RESUMO
Intensive cancer treatment with drug combination is widely exploited in the clinic but suffers from inconsistent pharmacokinetics among different therapeutic agents.To overcome it,the emerging nanomedicine offers an unparalleled opportunity for encapsulating multiple drugs in a nano-carrier.Herein,a two-step super-assembled strategy was performed to unify the pharmacokinetics of a pep-tide and a small molecular compound.In this proof-of-concept study,the bioinformatics analysis firstly revealed the potential synergies towards hepatoma therapy for the associative inhibition of exportin 1(XPO1)and ataxia telangiectasia mutated-Rad3-related(ATR),and then a super-assembled nano-pill(gold nano drug carrier loaded AZD6738 and 97-110 amino acids of apoptin(AP)(AA@G))was con-structed through camouflaging AZD6738(ATR small-molecule inhibitor)-binding human serum albumin onto the AP-Au supramolecular nanoparticle.As expected,both in vitro and in vivo experiment results verified that the AA@G possessed extraordinary biocompatibility and enhanced therapeutic effect through inducing cell cycle arrest,promoting DNA damage and inhibiting DNA repair of hepatoma cell.This work not only provides a co-delivery strategy for intensive liver cancer treatment with the clinical translational potential,but develops a common approach to unify the pharmacokinetics of peptide and small-molecular compounds,thereby extending the scope of drugs for developing the advanced com-bination therapy.
RESUMO
BackgroundThe outbreak of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-COV-2) has caused more than 2.5 million cases of Corona Virus Disease (COVID-19) in the world so far, with that number continuing to grow. To control the spread of the disease, screening large numbers of suspected cases for appropriate quarantine and treatment is a priority. Pathogenic laboratory testing is the gold standard but is time-consuming with significant false negative results. Therefore, alternative diagnostic methods are urgently needed to combat the disease. Based on COVID-19 radiographical changes in CT images, we hypothesized that Artificial Intelligences deep learning methods might be able to extract COVID-19s specific graphical features and provide a clinical diagnosis ahead of the pathogenic test, thus saving critical time for disease control. Methods and FindingsWe collected 1,065 CT images of pathogen-confirmed COVID-19 cases (325 images) along with those previously diagnosed with typical viral pneumonia (740 images). We modified the Inception transfer-learning model to establish the algorithm, followed by internal and external validation. The internal validation achieved a total accuracy of 89.5% with specificity of 0.88 and sensitivity of 0.87. The external testing dataset showed a total accuracy of 79.3% with specificity of 0.83 and sensitivity of 0.67. In addition, in 54 COVID-19 images that first two nucleic acid test results were negative, 46 were predicted as COVID-19 positive by the algorithm, with the accuracy of 85.2%. ConclusionThese results demonstrate the proof-of-principle for using artificial intelligence to extract radiological features for timely and accurate COVID-19 diagnosis. Author summaryTo control the spread of the COVID-19, screening large numbers of suspected cases for appropriate quarantine and treatment measures is a priority. Pathogenic laboratory testing is the gold standard but is time-consuming with significant false negative results. Therefore, alternative diagnostic methods are urgently needed to combat the disease. We hypothesized that Artificial Intelligences deep learning methods might be able to extract COVID-19s specific graphical features and provide a clinical diagnosis ahead of the pathogenic test, thus saving critical time. We collected 1,065 CT images of pathogen-confirmed COVID-19 cases along with those previously diagnosed with typical viral pneumonia. We modified the Inception transfer-learning model to establish the algorithm. The internal validation achieved a total accuracy of 89.5% with specificity of 0.88 and sensitivity of 0.87. The external testing dataset showed a total accuracy of 79.3% with specificity of 0.83 and sensitivity of 0.67. In addition, in 54 COVID-19 images that first two nucleic acid test results were negative, 46 were predicted as COVID-19 positive by the algorithm, with the accuracy of 85.2%. Our study represents the first study to apply artificial intelligence to CT images for effectively screening for COVID-19.
