Targeting efficiency and biodistribution of biotinylated-EGF-conjugated gelatin nanoparticles administered via aerosol delivery in nude mice with lung cancer

Nanomedicine for the management of lung and blood diseases

Nanotechnology provides a broad range of opportunities to develop new solutions for clinical problems. For the pulmonary field, nanotechnology promises better delivery of drugs and nucleic acid-based therapeutics to disease sites. Administration of therapeutics via inhalation provides the opportunity for direct delivery to the lung epithelium, the lining of the respiratory tract. By appropriate selection of particle size, deep lung delivery can be obtained with control of phagocytic uptake, the removal of particles by resident macrophages. Nanotechnology can also help in pulmonary therapies administered by intravenous and oral routes through targeting specific cell types and controlling bioavailability and release kinetics. In the hematology field, nanotechnology can counter multiple drug resistance in leukemia by blocking drug efflux from cancer cells, and provide effective delivery of siRNA into lymphocytes to block apoptosis in sepsis. Controlling the surface properties of materials on devices such as valves and stents promises improved biocompatibility by inhibition of thrombosis, the formation of blood clots, and regulating cell adhesion and activation. Nanoparticle-based thrombolytic agents have the potential to improve the effectiveness of clot removal. Treatment of both lung and blood diseases is also likely to benefit from nano-scaffold-based methods for controlling the differentiation and proliferation of stem and progenitor cells.

Multilevel binomial logistic prediction model for malignant pulmonary nodules based on texture features of CT image

PURPOSE

To introduce multilevel binomial logistic prediction model-based computer-aided diagnostic (CAD) method of small solitary pulmonary nodules (SPNs) diagnosis by combining patient and image characteristics by textural features of CT image.

MATERIALS AND METHODS

Describe fourteen gray level co-occurrence matrix textural features obtained from 2171 benign and malignant small solitary pulmonary nodules, which belongs to 185 patients. Multilevel binomial logistic model is applied to gain these initial insights.

RESULTS

Five texture features, including Inertia, Entropy, Correlation, Difference-mean, Sum-Entropy, and age of patients own aggregating character on patient-level, which are statistically different (P<0.05) between benign and malignant small solitary pulmonary nodules.

CONCLUSION

Some gray level co-occurrence matrix textural features are efficiently descriptive features of CT image of small solitary pulmonary nodules, which can profit diagnosis of earlier period lung cancer if combined patient-level characteristics to some extent.