Clin Oral Investig. 2024 Dec 2;28(12):677. doi: 10.1007/s00784-024-06068-5.

ABSTRACT

OBJECTIVES: The study aims to address the global burden of dental caries, a highly prevalent disease affecting billions of individuals, including both children and adults. Recognizing the significant health challenges posed by untreated dental caries, particularly in low- and middle-income countries, our goal is to improve early-stage detection. Though effective, traditional diagnostic methods, such as bitewing radiography, have limitations in detecting early lesions. By leveraging Artificial Intelligence (AI), we aim to enhance the accuracy and efficiency of caries detection, offering a transformative approach to dental diagnostics.

MATERIALS AND METHODS: This study proposes a novel deep learning-based approach for dental caries detection using the latest models, i.e., YOLOv7, YOLOv8, and YOLOv9. Trained on a dataset of over 3,200 images, the models address the shortcomings of existing detection methods and provide an automated solution to improve diagnostic accuracy.

RESULTS: The YOLOv7 model achieved a mean Average Precision (mAP) at 0.5 Intersection over Union (IoU) of 0.721, while YOLOv9 attained a mAP@50 IoU of 0.832. Notably, YOLOv8 outperformed both, with a mAP@0.5 of 0.982. This demonstrates robust detection capabilities across multiple categories, including caries," "Deep Caries," and "Exclusion."

CONCLUSIONS: This high level of accuracy and efficiency highlights the potential of integrating AI-driven systems into clinical workflows, improving diagnostic capabilities, reducing healthcare costs, and contributing to better patient outcomes, especially in resource-constrained environments.

CLINICAL RELEVANCE: Integrating these latest YOLO advanced AI models into dental diagnostics could transform the landscape of caries detection. Enhancing early-stage diagnosis accuracy can lead to more precise and cost-effective treatment strategies, with significant implications for improving patient outcomes, particularly in low-resource settings where traditional diagnostic capabilities are often limited.

PMID:39621193 | DOI:10.1007/s00784-024-06068-5

Insights Imaging. 2024 Dec 2;15(1):290. doi: 10.1186/s13244-024-01861-y.

ABSTRACT

OBJECTIVE: To develop a multi-scene model that can automatically segment acute vertebral compression fractures (VCFs) from spine radiographs.

METHODS: In this multicenter study, we collected radiographs from five hospitals (Hospitals A-E) between November 2016 and October 2019. The study included participants with acute VCFs, as well as healthy controls. For the development of the Positioning and Focus Network (PFNet), we used a training dataset consisting of 1071 participants from Hospitals A and B. The validation dataset included 458 participants from Hospitals A and B, whereas external test datasets 1-3 included 301 participants from Hospital C, 223 from Hospital D, and 261 from Hospital E, respectively. We evaluated the segmentation performance of the PFNet model and compared it with previously described approaches. Additionally, we used qualitative comparison and gradient-weighted class activation mapping (Grad-CAM) to explain the feature learning and segmentation results of the PFNet model.

RESULTS: The PFNet model achieved accuracies of 99.93%, 98.53%, 99.21%, and 100% for the segmentation of acute VCFs in the validation dataset and external test datasets 1-3, respectively. The receiver operating characteristic curves comparing the four models across the validation and external test datasets consistently showed that the PFNet model outperformed other approaches, achieving the highest values for all measures. The qualitative comparison and Grad-CAM provided an intuitive view of the interpretability and effectiveness of our PFNet model.

CONCLUSION: In this study, we successfully developed a multi-scene model based on spine radiographs for precise preoperative and intraoperative segmentation of acute VCFs.

CRITICAL RELEVANCE STATEMENT: Our PFNet model demonstrated high accuracy in multi-scene segmentation in clinical settings, making it a significant advancement in this field.

KEY POINTS: This study developed the first multi-scene deep learning model capable of segmenting acute VCFs from spine radiographs. The model's architecture consists of two crucial modules: an attention-guided module and a supervised decoding module. The exceptional generalization and consistently superior performance of our model were validated using multicenter external test datasets.

PMID:39621135 | DOI:10.1186/s13244-024-01861-y

Bioprocess Biosyst Eng. 2024 Dec 2. doi: 10.1007/s00449-024-03110-4. Online ahead of print.

ABSTRACT

Fast and accurate detection of infectious bacteria in wounds is crucial for effective clinical treatment. However, traditional methods take over 24 h to yield results, which is inadequate for urgent clinical needs. Here, we introduce a deep learning-driven framework that detects and classifies four bacteria commonly found in wounds: Acinetobacter baumannii (AB), Escherichia coli (EC), Pseudomonas aeruginosa (PA), and Staphylococcus aureus (SA). This framework leverages the pretrained ResNet50 deep learning architecture, trained on manually collected periodic bacterial colony-growth images from high-content imaging. In in vitro samples, our method achieves a detection rate of over 95% for early colonies cultured for 8 h, reducing detection time by more than 12 h compared to traditional Environmental Protection Agency (EPA)-approved methods. For colony classification, it identifies AB, EC, PA, and SA colonies with accuracies of 96%, 97%, 96%, and 98%, respectively. For mixed bacterial samples, it identifies colonies with 95% accuracy and classifies them with 93% precision. In mouse wound samples, the method identifies over 90% of developing bacterial colonies and classifies colony types with an average accuracy of over 94%. These results highlight the framework's potential for improving the clinical treatment of wound infections. Besides, the framework provides the detection results with key feature visualization, which enhance the prediction credibility for users. To summarize, the proposed framework enables high-throughput identification, significantly reducing detection time and providing a cost-effective tool for early bacterial detection.

PMID:39621107 | DOI:10.1007/s00449-024-03110-4

Abdom Radiol (NY). 2024 Dec 2. doi: 10.1007/s00261-024-04708-8. Online ahead of print.

ABSTRACT

Large language models (LLMs) and multi-modal large language models (MLLMs) represent the cutting-edge in artificial intelligence. This review provides a comprehensive overview of their capabilities and potential impact on radiology. Unlike most existing literature reviews focusing solely on LLMs, this work examines both LLMs and MLLMs, highlighting their potential to support radiology workflows such as report generation, image interpretation, EHR summarization, differential diagnosis generation, and patient education. By streamlining these tasks, LLMs and MLLMs could reduce radiologist workload, improve diagnostic accuracy, support interdisciplinary collaboration, and ultimately enhance patient care. We also discuss key limitations, such as the limited capacity of current MLLMs to interpret 3D medical images and to integrate information from both image and text data, as well as the lack of effective evaluation methods. Ongoing efforts to address these challenges are introduced.

PMID:39621074 | DOI:10.1007/s00261-024-04708-8

J Med Chem. 2024 Dec 2. doi: 10.1021/acs.jmedchem.4c02193. Online ahead of print.

ABSTRACT

Molecular property prediction with deep learning often employs self-supervised learning techniques to learn common knowledge through masked atom prediction. However, the common knowledge gained by masked atom prediction dramatically differs from the graph-level optimization objective of downstream tasks, which results in suboptimal problems. Particularly for properties with limited data, the failure to consider domain knowledge results in a direct search in an immense common space, rendering it infeasible to identify the global optimum. To address this, we propose MPCD, which enhances pretraining transferability by aligning the optimization objectives between pretraining and fine-tuning with domain knowledge. MPCD also leverages multitask learning to improve data utilization and model robustness. Technically, MPCD employs a relation-aware self-attention mechanism to capture molecules' local and global structures comprehensively. Extensive validation demonstrates that MPCD outperforms state-of-the-art methods for absorption, distribution, metabolism, excretion, and toxicity (ADMET) and physicochemical prediction across various data sizes.

PMID:39620982 | DOI:10.1021/acs.jmedchem.4c02193

Med Phys. 2024 Dec 2. doi: 10.1002/mp.17503. Online ahead of print.

ABSTRACT

BACKGROUND: Accurate musculoseletal ultrasound (MSKUS) image segmentation is crucial for diagnosis and treatment planning. Compared with traditional segmentation methods, deploying deep learning segmentation methods that balance segmentation efficiency, accuracy, and model size on edge devices has greater advantages.

PURPOSE: This paper aims to design a MSKUS image segmentation method that has fewer parameters, lower computation complexity and higher segmentation accuracy.

METHODS: In this study, an attention mechanism-based lightweight UNet (AML-UNet) is designed to segment target muscle regions in MSKUS images. To suppress the transmission of redundant feature, Channel Reconstruction and Spatial Attention Module is designed in the encoding path. In addition, considering the inherent characteristic of MSKUS image, Multiscale Aggregation Module is developed to replace the skip connection architecture of U-Net. Deep supervision is also introduced to the decoding path to refine predicted masks gradually. Our method is evaluated on two MSKUS 2D-image segmentation datasets, including 3917 MSKUS and 1534 images respectively. In the experiments, a five-fold cross-validation method is adopted in ablation experiments and comparison experiments. In addition, Wilcoxon Signed-Rank Test and Bonferroni correction are employed to validate the significance level. 0.01 was used as the statistical significance level in our paper.

RESULTS: AML-UNet yielded a mIoU of 84.17% and 90.14% on two datasets, representing a 3.38% ( p < 0.001 $p&lt;0.001$ ) and 3.48% ( p < 0.001 $p&lt;0.001$ ) over the Unext model. The number of parameters and FLOPs are only 0.21M and 0.96G, which are 1/34 and 1/29 of those in comparison with UNet.

CONCLUSIONS: Our proposed model achieved superior results with fewer parameters while maintaining segmentation efficiency and accuracy compared to other methods.

PMID:39620487 | DOI:10.1002/mp.17503

Microsc Res Tech. 2024 Dec 2. doi: 10.1002/jemt.24739. Online ahead of print.

ABSTRACT

Diabetes is a chronic disease that occurs when the body cannot regulate blood sugar levels. Nowadays, the screening tests for diabetes are developed using multivariate regression methods. An increasing amount of data is automatically collected to provide an opportunity for creating challenging and accurate prediction modes that are updated constantly with the help of machine learning techniques. In this manuscript, a Dual Multi Scale Attention Network optimized with Archerfish Hunting Optimization Algorithm is proposed for Diabetes Prediction (DMSAN-AHO-DP). Here, the data is gathered through PIMA Indian Diabetes Dataset (PIDD). The collected data is fed towards the preprocessing to remove the noise of input data and improves the data quality by using Contrast Limited Adaptive Histogram Equalization Filtering (CLAHEF) method. Then the preprocessed data are fed to Multi-Level Haar Wavelet Features Fusion Network (MHWFFN) based feature extraction. Then the extracted data is supplied to the Dual Multi Scale Attention Network (DMSAN) for diabetic or non-diabetic classification. The hyper parameter of Dual Multi Scale Attention Network is tuned with Archerfish Hunting Optimization (AHO) algorithm, which classifies diabetic or non-diabetic accurately. The proposed DMSAN-AHO-DP technique is implemented in Python. The efficacy of the DMSAN-AHO-DP approach is examined with some metrics, like Accuracy, F-scores, Sensitivity, Specificity, Precision, Recall, Computational time. The DMSAN-AHO-DP technique achieves 23.52%, 36.12%, 31.12% higher accuracy and 16.05%, 21.14%, 31.02% lesser error rate compared with existing models: Enhanced Deep Neural Network based Model for Diabetes Prediction (EDNN-DP), Indian PIMA Dataset using Deep Learning for Diabetes Prediction (ANN-DP), and Enhanced Support Vector Machine with Deep Neural Network Learning strategies for Diabetes Prediction (SVM-DNN-DP).

PMID:39620437 | DOI:10.1002/jemt.24739

Curr Diabetes Rev. 2024 Nov 29. doi: 10.2174/0115733998307556240819093038. Online ahead of print.

ABSTRACT

BACKGROUND: Recent research demonstrates that diabetes can lead to heart problems, neurological damage, and other illnesses.

METHOD: In this paper, we design a low-complexity Deep Learning (DL)-based model for the diagnosis of type 2 diabetes. In our experiments, we use the publicly available PIMA Indian Diabetes Dataset (PIDD). To obtain a low-complexity and accurate DL architecture, we perform an accuracy-versus-complexity study on several DL models.

RESULT: The results show that the proposed DL structure, including Convolutional Neural Networks and Multi-Layer Perceptron models (i.e., CNN+MLP model) outperforms other models with an accuracy of 93.89%.

CONCLUSION: With these features, the proposed hybrid model can be used in wearable devices and IoT-based health monitoring applications.

PMID:39620332 | DOI:10.2174/0115733998307556240819093038

J Orthop Res. 2024 Dec 2. doi: 10.1002/jor.26024. Online ahead of print.

ABSTRACT

Magnetic resonance imaging (MRI) of the knee is the recommended diagnostic method before invasive arthroscopy surgery. Nevertheless, interpreting knee MRI scans is a time-consuming process that is vulnerable to inaccuracies and inconsistencies. We proposed a multitask learning network MCSNetatt which efficiently introduces segmentation prior features and enhances classification results through multiscale feature fusion and spatial attention modules. The MRI studies and subsequent arthroscopic diagnosis of 259 knees were collected retrospectively. Models were trained based on multitask loss with coronal and sagittal sequences and fused using logistic regression (LR). We visualized the network's interpretability by the gradient-weighted class activation mapping method. The LR model achieved higher area under the curve and mean average precision of medial and lateral menisci than models trained on a single sagittal or coronal sequence. Our multitask model MCSNetat outperformed the single-task model CNet and two clinicians in classification, with accuracy, precision, recall, F1-score of 0.980, 1.000, 0.952, 0.976 for medial and 0.920, 0.905, 0.905, 0.905 for the lateral, respectively. With the assistance of model results and visualized saliency maps, both clinicians showed improvement in their diagnostic performance. Compared to the baseline segmentation model, our model improved dice similarity coefficient and the 95% Hausdorff distance (HD95) of the lateral meniscus for 2.3% and 0.860 mm in coronal images and 4.4% and 2.253 mm in sagittal images. Our multitask learning network quickly generated accurate clinicopathological classification and segmentation of knee MRI, demonstrating its potential to assist doctors in a clinical setting.

PMID:39620311 | DOI:10.1002/jor.26024

Front Radiol. 2024 Nov 15;4:1412404. doi: 10.3389/fradi.2024.1412404. eCollection 2024.

NO ABSTRACT

PMID:39620146 | PMC:PMC11608602 | DOI:10.3389/fradi.2024.1412404

Mar Life Sci Technol. 2024 Sep 27;6(4):631-650. doi: 10.1007/s42995-024-00252-y. eCollection 2024 Nov.

ABSTRACT

Genomic selection (GS) applied to the breeding of aquatic animals has been of great interest in recent years due to its higher accuracy and faster genetic progress than pedigree-based methods. The genetic analysis of complex traits in GS does not escape the current excitement around artificial intelligence, including a renewed interest in deep learning (DL), such as deep neural networks (DNNs), convolutional neural networks (CNNs), and autoencoders. This article reviews the current status and potential of DL applications in phenotyping, genotyping and genomic estimated breeding value (GEBV) prediction of GS. It can be seen from this article that CNNs obtain phenotype data of aquatic animals efficiently, and without injury; DNNs as single nucleotide polymorphism (SNP) variant callers are critical to have shown higher accuracy in assessments of genotyping for the next-generation sequencing (NGS); autoencoder-based genotype imputation approaches are capable of highly accurate genotype imputation by encoding complex genotype relationships in easily portable inference models; sparse DNNs capture nonlinear relationships among genes to improve the accuracy of GEBV prediction for aquatic animals. Furthermore, future directions of DL in aquaculture are also discussed, which should expand the application to more aquaculture species. We believe that DL will be applied increasingly to molecular breeding of aquatic animals in the future.

SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s42995-024-00252-y.

PMID:39620094 | PMC:PMC11602929 | DOI:10.1007/s42995-024-00252-y

Mar Life Sci Technol. 2024 Apr 29;6(4):736-749. doi: 10.1007/s42995-024-00222-4. eCollection 2024 Nov.

ABSTRACT

Improving the accuracy of fishing ground prediction for oceanic economic species has always been one of the most concerning issues in fisheries research. Recent studies have confirmed that deep learning has achieved superior results over traditional methods in the era of big data. However, the deep learning-based fishing ground prediction model with a single environment suffers from the problem that the area of the fishing ground is too large and not concentrated. In this study, we developed a deep learning-based fishing ground prediction model with multiple environmental factors using neon flying squid (Ommastrephes bartramii) in Northwest Pacific Ocean as an example. Based on the modified U-Net model, the approach involves the sea surface temperature, sea surface height, sea surface salinity, and chlorophyll a as inputs, and the center fishing ground as the output. The model is trained with data from July to November in 2002-2019, and tested with data of 2020. We considered and compared five temporal scales (3, 6, 10, 15, and 30 days) and seven multiple environmental factor combinations. By comparing different cases, we found that the optimal temporal scale is 30 days, and the optimal multiple environmental factor combination contained SST and Chl a. The inclusion of multiple factors in the model greatly improved the concentration of the center fishing ground. The selection of a suitable combination of multiple environmental factors is beneficial to the precise spatial distribution of fishing grounds. This study deepens the understanding of the mechanism of environmental field influence on fishing grounds from the perspective of artificial intelligence and fishery science.

PMID:39620085 | PMC:PMC11602920 | DOI:10.1007/s42995-024-00222-4

Eur J Radiol Open. 2024 Nov 15;13:100610. doi: 10.1016/j.ejro.2024.100610. eCollection 2024 Dec.

ABSTRACT

PURPOSE: To evaluate the effectiveness of a constructed deep learning model in predicting early recurrence after surgery in hepatocellular carcinoma (HCC) patients with solitary tumors ≤5 cm.

MATERIALS AND METHODS: Our study included a total of 331 HCC patients who underwent curative resection, with all patients having preoperative dynamic contrast-enhanced MRI (DCE-MRI). Patients who recurred within two years after surgery were defined as early recurrence. The enrolled patients were randomly divided into the training group and the testing group. A ResNet-based deep learning model with eight conventional neural network branches was built to predict the early recurrence status of these patients. Patient characteristics and laboratory tests were further filtered by regression models and then integrated with deep learning models to improve the prediction performance.

RESULTS: Among 331 HCC patients, 70 (21.1 %) experienced early recurrence. In multivariate Cox regression analysis, only tumor size (Hazard ratio (HR=1.394, 95 %CI:1.011-1.920, p value=0.043) and deep learning extracted image features (HR: 38440, 95 %CI:2321-636600, p value<0.001) were significant risk factors for early recurrence. In the training and testing cohort, the AUCs of the image-based deep learning prediction model were 0.839 and 0.833. By integrating tumor size with image-based deep learning model to construct a combined model, we found that the AUCs of the combined model to assess early recurrence in the training and validation cohort were 0.846 and 0.842. We further developed a nomogram to visualize the preoperative combined model, and the prediction performance of nomogram showed a good fitness in the testing cohort.

CONCLUSIONS: The proposed deep learning-based prediction model using DCE-MRI is useful for assessing early recurrence in HCC patients with single tumors ≤5 cm.

PMID:39619794 | PMC:PMC11607649 | DOI:10.1016/j.ejro.2024.100610

Proc AAAI Conf Artif Intell. 2024;38(14):15634-15643. doi: 10.1609/aaai.v38i14.29491. Epub 2024 Mar 24.

ABSTRACT

Utilizing electronic health records (EHR) for machine learning-driven clinical research has great potential to enhance outcome predictions and treatment personalization. Nonetheless, due to privacy and security concerns, the secondary use of EHR data is regulated, constraining researchers' access to EHR data. Generating synthetic EHR data with deep learning methods is a viable and promising approach to mitigate privacy concerns, offering not only a supplementary resource for downstream applications but also sidestepping the privacy risks associated with real patient data. While prior efforts have concentrated on EHR data synthesis, significant challenges persist: addressing the heterogeneity of features including temporal and non-temporal features, structurally missing values, and irregularity of the temporal measures, and ensuring rigorous privacy of the real data used for model training. Existing works in this domain only focused on solving one or two aforementioned challenges. In this work, we propose IGAMT, an innovative framework to generate privacy-preserved synthetic EHR data that not only maintains high quality with heterogeneous features, missing values, and irregular measures but also achieves differential privacy with enhanced privacy-utility trade-off. Extensive experiments prove that IGAMT significantly outperforms baseline and state-of-the-art models in terms of resemblance to real data and performance of downstream applications. Ablation studies also prove the effectiveness of the techniques applied in IGAMT.

PMID:39619768 | PMC:PMC11606572 | DOI:10.1609/aaai.v38i14.29491

Water Res X. 2024 Oct 22;25:100269. doi: 10.1016/j.wroa.2024.100269. eCollection 2024 Dec 1.

ABSTRACT

Short-term water demand forecasting (STWDF) for multiple spatially and temporally correlated District Metering Areas (DMAs) is an essential foundation for achieving more refined management of urban water supply networks. However, due to the greater uncertainty associated with specific DMA demand compared to overall water usage, accurately predicting STWDF poses significant challenges. This study introduces an innovative network architecture-the multi-scale correction module neural network, built upon Long Short-Term Memory (LSTM) networks and Convolutional Neural Networks (CNN) enhanced with Attention mechanisms-for simultaneous STWDF with a temporal resolution of one hour over a week for 10 DMAs located in a single city in northern Italy. This framework utilizes multivariate corrections to refine and enhance the output accuracy. The results reveal that, in comparison to traditional Gated Recurrent Unit or LSTM models, the proposed model with integrated correction modules, particularly those that leverage inter-DMA correlations, improves performance across all evaluation metrics by an average of 5 %-20 % per DMA. Additionally, it consistently delivers superior accuracy across three scenarios: single DMA forecasting, total water demand, and extreme conditions, while maintaining stable performance throughout. Furthermore, the interpretability analysis underscores the feasibility of this innovative structure and highlights the contribution of meteorological features to the predictive model in some DMA-level STWDF. The unified input-output framework elegantly simplifies the STWDF process across multiple DMAs, providing new insights and methodologies for future research in this domain.

PMID:39619677 | PMC:PMC11605409 | DOI:10.1016/j.wroa.2024.100269

Heliyon. 2024 Nov 5;10(22):e40142. doi: 10.1016/j.heliyon.2024.e40142. eCollection 2024 Nov 30.

ABSTRACT

This research delves into the obstacles and difficulties associated with predicting cryptocurrency movements in the volatile global financial market. This study develops and evaluates an advanced Deep Learning-Enhanced Temporal Fusion Transformer (ADE-TFT) model to estimate Bitcoin values more accurately. This research employs cutting-edge artificial intelligence (AI) and machine learning (ML) techniques to comprehensively examine various aspects of cryptocurrency forecasting, including geopolitical implications, market sentiment analysis, and pattern detection in transactional datasets. The study demonstrates that the ADE-TFT model outperforms its lower-layer counterparts in terms of forecasting accuracy, with reduced Mean Absolute Percentage Error (MAPE), Mean Squared Error (MSE), and Root Mean Square Error (RMSE) values, particularly when using a higher hidden layer configuration (h=8). The study emphasizes the importance of experimenting with different normalization strategies and utilizing various market-related data to enhance the model's performance. The results suggest that improving forecasting accuracy may require addressing these limitations and incorporating additional factors, such as market sentiment. By providing investors with more precise market predictions, the techniques and information presented in this research have the potential to significantly increase investor power in an unpredictable digital currency market, enabling wise investment choices.

PMID:39619580 | PMC:PMC11605417 | DOI:10.1016/j.heliyon.2024.e40142

ACS Omega. 2024 Nov 12;9(47):47180-47193. doi: 10.1021/acsomega.4c07689. eCollection 2024 Nov 26.

ABSTRACT

In the development of anticancer medications, vascular endothelial growth factor receptor 2 (VEGFR-2), which belongs to the protein tyrosine kinase family, emerges as one of the most significant targets of interest. The ongoing Food and Drug Administration (FDA) approval of novel therapeutic medicines toward VEGFR-2 emphasizes the urgent need to discover sophisticated molecular structures that are capable of reliably limiting VEGFR-2 activity. Recognizing the huge potential of deep-learning-based molecular model advancements, we focused our study on exploring the chemical space to find small molecules potentially inhibiting VEGFR-2. To achieve this goal, we utilized the junction tree variational autoencoder in combination with two optimization approaches on the latent space: the local Bayesian optimization on the initial data set and the gradient ascent on nine FDA-approved drugs targeting VEGFR-2. The optimization results yielded a set of 493 uncharted small molecules. Quantitative structure-activity relationship (QSAR) models and molecular docking were used to assess the generated molecules for their inhibitory potential using their predicted pIC50 and binding affinity. The QSAR model constructed on RDK7 fingerprints using the CatBoost algorithm achieved remarkable coefficients of determination (R 2) of 0.792 ± 0.075 and 0.859 with respect to internal and external validation. Molecular docking was implemented using the 4ASD complex with optimistic retrospective control results (the ROC-AUC value was 0.710 and the binding activity threshold was -7.90 kcal/mol). Newly generated molecules possessing acceptable results corresponding to both assessments were shortlisted and checked for interactions with the protein at the binding site on important residues, including Cys919, Asp1046, and Glu885.

PMID:39619551 | PMC:PMC11603221 | DOI:10.1021/acsomega.4c07689

Front Oncol. 2024 Nov 15;14:1464555. doi: 10.3389/fonc.2024.1464555. eCollection 2024.

ABSTRACT

PURPOSE: The aim of this study was to develop a novel approach for predicting the expression status of Epidermal Growth Factor Receptor (EGFR) and its subtypes in patients with Non-Small Cell Lung Cancer (NSCLC) using a Three-Dimensional Convolutional Neural Network (3D-CNN) ConvNeXt, radiomics features and clinical features.

MATERIALS AND METHODS: A total of 732 NSCLC patients with available CT imaging and EGFR expression data were included in this retrospective study. The region of interest (ROI) was manually segmented, and clinicopathological features were collected. Radiomic and deep learning features were extracted. The instances were randomly divided into training, validation, and test sets. Feature selection was performed, and XGBoost was used to create solo models and combined models to predict the presence of EGFR and subtypes mutations. The effectiveness of the models was assessed using ROC and PRC curves.

RESULTS: We established the following models: ModelCNN, Modelradiomic, Modelclinical, ModelCNN+radiomic, ModelCNN+clinical, Modelradiomic+clinical, and ModelCNN+radiomic+clinical, which were based on deep learning features, radiomic features, clinical data and combinations of these, respectively. In predicting EGFR mutations, ModelCNN+radiomic+clinical demonstrated superior performance compared to other prediction models, achieving an AUC of 0.801. For distinguishing between EGFR subtypes ex19del and L858R, ModelCNN+radiomic reached the highest AUC value of 0.775.

CONCLUSIONS: Both deep learning models and radiomic signature-based models offer reasonably accurate non-invasive predictions of EGFR status and its subtypes. Fusion models hold the potential to enhance noninvasive methods for predicting EGFR mutations and subtypes, presenting a more reliable prediction approach.

PMID:39619439 | PMC:PMC11604581 | DOI:10.3389/fonc.2024.1464555

Front Oncol. 2024 Nov 15;14:1488616. doi: 10.3389/fonc.2024.1488616. eCollection 2024.

ABSTRACT

INTRODUCTION: This study presented an end-to-end 3D deep learning model for the automatic segmentation of brain tumors.

METHODS: The MRI data used in this study were obtained from a cohort of 630 GBM patients from the University of Pennsylvania Health System (UPENN-GBM). Data augmentation techniques such as flip and rotations were employed to further increase the sample size of the training set. The segmentation performance of models was evaluated by recall, precision, dice score, Lesion False Positive Rate (LFPR), Average Volume Difference (AVD) and Average Symmetric Surface Distance (ASSD).

RESULTS: When applying FLAIR, T1, ceT1, and T2 MRI modalities, FusionNet-A and FusionNet-C the best-performing model overall, with FusionNet-A particularly excelling in the enhancing tumor areas, while FusionNet-C demonstrates strong performance in the necrotic core and peritumoral edema regions. FusionNet-A excels in the enhancing tumor areas across all metrics (0.75 for recall, 0.83 for precision and 0.74 for dice scores) and also performs well in the peritumoral edema regions (0.77 for recall, 0.77 for precision and 0.75 for dice scores). Combinations including FLAIR and ceT1 tend to have better segmentation performance, especially for necrotic core regions. Using only FLAIR achieves a recall of 0.73 for peritumoral edema regions. Visualization results also indicate that our model generally achieves segmentation results similar to the ground truth.

DISCUSSION: FusionNet combines the benefits of U-Net and SegNet, outperforming the tumor segmentation performance of both. Although our model effectively segments brain tumors with competitive accuracy, we plan to extend the framework to achieve even better segmentation performance.

PMID:39619438 | PMC:PMC11604588 | DOI:10.3389/fonc.2024.1488616

Glob Chang Biol. 2024 Dec;30(12):e17604. doi: 10.1111/gcb.17604.

ABSTRACT

Anthropogenic activities have altered approximately two-thirds of the Earth's land surface. Urbanization, industrialization, agricultural expansion, and deforestation are increasingly impacting the terrestrial landscapes, leading to shifts of areas in artificial surface (i.e., humanmade), cropland, pasture, forest, and barren land. Land use patterns and associated greenhouse gas (GHG) emissions play a critical role in global climate change. Here we synthesized 29 years of global historical data and demonstrated how land use impacts global GHG emissions using structural equation modeling. We then obtained predictive estimates of future global GHG emissions using a deep learning model. Our results show that, from 1992 to 2020, the global terrestrial areas covered by artificial surface and cropland have expanded by 133% and 6% because of population growth and socioeconomic development, resulting in 4.0% and 3.8% of declines in pasture and forest areas, respectively. Land use was significantly associated with GHG emissions (p < 0.05). Artificial surface dominates global GHG emissions, followed by cropland, pasture, and barren land. The increase in artificial surfaces has driven up global GHG emissions through the increase in energy consumption. Conversely, improved agricultural management practices have contributed to mitigating agricultural GHG emissions. Forest, on the other hand, serves as a sink of GHG. In total, global GHG emissions increased from 31 to 46 GtCO2eq from 1992 to 2020. Looking ahead, if current trends in global land use continue at the same rates, our model projects that global GHG emissions will reach 76 ± 8 GtCO2eq in 2050. In contrast, reducing the rates of land use change by half could limit global GHG emissions to 60 ± 3 GtCO2eq in 2050. Monitoring and analyzing these projections allow a better understanding of the potential impacts of various land use scenarios on global climate and planning for a sustainable future.

PMID:39614423 | DOI:10.1111/gcb.17604