Search Results (3,088)

N-Acetylaspartate (NAA), historically considered a brain-restricted neuro-metabolite, has emerged as a pivotal regulator in cancer biology. Governed by the biosynthetic enzyme N-acetyltransferase 8-like (NAT8L), the NAA axis exerts notable biological effects in malignancies. This review delineates the NAA axis as a context-dependent metabolic rheostat that is strategically hijacked by malignancies to orchestrate growth and immune evasion. Rather than a passive bystander, the NAA axis functions through distinct, histology-specific paradigms: it either serves as a metabolic reservoir to fuel lipid biomass expansion or is suppressed to redirect aspartate flux toward nucleotide biosynthesis, depending on the tumor’s unique genetic and metabolic bottlenecks. Beyond cell-intrinsic reprogramming, tumor-derived NAA is increasingly linked to immune evasion by impairing cytotoxic lymphocyte function and driving pro-tumorigenic M2-like macrophage polarization. By reconciling these disparate oncogenic roles, this review highlights the NAA axis as an important node of metabolic plasticity and evaluates its potential utility as a circulating biomarker and a stratifiable therapeutic target in diverse human malignancies. Full article

Background: Multiple sclerosis (MS) is a chronic autoimmune neurological disorder that affects the central nervous system. Its exact cause remains unknown, but it is believed to result from both genetic predisposition and environmental factors. The two MS phases include a progressive phase and a relapsing-remitting phase. Although the mechanisms driving relapse incidence remain unknown, efforts to identify the cause and prevent MS are ongoing. Methods: This narrative review summarizes the current evidence of cladribine’s role in managing the forms of relapsing MS. A focused literature search of six major databases was performed to identify relevant publications. Results: Cladribine has been used as a medication for hairy cell leukemia since 1993, and in 2017, it obtained approval for the treatment of MS in adults. In the treatment of relapsing-remitting MS, cladribine offers three significant benefits. First, it is reported to have a safe profile. Second, it requires only two brief annual treatment courses over two years. Third, it has a low cost. Furthermore, it has gained approval from the National Institute for Health and Care Excellence. Conclusion: Cladribine is highly effective against MS progression; it cuts the relapse rates by nearly half. Future research should focus on the long-term effect of cladribine use, specifically on the central nervous system, in the context of MS management. Full article

Sustainable supply chains are increasingly vulnerable to systemic risks, such as geopolitical conflicts at critical trade routes like the Strait of Hormuz or climate disasters, which reveal deep Environmental, Social, and Governance (ESG) weaknesses. Conventional optimization often fails in these “deep uncertainty” contexts, where reliable historical data are often scarce and qualitative factors are paramount. This study introduces a simulation-optimization framework that reframes risk management as a decision process rather than a purely computational one. Portfolios are parameterized across five key characteristics—prevention, vulnerability, resilience, recovery, and detection—to enable a genetic algorithm (GA) to generate a diverse ensemble of high-performing strategies. Instead of providing one “best” answer, the GA allows managers to evaluate multiple options against quantitative tail-risk measures and qualitative institutional factors. The framework produces a “trade-off map,” or Pareto frontier, visualizing the cost of protecting against downside risks. By adjusting the GA’s settings, decision makers can toggle between improving current plans and exploring new, structurally different strategies. The numerical results demonstrate that the GA consistently identifies high-performing portfolios, achieving at least 99.55% of the true optimal performance across all metrics while requiring only 25% of the computational evaluation budget of an exhaustive search space. Furthermore, the framework successfully generates a structurally diverse menu of near-optimal alternatives across all performance metrics, consistently outperforming Monte Carlo sampling in the quality of near-optimal solutions identified, particularly for tail-risk measures such as conditional value-at-risk. Ultimately, this approach integrates the manager’s professional judgment regarding non-quantifiable factors, such as political stability and social responsibility, with simulation data to support the selection of a robust, sustainable portfolio. Full article

In the context of the global energy transition and China’s “double carbon” strategy, power demand continues to grow, leading to expanded power plant construction that faces challenges such as spatial resource conflicts and multi-objective trade-offs. Multi-objective optimization methods are widely used for coordinating economic, environmental, and social objectives in power plant site selection. This paper systematically reviews the application of multi-objective optimization in power plant site selection using bibliometric and classification methods. The review covers four dimensions, namely, research trends, objective functions, constraints, and optimization algorithms, thereby constructing a reference framework. The study revealed that research began in 2008, with wind farm site selection now accounting for the greatest proportion. Objective functions can be summarized into four dimensions: energy and utilization, costs or benefits, engineering feasibility, and environmental and social impacts. There are six types of constraints for optimization: economic, technical, environmental, social, resource, and spatial. Genetic algorithms and their variants are the most widely used. This framework enhances the scientific rigor of power plant site selection and supports feasibility assessment, providing methodological references for site selection for power plants. Full article

Insect antimicrobial peptides (AMPs) are classically viewed as terminal effectors of innate immunity, but emerging evidence suggests that some can also shape defined neural states. In this Review, we argue that insect systems provide a powerful framework for resolving immune–brain communication at the level of individual peptide effectors, because genetically tractable innate-immune pathways allow pathway activation to be distinguished from peptide-specific effector function. Rather than surveying AMP families exhaustively, we focus on representative cases in which peptide identity, source, and timing can be linked to sleep, memory-related plasticity, and responses to acute injury. These studies show that the neural consequences of AMP induction cannot be inferred from pathway activation alone, but require peptide-level analysis of effector identity, cellular context, and exposure logic. This perspective also raises the question of translational potential. At present, direct biomedical development of endogenous insect AMPs in neural contexts remains limited, whereas more tangible applied interest has centered on insect venom peptides that share AMP-like physicochemical features. We therefore discuss insect venoms separately from endogenous AMP physiology. Venom peptides are not physiological equivalents of endogenous insect AMPs, but represent evolutionarily diversified AMP-like templates for scaffold discovery, mechanistic probing, and therapeutic engineering. Together, this review develops a peptide-level perspective on insect neuroimmune biology while highlighting insect venoms as a valuable, but highly constrained, source of templates for biomedical discovery. Full article

Rare genetic diseases are heterogeneous across mechanisms, trajectories, and treatment responses. To date, approved therapies remain available for only a small proportion of rare genetic diseases. Oligonucleotide-based RNA therapeutics, particularly antisense oligonucleotides (ASOs) and small interfering RNAs (siRNAs), offer a promising therapeutic avenue for rare genetic diseases with sequence-level precision. However, traditional preclinical paths may mis-predict human outcomes when disease biology diverges from animal models. New approach methodologies (NAMs), including patient-derived induced pluripotent stem cells (iPSCs), organoid models, and clinical-trials-in-a-dish (CTiD), aim to bring human biology earlier into the translational pipeline. NAMs enable variant-to-function studies, efficacy screening, and safety triage at clinically relevant speed and scale. While critics argue that NAMs are unvalidated and cannot replace preclinical animal models, proponents report that they are increasingly able to recapitulate human phenotypes and predict clinical liabilities, although their predictive validity remains context-dependent. Here, a front-loaded human filter refers to the use of human-derived systems early in development to support mechanistic interpretation, candidate prioritization, and early liability assessment before broader nonclinical evaluation. Recent studies pairing NAMs with ASOs support rapid, patient-specific preclinical screening in selected settings, while also showing the need for broader evidence on delivery, pharmacology, safety, and clinical relevance. This review places these developments within the translational realities of oligonucleotide-based therapeutics, including model fidelity, ASO chemistry and optimization, delivery challenges, pharmacology, regulatory pathways for individualized ASOs, and accessibility. We also propose a pragmatic validation framework to assess the scientific and translational credibility of NAMs across rare genetic diseases. Full article

In the context of the global transition towards energy systems with a high share of renewable energy, efficient and large-scale energy storage technologies are essential for improving the stability and flexibility of power grids. Phase change thermal energy storage has attracted considerable attention because of its high energy density and nearly isothermal heat release capability. However, its practical application remains constrained by the intrinsically low thermal conductivity of phase change materials (PCMs). For instance, 0.2–0.3 W/m·K for organic paraffins, 0.15–0.35 W/m·K for fatty acids, and 0.5–1.0 W/m·K for salt hydrates lead to slow charging and discharging rates. Incorporating metal foams into PCMs to form composite PCMs has emerged as a promising strategy, as metal foams can significantly improve effective thermal conductivity and enhance internal heat transfer. This paper systematically reviews recent advances in metal foam-enhanced phase change thermal energy storage, with particular emphasis on numerical modeling and structural optimization. First, the heat transfer enhancement mechanisms of metal foam/PCM composites are analyzed, together with the key performance indicators used to evaluate thermal storage performance. Second, material-level developments are reviewed, including pore structure parameters, interfacial engineering, and advanced fabrication methods. The review then discusses current structural design strategies, such as graded pore structures and partially filled configurations, as well as hybrid enhancement methods that combine passive and active heat transfer enhancement. Particular attention is paid to numerical modeling approaches at both pore and system scales, which are used to predict and optimize thermal behavior. In addition, optimization methods, including topology optimization, machine learning, and genetic algorithms, are examined for their potential to inversely design foam structures with tailored thermal performance. Finally, the key challenges in this field are summarized, and future research directions are proposed. These include multi-scale intelligent design, integration with complementary thermal management technologies, and the development of scalable solutions for engineering applications. This review aims to provide a systematic reference for achieving performance breakthroughs and promoting the practical deployment of phase change thermal energy storage technologies. Full article

Heritable thoracic aortic diseases (HTADs) comprise a genetically heterogeneous group of disorders predisposing patients to thoracic aortic aneurysm and dissection, yet current medical therapies remain limited to slowing disease progression rather than preventing aortic wall failure. Although pathogenic variants affect diverse genes encoding extracellular matrix (ECM) components, smooth muscle contractile proteins, and signaling molecules, these defects converge on disruption of the mechanobiological systems that maintain aortic wall integrity. The thoracic aorta functions as a mechanically integrated tissue in which endothelial cells, vascular smooth muscle cells, fibroblasts, immune cells and ECM continuously sense and respond to pulsatile biomechanical forces. Genetic perturbations affecting ECM architecture, contractile force generation, or growth factor signaling alter force transmission across this multicellular network, leading to maladaptive mechanotransduction, cellular phenotypic modulation, and progressive aneurysm formation. Using Marfan syndrome as a paradigmatic ECM-driven aortic disease, this review synthesizes current understanding of how altered biomechanics, biochemical signaling and immune responses reshape intercellular communication and activate disease-associated signaling pathways, including dysregulated TGF-β, nitric oxide, angiotensin receptor, calcium-dependent, and metabolic signaling. We highlight how single-cell transcriptomic analyses have elaborated changes in different cell-level functions including, ECM degradation, iron homeostasis, circadian/stress responses. Changes in iron metabolism in different cell types in the aorta suggest possible coordinated metabolic changes in aneurysm progression. These mechanistic insights enable the identification of cell-type–specific pathogenic programs and therapeutic discovery through systems-level approaches. We highlight the translational opportunities and challenges emerging from mouse models and human studies, emphasizing that therapeutic efficacy depends not only on pathway selection but also on disease stage, cellular context, and timing of intervention. Together, these findings support a model in which HTAD progression reflects dynamic, multicellular failure of mechanobiological homeostasis and provide a framework for the development of more precise, mechanism-based therapies. Full article

Uridine diphosphate glucuronosyltransferase 2B (UGT2B) enzymes constitute a critical subgroup of phase II metabolizing enzymes that modulate the clearance of steroid hormones, carcinogens, and numerous anticancer agents, thereby influencing cancer susceptibility, progression, and therapeutic outcomes. This review provides a comprehensive synthesis of the genetic, regulatory, and functional roles of UGT2B family members, particularly UGT2B4, UGT2B7, UGT2B10, UGT2B15, UGT2B17, and UGT2B28, in oncogenesis and cancer treatment. We summarize evidence from molecular, epidemiological, pharmacogenetic, and clinical studies demonstrating how UGT2B expression patterns, polymorphisms, copy number variations, epigenetic regulation, and microRNA-mediated control shape intratumoral hormone homeostasis, carcinogen detoxification, and drug resistance across multiple malignancies, including prostate, breast, lung, colorectal, hematological, and hormone-dependent cancers. UGT2B enzymes metabolize several widely used anticancer drugs and active metabolites, thereby affecting pharmacokinetics, efficacy, and toxicity. Understanding the context-specific roles of UGT2B family members offers a compelling opportunity for therapeutic exploitation. In particular, rational combination strategies incorporating UGT2B inhibitors or modulators alongside standard anticancer agents may enhance drug effectiveness without increasing dosage, while simultaneously enabling the dose reduction of the partner agent to mitigate dose-dependent toxicities. Such approaches are especially relevant for therapies with narrow therapeutic indices. Overall, this review highlights UGT2B enzymes as multifunctional determinants of cancer risk and treatment response and underscores their promise as biomarkers and actionable targets for precision oncology and optimized combination regimens. Full article

Sourdoughs are complex microbial ecosystems fundamental to traditional breadmaking. Despite extensive research, variability among traditional sourdough ecosystems remains a key area of investigation. In this context, 13 type I sourdough starters from Villaurbana (Sardinia, Italy) were characterised combining molecular and biochemical techniques. Bacterial and fungal communities were identified by NGS-based amplicon sequencing, while lactic and acetic acid, and residual sugars were quantified. The bacterial population was dominated by the genus Fructilactobacillus, whereas the fungal community spanned multiple genera. To explore the mycobiota, 130 yeast isolates were identified sequencing D1–D2 domain and through MALDI-TOF mass spectrometry. Six yeast species were detected: Saccharomyces cerevisiae was the dominant species (58.46%), followed by non-Saccharomyces yeasts (41.54%), including Torulaspora delbrueckii, Pichia fermentans, Wickerhamomyces anomalus, Maudiozyma humilis, and Monosporozyma unispora. Strain-level typing via microsatellite analysis revealed high intraspecific diversity among S. cerevisiae and T. delbrueckii isolates. S. cerevisiae strains were distributed across distinct genetic lineages, with some clustering alongside industrial reference strains and others displaying unique evolutionary trajectories. T. delbrueckii strains formed two clonal groups substantially divergent from reference strains. This study supports the restitution of characterized yeast strains to the community as a resource for establishing a new microbial consortium representative of Villaurbana sourdough tradition. Full article

The intersection of state power and literary dissent in the early Turkish Republic provides a critical context for understanding how modern nation-states manage intellectual opposition. Prevailing historiography often compartmentalizes the prominent socialist-realist writer Sabahattin Ali’s career into disconnected traditionalist and secular Marxist phases. Challenging this assumption of linear rupture, this study synthesizes genetic criticism, James C. Scott’s theory of hidden transcripts, and Lev Loseff’s concept of Aesopian language to reveal Ali’s underlying epistemological continuity. Examining an expanded corpus—including early poetry, coerced carceral revisions, private correspondence, and socio-realist prose—the analysis demonstrates that his early literary formation became a vital repertoire for negotiating state coercion. Findings show he strategically deployed the Sufi Ottoman zâhir-bâtın (external form versus inner essence) paradigm to camouflage Marxist commitments beneath a performative public transcript of nationalist compliance. Concurrently, he repurposed a transitional polysemic lexicon—navigating Sufi technical usage, classical literary inheritance, and broader Ottoman–Turkish colloquialisms—to articulate secular, revolutionary awakenings. Ultimately, while authoritarian coercion exacts a superficial public transcript of conformity, the hidden transcript of intellectual autonomy remains highly resilient, utilizing traditional theological frameworks for modern political evasion. Full article

Chronic lymphocytic leukemia (CLL) is a genetically heterogeneous disease in which TP53 alterations represent major adverse prognostic factors; this study aims to describe the clinical implications of complex TP53 disruption in a rare case context. Molecular and cytogenetic profiling was performed using MLPA for copy number variations and targeted next-generation sequencing for mutation detection, following DNA extraction from peripheral blood and standardized bioinformatic analysis pipelines. The patient exhibited concomitant del(13q14) and del(17p13), alongside two pathogenic TP53 mutations, indicating functional inactivation of p53 and a high-risk genomic profile; despite this, treatment with a Bruton’s tyrosine kinase inhibitor resulted in significant hematological improvement within six months. These findings highlight that adverse TP53 alterations override favorable cytogenetic markers and emphasize the necessity of comprehensive genomic testing to guide prognosis and personalized therapy in CLL. Full article

The impact of diet on human health is constantly being researched. Nutrition is one of the most powerful tools for influencing gene expression, and dietary habits can promote the expression of genetic predisposition to obesity, diabetes, cardiovascular disease, cancer, and neurodegenerative diseases (NDs). The dietary inflammatory index (DII) is a numerical score that assesses the pro-or anti-inflammatory potential of a given diet. According to high DII scores, a Western diet or a standard American diet (SAD) has proinflammatory properties. By disrupting the gut microbiome, SAD creates an unfavorable environment in the intestine that is associated with a low-grade systemic inflammatory response and oxidative changes that may promote the development of NDs. An increased intestinal permeability and loss of blood–brain barrier (BBB) integrity play key roles in the pathomechanisms of diet-dependent NDs, leading to proinflammatory signaling via the gut–brain axis. The aim of this narrative review is to present in detail the current state of knowledge on the function of the gut–brain axis depending on the pro-/anti-inflammatory potential of the diet, measured by the DII, in the context of the contributions of intestinal and BBB permeability disorders to the development of NDs. Full article

Background: Breast cancer is the most common malignancy among women and a leading cause of cancer-related deaths globally. Its development involves hormonal, genetic, environmental and inflammatory factors. Among environmental contributors, cadmium (Cd²⁺), a metalloestrogen known to induce redox imbalance, as well as estrogen metabolites, may exert divergent biological effects. Methods: This study investigated the effects of 17β-estradiol (E2) and its metabolites—2-methoxyestradiol (2-MeOE2) and 4-hydroxyestradiol (4-OHE2)—administered alone or in combination with CdCl₂, on estrogen receptor–-positive MCF-7 breast cancer cells and their doxorubicin-resistant cells (MCF-7/DOX). We evaluated cytotoxicity, interaction profiles (synergism/antagonism), and redox-related enzymes associated with drug resistance, including superoxide dismutase 1 (SOD1) and glutathione S-transferase pi (GST-pi). There are no known examples of these types of interactions, especially those involving estrogen metabolites with opposing biological activities—anticancer 2-MeOE2 and procarcinogenic—4-OHE2 in combination with cadmium. Cell viability was assessed after 48 h exposure to individual and combined treatments of CdCl₂. Interaction types (synergism/antagonism) were determined via the combination index method. Antioxidative enzymes were evaluated by quantitative and immunocytochemical analysis of SOD1, GST and GST-pi expression. Results: All tested compounds reduced cell viability in a concentration-dependent manner, with CdCl₂ showing the highest cytotoxicity. MCF-7 cell lines were generally more sensitive to CdCl₂, E2, and 2-MeOE2, whereas MCF-7/DOX cell lines exhibited greater sensitivity to 4-OHE2. Combination studies revealed predominantly antagonistic interactions, particularly for CdCl₂ + 2-MeOE2, suggesting a protective redox-modulating effect of this metabolite. Resistant cells consistently displayed higher SOD1 activity and GST-pi expression, indicating enhanced adaptive responses to oxidative stress. Conclusions: Our study underscores the importance of concentration-dependent interactions between environmental Cd²⁺ and pathways regulated by 17β-estradiol and its metabolites, particularly in the context of cytotoxicity and redox imbalance relevant to breast cancer progression and therapy resistance. Full article

Background/Objectives: Depression is a heterogeneous disorder shaped by both inherited liability and environmental exposures. Physical activity is a scalable, modifiable behavior consistently linked to lower depressive symptoms and reduced incident depression, but interpretation is complicated by measurement error, genetic confounding, and bidirectional pathways in which depression can also reduce activity. This systematic review synthesizes evidence on gene–physical activity interplay in depression across four complementary frameworks: (i) candidate–gene interaction studies, (ii) genome-wide susceptibility indexed by depression polygenic risk scores (PRS), (iii) lifestyle-context and activity-architecture analyses (e.g., timing and accumulation patterns), and (iv) Mendelian randomization (MR) studies testing bidirectional causal effects between activity-related traits and depression. Methods: A PRISMA-aligned search and narrative synthesis were conducted due to substantial heterogeneity in populations, exposure measurement (self-report vs. accelerometer), genetic approaches, and depression phenotypes. Twenty-seven studies met inclusion criteria. Results: Across designs, the most consistent pattern was that higher physical activity (or lower inactivity) tracked with lower depression risk or symptom burden even when genome-wide genetic susceptibility was modeled, supporting largely additive contributions of PRS and activity rather than strong, generalizable PRS × activity interactions. MR evidence most consistently supported a protective effect of physical activity on depression when activity was indexed by accelerometer-derived phenotypes, whereas self-reported activity instruments yielded weaker or more heterogeneous findings. Bidirectional genetic evidence also indicated that depression liability can causally suppress physical activity, consistent with a feedback loop relevant for prevention and intervention. Candidate-gene moderation effects were mixed and typically emerged only in specific contexts (e.g., stress history, developmental stage, sex, or treatment setting), underscoring limited replicability and sensitivity to how activity is operationalized. Conclusions: Overall, the literature supports physical activity as broadly protective across levels of genetic risk, while emphasizing that robust inference depends on objective exposure measurement, careful handling of confounding and reverse causation, and improved generalizability beyond predominantly European-ancestry genetic resources. Full article