Search Results (660)

Accurate and trustworthy prediction of lithium-ion battery aging remains challenging due to multi-mechanistic degradation, cell-to-cell variability, and distribution shift between laboratory calibration and deployment. Fractional-order models have been proposed to capture long-memory effects in electrochemical systems; however, it remains unclear when such memory is empirically identifiable and beneficial within the common prognostics abstraction of state-of-health (SOH) versus cycle index. This work develops a fully reproducible computational pipeline for mechanistic battery aging based on a Caputo fractional differential equation (FDE) and evaluates its cross-cell generalization on open NASA cycling data. Parameters are identified using bounded robust nonlinear least squares and validated under a strict transfer protocol: calibration on cells B0005/B0006 and evaluation on held-out cells B0007/B0018 without refitting. The fractional model is benchmarked against a classical ODE surrogate, an ECM-inspired resistance-proxy baseline, and one-step-ahead machine-learning predictors. Uncertainty quantification is performed via parameter bootstrap and subsequently calibrated using conformal correction to target nominal coverage under transfer. Results show that the fractional order tends to collapse toward the integer-order limit (α → 1) in this dataset, indicating limited evidence of additional long-memory at the SOH-versus-cycle level under the considered protocol, while robust identification remains essential for stability. Calibrated prediction intervals achieve near-nominal coverage on held-out cells, highlighting the importance of UQ calibration under cell-to-cell shift. The proposed scripts and environment specifications enable direct replication and facilitate future extensions to stress-aware fractional models and hybrid physics–ML approaches. Full article

A key limitation in contemporary HF management is the marked heterogeneity of the syndrome, driven by diverse pathophysiological mechanisms that are not fully captured by traditional classifications based on left ventricular ejection fraction. Precision medicine has emerged as a promising approach to address this heterogeneity by integrating clinical characteristics, circulating biomarkers, advanced imaging, and computational phenotyping strategies. However, current frameworks predominantly emphasize myocardial dysfunction, while the contribution of vascular abnormalities remains underrepresented. The interaction between the left ventricle and the arterial system plays a fundamental role in cardiovascular performance. Arterial stiffness, commonly assessed by pulse wave velocity (PWV), represents a key determinant of vascular aging and a robust predictor of cardiovascular risk. Increasing evidence suggests that vascular dysfunction contributes significantly to the pathophysiology and clinical expression of HF, particularly in phenotypes characterized by preserved ejection fraction. This review synthesizes current evidence on precision medicine in HF and highlights the emerging role of arterial stiffness and PWV in multidimensional patient phenotyping. We propose that integrating vascular parameters into existing phenotyping frameworks may enhance risk stratification, improve mechanistic understanding, and support the development of more personalized therapeutic strategies in heart failure. Unlike previous reviews that have addressed arterial stiffness or heart failure phenotyping separately, this work uniquely integrates ventricular–vascular interaction and pulse wave velocity into a comprehensive precision medicine framework for heart failure. By bridging vascular physiology with data-driven phenotyping strategies, this review provides a novel conceptual model for incorporating arterial stiffness into multidimensional patient characterization across the full spectrum of heart failure phenotypes. Full article

Trans-anethole oxygenase (TAO) exhibits broad arylpropene substrate specificity but has low activity in converting isoeugenol to high-value vanillin. Herein, we employed computer-aided rational design to engineer TAO from Pseudomonas putida (PpTAO) for enhanced catalytic efficiency toward isoeugenol. Structural modeling and AlphaFold 3 docking identified two key catalytic residues, Arg86 and His118. Through substrate channel engineering and computation-guided mutagenesis, a series of targeted variants were constructed. Three variants, H93A, Q207R/G249C, and I59T/F62T, showed significant improvements in whole-cell performance, with activity increases of 1.8-, 2.13-, and 4.83-fold over the wild type (WT), respectively. Purified enzyme kinetics corroborated these findings, as reflected in k_cat/K_m values that reached 1.6, 2.1, and 4.7 times that of the WT. Mechanistic molecular dynamics simulations revealed that H93A enhances activity by widening the access tunnel, whereas Q207R/G249C exerts beneficial distal effects. Notably, the I59T/F62T variant significantly increases substrate affinity by optimizing hydrophobic interactions within the binding pocket. These results validate the efficacy of computational modeling in enzyme redesign and provide a robust biocatalyst for the sustainable biosynthesis of vanillin. Full article

This study investigates shared molecular mechanisms between pyoderma gangrenosum (PG) and inflammatory bowel disease (IBD) and systematically evaluates the therapeutic potential of JAK inhibitors targeting this pathway. Despite the clear clinical comorbidity, the core inflammatory pathways driving cross-tissue associations between the two diseases remain unclear. Furthermore, systematic mechanistic evidence is lacking regarding whether JAK inhibitors act by regulating shared pathological pathways in patients with comorbidities. To address this, this study integrated PG skin and IBD intestinal transcriptome data, single-cell transcriptomic data, and genome-wide association study (GWAS) meta-data from public databases. It employed a multi-level computational biology approach combining Mendelian randomization, weighted gene co-expression network analysis, protein interaction network construction, molecular docking simulations, and system dynamics modeling. The results revealed that genetic analysis confirmed IBD as a causal risk factor for PG, precisely identifying six shared genetic loci. Transcriptomic analysis identified a cross-tissue conserved inflammatory module centered on the JAK-STAT pathway, with JAK2 and STAT3 identified as network hubs. Molecular docking predicted high affinity of baricitinib for both JAK1 and JAK2, while system dynamics modeling demonstrated that its intervention effectively suppresses signaling in the shared inflammatory network. This study reveals the molecular basis of the “gut–skin axis” comorbidity between PG and IBD from a multi-omics integration perspective. It provides predictive computational evidence for the use of JAK inhibitors in targeted comorbidity therapy. Baricitinib is identified as a particularly promising candidate. These findings advance the transition from empirical drug use to mechanism-guided precision treatment strategies. Although this study provides multiscale computational simulation evidence, the lack of direct experimental validation of these predicted results necessitates further confirmation through in vitro and in vivo experiments. Full article

This study aims to investigate the potential anticancer effects of erucin, an isothiocyanate derived from Eruca sativa, in triple-negative breast cancer (TNBC) by predicting molecular targets and evaluating its in vitro effects on TNBC cell proliferation, apoptosis and cell cycle distribution. Potential protein targets of erucin were identified using SwissTargetPrediction, resulting in 117 targets, of which 84 overlapped with TNBC-related genes sourced from GeneCards, DisGeNET, and OMIM. Protein–protein interaction analysis was performed to identify key hub genes. In vitro experiments were conducted using MDA-MB-231 TNBC cells to assess dose-dependent effects on cell viability. Flow cytometry was employed to evaluate apoptotic cell populations and cell cycle distribution. Protein–protein interaction analysis identified ten hub genes, including AKT1, STAT3, EGFR, and MMP9, representing highly connected nodes within the predicted interaction network. In vitro studies showed dose-dependent reduction in MDA-MB-231 cell viability following erucin treatment, with an IC₅₀ of approximately 48.87 µg/mL. Flow cytometry revealed increased apoptotic cell population and G1 phase accumulation. These findings suggest that erucin is associated with cytotoxic and antiproliferative effects in TNBC cells and may interact with multiple cancer-related targets. However, the identified molecular targets and pathways are based on computational predictions and require further experimental validation. Overall, this study provides a preliminary integrated framework linking computational predictions with experimental observations, which may support future mechanistic and preclinical investigations of erucin in TNBC. Full article

Geriatric pharmacotherapy is usually challenged by physiological senescence. For instance, progressive declines in organ function and alterations in body composition can complicate drug disposition. However, conventional pharmacometrics models commonly have limited capacity to map these high-dimensional, nonlinear relationships. In this review, we are examining the recent shift toward integrating machine learning (ML) with mechanistic pharmacokinetic (PK)/pharmacodynamic (PD) models to improve the accuracy and precision of dosing. Machine learning approaches like Random Forest and XGBoost consistently provided more accurate exposure predictions and significantly more efficient computational workflows than conventional methods. Nevertheless, concerns such as “black box” transparency and the potential of algorithmic bias toward specific patient demographics are challenging. It is important to incorporate explainability tools like SHAP, and adopting FAIR data principles is crucial for achieving professional trust and ensuring site-specific generalizability. Full article

Aqueous organic flow batteries are a promising technology for large-scale energy storage, owing to their safety, low cost, and tunable molecular properties. Battery performance is critically governed by the redox potential, solubility, and stability of organic active species, making molecular design a central research priority. Yet, many current systems still rely on inorganic metal-based materials, which face challenges such as high cost and sluggish kinetics. This review outlines a systematic molecular-engineering framework for designing novel redox species, offering strategies to tailor solubility, redox potential, and molecular size in both organic compounds. Recent advances in mechanistic insight, functionalization, and structure-dependent electrochemical performance are summarized. Computational chemistry and machine learning are highlighted for accelerating high-throughput screening and property prediction, speeding up molecular optimization. Small molecules (1–4 rings), including quinones (C=O), alloxazines, phenazines, and indigo derivatives, which undergo reversible redox reactions involving nitrogen and/or carbonyl groups, have been explored as anolytes and/or catholytes in aqueous redox flow batteries. Key challenges remain, including limited electrochemical stability windows, insufficient solubility, and poor molecular stability, leading to low energy density and cycling degradation. Improving anolyte performance by simultaneously lowering redox potential and enhancing solubility and stability is therefore crucial for advancing both organic and broader redox-active battery systems. Computational and machine learning approaches for identifying and refining electrolyte molecules are also addressed, enabling efficient screening and molecular modification toward high-performance flow batteries. Full article

Salmonella enterica infection remains a major threat to poultry health and food safety, largely due to its ability to invade the intestinal epithelium, modulate host immunity, and persist intracellularly. Curcumin, a bioactive phytochemical, has shown promising antimicrobial and immunomodulatory potential; however, its precise molecular interplay with host and pathogen systems remains unclear. An integrated computational pipeline was applied, combining target prediction, host immune network construction, Salmonella virulence interaction analysis, STRING-based PPI mapping, KEGG/GO enrichment, and molecular docking validation. Host immune hub genes and Salmonella virulence regulators were identified, followed by docking of curcumin to key host (AKT1, STAT3, TNF) and pathogen proteins (invA, phoP, ssrB). Host network analysis revealed enrichment in the PI3K–AKT, NF-κB, FoxO, and IL-10 signaling pathways, indicating roles in epithelial protection, immune regulation, inflammation suppression, and antioxidant defense. Salmonella virulence hubs were primarily associated with epithelial invasion, Type III secretion, intracellular survival, and global virulence reg-ulation. Docking analysis demonstrated a strong binding affinity of curcumin toward AKT1 (−7.4 kcal/mol), STAT3 (−6.5 kcal/mol) and TNF (−5.8 kcal/mol), supporting host immunomodulation and epithelial protection. Simultaneously, curcumin showed notable affinity for phoP (−6.8 kcal/mol), invA (−6.3 kcal/mol), and ssrB (−5.8 kcal/mol), suggesting the potential suppression of virulence signaling, invasion machinery, and intracellular persistence. This integrated host–pathogen systems analysis demonstrates that curcumin exerts a dual regulatory effect by enhancing host immune protection while concurrently disrupting Salmonella virulence mechanisms. These findings provide mechanistic insight supporting curcumin as a promising natural therapeutic candidate for controlling Salmonella infection in broilers. Full article

Efficient mixing is a persistent bottleneck in agricultural and agrochemical processing, where rapid and uniform mixing must be achieved under laminar flow with low energy input and gentle shear. Inspired by peristaltic transport in biological systems, this study investigates a bio-inspired flexible-wall squeezing mixer and establishes a two-dimensional computational framework to quantify how periodic wall deformation governs scalar homogenization in a flexible conduit. An Arbitrary Lagrangian–Eulerian dynamic mesh approach is implemented to resolve moving boundaries and to prescribe actuation, enabling the systematic evaluation of the separate and coupled effects of peak wall-normal velocity amplitude A and actuation frequency f on mixing performance. Mixing effectiveness is quantified using a variance-based mixing index MI and a sustained-threshold mixing time ts, and response surface methodology is employed to map the A–f design space and interpret the roles of time-dependent shear, interfacial stretching and folding, and vortex intensification. Relative to a non-actuated baseline, a peak wall-normal velocity amplitude of 3 × 10⁻³ m s⁻¹ at 2 Hz reduces t_s by 21.3%. At fixed f = 3 Hz, increasing A from 1 × 10⁻³ to 4 × 10⁻³ m s⁻¹ shortens t_s by 10.2%, while at fixed A = 3 × 10⁻³ m s⁻¹, raising f from 1 to 5 Hz further decreases t_s by 6.6% with diminishing gains at the lowest frequencies. The response surface identifies an operating optimum at A = 4 × 10⁻³ m s⁻¹ and f = 5 Hz, achieving a peak MI of 0.9557 and a minimum t_s of 7.81 s. A periodically squeezed physical mixing loop was further examined using fluorescence imaging to assess outlet homogeneity trends. The stabilized outlet coefficient of variation (CV) decreased from about 0.65 without squeezing to 0.60 at 1 Hz and 10 mm s⁻¹, 0.58 at 2 Hz and 10 mm s⁻¹, and 0.54 at 2 Hz and 30 mm s⁻¹, indicating that stronger and faster actuation improves outlet uniformity. The numerical and experimental results are therefore interpreted jointly as mechanistic and trend-level evidence, while a rigorous quantitative prediction for the cylindrical compliant device will require future three-dimensional, compliance-resolved simulations and broader experimental benchmarking. Full article

Multiple myeloma (MM) and osteosarcoma (OS) are two biologically distinct osseous malignancies with similar molecular networks that present translational challenges for their computational modeling. This comparative research analyzes MM and OS biology relevant to in silico approaches, focusing on PI3K-AKT-mTOR signaling, the RANK-RANKL-OPG axis, angiogenic factors (VEGF, TGFs), and immune mediators in MM, alongside the transcription factors (SOX9, RUNX2), signaling pathways (PI3K-AKT-mTOR, NOTCH), immune cell state (TAM2), and interleukins in OS. Based on this pathophysiologic foundation, the review outlines five computational paradigms: (i) mechanistic models; (ii) data-driven/machine learning schemes; (iii) hybrid mechanistic approaches; (iv) digital twins/virtual cohorts, and (v) MIDD/PBPK models for real-world applications. A cross-cancer comparison section summarizes common and distinct biological axes and their computational translation as well as the overlapping features from the bone microenvironment. For both MM and OS, the research assesses strengths, limitations, and data needs of current models, outlining the strategic objectives for next-generation multiscale, AI-enabled models providing a roadmap for tissue engineers, oncology scientists, and translational researchers to design clinically relevant preclinical tests and accelerate safer, more effective strategies for tumor-affected bones. The differences between MM and OS impose distinct biological constraints, so their comparisons are rare. Combining all these features with artificial intelligence capabilities will underpin a promising transition in the development of in silico adaptive and learning models. Full article

Background/Objectives: Cancer remains one of the leading global health burdens, mainly because of the lack of specificity and off-target toxicity associated with conventional therapeutic approaches. To move toward more efficient anticancer drug discovery, we have developed an advanced machine-learning-based architecture that allows for predictive modeling of anticancer small molecules. Methods: A total of 3600 compounds with experimentally validated IC₅₀ values were systematically processed to derive a comprehensive suite of molecular representations comprising 2D physicochemical descriptors, structural fingerprints, and hybrid descriptor sets generated via the Mordred and PaDEL frameworks. A total of six machine learning algorithms—Random Forest (RF), Extreme Gradient Boosting (XGB), Gradient Boosting (GB), Extra-Trees classifier (ET), Adaptive Boosting (AdaBoost), and Light Gradient Boosting Machine (LightGBM)—were trained and benchmarked via a rigorous model evaluation protocol incorporating 10-fold cross-validation along with multiple performance metrics. Ensemble voting strategies were also examined to assess potential performance. Result: Of all configurations, the XGB-Hybrid architecture emerged as the most robust and generalizable classifier with an AUC of 0.88 and accuracy of 79.11% on the independent test set. To ensure interpretability and mechanistic insight, SHAP-based feature analysis was conducted, by which feature contributions could be quantified and the molecular determinants most influential for anticancer activity discrimination were revealed. Altogether, the current study establishes an XGB-Hybrid framework as technically rigorous, interpretable, and high-performance predictive modeling with the ability to accelerate early-stage anticancer small molecule identification. Conclusions: The study has brought into focus the transformational effect of machine learning in modern computational oncology and rational drug design pipelines. Full article

Wound healing involves the coordinated regulation of inflammation, angiogenesis, and extracellular matrix remodeling, processes modulated by natural bioactives. In this context, Chelidonium majus L. (C. majus), a plant rich in alkaloids and flavonoids, remains mechanistically underexplored. This study, therefore, investigates its metabolites using an integrated computational–experimental approach and evaluates their applicability in sericin-based wound-healing systems. A curated database of 83 C. majus bioactive compounds was analyzed using cheminformatics and molecular docking against key wound-healing targets (iNOS, VEGF, MMP-3, and tyrosinase), followed by ADMET and toxicity prediction (StopTox). Selected plant–sericin formulations were subsequently evaluated for wound-healing activity using an in vitro fibroblast scratch assay. Docking revealed strong binding affinities for several metabolites, particularly protopine, kaempferol-3-rutinoside, cynaroside, hesperidin, quercetin-3-rhamnosylrutinoside, and vitexin, indicating multi-target modulation across inflammatory, proliferative, and remodeling phases of tissue repair. ADMET and toxicity analyses predicted favorable dermal safety and pharmacokinetic profiles for most compounds. Consistently, in vitro assays demonstrated that C. majus–sericin systems had fibroblast migration and wound closure in a concentration- and ratio-dependent manner, with improved healing kinetics observed at 150 µg/mL and for formulations containing higher relative proportions of both components. The experimental outcomes supported the pro-angiogenic and matrix-stabilizing mechanisms predicted in silico. Overall, C. majus metabolites exhibit polypharmacological wound-healing activity, supporting their integration into sericin-based systems as a promising strategy for topical therapies. Full article

Durum wheat production in the Mediterranean basin faces increasing climate variability and thus the need for computationally efficient tools to support agronomic decision-making at regional scale. Process-based crop models such as AquaCrop provide mechanistically sound yield estimates but require substantial computation time when screening large numbers of soil–climate–management combinations. The present study addresses this constraint by developing and evaluating five machine learning (ML) surrogate models—Linear Regression (LR), Multilayer Perceptron (MLP), Support Vector Machine for regression (SMOreg), RandomTree, and Reduced Error Pruning Tree (REPTree)—trained to emulate the AquaCrop-GIS response surface for durum wheat (Triticum durum Desf.) grain yield across the Capitanata plain (Southern Italy). A dataset of 342 instances was constructed by crossing 25 soil profiles, three sowing dates, and two irrigation regimes across 15 climatic grid cells (2014–2023), evaluated by stratified 10-fold cross-validation. The MLP achieved the highest accuracy (R = 0.983; R² = 0.966; RMSE = 0.083 t ha⁻¹); the four interpretable models were clustered at R = 0.891–0.907 (RMSE = 0.192–0.203 t ha⁻¹). All models converged on consistent agronomic signals: standard sowing (1 November) yielded +0.53 t ha⁻¹ over late sowing (15 November), supplemental irrigation added +0.17 t ha⁻¹, and fine-textured soils produced superior yields. The convergence of directional signals across linear, kernel-based, and tree-based architectures confirms that ML surrogates trained on process-model outputs can efficiently emulate AquaCrop response surfaces and deliver actionable management guidance for durum wheat producers and agricultural planners in Mediterranean dryland farming systems. Full article

Neuropsychiatric diseases are characterized by complex molecular underpinnings that remain challenging to fully elucidate. Molecular dynamics (MD) simulations have emerged as a powerful computational tool, providing a crucial bridge between static genetic data and the dynamic functional consequences of molecular alterations. This review offers a comprehensive overview of the application of MD simulations in studying the molecular basis of neuropsychiatric disorders. We highlight key applications, including the assessment of mutation pathogenicity in disease-associated proteins, the influence of post-translational modifications on protein function, folding, misfolding, and aggregation, and the characterization of psychopharmacological drug–target interactions at atomic resolution. Through relevant examples from research on psychiatric and neurodegenerative diseases, we illustrate how these computational methods are implemented to gain mechanistic insights. Importantly, this review traces the historical development of MD simulations in biological applications, critically examines the method’s limitations, and outlines future perspectives for simulating long-timescale physiological processes, large molecular ensembles, and even whole-cell environments. Ultimately, this work highlights MD simulations as a useful and complementary tool for modern neuropsychiatry research, capable of revealing disease mechanisms and guiding the development of novel therapeutic strategies. Full article

The degradation of plastic waste leads to the release of numerous chemical additives, including phthalate plasticizers, which have been implicated in the pathogenesis of metabolic disorders. Di (2-ethylhexyl) phthalate (DEHP) is a widely used plasticizer whose primary metabolite, mono (2-ethyl-5-carboxypentyl) phthalate (MECPP), has been associated with multiple metabolic diseases. In this study, we applied an integrated approach combining network toxicology and molecular docking to systematically investigate the potential mechanistic role of MECPP in metabolic dysregulation. Our strategy included multi-platform target prediction, disease gene association analysis, functional enrichment, protein–protein interaction network construction, and molecular docking analysis. The results suggested that MECPP may be associated with six common core targets, including BCL2, BCL2L1, MAPK14, MMP2, MMP9, and TNFRSF1A, which are mainly involved in apoptosis, inflammatory regulation, and extracellular matrix remodeling. Pathway enrichment analysis further indicated the potential involvement of several disease-overlapping pathways, including insulin resistance, neuroactive ligand–receptor interaction, efferocytosis, advanced glycation end product–receptor for advanced glycation end product (AGE–RAGE) signaling, phospholipase D signaling, and renin secretion. Overall, these findings suggest that MECPP may contribute to metabolic dysregulation through overlapping molecular mechanisms across multiple diseases. This study provides a computational basis for future experimental validation and environmental risk assessment. Full article