A. Smerat

Immobilized enzymes in continuous biotransformations: advances, challenges, and engineering perspectives for scalable biocatalytic processes.

Immobilized enzymes have become central to continuous biotransformations by enabling catalyst recovery, repeated utilization, simplified product separation, and stable long-term reactor operation. This study critically evaluates how immobilization strategies, support architectures, reactor configurations, and reaction media collectively govern enzyme stability, mass transfer, catalytic efficiency, and process productivity in continuous biocatalytic systems. Recent advances in adsorption, covalent immobilization, entrapment, encapsulation, cross-linked enzyme aggregates (CLEAs), membrane reactors, packed-bed reactors, monolithic reactors, and structured flow platforms are systematically assessed with emphasis on industrially relevant performance metrics. The analysis demonstrates that although immobilization substantially enhances operational stability and catalyst lifetime, overall process performance is dictated by the interplay between enzyme orientation, support microstructure, pore diffusion, substrate and product transport, water activity, cofactor availability, product inhibition, enzyme leaching, fouling, and hydrodynamic limitations. Despite significant progress, meaningful comparison across studies remains constrained by inconsistent reporting of key process parameters, including enzyme loading, immobilization efficiency, residence time, space-time yield (STY), turnover number (TON), operational half-life, catalyst productivity, pressure drop, and time-on-stream stability. The study identifies critical knowledge gaps in mechanistic understanding, standardized performance evaluation, and long-duration continuous operation. It proposes an integrated framework that combines rational immobilization design with transport modeling, in situ reaction diagnostics, and cofactor engineering.

Clinical translation of mRNA-based cancer vaccines for solid tumors.

Vaccines that use messenger RNA (mRNA) have become a promising platform that is transforming cancer immunotherapy. These mRNA vaccines can be generated and manufactured quickly due to their modular design and can also induce CD4⁺ T-cell and CD8⁺ T-cell responses, in contrast to conventional protein or peptide-based vaccines. Additionally, synthetic mRNA can be optimized through various strategies (e.g., codon optimization, chemical modifications, and polyepitopic design) to not only ensure efficient antigen expression and immune activation but also to mitigate excessive innate immune sensing. Technologies that deliver mRNA vaccines (lipid nanoparticles, dendritic cell-based formulations, self-adjuvanted mRNA constructs, and viral vector systems) have all supported the development of mRNA-vaccine clinical applications while providing unique advantages in stability, antigen presentation, or immunogenicity. Clinical trials in the early phases involving a variety of solid tumors, such as pancreatic cancer, glioblastoma, renal cell carcinoma, melanoma, and non-small cell lung cancer, show that mRNA vaccines can elicit durable T-cell responses, expand high-avidity T-cell clones, and, in some cases, prolong recurrence-free survival. However, the clinical benefit has been variable, often limited by tumor heterogeneity, immune evasion, and immunodominance. Combination strategies utilizing immune checkpoint inhibitors, chemotherapy, and adoptive T-cell therapy are under investigation. This review synthesizes evidence from published clinical studies and 65 registered clinical trials on mRNA-based cancer vaccines, summarizing key molecular principles, delivery strategies, clinical translation in solid tumors, and ongoing opportunities for future therapeutic development.

Comprehensive in silico genomics analysis of global trends and host-specific emergence of aminoglycoside resistance in Staphylococcus aureus: a One-Health perspective

Aminoglycosides remain clinically valuable against Staphylococcus aureus. Aminoglycoside resistance in S. aureus represents a critical One Health concern and is primarily driven by aminoglycoside-modifying enzymes (AMEs), which are frequently plasmid-encoded. Although regional studies have provided valuable insights, the global epidemiology of aminoglycoside resistance determinants remains poorly characterized because comprehensive data integrating human, animal, and environmental reservoirs are still lacking. This study addresses this gap by analyzing over 110,000 S. aureus genomes (2000–2025) to map the global resistome, quantify temporal and host-specific trends, and assess the association between genetic determinants and phenotypic resistance. We performed a retrospective One Health meta-analysis of 110,309 S. aureus genomes collected between 2000 and 2025 from 128 countries. Genomes were quality-filtered and aminoglycoside resistance determinants were identified using NCBI AMRFinderPlus (v4.0.23). Multilocus sequence typing and host-source harmonization (Human, Animal, Environment, Unknown) enabled clonal and reservoir stratification. Temporal trends in gene prevalence and resistance burden were modeled with robust regression. Geographic and host-associated structuring of key genes was assessed via χ2 and enrichment tests. Machine-learning models (elastic-net, random forests, XGBoost) were benchmarked for minimum inhibitory concentration (MIC) prediction via nested cross-validation, with performance evaluated by mean absolute error, RMSE, and SHAP-based feature importance. All analyses were conducted in R and Python using publicly available, de-identified genomic data. Aminoglycoside resistance-associated genes were dominated by modifying enzyme determinants, with ant(6)-Ia, ant(9)-Ia, aph(3’)-IIIa, sat4, aadD1, and aac(6’)-Ie/aph(2’’)-Ia occurring in 14–22% of isolates worldwide. Temporal analysis revealed significant declines in several major determinants, most notably ant(9)-Ia (–2.22 percentage points per year, p < 0.001), whereas apmA exhibited a non-significant decreasing trend in animal isolates. Host structuring was marked: human clinical isolates concentrated common determinants, while animal and environmental isolates harbored rare alleles (apmA, spw, str, spd). Geographic mapping confirmed near-universal distribution of common genes but focal restriction of rare ones. Publicly available phenotypic data indicated strong activity of amikacin, whereas gentamicin showed a distinct resistant subpopulation that closely corresponded with AME gene carriage. Genotype–phenotype analyses demonstrated strong concordance, with gene-rich complements predicting resistant MIC strata and absence of determinants predicting susceptibility. Analysis across different gene classes revealed frequent co-occurrence of aminoglycoside resistance genes with determinants from other classes, such as mecA, blaZ, and MLS_B, embedding them within multidrug-resistant (MDR) genomic contexts. Over 25 years, the prevalence of aminoglycoside resistance-associated genes in S. aureus has declined for several common determinants, while rare veterinary-linked alleles are emerging in animal isolates. Strong genotype–phenotype concordance supports genomic prediction for gentamicin and amikacin, where MIC data are available, although phenotypic confirmation remains essential. The frequent co-occurrence of aminoglycoside resistance genes with other antimicrobial resistance determinants indicates their integration within co-occurrence patterns of MDR genes, defined here as clusters of co-occurring resistance genes often carried on shared mobile genetic elements. These patterns highlight the need for integrated One Health surveillance combining clinical, veterinary, and environmental monitoring with plasmid-context resolution to anticipate emerging threats.

Impact of Four-Dose Vaccination on the Transmission Dynamics of a Fractional-Order Malaria Model in the Context of Sub-Saharan Africa

This study develops a comprehensive mathematical framework for analyzing the transmission dynamics of malaria, with particular emphasis on a four-dose vaccination strategy combined with treatment interventions as primary control mechanisms. The model stratifies the human population into nine compartments—susceptible, exposed, infected, under treatment, recovered, and four sequential vaccination classes (V 1 through V 4 )—together with susceptible and infected vector populations, all governed by a system of ordinary differential equations. Analysis of the diseasefree equilibrium demonstrates local asymptotic stability when the basic reproduction number R 0 < 1, while a unique endemic equilibrium exists and is stable when R 0 > 1. Sensitivity analysis identifies the mosquito biting rate, transmission probability, first-dose vaccination rate, and treatment rate as the most influential parameters governing malaria dynamics. Numerical simulations confirm that increasing vaccination coverage and completion rates across all four doses, in conjunction with optimized treatment, substantially reduces malaria incidence. To capture long-term memory effects inherent in malaria transmission—particularly non-exponential immunity waning and heterogeneous parasite development—a Caputo fractional-order version of the model is introduced, solved using the Adams–Bashforth predictor-corrector method. Comparison of the integer-order and fractional-order results reveals that memory effects slow the approach to equilibrium and may lead to higher endemic levels under identical intervention parameters, underscoring the value of the fractional extension. These findings emphasize that achieving high completion rates across all four vaccination doses, sustained treatment access, and integrated vector control are essential for meaningful progress toward malaria elimination in Sub- Saharan Africa.

Rural Agricultural Efficiency, Climate Sustainability, and Economic Performance: An Examination of Organic and Conventional Farming

Organic farming has emerged as a sustainable alternative to conventional farming, providing potential benefits in environmental conservation, crop quality, and market access. This study measures the impact of organic farming on agricultural productivity, resource utilisation, environmental sustainability, and trade competitiveness in Punjab, India. A comparative analysis of organic and conventional farming was conducted, focusing on key metrics such as crop yield per hectare, soil health, biological diversity, carbon footprint, and market access. As indicated by the research results, while conventional farming generated higher crop yield (e.g., the wheat crop: 4.47 vs. 3.82 t/ha, p = 0.034), organic farming improved in terms of resource utilisation, using less water (130.4 vs. 160.2 m3/t, p = 0.03) and energy consumption (225.6 vs. 270.8 kWh/t, p = 0.02). Organic farming was associated with better soil health (organic farming: 3.8% vs. 2.9%, p = 0.005) and superior biological diversity (species richness: 25 vs. 15, p = 0.008). Both findings were considered statistically significant. In terms of the marketplace, 68.2% of organic farming were able to access top marketplaces, whereas only 40.5% of conventional farming were (p = 0.01). By doing this, these individuals were able to negotiate higher costs (₹75.6 vs. ₹63.4/kg, p = 0.02). There is a clear result that organic farming products can be successful globally, as shown by the revealed comparative advantage (1.53).

Intelligent fault diagnosis and simulation-based modelling of grid-connected photovoltaic systems under desert dust conditions

Photovoltaic (PV) power plants operating in desert environments experience continuous efficiency losses because dust accumulation gradually reduces solar radiation reaching the module surface, leading to lower energy production even under favourable weather conditions. Accurate prediction of normal operating behaviour therefore provides a reference for distinguishing genuine faults from natural fluctuations in plant performance. This study proposes a data-driven framework for PV power prediction and residual-based fault diagnosis at the Aoulef PV power plant in southern Algeria. Artificial neural networks (ANN) and adaptive neuro-fuzzy inference systems (ANFIS) were developed from measured solar irradiance and ambient temperature to estimate the healthy-state PV power output. Model performance was assessed through regression analysis and statistical error indices, whereas fault detection relied on residuals computed from the difference between measured and predicted power. Both models achieved excellent prediction accuracy, with coefficients of determination approaching 0.99. The ANN produced lower prediction errors than the ANFIS, with a root mean square error of 0.009 and an mean absolute error of 0.004, which improved the sensitivity of the residual-based diagnosis. Dust accumulation, identified as fault F13, generated clear residual deviations that enabled automatic fault detection without interrupting plant operation. The findings indicate that the ANN framework combines high predictive accuracy with low computational demand, offering a practical and reliable solution for intelligent monitoring and maintenance of PV systems operating under harsh desert conditions.

Regulatory roles of pyroptosis-associated lncRnas in breast cancer progression and immune microenvironment.

Breast cancer progression and therapeutic response are profoundly influenced by tumor immune interactions, yet the molecular regulators linking cell death pathways with immune modulation remain incompletely understood. Pyroptosis, a gasdermin-mediated inflammatory form of programmed cell death, has emerged as a key determinant of tumor immunity, while long non-coding RNAs are increasingly recognized as critical upstream regulators of cancer signaling. Recent transcriptomic analyses have identified numerous pyroptosis-associated lncRNAs and generated prognostic signatures that stratify patients into distinct risk groups with significantly different survival outcomes. These signatures are closely associated with the tumor immune microenvironment: low-risk tumors exhibit increased infiltration of CD8+ T cells, NK cells, and B cells, together with elevated immune checkpoint expression, whereas high-risk tumors display immunosuppressive features, including M2 macrophage enrichment and higher tumor mutation burden. Mechanistic studies demonstrate that lncRNAs regulate pyroptosis through inflammasome activation, gasdermin-mediated signaling, and epigenetic modulation, thereby influencing tumor growth, metastasis, and therapeutic resistance. Despite these advances, clinical translation remains limited by dependence on retrospective transcriptomic datasets, insufficient mechanistic validation, lack of standardized assays, and scarce prospective clinical evidence. pyroptosis-associated lncRNAs represent a promising link between tumor progression and immune regulation, with considerable potential as prognostic biomarkers and therapeutic targets in breast cancer.

Level Graph of Turiyam Graphs: Concepts and Applications

This study introduces and investigates the concept of the (α, β, γ, δ)-level graph for a Turiyam graph, a novel extension of fuzzy and neutrosophic graphs that incorporates a fourth independent membership degree representing the unknown or liberal component. We defined the (α, β, γ, δ)-level graph of a turiyam graph as a crisp graph derived by applying specific threshold values (α, β, γ, δ) to its vertex and edge membership values. In addition, we discuss its application in social networks. This study is supported by illustrative examples.

Anti-PD-1 single-chain variable fragments in cancer immunotherapy: from molecular engineering to clinical translation

Mechanical and thermal properties prediction and optimization of multiscale high performance hybrid biopolymer nanocomposites

Machine learning-enhanced fluorescence signal processing of carbon quantum dots for high-accuracy chemical sensing

A comprehensive analysis of ML-driven methodologies for denoising, spectral decomposition, feature extraction, and high-accuracy classification in CQD fluorescence systems shows how ML enables ultra-low-level analyte detection, interpretable photophysical modeling, and real-time intelligent sensing across chemical and biological environments.

Genetic association of immunosenescence to immune decline

This review discusses how inherited variation in cytokine-regulatory pathways may influence IL-6, TNF-α, interferon, and TGF-β signaling, thereby contributing to inflammatory set points that favor chronic immune activation and inflammaging.