Development of Local Software for Automatic Measurement of Geometric Parameters in the Proximal Femur Using a Combination of a Deep Learning Approach and an Active Shape Model on X-ray Images.

Proximal femur geometry is an important risk factor for diagnosing and predicting hip and femur injuries. Hence, the development of an automated approach for measuring these parameters could help physicians with the early identification of hip and femur ailments. This paper presents a technique that combines the active shape model (ASM) and deep learning methodologies. First, the femur boundary is extracted by a deep learning neural network. Then, the femur's anatomical landmarks are fitted to the extracted border using the ASM method. Finally, the geometric parameters of the proximal femur, including femur neck axis length (FNAL), femur head diameter (FHD), femur neck width (FNW), shaft width (SW), neck shaft angle (NSA), and alpha angle (AA), are calculated by measuring the distances and angles between the landmarks. The dataset of hip radiographic images consisted of 428 images, with 208 men and 220 women. These images were split into training and testing sets for analysis. The deep learning network and ASM were subsequently trained on the training dataset. In the testing dataset, the automatic measurement of FNAL, FHD, FNW, SW, NSA, and AA parameters resulted in mean errors of 1.19%, 1.46%, 2.28%, 2.43%, 1.95%, and 4.53%, respectively.

Identification of Key Genes in Angiogenesis of Breast and Prostate Cancers in the Context of Different Cell Types.

INTRODUCTION: Angiogenesis involves the development of new blood vessels. Biochemical signals start this process in the body, which is followed by migration, growth, and differentiation of endothelial cells that line the inside wall of blood vessels. This process is vital for the growth of cancer cells and tumors. MATERIALS AND METHODS: We started our analysis by composing a list of genes that have a validated impact in humans with respect to angiogenesis-related phenotypes. Here, we have investigated the expression patterns of angiogenesis-related genes in the context of previously published single-cell RNA-Seq data from prostate and breast cancer samples. RESULTS: Using a protein-protein interaction network, we showed how different modules of angiogenesis-related genes are overexpressed in different cell types. In our results, genes, such as ACKR1, AQP1, and EGR1, showed a strong cell type-dependent overexpression pattern in the two investigated cancer types, which can potentially be helpful in the diagnosis and follow-up of patients with prostate and breast cancer. CONCLUSION: Our work demonstrates how different biological processes in distinct cell types contribute to the angiogenesis process, which can provide clues regarding the potential application of targeted inhibition of the angiogenesis process.

Evaluation of common protein biomarkers involved in the pathogenesis of respiratory diseases with proteomic methods: A systematic review.

AIM: Respiratory disease (RD) is one of the most common diseases characterized by lung dysfunction. Many diagnostic mechanisms have been used to identify the pathogenic agents of responsible for RD. Among these, proteomics emerges as a valuable diagnostic method for pinpointing the specific proteins involved in RD pathogenesis. Therefore, in this study, for the first time, we examined the protein markers involved in the pathogenesis of chronic obstructive pulmonary disease (COPD), idiopathic pulmonary fibrosis (IPF), asthma, bronchiolitis obliterans (BO), and chemical warfare victims exposed to mustard gas, using the proteomics method as a systematic study. MATERIALS AND METHODS: A systematic search was performed up to September 2023 on several databases, including PubMed, Scopus, ISI Web of Science, and Cochrane. In total, selected 4246 articles were for evaluation according to the criteria. Finally, 119 studies were selected for this systematic review. RESULTS: A total of 13,806 proteins were identified, 6471 in COPD, 1603 in Asthma, 5638 in IPF, three in BO, and 91 in mustard gas exposed victims. Alterations in the expression of these proteins were observed in the respective diseases. After evaluation, the results showed that 31 proteins were found to be shared among all five diseases. CONCLUSION: Although these 31 proteins regulate different factors and molecular pathways in all five diseases, they ultimately lead to the regulation of inflammatory pathways. In other words, the expression of some proteins in COPD and mustard-exposed patients increases inflammatory reactions, while in IPF, they cause lung fibrosis. Asthma, causes allergic reactions due to T-cell differentiation toward Th2.

Computer-assisted evaluation of small airway disease in CT scans of Iran-Iraq war victims of chemical warfare by a locally developed software: comparison between different quantitative methods.

OBJECTIVE: Diagnosis of small airway disease on computed tomography (CT) scans is challenging in patients with a history of chemical warfare exposure. We developed a software package based on different methodologies to identify and quantify small airway disease in CT images. The primary aim was to identify the best automatic methodology for detecting small airway disease in CT scans of Iran-Iraq War victims of chemical warfare. METHODS: This retrospective case-control study enrolled 46 patients with a history of chemical warfare exposure and 27 controls with inspiratory/expiratory (I/E) CT scans and spirometry tests. Image data were automatically segmented, and inspiratory images were registered into the expiratory images' frame using the locally developed software. Parametric response mapping (PRM) and air trapping index (ATI) mapping were performed on the CT images. Conventional QCT methods, including expiratory/inspiratory mean lung attenuation (E/I MLA) ratio, normal density E/I (ND E/I) MLA ratio, attenuation volume Index (AVI), %low attenuation areas (LAA) < -856 in exhale scans, and %LAA < -950 in inhale scans were also computed. QCT measurements were correlated with spirometry results and compared across the two study groups. RESULTS: The correlation analysis showed a significant negative relationship between three air trapping (AT) measurements (PRM, ATI, and %LAAExp < -856) and spirometry parameters (Fev1, Fvc, Fev1/Fvc, and MMEF). Moreover, %LAAExp < -856 had the highest significant negative correlation with Fev1/Fvc (r = -0.643, P-value < 0.001). Three AT measurements demonstrated a significant difference between the study groups. The E/I ratio was also significantly different between the two groups (P-value < 0.001). Binary logistic regression models showed PRMFsad, %LAAExp < -856, and ATI as significant and strong predictors of the study outcome. Optimal cut-points for PRMFsad = 19%, %LAAExp < -856 = 23%, and ATI = 27% were identified to classify the participants into two groups with high accuracy. CONCLUSION: QCT methods, including PRM, ATI, and %LAAExp < -856 can greatly advance the identification and quantification of SAD in chemical warfare victims. The results should be verified in well-designed prospective studies involving a large population.

A computational approach for the identification of key genes and biological pathways of chronic lung diseases: a systems biology approach.

BACKGROUND: Chronic lung diseases are characterized by impaired lung function. Given that many diseases have shared clinical symptoms and pathogenesis, identifying shared pathogenesis can help the design of preventive and therapeutic strategies. This study aimed to evaluate the proteins and pathways of chronic obstructive pulmonary disease (COPD), asthma, idiopathic pulmonary fibrosis (IPF), and mustard lung disease (MLD). METHODS AND RESULTS: After collecting the data and determining the gene list of each disease, gene expression changes were examined in comparison to healthy individuals. Protein-protein interaction (PPI) and pathway enrichment analysis were used to evaluate genes and shared pathways of the four diseases. There were 22 shared genes, including ACTB, AHSG, ALB, APO, A1, APO C3, FTH1, GAPDH, GC, GSTP1, HP, HSPB1, IGKC, KRT10, KRT9, LCN1, PSMA2, RBP4, 100A8, S100A9, TF, and UBE2N. The major biological pathways in which these genes are involved are inflammatory pathways. Some of these genes activate different pathways in each disease, leading to the induction or inhibition of inflammation. CONCLUSION: Identification of the genes and shared pathways of diseases can contribute to identifying pathogenesis pathways and designing preventive and therapeutic strategies.

Comparison of optimized machine learning approach to the understanding of medial tibial stress syndrome in male military personnel.

PURPOSE: This study investigates the applicability of optimized machine learning (ML) approach for the prediction of Medial tibial stress syndrome (MTSS) using anatomic and anthropometric predictors. METHOD: To this end, 180 recruits were enrolled in a cross-sectional study of 30 MTSS (30.36 ± 4.80 years) and 150 normal (29.70 ± 3.81 years). Twenty-five predictors/features, including demographic, anatomic, and anthropometric variables, were selected as risk factors. Bayesian optimization method was used to evaluate the most applicable machine learning algorithm with tuned hyperparameters on the training data. Three experiments were performed to handle the imbalances in the data set. The validation criteria were accuracy, sensitivity, and specificity. RESULTS: The highest performance (even 100%) was observed for the Ensemble and SVM classification models while using at least 6 and 10 most important predictors in undersampling and oversampling experiments, respectively. In the no-resampling experiment, the best performance (accuracy = 88.89%, sensitivity = 66.67%, specificity = 95.24%, and AUC = 0.8571) was achieved for the Naive Bayes classifier with the 12 most important features. CONCLUSION: The Naive Bayes, Ensemble, and SVM methods could be the primary choices to apply the machine learning approach in MTSS risk prediction. These predictive methods, alongside the eight common proposed predictors, might help to more accurately calculate the individual risk of developing MTSS at the point of care.

Design of a novel multi-epitope vaccine candidate against Chlamydia trachomatis using structural and nonstructural proteins: an immunoinformatics study.

Chlamydia trachomatis (C. trachomatis) is an obligate intracellular bacterium which causes eye and sexually transmitted infections. During pregnancy, the bacterium is associated with preterm complications, low weight of neonates, fetal demise and endometritis leading to infertility. The aim of our study was design of a multi-epitope vaccine (MEV) candidate against C. trachomatis. After protein sequence adoption from the NCBI, potential epitopes toxicity, antigenicity, allergenicity, MHC-I and MHC-II binding, cytotoxic T lymphocytes (CTLs), Helper T lymphocytes (HTLs) and interferon-γ (IFN-γ)- induction were predicted. The adopted epitopes were fused together using appropriate linkers. In the next step, the MEV structural mapping and characterization, three-dimensional (3D) structure homology modeling and refinement were also performed. The MEV candidate interaction with the toll-like receptor 4 (TLR4) was also docked. The immune responses simulation was assessed using the C-IMMSIM server. Molecular dynamic (MD) simulation verified the structural stability of the TLR4-MEV complex. The Molecular Mechanics Poisson-Boltzmann Surface Area (MMPBSA) approach demonstrated the MEV high affinity of binding to the TLR4, MHC-I and MHC-II. The MEV construct was also stable and water soluble and had enough antigenicity and lacked allergenicity with stimulation of T cells and B cells and INF-γ release. The immune simulation confirmed acceptable responses of both the humoral and cellular arms. It is proposed that in vitro and in vivo studies are needed to evaluate the findings of this study.Communicated by Ramaswamy H. Sarma.

Safety and immunogenicity of a recombinant receptor-binding domain-based protein subunit vaccine (Noora vaccine™) against COVID-19 in adults: A randomized, double-blind, placebo-controlled, Phase 1 trial.

The development of a safe and effective vaccine is essential to protect populations against coronavirus disease 2019 (COVID-19). There are several vaccine candidates under investigation with different mechanisms of action. In the present study, we have evaluated the safety and immunogenicity of a recombinant receptor-binding domain (RBD)-based protein subunit vaccine (Noora vaccine) against COVID-19 in adults. This Phase 1 trial is a randomized, double-blind, placebo-controlled study to evaluate the safety and immunogenicity of the recombinant RBD-based protein subunit vaccine (Noora vaccine) against COVID-19 in healthy adults volunteers. Eligible participants were included in this study after evaluating their health status and considering the exclusion criteria. They were then randomized into three groups and received three doses of vaccine (80 µg, 120 µg, and placebo) on Days 0, 21, and 35. Primary outcomes including solicited, unsolicited, and medically attended adverse events were recorded during this study. Secondary outcomes including the humoral and cellular immunity (including anti-RBD IgG antibody and neutralizing antibody) were measured on Days 0, 21, 28, 35, 42, and 49 by using the ELISA kit and the Virus Neutralization Test (VNT) was performed on day 49. Totally 70 cases were included in this Phase 1 trial and 60 of them completed the study. Safety assessments showed no severe adverse events. Local pain at the vaccine injection site occurred in 80% of the vaccinated volunteers. Induration and redness at the injection site were the other adverse reactions of this vaccine. There was no significant difference between the studied groups regarding adverse reactions. Anti-RBD IgG antibody and neutralizing antibody assessment showed significant seroconversion in comparison to the placebo group (80%, and 100% respectively, p < 0.001). The cellular immunity panel also showed mild to moderate induction of TH1 responses and the VNT showed 78% of seroprotection. The results of this Phase 1 trial showed acceptable safety without serious adverse events and significant seroconversions in the humoral and cellular immunity panel. The dose of 80 µg is an appropriate dose for injection in the next phases of the trial.

A Computer-based Analysis for Identification and Quantification of Small Airway Disease in Lung Computed Tomography Images: A Comprehensive Review for Radiologists.

Computed tomography (CT) imaging is being increasingly used in clinical practice for detailed characterization of lung diseases. Respiratory diseases involve various components of the lung, including the small airways. Evaluation of small airway disease on CT images is challenging as the airways cannot be visualized directly by a CT scanner. Small airway disease can manifest as pulmonary air trapping (AT). Although AT may be sometimes seen as mosaic attenuation on expiratory CT images, it is difficult to identify diffuse AT visually. Computer technology advances over the past decades have provided methods for objective quantification of small airway disease on CT images. Quantitative CT (QCT) methods are being rapidly developed to quantify underlying lung diseases with greater precision than subjective visual assessment of CT images. A growing body of evidence suggests that QCT methods can be practical tools in the clinical setting to identify and quantify abnormal regions of the lung accurately and reproducibly. This review aimed to describe the available methods for the identification and quantification of small airway disease on CT images and to discuss the challenges of implementing QCT metrics in clinical care for patients with small airway disease.

Global abundance of short tandem repeats is non-random in rodents and primates.

BACKGROUND: While of predominant abundance across vertebrate genomes and significant biological implications, the relevance of short tandem repeats (STRs) (also known as microsatellites) to speciation remains largely elusive and attributed to random coincidence for the most part. Here we collected data on the whole-genome abundance of mono-, di-, and trinucleotide STRs in nine species, encompassing rodents and primates, including rat, mouse, olive baboon, gelada, macaque, gorilla, chimpanzee, bonobo, and human. The collected data were used to analyze hierarchical clustering of the STR abundances in the selected species. RESULTS: We found massive differential STR abundances between the rodent and primate orders. In addition, while numerous STRs had random abundance across the nine selected species, the global abundance conformed to three consistent < clusters>, as follows: <rat, mouse>, <gelada, macaque, olive baboon>, and <gorilla, chimpanzee, bonobo, human>, which coincided with the phylogenetic distances of the selected species (p < 4E-05). Exceptionally, in the trinucleotide STR compartment, human was significantly distant from all other species. CONCLUSION: Based on hierarchical clustering, we propose that the global abundance of STRs is non-random in rodents and primates, and probably had a determining impact on the speciation of the two orders. We also propose the STRs and STR lengths, which predominantly conformed to the phylogeny of the selected species, exemplified by (t)10, (ct)6, and (taa4). Phylogenetic and experimental platforms are warranted to further examine the observed patterns and the biological mechanisms associated with those STRs.

Comparative proteomic analysis of mustard lung as a complicated disease using systems biology approach.

During Iraq-Iran conflict, chemical weapons, particularly SM gas, were used numerous times, whose aftereffects are still present. This study aimed to compare serum proteome in the chronic ML (n = 10) and HC (n = 10). TMT label-based quantitative proteomics was used to examine serums from two groups. Among total significant proteins, 14 proteins were upregulated (log2 ≥ FC 0.5, p 0.05), and 6 proteins were downregulated (log2 ≤ FC - 0.5, p 0.05). By helping PPI network, and EA, 11 main pathways connected to significantly different protein expression levels were discovered, including inflammatory and cell adhesion signaling pathways. It may be deduced that the wounded organs of exposed individuals experience poor repair cycles of cell degeneration and regeneration because certain repair signals were elevated while other structural and adhesion molecules were downregulated. The systems biology approach can help enhance our basic knowledge of biological processes, and contribute to a deeper understanding of pathophysiological mechanisms, as well as the identification of potential biomarkers of disease.

Proteomics analysis of chronic skin injuries caused by mustard gas.

Sulfur mustard (SM) is an alkylating and forming chemical that was widely used by Iraqi forces during the Iran-Iraq wars. One of the target organs of SM is the skin. Understanding the mechanisms involved in the pathogenesis of SM may help better identify complications and find appropriate treatments. The current study collected ten SM-exposed patients with long-term skin complications and ten healthy individuals. Proteomics experiments were performed using the high-efficiency TMT10X method to evaluate the skin protein profile, and statistical bioinformatics methods were used to identify the differentially expressed proteins. One hundred twenty-nine proteins had different expressions between the two groups. Of these 129 proteins, 94 proteins had increased expression in veterans' skins, while the remaining 35 had decreased expression. The hub genes included RPS15, ACTN1, FLNA, HP, SDHC, and RPL29, and three modules were extracted from the PPI network analysis. Skin SM exposure can lead to oxidative stress, inflammation, apoptosis, and cell proliferation.

Tear proteomics analysis of patient suffered from delayed mustard gas keratopathy.

Understanding the molecular and cellular mechanisms involved in the pathogenesis of ocular injured induced by mustard gas can help better identify complications and discover appropriate therapies. This study aimed to analyze the proteomics of tears of chemical warfare victims with mustard gas ocular injuries and compare it with healthy individuals. In this case-control research, 10 mustard gas victims with long-term ocular difficulties (Chronic) were included in the patient group, while 10 healthy persons who were age and sex matched to the patients were included in the control group. Schirmer strips were used to collect the tears of the participants. Proteomics experiments were performed using the high-efficiency TMT10X method to evaluate the tear protein profile, and statistical bioinformatics methods were used to identify the differently expressed proteins. 24 proteins had different expressions between the two groups. Among these 24 proteins, 8 proteins had increased expression in veterans' tears, while the remaining 16 proteins had decreased expression. Reactome pathways were used to look at proteins with various expressions, and 13 proteins were found to be engaged in the immune system, 9 of which were effective in the innate immune system, and 5 proteins were effective in the complement cascade. Ocular mustard gas exposure may cause a compromised immune system on the eye's surface, exposing the cornea to external and endogenous infections, and eventually causing corneal opacity and reduced vision.

Tandem repeats ubiquitously flank and contribute to translation initiation sites.

BACKGROUND: While the evolutionary divergence of cis-regulatory sequences impacts translation initiation sites (TISs), the implication of tandem repeats (TRs) in TIS selection remains largely elusive. Here, we employed the TIS homology concept to study a possible link between TRs of all core lengths and repeats with TISs. METHODS: Human, as reference sequence, and 83 other species were selected, and data was extracted on the entire protein-coding genes (n = 1,611,368) and transcripts (n = 2,730,515) annotated for those species from Ensembl 102. Following TIS identification, two different weighing vectors were employed to assign TIS homology, and the co-occurrence pattern of TISs with the upstream flanking TRs was studied in the selected species. The results were assessed in 10-fold cross-validation. RESULTS: On average, every TIS was flanked by 1.19 TRs of various categories within its 120 bp upstream sequence, per species. We detected statistically significant enrichment of non-homologous human TISs co-occurring with human-specific TRs. On the contrary, homologous human TISs co-occurred significantly with non-human-specific TRs. 2991 human genes had at least one transcript, TIS of which was flanked by a human-specific TR. Text mining of a number of the identified genes, such as CACNA1A, EIF5AL1, FOXK1, GABRB2, MYH2, SLC6A8, and TTN, yielded predominant expression and functions in the human brain and/or skeletal muscle. CONCLUSION: We conclude that TRs ubiquitously flank and contribute to TIS selection at the trans-species level. Future functional analyses, such as a combination of genome editing strategies and in vitro protein synthesis may be employed to further investigate the impact of TRs on TIS selection.

Causal Path of COPD Progression-Associated Genes in Different Biological Samples.

Chronic obstructive pulmonary disease (COPD) is a progressive inflammatory disease with pulmonary and extra-pulmonary complications. Due to the disease's systemic nature, many investigations investigated the genetic alterations in various biological samples. We aimed to infer causal genes in COPD's pathogenesis in different biological samples using elastic-net logistic regression and the Structural Equation Model. Samples of small airway epithelial cells, bronchoalveolar lavage macrophages, lung tissue biopsy, sputum, and blood samples were selected (135, 70, 235, 143, and 226 samples, respectively). Elastic-net Logistic Regression analysis was implemented to identify the most important genes involved in COPD progression. Thirty-three candidate genes were identified as essential factors in the pathogenesis of COPD and regulation of lung function. Recognized candidate genes in small airway epithelial (SAE) cells have the highest area under the ROC curve (AUC = 97%, SD = 3.9%). Our analysis indicates that macrophages and epithelial cells are more influential in COPD progression at the transcriptome level.

A complete, evidence-based review on novichok poisoning based on epidemiological aspects and clinical management.

Background: The whole world has learned about the existence of a highly toxic neuro-paralytic substance called Novichok. A wide range of neuro-paralytic toxins were used during the wars of decades ago, which also had harmful and irreversible effects. Fortunately, the establishment of conventions prohibiting the use of these weapons prevented the adverse clinical consequences of these compounds. What we did in the present study was to evaluate the clinical features of Novichok, how to manage exposure to it, and to evaluate the prognostic aspects associated with this poisoning agent. Methods: The manuscript especial databases including Medline, Web of knowledge, Google scholar, and Scopus were deeply searched by the two blinded investigators for all eligible studies based on the considered keywords. Initially 98 articles were initially collected by database searching that considering eligibility criteria, 83 articles were finally eligible for the final assessment. There is a lack of clinical trials and case-cohort studies on general population about treatment and side effects when it comes to human nerve agents and most of the data in our search is based on animal studies. Results: In evaluating various clinical, auto physiological and prognostic aspects of exposure to these substances, special attention was necessary to the following points. First, Novichok agents are considered more potent than other toxic agents. Pathophysiologically, these agents irreversibly bind acetylcholinesterase and produce a rapid cholinergic toxidrome which is responsible for the clinical manifestations as well as the potential dangerous and life threatening side effects caused by these agents. Uniquely, these agents are thought to also target every neuron in the central and peripheral nervous system. As a managerial and therapeutic approach, early and timely treatment of its related complication along with prevents massive exposure and decontamination in addition to rapid resuscitation can prohibit debilitating neuropathy and death due to facing it. Conclusion: The present review highlights the importance of recognizing the potential acute toxic effects of Novichok agents, diagnostic and therapeutic approaches (life-saving antidotal therapy) to complications and ultimately the application of guidelines to improve the prognosis of exposure to these agents for both victims and medical community.

Proposed minimal essential co-expression and physical interaction networks involved in the development of cognition impairment in human mid and late life.

AIM: The aim of this study was to identify the minimal essential co-expression and physical interaction networks involved in the development of cognition impairment in human mid and late life. METHODS: We searched the Online Mendelian Inheritance in Man (OMIM) database to extract the validated human genes annotated (until March 2020) for five major disorders of pathophysiological overlap and sequential chronological occurrence in human, including multiple sclerosis, type 2 diabetes mellitus, Alzheimer's disease, vascular dementia, and Lewy body dementia. Gene co-expression and physical interaction networks were subsequently constructed for the overlapping genes across the selected disorders. RESULTS: Remarkably, each of the gene co-expression and physical interaction networks consisted of single clusters (P = 0.0005 and P = 1 × 10-16, respectively). APP was the major hub in the integrated and tissue-specific co-expression networks, whereas insulin was the major hub in the physical interaction network. Several other hubs were identified across the identified networks, including TNF, VEGFA, GAPDH, and NOTCH1. CONCLUSION: We propose the minimal co-expression and physical interaction networks and their single clustering in the development of cognition impairment in human mid and late life. This is a pilot study, warranting identification of more risk genes, using additional validated databases in the future.

Genome-wide prediction and prioritization of human aging genes by data fusion: a machine learning approach.

BACKGROUND: Machine learning can effectively nominate novel genes for various research purposes in the laboratory. On a genome-wide scale, we implemented multiple databases and algorithms to predict and prioritize the human aging genes (PPHAGE). RESULTS: We fused data from 11 databases, and used Naïve Bayes classifier and positive unlabeled learning (PUL) methods, NB, Spy, and Rocchio-SVM, to rank human genes in respect with their implication in aging. The PUL methods enabled us to identify a list of negative (non-aging) genes to use alongside the seed (known age-related) genes in the ranking process. Comparison of the PUL algorithms revealed that none of the methods for identifying a negative sample were advantageous over other methods, and their simultaneous use in a form of fusion was critical for obtaining optimal results (PPHAGE is publicly available at https://cbb.ut.ac.ir/pphage). CONCLUSION: We predict and prioritize over 3,000 candidate age-related genes in human, based on significant ranking scores. The identified candidate genes are associated with pathways, ontologies, and diseases that are linked to aging, such as cancer and diabetes. Our data offer a platform for future experimental research on the genetic and biological aspects of aging. Additionally, we demonstrate that fusion of PUL methods and data sources can be successfully used for aging and disease candidate gene prioritization.

Link between short tandem repeats and translation initiation site selection.

BACKGROUND: Despite their vast biological implication, the relevance of short tandem repeats (STRs)/microsatellites to the protein-coding gene translation initiation sites (TISs) remains largely unknown. METHODS: We performed an Ensembl-based comparative genomics study of all annotated orthologous TIS-flanking sequences in human and 46 other species across vertebrates, on the genomic DNA and cDNA platforms (755,956 TISs), aimed at identifying human-specific STRs in this interval. The collected data were used to examine the hypothesis of a link between STRs and TISs. BLAST was used to compare the initial five amino acids (excluding the initial methionine), codons of which were flanked by STRs in human, with the initial five amino acids of all annotated proteins for the orthologous genes in other vertebrates (total of 5,314,979 pair-wise TIS comparisons on the genomic DNA and cDNA platforms) in order to compare the number of events in which human-specific and non-specific STRs occurred with homologous and non-homologous TISs (i.e., ≥ 50% and < 50% similarity of the five amino acids). RESULTS: We detected differential distribution of the human-specific STRs in comparison to the overall distribution of STRs on the genomic DNA and cDNA platforms (Mann Whitney U test p = 1.4 × 10-11 and p < 7.9 × 10-11, respectively). We also found excess occurrence of non-homologous TISs with human-specific STRs and excess occurrence of homologous TISs with non-specific STRs on both platforms (p < 0.00001). CONCLUSION: We propose a link between STRs and TIS selection, based on the differential co-occurrence rate of human-specific STRs with non-homologous TISs and non-specific STRs with homologous TISs.

Intelligent Diagnostic Assistant for Complicated Skin Diseases through C5's Algorithm.

INTRODUCTION: Intelligent Diagnostic Assistant can be used for complicated diagnosis of skin diseases, which are among the most common causes of disability. The aim of this study was to design and implement a computerized intelligent diagnostic assistant for complicated skin diseases through C5's Algorithm. METHOD: An applied-developmental study was done in 2015. Knowledge base was developed based on interviews with dermatologists through questionnaires and checklists. Knowledge representation was obtained from the train data in the database using Excel Microsoft Office. Clementine Software and C5's Algorithms were applied to draw the decision tree. Analysis of test accuracy was performed based on rules extracted using inference chains. The rules extracted from the decision tree were entered into the CLIPS programming environment and the intelligent diagnostic assistant was designed then. RESULTS: The rules were defined using forward chaining inference technique and were entered into Clips programming environment as RULE. The accuracy and error rates obtained in the training phase from the decision tree were 99.56% and 0.44%, respectively. The accuracy of the decision tree was 98% and the error was 2% in the test phase. CONCLUSION: Intelligent diagnostic assistant can be used as a reliable system with high accuracy, sensitivity, specificity, and agreement.