Background: The purpose of this study was to assess the occurrence of immunoglobulin E sensitization to common environmental allergens (atopy) and new allergic diseases among schoolchildren after starting school in a water-damaged school building. The staff and pupils of a Finnish elementary school with visible water damage and mold complained of respiratory and skin symptoms. The school building was examined and widespread moisture damage was found. A control school with no visible water damage was also examined. No indication of exceptional microbial growth was found in the samples taken from this school.
Methods: History of allergic diseases and the year of diagnosis were established by a questionnaire. IgE antibodies to the common environmental allergens were determined from randomly selcted groups from both schools.
Results: Elevated IgE values were significantly more common among the exposed children, as was the occurrence of new allergic diseases after the children started at the school.
Conclusions: The odds ratios for the IgE values of the study groups indicated a possible relationship between exposure to microorganisms and IgE sensitization. Exposure to spores, toxins, and other metabolites of molds may have complex results with unknown immunogenic effects that may act as a nonspecific trigger for allergic sensitization leading to the development of atopy.
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http://dx.doi.org/10.1034/j.1398-9995.2001.056002175.x | DOI Listing |
J Am Assoc Nurse Pract
January 2025
Division of Cardiology, Department of Medicine, Duke Health Integrated Practice, Duke University Health System, Durham, North Carolina.
Background: Increasing patient demand and clinician burnout in rheumatology practices have highlighted the need for more efficient models of care (MOC). Interprofessional collaboration is essential for improving patient outcomes and clinician satisfaction.
Local Problem: Our current MOC lacks standardization and formal integration of Nurse Practitioners (NPs) and Physician Assistants (PAs), resulting in reduced clinician satisfaction and limited patient access.
J Prim Care Community Health
January 2025
University of California, Davis, Division of Hospital Medicine, Sacramento, CA, USA.
Introduction: Nadezhda Clinic is a free student-run health clinic that provides culturally sensitive primary care services to the underserved Russian-speaking population of the greater Sacramento area. At the onset of the COVID-19 pandemic, the clinic suspended in-person services and solely offered telemedicine visits. Most patients were hesitant to utilize telemedicine due to poor technological literacy, privacy concerns, and a preference for in-person care.
View Article and Find Full Text PDFJ Pers Soc Psychol
January 2025
Marketing Division, Paul College of Business and Economics, University of New Hampshire.
What drives some people to save more effectively for their future than others? This multistudy investigation (N = 143,461) explores how dispositional optimism-the generalized tendency to hold positive expectations about the future-shapes individuals' financial decisions and outcomes. Leveraging both cross-sectional and longitudinal designs across several countries, our findings reveal that optimism significantly predicts greater savings over time, even when controlling for various demographic, psychological, and financial covariates. Furthermore, we find that the role of optimism varies based on socioeconomic circumstances: Among lower income individuals, optimism is more strongly associated with saving.
View Article and Find Full Text PDFJ Pers Soc Psychol
January 2025
Booth School of Business, The University of Chicago.
Face stereotypes are prevalent, consequential, yet oftentimes inaccurate. How do false first impressions arise and persist despite counter-evidence? Building on the overgeneralization hypothesis, we propose a domain-general cognitive mechanism: insufficient statistical learning, or Insta-learn. This mechanism posits that humans are quick statistical learners but insufficient samplers.
View Article and Find Full Text PDFMed Biol Eng Comput
January 2025
School of Control Science and Engineering, Tiangong University, Tianjin, 300387, China.
With the advancement of artificial intelligence technology, more and more effective methods are being used to identify and classify Electroencephalography (EEG) signals to address challenges in healthcare and brain-computer interface fields. The applications and major achievements of Graph Convolution Network (GCN) techniques in EEG signal analysis are reviewed in this paper. Through an exhaustive search of the published literature, a module-by-module discussion is carried out for the first time to address the current research status of GCN.
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