Dual targeting of Polo-like kinase 1 and baculoviral inhibitor of apoptosis repeat-containing 5 in TP53-mutated hepatocellular carcinoma

BACKGROUND Hepatocellular carcinoma(HCC),often diagnosed at advanced stages without curative therapies,is the fifth most common malignant cancer and the second leading cause of cancer-related mortality.Polo-like kinase 1(PLK1)is activated in the late G2 phase of the cell cycle and is required for entry to mitosis.Interestingly,PLK1 is overexpressed in many HCC patients and is highly associated with poor clinical outcome.Baculoviral inhibitor of apoptosis repeatcontaining 5(BIRC5)is also highly overexpressed in HCC and plays key roles in this malignancy.AIM To determine the expression patterns of PLK1 and BIRC5,as well as their correlation with p53 mutation status and patient clinical outcome.METHODS The expression patterns of PLK1 and BIRC5,and their correlation with p53 mutation status or patient clinical outcome were analyzed using a TCGA HCC dataset.Cell viability,cell apoptosis,and cell cycle arrest assays were conducted to investigate the efficacy of the PLK1 inhibitors volasertib and GSK461364 and the BIRC5 inhibitor YM155,alone or in combination.The in vivo efficacy of volasertib and YM155,alone or in combination,was assessed in p53-mutated Huh7-derived xenograft models in immune-deficient NSIG mice.RESULTS Our bioinformatics analysis using a TCGA HCC dataset revealed that PLK1 and BIRC5 were overexpressed in the same patient subset and their expression was highly correlated.The overexpression of both PLK1 and BIRC5 was more frequently detected in HCC with p53 mutations.High PLK1 or BIRC5 expression significantly correlated with poor clinical outcome.PLK1 inhibitors(volasertib and GSK461364)or a BIRC5 inhibitor(YM155)selectively targeted Huh7 cells with mutated p53,but not HepG2 cells with wild-type p53.The combination treatment of volasertib and YM155 synergistically inhibited the viability of Huh7 cells via apoptotic pathway.The efficacy of volasertib and YM155,alone or in combination,was validated in vivo in a Huh7-derived xenograft model.CONCLUSION PLK1 and BIRC5 are highly co-expressed in p53-mutated HCC and inhibition of both PLK1 and BIRC5 synergistically compromises the viability of p53-mutated HCC cells in vitro and in vivo.

The Effect of Bio-Oil on High-Temperature Performance of Bio-Oil Recycled Asphalt Binders

Bio-oil recycled asphalt binders in road engineering can help solve the problem of oil shortage and reduce the environmental pollution and sustainability.This paper investigated the road performance of the aged asphalt binder by adding bio-oil so that the aged asphalt binder could be reused to reach purpose of reuse.The residual soybean oil was selected as rejuvenator and blended with aged asphalt binder at 0%,2%,4%,and 6%,respectively.The results showed that bio-oil increased the penetration of aged asphalt binder,the penetration of bio-oil recycled asphalt binder with a bio-oil content of 6%reached the standard of 70#matrix asphalt binder.The addition of bio-oil reduced the viscosity,mixing and compaction temperature of aged asphalt binder.As a common knowledge,bio-oil helps to increase the lightweight components of the aged asphalt binder,which diminishes the high-temperature rutting resistance of bio-oil recycled asphalt binders.The high-temperature deformation resistance of bio-oil recycled asphalt binders had not decreased linearly with the bio-oil dosage.Meanwhile,the hightemperature performance of the bio-oil recycled asphalt binder with a 6%bio-oil was superior to matrix asphalt binder.Bio-oil increased the light components of the aged asphalt binder,thus reducing the high-temperature rheological properties of bio-oil recycled asphalt binders as the bio-oil dosage increases.The above test results showed that the bio-oil could restore the aged asphalt binder to the initial level to reach the reuse target.

Underlying mechanism of hemodynamics and intracranial aneurysm

In modern society,subarachnoid hemorrhage,mostly caused by intracranial aneurysm rupture,is accompanied by high disability and mortality rate,which has become a major threat to human health.Till now,the etiology of intracranial aneurysm has not been entirely clarified.In recent years,more and more studies focus on the relationship between hemodynamics and intracranial aneurysm.Under the physiological condition,the mechanical force produced by the stable blood flow in the blood vessels keeps balance with the structure of the blood vessels.When the blood vessels are stimulated by the continuous abnormal blood flow,the functional structure of the blood vessels changes,which becomes the pathophysiological basis of the inflammation and atherosclerosis of the blood vessels and further promotes the occurrence and development of the intracranial aneurysm.This review will focus on the relationship between hemodynamics and intracranial aneurysms,will discuss the mechanism of occurrence and development of intracranial aneurysms,and will provide a new perspective for the research and treatment of intracranial aneurysms.

Constructing public health evidence knowledge graph for decision-making support from COVID-19 literature of modelling study

The needs of mitigating COVID-19 epidemic prompt policymakers to make public health-related decision under the guidelines of science.Tremendous unstructured COVID-19 publications make it challenging for policymakers to obtain relevant evidence.Knowledge graphs(KGs)can formalize unstructured knowledge into structured form and have been used in supporting decision-making recently.Here,we introduce a novel framework that can ex-tract the COVID-19 public health evidence knowledge graph(CPHE-KG)from papers relating to a modelling study.We screen out a corpus of 3096 COVID-19 modelling study papers by performing a literature assessment process.We define a novel annotation schema to construct the COVID-19 modelling study-related IE dataset(CPHIE).We also propose a novel multi-tasks document-level information extraction model SS-DYGIE++based on the dataset.Leveraging the model on the new corpus,we construct CPHE-KG containing 60,967 entities and 51,140 rela-tions.Finally,we seek to apply our KG to support evidence querying and evidence mapping visualization.Our SS-DYGIE++(SpanBERT)model has achieved a F1 score of 0.77 and 0.55 respectively in document-level entity recognition and coreference resolution tasks.It has also shown high performance in the relation identification task.With evidence querying,our KG can present the dynamic transmissions of COVID-19 pandemic in different countries and regions.The evidence mapping of our KG can show the impacts of variable non-pharmacological interventions to COVID-19 pandemic.Analysis demonstrates the quality of our KG and shows that it has the potential to support COVID-19 policy making in public health.

Adaptively temporal graph convolution model for epidemic prediction of multiple age groups

Introduction:Multivariate time series prediction of infectious diseases is significant to public health,and the deep learning method has attracted increasing attention in this research field.Material and methods:An adaptively temporal graph convolution(ATGCN)model,which leams the contact patterns of multiple age groups in a graph-based approach,was proposed for COVID-19 and influenza prediction.We compared ATGCN with autoregressive models,deep sequence learning models,and experience-based ATGCN models in short-term and long-term prediction tasks.Results:Results showed that the ATGCN model performed better than the autoregressive models and the deep sequence learning models on two datasets in both short-term(12.5%and 10%improvements on RMSE)and longterm(12.4%and 5%improvements on RMSE)prediction tasks.And the RMSE of ATGCN predictions fluctuated least in different age groups of COVID-19(0.029±0.003)and influenza(0.059±0.008).Compared with the Ones-ATGCN model or the Pre-ATGCN model,the ATGCN model was more robust in performance,with RMSE of 0.0293 and 0.06 on two datasets when horizon is one.Discussion:Our research indicates a broad application prospect of deep learning in the field of infectious disease prediction.Transmission characteristics and domain knowledge of infectious diseases should be further applied to the design of deep learning models and feature selection.Conclusion:The ATGCN model addressed the multivariate time series forecasting in a graph-based deep learning approach and achieved robust prediction on the confirmed cases of multiple age groups,indicating its great potentials for exploring the implicit interactions of multivariate variables.