hybrid-petmri-imaging-of-alzheimer-s-disease-based-on-18f-av-1451

Hybrid PET/MRI Imaging of Alzheimer's Disease Based on 18F-AV-1451

Alzheimer&#39;s disease (AD) is a progressive and irreversible neurodegenerative disorder characterized by cognitive dysfunction, decline in daily living ...

improving-mri-negative-epilepsy-localization-synergy-dual-probe-petmr

Improving MRI-Negative Epilepsy Localization: Synergy of Dual-Probe PET/MR (18F-FDG/11C-FMZ)

The localization of refractory epilepsy, especially MRI-negative cases, is a critical challenge in diagnosing and treating neurological disorders. To ...

General Hospital of Northern Theater Command

Elucidation of post-translational modifications (PTMs), such as lysine acetylation (Kac), is crucial for understanding protein function and regulation. Although traditional experimental methods for identifying Kac sites are accurate, they are time-consuming and costly, leading to incomplete acetylome mapping. Computational approaches, particularly those incorporating machine learning, offer a rapid alternative, but face challenges owing to dataset imbalance, limited feature space, and the need for more effective feature-selection algorithms. To address these challenges, we present SIPSC-Kac, a novel computational method that integrates swarm intelligence algorithms with protein spatial characteristics to enhance the prediction of Kac sites. We used the AlphaFold system for spatial feature extraction and employed swarm intelligence for optimal feature selection, outperforming existing methods in terms of accuracy and computational efficiency. SIPSC-Kac demonstrated superior performance across multiple bacterial species, which was validated by its high performance in evaluation metrics. Our web server provides researchers with a user-friendly platform for Kac site prediction, thereby contributing to the advancement of bioinformatics and proteomic research. The SIPSC-Kac code and web server are accessible, thereby promoting broad applications in the scientific community.

SIPSC-Kac: Integrating swarm intelligence and protein spatial characteristics for enhanced lysine acetylation site identification.

In this study, a copolymer (PVA--PEG) of polyethylene glycol (PEG) and polyvinyl alcohol (PVA) was synthesized by grafting PEG chains onto PVA backbone. PVA--PEG was used as the carrier to prepare the silver nanoparticles/polymer composite (AgNPs/PVA--PEG) using a "one-pot" biological method in the presence of grape seeds extract as a reducing and stabilizing agent. In order to highlight the effect of the copolymer, the homo-polymers PVA and PEG were applied as the carriers to prepare the corresponding composites - AgNPs/PVA, and AgNPs/PEG, respectively using the same method. The prepared AgNPs/polymer products were characterized by UV absorption spectroscopy (UV-vis), scanning electron microscopy (SEM) and X-ray diffraction (XRD). The results show that the silver ions were successfully reduced by the grade seeds extract and the produced AgNPs are coated on the surface of AgNPs/PVA--PEG and AgNPs/PVA, but not for AgNPs/PEG. The prepared AgNPs are uniform and monodisperse, the particle size is small with mean diameter about 25.7 ± 2.3 nm and 54.2 ± 3.4 nm for AgNPs/PVA--PEG and AgNPs/PVA, respectively. The AgNPs/polymer composites exhibited superior antimicrobial effects against microorganisms ( and ). AgNPs/PVA--PEG demonstrated a better performance than AgNPs/PVA. AgNPs/PVA--PEG had a minimum inhibitory concentration (MIC) of 1.3 μg mL and a minimum inhibitory concentration (MBC) of 2.4 μg mL against the microorganisms. For anti-tumor effect, AgNPs/PVA--PEG also demonstrated a high cytotoxicity to the colorectal cancerous cells HCT116 and SW620. The IC50 values of AgNPs/PVA--PEG for HCT116 and SW620 cell lines were 25.4 and 37.6 μg mL, respectively, suggesting a good anticancer activity. All above results indicate that AgNPs/PVA--PEG composites have a significant potential for the control of microorganisms and inhibition of cancer cells.

Preparation of nanosilver/polymer composites and evaluation of their antimicrobial and antitumor effect.

BackgroundTau pathology is closely associated with brain atrophy and cognitive decline, but how it specifically influences local and distant gray matter volume (GMV) and cognitive function remains unclear.ObjectiveThis study aims to explore the spatial relationships between tau pathology, GMV and cognition using hybrid positron emission tomography/magnetic resonance imaging (PET/MRI).MethodsTwenty amyloid-β (Aβ)-positive Alzheimer's disease (AD) patients, 14 mild cognitive impairment (MCI) patients, and 22 Aβ-negative normal controls (NC) underwent standardized neuropsychological assessments and F-fortaucipir PET/MRI scans. We investigated the associations between regional tau standardized uptake value ratio (SUVR) and GMV in AD signature regions. Mediation analyses were conducted to explore the potential mediating effects of local and distant GMV in the relationship between tau pathology and cognition.ResultsThe study indicated that increased F-fortaucipir SUVR and decreased GMV were related to cognitive performance in MCI and AD patients. Compared to NC group, the number of brain regions with local and distant correlations between GMV and SUVR was greater in AD/MCI group. Mediation analysis revealed that GMV served as a significant mediator between tau pathology and cognition in local regions. Furthermore, distant effects were also observed, with hippocampal atrophy partially mediated the relationship between entorhinal cortex tau pathology and cognition. Meanwhile, medial parietal lobe atrophy partially mediated the relationship between medial temporal lobe tau deposition and cognition.ConclusionsOur findings provide an anatomically detailed insight into relationships between tau, GMV and cognition, especially in entorhinal cortex-hippocampus, temporal-parietal lobe cortical circuits.

Local and distant atrophy mediate the relationship between tau pathology and cognition in temporoparietal region in Alzheimer's disease.

Alzheimer's disease (AD) represents a significant challenge due to its progressive neurodegenerative impact, particularly within an aging global demographic. This underscores the critical need for developing sophisticated diagnostic tools for its early detection and precise monitoring. Within this realm, PET/MR imaging stands out as a potent dual-modality approach that transforms sensor data into detailed perceptual mappings, thereby enriching our grasp of brain pathophysiology. To capitalize on the strengths of PET/MR imaging in diagnosing AD, we have introduced a novel deep learning framework named "IT", which is inspired by the Transformer architecture. This innovative model adeptly captures both local and global characteristics within the imaging data, refining these features through advanced feature engineering techniques to achieve a synergistic integration. The efficiency of our model is underscored by robust experimental validation, wherein it delivers superior performance on a host of evaluative benchmarks, all while maintaining low demands on computational resources. Furthermore, the features we extracted resonate with established medical theories regarding feature distribution and usage efficiency, enhancing the clinical relevance of our findings. These insights significantly bolster the arsenal of tools available for AD diagnostics and contribute to the broader narrative of deciphering brain functionality through state-of-the-art imaging modalities.

IT: An interpretable transformer model for Alzheimer's disease prediction based on PET/MR images.

This study aims to develop and validate a novel two-step deep learning framework for the automated detection, segmentation, and classification of bone metastases in SPECT/CT imaging, accurately distinguishing malignant from benign lesions to improve early diagnosis and facilitate personalized treatment planning.

A Novel Two-step Classification Approach for Differentiating Bone Metastases From Benign Bone Lesions in SPECT/CT Imaging.

Cryo-electron microscopy (cryo-EM) is a powerful tool for high-resolution structural analysis of proteins and viruses. However, a major challenge in cryo-EM data processing is the presence of heterogeneous samples, IC contamination, and extraneous impurities, which hinder accurate target protein identification. To address this issue, we propose the Universal Model for Cryo-electron Microscopy Particle Picking (UM-CPP), a novel framework that integrates feature engineering with deep learning to enhance particle detection in cryo-EM micrographs. The key contribution of UM-CPP lies in its hybrid approach, which combines classical machine learning features with state-of-the-art deep learning techniques. This fusion enables robust and adaptable performance across diverse protein structures while maintaining high accuracy. In comparative evaluations, UM-CPP outperforms existing deep-learning-based methods in detection precision. Additionally, our model provides interpretable feature analysis, offering researchers deeper insights into the decision-making process of particle selectiona critical advancement for improving trust and usability in cryo-EM data analysis. By improving both accuracy and interpretability, UM-CPP advances the field of cryo-EM, facilitating more reliable and efficient structural studies of biological macromolecules.

UM-CPP: A Universal Model for Efficient Classification of Protein Particles in cryo-EM Micrographs with Feature Engineering.

This study aims to improve the diagnostic accuracy of SPECT/CT imaging in distinguishing benign from malignant bone lesions by integrating self-supervised deep learning and radiomics, reducing subjectivity in traditional image interpretation for more reliable clinical decision-making.

ArticleTotal : 2	Year
Publication title Cited by 1	2025
Publication title Cited by 1	2025

Article	Year
SIPSC-Kac: Integrating swarm intelligence and protein spatial characteristics for enhanced lysine acetylation site identification. International journal of biological macromolecules\| PubMed ID: 39515694	2024
Preparation of nanosilver/polymer composites and evaluation of their antimicrobial and antitumor effect. RSC advances\| PubMed ID: 40008016	2025
Local and distant atrophy mediate the relationship between tau pathology and cognition in temporoparietal region in Alzheimer's disease. Journal of Alzheimer's disease : JAD\| PubMed ID: 40116643	2025
IT: An interpretable transformer model for Alzheimer's disease prediction based on PET/MR images. NeuroImage\| PubMed ID: 40222500	2025
A Novel Two-step Classification Approach for Differentiating Bone Metastases From Benign Bone Lesions in SPECT/CT Imaging. Academic radiology\| PubMed ID: 40610298	2025
UM-CPP: A Universal Model for Efficient Classification of Protein Particles in cryo-EM Micrographs with Feature Engineering. ACS omega\| PubMed ID: 40686975	2025
Benign and malignant bone lesion diagnosis based on self-supervised and radiomics fusion using SPECT/CT images. Journal of bone oncology\| PubMed ID: 41277948	2025

Yingxin Dai

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