81 research outputs found

    Overexpression of CDC25C affects the cell cycle of ovarian granulosa cells from adult and young goats

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    Background: CDC25 is a dual-specificity phosphatase that was first identified in the yeast Schizosaccharomyces pombe as a cell cycle-defective mutant. Although CDC25 is involved in the cell cycle of ovarian granulosa cells, the CDC25 signaling pathway has not been clarified fully. To explore the role of CDC25C in the cell cycle of goat ovarian granulosa cells, a CDC25C-overexpressing vector, pCMV-HA-CDC25C, was constructed and transfected into granulosa cells from adult and young white goats from Jiangsu Nantong. RT-PCR was used to measure CDC25C, CDK1, and WEE1 gene expression levels, and flow cytometry was used to distinguish ovarian granulosa cells in different phases of the cell cycle. Progesterone and estradiol levels in transfected ovarian granulosa cells were also measured. Results: In adult goat follicular granulosa cells transfected with pCMV-HA-CDC25C, CDC25C expression increased significantly, which greatly increased the relative gene expression levels of both CDK1 and WEE1. Additionally, progesterone and estradiol levels were increased in goat follicular granulosa cells overexpressing CDC25C. And the cell cycle results showed that transfection of pCMV-HA-CDC25C leads to a higher proportion of cells in S phase compared to the no vector-transfected groups. Conclusions: The results of this study indicated that the overexpression of CDC25C may increase the gene expression levels of both WEE1 and CDK1 in S phase and accelerate the transition of cells from G1 phase to S phase

    The Neural Testbed: Evaluating Joint Predictions

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    Predictive distributions quantify uncertainties ignored by point estimates. This paper introduces The Neural Testbed: an open-source benchmark for controlled and principled evaluation of agents that generate such predictions. Crucially, the testbed assesses agents not only on the quality of their marginal predictions per input, but also on their joint predictions across many inputs. We evaluate a range of agents using a simple neural network data generating process. Our results indicate that some popular Bayesian deep learning agents do not fare well with joint predictions, even when they can produce accurate marginal predictions. We also show that the quality of joint predictions drives performance in downstream decision tasks. We find these results are robust across choice a wide range of generative models, and highlight the practical importance of joint predictions to the community

    Randomized Sharpness-Aware Training for Boosting Computational Efficiency in Deep Learning

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    By driving models to converge to flat minima, sharpness-aware learning algorithms (such as SAM) have shown the power to achieve state-of-the-art performances. However, these algorithms will generally incur one extra forward-backward propagation at each training iteration, which largely burdens the computation especially for scalable models. To this end, we propose a simple yet efficient training scheme, called Randomized Sharpness-Aware Training (RST). Optimizers in RST would perform a Bernoulli trial at each iteration to choose randomly from base algorithms (SGD) and sharpness-aware algorithms (SAM) with a probability arranged by a predefined scheduling function. Due to the mixture of base algorithms, the overall count of propagation pairs could be largely reduced. Also, we give theoretical analysis on the convergence of RST. Then, we empirically study the computation cost and effect of various types of scheduling functions, and give directions on setting appropriate scheduling functions. Further, we extend the RST to a general framework (G-RST), where we can adjust regularization degree on sharpness freely for any scheduling function. We show that G-RST can outperform SAM in most cases while saving 50\% extra computation cost

    nmPLS-Net: Segmenting Pulmonary Lobes Using nmODE

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    Pulmonary lobe segmentation is vital for clinical diagnosis and treatment. Deep neural network-based pulmonary lobe segmentation methods have seen rapid development. However, there are challenges that remain, e.g., pulmonary fissures are always not clear or incomplete, especially in the complex situation of the trilobed right pulmonary, which leads to relatively poor results. To address this issue, this study proposes a novel method, called nmPLS-Net, to segment pulmonary lobes effectively using nmODE. Benefiting from its nonlinear and memory capacity, we construct an encoding network based on nmODE to extract features of the entire lung and dependencies between features. Then, we build a decoding network based on edge segmentation, which segments pulmonary lobes and focuses on effectively detecting pulmonary fissures. The experimental results on two datasets demonstrate that the proposed method achieves accurate pulmonary lobe segmentation

    Functions of SURF4 gene in vivo

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    Biomarkers in aortic dissection: Diagnostic and prognostic value from clinical research

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    Abstract. Aortic dissection is a life-threatening condition for which diagnosis mainly relies on imaging examinations, while reliable biomarkers to detect or monitor are still under investigation. Recent advances in technologies provide an unprecedented opportunity to yield the identification of clinically valuable biomarkers, including proteins, ribonucleic acids (RNAs), and deoxyribonucleic acids (DNAs), for early detection of pathological changes in susceptible patients, rapid diagnosis at the bedside after onset, and a superior therapeutic regimen primarily within the concept of personalized and tailored endovascular therapy for aortic dissection

    Generating universal chimeric antigen receptor expressing cell products from induced pluripotent stem cells: beyond the autologous CAR-T cells

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    Abstract. Adoptive therapeutic immune cells, such as chimeric antigen receptor (CAR)-T cells and natural killer cells, have established a new generation of precision medicine based on which dramatic breakthroughs have been achieved in intractable lymphoma treatments. Currently, well-explored approaches focus on autologous cells due to their low immunogenicity, but they are highly restricted by the high costs, time consumption of processing, and the insufficiency of primary cells in some patients. Induced pluripotent stem cells (iPSCs) are cell sources that can theoretically produce indefinite well-differentiated immune cells. Based on the above facts, it may be reasonable to combine the iPSC technology and the CAR design to produce a series of highly controllable and economical “live” drugs. Manufacturing hypoimmunogenic iPSCs by inactivation or over-expression at the genetic level and then arming the derived cells with CAR have emerged as a form of “off-the-shelf” strategy to eliminate tumor cells efficiently and safely in a broader range of patients. This review describes the reasonability, feasibility, superiority, and drawbacks of such approaches, summarizes the current practices and relevant research progress, and provides insights into the possible new paths for personalized cell-based therapies

    Targeted therapeutic strategies for melanoma

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    Abstract. Melanoma accounts for a small proportion of skin cancers diagnosed each year, but it has a high degree of malignancy and rapid progression, resulting in a short survival period for patients. The incidence of melanoma continues to rise, and now melanoma accounts for 1.7% of cancer diagnoses worldwide and is the fifth most common cancer in the United States. With the development of high-throughput sequencing technologies, the understanding of the pathophysiology of melanoma had also been improved. The most common activating mutations in melanoma cells are BRAF, NRAS, and KIT mutations, which disrupt cell signaling pathways related to tumor proliferation. The progress has led to the emergence of molecularly targeted drugs, which extends the survival of patients with advanced melanoma. A large number of clinical trials have been conducted to confirm that targeted therapy for patients with advanced melanoma can improve progression-free survival and overall survival, and for stage III patients after radical tumor resection targeted therapy can reduce the recurrence of melanoma. Patients who were originally stage III or IV inoperable have the opportunity to achieve tumor radical resection after targeted therapy. This article reviewed the clinical trial data and summarized the clinical benefits and limitations of these therapies
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