J Pers Med. 2025 Mar 20;15(3):121. doi: 10.3390/jpm15030121.

ABSTRACT

(1) Background: Helminth infections are a widespread global health concern, with Ascaris and taeniasis representing two of the most prevalent infestations. Traditional diagnostic methods, such as egg-based microscopy, are fraught with challenges, including subjectivity and low throughput, often leading to misdiagnosis. This study evaluates the efficacy of advanced deep learning models in accurately classifying Ascaris lumbricoides and Taenia saginata eggs from microscopic images, proposing a technologically enhanced approach for diagnostics in clinical settings. (2) Methods: Three state-of-the-art deep learning models, ConvNeXt Tiny, EfficientNet V2 S, and MobileNet V3 S, are considered. A diverse dataset comprising images of Ascaris, Taenia, and uninfected eggs was utilized for training and validating these models by performing multiclass experiments. (3) Results: All models demonstrated high classificatory accuracy, with ConvNeXt Tiny achieving an F1-score of 98.6%, followed by EfficientNet V2 S at 97.5% and MobileNet V3 S at 98.2% in the experiments. These results prove the potential of deep learning in streamlining and improving the diagnostic process for helminthic infections. The application of deep learning models such as ConvNeXt Tiny, EfficientNet V2 S, and MobileNet V3 S shows promise for efficient and accurate helminth egg classification, potentially significantly enhancing the diagnostic workflow. (4) Conclusion: The study demonstrates the feasibility of leveraging advanced computational techniques in parasitology and points towards a future where rapid, objective, and reliable diagnostics are standard.

PMID:40137437 | DOI:10.3390/jpm15030121

J Funct Morphol Kinesiol. 2025 Feb 22;10(1):73. doi: 10.3390/jfmk10010073.

ABSTRACT

BACKGROUND/OBJECTIVES: Falls among the older adult population represent a significant public health concern, often leading to diminished quality of life and serious injuries that escalate healthcare costs, and they may even prove fatal. Accurate fall risk prediction is therefore crucial for implementing timely preventive measures. However, to date, there is no definitive metric to identify individuals with high risk of experiencing a fall. To address this, the present study proposes a novel approach that transforms biomechanical time-series data, derived from gait analysis, into visual representations to facilitate the application of deep learning (DL) methods for fall risk assessment.

METHODS: By leveraging convolutional neural networks (CNNs) and Siamese neural networks (SNNs), the proposed framework effectively addresses the challenges of limited datasets and delivers robust predictive capabilities.

RESULTS: Through the extraction of distinctive gait-related features and the generation of class-discriminative activation maps using Grad-CAM, the random forest (RF) machine learning (ML) model not only achieves commendable accuracy (83.29%) but also enhances explainability.

CONCLUSIONS: Ultimately, this study underscores the potential of advanced computational tools and machine learning algorithms to improve fall risk prediction, reduce healthcare burdens, and promote greater independence and well-being among the older adults.

PMID:40137325 | DOI:10.3390/jfmk10010073

J Imaging. 2025 Mar 20;11(3):91. doi: 10.3390/jimaging11030091.

ABSTRACT

Human activity recognition (HAR) has emerged as a transformative field with widespread applications, leveraging diverse sensor modalities to accurately identify and classify human activities. This paper provides a comprehensive review of HAR techniques, focusing on the integration of sensor-based, vision-based, and hybrid methodologies. It explores the strengths and limitations of commonly used modalities, such as RGB images/videos, depth sensors, motion capture systems, wearable devices, and emerging technologies like radar and Wi-Fi channel state information. The review also discusses traditional machine learning approaches, including supervised and unsupervised learning, alongside cutting-edge advancements in deep learning, such as convolutional and recurrent neural networks, attention mechanisms, and reinforcement learning frameworks. Despite significant progress, HAR still faces critical challenges, including handling environmental variability, ensuring model interpretability, and achieving high recognition accuracy in complex, real-world scenarios. Future research directions emphasise the need for improved multimodal sensor fusion, adaptive and personalised models, and the integration of edge computing for real-time analysis. Additionally, addressing ethical considerations, such as privacy and algorithmic fairness, remains a priority as HAR systems become more pervasive. This study highlights the evolving landscape of HAR and outlines strategies for future advancements that can enhance the reliability and applicability of HAR technologies in diverse domains.

PMID:40137203 | DOI:10.3390/jimaging11030091

J Imaging. 2025 Mar 19;11(3):88. doi: 10.3390/jimaging11030088.

ABSTRACT

The objective of this study is to propose an advanced image enhancement strategy to address the challenge of reducing radiation doses in pediatric renal scintigraphy. Data from a public dynamic renal scintigraphy database were used. Based on noisier images, four denoising neural networks (DnCNN, UDnCNN, DUDnCNN, and AttnGAN) were evaluated. To evaluate the quality of the noise reduction, with minimal detail loss, the kidney signal-to-noise ratio (SNR) and multiscale structural similarity (MS-SSIM) were used. Although all the networks reduced noise, UDnCNN achieved the best balance between SNR and MS-SSIM, leading to the most notable improvements in image quality. In clinical practice, 100% of the acquired data are summed to produce the final image. To simulate the dose reduction, we summed only 50%, simulating a proportional decrease in radiation. The proposed deep-learning approach for image enhancement ensured that half of all the frames acquired may yield results that are comparable to those of the complete dataset, suggesting that it is feasible to reduce patients' exposure to radiation. This study demonstrates that the neural networks evaluated can markedly improve the renal scintigraphic image quality, facilitating high-quality imaging with lower radiation doses, which will benefit the pediatric population considerably.

PMID:40137200 | DOI:10.3390/jimaging11030088

J Imaging. 2025 Mar 18;11(3):85. doi: 10.3390/jimaging11030085.

ABSTRACT

Semantic segmentation in deep learning is a crucial area of research within computer vision, aimed at assigning specific labels to each pixel in an image. The segmentation of crops, plants, and weeds has significantly advanced the application of deep learning in precision agriculture, leading to the development of sophisticated architectures based on convolutional neural networks (CNNs). This study proposes a segmentation algorithm for identifying plants and weeds using broadband multispectral images. In the first part of this algorithm, we utilize the PIF-Net model for feature extraction and fusion. The resulting feature map is then employed to enhance an optimized U-Net model for semantic segmentation within a broadband system. Our investigation focuses specifically on scenes from the CAVIAR dataset of multispectral images. The proposed algorithm has enabled us to effectively capture complex details while regulating the learning process, achieving an impressive overall accuracy of 98.2%. The results demonstrate that our approach to semantic segmentation and the differentiation between plants and weeds yields accurate and compelling outcomes.

PMID:40137197 | DOI:10.3390/jimaging11030085

J Imaging. 2025 Mar 18;11(3):84. doi: 10.3390/jimaging11030084.

ABSTRACT

Background: This study aims to objectively evaluate the overall quality of colonoscopies using a specially trained deep learning-based semantic segmentation neural network. This represents a modern and valuable approach for the analysis of colonoscopy frames. Methods: We collected thousands of colonoscopy frames extracted from a set of video colonoscopy files. A color-based image processing method was used to extract color features from specific regions of each colonoscopy frame, namely, the intestinal mucosa, residues, artifacts, and lumen. With these features, we automatically annotated all the colonoscopy frames and then selected the best of them to train a semantic segmentation network. This trained network was used to classify the four region types in a different set of test colonoscopy frames and extract pixel statistics that are relevant to quality evaluation. The test colonoscopies were also evaluated by colonoscopy experts using the Boston scale. Results: The deep learning semantic segmentation method obtained good results, in terms of classifying the four key regions in colonoscopy frames, and produced pixel statistics that are efficient in terms of objective quality assessment. The Spearman correlation results were as follows: BBPS vs. pixel scores: 0.69; BBPS vs. mucosa pixel percentage: 0.63; BBPS vs. residue pixel percentage: -0.47; BBPS vs. Artifact Pixel Percentage: -0.65. The agreement analysis using Cohen's Kappa yielded a value of 0.28. The colonoscopy evaluation based on the extracted pixel statistics showed a fair level of compatibility with the experts' evaluations. Conclusions: Our proposed deep learning semantic segmentation approach is shown to be a promising tool for evaluating the overall quality of colonoscopies and goes beyond the Boston Bowel Preparation Scale in terms of assessing colonoscopy quality. In particular, while the Boston scale focuses solely on the amount of residual content, our method can identify and quantify the percentage of colonic mucosa, residues, and artifacts, providing a more comprehensive and objective evaluation.

PMID:40137196 | DOI:10.3390/jimaging11030084

J Imaging. 2025 Mar 3;11(3):76. doi: 10.3390/jimaging11030076.

ABSTRACT

COVID-19 can cause acute infectious diseases of the respiratory system, and may probably lead to heart damage, which will seriously threaten human health. Electrocardiograms (ECGs) have the advantages of being low cost, non-invasive, and radiation free, and is widely used for evaluating heart health status. In this work, a lightweight deep learning network named GM-CBAM-ResNet is proposed for diagnosing COVID-19 based on ECG images. GM-CBAM-ResNet is constructed by replacing the convolution module with the Ghost module (GM) and adding the convolutional block attention module (CBAM) in the residual module of ResNet. To reveal the superiority of GM-CBAM-ResNet, the other three methods (ResNet, GM-ResNet, and CBAM-ResNet) are also analyzed from the following aspects: model performance, complexity, and interpretability. The model performance is evaluated by using the open 'ECG Images dataset of Cardiac and COVID-19 Patients'. The complexity is reflected by comparing the number of model parameters. The interpretability is analyzed by utilizing Gradient-weighted Class Activation Mapping (Grad-CAM). Parameter statistics indicate that, on the basis of ResNet19, the number of model parameters of GM-CBAM-ResNet19 is reduced by 45.4%. Experimental results show that, under less model complexity, GM-CBAM-ResNet19 improves the diagnostic accuracy by approximately 5% in comparison with ResNet19. Additionally, the interpretability analysis shows that CBAM can suppress the interference of grid backgrounds and ensure higher diagnostic accuracy under lower model complexity. This work provides a lightweight solution for the rapid and accurate diagnosing of COVD-19 based on ECG images, which holds significant practical deployment value.

PMID:40137188 | DOI:10.3390/jimaging11030076

J Imaging. 2025 Feb 26;11(3):72. doi: 10.3390/jimaging11030072.

ABSTRACT

In an era where security concerns are ever-increasing, the need for advanced technology to detect visible and concealed weapons has become critical. This paper introduces a novel two-stage method for concealed handgun detection, leveraging thermal imaging and deep learning, offering a potential real-world solution for law enforcement and surveillance applications. The approach first detects potential firearms at the frame level and subsequently verifies their association with a detected person, significantly reducing false positives and false negatives. Alarms are triggered only under specific conditions to ensure accurate and reliable detection, with precautionary alerts raised if no person is detected but a firearm is identified. Key contributions include a lightweight algorithm optimized for low-end embedded devices, making it suitable for wearable and mobile applications, and the creation of a tailored thermal dataset for controlled concealment scenarios. The system is implemented on a chest-worn Android smartphone with a miniature thermal camera, enabling hands-free operation. Experimental results validate the method's effectiveness, achieving an mAP@50-95 of 64.52% on our dataset, improving state-of-the-art methods. By reducing false negatives and improving reliability, this study offers a scalable, practical solution for security applications.

PMID:40137184 | DOI:10.3390/jimaging11030072

Metabolites. 2025 Mar 3;15(3):174. doi: 10.3390/metabo15030174.

ABSTRACT

Background/Objectives: Metabolomics has recently emerged as a key tool in the biological sciences, offering insights into metabolic pathways and processes. Over the last decade, network-based machine learning approaches have gained significant popularity and application across various fields. While several studies have utilized metabolomics profiles for sample classification, many network-based machine learning approaches remain unexplored for metabolomic-based classification tasks. This study aims to compare the performance of various network-based machine learning approaches, including recently developed methods, in metabolomics-based classification. Methods: A standard data preprocessing procedure was applied to 17 metabolomic datasets, and Bayesian neural network (BNN), convolutional neural network (CNN), feedforward neural network (FNN), Kolmogorov-Arnold network (KAN), and spiking neural network (SNN) were evaluated on each dataset. The datasets varied widely in size, mass spectrometry method, and response variable. Results: With respect to AUC on test data, BNN, CNN, FNN, KAN, and SNN were the top-performing models in 4, 1, 5, 3, and 4 of the 17 datasets, respectively. Regarding F1-score, the top-performing models were BNN (3 datasets), CNN (3 datasets), FNN (4 datasets), KAN (4 datasets), and SNN (3 datasets). For accuracy, BNN, CNN, FNN, KAN, and SNN performed best in 4, 1, 4, 4, and 4 datasets, respectively. Conclusions: No network-based modeling approach consistently outperformed others across the metrics of AUC, F1-score, or accuracy. Our results indicate that while no single network-based modeling approach is superior for metabolomics-based classification tasks, BNN, KAN, and SNN may be underappreciated and underutilized relative to the more commonly used CNN and FNN.

PMID:40137139 | DOI:10.3390/metabo15030174

Biomimetics (Basel). 2025 Mar 20;10(3):191. doi: 10.3390/biomimetics10030191.

ABSTRACT

Chemical oxygen demand (COD) is a critical parameter employed to assess the level of organic pollution in water. Accurate COD detection is essential for effective environmental monitoring and water quality assessment. Ultraviolet-visible (UV-Vis) spectroscopy has become a widely applied method for COD detection due to its convenience and the absence of the need for chemical reagents. This non-destructive and reagent-free approach offers a rapid and reliable means of analyzing water. Recently, deep learning has emerged as a powerful tool for automating the process of spectral feature extraction and improving COD prediction accuracy. In this paper, we propose a novel multi-scale one-dimensional convolutional neural network (MS-1D-CNN) fusion model designed specifically for spectral feature extraction and COD prediction. The architecture of the proposed model involves inputting raw UV-Vis spectra into three parallel sub-1D-CNNs, which independently process the data. The outputs from the final convolution and pooling layers of each sub-CNN are then fused into a single layer, capturing a rich set of spectral features. This fused output is subsequently passed through a Flatten layer followed by fully connected layers to predict the COD value. Experimental results demonstrate the effectiveness of the proposed method, as it was compared with three traditional methods and three deep learning methods on the same dataset. The MS-1D-CNN model showed a significant improvement in the accuracy of COD prediction, highlighting its potential for more reliable and efficient water quality monitoring.

PMID:40136845 | DOI:10.3390/biomimetics10030191

Cells. 2025 Mar 7;14(6):394. doi: 10.3390/cells14060394.

ABSTRACT

Pulmonary fibrosis due to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is the leading cause of death in patients with COVID-19. β-catenin, a key molecule in the Wnt/β-catenin signaling pathway, has been shown to be involved in the development of pulmonary fibrosis (e.g., idiopathic pulmonary fibrosis, silicosis). In this study, we developed a SARS-CoV-2-infected A549-hACE2 cell model to evaluate the efficacy of the A549-hACE2 monoclonal cell line against SARS-CoV-2 infection. The A549-hACE2 cells were then subjected to either knockdown or overexpression of the effector β-catenin, and the modified cells were subsequently infected with SARS-CoV-2. Additionally, we employed transcriptomics and raw letter analysis approaches to investigate other potential effects of β-catenin on SARS-CoV-2 infection. We successfully established a model of cellular fibrosis induced by SARS-CoV-2 infection in lung-derived cells. This model can be utilized to investigate the molecular biological mechanisms and cellular signaling pathways associated with virus-induced lung fibrosis. The results of our mechanistic studies indicate that β-catenin plays a significant role in lung fibrosis resulting from SARS-CoV-2 infection. Furthermore, the inhibition of β-catenin mitigated the accumulation of mesenchymal stroma in A549-hACE2 cells. Additionally, β-catenin knockdown was found to facilitate multi-pathway crosstalk following SARS-CoV-2 infection. The fact that β-catenin overexpression did not exacerbate cellular fibrosis may be attributed to the activation of PPP2R2B.

PMID:40136643 | DOI:10.3390/cells14060394

J Pers Med. 2025 Mar 5;15(3):103. doi: 10.3390/jpm15030103.

ABSTRACT

Background: In the United States, approximately 1 in 5 children experience comorbidities with mental illness, including depression and anxiety, which lead to poor general health outcomes. Adolescents with substance use disorders exhibit high rates of co-occurring mental illness, with over 60% meeting diagnostic criteria for another psychiatric condition in community-based treatment programs. Comorbidities are influenced by both genetic (DNA antecedents) and environmental (epigenetic) factors. Given the significant impact of psychiatric comorbidities on individuals' lives, this study aims to uncover common mechanisms through a Genome-Wide Association Study (GWAS) meta-meta-analysis. Methods: GWAS datasets were obtained for each comorbid phenotype, followed by a GWAS meta-meta-analysis using a significance threshold of p < 5E-8 to validate the rationale behind combining all GWAS phenotypes. The combined and refined dataset was subjected to bioinformatic analyses, including Protein-Protein Interactions and Systems Biology. Pharmacogenomics (PGx) annotations for all potential genes with at least one PGx were tested, and the genes identified were combined with the Genetic Addiction Risk Severity (GARS) test, which included 10 genes and eleven Single Nucleotide Polymorphisms (SNPs). The STRING-MODEL was employed to discover novel networks and Protein-Drug interactions. Results: Autism Spectrum Disorder (ASD) was identified as the top manifestation derived from the known comorbid interaction of anxiety, depression, and attention deficit hyperactivity disorder (ADHD). The STRING-MODEL and Protein-Drug interaction analysis revealed a novel network associated with these psychiatric comorbidities. The findings suggest that these interactions are linked to the need to induce "dopamine homeostasis" as a therapeutic outcome. Conclusions: This study provides a reliable genetic and epigenetic map that could assist healthcare professionals in the therapeutic care of patients presenting with multiple psychiatric manifestations, including anxiety, depression, and ADHD. The results highlight the importance of targeting dopamine homeostasis in managing ASD linked to these comorbidities. These insights may guide future pharmacogenomic interventions to improve clinical outcomes in affected individuals.

PMID:40137419 | DOI:10.3390/jpm15030103

Mar Drugs. 2025 Mar 7;23(3):115. doi: 10.3390/md23030115.

ABSTRACT

Marine microplastics (MPs) represent a novel ecological niche, populated by fungi with high potential for pharmaceutical discovery. This study explores the bioactivity of fungal strains isolated from MPs in Mediterranean sediments, focusing on their osteogenic and antiviral activities. Crude extracts prepared via solid-state and submerged-state fermentation were tested for their effects on extracellular matrix mineralization in vitro and bone growth in zebrafish larvae, and for their activity against the respiratory syncytial virus (RSV) and herpes simplex virus type 2 (HSV-2). Several extracts exhibited significant mineralogenic and osteogenic activities, with Aspergillus jensenii MUT6581 and Cladosporium halotolerans MUT6558 being the most performing ones. Antiviral assays identified extracts from A. jensenii MUT6581 and Bjerkandera adusta MUT6589 as effective against RSV and HSV-2 at different extents, with no cytotoxic effect. Although chemical profiling of A. jensenii MUT6581 extract led to the isolation of decumbenones A and B, they did not reproduce the observed bioactivities, suggesting the involvement of other active compounds or synergistic effects. These results highlight the plastisphere as a valuable resource for novel bioactive compounds and suggest the need for further fractionation and characterization to identify the molecules responsible for these promising activities.

PMID:40137301 | DOI:10.3390/md23030115

J Fungi (Basel). 2025 Mar 3;11(3):194. doi: 10.3390/jof11030194.

ABSTRACT

The majority of metagenomic studies are based on the study of bacterial biota. At the same time, the COVID-19 pandemic has prompted interest in the study of both individual fungal pathogens and fungal communities (i.e., the mycobiome) as a whole. Here, in this work, we investigated the human gut mycobiome during COVID-19. Stool samples were collected from patients at two time points: at the time of admission to the hospital (the first time point) and at the time of discharge from the hospital (the second time point). The results of this study revealed that Geotrichum sp. is more represented in a group of patients with COVID-19. Therefore, Geotrichum sp. is elevated in patients at the time of admission to the hospital and underestimated at the time of discharge. Additionally, the influence of factors associated with the diversity of fungal gut microbiota was separately studied, including disease severity and age factors.

PMID:40137232 | DOI:10.3390/jof11030194

Metabolites. 2025 Mar 11;15(3):192. doi: 10.3390/metabo15030192.

ABSTRACT

BACKGROUND: The metabolome of COVID-19 patients has been studied sparsely, with most research focusing on a limited number of plasma metabolites or small cohorts. This is the first study to test saliva metabolites in COVID-19 patients in a comprehensive way, revealing patterns significantly linked to disease and severity, highlighting saliva's potential as a non-invasive tool for pathogenesis or diagnostic studies.

METHODS: We included 30 asymptomatic subjects with no prior COVID-19 infection or vaccination, 102 patients with mild SARS-CoV-2 infection, and 61 hospitalized patients with confirmed SARS-CoV-2 status. Saliva samples were analyzed using hydrophilic interaction liquid chromatography-mass spectrometry (HILIC-MS/MS) in positive and negative ionization modes.

RESULTS: Significant differences in metabolites were identified in COVID-19 patients, with distinct patterns associated with disease severity. Dipeptides such as Val-Glu and Met-Gln were highly elevated in moderate cases, suggesting specific protease activity related to SARS-CoV-2. Acetylated amino acids like N-acetylserine and N-acetylhistidine increased in severe cases. Bacterial metabolites, including muramic acid and indole-3-carboxaldehyde, were higher in mild-moderate cases, indicating that oral microbiota differs according to disease severity. In severe cases, polyamines and organ-damage-related metabolites, such as N-acetylspermine and 3-methylcytidine, were significantly increased. Interestingly, most metabolites that were reduced in moderate cases were elevated in severe cases.

CONCLUSIONS: Saliva metabolomics offers insightful information that is potentially useful in studying COVID-19 severity and for diagnosis.

PMID:40137156 | DOI:10.3390/metabo15030192

Biosensors (Basel). 2025 Mar 8;15(3):175. doi: 10.3390/bios15030175.

ABSTRACT

Reporter systems are widely used to study biomolecular interactions and processes in vivo, representing one of the basic tools used to characterize synthetic regulatory circuits. Here, we developed a method that enables the monitoring of RNA-protein interactions through a reporter system in bacteria with high temporal resolution. For this, we used a Real-Time Protein Expression Assay (RT-PEA) technology for real-time monitoring of a fluorescent reporter protein, while having bacteria growing on solid media. Experimental results were analyzed by fitting a three-variable Gompertz growth model. To validate the method, the interactions between a set of RNA sequences and the RNA-binding protein (RBP) Musashi-1 (MSI1) were evaluated, as well as the allosteric modulation of the interaction by a small molecule (oleic acid). This new approach proved to be suitable to quantitatively characterize RNA-RBP interactions, thereby expanding the toolbox to study molecular interactions in living bacteria, including allosteric modulation, with special relevance for systems that are not suitable to be studied in liquid media.

PMID:40136972 | DOI:10.3390/bios15030175

Curr Oncol. 2025 Mar 19;32(3):179. doi: 10.3390/curroncol32030179.

ABSTRACT

Little is known about patient perspectives regarding consent for obtaining extra research-specific bone marrow (BM) samples during the diagnostic procedure for acute leukemia (AL). This study aimed to better understand patient experiences with consenting to provide these samples and identify potential areas for practice improvement. Semi-structured interviews were conducted with patients treated for AL, 4-6 years prior to the interviews, and healthcare professionals involved with obtaining patient consent and sample collection. A total of 17 patients (14 agreed to provide a sample and 3 did not have a sample in the biobank) and 5 healthcare professionals were interviewed, achieving data saturation. Patients supported increasing public knowledge about research and noted the importance of friends and family in providing emotional support and retaining information. Despite time pressure and anxiety, the decision to donate a research sample did not require much deliberation. Proximal factors informing decisions included impact on patient health and family and anticipated, procedure-associated pain; distal factors included altruism and trust in healthcare professionals. Key information included expected pain and management, the purpose of research samples, and sample security and privacy. Our findings suggest that BM research sample collection may be facilitated through optimizing the environment where information is provided and the type of information provided, including pain management options and the value of the samples for current and future research.

PMID:40136383 | DOI:10.3390/curroncol32030179

Plant Cell Environ. 2025 Mar 26. doi: 10.1111/pce.15488. Online ahead of print.

ABSTRACT

The antioxidative enzyme monodehydroascorbate reductase (MDHAR) is represented by five genes in Arabidopsis, including four that encode cytosolic and peroxisomal proteins. The in planta importance of these specific isoforms during oxidative stress remain to be characterised. T-DNA mutants for MDAR genes encoding cytosolic and peroxisomal isoforms were studied. To examine their roles in conditions of intracellular oxidative stress, mutants were crossed with a cat2 line lacking the major leaf catalase. Enzyme assays in mdar mutants and of recombinant MDHARs suggest that peroxisomal MDHAR1 and cytosolic MDHAR2 are major players in leaf NADH- and NADPH-dependent activities, respectively. All mutants showed a wild-type phenotype when grown in standard conditions. In the cat2 background, loss of peroxisomal MDHAR functions decreased growth whereas loss of the cytosolic MDHAR2 function had no effect on growth but annulled a large part of transcriptomic and phenotypic responses to oxidative stress. The effects of the mdar2 mutation included decreased salicylic acid accumulation and enhanced glutathione oxidation, and were reverted by complementation with the MDAR2 sequence. Together, the data show that the cytosolic MDHAR2 is dispensable in optimal conditions but essential to promote biotic defence responses triggered by oxidative stress.

PMID:40136012 | DOI:10.1111/pce.15488

J Biomol Struct Dyn. 2025 Mar 26:1-26. doi: 10.1080/07391102.2025.2480259. Online ahead of print.

ABSTRACT

Triple-negative breast cancer (TNBC), characterized by the absence of Estrogen Receptor (ER), Progesterone Receptor (PR), and amplified HER2, represents an aggressive subtype devoid of targeted therapies, contributing to heightened mortality rates. Matairesinol (MAT) has demonstrated anti-cancer, anti-inflammatory, immunomodulatory, anti-migratory, and antiangiogenic activities. This study investigates MAT's therapeutic potential for TNBC, employing network pharmacology, molecular docking, and molecular dynamics simulations. Through the integration of MAT and TNBC targets from public databases, we identified 47 potential therapeutic targets. Top 10 hub targets, including HIF1A, ESR1, AKT1, EGFR, HSP90AA1, Src, ERBB2, IGF1, ANXA5, and MAPK1, were revealed through protein-protein interaction analysis. Biological enrichments, encompassing GO and KEGG pathway analyses, unveiled insights into functional roles and associated pathways. The Compound-Targets-Pathways-Disease (C-T-P-D) network illustrated relationships between MAT, its targets, and pertinent pathways. Exploring protein-protein interactions with STRING, followed by validation and supplementation using the GeneMANIA-based functional association (GMFA) method and David web wizard, emphasized the MAPK signaling pathway as a more potential target of MAT against TNBC. The biological significance of these findings underscores MAT's potential as a multi-target inhibitor within multiple signaling pathways related to TNBC, showcasing its efficacy against TNBC. Molecular docking and dynamics simulations substantiated the interaction between MAT and the identified hub targets. In conclusion, our in-silico analysis proposes that MAT could mediate a multi-target and multi-pathway anti-TNBC effect with the MAPK pathway as its novel target pathway. These insights into the potential therapeutic mechanisms of MAT offer valuable directions for further research and the development of interventions against TNBC.

PMID:40135676 | DOI:10.1080/07391102.2025.2480259

J Am Chem Soc. 2025 Mar 26. doi: 10.1021/jacs.5c01526. Online ahead of print.

ABSTRACT

The dynamics of membranes are integral to regulating biological pathways in living systems, particularly in mediating intra- and extracellular communication between membraneless and membranized nano- and microcompartments. Mimicking these dynamics using biomimetic cell structures deepens our understanding of biologically driven processes, including morphological transformations, communication, and molecular sequestration within distinct environments (e.g., (membraneless) organelles, cytoplasm, cells, and the extracellular matrix). In this context, the demembranization of membranized coacervates represents a promising approach to endow them with additional functionalities and dynamic reconfiguration capabilities in response to external or biological stimuli. This versatility broadens their applicability in synthetic biology, systems biology, and biotechnology. Here, we present a strategy for controlled demembranization of membranized coacervate droplets. The membranized coacervates are created by coating membraneless coacervates with terpolymer-based nanoparticles to form a solid-like membrane. The addition of an anionic polysaccharide then triggers the demembranization process arising from electrostatic competition with the membrane components, resulting in polysaccharide-containing demembranized coacervate droplets. This membranization/demembranization process not only allows for the controlled structural reconfiguration of the coacervate entities but also varies their permeability toward (biological) (macro)molecules and nano- and microscale objects. Additionally, integrating an additional polymersome layer in this process facilitates the creation of bilayer and ″Janus-like″ membranized coacervates, advancing the development of coacervate protocells with hierarchical and asymmetric membrane structures. Our work highlights the control over both membranization and demembranization processes of coacervate protocells, establishing a platform for creating advanced protein-containing synthetic protocells with dynamic and diverse (membrane(less)) architectures.

PMID:40135632 | DOI:10.1021/jacs.5c01526