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This study developed a prognostic model for patients with colon adenocarcinoma (COAD) based on glycosylation-associated genes. By analyzing TCGA-COAD data, 110 key genes were identified, and a prognostic model incorporating five glycosylation-related genes was constructed. The model exhibits good predictive performance and is significantly associated with clinical features such as age, N stage, M stage, and lymph node count. The prognostic genes are involved in various biological processes and pathways, influence T cell differentiation, and may contribute to CRC development. High-risk patients show a higher degree of immune cell infiltration. This model aids in the early diagnosis, prognosis assessment, and treatment planning for CRC, and offers a direction for further research. [ABSTRACT FROM AUTHOR]

Online biodiversity information databases are the requirement of general bioinformatics systems due to the rapid changes in the technologies and Internet. Biodiversity surveys and genetic searches are very expensive and time consuming process as the scientists and researchers are busy in collecting and maintaining data; as a result of it, there is less time spends on research. The biodiversity data has been found in government institutes, laboratories and natural environmental museums. Moreover, these database systems are not well-matched with contemporary data types. The biodiversity information databases have proved so much helpful that it increases the knowledge, discoveries and conservations. Such new inventions and discoveries are in the priority list of patent led by WTO regime. Therefore, it is essential to have global database and a global web portal to collect and maintain the scattered information resources. [ABSTRACT FROM AUTHOR]

Abstract Background T-cell exhaustion (TEX), a condition characterized by impaired T-cell function, has been implicated in numerous pathological conditions, but its role in acute myocardial Infarction (AMI) remains largely unexplored. This research aims to identify and characterize all TEX-related genes for AMI diagnosis. Methods By integrating gene expression profiles, differential expression analysis, gene set enrichment analysis, protein-protein interaction networks, and machine learning algorithms, we were able to decipher the molecular mechanisms underlying TEX and its significant association with AMI. In addition, we investigated the diagnostic validity of the leading TEX-related genes and their interactions with immune cell profiles. Different types of candidate small molecule compounds were ultimately matched with TEX-featured genes in the “DrugBank” database to serve as potential therapeutic medications for future TEX-AMI basic research. Results We screened 1725 differentially expressed genes (DEGs) from 80 AMI samples and 71 control samples, identifying 39 differential TEX-related transcripts in total. Functional enrichment analysis identified potential biological functions and signaling pathways associated with the aforementioned genes. We constructed a TEX signature containing five hub genes with favorable prognostic performance using machine learning algorithms. In addition, the prognostic performance of the nomogram of these five hub genes was adequate (AUC between 0.815 and 0.995). Several dysregulated immune cells were also observed. Finally, six small molecule compounds which could be the future therapeutic for TEX in AMI were discovered. Conclusion Five TEX diagnostic feature genes, CD48, CD247, FCER1G, TNFAIP3, and FCGRA, were screened in AMI. Combining these genes may aid in the early diagnosis and risk prediction of AMI, as well as the evaluation of immune cell infiltration and the discovery of new therapeutics.

Background: Remimazolam is a new ultrashort-acting benzodiazepine for sedation and anesthesia. The effects of remimazolam and the mechanism by which it functions in cancer cells have not been determined. This research aimed to explore the mechanism of remimazolam action in colon cancer treatment, using bioinformatics analysis and in vitro experiments. Methods: Cell cycle progression, colony formation, self-renewal capacity, and apoptosis detection were performed in HCT8 cells treated with or without remimazolam. Transcriptome sequencing, Gene Ontology, Kyoto Encyclopedia of Genes and Genome, Protein-Protein Interaction, Gene Set Enrichment Analysis, Western blotting, and qPCR were performed to investigate the mechanism of action of remimazolam in HCT8 colon cancer cells. Results: Remimazolam promoted proliferation and cell-cycle progression of HCT8 cells. After remimazolam treatment, a total of 1,096 differentially expressed genes (DEGs) were identified: 673 genes were downregulated, and 423 genes were upregulated. The DEGs were enriched mainly in "DNA replication", "cell cycle", and "G1/S transition" related pathways. There were 15 DEGs verified by qPCR, and representative biomarkers were detected by Western Bloting. The remimazolam-mediated promotion of cell proliferation and cell cycle was reversed by G1T28, a CDK4/6 inhibitor. Conclusion: Remimazolam promoted cell-cycle progression and proliferation in HCT8 colon cancer cells, indicating that the long-term use of remimazolam has potential adverse effects in the anesthesia of patients with colon cancer. [ABSTRACT FROM AUTHOR]

Abstract Background Gastric cancer (CC) is a disease with high incidence and mortality rate. Immunotherapy is an important method for gastric cancer while lack of effective predictor. Integrins play an important role in the development. We aimed to explore the predictive value of β1 integrin (ITGB1) as a predictor of immunnotherapy in gastric cancer. Methods Differential expression analysis was conducted using the Gene Expression Profiling Interactive Analysis (GEPIA) 2.0 and GEO databases. GEPIA data were used to evaluate the prognostic value of ITGB1 in gastric cancer (GC). Transcriptomic and clinical data of GC and normal tissues were downloaded from The Cancer Genome Atlas database, and the TIMER database was used to evaluate the association between ITGB1 and immune infiltration. Time-dependent receiver operating characteristic (ROC) curve analysis was used to determine the prognostic value of ITGB1. To verify ITGB1 expression at the protein level, immunohistochemical staining was conducted. In addition, to analyze the correlation of ITGB1 with PD-1 and PD-L1, we examined levels of PD-1 and PD-L1 by IHC and determined the predictive value of ITGB1 for anti-PD-1 therapy in GC by ROC curve analysis. Results Compared with normal tissues, analysis of GEPIA and data at protein levels showed significantly higher expression of ITGB1 in GC. In addition, higher expression of ITGB1 was associated with worse pathological G-staging and tumor T-staging, which suggested that ITGB1 is a risk factor for poor prognosis in GC. The level of ITGB1 expression was positively correlated with CD8 + T cells, neutrophils, macrophages, and dendritic cells. ITGB1 expression was also correlated with PD-L1 expression, and this was further verified at the protein level by immunohistochemical analysis. The area under the ROC curve was 0.808. Conclusion ITGB1 may be a promising prognostic biomarker and effective predictor for anti-PD-1 therapy in GC. Trial registration Retrospectively registered.

Cervical cancer is one of the most common cancers in women and is the third leading cause of cancer-related mortality. Metabolic dysregulation is a common hallmark of cancer. Reactive oxygen species (ROS) are oxygen metabolism byproducts that are impacted by metabolic instability. ROS can trigger cellular apoptosis as a safety switch by controlling the intrinsic mitochondrial apoptosis pathway. MSRB3(methionine sulfoxide reductase B3) can inhibit ROS-mediated apoptosis in cervical cancer cells, promoting tumorigenesis and metastasis. In this article, the effect of miR-331-3p, and miR-338-3p on reducing gene expression is investigated. The unfavorable proteins in cervical cancer tissues were selected and obtained using the Human Protein Atlas (HPA) database. OncoDB was used to find differentially expressed genes (DEGs) linked with the cancer stage. A Venn diagram was created to identify genes that are both clinically unfavorable and linked with the cancer stage. STRING database was used to predict functional interactions of human proteins with specific genes. The upstream transcription factor, Gene Ontology analysis, and potential mechanism of MSRB3 and its interacting proteins were all investigated using Enrichr. HPA identified 223 genes linked with poor prognosis in cervical cancer patients. OncoDB was used to identify the top 1,000 DEGs related to cervical cancer patients' clinical stages. MSRB3 expression was shown to be correlated with cervical cancer patient survival and clinical stage. A tight relationship between MSRB3 expression and ROS was discovered by Reactome. According to STRING, MSRB3 interacts with two additional genes (TXN(Thioredoxin) and FXN(Frataxin)). According to GO analysis, these genes were engaged in protein repair pathways and had oxidoreductase activity. We discovered that MSRB3 expression is associated with a poor prognosis and survival in cervical cancer patients. Using the miRWalk database, we predicted that the miR-331-3p, miR-338-3p, and hsa-let-7b-3p have the potential to be involved in regulating MSRB3 Also, due to the TRRUST analysis, HSF2 is an upstream transcription factor of MSRB3, TXN, and FXN can be knocked down in order to suppress MSRB3 with two additional genes. Thus, treatment strategies that target MSRB3 expression may prone cervical cancer cells to apoptosis. [ABSTRACT FROM AUTHOR]

Background: Gastric cancer (CC) is a disease with high incidence and mortality rate. Immunotherapy is an important method for gastric cancer while lack of effective predictor. Integrins play an important role in the development. We aimed to explore the predictive value of β1 integrin (ITGB1) as a predictor of immunnotherapy in gastric cancer. Methods: Differential expression analysis was conducted using the Gene Expression Profiling Interactive Analysis (GEPIA) 2.0 and GEO databases. GEPIA data were used to evaluate the prognostic value of ITGB1 in gastric cancer (GC). Transcriptomic and clinical data of GC and normal tissues were downloaded from The Cancer Genome Atlas database, and the TIMER database was used to evaluate the association between ITGB1 and immune infiltration. Time-dependent receiver operating characteristic (ROC) curve analysis was used to determine the prognostic value of ITGB1. To verify ITGB1 expression at the protein level, immunohistochemical staining was conducted. In addition, to analyze the correlation of ITGB1 with PD-1 and PD-L1, we examined levels of PD-1 and PD-L1 by IHC and determined the predictive value of ITGB1 for anti-PD-1 therapy in GC by ROC curve analysis. Results: Compared with normal tissues, analysis of GEPIA and data at protein levels showed significantly higher expression of ITGB1 in GC. In addition, higher expression of ITGB1 was associated with worse pathological G-staging and tumor T-staging, which suggested that ITGB1 is a risk factor for poor prognosis in GC. The level of ITGB1 expression was positively correlated with CD8 + T cells, neutrophils, macrophages, and dendritic cells. ITGB1 expression was also correlated with PD-L1 expression, and this was further verified at the protein level by immunohistochemical analysis. The area under the ROC curve was 0.808. Conclusion: ITGB1 may be a promising prognostic biomarker and effective predictor for anti-PD-1 therapy in GC. Trial registration: Retrospectively registered. [ABSTRACT FROM AUTHOR]

Abstract Background Starch hydrolysates are energy sources for plant growth and development, regulate osmotic pressure and transmit signals in response to both biological and abiotic stresses. The α-amylase (AMY) and the β-amylase (BAM) are important enzymes that catalyze the hydrolysis of plant starch. Cassava (Manihot esculenta Crantz) is treated as one of the most drought-tolerant crops. However, the mechanisms of how AMY and BAM respond to drought in cassava are still unknown. Results Six MeAMY genes and ten MeBAM genes were identified and characterized in the cassava genome. Both MeAMY and MeBAM gene families contain four genes with alternative splicing. Tandem and fragment replications play important roles in the amplification of MeAMY and MeBAM genes. Both MeBAM5 and MeBAM10 have a BZR1/BES1 domain at the N-terminus, which may have transcription factor functions. The promoter regions of MeAMY and MeBAM genes contain a large number of cis-acting elements related to abiotic stress. MeAMY1, MeAMY2, MeAMY5, and MeBAM3 are proven as critical genes in response to drought stress according to their expression patterns under drought. The starch content, soluble sugar content, and amylase activity were significantly altered in cassava under different levels of drought stress. Conclusions These results provide fundamental knowledge for not only further exploring the starch metabolism functions of cassava under drought stress but also offering new perspectives for understanding the mechanism of how cassava survives and develops under drought.

The COVID-19 coronavirus, which primarily affects the lungs, is the source of the disease known as SARS-CoV-2. According to "Smoking and COVID-19: a scoping review," about 32% of smokers had a severe case of COVID-19 pneumonia at their admission time and 15% of non-smokers had this case of COVID-19 pneumonia. We were able to determine which genes were expressed differently in each group by comparing the expression of gene transcriptomic datasets of COVID-19 patients, smokers, and healthy controls. In all, 37 dysregulated genes are common in COVID-19 patients and smokers, according to our analysis. We have applied all important methods namely protein-protein interaction, hub-protein interaction, drug-protein interaction, tf-gene interaction, and gene-MiRNA interaction of bioinformatics to analyze to understand deeply the connection between both smoking and COVID-19 severity. We have also analyzed Pathways and Gene Ontology where 5 significant signaling pathways were validated with previous literature. Also, we verified 7 hub-proteins, and finally, we validated a total of 7 drugs with the previous study. [ABSTRACT FROM AUTHOR]

The strength of meta-analyses lies in the synthesis of data from multiple studies. Current guidelines require a thorough systematic search to maximize results, which usually includes searching multiple academic search systems (ASS). Google Scholar (GS) is considered a promising tool for searching the scientific literature. We aimed to determine whether GS is a valid and sufficient solitary data source for meta-analyses in the field of otolaryngology. Selected ENT-HNS journal was searched for meta-analyses published between 2010 and 2021 that adhered to the systematic reviews and meta-analyses guidelines and precisely followed the search algorithm. The latter was reproduced with GS, and the position of each enrolled study in each meta-analysis was determined. Ten meta-analyses were enrolled, the total number of search results ranged from 57 to 17,949. The number of GS search results was significantly greater than those of other ASS combinations (range 1,360–25,400, P =.006). The number of included papers for each meta-analysis ranged from 5 to 26. The position of all enrolled papers throughout GS searching was in the first 200 GS results in four of 10 meta-analyses. The reference lists of all included papers in the first 200 GS results identified 106 papers out of 108 (98%), while searching until the 500th GS output results identified 107 papers out of 108 papers (99%). GS can serve as a solitary ASS for systematic literature reviews and meta-analyses in the field of otolaryngology. Searching the first 500 or 200 results and including reference lists yields 99% and 98% coverage, respectively. [ABSTRACT FROM AUTHOR]

Background: Starch hydrolysates are energy sources for plant growth and development, regulate osmotic pressure and transmit signals in response to both biological and abiotic stresses. The α-amylase (AMY) and the β-amylase (BAM) are important enzymes that catalyze the hydrolysis of plant starch. Cassava (Manihot esculenta Crantz) is treated as one of the most drought-tolerant crops. However, the mechanisms of how AMY and BAM respond to drought in cassava are still unknown. Results: Six MeAMY genes and ten MeBAM genes were identified and characterized in the cassava genome. Both MeAMY and MeBAM gene families contain four genes with alternative splicing. Tandem and fragment replications play important roles in the amplification of MeAMY and MeBAM genes. Both MeBAM5 and MeBAM10 have a BZR1/BES1 domain at the N-terminus, which may have transcription factor functions. The promoter regions of MeAMY and MeBAM genes contain a large number of cis-acting elements related to abiotic stress. MeAMY1, MeAMY2, MeAMY5, and MeBAM3 are proven as critical genes in response to drought stress according to their expression patterns under drought. The starch content, soluble sugar content, and amylase activity were significantly altered in cassava under different levels of drought stress. Conclusions: These results provide fundamental knowledge for not only further exploring the starch metabolism functions of cassava under drought stress but also offering new perspectives for understanding the mechanism of how cassava survives and develops under drought. [ABSTRACT FROM AUTHOR]

Bizhi Tu,1,&ast; Yaya Jia,2,&ast; Jun Qian1 1Department of Orthopedics, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, People’s Republic of China; 2Department of Pediatrics, The Shanxi Medical University, Taiyuan, Shanxi, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Jun Qian, Department of Orthopedics, The First Affiliated Hospital of Anhui Medical University, 218 Jixi Road, Hefei, 230022, Anhui, People’s Republic of China, Tel +86-13955116514, Fax +86-551-62922025, Email qjpaper@sina.cnBackground: Human sarcomas (SARC) are a group of malignant tumors that originated from mesenchymal lineages with more than 60 subtypes. However, potential biomarkers for the diagnosis and prognosis of SARC remain to be investigated.Methods: We obtained three GSE raw matrix files (GSE39262, GSE21122, GSE48418) that related to various subtypes of sarcoma from the public GEO database and explored the widely differential expression genes in three obtained GSE files. Then common differential expression genes (CDGEs) were identified. We analyzed the correlation between the expression of the top five interacted genes of CDEGs and genome-wide differences, prognosis, genetic mutation, functional enrichment, immune infiltration, immune checkpoint, and marker genes’ expression of N6-methyladenosine (m6A) modification in SARC patients. Besides, a prognostic nomogram was constructed to predict the survival of SARC patients.Results: Among the three GSE files, 42 CDGEs were identified, and the top five interacted genes were ASPM, CCNB2, PRC1, AURKA, and SCM2. The expression levels of the five genes were higher in the SARC group than that in the normal group. The transcriptional level of CCNB2, PRC, and SCM2 was correlated to the worse survival of SARC. The constructed nomogram that combined CNB2, PRC1, and SCM2 showed a fairly good incredibility in predicting the survival of SARC (C-index: 0.711). Furthermore, the five genes were widely involved in immune infiltration, immune checkpoint, and m6A modification. In addition, we found a minor survival-related mutation rate (9%) of the five identified genes in SARC patients (p < 0.05).Conclusion: The results suggested the five identified genes widely participated in the prognosis, immune infiltration, immune checkpoint, and m6A modification of SARC patients. This study provided a theoretical basis for the research about the correlation between the level of five identified genes and sarcoma, but the further mechanism needs to be verified by experiments.Keywords: sarcoma, bioinformatics, prognostic values, differential expression gene, ASPM, CCNB2, PRC1, AURKA, SCM2

Black soldier fly larvae (BSFL) belong to the most widely reared insects as an alternative protein source at industrial scale. Bacteria in the larval gut can provide benefits for the animal, though some bacteria can also be pathogenic for the insect. Accurate characterization of the BSFL microbiota is important for the production of BSFL in terms of yield and microbiological safety. In this study, 16S ribosomal RNA gene sequence data sets from 11 studies were re-analysed to gain better insights in the BSFL gut microbiota, potential factors that influence their composition, and differences between the gut and the whole larvae microbiota. A core gut microbiota was found consisting of members of Enterococcus , Klebsiella, Morganella , Providencia , and Scrofimicrobium. Further, the factors 'Study', 'Age' and 'Feed' (i.e. rearing substrate of the larvae) significantly affected the microbiota gut composition. When compared to whole larvae, a significantly lower diversity was found for gut samples, suggesting that the larvae harboured additional microbes on their cuticle or in the insect body. Universal choices in insect sample type, primer selection and bio-informatics analysis pipeline can strengthen future meta-analyses and improve our understanding of the BSFL gut microbiota towards the optimization of insect rearing conditions and substrates. [ABSTRACT FROM AUTHOR]

Background and objective: Periodontitis is an inflammatory disease of the periodontium. However, the hub genes in periodontitis and their correlation with immune cells are not clear. This study aimed to identify hub genes and immune infiltration properties in periodontitis and to explore the correlation between hub genes and immune cells. Material and methods: Differentially expressed genes (DEGs) analysis and weighted gene co‐expression network analysis (WGCNA) were performed both on GSE10334 and GSE173078 datasets. Hub genes were identified via WGCNA and DEGs. The proportions of infiltrating immune cells were calculated by CIBERSORT algorithm, and single‐cell RNA‐sequencing dataset GSE164241 was used to explore cell‐type‐specific expression profiles of hub genes. Results: Eight hub genes (DERL3, FKBP11, LAX1, CD27, SPAG4, ST6GAL1, MZB1, and SEL1L3) were selected via WGCNA and DEGs by combining GSE10334 and GSE173078 datasets. CIBERSORT analysis showed a significant difference in the proportion of B cells, dendritic cells resting, and neutrophils in the gingival tissues between healthy and periodontitis patients, and expressions of these genes were highly correlated with the infiltration of B cells in periodontitis. Furthermore, real‐time quantitative PCR results further confirmed the overexpression of hub genes. Analysis of GSE164241dataset further identified that most of hub genes were mainly expressed in B cells. Conclusions: By integrating WGCNA, DEGs, and CIBERSORT analysis, eight genes were identified to be the hub genes of periodontitis and most of them were mainly expressed in B cells encouraging further researches on B cells in periodontitis pathogenesis. [ABSTRACT FROM AUTHOR]

The current global pandemic due to Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has taken a substantial number of lives across the world. Although few vaccines have been rolled-out, a number of vaccine candidates are still under clinical trials at various pharmaceutical companies and laboratories around the world. Considering the intrinsic nature of viruses in mutating and evolving over time, persistent efforts are needed to develop better vaccine candidates. In this study, various immuno-informatics tools and bioinformatics databases were deployed to derive consensus B-cell and T-cell epitope sequences of SARS-CoV-2 spike glycoprotein. This approach has identified four potential epitopes which have the capability to initiate both antibody and cell-mediated immune responses, are non-allergenic and do not trigger autoimmunity. These peptide sequences were also evaluated to show 99.82% of global population coverage based on the genotypic frequencies of HLA binding alleles for both MHC class-I and class-II and are unique for SARS-CoV-2 isolated from human as a host species. Epitope number 2 alone had a global population coverage of 98.2%. Therefore, we further validated binding and interaction of its constituent T-cell epitopes with their corresponding HLA proteins using molecular docking and molecular dynamics simulation experiments, followed by binding free energy calculations with molecular mechanics Poisson–Boltzmann surface area, essential dynamics analysis and free energy landscape analysis. The immuno-informatics pipeline described and the candidate epitopes discovered herein could have significant impact upon efforts to develop globally effective SARS-CoV-2 vaccines. [ABSTRACT FROM AUTHOR]

Objective To explore the role of Neurotropfin‐3 (NT‐3) in hypoxic‐ischemic encephalopathy (HIE) and determine its relative molecular mechanism. Methods The expression of NT‐3 was determined by qRT‐PCR in the cortex after HIE and immunofluorescence staining was used to detect the distribution of NT‐3. Then, the NT‐3 siRNA was used to validate the effect of NT‐3 for the neuroprotection by the measurement of cell apoptosis after oxygen‐glucose deprivation (OGD). Lastly, the molecular network mechanism was studied by NT‐3 interference experiment combined with bioinformatics technology. Results The expression of NT‐3 was significantly upregulated after HIE, and NT‐3 was co‐localized in the neurons of the cortex. Furthermore, the inhibition of NT‐3 increased neuronal apoptosis after OGD and elicited the down‐regulation of BDNF and NGF. Conclusion NT‐3 is a vital neurotrophic factor that can maintain neonatal survival in cortical neurons, and the underlying mechanism is associated with BDNF and NGF regulation.

While computational biology and bioinformatics became an inherent part of most life science degrees, it remains challenging to encourage students to employ these diverse skills in a practical research context. In this education paper, we present a method to enable students to acquire hands-on skills by the data-driven study of complex biomolecular datasets. The course starts with a series of introductory lectures, including a practical session on Cytoscape and associated computational network biology techniques. As we believe that enabling students to work on their own research questions will increase their motivation and interest, we introduced a recent hallmark virus-host protein-protein interaction article from which the database served as a starting point for the students to develop their research goal, which was presented and discussed in a first informal meeting. Over the next two months, about four interactive sessions were held, where students presented their problems, progress and challenges and exchanged ideas to help each other reach their goals. The role of the lecturers was to assist the students with independently collecting data and exploring new software tools for their research question. We present this concept and methods, and we share our experiences from an evaluation of the student learning outcomes. [ABSTRACT FROM AUTHOR]

Circular RNA (circRNA) is a novel regulatory non-coding RNA and participates in diverse physiological and pathological processes. However, the structures and molecular mechanisms of circRNAs remain unclear. In this study, taking advantage of openly databases and bioinformatics analysis, we observed lots of internal complementary base-pairing sequences (ICBPS) existed in plenty of circRNAs, especially in extremely long circRNAs (el-circRNAs, > 5,000 nt). The result indicated that circRNA may not be a simple circular structure. In addition, we put forward the hypothesis of “open-close effect” in the transition for specific circRNA from normal state to morbid state. Taken together, our results not only expand the knowledge of circRNAs, but also highlight the potential molecular mechanism of circRNAs.

Substantial research and monetary aids to healthcare establishments are provided by cloud computing. A benign position to store and handle vast genome data is offered by cloud services. Labs for gene sequencing send out raw and contingent data over the Internet to multiple sequence collections under conservative flow of gene information. The use of cloud services also reduces the storage costs of deoxyribonucleic acid (DNA) sequencing. Here, an efficient and new bio-informatics genomic system is proposed by the use of cloud services from Amazon to access the stored gene data and process it. A key task in bio-informatics is to locate protein-coding sections in a gene sequence based on three base periodicity (TBP) is for disease diagnosis and design drugs. Here, a novel cloud-based adaptive exon predictor (AEP) using Amazon cloud services is proposed to improve the accuracy in exon finding ability as well as aimed at superior convergence. Noise in the input gene sequence given to the proposed AEPs is pre-processed using normalized LMS filtering. Computational complexity can be reduced using proposed data normalized form of least logarithmic absolute difference (NLLAD) algorithm and its error normalized variants. It was shown that sign regressor NLLAD (SRNLLAD) dependent AEP is efficient in exon forecast applications using different metrics for a performance like sensitivity 0.8037, precision 0.8052 along with specificity 0.8146 by different gene sequences considered from the National Center for Biotechnology Information (NCBI) databank. The proposed AEPs have shown upright performance than typical LMS and other AEPs in terms of exon prediction accuracy, convergence, and computational complexity. Their less computational complexity will be found attractive, and they are suitable to use in bio-informatics nano devices.

Abstract Background Although significant advances have been made in understanding the mechanisms of macrophage response to Staphylococcus aureus infection, the molecular details are still elusive. Identification of the essential genes and biological processes of macrophages that are specifically changed at different durations of S. aureus exposure is of great clinical significance. Methods We aimed to identify the significantly changed genes and biological processes of S. aureus-exposed macrophages. We systematically analyzed the macrophage gene expression profile GSE 13670 database with 8 h, 24 h or 48 h S. aureus infection. The results were further confirmed by western blot and quantitative polymerase chain reaction (qPCR) analyses. Results After 8 h of S. aureus infection, the expression of 624 genes was significantly changed. Six hundred thirteen differentially expressed genes (DEGs) were identified after 24 h of S. aureus infection. Two hundred fifty-three genes were significantly changed after 48 h of S. aureus infection. STAT1 was consistently up-regulated in these three treatments. TP53, JAK2, CEBPA, STAT3, MYC, CTNNB1 and PRKCA were only identified in the 8 h or 24 h S. aureus infection groups. CTNNB1 and PRKCA were for the first time identified as potential essential genes in S. aureus infection of macrophages. In the Gene Ontology (GO) term analysis, the defense response was shown to be the most significantly changed biological process among all processes; KEGG pathway analysis identified the JAK-STAT signaling pathway involved in early infection. Conclusions Our systematic analysis identified unique gene expression profiles and specifically changed biological processes of the macrophage response to different S. aureus exposure times.

Circular RNA (circRNA) is a novel regulatory non-coding RNA and participates in diverse physiological and pathological processes. However, the structures and molecular mechanisms of circRNAs remain unclear. In this study, taking advantage of openly databases and bioinformatics analysis, we observed lots of internal complementary base-pairing sequences (ICBPS) existed in plenty of circRNAs, especially in extremely long circRNAs (el-circRNAs, > 5,000 nt). The result indicated that circRNA may not be a simple circular structure. In addition, we put forward the hypothesis of "open-close effect" in the transition for specific circRNA from normal state to morbid state. Taken together, our results not only expand the knowledge of circRNAs, but also highlight the potential molecular mechanism of circRNAs. [ABSTRACT FROM AUTHOR]

• A novel method for automated human vertebrae segmentation is proposed. • It combines the advantages of Algorithmic Feature Recognition Method and an enhanced Statistical Shape Model. • The method has been tested on L2 human vertebrae of male patients. • It has been stressed with low-density discretized models with missing morphological features. • The results show its robustness and effectiveness over the state-of-the-art. Human vertebrae are analysed and measured for various purposes, including to study anatomy, to diagnose illness, and to evaluate therapies. Achieving an accurate morphological and dimensional characterisation of three-dimensional (3D) vertebrae relies on the precise recognition of their features. Traditionally, these features, lacking defined edges, have been identified manually through a time-consuming, poorly repeatable, and poorly reproducible process. To the authors' knowledge, there is only one method published in the literature able to automatically recognise all the most important vertebral features: the algorithmic feature recognition method (AFRM). It requires a high-density point cloud as input, the presence of all morphological features, even if incomplete, and incurs high computational costs. This research aims to propose an improved version of the AFRM to overcome its limitations. The proposed approach combines the robustness of AFRM to provide the semantic segmentation of a 3D healthy human vertebra with the ability of the enhanced statistical shape model (eSSM) to transfer information among different models. Specifically, AFRM provides the semantic segmentation of the mean shape of the eSSM, while the latter transfers this information to target shapes. The eSSM was developed using 20 training samples of healthy adult male L2 vertebrae. The test samples included five healthy vertebrae and four vertebras with large missing parts. None of the test shapes were included in the training set. The novel approach could accurately recognise morphological features without the constraints that affect the AFRM. The proposed method guarantees reliable and automated segmentation through AFRM, exploiting the eSSM's ability to provide results even when dealing with sparsely populated or partially incomplete target models, significantly reducing the computational load. [ABSTRACT FROM AUTHOR]

Background: Adrenocortical carcinoma (ACC) is a rare endocrine malignancy with an unfavorable prognosis and limited treatment options. Nevertheless, no clinically applicable molecular markers have been identified for the progression of ACCs. DNA methylation alterations were found to contribute to the development of ACC in recent decades.Material and Methods: The aims of the current study was to identify the abnormally methylated differentially expressed genes (DEGs) in ACCs, and to elucidate the mechanistic basis for these changes. Analyses were conducted on gene expression and gene methylation profile datasets to identify the aberrantly methylated DEGs. The DAVID software was used to conduct the analyses of functional enrichment on screened genes. Finally, expression was validated, and the relationship between abnormally methylated DEGs and clinical features was determined via the Oncomine database and The Cancer Genome Atlas (TCGA). To further verify the altered expression and methylation status of our identified genes we also validated these changes at the tissue and cellular levels.Results: We screened and identified 92 differentially expressed genes and 802 abnormally methylated genes. Furthermore, seven aberrantly methylated and dysregulated genes were identified and validated, along with a number of functional enriched pathways. Among these seven genes, the expression or methylation status is significantly correlated with different pathological stages and overall rates of survival. In validation, the expression of seven genes were significantly altered and five genes were hypermethylated in ACC.Conclusions: Our study identified abnormally methylated DEGs and potentially affected pathways in ACCs, from which we could begin to understand the basic molecular mechanisms of these alterations. Moreover, these abnormally methylated genes might serve as therapeutic targets and biomarkers to allow ACC patients to be more precisely diagnosed and effectively treated.

Increased expression of GNAi2, which encodes the α-subunit of G-protein i2, has been correlated with the late-stage progression of ovarian cancer. GNAi2, also referred to as the proto-oncogene gip2, transduces signals from lysophosphatidic acid (LPA)-activated LPA-receptors to oncogenic cellular responses in ovarian cancer cells. To identify the oncogenic program activated by gip2, we carried out micro-array-based transcriptomic and bioinformatic analyses using the ovarian cancer cell-line SKOV3, in which the expression of GNAi2/gip2 was silenced by specific shRNA. A cut-off value of 5-fold change in gene expression (p < 0.05) indicated that a total of 264 genes were dependent upon gip2-expression with 136 genes coding for functional proteins. Functional annotation of the transcriptome indicated the hitherto unknown role of gip2 in stimulating the expression of oncogenic/growth-promoting genes such as KDR/VEGFR2, CCL20, and VIP. The array results were further validated in a panel of High-Grade Serous Ovarian Carcinoma (HGSOC) cell lines that included Kuramochi, OVCAR3, and OVCAR8 cells. Gene set enrichment analyses using DAVID, STRING, and Cytoscape applications indicated the potential role of the gip2-stimulated transcriptomic network involved in the upregulation of cell proliferation, adhesion, migration, cellular metabolism, and therapy resistance. The results unravel a multi-modular network in which the hub and bottleneck nodes are defined by ACKR3/CXCR7, IL6, VEGFA, CYCS, COX5B, UQCRC1, UQCRFS1, and FYN. The identification of these genes as the critical nodes in GNAi2/gip2 orchestrated onco-transcriptome establishes their role in ovarian cancer pathophysiology. In addition, these results also point to these nodes as potential targets for novel therapeutic strategies.

Analysis of protein plays a major role in bio-informatics domain. Protein sequence classification is most vital in this analysis of protein. In this scenario, different researchers proposed various techniques to classify protein sequence which is reviewed here at first. Different features extraction procedure from the protein sequence is a common part of all proposed classification model. But selection and arrangement of features are the most challenging task for classifying unknown protein sequences into its known families with high accuracy and preferable computational time. Analysis of recent trend points that traditional database system fails to classify a large amount of biological data which is overcome by data mining approach. New classification model involving four phases has been proposed here for classifying the unknown sequences into its family, generating knowledge-based as well as data analysis procedure. Here a hybrid algorithm with the combination of Fuzzy ARTMAP and neural network based algorithm is used. Neighbourhood analysis is also done in the final stage. This proposed model has implemented and validated with 497 different test sequences. Finally, it is also compared with the previous model and produce high accuracy level with low computation time than previous. [ABSTRACT FROM AUTHOR]

Modeling the interface region of a protein complex paves the way for understanding its dynamics and functionalities. Existing works model the interface region of a complex by using different approaches, such as, the residue composition at the interface region, the geometry of the interface residues, or the structural alignment of interface regions. These approaches are useful for ranking a set of docked conformation or for building scoring function for protein-protein docking, but they do not provide a generic and scalable technique for the extraction of interface patterns leading to functional motif discovery. In this work, we model the interface region of a protein complex by graphs and extract interface patterns of the given complex in the form of frequent subgraphs. To achieve this, we develop a scalable algorithm for frequent subgraph mining. We show that a systematic review of the mined subgraphs provides an effective method for the discovery of functional motifs that exist along the interface region of a given protein complex. In our experiments, we use three PDB protein structure datasets. The first two datasets are composed of PDB structures from different conformations of two dimeric protein complexes: HIV-1 protease (329 structures), and triosephosphate isomerase (TIM) (86 structures). The third dataset is a collection of different enzyme structures protein structures from the six top-level enzyme classes, namely: Oxydoreductase, Transferase, Hydrolase, Lyase, Isomerase, and Ligase. We show that for the first two datasets, our method captures the locking mechanism at the dimeric interface by taking into account the spatial positioning of the interfacial residues through graphs. Indeed, our frequent subgraph mining based approach discovers the patterns representing the dimerization lock which is formed at the base of the structure in 323 of the 329 HIV-1 protease structures. Similarly, for 86 TIM structures, our approach discovers the dimerization lock formation in 50 structures. For the enzyme structures, we show that we are able to capture the functional motifs (active sites) that are specific to each of the six top-level classes of enzymes through frequent subgraphs. [ABSTRACT FROM AUTHOR]

Background: Adrenocortical carcinoma (ACC) is a rare endocrine malignancy with an unfavorable prognosis and limited treatment options. Nevertheless, no clinically applicable molecular markers have been identified for the progression of ACCs. DNA methylation alterations were found to contribute to the development of ACC in recent decades. Material and Methods: The aims of the current study was to identify the abnormally methylated differentially expressed genes (DEGs) in ACCs, and to elucidate the mechanistic basis for these changes. Analyses were conducted on gene expression and gene methylation profile datasets to identify the aberrantly methylated DEGs. The DAVID software was used to conduct the analyses of functional enrichment on screened genes. Finally, expression was validated, and the relationship between abnormally methylated DEGs and clinical features was determined via the Oncomine database and The Cancer Genome Atlas (TCGA). To further verify the altered expression and methylation status of our identified genes we also validated these changes at the tissue and cellular levels. Results: We screened and identified 92 differentially expressed genes and 802 abnormally methylated genes. Furthermore, seven aberrantly methylated and dysregulated genes were identified and validated, along with a number of functional enriched pathways. Among these seven genes, the expression or methylation status is significantly correlated with different pathological stages and overall rates of survival. In validation, the expression of seven genes were significantly altered and five genes were hypermethylated in ACC. Conclusions: Our study identified abnormally methylated DEGs and potentially affected pathways in ACCs, from which we could begin to understand the basic molecular mechanisms of these alterations. Moreover, these abnormally methylated genes might serve as therapeutic targets and biomarkers to allow ACC patients to be more precisely diagnosed and effectively treated. [ABSTRACT FROM AUTHOR]

Solomon Marcus (1925–2016) was one of the founders of the Romanian theoretical computer science. His pioneering contributions to automata and formal language theories, mathematical linguistics and natural computing have been widely recognised internationally. In this paper we briefly present his publications in theoretical computer science and related areas, which consist in almost ninety papers. Finally we present a selection of ten Marcus books in these areas.

Cytochrome B is an important polypeptide of the mitochondria helpful in energy metabolism through oxidative phosphorylation. Cytochrome B plays an immense role in the reproduction of animals and due to its mutation prone nature, it can affect the basic physiology of animals. Cytochrome B affects reproductive system in males and equally plays an important role in transferring and providing energy in the development of the embryo, zygote, and oocytes precisely in females. The present study was conducted on Ghungroo pig to study their molecular and reproductive traits and the effect of the cytochrome B gene in the female reproduction of the Ghungroo pig. Although studies are available for cytochrome B gene analysis for evolutionary studies through phylogenetic analysis. This is the first report for the study of Cytochrome B gene on reproduction in pigs. Cytochrome B gene was sequenced and seven SNPs were observed out of which three were non-synonymous. INDEL mutation was detected in Variant B which had lead to Frame Shift mutation resulting in a stop codon AGA. The effect in the reproductive traits of the sow was studied due to the occurrence of nucleotide substitution. Bioinformatics analysis (I-mutant, PROVEAN, and SIFT) had revealed that the mutations were deleterious for the mutant type. Mutation leading to alterations in post-translational modification sites as phosphorylation site, leucine-rich nuclear export signal, occurrence of transmembrane helices, arginine and lysine peptide cleavage site for the mutant variant had resulted in a reduced physiological response. 3 D protein structure, (predicted through bioinformatics analysis) for cytochrome B has revealed distinct structural differences in mutated form with truncated protein by RMSD analysis through TM-Align software. Associated studies of genotype variants with reproductive traits have revealed the significant effect of variants of cytochrome B gene on reproductive traits namely litter size at first, second and third furrowing, piglet mortality, age at first furrowing and furrowing interval. Mitochondrial gene as Cytochrome B variants might be used as a marker for studying female reproduction of Ghungroo sow in future. [ABSTRACT FROM AUTHOR]

Aims Cohorts of millions of people's health records, whole genome sequencing, imaging, sensor, societal and publicly available data present a rapidly expanding digital trace of health. We aimed to critically review, for the first time, the challenges and potential of big data across early and late stages of translational cardiovascular disease research. Methods and results We sought exemplars based on literature reviews and expertise across the BigData@Heart Consortium. We identified formidable challenges including: data quality, knowing what data exist, the legal and ethical framework for their use, data sharing, building and maintaining public trust, developing standards for defining disease, developing tools for scalable, replicable science and equipping the clinical and scientific work force with new inter-disciplinary skills. Opportunities claimed for big health record data include: richer profiles of health and disease from birth to death and from the molecular to the societal scale; accelerated understanding of disease causation and progression, discovery of new mechanisms and treatment-relevant disease sub-phenotypes, understanding health and diseases in whole populations and whole health systems and returning actionable feedback loops to improve (and potentially disrupt) existing models of research and care, with greater efficiency. In early translational research we identified exemplars including: discovery of fundamental biological processes e.g. linking exome sequences to lifelong electronic health records (EHR) (e.g. human knockout experiments); drug development: genomic approaches to drug target validation; precision medicine: e.g. DNA integrated into hospital EHR for pre-emptive pharmacogenomics. In late translational research we identified exemplars including: learning health systems with outcome trials integrated into clinical care; citizen driven health with 24/7 multi-parameter patient monitoring to improve outcomes and population-based linkages Conclusion High volumes of inherently diverse ('big') EHR data are beginning to disrupt the nature of cardiovascular research and care. Such big data have the potential to improve our understanding of disease causation and classification relevant for early translation and to contribute actionable analytics to improve health and healthcare. of multiple EHR sources for higher resolution clinical epidemiology and public health. [ABSTRACT FROM AUTHOR]