Acetaminophen has been shown to influence cognitive and affective behavior possibly via alterations in serotonin function. This study builds upon this previous work by examining the relationship between acetaminophen and dual-learning systems, comprising reflective (rule-based) and reflexive (information-integration) processing. In a double-blind, placebo-controlled study, a sample of community-recruited adults (N = 87) were randomly administered acetaminophen (1000 mg) or placebo and then completed reflective-optimal and reflexive-optimal category learning tasks. For the reflective-optimal category learning task, acetaminophen compared to placebo was associated with enhanced accuracy prior to the first rule switch (but not overall accuracy), with needing fewer trials to reach criterion and with a faster learning rate. Acetaminophen modestly attenuated performance on the reflexive-optimal category learning task compared to placebo. These findings indirectly support two positions that have been proposed elsewhere. First, they are consistent with the view that acetaminophen has an influence on the serotonergic system. Second, the findings are consistent with a proposed link between elevated serotonin function and relative dominance of effortful, rule-based processing.
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http://dx.doi.org/10.1093/scan/nsy074 | DOI Listing |
NPJ Digit Med
January 2025
Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA.
The 12-lead electrocardiogram (ECG) is inexpensive and widely available. Whether conditions across the human disease landscape can be detected using the ECG is unclear. We developed a deep learning denoising autoencoder and systematically evaluated associations between ECG encodings and ~1,600 Phecode-based diseases in three datasets separate from model development, and meta-analyzed the results.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Hospital Pathology, College of Medicine, The Catholic University of Korea, 222 Banpo-daero, Seocho-gu, Seoul, 06591, Korea.
Recently, as the number of cancer patients has increased, much research is being conducted for efficient treatment, including the use of artificial intelligence in genitourinary pathology. Recent research has focused largely on the classification of renal cell carcinoma subtypes. Nonetheless, the broader categorization of renal tissue into non-neoplastic normal tissue, benign tumor and malignant tumor remains understudied.
View Article and Find Full Text PDFMeat Sci
January 2025
Department of Animal and Food Sciences, Texas Tech University, Lubbock, TX 79409, USA. Electronic address:
Steak samples were collected from the longissimus lumborum muscles of beef carcasses (Canada AA, n = 1505; Canada AAA, n = 1363) over a 3-year period. Steaks were aged for 14 d, and tenderness was determined by slice shear force (SSF). Metabolomic profiling of beef samples was performed using rapid evaporative ionization mass spectrometry (REIMS) (N = 2853).
View Article and Find Full Text PDFActa Otolaryngol
January 2025
Department of Otorhinolaryngology, Institute of Science Tokyo, Tokyo, Japan.
Background: Recent advances in artificial intelligence have facilitated the automatic diagnosis of middle ear diseases using endoscopic tympanic membrane imaging.
Aim: We aimed to develop an automated diagnostic system for middle ear diseases by applying deep learning techniques to tympanic membrane images obtained during routine clinical practice.
Material And Methods: To augment the training dataset, we explored the use of generative adversarial networks (GANs) to produce high-quality synthetic tympanic images that were subsequently added to the training data.
Sensors (Basel)
January 2025
Key Laboratory of Poyang Lake Wetland and Watershed Research, Ministry of Education, Jiangxi Normal University, Nanchang 330022, China.
Bird species detection is critical for applications such as the analysis of bird population dynamics and species diversity. However, this task remains challenging due to local structural similarities and class imbalances among bird species. Currently, most deep learning algorithms focus on designing local feature extraction modules while ignoring the importance of global information.
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