RESUMEN
Hepatocellular carcinoma （HCC） is one of the common malignant tumors of the digestive tract and seriously threatens the life of patients due to a high incidence rate， a high degree of malignancy， strong invasion and metastasis， and poor prognosis. At present， the main methods for the prevention and treatment of HCC include drugs， surgery， and interventional treatment， but all of these methods have certain adverse reactions and side effects. As an important intracellular signal transduction pathway in the human body， the JAK/STAT signaling pathway mainly exerts an anti-HCC effect by regulating cell invasion， metastasis， proliferation， growth， apoptosis， autophagy， angiogenesis， inflammation/immune response， iron metabolism， and drug resistance. Therefore， targeting the JAK/STAT signaling pathway plays an important role in the prevention and treatment of the development and progression of HCC. Traditional Chinese medicine has attracted wide attention due to its advantages of multiple targets， pathways， components， and levels in the treatment of HCC， and many cell or animal experiments on traditional Chinese medicine in the treatment of HCC have shown that the JAK/STAT signaling pathway is an important target for the prevention and treatment of HCC， with the effects of improving liver function， reducing HCC recurrence， and improving immunity. Based on this， this article analyzes the mechanism of action of the JAK/STAT signaling pathway in HCC， as well as the intervention effect of traditional Chinese medicine monomers， traditional Chinese medicine extracts， and traditional Chinese medicine compounds on the JAK/STAT signaling pathway， in order to provide theoretical basis and reference for the prevention and treatment of HCC and the research and development of new traditional Chinese medicine drugs.
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Hepatic fibrosis is a pathological reparative response of the liver to chronic injury and a crucial step in the progression of chronic liver disease， characterized mainly by the activation of hepatic stellate cells and diffuse deposition of extracellular matrix. Currently， there is no ideal specific drug for the treatment of liver fibrosis in clinical practice. In recent years， with the development and progress of traditional Chinese medicine （TCM） in the treatment of liver fibrosis， TCM has been widely recognized for its significant therapeutic effect and fewer adverse reactions. The phosphatidylinositol 3-kinase （PI3K）/protein kinase B （Akt） signaling pathway is an important pathway that affects the formation and development of liver fibrosis. It mainly plays a role in liver fibrosis by inhibiting the activation and proliferation of hepatic stellate cells， promoting their apoptosis， reducing oxidative stress in liver cells， decreasing the deposition of extracellular matrix， and enhancing liver cell autophagy. This article summarized the mechanisms by which Chinese medicinal monomers regulated the PI3K/Akt pathway to exert their effects on liver fibrosis and their synergistic effects with other signaling pathways， providing a theoretical basis and references for the development of new drugs for the treatment of liver fibrosis with TCM.
RESUMEN
The pathogenesis of nonalcoholic fatty liver disease (NAFLD) remains unclear, and currently no effective drugs have been approved for the treatment of NAFLD. Polygonum cuspidatum is a natural traditional Chinese medicine with a long history of application, and studies have shown that it plays an important role in the treatment of NAFLD. This article summarizes related research findings in the active components of Polygonum cuspidatum applied in the treatment of NAFLD, and it is found that the active components of Polygonum cuspidatum can improve insulin resistance, exert an anti-oxidative stress effect, regulate lipid metabolism, improve endoplasmic reticulum stress, and alleviate inflammatory infiltration by regulating the signaling pathways including Nrf2, AMPK, NF-κB, SIRT1, and PPARα, thereby exerting a preventive and therapeutic effect on NAFLD, so as to provide a basis and ideas for developing drugs for NAFLD and exploring related mechanisms.
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As the Covid-19 pandemic soars around the world, there is urgent need to forecast the expected number of cases worldwide and the length of the pandemic before receding and implement public health interventions for significantly stopping the spread of Covid-19. Widely used statistical and computer methods for modeling and forecasting the trajectory of Covid-19 are epidemiological models. Although these epidemiological models are useful for estimating the dynamics of transmission of epidemics, their prediction accuracies are quite low. Alternative to the epidemiological models, the reinforcement learning (RL) and causal inference emerge as a powerful tool to select optimal interventions for worldwide containment of Covid-19. Therefore, we formulated real-time forecasting and evaluation of multiple public health intervention problems into off-policy evaluation (OPE) and counterfactual outcome forecasting problems and integrated RL and recurrent neural network (RNN) for exploring public health intervention strategies to slow down the spread of Covid-19 worldwide, given the historical data that may have been generated by different public health intervention policies. We applied the developed methods to real data collected from January 22, 2020 to July 30, 2020 for real-time forecasting the confirmed cases of Covid-19 across the world. We observed that the number of new cases of Covid-19 worldwide reached a peak (407,205) on July 24, 2020 and forecasted that the number of laboratory-confirmed cumulative cases of Covid-19 will pass 20 million as of August 22, 2020. The results showed that outbreak of Covid-19 worldwide has peaked and is on the decline
RESUMEN
As of May 1, 2020, the number of cases of Covid-19 in the US passed 1,062,446, interventions to slow down the spread of Covid-19 curtailed most social activities. Meanwhile, an economic crisis and resistance to the strict intervention measures are rising. Some researchers proposed intermittent social distancing that may drive the outbreak of Covid-19 into 2022. Questions arise about whether we should maintain or relax quarantine measures. We developed novel artificial intelligence and causal inference integrated methods for real-time prediction and control of nonlinear epidemic systems. We estimated that the peak time of the Covid-19 in the US would be April 24, 2020 and its outbreak in the US will be over by the end of July and reach 1,551,901 cases. We evaluated the impact of relaxing the current interventions for reopening economy on the spread of Covid-19. We provide tools for balancing the risks of workers and reopening economy.
RESUMEN
As the Covid-19 pandemic soars around the world, there is urgent need to forecast the number of cases worldwide at its peak, the length of the pandemic before receding and implement public health interventions to significantly stop the spread of Covid-19. Widely used statistical and computer methods for modeling and forecasting the trajectory of Covid-19 are epidemiological models. Although these epidemiological models are useful for estimating the dynamics of transmission od epidemics, their prediction accuracies are quite low. To overcome this limitation, we formulated the real-time forecasting and evaluating multiple public health intervention problem into forecasting treatment response problem and developed recurrent neural network (RNN) for modeling the transmission dynamics of the epidemics and Counterfactual-RNN (CRNN) for evaluating and exploring public health intervention strategies to slow down the spread of Covid-19 worldwide. We applied the developed methods to the real data collected from January 22, 2020 to May 8, 2020 for real-time forecasting the confirmed cases of Covid-19 across the world.
RESUMEN
As COVID-19 evolves rapidly, the issues the governments of affected countries facing are whether and when to take public health interventions and what levels of strictness of these interventions should be, as well as when the COVID-19 spread reaches the stopping point after interventions are taken. To help governments with policy-making, we developed modified auto-encoders (MAE) method to forecast spread trajectory of Covid-19 of countries affected, under different levels and timing of intervention strategies. Our analysis showed public health interventions should be executed as soon as possible. Delaying intervention 4 weeks after March 8, 2020 would cause the maximum number of cumulative cases of death increase from 7,174 to 133,608 and the ending points of the epidemic postponed from Jun 25 to Aug 22.
