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.

Decision fusion in healthcare and medicine: a narrative review.

OBJECTIVE: To provide an overview of the decision fusion (DF) technique and describe the applications of the technique in healthcare and medicine at prevention, diagnosis, treatment and administrative levels. BACKGROUND: The rapid development of technology over the past 20 years has led to an explosion in data growth in various industries, like healthcare. Big data analysis within the healthcare systems is essential for arriving to a value-based decision over a period of time. Diversity and uncertainty in big data analytics have made it impossible to analyze data by using conventional data mining techniques and thus alternative solutions are required. DF is a form of data fusion techniques that could increase the accuracy of diagnosis and facilitate interpretation, summarization and sharing of information. METHODS: We conducted a review of articles published between January 1980 and December 2020 from various databases such as Google Scholar, IEEE, PubMed, Science Direct, Scopus and web of science using the keywords decision fusion (DF), information fusion, healthcare, medicine and big data. A total of 141 articles were included in this narrative review. CONCLUSIONS: Given the importance of big data analysis in reducing costs and improving the quality of healthcare; along with the potential role of DF in big data analysis, it is recommended to know the full potential of this technique including the advantages, challenges and applications of the technique before its use. Future studies should focus on describing the methodology and types of data used for its applications within the healthcare sector.

Validity and Reproducibility of a Semiquantitative Multiple-Choice Food Frequency Questionnaire in Iranian Adults.

Previous multiple-choice food-based food frequency questionnaires (FFQs) were not validated against weighed dietary records (WDRs) in Iran. This study investigated the validity and reproducibility of a multiple-choice semi-quantitative food frequency questionnaire (SQ-FFQ) in adults living in central Iran. Patients with diabetes and their spouses were asked to complete 3 SQ-FFQs by interview, and nine 3-day WDRs, over 9 months. They provided 2 blood samples to assess serum calcium, magnesium, zinc, and vitamin C levels. The Pearson and intraclass correlation coefficients were calculated to assess reproducibility and validity. The degree of misclassification was explored using a contingency table of quartiles which compare the information between third FFQ and WDRs. The method of triads was incorporated to assess validity coefficients between estimated intakes using third FFQ, WDRs, and biochemical markers and assumed true intakes. A total of 180 participants aged 48.9 ± 8.4 years completed the study. Compared to WDRs, FFQs overestimated all nutrient intakes except for iron. The median intraclass correlation between FFQs was 0.56. The median de-attenuated, age, sex, and education adjusted partial correlation coefficients for validity were 0.17 and 0.26 for FFQ1-WDRs and FFQ3-WDRs, respectively. The FFQ3 validity coefficients for vitamin C, calcium, magnesium, and zinc were 0.13, 0.62, 0.89, and 0.66, respectively, using the triads method. The median exact agreement and complete disagreement between FFQ3 and WDRs were 33% and 6%, respectively. The SQ-FFQ seems to be an acceptable tool to assess the long-term dietary intake for future large-scale studies in this population.

Home quarantine is a useful strategy to prevent the coronavirus outbreak: Identifying the reasons for non-compliance in some Iranians.

INTRODUCTION: The coronavirus outbreak has become a worrying issue and some people refuse to stay at home. Therefore, this study aims to identify the reasons behind some Iranian people's refusal to stay at home to prevent further virus transmission. METHOD: This cross-sectional study was conducted on postgraduate students in Iran. A questionnaire was designed based on 50 experts' opinions by using the Delphi method and 203 students completed the designed questionnaire in telegram groups. RESULTS: 35% of participants were upper 30 years of age, 70.4% were female, 74.4% had no coronavirus infection among their relatives, and 54.7% of them were Ph.D. candidates. The relations between "unclear accountability of events by some officials" and age as well as "failure to provide dissenting viewpoints and critical comments" and age were statistically significant (p = 0.027، p = 0.014). Moreover the relation between coronavirus infected relative and "persistent beliefs" was statistically significant (p = 0.014). The Chi-square test showed that gender, degree, resident and education province did not affect questions answering. The greatest agreement with questions is as following: lack of real situation understanding; 89.7%, people's livelihoods, and lack of government planning for low-income groups support; 86.7%, lack of people's knowledge concerning the coronavirus; 80.8%, lack of communicative educations for crisis situations; 79.8%, false assurance as well as minimizes the risks; 78.3%. CONCLUSION: Identifying the non-compliance factors with health recommendations can guide health care providers and managers to implementation of beneficial intervention.

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.

Relationship of zinc status with depression and anxiety among elderly population.

BACKGROUND & AIMS: Population aging is a global challenge. Old populations are prone to zinc deficiency. This study aimed to determine the association of zinc status with depression and anxiety among men and women aged 60+ years old. METHODS: In this cross-sectional study, 297 elderly participants (144 males and 153 females) were studied. The dietary and serum zinc were assessed using a three-day dietary record and an auto-analyzer, respectively. Depression and anxiety were measured using Geriatric Depression Scale and Hamilton Anxiety Rating Scale, respectively. Chi-squared test was used to compare qualitative variables. Multiple logistic regression analysis was conducted to assess relationship between zinc status and depression/anxiety. RESULTS: The total zinc deficiency based on serum values was 23.2%. Dietary intake of zinc in 72.4%of participants was less than of the Estimated Average Requirement (EAR). The total depression prevalence was 42.2%. Moreover, 52.5% of the participants suffered from anxiety. The odds of depression among participants in the third tertile of serum zinc concentration was 51% lower than those in the first tertile (OR = 0.49, CI = 0.25-0.96, p = 0.03). No significant relationship was found between zinc intake and depression. Furthermore, serum or dietary zinc levels were not related to anxiety. CONCLUSIONS: This study showed a considerable zinc deficiency and depression/anxiety in the old population. A significant relation was found between serum zinc concentration and depression. Further surveys, especially cohort studies and clinical trials are needed to confirm these results.

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.