Introduction: Laboratory test orders constitute an early outbreak data source. CDC receives laboratory order data in HL7 format from the Laboratory Corporation of America (LabCorp) and plans to use the data in the BioSense Early Event Detection and Situation Awareness System.
Methods: These LabCorp data contain information on tests ordered and include the type of test ordered and the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM)-coded reasons for the order. A consensus panel was formed to group test orders on the basis of expert opinion into eight standard syndrome categories to provide an additional data source for early outbreak detection. A laboratory order taxonomy was developed and used in the mapping consolidation phase. The five main classes of this taxonomy are miscellaneous functional tests, fluid screening tests, system-specific tests, tests for specific infections (by primary manifestation), and tests for specific noninfectious diseases.
Results: Summary of numbers of laboratory order codes in each syndrome category are fever (53), respiratory (53), gastrointestinal (27), neurological (35), rash (37), lymphadenitis (20), localized cutaneous lesion (11), and specific infection (63).
Conclusion: With the daily use of laboratory order data in BioSense, the actual distribution of laboratory order codes in syndrome groups can be evaluated, allowing modification of the mapping.
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Proc Natl Acad Sci U S A
January 2025
Institute of Science and Technology Austria, AT-3400 Klosterneuburg, Austria.
Biophysical constraints limit the specificity with which transcription factors (TFs) can target regulatory DNA. While individual nontarget binding events may be low affinity, the sheer number of such interactions could present a challenge for gene regulation by degrading its precision or possibly leading to an erroneous induction state. Chromatin can prevent nontarget binding by rendering DNA physically inaccessible to TFs, at the cost of energy-consuming remodeling orchestrated by pioneer factors (PFs).
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January 2025
Department of Physics, The University of Tokyo, Bunkyo-ku, Tokyo 113-0033, Japan.
The pseudogap phenomena have been a long-standing mystery of the cuprate high-temperature superconductors. The pseudogap in the electron-doped cuprates has been attributed to band folding due to antiferromagnetic (AFM) long-range order or short-range correlation. We performed an angle-resolved photoemission spectroscopy study of the electron-doped cuprates PrLaCeCuO showing spin-glass, disordered AFM behaviors, and superconductivity at low temperatures and, by measurements with fine momentum cuts, found that the gap opens on the unfolded Fermi surface rather than the AFM Brillouin zone boundary.
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Department of Neonatology/Neonatal Intensive Care Unit, University Hospital of Heraklion, School of Medicine, University of Crete, Heraklion, Crete, Greece.
Preterm births constitute a major public health issue and a chronic, cross-generational condition globally. Psychological and biological factors interact in a way that women from low socio-economic status (SES) are disproportionally affected by preterm delivery and at increased risk for the development of perinatal mental health problems. Low SES constitutes one of the most evident contributors to poor neurodevelopment of preterm infants.
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January 2025
Institute for Chemical Research, Kyoto University, Gokasho, Uji, Kyoto 611-0011, Japan.
High-performance and cost-effective hole-collecting materials (HCMs) are indispensable for commercially viable perovskite solar cells (PSCs). Here, we report an anchorable HCM composed of a triazatruxene core connected with three alkyl carboxylic acid groups (). In contrast to the phosphonic acid-containing tripodal analog (), molecules can form a hydrophilic monolayer on a transparent conducting oxide surface, which is beneficial for subsequent perovskite film deposition in the traditional layer-by-layer fabrication process.
View Article and Find Full Text PDFJ Am Chem Soc
January 2025
Department of Chemistry, University of Southern California, Los Angeles, California 90089, United States.
Generative artificial intelligence (AI) models trained on natural protein sequences have been used to design functional enzymes. However, their ability to predict individual reaction steps in enzyme catalysis remains unclear, limiting the potential use of sequence information for enzyme engineering. In this study, we demonstrated that sequence information can predict the rate of the S2 step of a haloalkane dehalogenase using a generative maximum-entropy (MaxEnt) model.
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