Sanitary Waters: Is It Worth Looking for Mycobacteria?

The freshwater environment is suitable for nontuberculous mycobacteria (NTMs) growth. Their high adaptability represents a considerable risk for sanitary water systems, which are a potential vector for NTMs transmission. This study investigated the occurrence of NTMs, such as , in hospital water systems to support the surveillance and control of potentially pathogenic NTMs.

sCD14-ST and Related Osteoimmunological Biomarkers: A New Diagnostic Approach to Osteomyelitis.

Osteomyelitis (OM) is a major challenge in orthopedic surgery. The diagnosis of OM is based on imaging and laboratory tests, but it still presents some limitations. Therefore, a deeper comprehension of the pathogenetic mechanisms could enhance diagnostic and treatment approaches.

Training in infection prevention and control: survey on the volume and on the learning demands of healthcare-associated infections control figures in the Emilia-Romagna Region (Northern Italy).

Background: In the context of Infections Prevention and Control activities, the training of healthcare-associated infection control figures is crucial; the COVID-19 pandemic further emphasized the necessity of ensuring a widespread and stable level of skills over time for such professionals. The present work aims to identify the number and training needs of the personnel working in the Emilia-Romagna Region's healthcare facilities as "healthcare-associated infection control figures".

Methods: Data were collected through a survey created by experts from the Regional Group "Training in the prevention and control of antibiotic resistance".

Gauge-Optimal Approximate Learning for Small Data Classification.

Small data learning problems are characterized by a significant discrepancy between the limited number of response variable observations and the large feature space dimension. In this setting, the common learning tools struggle to identify the features important for the classification task from those that bear no relevant information and cannot derive an appropriate learning rule that allows discriminating among different classes. As a potential solution to this problem, here we exploit the idea of reducing and rotating the feature space in a lower-dimensional gauge and propose the gauge-optimal approximate learning (GOAL) algorithm, which provides an analytically tractable joint solution to the dimension reduction, feature segmentation, and classification problems for small data learning problems.