Combining dosiomics and machine learning methods for predicting severe cardiac diseases in childhood cancer survivors: the French Childhood Cancer Survivor Study.

Background: Cardiac disease (CD) is a primary long-term diagnosed pathology among childhood cancer survivors. Dosiomics (radiomics extracted from the dose distribution) have received attention in the past few years to assess better the induced risk of radiotherapy (RT) than standard dosimetric features such as dose-volume indicators. Hence, using the spatial information contained in the dosiomics features with machine learning methods may improve the prediction of CD.

Mitochondrial complex I promotes kidney cancer metastasis.

Most kidney cancers are metabolically dysfunctional, but how this dysfunction affects cancer progression in humans is unknown. We infused C-labelled nutrients in over 80 patients with kidney cancer during surgical tumour resection. Labelling from [U-C]glucose varies across subtypes, indicating that the kidney environment alone cannot account for all tumour metabolic reprogramming.

Unitig-centered pan-genome machine learning approach for predicting antibiotic resistance and discovering novel resistance genes in bacterial strains.

In current genomic research, the widely used methods for predicting antimicrobial resistance (AMR) often rely on prior knowledge of known AMR genes or reference genomes. However, these methods have limitations, potentially resulting in imprecise predictions owing to incomplete coverage of AMR mechanisms and genetic variations. To overcome these limitations, we propose a pan-genome-based machine learning approach to advance our understanding of AMR gene repertoires and uncover possible feature sets for precise AMR classification.

Electron transport chain inhibition increases cellular dependence on purine transport and salvage.

Cancer cells reprogram their metabolism to support cell growth and proliferation in harsh environments. While many studies have documented the importance of mitochondrial oxidative phosphorylation (OXPHOS) in tumor growth, some cancer cells experience conditions of reduced OXPHOS in vivo and induce alternative metabolic pathways to compensate. To assess how human cells respond to mitochondrial dysfunction, we performed metabolomics in fibroblasts and plasma from patients with inborn errors of mitochondrial metabolism, and in cancer cells subjected to inhibition of the electron transport chain (ETC).

Mitochondrial metabolism in primary and metastatic human kidney cancers.

Most kidney cancers display evidence of metabolic dysfunction but how this relates to cancer progression in humans is unknown. We used a multidisciplinary approach to infuse C-labeled nutrients during surgical tumour resection in over 70 patients with kidney cancer. Labeling from [U-C]glucose varies across cancer subtypes, indicating that the kidney environment alone cannot account for all metabolic reprogramming in these tumours.

In vivo isotope tracing reveals a requirement for the electron transport chain in glucose and glutamine metabolism by tumors.

In mice and humans with cancer, intravenous C-glucose infusion results in C labeling of tumor tricarboxylic acid (TCA) cycle intermediates, indicating that pyruvate oxidation in the TCA cycle occurs in tumors. The TCA cycle is usually coupled to the electron transport chain (ETC) because NADH generated by the cycle is reoxidized to NAD by the ETC. However, C labeling does not directly report ETC activity, and other pathways can oxidize NADH, so the ETC's role in these labeling patterns is unverified.

Improving MGMT methylation status prediction of glioblastoma through optimizing radiomics features using genetic algorithm-based machine learning approach.

O6-Methylguanine-DNA-methyltransferase (MGMT) promoter methylation was shown in many studies to be an important predictive biomarker for temozolomide (TMZ) resistance and poor progression-free survival in glioblastoma multiforme (GBM) patients. However, identifying the MGMT methylation status using molecular techniques remains challenging due to technical limitations, such as the inability to obtain tumor specimens, high prices for detection, and the high complexity of intralesional heterogeneity. To overcome these difficulties, we aimed to test the feasibility of using a novel radiomics-based machine learning (ML) model to preoperatively and noninvasively predict the MGMT methylation status.

A Radiomics-Based Machine Learning Model for Prediction of Tumor Mutational Burden in Lower-Grade Gliomas.

Glioma is a Center Nervous System (CNS) neoplasm that arises from the glial cells. In a new scheme category of the World Health Organization 2016, lower-grade gliomas (LGGs) are grade II and III gliomas. Following the discovery of suppression of negative immune regulation, immunotherapy is a promising effective treatment method for lower-grade glioma patients.

Molecular subtype classification of low-grade gliomas using magnetic resonance imaging-based radiomics and machine learning.

In 2016, the World Health Organization (WHO) updated the glioma classification by incorporating molecular biology parameters, including low-grade glioma (LGG). In the new scheme, LGGs have three molecular subtypes: isocitrate dehydrogenase (IDH)-mutated 1p/19q-codeleted, IDH-mutated 1p/19q-noncodeleted, and IDH-wild type 1p/19q-noncodeleted entities. This work proposes a model prediction of LGG molecular subtypes using magnetic resonance imaging (MRI).

Compartmentalized metabolism supports midgestation mammalian development.

Mammalian embryogenesis requires rapid growth and proper metabolic regulation. Midgestation features increasing oxygen and nutrient availability concomitant with fetal organ development. Understanding how metabolism supports development requires approaches to observe metabolism directly in model organisms in utero.

Persea Americana Mill seed extracts: Understanding insights into the antioxidant and antityrosinase activities and effects on preserving qualities of whiteleg shrimp (Litopenaus vannamei) during refrigerated storage.

An ethanol extract of avocado seed (TN-1) and six smaller fractions (PD-1 to PD-6) were prepared. Most of the extracts exhibited scavenging DPPH radical, reducing Fe to Fe, and inhibiting polyphenoloxidase, consistently matching with their high polyphenolic content (p < 0.05).

A sequence-based prediction of Kruppel-like factors proteins using XGBoost and optimized features.

Krüppel-like factors (KLF) refer to a group of conserved zinc finger-containing transcription factors that are involved in various physiological and biological processes, including cell proliferation, differentiation, development, and apoptosis. Some bioinformatics methods such as sequence similarity searches, multiple sequence alignment, phylogenetic reconstruction, and gene synteny analysis have also been proposed to broaden our knowledge of KLF proteins. In this study, we proposed a novel computational approach by using machine learning on features calculated from primary sequences.

Radiomics-based machine learning model for efficiently classifying transcriptome subtypes in glioblastoma patients from MRI.

Background: In the field of glioma, transcriptome subtypes have been considered as an important diagnostic and prognostic biomarker that may help improve the treatment efficacy. However, existing identification methods of transcriptome subtypes are limited due to the relatively long detection period, the unattainability of tumor specimens via biopsy or surgery, and the fleeting nature of intralesional heterogeneity. In search of a superior model over previous ones, this study evaluated the efficiency of eXtreme Gradient Boosting (XGBoost)-based radiomics model to classify transcriptome subtypes in glioblastoma patients.

A Computational Framework Based on Ensemble Deep Neural Networks for Essential Genes Identification.

Essential genes contain key information of genomes that could be the key to a comprehensive understanding of life and evolution. Because of their importance, studies of essential genes have been considered a crucial problem in computational biology. Computational methods for identifying essential genes have become increasingly popular to reduce the cost and time-consumption of traditional experiments.

XGBoost Improves Classification of MGMT Promoter Methylation Status in IDH1 Wildtype Glioblastoma.

Approximately 96% of patients with glioblastomas (GBM) have IDH1 wildtype GBMs, characterized by extremely poor prognosis, partly due to resistance to standard temozolomide treatment. O6-Methylguanine-DNA methyltransferase (MGMT) promoter methylation status is a crucial prognostic biomarker for alkylating chemotherapy resistance in patients with GBM. However, MGMT methylation status identification methods, where the tumor tissue is often undersampled, are time consuming and expensive.

Using deep neural networks and biological subwords to detect protein S-sulfenylation sites.

Protein S-sulfenylation is one kind of crucial post-translational modifications (PTMs) in which the hydroxyl group covalently binds to the thiol of cysteine. Some recent studies have shown that this modification plays an important role in signaling transduction, transcriptional regulation and apoptosis. To date, the dynamic of sulfenic acids in proteins remains unclear because of its fleeting nature.

Using extreme gradient boosting to identify origin of replication in Saccharomyces cerevisiae via hybrid features.

DNA replication is a fundamental task that plays a crucial role in the propagation of all living things on earth. Hence, the accurate identification of its origin could be the key to giving an insightful understanding of the regulatory mechanism of gene expression. Indeed, with the robust development of computational techniques and the abundant biological sequencing data, it has become possible for scientists to identify the origin of replication accurately and promptly.

Gamma irradiation of cotton fabrics in AgNO₃ solution for preparation of antibacterial fabrics.

Silver nanoparticles (AgNPs) with diameter about 12 nm were immobilized on the surface of cotton fabrics by γ-irradiation of fabrics in the AgNO3 chitosan solution. Effects of absorbed dose, concentration of AgNO3 solution on immobilization of the AgNPs on fabrics were investigated. The optimal dose was selected to be 13.