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Compelling evidence has demonstrated the potential functions of circular RNAs (circRNAs) in breast cancer (BC) tumorigenesis. Nevertheless, the underlying mechanism by which circRNAs regulate BC progression is still unclear. The purpose of present research was to investigate the novel circRNA circRNF20 (hsa_circ_0087784) and its role in BC. CircRNA microarray sequencing revealed that circRNF20 was one of the upregulated transcripts in BC samples. Increased circRNF20 level predicted the poor clinical outcome in BC specimens. Functionally, circRNF20 promoted the proliferation and Warburg effect (aerobic glycolysis) of BC cells. Mechanistically, circRNF20 harbor miR-487a, acting as miRNA sponge, and then miR-487a targeted the 3’-UTR of hypoxia-inducible factor-1α (HIF-1α). Moreover, HIF-1α could bind with the promoter of hexokinase II (HK2) and promoted its transcription. In conclusion, this finding illustrates the vital roles of circRNF20 via the circRNF20/ miR-487a/HIF-1α/HK2 axis in breast cancer progress and Warburg effect, providing an interesting insight for the BC tumorigenesis.

Large-scale intact glycopeptide identification has been advanced by software tools. However, tools for quantitative analysis remain lagging behind, which hinders exploring the differential site-specific glycosylation. Here, we report pGlycoQuant, a generic tool for both primary and tandem mass spectrometry-based intact glycopeptide quantitation. pGlycoQuant advances in glycopeptide matching through applying a deep learning model that reduces missing values by 19–89% compared with Byologic, MSFragger-Glyco, Skyline, and Proteome Discoverer, as well as a Match In Run algorithm for more glycopeptide coverage, greatly expanding the quantitative function of several widely used search engines, including pGlyco 2.0, pGlyco3, Byonic and MSFragger-Glyco. Further application of pGlycoQuant to the N-glycoproteomic study in three different metastatic HCC cell lines quantifies 6435 intact N-glycopeptides and, together with in vitro molecular biology experiments, illustrates site 979-core fucosylation of L1CAM as a potential regulator of HCC metastasis. We expected further applications of the freely available pGlycoQuant in glycoproteomic studies. Software tools for larger-scale intact glycopeptide quantification lag far behind, which hinders exploring the differential sitespecific glycosylation. Here, the authors report pGlycoQuant, a generic tool with a deep learning model for quantitative glycoproteomics at intact glycopeptide level.

Photovoltaic panel defect detection presents significant challenges due to the wide range of defect scales, diverse defect types, and severe background interference, often leading to a high rate of false positives and missed detections. To address these challenges, this paper proposes the LEM-Detector, an efficient end-to-end photovoltaic panel defect detector based on the transformer architecture. To address the low detection accuracy for Crack and Star crack defects and the imbalanced dataset, a novel data augmentation method, the Linear Feature Augmentation (LFA) module, specifically designed for linear features, is introduced. LFA effectively improves model training performance and robustness. Furthermore, the Efficient Feature Enhancement Module (EFEM) is presented to enhance the receptive field, suppress redundant information, and emphasize meaningful features. To handle defects of varying scales, complementary semantic information from different feature layers is leveraged for enhanced feature fusion. A Multi-Scale Multi-Feature Pyramid Network (MMFPN) is employed to selectively aggregate boundary and category information, thereby improving the accuracy of multi-scale target recognition. Experimental results on a large-scale photovoltaic panel dataset demonstrate that the LEM-Detector achieves a detection accuracy of 94.7% for multi-scale defects, outperforming several state-of-the-art methods. This approach effectively addresses the challenges of photovoltaic panel defect detection, paving the way for more reliable and accurate defect identification systems. This research will contribute to the automatic detection of surface defects in industrial production, ultimately enhancing production efficiency.

The prevalence of intracranial aneurysm (IA) is increasing, and the consequences of its rupture are severe. This study aimed to reveal specific, sensitive, and non‐invasive biomarkers for diagnosis and classification of ruptured and unruptured IA, to benefit the development of novel treatment strategies and therapeutics altering the course of the disease. We first assembled an extensive candidate biomarker bank of IA, comprising up to 717 proteins, based on altered proteins discovered in the current tissue and serum proteomic analysis, as well as from previous studies. Mass spectrometry assays for hundreds of biomarkers were efficiently designed using our proposed deep learning‐based method, termed DeepPRM. A total of 113 potential markers were further quantitated in serum cohort I ( n  = 212) & II ( n  = 32). Combined with a machine‐learning‐based pipeline, we built two sets of biomarker combinations (P6 & P8) to accurately distinguish IA from healthy controls (accuracy: 87.50%) or classify IA rupture patients (accuracy: 91.67%) upon evaluation in the external validation set ( n  = 32). This extensive circulating biomarker development study provides valuable knowledge about IA biomarkers. Synopsis This study constructed a comprehensive mass spectrometry‐based proteomics strategy for serum protein biomarker discovery for intracranial aneurysm (IA). The presented workflow integrates the results of current proteome research and previously reported studies, yielding a comprehensive serum protein biomarker bank of IA. A highly efficient and timesaving PRM assay approach (DeepPRM) is proposed to facilitate targeted quantification of large‐scale candidate proteins. Machine learning on the serum proteome distinguishes IA from healthy controls with an accuracy of 87.50%, and ruptured from unruptured IA with an accuracy of 91.67%. Graphical Abstract This study constructed a comprehensive mass spectrometry‐based proteomics strategy for serum protein biomarker discovery for intracranial aneurysm (IA).

HCC is a highly lethal cancer characterised by significant sorafenib resistance, leading to poor patient outcomes. Recent studies have suggested that MED8 plays a role in enhancing tumour resistance to drugs, but its role in drug resistance in HCC has not yet been reported. This study found significantly higher MED8 expression in HCC tissues compared to adjacent noncancerous tissues. Increased MED8 expression in HCC correlates with poorer overall survival. Functional assays demonstrated that reduced MED8 expression inhibited HCC cell proliferation and epithelial-mesenchymal transition, promoted apoptosis, and increased sensitivity to sorafenib. Overexpression of MED8 elevated TRIP4 protein levels. TRIP4 overexpression negated the effects of MED8 knockdown, whereas TRIP4 suppression inhibited MED8-driven EMT. Mechanistically, MED8 interacts with TRIP4, reducing its ubiquitination and stabilising TRIP4 protein levels. Our findings indicate that the MED8-TRIP4 axis plays a role in sorafenib resistance in HCC and could serve as a therapeutic target for HCC treatment.

Trichosanthin (TCS) is a traditional Chinese herbal medicine used to treat some gynecological diseases. Its effective component has diverse biological functions, including antineoplastic activity. The human trophoblast cell line BeWo was chosen as an experimental model for in vitro testing of a drug screen for anticancer properties of TCS. The MTT method was used in this study to get a primary screen result. The result showed that 100 mM had the best IC50 value. Proteomics analysis was then performed for further investigation of the drug effect of TCS on the BeWo cell line. In this differential proteomic expression analysis, the total proteins extracted from the BeWo cell line and their protein expression level after the drug treatment were compared by 2DE. Then, 24 unique three-fold differentially expressed proteins (DEPs) were successfully identified by MALDI-TOF/TOF MS. Label-free proteomics was run as a complemental method for the same experimental procedure. There are two proteins that were identified in both the 2DE and label-free methods. Among those identified proteins, bioinformatics analysis showed the importance of pathway and signal transduction and gives us the potential possibility for the disease treatment hypothesis.

Defect detection on the surface of the steel strip is essential for the quality assurance of the steel strip. Precise localization and classification, the two significant tasks of defect detection, still need to be completed due to the diversity of defect scales. In this paper, a residual atrous spatial pyramid pooling (RASPP) module is first designed to enrich the multi-scale information of the feature maps and increase the receptive field of the feature maps. Secondly, a double pyramid network (DPN) that combines RASPP and feature pyramid is proposed to fuse multi-scale features further so that similar semantic features are shared among the features of each layer. Finally, DPN-Detector, an automatic surface defects detection network, is proposed, which embeds the DPN module into Faster R-CNN and replaces the original detection head with a designed double head. Experiments are carried out on the steel strip surface defect dataset (NEU-DET), and the results show that the mAP of DPN-Detector is as high as 80.93%, which is 3.52% higher than that of the baseline network Faster R-CNN. The classification accuracy is 74.64%, and the detection speed reaches 18.62 FPS. The proposed method performs better robustness, classification and regression capability than other steel strip defect detection methods.

The metabolism disorders are a common convergence of Alzheimer’s disease (AD) and type 2 diabetes mellitus (T2DM). The characteristics of AD are senile plaques and neurofibrillary tangles (NFTs) composed by deposits of amyloid−β (Aβ) and phosphorylated tau, respectively. Advanced glycation end−products (AGEs) are a stable modification of proteins by non−enzymatic reactions, which could result in the protein dysfunction. AGEs are associated with some disease developments, such as diabetes mellitus and AD, but the effects of the glycated γ2 subunit of AMPK on its activity and the roles in AD onset are unknown. Methods: We studied the effect of glycated γ2 subunit of AMPK on its activity in N2a cells. In 3 × Tg mice, we administrated L−arginine once every two days for 45 days and evaluated the glycation level of γ2 subunit and function of AMPK and alternation of pathologies. Results: The glycation level of γ2 subunit was significantly elevated in 3 × Tg mice as compared with control mice, meanwhile, the level of pT172−AMPK was obviously lower in 3 × Tg mice than that in control mice. Moreover, we found that arginine protects the γ2 subunit of AMPK from glycation, preserves AMPK function, and improves pathologies and cognitive deficits in 3 × Tg mice. Conclusions: Arginine treatment decreases glycated γ2 subunit of AMPK and increases p−AMPK levels in 3 × Tg mice, suggesting that reduced glycation of the γ2 subunit could ameliorate AMPK function and become a new target for AD therapy in the future.

Background The molecular signature underlying pancreatic ductal adenocarcinoma (PDAC) progression may include key proteins affecting the malignant phenotypes. Here, we aimed to identify the proteins implicated in PDAC with different tumour-node-metastasis (TNM) stages. Methods Eight-plex isobaric tags coupled with two-dimensional liquid chromatography–tandem mass spectrometry were used to analyse the proteome of PDAC tissues with different TNM stages. A loss-of-function study was performed to evaluate the oncogenic roles of WD repeat-containing protein 1 (WDR1) in PDAC. The molecular mechanism by which WDR1 promotes PDAC progression was studied by real-time qPCR, Western blotting, proximity ligation assay and co-immunoprecipitation. Results A total of 5036 proteins were identified, and 4708 proteins were quantified with high confidence. Compared with normal pancreatic tissues, 37 proteins were changed significantly in PDAC tissues of different stages. Moreover, 64 proteins were upregulated or downregulated in a stepwise manner as the TNM stages of PDAC increased, and 10 proteins were related to tumorigenesis. The functionally uncharacterised protein, WDR1, was highly expressed in PDAC and predicted a poor prognosis. WDR1 knockdown suppressed PDAC tumour growth and metastasis in vitro and in vivo. Moreover, WDR1 knockdown repressed the activity of the Wnt/β-Catenin pathway; ectopic expression of a stabilised form of β-Catenin restored the suppressive effects of WDR1 knockdown. Mechanistically, WDR1 interacted with USP7 to prevent ubiquitination-mediated degradation of β-Catenin. Conclusion Our study identifies several previous functional unknown proteins implicated in the progression of PDAC, and provides new insight into the oncogenic roles of WDR1 in PDAC development.

miR-34a is transcriptionally induced by the tumor suppressor gene p53, which is often downregulated in non-small cell lung cancer (NSCLC). To address whether the downstream signal of miR-34a is sufficient to induce apoptosis and to alter cellular radiosensitivity, a chemical synthetic miR-34a mimic was delivered into A549 and H1299 cells, with or without co-treatment of γ-irradiation. Results showed that ectopic expression of miR-34a induced dose-dependent cell growth inhibition and apoptosis in a p53-independent manner in both NSCLC cell lines. Interestingly, LyGDI was discovered as a new target gene of miR-34a, and downregulation of LyGDI promoted Rac1 activation and membrane translocation, resulting in cell apoptosis. Furthermore, restoration of miR-34a indirectly reduced cyclooxygenase-2 (COX-2) expression. Taken together, these results demonstrate that restoration of miR-34a expression enhances radiation-induced apoptosis, partly by suppressing the LyGDI signaling pathway, and miR-34a could possibly be used as a radiosensitizer for non-small cell lung cancer therapy.