AI Methods for Antimicrobial Peptides: Progress and Challenges.

Antimicrobial peptides (AMPs) are promising candidates to combat multidrug-resistant pathogens. However, the high cost of extensive wet-lab screening has made AI methods for identifying and designing AMPs increasingly important, with machine learning (ML) techniques playing a crucial role. AI approaches have recently revolutionised this field by accelerating the discovery of new peptides with anti-infective activity, particularly in preclinical mouse models.

Orthodontic movement after regenerative endodontic procedure in mature permanent tooth associated with dens invaginatus: a case report with 4-year follow-up.

Background: Regenerative endodontic procedures (REPs) are innovative treatments aimed at restoring damaged dental structures. However, the effect of orthodontic movement on REP-treated teeth is not well understood and may have significant long-term consequences. This study aimed to evaluate the impact of orthodontic movement on a mature permanent tooth associated with dens invaginatus that has undergone a regenerative endodontic procedure (REP).

Revolutionizing Endodontics: Innovative Approaches for Treating Mature Teeth With Closed Apices and Apical Lesions: A Report of Two Cases.

Introduction: Modern tissue engineering strategies have elucidated the potential of regenerative endodontic treatment (RET) as an alternative for treating mature teeth.

Methods: Here, we report two cases in which cell-based RET (CB-RET) using encapsulated allogeneic umbilical cord mesenchymal stem cells (UC-MSCs) in a platelet-poor plasma (PPP)-based scaffold was used in two mature teeth with pulp necrosis and apical periodontitis.

Results: After 5 years of follow-up, the healing response was satisfactory in both cases, with evidence of pulp revitalization.

Effect of Dentin Conditioning with EDTA and Diode Lasers on Expression of Odontoblast-like Cell Markers of Dental Pulp Stem Cells.

Regenerative endodontic procedures rely on the delivery of mesenchymal stem cells into the root canal and on the effect of local growth factors from the dentin and blood clot. The aim of this study was to assess the effect of dentin conditioning with ethylenediamine tetraacetic acid (EDTA) and diode lasers with different wavelengths (808 nm and 980 nm) on the expression of odontoblast-like cell markers. Forty dentin cylinders were divided into four groups according to the irrigation protocol: EDTA, EDTA + 808 nm diode laser, EDTA + 980 nm diode laser, and phosphate-buffered saline as the control group.

Cell-penetrating peptides predictors: A comparative analysis of methods and datasets.

Cell-Penetrating Peptides (CPP) are emerging as an alternative to small-molecule drugs to expand the range of biomolecules that can be targeted for therapeutic purposes. Due to the importance of identifying and designing new CPP, a great variety of predictors have been developed to achieve these goals. To establish a ranking for these predictors, a couple of recent studies compared their performances on specific datasets, yet their conclusions cannot determine if the ranking obtained is due to the model, the set of descriptors or the datasets used to test the predictors.

StarPep Toolbox: an open-source software to assist chemical space analysis of bioactive peptides and their functions using complex networks.

Motivation: Antimicrobial peptides (AMPs) are promising molecules to treat infectious diseases caused by multi-drug resistance pathogens, some types of cancer, and other conditions. Computer-aided strategies are efficient tools for the high-throughput screening of AMPs.

Results: This report highlights StarPep Toolbox, an open-source and user-friendly software to study the bioactive chemical space of AMPs using complex network-based representations, clustering, and similarity-searching models.

Overproduce and select, or determine optimal molecular descriptor subset via configuration space optimization? Application to the prediction of ecotoxicological endpoints.

Predicting the likely biological activity (or property) of compounds is a fundamental and challenging task in the drug discovery process. Current computational methodologies aim to improve their predictive accuracies by using deep learning (DL) approaches. However, non-DL based approaches for small- and medium-sized chemical datasets have demonstrated to be most suitable for.

Embedded-AMP: A Multi-Thread Computational Method for the Systematic Identification of Antimicrobial Peptides Embedded in Proteome Sequences.

Antimicrobial peptides (AMPs) have gained the attention of the research community for being an alternative to conventional antimicrobials to fight antibiotic resistance and for displaying other pharmacologically relevant activities, such as cell penetration, autophagy induction, immunomodulation, among others. The identification of AMPs had been accomplished by combining computational and experimental approaches and have been mostly restricted to self-contained peptides despite accumulated evidence indicating AMPs may be found embedded within proteins, the functions of which are not necessarily associated with antimicrobials. To address this limitation, we propose a machine-learning (ML)-based pipeline to identify AMPs that are embedded in proteomes.

Regenerative endodontic procedure combined with non-surgical root canal treatment in an immature permanent maxillary molar: A 7-year follow-up case report.

The aim of this report is to present the results of a personalised endodontic treatment for an immature molar, using regenerative endodontic procedure (REP) combined with non-surgical root canal treatment (NSRCT), with 7 years of follow-up. The tooth#3 presented combined endodontic pathologies in each root, with different pulpal and periapical status. A REP in the palatal (P) canal and a NSCRT in the mesio-buccal (MB) and disto-buccal (DB) canals were performed.

Handcrafted versus non-handcrafted (self-supervised) features for the classification of antimicrobial peptides: complementary or redundant?

Antimicrobial peptides (AMPs) have received a great deal of attention given their potential to become a plausible option to fight multi-drug resistant bacteria as well as other pathogens. Quantitative sequence-activity models (QSAMs) have been helpful to discover new AMPs because they allow to explore a large universe of peptide sequences and help reduce the number of wet lab experiments. A main aspect in the building of QSAMs based on shallow learning is to determine an optimal set of protein descriptors (features) required to discriminate between sequences with different antimicrobial activities.

The Four Pillars for Successful Regenerative Therapy in Endodontics: Stem Cells, Biomaterials, Growth Factors, and Their Synergistic Interactions.

Endodontics has made significant progress in regenerative approaches in recent years, thanks to advances in biologically based procedures or regenerative endodontic therapy (RET). In recent years, our profession has witnessed a clear conceptual shift in this therapy. RET was initially based on a blood clot induced by apical bleeding without harvesting the patient's cells or cell-free RET.

Antimicrobial peptides with cell-penetrating activity as prophylactic and treatment drugs.

Health is fundamental for the development of individuals and evolution of species. In that sense, for human societies is relevant to understand how the human body has developed molecular strategies to maintain health. In the present review, we summarize diverse evidence that support the role of peptides in this endeavor.

Regenerative Endodontic Procedure in an Immature Permanent Incisor with Internal Root Resorption: a Case Report.

This report describes a regenerative endodontic procedure of an immature permanent incisor with internal root resorption (IRR) and 4-years follow-up. A healthy 8-year-old man was referred for treatment of tooth #9 after a traumatic intrusion. The periapical radiograph showed an IRR and an open apex with periradicular lesion.

Do deep learning models make a difference in the identification of antimicrobial peptides?

In the last few decades, antimicrobial peptides (AMPs) have been explored as an alternative to classical antibiotics, which in turn motivated the development of machine learning models to predict antimicrobial activities in peptides. The first generation of these predictors was filled with what is now known as shallow learning-based models. These models require the computation and selection of molecular descriptors to characterize each peptide sequence and train the models.

Delivery of affordable and scalable encapsulated allogenic/autologous mesenchymal stem cells in coagulated platelet poor plasma for dental pulp regeneration.

The main goal of regenerative endodontics procedures (REPs) is to revitalize teeth by the regeneration of healthy dental pulp. In this study, we evaluated the potential of combining a natural and accessible biomaterial based on Platelet Poor Plasma (PPP) as a support for dental pulp stem cells (DPSC) and umbilical cord mesenchymal stem cells (UC-MSC). A comparison study between the two cell sources revealed compatibility with the PPP based scaffold with differences noted in the proliferation and angiogenic properties in vitro.

Improving Structure-Based Virtual Screening with Ensemble Docking and Machine Learning.

One of the main challenges of structure-based virtual screening (SBVS) is the incorporation of the receptor's flexibility, as its explicit representation in every docking run implies a high computational cost. Therefore, a common alternative to include the receptor's flexibility is the approach known as ensemble docking. Ensemble docking consists of using a set of receptor conformations and performing the docking assays over each of them.

Alignment-Free Antimicrobial Peptide Predictors: Improving Performance by a Thorough Analysis of the Largest Available Data Set.

In the last two decades, a large number of machine-learning-based predictors for the activities of antimicrobial peptides (AMPs) have been proposed. These predictors differ from one another in the learning method and in the training and testing data sets used. Unfortunately, the training data sets present several drawbacks, such as a low representativeness regarding the experimentally validated AMP space, and duplicated peptide sequences between negative and positive data sets.

Automatic construction of molecular similarity networks for visual graph mining in chemical space of bioactive peptides: an unsupervised learning approach.

The increasing interest in bioactive peptides with therapeutic potentials has been reflected in a large variety of biological databases published over the last years. However, the knowledge discovery process from these heterogeneous data sources is a nontrivial task, becoming the essence of our research endeavor. Therefore, we devise a unified data model based on molecular similarity networks for representing a chemical reference space of bioactive peptides, having an implicit knowledge that is currently not explicitly accessed in existing biological databases.

Relevant Features of Polypharmacologic Human-Target Antimicrobials Discovered by Machine-Learning Techniques.

Polypharmacologic human-targeted antimicrobials (polyHAM) are potentially useful in the treatment of complex human diseases where the microbiome is important (e.g., diabetes, hypertension).

Inflammatory biomarkers in dentinal fluid as an approach to molecular diagnostics in pulpitis.

Aim: To explore a set of inflammatory biomarkers obtained from dentinal fluid (DF) from patients with symptomatic irreversible pulpitis (IP), reversible pulpitis (RP) and normal pulp (NP).

Methodology: A cross-sectional exploratory study was performed, recruiting 64 patients on the basis of their respective pulp condition. DF samples were obtained from all patients (23, from IP patients; 20, from RP patients; and 21, from NP patients).

Mesenchymal Stem Cells Derived from Human Inflamed Dental Pulp Exhibit Impaired Immunomodulatory Capacity In Vitro.

Introduction: Dental pulp stem cells (DPSC) are very attractive in regenerative medicine. In this study, we focused on the characterization of the functional properties of mesenchymal stem cells derived from DPSCs. Currently, it is unknown whether inflammatory conditions present in an inflamed dental pulp tissue could alter the immunomodulatory properties of DPSCs.

Cell-Based Regenerative Endodontics for Treatment of Periapical Lesions: A Randomized, Controlled Phase I/II Clinical Trial.

A randomized controlled phase I/II clinical trial was designed to evaluate the safety and efficacy of encapsulated human umbilical cord mesenchymal stem cells in a plasma-derived biomaterial for regenerative endodontic procedures (REPs) in mature permanent teeth with apical lesions. The trial included 36 patients with mature incisors, canines, or mandibular premolars showing pulp necrosis and apical periodontitis. Patients were randomly and equally allocated between experimental (REP) or conventional root canal treatment (ENDO) groups.

An automatic representation of peptides for effective antimicrobial activity classification.

Antimicrobial peptides (AMPs) are a promising alternative to small-molecules-based antibiotics. These peptides are part of most living organisms' innate defense system. In order to computationally identify new AMPs within the peptides these organisms produce, an automatic AMP/non-AMP classifier is required.