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Background: The pervasiveness of the Internet in everyday life, especially among young people, has raised concerns about its effects on mental health, education, and, recently, oral health. Previous research has suggested a complex relationship between Problematic Internet Use (PIU), lifestyles, and oral health-related quality of life, highlighting the need to examine these interactions further. This study seeks to explore the PIU as a predictor of oral health-related quality of life and examine the mediating role of lifestyles between both in a sample of Peruvian schoolchildren., Methods: A cross-sectional study was carried out with 293 Peruvian students aged 12 to 17 years (M = 14.42, SD = 1.5), using structural equations to analyze the relationship between PIU, lifestyles, and quality of life related to oral health. The data collection procedure was through a face-to-face survey. Validated instruments measured PIU, lifestyles, and oral health-related quality of life. The study's theoretical model was analyzed through structural equation modeling with the MLR estimator. The fit assessment was performed using the comparative fit index (CFI), root mean square error of approximation (RMSEA), and standardized root mean square residual (SRMR)., Results: They indicated significant correlations between PIU, lifestyles, and oral health-related quality of life. A negative influence of PIU on lifestyles (β = -0.30, p < .001) and on oral health-related quality of life (β = -0.35, p < .001) was observed, as well as a positive relationship between PIU and oral health-related quality of life (β = 0.29, p < .001). The mediation of lifestyles was statistically significant, suggesting that they mediate the relationship between PIU and oral health-related quality of life., Conclusions: The study confirms that PIU can negatively affect adolescents' oral health-related quality of life, mediated by unhealthy lifestyles. It underlines the importance of promoting balanced Internet use and healthy lifestyles among young people to improve their oral well-being., (© 2024. The Author(s).)

The rapid emergence and spread of antimicrobial resistance across the globe have prompted the usage of bacteriophages (i.e. viruses that infect bacteria) in a variety of applications ranging from agriculture to biotechnology and medicine. In order to effectively guide the application of bacteriophages in these multifaceted areas, information about their host ranges-that is the bacterial strains or species that a bacteriophage can successfully infect and kill-is essential. Utilizing sixteen broad-spectrum (polyvalent) bacteriophages with experimentally validated host ranges, we here benchmark the performance of eleven recently developed computational host range prediction tools that provide a promising and highly scalable supplement to traditional, but laborious, experimental procedures. We show that machine- and deep-learning approaches offer the highest levels of accuracy and precision-however, their predominant predictions at the species- or genus-level render them ill-suited for applications outside of an ecosystems metagenomics framework. In contrast, only moderate sensitivity (<80 per cent) could be reached at the strain-level, albeit at low levels of precision (<40 per cent). Taken together, these limitations demonstrate that there remains room for improvement in the active scientific field of in silico host prediction to combat the challenge of guiding experimental designs to identify the most promising bacteriophage candidates for any given application., Competing Interests: None declared., (© The Author(s) 2024. Published by Oxford University Press.)

Human cytomegalovirus (HCMV) represents a major threat to human health, contributing to both birth defects in neonates as well as organ transplant failure and opportunistic infections in immunocompromised individuals. HCMV exhibits considerable interhost and intrahost diversity, which likely influences the pathogenicity of the virus. Therefore, understanding the relative contributions of various evolutionary forces in shaping patterns of variation is of critical importance both mechanistically and clinically. Herein, we present the individual components of an evolutionary baseline model for HCMV, with a particular focus on congenital infections for the sake of illustration-including mutation and recombination rates, the distribution of fitness effects, infection dynamics, and compartmentalization-and describe the current state of knowledge of each. By building this baseline model, researchers will be able to better describe the range of possible evolutionary scenarios contributing to observed variation as well as improve power and reduce false-positive rates when scanning for adaptive mutations in the HCMV genome., (© The Author(s) 2023. Published by Oxford University Press on behalf of Society for Molecular Biology and Evolution.)

Bacteriophages, infecting bacterial hosts in every environment on our planet, are a driver of adaptive evolution in bacterial communities. At the same time, the host range of many bacteriophages-and thus one of the selective pressures acting on complex microbial systems in nature-remains poorly characterized. Here, we computationally inferred the putative host ranges of 40 cluster P mycobacteriophages, including members from 6 subclusters (P1-P6). A series of comparative genomic analyses revealed that mycobacteriophages of subcluster P1 are restricted to the Mycobacterium genus, whereas mycobacteriophages of subclusters P2-P6 are likely also able to infect other genera, several of which are commonly associated with human disease. Further genomic analysis highlighted that the majority of cluster P mycobacteriophages harbor a conserved integration-dependent immunity system, hypothesized to be the ancestral state of a genetic switch that controls the shift between lytic and lysogenic life cycles-a temperate characteristic that impedes their usage in antibacterial applications., (© The Author(s) 2022. Published by Oxford University Press on behalf of Genetics Society of America.)

Here, we characterized the complete genome of the Siphoviridae BiggityBass, a lytic subcluster DR bacteriophage infecting Gordonia terrae CAG3. Its 63.2-kb genome contains 84 protein-coding genes, of which 40 could be assigned a putative function. BiggityBass is related most closely to AnClar and Yago84 with 90.61% and 90.52% nucleotide identity, respectively.

We characterized the complete genome of the cluster P mycobacteriophage Phegasus. Its 47.5-kb genome contains 81 protein-coding genes, 36 of which could be assigned a putative function. Phegasus is most closely related to two subcluster P1 bacteriophages, Mangethe and Majeke, with an average nucleotide identity of 99.63% each.

Bacteriophages infecting bacteria of the genus Gordonia have increasingly gained interest in the scientific community for their diverse applications in agriculture, biotechnology, and medicine, ranging from biocontrol agents in wastewater management to the treatment of opportunistic pathogens in pulmonary disease patients. However, due to the time and costs associated with experimental isolation and cultivation, host ranges for many bacteriophages remain poorly characterized, hindering a more efficient usage of bacteriophages in these areas. Here, we perform a series of computational genomic inferences to predict the putative host ranges of all Gordonia cluster DR bacteriophages known to date. Our analyses suggest that BiggityBass (as well as several of its close relatives) is likely able to infect host bacteria from a wide range of genera-from Gordonia to Nocardia to Rhodococcus, making it a suitable candidate for future phage therapy and wastewater treatment strategies.

Summary: Mutation accumulation (MA) is the most widely used method for directly studying the effects of mutation. By sequencing whole genomes from MA lines, researchers can directly study the rate and molecular spectra of spontaneous mutations and use these results to understand how mutation contributes to biological processes. At present there is no software designed specifically for identifying mutations from MA lines. Here we describe accuMUlate, a probabilistic mutation caller that reflects the design of a typical MA experiment while being flexible enough to accommodate properties unique to any particular experiment. Availability and implementation accuMUlate is available from https://github.com/dwinter/accuMUlate., Supplementary Information: Supplementary data are available at Bioinformatics online.

Background: Despite its documented importance, communication between clinicians and patients' families in the intensive care unit often fails to meet families' needs, and interventions to improve communication are needed. Use of a communication facilitator-an additional staff member-to improve communication between clinicians and patients' families is the focus of an ongoing randomized trial. The clinical team's acceptance of the communication facilitator as an integral part of the team is important., Objectives: To explore clinicians' perceptions of the usefulness of a communication facilitator in the intensive care unit., Methods: Fourteen semistructured qualitative interviews to assess perspectives of physicians, nurses, and social workers who had experience with the communication facilitator intervention on the intervention and the role of the facilitator. Methods based on grounded theory were used to analyze the data., Results: Clinicians perceived facilitators as (1) facilitating communication between patients' families and clinicians, (2) providing practical and emotional support for patients' families, and (3) providing practical and emotional support for clinicians. Clinicians were enthusiastic about the communication facilitator but concerned about overlapping or conflicting roles., Conclusions: Clinicians in the intensive care unit saw the facilitator intervention as enhancing communication and supporting both patients' families and clinicians. They also identified the importance of the facilitator within the interdisciplinary team. Negative perceptions about the use of a facilitator should be addressed before the intervention is implemented, in order to ensure its effectiveness., (©2014 American Association of Critical-Care Nurses.)