Recent advances and challenges in graphene-based nanocomposite scaffolds for tissue engineering application.

Graphene-based nanocomposites have recently attracted increasing attention in tissue engineering because of their extraordinary features. These biocompatible substances, in the presence of an apt microenvironment, can stimulate and sustain the growth and differentiation of stem cells into different lineages. This review discusses the characteristics of graphene and its derivatives, such as their excellent electrical signal transduction, carrier mobility, outstanding mechanical strength with improving surface characteristics, self-lubrication, antiwear properties, enormous specific surface area, and ease of functional group modification. Moreover, safety issues in the application of graphene and its derivatives in terms of biocompatibility, toxicity, and interaction with immune cells are discussed. We also describe the applicability of graphene-based nanocomposites in tissue healing and organ regeneration, particularly in the bone, cartilage, teeth, neurons, heart, skeletal muscle, and skin. The impacts of special textural and structural characteristics of graphene-based nanomaterials on the regeneration of various tissues are highlighted. Finally, the present review gives some hints on future research for the transformation of these exciting materials in clinical studies.

Molecular signaling pathways, pathophysiological features in various organs, and treatment strategies in SARS-CoV2 infection.

Cytokine storms and extra-activated cytokine signaling pathways can lead to severe tissue damage and patient death. Activation of inflammatory signaling pathways during Cytokine storms are an important factor in the development of acute respiratory syndrome (SARS-CoV-2), which is the major health problem today, causing systemic and local inflammation. Cytokine storms attract many inflammatory cells that attack the lungs and other organs and cause tissue damage. Angiotensin-converting enzyme 2 (ACE2) are expressed in a different type of tissues. inhibition of ACE2 activity impairs renin-angiotensin (RAS) function, which is related to the severity of symptoms and mortality rate in COVID-19 patients. Different signaling cascades are activated, affecting various organs during SARS-CoV-2 infection. Nowadays, there is no specific treatment for COVID-19, but scientists have recognized and proposed several treatment alternatives, including applying cytokine inhibitors, immunomodulators, and plasma therapy. Herein, we have provided the detailed mechanism of SARS-CoV-2 induced cytokine signaling and its connection with pathophysiological features in different organs. Possible treatment options to cope with the severe clinical manifestations of COVID-19 are also discussed.

The effects of Sorafenib and Natural killer cell co-injection in combinational treatment of hepatocellular carcinoma; an in vivo approach.

BACKGROUND: Natural killer cells (NKC) and Sorafenib (Sor) are two important agents for the treatment of hepatocellular carcinoma (HCC). Over the past decade, the interaction of Sor and NKC against HCC has been widely challenging. This study aimed to assess the efficacy of NKC & Sor for the treatment of HCC in vivo. METHODS: Subcutaneous xenograft models of HCC were established in nude mice. For safety assessment of treatment, the kidney and liver functions were analyzed. Paraffin embedded tumor sections were histopathologically studied and immunohistochemistry (IHC) tests were done to evaluate the angiogenesis (CD34) and proliferation (Ki67) indexes. The terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL) assay was performed to identify the tumor cells undergoing apoptosis. The serum levels of tumor necrosis factor-α (TNF-α) and interferon-γ (IFN-γ) were measured by enzyme-linked immunosorbent assay (ELISA) and expression levels of major inflammatory cytokines and cytoplasmic granules in xenograft HCC were quantified using real-time PCR. RESULTS: NKC & Sor significantly inhibited necrosis and apoptosis in tumor cells and increased angiogenesis and proliferation of HCC compared to the monotherapy of NKC or Sor alone. The serum levels of TNF-α, IFN-γ as well as the expression levels of TNF-α, IFN-γ, interleukins (ILs)-1, 6, 10, granzyme-B and perforin in the xenograft HCC tissues of the treated mice with NKC & Sor were significantly lower than those of treated with NKC or Sor alone. CONCLUSION: Therapy with the specific dosage of NKC & Sor could not inhibit the HCC xenograft growth rate through a synergistic effect in a mouse model of HCC.

Natural Killer Cell Expansion with Autologous Feeder Layer and Anti-CD3 Antibody for Immune Cell Therapy of Hepatocellular Carcinoma.

BACKGROUND: one of the promising approaches for treatment of some cancers is adoptive cell therapy using natural killer (NK) cells. Various methods have been investigated for ex vivo expansion of NK cells in large-scale, but most of them involved cancer or genetically modified cells as feeder layer and also some of them have the risk of T cell contamination and graft-versus-host disease (GVHD). METHOD: In this study, irradiated autologous peripheral blood mononuclear cells (PBMCs) as feeder layer with an anti-CD3 monoclonal antibody (mAb) were used. For activation and expansion of NK cells, human recombinant IL2 and IL15 were used. After co-culturing of expanded NK cells (eNKC) and isolated NK cells (iNKC) with hepatocellular carcinoma (HCC) cells, the viability of eNKC in compared to iNKC were analyzed by CCK-8 assay and degranulation of NK cells after co-culturing was assayed by measuring CD107a expression. Enzyme-Linked Immunosorbent Assay (ELISA) assay was used for the ability of NK cells to secretion of IFN-γ (interferon-γ) and TNF-α (Tumor Necrosis Factor-α) after co-culture with HCC cells. Real Time PCR analysis was used for expression of human Perforin and Granzyme B genes in the NK cells exposed to target HepG2 cells. RESULT: This method strongly expanded highly purified NK cells with powerful cytotoxicity against HCC cells. The expanded NK cells showed high level of expression of degranulation marker and human Perforin and Granzyme B genes, and also was secreted larger amounts of TNF-α and IFN-γ compared with fresh isolated NK cells. CONCLUSION: we proposed an effective method for expansion of cytotoxic NK cells using irradiated autologous PBMC as feeder layer for more successful transfer of allogeneic NK cell in immuno cell therapy of HCC.

Novel Combination of Mesenchymal Stem Cell-Conditioned Medium with Sorafenib Have Synergistic Antitumor Effect of Hepatocellular Carcinoma Cells

Objective: Hepatocellular carcinoma (HCC) is the most common liver malignancy. Sorafenib is the first-line systemic treatment for advanced HCCs. However, due to safety concerns, researchers are now looking for ways to boost the efficacy of the medication. One approach for reducing toxicity is combining sorafenib with other agents so that a lower dose of sorafenib is required. Mesenchymal stromal cells (MSCs) can have an inhibitory effect on HCC tumor growth. Mesenchymal Stem Cell-Conditioned Medium (MSC-CM) is the substance extracted from MSC culture and contains most of the potential cytokines secreted by MSCs. We, therefore, anticipated a synergistic Antitumor Effect of sorafenib in Combination with MSC-CM. In this study, we used HepG2 as our target cell lines. Methods: HepG2 cells were treated with sorafenib alone and with sorafenib + MSC-CM. CCK-8 assay was used to evaluate and compare the inhibition of cell growth between the two groups with different treatments. Results: The combination treatment of cell lines with sorafenib and MSC-CM had significantly reduced the values of IC50 compared to the use of sorafenib alone (3.4 vs. 2.7 respectively). Conclusion: This study suggests that a combination of sorafenib with MSC-CM can synergistically suppress the growth of HCC cells.

Combination therapy of sorafenib with mesenchymal stem cells as a novel cancer treatment regimen in xenograft models of hepatocellular carcinoma.

AIM: Hepatocellular carcinoma (HCC) is the most common liver malignancy and the second leading cause of cancer-related deaths in the world. Sorafenib is the first-line treatment of HCC. Although sorafenib has positive effects on the survival of patients, novel therapeutic strategies are needed to extend survival and improve the efficacy of sorafenib. This study combines sorafenib with mesenchymal stem cells (MSCs) as a new approach to enhance the efficacy of sorafenib. MATERIAL AND METHODS: A subcutaneous xenograft model of HCC, established by human HepG2 cell lines, was implanted into the flank of nude mice and was used to evaluate tumor growth after treatment with sorafenib alone or in combination with MSCs. The aspartate aminotransferase, alanine aminotransferase, blood urea nitrogen, and creatinine levels were measured for safety assessment. Histopathological studies were performed using hematoxylin and eosin staining, and immunohistochemistry tests were performed to evaluate proliferation (Ki67) and angiogenesis (CD34). The TUNEL assay was used to detect apoptosis and measure the expression of major inflammatory cytokines (IL-1a, IL-10, and TNF-α) with real-time polymerase chain reaction. RESULT: Sorafenib, in combination with MSCs, strongly inhibited tumor growth in the xenograft model. Furthermore, the combination therapy significantly inhibited HCC cell proliferation, decreased tumor angiogenesis, and induced apoptosis and maintained antitumor-associated anti-inflammatory effects of MSCs. CONCLUSION: This combination therapy strategy could be used as a new therapeutic approach to the treatment of HCC that significantly improves upon the results achieved using sorafenib as monotherapy.

Combinational immune-cell therapy of natural killer cells and sorafenib for advanced hepatocellular carcinoma: a review.

BACKGROUND: High prevalence of hepatocellular carcinoma (HCC) and typically poor prognosis of this disease that lead to late stage diagnosis when potentially curative therapies are least effective; therefore, development of an effective and systematic treatment is an urgent requirement. MAIN BODY: In this review, several current treatments for HCC patients and their advantages or disadvantages were summarized. Moreover, various recent preclinical and clinical studies about the performances of "two efficient agents, sorafenib or natural killer (NK) cells", against HCC cells were investigated. In addition, the focus this review was on the chemo-immunotherapy approach, correlation between sorafenib and NK cells and their effects on the performance of each other for better suppression of HCC. CONCLUSION: It was concluded that combinational therapy with sorafenib and NK cells might improve the outcome of applied therapeutic approaches for HCC patients. Finally, it was also concluded that interaction between sorafenib and NK cells is dose and time dependent, therefore, a careful dose and time optimizing is necessary for development of a combinational immune-cell therapy.

Characterization and Validation of Hepatocellular Carcinoma (HCC) Xenograft tumor as a Suitable Liver Cancer Model for Preclinical Mesenchymal Stem Cell Studies

Background: Hepatocellular carcinoma (HCC) is the fifth most diagnosed cancer and the third leading cause of cancer-related death. sorafenib is used as a standard therapy to treat HCC. mesenchymal stromal cells (MSCs) have also been used to suppress HCC. Here we investigate the development of a xenograft model of liver cancer to study the homing of hpMSC-GFP cells, tumor kinetics and molecular characterizations of HCC. Methods: To create xenograft models of HCC, HepG2 cell lines were inoculated into the flanks of 9 nude mice bilaterally. Animals were then divided into three groups: the first group received hpMSC-GFP systemically, the second received intra-tumoral hpMSC-GFP and the third received PBS. The first two groups were sacrificed after 72 hours of MSCs injection but the third group was followed up for forty days. One tumor from each animal was then transferred to formalin buffer for H&E staining and immunohistochemistry analysis (KI67 and CD34), and the other tumor was used for ex-vivo imaging. Blood samples were taken from all subjects before sacrificing them. Results: Histopathological fidelity of heterotopic HePG2 xenograft models to human HCC tumors was demonstrated. Biochemical evaluation suggested the health of the animal's liver and kidneys. Ex-vivo imaging illustrated homing of more hpMSC-GFP cells in tumor tissues derived from the group receiving intra-tumoral hpMSC-GFP. Conclusion: A standard method was used to inoculate tumor cells and the intervention was shown to be safe to liver and kidneys. Local injection of MSCs can be used as cell therapy to fight neoplasms.

Prediction of lung tumor types based on protein attributes by machine learning algorithms.

Early diagnosis of lung cancers and distinction between the tumor types (Small Cell Lung Cancer (SCLC) and Non-Small Cell Lung Cancer (NSCLC) are very important to increase the survival rate of patients. Herein, we propose a diagnostic system based on sequence-derived structural and physicochemical attributes of proteins that involved in both types of tumors via feature extraction, feature selection and prediction models. 1497 proteins attributes computed and important features selected by 12 attribute weighting models and finally machine learning models consist of seven SVM models, three ANN models and two NB models applied on original database and newly created ones from attribute weighting models; models accuracies calculated through 10-fold cross and wrapper validation (just for SVM algorithms). In line with our previous findings, dipeptide composition, autocorrelation and distribution descriptor were the most important protein features selected by bioinformatics tools. The algorithms performances in lung cancer tumor type prediction increased when they applied on datasets created by attribute weighting models rather than original dataset. Wrapper-Validation performed better than X-Validation; the best cancer type prediction resulted from SVM and SVM Linear models (82%). The best accuracy of ANN gained when Neural Net model applied on SVM dataset (88%). This is the first report suggesting that the combination of protein features and attribute weighting models with machine learning algorithms can be effectively used to predict the type of lung cancer tumors (SCLC and NSCLC).

Classification of lung cancer tumors based on structural and physicochemical properties of proteins by bioinformatics models.

Rapid distinction between small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC) tumors is very important in diagnosis of this disease. Furthermore sequence-derived structural and physicochemical descriptors are very useful for machine learning prediction of protein structural and functional classes, classifying proteins and the prediction performance. Herein, in this study is the classification of lung tumors based on 1497 attributes derived from structural and physicochemical properties of protein sequences (based on genes defined by microarray analysis) investigated through a combination of attribute weighting, supervised and unsupervised clustering algorithms. Eighty percent of the weighting methods selected features such as autocorrelation, dipeptide composition and distribution of hydrophobicity as the most important protein attributes in classification of SCLC, NSCLC and COMMON classes of lung tumors. The same results were observed by most tree induction algorithms while descriptors of hydrophobicity distribution were high in protein sequences COMMON in both groups and distribution of charge in these proteins was very low; showing COMMON proteins were very hydrophobic. Furthermore, compositions of polar dipeptide in SCLC proteins were higher than NSCLC proteins. Some clustering models (alone or in combination with attribute weighting algorithms) were able to nearly classify SCLC and NSCLC proteins. Random Forest tree induction algorithm, calculated on leaves one-out and 10-fold cross validation) shows more than 86% accuracy in clustering and predicting three different lung cancer tumors. Here for the first time the application of data mining tools to effectively classify three classes of lung cancer tumors regarding the importance of dipeptide composition, autocorrelation and distribution descriptor has been reported.

Correction: Classification of Lung Cancer Tumors Based on Structural and Physicochemical Properties of Proteins by Bioinformatics Models.

[This corrects the article DOI: 10.1371/journal.pone.0040017.].