Anesthesiol Clin. 2025 Sep;43(3):471-489. doi: 10.1016/j.anclin.2025.05.003. Epub 2025 Jul 3.

ABSTRACT

Recent breakthroughs in artificial intelligence (AI) have particularly shone in cardiothoracic anesthesia, where its ability to efficiently analyze complex datasets and process vast amounts of information in mere moments has captured considerable attention. For cardiothoracic anesthesiologists, the challenge of swiftly evaluating myriad variables is paramount to minimizing complications and optimizing patient outcomes. This article explores the current state of AI in cardiac anesthesia, illuminating the compelling evidence supporting its use and charting potential future paths for researchers and clinicians alike. We will also delve into the challenges in this dynamic field and propose inventive concepts for seamlessly integrating AI into future research initiatives.

PMID:40752948 | DOI:10.1016/j.anclin.2025.05.003

Anesthesiol Clin. 2025 Sep;43(3):405-418. doi: 10.1016/j.anclin.2025.04.003. Epub 2025 May 26.

ABSTRACT

The term "artificial intelligence" (AI) refers to the intelligence demonstrated by machines. It is generally defined as creating machines that mimic cognitive functions typically associated with the human mind. This article will define and explore the various levels of AI and examine its historical development. We will also analyze the attention AI has received from different countries. Next, we will investigate the fundamental technologies underpinning AI. After that, we will discuss how AI transforms health care. Finally, we will consider whether AI has the potential to replace physicians and explore its future in the health care sector.

PMID:40752944 | DOI:10.1016/j.anclin.2025.04.003

Surv Ophthalmol. 2025 Jul 31:S0039-6257(25)00128-6. doi: 10.1016/j.survophthal.2025.07.011. Online ahead of print.

ABSTRACT

Less than a decade has passed since deep learning (DL) was first applied in ophthalmology. With tremendous growth in this field since then, DL is expected to transform and enhance the efficiency of traditional ophthalmology practice. Central serous chorioretinopathy (CSC) is a common chorioretinal disorder whose etiopathogenesis remains largely unknown. The diagnosis and management of CSC rely heavily on multimodal imaging data, detailed analysis of which may exceed the capacity of many practices. In this comprehensive review, we examine how DL can address such issues through automated analysis of CSC-related imaging biomarkers, including subretinal fluid, pigment epithelial detachment, subretinal hyperreflective material, hyperreflective foci, retinal pigment epithelium atrophy, ellipsoid zone loss, and choroidal layer, sublayers, vessels, and neovascularization. Their prognostic yield and therapeutic implications are covered as well. We describe how DL enables rapid, noninvasive visualization of choroidal vasculature, a primary source of pathology in CSC, in unprecedented detail. We also review the state-of-the-art DL models designed for automated CSC diagnosis, classification, prognostication, and treatment outcome prediction based on imaging data. We highlight the challenges and gaps in this field, discuss some recommended counter measures, and suggest future research directions.

PMID:40752852 | DOI:10.1016/j.survophthal.2025.07.011

Int J Dev Neurosci. 2025 Aug;85(5):e70034. doi: 10.1002/jdn.70034.

ABSTRACT

BACKGROUND AND OBJECTIVE: Autism spectrum disorder (ASD) is a complex neurological condition that impacts cognitive, social and behavioural abilities. Early and accurate diagnosis is crucial for effective intervention and treatment. Traditional diagnostic methods lack accuracy, efficient feature selection and computational efficiency. This study proposes an integrated approach that combines the electromagnetic interaction algorithm (EIA) for feature selection with the adaptive kernel attention network (AKAttNet) for classification, aiming to improve ASD detection performance across multiple datasets.

METHODS: The proposed methodology consists of two core components: (1) EIA, which optimises feature selection by identifying the most relevant attributes for ASD classification, and (2) AKAttNet, a deep learning model leveraging adaptive kernel attention mechanisms to enhance classification accuracy. The framework is evaluated using four publicly available ASD datasets. The classification performance of AKAttNet is compared against traditional machine learning methods, including logistic regression (LR), support vector machine (SVM) and random forest (RF), as well as competing deep learning models. Statistical evaluation includes precision, recall (sensitivity), specificity and overall accuracy metrics.

RESULTS: The proposed model outperforms conventional machine learning and deep learning approaches, demonstrating higher classification accuracy and robustness across multiple datasets. AKAttNet, combined with EIA-based feature selection, achieves an accuracy improvement ranging from 0.901 to 0.9827, Cohen's kappa values between 0.7789 and 0.9685 and Jaccard similarity scores from 0.8041 to 0.9709 across four different datasets. Comparative analysis highlights the efficiency of the EIA algorithm in reducing feature dimensionality while maintaining high model performance. Additionally, the proposed method exhibits lower computational time and enhanced generalizability, making it a promising approach for ASD detection.

CONCLUSIONS: This study presents a practical ASD detection framework integrating EIA for feature selection with AKAttNet for classification. The results indicate that this hybrid approach enhances diagnostic accuracy while reducing computational overhead, making it a promising tool for early ASD diagnosis. The findings support the potential of deep learning and optimisation techniques in developing more efficient and reliable ASD screening systems. Future work can explore real-world clinical applications and further refinement of the feature selection process.

PMID:40751377 | DOI:10.1002/jdn.70034

J Oral Rehabil. 2025 Aug 1. doi: 10.1111/joor.70030. Online ahead of print.

ABSTRACT

BACKGROUND: The Test of Masticating and Swallowing Solids (TOMASS) is a validated screening tool for assessing masticatory and swallowing functions. However, the conventional TOMASS relies on operator-dependent methods, which limit its objectivity and efficiency. The neck-worn electronic stethoscope (NWES), a contact sensor positioned on the back of the neck, has recently been developed to automatically detect and monitor swallowing actions through deep learning-based analysis of collected sound data.

OBJECTIVE: This study piloted a semi-automated assessment approach using a NWES to objectively measure TOMASS parameters and examine the influence of age and gender.

METHODS: A total of 123 healthy adults (mean age: 58.7 ± 18.5 years) consumed two crackers while audio data recorded using a NWES and visual data were collected by smartphone. Measurements included discrete bite count, swallow count, oral processing and swallowing time (OPST), and first OPST (1st-OPST). Statistical analyses were conducted to assess gender- and age-related changes and differences.

RESULTS: The NWES enabled objective and precise TOMASS measurements. Age-related prolongation of OPST and 1st-OPST was observed, particularly in men (p < 0.001). Women exhibited fewer age-related changes in OPST, although swallow count tended to decrease with age (p < 0.001). Regarding gender differences, younger women demonstrated higher bite (2.3 [interquartile range (IQR): 1.0-3.0] vs. 1 [IQR: 1.0-2.0], p = 0.042) and swallow counts (2.5 [IQR: 2.0-2.5] vs. 2 [IQR: 1.0-2.0], p = 0.026) compared with men.

CONCLUSION: The NWES appeared suitable as an objective, efficient tool for automated TOMASS evaluation. Age-related changes in masticatory and swallowing performance differed according to gender, highlighting the need for tailored assessments. Future research on NWES-based TOMASS measurements should include diverse populations and extension to dysphagia and masticatory dysfunction.

PMID:40751301 | DOI:10.1111/joor.70030

Parasit Vectors. 2025 Aug 1;18(1):322. doi: 10.1186/s13071-025-06878-w.

ABSTRACT

BACKGROUND: Human intestinal parasitic infections (IPI) pose a significant global health issue caused by parasitic helminths and protozoa, affecting around 3.5 billion people worldwide, with more than 200,000 deaths annually. Despite advancements in molecular methods with higher sensitivity and specificity, the Kato-Katz or formalin-ethyl acetate centrifugation technique (FECT) remains the gold standard and a routine diagnostic procedure suitable for its simplicity and cost-effectiveness. However, these techniques have limitations that must be addressed. Thus, this study evaluated the performance of a deep-learning-based approach for intestinal parasite identification and compared it with that of human experts.

METHODS: Human experts performed FECT and Merthiolate-iodine-formalin (MIF) techniques to serve as ground truth and reference for parasite species. Subsequently, a modified direct smear was conducted to gather images for the training (80%) and testing (20%) datasets. State-of-the-art models, including YOLOv4-tiny, YOLOv7-tiny, YOLOv8-m, ResNet-50, and DINOv2 (base, small, and large), were employed and were operated using in-house CIRA CORE platform. Overall performance was evaluated using confusion matrices, the metrics of which were calculated on the basis of the one-versus-rest and micro-averaging approaches. Moreover, the receiver operating characteristic (ROC) and precision-recall (PR) curves were determined for visual comparison. Lastly, Cohen's Kappa and Bland-Altman analyses were used to statistically measure the significant differences and visualize the association levels between the human experts and the deep learning models' classification performance in intestinal parasite identification.

RESULTS: Findings demonstrated the potential of a deep-learning-based approach, particularly of models DINOv2-large (accuracy: 98.93%; precision: 84.52%; sensitivity: 78.00%; specificity: 99.57%; F1 score: 81.13%; AUROC: 0.97) and YOLOv8-m (accuracy: 97.59%; precision: 62.02%; sensitivity: 46.78%; specificity: 99.13%; F1 score: 53.33%; AUROC: 0.755; AUPR: 0.556) for their high metric values in intestinal parasite identification. Class-wise prediction showed high precision, sensitivity, and F1 scores for helminthic eggs and larvae due to more distinct morphology. Moreover, all models obtained a > 0.90 k score, which indicates a strong level of agreement compared with the medical technologists. The Bland-Altman analysis also presented the best agreement between FECT performed by medical technologist A and YOLOv4-tiny, while the MIF technique performed by medical technologist B and DINOv2-small demonstrated the best bias-free agreement, with mean differences of 0.0199 and -0.0080, and standard deviation differences of 0.6012 and 0.5588, respectively.

CONCLUSIONS: The results highlight the potential of integrating a deep-learning-based approach into parasite identification. The models showcased superiority in automated detection, suggesting a significant leap toward improving diagnostic procedures for IPI. This hybridization could enhance early detection and diagnosis, facilitating timely and targeted interventions to reduce the burden of IPI through more effective management and prevention strategies.

PMID:40751198 | DOI:10.1186/s13071-025-06878-w

Proteins. 2025 Aug 1. doi: 10.1002/prot.70033. Online ahead of print.

ABSTRACT

We present the methods and results of our protein complex and RNA structure predictions at CASP16. Our approach integrated multiple state-of-the-art deep learning models with a consensus-based scoring method. To enhance the depth of multiple sequence alignments (MSAs), we employed a large metagenomic sequence database. Model ranking was performed with a state-of-the-art consensus ranking method, to which we added more scoring terms. These predictions were further refined manually based on literature evidence. For RNA, we adopted an ensemble approach that incorporated multiple state-of-the-art methods, centered around our NuFold framework. As a result, our KiharaLab group ranked first in protein complex prediction and third in RNA structure prediction. A detailed analysis of targets that significantly differed from those of other groups highlighted both the strengths of our MSA and scoring strategies, as well as areas requiring further improvement.

PMID:40751131 | DOI:10.1002/prot.70033

Int J Comput Assist Radiol Surg. 2025 Aug 1. doi: 10.1007/s11548-025-03483-1. Online ahead of print.

ABSTRACT

PURPOSE: In order to automate the centerline extraction of the coronary tree, three challenges must be addressed: tracking branches automatically, passing through plaques successfully, and detecting endpoints accurately. This study aims to develop a method to solve the three challenges.

METHODS: We propose a branch-endpoint-aware coronary centerline extraction framework. The framework consists of a deep reinforcement learning-based tracker and a 3D dilated CNN-based detector. The tracker is designed to predict the actions of an agent with the objective of tracking the centerline. The detector identifies bifurcation points and endpoints, assisting the tracker in tracking branches and terminating the tracking process automatically. The detector can also estimate the radius values of the coronary artery.

RESULTS: The method achieves the state-of-the-art performance in both the centerline extraction and radius estimate. Furthermore, the method necessitates minimal user interaction to extract a coronary tree, a feature that surpasses other interactive methods.

CONCLUSION: The method can track branches automatically, pass through plaques successfully and detect endpoints accurately. Compared with other interactive methods that require multiple seeds, our method only needs one seed to extract the entire coronary tree.

PMID:40751109 | DOI:10.1007/s11548-025-03483-1

Sci Rep. 2025 Aug 1;15(1):28135. doi: 10.1038/s41598-025-12685-1.

ABSTRACT

In drug discovery, identifying molecules with desired pharmacological properties remains challenging, as conventional methods often rely on exhaustive trial-and-error and limited exploration of chemical space. Here, we present STELLA, a metaheuristics-based generative molecular design framework that combines an evolutionary algorithm for fragment-based chemical space exploration with a clustering-based conformational space annealing method for efficient multi-parameter optimization. Additionally, it leverages deep learning models for accurate prediction of pharmacological properties. Our case study, which focuses on docking score and quantitative estimate of drug-likeness as primary objectives, demonstrates that STELLA generates 217% more hit candidates with 161% more unique scaffolds and achieves more advanced Pareto fronts compared to REINVENT 4. In performance evaluations optimizing 16 properties simultaneously for MolFinder, REINVENT 4, and STELLA, STELLA consistently outperforms the control methods by achieving better average objective scores and exploring a broader region of chemical space. The results highlight STELLA's superior performance in both efficient exploration of chemical space and multi-parameter optimization, indicating that STELLA is a powerful tool for de novo molecular design.

PMID:40750989 | DOI:10.1038/s41598-025-12685-1

Sci Rep. 2025 Aug 1;15(1):28124. doi: 10.1038/s41598-025-12058-8.

ABSTRACT

Soil fauna play a critical role in maintaining ecosystem functions and assessing environmental health, making accurate and efficient detection essential. Therefore, this paper proposes an improved algorithm based on You Only Look Once (YOLO) v9, which enhances feature capture capability while reducing parameters by 33.6%. First, a dynamic local shuffle module (DLSConv) is proposed, which utilizes convolutions and adaptive shuffling, effectively enhancing information interaction and feature richness. Additionally, different efficient modules with multi-branch fusion structures, integrating DLSConv, are adopted for the Backbone and Neck to enhance feature extraction and fusion, while optimizing the feature maps fed into the detection head, thereby improving the network's ability to extract features and detect targets. Ablation experiments demonstrate that the model achieves a 2.3% improvement in F-score and 1.8% increase in mean average precision (mAP)@50. On the soil fauna dataset, it attains 94.3% in mAP@75, significantly outperforming the baseline in challenging scenarios. These results highlight the model's efficiency and reliability for soil fauna detection on resource-constrained devices. And this capability can significantly enhance ecological monitoring through scalable biodiversity assessment and empowers precision agriculture applications via actionable insights into soil health and faunal activity, underpinning sustainable land management practices.

PMID:40750964 | DOI:10.1038/s41598-025-12058-8

Sci Rep. 2025 Aug 1;15(1):28068. doi: 10.1038/s41598-025-10531-y.

ABSTRACT

Time-lapse imaging and deep-learning algorithms are promising tools to assess the most viable embryos and improve embryo selection in IVF laboratories. Here, we developed and validated a deep learning model based on self-supervised contrastive learning. The model was developed with a new approach based on matched KID (Known Implantation Data) embryos derived from the same cohort of a stimulation cycle, both judged to be of good quality according to classical morphological criteria and morphokinetics, transferred fresh or frozen, but with a different implantation fate (clinical pregnancy vs. failure of implantation). We used self-supervised contrastive learning to train convolutional neural networks to ensure an unbiased and comprehensive learning of the morphokinetics features of the embryos, followed by a Siamese neural network fine-tuning and an XGBoost final prediction model to prevent overfitting. 1580 embryo videos of 460 patients were included between January 2020 and February 2023. With the knowledge of the implantation outcome of a previous transfer of an embryo derived from the same stimulation cycle, this model could predict the pregnancy outcome of the subsequent transfer with an AUC of 0.57. Without any knowledge of transfer history, the model achieved a satisfactory performance in predicting implantation (AUC = 0.64). This model could be considered as an adjunct tool for biologists to better select embryos and reduce the number of useless transfers per patient, when a cohort with several embryos classified as good quality by classical criteria is obtained.

PMID:40750959 | DOI:10.1038/s41598-025-10531-y

Insights Imaging. 2025 Aug 1;16(1):165. doi: 10.1186/s13244-025-02045-y.

ABSTRACT

OBJECTIVES: Subvariants of testicular germ cell tumor (TGCT) significantly affect therapeutic strategies and patient prognosis. However, preoperatively distinguishing seminoma (SE) from non-seminoma (n-SE) remains a challenge. This study aimed to evaluate the performance of a deep learning-based super-resolution (SR) US radiomics model for SE/n-SE differentiation.

MATERIALS AND METHODS: This international multicenter retrospective study recruited patients with confirmed TGCT between 2015 and 2023. A pre-trained SR reconstruction algorithm was applied to enhance native resolution (NR) images. NR and SR radiomics models were constructed, and the superior model was then integrated with clinical features to construct clinical-radiomics models. Diagnostic performance was evaluated by ROC analysis (AUC) and compared with radiologists' assessments using the DeLong test.

RESULTS: A total of 486 male patients were enrolled for training (n = 338), domestic (n = 92), and international (n = 59) validation sets. The SR radiomics model achieved AUCs of 0.90, 0.82, and 0.91, respectively, in the training, domestic, and international validation sets, significantly surpassing the NR model (p < 0.001, p = 0.031, and p = 0.001, respectively). The clinical-radiomics model exhibited a significantly higher across both domestic and international validation sets compared to the SR radiomics model alone (0.95 vs 0.82, p = 0.004; 0.97 vs 0.91, p = 0.031). Moreover, the clinical-radiomics model surpassed the performance of experienced radiologists in both domestic (AUC, 0.95 vs 0.85, p = 0.012) and international (AUC, 0.97 vs 0.77, p < 0.001) validation cohorts.

CONCLUSIONS: The SR-based clinical-radiomics model can effectively differentiate between SE and n-SE.

CRITICAL RELEVANCE STATEMENT: This international multicenter study demonstrated that a radiomics model of deep learning-based SR reconstructed US images enabled effective differentiation between SE and n-SE.

KEY POINTS: Clinical parameters and radiologists' assessments exhibit limited diagnostic accuracy for SE/n-SE differentiation in TGCT. Based on scrotal US images of TGCT, the SR radiomics models performed better than the NR radiomics models. The SR-based clinical-radiomics model outperforms both the radiomics model and radiologists' assessment, enabling accurate, non-invasive preoperative differentiation between SE and n-SE.

PMID:40750949 | DOI:10.1186/s13244-025-02045-y

Commun Eng. 2025 Aug 1;4(1):140. doi: 10.1038/s44172-025-00477-4.

ABSTRACT

Adaptive modeling is imperative for analyzing nonlinear systems deployed in natural dynamic environments. It facilitates filtering, prediction, and automatic control of the target object in real time to respond to unpredictable and non-repetitive sudden physical impairment caused by ambient impacts, such as corrosion, thermal drift, interference, etc. Existing nonlinear modeling approaches, however, are too complex for online training or fall short in rapid model recalibration under such conditions. To address this challenge, here we present a strategy that applies a regulator to the Koopman operator, enabling real-time model adaptation for nonlinear systems. In our approach, the regulator is directly implemented in nonlinear state-space without disrupting the pre-trained black-box predictor. The proposed technique demonstrates efficacy in capturing a broad spectrum of nonlinear dynamics and exhibits rapid adaptability to system changes without requiring offline retraining. Furthermore, its lightweight implementation and high-speed performance make it well-suited for embedded systems and applications demanding fast model recalibration and robustness.

PMID:40750921 | DOI:10.1038/s44172-025-00477-4

Med Image Anal. 2025 Jul 26;105:103732. doi: 10.1016/j.media.2025.103732. Online ahead of print.

ABSTRACT

Fast radial-MRI approaches based on compressed sensing (CS) and deep learning (DL) often use non-uniform fast Fourier transform (NUFFT) as the forward imaging operator, which might introduce interpolation errors and reduce image quality. Using the polar Fourier transform (PFT), we developed fully polar CS and DL algorithms for fast 2D cardiac radial-MRI. Our methods directly reconstruct images in polar spatial space from polar k-space data, eliminating frequency interpolation and ensuring an easy-to-compute data consistency term for the DL framework via the variable splitting (VS) scheme. Furthermore, PFT reconstruction produces initial images with fewer artifacts in a reduced field of view, making it a better starting point for CS and DL algorithms, especially for dynamic imaging, where information from a small region of interest is critical, as opposed to NUFFT, which often results in global streaking artifacts. In the cardiac region, PFT-based CS technique outperformed NUFFT-based CS at acceleration rates of 5x (mean SSIM: 0.8831 vs. 0.8526), 10x (0.8195 vs. 0.7981), and 15x (0.7720 vs. 0.7503). Our PFT(VS)-DL technique outperformed the NUFFT(GD)-based DL method, which used unrolled gradient descent with the NUFFT as the forward imaging operator, with mean SSIM scores of 0.8914 versus 0.8617 at 10x and 0.8470 versus 0.8301 at 15x. Radiological assessments revealed that PFT(VS)-based DL scored 2.9±0.30 and 2.73±0.45 at 5x and 10x, whereas NUFFT(GD)-based DL scored 2.7±0.47 and 2.40±0.50, respectively. Our methods suggest a promising alternative to NUFFT-based fast radial-MRI for dynamic imaging, prioritizing reconstruction quality in a small region of interest over whole image quality.

PMID:40749276 | DOI:10.1016/j.media.2025.103732

Med Image Anal. 2025 Jul 29;105:103744. doi: 10.1016/j.media.2025.103744. Online ahead of print.

ABSTRACT

Deep learning-driven 3D medical image segmentation generally necessitates dense voxel-wise annotations, which are expensive and labor-intensive to acquire. Cross-annotation, which labels only a few orthogonal slices per scan, has recently emerged as a cost-effective alternative that better preserves the shape and precise boundaries of the 3D object than traditional weak labeling methods such as bounding boxes and scribbles. However, learning from such sparse labels, referred to as barely-supervised learning (BSL), remains challenging due to less fine-grained object perception, less compact class features and inferior generalizability. To tackle these challenges and foster collaboration between model training and human expertise, we propose a Multi-Faceted ConSistency learning (MF-ConS) framework with a Diversity and Uncertainty Sampling-based Active Learning (DUS-AL) strategy, specifically designed for the active BSL scenario. This framework combines a cross-annotation BSL strategy, where only three orthogonal slices are labeled per scan, with an AL paradigm guided by DUS to direct human-in-the-loop annotation toward the most informative volumes under a fixed budget. Built upon a teacher-student architecture, MF-ConS integrates three complementary consistency regularization modules: (i) neighbor-informed object prediction consistency for advancing fine-grained object perception by encouraging the student model to infer complete segmentation from masked inputs; (ii) prototype-driven consistency, which enhances intra-class compactness and discriminativeness by aligning latent feature and decision spaces using fused prototypes; and (iii) stability constraint that promotes model robustness against input perturbations. Extensive experiments on three benchmark datasets demonstrate that MF-ConS (DUS-AL) consistently outperforms state-of-the-art methods under extremely limited annotation.

PMID:40749274 | DOI:10.1016/j.media.2025.103744

Eur J Med Chem. 2025 Jul 25;298:118002. doi: 10.1016/j.ejmech.2025.118002. Online ahead of print.

ABSTRACT

The orthopoxvirus genus, particularly the monkeypox virus (MPXV), continues to pose a significant global public health threat. Therefore, the development of novel anti-orthopoxvirus agents remains an urgent priority. Machine learning has proven to be an effective approach for identifying potential drug candidates. In this study, we implemented a dual-view deep learning model that combines BERT and a graph neural network to analyze molecular sequences and structural graphs. The model was trained following a pre-training-then-fine-tuning paradigm and was subsequently applied to identify new molecules with potential anti-orthopoxvirus activity. Notably, a cinnamoyl anthranilic acid derivative (compound 6) was successfully predicted and demonstrated potent anti-orthopoxvirus effects both in vitro and in vivo. Furthermore, integrin subunit beta 3 (ITGB3) has been validated as one of the direct target protein of 6. In conclusion, we established a robust dual-view deep learning model for the discovery of novel anti-orthopoxvirus agents, and compound 6 is a promising candidate for orthopoxvirus treatment via ITGB3 targeting.

PMID:40749255 | DOI:10.1016/j.ejmech.2025.118002

PLoS One. 2025 Aug 1;20(8):e0327743. doi: 10.1371/journal.pone.0327743. eCollection 2025.

ABSTRACT

INTRODUCTION: Retinal diseases, a significant global health concern, often lead to severe vision impairment and blindness, resulting in substantial functional and social limitations. This study explored a novel goal of developing and comparing the performance of multiple state-of-the-art convolutional neural network (CNN) models for the automated detection and classification of retinal diseases using optical coherence tomography (OCT) images.

METHOD: The study utilized several models, including DenseNet121, ResNet50, Inception V3, MobileNet, and OCT images obtained from the WATBORG Eye Clinic, to detect and classify multiple retinal diseases such as glaucoma, macular edema, posterior vitreous detachment (PVD), and normal eye cases. The preprocessing techniques employed included data augmentation, resizing, and one-hot encoding. We also used the Gaussian Process-based Bayesian Optimization (GPBBO) approach to fine-tune the hyperparameters. Model performance was evaluated using the F1-Score, precision, recall, and area under the curve (AUC).

RESULT: All the CNN models evaluated in this study demonstrated a strong capability to detect and classify various retinal diseases with high accuracy. MobileNet achieved the highest accuracy at 96% and AUC of 0.975, closely followed by DenseNet121, which had 95% accuracy and an AUC of 0.963. Inception V3 and ResNet50, while not as high in accuracy, showed potential in specific contexts, with 83% and 79% accuracy, respectively.

CONCLUSION: These results underscore the potential of advanced CNN models for diagnosing retinal diseases. With the exception of ResNet50, the other CNN models displayed accuracy levels that are comparable to other state-of-the-art deep learning models. Notably, MobileNet and DenseNet121 showed considerable promise for use in clinical settings, enabling healthcare practitioners to make rapid and accurate diagnoses of retinal diseases. Future research should focus on expanding datasets, integrating multi-modal data, exploring hybrid models, and validating these models in clinical environments to further enhance their performance and real-world applicability.

PMID:40748964 | DOI:10.1371/journal.pone.0327743

ACS Synth Biol. 2025 Aug 1. doi: 10.1021/acssynbio.5c00250. Online ahead of print.

ABSTRACT

The 5'UTR sequence and N-terminal coding sequence (NCS) have been used to regulate gene expression in Corynebacterium glutamicum (C. glutamicum) microbial cell factories. However, there is currently insufficient research on the relationship between these expression element sequences and the protein expression rate in C. glutamicum. This study established a pattern between 5'UTR and NCS feature sequences and protein expression and validated their effects on protein expression. First, a 5'UTR library and a NCS library containing base N were constructed separately, and a continuous regulatory range across 5 orders of magnitude for the enhanced green fluorescent protein (eGFP) expression was achieved in both libraries by fluorescence activated cell sorting (FACS) and high-throughput sequencing. Next, the relationship between sequence information and protein expression was established based on the 5'UTR sequence and NCS sequence characteristics analysis in terms of CG content, minimum free energy (MFE), tRNA adaptability index, and deep learning. Moreover, four 5'UTR characteristic sequences and four NCS characteristic sequences were finally screened, which showed strong compatibility with different exogenous proteins. Furthermore, dynamic adjustment of eGFP fluorescence intensity from 45% to 511% was achieved through 16 different combinations of the screened four 5'UTR and four NCS sequences, confirming the synergistic effect of these two components. At the same time, these combinations also have a wide range of dynamic regulation of protein expression levels of other recombinant proteins such as mCherry and heavy chain antibody. This study provided a potential tool for finely regulating gene expression or protein production in C. glutamicum.

PMID:40748894 | DOI:10.1021/acssynbio.5c00250

PLoS One. 2025 Aug 1;20(8):e0328013. doi: 10.1371/journal.pone.0328013. eCollection 2025.

ABSTRACT

Financial data prediction and risk assessment represent a complex multi-task problem that requires effective handling of time-series data and multi-dimensional features. Traditional models struggle to simultaneously capture temporal dependencies, global information, and intricate nonlinear relationships, resulting in limited prediction accuracy. To address this challenge, we propose LTR-Net, a multi-module deep learning model that combines LSTM, Transformer, and ResNet. LTR-Net effectively processes the multi-dimensional features and dynamic changes in financial data by incorporating a temporal dependency modeling module, a global information capture module, and a deep feature extraction module. Experimental results demonstrate that LTR-Net significantly outperforms existing mainstream models, including LSTM, GRU, Transformer, and DeepAR, across multiple financial datasets. On the Kaggle Financial Distress Prediction Dataset and the Yahoo Finance Stock Market Data, LTR-Net exhibits higher accuracy, stability, and robustness across various metrics such as MSE, RMSE, MAE, and AUC. Ablation experiments further validate the indispensability of each module within LTR-Net, confirming the pivotal roles of the LSTM, Transformer, and ResNet modules in financial data analysis. LTR-Net not only enhances the accuracy of financial data prediction but also exhibits strong generalization capabilities, making it adaptable to data analysis and risk assessment tasks in other domains.

PMID:40748872 | DOI:10.1371/journal.pone.0328013

IEEE Trans Neural Netw Learn Syst. 2025 Aug 1;PP. doi: 10.1109/TNNLS.2025.3592788. Online ahead of print.

ABSTRACT

Predicting remaining useful life (RUL) plays a crucial role in the prognostics and health management of industrial systems that involve a variety of interrelated sensors. Given a constant stream of time-series sensory data from such systems, deep learning (DL) models have risen to prominence at identifying complex, nonlinear temporal dependencies in these data. In addition to the temporal dependencies of individual sensors, spatial dependencies emerge as important correlations among these sensors, which can be naturally modeled by a temporal graph that describes time-varying spatial relationships. However, the majority of existing studies have relied on capturing discrete snapshots of this temporal graph, a coarse-grained approach that leads to a loss of temporal information. Moreover, given the variety of heterogeneous sensors, it becomes vital that such inherent heterogeneity is leveraged for RUL prediction in temporal sensor graphs. To capture the nuances of the temporal and spatial relationships and heterogeneous characteristics in an interconnected graph of sensors, we introduce a novel model named temporal and heterogeneous graph neural networks (THGNNs). Specifically, THGNN aggregates historical data from neighboring nodes to accurately capture the temporal dynamics and spatial correlations within the stream of sensor data in a fine-grained manner. Moreover, the model leverages feature-wise linear modulation (FiLM) to address the diversity of sensor types, significantly improving the model's capacity to learn the heterogeneity in the data sources. Finally, we have validated the effectiveness of our approach through comprehensive experiments. Our empirical findings demonstrate significant advancements on the N-CMAPSS dataset, achieving improvements of up to 19.2% and 31.6% in terms of two different evaluation metrics over state-of-the-art methods.

PMID:40748812 | DOI:10.1109/TNNLS.2025.3592788