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Background: Globally, millions of children die as a result of diarrhoea and/or antimicrobial resistant infections. Diarrhoeagenic Escherichia coli (DEC) are responsible for a substantial proportion of cases of diarrhoea in South Africa and sub-Saharan Africa. Effective treatments (including the use of antimicrobials) are therefore essential., Methodology: E. coli isolated from children under the age of five were subjected to antimicrobial susceptibility testing using the Vitek 2® compact automated system (bioMérieux Inc., France) and categorized as multidrug or extensively drug resistant (MDR or XDR)., Results: Almost all isolates (164/166, 98.8 %) were categorized as MDR with 4.9 % (9/166) categorized as XDR. The majority of isolates (153/166, 92.2 %) were also phenotypically classified as extended-spectrum β-lactamase (ESBL) producers. More than half of these isolates (78/153, 51.0 %) were subjected to PCR for genes associated with ESBL production. More than half (45/78, 57.7 %) of the isolates tested were PCR positive for at least one ESBL gene or gene group and 11.5 % (9/78) were positive for two ESBL genes or gene groups., Discussion: There is a need to strengthen antimicrobial resistance surveillance in South Africa and improve infection prevention and control measures. There is also a need to review the current South African Treatment Guidelines as outlined by the South African Essential Drugs Programme., Competing Interests: Declaration of competing interest The authors have no conflicting financial or non-financial interests., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Nuclei detection and segmentation in hematoxylin and eosin-stained (H&E) tissue images are important clinical tasks and crucial for a wide range of applications. However, it is a challenging task due to nuclei variances in staining and size, overlapping boundaries, and nuclei clustering. While convolutional neural networks have been extensively used for this task, we explore the potential of Transformer-based networks in combination with large scale pre-training in this domain. Therefore, we introduce a new method for automated instance segmentation of cell nuclei in digitized tissue samples using a deep learning architecture based on Vision Transformer called CellViT. CellViT is trained and evaluated on the PanNuke dataset, which is one of the most challenging nuclei instance segmentation datasets, consisting of nearly 200,000 annotated nuclei into 5 clinically important classes in 19 tissue types. We demonstrate the superiority of large-scale in-domain and out-of-domain pre-trained Vision Transformers by leveraging the recently published Segment Anything Model and a ViT-encoder pre-trained on 104 million histological image patches - achieving state-of-the-art nuclei detection and instance segmentation performance on the PanNuke dataset with a mean panoptic quality of 0.50 and an F 1 -detection score of 0.83. The code is publicly available at https://github.com/TIO-IKIM/CellViT., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Selma Ugurel reports a relationship with Bristol Myers Squibb that includes: consulting or advisory, funding grants, speaking and lecture fees, and travel reimbursement. Selma Ugurel reports a relationship with Merck Serono that includes: consulting or advisory, funding grants, and speaking and lecture fees. Selma Ugurel reports a relationship with Merck Sharp & Dohme that includes: consulting or advisory, speaking and lecture fees, and travel reimbursement. Selma Ugurel reports a relationship with Novartis that includes: consulting or advisory, speaking and lecture fees, and travel reimbursement. Selma Ugurel reports a relationship with Almirall that includes: travel reimbursement. Selma Ugurel reports a relationship with IGEA Clinical Biophysics that includes: travel reimbursement. Selma Ugurel reports a relationship with Pierre Fabre that includes: travel reimbursement. Selma Ugurel reports a relationship with Sun Pharma that includes: travel reimbursement. Jens T. Siveke reports a relationship with AstraZeneca that includes: consulting or advisory and speaking and lecture fees. Jens T. Siveke reports a relationship with Bayer AG that includes: consulting or advisory and speaking and lecture fees. Jens T. Siveke reports a relationship with Boehringer Ingelheim that includes: consulting or advisory, funding grants, and speaking and lecture fees. Jens T. Siveke reports a relationship with Bristol-Myers Squibb that includes: consulting or advisory, funding grants, and speaking and lecture fees. Jens T. Siveke reports a relationship with Immunocore that includes: consulting or advisory and speaking and lecture fees. Jens T. Siveke reports a relationship with MSD Sharp Dohme that includes: consulting or advisory and speaking and lecture fees. Jens T. Siveke reports a relationship with Novartis that includes: consulting or advisory and speaking and lecture fees. Jens T. Siveke reports a relationship with Roche Genentech that includes: consulting or advisory, funding grants, and speaking and lecture fees. Jens T. Siveke reports a relationship with Servier that includes: consulting or advisory and speaking and lecture fees. Jens T. Siveke reports a relationship with Abalos Therapeutics that includes: funding grants. Jens T. Siveke reports a relationship with Celgene that includes: funding grants. Jens T. Siveke reports a relationship with Eisbach Bio that includes: funding grants. Jens T. Siveke reports a relationship with Pharma15 that includes: board membership and equity or stocks., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

When reproducing sounds over headphones, the simulated source can be externalized (i.e., perceived outside the head) or internalized (i.e., perceived within the head). Is it because it is perceived as more or less distant? To investigate this question, 18 participants evaluated distance and externalization for three types of sound (speech, piano, helicopter) in 27 conditions using nonindividualized stimuli. Distance and externalization ratings were significantly correlated across conditions and listeners, and when averaged across listeners or conditions. However, they were also decoupled in some circumstances: (1) Sound type had different effects on distance and externalization: the helicopter was evaluated as more distant, while speech was judged as less externalized. (2) Distance estimations increased with simulated distances even for stimuli judged as internalized. (3) Diotic reverberation influenced distance but not externalization. Overall, a source was not rated as externalized as soon as and only if its perceived distance exceeded a threshold (e.g., the head radius). These results suggest that distance and externalization are correlated but might not be aspects of a single perceptual continuum. In particular, a virtual source might be judged as both internalized and with a distance. Hence, it could be important to avoid using a scale related to distance when evaluating externalization., Competing Interests: Declaration of Conflicting InterestsThe authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.