The neuropeptide oxytocin (OXT) is suggested to exert an important role in human social behaviors by modulating the salience of social cues. To date, however, there is mixed evidence whether a single dose of OXT can improve the behavioral and neural sensitivity for emotional face processing. To overcome difficulties encountered with classic event-related potential studies assessing stimulus-saliency, we applied frequency-tagging EEG to implicitly assess the effect of a single dose of OXT (24 IU) on the neural sensitivity for positive and negative facial emotions. Neutral faces with different identities were presented at 6 Hz, periodically interleaved with an expressive face (angry, fearful, and happy, in separate sequences) every fifth image (i.e., 1.2 Hz oddball frequency). These distinctive frequency tags for neutral and expressive stimuli allowed direct and objective quantification of the neural expression-categorization responses. The study involved a double-blind, placebo-controlled, cross-over trial with 31 healthy adult men. Contrary to our expectations, we did not find an effect of OXT on facial emotion processing, neither at the neural, nor at the behavioral level. A single dose of OXT did not evoke social enhancement in general, nor did it affect social approach-avoidance tendencies. Possibly ceiling performances in facial emotion processing might have hampered further improvement.
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http://dx.doi.org/10.1111/psyp.14026 | DOI Listing |
J Am Chem Soc
January 2025
Molecular Sensing and Imaging Center, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing 210023, P. R. China.
Nanopore technology holds great potential for single-molecule identification. However, extracting meaningful features from ionic current signals and understanding the molecular mechanisms underlying the specific features remain unresolved. In this study, we uncovered a distinctive ionic current pattern in a K238Q aerolysin nanopore, characterized by transient spikes superimposed on two stable transition states.
View Article and Find Full Text PDFWorld J Orthop
December 2024
Department of Orthopaedic Surgery, Massachusetts General Hospital, Boston, MA 02114, United States.
Background: Pes planus (flatfoot) and pes cavus (high arch foot) are common foot deformities, often requiring clinical and radiographic assessment for diagnosis and potential subsequent management. Traditional diagnostic methods, while effective, pose limitations such as cost, radiation exposure, and accessibility, particularly in underserved areas.
Aim: To develop deep learning algorithms that detect and classify such deformities using smartphone cameras.
Rev Cardiovasc Med
December 2024
General Surgery Department, The First Hospital of Anhui University of Science & Technology (Huai Nan First People's Hospital), 232002 Huainan, Anhui, China.
Background: This study aimed to develop and evaluate the detection and classification performance of different deep learning models on carotid plaque ultrasound images to achieve efficient and precise ultrasound screening for carotid atherosclerotic plaques.
Methods: This study collected 5611 carotid ultrasound images from 3683 patients from four hospitals between September 17, 2020, and December 17, 2022. By cropping redundant information from the images and annotating them using professional physicians, the dataset was divided into a training set (3927 images) and a test set (1684 images).
Commun Biol
December 2024
Brain and Cognition, Faculty of Psychology and Educational Sciences, KU Leuven, Leuven, Belgium.
The functional organization of the human object vision pathway distinguishes between animate and inanimate objects. To understand animacy perception, we explore the case of zoomorphic objects resembling animals. While the perception of these objects as animal-like seems obvious to humans, such "Animal bias" is a striking discrepancy between the human brain and deep neural networks (DNNs).
View Article and Find Full Text PDFAnticancer Res
January 2025
Department of Surgery, Kuopio University Hospital and School of Medicine, University of Eastern Finland, Kuopio, Finland.
Background/aim: This study evaluated the diagnostic accuracy (DA) for colorectal adenomas (CRA), screened by fecal immunochemical test (FIT), using five artificial intelligence (AI) models: logistic regression (LR), support vector machine (SVM), neural network (NN), random forest (RF), and gradient boosting machine (GBM). These models were tested together with clinical features categorized as low-risk (lowR) and high-risk (highR).
Patients And Methods: The colorectal neoplasia (CRN) screening cohort of 5,090 patients included 222 CRA patients and 264 non-CRA patients.
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