Front Med (Lausanne). 2025 Mar 13;12:1507226. doi: 10.3389/fmed.2025.1507226. eCollection 2025.

ABSTRACT

INTRODUCTION: Pterygium, a prevalent ocular disorder, requires accurate severity assessment to optimize treatment and alleviate patient suffering. The growing patient population and limited ophthalmologist resources necessitate efficient AI-based diagnostic solutions. This study aims to develop an automated grading system combining deep learning and image processing techniques for precise pterygium evaluation.

METHODS: The proposed system integrates two modules: 1) A semantic segmentation module utilizing an improved TransUnet architecture for pixel-level pterygium localization, trained on annotated slit-lamp microscope images from clinical datasets. 2) A severity assessment module employing enhanced curve fitting algorithms to quantify pterygium invasion depth in critical ocular regions. The framework merges deep learning with traditional computational methods for comprehensive analysis.

RESULTS: The semantic segmentation model achieved an average Dice coefficient of 0.9489 (0.9041 specifically for pterygium class) on test datasets. In clinical validation, the system attained 0.9360 grading accuracy and 0.9363 weighted F1 score. Notably, it demonstrated strong agreement with expert evaluations (Kappa coefficient: 0.8908), confirming its diagnostic reliability.

DISCUSSION: The AI-based diagnostic method proposed in this study achieves automatic grading of pterygium by integrating semantic segmentation and curve fitting technology, which is highly consistent with the clinical evaluation of doctors. The quantitative evaluation framework established in this study is expected to meet multiple clinical needs beyond basic diagnosis. The construction of the data set should continue to be optimized in future studies.

PMID:40151829 | PMC:PMC11949100 | DOI:10.3389/fmed.2025.1507226

PeerJ Comput Sci. 2025 Mar 24;11:e2644. doi: 10.7717/peerj-cs.2644. eCollection 2025.

ABSTRACT

Social media platforms such as X, Facebook, and Instagram have become essential avenues for individuals to articulate their opinions, especially during global emergencies. These platforms offer valuable insights that necessitate analysis for informed decision-making and a deeper understanding of societal trends. Sentiment analysis is crucial for assessing public sentiment toward specific issues; however, applying it to dialectal Arabic presents considerable challenges in natural language processing. The complexity arises from the language's intricate semantic and morphological structures, along with the existence of multiple dialects. This form of analysis, also referred to as sentiment classification, opinion mining, emotion mining, and review mining, is the focus of this study, which analyzes tweets from three benchmark datasets: the Arabic Sentiment Tweets Dataset (ASTD), the A Twitter-based Benchmark Arabic Sentiment Analysis Dataset (ASAD), and the Tweets Emoji Arabic Dataset (TEAD). The research involves experimentation with a variety of comparative models, including machine learning, deep learning, transformer-based models, and a transformer-based ensemble model. Feature extraction for both machine learning and deep learning approaches is performed using techniques such as AraVec, FastText, AraBERT, and Term Frequency-Inverse Document Frequency (TF-IDF). The study compares machine learning models such as support vector machine (SVM), naïve Bayes (NB), decision tree (DT), and extreme gradient boosting (XGBoost) with deep learning models such as convolutional neural networks (CNN) and bidirectional long short-term memory (BLSTM) networks. Additionally, it explores transformer-based models such as CAMeLBERT, XLM-RoBERTa, and MARBERT, along with their ensemble configurations. The findings demonstrate that the proposed transformer-based ensemble model achieved superior performance, with average accuracy, recall, precision, and F1-score of 90.4%, 88%, 87.3%, and 87.7%, respectively.

PMID:40151815 | PMC:PMC11948314 | DOI:10.7717/peerj-cs.2644

3D Print Addit Manuf. 2025 Feb 13;12(1):11-22. doi: 10.1089/3dp.2023.0285. eCollection 2025 Feb.

ABSTRACT

Vat photopolymerization is characterized by its high precision and efficiency, making it a highly promising technique in ceramic additive manufacturing. However, the process faces a significant challenge in the form of recoating defects, necessitating real-time monitoring to maintain process stability. This article presents a defect detection method that leverages multi-image fusion and deep learning for identifying recoating defects in ceramic additive manufacturing. In the image fusion process, multiple single-channel recoating images captured by monitoring camera positioned near the photopolymerization equipment are merged with curing area mask image to create a three-channel color image. The recoating images suffer from perspective distortion due to their side view. To facilitate fusion with the curing area image, image rectification technique is applied to correct the perspective distortion, transforming the side view recoating images into a top-down view. Subsequently, the fused images are processed using a channel-wise YOLO (You Only Look Once, CW-YOLO) method to extract features, enabling the distinction of various types of defects. When compared with other deep learning models, CW-YOLO achieves higher detection accuracy while maintaining a detection rate of 103.58fps, meeting the requirements for real-time detection. Furthermore, the paper introduces the F1 score as a comprehensive evaluation metric, capturing both detection accuracy and recall rate. The results show that the F1 score is enhanced by approximately 10% after image fusion, demonstrating that the proposed method can significantly improve defect detection, particularly in cases involving difficult-to-distinguish defects like material shortages and scratches.

PMID:40151680 | PMC:PMC11937757 | DOI:10.1089/3dp.2023.0285

Front Vet Sci. 2025 Mar 13;12:1404564. doi: 10.3389/fvets.2025.1404564. eCollection 2025.

ABSTRACT

Intelligent management of large-scale farms necessitates efficient monitoring of individual livestock. To address this need, a three-phase intelligent monitoring system based on deep learning was designed, integrating a multi-part detection network for flock inventory counting, a facial classification model for facial identity recognition, and a facial expression analysis network for health assessment. For multi-part detection network, The YOLOv5s path aggregation network was modified by incorporating a multi-link convolution fusion block (MCFB) to enhance fine-grained feature extraction across objects of different sizes. To improve the detection of dense small targets, a Re-Parameterizable Convolution (RepConv) structure was introduced into the YOLOv5s head. For facial identity recognition, the sixth-stage structure in GhostNet was replaced with a four-layer spatially separable self-attention mechanism (SSSA) to strengthen key feature extraction. Additionally, model compression techniques were applied to optimize the facial expression analysis network for improved efficiency. A transfer learning strategy was employed for weight pre-training, and performance was evaluated using FPS, model weight, mean average precision (mAP), and test set accuracy. Experimental results demonstrated that the enhanced multi-part identification network effectively extracted features from different regions of the sheep flock, achieving an average detection accuracy of 95.84%, with a 2.55% improvement in mAP compared to YOLOv5s. The improved facial classification network achieved a test set accuracy of 98.9%, surpassing GhostNet by 3.1%. Additionally, the facial expression analysis network attained a test set accuracy of 99.2%, representing a 3.6% increase compared to EfficientNet. The proposed system significantly enhances the accuracy and efficiency of sheep flock monitoring by integrating advanced feature extraction and model optimization techniques. The improvements in facial classification and expression analysis further enable real-time health monitoring, contributing to intelligent livestock management.

PMID:40151568 | PMC:PMC11948620 | DOI:10.3389/fvets.2025.1404564

J Med Imaging (Bellingham). 2025 Mar;12(2):024002. doi: 10.1117/1.JMI.12.2.024002. Epub 2025 Mar 26.

ABSTRACT

PURPOSE: Previous studies on echocardiogram segmentation are focused on the left ventricle in parasternal long-axis views. Deep-learning models were evaluated on the segmentation of the ventricles in parasternal short-axis echocardiograms (PSAX-echo). Segmentation of the ventricles in complementary echocardiogram views will allow the computation of important metrics with the potential to aid in diagnosing cardio-pulmonary diseases and other cardiomyopathies. Evaluating state-of-the-art models with small datasets can reveal if they improve performance on limited data.

APPROACH: PSAX-echo was performed on 33 volunteer women. An experienced cardiologist identified end-diastole and end-systole frames from 387 scans, and expert observers manually traced the contours of the cardiac structures. Traced frames were pre-processed and used to create labels to train two domain-specific (Unet-Resnet101 and Unet-ResNet50), and four general-domain [three segment anything (SAM) variants, and the Detectron2] deep-learning models. The performance of the models was evaluated using the Dice similarity coefficient (DSC), Hausdorff distance (HD), and difference in cross-sectional area (DCSA).

RESULTS: The Unet-Resnet101 model provided superior performance in the segmentation of the ventricles with 0.83, 4.93 pixels, and 106 pixel 2 on average for DSC, HD, and DCSA, respectively. A fine-tuned MedSAM model provided a performance of 0.82, 6.66 pixels, and 1252 pixel 2 , whereas the Detectron2 model provided 0.78, 2.12 pixels, and 116 pixel 2 for the same metrics, respectively.

CONCLUSIONS: Deep-learning models are suitable for the segmentation of the left and right ventricles in PSAX-echo. We demonstrated that domain-specific trained models such as Unet-ResNet provide higher accuracy for echo segmentation than general-domain segmentation models when working with small and locally acquired datasets.

PMID:40151505 | PMC:PMC11943840 | DOI:10.1117/1.JMI.12.2.024002

Front Big Data. 2025 Mar 13;8:1455442. doi: 10.3389/fdata.2025.1455442. eCollection 2025.

ABSTRACT

The exponential growth of image and video data motivates the need for practical real-time content-based searching algorithms. Features play a vital role in identifying objects within images. However, feature-based classification faces a challenge due to uneven class instance distribution. Ideally, each class should have an equal number of instances and features to ensure optimal classifier performance. However, real-world scenarios often exhibit class imbalances. Thus, this article explores the classification framework based on image features, analyzing balanced and imbalanced distributions. Through extensive experimentation, we examine the impact of class imbalance on image classification performance, primarily on large datasets. The comprehensive evaluation shows that all models perform better with balancing compared to using an imbalanced dataset, underscoring the importance of dataset balancing for model accuracy. Distributed Gaussian (D-GA) and Distributed Poisson (D-PO) are found to be the most effective techniques, especially in improving Random Forest (RF) and SVM models. The deep learning experiments also show an improvement as such.

PMID:40151465 | PMC:PMC11948280 | DOI:10.3389/fdata.2025.1455442

Ophthalmol Sci. 2024 Dec 17;5(3):100685. doi: 10.1016/j.xops.2024.100685. eCollection 2025 May-Jun.

ABSTRACT

PURPOSE: Early glaucoma detection is key to preventing vision loss, but screening often requires specialized eye examination or photography, limiting large-scale implementation. This study sought to develop artificial intelligence models that use self-reported health data from surveys to prescreen patients at high risk for glaucoma who are most in need of glaucoma screening with ophthalmic examination and imaging.

DESIGN: Cohort study.

PARTICIPANTS: Participants enrolled from May 1, 2018, to July 1, 2022, in the nationwide All of Us Research Program who were ≥18 years of age, had ≥2 eye-related diagnoses in their electronic health record (EHR), and submitted surveys with self-reported health history.

METHODS: We developed models to predict the risk of glaucoma, as determined by EHR diagnosis codes, using 3 machine learning approaches: (1) penalized logistic regression, (2) XGBoost, and (3) a fully connected neural network. Glaucoma diagnosis was identified based on International Classification of Diseases codes extracted from EHR data. An 80/20 train-test split was implemented, with cross-validation employed for hyperparameter tuning. Input features included self-reported demographics, general health, lifestyle factors, and family and personal medical history.

MAIN OUTCOME MEASURES: Models were evaluated using standard classification metrics, including area under the receiver operating characteristic curve (AUROC).

RESULTS: Among the 8205 patients, 873 (10.64%) were diagnosed with glaucoma. Across models, AUROC scores for identifying which patients had glaucoma from survey health data ranged from 0.710 to 0.890. XGBoost achieved the highest AUROC of 0.890 (95% confidence interval [CI]: 0.860-0.910). Logistic regression followed with an AUROC of 0.772 (95% CI: 0.753-0.795). Explainability studies revealed that key features included traditionally recognized risk factors for glaucoma, such as age, type 2 diabetes, and a family history of glaucoma.

CONCLUSIONS: Machine and deep learning models successfully utilized health data from self-reported surveys to predict glaucoma diagnosis without additional data from ophthalmic imaging or eye examination. These models may eventually enable prescreening for glaucoma in a wide variety of low-resource settings, after which high-risk patients can be referred for targeted screening using more specialized ophthalmic examination or imaging.

FINANCIAL DISCLOSURES: The author(s) have no proprietary or commercial interest in any materials discussed in this article.

PMID:40151359 | PMC:PMC11946806 | DOI:10.1016/j.xops.2024.100685

Biomedicines. 2025 Mar 11;13(3):690. doi: 10.3390/biomedicines13030690.

ABSTRACT

Background: Cold exposure has an impact on various respiratory diseases. However, its relationship with idiopathic pulmonary fibrosis (IPF) remains to be elucidated. In this study, bioinformatics methods were utilized to explore the potential link between cold exposure and IPF. Methods: Cold exposure-related genes (CERGs) were identified using RNA-Seq data from mice exposed to cold versus room temperature conditions, along with cross-species orthologous gene conversion. Consensus clustering analysis was performed based on the CERGs. A prognostic model was established using univariate and multivariate risk analyses, as well as Lasso-Cox analysis. Differential analysis, WGCNA, and Lasso-Cox methods were employed to screen for signature genes. Results: This study identified 151 CERGs. Clustering analysis based on these CERGs revealed that IPF patients could be divided into two subgroups with differing severity levels. Significant differences were observed between these two subgroups in terms of hypoxia score, EMT score, GAP score, immune infiltration patterns, and mortality rates. A nine-gene prognostic model for IPF was established based on the CERG (AUC: 1 year: 0.81, 3 years: 0.79, 5 years: 0.91), which outperformed the GAP score (AUC: 1 year: 0.66, 3 years: 0.75, 5 years: 0.72) in prognostic accuracy. IPF patients were classified into high-risk and low-risk groups based on the RiskScore from the prognostic model, with significant differences observed between these groups in hypoxia score, EMT score, GAP score, immune infiltration patterns, and mortality rates. Ultimately, six high-risk signature genes associated with cold exposure in IPF were identified: GASK1B, HRK1, HTRA1, KCNN4, MMP9, and SPP1. Conclusions: This study suggests that cold exposure may be a potential environmental factor contributing to the progression of IPF. The prognostic model built upon cold exposure-related genes provides an effective tool for assessing the severity of IPF patients. Meanwhile, GASK1B, HRK1, HTRA1, KCNN4, MMP9, and SPP1 hold promise as potential biomarkers and therapeutic targets for IPF.

PMID:40149666 | DOI:10.3390/biomedicines13030690

Environ Microbiol. 2025 Apr;27(4):e70084. doi: 10.1111/1462-2920.70084.

ABSTRACT

Rice synthesises diterpenoid phytoalexins (DPs) which are known to operate in defence against foliar microbial pathogens and the root-knot nematode Meloidogyne graminicola. Here, we examined the role of DPs in shaping rice-associated root microbiomes in nematode-infested field soil. Further, we assessed how DPs affect interactions between the root microbiomes and M. graminicola. We used 16S and ITS2 rRNA gene amplicon analysis to characterise the root- and rhizosphere-associated microbiomes of DP knock-out rice mutants and their wild-type parental line, at an early (17 days) and late (28 days) stage of plant development in field soil. Disruption of DP synthesis resulted in distinct changes in the composition and structure of microbial communities both relative to the parental/wild-type line but also between individual mutants, indicating specificity in DP-microbe interactions. Moreover, the abundance of nematode-suppressive microbial taxa, including Streptomyces, Stenotrophomonas and Enterobacter was negatively correlated with that of Meloidogyne. Differential enrichment of microbial taxa in the roots of rice DP knock-out mutants versus wild-type suggests that DPs modulate specific taxa in the rice root microbiome. These findings indicate a role for DPs in plant-microbiome assembly and nematode interactions, further underscoring the potential of leveraging phytoalexins for sustainable management of crop diseases.

PMID:40151894 | DOI:10.1111/1462-2920.70084

Theor Biol Forum. 2024 Jul 1;117(1-2):61-68. doi: 10.19272/202411402005.

ABSTRACT

This review explores the critical role of information in biological regulation, extending beyond traditional concepts of homeostasis and homeorhesis. Information, recognized as a fundamental entity alongside matter and energy, governs the dynamic and adaptive processes of living systems. By proposing the concept of «homeoinformation », this paper highlights the continuous processing and integration of information as the foundation for stability and adaptation in life. This perspective offers a more comprehensive framework for understanding the complexity of biological systems and opens new avenues for research into the intricate dynamics of life.

PMID:40151861 | DOI:10.19272/202411402005

Theor Biol Forum. 2024 Jul 1;117(1-2):25-32. doi: 10.19272/202411402003.

ABSTRACT

Inheritance is a fundamental process that shapes the diversity of life on Earth. While DNA is commonly considered the primary carrier of genetic information, recent advances in molecular biology have shown that other forms of information, such as epigenetic modifications and non-coding RNAs, play important roles in inheritance. Here, we propose a theoretical framework that unifies the diverse sources of inheritance under the common concept of information. we argue that information, in its broadest sense, is the basis of inheritance.

PMID:40151859 | DOI:10.19272/202411402003

J Tradit Chin Med. 2025 Apr;45(2):376-384. doi: 10.19852/j.cnki.jtcm.2025.02.012.

ABSTRACT

OBJECTIVE: To investigate the mechanism underlying the effect of the Huanglian decoction (, HLD) on morphine tolerance (MT), using network pharmacology, and to verify these mechanisms in vitro and in vivo.

METHODS: Available biological data on each drug in the HLD were retrieved from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform. The target proteins of MT were retrieved from the GeneCards, PharmGkb, Therapeutic Target Database, DrugBank, and Online Mendelian Inheritance in Man databases. Information regarding MT and the drug targets was compared to obtain overlapping elements. This information was imported into the Search Tool for the Retrieval of Interacting Genes/Proteins platform to obtain a protein-protein interaction network diagram. Then, a "component-target" network diagram was constructed using screened drug components and target information, viaCytoscape (Institute for Systems Biology, Seattle, WA, USA). The database for annotation, visualization, and integrated discovery was used for Gene Ontology enrichment and Kyoto Encyclopedia of Genes and Genomes pathways analyses. Pathway information predicted by network pharmacology was verified using animal studies and cell experiments.

RESULTS: Network pharmacology analysis identified 22 active compounds of HLD and revealed that HLD partially ameliorated MT by modulating inflammatory, apoptosis, and nuclear factor kappa B (NF-κB) signaling pathways. Berberine (BBR), one of the main components of HLD, inhibited the development of MT in mice. BBR reduced cell viability while increasing B-cell lymphoma 2 (Bcl-2) protein expression and decreasing CD86, NF-κB, Bax, and Caspase-3 protein expression in brain vascular 2 (BV2) mcroglia cells treated with morphine. Additionally, BBR contributed to a reduction in pro-inflammatory cytokine release and apoptotic cell number.

CONCLUSIONS: BBR, a key component of HLD, effectively suppressed microglial activation and neuro-inflammation by regulating the NF-κB and apoptosis signaling pathways, thereby delaying MT. This study offers a novel approach to enhance the clinical analgesic efficacy of morphine.

PMID:40151124 | DOI:10.19852/j.cnki.jtcm.2025.02.012

Biomolecules. 2025 Mar 4;15(3):370. doi: 10.3390/biom15030370.

ABSTRACT

In the heart, Connexin 43 (Cx43) is involved in intercellular communication through gap junctions and exosomes. In addition, Cx43-formed hemichannels at the plasma membrane are important for ion homeostasis and cellular volume regulation. Through its localization within nuclei and mitochondria, Cx43 influences the function of the respective organelles. Several cardiovascular diseases such as heart failure, ischemia/reperfusion injury, hypertrophic cardiomyopathy and arrhythmias are characterized by Cx43 downregulation and a dysregulated Cx43 function. Accordingly, a putative therapeutic approach of these diseases would include the induction of Cx43 expression in the damaged heart, albeit such induction may have both beneficial and detrimental effects. In this review we discuss the consequences of increasing cardiac Cx43 expression, and discuss this manipulation as a strategy for the treatment of cardiovascular diseases.

PMID:40149906 | DOI:10.3390/biom15030370

Biomedicines. 2025 Mar 3;13(3):612. doi: 10.3390/biomedicines13030612.

ABSTRACT

Background: There is an urgent need to identify new biomarkers for early diagnosis and development of therapeutic strategies for diabetes mellitus (DM) patients who have invasive breast cancer (BC). We previously reported the increased activated form of 70 kDa ribosomal protein S6 kinase 1 (phospho-p70S6K1) in a triple-negative BC (TNBC) cell line MDA-MB-231 exposed to glycated albumin (GA) and in invasive ductal carcinoma tissues from T2DM patients, compared to untreated cells and their non-diabetic counterparts, respectively. Objective: We aimed to explore the function of p70S6K1 in GA-promoted TNBC progression. Methods: By employing small interference (si)RNA technology or blocking its kinase activity using its specific pharmacological inhibitor, we monitored cell invasion using Transwell® inserts and the expression levels of activated signaling proteins and cancer-related proteins using Western blot. Results: In silico analysis revealed that high mRNA levels of p70S6K1 were associated with an unfavorable prognosis and progression to advanced stages of TNBC in DM patients. The downregulation/blockade of p70S6K1 inhibited GA-promoted MDA-MB-231 cell invasion and the phosphorylation of protein S6 and ERK1/2, the p70S6K1 downstream effector, and the key oncogenic signaling protein, respectively. The suppression of the expression of GA-upregulated cancer proteins, including enolase-2, capping protein CapG, galectin-3, and cathepsin D, was observed after p70S6K1 downregulation/blockade. Further in silico validation analyses revealed increased gene expression of galectin-3 in DM TNBC patients, resulting in poor overall survival and disease-free survival. Conclusions: Targeting p70S6K1 may present a valuable therapeutic strategy, while galectin-3 could serve as a potential prognostic biomarker for invasive BC progression in DM patients.

PMID:40149589 | DOI:10.3390/biomedicines13030612

Genes (Basel). 2025 Feb 28;16(3):297. doi: 10.3390/genes16030297.

ABSTRACT

This study introduces a novel framework that simultaneously addresses the challenges of performance accuracy and result interpretability in transcriptomic-data-based classification. Background/objectives: In biological data classification, it is challenging to achieve both high performance accuracy and interpretability at the same time. This study presents a framework to address both challenges in transcriptomic-data-based classification. The goal is to select features, models, and a meta-voting classifier that optimizes both classification performance and interpretability. Methods: The framework consists of a four-step feature selection process: (1) the identification of metabolic pathways whose enzyme-gene expressions discriminate samples with different labels, aiding interpretability; (2) the selection of pathways whose expression variance is largely captured by the first principal component of the gene expression matrix; (3) the selection of minimal sets of genes, whose collective discerning power covers 95% of the pathway-based discerning power; and (4) the introduction of adversarial samples to identify and filter genes sensitive to such samples. Additionally, adversarial samples are used to select the optimal classification model, and a meta-voting classifier is constructed based on the optimized model results. Results: The framework applied to two cancer classification problems showed that in the binary classification, the prediction performance was comparable to the full-gene model, with F1-score differences of between -5% and 5%. In the ternary classification, the performance was significantly better, with F1-score differences ranging from -2% to 12%, while also maintaining excellent interpretability of the selected feature genes. Conclusions: This framework effectively integrates feature selection, adversarial sample handling, and model optimization, offering a valuable tool for a wide range of biological data classification problems. Its ability to balance performance accuracy and high interpretability makes it highly applicable in the field of computational biology.

PMID:40149449 | DOI:10.3390/genes16030297

Genes (Basel). 2025 Feb 27;16(3):293. doi: 10.3390/genes16030293.

ABSTRACT

Background/Objectives: microRNAs are small non-coding RNAs that regulate gene expression by inducing mRNA degradation or inhibiting translation. A growing body of evidence suggests that miRNAs may be utilized as anti-cancer therapeutics by targeting expression of key genes involved in cancerous transformation and progression. Renal cell cancer (RCC) is the most common kidney malignancy. The most efficient RCC treatments involve blockers of immune checkpoints, including antibodies targeting PD-L1 (Programmed Death Ligand 1). Interestingly, recent studies revealed the cross-kingdom horizontal transfer of plant miRNAs into mammalian cells, contributing to the modulation of gene expression by food ingestion. Here, we hypothesized that PD-L1 expression may be modulated by miRNAs originating from edible plants. Methods: To verify this hypothesis, we performed bioinformatic analysis to identify mes-miR395e from Manihot esculenta (cassava) as a promising candidate miRNA that could target PD-L1. To verify PD-L1 regulation mediated by the predicted plant miRNA, synthetic mes-miR395 mimics were transfected into cell lines derived from RCC tumors, followed by evaluation of PD-L1 expression using qPCR and Western blot. Results: Transfection of mes-miR395e mimics into RCC-derived cell lines confirmed that this miRNA decreases expression of PD-L1 in RCC cells at both mRNA and protein levels. Conclusions: This preliminary study shows the promise of plant miRNA as potential adjuvants supporting RCC treatment.

PMID:40149445 | DOI:10.3390/genes16030293

Genes (Basel). 2025 Feb 23;16(3):258. doi: 10.3390/genes16030258.

ABSTRACT

BACKGROUND/OBJECTIVES: Genes with similar expression patterns across multiple samples are considered coexpressed, and they may participate in similar biological processes or pathways. Gene coexpression networks depict the degree of similarity between the expression profiles of all genes in a set of samples. Gene coexpression tools allow for the prediction of functional gene partners or the assignment of roles to genes of unknown function. Weighted Gene Correlation Network Analysis (WGCNA) is an R package that provides a multitude of functions for constructing and analyzing a weighted or unweighted gene coexpression network.

METHODS: Previously preprocessed, high-quality gene expression data of 3500 samples of Affymetrix microarray technology from various tissues of the Arabidopsis thaliana plant model species were used to construct a weighted gene coexpression network, using WGCNA.

RESULTS: The gene dendrogram was used as the basis for the creation of a new Arabidopsis coexpression tool (ACT) version (ACT2.6). The dendrogram contains 21,273 leaves, each one corresponding to a single gene. Genes that are clustered in the same clade are coexpressed. WGCNA grouped the genes into 27 functional modules, all of which were positively or negatively correlated with specific tissues.

DISCUSSION: Genes known to be involved in common metabolic pathways were discovered in the same module. By comparing the current ACT version with the previous one, it was shown that the new version outperforms the old one in discovering the functional connections between gene partners. ACT2.6 is a major upgrade over the previous version and a significant addition to the collection of public gene coexpression tools.

PMID:40149410 | DOI:10.3390/genes16030258

Entropy (Basel). 2025 Mar 7;27(3):280. doi: 10.3390/e27030280.

ABSTRACT

The known laws of nature in the physical sciences are well expressed in the language of mathematics, a fact that caused Eugene Wigner to wonder at the "unreasonable effectiveness" of mathematical concepts to explain physical phenomena. The biological sciences, in contrast, have resisted the formulation of precise mathematical laws that model the complexity of the living world. The limits of mathematics in biology are discussed as stemming from the impossibility of constructing a deterministic "Laplacian" model and the failure of set theory to capture the creative nature of evolutionary processes in the biosphere. Indeed, biology transcends the limits of computation. This leads to a necessity of finding new formalisms to describe biological reality, with or without strictly mathematical approaches. In the former case, mathematical expressions that do not demand numerical equivalence (equations) provide useful information without exact predictions. Examples of approximations without equal signs are given. The ineffectiveness of mathematics in biology is an invitation to expand the limits of science and to see that the creativity of nature transcends mathematical formalism.

PMID:40149204 | DOI:10.3390/e27030280

Mol Neurodegener. 2025 Mar 28;20(1):38. doi: 10.1186/s13024-025-00826-z.

ABSTRACT

Amyotrophic Lateral Sclerosis (ALS) and Frontotemporal Dementia (FTD) are overwhelmingly linked to TDP-43 dysfunction. Mutations in TDP-43 are rare, indicating that the progressive accumulation of exogenous factors - such as cellular stressors - converge on TDP-43 to play a key role in disease pathogenesis. Post translational modifications such as SUMOylation play essential roles in response to such exogenous stressors. We therefore set out to understand how SUMOylation may regulate TDP-43 in health and disease. We find that TDP-43 is regulated dynamically via SUMOylation in response to cellular stressors. When this process is blocked in vivo, we note age-dependent TDP-43 pathology and sex-specific behavioral deficits linking TDP-43 SUMOylation with aging and disease. We further find that SUMOylation is correlated with human aging and disease states. Collectively, this work presents TDP-43 SUMOylation as an early physiological response to cellular stress, disruption of which may confer a risk for TDP-43 proteinopathy.

PMID:40149017 | DOI:10.1186/s13024-025-00826-z

New Phytol. 2025 Mar 27. doi: 10.1111/nph.70101. Online ahead of print.

ABSTRACT

Robinia pseudoacacia L. (black locust) is a nitrogen (N)-fixing legume tree with significant ecological and agricultural importance. Unlike well-studied herbaceous legumes, R. pseudoacacia is a perennial woody species, representing an understudied group of legume trees that establish symbiosis with Mesorhizobium. Understanding its genomic and transcriptional responses to nodulation provides key insights into N fixation in long-lived plants and their role in ecosystem N cycling. We assembled a high-quality 699.6-Mb reference genome and performed transcriptomic analyses comparing inoculated and noninoculated plants. Differential expression and co-expression network analyses revealed organ-specific regulatory pathways, identifying key genes associated with symbiosis, nutrient transport, and stress adaptation. Unlike Medicago truncatula, which predominantly responds to nodulation in roots, R. pseudoacacia exhibited stem-centered transcriptional reprogramming, with the majority of differentially expressed genes located in stems rather than in roots. Co-expression network analysis identified gene modules associated with "leghemoglobins", metal detoxification, and systemic nutrient allocation, highlighting a coordinated long-distance response to N fixation. This study establishes R. pseudoacacia as a genomic model for nodulating trees, providing essential resources for evolutionary, ecological, and applied research. These findings have significant implications for reforestation, phytoremediation, forestry, and sustainable N management, particularly in depleted, degraded, and contaminated soil ecosystems.

PMID:40149007 | DOI:10.1111/nph.70101