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This review aimed to identify newly discovered bacteria from individuals with periodontal/peri-implant diseases and organize them into new clusters (GF-MoR complexes) to update Socransky’s complexes (1998). For methodological development, the PCC (Population, Concept, Context) strategy was used for the focus question construction: “In patients with periodontal and/or peri-implant disease, what bacteria (microorganisms) were detected through laboratory assays?” The search strategy was applied to PubMed/MEDLINE, PubMed Central, and Embase. The search key terms, combined with Boolean markers, were (1) bacteria, (2) microbiome, (3) microorganisms, (4) biofilm, (5) niche, (6) native bacteria, (7) gingivitis), (8) periodontitis, (9) peri-implant mucositis, and (10) peri-implantitis. The search was restricted to the period 1998–2024 and the English language. The bacteria groups in the oral cavity obtained/found were retrieved and included in the GF-MoR complexes, which were based on the disease/condition, presenting six groups: (1) health, (2) gingivitis, (3) peri-implant mucositis, (4) periodontitis, (5) peri-implantitis, and (6) necrotizing and molar–incisor (M-O) pattern periodontitis. The percentual found per group refers to the number of times a specific bacterium was found to be associated with a particular disease. A total of 381 articles were found: 162 articles were eligible for full-text reading (k = 0.92). Of these articles, nine were excluded with justification, and 153 were included in this review (k = 0.98). Most of the studies reported results for the health condition, periodontitis, and peri-implantitis (3 out of 6 GF-MoR clusters), limiting the number of bacteria found in the other groups. Therefore, it became essential to understand that bacterial colonization is a dynamic process, and the bacteria present in one group could also be present in others, such as those observed with the bacteria found in all groups (Porphyromonas gingivalis, Tannarela forsythia, Treponema denticola, and Aggregatibacter actinomycetemcomitans) (GF-MoR’s red triangle). The second most observed bacteria were grouped in GF-MoR’s blue triangle: Porphyromonas spp., Prevotela spp., and Treponema spp., which were present in five of the six groups. The third most detected bacteria were clustered in the grey polygon (GF-MoR’s grey polygon): Fusobacterium nucleatum, Prevotella intermedia, Campylobacter rectus, and Eikenella corrodens. These three geometric shapes had the most relevant bacteria to periodontal and peri-implant diseases. Specifically, per group, GF-MoR’s health group had 58 species; GF-MoR’s gingivitis group presented 16 bacteria; GF-MoR’s peri-implant mucositis included 17 bacteria; GF-MoR’s periodontitis group had 101 different bacteria; GF-MoR’s peri-implantitis presented 61 bacteria; and the last group was a combination of necrotizing diseases and molar–incisor (M-I) pattern periodontitis, with seven bacteria. After observing the top seven bacteria of all groups, all of them were found to be gram-negative. Groups 4 and 5 (periodontitis and peri-implantitis) presented the same top seven bacteria. For the first time in the literature, GF-MoR’s complexes were presented, gathering bacteria data according to the condition found and including more bacteria than in Socransky’s complexes. Based on this understanding, this study could drive future research into treatment options for periodontal and peri-implant diseases, guiding future studies and collaborations to prevent and worsen systemic conditions. Moreover, it permits the debate about the evolution of bacterial clusters.

Emerging infectious diseases (EIDs) are newly emerging and reemerging infectious diseases. The National Institute of Allergy and Infectious Diseases identifies the following as emerging infectious diseases: SARS, MERS, COVID-19, influenza, fungal diseases, plague, schistosomiasis, smallpox, tick-borne diseases, and West Nile fever. The factors that should be taken into consideration are the genetic adaptation of microbial agents and the characteristics of the human host or environment. The new approach to identifying new possible pathogens will have to go through the One Health approach and omics integration data, which are capable of identifying high-priority microorganisms in a short period of time. New bioinformatics technologies enable global integration and sharing of surveillance data for rapid public health decision-making to detect and prevent epidemics and pandemics, ensuring timely response and effective prevention measures. Machine learning tools are being more frequently utilized in the realm of infectious diseases to predict sepsis in patients, diagnose infectious diseases early, and forecast the effectiveness of treatment or the appropriate choice of antibiotic regimen based on clinical data. We will discuss emerging microorganisms, omics techniques applied to infectious diseases, new computational solutions to evaluate biomarkers, and innovative tools that are useful for integrating omics data and electronic medical records data for the clinical management of emerging infectious diseases.

Analysis of genome sequencing data from >100,000 genomes of Mycobacterium tuberculosis complex using TB-Annotator software revealed a previously unknown lineage, proposed name L10, in central Africa. Phylogenetic reconstruction suggests L10 could represent a missing link in the evolutionary and geographic migration histories of M. africanum.

Heat shock protein 90 (Hsp90) is an ATP-dependent molecular chaperone which is essential in eukaryotes. It is required for the activation and stabilization of a wide variety of client proteins and many of them are involved in important cellular pathways. Since Hsp90 affects numerous physiological processes such as signal transduction, intracellular transport, and protein degradation, it became an interesting target for cancer therapy. Structurally, Hsp90 is a flexible dimeric protein composed of three different domains which adopt structurally distinct conformations. ATP binding triggers directionality in these conformational changes and leads to a more compact state. To achieve its function, Hsp90 works together with a large group of cofactors, termed co-chaperones. Co-chaperones form defined binary or ternary complexes with Hsp90, which facilitate the maturation of client proteins. In addition, posttranslational modifications of Hsp90, such as phosphorylation and acetylation, provide another level of regulation. They influence the conformational cycle, co-chaperone interaction, and inter-domain communications. In this review, we discuss the recent progress made in understanding the Hsp90 machinery.