Toothbrushes play a central role in oral hygiene and must be considered one of the most common articles of daily use. We analysed the bacterial colonization of used toothbrushes by next generation sequencing (NGS) and by cultivation on different media. Furthermore, we determined the occurrence of antibiotic resistance genes (ARGs) and the impact of different bristle materials on microbial growth and survival. NGS data revealed that , , , and comprise major parts of the toothbrush microbiome. The composition of the microbiome differed depending on the period of use or user age. While higher fractions of , , and were found after shorter periods, dominated on both toothbrushes used for more than four weeks and on toothbrushes of older users, while in-vitro tests revealed increasing counts of on all bristle materials as well. Compared to other environments, we found a rather low frequency of ARGs. We determined bacterial counts between 1.42 × 10 and 1.19 × 10 cfu/toothbrush on used toothbrushes and no significant effect of different bristles materials on bacterial survival or growth. Our study illustrates that toothbrushes harbor various microorganisms and that both period of use and user age might affect the microbial composition.
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http://dx.doi.org/10.3390/microorganisms8091379 | DOI Listing |
JMIR Form Res
January 2025
Section for Physiotherapy, Division of Medicine, Oslo University Hospital, Oslo, Norway.
Background: The use of mobile health interventions, such as apps, are proposed to meet the challenges faced by preventive health care services due to the increasing prevalence of type 2 diabetes (T2D). Thus, we developed and conducted initial feasibility testing of the Plunde app for promoting and monitoring individual goals related to lifestyle change for people at risk of T2D.
Objective: The primary aim of this study was to assess the feasibility of an app for promoting lifestyle change in people at risk of T2D.
Sci Rep
January 2025
Department of Anesthesiology and Surgical Intensive Care Unit, Kunming Children's Hospital, Kunming, Yunnan, China.
Metabolic syndrome (Mets) in adolescents is a growing public health issue linked to obesity, hypertension, and insulin resistance, increasing risks of cardiovascular disease and mental health problems. Early detection and intervention are crucial but often hindered by complex diagnostic requirements. This study aims to develop a predictive model using NHANES data, excluding biochemical indicators, to provide a simple, cost-effective tool for large-scale, non-medical screening and early prevention of adolescent MetS.
View Article and Find Full Text PDFUltrasound Med Biol
January 2025
School and Graduate Institute of Physical Therapy, College of Medicine, National Taiwan University, Taipei, Taiwan ROC; Center of Physical Therapy, National Taiwan University Hospital, Taipei, Taiwan ROC. Electronic address:
Objective: This study aimed to validate the ultrasound speckle tracking (UST) algorithm, determine the optimal probe location by comparing normalized cross-correlation (NCC) values of muscle displacement at two locations (proximal vs. middle) of the biceps femoris long head (BFlh) using the UST, and investigate the effects of Nordic hamstring curl exercise (NHE) training on BFlh displacement.
Methods: UST efficacy was verified with ex vivo uniaxial testing of porcine leg muscles.
Neural Netw
January 2025
School of Information Management and Engineering, Shanghai University of Finance and Economics, 200433 Shanghai, PR China. Electronic address:
Users may click on a news because they are interested in its content or because the news contains important information and is very popular. Modeling these two aspects is crucial for accurate news recommendation. Most existing studies focused on capturing users' preferences towards news content, and thus they are limited in investigating in depth users' preferences towards news popularity and independently capturing user content and popularity preferences.
View Article and Find Full Text PDFJ Am Med Inform Assoc
January 2025
Department of Cardiology, Royal North Shore Hospital, Sydney, NSW, Australia.
Objective: We aimed to develop a highly interpretable and effective, machine-learning based risk prediction algorithm to predict in-hospital mortality, intubation and adverse cardiovascular events in patients hospitalised with COVID-19 in Australia (AUS-COVID Score).
Materials And Methods: This prospective study across 21 hospitals included 1714 consecutive patients aged ≥ 18 in their index hospitalization with COVID-19. The dataset was separated into training (80%) and test sets (20%).
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