Metabolite signature of human malignant thyroid tissue: A systematic review and meta-analysis.

BACKGROUND: Thyroid cancer (TC) is the predominant malignancy within the endocrine system. However, the standard method for TC diagnosis lacks the capability to identify the pathological condition of all thyroid lesions. The metabolomics approach has the potential to manage this problem by identifying differential metabolites. AIMS: This study conducted a systematic review and meta-analysis of the NMR-based metabolomics studies in order to identify significant altered metabolites associated with TC. METHODS: A systematic search of published literature in any language in three databases including Embase, PubMed, and Scopus was conducted. Out of 353 primary articles, 12 studies met the criteria for inclusion in the systematic review. Among these, five reports belonging to three articles were eligible for meta-analysis. The correlation coefficient of the orthogonal partial least squares discriminant analysis, a popular model in the multivariate statistical analysis of metabolomic data, was chosen for meta-analysis. The altered metabolites were chosen based on the fact that they had been found in at least three studies. RESULTS: In total, 49 compounds were identified, 40 of which were metabolites. The increased metabolites in thyroid lesions compared normal samples included lactate, taurine, alanine, glutamic acid, glutamine, leucine, lysine, phenylalanine, serine, tyrosine, valine, choline, glycine, and isoleucine. Lipids were the decreased compounds in thyroid lesions. Lactate and alanine were increased in malignant versus benign thyroid lesions, while, myo-inositol, scyllo-inositol, citrate, choline, and phosphocholine were found to be decreased. The meta-analysis yielded significant results for three metabolites of lactate, alanine, and citrate in malignant versus benign specimens. DISCUSSION: In this study, we provided a concise summary of 12 included metabolomic studies, making it easier for future researchers to compare their results with the prior findings. CONCLUSION: It appears that the field of TC metabolomics will experience notable advancement, leading to the discovery of trustworthy diagnostic and prognostic biomarkers.

Engineering chimeric autoantibody receptor T cells for targeted B cell depletion in multiple sclerosis model: An in-vitro study.

Background: Recent evidence suggests that B cells and autoantibodies have a substantial role in the pathogenesis of Multiple sclerosis. T cells could be engineered to express chimeric autoantibody receptors (CAARs), which have an epitope of autoantigens in their extracellular domain acting as bait for trapping autoreactive B cells. This study aims to assess the function of designed CAAR T cells against B cell clones reactive to the myelin basic protein (MBP) autoantigen. Methods: T cells were transduced to express a CAAR consisting of MBP as the extracellular domain. experimental autoimmune encephalomyelitis (EAE) was induced by injecting MBP into mice. The cytotoxicity, proliferation, and cytokine production of the MBP-CAAR T cells were investigated in co-culture with B cells. Results: MBP-CAAR T cells showed higher cytotoxic activity against autoreactive B cells in all effector-to-target ratios compared to Mock T cell (empty vector-transduced T cell) and Un-T cells (un-transduced T cell). In co-cultures containing CAAR T cells, there was more proliferation and inflammatory cytokine release as compared to Un-T and Mock T cell groups. Conclusion: Based on these findings, CAAR T cells are promising for curing or modulating autoimmunity and can be served as a new approach for clone-specific B cell depletion therapy in multiple sclerosis.

High prevalence of Mucosa-Associated extended-spectrum ß-Lactamase-producing Escherichia coli and Klebsiella pneumoniae among Iranain patients with inflammatory bowel disease (IBD).

BACKGROUND: Several pieces of evidence suggest that certain pathobionts belonging to Enterobacterales are associated with the development and progression of inflammatory bowel diseases (IBD). Extended-spectrum ß-lactamases (ESBLs) ESBLs are frequently found in the Enterobacterales members, particularly in Escherichia coli and Klebsiella spp., and might trigger antibiotic-induced perturbations of the intestinal microbiota and led to more severe disease activity in IBD. Therefore, the severity of IBD could be influenced by ESBL-producing Enterobacterales, and hence, this study aimed to investigate the presence of ESBLs and carbapenemases among mucosa-associated E. coli and Klebsiella pneumoniae isolated from colonic biopsies of Iranian patients with IBD. METHODS: In this cross-sectional study, E. coli and K. pneumoniae were isolated from inflamed ileum and/or colon tissue of patients with IBD, including Ulcerative colitis (UC) and Crohn's disease (CD), during colonoscopy. Demographic data and clinical characteristics were recorded, and UC and CD disease activity and extent were evaluated according to the full Mayo score and Crohn's disease activity index (CDAI), respectively. Phenotypic and molecular detection of ESBL- and carbapenemase-producing E. coli and Klebsiella pneumoniae were carried out. Disease activity and other clinical and microbial features were compared in patients with and without gut colonization with ESBL producers. RESULTS: A total of 83 IBD patients, including 67 UC and 16 CD, were enrolled in the initial analysis. Intestinal colonization with ESBL-producing E. coli and/or Klebsiella pneumoniae was found in 37 (55.2%) of UC and 9 (56.2%) of DC patients - mostly harbored E. coli containing the blaCTX-M and blaTEM genes. UC patients with intestinal colonization with ESBL-producers had more severe disease compared with patients without colonization. Moreover, 10.2% of tested E. coli and 34.8% of K. pneumoniea were recognized as potential carbapenemase producers. CONCLUSION: Intestinal colonization with ESBL producers could arise disease activity in IBD patients. Further large-scale case-control studies should be performed to investigate the possible confounding factors that could contribute to this outcome.

Various concentrations of hesperetin induce different types of programmed cell death in human breast cancerous and normal cell lines in a ROS-dependent manner.

The polyphenolic component of citrus fruits, hesperetin (Hst), is a metabolite of hesperidin. In this study, we examined the effect of varying doses and exposure times of hesperetin on MCF-7 and MDA-MB-231 cancer cells, as well as MCF-10A normal cells. By using MTT assay, real-time PCR, western blot, and flow cytometry, we determined the effects of Hst on cell viability, ROS levels, and markers of cell death. Furthermore, molecular docking was used to identify Hst targets that might be involved in ROS-dependent cell death. According to the results, different concentrations of Hst induced different modes of cell death at specific ROS levels. Paraptosis occurred in all cell lines at concentration ranges of IC35 to IC60, and apoptosis occurred at concentrations greater than IC65. In addition, MDA-MB-231 cells were subjected to senescence at sub-toxic doses when treated for a long period of time. When Hst levels were higher, N-acetylcysteine (NAC)'s effect on neutralizing ROS was more pronounced. According to the docking results, Hst may interact with several proteins involved in the regulation of ROS. As an example, the interaction of CCS (Copper chaperone for superoxide dismutase) with Hst might interfere with its chaperone function in folding SOD-1 (superoxide dismutase enzyme), contributing to an increase in cytoplasmic ROS levels. Finally, depending on the ROS level, Hst induces various modes of cell death.

Systematic Review and Dose-Response Meta-Analysis on the Relationship between Different Gluten Doses and Risk of Coeliac Disease Relapse.

Gluten proteins are known as immunological triggers for inflammation resulting in mucosal lesions in patients with coeliac disease (CD). Adherence to a strict gluten-free diet (GFD) is currently known as the only effective treatment for CD. In this study, we performed a systematic review and dose-response meta-analysis on data from previous studies to investigate the association between different gluten doses administered and the risk of CD relapse. Electronic databases were systematically searched to retrieve studies that investigated the response of CD patients to different amounts of gluten intake and evaluated the clinical, serologic, and/or histologic evidence to recognize disease relapse. Study-specific relative risks (RRs) were combined using a random effects model. A total of 440 identified published papers were screened, of which 7 records were selected following full-text reviewing and eligibility assessment for dose-response meta-analysis. According to our analysis, the risk of CD relapse is estimated to be 0.2% (RR: 1.002; 95% CI: 1.001 to 1.004) following the consumption of 6 mg gluten/day, which was increased to 7% (RR: 1.07; 95% CI: 1.03 to 1.10), 50% (RR: 1.50; 95% CI: 1.23 to 1.82), 80% (RR: 1.80; 95% CI: 1.36 to 2.38), and 100% (RR: 2.00; 95% CI: 1.43 to 2.78) by the daily intake of 150, 881, 1276, and 1505 mg gluten, respectively. Although good adherence to a GFD can adequately control CD-related symptoms, disease relapse might happen even with a very low dose of gluten, and the duration of exposure to gluten is also an important matter. The current literature has substantial limitations, such as relying on the data from just a few countries that were different in terms of the amount of gluten administered, the duration of the challenge, etc. Therefore, more randomized clinical trials using a standardized gluten challenge protocol are needed to confirm the findings of the present study.

The use of integrated text mining and protein-protein interaction approach to evaluate the effects of combined chemotherapeutic and chemopreventive agents in cancer therapy.

Combining chemotherapeutic (CT) and chemopreventive (CP) agents for cancer treatment is controversial, and the issue has not yet been conclusively resolved. In this study, by integrating text mining and protein-protein interaction (PPI), the combined effects of these two kinds of agents in cancer treatment were investigated. First, text mining was performed by the Pathway Studio database to study the effects of various agents (CP and CT) on cancer-related processes. Then, each group's most important hub genes were obtained by calculating different centralities. Finally, the results of in silico analysis were validated by examining the combined effects of hesperetin (Hst) and vincristine (VCR) on MCF-7 cells. In general, the results of the in silico analysis revealed that the combination of these two kinds of agents could be useful for treating cancer. However, the PPI analysis revealed that there were a few important proteins that could be targeted for intelligent therapy while giving treatment with these agents. In vitro experiments confirmed the results of the in silico analysis. Also, Hst and VCR had good harmony in modulating the hub genes obtained from the in silico analysis and inducing apoptosis in the MCF-7 cell line.

Overrepresentation of Enterobacteriaceae and Escherichia coli is the major gut microbiome signature in Crohn's disease and ulcerative colitis; a comprehensive metagenomic analysis of IBDMDB datasets.

Objectives: A number of converging strands of research suggest that the intestinal Enterobacteriaceae plays a crucial role in the development and progression of inflammatory bowel disease (IBD), however, the changes in the abundance of Enterobacteriaceae species and their related metabolic pathways in Crohn's disease (CD) and ulcerative colitis (UC) compared to healthy people are not fully explained by comprehensive comparative metagenomics analysis. In the current study, we investigated the alternations of the Enterobacterales population in the gut microbiome of patients with CD and UC compared to healthy subjects. Methods: Metagenomic datasets were selected from the Integrative Human Microbiome Project (HMP2) through the Inflammatory Bowel Disease Multi'omics Database (IBDMDB). We performed metagenome-wide association studies on fecal samples from 191 CD patients, 132 UC patients, and 125 healthy controls (HCs). We used the metagenomics dataset to study bacterial community structure, relative abundance, differentially abundant bacteria, functional analysis, and Enterobacteriaceae-related biosynthetic pathways. Results: Compared to the gut microbiome of HCs, six Enterobacteriaceae species were significantly elevated in both CD and UC patients, including Escherichia coli, Klebsiella variicola, Klebsiella quasipneumoniae, Klebsiella pneumoniae, Proteus mirabilis, Citrobacter freundii, and Citrobacter youngae, while Klebsiella oxytoca, Morganella morganii, and Citrobacter amalonaticus were uniquely differentially abundant and enriched in the CD cohort. Four species were uniquely differentially abundant and enriched in the UC cohort, including Citrobacter portucalensis, Citrobacter pasteurii, Citrobacter werkmanii, and Proteus hauseri. Our analysis also showed a dramatically increased abundance of E. coli in their intestinal bacterial community. Biosynthetic pathways of aerobactin siderophore, LPS, enterobacterial common antigen, nitrogen metabolism, and sulfur relay systems encoded by E. coli were significantly elevated in the CD samples compared to the HCs. Menaquinol biosynthetic pathways were associated with UC that belonged to K. pneumoniae strains. Conclusions: In conclusion, compared with healthy people, the taxonomic and functional composition of intestinal bacteria in CD and UC patients was significantly shifted to Enterobacteriaceae species, mainly E. coli and Klebsiella species.

High Prevalence Rate of Microbial Contamination in Patient-Ready Gastrointestinal Endoscopes in Tehran, Iran: an Alarming Sign for the Occurrence of Severe Outbreaks.

An alarmingly increasing number of outbreaks caused by contaminated gastrointestinal (GI) endoscopes are being reported as a particularly concerning issue. This study is the first large-scale multicenter survey to evaluate the contamination of GI endoscopes in Tehran, Iran. This multicenter study was conducted among 15 tertiary referral and specialized gastrointestinal settings. Reprocessed GI endoscopes were sampled by the sequence of the flush-brush-flush method. Bacterial and viral contamination, as well as antimicrobial resistance, were explored by culture and molecular assays. A total of 133 reprocessed and ready-to-use GI endoscopes were investigated. In phase I and phase II, 47% and 32%, respectively, of the GI endoscopes were determined to be contaminated. GI flora was the most prevalent contaminant isolated from GI endoscopes, in which the most predominant bacteria were Pseudomonas aeruginosa, Escherichia coli, and Klebsiella pneumoniae, in both phase I and II evaluations. The majority of the isolated bacteria in the current study were considered multidrug-resistant organisms (MDROs). More importantly, we recovered carbapenem-resistant nonfermentative Gram-negative bacilli (CRNFGNB), carbapenem-resistant Enterobacterales (CRE), extended-spectrum ß-lactamase (ESBL)-producing Enterobacterales (ESBL-E), multidrug-resistant Clostridioides difficile, vancomycin-resistant Enterococcus (VRE), and drug-resistant Candida spp. Disconcertingly, our molecular assays revealed contamination of some reprocessed GI endoscopes with hepatitis B virus (HBV), hepatitis C virus (HCV), and even HIV. This multicenter study indicates a higher-than-expected contamination rate among reprocessed and ready-for-patient-use GI endoscopes, which suggests a higher-than-expected endoscopy-associated infection (EAI) risk, and potentially, morbidity and mortality rate, associated with endoscopy procedures in Tehran, Iran. IMPORTANCE In the light of severe outbreaks caused by multidrug-resistant microorganisms due to contaminated GI endoscopes, understanding to what extent GI endoscopes are inadequately reprocessed is crucial. Several studies assessed contamination of GI endoscopes with various outcomes across the world; however, the prevalence and risk factors of contaminated GI endoscopes and potential subsequent nosocomial spread are still unknown in Iran. The present study is the first large-scale multicenter survey to evaluate the microbial contamination of repossessed and ready-to-use GI endoscopes in Tehran, Iran. Our study showed a higher-than-expected contamination rate among reprocessed GI endoscopes, which suggests potential seeding of deadly but preventable outbreaks associated with endoscopy procedures in Iran. These results suggest that the current reprocessing and process control guidelines do not suffice in Iran. The current study is of particular importance and could provide insights into unrecognized and unidentified endoscopy-associated outbreaks in Iran.

Identifying Key Lysosome-Related Genes Associated with Drug-Resistant Breast Cancer Using Computational and Systems Biology Approach.

Background: Drug resistance in breast cancer is an unsolved problem in treating patients. It has been recently discussed that lysosomes contribute to the invasion and angiogenesis of cancer cells. There is evidence that lysosomes can also cause multi-drug resistance. We analyzed this emerging concept in breast cancer through computational and systems biology approaches. Objectives: We aimed to identify the key lysosome-related genes associated with drug-resistant breast cancer. Methods: All genes contributing to the structure and function of lysosomes were inquired through the Human Lysosome Gene Database. The prioritized top 51 genes from the provided lists of Endeavour, ToppGene, and GPSy as prioritization tools were selected. All lysosomal genes and 12 breast cancer-related genes aligned to identify the most similar genes to breast cancer-related genes. Different centralities were applied to score each human protein to calculate the most central lysosomal genes in the human protein-protein interaction (PPI) network. Common genes were extracted from the results of the mentioned methods as a selected gene set. For Gene Ontology enrichment, the selected gene set was analyzed by WebGestalt, DAVID, and KOBAS. The PPI network was constructed via the STRING database. The PPI network was analyzed utilizing Cytoscape for topology network interaction and CytoHubba to extract hub genes. Results: Based on biological studies, literature reviews, and comparing all mentioned analyzing methods, six genes were introduced as essential in breast cancer. This computational approach to all lysosome-related genes suggested that candidate genes include PRF1, TLR9, CLTC, GJA1, AP3B1, and RPTOR. The analyses of these six genes suggest that they may have a crucial role in breast cancer development, which has rarely been evaluated. These genes have a potential therapeutic implication for new drug discovery for chemo-resistant breast cancer. Conclusions: The present work focused on all the functional and structural lysosome-related genes associated with breast cancer. It revealed the top six lysosome hub genes that might serve as therapeutic targets in drug-resistant breast cancer. Since these genes play a pivotal role in the structure and function of lysosomes, targeting them can effectively overcome drug resistance.

Proteomic profiling of the rat hippocampus from the kindling and pilocarpine models of epilepsy: potential targets in calcium regulatory network.

Herein proteomic profiling of the rat hippocampus from the kindling and pilocarpine models of epilepsy was performed to achieve new potential targets for treating epileptic seizures. A total of 144 differently expressed proteins in both left and right hippocampi by two-dimensional electrophoresis coupled to matrix-assisted laser desorption-mass spectrometry were identified across the rat models of epilepsy. Based on network analysis, the majority of differentially expressed proteins were associated with Ca2+ homeostasis. Changes in ADP-ribosyl cyclase (ADPRC), lysophosphatidic acid receptor 3 (LPAR3), calreticulin, ubiquitin carboxyl-terminal hydrolase L1 (UCH-L1), synaptosomal nerve-associated protein 25 (SNAP 25) and transgelin 3 proteins were probed by Western blot analysis and validated using immunohistochemistry. Inhibition of calcium influx by 8-Bromo-cADP-Ribose (8-Br-cADPR) and 2-Aminoethyl diphenylborinate (2-APB) which act via the ADPRC and LPAR3, respectively, attenuated epileptic seizures. Considering a wide range of molecular events and effective role of calcium homeostasis in epilepsy, polypharmacy with multiple realistic targets should be further explored to reach the most effective treatments.

Comprehensive host-pathogen protein-protein interaction network analysis.

BACKGROUND: Infectious diseases are a cruel assassin with millions of victims around the world each year. Understanding infectious mechanism of viruses is indispensable for their inhibition. One of the best ways of unveiling this mechanism is to investigate the host-pathogen protein-protein interaction network. In this paper we try to disclose many properties of this network. We focus on human as host and integrate experimentally 32,859 interaction between human proteins and virus proteins from several databases. We investigate different properties of human proteins targeted by virus proteins and find that most of them have a considerable high centrality scores in human intra protein-protein interaction network. Investigating human proteins network properties which are targeted by different virus proteins can help us to design multipurpose drugs. RESULTS: As host-pathogen protein-protein interaction network is a bipartite network and centrality measures for this type of networks are scarce, we proposed seven new centrality measures for analyzing bipartite networks. Applying them to different virus strains reveals unrandomness of attack strategies of virus proteins which could help us in drug design hence elevating the quality of life. They could also be used in detecting host essential proteins. Essential proteins are those whose functions are critical for survival of its host. One of the proposed centralities named diversity of predators, outperforms the other existing centralities in terms of detecting essential proteins and could be used as an optimal essential proteins' marker. CONCLUSIONS: Different centralities were applied to analyze human protein-protein interaction network and to detect characteristics of human proteins targeted by virus proteins. Moreover, seven new centralities were proposed to analyze host-pathogen protein-protein interaction network and to detect pathogens' favorite host protein victims. Comparing different centralities in detecting essential proteins reveals that diversity of predator (one of the proposed centralities) is the best essential protein marker.

Alpha influenza virus infiltration prediction using virus-human protein-protein interaction network.

More than ten million deaths make influenza virus one of the deadliest of history. About half a million sever illnesses are annually reported consequent of influenza. Influenza is a parasite which needs the host cellular machinery to replicate its genome. To reach the host, viral proteins need to interact with the host proteins. Therefore, identification of host-virus protein interaction network (HVIN) is one of the crucial steps in treating viral diseases. Being expensive, time-consuming and laborious of HVIN experimental identification, force the researches to use computational methods instead of experimental ones to obtain a better understanding of HVIN. In this study, several features are extracted from physicochemical properties of amino acids, combined with different centralities of human protein-protein interaction network (HPPIN) to predict protein-protein interactions between human proteins and Alphainfluenzavirus proteins (HI-PPIs). Ensemble learning methods were used to predict such PPIs. Our model reached 0.93 accuracy, 0.91 sensitivity and 0.95 specificity. Moreover, a database including 694522 new PPIs was constructed by prediction results of the model. Further analysis showed that HPPIN centralities, gene ontology semantic similarity and conjoint triad of virus proteins are the most important features to predict HI-PPIs.

Parallelizing Assignment Problem with DNA Strands.

BACKGROUND: Many problems of combinatorial optimization, which are solvable only in exponential time, are known to be Non-Deterministic Polynomial hard (NP-hard). With the advent of parallel machines, new opportunities have been emerged to develop the effective solutions for NP-hard problems. However, solving these problems in polynomial time needs massive parallel machines and is not applicable up to now. OBJECTIVES: DNA (Deoxyribonucleic acid) computing provides a fantastic method to solve NP-hard problems in polynomial time. Accordingly, one of the famous NP-hard problems is assignment problem, which is designed to find the best assignment of n jobs to n persons in a way that it could maximize the profit or minimize the cost. MATERIAL AND METHODS: Applying bio molecular operations of Adelman Lipton model, a novel parallel DNA algorithm have been proposed for solving the assignment problem. RESULTS: The proposed algorithm can solve the problem in time complexity, and just O(n2) initial DNA strand in comparison with nn initial sequence, which is used by the other methods. CONCLUSIONS: In this article, using DNA computing, we proposed a parallel DNA algorithm to solve the assignment problem in linear time.

SARS-CoV-2-human protein-protein interaction network.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the novel coronavirus which caused the coronavirus disease 2019 pandemic and infected more than 12 million victims and resulted in over 560,000 deaths in 213 countries around the world. Having no symptoms in the first week of infection increases the rate of spreading the virus. The increasing rate of the number of infected individuals and its high mortality necessitates an immediate development of proper diagnostic methods and effective treatments. SARS-CoV-2, similar to other viruses, needs to interact with the host proteins to reach the host cells and replicate its genome. Consequently, virus-host protein-protein interaction (PPI) identification could be useful in predicting the behavior of the virus and the design of antiviral drugs. Identification of virus-host PPIs using experimental approaches are very time consuming and expensive. Computational approaches could be acceptable alternatives for many preliminary investigations. In this study, we developed a new method to predict SARS-CoV-2-human PPIs. Our model is a three-layer network in which the first layer contains the most similar Alphainfluenzavirus proteins to SARS-CoV-2 proteins. The second layer contains protein-protein interactions between Alphainfluenzavirus proteins and human proteins. The last layer reveals protein-protein interactions between SARS-CoV-2 proteins and human proteins by using the clustering coefficient network property on the first two layers. To further analyze the results of our prediction network, we investigated human proteins targeted by SARS-CoV-2 proteins and reported the most central human proteins in human PPI network. Moreover, differentially expressed genes of previous researches were investigated and PPIs of SARS-CoV-2-human network, the human proteins of which were related to upregulated genes, were reported.

AntAngioCOOL: computational detection of anti-angiogenic peptides.

BACKGROUND: Angiogenesis inhibition research is a cutting edge area in angiogenesis-dependent disease therapy, especially in cancer therapy. Recently, studies on anti-angiogenic peptides have provided promising results in the field of cancer treatment. METHODS: A non-redundant dataset of 135 anti-angiogenic peptides (positive instances) and 135 non anti-angiogenic peptides (negative instances) was used in this study. Also, 20% of each class were selected to construct an independent test dataset (see Additional files 1, 2). We proposed an effective machine learning based R package (AntAngioCOOL) to predict anti-angiogenic peptides. We have examined more than 200 different classifiers to build an efficient predictor. Also, more than 17,000 features were extracted to encode the peptides. RESULTS: Finally, more than 2000 informative features were selected to train the classifiers for detecting anti-angiogenic peptides. AntAngioCOOL includes three different models that can be selected by the user for different purposes; it is the most sensitive, most specific and most accurate. According to the obtained results AntAngioCOOL can effectively suggest anti-angiogenic peptides; this tool achieved sensitivity of 88%, specificity of 77% and accuracy of 75% on the independent test set. AntAngioCOOL can be accessed at https://cran.r-project.org/ . CONCLUSIONS: Only 2% of the extracted descriptors were used to build the predictor models. The results revealed that physico-chemical profile is the most important feature type in predicting anti-angiogenic peptides. Also, atomic profile and PseAAC are the other important features.

OligoCOOL: A mobile application for nucleotide sequence analysis.

Today smartphones are inseparable parts of modern life and are capable of performing many desktop computers' tasks such as scientific analysis with greater convenience. Here, we present OligoCOOL, which is an Android application for analyzing nucleic sequences. This application enables users to perform several common biomedical analyses for a given nucleotide sequence. OligoCOOL is a freely accessible Android app at http://bioinf.modares.ac.ir/software/OligoCOOL, which can be a suitable tool for the experimental design in the laboratories. This application also can be used to learn the basics of nucleotide sequence analysis. © 2019 International Union of Biochemistry and Molecular Biology, 47(2): 201-206, 2019.

Digging deeper into volatile organic compounds associated with cancer.

Volatile organic compounds (VOCs), produced and emitted through the metabolism of cancer cells or the body's immune system, are considered novel cancer biomarkers for diagnostic purposes. Of late, a large number of work has been done to find a relationship between VOCs' signature of body and cancer. Cancer-related VOCs can be used to detect several types of cancers at the earlier stages which in turn provide a significantly higher chance of survival. Here we aim to provide an updated picture of cancer-related VOCs based on recent findings in this field focusing on cancer odor database.

Cancer Odor Database (COD): a critical databank for cancer diagnosis research.

Database URL: http://bioinf.modares.ac.ir/software/cod/.