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 ...

therapeutic-effects-acorus-calamus-l-extract-on-radiation-induced

Therapeutic Effects of Acorus calamus L. Extract on Radiation-induced Skin Injury in a Rat Model

Radiation-induced skin injury is a common and debilitating complication in cancer therapy, often resulting in delayed wound healing and increased patient ...

General Hospital of Northern Theater Command

N-methylcytosine (4mC) is a DNA modification involving the addition of a methyl group to the fourth nitrogen atom of the cytosine base. This modification may influence gene regulation, providing potential insights into gene control mechanisms. Traditional laboratory methods for detecting 4mC DNA methylation have limitations, but the rise of artificial intelligence has introduced efficient computational strategies for 4mC site prediction. Despite this progress, challenges persist in terms of model performance and interpretability. To tackle these challenges, we propose DeepSF-4mC, a deep learning model specifically designed for predicting DNA cytosine 4mC methylation sites by leveraging sequence features. Our approach incorporates multiple encoding techniques to enhance prediction accuracy, increase model stability, and reduce the computational resources needed. Leveraging transfer learning, we harness existing models to enhance performance through learned representations or fine-tuning. Ensemble learning techniques combine predictions from multiple models, boosting robustness and accuracy. This research contributes to DNA methylation analysis and lays the groundwork for understanding 4mC's multifaceted role in biological processes. The web server for DeepSF-4mC is accessible at: http://deepsf-4mc.top/and the original code can be found at: https://github.com/754131799/DeepSF-4mC.

DeepSF-4mC: A deep learning model for predicting DNA cytosine 4mC methylation sites leveraging sequence features.

Enhancers are crucial in gene expression regulation, dictating the specificity and timing of transcriptional activity, which highlights the importance of their identification for unravelling the intricacies of genetic regulation. Therefore, it is critical to identify enhancers and their strengths. Repeated sequences in the genome are repeats of the same or symmetrical fragments. There has been a great deal of evidence that repetitive sequences contain enormous amounts of genetic information. Thus, We introduce the W2V-Repeated Index, designed to identify enhancer sequence fragments and evaluates their strength through the analysis of repeated K-mer sequences in enhancer regions. Utilizing the word2vector algorithm for numerical conversion and Manta Ray Foraging Optimization for feature selection, this method effectively captures the frequency and distribution of K-mer sequences. By concentrating on repeated K-mer sequences, it minimizes computational complexity and facilitates the analysis of larger K values. Experiments indicate that our method performs better than all other advanced methods on almost all indicators.

W2V-repeated index: Prediction of enhancers and their strength based on repeated fragments.

Rapid urbanization has highlighted ecological problems in the metropolitan area, with increasing landscape fragmentation and severe threats to ecosystem health (EH). Studying the spatio-temporal coupled relationship between landscape pattern and EH and its response to urbanization in the Fuzhou metropolitan area (FMA) can provide scientific reference for its long-term development planning. We examined the coupled relationship between landscape pattern and EH and its driving mechanism in the FMA at grid and township scales to address the gap. The results show that landscape heterogeneity, diversity, and dispersion are gradually increasing, and EH is rising progressively in the FMA from 2000 to 2020. The spatial distribution of landscape pattern indices and EH indicators showed a "high in the south and low in the north" trend. During the study period, the coupled relationship between landscape patterns and EH was increasingly powerful but with remarkable spatial heterogeneity. The study also found an inverted U-shaped relationship between urbanization and coupled relationships. Ecological landscapes' heterogeneity, diversity, and connectivity in low-urbanization areas are conducive to EH. The opposite is true for high-urbanization areas. This study provides a valuable reference for optimizing landscape planning and ecological management in metropolitan areas.

Coupled relationships between landscape pattern and ecosystem health in response to urbanization.

Three-dimensional bioprinting leverages computer-aided design to construct tissues and organs with specialized bioinks. A notable biomaterial for this purpose is chitosan, a natural polysaccharide sourced from crustacean exoskeletons. Chitosan's biocompatibility, biodegradability, non-toxicity, and ability to promote cell adhesion and proliferation make it an excellent component for bioinks. Initially, the rheological properties of chitosan presented challenges for its use in bioprinting. Enhancements in its printability and stability were achieved by integrating it with other natural or synthetic polymers, facilitating its successful application in bioprinting. Chitosan-based bioinks are particularly promising for controlled drug delivery. Incorporating pharmaceuticals directly into the bioink enables the printed structures to serve as localized, sustained-release systems. This approach offers multiple advantages, including precise drug delivery to targeted disease sites, increased therapeutic efficiency, and reduced systemic side effects. Moreover, bioprinting allows for the customization of drug delivery mechanisms to meet individual patient requirements. Although there have been considerable advancements, the use of chitosan-based bioinks in drug delivery is still an emerging field. This review highlights chitosan's essential role in both systemic and localized drug delivery, underscoring its significance and discussing ongoing trends in its application for pharmaceutical purposes.

Techniques and applications in 3D bioprinting with chitosan bio-inks for drug delivery: A review.

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.

 (), known for its aromatic leaves and rhizomes, is extensively used in traditional medicine to treat digestive issues, inflammation, pain, anxiety, and stress. The petroleum ether extract of  isolates specific bioactive compounds using petroleum ether, a nonpolar solvent effective in dissolving nonpolar plant compounds. This extract potentially offers antimicrobial, anti-inflammatory, and analgesic benefits. However, research on this extract is limited, necessitating further investigation to determine its safety, efficacy, and medical applications. This study aims to develop more efficient extraction methods for the petroleum ether extract, focusing on sesquiterpenes and isocalamendiol from . Additionally, the study will evaluate the extract's safety for topical use, providing insights into potential side effects and contraindications. This information is crucial for assessing the extract's suitability for topical treatments and guiding the development of new therapies and products.

Assessment of dermal safety of petroleum ether extract from  L.

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.

The causal relationship between keloid and mental disorders remains unclear. The aim of this study was to investigate whether keloid was causally associated with the risk of bipolar disorder (BD), anxiety, schizophrenia (SCZ), major depressive disorder (MDD) and post-traumatic stress disorder (PTSD) using a bidirectional Mendelian randomization (MR) analysis.

Association between keloid and mental disorders: perspective from genetic evidence.

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.

To evaluate the burden of bipolar disorder (BD) in adolescents and young adults at global, regional and national levels from 1990 to 2021.

The burden of bipolar disorder in adolescents and young adults: A global, regional, and national perspective from 1990 to 2021 with projections to 2040.

Bone metastasis, a frequent complication of advanced cancers, requires early, precise detection to enable timely interventions and improve patient outcomes. Computed tomography (CT), valued for non-invasive, high-resolution imaging, is essential for identifying bone metastatic lesions. However, these small, morphologically diverse, low-contrast lesions, combined with complex tumor microenvironments and computational limitations, challenge deep learning models' accuracy and real-time applicability. We propose BM-DETR, a Transformer-based model integrating spatial-contextual enhancement module (SCEM) to enhance low-contrast lesion features through channel attention and spatial mixing, AttentionUpsample for superior multi-scale feature fusion via dual-branch upsampling, and dilated transformer attention block (DTAB) to improve contextual modeling, addressing transformer limitations in local detail capture while optimizing efficiency. Evaluated on OsteoScan dataset, BM-DETR achieves mAP50 of 0.9376, and on BMSeg dataset, mAP50 of 0.9139, surpassing state-of-the-art methods. Balancing high accuracy with computational efficiency, BM-DETR's potential for edge deployment supports early screening and intelligent diagnosis of bone metastases. This work provides a robust foundation for automated lesion detection, advancing clinical translation of diagnostic systems.

Transformer-based multi-scale feature fusion for real-time CT bone metastasis detection.

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 : 3	Year
Publication title Cited by 1	2025
Publication title Cited by 1	2025
Publication title Cited by 1	2026

Article	Year
DeepSF-4mC: A deep learning model for predicting DNA cytosine 4mC methylation sites leveraging sequence features. Computers in biology and medicine\| PubMed ID: 38382385	2024
W2V-repeated index: Prediction of enhancers and their strength based on repeated fragments. Genomics\| PubMed ID: 39084477	2024
Coupled relationships between landscape pattern and ecosystem health in response to urbanization. Journal of environmental management\| PubMed ID: 39111014	2024
Techniques and applications in 3D bioprinting with chitosan bio-inks for drug delivery: A review. International journal of biological macromolecules\| PubMed ID: 39214837	2024
SIPSC-Kac: Integrating swarm intelligence and protein spatial characteristics for enhanced lysine acetylation site identification. International journal of biological macromolecules\| PubMed ID: 39515694	2024
Assessment of dermal safety of petroleum ether extract from L. Natural product research\| PubMed ID: 39731474	2024
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
Association between keloid and mental disorders: perspective from genetic evidence. Annals of general psychiatry\| PubMed ID: 40611270	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
The burden of bipolar disorder in adolescents and young adults: A global, regional, and national perspective from 1990 to 2021 with projections to 2040. Journal of affective disorders\| PubMed ID: 41110665	2026
Transformer-based multi-scale feature fusion for real-time CT bone metastasis detection. Bone\| PubMed ID: 41241185	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

Xiaodan Wu

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