Bioinform Adv. 2024 May 28;4(1):vbae078. doi: 10.1093/bioadv/vbae078. eCollection 2024.

ABSTRACT

MOTIVATION: Characterizing interactions at the protein-membrane interface is crucial as abnormal peripheral protein-membrane attachment is involved in the onset of many diseases. However, a limiting factor in studying and understanding protein-membrane interactions is that the membrane-binding domains of peripheral membrane proteins (PMPs) are typically unknown. By applying artificial intelligence techniques in the context of natural language processing (NLP), the accuracy and prediction time for protein-membrane interface analysis can be significantly improved compared to existing methods. Here, we assess whether NLP and protein language models (pLMs) can be used to predict membrane-interacting amino acids for PMPs.

RESULTS: We utilize available experimental data and generate protein embeddings from two pLMs (ProtTrans and ESM) to train classifier models. Overall, the results demonstrate the first proof of concept study and the promising potential of using deep learning and pLMs to predict protein-membrane interfaces for PMPs faster, with similar accuracy, and without the need for 3D structural data compared to existing tools.

AVAILABILITY AND IMPLEMENTATION: The code is available at https://github.com/zoecournia/pLM-PMI. All data are available in the Supplementary material.

PMID:39559823 | PMC:PMC11572487 | DOI:10.1093/bioadv/vbae078

IEEE Open J Eng Med Biol. 2024 May 27;5:837-845. doi: 10.1109/OJEMB.2024.3403948. eCollection 2024.

ABSTRACT

Goal: In patients with coronary artery disease, the prediction of future cardiac events such as myocardial infarction (MI) remains a major challenge. In this work, we propose a novel anatomy-informed multimodal deep learning framework to predict future MI from clinical data and Invasive Coronary Angiography (ICA) images. Methods: The images are analyzed by Convolutional Neural Networks (CNNs) guided by anatomical information, and the clinical data by an Artificial Neural Network (ANN). Embeddings from both sources are then merged to provide a patient-level prediction. Results: The results of our framework on a clinical study of 445 patients admitted with acute coronary syndromes confirms that multimodal learning increases the predictive power and achieves good performance (AUC: [Formula: see text] & F1-Score: [Formula: see text]), which outperforms the prediction obtained by each modality independently as well as that of interventional cardiologists (AUC: 0.54 & F1-Score: 0.18). Conclusions: To the best of our knowledge, this is the first attempt towards combining multimodal data through a deep learning framework for future MI prediction. Although it demonstrates the superiority of multi-modal approaches over single modality, the results do not yet meet the necessary criteria for practical application.

PMID:39559783 | PMC:PMC11573417 | DOI:10.1109/OJEMB.2024.3403948

IEEE Open J Eng Med Biol. 2024 May 20;5:867-876. doi: 10.1109/OJEMB.2024.3402151. eCollection 2024.

ABSTRACT

Goal: To evaluate the suitability of seismocardiogram (SCG) and gyrocardiogram (GCG) recorded at the skin level to classify aortic stenosis (AS) patients from healthy volunteers, and to determine the optimal sensor position for the classification. Methods: SCG and GCG were recorded along three axes at five chest locations of fifteen healthy subjects and AS patients. Signal frames underwent feature extraction in frequency and time-frequency domains. Then, binary classification was performed through three machine learning and three deep learning methods, considering SCG, GCG, and their combination. Results: The highest classification accuracies were achieved using Support Vector Machine (SVM) classifier, with the best sensor locations being at the mitral valve for SCG signals (92.3% accuracy) and at the pulmonary valve for GCG (92.1%). Combining SCG and GCG data allows for further improvement in the achievable accuracy (93.5%). Jointly exploiting SCG and GCG signals and both SVM- and ResNet18-based classifiers, 40 s of monitoring allows for reaching 97.2% accuracy with a single sensor on the pulmonary valve. Conclusions: Combining SCG and GCG with adequate machine learning and deep learning classifiers allows reliable classification of AS patients.

PMID:39559782 | PMC:PMC11573360 | DOI:10.1109/OJEMB.2024.3402151

Infect Dis Model. 2024 Oct 28;10(1):240-256. doi: 10.1016/j.idm.2024.10.007. eCollection 2025 Mar.

ABSTRACT

Despite the implementation of various initiatives, dengue remains a significant public health concern in Malaysia. Given that dengue has no specific treatment, dengue prediction remains a useful early warning mechanism for timely and effective deployment of public health preventative measures. This study aims to develop a comprehensive approach for forecasting dengue cases in Selangor, Malaysia by incorporating climate variables. An ensemble of Multiple Linear Regression (MLR) model, Long Short-Term Memory (LSTM), and Susceptible-Infected mosquito vectors, Susceptible-Infected-Recovered human hosts (SI-SIR) model were used to establish a relation between climate variables (temperature, humidity, precipitation) and mosquito biting rate. Dengue incidence subject to climate variability can then be projected by SI-SIR model using the forecasted mosquito biting rate. The proposed approach outperformed three alternative approaches and expanded the temporal horizon of dengue prediction for Selangor with the ability to forecast approximately 60 weeks ahead with a Mean Absolute Percentage Error (MAPE) of 13.97 for the chosen prediction window before the implementation of the Movement Control Order (MCO) in Malaysia. Extended validation across subsequent periods also indicates relatively satisfactory forecasting performance (with MAPE ranging from 13.12 to 17.09). This research contributed to the field by introducing a novel framework for the prediction of dengue cases over an extended temporal range.

PMID:39559512 | PMC:PMC11570709 | DOI:10.1016/j.idm.2024.10.007

Phys Imaging Radiat Oncol. 2024 Nov 3;32:100669. doi: 10.1016/j.phro.2024.100669. eCollection 2024 Oct.

ABSTRACT

BACKGROUND AND PURPOSE: The target structures for cervix brachytherapy are segmented by radiation oncologists using imaging and clinical information. At the first fraction, this is performed manually from scratch. For subsequent fractions the first fraction segmentations are rigidly propagated and edited manually. This process is time-consuming while patients wait immobilized. In this work, we evaluate the potential clinical impact of using population-based and patient-specific auto-segmentations as a starting point for target segmentation of the second fraction.

MATERIALS AND METHOD: For twenty-eight patients with locally advanced cervical cancer, treated with MRI-guided brachytherapy, auto-segmentations were retrospectively generated for the second fraction image using two approaches: 1) population-based model, 2) patient-specific models fine-tuned on first fraction information. A radiation oncologist manually edited the auto-segmentations to assess model-induced bias. Pairwise geometric and dosimetric comparisons were performed for the automatic, edited and clinical structures. The time spent editing the auto-segmentations was compared to the current clinical workflow.

RESULTS: The edited structures were more similar to the automatic than to the clinical structures. The geometric and dosimetric differences between the edited and the clinical structures were comparable to the inter-observer variability investigated in literature. Editing the auto-segmentations was faster than the manual segmentation performed during our clinical workflow. Patient-specific auto-segmentations required less edits than population-based structures.

CONCLUSIONS: Auto-segmentation introduces a bias in the manual delineations but this bias is clinically irrelevant. Auto-segmentation, particularly patient-specific fine-tuning, is a time-saving tool that can improve treatment logistics and therefore reduce patient burden during the second fraction of cervix brachytherapy.

PMID:39559487 | PMC:PMC11570852 | DOI:10.1016/j.phro.2024.100669

Digit Health. 2024 Nov 18;10:20552076241291306. doi: 10.1177/20552076241291306. eCollection 2024 Jan-Dec.

ABSTRACT

OBJECTIVE: The precise segmentation of kidneys from a 2D ultrasound (US) image is crucial for diagnosing and monitoring kidney diseases. However, achieving detailed segmentation is difficult due to US images' low signal-to-noise ratio and low-contrast object boundaries.

METHODS: This paper presents an approach called deep supervised attention with multi-loss functions (MLAU-Net) for US segmentation. The MLAU-Net model combines the benefits of attention mechanisms and deep supervision to improve segmentation accuracy. The attention mechanism allows the model to selectively focus on relevant regions of the kidney and ignore irrelevant background information, while the deep supervision captures the high-dimensional structure of the kidney in US images.

RESULTS: We conducted experiments on two datasets to evaluate the MLAU-Net model's performance. The Wuerzburg Dynamic Kidney Ultrasound (WD-KUS) dataset with annotation contained kidney US images from 176 patients split into training and testing sets totaling 44,880. The Open Kidney Dataset's second dataset has over 500 B-mode abdominal US images. The proposed approach achieved the highest dice, accuracy, specificity, Hausdorff distance (HD95), recall, and Average Symmetric Surface Distance (ASSD) scores of 90.2%, 98.26%, 98.93%, 8.90 mm, 91.78%, and 2.87 mm, respectively, upon testing and comparison with state-of-the-art U-Net series segmentation frameworks, which demonstrates the potential clinical value of our work.

CONCLUSION: The proposed MLAU-Net model has the potential to be applied to other medical image segmentation tasks that face similar challenges of low signal-to-noise ratios and low-contrast object boundaries.

PMID:39559387 | PMC:PMC11571257 | DOI:10.1177/20552076241291306

Digit Health. 2024 Nov 18;10:20552076241297053. doi: 10.1177/20552076241297053. eCollection 2024 Jan-Dec.

ABSTRACT

Endometrial cancer (EC), a growing malignancy among women, underscores an urgent need for early detection and intervention, critical for enhancing patient outcomes and survival rates. Traditional diagnostic approaches, including ultrasound (US), magnetic resonance imaging (MRI), hysteroscopy, and histopathology, have been essential in establishing robust diagnostic and prognostic frameworks for EC. These methods offer detailed insights into tumor morphology, vital for clinical decision-making. However, their analysis relies heavily on the expertise of radiologists and pathologists, a process that is not only time-consuming and labor-intensive but also prone to human error. The emergence of deep learning (DL) in computer vision has significantly transformed medical image analysis, presenting substantial potential for EC diagnosis. DL models, capable of autonomously learning and extracting complex features from imaging and histopathological data, have demonstrated remarkable accuracy in discriminating EC and stratifying patient prognoses. This review comprehensively examines and synthesizes the current literature on DL-based imaging techniques for EC diagnosis and management. It also aims to identify challenges faced by DL in this context and to explore avenues for its future development. Through these detailed analyses, our objective is to inform future research directions and promote the integration of DL into EC diagnostic and treatment strategies, thereby enhancing the precision and efficiency of clinical practice.

PMID:39559386 | PMC:PMC11571264 | DOI:10.1177/20552076241297053

Heliyon. 2024 Oct 18;10(21):e39579. doi: 10.1016/j.heliyon.2024.e39579. eCollection 2024 Nov 15.

ABSTRACT

Wind is a renewable energy source that is used to generate electricity. Wind power is one of the suitable solutions for global warming since it is free from pollution, doesn't cause greenhouse effects, and it is a natural source of energy. However, Wind power generation highly depends on weather conditions. It is very difficult to easily predict the amount of power generated from wind at a particular instant in time. Adama wind power farm is one of the wind farms in Ethiopia. There is no accurate and reliable forecasting model for the Adama wind farm that enables the forecasting of the power generated from the farm. The main objective of this research is to develop a wind power forecasting model for the Adama wind farm using deep learning techniques. Forecasting of wind power generation capacity involves appropriate modeling techniques that use past wind power generation data. The experiments have been conducted using Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (Bi-LSTM) and Gated Recurrent Unit (GRU). To achieve the highest forecasting accuracy, four years of data (from 2016 to 2019), with 5-min intervals, have been collected with a total of 163,802 rows. For hyperparameter optimization grid search and random search techniques have been utilized. The performances of the proposed deep learning models were investigated error metrics, including Mean Absolute Errors (MAE) and the Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE) and R squared (R2). Bi-LSTM outperforms the other two algorithms, scoring 0.644, 0.388, 0.769 and 0.978 MAE, MAPE, RMSE and R2 values respectively. Such wind power forecasting helps energy planners and regional power providers to compute power production and energy generated from other sources.

PMID:39559238 | PMC:PMC11570509 | DOI:10.1016/j.heliyon.2024.e39579

EClinicalMedicine. 2024 Nov 5;77:102888. doi: 10.1016/j.eclinm.2024.102888. eCollection 2024 Nov.

ABSTRACT

BACKGROUND: This study explores the potential of the deep learning-based convolutional neural network (CNN) to automatically recognize MMD using MRA images from atherosclerotic disease (ASD) and normal control (NC).

METHODS: In this retrospective study in China, 600 participants (200 MMD, 200 ASD and 200 NC) were collected from one institution as an internal dataset for training and 60 from another institution were collected as external testing set for validation. All participants were divided into training (N = 450) and validation sets (N = 90), internal testing set (N = 60), and external testing set (N = 60). The input to the CNN models comprised preprocessed MRA images, while the output was a tripartite classification label that identified the patient's diagnostic group. The performances of 3D CNN models were evaluated using a comprehensive set of metrics such as area under the curve (AUC) and accuracy. Gradient-weighted Class Activation Mapping (Grad-CAM) was used to visualize the CNN's decision-making process in MMD diagnosis by highlighting key areas. Finally, the diagnostic performances of the CNN models were compared with those of two experienced radiologists.

FINDINGS: DenseNet-121 exhibited superior discrimination capabilities, achieving a macro-average AUC of 0.977 (95% CI, 0.928-0.995) in the internal test sets and 0.880 (95% CI, 0.786-0.937) in the external validation sets, thus exhibiting comparable diagnostic capabilities to those of human radiologists. In the binary classification where ASD and NC were group together, with MMD as the separate group for targeted detection, DenseNet-121 achieved an accuracy of 0.967 (95% CI, 0.886-0.991). Additionally, the Grad-CAM results for the MMD, with areas of intense redness indicating critical areas identified by the model, reflected decision-making similar to human experts.

INTERPRETATION: This study highlights the efficacy of CNN model in the automated diagnosis of MMD on MRA images, easing the workload on radiologists and promising integration into clinical workflows.

FUNDING: National Natural Science Foundation of China, Tianjin Science and Technology Project and Beijing Natural Science Foundation.

PMID:39559186 | PMC:PMC11570825 | DOI:10.1016/j.eclinm.2024.102888

BMC Med Imaging. 2024 Nov 18;24(1):314. doi: 10.1186/s12880-024-01486-z.

ABSTRACT

In recent years, the incidence of nodular thyroid diseases has been increasing annually. Ultrasonography has become a routine diagnostic tool for thyroid nodules due to its high real-time capabilities and low invasiveness. However, thyroid images obtained from current ultrasound tests often have low resolution and are plagued by significant noise interference. Regional differences in medical conditions and varying levels of physician experience can impact the accuracy and efficiency of diagnostic results. With the advancement of deep learning technology, deep learning models are used to identify whether a nodule in a thyroid ultrasound image is benign or malignant. This helps to close the gap between doctors' experience and equipment differences, improving the accuracy of the initial diagnosis of thyroid nodules. To cope with the problem that thyroid ultrasound images contain complex background and noise as well as poorly defined local features. in this paper, we first construct an improved ResNet50 classification model that uses a two-branch input and incorporates a global attention lightening module. This model is used to improve the accuracy of benign and malignant nodule classification in thyroid ultrasound images and to reduce the computational effort due to the two-branch structure.We constructed a U-net segmentation model incorporating our proposed ACR module, which uses hollow convolution with different dilation rates to capture multi-scale contextual information for feature extraction of nodules in thyroid ultrasound images and uses the results of the segmentation task as an auxiliary branch for the classification task to guide the classification model to focus on the lesion region more efficiently in the case of weak local features. The classification model is guided to focus on the lesion region more efficiently, and the classification and segmentation sub-networks are respectively improved specifically for this study, which is used to improve the accuracy of classifying the benign and malignant nature of the nodules in thyroid ultrasound images. The experimental results show that the four evaluation metrics of accuracy, precision, recall, and f1 of the improved model are 96.01%, 93.3%, 98.8%, and 96.0%, respectively. The improvements were 5.7%, 1.6%, 13.1%, and 7.4%, respectively, compared with the baseline classification model.

PMID:39558260 | DOI:10.1186/s12880-024-01486-z

BMC Med Imaging. 2024 Nov 18;24(1):313. doi: 10.1186/s12880-024-01473-4.

ABSTRACT

BACKGROUND: Esophageal fistula (EF), a rare and potentially fatal complication, can be better managed with predictive models for personalized treatment plans in esophageal cancers. We aim to develop a clinical-deep learning radiomics model for effectively predicting the occurrence of EF.

METHODS: The study involved esophageal cancer patients undergoing radiotherapy or chemoradiotherapy. Arterial phase enhanced CT images were used to extract handcrafted and deep learning radiomic features. Along with clinical information, a 3-step feature selection method (statistical tests, Least Absolute Shrinkage and Selection Operator, and Recursive Feature Elimination) was used to identify five feature sets in training cohort for constructing random forest EF prediction models. Model performance was compared and validated in both retrospective and prospective test cohorts.

RESULTS: One hundred seventy five patients (122 in training and 53 in test cohort)were retrospectively collected from April 2018 to June 2022. An additional 27 patients were enrolled as a prospective test cohort from June 2022 to December 2023. Post-selection in the training cohort, five feature sets were used for model construction: clinical, handcrafted radiomic, deep learning radiomic, clinical-handcrafted radiomic, and clinical-deep learning radiomic. The clinical-deep learning radiomic model excelled with AUC of 0.89 (95% Confidence Interval: 0.83-0.95) in the training cohort, 0.81 (0.65-0.94) in the test cohort, and 0.85 (0.71-0.97) in the prospective test cohort. Brier-score and calibration curve analyses validated its predictive ability.

CONCLUSIONS: The clinical-deep learning radiomic model can effectively predict EF in patients with advanced esophageal cancer undergoing radiotherapy or chemoradiotherapy.

PMID:39558242 | DOI:10.1186/s12880-024-01473-4

BMC Med Imaging. 2024 Nov 18;24(1):312. doi: 10.1186/s12880-024-01469-0.

ABSTRACT

BACKGROUND AND PURPOSE: Tumor bed (TB) is the residual cavity of resected tumor after surgery. Delineating TB from CT is crucial in generating clinical target volume for radiotherapy. Due to multiple surgical effects and low image contrast, segmenting TB from soft tissue is challenging. In clinical practice, titanium clips were used as marks to guide the searching of TB. However, this information is limited and may cause large error. To provide more prior location information, the tumor regions on both pre-operative and post-operative CTs are both used by the deep learning model in segmenting TB from surrounding tissues.

MATERIALS AND METHODS: For breast cancer patient after surgery and going to be treated by radiotherapy, it is important to delineate the target volume for treatment planning. In clinical practice, the target volume is usually generated from TB by adding a certain margin. Therefore, it is crucial to identify TB from soft tissue. To facilitate this process, a deep learning model is developed to segment TB from CT with the guidance of prior tumor location. Initially, the tumor contour on the pre-operative CT is delineated by physician for surgical planning purpose. Then this contour is transformed to the post-operative CT via the deformable image registration between paired pre-operative and post-operative CTs. The original and transformed tumor regions are both used as inputs for predicting the possible region of TB by the deep-learning model.

RESULTS: Compared to the one without prior tumor contour information, the dice similarity coefficient of the deep-learning model with the prior tumor contour information is improved significantly (0.812 vs. 0.520, P = 0.001). Compared to the traditional gray-level thresholding method, the dice similarity coefficient of the deep-learning model with the prior tumor contour information is improved significantly (0.812 vs.0.633, P = 0.0005).

CONCLUSIONS: The prior tumor contours on both pre-operative and post-operative CTs provide valuable information in searching for the precise location of TB on post-operative CT. The proposed method provided a feasible way to assist auto-segmentation of TB in treatment planning of radiotherapy after breast-conserving surgery.

PMID:39558240 | DOI:10.1186/s12880-024-01469-0

Nucleic Acids Res. 2024 Nov 21:gkae1027. doi: 10.1093/nar/gkae1027. Online ahead of print.

ABSTRACT

Response to spatiotemporal variation in selection gradients resulted in signatures of polygenic adaptation in human genomes. We introduce RAISING, a two-stage deep learning framework that optimizes neural network architecture through hyperparameter tuning before performing feature selection and prediction tasks. We tested RAISING on published and newly designed simulations that incorporate the complex interplay between demographic history and selection gradients. RAISING outperformed Phylogenetic Generalized Least Squares (PGLS), ridge regression and DeepGenomeScan, with significantly higher true positive rates (TPR) in detecting genetic adaptation. It reduced computational time by 60-fold and increased TPR by up to 28% compared to DeepGenomeScan on published data. In more complex demographic simulations, RAISING showed lower false discoveries and significantly higher TPR, up to 17-fold, compared to other methods. RAISING demonstrated robustness with least sensitivity to demographic history, selection gradient and their interactions. We developed a sliding window method for genome-wide implementation of RAISING to overcome the computational challenges of high-dimensional genomic data. Applied to African, European, South Asian and East Asian populations, we identified multiple genomic regions undergoing polygenic selection. Notably, ∼70% of the regions identified in Africans are unique, with broad patterns distinguishing them from non-Africans, corroborating the Out of Africa dispersal model.

PMID:39558170 | DOI:10.1093/nar/gkae1027

Sci Rep. 2024 Nov 18;14(1):28428. doi: 10.1038/s41598-024-80132-8.

ABSTRACT

The current investigation proposes a novel hybrid methodology for the diagnosis of the foot fractures. The method uses a combination of deep learning methods and a metaheuristic to provide an efficient model for the diagnosis of the foot fractures problem. the method has been first based on applying some preprocessing steps before using the model for the features extraction and classification of the problem. the main model is based on a pre-trained ZFNet. The final layers of the network have been substituted using an extreme learning machine (ELM) in its entirety. The ELM part also optimized based on a new developed metaheuristic, called enhanced snow ablation optimizer (ESAO), to achieve better results. for validating the effectiveness of the proposed ZFNet/ELM/ESAO-based model, it has been applied to a standard benchmark from Institutional Review Board (IRB) and the findings have been compared to some different high-tech methods, including Decision Tree / K-Nearest Neighbour (DT/KNN), Linear discriminant analysis (LDA), Inception-ResNet Faster R-CNN architecture (FRCNN), Transfer learning‑based ensemble convolutional neural network (TL-ECNN), and combined model containing a convolutional neural network and long short-term memory (DCNN/LSTM). Final results show that using the proposed ZFNet/ELM/ESAO-based can be utilized as an efficient model for the diagnosis of the foot fractures.

PMID:39558102 | DOI:10.1038/s41598-024-80132-8

Nat Methods. 2024 Nov 18. doi: 10.1038/s41592-024-02505-1. Online ahead of print.

ABSTRACT

While advances in single-particle cryo-EM have enabled the structural determination of macromolecular complexes at atomic resolution, particle orientation bias (the 'preferred' orientation problem) remains a complication for most specimens. Existing solutions have relied on biochemical and physical strategies applied to the specimen and are often complex and challenging. Here, we develop spIsoNet, an end-to-end self-supervised deep learning-based software to address map anisotropy and particle misalignment caused by the preferred-orientation problem. Using preferred-orientation views to recover molecular information in under-sampled views, spIsoNet improves both angular isotropy and particle alignment accuracy during 3D reconstruction. We demonstrate spIsoNet's ability to generate near-isotropic reconstructions from representative biological systems with limited views, including ribosomes, β-galactosidases and a previously intractable hemagglutinin trimer dataset. spIsoNet can also be generalized to improve map isotropy and particle alignment of preferentially oriented molecules in subtomogram averaging. Therefore, without additional specimen-preparation procedures, spIsoNet provides a general computational solution to the preferred-orientation problem.

PMID:39558095 | DOI:10.1038/s41592-024-02505-1

Sci Rep. 2024 Nov 18;14(1):28435. doi: 10.1038/s41598-024-79602-w.

ABSTRACT

Synthetic data is becoming a valuable tool for computational pathologists, aiding in tasks like data augmentation and addressing data scarcity and privacy. However, its use necessitates careful planning and evaluation to prevent the creation of clinically irrelevant artifacts.This manuscript introduces a comprehensive pipeline for generating and evaluating synthetic pathology data using a diffusion model. The pipeline features a multifaceted evaluation strategy with an integrated explainability procedure, addressing two key aspects of synthetic data use in the medical domain.The evaluation of the generated data employs an ensemble-like approach. The first step includes assessing the similarity between real and synthetic data using established metrics. The second step involves evaluating the usability of the generated images in deep learning models accompanied with explainable AI methods. The final step entails verifying their histopathological realism through questionnaires answered by professional pathologists. We show that each of these evaluation steps are necessary as they provide complementary information on the generated data's quality.The pipeline is demonstrated on the public GTEx dataset of 650 Whole Slide Images (WSIs), including five different tissues. An equal number of tiles from each tissue are generated and their reliability is assessed using the proposed evaluation pipeline, yielding promising results.In summary, the proposed workflow offers a comprehensive solution for generative AI in digital pathology, potentially aiding the community in their transition towards digitalization and data-driven modeling.

PMID:39557989 | DOI:10.1038/s41598-024-79602-w

Sci Rep. 2024 Nov 18;14(1):28496. doi: 10.1038/s41598-024-79271-9.

ABSTRACT

Assessing localization of the transient receptor potential vanilloid-1 (TRPV1) in skin nerve fibers is crucial for understanding its role in peripheral neuropathy and pain. However, information on the specificity and sensitivity of TRPV1 antibodies used for immunofluorescence (IF) on human skin is currently lacking. To find a reliable TRPV1 antibody and IF protocol, we explored antibody candidates from different manufacturers, used rat DRG sections and human skin samples for screening and human TRPV1-expressing HEK293 cells for further validation. Final specificity assessment was done on human skin samples. Additionally, we developed two automated image analysis methods: a Python-based deep-learning approach and a Fiji-based machine-learning approach. These methods involve training a model or classifier for nerve fibers based on pre-annotations and utilize a nerve fiber mask to filter and count TRPV1 immunoreactive puncta and TRPV1 fluorescence intensity on nerve fibers. Both automated analysis methods effectively distinguished TRPV1 signals on nerve fibers from those in keratinocytes, demonstrating high reliability as evidenced by excellent intraclass correlation coefficient (ICC) values exceeding 0.75. This method holds the potential to uncover alterations in TRPV1 associated with neuropathic pain conditions, using a minimally invasive approach.

PMID:39557902 | DOI:10.1038/s41598-024-79271-9

Sci Rep. 2024 Nov 18;14(1):28501. doi: 10.1038/s41598-024-77549-6.

ABSTRACT

Autism spectrum disorder is a developmental condition that affects the social and behavioral abilities of growing children. Early detection of autism spectrum disorder can help children to improve their cognitive abilities and quality of life. The research in the area of autism spectrum disorder reports that it can be detected from cognitive tests and physical activities of children. The present research reports on the detection of autism spectrum disorder from the facial attributes of children. Children with autism spectrum disorder show ambiguous facial expressions which are different from the facial attributes of normal children. To detect autism spectrum disorder from facial images, this work presents an improvised variant of the YOLOv7-tiny model. The presented model is developed by integrating a pyramid of dilated convolutional layers in the feature extraction network of the YOLOv7-tiny model. Further, its recognition abilities are enhanced by incorporating an additional YOLO detection head. The developed model can detect faces with the presence of autism features by drawing bounding boxes and confidence scores. The entire work has been carried out on a self-annotated autism face dataset. The developed model achieved a mAP value of 79.56% which was better than the baseline YOLOv7-tiny and state-of-the-art YOLOv8 Small model.

PMID:39557896 | DOI:10.1038/s41598-024-77549-6

Sci Data. 2024 Nov 18;11(1):1242. doi: 10.1038/s41597-024-04062-w.

ABSTRACT

We present detailed annual land cover maps for the Baltic Sea region, spanning more than two decades (2000-2022). The maps provide information on eighteen land cover (LC) classes, including eight general LC types, eight major crop types and grassland, and two peat bog-related classes. Our maps represent the first homogenized annual dataset for the region and address gaps in current land use and land cover products, such as a lack of detail on crop sequences and peat bog exploitation. To create the maps, we used annual multi-temporal remote sensing data combined with a data encoding structure and deep learning classification. We obtained the training data from publicly available open datasets. The maps were validated using independent field survey data from the Land Use/Cover Area Frame Survey (LUCAS) and expert annotations from high-resolution imagery. The quantitative and qualitative results of the maps provide a reliable data source for monitoring agricultural transformations, peat bog exploitation, and restoration activities in the Baltic Sea region and its surrounding countries.

PMID:39557873 | DOI:10.1038/s41597-024-04062-w

Comput Biol Med. 2024 Nov 17;184:109411. doi: 10.1016/j.compbiomed.2024.109411. Online ahead of print.

ABSTRACT

Brain age, an emerging biomarker for brain diseases and aging, is typically predicted using single-modality T1-weighted structural MRI data. This study investigates the benefits of integrating structural MRI with diffusion MRI to enhance brain age prediction. We propose an attention-based deep learning model that fuses global-context information from structural MRI with local details from diffusion metrics. The model was evaluated using two large datasets: the Human Connectome Project (HCP, n = 1064, age 22-37) and the Cambridge Center for Aging and Neuroscience (Cam-CAN, n = 639, age 18-88). It was tested for generalizability and robustness on three independent datasets (n = 546, age 20-86), reproducibility on a test-retest dataset (n = 44, age 22-35), and longitudinal consistency (n = 129, age 46-92). We also examined the relationship between predicted brain age and behavioral measures. Results showed that the multimodal model improved prediction accuracy, achieving mean absolute errors (MAEs) of 2.44 years in the HCP dataset (sagittal plane) and 4.36 years in the Cam-CAN dataset (axial plane). The corresponding R2 values were 0.258 and 0.914, respectively, reflecting the model's ability to explain variance in the predictions across both datasets. Compared to single-modality models, the multimodal approach showed better generalization, reducing MAEs by 10-76 % and enhancing robustness by 22-82 %. While the multimodal model exhibited superior reproducibility, the sMRI model showed slightly better longitudinal consistency. Importantly, the multimodal model revealed unique associations between predicted brain age and behavioral measures, such as walking endurance and loneliness in the HCP dataset, which were not detected with chronological age alone. In the Cam-CAN dataset, brain age and chronological age exhibited similar correlations with behavioral measures. By integrating sMRI and dMRI through an attention-based model, our proposed approach enhances predictive accuracy and provides deeper insights into the relationship between brain aging and behavior.

PMID:39556917 | DOI:10.1016/j.compbiomed.2024.109411