Subcellular localization of APMCF1 and its biological significance of expression pattern in normal and malignant human tissues

<p>Abstract</p> <p>Background</p> <p>APMCF1 is a novel human gene first cloned from apoptotic MCF-7 cells. Our previous study found ectogenic APMCF1 could induce G1 arrest in hepatocarcinoma cell line HHCC. In order to search its broad expression profile for further understanding of its mechanism in tumor, we investigated a subcellular location of APMCF1 and performed an immunohistochemistry study including various tumor and normal tissues. Discovery from the expression characterization of AMPCF1 may have applicability in the analysis of its biological function in tumor.</p> <p>Methods</p> <p>We investigated subcellular localization of APMCF1 by transient transfection in green monkey kidney epithelial cells (COS-7) with a fusion protein vector pEGFP-APMCF1 and detected expression profile in a broad range of normal and malignant human tissues via tissue microarray (TMA) by immunohistochemistry with polyclonal antibody first produced in our laboratory.</p> <p>Results</p> <p>EGFP-APMCF1 was generally localized in the cytoplasm of COS-7 cell. Positive staining of APMCF1 was found in liver, lung, breast, colon, stomach, esophagus and testis, exhibited a ubiquitous expression pattern while its expression was up-regulated in tumor tissues compared with corresponding normal tissues. Normal brain neuron cells also showed expression of APMCF1, but negative in gliocyte cells and glioma. Both the normal and tumor tissues of ovary were absent of APMCF1 expression. Positive immunostaining for APMCF1 with large samples in liver, colon, esophagus, lung and breast carcinomas were 96% (51/53), 80% (44/55), 57% (30/53), 58% (33/57) and 34% (16/47) respectively.</p> <p>Conclusion</p> <p>These results revealed a cytoplastic expression pattern of APMCF1 and up-regulated in tumour tissues suggesting APMCF1 may have potential relationship with oncogenesis. The data presented should serve as a useful reference for further studies of APMCF1 functions in tumorigenesis and might provide a potential anti-tumor target.</p

Orbital-angular-momentum mode-group multiplexed transmission over a graded-index ring-core fiber based on receive diversity and maximal ratio combining

An orbital-angular-momentum (OAM) mode-group multiplexing (MGM) scheme using high-order mode groups (MGs) in a graded-index ring-core fiber (GIRCF) is proposed, in which a receive-diversity architecture is designed for each MG to suppress the mode partition noise resulting from random intra-group mode crosstalk. The signal-to-noise ratio (SNR) of the received signals is further improved by a simple maximal ratio combining (MRC) technique on the receiver side to efficiently take advantage of the diversity gain of the receiver. Intensity-modulated direct-detection (IM-DD) systems transmitting three OAM mode groups with total 100-Gb/s discrete multi-tone (DMT) signals over a 1-km GIRCF and two OAM mode groups with total 40-Gb/s DMT signals over an 18.4-km GIRCF are experimentally demonstrated, respectively, to confirm the feasibility of our proposed OAM-MGM scheme

MicroRNA-7 inhibits epithelial-to-mesenchymal transition and metastasis of breast cancer cells via targeting FAK expression

10.1371/journal.pone.0041523PLoS ONE78

Scalable mode division multiplexed transmission over a 10-km ring-core fiber using high-order orbital angular momentum modes

We propose and demonstrate a scalable mode division multiplexing scheme based on orbital angular momentum modes in ring core fibers. In this scheme, the high-order mode groups of a ring core fiber are sufficiently de-coupled by the large differential effective refractive index so that multiple-input multiple-output (MIMO) equalization is only used for crosstalk equalization within each mode group. We design and fabricate a graded-index ring core fiber that supports 5 mode groups with low inter-mode-group coupling, small intra-mode-group differential group delay, and small group velocity dispersion slope over the C-band for the high-order mode groups. We implement a two-dimensional wavelength- and mode-division multiplexed transmission experiment involving 10 wavelengths and 2 mode groups each with 4 OAM modes, transmitting 32 GBaud Nyquist QPSK signals over all 80 channels. An aggregate capacity of 5.12 Tb/s and an overall spectral efficiency of 9 bit/s/Hz over 10 km are realized, only using modular 4x4 MIMO processing with 15 taps to recover signals from the intra-mode-group mode coupling. Given the fixed number of modes in each mode group and the low inter-mode-group coupling in ring core fibres, our scheme strikes a balance in the trade-off between system capacity and digital signal processing complexity, and therefore has good potential for capacity upscaling at an expense of only modularly increasing the number of mode-groups with fixed-size (4x4) MIMO blocks

Changes in body weight and cardiovascular risk factors in a Chinese population with type 2 diabetes mellitus: a longitudinal study

IntroductionThe primary care management of blood glucose, blood pressure, lipid profiles, and body weight is important among patients with type 2 diabetes mellitus (T2DM) to prevent disease progression. Information on how weight changes would improve or deteriorate cardiovascular (CV) risk factors is warranted for making primary care recommendations. We aimed to investigate the changes in body weight and CV risk factors and to analyse their association in a Chinese population with T2DM.MethodsWe retrieved longitudinal data between 2020 and 2021 from 1,758 adult primary care patients enrolled in a diabetic retinopathy (DR) screening programme. Linear associations of changes in body weight with CV risk factors were explored. Multivariable logistic regression analysis was performed to examine associations between different weight change categories and the worsening of CV risk factors.ResultsThe mean age of all the participants was 63.71 years, and over half of participants were females. During a one-year follow-up period, 24.7% of patients had a weight loss of ≥3%, while 22.2% of patients had a weight gain of ≥3%. Patients who had a weight loss of ≥3% were more likely to prevent the worsening of haemoglobin A1c (HbA1c) and triglycerides, while those who had a weight gain of ≥3% tended to have worsened HbA1c, lipid profiles, and blood pressure.ConclusionResults from this real-world investigation suggested the concurrent need for weight loss intervention among patients who are overweight or obese and weight gain prevention among patients whose body weight falls within the normal range in the context of community-based diabetes management

CCND1 as a Predictive Biomarker of Neoadjuvant Chemotherapy in Patients with Locally Advanced Head and Neck Squamous Cell Carcinoma

BACKGROUND: Cyclin D1 (CCND1) has been associated with chemotherapy resistance and poor prognosis. In this study, we tested the hypothesis that CCND1 expression determines response and clinical outcomes in locally advanced head and neck squamous cell carcinoma (HNSCC) patients treated with neoadjuvant chemotherapy followed by surgery and radiotherapy. METHODOLOGY AND FINDINGS: 224 patients with HNSCC were treated with either cisplatin-based chemotherapy followed by surgery and radiotherapy (neoadjuvant group, n = 100) or surgery and radiotherapy (non-neoadjuvant group, n = 124). CCND1 expression was assessed by immunohistochemistry. CCND1 levels were analyzed with chemotherapy response, disease-free survival (DFS) and overall survival (OS). There was no significant difference between the neoadjuvant group and non-neoadjuvant group in DFS and OS (p = 0.929 and p = 0.760) when patients treated with the indiscriminate administration of cisplatin-based chemotherapy. However, in the neoadjuvant group, patients whose tumors showed a low CCND1 expression more likely respond to chemotherapy (p<0.001) and had a significantly better OS and DFS than those whose tumors showed a high CCND1 expression (73% vs 8%, p<0.001; 63% vs 6%, p<0.001). Importantly, patients with a low CCND1 expression in neoadjuvant group received more survival benefits than those in non-neoadjuvant group (p = 0.016), however patients with a high CCND1 expression and treated with neoadjuvant chemotherapy had a significantly poor OS compared to those treated with surgery and radiotherapy (p = 0.032). A multivariate survival analysis also showed CCND1 expression was an independent predictive factor (p<0.001). CONCLUSIONS: This study suggests that some but not all patients with HNSCC may benefit from neoadjuvant chemotherapy with cisplatin-based regimen and CCND1 expression may serve as a predictive biomarker in selecting patients undergo less than two cycles of neoadjuvant chemotherapy

Radiogenomics analysis reveals the associations of dynamic contrast-enhanced–MRI features with gene expression characteristics, PAM50 subtypes, and prognosis of breast cancer

BackgroundTo investigate reliable associations between dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) features and gene expression characteristics in breast cancer (BC) and to develop and validate classifiers for predicting PAM50 subtypes and prognosis from DCE-MRI non-invasively.MethodsTwo radiogenomics cohorts with paired DCE-MRI and RNA-sequencing (RNA-seq) data were collected from local and public databases and divided into discovery (n = 174) and validation cohorts (n = 72). Six external datasets (n = 1,443) were used for prognostic validation. Spatial–temporal features of DCE-MRI were extracted, normalized properly, and associated with gene expression to identify the imaging features that can indicate subtypes and prognosis.ResultsExpression of genes including RBP4, MYBL2, and LINC00993 correlated significantly with DCE-MRI features (q-value &lt; 0.05). Importantly, genes in the cell cycle pathway exhibited a significant association with imaging features (p-value &lt; 0.001). With eight imaging-associated genes (CHEK1, TTK, CDC45, BUB1B, PLK1, E2F1, CDC20, and CDC25A), we developed a radiogenomics prognostic signature that can distinguish BC outcomes in multiple datasets well. High expression of the signature indicated a poor prognosis (p-values &lt; 0.01). Based on DCE-MRI features, we established classifiers to predict BC clinical receptors, PAM50 subtypes, and prognostic gene sets. The imaging-based machine learning classifiers performed well in the independent dataset (areas under the receiver operating characteristic curve (AUCs) of 0.8361, 0.809, 0.7742, and 0.7277 for estrogen receptor (ER), human epidermal growth factor receptor 2 (HER2)-enriched, basal-like, and obtained radiogenomics signature). Furthermore, we developed a prognostic model directly using DCE-MRI features (p-value &lt; 0.0001).ConclusionsOur results identified the DCE-MRI features that are robust and associated with the gene expression in BC and displayed the possibility of using the features to predict clinical receptors and PAM50 subtypes and to indicate BC prognosis

An Explainable 3D Residual Self-Attention Deep Neural Network FOR Joint Atrophy Localization and Alzheimer's Disease Diagnosis using Structural MRI

Computer-aided early diagnosis of Alzheimer's disease (AD) and its prodromal form mild cognitive impairment (MCI) based on structure Magnetic Resonance Imaging (sMRI) has provided a cost-effective and objective way for early prevention and treatment of disease progression, leading to improved patient care. In this work, we have proposed a novel computer-aided approach for early diagnosis of AD by introducing an explainable 3D Residual Attention Deep Neural Network (3D ResAttNet) for end-to-end learning from sMRI scans. Different from the existing approaches, the novelty of our approach is three-fold: 1) A Residual Self-Attention Deep Neural Network has been proposed to capture local, global and spatial information of MR images to improve diagnostic performance; 2) An explanation method using Gradient-based Localization Class Activation mapping (Grad-CAM) has been introduced to improve the explainable of the proposed method; 3) This work has provided a full end-to-end learning solution for automated disease diagnosis. Our proposed 3D ResAttNet method has been evaluated on a large cohort of subjects from real datasets for two changeling classification tasks (i.e., Alzheimer's disease (AD) vs. Normal cohort (NC) and progressive MCI (pMCI) vs. stable MCI (sMCI)). The experimental results show that the proposed approach has a competitive advantage over the state-of-the-art models in terms of accuracy performance and generalizability. The explainable mechanism in our approach is able to identify and highlight the contribution of the important brain parts (e.g., hippocampus, lateral ventricle and most parts of the cortex) for transparent decisions.Comment: IEEE Journal of Biomedical and Health Informatics (2021

Has China’s Domestic Food Price Become More Stable? An Investigation Based on a Structural Break Regime Switching Model

The stability of grain prices relates closely to the development of China’s economy, social stability and quality of Chinese people’s life. However, with the gradual openness of China’s grain market and series of newly-issued China’s grain policies, the volatility characteristics of China’s grain price may experience some structural changes and whether it becomes more stable still remains controversial. In this paper, we investigate the fluctuation characteristics of some main grain prices during the past two decades by using Structural Break Regime Switching Model and the Structural Break Model. We find that China’s grain price has become more stable since 2004 with narrowing low and high growth regimes. The implementation of Minimum Purchasing Price Policy and the semi-separation of domestic and international grain markets may explain part of the reasons for the stabilization