Transcriptomic profiling identifies a nucleotide metabolism-related signature with prognostic power in gliomas.

OBJECTIVE: Nucleotide metabolic reprogramming as a hallmark of cancer is closely related to the occurrence and progression of cancer. We aimed to comprehensively analyze the nucleotide metabolism-related gene set and clinical significance in gliomas. METHODS: The RNA sequencing data of 702 gliomas from the Cancer Genome Atlas (TCGA) dataset were included as the training set, and the RNA sequencing data from the other three datasets (CGGA, GSE16011, and Rembrandt) were used as independent validation sets. Survival curve, Cox regression analysis, time-dependent ROC curve and nomogram model were performed to evaluate prognostic power of signature. R language was the main tool for bioinformatic analysis and graphical work. RESULTS: Based on the expression profiles of nucleotide metabolism-related genes, consensus clustering identified two robust clusters with different prognosis. We then developed a nucleotide metabolism-related signature that was closely related to clinical, pathological, and genomic characteristics of gliomas. And ROC curve showed that our signature was a potential biomarker for mesenchymal subtype. Survival curve and Cox regression analysis revealed signature as an independent prognostic factor for gliomas. In addition, we constructed a nomogram model to predict individual survival. Finally, functional analysis showed that nucleotide metabolism not only affected cell division and cell cycle, but also was associated with immune response in gliomas. CONCLUSION: We developed a nucleotide metabolism-related signature to predict prognosis and provided new insights into the role of nucleotide metabolism in gliomas.

A solid acid derived from fishbone catalyzes the hydrolysis of cellulose into nanocellulose.

The necessity to look into waste biomass resource regeneration has increased due to growing environmental and energy-related problems. This study successfully developed an innovative fishbone-derived carbon-based solid acid catalyst using the carbonation-sulfonation method, which was subsequently applied to catalyze the hydrolysis of cellulose to produce nanocellulose. The data analysis reveals that the sulfonation treatment affects the microstructure of the catalyst, resulting in a decline in its specific surface area (134.48 m2/g decreased to 9.66 m2/g). However, this treatment doesn't hinder the introduction of acidic functional groups. In particular, the solid acid catalyst derived from fishbone exhibited a total acid content of 3.76 mmol/g, with a concentration of -SO3H groups at 0.48 mmol/g. Furthermore, the solid acids originating from fishbones manifested remarkable thermal stability, exhibiting a mass loss of <15 % at temperatures up to 600 °C. Moreover, the catalyst displayed exceptional catalytic performance during the cellulose hydrolysis reaction, achieving an optimum nanocellulose yield of 45.7 % at an optimized reaction condition. An additional noteworthy feature is the solid acid catalyst's impressive recyclability, maintaining a nanocellulose yield of 44.87 % even after undergoing five consecutive usage cycles. This research outcome underscores an innovative approach to for the sustainable utilization of waste biomass resources.

Convolutional neural network-based magnetic resonance image differentiation of filum terminale ependymomas from schwannomas.

PURPOSE: Preoperative diagnosis of filum terminale ependymomas (FTEs) versus schwannomas is difficult but essential for surgical planning and prognostic assessment. With the advancement of deep-learning approaches based on convolutional neural networks (CNNs), the aim of this study was to determine whether CNN-based interpretation of magnetic resonance (MR) images of these two tumours could be achieved. METHODS: Contrast-enhanced MRI data from 50 patients with primary FTE and 50 schwannomas in the lumbosacral spinal canal were retrospectively collected and used as training and internal validation datasets. The diagnostic accuracy of MRI was determined by consistency with postoperative histopathological examination. T1-weighted (T1-WI), T2-weighted (T2-WI) and contrast-enhanced T1-weighted (CE-T1) MR images of the sagittal plane containing the tumour mass were selected for analysis. For each sequence, patient MRI data were randomly allocated to 5 groups that further underwent fivefold cross-validation to evaluate the diagnostic efficacy of the CNN models. An additional 34 pairs of cases were used as an external test dataset to validate the CNN classifiers. RESULTS: After comparing multiple backbone CNN models, we developed a diagnostic system using Inception-v3. In the external test dataset, the per-examination combined sensitivities were 0.78 (0.71-0.84, 95% CI) based on T1-weighted images, 0.79 (0.72-0.84, 95% CI) for T2-weighted images, 0.88 (0.83-0.92, 95% CI) for CE-T1 images, and 0.88 (0.83-0.92, 95% CI) for all weighted images. The combined specificities were 0.72 based on T1-WI (0.66-0.78, 95% CI), 0.84 (0.78-0.89, 95% CI) based on T2-WI, 0.74 (0.67-0.80, 95% CI) for CE-T1, and 0.81 (0.76-0.86, 95% CI) for all weighted images. After all three MRI modalities were merged, the receiver operating characteristic (ROC) curve was calculated, and the area under the curve (AUC) was 0.93, with an accuracy of 0.87. CONCLUSIONS: CNN based MRI analysis has the potential to accurately differentiate ependymomas from schwannomas in the lumbar segment.

Feasible Cluster Model Method for Simulating the Redox Potentials of Laccase CueO and Its Variant.

Laccases are regarded as versatile green biocatalysts, and recent scientific research has focused on improving their redox potential for broader industrial and environmental applications. The density functional theory (DFT) quantum mechanics approach, sufficiently rigorous and efficient for the calculation of electronic structures, is conducted to better comprehend the connection between the redox potential and the atomic structural feature of laccases. According to the crystal structure of wild type laccase CueO and its variant, a truncated miniature cluster model method was established in this research. On the basic of thermodynamic cycle, the overall Gibbs free energy variations before and after the one-electron reduction were calculated. It turned out that the trends of redox potentials to increase after variant predicted by the theoretical calculations correlated well with those obtained by experiments, thereby validating the feasibility of this cluster model method for simulating the redox potentials of laccases.

Process analysis of microplastic degradation using activated PMS and Fenton reagents.

The environmental degradation and physical aging of microplastics (MP) caused by oxidative stress have not been thoroughly elucidated. In this study, we used different oxidative agents (Fe2+-activated peroxymonosulfate and Fenton reagents) that can form free radicals to study the degradation mechanisms of nylon 6 (PA6) and polystyrene (PS) MPs. After 4 cycles of treatment, mass losses of 25.6% and 22.1% were obtained with PA6 and PS MPs, respectively. Scanning electron microscopy (SEM), Fourier-transform infrared (FTIR) spectroscopy, Raman spectroscopy, and X-ray photoelectron spectroscopy (XPS) were employed to confirm the MP oxidation, and polymer chain scissions. FTIR data indicated the existence of oxygen-containing chemical groups resulting from MPs oxidation, and chain scissions, such as CO, C-O, and O-CO. Raman spectroscopy suggested the presence of exposed aromatic groups, and weakening of the relative intensity of C-H related to the oxidation, and chain scission of the MPs. SEM imaging revealed visible morphological changes on the surface of MPs as a result of degradation. XPS indicated that the O/C ratio could be used as an indicator for the degree of MPs oxidation. By analyzing the degradation products with surface-enhanced Raman scattering (SERS) and gas chromatography-mass spectrometry, low-molecular-weight alkanes, alcohols, aldehydes, carboxylic acids derived from the derivatization of alcohols, were detected. These findings confirmed the advantage of using multiple analytic methods in tandem to evaluate the degradation of environmental MPs.

Quantum Mechanical Investigation of the Oxidative Cleavage of the C-C Backbone Bonds in Polyethylene Model Molecules.

Recalcitrant plastic waste has caused serious global ecological problems. There is an urgent need to develop environmentally friendly and efficient methods for degrading the highly stable carbon skeleton structure of plastics. To that end, we used a quantum mechanical calculation to thoroughly investigate the oxidative scission of the carbon-carbon (C-C) backbone in polyethylene (PE). Here, we studied the reaction path of C-C bond oxidation via hydroxyl radical in PE. The flexible force constants and fuzzy bond orders of the C-C bonds were calculated in the presence of one or more carbocations in the same PE carbon chain. By comparison, the strength of the C-C bond decreased when carbocation density increased. However, the higher the density of carbocations, the higher the total energy of the molecule and the more difficult it was to be generated. The results revealed that PE oxidized to alcohol and other products, such as carboxylic acid, aldehyde and ketone, etc. Moreover, the presence of carbocations was seen to promote the cleavage of C-C backbones in the absence of oxygen.