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Background: Beta-lactams remain the first-line treatment of infections despite the increasing global prevalence of penicillin-resistant/non-susceptible strains. We conducted a cross-sectional household survey in a rural community in northern Vietnam in 2018-2019 to provide prevalence estimates of penicillin non-susceptible (PNSP) carriage and to investigate behavioural and environmental factors associated with PNSP colonization. The data presented will inform the design of a large trial of population-based interventions targeting inappropriate antibiotic use.

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Purpose: The C-reactive protein (CRP)-albumin-lymphocyte (CALLY) index is a newly developed biomarker that combines measurements of CRP, serum albumin, and lymphocyte count. This index provides a thorough assessment of a patient's inflammation level, nutritional condition, and immunological function. The objective of this study is to examine the correlation between the CALLY index and all-cause mortality in COPD patients.

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The blood-brain-barrier prevents many imaging agents and therapeutics from being delivered to the brain that could fight central nervous system diseases such as Alzheimer's disease and strokes. However, techniques such as the use of stapled peptides or peptide shuttles may allow payloads through, with bioconjugation achieved bio-orthogonal tetrazine/norbornene click chemistry. A series of lanthanide-tetrazine probes have been synthesised herein which could be utilised in bio-orthogonal click chemistry with peptide-based delivery systems to deliver MRI agents through the blood-brain-barrier.

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Background: Ankylosing spondylitis (AS) is a chronic autoimmune disease characterized by inflammation of the sacroiliac joints and spine. Cuproptosis is a newly recognized copper-induced cell death mechanism. Our study explored the novel role of cuproptosis-related genes (CRGs) in AS, focusing on immune cell infiltration and molecular clustering.

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Background: Sepsis is a severe complication in leukemia patients, contributing to high mortality rates. Identifying early predictors of sepsis is crucial for timely intervention. This study aimed to develop and validate a predictive model for sepsis risk in leukemia patients using machine learning techniques.

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