Effect of cold atmospheric plasma on changing of biomolecular structures involved in apoptosis pathways of melanoma cancer.

BACKGROUND: Cold atmospheric plasma (CAP), is a technology based on non-thermal ionized gas that is used for cancer therapy in research. We evaluated the effect of CAP on malignant melanoma cancer cell line (B16) in comparison with normal cells (L929). METHODS: The effect of CAP on the cytotoxicity of B16 and L929 cell lines was assayed by the MTT method and inverted microscopy. The induction of apoptosis in cells was evaluated using a fluorescence microscope. FTIR monitored the CAP effect in biomacromolecules changes in these cell lines. QPCR assayed gene expression of BAX, BCL-2, and Caspase-3 (CASP-3). RESULTS: The results of the MTT test showed CAP has a cytotoxic effect on the B16 cancer cell line more than L929 normal cells (p < 0.0001). The results of invert and fluorescence microscopy showed CAP-induced apoptotic morphology on cancerous cells. FTIR spectroscopy indicated CAP changes biomacromolecules structure. Evaluation of gene expression showed CAP increased BAX and CASP-3 gene expression. Also, it decreased BCL-2 gene expression. CONCLUSIONS: Taken together, CAP may change biomacromolecule structures involved in apoptosis pathways, decrease proliferation and induce apoptosis in cancer cells.

Development and Validation of a Model for Endotracheal Intubation and Mechanical Ventilation Prediction in PICU Patients.

OBJECTIVES: To develop and externally validate an intubation prediction model for children admitted to a PICU using objective and routinely available data from the electronic medical records (EMRs). DESIGN: Retrospective observational cohort study. SETTING: Two PICUs within the same healthcare system: an academic, quaternary care center (36 beds) and a community, tertiary care center (56 beds). PATIENTS: Children younger than 18 years old admitted to a PICU between 2010 and 2022. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Clinical data was extracted from the EMR. PICU stays with at least one mechanical ventilation event (≥ 24 hr) occurring within a window of 1-7 days after hospital admission were included in the study. Of 13,208 PICU stays in the derivation PICU cohort, 1,175 (8.90%) had an intubation event. In the validation cohort, there were 1,165 of 17,841 stays (6.53%) with an intubation event. We trained a Categorical Boosting (CatBoost) model using vital signs, laboratory tests, demographic data, medications, organ dysfunction scores, and other patient characteristics to predict the need of intubation and mechanical ventilation using a 24-hour window of data within their hospital stay. We compared the CatBoost model to an extreme gradient boost, random forest, and a logistic regression model. The area under the receiving operating characteristic curve for the derivation cohort and the validation cohort was 0.88 (95% CI, 0.88-0.89) and 0.92 (95% CI, 0.91-0.92), respectively. CONCLUSIONS: We developed and externally validated an interpretable machine learning prediction model that improves on conventional clinical criteria to predict the need for intubation in children hospitalized in a PICU using information readily available in the EMR. Implementation of our model may help clinicians optimize the timing of endotracheal intubation and better allocate respiratory and nursing staff to care for mechanically ventilated children.

Improving mixed-integer temporal modeling by generating synthetic data using conditional generative adversarial networks: A case study of fluid overload prediction in the intensive care unit.

OBJECTIVE: The challenge of mixed-integer temporal data, which is particularly prominent for medication use in the critically ill, limits the performance of predictive models. The purpose of this evaluation was to pilot test integrating synthetic data within an existing dataset of complex medication data to improve machine learning model prediction of fluid overload. MATERIALS AND METHODS: This retrospective cohort study evaluated patients admitted to an ICU ≥ 72 h. Four machine learning algorithms to predict fluid overload after 48-72 h of ICU admission were developed using the original dataset. Then, two distinct synthetic data generation methodologies (synthetic minority over-sampling technique (SMOTE) and conditional tabular generative adversarial network (CTGAN)) were used to create synthetic data. Finally, a stacking ensemble technique designed to train a meta-learner was established. Models underwent training in three scenarios of varying qualities and quantities of datasets. RESULTS: Training machine learning algorithms on the combined synthetic and original dataset overall increased the performance of the predictive models compared to training on the original dataset. The highest performing model was the meta-model trained on the combined dataset with 0.83 AUROC while it managed to significantly enhance the sensitivity across different training scenarios. DISCUSSION: The integration of synthetically generated data is the first time such methods have been applied to ICU medication data and offers a promising solution to enhance the performance of machine learning models for fluid overload, which may be translated to other ICU outcomes. A meta-learner was able to make a trade-off between different performance metrics and improve the ability to identify the minority class.

Single domain antibodies specific for HER2 dimerization domain effectively disrupts HER2 dimerization.

Dimer-dependent phosphorylation of HER2 receptor is a key event for the signal transduction of HER family of receptors which correlates with tumor invasion and metastasis. New generation of therapies based on dimerization domain inhibition using monoclonal or fragment antibodies was introduced. A potent method for manufacturing antibodies and antibody fragments is the phage display antibody library method. A recombinant phage was generated using the phage display method from synthetic dAb library. Subtractive biopanning was performed on sepharose 4b resin. Evaluation of success of subtractive biopanning was confirmed by the PCR fingerprinting after the fourth round of biopanning. The fourth round of biopanning results in the isolation of several dimerization domain reactive clones based on the polyclonal phage ELISA results. Monoclonal phage cell ELISA was used to select the positive clones with the highest affinity, and they were subsequently employed for functional tests. Cell-ELISA, MTT assay and dimerization inhibition test revealed that the reactivity and specificity of the selected monoclonal phage to dimerization domain of HER2. Further, Annexin V/PI staining and gene expression analysis showed that increased apoptosis rates. Also, in silico binding of the selected clones to conformational structure of HER2 was applied, using protein-protein docking tool of the ICM-Pro software, and showed sdAbs were specifically interacted with dimerization domain of the receptor. In conclusion, we have identified a single domain targeting HER2 dimerization, which represents a promising therapeutic and diagnostic candidate for HER2-positive cancers. Purified sdAb needs to more research to evaluate it both in vivo and in vitro via functional tests to determine if it can be applied for treatment and diagnostics.

Improving irregular temporal modeling by integrating synthetic data to the electronic medical record using conditional GANs: a case study of fluid overload prediction in the intensive care unit.

Objective: The challenge of irregular temporal data, which is particularly prominent for medication use in the critically ill, limits the performance of predictive models. The purpose of this evaluation was to pilot test integrating synthetic data within an existing dataset of complex medication data to improve machine learning model prediction of fluid overload. Materials and Methods: This retrospective cohort study evaluated patients admitted to an ICU ≥ 72 hours. Four machine learning algorithms to predict fluid overload after 48-72 hours of ICU admission were developed using the original dataset. Then, two distinct synthetic data generation methodologies (synthetic minority over-sampling technique (SMOTE) and conditional tabular generative adversarial network (CT-GAN)) were used to create synthetic data. Finally, a stacking ensemble technique designed to train a meta-learner was established. Models underwent training in three scenarios of varying qualities and quantities of datasets. Results: Training machine learning algorithms on the combined synthetic and original dataset overall increased the performance of the predictive models compared to training on the original dataset. The highest performing model was the metamodel trained on the combined dataset with 0.83 AUROC while it managed to significantly enhance the sensitivity across different training scenarios. Discussion: The integration of synthetically generated data is the first time such methods have been applied to ICU medication data and offers a promising solution to enhance the performance of machine learning models for fluid overload, which may be translated to other ICU outcomes. A meta-learner was able to make a trade-off between different performance metrics and improve the ability to identify the minority class.

The effect of tranexamic acid on synovium of patients undergoing arthroplasty and anterior cruciate ligament reconstruction surgery.

Preoperative hemorrhage can be reduced using anti-fibrinolytic medicine tranexamic acid (TXA). During surgical procedures, local administration is being used more and more frequently, either as an intra-articular infusion or as a perioperative rinse. Serious harm to adult soft tissues can be detrimental to the individual since they possess a weak ability for regeneration. Synovial tissues and primary fibroblast-like synoviocytes (FLS) isolated from patients were examined using TXA treatment in this investigation. FLS is obtained from rheumatoid arthritis (RA), osteoarthritis (OA), and anterior cruciate ligament (ACL)-ruptured patients. The in vitro effect of TXA on primary FLS was investigated using 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) cell viability assays for cell death, annexin V/propidium iodide (PI) staining for apoptotic rate, real-time PCR for p65 and MMP-3 expression, and enzyme-linked immunosorbent assay (ELISA) for IL-6 measurement. MTT assays revealed a significant decrease in cell viability in FLS of all groups of patients following treatment with 0.8-60 mg/ml of TXA within 24 h. There was a significant increase in cell apoptosis after 24 h of exposure to TXA (15 mg/ml) in all groups, especially in RA-FLS. TXA increases the expression of MMP-3 and p65 expression. There was no significant change in IL-6 production after TXA treatment. An increase in receptor activator of nuclear factor kappa-Β ligand (RANK-L) production was seen only in RA-FLS. This study demonstrates that TXA caused significant synovial tissue toxicity via the increase in cell death and elevation of inflammatory and invasive gene expression in FLS cells.

Designing novel peptides for detecting the Omicron variant, specifying SARS-CoV-2, and simultaneously screening coronavirus infections.

In this study in silico a candidate diagnostic peptide-based tool was designed in four stages including diagnosis of coronavirus diseases, simultaneously identifying of COVID-19 and SARS from other members of this family, specific identification of SARS-CoV2, and diagnosis of COVID-19 Omicron. Designed candidate peptides consist of four immunodominant peptides from the proteins of the SARS-CoV-2 spike (S) and membrane (M). The tertiary structure of each peptide was predicted. The stimulation ability of the humoral immunity for each peptide was evaluated. Finally, in silico cloning was performed to develop an expression strategy for each peptide. These four peptides have suitable immunogenicity, appropriate construct, and the ability to be expressed in E.coli. These results must be experimentally validated in vitro and in vivo to ensure the immunogenicity of the kit.Communicated by Ramaswamy H. Sarma.

Association between angiotensin-converting enzyme-2 gene polymorphism (rs2106809) with severity and outcome of COVID-19 infection.

PURPOSE: Genetic factors play important role in the severity of the COVID-19 infection since SARS-CoV-2 binds to the ACE2 receptor on the surface of host cells. ACE2 polymorphisms that may influence the expression of ACE2 can alter patients' susceptibility to COVID-19 infection or increase the severity of the disease. This study aimed to investigate the association between ACE2 rs2106809 polymorphism and the severity of the COVID-19 infection. METHODS: In this cross-sectional study, ACE2 rs2106809 polymorphism was assessed in 142 COVID-19 patients. The disease was confirmed according to clinical symptoms, imaging, and laboratory findings. The severity of the disease was graded as severe versus non-severe based on the CDC. Genomic DNA was extracted from the whole blood and PCR- RFLP was performed to genotype the ACE2-rs2106809 with specific primers and Taq1 restriction enzyme. RESULTS: G/G genotype was significantly associated with COVID-19 severity (44.4% in severe vs. 17.5% in non-severe, OR: 4.1; 95%CI: 1.8-9.5, p = 0.0007). Patients with the G/G genotype need more mechanical ventilation (p = 0.021). ACE2 expression in patients carrying the A/G genotype was higher in the severe compared to the non-severe form of the disease (2.99 ± 0.99 vs. 2.21 ± 1.1), but it was not statistically significant (p = 0.9). CONCLUSION: The G allele and G/G genotype of ACE2 rs2106809 is associated with more severe COVID-19 and adverse disease outcomes.

Pharmacophenotype identification of intensive care unit medications using unsupervised cluster analysis of the ICURx common data model.

BACKGROUND: Identifying patterns within ICU medication regimens may help artificial intelligence algorithms to better predict patient outcomes; however, machine learning methods incorporating medications require further development, including standardized terminology. The Common Data Model for Intensive Care Unit (ICU) Medications (CDM-ICURx) may provide important infrastructure to clinicians and researchers to support artificial intelligence analysis of medication-related outcomes and healthcare costs. Using an unsupervised cluster analysis approach in combination with this common data model, the objective of this evaluation was to identify novel patterns of medication clusters (termed 'pharmacophenotypes') correlated with ICU adverse events (e.g., fluid overload) and patient-centered outcomes (e.g., mortality). METHODS: This was a retrospective, observational cohort study of 991 critically ill adults. To identify pharmacophenotypes, unsupervised machine learning analysis with automated feature learning using restricted Boltzmann machine and hierarchical clustering was performed on the medication administration records of each patient during the first 24 h of their ICU stay. Hierarchical agglomerative clustering was applied to identify unique patient clusters. Distributions of medications across pharmacophenotypes were described, and differences among patient clusters were compared using signed rank tests and Fisher's exact tests, as appropriate. RESULTS: A total of 30,550 medication orders for the 991 patients were analyzed; five unique patient clusters and six unique pharmacophenotypes were identified. For patient outcomes, compared to patients in Clusters 1 and 3, patients in Cluster 5 had a significantly shorter duration of mechanical ventilation and ICU length of stay (p < 0.05); for medications, Cluster 5 had a higher distribution of Pharmacophenotype 1 and a smaller distribution of Pharmacophenotype 2, compared to Clusters 1 and 3. For outcomes, patients in Cluster 2, despite having the highest severity of illness and greatest medication regimen complexity, had the lowest overall mortality; for medications, Cluster 2 also had a comparably higher distribution of Pharmacophenotype 6. CONCLUSION: The results of this evaluation suggest that patterns among patient clusters and medication regimens may be observed using empiric methods of unsupervised machine learning in combination with a common data model. These results have potential because while phenotyping approaches have been used to classify heterogenous syndromes in critical illness to better define treatment response, the entire medication administration record has not been incorporated in those analyses. Applying knowledge of these patterns at the bedside requires further algorithm development and clinical application but may have the future potential to be leveraged in guiding medication-related decision making to improve treatment outcomes.

Targeting the tumor microenvironment by liposomal Epacadostat in combination with liposomal gp100 vaccine.

Indoleamine-2,3-dioxygenase (IDO1) pathway has vital role in cancer immune escape and its upregulation leads to immunosuppressive environment which is associated with poor prognosis and progression in various cancers like melanoma. Previously, we showed the antitumoral efficacy of nanoliposomal form of Epacadostat (Lip-EPA), as an IDO1 inhibitor. Herein, we used Lip-EPA as a combination approach with liposomal gp100 (Lip-gp100) anti-cancer vaccine in melanoma model. Here, we showed that B16F10 tumor express IDO1 so using Lip-EPA will enhance the efficacy of vaccine therapy. The biodistribution of ICG-labelled liposomal form of EPA showed the remarkable accumulation of drug at tumor site. In an in vivo study, Lip-EPA enhanced the antitumor efficacy of Lip-gp100 in which the IDO mRNA expression was decreased (~ fourfold) in tumor samples. Also, we identified a significant increase in the number of infiltrated T lymphocytes (p < 0.0001) with enhanced in interferon gamma (IFN-γ) production (p < 0.0001). Additionally, Lip-EPA + Lip-gp100 significantly modulated intratumoral regulatory T cells which altogether resulted in the highest delay in tumor growth (TGD = 56.54%) and increased life span (ILS > 47.36%) in treated mice. Our study demonstrated that novel combination of Lip-EPA and Lip-gp100 was effective treatment with capability of being used in further clinical studies.

A cell-based subtractive panning strategy for selection of conformation-specific single-chain variable-fragment (scFv) against dimerization domain of EGFR.

BACKGROUND AND OBJECTIVE: Overexpression of EGFR, a member of the ErbB receptor family, has been observed in several cancers and causes resistance to therapeutic antibodies, such as Herceptin. In this study, we produced a recombinant single-chain variable fragment (scFv) antibody against the EGFR dimerization domain. METHODS: The recombinant scFv was generated using a cell-based subtractive panning strategy. Subtractive panning was performed on a genetically engineered, VERO/EGFR, cells as well as a triple-negative breast cancer, MDA-MB-468, cells. Phage cell-ELISA was used to monitor the binding of the selected scFvs to the dimerization domain of EGFR. Inhibition of EGFR and HER2 dimerization by the produced scFvs were finally evaluated using the dimerization inhibition test and the expression of apoptosis-related genes were measured using the quantitative RT-PCR. RESULTS: PCR fingerprinting results showed a uniform digestion pattern following the third round of panning that confirmed the success of subtractive panning. Moreover, cell-ELISA validated the reactivity of the produced scFvs to EGFR following stimulation with EGF. Dimerization inhibition test showed the capacity of the scFvs to inhibit EGFR and HER2 dimerization. Investigation of apoptosis-related genes showed that treatment with the scFv antibody caused increased Bax and decreased Bcl2 expression. CONCLUSIONS: Directed HER2 targeting was shown to be effective enough to block the functional domain of the cell receptor and its intracellular signaling pathway. The subtractive panning strategy used in this study could control the process of directed selection of specific antibodies against the dimerization domain of EGFR. Selected antibodies might then be functionally tested for antitumor effects in both in vitro and in vivo studies.

In Silico Analysis of a Candidate Multi-epitope Peptide Vaccine Against Human Brucellosis.

Brucellosis is one of the neglected endemic zoonoses in the world. Vaccination appears to be a promising health strategy to prevent it. This study used advanced computational techniques to develop a potent multi-epitope vaccine for human brucellosis. Seven epitopes from four main brucella species that infect humans were selected. They had significant potential to induce cellular and humoral responses. They showed high antigenic ability without the allergenic characteristic. In order to improve its immunogenicity, suitable adjuvants were also added to the structure of the vaccine. The physicochemical and immunological properties of the vaccine were evaluated. Then its two and three-dimensional structure was predicted. The vaccine was docked with toll-like receptor4 to assess its ability to stimulate innate immune responses. For successful expression of the vaccine protein in Escherichia coli, in silico cloning, codon optimization, and mRNA stability were evaluated. The immune simulation was performed to reveal the immune response profile of the vaccine after injection. The designed vaccine showed the high ability to induce immune response, especially cellular responses to human brucellosis. It showed the appropriate physicochemical properties, a high-quality structure, and a high potential for expression in a prokaryotic system.

Dimethyl Fumarate Inhibits Fibroblast Like Synoviocytes-mediated Inflammation and Joint Destruction in Rheumatoid Arthritis.

Rheumatoid arthritis (RA) as a chronic inflammatory disorder affects around 1% of the world population. Fibroblast-like synoviocyte (FLS), one of the main cells in RA pathogenesis is characterized by hyperproliferation and resistance to apoptosis resulting to synovial hyperplasia. Dimethyl fumarate (DMF) has been licensed for the treatment of multiple sclerosis (MS) and psoriasis; however, its role in RA is unknown. DMF has immunomodulatory properties and may be considered as therapeutic approach in RA treatment. In this study, we aimed to investigate the effect of DMF on controlling FLS-mediated synovial inflammation and joint destruction in RA. FLSs were isolated from synovial tissues of 8 patients with RA and treated with DMF. Apoptosis rate was analyzed by Annexin V-FITC. Cell proliferation was measured by carboxyfluorescein succinimidyl ester (CFSE) dye. The matrix metalloproteinase 3 (MMP3) and NF-кB pathway protein (p65) mRNA expression were evaluated by RT-PCR. Also, the IL-6 production and lactate release were measured in FLS supernatant. DMF treatment decreased the cell proliferation and increased apoptosis in a dose dependent manner. DMF-treated FLS showed a reduction in IL-6 and lactate release. Moreover, it was revealed that DMF inhibited the expression of p65 and MMP3. Our data demonstrate that DMF treatment suppresses the aggressive and inflammatory features of RA FLSs. Our Results suggest that DMF might be expected to be evaluated as a therapy for RA.

Combination effect of cold atmospheric plasma with green synthesized zero-valent iron nanoparticles in the treatment of melanoma cancer model.

Green synthesized zero-valent iron nanoparticles (nZVI) have high potential in cancer therapy. Cold atmospheric plasma (CAP) is also an emerging biomedical technique that has great potential to cure cancer. Therefore, the combined effect of CAP and nZVI might be promising in treatment of cancer. In this study, we evaluated the combined effect of CAP and nZVI on the metabolic activity of the surviving cells and induction of apoptosis in malignant melanoma in comparison with normal cells. Therefore, the effect of various time exposure of CAP radiation, different doses of nZVI, and the combined effect of CAP and nZVI were evaluated on the viability of malignant melanoma cells (B16-F10) and normal fibroblast cells (L929) at 24 h after treatment using MTT assay. Then, the effect of appropriate doses of each treatment on apoptosis was evaluated by fluorescence microscopy and flow cytometry with Annexin/PI staining. In addition, the expression of BAX, BCL2 and Caspase 3 (CASP3) was also assayed. The results showed although the combined effect of CAP and nZVI significantly showed cytotoxic effects and apoptotic activity on cancer cells, this treatment had no more effective compared to CAP or nZVI alone. In addition, evaluation of gene expression showed that combination therapy didn't improve expression of apoptotic genes in comparison with CAP or nZVI. In conclusion, combined treatment of CAP and nZVI does not seem to be able to improve the effect of monotherapy of CAP or nZVI. It may be due to the resistance of cancer cells to high ROS uptake or the accumulation of saturated ROS in cells, which prevents the intensification of apoptosis.

The combined effect of fish oil containing Omega-3 fatty acids and Lactobacillus plantarum on colorectal cancer.

Colorectal cancer (CRC) is one of the deadliest malignancies. Recent attempts have indicated the role of diet in the etiology of CRC. Natural dietary compounds such as probiotics and Omega-3 fatty acids that act synergistically can be beneficial in finding a tremendous solution against CRC. To date, the combined effect of fish oil containing Omega-3 fatty acids (Omega-3) and Lactobacillus plantarum (L. plantarum) on CRC has been left behind. We here evaluated the effects of co-encapsulation of Omega-3 and probiotic bacteria on CRC cell lines compared to normal cells. Omega-3 and L. plantarum bacteria were co-encapsulated in three ways, including gelatin-gum Arabic, gelatin-chitosan, and chitosan-gum Arabic complex coacervate microcapsules. After treatment of cells (Normal [L929] and colorectal [C26]) by L. plantarum, Omega-3, and microcapsules, viability and growth capacity of cell lines were measured using the MTT (3-[4, 5-dimethylthiazol-2-yl]-2, 5-diphenyltetrazolium bromide) assay. Isolated total RNA was used to evaluate the expression profile of BCL2-associated X protein (BAX), B-cell lymphoma 2 (BCL-2), and Caspase-3 (CASP3) genes by real-time polymerase chain reaction (PCR). Statistical analysis was performed with SPSS 25 software. A value of p < .05 was considered statistically significant. The results indicated a significant reduction in cell viability of C26 in a concentration-dependent manner in the treated cells with all treatments, except gelatin-gum Arabic microcapsules. The messenger RNA (mRNA) expression level of the BAX and CASP3 genes in C26 cells being treated with all treatments significantly increased than in untreated cells, and the expression level of the anti-apoptotic factor of the BCL-2 gene decreased in C26 cells simultaneously (p < .05). Although, the combined effect of Omega-3 and L. plantarum and microcapsulated treatments had no more effect on viability and apoptosis gene expression of cancer cells compared to Omega-3 or L. plantarum. In conclusion, combination therapy with fish oil containing Omega-3 and L. plantarum does not improve the anticancer effect of each alone.

Nucleolin; A tumor associated antigen as a potential lung cancer biomarker.

Lung cancer is a primary cause of mortality in many communities. The poor prognosis and clinical outcome of this cancer are mostly attributable to its advanced stage upon diagnosis, and as a result, it places a significant cost on public health across the globe. The majority of patients experience severe adverse effects from conventional therapies that involve nonspecific invasion of both healthy and malignant cells. Furthermore, no particular tumor marker has been developed to evaluate the patients' status and prognosis. NCL as one of the vital nuclear proteins is involved in various cellular activities, including ribosome assembly and rRNA processing. Research have shown that following malignant transformation in lung cancer cells, both the cytosolic and plasma membrane levels of this protein rise dramatically. Furthermore, signaling generated by the surface nucleolin significantly enhances tumor proliferation, differentiation, and angiogenesis. On the other hand, findings showed that altering the size and other properties of tumor cells may influence the expression pattern of nucleolin. Therefore, in the current study, we intend to review the role of nucleolin in the development and progression of lung cancer cells and also evaluate its potential as a prognostic, therapeutic as well as diagnostic marker in lung cancer patients.

Sida rhombifolia Exerts Anti-Proliferative and Pro-Apoptotic Effects in Human Liver Cancer HepG2 Cells in Vitro.

PURPOSE: Modern research revealed that plants belonging to the Sida rhombifolia family (Malvaceae) contain biologically active compounds that make them prone to discovering and developing anticancer drugs. This study aimed to evaluate the apoptosis effects of S. rhombifolia extracts in HepG2 Cell Line was performed. METHODS: The extractions were prepared, and an MTT assay was applied to evaluate its role in decreasing the viability of HepG2 and HFF cells. Phenolic compounds were analyzed using High-performance liquid chromatography (HPLC). FlowCytometry and RT-qPCR evaluated apoptosis was performed to measure the mRNA expression of pro-and anti-apoptotic mediators. RESULTS: The results can be summarized as EtOAc extract was more cytotoxic against the HepG2 cells (IC50= 364.3 µg/mL) compared to MeOH and HEX extracts (720.2 µg/mL) (560.4 µg/mL) with less cytotoxicity in HFF cells (353.2 µg/mL). The HPLC analysis results revealed most phenolic compounds, such as Epicatechin(1.3 mg/g). The EtOAc extract (300 µg/mL) induced 34% apoptosis in HepG2 cells. RT-qPCR data showed upregulation of the proapoptotic gene (Bax) and increased Bax/BCL-2 ratio by S. rhombifolia EtOAc extract (300 µg/mL). CONCLUSION: In conclusion, the EtOAc extract of S. rhombifolia is capable of inducing apoptosis in HepG2 cells through modulation of the mitochondrial pathway, which explains their antitumor activity.

The effect of immunomodulatory properties of naringenin on the inhibition of inflammation and oxidative stress in autoimmune disease models: a systematic review and meta-analysis of preclinical evidence.

BACKGROUND/OBJECTIVE: Naringenin is a member of the flavonoid family that can perform many biological processes to treat a wide range of inflammatory diseases and pathological conditions related to oxidative stress (OS). Naringenin immunomodulatory activities have been the subject of recent research as an effective alternative treatment for autoimmune disorders. The effects of naringenin on the levels of inflammatory biomarkers and OS factors in animal models of autoimmune disorders (ADs) were studied in this meta-analysis. METHODS: Up until January 2022, electronic databases such as Cochrane Library and EMBASE, PubMed, Web of Science, and Scopus were used to conduct a comprehensive literature search in English language. To evaluate the effect of naringenin on inflammatory mediators, such as TNF-α, IL-6, IL-ß, IFN-γ, NF-κB, and nitric oxide, and OS biomarkers, such as CAT, SOD, GPx, GSH and MDA, in AD models, we measured the quality assessment and heterogeneity test using the PRISMA checklist protocol and I2 statistic, respectively. A random-effects model was employed based on the heterogeneity test, and then pooled data were standardized as mean difference (SMD) with a 95% confident interval (CI). RESULTS: We excluded all clinical trials, cell experiment studies, animal studies with different parameters, non-autoimmune disease models, and an inadequate series of studies for quantitative synthesis. Finally, from 627 potentially reports, 12 eligible studies were included in the meta-analysis. Data were collected from several groups. Of these, 153 were in the naringenin group and 149 were in the control group. Our meta-analysis of the pooled data for the parameters of inflammation and OS indicated that naringenin significantly reduced the levels of NF-κB (SMD - 3.77, 95% CI [- 6.03 to - 1.51]; I2 = 80.1%, p = 0.002), IFN-γ (SMD - 6.18, 95% CI [- 8.73 to - 3.62]; I2 = 53.7%, p = 0.115), and NO (SMD - 3.97, 95% CI [- 5.50 to - 2.45]; I2 = 73.4%, p = 0.005), IL-1ß (SMD - 4.23, 95% CI [- 5.09 to - 3.37]; I2 = 0.0%, p = 0.462), IL-6 (SMD - 5.84, 95% CI [- 7.83 to - 3.85]; I2 = 86.5%, p < 0.001), and TNF-α (SMD - 5.10, 95% CI [- 6.34 to - 3.86]; I2 = 74.7%, p < 0.001). These findings also demonstrated the efficacy of naringenin on increasing the levels of CAT (SMD 4.19, 95% CI [1.33 to 7.06]; I2 = 79.9%, p = 0.007), GSH (SMD 4.58, 95% CI [1.64 to 7.51]; I2 = 90.5%, p < 0.001), and GPx (SMD 9.65, 95% CI [2.56 to 16.74]; I2 = 86.6%, p = 0.001) and decreasing the levels of MDA (SMD - 3.65, 95% CI [- 4.80 to - 2.51]; I2 = 69.4%, p = 0.001) than control groups. However, treatment with naringenin showed no statistically difference in SOD activity (SMD 1.89, 95% CI [- 1.11 to 4.89]; I2 = 93.6%, p < 0.001). CONCLUSION: Overall, our findings revealed the immunomodulatory potential of naringenin as an alternative treatment on inhibition of inflammation and OS in several autoimmune-related diseases. Nevertheless, regarding the limitation of clinical trials, strong preclinical models and clinical settings in the future are needed that address the effects of naringenin on ADs. Before large-scale clinical studies, precise human pharmacokinetic investigations are required to determine the dosage ranges and evaluate the initial safety profile of naringenin.

Naringenin induces intrinsic and extrinsic apoptotic signaling pathways in cancer cells: A systematic review and meta-analysis of in vitro and in vivo data.

Various reports show the beneficial effect of naringenin on the development of cancer. We hypothesized that naringenin suppresses cancer cells by activating intrinsic and extrinsic apoptosis pathways. This systematic review and meta-analysis was performed to reveal the effect of naringenin on cancer inhibition in vitro and in vivo by altering apoptotic factors. Literature search was carried out using electronic databases including PubMed, Web of Science, Scopus, Google Scholar, and Embase up to February 2021. The heterogeneity test of the included studies was performed using the PRISMA checklist protocol and I2 statistic, respectively. Pooled standard mean difference and effect size (ES) with 95% confident interval (CI) were used to evaluate each relationship. A total of 32 articles were enrolled in our final analysis. Meta-analysis of the pooled findings for apoptosis, viability percentage, and apoptotic factors determined that treatment with naringenin affects viability and apoptosis in cancer cells in vitro and in vivo. Moreover, the results of in vitro experiments showed that naringenin increases the activity of caspase-3 (ES, 5.04; 95% CI, 2.61-7.47; I2 = 99.9), caspase-9 (ES, 2.99; 95% CI, 2.47-3.51; I2 = 93.7%), caspase-8 (ES, 2.86; 95% CI, 1.11-4.61; I2 = 99.7%), and Bax expression (ES, 2.73; 95% CI, 1.91-3.55; I2 = 99.4%) in cancer cells. It also increased the apoptotic rate and the activity of caspase-3 and caspase-9 in tumor-bearing animals. Overall, our findings highlight the potential therapeutic effects of naringenin in cancer inhibition through caspases cascade.

The effects of apigenin administration on the inhibition of inflammatory responses and oxidative stress in the lung injury models: a systematic review and meta-analysis of preclinical evidence.

BACKGROUND/OBJECTIVE: Apigenin is a member of the flavonoid family that can regulate various biological processes, which is characterized as a treatment of different inflammatory disorders and pathological problems associated with oxidative stress (OS). Recent research has focused on apigenin immunomodulatory properties as a potential treatment for different types of lung injuries. This meta-analysis was designed to determine the impact of apigenin treatment on inflammatory markers and OS parameters in animal models of lung injuries. METHODS: The comprehensive literature search was conducted using electronic databases such as Google Scholar, PubMed, Web of Science, Scopus, and Embase up to August 2021. To assess apigenin's effect on inflammatory mediators and OS biomarkers in lung injury animal models, we used the I2 statistic to determine the heterogeneity. We then pooled data as standardized mean difference (SMD) with a 95% confidence interval (CI). RESULTS: Our meta-analysis of the pooled data for inflammatory biomarkers demonstrated that the apigenin administration significantly decreased the NF-κB expression (SMD - 1.60, 95% CI [- 2.93 to - 0.26]; I2 = 89.0%, p < 0.001), IL-1ß (SMD - 4.30, 95% CI [- 6.24 to - 2.37]; I2 = 67.3%, p = 0.047), IL-6 (SMD - 4.10, 95% CI [- 5.04 to - 3.16]; I2 = 72.6%, p < 0.001), TNF-α (SMD - 3.74, 95% CI [- 4.67 to - 2.82]; I2 = 84.1%, p < 0.001), and TNF-α gene expression (SMD - 3.44, 95% CI [- 4.44 to - 2.43]; I2 = 0.0%, p = 0.622). This study also indicated the efficacy of apigenin in increasing the level of CAT (SMD 4.56, 95% CI [3.57 to 5.55]; I2 = 15.3%, p = 3.15), GSH (SMD 5.12, 95% CI [3.53 to 6.70]; I2 = 77.6%, p < 0.001), and SOD (SMD 3.45, 95% CI [2.50 to 4.40]; I2 = 79.2%, p < 0.001), and decreasing the level of MDA (SMD - 3.87, 95% CI [- 5.25 to - 2.49]; I2 = 80.3%, p < 0.001) and MPO (SMD - 4.02, 95% CI [- 5.64 to - 2.40]; I2 = 88.9%, p < 0.001), TGF- ß (SMD - 3.81, 95% CI [- 4.91 to - 2.70]; I2 = 73.4%, p = 0.001) and W/D level (SMD - 3.22, 95% CI [- 4.47 to - 1.97]; I2 = 82.1%, p < 0.001) than control groups. CONCLUSION: Overall, our findings showed the immunomodulatory potential of apigenin as an alternative treatment for the suppression of inflammatory responses and OS in different types of lung injury diseases. Nevertheless, due to the paucity of clinical studies, reliable preclinical models, and clinical settings, evaluating the influence of apigenin on lung injury is required in the future. Before conducting large-scale clinical trials, detailed human pharmacokinetic studies are also needed to establish dosage ranges and determine the initial safety and tolerability of apigenin.