Exploring the Traditional Uses of Thymbra capitata Infusion in Algarve (Portugal): Anti-Inflammatory, Wound Healing, and Anti-Aging.

Inflammation plays a pivotal role in the resolution of infection or tissue damage. In addition, inflammation is considered a hallmark of aging, which in turn compromises wound healing. Thymbra capitata is an aromatic plant, whose infusion is traditionally used as an anti-inflammatory and wound-healing agent. In this study, a T. capitata infusion was prepared and characterized by HPLC-PDA-ESI-MSn and its safety profile determined by the resazurin metabolic assay. The anti-inflammatory potential was revealed in lipopolysaccharide (LPS)-stimulated macrophages by assessing nitric oxide (NO) release and levels of inducible nitric oxide synthase (iNOS) and the interleukin-1ß pro-form (pro-IL-1ß). Wound-healing capacity was determined using the scratch assay. The activity of senescence-associated ß-galactosidase was used to unveil the anti-senescent potential, along with the nuclear accumulation of yH2AX and p21 levels. The antiradical potential was assessed by DPPH and ABTS scavenging assays. The infusion contains predominantly rosmarinic acid and salvianolic acids. The extract decreased NO, iNOS, and pro-IL-1ß levels. Interestingly, the extract promoted wound healing and decreased ß-galactosidase activity, as well as yH2AX and p21 levels. The present work highlights strong antiradical, anti-inflammatory, and wound healing capacities, corroborating the traditional uses ascribed to this plant. We have described, for the first time for this extract, anti-senescent properties.

Advances in the Phytochemical Characterisation and Bioactivities of Salvia aurea L. Essential Oil.

The Salvia L. genus (Lamiaceae) is largely used in the pharmaceutical and food industry. Several species of biological relevance are extensively employed in traditional medicine, including Salvia aurea L. (syn. S. africana-lutea L.), which is used as a traditional skin disinfectant and in wounds as a healing remedy; nevertheless, these properties have not been validated yet. The aim of the present study is to characterise S. aurea essential oil (EO), unveiling its chemical composition and validating its biological properties. The EO was obtained by hydrodistillation and subsequently analysed by GC-FID and GC-MS. Different biological activities were assessed: the antifungal effect on dermatophytes and yeasts and the anti-inflammatory potential by evaluating nitric oxide (NO) production and COX-2 and iNOS protein levels. Wound-healing properties were assessed using the scratch-healing test, and the anti-aging capacity was estimated through the senescence-associated beta-galactosidase activity. S. aurea EO is mainly characterised by 1,8-cineole (16.7%), ß-pinene (11.9%), cis-thujone (10.5%), camphor (9.5%), and (E)-caryophyllene (9.3%). The results showed an effective inhibition of the growth of dermatophytes. Furthermore, it significantly reduced protein levels of iNOS/COX-2 and simultaneously NO release. Additionally, the EO exhibited anti-senescence potential and enhanced wound healing. Overall, this study highlights the remarkable pharmacological properties of Salvia aurea EO, which should be further explored in order to develop innovative, sustainable, and environmentally friendly skin products.

Characterizing Genomics Repositories Using Feature-Based Classification.

In the last decades, the field of metagenomics aided by NGS technologies has grown exponentially and is now a cornerstone tool in medicine. However, even with the current technologies, obtaining a conclusive identification of an organism can be challenging due to using reference-based methods. Consequently, when releasing a new repository of genomic data that contains de-novo sequences, it is problematic to characterize its content. In this paper, we propose a novel method for organism identification and the creation and characterization of genomic databases. For identification, we propose a three-step pipeline for reference-free reconstruction, reference-based classification and features-based classification. On the other hand, for content exposition and extraction, the sequences and their identification are aggregated into a web database catalogue.

The complexity landscape of viral genomes.

BACKGROUND: Viruses are among the shortest yet highly abundant species that harbor minimal instructions to infect cells, adapt, multiply, and exist. However, with the current substantial availability of viral genome sequences, the scientific repertory lacks a complexity landscape that automatically enlights viral genomes' organization, relation, and fundamental characteristics. RESULTS: This work provides a comprehensive landscape of the viral genome's complexity (or quantity of information), identifying the most redundant and complex groups regarding their genome sequence while providing their distribution and characteristics at a large and local scale. Moreover, we identify and quantify inverted repeats abundance in viral genomes. For this purpose, we measure the sequence complexity of each available viral genome using data compression, demonstrating that adequate data compressors can efficiently quantify the complexity of viral genome sequences, including subsequences better represented by algorithmic sources (e.g., inverted repeats). Using a state-of-the-art genomic compressor on an extensive viral genomes database, we show that double-stranded DNA viruses are, on average, the most redundant viruses while single-stranded DNA viruses are the least. Contrarily, double-stranded RNA viruses show a lower redundancy relative to single-stranded RNA. Furthermore, we extend the ability of data compressors to quantify local complexity (or information content) in viral genomes using complexity profiles, unprecedently providing a direct complexity analysis of human herpesviruses. We also conceive a features-based classification methodology that can accurately distinguish viral genomes at different taxonomic levels without direct comparisons between sequences. This methodology combines data compression with simple measures such as GC-content percentage and sequence length, followed by machine learning classifiers. CONCLUSIONS: This article presents methodologies and findings that are highly relevant for understanding the patterns of similarity and singularity between viral groups, opening new frontiers for studying viral genomes' organization while depicting the complexity trends and classification components of these genomes at different taxonomic levels. The whole study is supported by an extensive website (https://asilab.github.io/canvas/) for comprehending the viral genome characterization using dynamic and interactive approaches.

Chemical characterization and bioactivity of the essential oil from Santolina insularis, a Sardinian endemism.

Santolina insularis is a Sardinian endemism that is widely used in traditional medicine. The essential oil was obtained with a yield of 2.7% and is mainly characterized by ß-phellandrene (22.6%), myrcene (11.4%) and artemisia ketone (7.6%). The oil significantly reduced NO production without affecting macrophages viability. In addition, it substantially inhibited the expression of two key pro-inflammatory enzymes, iNOS and COX-2 (71% and 25% at 0.54 mg/mL). Furthermore, the oil had a promising antifungal activity being Cryptococcus neoformans (MIC = 0.13 mg/mL) and the majority of dermatophytes (MIC = 0.13 mg/mL) the most susceptible fungi. Moreover, it significantly decreased the yeast-to-hypha transition (80% inhibition at 0.13 mg/mL) on C. albicans long before showing inhibitory effects. Overall, these results show that S. insularis could be applied in the treatment of fungal infections and associated inflammatory response.

SCREEN-DR: Collaborative platform for diabetic retinopathy.

BACKGROUND AND OBJECTIVE: Diabetic retinopathy (DR) is the most prevalent microvascular complication of diabetes mellitus and can lead to irreversible visual loss. Screening programs, based on retinal imaging techniques, are fundamental to detect the disease since the initial stages are asymptomatic. Most of these examinations reflect negative cases and many have poor image quality, representing an important inefficiency factor. The SCREEN-DR project aims to tackle this limitation, by researching and developing computer-aided methods for diabetic retinopathy detection. This article presents a multidisciplinary collaborative platform that was created to meet the needs of physicians and researchers, aiming at the creation of machine learning algorithms to facilitate the screening process. METHODS: Our proposal is a collaborative platform for textual and visual annotation of image datasets. The architecture and layout were optimized for annotating DR images by gathering feedback from several physicians during the design and conceptualization of the platform. It allows the aggregation and indexing of imagiology studies from diverse sources, and supports the creation and annotation of phenotype-specific datasets to feed artificial intelligence algorithms. The platform makes use of an anonymization pipeline and role-based access control for securing personal data. RESULTS: The SCREEN-DR platform has been deployed in the production environment of the SCREEN-DR project at http://demo.dicoogle.com/screen-dr, and the source code of the project is publicly available. We provide a description of the platform's interface and use cases it supports. At the time of publication, four physicians have created a total of 1826 annotations for 701 distinct images, and the annotated data has been used for training classification models.

SCREEN-DR - Software Architecture for the Diabetic Retinopathy Screening.

Diabetic Retinopathy (DR) is a common complication of diabetes that may lead to blindness if not treated. However, since DR evolves without any symptoms in the initial stages, early detection and treatment can only be achieved through routine checks. This article presents the collaborative platform of the SCREEN-DR project that promotes partnership between physicians and researchers in the scope of a regional DR screening program. The role of researchers is to create classification algorithms to evaluate image quality, discard non-pathological cases, locate possible lesions and grade DR severity. Physicians are responsible for annotating datasets, including the visual delineation of lesions. The collaborative platform collects the studies, indexes the images metadata, and manages the creation of datasets and the respective annotation process. An advanced searching mechanism supports multimodal queries over annotated datasets and exporting of results for feeding artificial intelligence algorithms.

Controlled searching in reversibly de-identified medical imaging archives.

Nowadays, digital medical imaging in healthcare has become a fundamental tool for medical diagnosis. This growth has been accompanied by the development of technologies and standards, such as the DICOM standard and PACS. This environment led to the creation of collaborative projects where there is a need to share medical data between different institutions for research and educational purposes. In this context, it is necessary to maintain patient data privacy and provide an easy and secure mechanism for authorized personnel access. This paper presents a solution that fully de-identifies standard medical imaging objects, including metadata and pixel data, providing at the same time a reversible de-identifier mechanism that retains search capabilities from the original data. The last feature is important in some scenarios, for instance, in collaborative platforms where data is anonymized when shared with the community but searchable for data custodians or authorized entities. The solution was integrated into an open source PACS archive and validated in a multidisciplinary collaborative scenario.

Web Validation Service for Ensuring Adherence to the DICOM Standard.

The DICOM Standard has been fundamental for ensuring the interoperability of Picture Archive and Communications Systems (PACS). By compiling rigorously to the standard, medical imaging equipment and applications from different vendors can share their data, and create integrated workflows which contributes to better quality healthcare services. However, DICOM is a complex, flexible and very extensive standard. Thus, it is difficult to attest the conformity of data structures produced by DICOM applications resulting in unexpected behaviors, errors and malfunctions. Those situations may be critical for regular PACS operation, resulting in serious losses to the healthcare enterprise. Therefore, it is of paramount importance that application vendors and PACS administrators are confident that their applications follow the standard correctly. In this regard, we propose a method for validating the compliance of PACS application with the DICOM Standard. It can capture the intricate dependency structure of DICOM modules and data elements using a relatively simple description language. The modular nature of our method allows describing each DICOM module, their attributes, and dependencies on a re-usable basis. As a result, our validator is able to encompass the numerous modules present in DICOM, as well as keep up with the emergence of new ones.