Studies of individuals with amnestic mild cognitive impairment (aMCI) have detected hyperactivity in the hippocampus during task-related functional magnetic resonance imaging (fMRI). Such elevated activation has been localized to the hippocampal dentate gyrus/CA3 (DG/CA3) during performance of a task designed to detect the computational contributions of those hippocampal circuits to episodic memory. The current investigation was conducted to test the hypothesis that greater hippocampal activation in aMCI represents a dysfunctional shift in the normal computational balance of the DG/CA3 regions, augmenting CA3-driven pattern completion at the expense of pattern separation mediated by the dentate gyrus. We tested this hypothesis using an intervention based on animal research demonstrating a beneficial effect on cognition by reducing excess hippocampal neural activity with low doses of the atypical anti-epileptic levetiracetam. In a within-subject design we assessed the effects of levetiracetam in three cohorts of aMCI participants, each receiving a different dose of levetiracetam. Elevated activation in the DG/CA3 region, together with impaired task performance, was detected in each aMCI cohort relative to an aged control group. We observed significant improvement in memory task performance under drug treatment relative to placebo in the aMCI cohorts at the 62.5 and 125 mg BID doses of levetiracetam. Drug treatment in those cohorts increased accuracy dependent on pattern separation processes and reduced errors attributable to an over-riding effect of pattern completion while normalizing fMRI activation in the DG/CA3 and entorhinal cortex. Similar to findings in animal studies, higher dosing at 250 mg BID had no significant benefit on either task performance or fMRI activation. Consistent with predictions based on the computational functions of the DG/CA3 elucidated in basic animal research, these data support a dysfunctional encoding mechanism detected by fMRI in individuals with aMCI and therapeutic intervention using fMRI to detect target engagement in response to treatment.
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http://dx.doi.org/10.1016/j.nicl.2015.02.009 | DOI Listing |
BMC Bioinformatics
January 2025
College of Artificial Intelligence, Nanjing Agricultural University, Weigang No.1, Nanjing, 210095, Jiangsu, China.
Antimicrobial peptides (AMPs) have been widely recognized as a promising solution to combat antimicrobial resistance of microorganisms due to the increasing abuse of antibiotics in medicine and agriculture around the globe. In this study, we propose UniAMP, a systematic prediction framework for discovering AMPs. We observe that feature vectors used in various existing studies constructed from peptide information, such as sequence, composition, and structure, can be augmented and even replaced by information inferred by deep learning models.
View Article and Find Full Text PDFEur J Trauma Emerg Surg
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AO Research Institute Davos, Davos, Switzerland.
Background: Digitally Enhanced Hands-on Surgical Training (DEHST) platform was introduced to overcome the lack of training capabilities for the challenging task of freehand distal interlocking of intramedullary nails. It demonstrates high perceived realism for surgeons, and novices perform significantly better after DEHST training. However, characterization of how performance improves remained unexplored.
View Article and Find Full Text PDFTransl Psychiatry
January 2025
Department of Biomedical Engineering, National University of Singapore, Singapore, Singapore.
Depression treatment responses vary widely among individuals. Identifying objective biomarkers with predictive accuracy for therapeutic outcomes can enhance treatment efficiency and avoid ineffective therapies. This study investigates whether functional near-infrared spectroscopy (fNIRS) and clinical assessment information can predict treatment response in major depressive disorder (MDD) through machine-learning techniques.
View Article and Find Full Text PDFInt Dent J
January 2025
Department of Prosthodontics and Dental Implantology, College of Dentistry, King Faisal University, Al-Ahsa, Saudi Arabia. Electronic address:
The integration of artificial intelligence (AI) into dental imaging has led to significant advancements, particularly in the analysis of panoramic radiographs, also known as orthopantomograms (OPGs). One emerging application of AI is in determining gender from these radiographs, a task traditionally performed by forensic experts using manual methods. This systematic review and meta-analysis aim to evaluate the accuracy of AI algorithms in gender determination using OPGs, focusing on the reliability and potential clinical and forensic applications of these technologies.
View Article and Find Full Text PDFJ Am Soc Cytopathol
November 2024
Department of Pathology and Laboratory Medicine, University of Cincinnati College of Medicine, Cincinnati, Ohio. Electronic address:
Introduction: The United States Preventive Services Task Force (USPSTF) recommendation for cervical cancer screening includes the option to screen with high-risk human papilloma virus (hrHPV) alone, but some studies have reported that hrHPV testing alone missed precancerous and cancerous lesions. In this study, we evaluated the test performance characteristics of hrHPV in detecting cervical dysplasia with cervical cytology and biopsy as comparators.
Materials And Methods: We conducted a retrospective analysis of Papanicolaou smears between January and December 2019 performed at our institution with concurrent hrHPV and cytology testing.
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