Publications by authors named "S Bunk"
Article Synopsis
- ESBL-producing Enterobacterales (ESBL-PE) are common in long-term care facilities (LTCFs), prompting a study across six sites in Europe to assess how residents acquire these bacteria and the associated risk factors.
- Over 32 weeks, researchers screened 299 residents and found that 16.4% were colonized at the start, with a new acquisition rate of 0.79 per 1000 resident-days, influenced by factors like age, vascular disease, and antibiotic use.
- Key findings highlight the importance of infection control measures, such as ensuring hand sanitizers and adequate nurse staffing, as well as using genomic surveillance to inform strategies for managing ESBL-PE in LTCFs.
Article Synopsis
- AI in mammography shows promise through a study comparing AI-supported double reading to standard double reading among women aged 50-69 across 12 German sites.
- AI assistance led to a significantly higher breast cancer detection rate (6.7 per 1,000) than the control group's detection rate (5.7 per 1,000), with a 17.6% increase.
- The AI group had a slightly lower recall rate (37.4 per 1,000) compared to the control (38.3 per 1,000) and showed better positive predictive values for both recall (17.9% vs. 14.9%) and biopsy (64.5% vs. 59.2%), indicating improved
Contact inhibition of proliferation is a critical cell density control mechanism governed by the Hippo signalling pathway. The biochemical signalling underlying cell density-dependent cues regulating Hippo signalling and its downstream effectors, YAP, remains poorly understood. Here, we reveal that the tight junction protein ZO-2 is required for the contact-mediated inhibition of proliferation.
View Article and Find Full Text PDFBackground: Integrating artificial intelligence (AI) into mammography screening can support radiologists and improve programme metrics, yet the potential of different strategies for integrating the technology remains understudied. We compared programme-level performance metrics of seven AI integration strategies.
Methods: We performed a retrospective comparative evaluation of seven strategies for integrating AI into mammography screening using datasets generated from screening programmes in Germany (n=1 657 068), the UK (n=223 603) and Sweden (n=22 779).
Rationale Of The Trial: Although the use of engineered T cells in cancer immunotherapy has greatly advanced the treatment of hematological malignancies, reaching meaningful clinical responses in the treatment of solid tumors is still challenging. We investigated the safety and tolerability of IMA202 in a first-in-human, dose escalation basket trial in human leucocyte antigen A*02:01 positive patients with melanoma-associated antigen A1 (MAGEA1)-positive advanced solid tumors.
Trial Design: The 2+2 trial design was an algorithmic design based on a maximally acceptable dose-limiting toxicity (DLT) rate of 25% and the sample size was driven by the algorithmic design with a maximum of 16 patients.