Fashion industry emissions, resource use, and waste are attracting increasing consumer and government attention, with broad agreement that a new approach is required along the supply chain. Following the COVID-19 pandemic, a move to digitalisation facilitated an accelerating interest in digital applications, including immersive technologies such as augmented and virtual reality. This systematic literature review explores the intersecting topics of fashion, immersive technologies, and sustainability to determine the trends, examine the solutions offered, and discuss the implications of immersive technologies for sustainability. Four resources were consulted (Scholar, SCOPUS, WOS, and ProQuest), resulting in 74 articles for the review. Grey literature was included due to the currency of the topic and gaps in the available academic literature. The findings highlight immersive technology uses in the fashion industry, which are part of a move towards sustainability. These technologies are used to reduce online returns, educate consumers, reduce waste in design and manufacture, and remove the need for physical items. However, issues include high energy costs, consumer reluctance, and skill shortages. Results suggest future research and industry discussions should focus on empirical studies to measure the sustainability impact of immersive technologies, monitor technology diffusion and uptake, and measure the industry skills gap.

Metabolic pathway analysis, one of the most important fields in biochemistry, is pivotal to understanding the maintenance and modulation of the functions of an organism. Good comprehension of metabolic pathways is critical to understanding the mechanisms of some fundamental biological processes. Given a small molecule or an enzyme, how may one identify the metabolic pathways in which it may participate? Answering such a question is a first important step in understanding a metabolic pathway system. By utilizing the information provided by chemical-chemical interactions, chemical-protein interactions, and protein-protein interactions, a novel method was proposed by which to allocate small molecules and enzymes to 11 major classes of metabolic pathways. A benchmark dataset consisting of 3,348 small molecules and 654 enzymes of yeast was constructed to test the method. It was observed that the first order prediction accuracy evaluated by the jackknife test was 79.56% in identifying the small molecules and enzymes in a benchmark dataset. Our method may become a useful vehicle in predicting the metabolic pathways of small molecules and enzymes, providing a basis for some further analysis of the pathway systems.

Colorectal cancer is generally categorized into the following four stages according to its development or serious degree: Dukes A, B, C, and D. Since different stage of colorectal cancer actually corresponds to different activated region of the network, the transition of different network states may reflect its pathological changes. In view of this, we compared the gene expressions among the colorectal cancer patients in the aforementioned four stages and obtained the early and late stage biomarkers, respectively. Subsequently, the two kinds of biomarkers were both mapped onto the protein interaction network. If an early biomarker and a late biomarker were close in the network and also if their expression levels were correlated in the Dukes B and C patients, then a signal propagation path from the early stage biomarker to the late one was identified. Many transition genes in the signal propagation paths were involved with the signal transduction, cell communication, and cellular process regulation. Some transition hubs were known as colorectal cancer genes. The findings reported here may provide useful insights for revealing the mechanism of colorectal cancer progression at the cellular systems biology level.

Non-small cell lung cancer (NSCLC) is a potentially fatal disease and the incidence is increasing annually. In order to diagnose and treat NSCLC effectively, greater understanding of its molecular mechanism is required. In the present study, 36 NSCLC tissues and 10 normal tissues were selected. Reverse transcription‑quantitative polymerase chain reaction (RT‑qPCR) was used to analyze the CD44 mRNA expression level in NSCLC tissue and DNA sequencing was performed to further verify the CD44 expression level. Differentially expressed genes between tumor tissues and controls were determined by DNA sequencing and the Gene_act_net between CD44 and its associated genes was constructed. Gene Ontology (GO) term enrichment analysis of the differentially expressed genes was performed by the Biological Networks Gene Ontology tool. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was performed based on the Expression Analysis Systematic Explorer test applied in the Database for Annotation, Visualization and Integrated Discovery. RT‑qPCR results showed that CD34 was overexpressed in 21 of the 36 NSCLC tissues (58.3%). The Gene_act_net indicated that there were 20 differentially expressed genes with 17 upregulated and 3 downregulated. Among them, CD44, MET, ERBB2, EGFR, AKT1, IQGAP1 and STAT3 were associated with the occurrence and migration of NSCLC. In KEGG pathway analysis, extracellular matrix‑receptor interaction and hematopoietic cell lineage pathways were the most affected by overexpressed CD44; and thus may be important in the development and migration of NSCLC. In conclusion, CD44 was overexpressed in NSCLC and the overexpression was associated with the occurrence of NSCLC and migration of NSCLC cells.

The aim of this study was to identify critical gene pathways that are associated with lung cancer metastasis to the brain. The RNA-Seq approach was used to establish the expression profiles of a primary lung cancer, adjacent benign tissue, and metastatic brain tumor from a single patient. The expression profiles of these three types of tissues were compared to define differentially expressed genes, followed by serial-cluster analysis, gene ontology analysis, pathway analysis, and knowledge-driven network analysis. Reverse transcription–polymerase chain reaction (RT-PCR) was used to validate the expression of essential candidate genes in tissues from ten additional patients. Differential gene expression among these three types of tissues was classified into multiple clusters according to the patterns of their alterations. Further bioinformatic analysis of these expression profile data showed that the network of the signal transduction pathways related to actin cytoskeleton reorganization, cell migration, and adhesion was associated with lung cancer metastasis to the brain. The expression of ACTN4 (actinin, alpha 4), a cytoskeleton protein gene essential for cytoskeleton organization and cell motility, was significantly elevated in the metastatic brain tumor but not in the primary lung cancer tissue. The signaling pathways involved in the regulation of cytoskeleton reorganization, cell motility, and focal adhesion play a role in the process of lung cancer metastasis to the brain. The contribution of ACTN4 to the process of lung cancer metastasis to the brain could be mainly through regulation of actin cytoskeleton reorganization, cell motility, and focal adhesion.

Tumors are formed by the abnormal proliferation of somatic cells with disordered growth regulation under the influence of tumorigenic factors. Recently, the theory of “cancer drivers” connects tumor initiation with several specific mutations in the so-called cancer driver genes. According to the differentiation of four basic levels between tumor and adjacent normal tissues, the cancer drivers can be divided into the following: (1) Methylation level, (2) microRNA level, (3) mutation level and (4) mRNA level. In this study, a computational method is proposed to identify novel lung adenocarcinoma drivers based on dysfunctional genes on the methylation, microRNA, mutation and mRNA levels. First, a large network was constructed using protein-protein interactions. Next, we searched all of the shortest paths connecting dysfunctional genes on different levels and extracted new candidate genes lying on these paths. Finally, the obtained candidate genes were filtered by a permutation test and an additional strict selection procedure involving a betweenness ratio and an interaction score. Several candidate genes remained, which are deemed to be related to two different levels of cancer. The analyses confirmed our assertions that some have the potential to contribute to the tumorigenesis process on multiple levels.