Global challenges and solutions to achieving and sustaining measles and rubella elimination.

Measles and rubella have long been recognized as priorities for disease prevention because of their devastating consequences for child health; hence, all World Health Organization (WHO) regions currently have a goal to eliminate measles and four out of six WHO regions have a goal to eliminate rubella. Significant global progress has been made in the twenty-first century, with more than 40% of countries in the world verified by a Regional Verification Commission as having eliminated measles and more than 50% of countries having sustained rubella elimination. Making further progress will require addressing fundamental gaps in health systems, a particular challenge in the current global context where many countries face multiple barriers to both sustaining and achieving measles and rubella elimination.

Successful control of an environmental reservoir of NDM-producing Klebsiella pneumoniae associated with nosocomial transmissions in a low-incidence setting.

Background: The hospital wastewater system has been reported as a source of nosocomial acquisition of carbapenemase producing Enterobacteriaceae (CPE) in various settings. Cleaning and disinfection protocols or replacement of contaminated equipment often fail to eradicate these environmental reservoirs, which can lead to long-term transmission of CPE. We report a successful multimodal approach to control a New Delhi metallo-beta-lactamase positive Klebsiella pneumoniae (NDM-KP) nosocomial outbreak implicating contamination of sink traps in a low-incidence setting.

Automatic deep learning method for third lumbar selection and body composition evaluation on CT scans of cancer patients.

Introduction: The importance of body composition and sarcopenia is well-recognized in cancer patient outcomes and treatment tolerance, yet routine evaluations are rare due to their time-intensive nature. While CT scans provide accurate measurements, they depend on manual processes. We developed and validated a deep learning algorithm to automatically select and segment abdominal muscles [SM], visceral fat [VAT], and subcutaneous fat [SAT] on CT scans.

Impact of mutations in the mtrR, rpdlVD and rrl genes on azithromycin resistance in Neisseria gonorrhoeae.

Introduction: Bacterial sexually transmitted infections (STIs) pose a major public health problem. The emergence of antibiotic-resistant strains of Neisseria gonorrhoeae represents a serious threat to successful treatment and epidemiological control. The first extensively drug-resistant (XDR) strains (ceftriaxone-resistant and high-level azithromycin-resistant [HLR AZY]) have been reported.