Search Results (2,594)

Advantages in CMOS MOSFET-based electronics served as a basis for modern ubiquitous computerization. At the same time, theoretical and practical developments in material science, analytical chemistry and molecular biology have presented the possibility of applying Boolean logic and information theory findings on a molecular basis. Molecular computing, both organic and inorganic, has the advantages of high computational density, scalability, energy efficiency and parallel computing. Carbon-based and carbohydrate molecular machines are potentially biocompatible and well-suited for biomedical tasks. Molecular computing-enabled sensors, medication-delivery molecular machines, and diagnostic and therapeutic nanobots are at the cutting edge of medical research. Highly focused diagnostics, precision medicine, and personalized treatment can be achieved with molecular computing tools and machinery. At the same time, traditional electronics and AI advancements create a highly effective computerized environment for analyzing big data, assist in diagnostics with sophisticated pattern recognition and step in as a medical routine aid. The combination of the advantages of MOSFET-based electronics and molecular computing creates an opportunity for next-generation healthcare. Full article

Macroalgal polysaccharides represent a diverse group of structurally complex biopolymers with significant potential in biomedicine and functional food applications. This review provides a comprehensive examination of their structural features, biological activities, and molecular targets, with an emphasis on precision applications. Key polysaccharides such as alginates, carrageenans, fucoidans, ulvans, and laminarans are highlighted, focusing on their unique chemical backbones, degrees of sulfation, and branching patterns that underlie their bioactivity. Special attention is given to their roles in modulating inflammation, oxidative stress, apoptosis, gut microbiota, and metabolic pathways. Comparative assessment of extraction strategies, structure–function relationships, and bioactivity data highlights the importance of tailoring polysaccharide processing methods to preserve bioefficacy. Emerging insights from computational modelling and receptor-binding studies reveal promising interactions with immune and apoptotic signalling cascades, suggesting new therapeutic opportunities. Finally, the review outlines challenges related to standardisation, scalability, and regulatory approval, while proposing avenues for future research toward clinical translation and industrial innovation. By integrating structural biology, pharmacology, and nutraceutical sciences, this work underscores the potential of macroalgal polysaccharides as precision agents in health-promoting formulations and next-generation functional foods. Full article

Background: Tumor-associated macrophages (TAMs) are essential regulators of the glioblastoma (GBM) microenvironment; their functional heterogeneity and interaction networks are not fully elucidated. We identify RNF135 as a novel TAM-enriched gene associated with immune activation and adverse prognosis in GBM. Methods: To evaluate RNF135’s expression profile, prognostic significance, and functional pathways, extensive transcriptome analyses from TCGA and CGGA cohorts were conducted. The immunological landscape and cellular origin of RNF135 were outlined using single-cell RNA-seq analyses and bulk RNA-seq immune deconvolution (MCP-counter, xCell and ssGSEA). Cell–cell communication networks between tumor cells and RNF135-positive and -negative tumor-associated macrophage subsets were mapped using CellChat. Results: RNF135 predicted a poor overall survival and was markedly upregulated in GBM tissues. Functional enrichment analyses showed that increased cytokine signaling, interferon response, and innate immune activation were characteristics of RNF135-high samples. Immune infiltration profiling showed a strong correlation between the abundance of T cells and macrophages and RNF135 expression. According to the single-cell analyses, RNF135 was primarily expressed in TAMs, specifically in proliferation, phagocytic, and transitional subtypes. RNF135-positive TAMs demonstrated significantly improved intercellular communication with aggressive tumor subtypes in comparison to RNF135-negative TAMs. This was facilitated by upregulated signaling pathways such as MHC-II, CD39, ApoE, and most notably, the CCL signaling axis. The CCL3/CCL3L3–CCR1 ligand–receptor pair was identified as a major mechanistic driver of TAM–TAM crosstalk. High RNF135 expression was also linked to greater sensitivity to Selumetinib, a selective MEK1/2 inhibitor that targets the MAPK/ERK pathway, according to drug sensitivity analysis. Conclusions: RNF135 defines a TAM phenotype in GBM that is both immunologically active and immunosuppressive. This phenotype promotes inflammatory signaling and communication between cells in the tumor microenvironment. Targeting the CCL–CCR1 axis or combining RNF135-guided immunomodulation with certain inhibitors could be a promising therapeutic strategies for GBM. Full article

Cognition is often modeled in terms of abstract reasoning and neural computation, yet a growing body of theoretical and experimental work suggests that the roots of cognition lie in fundamental embodied regulatory processes. This article presents a theory of cognition grounded in sensing, feeling, and affect—capacities that precede neural systems and are observable in even the simplest living organisms. Based on the info-computational framework, this entry outlines how cognition and proto-subjectivity co-emerge in biological systems. Embodied appraisal—the system’s ability to evaluate internal and external conditions in terms of valence (positive/negative; good/bad)—and the capacity to regulate accordingly are described as mutually constitutive processes observable at the cellular level. This concept reframes cognition not as abstract symbolic reasoning but as value-sensitive, embodied information dynamics resulting from self-regulating engagement with the environment that spans scales from unicellular organisms to complex animals. In this context, information is physically instantiated, and computation is the dynamic, self-modifying process by which organisms regulate and organize themselves. Cognition thus emerges from the dynamic coupling of sensing, internal evaluation, and adaptive morphological (material shape-based) activity. Grounded in findings from developmental biology, bioelectric signaling, morphological computation, and basal cognition, this account situates intelligence as an affect-driven regulatory capacity intrinsic to biological life. While focused on biological systems, this framework also offers conceptual insights for developing more adaptive and embodied forms of artificial intelligence. Future experiments with minimal living systems or synthetic agents may help operationalize and test the proposed mechanisms of proto-subjectivity and affect regulation. Full article

Background: Very preterm infants (VPIs) are born with an undeveloped immune system and are more susceptible to infection. Acquired immune responses must develop in a complex nutritional and metabolic environment as these babies transition from parenteral to enteral nutrition. We explored the feasibility of a multi-omics approach to investigate the changes in metabolic and molecular profiles between day 3 and day 10 of life. Methods: Blood and plasma samples were collected at day 3 and day 10 of life from eight infants born <29 weeks’ gestation and used to perform microarray transcriptomics and ¹H NMR metabolomics. All data were analysed using univariate statistics and mapped to biological pathways and molecular functions using an assortment of databases. Results: We found 1185 genes differentially expressed. The expression patterns found mapped to different immune function, maturation, and development pathways as well as providing mechanistic insights into metabolic changes, notably the downregulation of the metallothionein pathways. We found five metabolites that presented significant differential abundance. These linked to sugar and fat metabolic pathways, known to be altered in the preterm infants. Conclusions: We show that a multi-omics approach is feasible in VPIs and can identify simultaneous changes in the complex metabolic processes and immune adaptation that occur in the first ten days of life. Full article

Non-perturbative approaches to linear and nonlinear responses (NLR) of atoms, molecules, and molecular aggregates are reviewed in relation to low and high harmonic generations (HG) by laser fields. These response properties are effective for the generation of entangled light pairs for quantum information processing by spontaneous parametric downconversion (SPDC) and stimulated four-wave mixing (SFWM). Quasi-energy derivative (QED) methods, such as QED Møller–Plesset (MP) perturbation, are reviewed as time-dependent variational methods (TDVP), providing analytical expressions of time-dependent linear and nonlinear responses of open-shell atoms, molecules, and molecular aggregates. Numerical Liouville methods for the low HG (LHG) and high HG (HHG) regimes are reviewed to elucidate the NLR of molecules in both LHG and HHG regimes. Three-step models for the generation of HHG in the latter regime are reviewed in relation to developments of attosecond science and spectroscopy. Orbital tomography is also reviewed in relation to the theoretical and experimental studies of the amplitudes and phases of wave functions of open-shell atoms and molecules, such as molecular oxygen, providing the Dyson orbital explanation. Interactions between quantum lights and molecules are theoretically examined in relation to derivations of several distribution functions for quantum information processing, quantum dynamics of molecular aggregates, and future developments of quantum molecular devices such as measurement-based quantum computation (MBQC). Quantum dynamics for energy transfer in dendrimer and related light-harvesting antenna systems are reviewed to examine the classical and quantum dynamics behaviors of photosynthesis. It is shown that quantum coherence plays an important role in the well-organized arrays of chromophores. Finally, applications of quantum optics to molecular quantum information and quantum biology are examined in relation to emerging interdisciplinary frontiers. Full article

Recent progress in tumor immunotherapy highlights the important role of the immune system in combating various cancers. Traditionally designed to protect against infectious diseases, vaccines are now being adapted to stimulate immune responses against tumor-specific neoantigens. Both preclinical studies and clinical trials have explored innovative approaches for identifying neoantigens and optimizing vaccine design, advancing the field of personalized oncology. Among these, DNA-based vaccines have become a particularly attractive approach for cancer immunotherapy. This evolution has been driven by improvements in molecular biology techniques, including more precise methods for detecting tumor-specific mutations, computational tools for predicting immunogenic antigens, and novel platforms for delivering nucleic acid vaccines. Personalized DNA vaccines are typically developed through a complex, multi-step process that involves sequencing a patient’s tumor, computational analysis to identify potential targets, and custom vaccine production. In this review, we examine the use of both shared tumor antigens and individualized neoantigens in cancer vaccine development. We outline strategies for neoantigen identification that provide insights into tumor-specific alterations. Furthermore, we highlight recent advances in DNA vaccine technologies, address the current limitations facing cancer vaccines, propose strategies to overcome these challenges, and consider key clinical and technical factors for successful implementation. Full article

Background/Objectives: The integration of artificial intelligence (AI) into obstetric care poses significant potential to enhance clinical decision-making and optimize maternal and neonatal outcomes. Traditional prediction methods in maternal-foetal medicine often rely on subjective clinical judgment and limited statistical models, which may not fully capture complex patient data. By integrating computational innovation with mechanistic biology and rigorous clinical validation, AI can finally fulfil the promise of precision obstetrics by transforming pregnancy complications into a preventable, personalised continuum of care. This study aims to map the current landscape of AI applications across the continuous spectrum of maternal–foetal health, identify the types of models used, and compare clinical targets and performance, potential pitfalls, and strategies to translate innovation into clinical impact. Methods: A literature search of peer-reviewed studies that employ AI for prediction, diagnosis, or decision support in Obstetrics was conducted. AI algorithms were categorised by application area: foetal monitoring, prediction of preterm birth, prediction of pregnancy complications, and/or labour and delivery. Results: AI-driven models consistently demonstrate superior performance to traditional approaches. Nevertheless, their widespread clinical adoption is hindered by limited dataset diversity, “black-box” algorithms, and inconsistent reporting standards. Conclusions: AI holds transformative potential to improve maternal and neonatal outcomes through earlier diagnosis, personalised risk assessment, and automated monitoring. To fulfil this promise, the field must prioritize the creation of large, diverse, open-access datasets, mandate transparent, explainable model architectures, and establish robust ethical and regulatory frameworks. By addressing these challenges, AI can become an integral, equitable, and trustworthy component of Obstetric care worldwide. Full article

In avian biology, the radius of curvature, or R, has hardly ever been used to study the mechanics of birds’ egg shape formation. However, it is essential for introducing important details about the form, function, and performance of an object, which is useful in biomedicine, manufacturing, and precision design. In order to determine a possible biological mechanism and the location of load application that creates the distinctive asymmetric egg shape in nature, the goal of this study was to develop a formula for computing R at any point over an egg contour. We derived a relatively simple means of computing R and identified the location that muscular compression is carried out to give the egg its characteristic form. This location (x/L), the angle (α) of compression and the relative magnitude of the load proportional to R can help identify a specific section of the oviduct and the squeezing muscle involved. Novel equations for computing R, x/L and α were proposed, based on standard geometric parameters. Our findings demonstrate how the theoretical knowledge of physical, mechanical and mathematical processes can contribute to the solution of biological problems and resonates with the fields of egg-inspired engineering. Full article

Taeniasis and neurocysticercosis (NCC), caused by Taenia solium, are significant public health concerns recognised by the World Health Organization (WHO) in developing countries across the Americas, Asia, and Africa. Taeniasis occurs in humans after consuming undercooked pork containing the larval stage (Cysticerci), which matures into the adult reproductive form in the intestine, releasing eggs through faeces. Accidental ingestion of these eggs by humans is the primary cause of NCC, a principal contributor to acquired epilepsy in endemic regions. Interrupting this transmission cycle is crucial to reducing the incidence of human NCC and porcine cysticercosis, thereby underscoring the need for accurate diagnosis and timely treatment of taeniasis. Current diagnostic tests for taeniasis, including microscopy, serology, copro-DNA, and coproantigen assays, exhibit variability in sensitivity, reproducibility, cross-reactivity, and accessibility. To overcome these limitations, bioinformatics tools were integrated with recombinant DNA technology to identify protein sequences with immunological potential. These sequences were evaluated in silico and used to construct an expression system. Subsequently, the antigens were expressed in a eukaryotic system, yielding two purified recombinant protein variants of 21 and 30 kDa. Their purification validated via Western blotting of the molecular tag, paves the way for the development of a direct immunological assay for the specific detection of Taenia solium carriers. Full article

Endocrine cells are dedicated to the production and processing of hormones, from peptides to small molecules, to regulate key physiological processes, including glucose homeostasis and metabolism. Because of this relatively high productivity, endocrine cells must handle a variety of stresses from oxidative stress to the unfolded protein response of the endoplasmic reticulum (UPR^ER). While much is known about the major pathways regulating the UPR^ER, the roles of endocrine cell type-specific, context-dependent, and time-dependent transcriptional changes are not well explored. To identify unique and shared responses to the UPR^ER across a subset of endocrine cell types, we tested representative lines for β-cells (insulin), α-cells (glucagon), δ-cells (somatostatin), X/A-cells (ghrelin), L-cells (glucagon-like peptide 1 (GLP1)), and thyrotropes (thyroid hormone and thyroglobulin). We exposed each cell type to the canonical ER stressor thapsigargin for 6 and 24 h, or vehicle for 24 h, and performed mRNA sequencing. Analysis of the data showed all lines responded to thapsigargin. Comparisons of differentially expressed genes between each line revealed both shared and unique transcriptional signatures. These data represent a valuable mineable set of candidate genes that may have cell type-specific functions during the UPR^ER and have the potential to lead to a new understanding of how different endocrine cells mitigate or succumb to ER stress. Full article

Background/Objectives: Human ovarian granulosa cells (hGCs) are crucial to ovarian follicle development and function, exhibiting multipotency and the ability to differentiate into neuronal cells, chondrocytes, and osteoblasts in vitro. 3,4,5,4′-tetramethoxystilbene (DMU-212) is a methylated derivative of resveratrol, a natural polyphenol found in grapes and berries, with a wide spectrum of biological activities, including notable anticancer properties. Interestingly, DMU-212 exhibits cytotoxic effects predominantly on cancer cells while sparing non-cancerous ones, and evidence suggests that similar to resveratrol, it may also promote hGC differentiation. This study aimed to investigate the effects of the liposomal formulation of this methylated resveratrol analog—lipDMU-212—on the osteogenic differentiation ability of hGCs in a primary three-dimensional cell culture model. Methods: lipDMU-212 was formulated using the thin-film hydration method. GC spheroids’ viability was evaluated after exposure to lipDMU-212, an osteoinductive medium, or both. Osteogenic differentiation was confirmed using Alizarin Red staining and quantified by measuring Alkaline Phosphatase (ALP) activity on days 1, 7, and 15. RNA sequencing (RNA-seq) was performed to explore molecular mechanisms underlying lipDMU-212-induced differentiation. Results: lipDMU-212 promoted osteogenic differentiation of hGCs in the 3D cell culture model, as evidenced by increased mineralization and a ~4-fold increase in ALP activity compared with the control. RNA-seq revealed up-regulation of genes related to cell differentiation and cellular identity. Furthermore, JUN (+2.82, p = 0.003), LRP1 (+2.06, p = 0.05), AXIN1 (+3.02, p = 0.03), and FYN (+3.30, p = 0.01) were up-regulated, indicating modulation of the Wnt/β-catenin signaling pathway, a key regulator of osteoblast differentiation. Conclusions: The ability of GCs to differentiate into diverse tissue-specific cell types underscores their potential in regenerative medicine. This study contributes to the understanding of lipDMU-212’s role in osteogenic differentiation and highlights its potential in developing future therapies for degenerative bone diseases. Full article

Background: Cancer stem cells (CSCs) are key drivers of tumorigenesis and metastasis. However, the precise roles of CSC-associated genes in these processes remain unclear. Methods: This study integrates cancer stem cell biomarkers and clinical data from The Cancer Genome Atlas (TCGA) specific to bladder cancer (BLCA). By combining differentially expressed genes (DEGs) from TCGA-BLCA samples with CSC-related biomarkers, we conducted comprehensive functional analyses and developed an 8-gene prognostic signature through Cox regression, least absolute shrinkage and selection operator (LASSO) analysis, and multivariate Cox regression. This model was validated with GEO datasets (GSE13507 and GSE32894), and the single-cell RNA seq dataset GSE222315 was subsequently analyzed to characterize the signature genes and elucidate their interactions. And a nomogram was created to stratify TCGA-BLCA patients into risk categories. The ‘oncoPredict’ algorithm based on the GDSC2 dataset assessed drug sensitivity in BLCA. Result: From the TCGA cohort, 665 CSC-related genes were identified, with 120 showing significant differential expression. The 8-gene signature (ALDH1A1, CBX7, CSPG4, DCN, FASN, INHBB, MYC, NCAM1) demonstrated strong predictive power for overall survival in both TCGA and GEO cohorts, as confirmed by Kaplan–Meier and ROC analyses. The nomogram, integrating age, tumor stage and risk scores, demonstrated high predictive accuracy. Additionally, the oncoPredict algorithm indicated varying drug sensitivities across patient groups. Based on retrospective data, we identified a novel CSC-related prognostic signature for BLCA. This finding suggests that targeting these genes could offer promising therapeutic strategies. Full article

Mamba (Dendroaspis species) snakebites are critical medical emergencies across sub-Saharan Africa. Envenomings can result in the rapid onset of complex neurotoxic symptoms, often leading to high rates of mortality without timely intervention with antivenom. The ancestral state of mambas is the green coloured, forest dwelling type, with the tan/grey coloured, savannah dwelling D. polylepis (Black Mamba) representing a derived state both ecologically and morphologically. However, it has not been tested whether these changes are paralleled by changes in venom biochemistry or if there are differential molecular evolutionary patterns. To fill these knowledge gaps, this study evaluated the neurotoxic effects of all Dendroaspis species venoms using the chick biventer cervicis nerve-muscle preparation, assessed the neutralizing efficacy of three antivenoms commercially available in Africa, and reconstructed the molecular evolutionary history of the toxin types to ascertain whether some were unique to particular species. All Dendroaspis venoms demonstrated potent flaccid-paralysis due to postsynaptic neurotoxicity. The only exception was D. angusticeps venom, which conversely exhibited spastic-paralysis due to presynaptic/synaptic neurotoxicity characterised by potentiation of acetylcholine presynaptic release and sustained synaptic activity of this neurotransmitter. Antivenom efficacy varied significantly. All three antivenoms neutralized to some degree the flaccid-paralysis postsynaptic effects for all species, with D. viridis venom being the best neutralized, and this pattern extended to all the antivenoms. However, neutralisation of flaccid-paralysis postsynaptic effects unmasked spastic-paralysis presynaptic/synaptic neurotoxicity within non-angusticeps venoms. Spastic-paralysis presynaptic effects were poorly neutralized for all species by all antivenoms, consistent with prior clinical reports of poor neutralisation of spastic-paralytic effects. Geographic variation in D. polylepis venom was evident for the relative neutralisation of both spastic-paralysis presynaptic/synaptic and flaccid-paralysis postsynaptic/synaptic neurotoxic pathophysiological effects, with differential neutralization capabilities noted between the Kenyan and South African populations studied. Molecular phylogenetic analyses confirmed spastic-paralysis and flaccid- paralysis toxins to be a trait that emerged in the Dendroaspis last common ancestor, with all species sharing all toxin types. Therefore, differences in venoms’ pathophysiological actions between species are due to differential expression of toxin isoforms rather than the evolution of species-specific novel toxins. Our findings highlight the synergistic nature of flaccid-paralysis postsynaptic and spastic-paralysis presynaptic/synaptic toxins, while contributing significant clinical and evolutionary knowledge of Dendroaspis venoms. These data are crucial for the continued development of more effective therapeutic interventions to improve clinical outcomes and for evidence-based design of clinical management strategies for the envenomed patient. Full article

Background: Prostate cancer is the most commonly occurring cancer amongst men. The detection and treatment of this cancer is therefore of great importance. The severity level of this cancer, which is established as a score in the Gleason Grading Group (GGC), guides the treatment of the cancer. Methods: In this paper, traditional machine learning (ML) classification methods such as Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and XGBoost (XGB), which have recently been shown to accurately identifying biomarkers for computational biology, are leveraged to find potential biomarkers for the different GGC scores. A ML framework that maps the Gleason Grading Group (GGG) into five severity levels—low, intermediate-low, intermediate, intermediate-high, and high—has been developed using the above methods. The microarray data for this ML method have been derived from immunohistochemical tests. The study includes severity level-wise biomarker identification, incorporating missing value imputation, class imbalance handling using the SMOTE-Tomek link method, and stratified k-fold validation to ensure robust biomarker selection. Results: The framework is evaluated on prostate cancer tissue microarray gene expression data from 1119 samples. A combination of high-aggressive and low-aggressive signatures are used in four experimental setups. The results demonstrate the effectiveness of the approach in distinguishing between critical biomarkers with highly accurate models, obtaining 96.85% accuracy using the XGBoost method. Conclusions: Leveraging ML gives a potential ground to involve the domain experts and the satisfactory results have approved that. For the future physician-in-the-loop approach can be tested to ensure further diagnosis impact. Full article