Clinicopathological and prognostic value of epithelial cell adhesion molecule in solid tumours: a meta-analysis

BackgroundMalignant tumors, mainly solid tumors, are a significant obstacle to the improvement of life expectancy at present. Epithelial cell adhesion molecule (EpCAM), a cancer stem cell biomarker, showed widespread expression in most normal epithelial cells and most cancers. Although the clinical significance of EpCAM in various malignant solid tumors has been studied extensively, the latent relationships between EpCAM and pathological and clinical characteristics in solid tumors and differences in the roles of EpCAM among tumors have not been clearly determined. The destination point of this study was to analyze the value of EpCAM in solid tumors in clinicopathological and prognostic dimension using a meta-analysis approach.Method and materialsA comprehensive and systematic search of the researches published up to March 7th, 2022, in PubMed, EMBASE, Web of Science, Cochrane library and PMC databases was performed. The relationships between EpCAM overexpression, clinicopathological characteristics, and survival outcomes were analyzed. Pooled hazard ratios (HRs) with 95% confidence intervals (CIs) and odds ratios (ORs) were estimated as indicators of the degree of correlation. This research was registered on PROSPERO (International prospective register of systematic reviews), ID: CRD42022315070.ResultsIn total, 57 articles and 14184 cases were included in this study. High EpCAM expression had a significant coherence with a poorer overall survival (OS) (HR: 1.30, 95% CI: 1.08–1.58, P < 0.01) and a worse disease-free survival (DFS) (HR: 1.58, 95% CI: 1.28–1.95, P < 0.01), especially of gastrointestinal tumors’ OS (HR: 1.50, 95% CI: 1.15–1.95, P < 0.01), and DFS (HR: 1.84, 95% CI: 1.52–2.33, P < 0.01). The DFS of head and neck tumors (HR: 2.33, 95% CI: 1.51–3.61, P < 0.01) was also associated with the overexpression of EpCAM. There were no positive relationships between the overexpression of EpCAM and sex (RR: 1.03, 95% CI: 0.99–1.07, P = 0.141), T classification (RR: 0.93, 95% CI: 0.82–1.06, P = 0.293), lymph node metastasis (RR: 0.85, 95% CI: 0.54–1.32, P = 0.461), distant metastasis (RR: 0.97, 95% CI: 0.84–1.10, P = 0.606), vascular infiltration (RR: 1.05, 95% CI: 0.85–1.29, P = 0.611), and TNM stage (RR: 0.93, 95% CI: 0.83–1.04, P = 0.187). However, the overexpression of EpCAM exhibited a significant association with the histological grades (RR: 0.88, 95% CI: 0.80–0.97, P < 0.01).ConclusionBased on pooled HRs, the positive expression of EpCAM was totally correlated to a worse OS and DFS in solid tumors. The expression of EpCAM was related to a worse OS in gastrointestinal tumors and a worse DFS in gastrointestinal tumors and head and neck tumors. Moreover, EpCAM expression was correlated with the histological grade. The results presented pointed out that EpCAM could serve as a prognostic biomarker for gastrointestinal and head and neck tumors.Systematic review registrationhttps://www.crd.york.ac.uk/prospero, identifier CRD42022315070

Dynamic Position-aware Network for Fine-grained Image Recognition

Most weakly supervised fine-grained image recognition (WFGIR) approaches predominantly focus on learning the discriminative details which contain the visual variances and position clues. The position clues can be indirectly learnt by utilizing context information of discriminative visual content. However, this will cause the selected discriminative regions containing some non-discriminative information introduced by the position clues. These analysis motivate us to directly introduce position clues into visual content to only focus on the visual variances, achieving more precise discriminative region localization. Though important, position modelling usually requires significant pixel/region annotations and therefore is labor-intensive. To address this issue, we propose an end-to-end Dynamic Position-aware Network (DP-Net) to directly incorporate the position clues into visual content and dynamically align them without extra annotations, which eliminates the effect of position information for visual variances of subcategories. In particular, the DP-Net consists of: 1) Position Encoding Module, which learns a set of position-aware parts by directly adding the learnable position information into the horizontal/vertical visual content of images; 2) Position-vision Aligning Module, which dynamically aligns both visual content and learnable position information via performing graph convolution on position-aware parts; 3) Position-vision Reorganization Module, which projects the aligned position clues and visual content into the Euclidean space to construct a position-aware feature maps. Finally, the position-aware feature maps are used which is implicitly applied the aligned visual content and position clues for more accurate discriminative regions localization. Extensive experiments verify that DP-Net yields the best performance under the same settings with most competitive approaches, on CUB Bird, Stanford-Cars, and FGVC Aircraft datasets

Fine-Grained Retrieval Prompt Tuning

Fine-grained object retrieval aims to learn discriminative representation to retrieve visually similar objects. However, existing top-performing works usually impose pairwise similarities on the semantic embedding spaces or design a localization sub-network to continually fine-tune the entire model in limited data scenarios, thus resulting in convergence to suboptimal solutions. In this paper, we develop Fine-grained Retrieval Prompt Tuning (FRPT), which steers a frozen pre-trained model to perform the fine-grained retrieval task from the perspectives of sample prompting and feature adaptation. Specifically, FRPT only needs to learn fewer parameters in the prompt and adaptation instead of fine-tuning the entire model, thus solving the issue of convergence to suboptimal solutions caused by fine-tuning the entire model. Technically, a discriminative perturbation prompt (DPP) is introduced and deemed as a sample prompting process, which amplifies and even exaggerates some discriminative elements contributing to category prediction via a content-aware inhomogeneous sampling operation. In this way, DPP can make the fine-grained retrieval task aided by the perturbation prompts close to the solved task during the original pre-training. Thereby, it preserves the generalization and discrimination of representation extracted from input samples. Besides, a category-specific awareness head is proposed and regarded as feature adaptation, which removes the species discrepancies in features extracted by the pre-trained model using category-guided instance normalization. And thus, it makes the optimized features only include the discrepancies among subcategories. Extensive experiments demonstrate that our FRPT with fewer learnable parameters achieves the state-of-the-art performance on three widely-used fine-grained datasets

Dissolution of Microcrystalline Cellulose in Phosphoric Acid—Molecular Changes and Kinetics

In this study, we aimed to dissolve microcrystalline cellulose (MCC) with phosphoric acid to obtain high-quality fermentable saccharides. MCC was directly dissolved in phosphoric acid (the concentration was 83%) for 10 hours at temperatures of 30, 50, and 70 °C. The structural changes of MCC were determined in detail with X-ray powder diffraction, solid-state cross-polarization magic angle spinning 13C-NMR, and X-ray photoelectron spectroscopy. The kinetics of MCC decrystallization during treatment with phosphoric acid was also compared at 30, 50, and 70 °C. With the assumption of first order kinetics, the Arrhenius parameters of K, A0 and Ea were calculated. The rate constants of decrystallization reaction (K) were 0.06, 0.17, and 0.12 h-1 respectively. The pre-exponential factor (A0) was 1.2 × 106 h-1, and the activation energy (Ea) was 42.4 kJ/mol

A Dual‐Kinetic Control Strategy for Designing Nano‐Metamaterials: Novel Class of Metamaterials with Both Characteristic and Whole Sizes of Nanoscale

Abstract Increasingly intricate in their multilevel multiscale microarchitecture, metamaterials with unique physical properties are challenging the inherent constraints of natural materials. Their applicability in the nanomedicine field still suffers because nanomedicine requires a maximum size of tens to hundreds of nanometers; however, this size scale has not been achieved in metamaterials. Therefore, “nano‐metamaterials,” a novel class of metamaterials, are introduced, which are rationally designed materials with multilevel microarchitectures and both characteristic sizes and whole sizes at the nanoscale, investing in themselves remarkably unique and significantly enhanced material properties as compared with conventional nanomaterials. Microarchitectural regulation through conventional thermodynamic strategy is limited since the thermodynamic process relies on the frequency‐dependent effective temperature, Teff(ω), which limits the architectural regulation freedom degree. Here, a novel dual‐kinetic control strategy is designed to fabricate nano‐metamaterials by freezing a high‐free energy state in a Teff(ω)‐constant system, where two independent dynamic processes, non‐solvent induced block copolymer (BCP) self‐assembly and osmotically driven self‐emulsification, are regulated simultaneously. Fe3+‐“onion‐like core@porous corona” (Fe3+‐OCPCs) nanoparticles (the products) have not only architectural complexity, porous corona and an onion‐like core but also compositional complexity, Fe3+ chelating BCP assemblies. Furthermore, by using Fe3+‐OCPCs as a model material, a microstructure‐biological performance relationship is manifested in nano‐metamaterials

ZEB1 Transcriptionally Activates PHGDH to Facilitate Carcinogenesis and Progression of HCCSummary

Background & Aims: Phosphoglycerate dehydrogenase (PHGDH), the rate-limiting enzyme of the de novo serine synthesis pathway (SSP), has been implicated in the carcinogenesis and metastasis of hepatocellular carcinoma (HCC) because of its excessive expression and promotion of SSP. In previous experiments we found that SSP flux was diminished by knockdown of zinc finger E-box binding homeobox 1 (ZEB1), a stimulator of HCC metastasis, but the underlying mechanism remains largely unknown. Here, we aimed to determine how SSP flux is regulated by ZEB1 and the contribution of such regulation to carcinogenesis and progression of HCC. Methods: We used genetic mice with Zeb1 knockout in liver specifically to determine whether Zeb1 deficiency impacts HCC induced by the carcinogen diethylnitrosamine plus CCl4. We explored the regulatory mechanism of ZEB1 in SSP flux using uniformly-labeled [13C]-glucose tracing analyses, liquid chromatography–mass spectrometry, real-time quantitative polymerase chain reaction, luciferase report assay, and chromatin immunoprecipitation assay. We determined the contribution of the ZEB1–PHGDH regulatory axis to carcinogenesis and metastasis of HCC by cell counting assay, methyl thiazolyl tetrazolium (MTT) assay, scratch wound assay, Transwell assay, and soft agar assay in vitro, orthotopic xenograft, bioluminescence, and H&E assays in vivo. We investigated the clinical relevance of ZEB1 and PHGDH by analyzing publicly available data sets and 48 pairs of HCC clinical specimens. Results: We identified that ZEB1 activates PHGDH transcription by binding to a nonclassic binding site within its promoter region. Up-regulated PHGDH augments SSP flux to enable HCC cells to be more invasive, proliferative, and resistant to reactive oxygen species and sorafenib. Orthotopic xenograft and bioluminescence assays have shown that ZEB1 deficiency significantly impairs the tumorigenesis and metastasis of HCC, and such impairments can be rescued to a large extent by exogenous expression of PHGDH. These results were confirmed by the observation that conditional knockout of ZEB1 in mouse liver dramatically impedes carcinogenesis and progression of HCC induced by diethylnitrosamine/CCl4, as well as PHGDH expression. In addition, analysis of The Cancer Genome Atlas database and clinical HCC samples showed that the ZEB1–PHGDH regulatory axis predicts poor prognosis of HCC. Conclusions: ZEB1 plays a crucial role in stimulating carcinogenesis and progression of HCC by activating PHGDH transcription and subsequent SSP flux, deepening our knowledge of ZEB1 as a transcriptional factor in fostering the development of HCC via reprogramming the metabolic pathway