Small Sci. 2023 Jun 11;3(8):2300031. doi: 10.1002/smsc.202300031. eCollection 2023 Aug.

ABSTRACT

Recent advancements in transmission electron microscopy (TEM) have enabled the study of atomic structures of materials at unprecedented scales as small as tens of picometers (pm). However, accurately detecting atomic positions from TEM images remains a challenging task. Traditional Gaussian fitting and peak-finding algorithms are effective under ideal conditions but perform poorly on images with strong background noise or contamination areas (shown as ultrabright or ultradark contrasts). Moreover, these traditional algorithms require parameter tuning for different magnifications. To overcome these challenges, AtomID-Net is presented, a deep neural network model for atomic detection from multiscale low-SNR experimental images of scanning TEM (scanning transmission electron microscopy (STEM)). The model is trained on real images, which allows the robust and efficient detection of atomic positions, even in the presence of background noise and contamination. The evaluation on a test set of 50 images with a resolution of 800 × 800 yields an average F1-Score of 0.964, which demonstrates significant improvements over existing peak-finding algorithms.

PMID:40213610 | PMC:PMC11935788 | DOI:10.1002/smsc.202300031

Small Sci. 2024 Mar 19;4(5):2300185. doi: 10.1002/smsc.202300185. eCollection 2024 May.

ABSTRACT

Herein, state-of-the-art multiscale modeling methods have been described. This research includes notable molecular, micro-, meso-, and macroscale models for hard (polymer, metal, yarn, fiber, fiber-reinforced polymer, and polymer matrix composites) and soft (biological tissues such as brain white matter [BWM]) composite materials. These numerical models vary from molecular dynamics simulations to finite-element (FE) analyses and machine learning/deep learning surrogate models. Constitutive material models are summarized, such as viscoelastic hyperelastic, and emerging models like fractional viscoelastic. Key challenges such as meshing, data variability and material nonlinearity-driven uncertainty, limitations in terms of computational resources availability, model fidelity, and repeatability are outlined with state-of-the-art models. Latest advancements in FE modeling involving meshless methods, hybrid ML and FE models, and nonlinear constitutive material (linear and nonlinear) models aim to provide readers with a clear outlook on futuristic trends in composite multiscale modeling research and development. The data-driven models presented here are of varying length and time scales, developed using advanced mathematical, numerical, and huge volumes of experimental results as data for digital models. An in-depth discussion on data-driven models would provide researchers with the necessary tools to build real-time composite structure monitoring and lifecycle prediction models.

PMID:40213577 | PMC:PMC11935080 | DOI:10.1002/smsc.202300185

Small Sci. 2024 Aug 10;4(11):2400252. doi: 10.1002/smsc.202400252. eCollection 2024 Nov.

ABSTRACT

Developing scalable and accurate predictive analytical methods for the classification of protein-DNA binding is critical for advancing our understanding of molecular biology, disease mechanisms, and a wide spectrum of biotechnological and medical applications. It is discovered that histone-DNA interactions can be stratified based on stain patterns created by the deposition of various nucleoprotein solutions onto a substrate. In this study, a deep-learning neural network is applied to categorize polarized light microscopy images of drying droplet deposits originating from different histone-DNA mixtures. These DNA stain patterns featured high reproducibility across different species and thus enabled comprehensive DNA categorization (100% accuracy) and accurate prediction of their respective binding affinities to histones. Eukaryotic DNA, which has a higher binding affinity to mammalian histones than prokaryotic DNA, is associated with a higher overall prediction accuracy. For a given species, the average prediction accuracy increased with DNA size. To demonstrate generalizability, a pre-trained CNN is challenged with unknown images that originated from DNA samples of species not included in the training set. The CNN classified these unknown histone-DNA samples as either strong or medium binders with 84.4% and 96.25% accuracy, respectively.

PMID:40213456 | PMC:PMC11935254 | DOI:10.1002/smsc.202400252

JAMIA Open. 2025 Apr 10;8(2):ooaf026. doi: 10.1093/jamiaopen/ooaf026. eCollection 2025 Apr.

ABSTRACT

OBJECTIVES: Recent advances in deep learning show significant potential in analyzing continuous monitoring electronic health records (EHR) data for clinical outcome prediction. We aim to develop a Transformer-based, Encounter-level Clinical Outcome (TECO) model to predict mortality in the intensive care unit (ICU) using inpatient EHR data.

MATERIALS AND METHODS: The TECO model was developed using multiple baseline and time-dependent clinical variables from 2579 hospitalized COVID-19 patients to predict ICU mortality and was validated externally in an acute respiratory distress syndrome cohort (n = 2799) and a sepsis cohort (n = 6622) from the Medical Information Mart for Intensive Care IV (MIMIC-IV). Model performance was evaluated based on the area under the receiver operating characteristic (AUC) and compared with Epic Deterioration Index (EDI), random forest (RF), and extreme gradient boosting (XGBoost).

RESULTS: In the COVID-19 development dataset, TECO achieved higher AUC (0.89-0.97) across various time intervals compared to EDI (0.86-0.95), RF (0.87-0.96), and XGBoost (0.88-0.96). In the 2 MIMIC testing datasets (EDI not available), TECO yielded higher AUC (0.65-0.77) than RF (0.59-0.75) and XGBoost (0.59-0.74). In addition, TECO was able to identify clinically interpretable features that were correlated with the outcome.

DISCUSSION: The TECO model outperformed proprietary metrics and conventional machine learning models in predicting ICU mortality among patients with COVID-19, widespread inflammation, respiratory illness, and other organ failures.

CONCLUSION: The TECO model demonstrates a strong capability for predicting ICU mortality using continuous monitoring data. While further validation is needed, TECO has the potential to serve as a powerful early warning tool across various diseases in inpatient settings.

PMID:40213364 | PMC:PMC11984207 | DOI:10.1093/jamiaopen/ooaf026

Comput Struct Biotechnol J. 2025 Mar 17;27:1187-1203. doi: 10.1016/j.csbj.2025.03.021. eCollection 2025.

ABSTRACT

The Transformer is a deep neural network based on the self-attention mechanism, designed to handle sequential data. Given its tremendous advantages in natural language processing, it has gained traction for other applications. As the primary structure of RNA is a sequence of nucleotides, researchers have applied Transformers to predict secondary and tertiary structures from RNA sequences. The number of Transformer-based models in structure prediction tasks is rapidly increasing as they have performed on par or better than other deep learning networks, such as Convolutional and Recurrent Neural Networks. This article thoroughly examines Transformer-based RNA structure prediction models. Through an in-depth analysis of the models, we aim to explain how their architectural innovations improve their performances and what they still lack. As Transformer-based techniques for RNA structure prediction continue to evolve, this review serves as both a record of past achievements and a guide for future avenues.

PMID:40213272 | PMC:PMC11982051 | DOI:10.1016/j.csbj.2025.03.021

Data Brief. 2025 Mar 19;60:111483. doi: 10.1016/j.dib.2025.111483. eCollection 2025 Jun.

ABSTRACT

In the realm of evaluating vegetable freshness, automated methods that assess external morphology, texture, and colour have emerged as efficient and cost-effective tools. These methods play a crucial role in sorting high-quality vegetables for both export and local consumption, significantly impacting the revenue of the food industry worldwide. Researchers have recognized the importance of this area, leading to the development of various automated techniques, particularly leveraging advanced deep learning technologies to categorize vegetables into specific classes. However, the effectiveness of these methods heavily relies on the databases used for training and validation, posing a challenge due to the lack of suitable datasets.

PMID:40213046 | PMC:PMC11985062 | DOI:10.1016/j.dib.2025.111483

Head Neck Tumor Segm MR Guid Appl (2024). 2025;15273:136-153. doi: 10.1007/978-3-031-83274-1_10. Epub 2025 Mar 3.

ABSTRACT

Radiation therapy (RT) is essential in treating head and neck cancer (HNC), with magnetic resonance imaging (MRI)-guided RT offering superior soft tissue contrast and functional imaging. However, manual tumor segmentation is time-consuming and complex, and therefore remains a challenge. In this study, we present our solution as team TUMOR to the HNTS-MRG24 MICCAI Challenge which is focused on automated segmentation of primary gross tumor volumes (GTVp) and metastatic lymph node gross tumor volume (GTVn) in pre-RT and mid-RT MRI images. We utilized the HNTS-MRG2024 dataset, which consists of 150 MRI scans from patients diagnosed with HNC, including original and registered pre-RT and mid-RT T2-weighted images with corresponding segmentation masks for GTVp and GTVn. We employed two state-of-the-art models in deep learning, nnUNet and MedNeXt. For Task 1, we pretrained models on pre-RT registered and mid-RT images, followed by fine-tuning on original pre-RT images. For Task 2, we combined registered pre-RT images, registered pre-RT segmentation masks, and mid-RT data as a multi-channel input for training. Our solution for Task 1 achieved 1st place in the final test phase with an aggregated Dice Similarity Coefficient of 0.8254, and our solution for Task 2 ranked 8th with a score of 0.7005. The proposed solution is publicly available at Github Repository.

PMID:40213035 | PMC:PMC11982674 | DOI:10.1007/978-3-031-83274-1_10

Head Neck Tumor Segm MR Guid Appl (2024). 2025;15273:65-74. doi: 10.1007/978-3-031-83274-1_4. Epub 2025 Mar 3.

ABSTRACT

Accurate segmentation of tumors in MRI-guided radiation therapy (RT) is crucial for effective treatment planning, particularly for complex malignancies such as head and neck cancer (HNC). This study presents a comparative analysis between two state-of-the-art deep learning models, nnU-Net v2 and STU-Net, for automatic tumor segmentation in pre-RT MRI images. While both models are designed for medical image segmentation, STU-Net introduces critical improvements in scalability and transferability, with parameter sizes ranging from 14 million to 1.4 billion. Leveraging large-scale pre-training on datasets such as TotalSegmentator, STU-Net captures complex and variable tumor structures more effectively. We modified the default nnU-Net v2 by adding additional convolutional layers to both the encoder and decoder, improving its performance for MRI data. Based on our experimental results, STU-Net demonstrated better performance than nnU-Net v2 in the head and neck tumor segmentation challenge. These findings suggest that integrating advanced models like STU-Net into clinical work-flows could remarkably enhance the precision of RT planning, potentially improving patient outcomes. Ultimately, the performance of the fine-tuned STU-Net-B model submitted for the final evaluation phase of Task 1 in this challenge achieved a DSCagg-GTVp of 0.76, a DSCagg-GTVn of 0.85, and an overall DSCagg-mean score of 0.81, securing ninth place in the Task 1 rankings. The described solution is by team SZTU-SingularMatrix for Head and Neck Tumor Segmentation for MR-Guided Applications (HNTS-MRG) 2024 challenge. Link to the trained model weights: https://github.com/Duskwang/Weight/releases.

PMID:40213034 | PMC:PMC11983000 | DOI:10.1007/978-3-031-83274-1_4

Front Digit Health. 2025 Mar 27;7:1557467. doi: 10.3389/fdgth.2025.1557467. eCollection 2025.

ABSTRACT

BACKGROUND: Type 2 Diabetes Mellitus (T2DM) remains a critical global health challenge, necessitating robust predictive models to enable early detection and personalized interventions. This study presents a comprehensive bibliometric and systematic review of 33 years (1991-2024) of research on machine learning (ML) and artificial intelligence (AI) applications in T2DM prediction. It highlights the growing complexity of the field and identifies key trends, methodologies, and research gaps.

METHODS: A systematic methodology guided the literature selection process, starting with keyword identification using Term Frequency-Inverse Document Frequency (TF-IDF) and expert input. Based on these refined keywords, literature was systematically selected using PRISMA guidelines, resulting in a dataset of 2,351 articles from Web of Science and Scopus databases. Bibliometric analysis was performed on the entire selected dataset using tools such as VOSviewer and Bibliometrix, enabling thematic clustering, co-citation analysis, and network visualization. To assess the most impactful literature, a dual-criteria methodology combining relevance and impact scores was applied. Articles were qualitatively assessed on their alignment with T2DM prediction using a four-point relevance scale and quantitatively evaluated based on citation metrics normalized within subject, journal, and publication year. Articles scoring above a predefined threshold were selected for detailed review. The selected literature spans four time periods: 1991-2000, 2001-2010, 2011-2020, and 2021-2024.

RESULTS: The bibliometric findings reveal exponential growth in publications since 2010, with the USA and UK leading contributions, followed by emerging players like Singapore and India. Key thematic clusters include foundational ML techniques, epidemiological forecasting, predictive modelling, and clinical applications. Ensemble methods (e.g., Random Forest, Gradient Boosting) and deep learning models (e.g., Convolutional Neural Networks) dominate recent advancements. Literature analysis reveals that, early studies primarily used demographic and clinical variables, while recent efforts integrate genetic, lifestyle, and environmental predictors. Additionally, literature analysis highlights advances in integrating real-world datasets, emerging trends like federated learning, and explainability tools such as SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations).

CONCLUSION: Future work should address gaps in generalizability, interdisciplinary T2DM prediction research, and psychosocial integration, while also focusing on clinically actionable solutions and real-world applicability to combat the growing diabetes epidemic effectively.

PMID:40212895 | PMC:PMC11983615 | DOI:10.3389/fdgth.2025.1557467

Front Plant Sci. 2025 Mar 27;16:1491170. doi: 10.3389/fpls.2025.1491170. eCollection 2025.

ABSTRACT

High-quality 3D reconstruction and accurate 3D organ segmentation of plants are crucial prerequisites for automatically extracting phenotypic traits. In this study, we first extract a dense point cloud from implicit representations, which derives from reconstructing the maize plants in 3D by using the Nerfacto neural radiance field model. Second, we propose a lightweight point cloud segmentation network (PointSegNet) specifically for stem and leaf segmentation. This network includes a Global-Local Set Abstraction (GLSA) module to integrate local and global features and an Edge-Aware Feature Propagation (EAFP) module to enhance edge-awareness. Experimental results show that our PointSegNet achieves impressive performance compared to five other state-of-the-art deep learning networks, reaching 93.73%, 97.25%, 96.21%, and 96.73% in terms of mean Intersection over Union (mIoU), precision, recall, and F1-score, respectively. Even when dealing with tomato and soybean plants, with complex structures, our PointSegNet also achieves the best metrics. Meanwhile, based on the principal component analysis (PCA), we further optimize the method to obtain the parameters such as leaf length and leaf width by using PCA principal vectors. Finally, the maize stem thickness, stem height, leaf length, and leaf width obtained from our measurements are compared with the manual test results, yielding R 2 values of 0.99, 0.84, 0.94, and 0.87, respectively. These results indicate that our method has high accuracy and reliability for phenotypic parameter extraction. This study throughout the entire process from 3D reconstruction of maize plants to point cloud segmentation and phenotypic parameter extraction, provides a reliable and objective method for acquiring plant phenotypic parameters and will boost plant phenotypic development in smart agriculture.

PMID:40212877 | PMC:PMC11983422 | DOI:10.3389/fpls.2025.1491170

Front Genet. 2025 Mar 27;16:1556495. doi: 10.3389/fgene.2025.1556495. eCollection 2025.

ABSTRACT

Idiopathic Pulmonary Fibrosis (IPF) is a progressive interstitial lung disease characterized by unknown etiology and limited therapeutic options. Recent studies implicate exosomal non-coding RNAs (ncRNAs) as crucial regulators in IPF. These ncRNAs, including long non-coding RNAs (lncRNAs), microRNAs (miRNAs), and circular RNAs (circRNAs), are involved in cellular processes through various mechanisms of selective packaging, intercellular communication, and signaling pathway integration. LncRNAs such as LINC00470 and PVT1 exhibit pro-fibrotic effects, while others like lnc-DC and THRIL show inhibitory roles; some, including UCA1 and MALAT1, demonstrate bidirectional regulation. In miRNAs, pro-fibrotic agents (e.g., miR-486, miR-223) contrast with inhibitory miRNAs (e.g., miR-34a, miR-126), while miR-21 and miR-155 display dual functions. Similarly, circRNAs such as circ_0000479 and circ_0026344 promote fibrosis, whereas circ_0000072 and circ_0000410 act as inhibitors, with certain circRNAs (e.g., circ_002178 and circ_0001246) exhibiting complex regulatory effects. Exosomal ncRNAs modulate key pathways, including TGF-β and Wnt/β-catenin, influencing IPF progression. Despite their potential, challenges remain in exosome isolation, functional characterization of ncRNAs, and clinical translation. Addressing these barriers through innovative research strategies is essential to leverage exosomal ncRNAs in the management and treatment of IPF. This review comprehensively examines the roles of exosomal ncRNAs in IPF, elucidates their mechanisms and pathway interactions, and discusses future perspectives to enhance understanding and therapeutic strategies for this disease.

PMID:40212286 | PMC:PMC11983508 | DOI:10.3389/fgene.2025.1556495

Front Bioeng Biotechnol. 2025 Mar 27;13:1527084. doi: 10.3389/fbioe.2025.1527084. eCollection 2025.

ABSTRACT

INTRODUCTION: Genetically enhanced microorganisms have wide applications in different fields and the increasing availability of omics data has enabled the development of genome-scale metabolic models (GEMs), which are essential tools in synthetic biology. Bartonella quintana str. Toulouse, a facultative intracellular parasite, presents a small genome and the ability to grow in axenic culture, making it a potential candidate for genome reduction and synthetic biology applications. This study aims to reconstruct and analyze the metabolic network of B. quintana to optimize its growth conditions for laboratory use.

METHODS: A metabolic reconstruction of B. quintana was performed using genome annotation tools (RAST and ModelSEED), followed by refinement using multiple databases (KEGG, BioCyc, BRENDA). Flux Balance Analysis (FBA) was conducted to optimize biomass production, and in-silico knockouts were performed to evaluate growth yield under different media conditions. Additionally, experimental validation was carried out by testing modified culture media and performing proteomic analyses to identify metabolic adaptations.

RESULTS: FBA simulations identified key metabolic requirements, including 2-oxoglutarate as a crucial compound for optimal growth. In-silico knockouts of transport genes revealed their essentiality in nutrient uptake. Experimental validation confirmed the role of 2-oxoglutarate and other nutrients in improving bacterial growth, though unexpected decreases in viability were observed under certain supplemented conditions. Proteomic analysis highlighted differential expression of proteins associated with cell wall integrity and metabolic regulation.

DISCUSSION: This study represents a step toward developing B. quintana as a viable chassis for synthetic biology applications. The reconstructed metabolic model provides a comprehensive understanding of B. quintana's metabolic capabilities, identifying essential pathways and growth limitations. While metabolic predictions align with experimental results in key aspects, further refinements are needed to enhance model accuracy and optimize growth conditions.

PMID:40213639 | PMC:PMC11983613 | DOI:10.3389/fbioe.2025.1527084

Comput Struct Biotechnol J. 2025 Mar 19;27:1204-1214. doi: 10.1016/j.csbj.2025.03.011. eCollection 2025.

ABSTRACT

Non-invasive force inference based on imaging data has significantly advanced our understanding of the mechanical cues driving morphogenesis. In 2D studies of confluent tissues, these methods allow for the computation of forces acting on cells by analyzing their geometrical features. Here, we present a novel approach for 3D force and energy inference in curved epithelia. Specifically, we focus on tubular epithelia, which form the foundation of many vital organs, including the lungs, kidneys, and vasculature. Our technique analyzes the average mechanical behavior of cells along their apico-basal axis and is based on an optimal parametrization of a vertex model aimed at obtaining effective tissue parameters. We apply our method to in silico data to investigate the mechanical consequences of different 3D cellular packing scenarios. Our results reveal that in squamous epithelia, prismatic cellular shapes are mechanically stable. However, in cubic/columnar tubes, prismatic shapes are incompatible with the adhesion required to maintain tissue integrity. In conclusion, this study indicates that in cubic/columnar epithelia, stability can only be achieved if cells undergo apico-basal intercalations and adopt an alternative shape: the scutoid.

PMID:40213271 | PMC:PMC11982039 | DOI:10.1016/j.csbj.2025.03.011

NPJ Metab Health Dis. 2025;3(1):13. doi: 10.1038/s44324-025-00056-3. Epub 2025 Apr 8.

ABSTRACT

Consumption of ultra-processed foods (UPFs) increases overall caloric intake and is associated with obesity, cardiovascular disease, and brain pathology. There is scant evidence as to why UPF consumption leads to increased caloric intake and whether the negative health consequences are due to adiposity or characteristics of UPFs. Using the UK Biobank sample, we probed the associations between UPF consumption, adiposity, metabolism, and brain structure. Our analysis reveals that high UPF intake is linked to adverse adiposity and metabolic profiles, alongside cellularity changes in feeding-related subcortical brain areas. These are partially mediated by dyslipidemia, systemic inflammation and body mass index, suggesting that UPFs exert effects on the brain beyond just contributing to obesity. This dysregulation of the network of subcortical feeding-related brain structures may create a self-reinforcing cycle of increased UPF consumption.

PMID:40213086 | PMC:PMC11978510 | DOI:10.1038/s44324-025-00056-3

iScience. 2025 Mar 12;28(4):112097. doi: 10.1016/j.isci.2025.112097. eCollection 2025 Apr 18.

ABSTRACT

Innate defensive behaviors are essential for survival, allowing animals to appropriately respond to predatory threats. The anterior hypothalamic nucleus (AHN), a key region in the medial hypothalamic defense system, contains both GABAergic and glutamatergic neurons, reflecting a sophisticated balance between inhibitory and excitatory signaling. However, the specific behavioral functions of these neuronal populations have not been systemically examined. Here, we utilized fiber photometry and optogenetic stimulation to investigate the roles of AHN GABAergic, glutamatergic, and CaMKIIa+ neuronal activities in mediating innate defensive behaviors. Our results indicate that AHN GABAergic neurons mediate anxiety-associated investigatory behaviors, while AHN glutamatergic neurons drive escape and freezing responses. The AHN CaMKIIa+ neurons, which exhibit significant heterogeneity, suggest a more nuanced role, potentially balancing escape and freezing responses. Our study provides a foundation for future investigations into the neural circuits underlying innate defensive behaviors and its dysregulation in neuropsychiatric conditions including PTSD and panic disorder.

PMID:40212593 | PMC:PMC11985144 | DOI:10.1016/j.isci.2025.112097

Front Artif Intell. 2025 Mar 19;8:1528562. doi: 10.3389/frai.2025.1528562. eCollection 2025.

ABSTRACT

INTRODUCTION: The biomedical literature is the go-to source of information regarding relationships between biological entities, including genes, diseases, cell types, and drugs, but the rapid pace of publication makes an exhaustive manual exploration impossible. In order to efficiently explore an up-to-date repository of millions of abstracts, we constructed an efficient and modular natural language processing pipeline and applied it to the entire PubMed abstract corpora.

METHODS: We developed SciLinker using open-source libraries and pre-trained named entity recognition models to identify human genes, diseases, cell types and drugs, normalizing these biological entities to the Unified Medical Language System (UMLS). We implemented a scoring schema to quantify the statistical significance of entity co-occurrences and applied a fine-tuned PubMedBERT model for gene-disease relationship extraction.

RESULTS: We identified and analyzed over 30 million association sentences, including more than 11 million gene-disease co-occurrence sentences, revealing more than 1.25 million unique gene-disease associations. We demonstrate SciLinker's ability to extract specific gene-disease relationships using osteoporosis as a case study. We show how such an analysis benefits target identification as clinically validated targets are enriched in SciLinker-derived disease-associated genes. Moreover, this co-occurrence data can be used to construct disease-specific networks, providing insights into significant relationships among biological entities from scientific literature.

CONCLUSION: SciLinker represents a novel text mining approach that extracts and quantifies associations between biomedical entities through co-occurrence analysis and relationship extraction from PubMed abstracts. Its modular design enables expansion to additional entities and text corpora, making it a versatile tool for transforming unstructured biomedical data into actionable insights for drug discovery.

PMID:40212086 | PMC:PMC11983328 | DOI:10.3389/frai.2025.1528562

Small Sci. 2024 Jul 8;4(9):2400084. doi: 10.1002/smsc.202400084. eCollection 2024 Sep.

ABSTRACT

Mesoporous silica nanoparticles (MSNs) have emerged as promising drug carriers that can facilitate targeted anticancer drug delivery, but efficiency studies relying on active targeting mechanisms remain elusive. This study implements in vitro 3D cocultures, so-called microtissues, to model a physiologically relevant tumor microenvironment (TME) to examine the impact of surface-modified MSNs without targeting ligands on the internalization, cargo delivery, and cargo release in tumor cells and cancer-associated fibroblasts. Among these, acetylated MSNs most effectively localized in tumor cells in a 3D setting containing collagen, while other MSNs did so to a lesser degree, most likely due to remaining trapped in the extracellular matrix of the TME. Confocal imaging of hydrophobic model drug-loaded MSNs demonstrated effective cargo release predominantly in tumor cells, both in 2D and 3D cocultures. MSN-mediated delivery of an anticancer drug in the microtissues exhibited a significant reduction in tumor organoid size and enhanced the tumor-specific cytotoxic effects of a γ-secretase inhibitor, compared to the highly hydrophobic drug in free form. This inherent targeting potential suggests reduced off-target effects and increased drug efficacy, showcasing the promise of surface modification of MSNs as a means of direct cell-specific targeting and delivery for precise and successful targeted drug delivery.

PMID:40212075 | PMC:PMC11935100 | DOI:10.1002/smsc.202400084

Bioessays. 2025 Apr 10:e202400175. doi: 10.1002/bies.202400175. Online ahead of print.

ABSTRACT

The assembly of Cullin3-based RING E3 ubiquitin ligase (CRL3) complexes is orchestrated in two consecutive steps: the formation of the dimeric BTB domain core and the recruitment of CUL3-RBX1 subunits. Each step is tightly regulated to ensure the formation of complete and functional dimeric CRL3s. The first assembly step is regulated by two mechanisms: "co-co assembly" and proteasome-dependent degradation of aberrant heterodimers. The second step is facilitated by a conserved CUL3 N-terminal assembly (NA) motif. The CUL3 NA motif contributes to the assembly of CRL3s in two aspects: interacting with both BTB domain-containing protein protomers to facilitate complete dimeric assembly, and enhancing the stability of CRL3s by overcoming the tensions generated by conformational entropy during ubiquitin transfer. Given that all Cullin proteins contain N-terminal extensions, we postulate that these extensions, similar to the CUL3 NA motif-contributed assembly, play an important role in the functional regulation of CRLs and thus warrant further investigation.

PMID:40211562 | DOI:10.1002/bies.202400175

Thyroid Res. 2025 Apr 11;18(1):15. doi: 10.1186/s13044-025-00230-1.

ABSTRACT

BACKGROUND: Papillary thyroid cancer (PTC) is the most prevalent follicular cell-derived subtype of thyroid cancer. A systems biology approach to PTC can elucidate the mechanism by which molecular components work and interact with one another to decipher a panoramic view of the pathophysiology.

METHODOLOGY: PTC associated genes and transcriptomic data were retrieved from DisGeNET and Gene Expression Omnibus database respectively. Published proteomic and metabolomic datasets in PTC from EMBL-EBI were used. Gene Ontology and pathway analyses were performed with SNPs, differentially expressed genes (DEGs), proteins, and metabolites linked to PTC. The effect of a nucleotide substitution on a protein's function was investigated. Additionally, significant transcription factors (TFs) and kinases were identified. An integrated strategy was used to analyse the multi-omics data to determine the key deregulated pathways in PTC carcinogenesis.

RESULTS: Pathways linked to carbohydrate, protein, and lipid metabolism, along with the immune response, signaling, apoptosis, gene expression, epithelial-mesenchymal transition (EMT), and disease onset, were identified as significant for the clinical and functional aspects of PTC. Glyoxylate and dicarboxylate metabolism and citrate cycle were the most common pathways among the PTC omics datasets. Commonality analysis deciphered five TFs and fifty-seven kinases crucial for PTC genesis and progression. Core deregulated pathways, TFs, and kinases modulate critical biological processes like proliferation, angiogenesis, immune infiltration, invasion, autophagy, EMT, and metastasis in PTC.

CONCLUSION: Identified dysregulated pathways, TFs and kinases are critical in PTC and may help in systems level understanding and device specific experiments, biomarkers, and drug targets for better management of PTC.

PMID:40211357 | DOI:10.1186/s13044-025-00230-1

J Transl Med. 2025 Apr 10;23(1):425. doi: 10.1186/s12967-025-06446-x.

ABSTRACT

Systems biology is a holistic approach to biological sciences that combines experimental and computational strategies, aimed at integrating information from different scales of biological processes to unravel pathophysiological mechanisms and behaviours. In this scenario, high-throughput technologies have been playing a major role in providing huge amounts of omics data, whose integration would offer unprecedented possibilities in gaining insights on diseases and identifying potential biomarkers. In the present review, we focus on strategies that have been applied in literature to integrate genomics, transcriptomics, proteomics, and metabolomics in the year range 2018-2024. Integration approaches were divided into three main categories: statistical-based approaches, multivariate methods, and machine learning/artificial intelligence techniques. Among them, statistical approaches (mainly based on correlation) were the ones with a slightly higher prevalence, followed by multivariate approaches, and machine learning techniques. Integrating multiple biological layers has shown great potential in uncovering molecular mechanisms, identifying putative biomarkers, and aid classification, most of the time resulting in better performances when compared to single omics analyses. However, significant challenges remain. The high-throughput nature of omics platforms introduces issues such as variable data quality, missing values, collinearity, and dimensionality. These challenges further increase when combining multiple omics datasets, as the complexity and heterogeneity of the data increase with integration. We report different strategies that have been found in literature to cope with these challenges, but some open issues still remain and should be addressed to disclose the full potential of omics integration.

PMID:40211300 | DOI:10.1186/s12967-025-06446-x