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Genomic analysis and potential polyhydroxybutyrate (PHB) production from Bacillus strains isolated from extreme environments in Mexico.
BMC Microbiol
January 2025
Unidad de Manipulación Genética, Facultad de Ciencias Biológicas, Departamento de Microbiología e Inmunología, Universidad Autónoma de Nuevo León, Monterrey, Nuevo León, México.
Background: Plastic pollution is a significant environmental problem caused by its high resistance to degradation. One potential solution is polyhydroxybutyrate (PHB), a microbial biodegradable polymer. Mexico has great uncovered microbial diversity with high potential for biotechnological applications.
View Article and Find Full Text PDFPosttraumatic stress disorder in disaster-exposed youth: examining diagnostic concordance and model fit using ICD-11 and DSM-5 criteria.
BMC Pediatr
January 2025
Department of Psychology, University of South Dakota, 414 E. Clark St, Vermillion, SD, USA.
Background: Competing definitions of posttraumatic stress disorder (PTSD) have been proposed by ICD-11 and DSM-5; it is unclear which diagnostic model works best for children and adolescents. Although other studies have predicted the impact of these models by approximating the criteria using older measures, this study advances the research by comparing measures designed to assess ICD-11 and DSM-5 criteria in hurricane-exposed youth. This study evaluates ICD-11 and DSM-5 (both the standard and preschool-age) diagnostic models by identifying diagnostic rates, evaluating diagnostic concordance, investigating the predictive value of constructs associated with PTSD (demographics, disaster threat and exposure, functional impairment), and examining model fit.
View Article and Find Full Text PDFAn integrated framework to guide evidence-informed public health policymaking.
J Public Health Policy
January 2025
Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Australia.
Evidence-informed policymaking emphasizes that policy decisions should be informed by the best available evidence from research and follow a systematic and transparent approach. For public health policymaking we can learn from existing practices of transparent, evidence-informed decision-making for clinical practice, medicines, and medical technology. We review existing evidence-to-decision frameworks, as well as frameworks and theories for policymaking to address the political dimension of policymaking, and use this analysis to propose an integrated framework to guide evidence-informed policymaking.
View Article and Find Full Text PDFGuidelines for the diagnosis and treatment of chronic lymphocytic leukemia and small lymphocytic lymphoma from Chronic Lymphocytic Leukemia Spanish Group (GELLC).
Med Clin (Barc)
January 2025
Servicio de Hematología, Hospital Universitario Vall d'Hebron, Barcelona, España.
Introduction: Chronic lymphocytic leukemia (CLL) is the most common form of leukemia in adults in Western countries, with a median age at diagnosis of 72 years. This guide, developed by the Spanish Group for Chronic Lymphocytic Leukemia (GELLC), addresses the most relevant aspects of CLL, with the objectives of facilitating and aiding the diagnostic process, establishing therapeutic recommendations for choosing the best treatment for each type of patient, as well as standardizing the management of CLL and ensuring equity across different hospitals in terms of the use of the various available treatment regimens.
Methodology: The references obtained were classified according to the level of evidence and following the criteria established by the Agency for Health Research and Quality, and the recommendations were classified according to the criteria of the National Comprehensive Cancer Network (NCCN).
Machine learning in public health informatics: evidence that complex sampling structures may not be needed for prediction models with imbalanced outcomes.
Ann Epidemiol
January 2025
Department of Orthopaedic Surgery, Thompson Laboratory for Regenerative Orthopaedics, School of Medicine, University of Missouri, 1100 Virginia Avenue, Columbia MO, 65211, USA. Electronic address:
Purpose: The objective of this study is to investigate the predictive ability of machine learning models for imbalanced outcomes from national survey data without the use of sampling weights.
Methods: We evaluated the predictive performance of machine learning models on imbalanced outcomes from the US National Health and Nutrition Examination Survey (USNHANES) without using sampling weights. Four machine learning models (support vector machine, random forest, least absolute shrinkage and selection operator regression, and deep neural network) were compared with a logistic model that incorporates the survey's complex sampling design.
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