Estimation of natural radioactivity in the soil samples around the uranium site, royal village, Khandela, Sikar district, Rajasthan.

The measurements of activity concentration of radium ( 226Ra ), thorium ( 232Th) and potassium ( 40K ) natural radionuclides using high purity germanium ( HPGe ) detector to assess harmful effects on people residing around Royal Uranium Site, Sikar, Rajasthan, India. The activity concentrations range from 29.03 ± 3.72 to 69.95 ± 4.07 Bq/kg for 226Ra with a mean value of 47.01 Bq/kg, 57.99 ± 6.13 to 113.94 ± 6.54 Bq/kg with a mean value of 86.56 Bq/kg for 232Th,678.19 ± 76.36 to 1426.55 ± 81.32 Bq/kg for 40K with a mean value of 1195 Bq/kg. Average Radium Equivalent Activity was measured 261.59 ± 35.48 Bq/kg. The total outdoor absorbed gamma dose rate ranged from 78.42 to 157.91 nGy/h with a mean value of 122.12 nGy/h.The average annual effective dose equivalent outdoors and indoors was found 0.75 mSv.Mean external (Hex) and internal (Hin) hazard indices are measured 0.70 and 0.82, respectively, for the study area.

Logic-based modeling and drug repurposing for the prediction of novel therapeutic targets and combination regimens against E2F1-driven melanoma progression.

Melanoma presents increasing prevalence and poor outcomes. Progression to aggressive stages is characterized by overexpression of the transcription factor E2F1 and activation of downstream prometastatic gene regulatory networks (GRNs). Appropriate therapeutic manipulation of the E2F1-governed GRNs holds the potential to prevent metastasis however, these networks entail complex feedback and feedforward regulatory motifs among various regulatory layers, which make it difficult to identify druggable components. To this end, computational approaches such as mathematical modeling and virtual screening are important tools to unveil the dynamics of these signaling networks and identify critical components that could be further explored as therapeutic targets. Herein, we integrated a well-established E2F1-mediated epithelial-mesenchymal transition (EMT) map with transcriptomics data from E2F1-expressing melanoma cells to reconstruct a core regulatory network underlying aggressive melanoma. Using logic-based in silico perturbation experiments of a core regulatory network, we identified that simultaneous perturbation of Protein kinase B (AKT1) and oncoprotein murine double minute 2 (MDM2) drastically reduces EMT in melanoma. Using the structures of the two protein signatures, virtual screening strategies were performed with the FDA-approved drug library. Furthermore, by combining drug repurposing and computer-aided drug design techniques, followed by molecular dynamics simulation analysis, we identified two potent drugs (Tadalafil and Finasteride) that can efficiently inhibit AKT1 and MDM2 proteins. We propose that these two drugs could be considered for the development of therapeutic strategies for the management of aggressive melanoma.

LncRNA-SLC16A1-AS1 induces metabolic reprogramming during Bladder Cancer progression as target and co-activator of E2F1.

Long non-coding RNAs (lncRNAs) have emerged as integral components of E2F1-regulated gene regulatory networks (GRNs), but their implication in advanced or treatment-refractory malignancy is unknown. Methods: We combined high-throughput transcriptomic approaches with bioinformatics and structure modeling to search for lncRNAs that participate in E2F1-activated prometastatic GRNs and their phenotypic targets in the highly-relevant case of E2F1-driven aggressive bladder cancer (BC). RNA immunoprecipitation was performed to verify RNA-protein interactions. Functional analyses including qRT-PCR, immunoblotting, luciferase assays and measurement of extracellular fluxes were conducted to validate expression and target gene regulation. Results: We identified E2F1-responsive lncRNA-SLC16A1-AS1 and its associated neighboring protein-coding gene, SLC16A1/MCT1, which both promote cancer invasiveness. Mechanistically, upon E2F1-mediated co-transactivation of the gene pair, SLC16A1-AS1 associates with E2F1 in a structure-dependent manner and forms an RNA-protein complex that enhances SLC16A1/MCT1 expression through binding to a composite SLC16A1-AS1:E2F1-responsive promoter element. Moreover, SLC16A1-AS1 increases aerobic glycolysis and mitochondrial respiration and fuels ATP production by fatty acid ß-oxidation. These metabolic changes are accompanied by alterations in the expression of the SLC16A1-AS1:E2F1-responsive gene PPARA, a key mediator of fatty acid ß-oxidation. Conclusions: Our results unveil a new gene regulatory program by which E2F1-induced lncRNA-SLC16A1-AS1 forms a complex with its transcription factor that promotes cancer metabolic reprogramming towards the acquisition of a hybrid oxidative phosphorylation/glycolysis cell phenotype favoring BC invasiveness.

WITHDRAWN: The Atlas of Inflammation Resolution (AIR).

The Publisher regrets that this article is an accidental duplication of an article that has already been published, https://doi.org/10.1016/j.mam.2020.100894. The duplicate article has therefore been withdrawn. The full Elsevier Policy on Article Withdrawal can be found at https://www.elsevier.com/about/our-business/policies/article-withdrawal.

The Atlas of Inflammation Resolution (AIR).

Acute inflammation is a protective reaction by the immune system in response to invading pathogens or tissue damage. Ideally, the response should be localized, self-limited, and returning to homeostasis. If not resolved, acute inflammation can result in organ pathologies leading to chronic inflammatory phenotypes. Acute inflammation and inflammation resolution are complex coordinated processes, involving a number of cell types, interacting in space and time. The biomolecular complexity and the fact that several biomedical fields are involved, make a multi- and interdisciplinary approach necessary. The Atlas of Inflammation Resolution (AIR) is a web-based resource capturing an essential part of the state-of-the-art in acute inflammation and inflammation resolution research. The AIR provides an interface for users to search thousands of interactions, arranged in inter-connected multi-layers of process diagrams, covering a wide range of clinically relevant phenotypes. By mapping experimental data onto the AIR, it can be used to elucidate drug action as well as molecular mechanisms underlying different disease phenotypes. For the visualization and exploration of information, the AIR uses the Minerva platform, which is a well-established tool for the presentation of disease maps. The molecular details of the AIR are encoded using international standards. The AIR was created as a freely accessible resource, supporting research and education in the fields of acute inflammation and inflammation resolution. The AIR connects research communities, facilitates clinical decision making, and supports research scientists in the formulation and validation of hypotheses. The AIR is accessible through https://air.bio.informatik.uni-rostock.de.

An integrative network-driven pipeline for systematic identification of lncRNA-associated regulatory network motifs in metastatic melanoma.

BACKGROUND: Melanoma phenotype and the dynamics underlying its progression are determined by a complex interplay between different types of regulatory molecules. In particular, transcription factors (TFs), microRNAs (miRNAs), and long non-coding RNAs (lncRNAs) interact in layers that coalesce into large molecular interaction networks. Our goal here is to study molecules associated with the cross-talk between various network layers, and their impact on tumor progression. RESULTS: To elucidate their contribution to disease, we developed an integrative computational pipeline to construct and analyze a melanoma network focusing on lncRNAs, their miRNA and protein targets, miRNA target genes, and TFs regulating miRNAs. In the network, we identified three-node regulatory loops each composed of lncRNA, miRNA, and TF. To prioritize these motifs for their role in melanoma progression, we integrated patient-derived RNAseq dataset from TCGA (SKCM) melanoma cohort, using a weighted multi-objective function. We investigated the expression profile of the top-ranked motifs and used them to classify patients into metastatic and non-metastatic phenotypes. CONCLUSIONS: The results of this study showed that network motif UCA1/AKT1/hsa-miR-125b-1 has the highest prediction accuracy (ACC = 0.88) for discriminating metastatic and non-metastatic melanoma phenotypes. The observation is also confirmed by the progression-free survival analysis where the patient group characterized by the metastatic-type expression profile of the motif suffers a significant reduction in survival. The finding suggests a prognostic value of network motifs for the classification and treatment of melanoma.

C. elegans protein interaction network analysis probes RNAi validated pro-longevity effect of nhr-6, a human homolog of tumor suppressor Nr4a1.

Protein-protein interaction (PPI) studies are gaining momentum these days due to the plethora of various high-throughput experimental methods available for detecting PPIs. Proteins create complexes and networks by functioning in harmony with other proteins and here in silico network biology hold the promise to reveal new functionality of genes as it is very difficult and laborious to carry out experimental high-throughput genetic screens in living organisms. We demonstrate this approach by computationally screening C. elegans conserved homologs of already reported human tumor suppressor and aging associated genes. We select by this nhr-6, vab-3 and gst-23 as predicted longevity genes for RNAi screen. The RNAi results demonstrated the pro-longevity effect of these genes. Nuclear hormone receptor nhr-6 RNAi inhibition resulted in a C. elegans phenotype of 23.46% lifespan reduction. Moreover, we show that nhr-6 regulates oxidative stress resistance in worms and does not affect the feeding behavior of worms. These findings imply the potential of nhr-6 as a common therapeutic target for aging and cancer ailments, stressing the power of in silico PPI network analysis coupled with RNAi screens to describe gene function.

Drug Repositioning Inferred from E2F1-Coregulator Interactions Studies for the Prevention and Treatment of Metastatic Cancers.

Metastasis management remains a long-standing challenge. High abundance of E2F1 triggers tumor progression by developing protein-protein interactions (PPI) with coregulators that enhance its potential to activate a network of prometastatic transcriptional targets. Methods: To identify E2F1-coregulators, we integrated high-throughput Co-immunoprecipitation (IP)/mass spectometry, GST-pull-down assays, and structure modeling. Potential inhibitors of PPI discovered were found by bioinformatics-based pharmacophore modeling, and transcriptome profiling was conducted to screen for coregulated downstream targets. Expression and target gene regulation was validated using qRT-PCR, immunoblotting, chromatin IP, and luciferase assays. Finally, the impact of the E2F1-coregulator complex and its inhibiting drug on metastasis was investigated in vitro in different cancer entities and two mouse metastasis models. Results: We unveiled that E2F1 forms coactivator complexes with metastasis-associated protein 1 (MTA1) which, in turn, is directly upregulated by E2F1. The E2F1:MTA1 complex potentiates hyaluronan synthase 2 (HAS2) expression, increases hyaluronan production and promotes cell motility. Disruption of this prometastatic E2F1:MTA1 interaction reduces hyaluronan synthesis and infiltration of tumor-associated macrophages in the tumor microenvironment, thereby suppressing metastasis. We further demonstrate that E2F1:MTA1 assembly is abrogated by small-molecule, FDA-approved drugs. Treatment of E2F1/MTA1-positive, highly aggressive, circulating melanoma cells and orthotopic pancreatic tumors with argatroban prevents metastasis and cancer relapses in vivo through perturbation of the E2F1:MTA1/HAS2 axis. Conclusion: Our results propose argatroban as an innovative, E2F-coregulator-based, antimetastatic drug. Cancer patients with the infaust E2F1/MTA1/HAS2 signature will likely benefit from drug repositioning.

Recent advancement in the early detection of melanoma using computerized tools: An image analysis perspective.

BACKGROUND: The paper reviews the advancement of tools and current technologies for the detection of melanoma. We discussed several computational strategies from pre- to postprocessing image operations, descriptors, and popular classifiers to diagnose a suspected skin lesion based on its virtual similarity to the malignant lesion with known histopathology. We reviewed the current state of smart phone-based apps as diagnostic tools for screening. METHODS: A literature survey was conducted using a combination of keywords in the bibliographic databases: PubMed, AJCC, PH2, EDRA, and ISIC melanoma project. A number of melanoma detection apps were downloaded for two major mobile operating systems, iOS and Android; their important uses, key challenges, and various expert opinions were evaluated and also discussed. RESULTS: We have provided an overview of research on the computer-aided diagnosis methods to estimate melanoma risk and early screening. Dermoscopic images are the most viable option for the advent of new image processing technologies based on which many of the skin cancer detection apps are being developed recently. We have categorized and explored their potential uses, evaluation criteria, limitations, and other details. CONCLUSION: Such advancements are helpful in the sense they are raising awareness. Diagnostic accuracy is the major issue of smart phone-based apps and it cannot replace an adequate clinical experience and biopsy procedures.

Integrative workflows for network analysis.

Due to genetic heterogeneity across patients, the identification of effective disease signatures and therapeutic targets is challenging. Addressing this challenge, we have previously developed a network-based approach, which integrates heterogeneous sources of biological information to identify disease specific core-regulatory networks. In particular, our workflow uses a multi-objective optimization function to calculate a ranking score for network components (e.g. feedback/feedforward loops) based on network properties, biomedical and high-throughput expression data. High ranked network components are merged to identify the core-regulatory network(s) that is then subjected to dynamical analysis using stimulus-response and in silico perturbation experiments for the identification of disease gene signatures and therapeutic targets. In a case study, we implemented our workflow to identify bladder and breast cancer specific core-regulatory networks underlying epithelial-mesenchymal transition from the E2F1 molecular interaction map.In this study, we review our workflow and described how it has developed over time to understand the mechanisms underlying disease progression and prediction of signatures for clinical decision making.

Impact of graphene-based nanomaterials (GBNMs) on the structural and functional conformations of hepcidin peptide.

Graphene-based nanomaterials (GBNMs) are widely used in various industrial and biomedical applications. GBNMs of different compositions, size and shapes are being introduced without thorough toxicity evaluation due to the unavailability of regulatory guidelines. Computational toxicity prediction methods are used by regulatory bodies to quickly assess health hazards caused by newer materials. Due to increasing demand of GBNMs in various size and functional groups in industrial and consumer based applications, rapid and reliable computational toxicity assessment methods are urgently needed. In the present work, we investigate the impact of graphene and graphene oxide nanomaterials on the structural conformations of small hepcidin peptide and compare the materials for their structural and conformational changes. Our molecular dynamics simulation studies revealed conformational changes in hepcidin due to its interaction with GBMNs, which results in a loss of its functional properties. Our results indicate that hepcidin peptide undergo severe structural deformations when superimposed on the graphene sheet in comparison to graphene oxide sheet. These observations suggest that graphene is more toxic than a graphene oxide nanosheet of similar area. Overall, this study indicates that computational methods based on structural deformation, using molecular dynamics (MD) simulations, can be used for the early evaluation of toxicity potential of novel nanomaterials.

A web platform for the network analysis of high-throughput data in melanoma and its use to investigate mechanisms of resistance to anti-PD1 immunotherapy.

Cellular phenotypes are established and controlled by complex and precisely orchestrated molecular networks. In cancer, mutations and dysregulations of multiple molecular factors perturb the regulation of these networks and lead to malignant transformation. High-throughput technologies are a valuable source of information to establish the complex molecular relationships behind the emergence of malignancy, but full exploitation of this massive amount of data requires bioinformatics tools that rely on network-based analyses. In this report we present the Virtual Melanoma Cell, an online tool developed to facilitate the mining and interpretation of high-throughput data on melanoma by biomedical researches. The platform is based on a comprehensive, manually generated and expert-validated regulatory map composed of signaling pathways important in malignant melanoma. The Virtual Melanoma Cell is a tool designed to accept, visualize and analyze user-generated datasets. It is available at: https://www.vcells.net/melanoma. To illustrate the utilization of the web platform and the regulatory map, we have analyzed a large publicly available dataset accounting for anti-PD1 immunotherapy treatment of malignant melanoma patients.

MiR-205-5p and miR-342-3p cooperate in the repression of the E2F1 transcription factor in the context of anticancer chemotherapy resistance.

High rates of lethal outcome in tumour metastasis are associated with the acquisition of invasiveness and chemoresistance. Several clinical studies indicate that E2F1 overexpression across high-grade tumours culminates in unfavourable prognosis and chemoresistance in patients. Thus, fine-tuning the expression of E2F1 could be a promising approach for treating patients showing chemoresistance. Methods: We integrated bioinformatics, structural and kinetic modelling, and experiments to study cooperative regulation of E2F1 by microRNA (miRNA) pairs in the context of anticancer chemotherapy resistance. Results: We showed that an enhanced E2F1 repression efficiency can be achieved in chemoresistant tumour cells through two cooperating miRNAs. Sequence and structural information were used to identify potential miRNA pairs that can form tertiary structures with E2F1 mRNA. We then employed molecular dynamics simulations to show that among the identified triplexes, miR-205-5p and miR-342-3p can form the most stable triplex with E2F1 mRNA. A mathematical model simulating the E2F1 regulation by the cooperative miRNAs predicted enhanced E2F1 repression, a feature that was verified by in vitro experiments. Finally, we integrated this cooperative miRNA regulation into a more comprehensive network to account for E2F1-related chemoresistance in tumour cells. The network model simulations and experimental data indicate the ability of enhanced expression of both miR-205-5p and miR-342-3p to decrease tumour chemoresistance by cooperatively repressing E2F1. Conclusions: Our results suggest that pairs of cooperating miRNAs could be used as potential RNA therapeutics to reduce E2F1-related chemoresistance.

Interaction of anthraquinones of Cassia occidentalis seeds with DNA and Glutathione.

Consumption of Cassia occidentalis (CO) seeds has been associated with the hepatomyoencephalopathy (HME) in children. Recently, we have characterized the toxic anthraquinones (AQs) such as Emodin, Rhein, Aloe-emodin, Chrysophanol and Physcion in CO seeds and detected these moieties in the bio fluids of CO poisoning cases. As AQs were detected in the serum of HME patients, their interaction with key biomolecules including protein, DNA and glutathione (GSH) is imperative. In this regard, we have previously reported the interaction of these AQs with serum albumin protein and their subsequent biological effects. However, the interaction of these AQs with DNA and GSH remained unexplored. In the present work, we have studied the binding of these AQs of CO seeds with DNA and GSH by fluorescence spectroscopy, UV-vis spectral analysis, molecular docking, and biochemical studies. Results indicated a higher binding affinity for Emodin (Ka = 3.854 × 104 L mol-1 S-1), Aloe-emodin (Ka = 0.961 × 104 L mol-1 S-1) and Rhein (Ka = 0.034 × 104 L mol-1 S-1) towards calf thymus DNA may be associated with their higher cytotoxicity. Alternatively, Physcion and Chrysophanol which showed less cytotoxicity in our earlier studies exhibited very low DNA binding. The binding pattern of all these AQs is consistent with the in-silico data. Absorption spectroscopy studies indicated the possible formation of GSH conjugate with Aloe-emodin and Physcion. Further biochemical measurement of GSH and GSSG (Glutathione disulfide) following incubation with AQs indicated that Aloe-emodin (28%) and Rhein (30%) oxidizes GSH to GSSG more as compared to other AQs. Taken together, these results suggest that the higher cytotoxicity of Rhein, Emodin and Aloe-emodin may be attributed to their potent DNA and GSH binding affinity.

A Network-Based Integrative Workflow to Unravel Mechanisms Underlying Disease Progression.

Unraveling mechanisms underlying diseases has motivated the development of systems biology approaches. The key challenges for the development of mathematical models and computational tool are (1) the size of molecular networks, (2) the nonlinear nature of spatio-temporal interactions, and (3) feedback loops in the structure of interaction networks. We here propose an integrative workflow that combines structural analyses of networks, high-throughput data, and mechanistic modeling. As an illustration of the workflow, we use prostate cancer as a case study with the aim of identifying key functional components associated with primary to metastasis transitions. Analysis carried out by the workflow revealed that HOXD10, BCL2, and PGR are the most important factors affected in primary prostate samples, whereas, in the metastatic state, STAT3, JUN, and JUNB are playing a central role. The identified key elements of each network are validated using patient survival analysis. The workflow presented here allows experimentalists to use heterogeneous data sources for the identification of diagnostic and prognostic signatures.

Unraveling a tumor type-specific regulatory core underlying E2F1-mediated epithelial-mesenchymal transition to predict receptor protein signatures.

Cancer is a disease of subverted regulatory pathways. In this paper, we reconstruct the regulatory network around E2F, a family of transcription factors whose deregulation has been associated to cancer progression, chemoresistance, invasiveness, and metastasis. We integrate gene expression profiles of cancer cell lines from two E2F1-driven highly aggressive bladder and breast tumors, and use network analysis methods to identify the tumor type-specific core of the network. By combining logic-based network modeling, in vitro experimentation, and gene expression profiles from patient cohorts displaying tumor aggressiveness, we identify and experimentally validate distinctive, tumor type-specific signatures of receptor proteins associated to epithelial-mesenchymal transition in bladder and breast cancer. Our integrative network-based methodology, exemplified in the case of E2F1-induced aggressive tumors, has the potential to support the design of cohort- as well as tumor type-specific treatments and ultimately, to fight metastasis and therapy resistance.Deregulation of E2F family transcription factors is associated with cancer progression and metastasis. Here, the authors construct a map of the regulatory network around the E2F family, and using gene expression profiles, identify tumour type-specific regulatory cores and receptor expression signatures associated with epithelial-mesenchymal transition in bladder and breast cancer.

Identification of Antineoplastic Targets with Systems Approaches, Using Resveratrol as an In-Depth Case Study.

The identification and validation of novel drug-target combinations are key steps in the drug discovery processes. Cancer is a complex disease that involves several genetic and environmental factors. High-throughput omics technologies are now widely available, however the integration of multi-omics data to identify viable anticancer drug-target combinations, that allow for a better clinical outcome when considering the efficacy-toxicity spectrum, is challenging. This review article provides an overview of systems approaches which help to integrate a broad spectrum of technologies and data. We focus on network approaches and investigate anticancer mechanism and biological targets of resveratrol using reverse pharmacophore mapping as an in-depth case study. The results of this case study demonstrate the use of systems approaches for a better understanding of the behavior of small molecule inhibitors in receptor binding sites. The presented network analysis approach helps in formulating hypotheses and provides mechanistic insights of resveratrol in neoplastic transformations.

Aryl Hydrocarbon Receptor Activation Contributes to Benzanthrone-Induced Hyperpigmentation via Modulation of Melanogenic Signaling Pathways.

Benzanthrone (BA), an oxidized polycyclic aromatic hydrocarbon (PAH), has been found to be a potential health threat to occupational workers involved in dye manufacturing factories. It has been observed that occupational workers become exposed to BA either during manufacturing, pulverization, or storage and developed various kinds of skin diseases like contact dermatitis, itching, erythema, roughness, and foremost, hyperpigmentation. It has been shown that some environmental organic pollutants (POPs) like dioxins, furans, and polychlorinated biphenyls (PCBs) may act as ligands for the aryl hydrocarbon receptor (AhR) and regulate hyperpigmentation. Here, we hypothesized that BA may also act as a ligand for AhR and possibly regulate the melanogenic pathway to induced hyperpigmentation. Our computation results indicate that BA has a high binding affinity toward AhR for the initiation of melanogenic signaling. Following the in silico predictions, we used primary mouse melanocytes (PMMs) and confirmed that exposure to BA (5, 10, and 25 µM) resulted in an increase in AhR expression, tyrosinase activity, and melanin synthesis. Moreover, to study the physiological relevance of these findings, C57BL/6 mice were topically exposed to BA, and enhanced pigmentation and melanin synthesis were observed. Furthermore, the study was extended to assess the mechanistic aspects involved in BA-induced hyperpigmentation in PMMs as well as in mouse skin. Our results suggest that BA exposure initiates AhR signaling and increases tyrosinase enzyme activity and melanin synthesis. Moreover, the genes that regulate the melanin synthesis, such as TRP-1, TRP-2 and the transcription factor MITF, were also found to be increased. Thus, altogether, we suggest that BA-AhR interactions are critical for BA-induced hyperpigmentation.

MicroRNA and Transcription Factor Gene Regulatory Network Analysis Reveals Key Regulatory Elements Associated with Prostate Cancer Progression.

Technological and methodological advances in multi-omics data generation and integration approaches help elucidate genetic features of complex biological traits and diseases such as prostate cancer. Due to its heterogeneity, the identification of key functional components involved in the regulation and progression of prostate cancer is a methodological challenge. In this study, we identified key regulatory interactions responsible for primary to metastasis transitions in prostate cancer using network inference approaches by integrating patient derived transcriptomic and miRomics data into gene/miRNA/transcription factor regulatory networks. One such network was derived for each of the clinical states of prostate cancer based on differentially expressed and significantly correlated gene, miRNA and TF pairs from the patient data. We identified key elements of each network using a network analysis approach and validated our results using patient survival analysis. We observed that HOXD10, BCL2 and PGR are the most important factors affected in primary prostate samples, whereas, in the metastatic state, STAT3, JUN and JUNB are playing a central role. Benefiting integrative networks our analysis suggests that some of these molecules were targeted by several overexpressed miRNAs which may have a major effect on the dysregulation of these molecules. For example, in the metastatic tumors five miRNAs (miR-671-5p, miR-665, miR-663, miR-512-3p and miR-371-5p) are mainly responsible for the dysregulation of STAT3 and hence can provide an opportunity for early detection of metastasis and development of alternative therapeutic approaches. Our findings deliver new details on key functional components in prostate cancer progression and provide opportunities for the development of alternative therapeutic approaches.

RETRACTED: Ambient UV-B exposure reduces the binding of ofloxacin with bacterial DNA gyrase and induces DNA damage mediated apoptosis.

This article has been retracted: please see Elsevier Policy on Article Withdrawal (http://www.elsevier.com/locate/withdrawalpolicy). This article has been retracted at the request of the Editor-in-Chief. The study is retracted due to image duplication reasons: The article contains an image that had already appeared in Free Radic Res, 48.3 (2014): 333­346. DOI 10.3109/10715762.2013.869324. The images are used in both papers but to conclude something entirely different, and suggested that the images have an entirely different biological meaning and treatment. Duplicating images in this way is ethically not acceptable.