The anticancer effects of biodegradable nanomagnetic dual natural components on the leptin gene expression in lung cancer.
In this study, evaluated CUR-SIL dual drug-loaded poly (ɛ-caprolactone) [PCL]-co-poly ethylene glycol (PEG) magnetic nanoparticles (MNPs) were used to determine the inhibitory effect on LEP gene expression. The physicochemical properties of free and CUR-SIL-loaded PCL-PEG were fully characterized. The cytotoxic effect of CUR-SIL-loaded PCL-PEG magnetic nanoparticles was determined by MTT assay. Afterward, the inhibition of LEP gene expression was specified through real-time PCR. Results indicated that CUR-SIL cytotoxicity is time- and dose-dependent. CUR-SIL loaded MNPs showed the IC50 limit in lower concentrations in comparison to net CUR-SIL. CUR-SIL loaded MNPs reduced LEP expression more than net CUR-SIL. These results revealed the possibilities of CUR-SIL-loaded MNPs as a natural and effective antitumor drug delivery system to fight lung tumors. PMID: 26593227 [PubMed - as supplied by publisher]
Radiology reports created with help from artificial intelligence (AI) are more...Read more on AuntMinnie.comRelated Reading: C-MIMI: AI peer review can spot missed lung cancer C-MIMI: Use of AI in radiology is evolving SNMMI 2020: AI analysis of PET could help classify prostate cancer SIIM 2020: Human element shouldn't be neglected with AI AI Metrics joins forces with Imaging Biometrics
In conclusion, the developed chip has a potential in lung tumor genotyping and therapy monitoring for precision medicine, even other tumors. PMID: 32928434 [PubMed - in process]
Cancers, Vol. 12, Pages 2654: Analysis of Bone Scans in Various Tumor Entities Using a Deep-Learning-Based Artificial Neural Network Algorithm—Evaluation of Diagnostic Performance Cancers doi: 10.3390/cancers12092654 Authors: Jan Wuestemann Sebastian Hupfeld Dennis Kupitz Philipp Genseke Simone Schenke Maciej Pech Michael C. Kreissl Oliver S. Grosser The bone scan index (BSI), initially introduced for metastatic prostate cancer, quantifies the osseous tumor load from planar bone scans. Following the basic idea of radiomics, this method incorporates specific deep-learning techniques (artificial n...
A peer review process that's driven by artificial intelligence (AI) can identify...Read more on AuntMinnie.comRelated Reading: C-MIMI: Use of AI in radiology is evolving Medical imaging AI market projected for strong growth Can AI diagnose heart failure on chest x-rays? AI aids classification of indeterminate lung nodules Ferrum deploys AI platform for lung cancer detection
Artificial intelligence tools being developed at Case Western Reserve University in Cleveland to fight lung cancer are a step closer to human clinical trials, thanks to recent agreements with two pharmaceutical companies.
Publication date: August 2020Source: Artificial Intelligence in Medicine, Volume 108Author(s): Anna Meldo, Lev Utkin, Maxim Kovalev, Ernest Kasimov
In conclusion, using a large cohort with rich health and DNA methylation data, we provide the first comparison of six major epigenetic measures of biological ageing with respect to their associations with leading causes of mortality and disease burden. DNAm GrimAge outperformed the other measures in its associations with disease data and associated clinical traits. This may suggest that predicting mortality, rather than age or homeostatic characteristics, may be more informative for common disease prediction. Thus, proteomic-based methods (as utilised by DNAm GrimAge) using large, physiologically diverse protein sets for p...
PMID: 32866415 [PubMed - as supplied by publisher]
Publication date: Available online 28 August 2020Source: Artificial Intelligence in MedicineAuthor(s): Anna Meldo, Lev Utkin, Maxim Kovalev, Ernest Kasimov
Conclusion For patients with central NSCLC with extensive tumor invasion, thus inability to tolerate sleeve resection or pneumonectomy, autologous lung transplantation can preserve lung function to the greatest extent with a complete tumor resection and improve postoperative quality of life. DOI: 10.3779/j.issn.1009-3419.2020.103.12