Long-term prognosis of idiopathic ventricular fibrillation: An eighteen-year experience from a tertiary center.

INTRODUCTION AND OBJECTIVES: Idiopathic ventricular fibrillation (IVF) is diagnosed in patients who survive sudden cardiac arrest (SCA), preferably with documented ventricular fibrillation (VF), without any identifiable structural or electrical abnormality. Current evidence provides limited guidance on the diagnosis and follow-up of these patients. Our aim was to assess the clinical outcomes of survivors of an aborted SCA attributed to IVF. METHODS: We retrospectively collected clinical data from all patients who survived SCA and implanted a cardiac defibrillator (ICD) between 2005 and 2023. RESULTS: A total of 38 patients, 36.8% female, with a mean age of 44±14 years old were included. Median follow-up time was 8.7 years (interquartile range (IQR) 4.7-14.7 years). All patients underwent a comprehensive diagnostic evaluation that excluded structural and coronary disease. During follow-up, underlying diagnoses were established in 34.2% of the whole cohort. Genetic testing, performed in 37.2%, revealed underlying diagnoses in 57.1% of those tested, compared to only 26.3% of patients who did not undergo genetic testing [p=0.035, OR=5.1 (95% confidence interval (CI) 1.2-21.5)]. Mortality was 10.5% (due to non-arrhythmic causes) and 36.8% patients received appropriate therapies with a median time to first ICD therapy of 39 [5.4-47.3] months. CONCLUSION(S): Etiological diagnosis and recurrence prediction in patients with IVF remains challenging, even with extensive diagnostic evaluation and long-term follow-up. In our study, genetic testing enhanced diagnostic yield. Consistent with previous findings, our cohort experienced a notable arrhythmic recurrence, with no cardiac deaths, underlining the pivotal role of ICD implantation in these patients.

Reconstruction of cell-specific models capturing the influence of metabolism on DNA methylation in cancer.

The imbalance of epigenetic regulatory mechanisms such as DNA methylation, which can promote aberrant gene expression profiles without affecting the DNA sequence, may cause the deregulation of signaling, regulatory, and metabolic processes, contributing to a cancerous phenotype. Since some metabolites are substrates and cofactors of epigenetic regulators, their availability can be affected by characteristic cancer cell metabolic shifts, feeding cancer onset and progression through epigenetic deregulation. Hence, there is a need to study the influence of cancer metabolic reprogramming in DNA methylation to design new effective treatments. In this study, a generic Genome-Scale Metabolic Model (GSMM) of a human cell, integrating DNA methylation or demethylation reactions, was obtained and used for the reconstruction of Genome-Scale Metabolic Models enhanced with Enzymatic Constraints using Kinetic and Omics data (GECKOs) of 31 cancer cell lines. Furthermore, cell-line-specific DNA methylation levels were included in the models, as coefficients of a DNA composition pseudo-reaction, to depict the influence of metabolism over global DNA methylation in each of the cancer cell lines. Flux simulations demonstrated the ability of these models to provide simulated fluxes of exchange reactions similar to the equivalent experimentally measured uptake/secretion rates and to make good functional predictions. In addition, simulations found metabolic pathways, reactions and enzymes directly or inversely associated with the gene promoter methylation. Two potential candidates for targeted cancer epigenetic therapy were identified.

The Characterization and Cytotoxic Evaluation of Chondrosia reniformis Collagen Isolated from Different Body Parts (Ectosome and Choanosome) Envisaging the Development of Biomaterials.

Chondrosia reniformis is a collagen-rich marine sponge that is considered a sustainable and viable option for producing an alternative to mammalian-origin collagens. However, there is a lack of knowledge regarding the properties of collagen isolated from different sponge parts, namely the outer region, or cortex, (ectosome) and the inner region (choanosome), and how it affects the development of biomaterials. In this study, a brief histological analysis focusing on C. reniformis collagen spatial distribution and a comprehensive comparative analysis between collagen isolated from ectosome and choanosome are presented. The isolated collagen characterization was based on isolation yield, Fourier-transformed infrared spectroscopy (FTIR), circular dichroism (CD), SDS-PAGE, dot blot, and amino acid composition, as well as their cytocompatibility envisaging the development of future biomedical applications. An isolation yield of approximately 20% was similar for both sponge parts, as well as the FTIR, CD, and SDS-PAGE profiles, which demonstrated that both isolated collagens presented a high purity degree and preserved their triple helix and fibrillar conformation. Ectosome collagen had a higher OHpro content and possessed collagen type I and IV, while the choanosome was predominately constituted by collagen type IV. In vitro cytotoxicity assays using the L929 fibroblast cell line displayed a significant cytotoxic effect of choanosome collagen at 2 mg/mL, while ectosome collagen enhanced cell metabolism and proliferation, thus indicating the latter as being more suitable for the development of biomaterials. This research represents a unique comparative study of C. reniformis body parts, serving as a support for further establishing this marine sponge as a promising alternative collagen source for the future development of biomedical applications.

BioISO: An Objective-Oriented Application for Assisting the Curation of Genome-Scale Metabolic Models.

As the reconstruction of Genome-Scale Metabolic Models (GEMs) becomes standard practice in systems biology, the number of organisms having at least one metabolic model is peaking at an unprecedented scale. The automation of laborious tasks, such as gap-finding and gap-filling, allowed the development of GEMs for poorly described organisms. However, the quality of these models can be compromised by the automation of several steps, which may lead to erroneous phenotype simulations. Biological networks constraint-based In Silico Optimisation (BioISO) is a computational tool aimed at accelerating the reconstruction of GEMs. This tool facilitates manual curation steps by reducing the large search spaces often met when debugging in silico biological models. BioISO uses a recursive relation-like algorithm and Flux Balance Analysis (FBA) to evaluate and guide debugging of in silico phenotype simulations. The potential of BioISO to guide the debugging of model reconstructions was showcased and compared with the results of two other state-of-the-art gap-filling tools (Meneco and fastGapFill). In this assessment, BioISO is better suited to reducing the search space for errors and gaps in metabolic networks by identifying smaller ratios of dead-end metabolites. Furthermore, BioISO was used as Meneco's gap-finding algorithm to reduce the number of proposed solutions for filling the gaps.

Orogenital and anal infection by Chlamydia trachomatis, Neisseria gonorrhoeae, Mycoplasma genitalium, and other sexually transmitted infections in men who have sex with men in Lisbon.

BACKGROUND: Men who have sex with men (MSM) are at risk for sexually transmitted infections (STIs), but more data on extragenital carriage are needed. AIM: We assessed the genital and extragenital prevalence of bacterial and other STIs in MSM in a Lisbon sexual health clinic. METHODS: We screened oral, anal, and urine samples of MSM visiting the GAT-CheckpointLX clinic June 2017-December 2021 for Chlamydia trachomatis (including lymphogranuloma venereum, LGV), Neisseria gonorrhoeae, Mycoplasma genitalium, Trichomonas vaginalis, Mycoplasma hominis, Ureaplasma urealyticum, and U. parvum. Ano-oro-genital lesions were tested for LGV, Treponema pallidum, and Herpes Simplex Virus. Blood was tested for HIV and T. pallidum antibodies. RESULTS: N. gonorrhoeae was found in 16.6% of the MSM followed by C. trachomatis (13.2%), M. genitalium (10.3%) and T. vaginalis (0.2%). The most frequent occurrence was anorectal (C. trachomatis, M. genitalium) and oral (N. gonorrhoeae). We found high carriage of U. urealyticum (36.1%) and M. hominis (22.1%). LGV was detected in 21.8% of chlamydia-positive anorectal swabs. Syphilis was detected in 22.6% of tested MSM, while 13.8% had HIV. Gonorrhoea and chlamydia were significantly more prevalent in MSM with concomitant HIV or syphilis. CONCLUSION: The substantial extragenital prevalence of bacterial STIs in MSM, and HIV and syphilis coinfections, suggest screening has value in identifying hidden carriage and in contributing for providing better care.

The first multi-tissue genome-scale metabolic model of a woody plant highlights suberin biosynthesis pathways in Quercus suber.

Over the last decade, genome-scale metabolic models have been increasingly used to study plant metabolic behaviour at the tissue and multi-tissue level under different environmental conditions. Quercus suber, also known as the cork oak tree, is one of the most important forest communities of the Mediterranean/Iberian region. In this work, we present the genome-scale metabolic model of the Q. suber (iEC7871). The metabolic model comprises 7871 genes, 6231 reactions, and 6481 metabolites across eight compartments. Transcriptomics data was integrated into the model to obtain tissue-specific models for the leaf, inner bark, and phellogen, with specific biomass compositions. The tissue-specific models were merged into a diel multi-tissue metabolic model to predict interactions among the three tissues at the light and dark phases. The metabolic models were also used to analyse the pathways associated with the synthesis of suberin monomers, namely the acyl-lipids, phenylpropanoids, isoprenoids, and flavonoids production. The models developed in this work provide a systematic overview of the metabolism of Q. suber, including its secondary metabolism pathways and cork formation.

Viral genetic clustering and transmission dynamics of the 2022 mpox outbreak in Portugal.

Pathogen genome sequencing during epidemics enhances our ability to identify and understand suspected clusters and investigate their relationships. Here, we combine genomic and epidemiological data of the 2022 mpox outbreak to better understand early viral spread, diversification and transmission dynamics. By sequencing 52% of the confirmed cases in Portugal, we identified the mpox virus sublineages with the highest impact on case numbers and fitted them into a global context, finding evidence that several international sublineages probably emerged or spread early in Portugal. We estimated a 62% infection reporting rate and that 1.3% of the population of men who have sex with men in Portugal were infected. We infer the critical role played by sexual networks and superspreader gatherings, such as sauna attendance, in the dissemination of mpox virus. Overall, our findings highlight genomic epidemiology as a tool for the real-time monitoring and control of mpox epidemics, and can guide future vaccine policy in a highly susceptible population.

Bilateral Synchronous Olecranon Apophyseal Fracture in a Teenager: A Case Report.

Introduction: While elbow fractures are frequently observed in children, isolated apophyseal detachments of the olecranon are rare, accounting for just 1.7% of all elbow fractures in healthy children. These fractures have been found to have a large positive likelihood ratio for osteogenesis imperfecta (OI). Within the population of patients with OI, the incidence is 13.5%, with 6% sustaining bilateral fractures. There are, however, very few reports of synchronous bilateral fractures. Case Report: A 15-year-old boy sustained a bilateral olecranon apophyse fracture, AO/OTA 21u-M/7 (displaced on the right side and minimal displacement on the left) after a low-energy fall. The patient was submitted on the same day to surgical treatment (open reduction and internal fixation with AO tension band wiring technique) on the right elbow and nonoperative treatment with a cast on the left side. Exome sequencing excluded mutations related to OI. Conclusion: Apophyseal avulsion fractures of the olecranon may not be pathognomonic of OI, However, orthopedists should exercise caution and remain alert to the potential occurrence in patients who experience displaced, isolated olecranon fractures due to low-energy mechanisms, particularly if they have a history of previous fractures. The clinical genetic evaluation was recommended because of clinical suspicion of OI and because patient management can be significantly influenced by genetic testing, particularly when a genetic syndrome is identified.

Recent advances in data- and knowledge-driven approaches to explore primary microbial metabolism.

With the rapid progress in metabolomics and sequencing technologies, more data on the metabolome of single microbes and their communities become available, revealing the potential of microorganisms to metabolize a broad range of chemical compounds. The analysis of microbial metabolomics datasets remains challenging since it inherits the technical challenges of metabolomics analysis, such as compound identification and annotation, while harboring challenges in data interpretation, such as distinguishing metabolite sources in mixed samples. This review outlines the recent advances in computational methods to analyze primary microbial metabolism: knowledge-based approaches that take advantage of metabolic and molecular networks and data-driven approaches that employ machine/deep learning algorithms in combination with large-scale datasets. These methods aim at improving metabolite identification and disentangling reciprocal interactions between microbes and metabolites. We also discuss the perspective of combining these approaches and further developments required to advance the investigation of primary metabolism in mixed microbial samples.

A systematic evaluation of deep learning methods for the prediction of drug synergy in cancer.

One of the main obstacles to the successful treatment of cancer is the phenomenon of drug resistance. A common strategy to overcome resistance is the use of combination therapies. However, the space of possibilities is huge and efficient search strategies are required. Machine Learning (ML) can be a useful tool for the discovery of novel, clinically relevant anti-cancer drug combinations. In particular, deep learning (DL) has become a popular choice for modeling drug combination effects. Here, we set out to examine the impact of different methodological choices on the performance of multimodal DL-based drug synergy prediction methods, including the use of different input data types, preprocessing steps and model architectures. Focusing on the NCI ALMANAC dataset, we found that feature selection based on prior biological knowledge has a positive impact-limiting gene expression data to cancer or drug response-specific genes improved performance. Drug features appeared to be more predictive of drug response, with a 41% increase in coefficient of determination (R2) and 26% increase in Spearman correlation relative to a baseline model that used only cell line and drug identifiers. Molecular fingerprint-based drug representations performed slightly better than learned representations-ECFP4 fingerprints increased R2 by 5.3% and Spearman correlation by 2.8% w.r.t the best learned representations. In general, fully connected feature-encoding subnetworks outperformed other architectures. DL outperformed other ML methods by more than 35% (R2) and 14% (Spearman). Additionally, an ensemble combining the top DL and ML models improved performance by about 6.5% (R2) and 4% (Spearman). Using a state-of-the-art interpretability method, we showed that DL models can learn to associate drug and cell line features with drug response in a biologically meaningful way. The strategies explored in this study will help to improve the development of computational methods for the rational design of effective drug combinations for cancer therapy.

Increasing and sustaining blood-borne virus screening in Spain and Portugal throughout the COVID-19 pandemic: a multi-center quality improvement intervention.

Background: Around 57,000 people in Spain and Portugal currently living with HIV or chronic hepatitis C are unaware of their infection. The COVID-19 pandemic severely disrupted screening efforts for these infections. We designed an intervention to increase and sustain opportunistic blood-borne virus (BBV) screening and linkage to care (SLTC) by implementing the TEST model. Methods: The Plan Do Study Act (PDSA) method of quality improvement (QI) was implemented in 8 healthcare organizations (HCOs), including four hospitals, two clusters of community health centers, and two community-based organizations (CBOs). Baseline assessment included a review of BBV SLTC practices, testing volume, and results 12 months before the intervention. Changes in BBV testing rates over time were measured before, during, and after the COVID-19 lockdowns in 2020. A mixed ANOVA model was used to analyze the possible effect on testing volumes among HCOs over the three study periods. Intervention: BBV testing was integrated into normal clinical flow in all HCOs using existing clinical infrastructure and staff. Electronic health record (EHR) systems were modified whenever possible to streamline screening processes, implement systemic institutional policy changes, and promote QI. Results: Two years after the launch of the intervention in screening practices, testing volumes increased by 116%, with formal healthcare settings recording larger increases than CBOs. The start of the COVID-19 lockdowns was accompanied by a global 60% decrease in testing in all HCOs. Screening emergency department patients or using EHR systems to automate screening showed the highest resilience and lowest reduction in testing. HCOs recovered 77% of their testing volume once the lockdowns were lifted, with CBOs making the fullest recovery. Globally, enhanced screening techniques enabled HCOs to diagnose a total of 1,860 individuals over the research period. Conclusions: Implementation of the TEST model enabled HCOs to increase and sustain BBV screening, even during COVID-19 lockdowns. Although improvement in screening was noted in all HCOs, additional work is needed to develop strong patient linkage to care models in challenging times, such as global pandemics.

Evaluating molecular representations in machine learning models for drug response prediction and interpretability.

Machine learning (ML) is increasingly being used to guide drug discovery processes. When applying ML approaches to chemical datasets, molecular descriptors and fingerprints are typically used to represent compounds as numerical vectors. However, in recent years, end-to-end deep learning (DL) methods that can learn feature representations directly from line notations or molecular graphs have been proposed as alternatives to using precomputed features. This study set out to investigate which compound representation methods are the most suitable for drug sensitivity prediction in cancer cell lines. Twelve different representations were benchmarked on 5 compound screening datasets, using DeepMol, a new chemoinformatics package developed by our research group, to perform these analyses. The results of this study show that the predictive performance of end-to-end DL models is comparable to, and at times surpasses, that of models trained on molecular fingerprints, even when less training data is available. This study also found that combining several compound representation methods into an ensemble can improve performance. Finally, we show that a post hoc feature attribution method can boost the explainability of the DL models.

A pipeline for the reconstruction and evaluation of context-specific human metabolic models at a large-scale.

Constraint-based (CB) metabolic models provide a mathematical framework and scaffold for in silico cell metabolism analysis and manipulation. In the past decade, significant efforts have been done to model human metabolism, enabled by the increased availability of multi-omics datasets and curated genome-scale reconstructions, as well as the development of several algorithms for context-specific model (CSM) reconstruction. Although CSM reconstruction has revealed insights on the deregulated metabolism of several pathologies, the process of reconstructing representative models of human tissues still lacks benchmarks and appropriate integrated software frameworks, since many tools required for this process are still disperse across various software platforms, some of which are proprietary. In this work, we address this challenge by assembling a scalable CSM reconstruction pipeline capable of integrating transcriptomics data in CB models. We combined omics preprocessing methods inspired by previous efforts with in-house implementations of existing CSM algorithms and new model refinement and validation routines, all implemented in the Troppo Python-based open-source framework. The pipeline was validated with multi-omics datasets from the Cancer Cell Line Encyclopedia (CCLE), also including reference fluxomics measurements for the MCF7 cell line. We reconstructed over 6000 models based on the Human-GEM template model for 733 cell lines featured in the CCLE, using MCF7 models as reference to find the best parameter combinations. These reference models outperform earlier studies using the same template by comparing gene essentiality and fluxomics experiments. We also analysed the heterogeneity of breast cancer cell lines, identifying key changes in metabolism related to cancer aggressiveness. Despite the many challenges in CB modelling, we demonstrate using our pipeline that combining transcriptomics data in metabolic models can be used to investigate key metabolic shifts. Significant limitations were found on these models ability for reliable quantitative flux prediction, thus motivating further work in genome-wide phenotype prediction.

merlin, an improved framework for the reconstruction of high-quality genome-scale metabolic models.

Genome-scale metabolic models have been recognised as useful tools for better understanding living organisms' metabolism. merlin (https://www.merlin-sysbio.org/) is an open-source and user-friendly resource that hastens the models' reconstruction process, conjugating manual and automatic procedures, while leveraging the user's expertise with a curation-oriented graphical interface. An updated and redesigned version of merlin is herein presented. Since 2015, several features have been implemented in merlin, along with deep changes in the software architecture, operational flow, and graphical interface. The current version (4.0) includes the implementation of novel algorithms and third-party tools for genome functional annotation, draft assembly, model refinement, and curation. Such updates increased the user base, resulting in multiple published works, including genome metabolic (re-)annotations and model reconstructions of multiple (lower and higher) eukaryotes and prokaryotes. merlin version 4.0 is the only tool able to perform template based and de novo draft reconstructions, while achieving competitive performance compared to state-of-the art tools both for well and less-studied organisms.

Non-Compaction Cardiomyopathy and Multiple Sclerosis: Associated or Independent Diseases? A Case Report.

Non-compaction cardiomyopathy (NCCM) is associated with neuromuscular disorders; however, there has been little investigation on its association with other neurological diseases, such as multiple sclerosis. We present the case of a 46-year-old woman with a history of multiple sclerosis who developed heart failure and was diagnosed with non-compaction cardiomyopathy.

UPIMAPI, reCOGnizer and KEGGCharter: Bioinformatics tools for functional annotation and visualization of (meta)-omics datasets.

Omics and meta-omics technologies are powerful approaches to explore microorganisms' functions, but the sheer size and complexity of omics datasets often turn the analysis into a challenging task. Software developed for omics and meta-omics analyses, together with knowledgebases encompassing information on genes, proteins, taxonomic and functional annotation, among other types of information, are valuable resources for analyzing omics data. Although several bioinformatics resources are available for meta-omics analyses, many require significant computational expertise. Web interfaces are more user-friendly, but often struggle to handle large data files, such as those obtained in metagenomics, metatranscriptomics, or metaproteomics experiments. In this work, we present three novel bioinformatics tools, which are available through user-friendly command-line interfaces, can be run sequentially or stand-alone, and combine popular resources for functional annotation. UPIMAPI performs sequence homology-based annotation and obtains data from UniProtKB (e.g., protein names, EC numbers, Gene Ontology, Taxonomy, cross-references to external databases). reCOGnizer performs multithreaded domain homology-based annotation of protein sequences with several functional databases (i.e., CDD, NCBIfam, Pfam, Protein Clusters, SMART, TIGRFAM, COG and KOG) and in addition, obtains information on domain names and descriptions and EC numbers. KEGGCharter represents omics results, including differential gene expression, in KEGG metabolic pathways. In addition, it shows the taxonomic assignment of the enzymes represented, which is particularly useful in metagenomics studies in which several microorganisms are present. reCOGnizer, UPIMAPI and KEGGCharter together provide a comprehensive and complete functional characterization of large datasets, facilitating the interpretation of microbial activities in nature and in biotechnological processes.

Exploring synergies between plant metabolic modelling and machine learning.

As plants produce an enormous diversity of metabolites to help them adapt to the environment, the study of plant metabolism is of utmost importance to understand different plant phenotypes. Omics data have been generated at an unprecedented rate for several organisms, including plants, and are widely used to study the central dogma of molecular biology, connecting the genome to phenotypes. Constraint-based modelling (CBM) methods, working over genome-scale metabolic models (GSMMs), have been crucial for organising and analysing omics data by integrating them with biochemical knowledge. In 2009, the first plant GSMM was reconstructed and, since then, several advances have been made, including the creation of context- and multi-tissue models that have supported the study of plant metabolism. Nevertheless, plant metabolic modelling remains very challenging. In parallel, as omics datasets are complex and heterogeneous, machine learning (ML) models have been applied in their interpretation to foster knowledge discovery. Recently, the first studies combining both CBM and ML approaches have emerged and have shown promising results. Here, we present the major advances in plant metabolic modelling and review the main CBM-ML hybrid studies. Finally, we discuss the application of machine learning to address the unique challenges of plant metabolic modelling.

Biomaterials and Bioactive Natural Products from Marine Invertebrates: From Basic Research to Innovative Applications.

Aquatic invertebrates are a major source of biomaterials and bioactive natural products that can find applications as pharmaceutics, nutraceutics, cosmetics, antibiotics, antifouling products and biomaterials. Symbiotic microorganisms are often the real producers of many secondary metabolites initially isolated from marine invertebrates; however, a certain number of them are actually synthesized by the macro-organisms. In this review, we analysed the literature of the years 2010-2019 on natural products (bioactive molecules and biomaterials) from the main phyla of marine invertebrates explored so far, including sponges, cnidarians, molluscs, echinoderms and ascidians, and present relevant examples of natural products of interest to public and private stakeholders. We also describe omics tools that have been more relevant in identifying and understanding mechanisms and processes underlying the biosynthesis of secondary metabolites in marine invertebrates. Since there is increasing attention on finding new solutions for a sustainable large-scale supply of bioactive compounds, we propose that a possible improvement in the biodiscovery pipeline might also come from the study and utilization of aquatic invertebrate stem cells.

RNAP II produces capped 18S and 25S ribosomal RNAs resistant to 5'-monophosphate dependent processive 5' to 3' exonuclease in polymerase switched Saccharomyces cerevisiae.

BACKGROUND: We have previously found that, in the pathogenic yeast Candida albicans, 18S and 25S ribosomal RNA components, containing more than one phosphate on their 5'-end were resistant to 5'-monophosphate requiring 5' → 3″ exonuclease. Several lines of evidence pointed to RNAP II as the enzyme producing them. RESULTS: We now show the production of such 18S and 25S rRNAs in Saccharomyces cerevisiae that have been permanently switched to RNAP II (due to deletion of part of RNAP I upstream activator alone, or in combination with deletion of one component of RNAP I itself). They contain more than one phosphate at their 5'-end and an anti-cap specific antibody binds to them indicating capping of these molecules. These molecules are found in RNA isolated from nuclei, therefore are unlikely to have been modified in the cytoplasm. CONCLUSIONS: Our data confirm the existence of such molecules and firmly establish RNAP II playing a role in their production. The fact that we see these molecules in wild type Saccharomyces cerevisiae indicates that they are not only a result of mutations but are part of the cells physiology. This adds another way RNAP II is involved in ribosome production in addition to their role in the production of ribosome associated proteins.

Antibacterial Activity of Isobavachalcone (IBC) Is Associated with Membrane Disruption.

Isobavachalcone (IBC) is a natural prenylated chalcone with a broad spectrum of pharmacological properties. In this work, we newly synthesized and investigated the antibacterial activity of IBC against Gram-positive, Gram-negative and mycobacterial species. IBC was active against Gram-positive bacteria, mainly against Methicillin-Susceptible Staphylococcus aureus (MSSA) and Methicillin-Resistant Staphylococcus aureus (MRSA), with minimum inhibitory concentration (MIC) values of 1.56 and 3.12 µg/mL, respectively. On the other hand, IBC was not able to act against Gram-negative species (MIC > 400 µg/mL). IBC displayed activity against mycobacterial species (MIC = 64 µg/mL), including Mycobacterium tuberculosis, Mycobacterium avium and Mycobacterium kansasii. IBC was able to inhibit more than 50% of MSSA and MRSA biofilm formation at 0.78 µg/mL. Its antibiofilm activity was similar to vancomycin, which was active at 0.74 µg/mL. In order to study the mechanism of the action by fluorescence microscopy, the propidium iodide (PI) and SYTO9 fluorophores indicated that IBC disrupted the membrane of Bacillus subtilis. Toxicity assays using human keratinocytes (HaCaT cell line) showed that IBC did not have the capacity to reduce the cell viability. These results suggested that IBC is a promising antibacterial agent with an elucidated mode of action and potential applications as an antibacterial drug and a medical device coating.