Search Results (4,426)

Metabolic dysfunction-associated steatotic liver disease (MASLD) begins with hepatic lipid accumulation and triggers insulin resistance. Hibiscus leaf extract exhibits antioxidant and anti-atherosclerotic activities, and is rich in (−)-epicatechin gallate (ECG). Despite ECG’s well-known pharmacological activities and its total antioxidant capacity being stronger than that of other catechins, its regulatory effects on MASLD have not been fully described previously. Therefore, this study attempted to evaluate the anti-MASLD potential of ECG isolated from Hibiscus leaves on abnormal lipid and glucose metabolism in hepatocytes. First, oleic acid (OA) was used as an experimental model to induce lipid dysmetabolism in human primary hepatocytes. Treatment with ECG can significantly (p < 0.05) reduce the OA-induced cellular lipid accumulation. Nile red staining revealed, compared to the OA group, the inhibition percentages of 29, 61, and 82% at the tested doses of ECG, respectively. The beneficial effects of ECG were associated with the downregulation of SREBPs/HMGCR and upregulation of PPARα/CPT1 through targeting AMPK. Also, ECG at 0.4 µM produced a significant (p < 0.01) decrease in oxidative stress by 83%, and a marked (p < 0.05) increase in glycogen synthesis by 145% on the OA-exposed hepatocytes with insulin signaling blockade. Mechanistic assays indicated lipid and glucose metabolic homeostasis of ECG might be mediated via regulation of lipogenesis, fatty acid β-oxidation, and insulin resistance, as confirmed by an AMPK inhibitor. These results suggest ECG is a dual modulator of lipid and carbohydrate dysmetabolism in hepatocytes. Full article

If we do not urgently educate current and future medical professionals to critically evaluate and distinguish credible AI-assisted diagnostic tools from those whose performance is artificially inflated by data leakage or improper validation, we risk undermining clinician trust in all AI diagnostics and jeopardizing future advances in patient care. For instance, machine learning models have shown high accuracy in diagnosing Parkinson’s Disease when trained on clinical features that are themselves diagnostic, such as tremor and rigidity. This study systematically investigates the impact of data leakage and feature selection on the true clinical utility of machine learning models for early Parkinson’s Disease detection. We constructed two experimental pipelines: one excluding all overt motor symptoms to simulate a subclinical scenario and a control including these features. Nine machine learning algorithms were evaluated using a robust three-way data split and comprehensive metric analysis. Results reveal that, without overt features, all models exhibited superficially acceptable F1 scores but failed catastrophically in specificity, misclassifying most healthy controls as Parkinson’s Disease. The inclusion of overt features dramatically improved performance, confirming that high accuracy was due to data leakage rather than genuine predictive power. These findings underscore the necessity of rigorous experimental design, transparent reporting, and critical evaluation of machine learning models in clinically realistic settings. Our work highlights the risks of overestimating model utility due to data leakage and provides guidance for developing robust, clinically meaningful machine learning tools for early disease detection. Full article

Cell confluence and number are critical indicators for assessing cellular growth status, contributing to disease diagnosis and the development of targeted therapies. Accurate and efficient cell segmentation is essential for quantifying these indicators. However, current segmentation methodologies still encounter significant challenges in addressing multi-scale heterogeneity, poorly delineated boundaries under limited annotation, and the inherent trade-off between computational efficiency and segmentation accuracy. We propose an innovative network architecture. First, a preprocessing pipeline combining contrast-limited adaptive histogram equalization (CLAHE) and Gaussian blur is introduced to balance noise suppression and local contrast enhancement. Second, a bidirectional feature pyramid network (BiFPN) is incorporated, leveraging cross-scale feature calibration to enhance multi-scale cell recognition. Third, adaptive kernel convolution (AKConv) is developed to capture the heterogeneous spatial distribution of glioma stem cells (GSCs) through dynamic kernel deformation, improving boundary segmentation while reducing model complexity. Finally, a probability density-guided non-maximum suppression (Soft-NMS) algorithm is proposed to alleviate cell under-detection. Experimental results demonstrate that the model achieves 95.7% mAP50 (box) and 95% mAP50 (mask) on the GSCs dataset with an inference speed of 38 frames per second. Moreover, it simultaneously supports dual-modality output for cell confluence assessment and precise counting, providing a reliable automated tool for tumor microenvironment research. Full article

Accurate detection of Alzheimer’s disease (AD) is critical yet challenging for early medical intervention. Deep learning methods, especially convolutional neural networks (CNNs), have shown promising potential for improving diagnostic accuracy using magnetic resonance imaging (MRI). This study aims to identify the most informative combination of MRI slice orientation and anatomical location for AD classification. We propose an automated framework that first selects the most relevant slices using a feature entropy-based method applied to activation maps from a pretrained CNN model. For classification, we employ a lightweight CNN architecture based on depthwise separable convolutions to efficiently analyze the selected 2D MRI slices extracted from preprocessed 3D brain scans. To further interpret model behavior, an attention mechanism is integrated to analyze which feature level contributes the most to the classification process. The model is evaluated on three binary tasks: AD vs. mild cognitive impairment (MCI), AD vs. cognitively normal (CN), and MCI vs. CN. The experimental results show the highest accuracy (97.4%) in distinguishing AD from CN when utilizing the selected slices from the ninth axial segment, followed by the tenth segment of coronal and sagittal orientations. These findings demonstrate the significance of slice location and orientation in MRI-based AD diagnosis and highlight the potential of lightweight CNNs for clinical use. Full article

Allicin (ALC), a naturally occurring organosulfur compound derived from garlic (Allium sativum), exhibits potential neuroprotective properties. Parkinson’s disease (PD) is a progressive neurodegenerative disease characterized by degeneration of dopaminergic neurons and motor dysfunction. This study utilized bioinformatics and network pharmacology methods to predict the anti-PD mechanism of ALC and established in vivo and in vitro PD models using 6-hydroxydopamine (6-OHDA) for experimental verification. Network pharmacological analysis indicates that apoptosis regulation and the PKA/p-CREB/BDNF signaling pathway are closely related to the anti-PD effect of ALC, and protein kinase A (PKA) and dopamine transporter (DAT) are key molecular targets. The experimental results show that ALC administration can alleviate the cytotoxicity of SH-SY5Y induced by 6-OHDA and simultaneously improve the motor dysfunction and dopaminergic neuron loss in PD mice. In addition, ALC can also activate the PKA/p-CREB/BDNF signaling pathway and increase the DAT level in brain tissue, regulate the expression of BAX and Bcl-2, and reduce neuronal apoptosis. These results indicate that ALC can exert anti-PD effects by up-regulating the PKA/p-CREB/BDNF/DAT signaling pathway and inhibiting neuronal apoptosis, providing theoretical support for the application of ALC in PD. Full article

Hormone-dependent breast cancer, particularly in its treatment-resistant forms, remains a significant therapeutic challenge. In this study, we applied a fully computational strategy to design steroid-based compounds capable of simultaneously targeting three key receptors involved in disease progression: progesterone receptor (PR), estrogen receptor alpha (ER-α), and HER2. Using a robust 3D-QSAR model (R² = 0.86; Q²_LOO = 0.86) built from 52 steroidal structures, we identified molecular features associated with high anticancer potential, specifically increased polarizability and reduced electronegativity. From a virtual library of 271 DFT-optimized analogs, 31 compounds were selected based on predicted potency (pIC₅₀ > 7.0) and screened via molecular docking against PR (PDB 2W8Y), HER2 (PDB 7JXH), and ER-α (PDB 6VJD). Seven candidates showed strong binding affinities (ΔG ≤ −9 kcal/mol for at least two targets), with Estero-255 emerging as the most promising. This compound demonstrated excellent conformational stability, a robust hydrogen-bonding network, and consistent multitarget engagement. Molecular dynamics simulations over 100 nanoseconds confirmed the structural integrity of the top ligands, with low RMSD values, compact radii of gyration, and stable binding energy profiles. Key interactions included hydrophobic contacts, π–π stacking, halogen–π interactions, and classical hydrogen bonds with conserved residues across all three targets. These findings highlight Estero-255, alongside Estero-261 and Estero-264, as strong multitarget candidates for further development. By potentially disrupting the PI3K/AKT/mTOR signaling pathway, these compounds offer a promising strategy for overcoming resistance in hormone-driven breast cancer. Experimental validation, including cytotoxicity assays and ADME/Tox profiling, is recommended to confirm their therapeutic potential. Full article

Type 10 17β-hydroxysteroid dehydrogenase (17β-HSD10) is the HSD17B10 gene product. It plays an appreciable part in the carcinogenesis and pathogenesis of neurodegeneration, such as Alzheimer’s disease and infantile neurodegeneration. This mitochondrial, homo-tetrameric protein is a central hub in various metabolic pathways, e.g., branched-chain amino acid degradation and neurosteroid metabolism. It can bind to other proteins carrying out diverse physiological functions, e.g., tRNA maturation. It has also previously been proposed to be an Aβ-binding alcohol dehydrogenase (ABAD) or endoplasmic reticulum-associated Aβ-binding protein (ERAB), although those reports are controversial due to data analyses. For example, the reported k_m value of some substrate of ABAD/ERAB was five times higher than its natural solubility in the assay employed to measure k_m. Regarding any reported “one-site competitive inhibition” of ABAD/ERAB by Aβ, the k_i value estimations were likely impacted by non-physiological concentrations of 2-octanol at high concentrations of vehicle DMSO and, therefore, are likely artefactual. Certain data associated with ABAD/ERAB were found not reproducible, and multiple experimental approaches were undertaken under non-physiological conditions. In contrast, 17β-HSD10 studies prompted a conclusion that Aβ inhibited 17β-HSD10 activity, thus harming brain cells, replacing a prior supposition that “ABAD” mediates Aβ neurotoxicity. Furthermore, it is critical to find answers to the question as to why elevated levels of 17β-HSD10, in addition to Aβ and phosphorylated Tau, are present in the brains of AD patients and mouse AD models. Addressing this question will likely prompt better approaches to develop treatments for Alzheimer’s disease. Full article

Diclofenac, an aryl-acetic acid derivative from the non-steroidal anti-inflammatory drug class, is the subject of multiple non-clinical and clinical studies regarding its usefulness in treating some dermatologic pathologies with an inflammatory, auto-immune, or proliferative component. Diclofenac is now approved for the topical treatment of actinic keratoses (AK), pre-malignant entities that have the risk of transformation into skin carcinomas. The hypothesis that diclofenac increases granular layer development in the mice tail model, having an anti-psoriatic effect, was demonstrated in a previous study in which 1% and 2% diclofenac ointment was evaluated. The aim of the present study was to perform experimental research on the topical effect of diclofenac in the mice tail model, by testing 4% and 8% diclofenac ointment, which is presented in the first part of the manuscript. In the second part of the manuscript, we also aimed to conduct a literature review regarding topical diclofenac uses in specific dermatological entities by evaluating the articles published in PubMed and Scopus databases during 2014–2025. The studies regarding the efficacy of topical diclofenac in dermatological diseases such as AK and field cancerization, actinic cheilitis, basal cell carcinoma, Bowen disease, Darier disease, seborrheic keratoses, and porokeratosis, were analyzed. The results of the experimental work showed a significant effect of 4% and 8% diclofenac ointment on orthokeratosis degree when compared to the negative control groups. Diclofenac in the concentration of 4% and 8% significantly increased the orthokeratosis degree compared to the negative control with untreated mice (p = 0.006 and p = 0.011, respectively, using the Kruskal–Wallis test) and to the negative control with vehicle (p = 0.006 and p = 0.011, respectively, using the Kruskal–Wallis test). The mean epidermal thickness was increased for the diclofenac groups, but not significantly when compared to the control groups. The results are concordant with our previous experiment, emphasizing the need for future clinical trials on the use of topical diclofenac in psoriasis. Full article

Background: Despite distinct etiologies, type 1 diabetes (T1D) and type 2 diabetes (T2D) share chronic inflammation as a core feature. Interleukins, key immune mediators, play important yet still not fully understood roles in the development and complications of both conditions. Objective: This narrative review aims to provide a comprehensive and critical synthesis of current evidence on the role of key interleukins in T1D and T2D, highlighting their immunological functions, genetic associations, clinical correlations, and translational potential. Methods: A targeted literature search was conducted in PubMed, Google Scholar, and ScienceDirect up to January 2025, focusing on English-language clinical and experimental studies involving interleukins and their relevance to T1D and T2D. Reference lists were manually screened for additional sources. Interleukins (ILs) were reviewed individually to assess their immunobiology, disease specificity, and biomarker or therapeutic value. Findings: Pro-inflammatory cytokines such as IL-1β, IL-6, and IL-17 contribute to islet inflammation, insulin resistance, and microvascular damage in both T1D and T2D. Anti-inflammatory mediators including IL-4, IL-10, and IL-13 exhibit protective effects but vary in expression across disease stages. Less-characterized interleukins such as IL-3, IL-5, IL-9, and IL-27 demonstrate dual or context-dependent roles, particularly in shaping immune tolerance and tissue-specific complications such as nephropathy and neuropathy. Polymorphisms in IL-10 and IL-6 genes further suggest genetic contributions to interleukin dysregulation and metabolic dysfunction. Despite promising insights, translational gaps persist due to overreliance on preclinical models and limited longitudinal clinical data. Conclusions: Interleukins represent a mechanistic bridge linking immune dysregulation to metabolic derangements in both T1D and T2D. While their diagnostic and therapeutic potential is increasingly recognized, future research must address current limitations through isoform-specific targeting, context-aware interventions, and validation in large-scale, human cohorts. A unified interleukin-based framework may ultimately advance personalized strategies for diabetes prevention and treatment. Full article

Sepsis is a fetal disease that requires a clear diagnostic biomarker for timely antibiotic treatment. Recent research has identified a pyroptosis-inducing epitope known as P5-5 in tetranectin (TN), a plasma protein produced by monocytes. Previously, we produced a 12F1 monoclonal antibody against the P5-5 and discovered that it could not only diagnose the presence but also monitor the progress of sepsis in the clinic. In the current study, we further investigated the structure site of the P5-5 and the recognition mechanism between the 12F1 mAb and the P5-5 epitope. To this end, 10 amino acids (NDALYEYLRQ) in the P5-5 were individually mutated to alanine, and their binding to the mAb was tested to confirm the most significant antigenic recognition sites. In the meanwhile, the spatial conformation of 12F1 mAb variable regions was modeled, and the molecular recognition mechanisms in detail of the mAb to the P5-5 epitope were further studied by molecular docking. Following epitope prediction and experimental verification, we demonstrated that the motif “DALYEYL” in the epitope sequence position 2−8 of TN-P5-5 is the major binding region for mAb recognition, in which two residues (4L and 8L) were essential for the interaction between the P5-5 epitope and the 12F1 mAb. Therefore, our study greatly narrowed down the previously reported motif from ten to seven amino acids and identified two Leu as critical contact residues. Finally, a single-chain variable fragment (scFv) from the 12F1 hybridoma was constructed, and it was confirmed that the identified motif and residues are prerequisites for the strong binding between P5-5 and 12F1. Altogether, the data of the present work could serve as a theoretic guide for the clinical design of biosynthetic drugs by artificial intelligence to treat sepsis. Full article

The C-terminus of the BoNT/A heavy chain (BoNT/AHC) mediates binding to its receptor, SV2, a critical step for toxicity. Antibody inhibition of this interaction enhances neuronal survival. We previously identified a functional anti-BoNT/AHC nanobody, HM. To extend its in vivo half-life, we designed and prepared two Fc-optimized nanoparticles, HM-Fc5 and HM-Fc6. Structural modeling (homology/docking) of the HM Fv-AHC complex predicted that HM engages key AHC residues (Tyr¹¹⁵⁵, Phe¹¹⁶⁰, Ile¹¹⁶¹, Val¹¹⁸⁴, Asn¹¹⁸⁸, Lys¹¹⁸⁹, Glu¹¹⁹⁰), which overlap with the SV2 binding site. This suggests HM’s protective mechanism involves blocking toxin-receptor binding and cellular entry. HM-Fc5 and HM-Fc6 retained the stability and function of the parental HM antibody while exhibiting prolonged in vivo half-life. These optimized nanobodies offer economical candidates potentially enabling longer dosing intervals, beneficial for prophylaxis or chronic disease treatment. Significance Statement: The purpose of the study is to design and prepare two Fc optimized nanoparticles, HM-Fc5 and HM-Fc6, and predict the key residues involved in the interaction between HMs and AHC. The experimental results showed that HM-Fc5 and HM-Fc6 have the same stability as the parent HM antibody but have a longer half-life in vivo. The key residues Tyr¹¹⁵⁵, Phe¹¹⁶⁰, Ile¹¹⁶¹, Val¹¹⁸⁴, Asn¹¹⁸⁸, Lys¹¹⁸⁹, and Glu¹¹⁹⁰ overlap with the SV2 binding site. Our experimental results indicate that these nanobody candidates are not only more economical and convenient, but may also have longer dosing intervals, providing strong evidence and reference for prolonging the in vivo half-life of nanomaterials. Full article

Cerebral malaria (CM) is the severe progression of an infection with Plasmodium falciparum, causing detrimental damage to brain tissue and is the most frequent cause of Plasmodium falciparum mortality. The critical role of brain-infiltrating CD8⁺ T cells in the pathophysiology of CM having been revealed, our investigation focuses on the role of NR2F6, an established immune checkpoint, as a candidate driver of CM pathology. We employed an experimental mouse model of CM based on Plasmodium berghei ANKA (PbA) infection to compare the relative susceptibility of Nr2f6-knock-out and wild-type C57BL6/N mice. As a remarkable result, Nr2f6 deficiency confers a significant survival benefit. In terms of mechanism, we detected less severe endotheliopathy and, hence, less damage to the blood–brain barrier (BBB), accompanied by decreased sequestered parasites and less cytotoxic T-lymphocytes within the brain, manifesting in a better disease outcome. We present evidence that NR2F6 deficiency renders mice more resistant to experimental cerebral malaria (ECM), confirming a causal and non-redundant role for NR2F6 in the progression of ECM disease. Consequently, pharmacological inhibitors of the NR2F6 pathway could be of use to bolster BBB integrity and protect against CM. Full article

The application of image recognition technology plays a vital role in agricultural disease identification. Existing approaches primarily rely on image classification, object detection, or semantic segmentation. However, a major challenge in current semantic segmentation methods lies in accurately identifying small target objects. In this study, common tobacco leaf diseases—such as frog-eye disease, climate spots, and wildfire disease—are characterized by small lesion areas, with an average target size of only 32 pixels. This poses significant challenges for existing techniques to achieve precise segmentation. To address this issue, we propose integrating two attention mechanisms, namely cross-feature map attention and dual-branch attention, which are incorporated into the semantic segmentation network to enhance performance on small lesion segmentation. Moreover, considering the lack of publicly available datasets for tobacco leaf disease segmentation, we constructed a training dataset via image splicing. Extensive experiments were conducted on baseline segmentation models, including UNet, DeepLab, and HRNet. Experimental results demonstrate that the proposed method improves the mean Intersection over Union (mIoU) by 4.75% on the constructed dataset, with only a 15.07% increase in computational cost. These results validate the effectiveness of our novel attention-based strategy in the specific context of tobacco leaf disease segmentation. Full article

The production of winter wheat, spring barley, spring oilseed rape, and field beans requires detailed experimental data studies to analyze the quality and productivity of spring barley grain under different cultivation and tillage conditions. As the world’s population grows, more food is required to maintain a stable food supply chain. For many years, intensive farming systems have been used to meet this need. Today, intensive climate change events and other global environmental challenges are driving a shift towards sustainable use of natural resources and simplified cultivation methods that produce high-quality and productive food. It is important to study different tillage systems in order to understand how these methods can affect the chemical composition and nutritional value of the grain. Both agronomic and economic aspects contribute to the complexity of this field and their analysis will undoubtedly contribute to the development of more efficient agricultural practice models and the promotion of more conscious consumption. An appropriate tillage system should be oriented towards local climatic characteristics and people’s needs. The impact of reduced tillage on these indicators in spring barley production is still insufficiently investigated and requires further analysis at a global level. This study was carried out at Vytautas Magnus University Agriculture Academy (Lithuania) in 2022–2024. Treatments were arranged using a split-plot design. Based on a long-term tillage experiment, five tillage systems were tested: deep and shallow plowing, deep cultivation–chiseling, shallow cultivation–disking, and no-tillage. The results show that in 2022–2024, the hectoliter weight and moisture content of spring barley grains increased, but protein content and germination decreased in shallowly plowed fields. In deep cultivation–chiseling fields, the protein content (0.1–1.1%) of spring barley grains decreased, and in shallow cultivation–disking fields, the moisture content (0.2–0.3%) decreased. In all fields, the simplified tillage systems applied reduced spring barley germination (0.4–16.7%). Tillage systems and meteorological conditions are the two main forces shaping the quality indicators of spring barley grains. Properly selected tillage systems and favorable climatic conditions undoubtedly contribute to better grain properties and higher yields, while reducing the risk of disease spread. Full article

Precisely recognizing diseases in tomato leaves plays a crucial role in enhancing the health, productivity, and quality of tomato crops. However, disease identification methods that rely on single-mode information often face the problems of insufficient accuracy and weak generalization ability. Therefore, this paper proposes a tomato leaf disease recognition framework FCMNet based on multimodal fusion, which combines tomato leaf disease image and text description to enhance the ability to capture disease characteristics. In this paper, the Fourier-guided Attention Mechanism (FGAM) is designed, which systematically embeds the Fourier frequency-domain information into the spatial-channel attention structure for the first time, enhances the stability and noise resistance of feature expression through spectral transform, and realizes more accurate lesion location by means of multi-scale fusion of local and global features. In order to realize the deep semantic interaction between image and text modality, a Cross Vision–Language Alignment module (CVLA) is further proposed. This module generates visual representations compatible with Bert embeddings by utilizing block segmentation and feature mapping techniques. Additionally, it incorporates a probability-based weighting mechanism to achieve enhanced multimodal fusion, significantly strengthening the model’s comprehension of semantic relationships across different modalities. Furthermore, to enhance both training efficiency and parameter optimization capabilities of the model, we introduce a Multi-strategy Improved Coati Optimization Algorithm (MSCOA). This algorithm integrates Good Point Set initialization with a Golden Sine search strategy, thereby boosting global exploration, accelerating convergence, and effectively preventing entrapment in local optima. Consequently, it exhibits robust adaptability and stable performance within high-dimensional search spaces. The experimental results show that the FCMNet model has increased the accuracy and precision by 2.61% and 2.85%, respectively, compared with the baseline model on the self-built dataset of tomato leaf diseases, and the recall and F1 score have increased by 3.03% and 3.06%, respectively, which is significantly superior to the existing methods. This research provides a new solution for the identification of tomato leaf diseases and has broad potential for agricultural applications. Full article