Systematic reviews represent a powerful instrument to summarize the existing evidence in medical literature. However, articles for a systematic review are hard to identify, and mostly require a structured search of the literature through a number of databases, using keyword-based search strategies, followed by the painstaking manual selection of pieces of evidence that are pertinent to the query. A.I. algorithms may offer solutions to reduce the workload on investigators. We applied BERTopic, a newer and much-recognized transformer-based topic-modeling algorithm, to two datasets of 6137 and 5309 articles of newly published systematic reviews in the area of peri-implantitis and bone regeneration in implant dentistry. In the two datasets, BERTopic identified 14 and 22 clusters, respectively, and it automatically created labels describing the nature of the topics for each individual cluster based on semantic interpretation of their titles. Most themes regarded the query theme, but in both conditions, BERTopic also uncovered articles related to off-themes, which composed around 30% of the dataset—with sensitivity up to 0.79 and specificities of at least 0.99. Our results suggest that adding a topic-modeling step to the screening process could potentially save working hours for researchers involved in systematic reviews of the literature.

This study assessed the associations between host genetic variants and subgingival microbiota in patients with metabolic syndrome (MetS); 103 patients with MetS underwent medical and periodontal examinations and had blood and subgingival plaque samples taken. DNA was extracted and processed (assessing a panel of selected single nucleotide polymorphisms (SNPs) first (hypothesis-testing), and then expanding to a discovery phase. The subgingival plaque microbiome from these patients was profiled. Analysis of associations between host genetic and microbial factors was performed, stratified for periodontal diagnosis. Specific SNPs within RUNX2, CAMTA1 and VDR genes were associated with diversity metrics, with no genome-wide associations detected for periodontitis severity or Mets components at p<10-7. Severe periodontitis was associated with pathogenetic genera and species. Some SNPs correlated with specific bacterial genera, as well as with microbial taxa, notably VDR (rs12717991) with Streptococcus mutans and RUNX2 (rs3749863) with Porphyromonas gingivalis. This is the first study exploring the role of host genotype variation in subgingival microbial dysbiosis in patients with MetS and periodontitis. In conclusion, variation in host genotypes may play a role in the dysregulated immune responses characterizing periodontitis and thus the oral microbiome, suggesting that systemic health-associated host traits further interact with oral health and microbiome.

As the number of publications is quickly growing in any area of science, the need to efficiently find relevant information amidst a large number of similarly themed articles becomes very important. Semantic searching through text documents has the potential to overcome the limits of keyword-based searches, especially since the introduction of attention-based transformers, which can capture contextual nuances of meaning in single words, sentences or whole documents. The deployment of these computational tools has been made simpler and accessible to investigators in every field of research thanks to a growing number of dedicated libraries, but a knowledge of how meaning representation strategies work is crucial to make the most out of these instruments. The present work aims at introducing the technical evolution of the meaning representation systems, from vectors to embeddings and transformers tailored to life science investigators with no previous knowledge of natural language processing.