66 research outputs found
Functional role of MicroRNA/PI3K/AKT axis in osteosarcoma
Osteosarcoma (OS) is a primary malignant bone tumor that occurs in children and adolescents, and the PI3K/AKT pathway is overactivated in most OS patients. MicroRNAs (miRNAs) are highly conserved endogenous non-protein-coding RNAs that can regulate gene expression by repressing mRNA translation or degrading mRNA. MiRNAs are enriched in the PI3K/AKT pathway, and aberrant PI3K/AKT pathway activation is involved in the development of osteosarcoma. There is increasing evidence that miRNAs can regulate the biological functions of cells by regulating the PI3K/AKT pathway. MiRNA/PI3K/AKT axis can regulate the expression of osteosarcoma-related genes and then regulate cancer progression. MiRNA expression associated with PI3K/AKT pathway is also clearly associated with many clinical features. In addition, PI3K/AKT pathway-associated miRNAs are potential biomarkers for osteosarcoma diagnosis, treatment and prognostic assessment. This article reviews recent research advances on the role and clinical application of PI3K/AKT pathway and miRNA/PI3K/AKT axis in the development of osteosarcoma
SARS Pandemic Exposure Impaired Early Childhood Development in China
Social and mental stressors associated with the pandemic of a novel infectious disease, e.g., COVID-19 or SARS may promote long-term effects on child development. However, reports aimed at identifying the relationship between pandemics and child health are limited. A retrospective study was conducted to associate the SARS pandemic in 2003 with development milestones or physical examinations among longitudinal measurements of 14,647 children. Experiencing SARS during childhood was associated with delayed milestones, with hazard ratios of 3.17 (95% confidence intervals CI: 2.71, 3.70), 3.98 (3.50, 4.53), 4.96 (4.48, 5.49), or 5.57 (5.00, 6.20) for walking independently, saying a complete sentence, counting 0–10, and undressing him/herself for urination, respectively. These results suggest relevant impacts from COVID-19 on child development should be investigated
ZS-SRT: An Efficient Zero-Shot Super-Resolution Training Method for Neural Radiance Fields
Neural Radiance Fields (NeRF) have achieved great success in the task of
synthesizing novel views that preserve the same resolution as the training
views. However, it is challenging for NeRF to synthesize high-quality
high-resolution novel views with low-resolution training data. To solve this
problem, we propose a zero-shot super-resolution training framework for NeRF.
This framework aims to guide the NeRF model to synthesize high-resolution novel
views via single-scene internal learning rather than requiring any external
high-resolution training data. Our approach consists of two stages. First, we
learn a scene-specific degradation mapping by performing internal learning on a
pretrained low-resolution coarse NeRF. Second, we optimize a super-resolution
fine NeRF by conducting inverse rendering with our mapping function so as to
backpropagate the gradients from low-resolution 2D space into the
super-resolution 3D sampling space. Then, we further introduce a temporal
ensemble strategy in the inference phase to compensate for the scene estimation
errors. Our method is featured on two points: (1) it does not consume
high-resolution views or additional scene data to train super-resolution NeRF;
(2) it can speed up the training process by adopting a coarse-to-fine strategy.
By conducting extensive experiments on public datasets, we have qualitatively
and quantitatively demonstrated the effectiveness of our method
Validation of the GALAD model and establishment of a new model for HCC detection in Chinese patients
BackgroundGALAD model is a statistical model used to estimate the possibility of hepatocellular carcinoma (HCC) in patients with chronic liver disease. Many studies with other ethnic populations have shown that it has high sensitivity and specificity. However, whether this model can be used for Chinese patients remains to be determined. Our study was conducted to verify the performance of GALAD model in a Chinese cohort and construct a new model that is more appropriately for Chinese populations.MethodsThere are total 512 patients enrolled in the study, which can be divided into training set and validation set. 80 patients with primary liver cancer, 139 patients with chronic liver disease and 87 healthy people were included in the training set. Through the ROC(receiver operating characteristic) curve analysis, the recognition performance of GALAD model for liver cancer was evaluated, and the GAADPB model was established by logistic regression, including gender, age, AFP, DCP, total protein, and total bilirubin. The validation set (75 HCC patients and 130 CLD patients) was used to evaluate the performance of the GAADPB model.ResultThe GALAD and GAADPB achieved excellent performance (area under the receiver operating characteristic curve [AUC], 0.925, 0.945), and were better than GAAP, Doylestown, BALAD-2, aMAP, AFP, AFP-L3%, DCP and combined detection of AFP, AFP-L3 and DCP (AUCs: 0.894, 0.870, 0.648, 0.545, 0.879, 0.782, 0.820 and 0.911) for detecting HCC from CLD in the training set. As for early stage of HCC (BCLC 0/A), GAADPB had the best sensitivity compared to GALAD, ADP and DCP (56.3%, 53.1%, 40.6%, 50.0%). GAADPB had better performance than GALAD in the test set, AUC (0.896 vs 0.888).ConclusionsThe new GAADPB model was powerful and stable, with better performance than the GALAD and other models, and it also was promising in the area of HCC prognosis prediction. Further study on the real-world HCC patients in China are needed
Digital Twin Brain: a simulation and assimilation platform for whole human brain
In this work, we present a computing platform named digital twin brain (DTB)
that can simulate spiking neuronal networks of the whole human brain scale and
more importantly, a personalized biological brain structure. In comparison to
most brain simulations with a homogeneous global structure, we highlight that
the sparseness, couplingness and heterogeneity in the sMRI, DTI and PET data of
the brain has an essential impact on the efficiency of brain simulation, which
is proved from the scaling experiments that the DTB of human brain simulation
is communication-intensive and memory-access intensive computing systems rather
than computation-intensive. We utilize a number of optimization techniques to
balance and integrate the computation loads and communication traffics from the
heterogeneous biological structure to the general GPU-based HPC and achieve
leading simulation performance for the whole human brain-scaled spiking
neuronal networks. On the other hand, the biological structure, equipped with a
mesoscopic data assimilation, enables the DTB to investigate brain cognitive
function by a reverse-engineering method, which is demonstrated by a digital
experiment of visual evaluation on the DTB. Furthermore, we believe that the
developing DTB will be a promising powerful platform for a large of research
orients including brain-inspiredintelligence, rain disease medicine and
brain-machine interface.Comment: 12 pages, 11 figure
Search for light dark matter from atmosphere in PandaX-4T
We report a search for light dark matter produced through the cascading decay
of mesons, which are created as a result of inelastic collisions between
cosmic rays and Earth's atmosphere. We introduce a new and general framework,
publicly accessible, designed to address boosted dark matter specifically, with
which a full and dedicated simulation including both elastic and quasi-elastic
processes of Earth attenuation effect on the dark matter particles arriving at
the detector is performed. In the PandaX-4T commissioning data of 0.63
tonneyear exposure, no significant excess over background is observed.
The first constraints on the interaction between light dark matter generated in
the atmosphere and nucleus through a light scalar mediator are obtained. The
lowest excluded cross-section is set at for
dark matter mass of MeV and mediator mass of 300 MeV. The
lowest upper limit of to dark matter decay branching ratio is
A Search for Light Fermionic Dark Matter Absorption on Electrons in PandaX-4T
We report a search on a sub-MeV fermionic dark matter absorbed by electrons
with an outgoing active neutrino using the 0.63 tonne-year exposure collected
by PandaX-4T liquid xenon experiment. No significant signals are observed over
the expected background. The data are interpreted into limits to the effective
couplings between such dark matter and electrons. For axial-vector or vector
interactions, our sensitivity is competitive in comparison to existing
astrophysical bounds on the decay of such dark matter into photon final states.
In particular, we present the first direct detection limits for an axial-vector
(vector) interaction which are the strongest in the mass range from 25 to 45
(35 to 50) keV/c
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