Search Results (310)

Background: Despite extensive phytochemical research on white tea varieties, flavonoid profiling in Baihao Yinzhen remains scarce. The development of green and efficient extraction methods is essential to facilitate its potential application in cosmetic formulations. Methods: A deep eutectic solvent-based ultrasound-assisted extraction (DES-UAE) was developed for Baihao Yinzhen flavonoids. After screening of 14 DESs and optimizing the conditions via single-factor and response surface methodology, the extracts were analyzed by UPLC-MS. Anti-inflammatory activity was assessed in LPS-induced RAW264.7 cells by measuring TNF-α and IL-6 levels, with molecular docking simulating flavonoid–cytokine interactions; Results: Among 14 tested deep eutectic solvents, hydroxypropyl-β-cyclodextrin/lactic acid (HP-β-CD/La) was identified as the most effective solvent for flavonoid extraction. Under optimized conditions (HBD/HBA mass ratio 3:1, temperature 60 °C, water content 40%, solid–liquid ratio 1:19, extraction time 62 min), the maximum flavonoid yield reached 108.72 mg RE/g DW. The DES extract (2.5 μg/mL) significantly suppressed TNF-α and IL-6 secretion in LPS-stimulated RAW264.7 cells compared to the water extract. UPLC-MS identified five major flavonoid glycosides, and molecular docking revealed their strong binding affinities with TNF-α and IL-6 proteins. Conclusions: DES-UAE provides an efficient green method for flavonoid extraction. The extract demonstrates significant anti-inflammatory activity, supporting its potential as a natural cosmetic ingredient. This study aimed to develop an efficient and green DES-UAE method for the extraction of flavonoids from Baihao Yinzhen, in order to evaluate the antioxidant and anti-inflammatory activities of the extract and to explore the potential interaction mechanisms of key flavonoids with inflammatory targets via molecular docking. Full article

Deep-sea mussels of the genus Bathymodiolus exhibit adaptability to nutrient-poor deep-sea environments by establishing nutritional intracellular symbiosis with chemosynthetic bacteria harbored within the gill epithelial cells. However, this poses a conflict for the innate immune system of the host, which must balance the tolerance of beneficial symbiotic bacteria with the need to eliminate exogenous microbes. This review synthesizes existing knowledge and recent findings on Bathymodiolus japonicus to outline the cellular and molecular mechanisms governing this symbiotic relationship. In the host immune system, hemocytes are responsible for systemic defense, whereas gill cells are involved in local symbiotic acceptance. Central to the establishment of symbiosis is the host’s phagocytic system, which non-selectively engulfs bacteria but selectively retains symbionts. We highlight a series of cellular events in gill cells involving the engulfment, selection, retention and/or digestion of symbionts, and the regulatory mechanism of phagocytosis through mechanistic target of rapamycin complex 1, which connects bacterial nutrient supply with host immune and metabolic responses. This integrated model of symbiosis regulation, which links immunity, metabolism, and symbiosis, provides a fundamental framework for understanding how hosts establish and maintain a stable coexistence with microbes, offering a new perspective on symbiotic strategies in diverse organisms. Full article

Background: The pathological response to neoadjuvant chemotherapy (NAC) is an established predictor of long-term outcomes in breast cancer. However, conventional binary assessment based solely on pathological complete response (pCR) fails to capture prognostic heterogeneity across molecular subtypes. This study aimed to develop an interpretable deep learning model that integrates multiple clinical and pathological variables to predict both recurrence and metastasis development following NAC treatment. Methods: We conducted a retrospective analysis of 832 breast cancer patients who received NAC between 2013 and 2022. The analysis incorporated five key variables: tumor size changes, nodal status, Ki-67 index, Miller–Payne grade, and molecular subtype. A Multi-Layer Perceptron (MLP) model was implemented on the PyTorch platform and systematically benchmarked against SVM, Random Forest, and XGBoost models using five-fold cross-validation. Model performance was assessed by calculating the area under the curve (AUC), accuracy, precision, recall, and F1-score, and by analyzing confusion matrices. Results: The MLP model achieved AUC values of 0.86 (95% CI: 0.82–0.93) for HER2-positive cases, 0.82 (95% CI: 0.70–0.92) for triple-negative cases, and 0.76 (95% CI: 0.66–0.82) for HR+/HER2-negative cases. SHAP analysis identified post-NAC tumor size, Ki-67 index, and Miller–Payne grade as the most influential predictors. Notably, patients who achieved pCR still had a 12% risk of developing recurrence, highlighting the necessity for ongoing risk assessment beyond binary response evaluation. Conclusions: The proposed deep learning system provides precise and interpretable risk assessment for NAC patients, facilitating individualized treatment approaches and post-treatment monitoring plans. Full article

Rheumatoid arthritis (RA) is a heterogeneous autoimmune disease characterized by variable clinical manifestations and a complex, often unpredictable disease trajectory, which hinders early diagnosis and personalized treatment. This review highlights recent breakthroughs in biomarker discovery, emphasizing the transformative impact of multi-omics technologies and deep profiling of the synovial microenvironment. Advances in genomics and transcriptomics have identified key genetic variants and expression signatures associated with disease susceptibility, progression, and therapeutic response. Complementary insights from proteomics and metabolomics have elucidated dynamic molecular patterns linked to inflammation and joint destruction. Concurrently, microbiome research has positioned gut microbiota as a compelling source of non-invasive biomarkers with both diagnostic and immunomodulatory relevance. The integration of these diverse data modalities through advanced bioinformatics platforms enables the construction of comprehensive biomarker panels, offering a multidimensional molecular portrait of RA. When coupled with synovial tissue profiling, these approaches facilitate the identification of spatially resolved biomarkers essential for localized disease assessment and precision therapeutics. These innovations are transforming RA care by enabling earlier detection, improved disease monitoring, and personalized treatment strategies that aim to optimize patient outcomes. Full article

The inner mitochondrial membrane proteins ATP/ADP carrier protein 1 (AAC1) and Uncoupling protein 1 (UCP1) belong to the SLC25 mitochondrial carrier family. AAC1 is responsible for ATP/ADP exchange, while UCP1-dependent proton transport, which also requires small molecules known as activators, is the basis of brown fat thermogenesis. Arachidonic acid (AA) is an endogenous activator capable of inducing proton transport in both proteins. As such, both AAC1- and UCP1-dependent proton transport are potential targets of weight loss drugs. While AAC1 structures have long been available, only recently have structures of UCP1 been determined. Unfortunately, no AA-bound structure of either protein is available. To explore their interactions with AA, we performed molecular dynamics (MD) simulations of both proteins. Six parallel simulations of each protein were run with an average length of just over 6 μs, for a total of 75 μs of aggregate simulation across both proteins. AA bound deeply between transmembrane helix (TM) helices or in the central cavity of AAC1 in 14 events and between TM helices of UCP1 in 6 events. All AA involved in these deep binding events came from the intermembrane space-facing (C) leaflet. In AAC1, AA most often bound between TM1/TM2 and TM5/TM6. In four cases the fatty acid bound at the bottom of the central cavity rather than in an interhelical groove. In UCP1, all but one deeply bound AA sat between TM5 and TM6. No AA fully entered the cavity as observed in AAC1. In addition to entering the proteins, AAs were enriched around them in the surrounding membrane adjacent to the TM helices. While both protein structures exhibit hydrophobic stretches separating the intermembrane space (IMS) from the matrix, water wires formed through both AAC1 and UCP1, connecting the bulk water in both regions. Grotthuss shuttling along water wires has been proposed as a possible mechanism of AAC1/UCP1-dependent proton transport, but water wires are not present in experimental structures and have not previously been reported in MD simulations. Calculations of electric potentials along these water wires find a large 0.75–1 V electrostatic barrier along water wires through AAC1 and a substantially smaller such barrier of ~0.5 V through UCP1. Full article

Protein–protein interactions (PPIs) are significant in understanding the complex molecular processes of plant growth, disease resistance, and stress responses. Machine learning (ML) has recently emerged as a powerful tool that can predict and analyze PPIs, offering complementary insights into traditional experimental approaches. It also accounts for proteoforms, distinct molecular variants of proteins arising from alternative splicing, or genetic variations and modifications, which can significantly influence PPI dynamics and specificity in rice. This review presents a comprehensive summary of ML-based methods for PPI predictions in rice (Oryza sativa) based on recent developments in algorithmic innovation, feature extraction processes, and computational resources. We present applications of these models in the discovery of candidate genes, unknown protein annotations, identification of plant–pathogen interactions, and precision breeding. Case studies demonstrate the utility of ML-based methods in improving rice resistance to abiotic and biotic stresses. Additionally, this review highlights key challenges like data limits, model generalizability, and future directions like multi-omics, deep learning and artificial intelligence (AI). This review provides a roadmap for researchers aiming to use ML to generate predictive and mechanistic insights on rice PPI networks, hence helping to achieve enhanced crop improvement programs. Full article

Leukemia stem cells (LSCs) in numerous hematologic malignancies are generally believed to be responsible for disease initiation, progression/relapse and resistance to chemotherapy. It has been shown that non-leukemic hematopoietic cells are affected molecularly and biologically by leukemia cells in the same bone marrow environment where both non-leukemic hematopoietic stem cells (HSCs) and LSCs reside. We believe the molecular and biological changes of these non-leukemic HSCs should be accompanied by the morphological changes of these cells. On the other hand, the quantity of these non-leukemic HSCs with morphological changes should reflect disease severity, prognosis and therapy responses. Thus, identification of non-leukemic HSCs in the leukemia bone marrow environment and monitoring of their quantity before, during and after treatments will potentially provide valuable information for correctly handling treatment plans and predicting outcomes. However, we have known that these morphological changes at the stem cell level cannot be extracted and identified by microscopic visualization with human eyes. In this study, we chose polycythemia vera (PV) as a disease model (a type of human myeloproliferative neoplasms derived from a hematopoietic stem cell harboring the JAK2V617F oncogene) to determine whether we can use artificial intelligence (AI) deep learning to identify and quantify non-leukemic HSCs obtained from bone marrow of JAK2V617F knock-in PV mice by analyzing single-cell images. We find that non-JAK2V617F-expressing HSCs are distinguishable from LSCs in the same bone marrow environment by AI with high accuracy (>96%). More importantly, we find that non-JAK2V617F-expressing HSCs from the leukemia bone marrow environment of PV mice are morphologically distinct from normal HSCs from a normal bone marrow environment of normal mice by AI with an accuracy of greater than 98%. These results help us prove the concept that non-leukemic HSCs undergo AI-recognizable morphological changes in the leukemia bone marrow environment and possess unique morphological features distinguishable from normal HSCs, providing a possibility to assess therapy responses and disease prognosis through identifying and quantitating these non-leukemic HSCs in patients. Full article

Objectives: Endometrial cancer is the most common gynaecologic malignancy, and its mortality rate is rising. Advanced or recurrent disease remains challenging because historically there have been limited therapeutic options. We aim to describe a complete and durable response to the HER2-directed antibody–drug conjugate trastuzumab deruxtecan (T-DXd) in a heavily pretreated patient with HER2-positive, mismatch-repair-deficient metastatic serous endometrial cancer. Methods: A 72-year-old woman underwent hysterectomy, bilateral salpingo-oophorectomy, and staging procedures for FIGO stage IIIA, high-grade serous papillary endometrial carcinoma. Tumour profiling revealed dMMR, a p53 abnormal pattern, and HER2 overexpression (IHC 3+). She received carboplatin/paclitaxel plus avelumab, followed by pegylated liposomal doxorubicin and weekly paclitaxel. After progression on paclitaxel, off-label T-DXd was initiated. Molecular data (FoundationOne CDx) were collected, along with and serial imaging and CA125 assessments. Results: The patient developed cough after two cycles of T-DXd; interstitial lung disease was excluded, and treatment resumed with steroid cover. By December 2024, PET/CT demonstrated complete metabolic response, with resolution of vaginal-vault and para-aortic lesions and normalisation of CA125. Real-world progression-free survival exceeded eight months, with ongoing symptom improvement. Treatment was generally well tolerated; the principal adverse event was grade 3 neutropenia requiring dose reduction. No cardiotoxicity or interstitial lung disease occurred. Conclusions: This case illustrates that T-DXd can induce deep and durable remission in HER2-positive, dMMR metastatic serous endometrial cancer after multiple lines of therapy. It adds real-world evidence supporting further investigation of HER2-directed antibody–drug conjugates in gynaecologic malignancies, and underscores the need for confirmatory trials and refined biomarker-driven patient selection. Full article

Molecular dynamics (MD) can dynamically reveal the structural evolution and mechanical response of Zirconium (Zr) at the atomic scale under complex service conditions such as high temperature, stress, and irradiation. However, traditional empirical potentials are limited by their fixed function forms and parameters, making it difficult to accurately describe the multi-body interactions of Zr under conditions such as multi-phase structures and strong nonlinear deformation, thereby limiting the accuracy and generalization ability of simulation results. This paper combines high-throughput first-principles calculations (DFT) with the machine learning method to develop the Deep Potential (DP) for Zr. The developed DP of Zr was verified by performing molecular dynamic simulations on lattice constants, surface energies, grain boundary energies, melting point, elastic constants, and tensile responses. The results show that the DP model achieves high consistency with DFT in predicting multiple key physical properties, such as lattice constants and melting point. Also, it can accurately capture atomic migration, local structural evolution, and crystal structural transformations of Zr under thermal excitation. In addition, the DP model can accurately capture plastic deformation and stress softening behavior in Zr under large strains, reproducing the characteristics of yielding and structural rearrangement during tensile loading, as well as the stress-induced phase transition of Zr from HCP to FCC, demonstrating its strong physical fidelity and numerical stability. Full article

Background: Biopsy remains the gold standard for characterizing breast cancer, but it is invasive, costly, and may not fully capture tumor heterogeneity. Advances in artificial intelligence (AI) now allow for the extraction of biological and clinical information from medical images, raising the possibility of using imaging as a non-invasive alternative. Methods: A semi-systematic review was conducted to identify AI-based approaches applied to mammography (MM) and breast tomosynthesis (BT) for tumor subtyping, staging, and prognosis. A PubMed search retrieved 1091 articles, of which 81 studies met inclusion criteria (63 MM, 18 BT). Studies were analyzed by clinical target, modality, AI pipeline, number of cases, dataset type, and performance metrics (AUC, accuracy, or C-index). Results: Most studies focused on tumor subtyping, particularly receptor status and molecular classification. Contrast-enhanced spectral mammography (CESM) was frequently used in radiomics pipelines, while end-to-end deep learning (DL) approaches were increasingly applied to MM. Deep models achieved strong performance for ER/PR and HER2 status prediction, especially in large datasets. Fewer studies addressed staging or prognosis, but promising results were obtained for axillary lymph node (ALN) metastasis and pathological complete response (pCR). Multimodal and longitudinal approaches—especially those combining MM or BT with MRI or ultrasound—show improved accuracy but remain rare. Public datasets were used in only a minority of studies, limiting reproducibility. Conclusions: AI models can predict key tumor characteristics directly from MM and BT, showing promise as non-invasive tools to complement or even replace biopsy. However, challenges remain in terms of generalizability, external validation, and clinical integration. Future work should prioritize standardized annotations, larger multicentric datasets, and integration of histological or transcriptomic validation to ensure robustness and real-world applicability. Full article

Breast cancer is one of the major health threats to women worldwide; thus, a need has arisen to reduce the number of instances and deaths through new methods of diagnostic monitoring and treatment. The present review is the synthesis of the recent clinical studies and technological advances in the application of magnetic resonance imaging (MRI) to monitor the pharmacological treatment of breast cancer. The specific focus is on high-risk groups (carriers of BRCA mutations and recipients of neoadjuvant chemotherapy) and the use of novel MRI methods (dynamic contrast-enhanced (DCE) MRI, diffusion-weighted imaging (DWI), and radiomics tools). All the reviewed studies show that MRI is more sensitive (up to 95%) and specific than conventional imaging in detecting malignancy particularly in dense breast tissue. Moreover, MRI can be used to assess the response and residual disease in a tumor early and accurately for personalized treatment, de-escalate unneeded interventions, and maximize positive outcomes. AI-based radiomics combined with deep-learning models also expand the ability to predict the therapeutic response and molecular subtypes, and can mitigate the risk of overfitting models when using complex methods of modeling. Other developments are hybrid PET/MRI, image guidance during surgery, margin assessment intraoperatively, three-dimensional surgical templates, and the utilization of MRI in surgery planning and reducing reoperation. Although economic factors will always play a role, the diagnostic and prognostic accuracy and capability to aid in targeted treatment makes MRI a key tool for modern breast cancer. The growing complement of MRI and novel curative approaches indicate that breast cancer patients may experience better survival and recuperation, fewer recurrences, and a better quality of life. Full article

Introduction: Tumor necrosis factor-α (TNF-α) is a key regulator of inflammatory responses, and its biological activity is dependent on proteolytic processing by the tumor necrosis factor-α-converting enzyme (TACE), also known as ADAM17. Aberrant TACE activity has been associated with various inflammatory and immune-mediated diseases, positioning it as a compelling target for therapeutic intervention. Methods: While our previous study explored TACE inhibition via repositioned FDA-approved drugs, the present study aims to examine previously untested chemical scaffolds from the Enamine compound library, seeking first-in-class TACE inhibitors. We employed an integrated in silico workflow that combined ligand-based virtual screening using a graph convolutional network (GCN) model trained on known TACE inhibitors with structure-based methodologies, including molecular docking, molecular dynamics (MD) simulations, and binding free energy calculations. Results: Several enamine-derived compounds demonstrated strong predicted inhibitory potential, favorable docking scores, and stable interactions with the TACE active site. Among them, Z1459964184, Z2242870510, and Z1450394746 emerged as lead candidates based on their highly stable 300 ns RMSD and robust hydrogen bonding profile as compared to the reference compound BMS-561392. Conclusions: This study highlights the utilization of deep learning-driven screening combined with extended 300 ns molecular simulations to identify novel small-molecule scaffolds for TACE inhibition and supports further exploration of these hits as potential anti-inflammatory therapeutics. Full article

Brain gliomas are highly infiltrative and heterogenous tumors, whose early and accurate detection as well as therapeutic management are challenging. Artificial intelligence (AI) has the potential to redefine the landscape in neuro-oncology and can enhance glioma detection, imaging segmentation, and non-invasive molecular characterization better than conventional diagnostic modalities through deep learning-driven radiomics and radiogenomics. AI algorithms have been shown to predict genotypic and phenotypic glioma traits with remarkable accuracy and facilitate patient-tailored therapeutic decision-making. Such algorithms can be incorporated into surgical planning to optimize resection extent while preserving eloquent cortical structures through preoperative imaging fusion and intraoperative augmented reality-assisted navigation. Beyond resection, AI may assist in radiotherapy dose distribution optimization, thus ensuring maximal tumor control while minimizing surrounding tissue collateral damage. AI-guided molecular profiling and treatment response prediction models can facilitate individualized chemotherapy regimen tailoring, especially for glioblastomas with MGMT promoter methylation. Applications in immunotherapy are emerging, and research is focusing on AI to identify tumor microenvironment signatures predictive of immune checkpoint inhibition responsiveness. AI-integrated prognostic models incorporating radiomic, histopathologic, and clinical variables can additionally improve survival stratification and recurrence risk prediction remarkably, to refine follow-up strategies in high-risk patients. However, data heterogeneity, algorithmic transparency concerns, and regulatory challenges hamstring AI implementation in neuro-oncology despite its transformative potential. It is therefore imperative for clinical translation to develop interpretable AI frameworks, integrate multimodal datasets, and robustly validate externally. Future research should prioritize the creation of generalizable AI models, combine larger and more diverse datasets, and integrate multimodal imaging and molecular data to overcome these obstacles and revolutionize AI-assisted patient-specific glioma management. Full article

Proteins are dynamic macromolecules whose functions are intricately linked to their structural flexibility. Recent breakthroughs in deep learning have enabled accurate prediction of static protein structures. However, understanding protein function is more complex. It often requires access to a diverse ensemble of conformations. Traditional sampling techniques exist to help with this. These include molecular dynamics and Monte Carlo simulations. These techniques can explore conformational landscapes. However, they have limitations as they are often limited by high computational cost and suffer from slow convergence. In response, deep generative models (DGMs) have emerged as a powerful alternative for efficient and scalable protein conformation sampling. Leveraging architectures such as variational autoencoders, normalizing flows, generative adversarial networks, and diffusion models, DGMs can learn complex, high-dimensional distributions over protein conformations directly from data. This survey on generative models for protein conformation sampling provides a comprehensive overview of recent advances in this emerging field. We categorize existing models based on generative architecture, structural representation, and target tasks. We also discuss key datasets, evaluation metrics, limitations, and opportunities for integrating physics-based knowledge with data-driven models. By bridging machine learning and structural biology, DGMs are poised to transform our ability to model, design, and understand dynamic protein behavior. Full article

Climate change is intensifying the co-occurrence of drought and heat stresses, which substantially constrain global crop yields and threaten food security. Developing climate–resilient crop varieties requires a comprehensive understanding of the physiological and molecular mechanisms underlying combined drought–heat stress tolerance. This review systematically summarizes recent advances in integrating multi-scale remote-sensing phenomics with multi-omics approaches—genomics, transcriptomics, proteomics, and metabolomics—to elucidate stress response pathways and identify adaptive traits. High-throughput phenotyping platforms, including satellites, UAVs, and ground-based sensors, enable non-invasive assessment of key stress indicators such as canopy temperature, vegetation indices, and chlorophyll fluorescence. Concurrently, omics studies have revealed central regulatory networks, including the ABA–SnRK2 signaling cascade, HSF–HSP chaperone systems, and ROS-scavenging pathways. Emerging frameworks integrating genotype × environment × phenotype (G × E × P) interactions, powered by machine learning and deep learning algorithms, are facilitating the discovery of functional genes and predictive phenotypes. This “pixels-to-proteins” paradigm bridges field-scale phenotypes with molecular responses, offering actionable insights for breeding, precision management, and the development of digital twin systems for climate-smart agriculture. We highlight current challenges, including data standardization and cross-platform integration, and propose future research directions to accelerate the deployment of resilient crop varieties. Full article