Deep radial basis function networks with subcategorization for mitosis detection in breast histopathology images.

Due to the intra-class diversity of mitotic cells and the morphological overlap with similarly looking imposters, automatic mitosis detection in histopathology slides is still a challenging task. In this paper, we propose a novel mitosis detection model in a weakly supervised way, which consists of a candidate proposal network and a verification network. The candidate proposal network based on patch learning aims to separate both mitotic cells and their mimics from the background as candidate objects, which substantially reduces missed detections in the screening process of candidates.

Role of mA modifications in immune evasion and immunotherapy.

RNA modification has garnered increasing attention in recent years due to its pivotal role in tumorigenesis and immune surveillance. N-methyladenosine (mA) modification is the most prevalent RNA modification, which can affect the expression of RNA by methylating adenylate at the sixth N position to regulate the occurrence and development of tumors. Dysregulation of mA affects the activation of cancer-promoting pathways, destroys immune cell function, maintains immunosuppressive microenvironment, and promotes tumor cell growth.

Palmitic acid-capped MIL-101-Al as a nano-adjuvant to amplify immune responses against .

As a highly contagious opportunistic pathogen, (. ) is one of the main causes of healthcare-associated infections. The drug-resistant nature of .

Nuclei detection in breast histopathology images with iterative correction.

This work presents a deep network architecture to improve nuclei detection performance and achieve the high localization accuracy of nuclei in breast cancer histopathology images. The proposed model consists of two parts, generating nuclear candidate module and refining nuclear localization module. We first design a novel patch learning method to obtain high-quality nuclear candidates, where in addition to categories, location representations are also added to the patch information to implement the multi-task learning process of nuclear classification and localization; meanwhile, the deep supervision mechanism is introduced to obtain the coherent contributions from each scale layer.

Functional Connectivity Networks with Latent Distributions for Mild Cognitive Impairment Identification.

This work presents a novel approach to estimate brain functional connectivity networks via generative learning. Due to the complexity and variability of rs-fMRI signal, we consider it as a random variable, and utilize variational autoencoder networks to encode it as a confidence distribution in the latent space rather than as a fixed vector, so as to establish the relationship between them. First, the mean time series of each brain region of interest is mapped into a multivariate Gaussian distribution.

BAK plays a key role in A-1331852-induced apoptosis in senescent chondrocytes.

Osteoarthritis occurs when the number of senescent chondrocytes in the joints reaches an intolerable level. The purpose of our study was to explore the therapeutic effect and mechanism of action of A-1331852 in osteoarthritis. Doxorubicin and etoposide were used to induce cell senescence as determined by the cessation of cell proliferation, augmented senescence-associated beta-galactosidase (SA-β-Gal) staining, and increased p53 expression levels.

Bim- and Bax-mediated mitochondrial pathway dominates abivertinib-induced apoptosis and ferroptosis.

Abivertinib (AC) is a novel epidermal growth factor receptor tyrosine kinase inhibitor with highly efficient antitumor activity. Here, we report the capacity of AC to induce both reactive oxygen species (ROS)-dependent apoptosis and ferroptosis in tumor cells. Our data showed that AC induced iron- and ROS-dependent cytotoxicity in MCF7, HeLa, and A549 cell lines.

Bak instead of Bax plays a key role in metformin-induced apoptosis s in HCT116 cells.

Metformin (Met) exhibits anticancer ability in various cancer cell lines. This report aims to explore the exact molecular mechanism of Met-induced apoptosis in HCT116 cells, a human colorectal cancer cell line. Met-induced reactive oxygen species (ROS) increase and ROS-dependent cell death accompanied by plasma membrane blistering, mitochondrial swelling, loss of mitochondrial membrane potential, and release of cytochrome c.

Evaluating the inhibitory priority of Bcl-xL to Bad, tBid and Bax by using live-cell imaging assay.

Molecular regulatory network among the B cell leukemia-2 (Bcl-2) family proteins is a research hotspot on apoptosis. The inhibitory priority of anti-apoptotic Bcl-2 family proteins (such as Bcl-xL) to pro-apoptotic Bcl-2 family proteins (such as Bad, tBid and Bax) determines the outcome of their interactions. Based on over-expression model system, we here evaluate the inhibitory priority of Bcl-xL to Bad, tBid and Bax by using live-cell imaging assay on cell viability.

Measuring calibration factors by imaging a dish of cells expressing different tandem constructs plasmids.

Three-cube Förster resonance energy transfer (FRET) method is the most extensively applied approach for live-cell FRET quantification. Reliable measurements of calibration factors are crucial for quantitative FRET measurement. We here proposed a modified TA-G method (termed as mTA-G) to simultaneously obtain the FRET-sensitized quenching transition factor (G) and extinction coefficients ratio (γ) between donor and acceptor.

Development and validation of a nomogram to predict overall survival for patients with metastatic renal cell carcinoma.

Background: Heterogeneity of metastatic renal cell carcinoma (RCC) constraints accurate prognosis prediction of the tumor. We therefore aimed at developing a novel nomogram for accurate prediction of overall survival (OS) of patients with metastatic RCC.

Methods: We extracted 2010 to 2016 data for metastatic RCC patients in the Surveillance, Epidemiology, and End Results (SEER) database, and randomly stratified them equally into training and validation sets.

Nomogram prediction of overall survival based on log odds of positive lymph nodes for patients with penile squamous cell carcinoma.

Purpose: This study aimed to establish a nomogram to predict the long-term overall survival (OS) for patients with penile squamous cell carcinoma (PSCC).

Method: The PSCC patients receiving regional lymph node dissection (RLND) were enrolled from the Surveillance, Epidemiology, and End Results (SEER) database between 2004 and 2015. The dataset of all eligible patients were used to develop the predictive model.

Inhibition of HDAC6 expression decreases brain injury induced by APOE4 and Aβ co‑aggregation in rats.

The present study aimed to explore the effects of histone deacetylase 6 (HDAC6) on brain injury in rats induced by apolipoprotein E4 (APOE4) and amyloid β protein alloform 1‑40 (Aβ1‑40) copolymerization. The rats were randomly divided into four groups: Control group, sham group, APOE4 + Aβ1‑40 co‑injection group (model group) and HDAC6 inhibitor group (HDAC6 group). The brain injury model was established by co‑injection of APOE4 + Aβ1‑40.

Long non-coding RNA CRNDE promotes heptaocellular carcinoma cell proliferation by regulating PI3K/Akt /β-catenin signaling.

Hepatocellular carcinoma (HCC) is one of the common malignant cancer worldwide, and its molecular pathogenesis remains elusive and recently long non-coding RNAs (lncRNAs) have been reported that play divergent roles in HCC tumorigenesis and development. In current study, we found a lncRNA, CRNDE is more commonly up-regulated in HCC malignant tissues and associated with poor clinical outcomes. Furthermore, both loss- and gain-functions assays revealed that CRNDE promotes HCC cell proliferation and growth in vitro and in vivo.

A preliminary study on the anticancer efficacy of Caulis spatholobi compound 1802.

In recent years, antitumor and antiviral effect of Caulis spatholobi becomes a hot topic of medical drug research. Experiment shows that the water extract of Caulis spatholobi compound 1802 showed the effect of inhibiting tumor growth, the difference was statistically significant (P<0.05); the rate of tumor inhibition was highest in the high dose group of compound 1802, which could reach 41.

Evolutionary conservation and functional divergence of the LFK gene family play important roles in the photoperiodic flowering pathway of land plants.

ZEITLUPE (ZTL), LOV KELCH PROTEIN 2 (LKP2), and FLAVIN-BINDING KELCH REPEAT F-BOX 1 (FKF1)-blue-light photoreceptors-play important roles in regulating the circadian clock and photoperiodic flowering pathway in plants. In this study, phylogenetic analysis revealed that the LOV (Light, Oxygen, or Voltage) and Kelch repeat-containing F-box (LFK) gene family can be classified into two clades, ZTL/LKP2 and FKF1, with clear differentiation between monocots and dicots within each clade. The LFK family genes underwent strong purifying selection; however, signatures of positive selection to adapt to local conditions still existed in 18 specific codons.

Curcumol inhibits the proliferation of gastric adenocarcinoma MGC-803 cells via downregulation of IDH1.

Curcumol, a polyphenol compound derived from the rhizome of Curcuma, has been established as an antitumor compound against multiple types of cancer, including gastric (GC), lung, liver and breast cancer. However, the molecular mechanisms undelying its anticancer activity in GC are still unclear. In this study, the antitumor efficacy of curcumol was ascertained in human gastric adenocarcinoma MGC-803 cells.

Medical Image Retrieval Using Multi-Texton Assignment.

In this paper, we present a multi-texton representation method for medical image retrieval, which utilizes the locality constraint to encode each filter bank response within its local-coordinate system consisting of the k nearest neighbors in texton dictionary and subsequently employs spatial pyramid matching technique to implement feature vector representation. Comparison with the traditional nearest neighbor assignment followed by texton histogram statistics method, our strategies reduce the quantization errors in mapping process and add information about the spatial layout of texton distributions and, thus, increase the descriptive power of the image representation. We investigate the effects of different parameters on system performance in order to choose the appropriate ones for our datasets and carry out experiments on the IRMA-2009 medical collection and the mammographic patch dataset.

Medical image classification via multiscale representation learning.

Multiscale structure is an essential attribute of natural images. Similarly, there exist scaling phenomena in medical images, and therefore a wide range of observation scales would be useful for medical imaging measurements. The present work proposes a multiscale representation learning method via sparse autoencoder networks to capture the intrinsic scales in medical images for the classification task.

Computational Model Based on Neural Network of Visual Cortex for Human Action Recognition.

In this paper, we propose a bioinspired model for human action recognition through modeling neural mechanisms of information processing in two visual cortical areas: the primary visual cortex (V1) and the middle temporal cortex (MT) dedicated to motion. This model, named V1-MT, is composed of V1 and MT models (layers) corresponding to their cortical areas, which are built with layered spiking neural networks (SNNs). Some neuron properties in V1 and MT, such as direction and speed selectivity, spatiotemporal inseparability, and center surround suppression, are integrated into SNNs.