Objectives: Many travellers to low-income countries return home colonized at the intestinal level with extended-spectrum cephalosporin-resistant (ESC-R) and/or colistin-resistant (CST-R) Escherichia coli (Ec) strains. However, nothing is known about the local sources responsible for the transmission of these pathogens to the travellers.
Methods: We compared the ESC-R- and CST-R-Ec strains found in the pre- (n = 23) and post-trip (n = 37) rectal swabs of 37 travellers from Switzerland to Zanzibar with those (i) contemporarily isolated from local people, poultry, retailed chicken meat (n = 31), and (ii) from other sources studied in the recent past (n = 47). WGS and core-genome analyses were implemented.
Results: Twenty-four travellers returned colonized with ESC-R- (n = 29) and/or CST-R- (n = 8) Ec strains. Almost all ESC-R-Ec were CTX-M-15 producers and belonged to heterogeneous STs/core-genome STs (cgSTs), while mcr-positive strains were not found. Based on the strains' STs/cgSTs, only 20 subjects were colonized with ESC-R- and/or CST-R-Ec that were not present in their gut before the journey. Single nucleotide variant (SNV) analysis showed that three of these 20 travellers carried ESC-R-Ec (ST3489, ST3580, ST361) identical (0-20 SNVs) to those found in local people, chicken meat, or poultry. Three further subjects carried ESC-R-Ec (ST394, ST648, ST5173) identical or highly related (15-55 SNVs) to those previously reported in local people, fish, or water.
Conclusions: This is the first known study comparing the ESC-R- and/or CST-R-Ec strains obtained from travellers and local sources using solid molecular methods. We showed that for at least one-third of the returning travellers the acquired antibiotic-resistant Ec had a corresponding strain among resident people, food, animal and/or environmental sources.
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http://dx.doi.org/10.1093/jac/dkaa457 | DOI Listing |
Viruses
December 2024
Infectious Diseases Department, Royal Adelaide Hospital, Central Adelaide Local Health Network, Adelaide 5000, Australia.
Background: Point-of-care hepatitis C virus (HCV) testing streamlines testing and treatment pathways. In this study, we established an HCV model of care in a homelessness service by offering antibody and RNA point-of-care testing.
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Viruses
November 2024
Faculty of Medical and Health Sciences, Tel Aviv University, Tel Aviv 6997801, Israel.
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View Article and Find Full Text PDFVaccines (Basel)
November 2024
Department of Infectious Diseases, National Institute of Health Doctor Ricardo Jorge, Public Health Centre Doutor Gonçalves Ferreira, Rua Alexandre Herculano 321, 4000-055 Porto, Portugal.
A vaccination programme against severe acute respiratory syndrome coronavirus 2 was initiated in Portugal in December 2020. In this study, we report the findings of a prospective cohort study implemented with the objective of monitoring antibody production in response to COVID-19 vaccination. The humoral immune response to vaccination was followed up using blood samples collected from 191 healthcare workers.
View Article and Find Full Text PDFSensors (Basel)
December 2024
Institute of Computer Science, Zurich University of Applied Sciences, 8400 Winterthur, Switzerland.
Simultaneous localization and mapping (SLAM) techniques can be used to navigate the visually impaired, but the development of robust SLAM solutions for crowded spaces is limited by the lack of realistic datasets. To address this, we introduce InCrowd-VI, a novel visual-inertial dataset specifically designed for human navigation in indoor pedestrian-rich environments. Recorded using Meta Aria Project glasses, it captures realistic scenarios without environmental control.
View Article and Find Full Text PDFSensors (Basel)
December 2024
School of Electronic Information Engineering, Taiyuan University of Science and Technology, Taiyuan 030024, China.
Human pose estimation is an important research direction in the field of computer vision, which aims to accurately identify the position and posture of keypoints of the human body through images or videos. However, multi-person pose estimation yields false detection or missed detection in dense crowds, and it is still difficult to detect small targets. In this paper, we propose a Mamba-based human pose estimation.
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