Front Oncol. 2024 Nov 12;14:1443029. doi: 10.3389/fonc.2024.1443029. eCollection 2024.

ABSTRACT

OBJECTIVES: The aim of this study is to develop an ultrasound-based fusion model of clinical, radiomics and deep learning (CRDL) for accurate diagnosis of benign and malignant soft tissue tumors (STTs).

METHODS: In this retrospective study, ultrasound images and clinical data of patients with STTs from two hospitals were collected between January 2021 and December 2023. Radiomics features and deep learning features were extracted from the ultrasound images, and the optimal features were selected to construct fusion models using support vector machines. The predictive performance of the model was evaluated based on three aspects: discrimination, calibration and clinical usefulness. The DeLong test was used to compare whether there was a significant difference in AUC between the models. Finally, two radiologists who were unaware of the clinical information performed an independent diagnosis and a model-assisted diagnosis of the tumor to compare the performance of the two diagnoses.

RESULTS: A training cohort of 516 patients from Hospital-1 and an external validation cohort of 78 patients from Hospital-2 were included in the study. The Pre-FM CRDL showed the best performance in predicting STTs, with area under the curve (AUC) of 0.911 (95%CI: 0.894-0.928) and 0.948 (95%CI: 0.906-0.990) for training cohort and external validation cohort, respectively. The DeLong test showed that the Pre-FM CRDL significantly outperformed the clinical models (P< 0.05). In addition, the Pre-FM CRDL can improve the diagnostic accuracy of radiologists.

CONCLUSION: This study demonstrates the high clinical applicability of the fusion model in the differential diagnosis of STTs.

PMID:39600644 | PMC:PMC11588752 | DOI:10.3389/fonc.2024.1443029

Bioinform Adv. 2024 Nov 22;4(1):vbae169. doi: 10.1093/bioadv/vbae169. eCollection 2024.

ABSTRACT

SUMMARY: Although multiple neural networks have been proposed for detecting secondary structures from medium-resolution (5-10 Å) cryo-electron microscopy (cryo-EM) maps, the loss functions used in the existing deep learning networks are primarily based on cross-entropy loss, which is known to be sensitive to class imbalances. We investigated five loss functions: cross-entropy, Focal loss, Dice loss, and two combined loss functions. Using a U-Net architecture in our DeepSSETracer method and a dataset composed of 1355 box-cropped atomic-structure/density-map pairs, we found that a newly designed loss function that combines Focal loss and Dice loss provides the best overall detection accuracy for secondary structures. For β-sheet voxels, which are generally much harder to detect than helix voxels, the combined loss function achieved a significant improvement (an 8.8% increase in the F1 score) compared to the cross-entropy loss function and a noticeable improvement from the Dice loss function. This study demonstrates the potential for designing more effective loss functions for hard cases in the segmentation of secondary structures. The newly trained model was incorporated into DeepSSETracer 1.1 for the segmentation of protein secondary structures in medium-resolution cryo-EM map components. DeepSSETracer can be integrated into ChimeraX, a popular molecular visualization software.

AVAILABILITY AND IMPLEMENTATION: https://www.cs.odu.edu/∼bioinfo/B2I_Tools/.

PMID:39600382 | PMC:PMC11590252 | DOI:10.1093/bioadv/vbae169

Acute Crit Care. 2024 Nov 25. doi: 10.4266/acc.2024.01200. Online ahead of print.

ABSTRACT

BACKGROUND: Optimal sedation assessment in critically ill children remains challenging due to the subjective nature of behavioral scales and intermittent evaluation schedules. This study aimed to develop a deep learning model based on heart rate variability (HRV) parameters and vital signs to predict effective and safe sedation levels in pediatric patients.

METHODS: This retrospective cross-sectional study was conducted in a pediatric intensive care unit at a tertiary children's hospital. We developed deep learning models incorporating HRV parameters extracted from electrocardiogram waveforms and vital signs to predict Richmond Agitation-Sedation Scale (RASS) scores. Model performance was evaluated using the area under the receiver operating characteristic curve (AUROC) and area under the precision-recall curve (AUPRC). The data were split into training, validation, and test sets (6:2:2), and the models were developed using a 1D ResNet architecture.

RESULTS: Analysis of 4,193 feature sets from 324 patients achieved excellent discrimination ability, with AUROC values of 0.867, 0.868, 0.858, 0.851, and 0.811 for whole number RASS thresholds of -5 to -1, respectively. AUPRC values ranged from 0.928 to 0.623, showing superior performance in deeper sedation levels. The HRV metric SDANN2 showed the highest feature importance, followed by systolic blood pressure and heart rate.

CONCLUSIONS: A combination of HRV parameters and vital signs can effectively predict sedation levels in pediatric patients, offering the potential for automated and continuous sedation monitoring in pediatric intensive care settings. Future multi-center validation studies are needed to establish broader applicability.

PMID:39600246 | DOI:10.4266/acc.2024.01200

Hum Brain Mapp. 2024 Dec 1;45(17):e26783. doi: 10.1002/hbm.26783.

ABSTRACT

Multimodal neuroimaging is an emerging field that leverages multiple sources of information to diagnose specific brain disorders, especially when deep learning-based AI algorithms are applied. The successful combination of different brain imaging modalities using deep learning remains a challenging yet crucial research topic. The integration of structural and functional modalities is particularly important for the diagnosis of various brain disorders, where structural information plays a crucial role in diseases such as Alzheimer's, while functional imaging is more critical for disorders such as schizophrenia. However, the combination of functional and structural imaging modalities can provide a more comprehensive diagnosis. In this work, we present MultiViT, a novel diagnostic deep learning model that utilizes vision transformers and cross-attention mechanisms to effectively fuse information from 3D gray matter maps derived from structural MRI with functional network connectivity matrices obtained from functional MRI using the ICA algorithm. MultiViT achieves an AUC of 0.833, outperforming both our unimodal and multimodal baselines, enabling more accurate classification and diagnosis of schizophrenia. In addition, using vision transformer's unique attentional maps in combination with cross-attentional mechanisms and brain function information, we identify critical brain regions in 3D gray matter space associated with the characteristics of schizophrenia. Our research not only significantly improves the accuracy of AI-based automated imaging diagnostics for schizophrenia, but also pioneers a rational and advanced data fusion approach by replacing complex, high-dimensional fMRI information with functional network connectivity, integrating it with representative structural data from 3D gray matter images, and further providing interpretative biomarker localization in a 3D structural space.

PMID:39600159 | DOI:10.1002/hbm.26783

J Dermatol. 2024 Nov 26. doi: 10.1111/1346-8138.17556. Online ahead of print.

NO ABSTRACT

PMID:39600150 | DOI:10.1111/1346-8138.17556

BMC Med Imaging. 2024 Nov 26;24(1):318. doi: 10.1186/s12880-024-01498-9.

ABSTRACT

Virtual histopathology is an emerging technology in medical imaging that utilizes advanced computational methods to analyze tissue images for more precise disease diagnosis. Traditionally, histopathology relies on manual techniques and expertise, often resulting in time-consuming processes and variability in diagnoses. Virtual histopathology offers a more consistent, and automated approach, employing techniques like machine learning, deep learning, and image processing to simulate staining and enhance tissue analysis. This review explores the strengths, limitations, and clinical applications of these methods, highlighting recent advancements in virtual histopathological approaches. In addition, important areas are identified for future research to improve diagnostic accuracy and efficiency in clinical settings.

PMID:39593024 | DOI:10.1186/s12880-024-01498-9

BMC Med Imaging. 2024 Nov 26;24(1):320. doi: 10.1186/s12880-024-01489-w.

ABSTRACT

BACKGROUND: A deep learning (DL) model that can automatically detect and classify cervical canal and neural foraminal stenosis using cervical spine magnetic resonance imaging (MRI) can improve diagnostic accuracy and efficiency.

METHODS: A method comprising region-of-interest (ROI) detection and cascade prediction was formulated for diagnosing cervical spinal stenosis based on a DL model. First, three part-specific convolutional neural networks were employed to detect the ROIs in different parts of the cervical MR images. Cascade prediction of the stenosis categories was subsequently performed to record the stenosis level and position on each patient slice. Finally, the results were combined to obtain a patient-level diagnostic report. Performance was evaluated based on the accuracy (ACC), area under the curve (AUC), sensitivity, specificity, F1 Score, diagnosis time of the DL model, and recall rate for ROI detection localization.

RESULTS: The average recall rate of the ROI localization was 89.3% (neural foramen) and 99.7% (central canal) under the five-fold cross-validation of the DL model. In the dichotomous classification (normal or mild vs. moderate or severe), the ACC and AUC of the DL model were comparable to those of the radiologists, and the F1 score (84.8%) of the DL model was slightly higher than that of the radiologists (83.8%) for the central canal. Diagnosing whether the central canal or neural foramen of a slice is narrowed in the cervical MRI scan required an average of 15 and 0.098 s for the radiologists and DL model, respectively.

CONCLUSIONS: The DL model demonstrated comparable performance with subspecialist radiologists for the detection and classification of central canal and neural foraminal stenosis on cervical spine MRI. Moreover, the DL model demonstrated significant timesaving ability.

PMID:39593012 | DOI:10.1186/s12880-024-01489-w

Curr Med Imaging. 2024;20(1):e15734056256872. doi: 10.2174/0115734056256872240909112137.

ABSTRACT

INTRODUCTION: Multimodal medical image fusion techniques play an important role in clinical diagnosis and treatment planning. The process of combining multimodal images involves several challenges depending on the type of modality, transformation techniques, and mapping of structural and metabolic information.

METHODS: Artifacts can form during data acquisition, such as minor movement of patients, or data pre-processing, registration, and normalization. Unlike single-modality images, the detection of an artifact is a more challenging task in complementary fused multimodal images. Many medical image fusion techniques have been developed by various researchers, but not many have tested the robustness of their fusion approaches. The main objective of this study is to identify and classify the noise and artifacts present in the fused MRI-SPECT brain images using transfer learning by fine-tuned CNN networks. Deep neural network-based techniques are capable of detecting minor amounts of noise in images. In this study, three pre-trained convolutional neural network-based models (ResNet50, DenseNet 169, and InceptionV3) were used to detect artifacts and various noises including Gaussian, Speckle, Random, and mixed noises present in fused MRI -SPECT brain image datasets using transfer learning.

RESULTS: The five-fold stratified cross-validation (SCV) is used to evaluate the performance of networks. The obtained performance results for the pretrained DenseNet169 model for various folds were greater as compared with the rest of the models; the former had an average accuracy of five-fold of 93.8±5.8%, 98%±3.9%, 97.8±1.64%, and 93.8±5.8%, whereas InceptionNetV3 had a value of 90.6±9.8%, 98.8±1.6%, 91.4±9.74%, and 90.6±9.8%, and ResNet50 had a value of 75.8±21%.84.8±7.6%, 73.8±22%, and 75.8±21% for Gaussian, speckle, random and mixed noise, respectively.

CONCLUSION: Based on the performance results obtained, the pre-trained DenseNet 169 model provides the highest accuracy among the other four used models.

PMID:39592903 | DOI:10.2174/0115734056256872240909112137

Curr Med Imaging. 2024;20(1):e15734056315235. doi: 10.2174/0115734056315235240820080406.

ABSTRACT

INTRODUCTION: This research assesses HRNet and ResNet architectures for their precision in localizing hand acupoints on 2D images, which is integral to automated acupuncture therapy.

OBJECTIVES: The primary objective was to advance the accuracy of acupoint detection in traditional Korean medicine through the application of these advanced deep-learning models, aiming to improve treatment efficacy.

BACKGROUND: Acupoint localization in traditional Korean medicine is crucial for effective treatment, and the study aims to enhance this process using advanced deep-learning models.

METHODS: The study employs YOLOv3, YOLOF, and YOLOX-s for object detection within a top-down framework, comparing HRNet and ResNet architectures. These models were trained and tested using datasets annotated by technicians and their mean values, with performance evaluated based on Average Precision at two IoU thresholds.

RESULTS: HRNet consistently demonstrated lower mean distance errors across various acupoints compared to ResNet, particularly at a 256x256 pixel resolution. Notably, the HRNet-w48 model surpassed human annotators, including medical experts, in localization accuracy.

CONCLUSION: HRNet's superior performance in acupoint localization suggests its potential to improve the precision and efficacy of acupuncture treatments. The study highlights the promising role of machine learning in enhancing traditional medical practices and underscores the importance of accurate acupoint localization in clinical acupuncture.

PMID:39592902 | DOI:10.2174/0115734056315235240820080406

Sci Rep. 2024 Nov 26;14(1):29330. doi: 10.1038/s41598-024-80842-z.

ABSTRACT

The proliferation of multimedia-based deepfake content in recent years has posed significant challenges to information security and authenticity, necessitating the use of methods beyond dependable dynamic detection. In this paper, we utilize the powerful combination of Deep Generative Adversarial Networks (GANs) and Transfer Learning (TL) to introduce a new technique for identifying deepfakes in multimedia systems. Each of the GAN architectures may be customized to detect subtle changes in different multimedia formats by combining their advantages. A multi-collaborative framework called "MCGAN" is developed because it contains audio, video, and image files. This framework is compared to other state-of-the-art techniques to estimate the overall fluctuation based on performance, improving the accuracy rate by up to 17.333% and strengthening the deepfake detection hierarchy. In order to accelerate the training process overall and enable the system to respond rapidly to novel patterns that indicate deepfakes, TL employs the pre-train technique on the same databases. When it comes to identifying the contents of deepfakes, the proposed method performs quite well. In a range of multimedia scenarios, this enhances real-time detection capabilities while preserving a high level of accuracy. A progressive hierarchy that ensures information integrity in the digital world and related research is taken into consideration in this development.

PMID:39592806 | DOI:10.1038/s41598-024-80842-z

Sci Rep. 2024 Nov 26;14(1):29386. doi: 10.1038/s41598-024-80640-7.

ABSTRACT

In recent times, characterized by the rapid advancement of science and technology, the educational system has continuously evolved. Within this modern educational landscape, Science, Technology, Engineering, Arts, and Mathematics (STEAM) education has emerged as a prominent pedagogical paradigm, gaining substantial popularity in college-level instruction and capturing widespread societal attention. Notably, the cultivation of audio-visual aesthetic proficiency occupies a central role within this educational approach, prioritizing the enhancement of aesthetic sensibilities. By ingeniously amalgamating scientific knowledge with emotional expression, this research assumes a crucial facet in the holistic development of individuals. The research aims to explore the cultivation of students' audio-visual aesthetic abilities in university-level vocal music education by integrating deep learning and STEAM education principles. Drawing upon an extensive review of relevant literature, this research synthesizes the principles of STEAM education with those of deep learning, while considering the current cultural and societal context and the distinct realities faced by contemporary college students. Consequently, this research posits a novel conceptual framework for curriculum design and proposes a three-stage teaching process model. To substantiate the efficacy of this innovative educational model, an empirical investigation employing a questionnaire survey is conducted to assess its teaching effectiveness, confirming the marked superiority of this pioneering pedagogical approach. The results demonstrate that the new teaching model has led to notable enhancements in students' audio-visual aesthetic abilities, self-confidence in learning, and learning efficiency. Additionally, compared to traditional educational methods, the curriculum primarily, which focused on STEAM education with the project as its core, emphasizes the logic of the learning process and its connection with other disciplines. In conclusion, the three-stage educational model combining STEAM education and deep learning fully considers students' learning situations and utilizes the analytical capabilities of computers for educational purposes. This learner-centric approach significantly augments teaching efficiency and flexibility. Finally, the research concludes by summarizing its contributions and limitations, offering practical recommendations for the field. This research provides new insights and references for the practice and improvement of audio-visual aesthetic education in higher education institutions.

PMID:39592803 | DOI:10.1038/s41598-024-80640-7

Sci Rep. 2024 Nov 26;14(1):29390. doi: 10.1038/s41598-024-79011-z.

ABSTRACT

Recommendation Systems (RS) play a crucial role in delivering personalized item suggestions, yet traditional methods often struggle with accuracy, scalability, efficiency, and cold-start challenges. This paper presents the HRS-IU-DL model, a novel hybrid recommendation system that advances the field by integrating multiple sophisticated techniques to enhance both accuracy and relevance. The proposed model uniquely combines user-based and item-based Collaborative Filtering (CF) to effectively analyze user-item interactions, Neural Collaborative Filtering (NCF) to capture complex non-linear relationships, and Recurrent Neural Networks (RNN) to identify sequential patterns in user behavior. Furthermore, it incorporates Content-Based Filtering (CBF) with Term Frequency-Inverse Document Frequency (TF-IDF) for in-depth analysis of item attributes. A key contribution of this work is the innovative fusion of CF, NCF, RNN, and CBF, which collectively address significant challenges such as data sparsity, the cold-start problem, and the increasing demand for personalized recommendations. Additionally, the model employs N-Sample techniques to recommend the top 10 similar items based on user-specified genres, leveraging methods like Cosine Similarity, Singular Value Decomposition (SVD), and TF-IDF. The HRS-IU-DL model is rigorously evaluated on the publicly available Movielens 100k dataset using train-test splits. Performance is assessed using metrics such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Precision, and Recall. The results demonstrate that the HRS-IU-DL model not only outperforms state-of-the-art approaches but also achieves substantial improvements across these evaluation metrics, highlighting its contribution to the advancement of RS technology.

PMID:39592677 | DOI:10.1038/s41598-024-79011-z

Sci Rep. 2024 Nov 26;14(1):29382. doi: 10.1038/s41598-024-79361-8.

ABSTRACT

Echocardiography is the gold standard of diagnosis and evaluation of patent ductus arteriosus (PDA), a common condition among preterm infants that can cause hemodynamic abnormalities and increased mortality rates, but this technique requires a skilled specialist and is not always available. Meanwhile, chest X-ray (CXR) imaging is also known to exhibit signs of PDA and is a routine imaging modality in neonatal intensive care units. In this study, we aim to find and objectively define CXR image features that are associated with PDA by training and visually analyzing a deep learning model. We first collected 4617 echocardiograms from neonatal intensive care unit patients and 17,448 CXR images that were taken 4 days before to 3 days after the echocardiograms were obtained. We trained a deep learning model to predict the presence of severe PDA using the CXR images, and then visualized the model using GradCAM++ to identify the regions of the CXR images important for the model's prediction. The visualization results showed that the model focused on the regions around the upper thorax, lower left heart, and lower right lung. Based on these results, we hypothesized and evaluated three radiographic features of PDA: cardiothoracic ratio, upper heart width to maximum heart width ratio, and upper heart width to thorax width ratio. We then trained an XGBoost model to predict the presence of severe PDA using these radiographic features combined with clinical features. The model achieved an AUC of 0.74, with a high specificity of 0.94. Our study suggests that the proposed radiographic features of CXR images can be used as an auxiliary tool to predict the presence of PDA in preterm infants. This can be useful for the early detection of PDA in neonatal intensive care units in cases where echocardiography is not available.

PMID:39592675 | DOI:10.1038/s41598-024-79361-8

Sci Rep. 2024 Nov 26;14(1):29285. doi: 10.1038/s41598-024-79632-4.

ABSTRACT

With the fast-growing interconnection of smart technologies, the Industrial Internet of Things (IIoT) has revolutionized how industries work by connecting devices and sensors and automating regular operations via the Internet of Things (IoTs). IoT devices provide seamless diversity and connectivity in different application domains. This system and its transmission channels are subjected to targeted cyberattacks due to their round-the-clock connectivity. Accordingly, a multilevel security solution is needed to safeguard the industrial system. By analyzing the data packet, the Intrusion Detection System (IDS) counteracts the cyberattack for the targeted attack in the IIoT platform. Various research has been undertaken to address the concerns of cyberattacks on IIoT networks using machine learning (ML) and deep learning (DL) approaches. This study introduces a new Bayesian Machine Learning with the Sparrow Search Algorithm for Cyberattack Detection (BMLSSA-CAD) technique in the IIoT networks. The proposed BMLSSA-CAD technique aims to enhance security in IIoT networks by detecting cyberattacks. In the BMLSSA-CAD technique, the min-max scaler normalizes the input dataset. Additionally, the method utilizes the Chameleon Optimization Algorithm (COA)-based feature selection (FS) approach to identify the optimal feature set. The BMLSSA-CAD technique uses the Bayesian Belief Network (BBN) model for cyberattack detection. The hyperparameter tuning process employs the sparrow search algorithm (SSA) model to enhance the BBN model performance. The performance of the BMLSSA-CAD method is examined using UNSWNB51 and UCI SECOM datasets. The experimental validation of the BMLSSA-CAD method highlighted superior accuracy outcomes of 97.84% and 98.93% compared to recent techniques on the IIoT platform.

PMID:39592667 | DOI:10.1038/s41598-024-79632-4

Sci Rep. 2024 Nov 26;14(1):29397. doi: 10.1038/s41598-024-80239-y.

ABSTRACT

Synthetic Aperture Radar (SAR) integrated with deep learning has been widely used in several military and civilian applications, such as border patrolling, to monitor and regulate the movement of people and goods across land, air, and maritime borders. Amongst these, maritime borders confront different threats and challenges. Therefore, SAR-based ship detection becomes essential for naval surveillance in marine traffic management, oil spill detection, illegal fishing, and maritime piracy. However, the model becomes insensitive to small ships due to the wide-scale variance and uneven distribution of ship sizes in SAR images. This increases the difficulties associated with ship recognition, which triggers several false alarms. To effectively address these difficulties, the present work proposes an ensemble model (eYOLO) based on YOLOv4 and YOLOv5. The model utilizes a weighted box fusion technique to fuse the outputs of YOLOv4 and YOLOv5. Also, a generalized intersection over union loss has been adopted in eYOLO which ensures the increased generalization capability of the model with reduced scale sensitivity. The model has been developed end-to-end, and its performance has been validated against other reported results using an open-source SAR-ship dataset. The obtained results authorize the effectiveness of eYOLO in multi-scale ship detection with an F1 score and mAP of 91.49% and 92.00%, respectively. This highlights the efficacy of eYOLO in multi-scale ship detection using SAR imagery.

PMID:39592646 | DOI:10.1038/s41598-024-80239-y

Comput Biol Med. 2024 Nov 25;184:109399. doi: 10.1016/j.compbiomed.2024.109399. Online ahead of print.

ABSTRACT

Physical rehabilitation plays a critical role in enhancing health outcomes globally. However, the shortage of physiotherapists, particularly in developing countries where the ratio is approximately ten physiotherapists per million people, poses a significant challenge to effective rehabilitation services. The existing literature on rehabilitation often falls short in data representation and the employment of diverse modalities, limiting the potential for advanced therapeutic interventions. To address this gap, This study integrates Computer Vision and Human Activity Recognition (HAR) technologies to support home-based rehabilitation. The study mitigates this gap by exploring various modalities and proposing a framework for data representation. We introduce a novel framework that leverages both Continuous Wavelet Transform (CWT) and Mel-Frequency Cepstral Coefficients (MFCC) for skeletal data representation. CWT is particularly valuable for capturing the time-frequency characteristics of dynamic movements involved in rehabilitation exercises, enabling a comprehensive depiction of both temporal and spectral features. This dual capability is crucial for accurately modelling the complex and variable nature of rehabilitation exercises. In our analysis, we evaluate 20 CNN-based models and one Vision Transformer (ViT) model. Additionally, we propose 12 hybrid architectures that combine CNN-based models with ViT in bi-model and tri-model configurations. These models are rigorously tested on the UI-PRMD and KIMORE benchmark datasets using key evaluation metrics, including accuracy, precision, recall, and F1-score, with 5-fold cross-validation. Our evaluation also considers real-time performance, model size, and efficiency on low-power devices, emphasising practical applicability. The proposed fused tri-model architectures outperform both single-architectures and bi-model configurations, demonstrating robust performance across both datasets and making the fused models the preferred choice for rehabilitation tasks. Our proposed hybrid model, DenMobVit, consistently surpasses state-of-the-art methods, achieving accuracy improvements of 2.9% and 1.97% on the UI-PRMD and KIMORE datasets, respectively. These findings highlight the effectiveness of our approach in advancing rehabilitation technologies and bridging the gap in physiotherapy services.

PMID:39591669 | DOI:10.1016/j.compbiomed.2024.109399

Biomed Pharmacother. 2024 Nov 25;181:117688. doi: 10.1016/j.biopha.2024.117688. Online ahead of print.

ABSTRACT

The dual-specificity tyrosine-phosphorylation-regulated kinase 1 A (DYRK1A) presents a promising therapeutic target for neurological diseases. However, current inhibitors lack selectivity, which can lead to unexpected side effects and increase the difficulty of studying DYRK1A. Therefore, identifying selective inhibitors targeting DYRK1A is essential for reducing side effects and facilitating neurological disease research. This study aimed to discover DYRK1A inhibitors through a screening pipeline incorporating a deep neural network (DNN) model. Herein, we report an optimized model with an accuracy of 0.93 on a testing set. The pipeline was then performed to identify potential DYRK1A inhibitors from the National Cancer Institute (NCI) library. Four novel DYRK1A inhibitors were identified, and compounds NSC657702 and NSC31059 were noteworthy for their potent inhibition, with IC50 values of 50.9 and 39.5 nM, respectively. NSC31059 exhibited exceptional selectivity across 70 kinases. The compounds also significantly reduced DYRK1A-induced tau phosphorylation at key sites associated with the pathology of neurodegenerative diseases. Moreover, they promoted tubulin polymerization, suggesting a role in microtubule stabilization. Cytotoxicity assessments further confirmed the neuronal safety of the compounds. Together, the results demonstrated a promising screening pipeline and novel DYRK1A inhibitors as candidates for further optimization and development.

PMID:39591664 | DOI:10.1016/j.biopha.2024.117688

PLoS One. 2024 Nov 26;19(11):e0313308. doi: 10.1371/journal.pone.0313308. eCollection 2024.

ABSTRACT

Artificial Intelligence (AI)-based deep learning methods for predicting protein structures are reshaping knowledge development and scientific discovery. Recent large-scale application of AI models for protein structure prediction has changed perceptions about complicated biological problems and empowered a new generation of structure-based hypothesis testing. It is well-recognized that proteins have a modular organization according to archetypal folds. However, it is yet to be determined if predicted structures are tuned to one conformation of flexible proteins or if they represent average conformations. Further, whether or not the answer is protein fold-dependent. Therefore, in this study, we analyzed 2878 proteins with at least ten distinct experimental structures available, from which we can estimate protein topological rigidity verses heterogeneity from experimental measurements. We found that AlphaFold v2 (AF2) predictions consistently return one specific form to high accuracy, with 99.68% of distinct folds (n = 623 out of 628) having an experimental structure within 2.5Å RMSD from a predicted structure. Yet, 27.70% and 10.82% of folds (174 and 68 out of 628 folds) have at least one experimental structure over 2.5Å and 5Å RMSD, respectively, from their AI-predicted structure. This information is important for how researchers apply and interpret the output of AF2 and similar tools. Additionally, it enabled us to score fold types according to how homogeneous versus heterogeneous their conformations are. Importantly, folds with high heterogeneity are enriched among proteins which regulate vital biological processes including immune cell differentiation, immune activation, and metabolism. This result demonstrates that a large amount of protein fold flexibility has already been experimentally measured, is vital for critical cellular processes, and is currently unaccounted for in structure prediction databases. Therefore, the structure-prediction revolution begets the protein dynamics revolution!

PMID:39591473 | DOI:10.1371/journal.pone.0313308

PLoS One. 2024 Nov 26;19(11):e0313000. doi: 10.1371/journal.pone.0313000. eCollection 2024.

ABSTRACT

Mitochondria are intracellular organelles that act as powerhouses by breaking down nutrition molecules to produce adenosine triphosphate (ATP) as cellular fuel. They have their own genetic material called mitochondrial DNA. Alterations in mitochondrial DNA can result in primary mitochondrial diseases, including neurodegenerative disorders. Early detection of these abnormalities is crucial in slowing disease progression. With recent advances in data acquisition techniques such as focused ion beam scanning electron microscopy, it has become feasible to capture large intracellular organelle volumes at data rates reaching 4Tb/minute, each containing numerous cells. However, manually segmenting large data volumes (gigapixels) can be time-consuming for pathologists. Therefore, there is an urgent need for automated tools that can efficiently segment mitochondria with minimal user intervention. Our article proposes an ensemble of two automatic segmentation pipelines to predict regions of interest specific to mitochondria. This architecture combines the predicted outputs from both pipelines using an ensemble learning-based entropy-weighted fusion technique. The methodology minimizes the impact of individual predictions and enhances the overall segmentation results. The performance of the segmentation task is evaluated using various metrics, ensuring the reliability of our results. We used four publicly available datasets to evaluate our proposed method's effectiveness. Our proposed fusion method has achieved a high score in terms of the mean Jaccard index and dice coefficient for all four datasets. For instance, in the UroCell dataset, our proposed fusion method achieved scores of 0.9644 for the mean Jaccard index and 0.9749 for the Dice coefficient. The mean error rate and pixel accuracy were 0.0062 and 0.9938, respectively. Later, we compared it with state-of-the-art methods like 2D and 3D CNN algorithms. Our ensemble approach shows promising segmentation efficiency with minimal intervention and can potentially aid in the early detection and mitigation of mitochondrial diseases.

PMID:39591424 | DOI:10.1371/journal.pone.0313000

PLOS Digit Health. 2024 Nov 26;3(11):e0000668. doi: 10.1371/journal.pdig.0000668. eCollection 2024 Nov.

ABSTRACT

Locomotive Syndrome (LS) is defined by decreased walking and standing abilities due to musculoskeletal issues. Early diagnosis is vital as LS can be reversed with appropriate intervention. Although diagnosing LS using standardized charts is straightforward, the labor-intensive and time-consuming nature of the process limits its widespread implementation. To address this, we introduced a Deep Learning (DL)-based computer vision model that employs OpenPose for pose estimation and MS-G3D for spatial-temporal graph analysis. This model objectively assesses gait patterns through single-camera video captures, offering a novel and efficient method for LS prediction and analysis. Our model was trained and validated using a dataset of 186 walking videos, plus 65 additional videos for external validation. The model achieved an average sensitivity of 0.86, demonstrating high effectiveness in identifying individuals with LS. The model's positive predictive value was 0.85, affirming its reliable LS detection, and it reached an overall accuracy rate of 0.77. External validation using an independent dataset confirmed strong generalizability with an Area Under the Curve of 0.75. Although the model accurately diagnosed LS cases, it was less precise in identifying non-LS cases. This study pioneers in diagnosing LS using computer vision technology for pose estimation. Our accessible, non-invasive model serves as a tool that can accurately diagnose the labor-intensive LS tests using only visual assessments, streamlining LS detection and expediting treatment initiation. This significantly improves patient outcomes and marks a crucial advancement in digital health, addressing key challenges in management and care of LS.

PMID:39591393 | DOI:10.1371/journal.pdig.0000668