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Background Epidemiologic studies continue to emphasize that increasing patients with inflammatory bowel disease (IBD) develop to colorectal cancer (CRC). Although the function and mechanisms of IBD-associated genes (IBDGs) in CRC tumorigenesis have been extensively researched, the implications of IBDGs in the prognosis value and tumor immunity of CRC remain unclear. Results In this study, the expression, pathological stages and prognostic value of IBDGs in CRC were systematically analyzed, and 7 prognostic genes including CDH1, CCL11, HLA–DRA, NOS2, NAT2, TIMP1 and TP53 were screened through LASSO–Cox regression analysis. Then, a prognostic signature was established based on the 7 prognostic genes, and the model exhibited a good ability in risk stratification of CRC patients. Subsequent results showed that the genetic alterations of the 7 prognostic genes exhibited more significant and extensive influence on immune cells infiltration in colon adenocarcinoma than that in rectal adenocarcinoma. Meanwhile, immune cells infiltration also showed a significant difference between low-risk group and high-risk group. What’s more, 7 prognostic genes-based risk stratification was associated with microsatellite instability, and its prognostic characteristics were significantly negatively correlated with mismatch repair genes. Conclusions This study provided a promising insight that the 7 IBDGs could be used as valuable biomarkers for prognostic diagnosis and personalized immunotherapy of CRC patients.

Two-dimensional (2D) semiconductors hold great promises for ultra-scaled transistors. In particular, the gate length of MoS 2 transistor has been scaled to 1 nm and 0.3 nm using single wall carbon nanotube and graphene, respectively. However, simultaneously scaling the channel length of these short-gate transistor is still challenging, and could be largely attributed to the processing difficulties to precisely align source-drain contact with gate electrode. Here, we report a self-alignment process for realizing ultra-scaled 2D transistors. By mechanically folding a graphene/BN/MoS 2 heterostructure, source-drain metals could be precisely aligned around the folded edge, and the channel length is only dictated by heterostructure thickness. Together, we could realize sub-1 nm gate length and sub-50 nm channel length for vertical MoS 2 transistor simultaneously. The self-aligned device exhibits on-off ratio over 10 5 and on-state current of 250 μA/μm at 4 V bias, which is over 40 times higher compared to control sample without self-alignment process. The simultaneous scaling down of the channel length and gate length of 2D transistors remains challenging. Here, the authors report a self-alignment process to fabricate vertical MoS 2 transistors with sub-1 nm gate length and sub−50 nm channel length, exhibiting on-off ratios over 10 5 and on-state currents of 250 μA/μm at 4 V bias.

Multi-station radar can provide better performance against deception jamming, but the harsh detection requirements and risk of network destruction undermine the practicability of the multi-station radar. Therefore, it is necessary to further explore the anti-deception jamming performance of a single-station radar. This paper introduces a novel method, based on parameter estimation with a virtual multi-station system, to discriminate range deceptive jamming. The system consists of a single-station radar assisted by the reconfigurable intelligent surfaces (RIS). A unified parameter estimation model for true and false targets is established, and the convex optimization method is applied to estimate the target location and deception range. The Cramer–Rao lower bound (CRLB) of the target localization and the measured deception range is then derived. By using the measured deception range and its CRLB, an optimal discrimination algorithm in accordance with the Neyman–Pearson lemma is designed. Simulation results demonstrate the feasibility of the proposed method and analyze the effects of factors such as signal-to-noise ratio (SNR), deception range, jammer location, and the RISs station arrangement on the discrimination performance.

Battlefield surveillance radar is usually 2-D radar, which cannot realize target three-dimensional localization, leading to poor resolution for the air target in the elevation dimension. Previous researchers have used the Traditional Height Finder Radar (HFR) or multiple 2-D radar networking to estimate the target three-dimensional location. However, all of them face the problems of high cost, poor real-time performance and high requirement of space–time registration. In this paper, Reconfigurable Intelligent Surfaces (RISs) with low cost are introduced into the 2-D radar to realize the target three-dimensional localization. Taking advantage of the wide beam of 2-D radar in the elevation dimension, several Unmanned Aerial Vehicles (UAVs) carrying RISs are set in the receiving beam to form multiple auxiliary measurement channels. In addition, the traditional 2-D radar measurements combined with the auxiliary channel measurements are used to realize the target three-dimensional localization by solving a nonlinear least square problem with a convex optimization method. For the proposed RIS-assisted target three-dimensional localization problem, the Cramer–Rao Lower Bound (CRLB) is derived to measure the target localization accuracy. Simulation results verify the effectiveness of the proposed 3-D localization method, and the influences of the number, the positions and the site errors of the RISs on the localization accuracy are covered.

A reconfigurable intelligent surface (RIS) is a promising technology for wireless communication and radar detection, owing to its superior ability to realize smart radio environments. Inspired by previous studies on RISs, this study deals with the use of RISs for radar electronic counter-countermeasures (ECCMs) in deception jamming scenarios. At first, a RIS was applied to a monostatic radar, constructing a virtual multi-radar system combined with multi-beam receiving technology. Then, a data-fusion-based deception ECCM method for the proposed virtual multi-radar system was studied to discriminate the active false targets generated by deception jamming. A theoretical analysis of the target discrimination probability was derived. Because the location of RISs is the key to determining the target discrimination ability, the location optimization of the RIS was considered based on the theoretical analysis. Simulation results corroborate the deception ECCM ability of the proposed RIS-assisted virtual multi-radar system, enhancing the survivability of a radar system in a complex electromagnetic environment.

Background Identifying reliable biomarkers could effectively predict esophagus carcinoma (EC) patients with poor prognosis. In this work, we constructed an immune-related gene pairs (IRGP) signature to evaluate the prognosis of EC. Results The IRGP signature was trained by the TCGA cohort and validated by three GEO datasets, respectively. Cox regression model together with LASSO was applied to construct the overall survival (OS) associated IRGP. 21 IRGPs consisting of 38 immune-related genes were included in our signature, according to which patients were stratified into high- and low-risk groups. The results of Kaplan-Meier survival analyses indicated that high-risk EC patients had worse OS than low-risk group in the training set, meta-validation set and all independent validation datasets. After adjustment in multivariate Cox analyses, our signature continued to be an independent prognostic factor of EC and the signature-based nomogram could effectively predict the prognosis of EC sufferers. Besides, Gene Ontology analysis revealed this signature is related to immunity. ‘CIBERSORT’ analysis revealed the infiltration levels of plasma cells and activated CD4 memory T cells in two risk groups were significantly different. Ultimately, we validated the expression levels of six selected genes from IRGP index in KYSE-150 and KYSE-450. Conclusions This IRGP signature could be applied to select EC patients with high mortality risk, thereby improving prospects for the treatment of EC.

MicroRNAs (miRNAs), a group of small noncoding RNAs (approximately 20-24 nucleotides), act as essential regulators affecting endogenous gene expression in plants. MiR482/2118 is a unique miRNA superfamily in plants and represses NUCLEOTIDE BINDING SITE-LEUCINE-RICH REPEAT ( NBS-LRR ) genes to function in plant resistance to pathogens. In addition, over the past several years, it has been found that miR482/2118 not only targets NBS-LRR s but also acts on other molecular mechanisms to affect plant resistance. miR482/2118-5ps, phased small interfering RNAs (phasiRNAs) and long noncoding RNAs (lncRNAs) play important roles in plant disease resistance. This review summarizes the current knowledge of the interactions and links between miR482/2118 and its new interacting molecules, miR482/2118-5p, phasiRNAs and lncRNAs, in plant disease resistance. Here, we aim to provide a comprehensive view describing the new molecular mechanism associated with miR482/2118 in the plant immune system.

In this paper, two new iridium (III) complexes, [Ir(ppy)2(ipbp)](PF6) (Ir1) (ppy = 2-phenylpyridine, ipbp = 3-(1H-imidazo[4,5-f][1,10]phenanthrolin-2yl)-4H-chromen-4-one) and [Ir(bzq)2(ipbp)](PF6) (Ir2) (bzq = benzo[h]quinolone), were synthesized and characterized. The cytotoxicity of the complexes against human colon cancer HCT116 and normal LO2 cells was evaluated by the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) method. The complexes Ir1 and Ir2 show high cytotoxic efficacy toward HCT116 cells with a low IC50 value of 1.75 ± 0.10 and 6.12 ± 0.2 µM. Interestingly, Ir1 only kills cancer cells, not normal LO2 cells (IC50 > 200 µM). The inhibition of cell proliferation and migration were investigated by multiple tumor spheroid (3D) and wound healing experiments. The cellular uptake was explored under a fluorescence microscope. The intracellular reactive oxygen species (ROS), change of mitochondrial membrane potential, glutathione (GSH) and adenine nucleoside triphosphate (ATP) were studied. Apoptosis and cell cycle arrest were performed by flow cytometry. The results show that the complexes induce early apoptosis and inhibit the cell proliferation at the G0/G1 phase. Additionally, the apoptotic mechanism was researched by Western blot analysis. The results obtained demonstrate that the complexes cause apoptosis in HCT116 cells through ROS-mediated mitochondrial dysfunction and the inhibition of PI3K/AKT signaling pathways.

Summary Background Oxidative stress process plays a key role in aging and cancer; however, currently, there is paucity of machine-learning model studies investigating the relationship between oxidative stress and prognosis of elderly patients with esophageal squamous cancer (ESCC). Methods This study included elderly patients with ESCC who underwent curative ESCC resection surgery continuously from January 2013 to December 2020 and were stratified into the training and external validation cohorts. Using Cox stepwise regression analysis based on Akaike information criterion, the relationship between oxidative stress biomarkers and prognosis was explored, and a geriatric ESCC-related oxidative stress score (OSS) was constructed. To construct a predictive model for 3-year overall survival (OS), machine-learning strategies including decision tree (DT), random forest (RF), and support vector machine (SVM) were employed. These machine-learning strategies play a key role in data mining and pattern recognition tasks. Each model was tested in the external validation cohort through 1000 resampling iterations. Validation was conducted using receiver operating characteristic area under the curve (AUC) and calibration plots. Results The training cohort and validation cohort consisted of 340 and 145 patients, respectively. In the training cohort, the 3-year OS rate for patients was 59.2%. We constructed the OSS based on systemic oxidative stress biomarkers using the training cohort. The study found that pathological N stage, pathological T stage, tumor histological type, lymphovascular invasion, CEA, OSS, CA 19 − 9, and the amount of bleeding were the most important factors influencing the 3-year OS. These eight important features were included in training the RF, DT, and SVM and trained on the training cohort and validated cohort, respectively. In the training cohort, the RF model demonstrated the highest predictive performance with an AUC of 0.975 (0.962–0.987), while the DT model is 0.784 (0.739–0.830) and the SVM is 0.879 (0.843–0.916). In the external validation cohort, the RF model again exhibited the highest performance with an AUC of 0.791 (0.717–0.864), compared to the DT model with an AUC of 0.717 (0.640–0.794) and 0.779 (0.702–0.856) in SVM. Conclusions The random forest clinical prediction model constructed based on OSS can effectively predict the prognosis of elderly patients with ESCC after curative surgery.

As two pivotal regulatory factors in cancer biology, oxidative stress and inflammation interact dynamically through complex network mechanisms to influence tumor initiation, progression, and treatment resistance. Oxidative stress induces genomic instability, oncogenic signaling activation, and tumor microenvironment (TME) remodeling via the abnormal accumulation of reactive oxygen species (ROS) or reactive nitrogen species (RNS). Conversely, inflammation sustains malignant phenotypes by releasing pro-inflammatory cytokines and chemokines and promoting immune cell infiltration. These processes create a vicious cycle via positive feedback loops whereby oxidative stress initiates inflammatory signaling, while the inflammatory milieu further amplifies ROS/RNS production, collectively promoting proliferation, migration, angiogenesis, drug resistance, and immune evasion in tumor cells. Moreover, their crosstalk modulates DNA damage repair, metabolic reprogramming, and drug efflux pump activity, significantly impacting the sensitivity of cancer cells to chemotherapy, radiotherapy, and targeted therapies. This review systematically discusses these advances and the molecular mechanisms underlying the interplay between oxidative stress and inflammation in cancer biology. It also explores their potential as diagnostic biomarkers and prognostic indicators and highlights novel therapeutic strategies targeting the oxidative stress–inflammation axis. The goal is to provide a theoretical framework and translational roadmap for developing synergistic anti-tumor therapies.