Integrating bioinformatics and machine learning methods to analyze diagnostic biomarkers for HBV-induced hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is a malignant tumor. It is estimated that approximately 50-80% of HCC cases worldwide are caused by hepatitis b virus (HBV) infection, and other pathogenic factors have been shown to promote the development of HCC when coexisting with HBV. Understanding the molecular mechanisms of HBV-induced hepatocellular carcinoma (HBV-HCC) is crucial for the prevention, diagnosis, and treatment of the disease. In this study, we analyzed the molecular mechanisms of HBV-induced HCC by combining bioinformatics and deep learning methods. Firstly, we collected a gene set related to HBV-HCC from the GEO database, performed differential analysis and WGCNA analysis to identify genes with abnormal expression in tumors and high relevance to tumors. We used three deep learning methods, Lasso, random forest, and SVM, to identify key genes RACGAP1, ECT2, and NDC80. By establishing a diagnostic model, we determined the accuracy of key genes in diagnosing HBV-HCC. In the training set, RACGAP1(AUC:0.976), ECT2(AUC:0.969), and NDC80 (AUC: 0.976) showed high accuracy. They also exhibited good accuracy in the validation set: RACGAP1(AUC:0.878), ECT2(AUC:0.731), and NDC80(AUC:0.915). The key genes were found to be highly expressed in liver cancer tissues compared to normal liver tissues, and survival analysis indicated that high expression of key genes was associated with poor prognosis in liver cancer patients. This suggests a close relationship between key genes RACGAP1, ECT2, and NDC80 and the occurrence and progression of HBV-HCC. Molecular docking results showed that the key genes could spontaneously bind to the anti-hepatocellular carcinoma drugs Lenvatinib, Regorafenib, and Sorafenib with strong binding activity. Therefore, ECT2, NDC80, and RACGAP1 may serve as potential biomarkers for the diagnosis of HBV-HCC and as targets for the development of targeted therapeutic drugs.

Superstrong, sustainable, origami wood paper enabled by dual-phase nanostructure regulation in cell walls.

Constructing a crystalline-amorphous hybrid structure is an effective strategy to overcome the conflict between the strength and toughness of materials. However, achieving such a material structure often involves complex, energy-intensive processing. Here, we leverage the natural wood featuring coexisting crystalline and amorphous regions to achieve superstrong and ultratough wood paper (W-paper) via a dual-phase nanostructure regulation strategy. After partially removing amorphous hemicellulose and eliminating most lignin, the treated wood can self-densify through an energy-efficient air drying, resulting in a W-paper with high tensile strength, toughness, and folding endurance. Coarse-grained molecular dynamics simulations reveal the underlying deformation mechanism of the crystalline and amorphous regions inside cell walls and the failure mechanism of the W-paper under tension. Life cycle assessment reveals that W-paper shows a lower environmental impact than commercial paper and common plastics. This dual-phase nanostructure regulation based on natural wood may provide valuable insights for developing high-performance and sustainable film materials.

Imatinib-induced ulcerative colitis.

INTRODUCTION: Imatinib, a tyrosine kinase inhibitor, is the first-line therapy for patients with KIT mutation in gastrointestinal stromal tumor (GIST). Nausea, vomiting, diarrhea, dyspepsia and abdominal pain are common gastrointestinal adverse reactions of imatinib, but imatinib-induced ulcerative colitis (UC) is rarely reported. CASE REPORT: We presented a case of UC induced by imatinib in a 56-year-old male patient who experienced this adverse event after 5 years of imatinib 400 mg/d treatment following GIST resection. MANAGEMENT AND OUTCOME: The patient's diarrhea and bloody stools showed significant improvement following the discontinuation of imatinib therapy and administration of antidiarrheal medications. Then, imatinib was restarted at a daily dosage of 400 mg. DISCUSSION: UC is a rare adverse event associated with imatinib. Physicians should consider the possibility of UC induced by imatinib when patients present with diarrhea and bloody stool after receiving imatinib treatment. This case offered objective evidence of UC induced by imatinib.

The Critical Roles of Water in the Processing, Structure, and Properties of Nanocellulose.

The cellulose industry depends heavily on water owing to the hydrophilic nature of cellulose fibrils and its potential for sustainable and innovative production methods. The emergence of nanocellulose, with its excellent properties, and the incorporation of nanomaterials have garnered significant attention. At the nanoscale level, nanocellulose offers a higher exposure of hydroxyl groups, making it more intimate with water than micro- and macroscale cellulose fibers. Gaining a deeper understanding of the interaction between nanocellulose and water holds the potential to reduce production costs and provide valuable insights into designing functional nanocellulose-based materials. In this review, water molecules interacting with nanocellulose are classified into free water (FW) and bound water (BW), based on their interaction forces with surface hydroxyls and their mobility in different states. In addition, the water-holding capacity of cellulosic materials and various water detection methods are also discussed. The review also examines water-utilization and water-removal methods in the fabrication, dispersion, and transport of nanocellulose, aiming to elucidate the challenges and tradeoffs in these processes while minimizing energy and time costs. Furthermore, the influence of water on nanocellulose properties, including mechanical properties, ion conductivity, and biodegradability, are discussed. Finally, we provide our perspective on the challenges and opportunities in developing nanocellulose and its interplay with water.

Research progress of N6-methyladenosine in colorectal cancer: A review.

Colorectal cancer is the third most common malignant tumor worldwide, causing serious harm to human health. Epigenetic modification, especially RNA methylation modification, plays a critical role in the occurrence and development of colorectal cancer via post-transcriptional regulation of mRNA and non-coding RNA expression. Among these, N6-methyladenosine (m6A) is the most common chemical modification in mammals, which plays an important role in the progress of cancer, including colorectal cancer. m6A is a dynamic and reversible process and is mainly regulated by m6A methyltransferase ("writers"), m6A demethylases ("erasers"), and m6A binding proteins ("readers"). Herein, we reviewed recent advances in the role of m6A modification in colorectal cancer and focused on the factors affecting m6A modification. Furthermore, we discussed the clinical application of m6A modifications for colorectal cancer diagnosis, prognosis, and treatment and provided guides in clinical practice. m6A modification and m6A regulators play significant roles in the occurrence and development of colorectal cancer by regulating the stability and translation of mRNAs, the maturation of miRNAs, and the function of lncRNAs. m6A regulators can play biological roles in colorectal cancer through m6A-dependent manner or m6A-independent manner. Multiplies of internal factors, including miRNAs and lncRNAs, and external factors can also regulate the m6A modification by completing with m6A regulators in a base complement manner, regulating the expression of m6A and mutating the m6A site. m6A regulators and m6A modificantion are diagnostic and prognostic markers for CRC. Therefore, m6A regulators and m6A modificantion may be potential therapeutic target for CRC in the future.

Interface Engineering Between Multi-Elemental Alloy Nanoparticles and a Carbon Support Toward Stable Catalysts.

Multi-elemental alloy (MEA) nanoparticles have recently received notable attention owing to their high activity and superior phase stability. Previous syntheses of MEA nanoparticles mainly used carbon as the support, owing to its high surface area, good electrical conductivity, and tunable defective sites. However, the interfacial stability issue, such as nanoparticle agglomeration, remains outstanding due to poor interfacial binding between MEA and carbon. Such a problem often causes performance decay when MEA nanoparticles are used as catalysts, hindering their practical applications. Herein, an interface engineering strategy is developed to synthesize MEA-oxide-carbon hierarchical catalysts, where the oxide on carbon helps disperse and stabilize the MEA nanoparticles toward superior thermal and electrochemical stability. Using several MEA compositions (PdRuRh, PtPdIrRuRh, and PdRuRhFeCoNi) and oxides (TiO2 and Cr2 O3 ) as model systems, it is shown that adding the oxide renders superior interfacial stability and therefore excellent catalytic performance. Excellent thermal stability is demonstrated under transmission electron microscopy with in situ heating up to 1023 K, as well as via long-term cycling (>370 hours) of a Li-O2 battery as a harsh electrochemical condition to challenge the catalyst stability. This work offers a new route toward constructing efficient and stable catalysts for various applications.

Machine Learning Accelerated, High Throughput, Multi-Objective Optimization of Multiprincipal Element Alloys.

Multiprincipal element alloys (MPEAs) have gained surging interest due to their exceptional properties unprecedented in traditional alloys. However, identifying an MPEA with desired properties from a huge compositional space via a cost-effective design remains a grand challenge. To address this challenge, the authors present a highly efficient design strategy of MPEAs through a coherent integration of molecular dynamics (MD) simulation, machine learning (ML) algorithms, and genetic algorithm (GA). The ML model can be effectively trained from 54 MD simulations to predict the stiffness and critical resolved shear stress (CRSS) of CoNiCrFeMn alloys with a relative error of 2.77% and 2.17%, respectively, with a 12 600-fold reduction of computation time. Furthermore, by combining the highly efficient ML model and a multi-objective GA, one can predict 100 optimal compositions of CoNiCrFeMn alloys with simultaneous high stiffness and CRSS, as verified by 100 000 ML-accelerated predictions. The highly efficient and precise design strategy can be readily adapted to identify MPEAs of other principal elements and thus substantially accelerate the discovery of other high-performance MPEA materials.

Machine learning-accelerated prediction of overpotential of oxygen evolution reaction of single-atom catalysts.

The oxygen evolution reaction (OER) is a critical reaction for energy-related applications, yet suffers from its slow kinetics and large overpotential. It is desirable to develop effective OER electrocatalysts, such as single-atom catalysts (SACs). Here, we demonstrate machine learning (ML)-accelerated prediction of OER overpotential of all transition metals. Based on density functional theory (DFT) calculations of 15 species of SACs, we design a topological information-based ML model to map the OER overpotentials with atomic properties of the corresponding SACs. The trained ML model not only yields remarkable prediction precision (relative error of 6.49%) but also enables a 130,000-fold reduction of prediction time in comparison with pure DFT calculation. Furthermore, an intrinsic descriptor that correlates the overpotential of an SAC with its atomic properties is revealed. The approach and results from this study can be readily applicable to screen other SACs and significantly accelerate the design of high-performance catalysts for many other reactions.

High temperature shockwave stabilized single atoms.

The stability of single-atom catalysts is critical for their practical applications. Although a high temperature can promote the bond formation between metal atoms and the substrate with an enhanced stability, it often causes atom agglomeration and is incompatible with many temperature-sensitive substrates. Here, we report using controllable high-temperature shockwaves to synthesize and stabilize single atoms at very high temperatures (1,500-2,000 K), achieved by a periodic on-off heating that features a short on state (55 ms) and a ten-times longer off state. The high temperature provides the activation energy for atom dispersion by forming thermodynamically favourable metal-defect bonds and the off-state critically ensures the overall stability, especially for the substrate. The resultant high-temperature single atoms exhibit a superior thermal stability as durable catalysts. The reported shockwave method is facile, ultrafast and universal (for example, Pt, Ru and Co single atoms, and carbon, C3N4 and TiO2 substrates), which opens a general route for single-atom manufacturing that is conventionally challenging.