An intuitionistic approach for the predictability of anti-angiogenic inhibitors in cancer diagnosis

Interpretable machine learning identifies metabolites associated with glomerular filtration rate in type 2 diabetes patients

Objective

The co-occurrence of kidney disease in patients with type 2 diabetes (T2D) is a major public health challenge. Although early detection and intervention can prevent or slow down the progression, the commonly used estimated glomerular filtration rate (eGFR) based on serum creatinine may be influenced by factors unrelated to kidney function. Therefore, there is a need to identify novel biomarkers that can more accurately assess renal function in T2D patients. In this study, we employed an interpretable machine-learning framework to identify plasma metabolomic features associated with GFR in T2D patients.

Methods

We retrieved 1626 patients with type 2 diabetes (T2D) in Liaoning Medical University First Affiliated Hospital (LMUFAH) as a development cohort and 716 T2D patients in Second Affiliated Hospital of Dalian Medical University (SAHDMU) as an external validation cohort. The metabolite features were screened by the orthogonal partial least squares discriminant analysis (OPLS-DA). We compared machine learning prediction methods, including logistic regression (LR), support vector machine (SVM), random forest (RF), and eXtreme Gradient Boosting (XGBoost). The Shapley Additive exPlanations (SHAP) were used to explain the optimal model.

Results

For T2D patients, compared with the normal or elevated eGFR group, glutarylcarnitine (C5DC) and decanoylcarnitine (C10) were significantly elevated in GFR mild reduction group, and citrulline and 9 acylcarnitines were also elevated significantly (FDR<0.05, FC > 1.2 and VIP > 1) in moderate or severe reduction group. The XGBoost model with metabolites had the best performance: in the internal validate dataset (AUROC=0.90, AUPRC=0.65, BS=0.064) and external validate cohort (AUROC=0.970, AUPRC=0.857, BS=0.046). Through the SHAP method, we found that C5DC higher than 0.1μmol/L, Cit higher than 26 μmol/L, triglyceride higher than 2 mmol/L, age greater than 65 years old, and duration of T2D more than 10 years were associated with reduced GFR.

Conclusion

Elevated plasma levels of citrulline and a panel of acylcarnitines were associated with reduced GFR in T2D patients, independent of other conventional risk factors.

PMFFNet: A hybrid network based on feature pyramid for ovarian tumor segmentation

Ovarian cancer is a highly lethal malignancy in the field of oncology. Generally speaking, the segmentation of ovarian medical images is a necessary prerequisite for the diagnosis and treatment planning. Therefore, accurately segmenting ovarian tumors is of utmost importance. In this work, we propose a hybrid network called PMFFNet to improve the segmentation accuracy of ovarian tumors. The PMFFNet utilizes an encoder-decoder architecture. Specifically, the encoder incorporates the ViTAEv2 model to extract inter-layer multi-scale features from the feature pyramid. To address the limitation of fixed window size that hinders sufficient interaction of information, we introduce Varied-Size Window Attention (VSA) to the ViTAEv2 model to capture rich contextual information. Additionally, recognizing the significance of multi-scale features, we introduce the Multi-scale Feature Fusion Block (MFB) module. The MFB module enhances the network's capacity to learn intricate features by capturing both local and multi-scale information, thereby enabling more precise segmentation of ovarian tumors. Finally, in conjunction with our designed decoder, our model achieves outstanding performance on the MMOTU dataset. The results are highly promising, with the model achieving scores of 97.24%, 91.15%, and 87.25% in mACC, mIoU, and mDice metrics, respectively. When compared to several Unet-based and advanced models, our approach demonstrates the best segmentation performance.

Brain tumor classification for MRI images using dual-discriminator conditional generative adversarial network

This research focuses on improving the detection and classification of brain tumors using a method called Brain Tumor Classification using Dual-Discriminator Conditional Generative Adversarial Network (DDCGAN) for MRI images. The proposed system is implemented in the MATLAB programming language. In this study, images of the brain are taken from a dataset and processed to remove noise and enhance image quality. The brain pictures are taken from Brats MRI image dataset. The images are preprocessed using Structural interval gradient filtering to remove noises and improve the quality of the image. The preprocessing outcomes are given to feature extraction. The features are extracted by Empirical wavelet transform (EWT) and the extracted features are given to the Dual-discriminator conditional generative adversarial network (DDCGAN) for recognizing the brain tumor, which classifies the brain images into glioma, meningioma, pituitary gland, and normal. Then, the weight parameter of DDCGAN is optimized by utilizing Border Collie Optimization (BCO), which is a met a heuristic approach to handle the real world optimization issues. It maximizes the detection accurateness and reduced computational time. Implemented in MATLAB, the experimental results demonstrate that the proposed system achieves a high sensitivity of 99.58%. The BCO-DDCGAN-MRI-BTC method outperforms existing techniques in terms of precision and sensitivity when compared to methods like Kernel Basis SVM (KSVM-HHO-BTC), Joint Training of Two-Channel Deep Neural Network (JT-TCDNN-BTC), and YOLOv2 including Convolutional Neural Network (YOLOv2-CNN-BTC). The research findings indicate that the proposed method enhances the accuracy of brain tumor classification while reducing computational time and errors.

Cell Line-Dependent Adhesion and Inhibition of Proliferation on Carbon-Based Nanofilms

Introduction

Disorganisation of the extracellular matrix (ECM) is strongly connected to tumor progression. Even small-scale changes can significantly influence the adhesion and proliferation of cancer cells. Therefore, the use of biocompatible nanomaterials capable of supporting and partially replenishing degraded ECM might be essential to recover the niche after tumor resection. The objective of this study was to evaluate the influence of graphene, graphene oxide, fullerene, and diamond nanofilms on breast cancer and glioblastoma grade IV cell lines.

Methods

Nanomaterials were characterized using SEM and TEM techniques; zeta potential analysis was also performed. Nanofilms of graphene, fullerene, and diamond nanoparticles were also characterized using AFM. The toxicity was tested on breast cancer MDA.MB.231 and glioblastoma grade IV U-87 MG cell lines, using LDH assay and by counting stained dead cells in bioprinted 3D models. The following parameters were analyzed: proliferation, adhesion to the nanofilm, and adhesion to particular ECM components covered with diamond nanoparticles.

Results and Discussion

Our studies demonstrated that nanofilms of graphene and diamond nanoparticles are characterized by cell-specific toxicity. Those nanomaterials were non-toxic to MDA.MB.231 cells. After applying bioprinted 3D models, diamond nanoparticles were not toxic for both cell lines. Nanofilms made of diamond nanoparticles and graphene inhibit the proliferation of MDA.MB.231 cells after 48 and 72 hours. Increased adhesion on nanofilm made of diamond nanoparticles was only observed for MDA.MB.231 cells after 30 and 60 minutes from seeding the cells. However, analysis of adhesion to certain ECM components coated with diamond nanoparticles revealed enhanced adhesion to tenascin and vitronectin for both tested cell lines.

Conclusion

Our studies show that nanofilm made of diamond nanoparticles is a non-toxic and pro-adhesive nanomaterial that might stabilize and partially replenish the niche after breast tumor resection as it enhances the adhesion of breast cancer cells and inhibits their proliferation.