RadPhysBio: A Radiobiological Database for the Prediction of Cell Survival upon Exposure to Ionizing Radiation

Based on the need for radiobiological databases, in this work, we mined experimental ionizing radiation data of human cells treated with X-rays, γ-rays, carbon ions, protons and α-particles, by manually searching the relevant literature in PubMed from 1980 until 2024. In order to calculate normal and tumor cell survival α and β coefficients of the linear quadratic (LQ) established model, as well as the initial values of the double-strand breaks (DSBs) in DNA, we used WebPlotDigitizer and Python programming language. We also produced complex DNA damage results through the fast Monte Carlo code MCDS in order to complete any missing data. The calculated α/β values are in good agreement with those valued reported in the literature, where α shows a relatively good association with linear energy transfer (LET), but not β. In general, a positive correlation between DSBs and LET was observed as far as the experimental values are concerned. Furthermore, we developed a biophysical prediction model by using machine learning, which showed a good performance for α, while it underscored LET as the most important feature for its prediction. In this study, we designed and developed the novel radiobiological 'RadPhysBio' database for the prediction of irradiated cell survival (α and β coefficients of the LQ model). The incorporation of machine learning and repair models increases the applicability of our results and the spectrum of potential users.

Dual Targeting of DNA Damage Response Proteins Implicated in Cancer Radioresistance

Ionizing radiation can induce different types of DNA lesions, leading to genomic instability and ultimately cell death. Radiation therapy or radiotherapy, a major modality in cancer treatment, harnesses the genotoxic potential of radiation to target and destroy cancer cells. Nevertheless, cancer cells have the capacity to develop resistance to radiation treatment (radioresistance), which poses a major obstacle in the effective management of cancer. It has been shown that administration of platinum-based drugs to cancer patients can increase tumor radiosensitivity, but despite this, it is associated with severe adverse effects. Several lines of evidence support that activation of the DNA damage response and repair machinery in the irradiated cancer cells enhances radioresistance and cellular survival through the efficient repair of DNA lesions. Therefore, targeting of key DNA damage repair factors would render cancer cells vulnerable to the irradiation effects, increase cancer cell killing, and reduce the risk of side effects on healthy tissue. Herein, we have employed a computer-aided drug design approach for generating ab initio a chemical compound with drug-like properties potentially targeting two proteins implicated in multiple DNA repair pathways. The findings of this study could be taken into consideration in clinical decision-making in terms of co-administering radiation with DNA damage repair factor-based drugs.

Soft Tissue Ewing Sarcoma Cell Drug Resistance Revisited: A Systems Biology Approach

Ewing sarcoma is a rare type of cancer that develops in the bones and soft tissues. Drug therapy represents an extensively used modality for the treatment of sarcomas. However, cancer cells tend to develop resistance to antineoplastic agents, thereby posing a major barrier in treatment effectiveness. Thus, there is a need to uncover the molecular mechanisms underlying chemoresistance in sarcomas and, hence, to enhance the anticancer treatment outcome. In this study, a differential gene expression analysis was conducted on high-throughput transcriptomic data of chemoresistant versus chemoresponsive Ewing sarcoma cells. By applying functional enrichment analysis and protein-protein interactions on the differentially expressed genes and their corresponding products, we uncovered genes with a hub role in drug resistance. Granted that non-coding RNA epigenetic regulators play a pivotal role in chemotherapy by targeting genes associated with drug response, we investigated the non-coding RNA molecules that potentially regulate the expression of the detected chemoresistance genes. Of particular importance, some chemoresistance-relevant genes were associated with the autonomic nervous system, suggesting the involvement of the latter in the drug response. The findings of this study could be taken into consideration in the clinical setting for the accurate assessment of drug response in sarcoma patients and the application of tailored therapeutic strategies.

An in silico approach to the identification of diagnostic and prognostic markers in low-grade gliomas

Low-grade gliomas (LGG) are central nervous system Grade I tumors, and as they progress they are becoming one of the deadliest brain tumors. There is still great need for timely and accurate diagnosis and prognosis of LGG. Herein, we aimed to identify diagnostic and prognostic biomarkers associated with LGG, by employing diverse computational approaches. For this purpose, differential gene expression analysis on high-throughput transcriptomics data of LGG versus corresponding healthy brain tissue, derived from TCGA and GTEx, respectively, was performed. Weighted gene co-expression network analysis of the detected differentially expressed genes was carried out in order to identify modules of co-expressed genes significantly correlated with LGG clinical traits. The genes comprising these modules were further used to construct gene co-expression and protein-protein interaction networks. Based on the network analyses, we derived a consensus of eighteen hub genes, namely, CD74, CD86, CDC25A, CYBB, HLA-DMA, ITGB2, KIF11, KIFC1, LAPTM5, LMNB1, MKI67, NCKAP1L, NUSAP1, SLC7A7, TBXAS1, TOP2A, TYROBP, and WDFY4. All detected hub genes were up-regulated in LGG, and were also associated with unfavorable prognosis in LGG patients. The findings of this study could be applicable in the clinical setting for diagnosing and monitoring LGG.

"In the light of evolution:" keratins as exceptional tumor biomarkers

Keratins (KRTs) are the intermediate filament-forming proteins of epithelial cells, classified, according to their physicochemical properties, into "soft" and "hard" keratins. They have a key role in several aspects of cancer pathophysiology, including cancer cell invasion and metastasis, and several members of the KRT family serve as diagnostic or prognostic markers. The human genome contains both, functional KRT genes and non-functional KRT pseudogenes, arranged in two uninterrupted clusters on chromosomes 12 and 17. This characteristic renders KRTs ideal for evolutionary studies. Herein, comprehensive phylogenetic analyses of KRT homologous proteins in the genomes of major taxonomic divisions were performed, so as to fill a gap in knowledge regarding the functional implications of keratins in cancer biology among tumor-bearing species. The differential expression profiles of KRTs in diverse types of cancers were investigated by analyzing high-throughput data, as well. Several KRT genes, including the phylogenetically conserved ones, were found to be deregulated across several cancer types and to participate in a common protein-protein interaction network. This indicates that, at least in cancer-bearing species, these genes might have been under similar evolutionary pressure, perhaps to support the same important function(s). In addition, semantic relations between KRTs and cancer were detected through extensive text mining. Therefore, by applying an integrative in silico pipeline, the evolutionary history of KRTs was reconstructed in the context of cancer, and the potential of using non-mammalian species as model organisms in functional studies on human cancer-associated KRT genes was uncovered.

A Counterintuitive Neutrophil-Mediated Pattern in COVID-19 Patients Revealed through Transcriptomics Analysis

The COVID-19 pandemic has persisted for almost three years. However, the mechanisms linked to the SARS-CoV-2 effect on tissues and disease severity have not been fully elucidated. Since the onset of the pandemic, a plethora of high-throughput data related to the host transcriptional response to SARS-CoV-2 infections has been generated. To this end, the aim of this study was to assess the effect of SARS-CoV-2 infections on circulating and organ tissue immune responses. We profited from the publicly accessible gene expression data of the blood and soft tissues by employing an integrated computational methodology, including bioinformatics, machine learning, and natural language processing in the relevant transcriptomics data. COVID-19 pathophysiology and severity have mainly been associated with macrophage-elicited responses and a characteristic "cytokine storm". Our counterintuitive findings suggested that the COVID-19 pathogenesis could also be mediated through neutrophil abundance and an exacerbated suppression of the immune system, leading eventually to uncontrolled viral dissemination and host cytotoxicity. The findings of this study elucidated new physiological functions of neutrophils, as well as tentative pathways to be explored in asymptomatic-, ethnicity- and locality-, or staging-associated studies.

p73 isoforms meet evolution of metastasis

Cancer largely adheres to Darwinian selection. Evolutionary forces are prominent during metastasis, the final and incurable disease stage, where cells acquire combinations of advantageous phenotypic features and interact with a dynamically changing microenvironment, in order to overcome the metastatic bottlenecks, while therapy exerts additional selective pressures. As a strategy to increase their fitness, tumors often co-opt developmental and tissue-homeostasis programs. Herein, 25 years after its discovery, we review TP73, a sibling of the cardinal tumor-suppressor TP53, through the lens of cancer evolution. The TP73 gene regulates a wide range of processes in embryonic development, tissue homeostasis and cancer via an overwhelming number of functionally divergent isoforms. We suggest that TP73 neither merely mimics TP53 via its p53-like tumor-suppressive functions, nor has black-or-white-type effects, as inferred by the antagonism between several of its isoforms in processes like apoptosis and DNA damage response. Rather, under dynamic conditions of selective pressure, the various p73 isoforms which are often co-expressed within the same cancer cells may work towards a common goal by simultaneously activating isoform-specific transcriptional and non-transcriptional programs. Combinatorial co-option of these programs offers selective advantages that overall increase the likelihood for successfully surpassing the barriers of the metastatic cascade. The p73 functional pleiotropy-based capabilities might be present in subclonal populations and expressed dynamically under changing microenvironmental conditions, thereby supporting clonal expansion and propelling evolution of metastasis. Deciphering the critical p73 isoform patterns along the spatiotemporal axes of tumor evolution could identify strategies to target TP73 for prevention and therapy of cancer metastasis.

Radiation Type- and Dose-Specific Transcriptional Responses across Healthy and Diseased Mammalian Tissues

Ionizing radiation (IR) is a genuine genotoxic agent and a major modality in cancer treatment. IR disrupts DNA sequences and exerts mutagenic and/or cytotoxic properties that not only alter critical cellular functions but also impact tissues proximal and distal to the irradiated site. Unveiling the molecular events governing the diverse effects of IR at the cellular and organismal levels is relevant for both radiotherapy and radiation protection. Herein, we address changes in the expression of mammalian genes induced after the exposure of a wide range of tissues to various radiation types with distinct biophysical characteristics. First, we constructed a publicly available database, termed RadBioBase, which will be updated at regular intervals. RadBioBase includes comprehensive transcriptomes of mammalian cells across healthy and diseased tissues that respond to a range of radiation types and doses. Pertinent information was derived from a hybrid analysis based on stringent literature mining and transcriptomic studies. An integrative bioinformatics methodology, including functional enrichment analysis and machine learning techniques, was employed to unveil the characteristic biological pathways related to specific radiation types and their association with various diseases. We found that the effects of high linear energy transfer (LET) radiation on cell transcriptomes significantly differ from those caused by low LET and are consistent with immunomodulation, inflammation, oxidative stress responses and cell death. The transcriptome changes also depend on the dose since low doses up to 0.5 Gy are related with cytokine cascades, while higher doses with ROS metabolism. We additionally identified distinct gene signatures for different types of radiation. Overall, our data suggest that different radiation types and doses can trigger distinct trajectories of cell-intrinsic and cell-extrinsic pathways that hold promise to be manipulated toward improving radiotherapy efficiency and reducing systemic radiotoxicities.

Identification of neoplasm-specific signatures of miRNA interactions by employing a systems biology approach

MicroRNAs represent major regulatory components of the disease epigenome and they constitute powerful biomarkers for the accurate diagnosis and prognosis of various diseases, including cancers. The advent of high-throughput technologies facilitated the generation of a vast amount of miRNA-cancer association data. Computational approaches have been utilized widely to effectively analyze and interpret these data towards the identification of miRNA signatures for diverse types of cancers. Herein, a novel computational workflow was applied to discover core sets of miRNA interactions for the major groups of neoplastic diseases by employing network-based methods. To this end, miRNA-cancer association data from four comprehensive publicly available resources were utilized for constructing miRNA-centered networks for each major group of neoplasms. The corresponding miRNA-miRNA interactions were inferred based on shared functionally related target genes. The topological attributes of the generated networks were investigated in order to detect clusters of highly interconnected miRNAs that form core modules in each network. Those modules that exhibited the highest degree of mutual exclusivity were selected from each graph. In this way, neoplasm-specific miRNA modules were identified that could represent potential signatures for the corresponding diseases.

Ginger for Healthy Ageing: A Systematic Review on Current Evidence of Its Antioxidant, Anti-Inflammatory, and Anticancer Properties

The world's population is ageing at an accelerated pace. Ageing is a natural, physiological but highly complex and multifactorial process that all species in the Tree of Life experience over time. Physical and mental disabilities, and age-related diseases, would increase along with the increasing life expectancy. Ginger (Zingiber officinale) is a plant that belongs to the Zingiberaceae family, native to Southeast Asia. For hundreds of years, ginger has been consumed in various ways by the natives of Asian countries, both as culinary and medicinal herb for the treatment of many diseases. Mounting evidence suggests that ginger can promote healthy ageing, reduce morbidity, and prolong healthy lifespan. Ginger, a well-known natural product, has been demonstrated to possess antioxidant, anti-inflammatory, anticancer, and antimicrobial properties, as well as an outstanding antiviral activity due to a high concentration of antiviral compounds. In this review, the current evidence on the potential role of ginger and its active compounds in the prevention of ageing is discussed.

Novel integrated workflow allows production and in-depth quality assessment of multifactorial reprogrammed skeletal muscle cells from human stem cells

Skeletal muscle tissue engineering aims at generating biological substitutes that restore, maintain or improve normal muscle function; however, the quality of cells produced by current protocols remains insufficient. Here, we developed a multifactor-based protocol that combines adenovector (AdV)-mediated MYOD expression, small molecule inhibitor and growth factor treatment, and electrical pulse stimulation (EPS) to efficiently reprogram different types of human-derived multipotent stem cells into physiologically functional skeletal muscle cells (SMCs). The protocol was complemented through a novel in silico workflow that allows for in-depth estimation and potentially optimization of the quality of generated muscle tissue, based on the transcriptomes of transdifferentiated cells. We additionally patch-clamped phenotypic SMCs to associate their bioelectrical characteristics with their transcriptome reprogramming. Overall, we set up a comprehensive and dynamic approach at the nexus of viral vector-based technology, bioinformatics, and electrophysiology that facilitates production of high-quality skeletal muscle cells and can guide iterative cycles to improve myo-differentiation protocols.

Evaluation of intraoperative ketamine on the prevention of severe rebound pain upon cessation of peripheral nerve block: a prospective randomised, double-blind, placebo-controlled study

BACKGROUND

Pain after resolution of peripheral nerve block, known as 'rebound pain' (RP), is a major problem in outpatient surgery. The primary objective was to evaluate the benefit of intraoperative ketamine at an anti-hyperalgesic dose on the incidence of rebound pain after upper limb surgery under axillary plexus block in ambulatory patients. The secondary objective was to better understand the rebound pain phenomenon (individual risk factors).

METHODS

In this prospective, double-blind study, patients were randomised to receive either a single dose of i.v. ketamine (0.3 mg kg^-1) or a placebo. Preoperative mechanical temporal summation and central sensitization inventory were applied to question underlying central sensitisation. Pain catastrophising and Douleur Neuropathique 4 questionnaires were used. Rebound pain was defined as pain intensity score >7 (numeric rating scale, 0-10) after block resolution. Postoperative pain was recorded at Days 1, 4, and 30 after discharge.

RESULTS

A total of 109 subjects completed the study, and 40.4% presented with rebound pain. Ketamine administration did not reduce rebound pain incidence or intensity. Temporal summation and central sensitisation inventory scores did not differ between subjects with and without rebound pain. The predictive risk factors were bone surgery (odds ratio [OR]=5.2; confidence interval [CI], 1.9-14.6), severe preoperative pain (OR=4.2; CI, 1.5-11.7), and high pain catastrophising (OR=4.8; CI, 1.0-22.3). At Day 30, the average daily pain was higher in the rebound pain group involving neuropathic characteristics.

CONCLUSION

Ketamine at an anti-hyperalgesic dose showed no benefit on rebound pain development. Although central sensitisation might not be involved, preoperative pain intensity, and catastrophising stand as risk factors. Because rebound pain remains frequent despite adequate procedure-specific postoperative analgesia, future studies should focus on patient-specific pain management.

Thrombocytopenia in COVID‑19 and vaccine‑induced thrombotic thrombocytopenia

The highly heterogeneous symptomatology and unpredictable progress of COVID-19 triggered unprecedented intensive biomedical research and a number of clinical research projects. Although the pathophysiology of the disease is being progressively clarified, its complexity remains vast. Moreover, some extremely infrequent cases of thrombotic thrombocytopenia following vaccination against SARS-CoV-2 infection have been observed. The present study aimed to map the signaling pathways of thrombocytopenia implicated in COVID-19, as well as in vaccine-induced thrombotic thrombocytopenia (VITT). The biomedical literature database, MEDLINE/PubMed, was thoroughly searched using artificial intelligence techniques for the semantic relations among the top 50 similar words (>0.9) implicated in COVID-19-mediated human infection or VITT. Additionally, STRING, a database of primary and predicted associations among genes and proteins (collected from diverse resources, such as documented pathway knowledge, high-throughput experimental studies, cross-species extrapolated information, automated text mining results, computationally predicted interactions, etc.), was employed, with the confidence threshold set at 0.7. In addition, two interactomes were constructed: i) A network including 119 and 56 nodes relevant to COVID-19 and thrombocytopenia, respectively; and ii) a second network containing 60 nodes relevant to VITT. Although thrombocytopenia is a dominant morbidity in both entities, three nodes were observed that corresponded to genes (AURKA, CD46 and CD19) expressed only in VITT, whilst ADAM10, CDC20, SHC1 and STXBP2 are silenced in VITT, but are commonly expressed in both COVID-19 and thrombocytopenia. The calculated average node degree was immense (11.9 in COVID-19 and 6.43 in VITT), illustrating the complexity of COVID-19 and VITT pathologies and confirming the importance of cytokines, as well as of pathways activated following hypoxic events. In addition, PYCARD, NLP3 and P2RX7 are key potential therapeutic targets for all three morbid entities, meriting further research. This interactome was based on wild-type genes, revealing the predisposition of the body to hypoxia-induced thrombosis, leading to the acute COVID-19 phenotype, the 'long-COVID syndrome', and/or VITT. Thus, common nodes appear to be key players in illness prevention, progression and treatment.

In Silico Investigation of the Biological Implications of Complex DNA Damage with Emphasis in Cancer Radiotherapy through a Systems Biology Approach

Different types of DNA lesions forming in close vicinity, create clusters of damaged sites termed as “clustered/complex DNA damage” and they are considered to be a major challenge for DNA repair mechanisms resulting in significant repair delays and induction of genomic instability. Upon detection of DNA damage, the corresponding DNA damage response and repair (DDR/R) mechanisms are activated. The inability of cells to process clustered DNA lesions efficiently has a great impact on the normal function and survival of cells. If complex lesions are left unrepaired or misrepaired, they can lead to mutations and if persistent, they may lead to apoptotic cell death. In this in silico study, and through rigorous data mining, we have identified human genes that are activated upon complex DNA damage induction like in the case of ionizing radiation (IR) and beyond the standard DNA repair pathways, and are also involved in cancer pathways, by employing stringent bioinformatics and systems biology methodologies. Given that IR can cause repair resistant lesions within a short DNA segment (a few nm), thereby augmenting the hazardous and toxic effects of radiation, we also investigated the possible implication of the most biologically important of those genes in comorbid non-neoplastic diseases through network integration, as well as their potential for predicting survival in cancer patients.

Evaluation of virtual reality combining music and a hypnosis session to reduce anxiety before hand surgery under axillary plexus block: A prospective study

AIMS

Preoperative anxiety, which can affect postoperative recovery, is often present in patients undergoing surgery under loco-regional anaesthesia (LRA). Minimising preoperative anxiety with premedication can be effective but results in drug-related side effects. Therefore, the use of non-pharmacological techniques should be encouraged.

METHODS

We evaluated whether a virtual reality (VR) incorporating music and a hypnosis session, provided during the performance of LRA, can reduce preoperative anxiety. Fifty patients scheduled for elective hand surgery under an axillary plexus block were enrolled (March-June 2019). The primary outcome measure was the change in the Amsterdam Anxiety and Preoperative Information Scale (APAIS) questionnaire 5 min after the VR session as compared to before the VR session. The secondary outcome measures were the visual analog scale (VAS) for anxiety before and 2 h after the surgery and the Evaluation du Vécu de l'ANesthésie-LocoRégionale (EVAN-LR) satisfaction score.

RESULTS

Data from 48 patients were analysed. The APAIS score as well as VAS for anxiety were significantly reduced after a VR session (p < .001 for both scores). Patients were very satisfied (EVAN-LR: 92 (88, 94)).

CONCLUSIONS

The use of VR incorporating music and a hypnosis session could be an effective tool in the management of a patient's preoperative anxiety during the performance of an axillary plexus block.

Applications of Healthcare Robots in Combating the COVID-19 Pandemic

Due to the increasing number of COVID-19 cases, there is a remarkable demand for robots, especially in the clinical sector. SARS-CoV-2 mainly propagates due to close human interactions and contaminated surfaces, and hence, maintaining social distancing has become a mandatory preventive measure. This generates the need to treat patients with minimal doctor-patient interaction. Introducing robots in the healthcare sector protects the frontline healthcare workers from getting exposed to the coronavirus as well as decreases the need for medical personnel as robots can partially take over some medical roles. The aim of this paper is to highlight the emerging role of robotic applications in the healthcare sector and allied areas. To this end, a systematic review was conducted regarding the various robots that have been implemented worldwide during the COVID-19 pandemic to attenuate and contain the virus. The results obtained from this study reveal that the implementation of robotics into the healthcare field has a substantial effect in controlling the spread of SARS-CoV-2, as it blocks coronavirus propagation between patients and healthcare workers, along with other advantages such as disinfection or cleaning.

In Silico Methods for the Identification of Diagnostic and Favorable Prognostic Markers in Acute Myeloid Leukemia

Acute myeloid leukemia (AML), the most common type of acute leukemia in adults, is mainly asymptomatic at early stages and progresses/recurs rapidly and frequently. These attributes necessitate the identification of biomarkers for timely diagnosis and accurate prognosis. In this study, differential gene expression analysis was performed on large-scale transcriptomics data of AML patients versus corresponding normal tissue. Weighted gene co-expression network analysis was conducted to construct networks of co-expressed genes, and detect gene modules. Finally, hub genes were identified from selected modules by applying network-based methods. This robust and integrative bioinformatics approach revealed a set of twenty-four genes, mainly related to cell cycle and immune response, the diagnostic significance of which was subsequently compared against two independent gene expression datasets. Furthermore, based on a recent notion suggesting that molecular characteristics of a few, unusual patients with exceptionally favorable survival can provide insights for improving the outcome of individuals with more typical disease trajectories, we defined groups of long-term survivors in AML patient cohorts and compared their transcriptomes versus the general population to infer favorable prognostic signatures. These findings could have potential applications in the clinical setting, in particular, in diagnosis and prognosis of AML.

Polycystic οvary syndrome revisited: An interactions network approach

BACKGROUND

The polycystic ovary syndrome (PCOS) has genetic, epigenetic, metabolic and reproductive aspects, while its complex pathophysiology has not been conclusively deciphered.

AIM

The goal of this research was to screen the gene/gene products associated with PCOS and to predict any possible interactions with the highest possible fidelity.

MATERIALS AND METHODS

STRING v10.5 database and a confidence level of 0.7 were used.

RESULTS

A highly interconnected network of 48 nodes was created, where insulin (INS) appears to be the major hub. INS upstream and downstream defects were analysed and revealed that only the kisspeptin- and glucagon-coding genes were upstream of INS.

CONCLUSION

A metabolic dominance was inferred and discussed herein with its implications in puberty, obesity, infertility and cardiovascular function. This study, thus, may contribute to the resolution of a scientific conflict between the USA and EU definitions of the syndrome and/or provide a new P4 medicine approach.

Bioinformatic approaches to the investigation of the atavistic genes implicated in cancer

Introduction: Cancer is a widespread phenomenon occurring across multicellular organisms and represents a condition of atavism, wherein cells follow a path of reverse evolution that unlocks a toolkit of ancient pre-existing adaptations by disturbing hub genes of the human gene network. This results to a primitive cellular phenotype which resembles a unicellular life form. Methods: In the present study, we have employed bioinformatic approaches for the in-depth investigation of twelve atavistic hub genes (ACTG1, CTNNA1, CTNND1, CTTN, DSP, ILK, PKN2, PKP3, PLEC, RCC2, TLN1 and VASP), which exhibit highly disrupted interactions in diverse types of cancer and are associated with the formation of metastasis. To this end, phylogenetic analyses were conducted towards unravelling the evolutionary history of those hubs and tracing the origin of cancer in the Tree of Life. Results: Based on our results, most of those genes are of unicellular origin, and some of them can be traced back to the emergence of cellular life itself (atavistic theory). Our findings indicate how deep the evolutionary roots of cancer actually are, and may be exploited in the clinical setting for the design of novel therapeutic approaches and, particularly, in overcoming resistance to antineoplastic treatment.

Mining Natural Products with Anticancer Biological Activity through a Systems Biology Approach

Natural products, like turmeric, are considered powerful antioxidants which exhibit tumor-inhibiting activity and chemoradioprotective properties. Nowadays, there is a great demand for developing novel, affordable, efficacious, and effective anticancer drugs from natural resources. In the present study, we have employed a stringent in silico methodology to mine and finally propose a number of natural products, retrieved from the biomedical literature. Our main target was the systematic search of anticancer products as anticancer agents compatible to the human organism for future use. In this case and due to the great plethora of such products, we have followed stringent bioinformatics methodologies. Our results taken together suggest that natural products of a great diverse may exert cytotoxic effects in a maximum of the studied cancer cell lines. These natural compounds and active ingredients could possibly be combined to exert potential chemopreventive effects. Furthermore, in order to substantiate our findings and their application potency at a systems biology level, we have developed a representative, user-friendly, publicly accessible biodatabase, NaturaProDB, containing the retrieved natural resources, their active ingredients/fractional mixtures, the types of cancers that they affect, and the corresponding experimentally verified target genes.

A Systems-Based Key Innovation-Driven Approach Infers Co-option of Jaw Developmental Programs During Cancer Progression

Cancer acquires metastatic potential and evolves via co-opting gene regulatory networks (GRN) of embryonic development and tissue homeostasis. Such GRNs are encoded in the genome and frequently conserved among species. Considering that all metazoa have evolved from a common ancestor via major macroevolutionary events which shaped those GRNs and increased morphogenetic complexity, we sought to examine whether there are any key innovations that may be consistently and deterministically linked with metastatic potential across the metazoa clades. To address tumor evolution relative to organismal evolution, we revisited and retrospectively juxtaposed seminal laboratory and field cancer studies across taxa that lie on the evolutionary lineage from cnidaria to humans. We subsequently applied bioinformatics to integrate species-specific cancer phenotypes, multiomics data from up to 42 human cancer types, developmental phenotypes of knockout mice, and molecular phylogenetics. We found that the phenotypic manifestations of metastasis appear to coincide with agnatha-to-gnathostome transition. Genes indispensable for jaw development, a key innovation of gnathostomes, undergo mutations or methylation alterations, are aberrantly transcribed during tumor progression and are causatively associated with invasion and metastasis. There is a preference for deregulation of gnathostome-specific versus pre-gnathostome genes occupying hubs of the jaw development network. According to these data, we propose our systems-based model as an in silico tool the prediction of likely tumor evolutionary trajectories and therapeutic targets for metastasis prevention, on the rationale that the same genes which are essential for key innovations that catalyzed vertebrate evolution, such as jaws, are also important for tumor evolution.

Investigating Molecular Determinants of Cancer Cell Resistance to Ionizing Radiation Through an Integrative Bioinformatics Approach

Eradication of cancer cells through exposure to high doses of ionizing radiation (IR) is a widely used therapeutic strategy in the clinical setting. However, in many cases, cancer cells can develop remarkable resistance to radiation. Radioresistance represents a prominent obstacle in the effective treatment of cancer. Therefore, elucidation of the molecular mechanisms and pathways related to radioresistance in cancer cells is of paramount importance. In the present study, an integrative bioinformatics approach was applied to three publicly available RNA sequencing and microarray transcriptome datasets of human cancer cells of different tissue origins treated with ionizing radiation. These data were investigated in order to identify genes with a significantly altered expression between radioresistant and corresponding radiosensitive cancer cells. Through rigorous statistical and biological analyses, 36 genes were identified as potential biomarkers of radioresistance. These genes, which are primarily implicated in DNA damage repair, oxidative stress, cell pro-survival, and apoptotic pathways, could serve as potential diagnostic/prognostic markers cancer cell resistance to radiation treatment, as well as for therapy outcome and cancer patient survival. In addition, our findings could be potentially utilized in the laboratory and clinical setting for enhancing cancer cell susceptibility to radiation therapy protocols.

Nodal tumor volume as a prognostic factor for head and neck squamous cell carcinoma: a systematic review

Introduction: Several studies suggest that there is an association between the metastatic nodal tumor volume and the clinical outcome in patients with solid cancers. However, despite the prognostic potential of nodal volume, a standardized method for estimating the nodal volumetric parameters is lacking. Herein, we conducted a systematic review of the published scientific literature towards investigating the prognostic value of nodal volume in the carcinomas of head and neck, taking into consideration the primary tumor site and the human papillomavirus (HPV) status. Methodological issues: For this purpose, the biomedical literature database PubMed/MEDLINE was searched for studies relevant to the relationship of nodal volume to the treatment outcome and survival in head and neck squamous cell carcinoma (HNSCC) patients. Collectively, based on stringent inclusion/exclusion criteria, 23 eligible studies were included in the present systematic review. Results: On the basis of our findings, nodal volume is suggested to be strongly associated with clinical outcomes in HNSCC patients. Of particular note, there is an indication that nodal volume is an independent factor for further risk stratification for recurrence-free survival in patients with squamous cell carcinoma of the pharynx (oropharynx and hypopharynx). Extranodal extension (ENE) and HPV status should be also taken into consideration in further studies.

Neural Networks Recapitulation by Cancer Cells Promotes Disease Progression: A Novel Role of p73 Isoforms in Cancer-Neuronal Crosstalk

Simple Summary

Cancer is initiated by alterations in specific genes. However, at late stages, cancer cells become metastatic not necessarily through continuous accumulation of additional mutations, but by hijacking programs of normal embryonic development and reactivating them in an unusual place, at the wrong time. Here, we applied computational and experimental approaches to show that these malignant reactivations include genes that are crucial for the development and function of the nervous system. We use the paradigm of melanoma transition from less invasive to highly aggressive stages in order to show that major players of metastasis, such as TP73 gene products, are implicated in this process. This work provides evidence for interactions between cancer cells and the neuronal system, which may have important future implications for metastasis prevention and cancer management.

Abstract

Mechanisms governing tumor progression differ from those of initiation. One enigmatic prometastatic process is the recapitulation of pathways of neural plasticity in aggressive stages. Cancer and neuronal cells develop reciprocal interactions via mutual production and secretion of neuronal growth factors, neurothrophins and/or axon guidance molecules in the tumor microenvironment. Understanding cancer types where this process is active, as well as the drivers, markers and underlying mechanisms, has great significance for blocking tumor progression and improving patient survival. By applying computational and systemic approaches, in combination with experimental validations, we provide compelling evidence that genes involved in neuronal development, differentiation and function are reactivated in tumors and predict poor patient outcomes across various cancers. Across cancers, they co-opt genes essential for the development of distinct anatomical parts of the nervous system, with a frequent preference for cerebral cortex and neural crest-derived enteric nerves. Additionally, we show that p73, a transcription factor with a dual role in neuronal development and cancer, simultaneously induces neurodifferentiation and stemness markers during melanoma progression. Our data yield the basis for elucidating driving forces of the nerve–tumor cell crosstalk and highlight p73 as a promising regulator of cancer neurobiology.

Codon Usage and Phenotypic Divergences of SARS-CoV-2 Genes

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which first occurred in Wuhan (China) in December of 2019, causes a severe acute respiratory illness with a high mortality rate, and has spread around the world. To gain an understanding of the evolution of the newly emerging SARS-CoV-2, we herein analyzed the codon usage pattern of SARS-CoV-2. For this purpose, we compared the codon usage of SARS-CoV-2 with that of other viruses belonging to the subfamily of Orthocoronavirinae. We found that SARS-CoV-2 has a high AU content that strongly influences its codon usage, which appears to be better adapted to the human host. We also studied the evolutionary pressures that influence the codon usage of five conserved coronavirus genes encoding the viral replicase, spike, envelope, membrane and nucleocapsid proteins. We found different patterns of both mutational bias and natural selection that affect the codon usage of these genes. Moreover, we show here that the two integral membrane proteins (matrix and envelope) tend to evolve slowly by accumulating nucleotide mutations on their corresponding genes. Conversely, genes encoding nucleocapsid (N), viral replicase and spike proteins (S), although they are regarded as are important targets for the development of vaccines and antiviral drugs, tend to evolve faster in comparison to the two genes mentioned above. Overall, our results suggest that the higher divergence observed for the latter three genes could represent a significant barrier in the development of antiviral therapeutics against SARS-CoV-2.

Codon usage bias in radioresistant bacteria

The relationship between patterns of codon usage bias (CUB), the preferential usage of synonimous nucleotide triplets encoding the same amino acid, and radioresistance was investigated int he genomes of 16 taxonomically distinct radioresistant prokaryotic organisms and in a control set of 11 non-radioresistant bacteria. The radioresistant species were found to be strongly biased towards G and C in the third synonimous codon position. ENC and neutrality plots also sugest that CUB in radioresistant bacteria is mainly affected by mutational bias. Furthermore, the availability of tRNA gene copy number was analyzed and it was found that nine radioresistant species have the sam number of tRNA gene copies for each codon. This suggests that tRNA gene copies and codon bias co-evolved in a specific way in radioresistant species.

In Silico Phylogenetic and Structural Analyses of Plant Endogenous Danger Signaling Molecules upon Stress

The plant innate immune system has two major branches, the pathogen-triggered immunity and the effector-triggered immunity (ETI). The effectors are molecules released by plant attackers to evade host immunity. In addition to the foreign intruders, plants possess endogenous instigators produced in response to general cellular injury termed as damage-associated molecular patterns (DAMPs). In plants, DAMPs or alarmins are released by damaged, stressed, or dying cells following abiotic stress such as radiation, oxidative and drought stresses. In turn, a cascade of downstream signaling events is initiated leading to the upregulation of defense or response-related genes. In the present study, we have investigated more thoroughly the conservation status of the molecular mechanisms implicated in the danger signaling primarily in plants. Towards this direction, we have performed in silico phylogenetic and structural analyses of the associated biomolecules in taxonomically diverse plant species. On the basis of our results, the defense mechanisms appear to be largely conserved within the plant kingdom. Of note, the sequence and/or function of several components of these mechanisms was found to be conserved in animals, as well. At the same time, the molecules involved in plant defense were found to form a dense protein-protein interaction (PPi) network, suggesting a crosstalk between the various defense mechanisms to a variety of stresses, like oxidative stress.

Prediction of Gold Nanoparticle and Microwave-Induced Hyperthermia Effects on Tumor Control via a Simulation Approach

Hyperthermia acts as a powerful adjuvant to radiation therapy and chemotherapy. Recent advances show that gold nanoparticles (Au-NPs) can mediate highly localized thermal effects upon interaction with laser radiation. The purpose of the present study was to investigate via in silico simulations the mechanisms of Au-NPs and microwave-induced hyperthermia, in correlation to predictions of tumor control (biological endpoints: tumor shrinkage and cell death) after hyperthermia treatment. We also study in detail the dependence of the size, shape and structure of the gold nanoparticles on their absorption efficiency, and provide general guidelines on how one could modify the absorption spectrum of the nanoparticles in order to meet the needs of specific applications. We calculated the hyperthermia effect using two types of Au-NPs and two types of spherical tumors (prostate and melanoma) with a radius of 3 mm. The plasmon peak for the 30 nm Si-core Au-coated NPs and the 20 nm Au-NPs was found at 590 nm and 540 nm, respectively. Considering the plasmon peaks and the distribution of NPs in the tumor tissue, the induced thermal profile was estimated for different intervals of time. Predictions of hyperthermic cell death were performed by adopting a three-state mathematical model, where “three-state” includes (i) alive, (ii) vulnerable, and (iii) dead states of the cell, and it was coupled with a tumor growth model. Our proposed methodology and preliminary results could be considered as a proof-of-principle for the significance of simulating accurately the hyperthermia-based tumor control involving the immune system. We also propose a method for the optimization of treatment by overcoming thermoresistance by biological means and specifically through the targeting of the heat shock protein 90 (HSP90), which plays a critical role in the thermotolerance of cells and tissues.

Bipartite graphs in systems biology and medicine: a survey of methods and applications

The latest advances in high-throughput techniques during the past decade allowed the systems biology field to expand significantly. Today, the focus of biologists has shifted from the study of individual biological components to the study of complex biological systems and their dynamics at a larger scale. Through the discovery of novel bioentity relationships, researchers reveal new information about biological functions and processes. Graphs are widely used to represent bioentities such as proteins, genes, small molecules, ligands, and others such as nodes and their connections as edges within a network. In this review, special focus is given to the usability of bipartite graphs and their impact on the field of network biology and medicine. Furthermore, their topological properties and how these can be applied to certain biological case studies are discussed. Finally, available methodologies and software are presented, and useful insights on how bipartite graphs can shape the path toward the solution of challenging biological problems are provided.

Identification of gene expression profiles in myocardial infarction: a systematic review and meta-analysis

BACKGROUND

Myocardial infarction (MI) is a multifactorial disease with complex pathogenesis, mainly the result of the interplay of genetic and environmental risk factors. The regulation of thrombosis, inflammation and cholesterol and lipid metabolism are the main factors that have been proposed thus far to be involved in the pathogenesis of MI. Traditional risk-estimation tools depend largely on conventional risk factors but there is a need for identification of novel biochemical and genetic markers. The aim of the study is to identify differentially expressed genes that are consistently associated with the incidence myocardial infarction (MI), which could be potentially incorporated into the traditional cardiovascular diseases risk factors models.

METHODS

The biomedical literature and gene expression databases, PubMed and GEO, respectively, were searched following the PRISMA guidelines. The key inclusion criteria were gene expression data derived from case-control studies on MI patients from blood samples. Gene expression datasets regarding the effect of medicinal drugs on MI were excluded. The t-test was applied to gene expression data from case-control studies in MI patients.

RESULTS

A total of 162 articles and 174 gene expression datasets were retrieved. Of those a total of 4 gene expression datasets met the inclusion criteria, which contained data on 31,180 loci in 93 MI patients and 89 healthy individuals. Collectively, 626 differentially expressed genes were detected in MI patients as compared to non-affected individuals at an FDR q-value = 0.01. Of those, 88 genes/gene products were interconnected in an interaction network. Totally, 15 genes were identified as hubs of the network.

CONCLUSIONS

Functional enrichment analyses revealed that the DEGs and that they are mainly involved in inflammatory/wound healing, RNA processing/transport mechanisms and a yet not fully characterized pathway implicated in RNA transport and nuclear pore proteins. The overlap between the DEGs identified in this study and the genes identified through genetic-association studies is minimal. These data could be useful in future studies on the molecular mechanisms of MI as well as diagnostic and prognostic markers.

Inter-species functional interactome of nuclear steroid receptors (R1)

Steroids exert their actions by binding to the glucocorticoid, mineralocorticoid, androgen, estrogen and progesterone classes of receptors. Despite an exponential increase in our knowledge of steroid receptors, their interactions with other molecules, subcellular location and functions still need further elucidation. To unravel the mechanism(s) of action of the steroid hormones, as well as the function of their cognate nuclear receptors, an interaction network was created (henceforth referred to as "R1 Interactome")- illustrating that robust interactions have been preserved in rodents, frog, zebra fish and drosophila. The generated interactome of the retrieved orthologs across species revealed: a. interactions among surface-cytosol-nuclear receptors, and/or orphan receptors and genes, and b. nuclear corepressor 1 (NCOR1) as a major "hub", through which most steroid receptors interact. These mechanisms (i) integrate social behavior and environmental stimuli with intrinsic cellular functions, (ii) provide an explanatory mechanism of the major Public Health problem of "non-ionizing" radiation impact, surpassing the existing conflict over the "thermal"/ "non- thermal" consequences of radiation, linking all the so far proposed mechanisms, and addressing all reported effects in humans, rodents and insects, and (iii) reveal biologically or clinically important pathways and/or regulatory networks.

Methods of Analysis and Meta-Analysis for Identifying Differentially Expressed Genes

Microarray approaches are widely used high-throughput techniques to assess simultaneously the expression of thousands of genes under certain conditions and study the effects of certain treatments, diseases, and developmental stages. The traditional way to perform such experiments is to design oligonucleotide hybridization probes that correspond to specific genes and then measure the expression of the genes in order to determine which of them are up- or down-regulated compared to a condition that is used as a control. Hitherto, individual experiments cannot capture the bigger picture of how a biological system works and, therefore, data integration from multiple experimental studies and external data repositories is necessary to understand the function of genes and their expression patterns under certain conditions. Therefore, the development of methods for handling, integrating, comparing, interpreting and visualizing microarray data is necessary. The selection of an appropriate method for analysing microarray datasets is not an easy task. In this chapter, we provide an overview of the various methods developed for microarray data analysis, as well as suggestions for choosing the appropriate method for microarray meta-analysis.

RecA: a universal drug target in pathogenic bacteria

The spread of bacterial infectious diseases due to the development of resistance to antibiotic drugs in pathogenic bacteria is an emerging global concern. Therefore, the efficacious management and prevention of bacterial infections are major public health challenges. RecA is a pleiotropic recombinase protein that has been demonstrated to be implicated strongly in the bacterial drug resistance, survival and pathogenicity. In this minireview, RecA's role in the development of antibiotic resistance and its potential as an antimicrobial drug target are discussed.

Network analysis of genes and their association with diseases

A plethora of network-based approaches within the Systems Biology universe have been applied, to date, to investigate the underlying molecular mechanisms of various human diseases. In the present study, we perform a bipartite, topological and clustering graph analysis in order to gain a better understanding of the relationships between human genetic diseases and the relationships between the genes that are implicated in them. For this purpose, disease-disease and gene-gene networks were constructed from combined gene-disease association networks. The latter, were created by collecting and integrating data from three diverse resources, each one with different content covering from rare monogenic disorders to common complex diseases. This data pluralism enabled us to uncover important associations between diseases with unrelated phenotypic manifestations but with common genetic origin. For our analysis, the topological attributes and the functional implications of the individual networks were taken into account and are shortly discussed. We believe that some observations of this study could advance our understanding regarding the etiology of a disease with distinct pathological manifestations, and simultaneously provide the springboard for the development of preventive and therapeutic strategies and its underlying genetic mechanisms.

Progression of mouse skin carcinogenesis is associated with the orchestrated deregulation of mir-200 family members, mir-205 and their common targets

MicroRNAs are small, non-coding RNAs which regulate post-transcriptionally hundreds of target mRNAs. Given that their expression is deregulated in several cancer types, they represent potential diagnostic, prognostic, and predictive biomarkers, as well as next-generation therapeutic targets. Nevertheless, the involvement of miRNAs in non-melanoma skin cancer, a cancer type with increasing prevalence, is not extensively studied, and their comprehensive characterization as regard to the initiation, promotion, and progression stages is missing. To this end, we exploited a well-established multistage mouse skin carcinogenesis model in order to identify miRNAs consistently implicated in different stages of skin carcinogenesis. The cell lines comprising this model were subjected to miRNA expression profiling using microarrays, followed by bioinformatics analysis and validation with Q-PCR, as well as treatment with miRNA modulators. We showed that among all deregulated miRNAs in our system, only a functionally coherent group consisting of the miR-200 family members and miR-205-5p displays a pattern of progressive co-downregulation from the early toward the most aggressive stages of carcinogenesis. Their overlapping, co-regulated putative targets are potentially inter-associated and, of these, the EMT-related Rap1a is overexpressed toward aggressive stages. Ectopic expression of miR-205-5p in spindle cancer cells reduces Rap1a, mitigates cell invasiveness, decreases proliferation, and delays tumor onset. We conclude that deregulation of this miRNA group is primarily associated with aggressive phenotypes of skin cancer cells. Restoration of the miR-205-5p member of this group in spindle cells reduces the expression of critical, co-regulated targets that favor cancer progression, thus reversing the EMT characteristics. © 2015 Wiley Periodicals, Inc.

Functions, divergence and clinical value of TAp73 isoforms in cancer

The p73 gene encodes the tumour suppressive full-length TAp73 and N-terminal-truncated DNp73 isoforms that act as dominant negative inhibitors of TAp73. The overall effect of p73 in oncogenesis is thought to depend on the TAp73 to DNp73 isoforms' ratio. TAp73 isoforms include a number of C-terminal variants as a result of alternative splicing in 3'-end. TAp73 isoforms protect cells from oncogenic alterations in a multifaceted way since they are implicated in the suppression of all demonstrated hallmarks and enabling characteristics of cancer. Their best established role is in apoptosis, a process which seems to be differently affected by each TAp73 C-terminal variant. Based on previous findings and our thorough bioinformatics analysis, we highlight that TAp73 variants are functionally non-equivalent, since they present major differences in their transactivation efficiencies, protein interactions, response to DNA damage and apoptotic effects that are attributable to the primary structure of their C terminus. In this review, we summarise these differences and we unveil the link between crucial C-terminal motifs/residues and the oncosuppressive potential of TAp73 isoforms, emphasising on the importance of considering C terminus during the development of p73-based anticancer biologics.

Human cancer databases (review)

Cancer is one of the four major non‑communicable diseases (NCD), responsible for ~14.6% of all human deaths. Currently, there are >100 different known types of cancer and >500 genes involved in cancer. Ongoing research efforts have been focused on cancer etiology and therapy. As a result, there is an exponential growth of cancer‑associated data from diverse resources, such as scientific publications, genome‑wide association studies, gene expression experiments, gene‑gene or protein‑protein interaction data, enzymatic assays, epigenomics, immunomics and cytogenetics, stored in relevant repositories. These data are complex and heterogeneous, ranging from unprocessed, unstructured data in the form of raw sequences and polymorphisms to well‑annotated, structured data. Consequently, the storage, mining, retrieval and analysis of these data in an efficient and meaningful manner pose a major challenge to biomedical investigators. In the current review, we present the central, publicly accessible databases that contain data pertinent to cancer, the resources available for delivering and analyzing information from these databases, as well as databases dedicated to specific types of cancer. Examples for this wealth of cancer‑related information and bioinformatic tools have also been provided.

A comprehensive phylogenetic and structural analysis of the carcinoembryonic antigen (CEA) gene family

The carcinoembryonic antigen (CEA) gene family belongs to the immunoglobulin (Ig) superfamily and codes for a vast number of glycoproteins that differ greatly both in amino acid composition and function. The CEA family is divided into two groups, the carcinoembryonic antigen-related cell adhesion molecules (CEACAMs) and the pregnancy-specific glycoproteins. The CEA family members are implicated in pleiotropic (patho)physiological functions including cell-cell adhesion, pregnancy, immunity, neovascularization, regulation of insulin homeostasis, and carcinogenesis. In general, the CEA-encoded proteins are composed of an extracellular region with Ig variable and constant-like domains and a cytoplasmic region containing signaling motifs. Of particular interest, the well-studied human and mouse CEA genes are arranged in clusters in a single chromosome. Taking into account this characteristic, we made an effort to reconstruct the evolutionary history of the CEA gene family. Toward this end, the publicly available genomes were searched extensively for CEA homologs. The domain organization of the retrieved protein sequences was analyzed, and, subsequently, comprehensive phylogenetic analyses of the entire length CEA homologous proteins were performed. A series of evolutionarily conserved amino acid residues, functionally important, were identified. The relative positioning of these residues on the modeled tertiary structure of novel CEA protein domains revealed that they are, also, spatially conserved. Furthermore, the chromosomal arrangement of CEA genes was examined, and it was found that the CEA genes are preserved in terms of position, transcriptional orientation, and number in all species under investigation.

A comprehensive phylogenetic analysis of deadenylases

Deadenylases catalyze the shortening of the poly(A) tail at the messenger ribonucleic acid (mRNA) 3′-end in eukaryotes. Therefore, these enzymes influence mRNA decay, and constitute a major emerging group of promising anti-cancer pharmacological targets. Herein, we conducted full phylogenetic analyses of the deadenylase homologs in all available genomes in an effort to investigate evolutionary relationships between the deadenylase families and to identify invariant residues, which probably play key roles in the function of deadenylation across species. Our study includes both major Asp-Glu-Asp-Asp (DEDD) and exonuclease-endonuclease-phospatase (EEP) deadenylase superfamilies. The phylogenetic analysis has provided us with important information regarding conserved and invariant deadenylase amino acids across species. Knowledge of the phylogenetic properties and evolution of the domain of deadenylases provides the foundation for the targeted drug design in the pharmaceutical industry and modern exonuclease anti-cancer scientific research.

Unraveling microalgal molecular interactions using evolutionary and structural bioinformatics

Microalgae are unicellular microorganisms indispensible for environmental stability and life on earth, because they produce approximately half of the atmospheric oxygen, with simultaneously feeding on the harmful greenhouse gas carbon dioxide. Using gene fusion analysis, a series of five fusion/fission events was identified, that provided the basis for critical insights to their evolutionary history. Moreover, the three-dimensional structures of both the fused and the component proteins were predicted, allowing us to envisage putative protein-protein interactions that are invaluable for the efficient usage, handling and exploitation of microalgae. Collectively, our proposed approach on the five fusion/fission alga protein events contributes towards the expansion of the microalgae knowledgebase, bridging protein evolution of the ancient microalgal species and the rapidly evolving, modern, bioinformatics field.