Using Deep-Learning-Based Artificial Intelligence Technique to Automatically Evaluate the Collateral Status of Multiphase CTA in Acute Ischemic Stroke.

BACKGROUND: Collateral status is an important predictor for the outcome of acute ischemic stroke with large vessel occlusion. Multiphase computed-tomography angiography (mCTA) is useful to evaluate the collateral status, but visual evaluation of this examination is time-consuming. This study aims to use an artificial intelligence (AI) technique to develop an automatic AI prediction model for the collateral status of mCTA. METHODS: This retrospective study enrolled subjects with acute ischemic stroke receiving endovascular thrombectomy between January 2015 and June 2020 in a tertiary referral hospital. The demographic data and images of mCTA were collected. The collateral status of all mCTA was visually evaluated. Images at the basal ganglion and supraganglion levels of mCTA were selected to produce AI models using the convolutional neural network (CNN) technique to automatically predict the collateral status of mCTA. RESULTS: A total of 82 subjects were enrolled. There were 57 cases randomly selected for the training group and 25 cases for the validation group. In the training group, there were 40 cases with a positive collateral result (good or intermediate) and 17 cases with a negative collateral result (poor). In the validation group, there were 21 cases with a positive collateral result and 4 cases with a negative collateral result. During training for the CNN prediction model, the accuracy of the training group could reach 0.999 ± 0.015, whereas the prediction model had a performance of 0.746 ± 0.008 accuracy on the validation group. The area under the ROC curve was 0.7. CONCLUSIONS: This study suggests that the application of the AI model derived from mCTA images to automatically evaluate the collateral status is feasible.

Prediction of Cognitive Degeneration in Parkinson's Disease Patients Using a Machine Learning Method.

This study developed a predictive model for cognitive degeneration in patients with Parkinson's disease (PD) using a machine learning method. The clinical data, plasma biomarkers, and neuropsychological test results of patients with PD were collected and utilized as model predictors. Machine learning methods comprising support vector machines (SVMs) and principal component analysis (PCA) were applied to obtain a cognitive classification model. Using 32 comprehensive predictive parameters, the PCA-SVM classifier reached 92.3% accuracy and 0.929 area under the receiver operating characteristic curve (AUC). Furthermore, the accuracy could be increased to 100% and the AUC to 1.0 in a PCA-SVM model using only 13 carefully chosen features.

Inhibition of proliferation and migration of melanoma cells by ketoconazole and Ganoderma immunomodulatory proteins.

Ketoconazole, an antifungal agent, has been used to inhibit hormone synthesis in types of prostate and breast cancer. Immunomodulatory proteins of Ganoderma microsporum (GMI) inhibit the tumor necrosis factor-α- and epidermal growth factor-induced metastatic ability of lung cancer cells. Cutaneous malignant melanoma is a highly invasive and metastatic skin cancer. However, to the best of our knowledge, there is limited understanding regarding the effects of ketoconazole and GMI on melanoma. The current study aimed to investigate the inhibitory effects of GMI combined with ketoconazole on melanoma survival and metastasis. The effects of GMI combined with ketoconazole on the viability, migration and protein expression of melanoma cells were determined by MTT assay, Boyden chamber assay and western blot analysis, respectively. The expression of monocyte chemoattractant protein-1 (MCP-1) was investigated by enzyme-linked immunoabsorbent assay. The present results indicate that ketoconazole enhances the GMI-induced decrease in proliferation and migration of A375.S2 melanoma cells in a concentration-dependent manner. Ketoconazole was identified to reduce the level of GMI-induced phosphorylated-adenosine monophosphate-activated protein kinase (p-AMPK)-α and autophagy; however, ketoconazole did not affect p-AMPK-ß levels in A375.S2 cells. In addition, ketoconazole and dorsomorphin dihydrochloride, an AMPK inhibitor, were revealed to reduce MCP-1 secretion in A375.S2 cells. In summary, the present study revealed that ketoconazole enhances GMI-inhibited proliferation and migration of A375.S2 melanoma cancer cells, and inhibits the secretion of MCP-1.

Ganoderma immunomodulatory protein and chidamide down-regulate integrin-related signaling pathway result in migration inhibition and apoptosis induction.

BACKGROUND: In terms of melanoma, recent advances have been made in target therapies and immune checkpoint inhibitors, but durable remission is rare. Ganoderma immunomodulatory proteins (GMI) induce a cytotoxic effect in cancer cells via autophagy. However, the role of GMI in melanoma is not clear. PURPOSE: The aims of this study are to investigate the inhibiting effects of GMI combined with chidamide on survival and metastases of melanoma cells via integrin-related signaling pathway and to propose strategies for combining GMI and chidamide using animal model. METHODS: Cell viability was measured by cell CCK-8. The activities of apoptosis- and migration-related proteins were detected on Western blot. Flow cytometry was used to analyze cell cycle distribution and sub-G1 fraction in treated melanoma cells. To evaluate the activity of combination GMI and chidamide treatment, an in vivo anti-tumor metastasis study was performed. RESULTS: GMI combined with chidamide additively induced apoptosis. GMI inhibited the expressions of Integrin α5, αV, ß1, and ß3. The level of p-FAK was inhibited by GMI. Combination treatment of GMI and chidamide decreased survivin and increased cleaved caspase-7 and LC3 II/I. Integrin-αV overexpression activated p-FAK pathways in A375.S2 cells. GMI significantly inhibited cell growth and migration of A375.S2 cells on wound healing assay. In vivo, GMI combined with chidamide suppressed distal tumor metastasis. CONCLUSION: GMI inhibits the migration and growth of melanoma cells via integrin-related signaling pathway. GMI and chidamide induces apoptosis. In vivo, GMI and chidamide additively reduce distant metastases. GMI and chidamide are potential immunotherapeutic adjuvant for metastatic melanoma.

Polyurethane-based drug delivery systems.

Polyurethanes (PUs) are formed by a reaction between isocyanates and diols to yield polymers with urethane bonds (-NH-COO-) in their main chain. A great variety of building blocks is commercially available that allows the chemical and physical properties of PUs to be tailored to their target applications, particularly for the biomedical and pharmaceutical fields. This article reviews the synthesis and characterization of PUs and PU-copolymers, as well as their in vitro and in vivo biodegradability and biocompatibility. Particular emphasis is placed on the use of PUs for the controlled release of drugs and for the (targeted) delivery of biotherapeutics.