Search Results (667)

This study analyzed Porcine epidemic diarrhea virus (PEDV) spike protein sequences and structures in Thailand from 2008 to 2024 to provide predicted structural templates that could inform regional vaccine selection and planned exposure frameworks. Using an in-silico approach, the researchers reduced sequence redundancy via CD-HIT (v4.8.1), established evolutionary lineages with BEAST (v1.10.4), and reconstructed protein structures using SWISS-MODEL. Structural comparisons and clustering were performed using DALI Z-scores and DBSCAN (v1.2.2), while Discotope 3 (v3.0) and ElliPro mapped B-cell epitope landscapes against a G1 reference strain. The results revealed a major lineage shift from G2a to G2b strains around 2017, with the spike proteins categorized into 14 subtypes and 6 eigenvalue clusters. Notably, minor amino acid substitutions altered properties such as hydrophobicity without disrupting the core structure, and certain deletions caused minimal structural deviations, indicating that sequence data or predicted structures alone do not fully dictate viral virulence or immunogenicity. Furthermore, primitive TH2 strains shared evolutionary links with G1 or US-InDel strains despite their G2 classification, identifying Cluster 1 as a potential ancestral structural type. In conclusion, this updated analysis provides crucial baseline data to optimize regional PEDV preventative measures, though further rigorous structural investigations are needed to definitively link specific spike alterations to virulence and host immune response. Full article

Objective, continuous assessment of real-world mobility using wearables has significant potential to transform clinical research and practice, yet the field lacks standardised, open-source tools that enable reproducible algorithm real-world validation, across multiple clinical cohorts. This would improve transparency around definitions and performance, thereby enhancing interpretation and more meaningful comparison across studies. The Mobilise-D consortium validated a comprehensive analytical pipeline for estimating digital mobility outcomes from wearables, originally implemented in a combination of MATLAB, R, and Python codes. To overcome the licencing, reproducibility, and accessibility limitations of this implementation, the pipeline has been re-implemented and re-validated, against gold standards, as the open-source mobgap Python package. Here, we describe the mobgap ecosystem, detail how algorithms can be integrated and benchmarked in a reproducible way and present a re-validation of the pipeline against reference data across six clinical cohorts under real-world conditions. Validation results showed that across all cohorts, walking speed was estimated with an absolute error of 0.10 m/s and an intraclass correlation coefficient (ICC) of 0.81, demonstrating comparable or superior performance to the original implementation. Mobgap (v1.2) is openly available and is intended to serve as a reproducible reference implementation and benchmarking platform for researchers developing or validating mobility analysis algorithms using wearable data. Full article

Epilepsy is a chronic neurological disorder prevalent worldwide, characterized by recurrent episodes of epileptic seizures. The primary current treatment approach is pharmacological, aimed at reducing the intensity and frequency of seizures, though it does not provide a cure. Neuroinflammation plays a central role in epilepsy by activating glial cells and stimulating the release of inflammatory mediators, further disrupting the balance between excitation and inhibition, thereby promoting the onset and recurrence of seizures. Furthermore, persistent inflammatory processes induce synaptic remodeling and the formation of dysfunctional neural circuits, establishing a pathological cycle in which inflammation and epileptic activity feed into each other. In this regard, natural products represent an important avenue for the discovery of new treatments. Thus, this review aimed to relate the role of the main inflammatory targets (Inflammasome/NLRP3, NF-κB, MAPK, mTOR, COX-2/PGE2, and TLR4/HMGB1) to epilepsy and to investigate in the literature natural products acting through these pathways in the treatment of epileptic seizures. Consequently, inflammatory pathways have emerged as critical targets in epilepsy, highlighting the importance of strategies capable of modulating neuroinflammatory processes. In this context, natural products stand out as promising therapeutic alternatives, given their multitarget mechanisms of action, potential to attenuate neuroinflammation and neuronal hyperexcitability. Full article

Background/Objectives: Cardiovascular diseases (CVDs) and cancer remain major global health challenges and are among the leading causes of mortality worldwide, including in Kuwait. Medicinal plants are important sources of bioactive compounds with therapeutic potential. This study aimed to identify the phytochemical constituents of Curcuma longa L. root extract and evaluate their potential cardioprotective and anticancer activities using integrated computational approaches. Methods: Phytochemical profiling of Curcuma longa root extract was performed using gas chromatography–mass spectrometry (GC–MS). The identified compounds were evaluated through molecular docking against selected cardiovascular- and cancer-related targets, including HMG-CoA reductase, phosphoinositide 3-kinase (PI3K), cyclin-dependent kinase 6 (CDK6), and HER2 kinase receptors. Protein–ligand interactions were analyzed to determine binding stability. Biological activity prediction and pharmacokinetic properties were assessed using PASS prediction and SwissADME tools, while density functional theory (DFT) calculations were conducted to investigate electronic and quantum chemical characteristics associated with ligand reactivity. Results: GC–MS analysis identified seventeen phytochemical constituents with retention times ranging from 7.57 to 32.70 min. The major compounds detected were 2-oxo-cyclooctaneacetic acid (30.88%), curlone (20.99%), and tumerone (13.85%). Molecular docking revealed favorable binding affinities for α-curcumene, caryophyllene, bergamotene, cyclohexene derivatives, tumerone, curlone, and (6R,7R)-bisabolone against the selected targets, with interaction profiles comparable to reference drugs. PASS and SwissADME analyses indicated promising biological activities, acceptable drug-likeness, and favorable pharmacokinetic properties. DFT analysis demonstrated that curlone and tumerone possessed stable electronic configurations and favorable reactivity profiles. Conclusions: The findings suggest that bioactive compounds from Curcuma longa may serve as promising lead candidates for the development of cardioprotective and anticancer agents. However, further experimental validation through in vitro and in vivo studies is required to confirm these computational predictions. Full article

Background and Objectives: Sjögren’s disease (SjD) is a chronic autoimmune disorder in which the immune system attacks the glands that produce tears and saliva, leading to symptoms such as dry eyes and dry mouth. If left untreated, SjD can also cause inflammation and damage to other parts of the body, including the skin, lungs, kidneys, and nervous system, and increase the risk of developing lymphoma. The human leukocyte antigen (HLA) class II molecule HLA-DR3 is strongly associated with SjD. Materials and Methods: To investigate how post-translational modifications (PTMs) influence the presentation of SjD-associated autoantigens by HLA-DR3, we employed a computational framework to determine the binding of PTM-mimic peptides to HLA-DR3. We further supported the in-silico results with in-vitro experiments. Results: Our analysis revealed that PTM-mimic substitutions at canonical anchor positions rarely improved predicted binding affinity using the Stabilized Matrix Method, with most modifications resulting in reduced affinity. However, a comprehensive analysis of full-length SjD-associated autoantigen sequences (Ro60, Ro52, La) identified discrete regions with high densities of PTM-eligible anchor sites, specifically, the Ro60 HEAT solenoid, Ro52 RING/B-box/PRY-SPRY modules, and the La motif-RRM1 region, suggesting that PTMs may alter epitope presentation in a sequence-dependent manner. Experimental validation of selected PTM-mimic peptides showed enhanced T cell responses, which were associated with increased binding affinity to HLA-DR3. Structural modeling of a representative complex revealed that PTM-mimic peptides adopt a slightly shifted backbone orientation and altered side-chain positioning, leading to a larger peptide–DR3 interaction interface. Conclusions: These findings provide new insights into the role of PTMs in shaping the immunogenicity of SjD-associated autoantigens and highlight the potential for PTM-mimic peptides to modulate T cell responses in SjD. Full article

Background: The current study integrates AlphaFold2-based structural modeling of DNA-PKcs with an experimentally resolved DNA-PKcs structure, large-scale molecular docking of over 1500 known DNA-PK inhibitors, aiming to identify clinically relevant candidates for next-generation pharmacological delivery methods, evaluate receptor model suitability, and predict how potential radio-halogenation may influence binding behavior and translational radiotherapeutic potential of current clinically relevant candidates. AlphaFold2-based prediction of the DNA-PKcs structure was the starting point, followed by comparison of the AI-derived model with an experimentally resolved structure to evaluate the model’s accuracy and docking suitability. A large database of 1369 known DNA-PK inhibitors was then screened by molecular docking, after which six clinically relevant compounds were analyzed for molecular dynamics. Finally, a focused docking study was conducted on 67 possible radio-halogenated derivatives derived from these clinically relevant scaffolds to investigate the effect of potential iodination, bromination, or astatination on binding behavior. These findings indicate that large-scale docking can be used to comparatively rank DNA-PK-targeting compounds and explore the impact of theoretical radiohalogenation on predicted binding behavior. However, the results also underline the limitations of current receptor models and molecular simulation workflows, supporting the need for experimental validation before translational conclusions are drawn. Full article

Background/Objectives: Tuberculosis (TB), caused by Mycobacterium tuberculosis, remains a major global health challenge, exacerbated by the emergence of multidrug-resistant strains and limited efficacy of existing therapies. Given the involvement of multiple essential mycobacterial proteins, multitarget drug discovery represents a rational therapeutic strategy. Methods: In this study, an integrated in silico pipeline combining machine learning–based quantitative structure–activity relationship modeling, graph neural network–driven drug–target affinity prediction, molecular docking, molecular dynamics (MD) simulations, and pharmacokinetic–toxicity profiling was employed to identify potential antitubercular leads from natural products. Results: A curated library of over 0.69 million compounds from the COCONUT database was systematically screened against seven essential M. tuberculosis protein targets. Machine learning and heterogeneous graph neural network models effectively captured complex ligand–protein interaction patterns, enabling high-confidence multitarget prioritization. Structure-based docking and MM-GBSA analyses revealed favorable binding affinities, further supported by 100 ns Molecular Dynamics simulations demonstrating stable binding and conformational integrity. In silico ADMET and toxicity predictions identified pharmacokinetically balanced candidates, while density functional theory calculations corroborated favorable electronic properties. Conclusions: Notably, a myricetin-based flavonoid glycoside exhibited consistent multitarget binding and dynamic stability across all targets. Overall, this study underscores the potential of integrated artificial intelligence and structure-based approaches in accelerating natural product-based antitubercular drug discovery and supports further experimental validation of prioritized leads. Full article

Background/Objectives: Patients with breast cancer show substantial heterogeneity in terms of psychological adjustment following diagnosis. We aimed to characterize longitudinal trajectories of quality of life (QoL) and depressive symptoms during the first 18 months post-diagnosis and to identify robust clinical, psychosocial, and behavioral predictors associated with distinct adjustment pathways. Methods: Women (N = 538; mean age 55.4 years; range 40–70) with operable breast cancer (stages I–III) were drawn from the multicenter BOUNCE cohort. QoL (Global Health Status/QoL scale of the European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire Core 30) and depressive symptoms (depression subscale of the Hospital Anxiety and Depression Scale) were assessed at baseline and months 3, 6, 9, 12, 15 and 18. Latent class growth analysis and growth mixture modeling identified distinct trajectory classes. Associations between early predictors and trajectory membership were examined using logistic regression combined with elastic net regularization. Results: Depression trajectories demonstrated heterogeneity, with groups characterized by persistent resilience (59.7%), stable moderate/high (25.3%), delayed onset (5.0%), and recovery (10.0%). QoL trajectories ranged from stable excellent (13.2%) and stable high (40.7%) to moderate (31.4%) and persistent low/deteriorating (6.9%), as well as a distinct recovering trajectory (7.8%). Trajectory differentiation was primarily driven by psychological resources, symptom burden, functional status, and coping processes, alongside specific contributions from clinical factors. Conclusions: Distinct subgroups of women with breast cancer follow divergent adjustment pathways. These findings highlight the multidimensional nature of resilience and support the need for tailored interventions that promote long-term well-being beyond simple risk reduction. Full article

Background and Objectives: Functional decline and sarcopenia are major aging-related concerns. While protein intake is known to influence muscle health, its longitudinal impact on strength and physical function across age and gender remains underexplored. We assessed whether low protein intake correlate with future onset of low handgrip strength (HGS) and physical impairments in older adults using SHARE data. Methods: We analyzed 38,073 adults aged ≥50 years from 27 European countries using SHARE Waves 8 (2019/20) and 9 (2021/22). A protein intake index was derived from the frequency of consuming dairy, legumes/eggs, and meat/fish/poultry. Low intake was defined as the lowest decile. Logistic regression models, adjusted for age, gender, country, and baseline health, examined associations with low HGS and ten physical difficulties, stratified by age (50–65 vs. ≥66 years) and gender. Results: Low protein intake is associated with higher odds of low HGS in men (OR = 1.39 for 50–65; OR = 1.35 for ≥66) and older women (OR = 1.21). It was also associated with higher odds of mobility-related limitations, including walking 100 m (ORs = 1.25–1.53), stooping/kneeling (ORs = 1.20–1.19 in women), and reaching overhead (ORs = 1.19–1.33). Strength-related tasks, such as pushing/pulling large objects were more affected in men (ORs = 1.44 and 1.21). Notably, women aged 50–65 had over twice the odds of toileting difficulty (OR = 2.27) and significantly higher odds of difficulty shopping (OR = 1.65). These patterns highlight gender- and age-specific vulnerabilities. Conclusions: Low protein intake is associated with modest but consistent increases in the risk of reduced muscle strength and functional difficulties in older adults. Tailored nutritional strategies may mitigate age- and gender-specific risks to physical independence. Full article

The purpose of this investigation was to develop and physicochemically characterize two natural O/W cosmetic cream prototypes (LC1, LC2) containing 5% (w/w) of a Vaccinium vitis-idaea (lingonberry) fruit extract (LE) together with their corresponding blank formulations (LC1-BL, LC2-BL). The extract was obtained by hydroalcoholic maceration followed by solvent removal and was characterized for total phenolic, flavonoid, and monomeric anthocyanin content. Its antioxidant capacity was evaluated using DPPH, FRAP, CUPRAC, and ABTS assays. The phenolic profile was further explored by HPLC–DAD–ESI(+), enabling tentative identification of phenolic subclasses previously reported in the literature to be associated with antioxidant properties. The prepared creams were evaluated for qualitative organoleptic properties, pH, texture (hardness, adhesiveness, and spreadability), viscosity, and accelerated conditions of stability. All formulations were stable, and no phase separation occurred; however, the addition of the extract modified their color and odor and decreased the pH to values within the physiological skin pH range. An in-silico safety evaluation of the constituents (MoS and TTC) found a good toxicological profile at concentrations employed. Overall, the results support the feasibility of incorporating lingonberry fruit extract into O/W cosmetic cream systems and demonstrate that appropriate formulation design allows the development of stable products with defined physicochemical and mechanical characteristics. Full article

The continuous-flow technologies in organic synthesis for the production of active pharmaceutical ingredients (APIs) are nowadays more and more applied. In-silico process design is a powerful tool able to support organic synthesis in the field of scale-up and process development. Process design feasibility and reliability depend on the availability of a well-defined chemical reaction kinetic scheme, information which is usually derived from experimental datasets collected on purpose. The latter approach is time-consuming and demanding in terms of resources. Different possibilities are here proposed to valorize widely available experimental data from explorative works with different approaches, depending on the nature, richness, and structure of the datasets. The kinetic parameters (i.e., reaction order, kinetic constant, and activation energy) of some interesting organic reactions have been approximately estimated by applying different computational methodologies, thanks to built-in experimental databases. The numerical algebra approach dealing with linear and non-linear regression analysis for the kinetic parameters has been initially considered and related to the database information for oseltamivir synthesis. The Bayesian statistic was applied to the ibuprofen case through the application of the Markov Chain Monte Carlo (MCMC) method for reaction order estimation. At last, a Machine Learning (ML) approach has been applied to the Rolipram and Pregabalin case study. The in-house developed T-ReX experimental kinetic constant database was exploited, with application of the k-Nearest neighbor algorithm for classification and regular expression pattern recognition. Advantages and limitations of the three approaches are discussed. Full article

Background/Objectives: In pregnancy, beta-cell function is of interest since not only insulin resistance but also beta-cell dysfunction is common, especially when gestational diabetes mellitus (GDM) occurs. Typically, model-based beta-cell function is assessed with (at least) five-sample oral glucose tolerance test (OGTT). The aim of this study was to investigate whether the clinically common three-sample OGTT is sufficient for model-based beta-cell function assessment in pregnancy. Methods: We studied a group of pregnant women undergoing a 2 h five-sample OGTT with glucose, insulin, and C-peptide measurement at early and/or mid-pregnancy, for a total of 152 OGTTs. The five-sample OGTT was used for model-based beta-cell function assessment, yielding three beta-cell function parameters, i.e., glucose sensitivity (GSENS), potentiation factor ratio (PFR), and rate sensitivity (RSENS). GSENS, PFR, and RSENS assessment was repeated with the three-sample OGTT (at 0, 60, 120 min) and related values were compared to those from the five-sample OGTT (reference). Results: We found that, for GSENS, regression and Bland–Altman analyses showed satisfactory results (conditional and marginal R² values: 0.56 and 0.75, p < 0.0001, and limits of agreement containing 94.2% of samples). Moreover, five-sample and three-sample OGTT GSENS versions were fully consistent in patient subgroup analyses. Results for PFR were less satisfactory but acceptable, whereas those for RSENS were not reliable. Conclusions: The three-sample OGTT is acceptable for model-based beta-cell function assessment in pregnancy, although not for all parameters. Our methodology may be used to explore the effect of time sample reduction in other in-silico models. Full article

Background: Cholesterol imbalances and elevated blood pressure (BP) are closely interrelated risk factors for cardiovascular disease (CVD) and are subject to genetic influences. We sought to identify novel associations between candidate genetic coding variants and CVD traits in our isolated study cohort and validate them in a general population cohort. Methods: We leveraged the population genetic features of the Norfolk Island Health Study (NIHS, n = 601), to identify candidate functional variants which were analysed for association with CVD and metabolic syndrome traits. We followed up suggestive variant-trait associations in the 2022 release of UK Biobank whole exome data (n = 200,625). Results: We identified a novel ten-base-pair in-frame missense multi-nucleotide variant (MNV), tagged by rs35724886, in the lipid metabolism gene ACOT4, which was associated with cholesterol levels and blood pressure. The MNV was associated with a lower incidence of ‘elevated BP’—systolic BP ≥ 130 mmHg or diastolic BP ≥ 80 mmHg—(OR: 0.70; 95% CI: 0.51, 0.97; p = 0.03), and higher total cholesterol levels (β = 0.08; p = 0.04) in the NIHS. Validation in the UK Biobank revealed consistent associations between the MNV (proxied by rs35725886) and lower incidence of ‘elevated BP’ (p = 0.0001), higher total cholesterol (p = 0.01), and reduced use of medication for managing blood pressure (p = 1.8 × 10⁻⁶) and cholesterol (p = 0.002). Structural modelling and in-silico predictions suggested that the MNV introduced destabilising changes in the ACOT4 protein, likely influencing peroxisomal lipid metabolism pathways critical to CVD risk. Conclusions: This study identified a coding MNV with potential implications for understanding the genetic regulation of lipid metabolism and its impact on cardiovascular health. Full article

Environmental DNA (eDNA) metabarcoding is an innovative tool that is transforming ecological research. It offers a simple and effective method for simultaneously detecting numerous species across a wide range of environments. The method relies on assigning DNA sequences sampled from the environment to taxa, which is straightforward for species that have already been sequenced and are represented in reference databases. However, existing bioinformatics tools often fail to deliver accurate, fine-grained assignments when target species are absent from these databases. This limitation arises from handcrafted classification thresholds that do not account for nucleotide positional information. Here, we propose a deep neural architecture specifically designed to exploit both nucleotide identity and positional patterns in short TELEO sequences. Using an in-silico validation framework based on NCBI genbank sequences, we compare our approach with several state-of-the-art bioinformatics tools (Obitools, Kraken2, Lolo), as well as alternative sequence embedding methods, under controlled conditions. Our approach yields significantly higher classification accuracy at the genus and family levels, achieving average accuracies of 94.7% at the genus level and 86.5% at the family level, substantially outperforming the tested reference-based pipelines. The method remains robust with limited training data and shows improved performance when nucleotide positional information is preserved through sequence alignment. These results demonstrate the potential of AI-powered eDNA metabarcoding to complement existing taxonomic assignment tools, particularly in contexts where reference databases are incomplete or species-level resolution is not achievable, thereby supporting biodiversity monitoring and ecosystem management. Full article