Developing a Breast Cancer Resistance Protein Substrate Prediction System Using Deep Features and LDA

Breast cancer resistance protein(BCRP)is an important resistance protein that significantly impacts anticancer drug discovery,treatment,and rehabilitation.Early identification of BCRP substrates is quite a challenging task.This study aims to predict early substrate structure,which can help to optimize anticancer drug development and clinical diagnosis.For this study,a novel intelligent approach-based methodology is developed by modifying the ResNet101 model using transfer learning(TL)for automatic deep feature(DF)extraction followed by classification with linear discriminant analysis algorithm(TLRNDF-LDA).This study utilized structural fingerprints,which are exploited by DF contrary to conventional molecular descriptors.The proposed in silico model achieved an outstanding accuracy performance of 98.56%on test data compared to other state-of-the-art approaches using standard quality measures.Furthermore,the model’s efficacy is validated via a statistical analysisANOVAtest.It is demonstrated that the developedmodel can be used effectively for early prediction of the substrate structure.The pipeline of this study is flexible and can be extended for in vitro assessment efficacy of anticancer drug response,identification of BCRP functions in transport experiments,and prediction of prostate or lung cancer cell lines.

End-to-End 2D Convolutional Neural Network Architecture for Lung Nodule Identification and Abnormal Detection in Cloud

The extent of the peril associated with cancer can be perceivedfrom the lack of treatment, ineffective early diagnosis techniques, and mostimportantly its fatality rate. Globally, cancer is the second leading cause ofdeath and among over a hundred types of cancer;lung cancer is the secondmost common type of cancer as well as the leading cause of cancer-relateddeaths. Anyhow, an accurate lung cancer diagnosis in a timely manner canelevate the likelihood of survival by a noticeable margin and medical imagingis a prevalent manner of cancer diagnosis since it is easily accessible to peoplearound the globe. Nonetheless, this is not eminently efficacious consideringhuman inspection of medical images can yield a high false positive rate. Ineffectiveand inefficient diagnosis is a crucial reason for such a high mortalityrate for this malady. However, the conspicuous advancements in deep learningand artificial intelligence have stimulated the development of exceedinglyprecise diagnosis systems. The development and performance of these systemsrely prominently on the data that is used to train these systems. A standardproblem witnessed in publicly available medical image datasets is the severeimbalance of data between different classes. This grave imbalance of data canmake a deep learning model biased towards the dominant class and unableto generalize. This study aims to present an end-to-end convolutional neuralnetwork that can accurately differentiate lung nodules from non-nodules andreduce the false positive rate to a bare minimum. To tackle the problem ofdata imbalance, we oversampled the data by transforming available images inthe minority class. The average false positive rate in the proposed method isa mere 1.5 percent. However, the average false negative rate is 31.76 percent.The proposed neural network has 68.66 percent sensitivity and 98.42 percentspecificity.

Drug Response Prediction of Liver Cancer Cell Line Using Deep Learning

Cancer is the second deadliest human disease worldwide with high mortality rate.Rehabilitation and treatment of this disease requires precise and automatic assessment of effective drug response and control system.Prediction of treated and untreated cancerous cell line is one of the most challenging problems for precise and targeted drug delivery and response.A novel approach is proposed for prediction of drug treated and untreated cancer cell line automatically by employing modified Deep neural networks.Human hepatocellular carcinoma(HepG2)cells are exposed to anticancer drug functionalized CFO@BTO nanoparticles developed by our lab.Prediction models are developed by modifying ResNet101 and exploiting the transfer learning concept.Last three layers of ResNet101 are re-trained for the identification of drug treated cancer cells.Transfer learning approach in an appropriate choice especially when there is limited amount of annotated data.The proposed technique is validated on acquired 203 fluorescentmicroscopy images of human HepG2 cells treated with drug functionalized cobalt ferrite@barium titanate(CFO@BTO)magnetoelectric nanoparticles in vitro.The developed approach achieved high prediction with accuracy of 97.5%and sensitivity of 100%and outperformed other approaches.The high performance reveals the effectiveness of the approach.It is scalable and fully automatic prediction approach which can be extended for other similar cell diseases such as lung,brain tumor and breast cancer.

New Applications to Solitary Wave Ansatz

In this article, the solitary wave and shock wave solitons for nonlinear Ostrovsky equation and Potential Kadomstev-Petviashvili equations have been obtained. The solitary wave ansatz is used to carry out the solutions.

Binary phase solid-state photopolymerization of acrylates： design, characterization and biomineralization of 3D scaffolds for tissue engineering

Porous polymer scaffolds designed by the cryogel method are attractive materials for a range of tissue engineering applications. However, the use of toxic cross- linker for retaining the pore structure limits their clinical applications. In this research, acrylates （HEA/PEGDA, HEMA/PEGDA and PEGDA） were used in the low-temperature solid-state photopolymerization to produce porous scaffolds with good structural retention. The morphology, pore diameter, mineral deposition and water absorption of the scaffold were characterized by SEM and water absorption test respectively. Elemental analysis and cytotoxicity of the biomineralized scaffold were revealed by using XRD and MTT assay test. The PEGDA-derived scaffold showed good water absorption ability and a higher degree of porosity with larger pore size compared to others. XRD patterns and IR results confirmed the formation of hydroxyapatite crystals from an alternative socking process. The overall cell proliferation was excellent, where PEGDA-derived scaffold had the highest and the most uniform cell growth, while HEMAJPEGDA scaffold showed the least. These results suggest that the cell proliferation and adhesion are directly proportional to the pore size, the shape and the porosity of scaffolds.

煤炭腐植酸作为土壤改良剂对土壤物理性质和小麦产量的影响

在巴基斯坦旁遮普的波特瓦地区(33°N,74°E),集约化土壤耕作,土壤侵蚀和低量作物残体的投入是导致土壤结构退化的原因。结构不稳定的土壤很容易受到侵蚀,反过来,土壤侵蚀又会造成作物产量的下降。因此,为了改善土壤的物理性状,在巴基斯坦旁遮普的干旱地区进行了田间试验。试验地点位于大学(拉瓦尔品第PMAS干旱农业大学)研究农场的园区内。2种不同等级(实验室级和商品级)的腐植酸(HA)各分8个水平,施用2年,处理分别为HL_0(对照,不施腐植酸),HL_1 10 kg HA/hm^2,HL_2 20 kg HA/hm^2,HL_3 30 kg HA/hm^2,HL_4 60 kg HA/hm^2,HL_5 90 kg HA/hm^2,HL_6 120 kg HA/hm^2和HL_7 150 kg HA/hm^2,各处理同时配合施用N-P-K(120-90-60 kg/hm^2)。试验期间,检测土壤总有机碳、饱和导水率、团聚体稳定性、容重、土壤含水量和作物产量。试验结果表明,腐植酸能通过影响土壤总有机碳、饱和导水率、团聚体稳定性、容重和土壤含水量等指标来改善土壤的物理性状。2年试验结果均表现为,实验室级的腐植酸比商品级的腐植酸能更好地改善土壤物理性状,从而提高小麦产量。2种不同级别的腐植酸各施用水平与对照相比,差异均显著。在120 kg/hm^2和150 kg/hm^2的腐植酸施用水平下,大多数指标均显示出了相似的结果,因此,从经济角度考虑,120 k g/hm^2的腐植酸用量为最佳施用量。