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BackgroundDrug resistance is a major challenge in cancer therapeutics. Abundant evidence indicates that DNA repair systems are enhanced after repetitive chemotherapeutic treatments, rendering cancers cells drug-resistant. Flap endonuclease 1 (FEN1) plays critical roles in DNA replication and repair and in counteracting replication stress, which is a key mechanism for many chemotherapeutic drugs to kill cancer cells. FEN1 was previously shown to be upregulated in response to DNA damaging agents. However, it is unclear about the transcription factors that regulate FEN1 expression in human cancer. More importantly, it is unknown whether up-regulation of FEN1 has an adverse impact on the prognosis of chemotherapeutic treatments of human cancers.MethodsTo reveal regulation mechanism of FEN1 expression, we search and identify FEN1 transcription factors or repressors and investigate their function on FEN1 expression by using a combination of biochemical, molecular, and cellular approaches. Furthermore, to gain insights into the impact of FEN1 levels on the response of human cancer to therapeutic treatments, we determine FEN1 levels in human breast cancer specimens and correlate them to the response to treatments and the survivorship of corresponding breast cancer patients.ResultsWe observe that FEN1 is significantly up-regulated upon treatment of chemotherapeutic drugs such as mitomycin C (MMC) and Taxol in breast cancer cells. We identify that the transcription factor/repressor YY1 binds to the FEN1 promoter and suppresses the expression of FEN1 gene. In response to the drug treatments, YY1 is dissociated from the FEN1 promoter region leading over-expression of FEN1. Overexpression of YY1 in the cells results in down-regulation of FEN1 and sensitization of the cancer cells to MMC or taxol. Furthermore, we observe that the level of FEN1 is inversely correlated with cancer drug and radiation resistance and with survivorship in breast cancer patients.ConclusionAltogether, our current data indicate that YY1 is a transcription repressor of FEN1 regulating FEN1 levels in response to DNA damaging agents. FEN1 is up-regulated in human breast cancer and its levels inversely correlated with cancer drug and radiation resistance and with survivorship in breast cancer patients.Electronic supplementary materialThe online version of this article (doi:10.1186/s12885-015-1043-1) contains supplementary material, which is available to authorized users.

Background Gut microbiota (GMB) alteration has been reported to influence the Alzheimer’s disease (AD) pathogenesis through immune, endocrine, and metabolic pathways. This study aims to investigate metabolic output of the dysbiosis of GMB in AD pathogenesis. In this study, the fecal microbiota and metabolome from 21 AD participants and 44 cognitively normal control participants were measured. Untargeted GMB taxa was analyzed through 16S ribosomal RNA gene profiling based on next-generation sequencing and fecal metabolites were quantified by using ultrahigh performance liquid chromatography-mass spectrometry (UPLC-MS). Results Our analysis revealed that AD was characterized by 15 altered gut bacterial genera, of which 46.7% (7/15 general) was significantly associated with a series of metabolite markers. The predicted metabolic profile of altered gut microbial composition included steroid hormone biosynthesis, N-Acyl amino acid metabolism and piperidine metabolism. Moreover, a combination of 2 gut bacterial genera (Faecalibacterium and Pseudomonas) and 4 metabolites (N-Docosahexaenoyl GABA, 19-Oxoandrost-4-ene-3,17-dione, Trigofoenoside F and 22-Angeloylbarringtogenol C) was able to discriminate AD from NC with AUC of 0.955 in these 65 subjects. Conclusions These findings demonstrate that gut microbial alterations and related metabolic output changes may be associated with pathogenesis of AD, and suggest that fecal markers might be used as a non-invasive examination to assist screening and diagnosis of AD.

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Short-term memory (STM) is crucial for animals to hold information for a small period of time. Persistent or recurrent neural activity, together with neural oscillations, is known to encode the STM at the cellular level. However, the coding mechanisms at the microcircuitry level remain a mystery. Here, we performed two-photon imaging on behaving mice to monitor the activity of neuronal microcircuitry. We discovered a neuronal subpopulation in the medial prefrontal cortex (mPFC) that exhibited emergent properties in a context-dependent manner underlying a STM-like behavior paradigm. These neuronal subpopulations exclusively comprise excitatory neurons and mainly represent a group of neurons with stronger functional connections. Microcircuitry plasticity was maintained for minutes and was absent in an animal model of Alzheimer's disease (AD). Thus, these results point to a functional coding mechanism that relies on the emergent behavior of a functionally defined neuronal assembly to encode STM.

Background Traditional diagnosis methods for lymph node metastases are labor-intensive and time-consuming. As a result, diagnostic systems based on deep learning (DL) algorithms have become a hot topic. However, current research lacks testing with sufficient data to verify performance. The aim of this study was to develop and test a deep learning system capable of identifying lymph node metastases. Methods 921 whole-slide images of lymph nodes were divided into two cohorts: training and testing. For lymph node quantification, we combined Faster RCNN and DeepLab as a cascade DL algorithm to detect regions of interest. For metastatic cancer identification, we fused Xception and DenseNet-121 models and extracted features. Prospective testing to verify the performance of the diagnostic system was performed using 327 unlabeled images. We further validated the proposed system using Positive Predictive Value (PPV) and Negative Predictive Value (NPV) criteria. ResultsWe developed a DL-based system capable of automated quantification and identification of metastatic lymph nodes. The accuracy of lymph node quantification was shown to be 97.13%. The PPV of the combined Xception and DenseNet-121 model was 93.53%, and the NPV was 97.99%. Our experimental results show that the differentiation level of metastatic cancer affects the recognition performance. Conclusions The diagnostic system we established reached a high level of efficiency and accuracy of lymph node diagnosis. This system could potentially be implemented into clinical workflow to assist pathologists in making a preliminary screening for lymph node metastases in gastric cancer patients.

Background Early diagnosis of Peritoneal metastasis (PM) is clinically significant regarding optimal treatment selection and avoidance of unnecessary surgical procedures. Cytopathology plays an important role in early screening of PM. We aimed to develop a deep learning (DL) system to achieve intelligent cytopathology interpretation, especially in ascites cytopathology. Methods The original ascites cytopathology image dataset consists of 139 patients' original hematoxylin-eosin (HE) and Papanicolaou (PAP) Staining images. DL system was developed using transfer learning (TL) to achieve cell detection and classification. Pre-trained alexnet, vgg16, goolenet, resnet18 and resnet50 models were studied. Cell detection dataset consists of 176 cropped images with 6573 annotated cell bounding boxes. Cell classification data set consists of 487 cropped images with 18,558 and 6089 annotated malignant and benign cells in total, respectively. Results We established a novel ascites cytopathology image dataset and achieved automatically cell detection and classification. DetectionNet based on Faster R-CNN using pre-trained resnet18 achieved cell detection with 87.22% of cells' Intersection of Union (IoU) bigger than the threshold of 0.5. The mean average precision (mAP) was 0.8316. The Classifica-tionNet based on resnet50 achieved the greatest performance in cell classification with AUC = 0.8851, Precision = 96.80%, FNR = 4.73%. The DL system integrating the separately trained DetectionNet and Classificationnet showed great performance in the cytopathology image interpretation. Conclusions We demonstrate that the integration of DL can improve the efficiency of healthcare. The DL system we developed using TL techniques achieved accurate cytopathology interpretation, and had great potential to be integrated into clinician workflow.

Tidal current-induced cyclonic eddies cause cold-water upwelling and periodic sea surface temperature (SST) drops around the coral reef area in Nanwan Bay, which is located at the southern tip of Taiwan. This study used Himawari-8 satellite data and tide gauge and coastal ocean dynamics application radar (CODAR) data to analyze the characteristics of the SST drops and cyclonic eddy propagation and used an oceanic general circulation model (OGCM) to simulate the tidal current flowing process. According to the CODAR data analysis, the mixed primary semidiurnal tide had an average current velocity of 0.3-0.4 m s −1 throughout the bay, and the average life cycle of a cyclonic eddy is 6.6 hr, with a propagation speed of 0.35 m s −1 . The SST drop during the spring tide period was greater than that during the neap tide period, and the SST dropped in both summer and winter. The average daily SST drop in the summer reached 2°C with a maximum observed value of 4.7°C, and the SST drop rate was 0.3-0.5°C hr −1 . The annual mean chlorophyll-a concentration was 0.25 mg m −3 . This study explored the special properties of the Nanwan Bay coral reef area from the perspective of ocean physics to allow ecologists to facilitate the implementation of long-term conservation and monitoring programs.Plain Language Summary In recent years, the increased global sea temperatures have caused severe coral bleaching; however, Nanwan Bay has suffered a comparatively smaller impact. To understand why, we analyzed satellite and radar data. We found that Nanwan Bay experienced an almost daily sea surface temperature (SST) drop during the summer and that this periodic variation was caused by a cyclonic eddy upwelling cold water to the surface. In the summer, the SST drop was 2°C on average and dropped at a rate of 0.3-0.5°C hr −1 . The observational record indicated that the water cooled as much as 4.7°C in 1 day. These eddies were caused by the unique topography in this area of the bay; they survived an average of 6.6 hr and moved at a rate of 0.35 m s −1 . The formed eddies disappeared in the bay or flowed southward out of the bay. With this study, we hope to help marine ecologists understand the Nanwan Bay environment from a geophysical perspective so that they have sufficient background knowledge when investigating coral biology and coral reef ecology in the future.