Preparation and characterization of a nanohydroxyapatite and sodium fluoride loaded chitosan-based in situ forming gel for enamel biomineralization

The development of remineralizing smart biomaterials is a contemporary approach to caries prevention. The present study aimed at formulation preparation and characterization of a thermoresponsive oral gel based on poloxamer and chitosan loaded with sodium fluoride (NaF) and nanohydroxyapatite (nHA) to treat demineralization. The chemical structure and morphology of the formulation were characterized using FTIR and FESEM-EDS tests. Hydrogel texture, rheology, and stability were also examined. The hydrogel was in a sol state at room temperature and became gel after being placed at 37 °C with no significance different in gelation time with the formulation without nHA and NaF as observed by t-test. The FTIR spectrum of nHA/NaF/chitosan-based hydrogel indicated the formation of physical crosslinking without any chemical interactions between the hydrogel components. The FESEM-EDS results demonstrated the uniform distribution of each element within the hydrogel matrix, confirming the successful incorporation of nHA and NaF in the prepared gel. The hardness, hydrogel’s adhesiveness, and cohesiveness were 0.9 mJ, 1.7 mJ, and 0.37, respectively, indicating gel stability and the acceptable retention time of hydrogels. The formulation exhibited a non-Newtonian shear-thinning pseudoplastic and thixotropic behavior with absolute physical stability. Within the limitation of in vitro studies, nHA/NaF/chitosan-based in situ forming gel demonstrated favorable properties, which could be trasnsorm into a gel state in oral cavity due to poloxamer and chitosan and can prevent dental caries due to nHA and NaF. We propose this formulation as a promising dental material in tooth surface remineralization

Tissue-engineered small-diameter vascular grafts containing novel copper-doped bioactive glass biomaterials to promote angiogenic activity and endothelial regeneration

Abstract Small-diameter vascular grafts frequently fail because of obstruction and infection. Despite the wide range of commercially available vascular grafts, the anatomical uniqueness of defect sites demands patient-specific designs. This study aims to increase the success rate of implantation by fabricating bilayer vascular grafts containing bioactive glasses (BGs) and modifying their composition by removing hemostatic ions to make them blood-compatible and to enhance their antibacterial and angiogenesis properties. The porous vascular graft tubes were 3D printed using polycaprolactone, polyglycerol sebacate, and the modified BGs. The polycaprolactone sheath was then wrapped around the 3D-printed layer using the electrospinning technique to prevent blood leakage. The results demonstrated that the incorporation of modified BGs into the polymeric matrix not only improved the mechanical properties of the vascular graft but also significantly enhanced its antibacterial activity against both gram-negative and gram-positive strains. In addition, no hemolysis or platelet activity was detected after incorporating modified BGs into the vascular grafts. Copper-releasing vascular grafts significantly enhanced endothelial cell proliferation, motility, and VEGF secretion. Additionally, In vivo angiogenesis (CD31 immunofluorescent staining) and gene expression experiments showed that copper-releasing vascular grafts considerably promoted the formation of new blood vessels, low-grade inflammation (decreased expression of IL-1β and TNF-α), and high-level angiogenesis (increased expression of angiogenic growth factors including VEGF, PDGF-BB, and HEBGF). These observations indicate that the use of BGs with suitable compositional modifications in vascular grafts may promote the clinical success of patient-specific vascular prostheses by accelerating tissue regeneration without any coagulation problems

Additive manufacturing of bioactive glass biomaterials

Abstract Tissue engineering (TE) and regenerative medicine have held great promises for the repair and regeneration of damaged tissues and organs. Additive manufacturing has recently appeared as a versatile technology in TE strategies that enables the production of objects through layered printing. By applying 3D printing and bioprinting, it is now possible to make tissue-engineered constructs according to desired thickness, shape, and size that resemble the native structure of lost tissues. Up to now, several organic and inorganic materials were used as raw materials for 3D printing; bioactive glasses (BGs) are among the most hopeful substances regarding their excellent properties (e.g., bioactivity and biocompatibility). In addition, the reported studies have confirmed that BG-reinforced constructs can improve osteogenic, angiogenic, and antibacterial activities. This review aims to provide an up-to-date report on the development of BG-containing raw biomaterials that are currently being employed for the fabrication of 3D printed scaffolds used in tissue regeneration applications with a focus on their advantages and remaining challenges

Dual targeting of TGF-β and PD-L1 inhibits tumor growth in TGF-β/PD-L1-driven colorectal carcinoma

Immunosuppressive factors within the tumor microenvironment (TME), such as Transforming growth factor beta (TGF-β), constitute a crucial hindrance to immunotherapeutic approaches in colorectal cancer (CRC). Furthermore, immune checkpoint factors (e.g., programmed death-ligand 1 [PD-L1]) inhibit T-cell proliferation and activation. To cope with the inhibitory effect of immune checkpoints, the therapeutic value of dual targeting PD-L1 and TGF-β pathways via M7824 plus 5-FU in CRC has been evaluated. Integrative-systems biology approaches and RNAseq were used to assess the differential level of genes associated with 88 metastatic-CRC patients. The level of PD-L1 and TGF-β was evaluated in a validation cohort. The anti-proliferative, migratory, and apoptotic effects of PD-L1/TGF-β inhibitor, M7824, were assessed by MTT, wound-healing assay, and flow cytometry. Anti-tumor activity was assessed in a xenograft model, followed by biochemical studies and histological staining, and gene/protein expression analyses by RT-PCR and ELISA/IHC. The result of differentially expressed genes (DEGs) analysis showed 1268 upregulated and 1074 downregulated genes in CRC patients. Among the highest scoring genes and dysregulated pathways associated with CRC, PD-L1, and TGF-β were identified and further validated in 92 CRC patients. Targeting of PD-L1-TGF-β inhibited cell growth and migration, associated with modulation of CyclinD1 and MMP9. Furthermore, M7824 inhibited tumor growth via targeting TGF-β and PD-L1 pathways, resulting in modulation of inflammatory response and fibrosis via TNF-α/IL6/CD4–8 and COL1A1/1A2, respectively. In conclusion, our data illustrated that co-targeting PD-L1 and TGF-β pathways increased the effect of Fluorouracil (5-FU) and reduced the tumor growth in PD-L1/TGF-β expressing tumors, providing a new therapeutic option in the treatment of CRC.</p

Differentiation of COVID‐19 pneumonia from other lung diseases using CT radiomic features and machine learning : A large multicentric cohort study

To derive and validate an effective machine learning and radiomics‐based model to differentiate COVID‐19 pneumonia from other lung diseases using a large multi‐centric dataset. In this retrospective study, we collected 19 private and five public datasets of chest CT images, accumulating to 26 307 images (15 148 COVID‐19; 9657 other lung diseases including non‐COVID‐19 pneumonia, lung cancer, pulmonary embolism; 1502 normal cases). We tested 96 machine learning‐based models by cross‐combining four feature selectors (FSs) and eight dimensionality reduction techniques with eight classifiers. We trained and evaluated our models using three different strategies: #1, the whole dataset (15 148 COVID‐19 and 11 159 other); #2, a new dataset after excluding healthy individuals and COVID‐19 patients who did not have RT‐PCR results (12 419 COVID‐19 and 8278 other); and #3 only non‐COVID‐19 pneumonia patients and a random sample of COVID‐19 patients (3000 COVID‐19 and 2582 others) to provide balanced classes. The best models were chosen by one‐standard‐deviation rule in 10‐fold cross‐validation and evaluated on the hold out test sets for reporting. In strategy#1, Relief FS combined with random forest (RF) classifier resulted in the highest performance (accuracy = 0.96, AUC = 0.99, sensitivity = 0.98, specificity = 0.94, PPV = 0.96, and NPV = 0.96). In strategy#2, Recursive Feature Elimination (RFE) FS and RF classifier combination resulted in the highest performance (accuracy = 0.97, AUC = 0.99, sensitivity = 0.98, specificity = 0.95, PPV = 0.96, NPV = 0.98). Finally, in strategy #3, the ANOVA FS and RF classifier combination resulted in the highest performance (accuracy = 0.94, AUC =0.98, sensitivity = 0.96, specificity = 0.93, PPV = 0.93, NPV = 0.96). Lung radiomic features combined with machine learning algorithms can enable the effective diagnosis of COVID‐19 pneumonia in CT images without the use of additional tests

COVID-19 prognostic modeling using CT radiomic features and machine learning algorithms: Analysis of a multi-institutional dataset of 14,339 patients

Background: We aimed to analyze the prognostic power of CT-based radiomics models using data of 14,339 COVID-19 patients. Methods: Whole lung segmentations were performed automatically using a deep learning-based model to extract 107 intensity and texture radiomics features. We used four feature selection algorithms and seven classifiers. We evaluated the models using ten different splitting and cross-validation strategies, including non-harmonized and ComBat-harmonized datasets. The sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were reported. Results: In the test dataset (4,301) consisting of CT and/or RT-PCR positive cases, AUC, sensitivity, and specificity of 0.83 ± 0.01 (CI95%: 0.81-0.85), 0.81, and 0.72, respectively, were obtained by ANOVA feature selector + Random Forest (RF) classifier. Similar results were achieved in RT-PCR-only positive test sets (3,644). In ComBat harmonized dataset, Relief feature selector + RF classifier resulted in the highest performance of AUC, reaching 0.83 ± 0.01 (CI95%: 0.81-0.85), with a sensitivity and specificity of 0.77 and 0.74, respectively. ComBat harmonization did not depict statistically significant improvement compared to a non-harmonized dataset. In leave-one-center-out, the combination of ANOVA feature selector and RF classifier resulted in the highest performance. Conclusion: Lung CT radiomics features can be used for robust prognostic modeling of COVID-19. The predictive power of the proposed CT radiomics model is more reliable when using a large multicentric heterogeneous dataset, and may be used prospectively in clinical setting to manage COVID-19 patients.</p