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Doxorubicin (DOX) is the most commonly used anticancer drug; however, it has limited use because prolonged administration may result in severe cardiotoxicity. Simvastatin (SIM), generally prescribed for hypercholesterolaemia, has also shown salubrious results in the monotherapy or combinational drug therapy of different cancers in various models. Nanoparticle drug delivery systems are a novel way of improving therapeutics and also improving the absorption and specificity of drugs towards tumour cells. In this study, we exploited this technology to increase drug specificity and minimize imminent adverse effects. In this study, the antitumour activity of the combination formulas of DOX and SIM, either loaded in water (DOX‐SIM‐Solution) or nanoemulsions (NEs) (DOX‐SIM‐NE), was evaluated in a Swiss albino mouse model of Ehrlich ascites carcinoma. The anticancer effect was assessed by quantifying the change in body weight, mean survival time, and percent increase in lifespan (%ILS), determining haematological and serum biochemical parameters (liver function test, kidney function test and lipid profile parameters) as well as studying the histopathological alterations in liver tissues. We observed a clear increase in %ILS of the DOX‐SIM‐Solution group (265.30) that was double the %ILS of the DOX‐SIM‐NE group (134.70). However, DOX‐SIM‐NE had a non‐toxic effect on the haematological parameters, whereas DOX‐SIM‐Solution increased the levels of haemoglobin and lymphocytes. Furthermore, the encapsulation of SIM and DOX into NEs improved the levels of all serum biochemical parameters compared to the DOX‐SIM‐Solution. A reduction in the side effects of DOX‐SIM‐NE on the liver was also established using light microscopy, which revealed that the morphologies of the hepatocytes of the mice were less affected by administration of the DOX‐SIM‐NE treatment than with the DOX‐SIM‐Solution treatment. The study showed that incorporating SIM into the DOX‐loaded‐NE formulation remarkably improved its efficiency and simultaneously reduced its adverse effects.  相似文献   
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Background

Recently developed convolutional neural network (CNN) models determine bone age more accurately than radiologists.

Objective

The purpose of this study was to determine whether a CNN and radiologists can accurately predict bone age from radiographs using only the index finger rather than the whole hand.

Materials and methods

We used a public anonymized dataset provided by the Radiological Society of North America (RSNA) pediatric bone age challenge. The dataset contains 12,611 hand radiographs for training and 200 radiographs for testing. The index finger was cropped from these images to create a second dataset. Separate CNN models were trained using the whole-hand radiographs and the cropped second-digit dataset using the consensus ground truth provided by the RSNA bone age challenge. Bone age determination using both models was compared with ground truth as provided by the RSNA dataset. Separately, three pediatric radiologists determined bone age from the whole-hand and index-finger radiographs, and the consensus was compared to the ground truth and CNN-model-determined bone ages.

Results

The mean absolute difference between the ground truth and CNN bone age for whole-hand and index-finger was similar (4.7 months vs. 5.1 months, P=0.14), and both values were significantly smaller than that for radiologist bone age determination from the single-finger radiographs (8.0 months, P<0.0001).

Conclusion

CNN-model-determined bone ages from index-finger radiographs are similar to whole-hand bone age interpreted by radiologists in the dataset, as well as a model trained on the whole-hand radiograph. In addition, the index-finger model performed better than the ground truth compared to subspecialty trained pediatric radiologists also using only the index finger to determine bone age. The radiologist interpreting bone age can use the second digit as a reliable starting point in their search pattern.

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