Search Results (43)

Acute myeloid leukemia (AML) presents significant challenges for CAR T-cell therapy due to its pronounced heterogeneity and the lack of leukemia-specific surface antigens. Frequently targeted antigens, such as CD33, CD123, and CLL-1, are also present on normal hematopoietic progenitors, resulting in on-target, off-tumor toxicity and restricting clinical translation. To address these challenges, logic-gated CAR T-cell strategies have been developed to enable combinatorial antigen recognition. These approaches incorporate engineered circuits, including AND, OR, and NOT gates, as well as synNotch receptors, split-CAR configurations, and inhibitory platforms (iCARs and Tmod), to improve discrimination between leukemic and normal cells. In AML, CAR T-cell efficacy and persistence are further affected by the immunosuppressive bone marrow and lymphoid microenvironment, which involves immune cell trafficking, cytokine signaling, and lymphatic immune regulation. Preclinical studies employing dual-target strategies, such as CD33/CD123 and CLL-1/CD123, have shown improved antileukemic efficacy with reduced hematopoietic toxicity. This review summarizes the molecular principles underlying logic-gated CAR-T systems and examines their translational application in AML. Additionally, it highlights emerging evidence connecting the regulation of lymphatic and immune microenvironments to CAR T-cell persistence, trafficking, and toxicity and discusses future strategies, such as single-cell antigen mapping, computational circuit engineering, and synthetic immune programming, to enhance the precision and clinical feasibility of next-generation AML immunotherapies. Full article

Plant-parasitic nematodes (PPNs) rank among the most economically destructive soilborne pathogens worldwide, causing annual crop losses estimated at USD 125–175 billion. Traditional management of plant parasitic nematodes has depended significantly on synthetic nematicides; however, increasing regulatory constraints, environmental pollution, and the rise of resistant nematode populations have generated an urgent need for sustainable alternatives. Humic substances (HS), comprising humic acids, fulvic acids, and humins derived primarily from leonardite and lignite, represent biologically active components of soil organic matter. Their different functional groups, like carboxylic, phenolic, and carbonyl groups, have direct nematicidal and nematostatic effects by stopping eggs from hatching, slowing down juvenile development, and lowering infectivity. They also indirectly improve soil structure, nutrient bioavailability, and the composition of the rhizosphere microbiome. Plant growth-promoting rhizobacteria (PGPR), particularly Bacillus spp. and Pseudomonas spp., suppress PPN populations through antibiotic biosynthesis, cuticle-degrading hydrolytic enzymes, nematostatic volatile organic compounds, and elicitation of induced systemic resistance (ISR). This review methodically analyzes the individual and synergistic processes by which HS and PGPR inhibit PPNs and enhance plant growth. Humic compounds strongly promote PGPR rhizosphere colonization, augmenting microbial metabolic activity and bioinoculant stability, hence producing combinatorial suppressive effects unattainable by either input independently. The combined HS-PGPR approach is reliable and environmentally sustainable for comprehensive nematode control, requiring multidisciplinary research to achieve global sustainable agriculture. Full article

Petroleum coke quality strongly influences refinery economics and downstream energy use, yet real-time control is constrained by slow quality assays and a 24–48 h lag in laboratory results. This study introduces a physics-informed combinatorial digital twin for value-optimized coking, aimed at improving energy efficiency and environmental performance through adaptive quality forecasting. The approach builds a modular library of 32 candidate equations grouped into eight quality parameters and links them via cross-parameter dependencies. A two-level optimization scheme is applied: a genetic algorithm selects the best model combination, while a secondary loop tunes parameters under a multi-objective fitness function balancing accuracy, interpretability, and computational cost. Validation on five clustered operating regimes (industrial patterns augmented with noise-perturbed synthetic data) shows that optimal model ensembles outperform single best models, achieving typical cluster errors of ~7–13% NMAE. The developed digital twin framework enables accurate prediction of coke quality parameters that are critical for its energy applications, such as volatile matter and sulfur content, which serve as direct proxies for estimating the net calorific value and environmental footprint of coke as a fuel. Full article

Grammar induction runs into a serious problem due to the exponential growth of the number of possible derivation trees as sentence length increases, which makes unsupervised parsing both computationally demanding and highly indeterminate. This paper proposes a mathematics-based approach that alleviates this combinatorial complexity by introducing structural constraints based on Catalan and Fuss–Catalan numbers. By limiting the depth of the tree, the degree of branching and the form of derivation, the method significantly narrows the search space, while retaining the full generative power of context-free grammars. A filtering algorithm guided by Catalan structures is developed that incorporates these combinatorial constraints directly into the execution process, with formal analysis showing that the search complexity, under realistic assumptions about depth and richness, decreases from exponential to approximately polynomial. Experimental results on synthetic and natural-language datasets show that the Catalan-constrained model reduces candidate derivation trees by approximately 60%, improves F1 accuracy over unconstrained and depth-bounded baselines, and nearly halves average parsing time. Qualitative evaluation further indicates that the induced grammars exhibit more balanced and linguistically plausible structures. These findings demonstrate that Catalan-based structural constraints provide an elegant and effective mechanism for controlling ambiguity in grammar induction, bridging formal combinatorics with practical syntactic learning. Full article

For network graphs, numerous graph features are intimately linked to eigenvalues of the Laplacian matrix, such as connectivity and diameter. Thus, it is very important to solve eigenvalues of the Laplacian matrix for graphs. Similarly, for higher-order networks, eigenvalues of combinatorial Laplacian matrices are also important for invariants of graphs. However, for large-scale networks, it is difficult to calculate eigenvalues of the Laplacian matrix directly because it is either very difficult to obtain the whole network structure or requires a lot of computing resources. Therefore, this article makes the following contributions. Firstly, this paper proposes a random walk approach for estimating the bounds of the greatest eigenvalues of Laplacian matrices for large-scale networks. Considering the relationship between the spectral moments of the adjacency matrix and the closed paths in the network, we utilize the relationship between the adjacency matrix and the Laplacian matrix to establish the relationship between the Laplacian matrix and the closed paths. Then, we employ equiprobable random walks to sample the large graph to obtain the small graph. Through algebraic topology knowledge, we obtain the bounds of the largest eigenvalue of the Laplacian matrix of the large graph by using Laplacian spectral moments of the small graph. Secondly, for high-order networks, this paper proposes a method based on random walks and graph transformations. The graph transformation we propose mainly converts graphs with second-order simplices into ordinary weighted graphs, thereby transforming the problem of solving the spectral moments of the second-order combined Laplacian matrix into solving the spectral moments of the adjacency matrix. Then, we use the aforementioned random walk method to solve bounds of the greatest eigenvalue of the second-order combinatorial Laplacian matrix. Finally, by comparing the proposed method with existing algorithms in synthetic and real networks, its accuracy and superiority are demonstrated. Full article

Cancer progression and therapeutic resistance are driven by complex molecular interactions between genomic instability and immune modulation. Defects in the DNA damage response (DDR) not only promote tumor heterogeneity but also shape the tumor immune landscape through the generation of neoantigens, activation of the cGAS–STING pathway, and modulation of immune checkpoints. This review provides an integrative overview of the molecular mechanisms linking DDR dysfunction to immune crosstalk, emphasizing how these processes influence tumor evolution and response to therapy. We discuss emerging therapeutic strategies that exploit DDR–immune interactions, including PARP and ATR inhibitors, synthetic lethality approaches, and combination regimens with immune checkpoint blockade. Understanding the bidirectional connection between DNA repair pathways and immune signaling unveils new translational opportunities for precision oncology and offers a framework for developing combinatorial therapies capable of overcoming resistance and improving long-term cancer control. Full article

Aperiodic tessellations of polykite unitiles, such as hats and turtles, and the recently introduced hares, red squirrels, and gray squirrels, have attracted significant interest due to their structural and combinatorial properties. Our primary objective here is to learn how we could build a self-assembling polyhedron that would have an aperiodic tessellation of its surface using only a single type of polykite unitile. Such a structure would be analogous to some viral capsids that have been reported to have a quasicrystal configuration of capsomeres. We report on our use of a graph–theoretic approach to examine the adjacency and symmetry constraints of these unitiles in tessellations because by using graph theory rather than the usual geometric description of polykite unitiles, we are able (1) to identify which particular vertices and/or edges join one another in aperiodic tessellations; (2) to take advantage of being scale invariant; and (3) to use the deformability of shapes in moving from the plane to the sphere. We systematically classify their connectivity patterns and structural characteristics by utilizing Hamiltonian cycles of vertex degrees along the perimeters of the unitiles. In addition, we applied Blumeyer’s 2 × 2 classification framework to investigate the influence of chirality and periodicity, while Heesch numbers of corona structures provide further insights into tiling patterns. Furthermore, we analyzed the distribution of polykite unitiles with Voronoi tessellations and their Delaunay triangulations. The results of this study contribute to a better understanding of self-assembling structures with potential applications in biomimetic materials, nanotechnology, and synthetic biology. Full article

Spinal cord injury (SCI) remains a devastating neurological condition characterized by loss of sensory, motor and autonomic function. Despite decades of research, no FDA-approved regenerative therapies currently exist to restore lost function following SCI. Schwann cells (SCs) support axon regeneration, remyelination, and neuroprotection after SCI, with their therapeutic potential validated in clinical trials demonstrating safe and feasible transplantation in humans. Although SC transplantation has shown promising results, challenges remain, including modest graft survival, limited host integration, and restricted migration that collectively contribute to constrain efficacy. To address these limitations, biomaterial scaffolds have been explored as synergistic platforms to enhance SC delivery and function. When combined with natural or synthetic biomaterials such as hydrogels, nanofiber scaffolds, or ECM-mimetic matrices, SCs demonstrate improved survival, retention, spatial distribution, and regenerative activity. The intrinsic regenerative properties of SCs, first demonstrated in models of peripheral nerve injury, make them particularly well-suited for neural repair of the central nervous system (CNS) compared to other cell types and their effectiveness can be enhanced synergistically when combined with biomaterials. These constructs not only provide structural support but also modulate the lesion microenvironment, enhance axon growth and improve SC integration with host tissue. Combinatorial approaches incorporating biomaterials with SCs are emerging as next-generation strategies to optimize repair for clinical translation. This review focuses on current progress in SC-based therapies combined with biomaterials, highlighting key preclinical advances, clinical translation efforts, and the path forward toward effective regenerative interventions for SCI. Full article

Forming interdisciplinary research teams is challenging, especially when the pool of candidates is large and/or the addressed research projects require multi-disciplinary expertise. Based on their previous research outputs, like published work, a data-driven team formation procedure selects the researchers that are likely to work well together while covering all areas and offering all skills required by the multi-disciplinary topic. The description of the research team formation problem proposed in this paper uses novel quantitative metrics about the team candidates computed from bibliographic metadata records. The proposed methodology first analyzes the metadata fields that provide useful information and then computes four synthetic indicators regarding candidates’ skills and their interpersonal traits. Interdisciplinary teams are formed by solving a complex combinatorial multi-objective weighted set cover optimization problem, defined as equations involving the synthetic indicators. Problem solving uses the NSGA-II genetic algorithm. The proposed methodology is validated and compared with other similar approaches using a dataset on researchers from Politehnica University of Timisoara extracted from the IEEE Xplore database. Experimental results show that the method can identify potential research teams in situations for which other related algorithms fail. Full article

Solid tumors pose significant therapeutic challenges due to their resistance to conventional treatments and the complexity of the tumor microenvironment. Cell-based immunotherapies offer a promising approach, enabling precise, personalized treatment through immune system modulation. This review explores several emerging cellular therapies for solid tumors, including tumor-infiltrating lymphocytes, T cell receptor-engineered T cells, CAR T cells, CAR natural killer cells, and macrophages. Tumor-infiltrating lymphocytes and their modified versions, T cell receptor-engineered T cells and CAR T cells, provide personalized immune responses, although their effectiveness can be limited by factors like variation in tumor antigens and the suppressive nature of the tumor environment. Natural killer cells engineered with chimeric receptors offer safer, non-major histocompatibility complex-restricted targeting, while modified macrophages exploit their natural ability to enter tumors and reshape the immune landscape. CAR-modified macrophages and macrophages conjugated with drugs are also considered as therapy for solid tumors. The review also examines the implications of autologous versus allogeneic cell sources. Autologous therapies ensure immunologic compatibility but are limited by scalability and manufacturing constraints. Allogeneic approaches offer “off-the-shelf” potential but require gene editing to avoid immune rejection. Integrating synthetic biology, gene editing, and combinatorial strategies will be essential to enhance efficacy and expand the clinical utility of cellular immunotherapies for solid tumors. Full article

Background/objectives: Streptococcus agalactiae (or Group B Streptococcus, GBS) is a major cause of neonatal meningitis globally. There are 10 serotypes of GBS, which are distinguished by their capsular polysaccharide (CPS) structure, with serotypes Ia, Ib, II, III, IV and V responsible for up to 99% of infections. Currently, there are no licensed vaccines against GBS. The most developed candidates are glycoconjugate vaccines, which can be highly effective but are also expensive to produce by existing approaches and unaffordable for many parts of the world. Biosynthesis of recombinant glycans and glycoconjugates in tractable strains of bacteria offers a low-cost alternative approach to current chemical conjugation methods. Methods: In this study, we apply combinatorial hierarchical DNA assembly to the heterologous biosynthesis of GBS III, IV and V CPSs in E. coli. Each gene was removed from its native regulation, paired with synthetic regulatory elements and rebuilt from the bottom up to generate libraries of reconstituted pathways. These pathways were screened for glycan biosynthesis using serotype-specific antisera. Results: We identified several configurations that successfully biosynthesised the GBS CPSs. Furthermore, we exploited the conserved nature of the GBS CPS biosynthesis loci and the flexibility of modular DNA assembly by constructing hybrid pathways from a minimal pool of glycosyltransferase genes. We show that transferase genes with homologous function can be used interchangeably between pathways, obviating the need to clone a complete locus for each new CPS assembly. Conclusions: In conclusion, we report the first demonstration of heterologous GBS CPS IV and V biosynthesis in E. coli, a key milestone towards the development of low-cost recombinant multivalent GBS glycoconjugate vaccines. Full article

Advances in combinatorial synthesis and high-throughput screening methods have led to renewed interest in synthetic plant immunity activators as well as priming agents. 3,5-Dichloroanthranilic acid (3,5-DCAA) is a derivative of anthranilic acid that has shown potency in activating defence mechanisms in Arabidopsis and barley. Chemical biology, which is the interface of chemistry and biology, can make use of metabolomic approaches and tools to better understand molecular mechanisms operating in complex biological systems. Here we report on the untargeted metabolomic profiling of barley seedlings treated with 3,5-DCAA to gain deeper insights into the mechanism of action of this resistance inducer. Histochemical analysis revealed the production of reactive oxygen species in the leaves upon 3,5-DCAA infiltration. Subsequently, methanolic extracts from different time periods (12, 24, and 36 h post-treatment) were analysed by ultra-high-performance liquid chromatography hyphenated to a high-resolution mass spectrometer. Both unsupervised and supervised chemometric methods were used to reveal hidden patterns and highlight metabolite variables associated with the treatment. Based on the metabolites identified, both the phenylpropanoid and octadecanoid pathways appear to be main routes activated by 3,5-DCAA. Different classes of responsive metabolites were annotated with flavonoids, more specifically flavones, which were the most dominant. Given the limited understanding of this inducer, this study offers a metabolomic analysis of the response triggered by its foliar application in barley. This additional insight could help make informed decisions for the development of more effective strategies for crop protection and improvement, ultimately contributing to crop resilience and agricultural sustainability. Full article

Cruzipain (CZP), the major cysteine protease present in T. cruzi, the ethiological agent of Chagas disease, has attracted particular attention as a therapeutic target for the development of targeted covalent inhibitors (TCI). The vast chemical space associated with the enormous molecular diversity feasible to explore by means of modern synthetic approaches allows the design of CZP inhibitors capable of exhibiting not only an efficient enzyme inhibition but also an adequate translation to anti-T. cruzi activity. In this work, a computer-aided design strategy was developed to combinatorially construct and screen large libraries of 1,4-disubstituted 1,2,3-triazole analogues, further identifying a selected set of candidates for advancement towards synthetic and biological activity evaluation stages. In this way, a virtual molecular library comprising more than 75 thousand diverse and synthetically feasible analogues was studied by means of molecular docking and molecular dynamic simulations in the search of potential TCI of CZP, guiding the synthetic efforts towards a subset of 48 candidates. These were synthesized by applying a Cu(I)-catalyzed azide-alkyne cycloaddition (CuAAC) centered synthetic scheme, resulting in moderate to good yields and leading to the identification of 12 hits selectively inhibiting CZP activity with IC₅₀ in the low micromolar range. Furthermore, four triazole derivatives showed good anti-T. cruzi inhibition when studied at 50 $\mathsf{\mu }$M; and Ald-6 excelled for its high antitrypanocidal activity and low cytotoxicity, exhibiting complete in vitro biological activity translation from CZP to T. cruzi. Overall, not only Ald-6 merits further advancement to preclinical in vivo studies, but these findings also shed light on a valuable chemical space where molecular diversity might be explored in the search for efficient triazole-based antichagasic agents. Full article

2,3-butanediol (2,3-BD) is a versatile bio-based platform chemical. An artificial four-enzyme synthetic biosystem composed of ethanol dehydrogenase, NADH oxidase, formolase and 2,3-butanediol dehydrogenase was designed for upgrading ethanol to 2,3-BD in our previous study. However, a key challenge in developing in vitro enzymatic systems for 2,3-BD synthesis is the relatively sluggish catalytic efficiency of formolase, which catalyzes the rate-limiting step in such systems. Herein, this study reports how engineering the tunnel and substrate binding pocket of FLS improved its catalytic performance. A series of single-point and combinatorial variants were successfully obtained which displayed both higher catalytic efficiency and better substrate tolerance than wild-type FLS. Subsequently, a cell-free biosystem based on the FLS:I28V/L482E enzyme was implemented for upgrading ethanol to 2,3-BD. Ultimately, this system achieved efficient production of 2,3-BD from ethanol by the fed-batch method, reaching a concentration of 1.39 M (124.83 g/L) of the product and providing both excellent productivity and yield values of 5.94 g/L/h and 92.7%, respectively. Taken together, this modified enzymatic catalysis system provides a highly promising alternative approach for sustainable and cost-competitive production of 2,3-BD. Full article

Biomimetic ligands are synthetic compounds that mimic the structure and binding properties of natural biological ligands. The first uses of textile dyes as pseudo-affinity ligands paved the way for the rational design and de novo synthesis of low-cost, non-toxic and highly stable triazine-scaffolded affinity ligands. A novel method to assess and enhance protein stability, employing triazine-based biomimetic ligands and using cutinase from Fusarium solani pisi as a protein model, has been previously reported. This innovative approach combined the concepts of molecular modeling and solid-phase combinatorial chemistry to design, synthesize and screen biomimetic compounds able to bind cutinase through complementary affinity-like interactions while maintaining its biological functionality. The screening of a 36-member biased combinatorial library enabled the identification of promising lead ligands. The immobilization/adsorption of cutinase onto a particular lead (ligand 3′/11) led to a noteworthy enhancement in thermal stability within the temperature range of 60–80 °C. In the present study, similar triazine-based compounds, sourced from the same combinatorial library and mimicking dipeptides of diverse amino acids, were selected and studied to determine their effectiveness in binding and/or improving the thermal stability of several lipases, enzymes which are closely related in function to cutinases. Three ligands with different compositions were screened for their potential thermostabilizing effect on different lipolytic enzymes at 60 °C. An entirely distinct enzyme, invertase from Saccharomyces cerevisiae, was also assessed for binding to the same ligands and functioned as a ‘control’ for the experiments with lipases. The high binding yield of ligand 3′/11 [4-({4-chloro-6-[(2-methylbutyl)amino]-1,3,5-triazin-2-yl}amino)benzoic acid] to cutinase was confirmed, and the same ligand was tested for its ability to bind lipases from Aspergillus oryzae (AOL), Candida rugosa (CRL), Chromobacterium viscosum (CVL), Rhizomucor miehei (RML) and Rhizopus niveus (RNL). The enzymes CRL, CVL, RNL and invertase showed significant adsorption yields to ligand 3′/11—32, 29, 36 and 94%, respectively, and the thermal stability at 60 °C of free and adsorbed enzymes was studied. CVL and RNL were also stabilized by adsorption to ligand 3′/11. In the case of CRL and invertase, which bound but were not stabilized by ligand (3′/11), other ligands from the original combinatorial library were tested. Between the two alternative ligands, one was effective at stabilizing C. rugosa lipase, while none stabilized invertase. Full article