Med Image Anal. 2025 Apr 5;102:103567. doi: 10.1016/j.media.2025.103567. Online ahead of print.

ABSTRACT

Artifacts in histology images pose challenges for accurate diagnosis with deep learning models, often leading to misinterpretations. Existing artifact restoration methods primarily rely on Generative Adversarial Networks (GANs), which approach the problem as image-to-image translation. However, those approaches are prone to mode collapse and can unexpectedly alter morphological features or staining styles. To address the issue, we propose ArtiDiffuser, a counterfactual diffusion model tailored to restore only artifact-distorted regions while preserving the integrity of the rest of the image. Additionally, we show an innovative perspective by addressing the misdiagnosis stemming from artifacts via artifact synthesis as data augmentation, and thereby leverage ArtiDiffuser to unify the artifact synthesis and the restoration capabilities. This synergy significantly surpasses the performance of conventional methods which separately handle artifact restoration or synthesis. We propose a Swin-Transformer denoising network backbone to capture both local and global attention, further enhanced with a class-guided Mixture of Experts (MoE) to process features related to specific artifact categories. Moreover, it utilizes adaptable class-specific tokens for enhanced feature discrimination and a mask-weighted loss function to specifically target and correct artifact-affected regions, thus addressing issues of data imbalance. In downstream applications, ArtiDiffuser employs a consistency regularization strategy that assures the model's predictive accuracy is maintained across original and artifact-augmented images. We also contribute the first comprehensive histology dataset, comprising 723 annotated patches across various artifact categories, to facilitate further research. Evaluations on four distinct datasets for both restoration and synthesis demonstrate ArtiDiffuser's effectiveness compared to GAN-based approaches, used for either pre-processing or augmentation. The code is available at https://github.com/wagnchogn/ArtiDiffuser.

PMID:40188685 | DOI:10.1016/j.media.2025.103567

Environ Int. 2025 Mar 30;198:109427. doi: 10.1016/j.envint.2025.109427. Online ahead of print.

ABSTRACT

Wastewater-based epidemiology (WBE) represents a powerful technique for quantifying the attenuation characteristics and consumption of pharmaceuticals. In addition to WBE, no further methods have been developed to assess the wastewater stability related to antidepressants (ADs). In this study, the biodegradability, solubility, and adsorption or partition of 66 ADs were objectively scored according to the relevant guidelines of the Organisation for Economic Cooperation and Development. An assessment framework and the MSSL-RealFormer classification model of ADs wastewater stability were constructed based on physicochemical properties to predict the ADs wastewater stability and the quantitative structure-activity relationship. The constructed MSSL-RealFormer classification model exhibited a markedly higher prediction accuracy than traditional methods. Furthermore, 15 high-stable ADs in wastewater with low biodegradability, high solubility, and low adsorption or partition were identified. SHapley Additive exPlanation method demonstrated that group hydrophobicity, electrostatic and van der Waals forces exerted a significant influence on the ADs wastewater stability. And molecular stability was found to be significantly correlated with the ADs wastewater stability. A combination of density functional theory and MSSL-RealFormer classification model was employed to identify 17 high-stable transformation products of nine medium- and low-stable ADs in wastewater. The Ecological Structure Activity Relationships model demonstrated that bupropion, tapentadol and chlorpheniramine exhibited significant acute toxicity to the aquatic food chain. In this study, a novel deep learning model was constructed to rapidly screen the correlation between the ADs wastewater stability and their molecular structures. It is anticipated to prove a favorable tool for optimizing the wastewater stability screening of pharmaceuticals.

PMID:40188602 | DOI:10.1016/j.envint.2025.109427

Comput Methods Programs Biomed. 2025 Mar 27;265:108728. doi: 10.1016/j.cmpb.2025.108728. Online ahead of print.

ABSTRACT

BACKGROUND AND OBJECTIVE: In the medical field of digital pathology, many tasks rely on visual assessments of tissue patterns or cells, presenting an opportunity to apply computer vision methods. However, acquiring a substantial number of annotations for developing deep learning algorithms remains a bottleneck. The annotation process is inherently biased due to various constraints, including labor shortages, high costs, time inefficiencies, and a strongly imbalanced distribution of labels. This study explores available solutions for reducing the costs of annotation bootstrapping in the challenging task of follicular lymphoma diagnosis.

METHODS: We compare three distinct approaches to annotation bootstrapping: extensive manual annotations, active learning, and weak supervision. We propose a hybrid architecture for centroblast and centrocyte detection from whole slide images, based on a custom cell encoder and contextual encoding derived from foundation models for digital pathology. We collected a dataset of 41 whole slide images scanned with a 20x objective lens and resolution 0.24μm/pixel, from which 12,704 cell annotations were gathered.

RESULTS: Applying our proposed active learning workflow led to an almost twofold increase in the number of samples within the minority class. The best bootstrapping method improved the overall performance of the detection algorithm by 18 percentage points, yielding a macro-averaged F1-score, precision, and recall of 63%.

CONCLUSIONS: The results of this study may find applications in other digital pathology problems, particularly for tasks involving a lack of homogeneous cell clusters within whole slide images.

PMID:40188578 | DOI:10.1016/j.cmpb.2025.108728

BMC Med. 2025 Apr 7;23(1):200. doi: 10.1186/s12916-025-04022-0.

ABSTRACT

BACKGROUND: Influenza A virus (IAV) is a major cause of seasonal and global pandemics, posing serious health risks. Repositioning approved drugs offers an efficient antiviral strategy, particularly as calcium (Ca2⁺) is crucial for IAV infection, making Ca2⁺ channel blockers (CCBs) promising candidates for antiviral agents.

METHODS: The in vitro antiviral activity of cilnidipine was evaluated using MTT assays, qRT-PCR, plaque assays, and western blotting. Mechanistic studies involved time-of-addition, viral internalization, pseudovirus neutralization, and HA (hemagglutinin) syncytium assays. For in vivo analysis, BALB/c mice were intranasally infected to evaluate the effects of cilnidipine on viral titer, lung index, pulmonary inflammatory mediators, and survival rate.

RESULTS: In vitro, cilnidipine exhibits antiviral activity against IAV during the early stages of infection. It disrupts clathrin- and caveolin-mediated endocytosis to inhibit the internalization of IAV and interacts with the viral HA2 subunit to impede virus membrane fusion. Additionally, cilnidipine suppresses the PI3K-AKT and p38 MAPK pathways activated by IAV infections. In vivo, cilnidipine reduces virus titers and lung index, ameliorates lung pathology, and inhibits pulmonary inflammatory mediator expression, improving survival rates.

CONCLUSIONS: These findings highlight the promising anti-IAV properties of cilnidipine both in vitro and in vivo, suggesting its potential as a clinical agent for emergencies against influenza outbreaks.

PMID:40189517 | DOI:10.1186/s12916-025-04022-0

J Microbiol Methods. 2025 Apr 4:107125. doi: 10.1016/j.mimet.2025.107125. Online ahead of print.

ABSTRACT

Conventional clinical microbiological techniques are enhanced by the introduction of artificial intelligence (AI). Comprehensive data processing and analysis enabled the development of curated datasets that has been effectively used in training different AI algorithms. Recently, a number of machine learning (ML) and deep learning (DL) algorithms are developed and evaluated using diverse microbiological datasets. These datasets included spectral analysis (Raman and MALDI-TOF spectroscopy), microscopic images (Gram and acid fast stains), and genomic and protein sequences (whole genome sequencing (WGS) and protein data banks (PDBs)). The primary objective of these algorithms is to minimize the time, effort, and expenses linked to conventional analytical methods. Furthermore, AI algorithms are incorporated with quantitative structure-activity relationship (QSAR) models to predict novel antimicrobial agents that address the continuing surge of antimicrobial resistance. During the COVID-19 pandemic, AI algorithms played a crucial role in vaccine developments and the discovery of new antiviral agents, and introduced potential drug candidates via drug repurposing. However, despite their significant benefits, the implementation of AI encounters various challenges, including ethical considerations, the potential for bias, and errors related to data training. This review seeks to provide an overview of the most recent applications of artificial intelligence in clinical microbiology, with the intention of educating a wider audience of clinical practitioners regarding the current uses of machine learning algorithms and encouraging their implementation. Furthermore, it will discuss the challenges related to the incorporation of AI into clinical microbiology laboratories and examine future opportunities for AI within the realm of infectious disease epidemiology.

PMID:40188989 | DOI:10.1016/j.mimet.2025.107125

Respir Med. 2025 Apr 4:108076. doi: 10.1016/j.rmed.2025.108076. Online ahead of print.

ABSTRACT

The European Cystic Fibrosis Society-Clinical Trials Network (ECFS-CTN) herein proposes guidance for the use of chest CT-scans for the regular monitoring of lung disease in CF. Statements were completed in a 3-step process: the questions were identified via an anonymous online survey, followed by a comprehensive literature search, and a final Delphi process. The guidance recommends the use of ultra-low dose CT scans (effective radiation dose, 0.08 mSv; equivalent to 2 to 4 chest X-rays), tracking of patients' cumulative radiation and effective communication strategies using "de-medicalised" information for shared decision making. Chest CT scans (with lung volume monitoring) are not recommended systematically in both children and adults. Ultimate responsibility for justifying a chest CT scan lies with the individual professionals directly involved, the final decision being influenced by indications, costs, expertise, available material, resources and/or the patient's values, as well as possible impact on treatment modalities.

PMID:40189162 | DOI:10.1016/j.rmed.2025.108076

Respir Med. 2025 Apr 4:108087. doi: 10.1016/j.rmed.2025.108087. Online ahead of print.

ABSTRACT

BACKGROUND: Diagnostic tests for cystic fibrosis (CF) are not readily available in resource-limited settings, which leads to delay in diagnosis and treatment of children with CF.

METHODS: In a multicentric prospective study, children with recurrent/ persistent pneumonia, failure to thrive, or steatorrhea with suspicion of CF were enrolled. Sweat chloride concentration was measured to confirm the diagnosis of CF. Diagnostic accuracy of various clinical features, ancillary laboratory investigations (serum electrolytes, blood gas, stool fat globules), and aquagenic wrinkling for CF diagnosis was estimated. CF clinical diagnostic score (CF-CDS) was developed by combining significant parameters in stepwise logistic regression.

RESULTS: Of 860 children enrolled, 313 (36.7%) were diagnosed with CF. History of a sibling with CF, clubbing, hyponatremia, metabolic alkalosis, stool fat globule positivity, sputum Pseudomonas isolation, and aquagenic wrinkling within 3 minutes were found to be independently associated with a diagnosis of CF. CF-CDS score developed by combining these parameters demonstrated excellent diagnostic accuracy for diagnosis of CF [AUROC of 0.923 (95%CI: 0.899, 0.946)]. At a cut-off of ≥ 2.5, CF-CDS had sensitivity and specificity of 87.64% and 81.02%, respectively.

CONCLUSION: CF-CDS has excellent diagnostic accuracy for diagnosis of CF in children and can be used to decide on starting treatment of CF pending confirmatory tests when confirmatory tests are not readily available.

PMID:40189161 | DOI:10.1016/j.rmed.2025.108087

ISME J. 2025 Apr 6:wraf065. doi: 10.1093/ismejo/wraf065. Online ahead of print.

ABSTRACT

Virulent bacteriophages (or phages) are viruses that specifically infect and lyse a bacterial host. When multiple phages co-infect a bacterial host, the extent of lysis, dynamics of bacteria-phage and phage-phage interactions are expected to vary. The objective of this study is to identify the factors influencing the interaction of two virulent phages with different Pseudomonas aeruginosa growth states (planktonic, an infected epithelial cell line, and biofilm) by measuring the bacterial time-kill and individual phage replication kinetics. A single administration of phages effectively reduced P. aeruginosa viability in planktonic conditions and infected human lung cell cultures, but phage-resistant variants subsequently emerged. In static biofilms, the phage combination displayed initial inhibition of biofilm dispersal, but sustained control was achieved only by combining phages and meropenem antibiotic. In contrast, adherent biofilms showed tolerance to phage and/or meropenem, suggesting a spatio-temporal variation in the phage-bacterial interaction. The kinetics of adsorption of each phage to P. aeruginosa during single- or co-administration were comparable. However, the phage with the shorter lysis time depleted bacterial resources early and selected a specific nucleotide polymorphism that conferred a competitive disadvantage and cross-resistance to the second phage. The extent and strength of this phage-phage competition and genetic loci conferring phage resistance, are, however, P. aeruginosa genotype dependent. Nevertheless, adding phages sequentially resulted in their unimpeded replication with no significant increase in bacterial host lysis. These results highlight the interrelatedness of phage-phage competition, phage resistance and specific bacterial growth state (planktonic/biofilm) in shaping the interplay among P. aeruginosa and virulent phages.

PMID:40188480 | DOI:10.1093/ismejo/wraf065

Sci Rep. 2025 Apr 7;15(1):11813. doi: 10.1038/s41598-025-96234-w.

ABSTRACT

Alzheimer's disease (AD) subjects usually show more profound morphological changes with time compared to cognitively normal (CN) individuals. These changes are the combination of two major biological processes: normal aging and AD pathology. Investigating normal aging and residual morphological changes separately can increase our understanding of the disease. This paper proposes two scores, the aging score (AS) and the AD-specific score (ADS), whose purpose is to measure these two components of brain atrophy independently. For this, in the first step, we estimate the atrophy due to the normal aging of CN subjects by computing the expected deformation required to match imaging templates generated at different ages. We used a state-of-the-art generative deep learning model for generating such imaging templates. In the second step, we apply deep learning-based diffeomorphic registration to align the given image of a subject with a reference imaging template. Parametrization of this deformation field is then decomposed voxel-wise into their parallel and perpendicular components with respect to the parametrization of the expected atrophy of CN individuals in one year computed in the first step. AS and ADS are the normalized scores of these two components, respectively. We evaluated these two scores on the OASIS-3 dataset with 1,014 T1-weighted MRI scans. Of these, 326 scans were from CN subjects, and 688 scans were from subjects diagnosed with AD at various stages of clinical severity, as defined by clinical dementia rating (CDR) scores. Our results reveal that AD is marked by both disease-specific brain changes and an accelerated aging process. Such changes affect brain regions differently. Moreover, the proposed scores were sensitive to detect changes in the early stages of the disease, which is promising for its potential future use in clinical studies. Our code is freely available at https://github.com/Fjr9516/DBM_with_DL .

PMID:40189702 | DOI:10.1038/s41598-025-96234-w

Sci Rep. 2025 Apr 6;15(1):11781. doi: 10.1038/s41598-025-96297-9.

ABSTRACT

Mandarin orange is a popular fruit in China and known worldwide for its unique flavor and nutritional benefits. As consumer demand for fruit quality increases, the fine assessment and grading of fruit sweetness-especially through non-destructive testing techniques-are becoming increasingly important in agriculture and commerce. In this paper, a new Attention for Orange (AO) attention mechanism and Multiscale Feature Optimization (MFO) feature extraction module are designed and combined with VGG13 convolutional neural network (CNN), innovatively proposed VGG-MFO-Orange CNN model for accurately classifying mandarin oranges with different sweetness. First, a sample of Linhai mandarin oranges was collected, and a sweetness triple classification dataset with 5022 images was formed, utilizing image acquisition and sugar detection. The proposed model was then trained against six influential classical CNN models: DenseNet121, MobileNet_v2, ResNet50, ShuffleNet, VGG13, and VGG13_bn. The experimental results showed that our model achieved an accuracy of 86.8% on the validation set, which was significantly better than the other six models. It also demonstrated excellent generalization ability and effectiveness in predicting the sweetness of Linhai mandarin oranges. Therefore, our model can provide an efficient means of fruit grading for agricultural production, contribute to agricultural modernization, and enhance the competitiveness of agricultural products in the market.

PMID:40189693 | DOI:10.1038/s41598-025-96297-9

Sci Rep. 2025 Apr 6;15(1):11765. doi: 10.1038/s41598-025-96071-x.

ABSTRACT

Hyperthermia (HT) in combination with radio- and/or chemotherapy has become an accepted cancer treatment for distinct solid tumour entities. In HT, tumour tissue is exogenously heated to temperatures between 39 and 43 °C for 60 min. Temperature monitoring can be performed non-invasively using dynamic magnetic resonance imaging (MRI). However, the slow nature of MRI leads to motion artefacts in the images due to the movements of patients during image acquisition. By discarding parts of the data, the speed of the acquisition can be increased - known as undersampling. However, due to the invalidation of the Nyquist criterion, the acquired images might be blurry and can also produce aliasing artefacts. The aim of this work was, therefore, to reconstruct highly undersampled MR thermometry acquisitions with better resolution and with fewer artefacts compared to conventional methods. The use of deep learning in the medical field has emerged in recent times, and various studies have shown that deep learning has the potential to solve inverse problems such as MR image reconstruction. However, most of the published work only focuses on the magnitude images, while the phase images are ignored, which are fundamental requirements for MR thermometry. This work, for the first time, presents deep learning-based solutions for reconstructing undersampled MR thermometry data. Two different deep learning models have been employed here, the Fourier Primal-Dual network and the Fourier Primal-Dual UNet, to reconstruct highly undersampled complex images of MR thermometry. MR images of 44 patients with different sarcoma types who received HT treatment in combination with radiotherapy and/or chemotherapy were used in this study. The method reduced the temperature difference between the undersampled MRIs and the fully sampled MRIs from 1.3 to 0.6 °C in full volume and 0.49 °C to 0.06 °C in the tumour region for a theoretical acceleration factor of 10.

PMID:40189690 | DOI:10.1038/s41598-025-96071-x

Sci Rep. 2025 Apr 6;15(1):11754. doi: 10.1038/s41598-025-95871-5.

ABSTRACT

To investigate the potential of employing artificial intelligence (AI) -driven breast ultrasound analysis models for the classification of glandular tissue components (GTC) in dense breast tissue. A total of 1,848 healthy women with mammograms classified as dense breast were enrolled in this prospective study. Residual Network (ResNet) 101 classification model and ResNet with fully Convolutional Networks (ResNet + FCN) segmentation model were trained. The better effective model was selected to appraise the classification performance of 3 breast radiologists and 3 non-breast radiologists. The evaluation metrics included sensitivity, specificity, and positive predictive value (PPV). The ResNet101 model demonstrated superior performance compared to the ResNet + FCN model. It significantly enhanced the classification sensitivity of all radiologists by 0.060, 0.021, 0.170, 0.009, 0.052, and 0.047, respectively. For P1 to P4 glandular, the PPVs of all radiologists increased by 0.154, 0.178, 0.027, and 0.109 with Ai-assisted. Notably, the non-breast radiologists experienced a particularly substantial rise in PPV (p < 0.01). This study trained ResNet 101 deep learning model is a reliable and accurate system for assisting different experienced radiologists differentiate dense breast glandular tissue components in ultrasound images.

PMID:40189689 | DOI:10.1038/s41598-025-95871-5

Sci Rep. 2025 Apr 6;15(1):11750. doi: 10.1038/s41598-025-95985-w.

ABSTRACT

Cassava is a tuberous edible plant native to the American tropics and is essential for its versatile applications including cassava flour, bread, tapioca, and laundry starch. Cassava leaf diseases reduce crop yields, elevate production costs, and disrupt market stability. This places significant burdens on farmers and economies while highlighting the need for effective management strategies. Traditional methods of manual disease diagnosis are costly, labor-intensive, and time-consuming. This research aims to address the challenge of accurate disease classification by overcoming the limitations of existing methods, which encounter difficulties with the complexity and variability of leaf disease symptoms. To the best of our knowledge, this is the first study to propose a novel dual-track feature aggregation architecture that integrates the Residual Inception Positional Encoding Attention (RIPEA) Network with EfficientNet for the classification of cassava leaf diseases. The proposed model employs a dual-track feature aggregation architecture which integrates the RIPEA Network with EfficientNet. The RIPEA track extracts significant features by leveraging residual connections for preserving gradients and uses multi-scale feature fusion for combining fine-grained details with broader patterns. It also incorporates Coordinate and Mixed Attention mechanisms which focus on cross-channel and long-range dependencies. The extracted features from both tracks are aggregated for classification. Furthermore, it incorporates an image augmentation method and a cosine decay learning rate schedule to improve model training. This improves the ability of the model to accurately differentiate between Cassava Bacterial Blight (CBB), Brown Streak Disease (CBSD), Green Mottle (CGM), Mosaic Disease (CMD), and healthy leaves, addressing both local textures and global structures. Additionally, to enhance the interpretability of the model, we apply Grad-CAM to provide visual explanations for the model's decision-making process, helping to understand which regions of the leaf images contribute to the classification results. The proposed network achieved a classification accuracy of 93.06%.

PMID:40189680 | DOI:10.1038/s41598-025-95985-w

Sci Rep. 2025 Apr 6;15(1):11757. doi: 10.1038/s41598-025-96385-w.

ABSTRACT

Coherent beam combination has emerged as a promising strategy for overcoming the power limitations of individual fibre lasers. This approach relies on maintaining precise phase difference between the constituent beamlets, which are typically established using phase retrieval algorithms. However, phase locking is often studied under the assumption that the power levels of the beamlets remain stable, an idealisation that does not hold always in practical applications. Over the operational lifetime of fibre lasers, power degradation inevitably occurs, introducing additional challenges to phase retrieval. To address this, we propose a deep learning algorithm for single-step simultaneous phase and amplitude identification, directly from a single camera observation of the intensity distribution of the combined beam. By leveraging its ability to detect and interpret subtle variations in intensity interference patterns, the deep learning approach can accurately disentangle phase and power contributions, even in the presence of significant power fluctuations. Using a spatial light modulator, we systematically investigate the impact of power-level fluctuations on phase retrieval within a simulated coherent beam combination system. Furthermore, we explore the scalability of this deep learning approach by evaluating its ability to achieve the required phase and amplitude precision as the number of beamlets increases.

PMID:40189661 | DOI:10.1038/s41598-025-96385-w

Sci Rep. 2025 Apr 6;15(1):11747. doi: 10.1038/s41598-025-96053-z.

ABSTRACT

The utility of CT-derived parameters for hepatic steatosis assessment has primarily focused on non-alcoholic fatty liver disease. This study aimed to evaluate their applicability in chronic hepatitis B (CHB) through a retrospective analysis of 243 CHB patients. Using deep-learning-based 3D organ segmentation on abdominal CT scans at 100 kVp, the mean volumetric CT attenuation of the liver and spleen was automatically measured on pre-contrast (liver (L)_pre and spleen (S)_pre) and post-contrast (L_post and S_post) portal venous phase images. To identify mild, moderate, and severe steatosis (S1, S2, and S3 based on the controlled attenuation parameter), L_pre showed areas under the receiver operating characteristic curve (AUROCs) of 0.695, 0.779, and 0.795, significantly higher than L-S_pre (0.633, 0.691, and 0.732; Ps = 0.02, 0.003, and 0.03). Post-contrast parameters demonstrated slightly lower AUROCs than their pre-contrast counterparts (Ps = 0.15-0.81). Concomitant hepatic fibrosis influenced diagnostic performance, with CT parameters performing better in patients without severe fibrosis than those with (F3-4 on transient elastography), though statistical significance was only observed for L-S_post in severe steatosis (P = 0.037). In conclusion, CT attenuation-based parameters extracted through automated 3D analysis show promise as a tool for assessing hepatic steatosis in patients with CHB.

PMID:40189652 | DOI:10.1038/s41598-025-96053-z

Sci Rep. 2025 Apr 6;15(1):11744. doi: 10.1038/s41598-025-95819-9.

ABSTRACT

Neural style transfer (NST) has opened new possibilities for digital art by enabling the blending of distinct artistic styles with content from various images. However, traditional NST methods often need help balancing style fidelity and content preservation, and many models need more computational efficiency, limiting their applicability for real-time applications. This study aims to enhance the efficiency and quality of NST by proposing a refined model that addresses key challenges in content retention, style fidelity, and computational performance. Specifically, the research explores techniques to improve the visual coherence of style transfer, ensuring consistency and accessibility for practical use. The proposed model integrates Adaptive Instance Normalization (AdaIN) and Gram matrix-based style representation within a convolutional neural network (CNN) architecture. The model is evaluated using quantitative metrics such as content loss, style loss, Structural Similarity Index (SSIM), and processing time, along with a qualitative assessment of content and style consistency across various image pairs. The proposed model significantly improves content and style balance, with content and style loss values reduced by 15% compared to baseline models. The optimal configuration yields an SSIM score of 0.88 for medium style intensity, maintaining structural integrity while achieving stylistic effects. Additionally, the model's processing time is reduced by 76%, making it suitable for near-real-time applications. Style fidelity scores remain high across various artistic styles, with minimal loss in content retention. The refined NST model balances style and content effectively, enhancing visual quality and computational efficiency. These advancements make NST more accessible for real-time artistic applications, providing a versatile digital art, design, and multimedia production tool.

PMID:40189651 | DOI:10.1038/s41598-025-95819-9

Sci Rep. 2025 Apr 6;15(1):11778. doi: 10.1038/s41598-025-96523-4.

ABSTRACT

Agriculture is an essential foundation that supports numerous economies, and the longevity of the coffee business is of paramount significance. Controlling and safeguarding coffee farms from harmful pests, including the Coffee Berry Borer, Mealybugs, Scales, and Leaf Miners, which may drastically affect crop productivity and quality. Standard methods for detecting pest diseases sometimes need specialized knowledge or thorough analysis, leading to a substantial commitment of time and effort. To address this challenge, researchers have explored the use of computer vision and deep learning techniques for the automated detection of plant pest diseases. This paper presents a novel strategy for the early detection of coffee crop killers using Hybrid Vision Graph Neural Networks (HV-GNN) in coffee plantations. The model was trained and validated using a curated dataset of 2850 labelled coffee plant images, which included diverse insect infestations. The HV-GNN design allows the model to recognize individual pests within images and capture the complex relationships between them, potentially leading to improved detection accuracy. HV-GNN proficiently detect pests by analyzing their visual characteristics and elucidating the interconnections among pests in images. Experimental findings indicate that HV-GNN attain a detection accuracy of 93.6625%, exceeding that of leading models. The increased accuracy underscores the feasibility of practical implementation, enabling proactive pest control to protect coffee farms and improve agricultural output.

PMID:40189644 | DOI:10.1038/s41598-025-96523-4

Sci Rep. 2025 Apr 6;15(1):11772. doi: 10.1038/s41598-025-91225-3.

ABSTRACT

Image observation method is a key method for shale reservoir evaluation. At the micro scale, scanning electron microscope images can be used to accurately understand the structural characteristics of shale. Most of the current research is to artificially identify the microstructure of shale. This approach has subjective limitations and makes it difficult to process images in batches on a large scale. We take the scanning electron microscope image as the research object, and the shale deep learning theory as the research method to realize the intelligent identification of microscopic substances in the shale scanning electron microscope image. The results show that the improved deep learning model performs better than other deep learning models. The maximum values of Precision, Recall, mAP50 and mAP50-95 reached 0.94442, 0.91695, 0.9579 and 0.71547, respectively. The functions of the optimized Yolov8 model were integrated with SEM technology. In engineering practice, it can assist researchers to quickly locate object substances and obtain high-quality SEM images, effectively improving the efficiency and accuracy of reservoir evaluation. In addition, this technology has great potential for development, and it is expected to play an important role in expanding to a variety of fields such as medicine and materials science by changing the test object.

PMID:40189619 | DOI:10.1038/s41598-025-91225-3

World J Microbiol Biotechnol. 2025 Apr 7;41(4):127. doi: 10.1007/s11274-025-04350-6.

ABSTRACT

Rhodococcus sp. DK17 has been previously isolated from oil-contaminated soil and studied for its ability to degrade various monocyclic alkylbenzenes. This study investigated the decomposition of kraft lignin (genes, enzymes, and metabolic pathways) by DK17, using whole-genome sequencing data, as a potential biocatalyst for biotechnological lignin valorization. DK17 used kraft lignin and its main degradative metabolites, such as vanillin and vanillic acid, as growth substrates. High-performance liquid chromatography revealed that DK17 converted dehydrodivanillin (a representative lignin model compound). Quantitative polymerase chain reaction of mRNAs from DK17 cells induced in the presence of lignin showed that the putative genes coding for two copies of dye-decolorizing peroxidases (dypB1 and dypB2) were upregulated 1.6- and 2.4-fold after 5 and 24 h of induction, respectively, compared with glucose-induced cells. Vanillic acid induced dypB1 and dypB2 at lower levels than lignin by 1.4- and 1.6-fold after 5 and 24 h of induction, respectively. Computational homology analysis using the DypB1 and DypB2 protein sequences also predicted their initial roles in lignin decomposition. The duplicated dyp genes are believed to allow DK17 to achieve prolonged and continuous initial lignin decomposition, cleaving C-C and C-O-C linkages in the main lignin structure, the arylglycerol-β-aryl ether. Based on the above data, DK17 appears to initiate oxidative lignin decomposition using DyPs, producing smaller metabolites, such as vanillin and vanillic acid, which could be accumulated as value-added bioproducts (in metabolically engineered mutant strains) or further degraded for cell growth (in wild-type strains) via an ortho-ring cleavage pathway.

PMID:40189716 | DOI:10.1007/s11274-025-04350-6

Sci Rep. 2025 Apr 6;15(1):11789. doi: 10.1038/s41598-025-95537-2.

ABSTRACT

Nutraceuticals exert a series of health benefits, including protection against cardiovascular diseases. In this study, naringin, naringenin, and quercetin were tested for their safety and efficacy in ameliorating angiotensin (Ang) II-induced cardiac hypertrophy through carbonic anhydrase II (CA-II) inhibition. In silico molecular docking and MD simulations exhibited that naringin strongly binds CA-II with a docking score of -9.55 kcal/mol and hydrogen bonding energy of -6.07 kcal/mol. Naringin formed stable hydrogen bond interactions with Asn62, Trp5, and N-acetyl His4 via catalytic water molecule, and a continuous interaction via major water bridge with N-acetyl His4, His4, and Trp5. Moreover, naringin effectively inhibited CA-II activity with an IC50 value of 82.99 ± 4.92 nM, followed by naringenin and quercetin. Of note, all the tested nutraceuticals were found to be safe as evident from the cell viability assays. Further, naringin effectively attenuated cardiac hypertrophy, as indicated by the reductions in the Ang II-induced increases in cell surface area of H9c2 cardio myoblasts (165.6 ± 1.26% Ang II vs. 109.8 ± 1.88% Ang II + naringin), followed by naringenin and quercetin. Furthermore, naringin significantly inhibited CA-II activity (191.77 ± 7.69% Ang II vs. 120.16 ± 5.52% Ang II + naringin) and suppressed Ang II-induced CA-II and Na+/H+ exchanger 1 (NHE1) protein expression. Besides, naringin suppressed Ang II-induced CA-II, NHE1, Na+/Ca2+ exchanger 1 (NCX1), and angiotensin-converting enzyme (ACE1) mRNA expression. Collectively, naringin when compared to naringenin and quercetin effectively attenuated Ang II-induced cardio myoblast hypertrophy, CA-II activity, CA-II, and NHE1 expression. The naringin-mediated attenuation of cardiac hypertrophy might be through the inhibition of CA-II enzyme activity, and the suppression of NHE1, and NCX1.

PMID:40189613 | DOI:10.1038/s41598-025-95537-2