PLoS One. 2025 Jan 3;20(1):e0315130. doi: 10.1371/journal.pone.0315130. eCollection 2025.

ABSTRACT

In athletes' competitions and daily training, in order to further strengthen the athletes' sports level, it is usually necessary to analyze the athletes' sports actions at a specific moment, in which it is especially important to quickly and accurately identify the categories and positions of the athletes, sports equipment, field boundaries and other targets in the sports scene. However, the existing detection methods failed to achieve better detection results, and the analysis found that the reasons for this phenomenon mainly lie in the loss of temporal information, multi-targeting, target overlap, and coupling of regression and classification tasks, which makes it more difficult for these network models to adapt to the detection task in this scenario. Based on this, we propose for the first time a supervised object detection method for scenarios in the field of motion management. The main contributions of this method include: designing a TSM module that combines temporal offset operation and spatial convolution operation to enhance the network structure's ability to capture temporal information in the motion scene; designing a deformable attention mechanism that enhances the feature extraction capability of individual target actions in the motion scene; designing a decoupling structure that decouples the regression task from the classification task; and using the above approach for object detection in motion management scenarios. The accuracy of target detection in this scenario is greatly. To evaluate the effectiveness of our designed network and proposed methodology, we conduct experiments on open-source datasets. The final comparison experiment shows that our proposed method outperforms all the other seven common target detection networks on the same dataset with a map_0.5 score of 92.298%. In the ablation experiments, the reduction of each module reduces the accuracy of detection. The two types of experiments prove that the proposed method is effective and can achieve better results when applied to motion management detection scenarios.

PMID:39752546 | DOI:10.1371/journal.pone.0315130

Proc Natl Acad Sci U S A. 2025 Jan 7;122(1):e2321319121. doi: 10.1073/pnas.2321319121. Epub 2025 Jan 3.

ABSTRACT

We study image segmentation using spatiotemporal dynamics in a recurrent neural network where the state of each unit is given by a complex number. We show that this network generates sophisticated spatiotemporal dynamics that can effectively divide an image into groups according to a scene's structural characteristics. We then demonstrate a simple algorithm for object segmentation that generalizes across inputs ranging from simple geometric objects in grayscale images to natural images. Using an exact solution of the recurrent network's dynamics, we present a precise description of the mechanism underlying object segmentation in the network dynamics, providing a clear mathematical interpretation of how the algorithm performs this task. Object segmentation across all images is accomplished with one recurrent neural network that has a single, fixed set of weights. This demonstrates the expressive potential of recurrent neural networks when constructed using a mathematical approach that brings together their structure, dynamics, and computation.

PMID:39752524 | DOI:10.1073/pnas.2321319121

PLoS One. 2025 Jan 3;20(1):e0307900. doi: 10.1371/journal.pone.0307900. eCollection 2025.

ABSTRACT

Accurate diagnosis of pancreatic cancer using CT scan images is critical for early detection and treatment, potentially saving numerous lives globally. Manual identification of pancreatic tumors by radiologists is challenging and time-consuming due to the complex nature of CT scan images and variations in tumor shape, size, and location of the pancreatic tumor also make it challenging to detect and classify different types of tumors. Thus, to address this challenge we proposed a four-stage framework of computer-aided diagnosis systems. In the preprocessing stage, the input image resizes into 227 × 227 dimensions then converts the RGB image into a grayscale image, and enhances the image by removing noise without blurring edges by applying anisotropic diffusion filtering. In the segmentation stage, the preprocessed grayscale image a binary image is created based on a threshold, highlighting the edges by Sobel filtering, and watershed segmentation to segment the tumor region and we also implement the U-Net method for segmentation. Then refine the geometric structure of the image using morphological operation and extracting the texture features from the image using a gray-level co-occurrence matrix computed by analyzing the spatial relationship of pixel intensities in the refined image, counting the occurrences of pixel pairs with specific intensity values and spatial relationships. The detection stage analyzes the tumor region's extracted features characteristics by labeling the connected components and selecting the region with the highest density to locate the tumor area, achieving a good accuracy of 99.64%. In the classification stage, the system classifies the detected tumor into the normal, pancreatic tumor, then into benign, pre-malignant, or malignant using a proposed reduced 11-layer AlexNet model. The classification stage attained an accuracy level of 98.72%, an AUC of 0.9979, and an overall system average processing time of 1.51 seconds, demonstrating the capability of the system to effectively and efficiently identify and classify pancreatic cancers.

PMID:39752442 | DOI:10.1371/journal.pone.0307900

PLoS One. 2025 Jan 3;20(1):e0316089. doi: 10.1371/journal.pone.0316089. eCollection 2025.

ABSTRACT

Optical Coherence Tomography (OCT) offers high-resolution images of the eye's fundus. This enables thorough analysis of retinal health by doctors, providing a solid basis for diagnosis and treatment. With the development of deep learning, deep learning-based methods are becoming more popular for fundus OCT image segmentation. Yet, these methods still encounter two primary challenges. Firstly, deep learning methods are sensitive to weak edges. Secondly, the high cost of annotating medical image data results in a lack of labeled data, leading to overfitting during model training. To tackle these challenges, we introduce the Multi-Task Attention Mechanism Network with Pruning (MTAMNP), consisting of a segmentation branch and a boundary regression branch. The boundary regression branch utilizes an adaptive weighted loss function derived from the Truncated Signed Distance Function(TSDF), improving the model's capacity to preserve weak edge details. The Spatial Attention Based Dual-Branch Information Fusion Block links these branches, enabling mutual benefit. Furthermore, we present a structured pruning method grounded in channel attention to decrease parameter count, mitigate overfitting, and uphold segmentation accuracy. Our method surpasses other cutting-edge segmentation networks on two widely accessible datasets, achieving Dice scores of 84.09% and 93.84% on the HCMS and Duke datasets.

PMID:39752440 | DOI:10.1371/journal.pone.0316089

PLoS One. 2025 Jan 3;20(1):e0314944. doi: 10.1371/journal.pone.0314944. eCollection 2025.

ABSTRACT

Medical volume data are rapidly increasing, growing from gigabytes to petabytes, which presents significant challenges in organisation, storage, transmission, manipulation, and rendering. To address the challenges, we propose an end-to-end architecture for data compression, leveraging advanced deep learning technologies. This architecture consists of three key modules: downsampling, implicit neural representation (INR), and super-resolution (SR). We employ a trade-off point method to optimise each module's performance and achieve the best balance between high compression rates and reconstruction quality. Experimental results on multi-parametric MRI data demonstrate that our method achieves a high compression rate of up to 97.5% while maintaining superior reconstruction accuracy, with a Peak Signal-to-Noise Ratio (PSNR) of 40.05 dB and Structural Similarity Index (SSIM) of 0.96. This approach significantly reduces GPU memory requirements and processing time, making it a practical solution for handling large medical datasets.

PMID:39752347 | DOI:10.1371/journal.pone.0314944

Bioinformatics. 2025 Jan 3:btae658. doi: 10.1093/bioinformatics/btae658. Online ahead of print.

ABSTRACT

MOTIVATION: Skeletal muscle cells (skMCs) combine together to create long, multi-nucleated structures called myotubes. By studying the size, length, and number of nuclei in these myotubes, we can gain a deeper understanding of skeletal muscle development. However, human experimenters may often derive unreliable results owing to the unusual shape of the myotube, which causes significant measurement variability.

RESULTS: We propose a new method for quantitative analysis of the dexamethasone side effect on human-derived young and aged skeletal muscle by simultaneous myotube and nuclei segmentation using deep learning combined with post-processing techniques. The deep learning model outputs myotube semantic segmentation, nuclei semantic segmentation, and nuclei center, and post-processing applies a watershed algorithm to accurately distinguish overlapped nuclei and identify myotube branches through skeletonization. To evaluate the performance of the model, the myotube diameter and the number of nuclei were calculated from the generated segmented images and compared with the results calculated by human experimenters. In particular, the proposed model produced outstanding outcomes when comparing human-derived primary young and aged skMCs treated with dexamethasone. The proposed standardized and consistent automated image segmentation system for myotubes is expected to help streamline the drug-development process for skeletal muscle diseases.

AVAILABILITY AND IMPLEMENTATION: The code and the data are available at https://github.com/tdn02007/QA-skMCs-Seg.

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PMID:39752317 | DOI:10.1093/bioinformatics/btae658

J Phys Chem A. 2025 Jan 3. doi: 10.1021/acs.jpca.4c05519. Online ahead of print.

ABSTRACT

Artificial intelligence technology has introduced a new research paradigm into the fields of quantum chemistry and materials science, leading to numerous studies that utilize machine learning methods to predict molecular properties. We contend that an exemplary deep learning model should not only achieve high-precision predictions of molecular properties but also incorporate guidance from physical mechanisms. Here, we propose a framework for predicting molecular properties based on data-driven electron density images, referred to as D3-ImgNet. This framework integrates group theory, density functional theory-related mechanisms, deep learning techniques, and multiobjective optimization mechanisms, embodying a methodological fusion of data analytics and system optimization. Initially, we focus on atomization energies as the primary target of our study, using the QM9 data set to demonstrate the framework's ability to predict molecular atomization energies with high accuracy and excellent exploration performance. We then further evaluate its predictive capabilities for dipole moments and forces with the QM9X data set, achieving satisfactory results. Additionally, we tested the D3-ImgNet framework on the SN2 reaction data set to demonstrate its ability to precisely predict the minimum energy paths of SN2 chemical reactions, showcasing its portability and adaptability in chemical reaction modeling. Finally, visualizations of the electronic density generated by the framework faithfully replicate the physical phenomenon of electron density transfer. We believe that this framework has the potential to accelerate property predictions and high-throughput screening of functional materials.

PMID:39752232 | DOI:10.1021/acs.jpca.4c05519

Int J Comput Assist Radiol Surg. 2025 Jan 3. doi: 10.1007/s11548-024-03294-w. Online ahead of print.

ABSTRACT

PURPOSE: Thyroid nodules are common, and ultrasound-based risk stratification using ACR's TIRADS classification is a key step in predicting nodule pathology. Determining thyroid nodule contours is necessary for the calculation of TIRADS scores and can also be used in the development of machine learning nodule diagnosis systems. This paper presents the development, validation, and multi-institutional independent testing of a machine learning system for the automatic segmentation of thyroid nodules on ultrasound.

METHODS: The datasets, containing a total of 1595 thyroid ultrasound images from 520 patients with thyroid nodules, were retrospectively collected under IRB approval from University of Chicago Medicine (UCM) and Weill Cornell Medical Center (WCMC). Nodules were manually contoured by a team of UCM and WCMC physicians for ground truth. An AttU-Net, a U-Net architecture with additional attention weighting functions, was trained for the segmentations. The algorithm was validated through fivefold cross-validation by nodule and was tested on two independent test sets: one from UCM and one from WCMC. Dice similarity coefficient (DSC) and percent Hausdorff distance (%HD), Hausdorff distance reported as a percent of the nodule's effective diameter, served as the performance metrics.

RESULTS: On multi-institutional independent testing, the AttU-Net yielded average DSCs (std. deviation) of 0.915 (0.04) and 0.922 (0.03) and %HDs (std. deviation) of 12.9% (4.6) and 13.4% (6.3) on the UCM and WCMC test sets, respectively. Similarity testing showed the algorithm's performance on the two institutional test sets was equivalent up to margins of Δ DSC ≤ 0.013 and Δ %HD ≤ 1.73%.

CONCLUSIONS: This work presents a robust automatic thyroid nodule segmentation algorithm that could be implemented for risk stratification systems. Future work is merited to incorporate this segmentation method within an automatic thyroid classification system.

PMID:39751996 | DOI:10.1007/s11548-024-03294-w

Alzheimers Dement. 2024 Dec;20 Suppl 1:e085828. doi: 10.1002/alz.085828.

ABSTRACT

BACKGROUND: Amyloid-β accumulation is a pivotal factor in Alzheimer's disease (AD) progression. As treatment for AD has not been successful yet, the most effective approach lies in early diagnosis and the subsequent delay of disease progression. Hence, this study introduces a deep learning model to predict amyloid-β accumulation in the brain.

METHOD: We mathematically modeled the diffusion of amyloid-β based on its biological traits, encompassing generation, clearance, and diffusion. We converted the model into a deep learning framework with multi-layer perceptron (MLP) and graph convolutional neural network (GCN) (Kipf et al., 2016) to forecast the accumulation of the protein. We extracted the necessary information from various neuroimage data, including T1 structural magnetic resonance (MR) images, 18F-Florbetapir positron emission tomography (PET) scans, and diffusion weighted MR images (DWI), to simulate the diffusion of the protein (Figure 1). We used longitudinal data of 146 subjects, incorporating 436 data points.

RESULT: The proposed model accurately predicted amyloid-β after 2 years (Figure 2), showing a high correlation in the test dataset (median = 0.8273, IQR = [0.7708, 0.8692]), outperforming the previous model (average 0.58) (Kim et al., 2019). We examined generation and clearance terms, mapping top 30% ROIs onto the brain by averaging each term across subjects (Figure 3). The regions with early AD amyloid-β accumulation are believed to be related to the default mode network and prefrontal network (Palmqvist et al., 2017) supported by Figure 3a. The effectiveness of amyloid-β clearance may be influenced by brain activity (Mergenthaler et al., 2013; Ullah et al., 2023). Earlier studies reported diminished metabolism in specific regions during the early AD (Chételat et al., 2020; Kantarci et al., 2021). The proposed model identified high clearance regions (Figure 3b), aligning with regions showing normal metabolism.

CONCLUSION: We introduced a deep learning model that simulates the diffusion of amyloid-β with strong predictive performance and interpretation. While parameters were optimized for the entire group, accuracy varied for some subjects. Also, further investigation is needed to interpret each term comprehensively. Despite the need for individual optimization and additional interpretative analysis, the model may contribute to the diagnosis of AD.

PMID:39751753 | DOI:10.1002/alz.085828

Brief Bioinform. 2024 Nov 22;26(1):bbae691. doi: 10.1093/bib/bbae691.

ABSTRACT

The identification of cancer driver genes is crucial for understanding the complex processes involved in cancer development, progression, and therapeutic strategies. Multi-omics data and biological networks provided by numerous databases enable the application of graph deep learning techniques that incorporate network structures into the deep learning framework. However, most existing methods do not account for the heterophily in the biological networks, which hinders the improvement of model performance. Meanwhile, feature confusion often arises in models based on graph neural networks in such graphs. To address this, we propose a Simplified Graph neural network for identifying Cancer Driver genes in heterophilic networks (SGCD), which comprises primarily two components: a graph convolutional neural network with representation separation and a bimodal feature extractor. The results demonstrate that SGCD not only performs exceptionally well but also exhibits robust discriminative capabilities compared to state-of-the-art methods across all benchmark datasets. Moreover, subsequent interpretability experiments on both the model and biological aspects provide compelling evidence supporting the reliability of SGCD. Additionally, the model can dissect gene modules, revealing clearer connections between driver genes in cancers. We are confident that SGCD holds potential in the field of precision oncology and may be applied to prognosticate biomarkers for a wide range of complex diseases.

PMID:39751645 | DOI:10.1093/bib/bbae691

Alzheimers Dement. 2024 Dec;20 Suppl 1:e085804. doi: 10.1002/alz.085804.

ABSTRACT

BACKGROUND: Cerebral small vessel disease (CSVD), which includes cerebral amyloid angiopathy (CAA) and arteriolosclerosis, often co-occurs with Alzheimer's disease (AD) pathology. The medial temporal lobe (MTL) is susceptible to hosting multiple AD pathologies, such as neurofibrillary tangles (NFTs), amyloid-β plaques, phospho-Tar-DNA-Binding-Protein-43 (pTDP-43), as well as CSVD. Whether a causal relationship between these pathologies exists remains largely unknown, but one potential linking mechanism is the dysfunction of perivascular clearance. Our objective was to examine the burden of CSVD in the MTL of a pathological AD cohort and to establish the associations between CSVD and AD-related pathologies, as well as between CSVD and enlarged perivascular spaces (EPVS), a potential indicator of clearance dysfunction.

METHOD: The study included 156 autopsy cases (mean age at death 79.4±10.9 years, 90 females) from the Massachusetts Alzheimer's Disease Research Center (MADRC). One hemisphere was preserved in formalin, and 5 µm-thick sections were cut from predefined regions of the hippocampal body and entorhinal cortex. These sections were subsequently stained using luxol fast blue with hematoxylin&eosin (LHE), and antibodies against amyloid-β, hyperphosphorylated tau (At8), and pTDP-43, following standard histological and immunohistochemical protocols. Utilizing deep-learning models (Aiforia®), we computed the burden of CAA, amyloid-β plaques, NFTs, and pTDP-43 inclusions (Figure 1). Additionally, the severity of arteriolosclerosis and the % area of EPVS were evaluated on the LHE sections.

RESULT: In linear mixed effects models CAA was positively associated with the density of NFTs (Est = 7.21; p = 0.024; R2 = 69%) and amyloid-β plaque burden (Est = 3.01; p<0.001; R2 = 61%) in all regions of interest. Arteriolosclerosis had no direct effect on parenchymal AD-related pathologies but showed a positive interaction with CAA in the association with PVS enlargement. There was no relationship between pTDP-43 inclusions and arteriolosclerosis.

CONCLUSION: These results point towards an association between microvascular pathology and AD-related pathology, possibly mediated by clearance dysfunction.

PMID:39751386 | DOI:10.1002/alz.085804

Alzheimers Dement. 2024 Dec;20 Suppl 1:e092140. doi: 10.1002/alz.092140.

ABSTRACT

BACKGROUND: Assessing tau accumulation in early affected areas like the lateral entorhinal cortex (EC) and inferior temporal gyrus (ITG) enables early prediction of disease progression and cognitive decline. However, positron emission tomography (PET) imaging poses radiation exposure and cost concerns. This research aims to develop a deep learning model predicting tau positivity in these regions using MRI.

METHOD: In this study, we used the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort, of which dataset was partitioned into train, validation, and test sets (8:1:1 ratio), encompassing a total of 1010 scans, all of whom underwent T1-weighted magnetic resonance imaging (MRI) and [18F] flortaucipir-PET imaging. For the T1-weighted MRI images, FreeSurfer v7.2 was employed to perform pre-processing and extract cortical thickness measurements. Simultaneously, [18F] flortaucipir-PET imaging was processed to compute voxel-wise regions of interest (ROIs) for 66 specific brain regions. Regional tau positivity was established using a cutoff at a z-score of 1.25, with a focus on cognitive normal (CN) subjects within the train set. To predict early tau accumulation regions, we developed an attention mechanism-based encoder-decoder model by adopting a Transformer model into our problem setting, performing sequential predictions for each of the 66 regions. Notably, the model's predictive performance in initial regions significantly influences subsequent predictions. Consequently, we implemented a prioritization strategy, emphasizing predictions from areas where the model demonstrated high accuracy. This approach was designed to enhance the overall predictive accuracy of the model.

RESULT: Predicting five early tau accumulation regions per hemisphere, our model achieved an average AUC of 0.84 and accuracy of 84% for the test dataset (112 participants). Notably, in critical early disease progression regions (fusiform gyrus and ITG), AUC values of 0.84, 0.85, and accuracies of 84.4%, 84% were observed. Furthermore, the proposed prioritization strategy improved performance compared to predictions using vanilla attention-based model.

CONCLUSION: We developed an attention mechanism-based architecture with an encoder-decoder structure. By predicting outcomes not only based on cortical thickness values but also their cross-attention-based contexture information, we could achieve highly accurate tau prediction in early and challenging regions.

PMID:39751223 | DOI:10.1002/alz.092140

Alzheimers Dement. 2024 Dec;20 Suppl 1:e088814. doi: 10.1002/alz.088814.

ABSTRACT

BACKGROUND: Alzheimer's disease (AD) has been associated with speech and language impairment. Recent progress in the field has led to the development of automated AD detection using audio-based methods, because it has a great potential for cross-linguistic detection. In this investigation, we utilised a pretrained deep learning model to automatically detect AD, leveraging acoustic data derived from Chinese speech.

METHOD: Speech samples from a picture description task were obtained from 81 native Chinese speakers in Taiwan. This group included 34 normal controls (NC) (14 females; age range: 61-89 years; average age: 73.9 years; SD: 6.7) and 47 patients diagnosed with early AD (24 females; age range: 59-89 years; average age: 76.8 years; SD: 7.6). The audio data were first segmented into 6-second clips, resulting in a total of 1117 clips for the NC group and 1770 for the AD group. Due to the data imbalance, we equalized the groups by randomly selecting 1117 clips from the AD group. The dataset was then divided into a training-to-testing ratio of 8 to 2. The training clips were initially processed using a pre-trained Wav2vec2 model to generate internal acoustic representations. Subsequently, these acoustic representations, now serving as feature data, were input into a two-layer fully connected neural network for additional training and classification.

RESULT: The model achieved a training accuracy of 83%. Notable metrics for test performance were observed as follows: Accuracy - 81.25%, Precision - 80.92%, Recall - 81.79%, and F1 Score - 81.35%. These findings suggest a promising capability of wav2vec2 in Alzheimer's Disease detection, demonstrating a commendable balance between precision and recall.

CONCLUSION: The pre-trained Wav2vec2 model emerges as a promising tool for AD detection through Chinese speech data. This research sets the foundation for additional investigation into the model's potential for cross-linguistic detection using speech data. Additionally, future work can also be conducted to investigate what acoustic features are used and their significances in classification process.

PMID:39751145 | DOI:10.1002/alz.088814

Alzheimers Dement. 2024 Dec;20 Suppl 1:e089241. doi: 10.1002/alz.089241.

ABSTRACT

BACKGROUND: Early detection and accurate forecasting of AD progression are crucial for timely intervention and management. This study leverages multi-modal data, including MRI scans, brain volumetrics, and clinical notes, utilizing Machine Learning (ML), Deep Learning (DL) and a range of ensemble methods to enhance the forecasting accuracy of Alzheimer's disease.

METHOD: We utilize the OASIS-3 longitudinal dataset, tracking 1,098 patients over 30 years. From OASIS-3, we combined three modalities - MRI scans, Freesurfer brain volumetrics, and Clinical Data from the Alzheimer's Disease Research Center (ADRC). We use Convolutional Neural Networks (CNNs), specifically MobileNetV2, ResNet101, ResNet152 and ResNet200 for MRIs and Machine Learning (ML) techniques (Random Forest and K Nearest Neighbors) for Freesurfer featurized brain volumetrics and clinical data. Individual models were tuned for each modality, with the best models combined via ensembles to predict each patient's future Clinical Dementia Rating (CDR). Ensembles evaluated included: aggressive (any modality predicting positive), conservative (all modalities predicting positive), conditional ensembles (majority voting and ADRC or both MRI/Freesurfer), and custom machine learning models built to integrate the modality predictions based on the confidence values returned from the MRI model and the predictions of other models. The figure below shows our experimental pipeline.

RESULT: The study achieved >95% accuracy in predicting future CDR. Ensembles notably reduced harmful False Negatives by 2x-15x, compared to individual modalities, while incurring nominal increases in False Positives. The machine learning trained ensemble demonstrated improved accuracy over the best individual modality predictions The results highlight the potential of multi-modal AI ensemble methods in improving the accuracy of early AD detection and prognosis. The figures below show comparative accuracy and false positives/false negatives rates for each ensemble as compared to the individual modalities.

CONCLUSION: This work demonstrates the potential efficacy of multi-modal data integration via ensemble learning in forecasting Alzheimer's disease, significantly outperforming single-modality methods. It underscores the importance of leveraging diverse data sources and advanced analytical techniques for early diagnosis and intervention in Alzheimer's care, paving the way for future research to explore additional modalities and methods for even greater accuracy and clinical utility.

PMID:39751131 | DOI:10.1002/alz.089241

Alzheimers Dement. 2024 Dec;20 Suppl 1:e088832. doi: 10.1002/alz.088832.

ABSTRACT

BACKGROUND: Alzheimer's disease (AD) related pathologies (i.e., neurofibrillary tangles [NFTs], amyloid-β plaques, and phosphorylated-TAR-DNA-binding-protein-43 [pTDP-43]) differ across sexes. However, the interaction between sex and cerebral small vessel disease (CSVD) (i.e., cerebral amyloid angiopathy [CAA] and arteriolosclerosis) on AD-related pathologies has been less well characterized. The medial temporal lobe (MTL) is a crucial region in AD pathophysiology which harbors AD-pathologies at an early disease stage and is vascularized by small vessels prone to CSVD. We therefore aimed to analyze the relationship between sex and CSVD on AD-related pathologies in the MTL of a pathological AD cohort.

METHODS: The study included autopsy cases from the Massachusetts AD Research Center. One hemisphere was formalin-fixed, samples from pre-defined regions (hippocampal body and entorhinal cortex) were stained for luxol fast blue with hematoxylin&eosin, amyloid-b, At8, and pTDP-43. Deep-learning models (Aiforiaâ) were used to obtain quantitative measures for burden of CAA, NFTs, and pTDP-43 inclusions. Arteriolosclerosis (grade 0-3) was determined in vessels >20mm Ø. Mixed effect models explored age and sex (fixed terms) across regions of interest (ROIs: hippocampus, parahippocampal gyrus, entorhinal cortex, amygdala) of the MTL on CSVD subtypes. Furthermore, interactions between sex and CSVD subtypes on AD-related pathologies were tested. Finally, ApoE effect was evaluated in a subgroup with available genotyping.

RESULTS: 157 autopsy cases (80.8±12.6y, 91 females) were included, ApoE status was available in 66/157. Females had higher arteriolosclerosis severity (odds ratio for grade 2/3 = 1.56, 95% confidence interval [CI] 1.11; 2.20, p = 0.01) and lower CAA burden (b = -0.08, 95%CI -0.12; -0.04, p<0.001), when adjusting for age and ROIs. Moreover, females had lower burden of amyloid-b plaques (b = -0.25, 95%CI -0.46; -0.04, p = 0.02) and higher density of NFTs (b = 5.16, 95%CI 1.90; 8.41, p = 0.002). Inclusion of ApoE-status confirmed these findings with higher effect-size, and ApoE4 genotype interacted with female sex predicting higher NFT density when adjusting for arteriolosclerosis and CAA respectively (b = 16.2, 95%CI 4.2; 28.2, p = 0.008 and b = 16.3, 95%CI 3.37; 29.22, p = 0.01). No interaction effect was found between sex and CSVD on AD-pathology.

CONCLUSIONS: In this cohort, sex differentially affected microvascular and AD-related pathologies in the MTL. ApoE4 genotype might act as an effect modifier of sex.

PMID:39751121 | DOI:10.1002/alz.088832

Alzheimers Dement. 2024 Dec;20 Suppl 1:e089093. doi: 10.1002/alz.089093.

ABSTRACT

BACKGROUND: Cell-type specific expression quantitative trait loci (eQTLs) can help dissect cellular heterogeneity in the impact of genetic variation on gene expression for Alzheimer's disease (AD) and AD-related dementia (ADRD). However, due to the high cost and stringent sample collection criteria, it is challenging to obtain large single-nuclei RNA sequencing (snRNA-seq) data with sufficient cohort size to match genotyping data to systematically identify human brain-specific eQTLs for AD/ADRD.

METHOD: In this study, we presented a deep learning-based deconvolution framework on large-scale bulk RNA sequencing (RNA-seq) data to infer cell-type specific eQTLs in the human brains with AD/ADRD. Specifically, we first predicted the brain cell-type specific gene expression for the harmonized bulk RNA dataset (n = 1,092) from Religious Orders Study and Memory and Aging Project (ROS/MAP). We then incorporated the inferred cell-type specific gene expression with matched whole genome sequencing (WGS) data from ROS/MAP to identify the brain cell-type specific eQTLs. These cell-type specific eQTLs were further colocalized with AD genome-wide association study (GWAS) findings to discover potential risk genes and druggable targets for AD.

RESULT: We identified 44,504 genome-wide significant cell-type specific cis-eQTLs (window size = 1Mb, p < 5 × 10-8) from eight brain cell types, including excitatory neurons, inhibitory neurons, microglia, oligodendrocytes, oligodendrocyte precursor cells, astrocytes, and endothelium. Approximate 2,732 eQTLs in astrocytes and 7,628 in excitatory neurons are identical to the results from a large existing snRNA-seq data, associated with the regulation of multiple genes (eGenes, e.g., ARL17B, LRRC37A2, ERAP2, PILRB, ZNF266). We illustrated that the GWAS variant rs199456 (AD GWAS: p = 2.57 × 10-9) co-localized with its regulatory effect on LRRC37A2, LRRC37A and ARL17B gene expression in excitatory neurons.

CONCLUSION: In summary, this study presented comprehensive brain cell type-specific eQTL analysis and identified potential eQTL-regulated likely causal genes from AD GWAS findings using a deep learning-based deconvolution framework. It offers an opportunity to efficiently discover the effect of GWAS loci on gene expression at cell type-specific manners using genotyping data and matched bulk RNA-seq data instead of costly snRNA-seq data. Functional validation of candidate eQTLs and associated genes are warranted in the future.

PMID:39751088 | DOI:10.1002/alz.089093

Alzheimers Dement. 2024 Dec;20 Suppl 1:e089010. doi: 10.1002/alz.089010.

ABSTRACT

BACKGROUND: Alzheimer's Disease (AD) is a widespread neurodegenerative disease with Mild Cognitive Impairment (MCI) acting as an interim phase between normal cognitive state and AD. The irreversible nature of AD and the difficulty in early prediction present significant challenges for patients, caregivers, and the healthcare sector. Deep learning (DL) methods such as Recurrent Neural Networks (RNN) have been utilized to analyze Electronic Health Records (EHR) to model disease progression and predict diagnosis. However, these models do not address some inherent irregularities in EHR data such as irregular time intervals between clinical visits. Furthermore, most DL models are not interpretable. To address these issues, we developed a novel DL architecture called Time-Aware RNN (TA-RNN) to predict MCI to AD conversion at the next clinical visit.

METHOD: TA-RNN comprises of a time embedding layer, attention-based RNN, and prediction layer based on multi-layer perceptron (MLP) (Figure 1). For interpretability, a dual-level attention mechanism within the RNN identifies significant visits and features impacting predictions. TA-RNN addresses irregular time intervals by incorporating time embedding into longitudinal cognitive and neuroimaging data based on attention weights to create a patient embedding. The MLP, trained on demographic data and the patient embedding predicts AD conversion. TA-RNN was evaluated on Alzheimer's Disease Neuroimaging Initiative (ADNI) and National Alzheimer's Coordinating Center (NACC) datasets based on F2 score and sensitivity.

RESULT: Multiple TA-RNN models were trained with two, three, five, or six visits to predict the diagnosis at the next visit. In one setup, the models were trained and tested on ADNI. In another setup, the models were trained on the entire ADNI dataset and evaluated on the entire NACC dataset. The results indicated superior performance of TA-RNN compared to state-of-the-art (SOTA) and baseline approaches for both setups (Figure 2A and 2B). Based on attention weights, we also highlighted significant visits (Figure 3A) and features (Figure 3B) and observed that CDRSB and FAQ features and the most recent visit had highest influence in predictions.

CONCLUSION: We propose TA-RNN, an interpretable model to predict MCI to AD conversion while handling irregular time intervals. TA-RNN outperformed SOTA and baseline methods in multiple experiments.

PMID:39751068 | DOI:10.1002/alz.089010

Brief Bioinform. 2024 Nov 22;26(1):bbae686. doi: 10.1093/bib/bbae686.

ABSTRACT

Recent studies suggest cGAS-STING pathway may play a crucial role in the genesis and development of hepatocellular carcinoma (HCC), closely associated with classical pathways and tumor immunity. We aimed to develop models predicting survival and anti-PD-1/PD-L1 outcomes centered on the cGAS-STING pathway in HCC. We identified classical pathways highly correlated with cGAS-STING pathway and constructed transformer survival model preserving raw structure of pathways. We also developed explainable XGBoost model for predicting anti-PD-1/PD-L1 outcomes using SHAP algorithm. We trained and validated transformer survival model on pan-cancer cohort and tested it on three independent HCC cohorts. Using 0.5 as threshold across cohorts, we divided each HCC cohort into two groups and calculated P values with log-rank test. TCGA-LIHC: C-index = 0.750, P = 1.52e-11; ICGC-LIRI-JP: C-index = 0.741, P = .00138; GSE144269: C-index = 0.647, P = .0233. We trained and validated [area under the receiver operating characteristic curve (AUC) = 0.777] XGBoost model on immunotherapy datasets and tested it on GSE78220 (AUC = 0.789); we also tested XGBoost model on HCC anti-PD-L1 cohort (AUC = 0.719). Our deep learning model and XGBoost model demonstrate potential in predicting survival risks and anti-PD-1/PD-L1 outcomes in HCC. We deployed these two prediction models to the GitHub repository and provided detailed instructions for their usage: deep learning survival model, https://github.com/mlwalker123/CSP_survival_model; XGBoost immunotherapy model, https://github.com/mlwalker123/CSP_immunotherapy_model.

PMID:39749665 | DOI:10.1093/bib/bbae686

J Chem Inf Model. 2025 Jan 3. doi: 10.1021/acs.jcim.4c01737. Online ahead of print.

ABSTRACT

As combination therapy becomes more common in clinical applications, predicting adverse effects of combination medications is a challenging task. However, there are three limitations of the existing prediction models. First, they rely on a single view of the drug and cannot fully utilize multiview information, resulting in limited performance when capturing complex structures. Second, they ignore subgraph information at different scales, which limits the ability to model interactions between subgraphs. Third, there has been limited research on effectively integrating multiview features of molecules. Therefore, we propose ComNet, a deep learning model that improves the accuracy of side effect prediction by integrating multiview features of drugs. First, to capture diverse features of drugs, a multiview feature extraction module is proposed, which not only uses molecular fingerprints but also extracts semantic information on SMILES and spatial information on 3D conformations. Second, to enhance the modeling ability of complex structures, a multiscale subgraph fusion mechanism is proposed, which can fuse local and global graph structures of drugs. Finally, a multiview feature fusion mechanism is proposed, which uses an attention mechanism to adaptively adjust the weights of different views to achieve multiview data fusion. Experiments on several publicly available data sets show that ComNet performs better than existing methods in various complex scenarios, especially in cold-start scenarios. Ablation experiments show that each core structure in ComNet contributes to the overall performance. Further analysis shows that ComNet not only converges rapidly and has good generalization ability but also identifies different substructures in the molecule. Finally, a case study on a self-collected data set validates the superior performance of ComNet in practical applications.

PMID:39749659 | DOI:10.1021/acs.jcim.4c01737

Front Comput Neurosci. 2024 Dec 19;18:1523973. doi: 10.3389/fncom.2024.1523973. eCollection 2024.

NO ABSTRACT

PMID:39749286 | PMC:PMC11693727 | DOI:10.3389/fncom.2024.1523973