiding potential risks associated with the use of high intensity electric pulses.     

The efficient and unbiased estimation of nuclear size variability using the ‘selector’

The selector was used to make an unbiased estimation of nuclear size variability in one benign naevocellular skin tumour and one cutaneous malignant melanoma. The results showed that the estimates obtained using the selector were comparable to those obtained using the more time consuming Cavalieri-disector approach. Employing ‘optical sections’, the selector was found to be between five and ten times more efficient than the Cavalieri-disector method when using physical sections.     

Brain Tumor Segmentation in Multimodal MRI Using U-Net Layered Structure

The brain tumour is the mass where some tissues become old or damaged, but they do not die or not leave their space. Mainly brain tumour masses occur due to malignant masses. These tissues must die so that new tissues are allowed to be born and take their place. Tumour segmentation is a complex and time-taking problem due to the tumour’s size, shape, and appearance variation. Manually finding such masses in the brain by analyzing Magnetic Resonance Images (MRI) is a crucial task for experts and radiologists. Radiologists could not work for large volume images simultaneously, and many errors occurred due to overwhelming image analysis. The main objective of this research study is the segmentation of tumors in brain MRI images with the help of digital image processing and deep learning approaches. This research study proposed an automatic model for tumor segmentation in MRI images. The proposed model has a few significant steps, which first apply the pre-processing method for the whole dataset to convert Neuroimaging Informatics Technology Initiative (NIFTI) volumes into the 3D NumPy array. In the second step, the proposed model adopts U-Net deep learning segmentation algorithm with an improved layered structure and sets the updated parameters. In the third step, the proposed model uses state-of-the-art Medical Image Computing and Computer-Assisted Intervention (MICCAI) BRATS 2018 dataset with MRI modalities such as T1, T1Gd, T2, and Fluid-attenuated inversion recovery (FLAIR). Tumour types in MRI images are classified according to the tumour masses. Labelling of these masses carried by state-of-the-art approaches such that the first is enhancing tumour (label 4), edema (label 2), necrotic and non-enhancing tumour core (label 1), and the remaining region is label 0 such that edema (whole tumour), necrosis and active. The proposed model is evaluated and gets the Dice Coefficient (DSC) value for High-grade glioma (HGG) volumes for their test set-a, test set-b, and test set-c 0.9795, 0.9855 and 0.9793, respectively. DSC value for the Low-grade glioma (LGG) volumes for the test set is 0.9950, which shows the proposed model has achieved significant results in segmenting the tumour in MRI using deep learning approaches. The proposed model is fully automatic that can implement in clinics where human experts consume maximum time to identify the tumorous region of the brain MRI. The proposed model can help in a way it can proceed rapidly by treating the tumor segmentation in MRI.     

Optimal control of some simplified models of tumour growth

In this article we study some optimal control problems for a system of PDEs that describes the growth of a spherical tumour influenced by the mechanical action of chemicals. We make two different choices of the cost functional. We prove existence results, deduce the associated optimality systems and present iterative algorithms for the computation of the solution.     

癌性腹水患者外周血THl、TH2细胞免疫反应状态的研究

目的:研究辅助性T细胞(TH1、TH2)在癌性腹水患者中的作用及临床意义。方法:采用ELISA(酶联免疫吸附法)检测患者外周血和腹水中-干扰素(-INF)、白细胞介素(IL-4)水平,从而判断THl、TH2细胞的活性。结果:癌性腹水患者外周血THl细胞的活性较正常人明显降低,TH2细胞活性明显增强。治疗无效者外周血THl细胞的活性进一步下降,TH2细胞活性进一步增强,而治疗有效者外周血THl细胞功能逐渐增强,TH2细胞功能逐渐下降,其水平均接近于正常对照组。结论:外周血辅助性T细胞(TH1、TH2)的活性对癌性腹水患者的疗效及预后判断具有一定的价值。     

激光照射对荷瘤小鼠淋巴细胞活性的影响

采用MTT还原分析法研究氦氖激光照射对SP2 /O骨髓瘤小鼠脾细胞、胸腺细胞的增殖活性的影响。结果表明激光具有增强荷瘤小鼠免疫细胞活性的功能。激光组与对照组相比P <0 0 1,差异明显。由此证明氦氖激光照射免疫器官区 (脾区 )具有活化淋巴细胞 ,促进淋巴细胞DNA合成 ,提高机体免疫力的作用。     

斯普林联合化疗治疗中、晚期恶性肿瘤的临床观察

目的 :观察斯普林联合化疗治疗恶性肿瘤的疗效。方法 :84例中、晚期恶性肿瘤患者随机分成两组。观察组 :43例各类中、晚期恶性肿瘤患者应用斯普林联合化疗药治疗 ,斯普林每日静点 8ml,连用 3周。两组化疗用药基本相似。评价治疗前后血象变化、病人全身状况改善情况 (神疲、乏力 ,食欲减退 ,恶心、呕吐 ,腹胀、便秘 ,心悸、失眠 ) ,生活质量提高等方面。结果 :观察组血象WBC在治疗前后无明显变化 (P >0 .0 5 ) ,而对照组治疗后血象WBC明显下降 ,较治疗前有显著差异 (P <0 .0 5 )。两组治疗后血象改变具有统计学意义 (P <0 .0 5 )。观察组治疗后病人全身状况得到改善 ,生活质量明显提高 ,与对照组比较有显著差异 (P <0 .0 5 )。结论 ,斯普林具有良好稳定化疗病人血象 ,改善病人全身状况 ,提高病人生活质量的作用     

迈入新世纪的硼中子俘获疗法（BNCT）

扼要叙述进入21世纪之际,硼中子俘获疗法(boron neutorn capture therapy,BNCT)在国际范围内的一些显著进展,包括BNCT的临床定位、肿瘤复发的探索、硼浓度的定量探测、靶向掺硼药物的开发以及我国医院中子照射器的问世.这些BNCT长期开发中的瓶颈趋于缓解,预示了BNCT个性化与例行化的前景更为清晰.