Microwave‐Assisted Pyrolysis of Biomass for Bio‐Oil Production

Microwave‐assisted pyrolysis (MAP) is a new thermochemical process that converts biomass to bio‐oil. Compared with the conventional electrical heating pyrolysis, MAP is more rapid, efficient, selective, controllable, and flexible. This chapter provides an up‐to‐date knowledge of bio‐oil production from microwave‐assisted pyrolysis of biomass. The chemical, physical, and energy properties of bio‐oils obtained from microwave‐assisted pyrolysis of biomass are described in comparison with those from conventional pyrolysis, the characteristics of microwave‐assisted pyrolysis as affected by biomass feedstock properties, microwave heating operations, use of exogenous microwave absorbents, and catalysts are discussed. With the advantages it offers and the further research and development recommended, microwave‐assisted pyrolysis has a bright future in production of bio‐oils that can effectively narrow the energy gap and reduce negative environmental impacts of our energy production and application practice

Lack of Cul4b, an E3 Ubiquitin Ligase Component, Leads to Embryonic Lethality and Abnormal Placental Development

Cullin-RING ligases (CRLs) complexes participate in the regulation of diverse cellular processes, including cell cycle progression, transcription, signal transduction and development. Serving as the scaffold protein, cullins are crucial for the assembly of ligase complexes, which recognize and target various substrates for proteosomal degradation. Mutations in human CUL4B, one of the eight members in cullin family, are one of the major causes of X-linked mental retardation. We here report the generation and characterization of Cul4b knockout mice, in which exons 3 to 5 were deleted. In contrast to the survival to adulthood of human hemizygous males with CUL4B null mutation, Cul4b null mouse embryos show severe developmental arrest and usually die before embryonic day 9.5 (E9.5). Accumulation of cyclin E, a CRL (CUL4B) substrate, was observed in Cul4b null embryos. Cul4b heterozygotes were recovered at a reduced ratio and exhibited a severe developmental delay. The placentas in Cul4b heterozygotes were disorganized and were impaired in vascularization, which may contribute to the developmental delay. As in human CUL4B heterozygotes, Cul4b null cells were selected against in Cul4b heterozygotes, leading to various degrees of skewed X-inactivation in different tissues. Together, our results showed that CUL4B is indispensable for embryonic development in the mouse

Identification and Functional Analysis of Tartrate-Resistant Acid Phosphatase Type 5b (TRAP5b) in <i>Oreochromis niloticus</i>

Tartrate-resistant acid phosphatase type 5 (TRAP5) is an enzyme that is highly expressed in activated macrophages and osteoclasts and plays important biological functions in mammalian immune defense systems. In the study, we investigated the functions of tartrate-resistant acid phosphatase type 5b from Oreochromis niloticus (OnTRAP5b). The OnTRAP5b gene has an open reading frame of 975 bp, which encodes a mature peptide consisting of 302 amino acids with a molecular weight of 33.448 kDa. The OnTRAP5b protein contains a metallophosphatase domain with metal binding and active sites. Phylogenetic analysis revealed that OnTRAP5b is clustered with TRAP5b of teleost fish and shares a high amino acid sequence similarity with other TRAP5b in teleost fish (61.73–98.15%). Tissues expression analysis showed that OnTRAP5b was most abundant in the liver and was also widely expressed in other tissues. Upon challenge with Streptococcus agalactiae and Aeromonas hydrophila in vivo and in vitro, the expression of OnTRAP5b was significantly up-regulated. Additionally, the purified recombinant OnTRAP5b ((r)OnTRAP5) protein exhibited optimal phosphatase activity at pH 5.0 and an ideal temperature of 50 °C. The Vmax, Km, and kcat of purified (r)OnTRAP5b were found to be 0.484 μmol × min−1 × mg−1, 2.112 mM, and 0.27 s−1 with respect to pNPP as a substrate, respectively. Its phosphatase activity was differentially affected by metal ions (K+, Na+, Mg2+, Ca2+, Mn2+, Cu2+, Zn2+, and Fe3+) and inhibitors (sodium tartrate, sodium fluoride, and EDTA). Furthermore, (r)OnTRAP5b was found to promote the expression of inflammatory-related genes in head kidney macrophages and induce reactive oxygen expression and phagocytosis. Moreover, OnTRAP5b overexpression and knockdown had a significant effect on bacterial proliferation in vivo. When taken together, our findings suggest that OnTRAP5b plays a significant role in the immune response against bacterial infection in Nile tilapia

Depletion of the N 6 -Methyladenosine (m6A) reader protein IGF2BP3 induces ferroptosis in glioma by modulating the expression of GPX4

Abstract Emerging evidence highlights the multifaceted contributions of m6A modifications to glioma. IGF2BP3, a m6A modification reader protein, plays a crucial role in post-transcriptional gene regulation. Though several studies have identified IGF2BP3 as a poor prognostic marker in glioma, the underlying mechanism remains unclear. In this study, we demonstrated that IGF2BP3 knockdown is detrimental to cell growth and survival in glioma cells. Notably, we discovered that IGF2BP3 regulated ferroptosis by modulating the protein expression level of GPX4 through direct binding to a specific motif on GPX4 mRNA. Strikingly, the m6A modification at this motif was found to be critical for GPX4 mRNA stability and translation. Furthermore, IGF2BP3 knockdown glioma cells were incapable of forming tumors in a mouse xenograft model and were more susceptible to phagocytosis by microglia. Our findings shed light on an unrecognized regulatory function of IGF2BP3 in ferroptosis. The identification of a critical m6A site within the GPX4 transcript elucidates the significance of post-transcriptional control in ferroptosis

Identification of a copper metabolism‐related gene signature for predicting prognosis and immune response in glioma

Abstract Background Gliomas are highly refractory intracranial cancers characterized by genetic and transcriptional heterogeneity. However, therapeutic options are limited. In the last years, copper‐induced cell death is becoming a prospective treatment strategy for gliomas and other solid tumors, but copper metabolism‐related genes associated with cancer development remain unclear. Methods We first collected gene expression data from The Cancer Genome Atlas (TCGA) to identify significantly differentially expressed copper metabolism‐related genes in gliomas. Using these genes, we performed COX regression and Least Absolute Shrinkage and Selection Operator (LASSO) regression to construct the prognostic model. The prognostic value of the model was further validated by CGGA testing set. Subsequently, functional analyses were carried out, including gene set enrichment analysis (GSEA), immune infiltration analysis, and mutation analysis. Finally, the expression levels of these genes were verified by immunohistochemical analysis. Results The prognostic model consisted of 7 genes: CDK1, LOXL2, LOXL3, NFE2L2, SLC31A1, SUMF1 and FDX1. According to this prognosis model, glioma patients could be split into the high‐risk group or low‐risk group, and the low‐risk group showed significantly better prognostic survival (p < 0.001). Moreover, the high‐risk group had higher levels of immune cell infiltration, immune checkpoint genes expression, and higher tumor mutational burden (TMB), which indicates that they might benefit more from immunotherapy. Finally, we confirmed the expression level of FDX1, SUMF1, and SLC31A1 protein as significantly different in glioblastoma, lower‐grade glioma, and non‐tumor brain tissues by immunohistochemical analysis, and the high expression of FDX1 and SLC31A1 protein was related to poor survival in glioma patients. Conclusions Our study could contribute to the prognosis prediction and decision‐making in patients with gliomas

Computed tomography-based radiomic model at node level for the prediction of normal-sized lymph node metastasis in cervical cancer

Purpose: Radiomic models have been demonstrated to have acceptable discrimination capability for detecting lymph node metastasis (LNM). We aimed to develop a computed tomography–based radiomic model and validate its usefulness in the prediction of normal-sized LNM at node level in cervical cancer. Methods: A total of 273 LNs of 219 patients from 10 centers were evaluated in this study. We randomly divided the LNs from the 2 centers with the largest number of LNs into the training and internal validation cohorts, and the rest as the external validation cohort. Radiomic features were extracted from the arterial and venous phase images. We trained an artificial neural network (ANN) to develop two single-phase models. A radiomic model reflecting the features of two-phase images was also built for directly predicting LNM in cervical cancer. Moreover, four state-of-the-art methods were used for comparison. The performance of all models was assessed using the area under the receiver operating characteristic curve (AUC). Results: Among the models we built, the models combining the features of two phases surpassed the single-phase models, and the models generated by ANN had better performance than the others. We found that the radiomic model achieved the highest AUCs of 0.912 and 0.859 in the training and internal validation cohorts, respectively. In the external validation cohort, the AUC of the radiomic model was 0.800. Conclusion: We constructed a radiomic model that exhibited great ability in the prediction of LNM. The application of the model could optimize clinical staging and decision-making

Predicting response to immunotherapy in advanced non-small-cell lung cancer using tumor mutational burden radiomic biomarker

Background Tumor mutational burden (TMB) is a significant predictor of immune checkpoint inhibitors (ICIs) efficacy. This study investigated the correlation between deep learning radiomic biomarker and TMB, including its predictive value for ICIs treatment response in patients with advanced non-small-cell lung cancer (NSCLC).Methods CT images from 327 patients with TMB data (TMB median=6.067 mutations per megabase (range: 0 to 42.151)) were retrospectively collected and randomly divided into a training (n=236), validation (n=26), and test cohort (n=65). We used 3D-densenet to estimate the target tumor area, which used 1020 deep learning features to distinguish High-TMB from Low-TMB patients and establish the TMB radiomic biomarker (TMBRB). The TMBRB was developed in the training cohort combined with validation cohort and evaluated in the test cohort. The predictive value of TMBRB was assessed in a cohort of 123 NSCLC patients who had received ICIs (survival median=462 days (range: 16 to 1128)).Results TMBRB discriminated between High-TMB and Low-TMB patients in the training cohort (area under the curve (AUC): 0.85, 95% CI: 0.84 to 0.87))and test cohort (AUC: 0.81, 95% CI: 0.77 to 0.85). In this study, the predictive value of TMBRB was better than that of a histological subtype (AUC of training cohort: 0.75, 95% CI: 0.72 to 0.77; AUC of test cohort: 0.71, 95% CI: 0.66 to 0.76) or Radiomic model (AUC of training cohort: 0.75, 95% CI: 0.72 to 0.77; AUC of test cohort: 0.74, 95% CI: 0.69 to 0.79). When predicting immunotherapy efficacy, TMBRB divided patients into a high- and low-risk group with distinctly different overall survival (OS; HR: 0.54, 95% CI: 0.31 to 0.95; p=0.030) and progression-free survival (PFS; HR: 1.78, 95% CI: 1.07 to 2.95; p=0.023). Moreover, TMBRB had a better predictive ability when combined with the Eastern Cooperative Oncology Group performance status (OS: p=0.007; PFS: p=0.003). Visual analysis revealed that tumor microenvironment was important for predicting TMB.Conclusion By combining deep learning technology and CT images, we developed an individual non-invasive biomarker that could distinguish High-TMB from Low-TMB, which might inform decisions on the use of ICIs in patients with advanced NSCLC