The Therapeutic Application of Hydrogen in Cancer: The Potential and Challenges.

Hydrogen therapy has emerged as a possible approach for both preventing and treating cancer. Cancers are often associated with oxidative stress and chronic inflammation. Hydrogen, with its unique physiological functions and characteristics, exhibits antioxidant, anti-inflammatory, and anti-apoptotic properties, making it an attractive candidate for cancer treatment. Through its ability to mitigate oxidative damage, modulate inflammatory responses, and sustain cellular viability, hydrogen demonstrates significant potential in preventing cancer recurrence and improving treatment outcomes. Preclinical studies have shown the efficacy of hydrogen therapy in several cancer types, highlighting its ability to enhance the effectiveness of conventional treatments while reducing associated side effects. Furthermore, hydrogen therapy has been found to be safe and well-tolerated in clinical settings. Nonetheless, additional investigations are necessary to improve a comprehensive understanding of the mechanisms underlying hydrogen's therapeutic potential and refine the administration and dosage protocols. However, further clinical trials are still needed to explore its safety profile and capacity. In aggregate, hydrogen therapy represents an innovative and promising treatment for several malignancies.

The prognostic, diagnostic, and therapeutic impact of Long noncoding RNAs in gastric cancer.

Gastric cancer (GC), ranking as the third deadliest cancer globally, faces challenges of late diagnosis and limited treatment efficacy. Long non-coding RNAs (lncRNAs) emerge as valuable treasured targets for cancer prognosis, diagnosis, and therapy, given their high specificity, convenient non-invasive detection in body fluids, and crucial roles in diverse physiological and pathological processes. Research indicates the significant involvement of lncRNAs in various aspects of GC pathogenesis, including initiation, metastasis, and recurrence, underscoring their potential as novel diagnostic and prognostic biomarkers, as well as therapeutic targets for GC. Despite existing challenges in the clinical application of lncRNAs in GC, the evolving landscape of lncRNA molecular biology holds promise for advancing the survival and treatment outcomes of gastric cancer patients. This review provides insights into recent studies on lncRNAs in gastric cancer, elucidating their molecular mechanisms and exploring the potential clinical applications in GC.

Bioinformatics analysis and machine learning approach applied to the identification of novel key genes involved in non-alcoholic fatty liver disease.

Non-alcoholic fatty liver disease (NAFLD) comprises a range of chronic liver diseases that result from the accumulation of excess triglycerides in the liver, and which, in its early phases, is categorized NAFLD, or hepato-steatosis with pure fatty liver. The mortality rate of non-alcoholic steatohepatitis (NASH) is more than NAFLD; therefore, diagnosing the disease in its early stages may decrease liver damage and increase the survival rate. In the current study, we screened the gene expression data of NAFLD patients and control samples from the public dataset GEO to detect DEGs. Then, the correlation betweenbetween the top selected DEGs and clinical data was evaluated. In the present study, two GEO datasets (GSE48452, GSE126848) were downloaded. The dysregulated expressed genes (DEGs) were identified by machine learning methods (Penalize regression models). Then, the shared DEGs between the two training datasets were validated using validation datasets. ROC-curve analysis was used to identify diagnostic markers. R software analyzed the interactions between DEGs, clinical data, and fatty liver. Ten novel genes, including ABCF1, SART3, APC5, NONO, KAT7, ZPR1, RABGAP1, SLC7A8, SPAG9, and KAT6A were found to have a differential expression between NAFLD and healthy individuals. Based on validation results and ROC analysis, NR4A2 and IGFBP1b were identified as diagnostic markers. These key genes may be predictive markers for the development of fatty liver. It is recommended that these key genes are assessed further as possible predictive markers during the development of fatty liver.

Down regulation of Cathepsin W is associated with poor prognosis in pancreatic cancer.

Pancreatic ductal adenocarcinoma (PDAC) is associated with a very poor prognosis. Therefore, there has been a focus on identifying new biomarkers for its early diagnosis and the prediction of patient survival. Genome-wide RNA and microRNA sequencing, bioinformatics and Machine Learning approaches to identify differentially expressed genes (DEGs), followed by validation in an additional cohort of PDAC patients has been undertaken. To identify DEGs, genome RNA sequencing and clinical data from pancreatic cancer patients were extracted from The Cancer Genome Atlas Database (TCGA). We used Kaplan-Meier analysis of survival curves was used to assess prognostic biomarkers. Ensemble learning, Random Forest (RF), Max Voting, Adaboost, Gradient boosting machines (GBM), and Extreme Gradient Boosting (XGB) techniques were used, and Gradient boosting machines (GBM) were selected with 100% accuracy for analysis. Moreover, protein-protein interaction (PPI), molecular pathways, concomitant expression of DEGs, and correlations between DEGs and clinical data were analyzed. We have evaluated candidate genes, miRNAs, and a combination of these obtained from machine learning algorithms and survival analysis. The results of Machine learning identified 23 genes with negative regulation, five genes with positive regulation, seven microRNAs with negative regulation, and 20 microRNAs with positive regulation in PDAC. Key genes BMF, FRMD4A, ADAP2, PPP1R17, and CACNG3 had the highest coefficient in the advanced stages of the disease. In addition, the survival analysis showed decreased expression of hsa.miR.642a, hsa.mir.363, CD22, BTNL9, and CTSW and overexpression of hsa.miR.153.1, hsa.miR.539, hsa.miR.412 reduced survival rate. CTSW was identified as a novel genetic marker and this was validated using RT-PCR. Machine learning algorithms may be used to Identify key dysregulated genes/miRNAs involved in the disease pathogenesis can be used to detect patients in earlier stages. Our data also demonstrated the prognostic and diagnostic value of CTSW in PDAC.

The Prognostic Value of ASPHD1 and ZBTB12 in Colorectal Cancer: A Machine Learning-Based Integrated Bioinformatics Approach.

Introduction: Colorectal cancer (CRC) is a common cancer associated with poor outcomes, underscoring a need for the identification of novel prognostic and therapeutic targets to improve outcomes. This study aimed to identify genetic variants and differentially expressed genes (DEGs) using genome-wide DNA and RNA sequencing followed by validation in a large cohort of patients with CRC. Methods: Whole genome and gene expression profiling were used to identify DEGs and genetic alterations in 146 patients with CRC. Gene Ontology, Reactom, GSEA, and Human Disease Ontology were employed to study the biological process and pathways involved in CRC. Survival analysis on dysregulated genes in patients with CRC was conducted using Cox regression and Kaplan-Meier analysis. The STRING database was used to construct a protein-protein interaction (PPI) network. Moreover, candidate genes were subjected to ML-based analysis and the Receiver operating characteristic (ROC) curve. Subsequently, the expression of the identified genes was evaluated by Real-time PCR (RT-PCR) in another cohort of 64 patients with CRC. Gene variants affecting the regulation of candidate gene expressions were further validated followed by Whole Exome Sequencing (WES) in 15 patients with CRC. Results: A total of 3576 DEGs in the early stages of CRC and 2985 DEGs in the advanced stages of CRC were identified. ASPHD1 and ZBTB12 genes were identified as potential prognostic markers. Moreover, the combination of ASPHD and ZBTB12 genes was sensitive, and the two were considered specific markers, with an area under the curve (AUC) of 0.934, 1.00, and 0.986, respectively. The expression levels of these two genes were higher in patients with CRC. Moreover, our data identified two novel genetic variants-the rs925939730 variant in ASPHD1 and the rs1428982750 variant in ZBTB1-as being potentially involved in the regulation of gene expression. Conclusions: Our findings provide a proof of concept for the prognostic values of two novel genes-ASPHD1 and ZBTB12-and their associated variants (rs925939730 and rs1428982750) in CRC, supporting further functional analyses to evaluate the value of emerging biomarkers in colorectal cancer.

Breast cancer prediction using different machine learning methods applying multi factors.

OBJECTIVE: Breast cancer (BC) is a multifactorial disease and is one of the most common cancers globally. This study aimed to compare different machine learning (ML) techniques to develop a comprehensive breast cancer risk prediction model based on features of various factors. METHODS: The population sample contained 810 records (115 cancer patients and 695 healthy individuals). 45 attributes out of 85 were selected based on the opinion of experts. These selected attributes are in genetic, biochemical, biomarker, gender, demographic and pathological factors. 13 Machine learning models were trained with proposed attributes and coefficient of attributes and internal relationships were calculated. RESULT: Compared to other methods random forest (RF) has higher performance (accuracy 99.26%, precision 99%, and area under the curve (AUC) 99%). The results of assessing the impact and correlation of variables using the RF method based on PCA indicated that pathology, biomarker, biochemistry, gene, and demographic factors with a coefficient of 0.35, 0.23, 0.15, 0.14, and 0.13 respectively, affected the risk of BC (r2 = 0.54). CONCLUSION: Breast cancer has several risk factors. Medical experts use these risk factors for early diagnosis. Therefore, identifying related risk factors and their effect can increase the accuracy of diagnosis. Considering the broad features for predicting breast cancer leads to the development of a comprehensive prediction model. In this study, using RF technique a breast cancer prediction model with 99.3% accuracy was developed based on multifactorial features.

Extracellular vesicles: Emerging mediators of cell communication in gastrointestinal cancers exhibiting metabolic abnormalities.

There is a complex interaction between pro-tumoural and anti-tumoural networks in the tumour microenvironment (TME). Throughout tumourigenesis, communication between malignant cells and various cells of the TME contributes to metabolic reprogramming. Tumour Dysregulation of metabolic pathways offer an evolutional advantage in the TME and enhance the tumour progression, invasiveness, and metastasis. Therefore, understanding these interactions within the TME is crucial for the development of innovative cancer treatments. Extracellular vesicles (EVs) serve as carriers of various materials that include microRNAs, proteins, and lipids that play a vital role in the communication between tumour cells and non-tumour cells. EVs are actively involved in the metabolic reprogramming process. This review summarized recent findings regarding the involvement of EVs in the metabolic reprogramming of various cells in the TME of gastrointestinal cancers. Additionally, we highlight identified microRNAs involved in the reprogramming process in this group of cancers and explained the abnormal tumour metabolism targeted by exosomal cargos as well as the novel potential therapeutic approaches.

Prognostic value of primary tumor location in colorectal cancer: an updated meta-analysis.

The clinical, histological, and molecular differences between right-sided colon cancer (RCC) and left-sided colon cancer (RCC) have received considerable attention. Over the past decade, many articles have been published concerning the association between primary tumor location (PTL) of colorectal cancer and survival outcomes. Therefore, there is a growing need for an updated meta-analysis integrating the outcomes of recent studies to determine the prognostic role of right vs left-sidedness of PTL in patients with colorectal cancer. We conducted a comprehensive database review using PubMed, SCOPUS, and Cochrane library databases from February 2016 to March 2023 for prospective or retrospective studies reporting data on overall survival (OS) and cancer-specific survival (CSS) of RCC compared with LCC. A total of 60 cohort studies comprising 1,494,445 patients were included in the meta-analysis. We demonstrated that RCC is associated with a significantly increased risk of death compared with LCC by 25% (hazard ratio (HR), 1.25; 95% confidence interval (CI), 1.19-1.31; I2 = 78.4%; Z = 43.68). Results showed that patients with RCC have a worse OS compared with LCC only in advanced stages (Stage III: HR, 1.275; 95% CI 1.16-1.4; P = 0.0002; I2 = 85.8%; Stage IV: HR, 1.34; 95% CI 1.25-1.44; P < 0.0001; I2 = 69.2%) but not in primary stages (Stage I/II: HR, 1.275; 95% CI 1.16-1.4; P = 0.0002; I2 = 85.8%). Moreover, a meta-analysis of 13 studies including 812,644 patients revealed that there is no significant difference in CSS between RCC and LCC (HR, 1.121; 95% CI 0.97-1.3; P = 0.112). Findings from the present meta-analysis highlight the importance of PTL in clinical decision-making for patients with CRC, especially in advanced stages. We provide further evidence supporting the hypothesis that RCC and LCC are distinct disease entities that should be managed differently.

Association of dietary intake and cervical cancer: a prevention strategy.

INTRODUCTION: Cervical cancer is one of lethal cancers in women. As a global concern, identifying important factors of cancer is a useful strategy for prevention. Due to the role of diet/nutrition factors for cancer, the purpose of our study was to determine the impact of 150 nutrition/vitamin factors and 50 non-nutritional factor in cervical cancer and phase. METHODS: Population samples of 2088 healthy subjects and patients with cervical cancer were investigated. 200 factors such as vitamin E, B1, B6, fruits, HPV, and age were gathered. Deep learning, Decision tree, and correlation matrix were used for modeling and identifying important factors. SPSS 26, R4.0.3, and Rapid miner were utilized for implementation. RESULTS: Our findings indicated that zinc, Iron, Niacin, Potassium, Phosphorous, and Cooper have a beneficial impact in reducing the risk of cervical cancer and progression of phase in Iranian women, as well as Salt, snacks and milk Were identified as high-risk food factors (P value < 0.05 and coefficient correlation > 0.6). Also, alcohol, and sex patient with two groups, HPV positive have an impact on cervical cancer incidence. Phosphorus and selenium in the Micronutrients category (R2 = 0.85, AUC = 0.993) and polyunsaturated fatty acid and salt in the Macronutrients category and other categories of nutrients were identified as the most effective factors in cervical cancer using deep learning (R2 = 0.93, AUC = 0.999). CONCLUSIONS: A diet and rich nutrition can be helpful for the prevention of cervix cancer and may reduce the risk of disease. Additional research is necessary for different countries.

Identification of ZMYND19 as a novel biomarker of colorectal cancer: RNA-sequencing and machine learning analysis.

Colorectal cancer (CRC) is the third most common cause of cancer-related deaths. The five-year relative survival rate for CRC is estimated to be approximately 90% for patients diagnosed with early stages and 14% for those diagnosed at an advanced stages of disease, respectively. Hence, the development of accurate prognostic markers is required. Bioinformatics enables the identification of dysregulated pathways and novel biomarkers. RNA expression profiling was performed in CRC patients from the TCGA database using a Machine Learning approach to identify differential expression genes (DEGs). Survival curves were assessed using Kaplan-Meier analysis to identify prognostic biomarkers. Furthermore, the molecular pathways, protein-protein interaction, the co-expression of DEGs, and the correlation between DEGs and clinical data have been evaluated. The diagnostic markers were then determined based on machine learning analysis. The results indicated that key upregulated genes are associated with the RNA processing and heterocycle metabolic process, including C10orf2, NOP2, DKC1, BYSL, RRP12, PUS7, MTHFD1L, and PPAT. Furthermore, the survival analysis identified NOP58, OSBPL3, DNAJC2, and ZMYND19 as prognostic markers. The combineROC curve analysis indicated that the combination of C10orf2 -PPAT- ZMYND19 can be considered as diagnostic markers with sensitivity, specificity, and AUC values of 0.98, 1.00, and 0.99, respectively. Eventually, ZMYND19 gene was validated in CRC patients. In conclusion, novel biomarkers of CRC have been identified that may be a promising strategy for early diagnosis, potential treatment, and better prognosis.

Machine learning algorithms reveal potential miRNAs biomarkers in gastric cancer.

Gastric cancer is the high mortality rate cancers globally, and the current survival rate is 30% even with the use of combination therapies. Recently, mounting evidence indicates the potential role of miRNAs in the diagnosis and assessing the prognosis of cancers. In the state-of-art research in cancer, machine-learning (ML) has gained increasing attention to find clinically useful biomarkers. The present study aimed to identify potential diagnostic and prognostic miRNAs in GC with the application of ML. Using the TCGA database and ML algorithms such as Support Vector Machine (SVM), Random Forest, k-NN, etc., a panel of 29 was obtained. Among the ML algorithms, SVM was chosen (AUC:88.5%, Accuracy:93% in GC). To find common molecular mechanisms of the miRNAs, their common gene targets were predicted using online databases such as miRWalk, miRDB, and Targetscan. Functional and enrichment analyzes were performed using Gene Ontology (GO) and Kyoto Database of Genes and Genomes (KEGG), as well as identification of protein-protein interactions (PPI) using the STRING database. Pathway analysis of the target genes revealed the involvement of several cancer-related pathways including miRNA mediated inhibition of translation, regulation of gene expression by genetic imprinting, and the Wnt signaling pathway. Survival and ROC curve analysis showed that the expression levels of hsa-miR-21, hsa-miR-133a, hsa-miR-146b, and hsa-miR-29c were associated with higher mortality and potentially earlier detection of GC patients. A panel of dysregulated miRNAs that may serve as reliable biomarkers for gastric cancer were identified using machine learning, which represents a powerful tool in biomarker identification.

Circulating Tumor Cells and Cell-free Nucleic Acids as Biomarkers in Colorectal Cancer.

Colorectal cancer (CRC) is currently the second most prevalent cancer diagnosed in women and the third most common kind of cancer in men. Despite tremendous efforts and advancements in diagnostic approaches and treatment options, the mortality rate of CRC accounts for around one million each year globally. The five-year survival rate of CRC is reported to be approximately 14 percent for patients diagnosed at an advanced stage. Due to its significant associated mortality and morbidity, diagnostic tools to identify the disease at its early stages are urgently required. Early diagnosis may lead to better outcomes. The gold standard approach for CRC diagnosis is colonoscopy with biopsy. However, it is an invasive process with a risk of complications and discomfort for the patient. Moreover, it is usually performed in symptomatic or high-risk individuals and therefore, asymptomatic patients might be missed. Thus, alternative non-invasive diagnostic techniques are required to improve CRC outcomes. The new era of personalized medicine is identifying novel biomarkers associated with overall survival and clinical outcomes. Recently, liquid biopsy, a minimally invasive analysis of body fluid biomarkers, has gained attention for diagnosis, evaluation of prognosis, and follow-up of patients with CRC. Several previous studies have demonstrated that this novel approach allows for better understanding of CRC tumor biology and leads to an improvement in clinical outcomes. Here, we explain the enrichment and detection methods of circulating biomarkers, including CTCs, ctDNA, miRNA, lncRNA, and circRNA. Furthermore, we provide an overview on their clinical potential as diagnostic, prognostic, and predictive biomarkers for CRC.

Integrated analysis of multi-omics data for the discovery of biomarkers and therapeutic targets for colorectal cancer.

The considerable burden of colorectal cancer and the rising trend in young adults emphasize the necessity of understanding its underlying mechanisms, providing new diagnostic and prognostic markers, and improving therapeutic approaches. Precision medicine is a new trend all over the world and identification of novel biomarkers and therapeutic targets is a step forward towards this trend. In this context, multi-omics data and integrated analysis are being investigated to develop personalized medicine in the management of colorectal cancer. Given the large amount of data from multi-omics approach, data integration and analysis is a great challenge. In this Review, we summarize how statistical and machine learning techniques are applied to analyze multi-omics data and how it contributes to the discovery of useful diagnostic and prognostic biomarkers and therapeutic targets. Moreover, we discuss the importance of these biomarkers and therapeutic targets in the clinical management of colorectal cancer in the future. Taken together, integrated analysis of multi-omics data has great potential for finding novel diagnostic and prognostic biomarkers and therapeutic targets, however, there are still challenges to overcome in future studies.

Therapeutic Potential of Herbal Medicine against Non-alcoholic Fatty Liver Disease.

Non-alcoholic fatty liver disease (NAFLD) is a common metabolic disorder associated with obesity, diabetes mellitus, dyslipidemia, and cardiovascular disease. A "multiple hit" model has been a widely accepted explanation for the disease's complicated pathogenesis. Despite advances in our knowledge of the processes underlying NAFLD, no conventional pharmaceutical therapy exists. The only currently approved option is to make lifestyle modifications, such as dietary and physical activity changes. The use of medicinal plants in the treatment of NAFLD has recently gained interest. Thus, we review the current knowledge about these agents based on clinical and preclinical studies. Moreover, the association between NAFLD and colorectal cancer (CRC), one of the most common and lethal malignancies, has recently emerged as a new study area. We overview the shared dysregulated pathways and the potential therapeutic effect of herbal medicines for CRC prevention in patients with NAFLD.

Deep Learning for Acute Myeloid Leukemia Diagnosis.

By changing the lifestyle and increasing the cancer incidence, accurate diagnosis becomes a significant medical action. Today, DNA microarray is widely used in cancer diagnosis and screening since it is able to measure gene expression levels. Analyzing them by using common statistical methods is not suitable because of the high gene expression data dimensions. So, this study aims to use new techniques to diagnose acute myeloid leukemia. In this study, the leukemia microarray gene data, contenting 22283 genes, was extracted from the Gene Expression Omnibus repository. Initial preprocessing was applied by using a normalization test and principal component analysis in Python. Then DNNs neural network designed and implemented to the data and finally results cross-validated by classifiers. The normalization test was significant (P>0.05) and the results show the PCA gene segregation potential and independence of cancer and healthy cells. The results accuracy for single-layer neural network and DNNs deep learning network with three hidden layers are 63.33 and 96.67, respectively. Using new methods such as deep learning can improve diagnosis accuracy and performance compared to the old methods. It is recommended to use these methods in cancer diagnosis and effective gene selection in various types of cancer.

Effects of synbiotic supplementation on gut microbiome, serum level of TNF-α, and expression of microRNA-126 and microRNA-146a in patients with type 2 diabetes mellitus: study protocol for a double-blind controlled randomized clinical trial.

BACKGROUND: The dramatic increase in the prevalence of type 2 diabetes mellitus (T2DM) is a global major challenge to health. Circulating microRNAs have been suggested as promising biomarkers for different disorders such as diabetes. Imbalances in the gut microbiome have been revealed to contribute to the progression of multiple diseases including T2DM. Recently, the consumption of probiotics and synbiotics in the treatment of various diseases has shown a substantial growth. The anti-diabetes and anti-inflammatory effects of synbiotics have been indicated, which may be due to their beneficial effects on the gut microbiome. However, further research is needed to assess the effects of synbiotics on the microbiota and their impacts on expression of microRNAs relating to T2DM. Thus, we will aim to assess the effects of synbiotics on microbiota, serum level of tumor necrosis factor-α (TNF-α), and expression of microRNA-126 and microRNA-146a in patients with T2DM. METHODS: Seventy-two patients with T2DM will be recruited in this double-blind randomized parallel placebo-controlled clinical trial. After block matching based on age and sex, participants will be randomly assigned to receive 1000 mg/day synbiotic (Familact) or placebo for 12 weeks. The microRNA-126 and microRNA-146a expression levels will be measured by real-time polymerase chain reaction and serum TNF-α level will be assessed by enzyme-linked immunosorbent assay kit at the beginning and at the end of the study. Determination of the gut microbiota will be done by quantitative polymerase chain reaction methods at baseline and at the end of the trial. Biochemical assessments (glycemic and lipid profiles) will also be conducted at onset and end of the study. DISCUSSION: This is the first randomized controlled trial that will determine the effect of synbiotic supplementation on the gut microbiota and its probable impacts on serum levels of TNF-α and expression of related microRNAs in patients with T2DM. TRIAL REGISTRATION: Iranian Registry of Clinical Trials: IRCT20180624040228N2. Registered on 27 March 2019. http://www.irct.ir/trial/38371.

Health Education Serious Games Targeting Health Care Providers, Patients, and Public Health Users: Scoping Review.

BACKGROUND: Serious educational games have shown effectiveness in improving various health outcomes. Previous reviews of health education games have focused on specific diseases, certain medical subjects, fixed target groups, or limited outcomes of interest. Given the recent surge in health game studies, a scoping review of health education games is needed to provide an updated overview of various aspects of such serious games. OBJECTIVE: This study aimed to conduct a scoping review of the design and evaluation of serious educational games for health targeting health care providers, patients, and public (health) users. METHODS: We identified 2313 studies using a unique combination of keywords in the PubMed and ScienceDirect databases. A total of 161 studies were included in this review after removing duplicates (n=55) and excluding studies not meeting our inclusion criteria (1917 based on title and abstract and 180 after reviewing the full text). The results were stratified based on games targeting health care providers, patients, and public users. RESULTS: Most health education games were developed and evaluated in America (82/161, 50.9%) and Europe (64/161, 39.8%), with a considerable number of studies published after 2012. We discovered 58.4% (94/161) of studies aiming to improve knowledge learning and 41.6% (67/161) to enhance skill development. The studies targeted various categories of end users: health care providers (42/161, 26.1%), patients (38/161, 23.6%), public users (75/161, 46.6%), and a mix of users (6/161, 3.7%). Among games targeting patients, only 13% (6/44) targeted a specific disease, whereas a growing majority targeted lifestyle behaviors, social interactions, cognition, and generic health issues (eg, safety and nutrition). Among 101 studies reporting gameplay specifications, the most common gameplay duration was 30 to 45 min. Of the 61 studies reporting game repetition, only 14% (9/61) of the games allowed the users to play the game with unlimited repetitions. From 32 studies that measured follow-up duration after the game intervention, only 1 study reported a 2-year postintervention follow-up. More than 57.7% (93/161) of the games did not have a multidisciplinary team to design, develop, or assess the game. CONCLUSIONS: Serious games are increasingly used for health education targeting a variety of end users. This study offers an updated scoping review of the studies assessing the value of serious games in improving health education. The results showed a promising trend in diversifying the application of health education games that go beyond a specific medical condition. However, our findings indicate the need for health education game development and adoption in developing countries and the need to focus on multidisciplinary teamwork in designing effective health education games. Furthermore, future health games should expand the duration and repetition of games and increase the length of the follow-up assessments to provide evidence on long-term effectiveness.

Effect of saffron (Crocus sativus L.) on lipid profile, glycemic indices and antioxidant status among overweight/obese prediabetic individuals: A double-blinded, randomized controlled trial.

OBJECTIVE: To investigate the effects of saffron (Crocus sativus L.) on lipid profile, glycemic and antioxidant status in overweight/obese individuals with prediabetes. METHODS: In this randomized, double-blind, placebo-controlled trial, the prediabetic patients were randomly assigned to receive saffron (15 mg/d) pills or placebo for eight weeks. Serum levels of lipid profile, fasting blood sugar (FBS), glycosylated hemoglobin (HbA1c), blood urea nitrogen (BUN), creatinine, and diphenylpycrylhydrazyl (DPPH) radical scavenging activity were assessed biochemically at baseline and at 8 weeks after treatment. The adverse events, if any, were also recorded. RESULTS: Seventy-five of participants (36 in treatment and 39 in placebo groups) completed the study. Within-group comparisons revealed a significant effect of saffron supplementation on FBS (118.11 ± 3.55 vs. 109.14 ± 6.23), HbA1c (5.85 ± 0.12 vs. 5.70 ± 0.11), and DPPH (11.06 ± 3.24 vs. 13.46 ± 3.33) levels (P < 0.005 for all). In adjusting models, there was a significant reduction in FBS by -7.97 mg/dL, and HbA1c by -0.15% in saffron group compared to placebo. Moreover, saffron intake tended to increase in DPPH radical scavenging activity (2.4% vs. -0.85% in saffron and placebo groups, respectively). However, no significant changes in anthropometric measures, lipid profile, and renal markers were observed after saffron intake compared with placebo. CONCLUSION: Saffron supplementation could improve glycemic and antioxidant indices in overweight/obese individuals with prediabetes, however, no beneficial effect was observed on lipid profile and anthropometric parameters. (IRCT20120913010826N19).

The effect of resveratrol supplementation on the expression levels of factors associated with cellular senescence and sCD163/sTWEAK ratio in patients with type 2 diabetes mellitus: study protocol for a double-blind controlled randomised clinical trial.

INTRODUCTION: Over the past decades, the number of people with type 2 diabetes (T2D) has increased globally. One of the major complications in these patients is cardiovascular disease; it seems that the cell proliferation inhibition can improve vascular function in these patients. It is proposed that peroxisome proliferator-activated receptor alpha (PPARα) can induce cell cycle arrest via cyclin-dependent kinase inhibitor 2A (p16) activation. Also, it has been shown that phosphorylated tumour suppressor protein p53 is involved in cell senescence by cyclin-dependent kinase inhibitor 1 (p21) upregulation. Resveratrol is a natural polyphenol and appears to improve the vascular function through the mentioned pathways. We will aim to evaluate the effects of resveratrol supplementation on mRNA expression of PPARα, p53, p21 and p16 in patients with T2D. We will also measure serum levels of cluster of differentiation 163 (CD163) and tumour necrosis factor (TNF)-like weak inducer of apoptosis (TWEAK) as the indicators of cardiovascular status. METHODS AND ANALYSIS: Seventy-two subjects suffering from T2D will participate in this double-blind randomised parallel placebo-controlled clinical trial. Participants will be randomly assigned to receive 1000 mg/day trans-resveratrol or placebo (methyl cellulose) for 8 weeks. The mRNA expression levels of PPARα, p53, p21 and p16 genes will be assessed using real-time PCR and serum CD163 and TWEAK levels will be measured using commercially available ELISA kits at baseline and the end of the study. Clinical outcome parameters (glycaemic and lipid profiles and body composition) will also be measured before and after study duration. ETHICS AND DISSEMINATION: The study is performed in agreement with the Declaration of Helsinki and is approved by the Ethics Committee of the Shahid Sadoughi University of Medical Sciences (no: ir.ssu.sph.rec.1396.120). The results will be published in scientific journals. TRIAL REGISTRATION NUMBER: IRCT20171118037528N1; Pre-results.

Preparation of Iron-Containing Schiff Base and Ionic Liquid Based Bifunctional Periodic Mesoporous Organosilica and Its Application in the Synthesis of 3,4-Dihydropyrimidinones.

A novel iron-containing Schiff base and ionic liquid based bifunctional periodic mesoporous organosilica (Fe@SBIL-BPMO) was prepared, characterized, and its catalytic application was developed. The SBIL-BPMO was first prepared by the grafting of 3-aminopropyltrimethoxysilane on an ionic-liquid-based PMO followed by treatment with 2-hydroxybenzaldehyde in toluene at reflux. This material was then reacted with Fe(NO3 )3 ⋅9 H2 O to afford the Fe@BPMO-SBIL nanocatalyst. The catalyst was characterized by thermogravimetric analysis, nitrogen sorption experiments, diffuse reflectance FTIR spectroscopy, low-angle powder XRD, and TEM. The Fe@SBIL-BPMO catalyst was successfully applied in the one-pot synthesis of 3,4-dihydropyrimidinone/thione derivatives under solvent-free reactions. The stability, reactivity, and reusability of the catalyst under the reaction conditions have also been investigated.