Search Results (243)

Usnic acid (UA), a prominent lichen secondary metabolite, exhibits a unique dual therapeutic profile in dermatology, though its clinical translation is limited by systemic hepatotoxicity and poor solubility. This review comprehensively evaluates the topical efficacy, molecular mechanisms, and advanced formulation strategies of UA enantiomers and UA-rich extracts. A literature search across PubMed, Scopus, and Google Scholar identified 36 original publications focusing on anti-melanoma activity, photoprotection, and tissue regeneration. In vitro studies demonstrate that UA induces apoptosis in resistant melanoma cell lines (A375, HTB-140) via extrinsic/intrinsic pathways, with (−)-UA effectively overcoming doxorubicin resistance. Conversely, in non-cancerous models, low concentrations of UA accelerate wound and burn healing by upregulating vascular endothelial growth factor (VEGF), stimulating fibroblast proliferation, and optimizing extracellular matrix remodeling while preventing hypertrophic scarring. To mitigate skin sensitization and systemic risks, advanced drug delivery systems—including liposomes, nanoemulsions, chitosan nanogels, and electrospun scaffolds—have been developed, significantly enhancing skin permeability and localized dermal retention. Ultimately, the development of bio-functionalized smart dressings and targeted nano-formulations represents the most viable path toward unlocking the full clinical potential of UA in modern dermatological and oncological care. Full article

Background and Objectives: Temporomandibular disorders (TMDs) encompass a broad spectrum of functional and structural abnormalities of the temporomandibular joint (TMJ). Conventional diagnostic tools, although essential, often fail to capture the underlying biochemical mechanisms driving disease progression. Synovial fluid (SF), by virtue of its direct proximity to intra-articular tissues, represents an accessible biological matrix for identifying molecular signatures of inflammation, cartilage degradation, lubrication failure, oxidative stress, and angiogenic activation. The objective of this review is to synthesize current evidence on SF proteomics in TMD and evaluate its potential translational value in precision medicine. Materials and Methods: A narrative review of the literature was conducted on PubMed to identify human studies focused on SF proteomic and biochemical biomarkers in TMD. Eligible studies included original research articles assessing SF composition in relation to specific TMJ pathologies, diagnostic categories, or clinical phenotypes. Extracted data included study design, sample characteristics, analytic methodology, biomarkers investigated, and key findings. Google Gemini (Google LLC, Mountain View, CA, USA) was used as an AI-assisted tool to support language editing and manuscript writing during the preparation of this article. The use of this tool was limited to linguistic refinement; all scientific content, data interpretation, and conclusions were formulated and verified by the authors. Results: Across the analyzed studies, TMD phenotypes—particularly disc displacement with or without reduction (DDwR, DDwoR) and osteoarthritis (OA)—were characterized by consistent alterations in cytokines (IL-1β, IL-6, IL-8, TNF-α), extracellular matrix (ECM) components (aggrecan, glycosaminoglycans (GAGs), decorin, MMP-2, MMP-9), lubrication molecules (lubricin/PRG4), oxidative stress mediators (myeloperoxidase (MPO), nitric oxide (NO), glutathione peroxidase (GPX)), adipokines (chemerin, resistin, adiponectin), and angiogenic factors (vascular endothelial growth factor (VEGF), fibroblast growth factor-2 (FGF-2)). Recent liquid chromatography–tandem mass spectrometry (LC–MS/MS) analyses further revealed phenotype-specific protein clusters and pathways related to inflammation, ferroptosis, hypoxia signaling, and proteoglycan metabolism. Conclusions: Current evidence suggests that SF proteomics and multi-analyte biomarker profiling offer a promising, hypothesis-generating approach for understanding the biological mechanisms underlying TMD. The integration of proteomic, metabolic, and inflammatory markers holds future potential for diagnostic panel development; however, prospective clinical validation is still required before SF-based molecular profiling can be implemented as a precision medicine tool in TMJ disorders. Full article

Triple-negative breast cancer (TNBC) lacks effective molecular targets, leading to poor prognosis. Previous computational methods to identify targets have suffered from low druggability, high complexity, and lack of robust validation. We propose a hybrid methodology combining Boolean network modeling with semidefinite programming (SDP) to analyze a TNBC cell line network. The resulting therapeutic pair underwent a multi-level validation framework, including Boolean simulations, statistical uncertainty quantification (bootstrap), sensitivity analysis, and orthogonal computational support from AlphaGenome, a deep learning model from Google DeepMind. Our analysis identified TK1 and VIM as a computationally robust therapeutic pair. Dual inhibition achieved 99.03% similarity to the apoptotic state with a 95% confidence interval of [98.79%, 99.26%], and was statistically superior to alternative pairs ($p<0.001$). The selection remained optimal across all tested model parameters, demonstrating high robustness. Importantly, the pair has full druggability because both targets have available specific inhibitors. Orthogonal computational evidence from AlphaGenome, stratified by mammary compartment, indicated that both targets exhibit moderate baseline expression in normal mammary epithelium (TK1 = 0.159, VIM = 0.143 in normalized RNA-seq units; n = 13 tracks per gene), with VIM showing a 2.2-fold higher expression in mammary stroma than in epithelium—a gradient consistent with its established role as a mesenchymal marker. Promoter-variant proxy analysis indicated near-zero transcriptomic perturbation upon simulated inhibition of either target in normal mammary epithelium (mean $|{log}_2\mathrm{FC}|<0.001$), supporting a favorable therapeutic window. Our methodology identified TK1–VIM as a computationally robust, druggable therapeutic candidate pair with biologically plausible mechanism of action. Gene-variability analysis identified TK1 and VIM as the highest-scoring candidates, with SDP optimization providing complementary, independent confirmation of this selection. This work provides a computationally grounded candidate strategy and a rigorous methodological benchmark for computational drug target identification; experimental validation remains an essential next step before clinical translation. Full article

Background/Objectives: Rifampicin-resistant tuberculosis (RR-TB) and HIV co-infection remain major contributors to morbidity and mortality, particularly in high-burden settings. HIV-related clinical factors, including viral suppression, CD4-defined immune status, HIV drug resistance, virological failure, and ART failure, may influence RR-TB treatment response; however, existing evidence remains fragmented. This systematic review and meta-analysis protocol aims to synthesize evidence on the impact of HIV viral suppression, immune status, and HIV drug resistance/ART resistance status on RR-TB treatment outcomes. Methods: This protocol was developed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols guidelines. Published peer-reviewed studies and relevant grey literature from January 2005 to December 2025 will be searched in PubMed/MEDLINE, Cochrane Library, Embase, Web of Science, ScienceDirect, EBSCOhost, PsycINFO, Google Scholar, and other relevant sources. No language restriction will be applied at the search stage. Where feasible, non-English records will be translated for title/abstract and full-text screening. Two reviewers will independently screen studies, extract data, and assess study quality, with disagreements resolved by a third reviewer. Study-level risk of bias will be assessed using design-appropriate tools, and the certainty of evidence for each outcome will be evaluated using GRADE. Results: Evidence will be synthesized narratively and, where studies are sufficiently homogeneous, quantitatively through meta-analysis. Outcomes of interest will include treatment success, treatment failure, mortality, treatment completion, microbiological cure, and adverse events. Subgroup analyses will be considered by viral suppression status, CD4-defined immune status, HIV drug resistance/ART resistance status, geographic region, and treatment regimen where data permit. Conclusions: This review will provide evidence on how HIV viral suppression, immune status, and HIV drug resistance/ART resistance influence RR-TB treatment outcomes. The findings may inform integrated TB/HIV care, clinical monitoring, and treatment strategies for individuals co-infected with HIV and RR-TB. Full article

Climate hazards increasingly unfold as information crises alongside physical impacts, producing rapid shifts in what people search for and discuss online. This case study demonstrates how AI-supported analysis of online data can complement conventional disaster intelligence by providing a scalable social sensing layer for climate hazard resilience in Galicia. It integrates Google Trends as a proxy for changing public attention and information demand, and YouTube videos and comment threads to capture public sensemaking and resilience-relevant signals. Monthly Google Trends series were used for eight hazards, with floods showing the highest mean interest, followed by wildfires and heatwaves. For the three highest-salience hazards, the study analyzed YouTube comments using gpt-5-mini to extract sentiment, emotions, topics, institutional trust cues, collective efficacy cues, calls to action, impacts, vulnerable groups, and coping actions. The corpus included 184 heatwave comments, 20,427 wildfire comments, and 4882 flood comments. Across hazards, discourse is predominantly negative but differs in structure. Heatwave threads skew toward mockery and normalization, wildfire threads center on anger, governance and low institutional trust, and flood threads combine solidarity with demands for localized warnings and guidance. The study translates comment-level signals into traceable policy recommendations emphasizing actionable risk communication, early warning and response capacity, and trust-building practices. The study concludes with an operational pipeline concept for continuous monitoring and dashboard-based decision support, while emphasizing limitations related to Google Trends sampling and normalization, platform and API biases, and model-mediated uncertainty. Full article

Imaging of infections has the potential to improve clinical outcomes, but pathogen-specific imaging strategies are currently unavailable. Given their target specificity, antimicrobials may be useful as molecular imaging ligands to target infections. Despite substantial development efforts, no antimicrobial-based ligands are approved for clinical use. This scoping review comprehensively surveys radiolabeled antimicrobials across antibacterial, antimycobacterial, antiviral, and antifungal drug classes, examining their progression through the translational pipeline. The review utilized PubMed and Google Scholar databases (1970–2025), following PRISMA Extension for Scoping Reviews (PRISMA-ScR) guidelines. Two reviewers independently screened titles, abstracts, and full-text articles; data were extracted, and content duplicates were removed. In total, 143 preclinical and 25 clinical articles met the selection criteria. In clinical studies, most tracers showed suboptimal specificity for infections, while some proved useful for pharmacokinetic characterization. Among preclinical studies, radiolabeled plazomicin and echinocandins (caspofungin and anidulafungin) exhibited the greatest number of preferred characteristics. In conclusion, ideal antimicrobial pharmacologic properties can be counterproductive for imaging, where rapid background clearance and a high target-to-non-target ratio (T/NT) are essential. Many radioligands demonstrate good tissue penetration but suboptimal washout, limiting their diagnostic value. In vivo pharmacokinetic applications during active infections are promising, though significant challenges remain for infection imaging. Full article

Background/Objective: Intracranial arteriovenous malformations (AVMs) are high-flow cerebrovascular lesions associated with a significant risk of intracranial hemorrhage, neurological morbidity, and mortality. Current management strategies, including microsurgical resection, endovascular embolization, stereotactic radiosurgery, and conservative observation, remain limited by procedural risk and uncertain long-term outcomes. Beta-blockers, particularly propranolol, have recently attracted interest as potential adjunctive therapies because of their vasoconstrictive, antiangiogenic, and vascular remodeling properties. This review evaluates the mechanistic rationale and current evidence regarding beta-blocker use in intracranial AVMs. Methods: A comprehensive literature review was conducted using PubMed, Scopus, and Google Scholar databases through January 2026 using combinations of the terms “arteriovenous malformation,” “AVM,” “beta-blocker,” “propranolol,” “angiogenesis,” “hemorrhage,” and “cerebral cavernous malformation.” Eligible studies included experimental investigations, translational studies, observational cohorts, case reports, clinical trials, systematic reviews, and meta-analyses evaluating beta-blocker use in intracranial AVMs or related vascular malformations. Studies unrelated to cerebrovascular lesions, duplicate reports, and non-English publications were excluded. Given the heterogeneity and limited volume of available AVM-specific literature, findings were synthesized narratively rather than through formal systematic review methodology. Discussion: Preclinical studies suggest that beta-blockers modulate molecular pathways implicated in AVM pathophysiology, including VEGF, HIF-1α, SDF1α/CXCR4, MMP-9, and Notch-associated signaling. These mechanisms may reduce abnormal angiogenesis, endothelial instability, and pathological vascular remodeling. Clinical evidence, however, remains limited to retrospective studies, perioperative reports, and indirect evidence from cerebral cavernous malformations. Observational studies have reported associations between beta-blocker exposure and certain favorable AVM characteristics, including lower rates of hemorrhagic presentation and less complex angioarchitecture. However, these findings are highly susceptible to confounding, reverse causation, and selection bias and should not be interpreted as evidence of disease modification. Conclusions: Beta-blockers cannot currently be recommended as definitive therapy for intracranial AVMs. Their established role remains perioperative hemodynamic control, while potential disease-modifying effects require validation through prospective studies and randomized clinical trials. Full article

Background/Objectives: Excitotoxicity, primarily caused by excessive glutamate signaling, is a significant contributor to the aetiology of several neurological disorders. Docosahexaenoic acid (DHA), a long-chain omega-3 polyunsaturated fatty acid, is known for its neuroprotective properties, including antioxidants and anti-inflammatory effects. However, the existing literature has not sufficiently reviewed its specific role in glutamate-induced excitotoxicity. This systematic review aimed to provide comprehensive information from the literature on the neuroprotective effects of DHA in models of glutamate-induced neurotoxicity. Methods: A systematic search was conducted in the Cochrane Library, Scopus, Web of Science, PubMed, ScienceDirect, and Google Scholar, following PRISMA 2020 guidelines. The following keywords were used: DHA OR docosahexaenoic acid AND excitotoxicity OR glutamate-induced excitotoxicity OR glutamate-induced neurotoxicity. A total of 475 articles were screened, and 13 original articles published between 2003 and 2025 were included for data extraction. These studies included nine in vivo animal studies, three ex vivo studies, and one in vitro study. The risk of bias was assessed using SYRCLE’s methodology. Results: Our findings demonstrate that DHA provides substantial neuroprotection against excitotoxicity through antioxidative, anti-inflammatory, and anti-apoptotic mechanisms. Furthermore, DHA enhances neuronal function and cognitive performance by modulating neurotransmitter levels and glutamate-related signaling pathways. Despite these positive outcomes, heterogeneity across studies suggests that the neuroprotective properties of DHA may be affected by various parameters, such as the source of DHA, treatment dose and duration, age and experimental design. Conclusions: Although previous studies have demonstrated the benefits of DHA in preclinical and clinical settings of neurological disorders, further clinical studies focusing on the modulation of excitotoxicity by DHA are needed to validate its translational efficacy and therapeutic significance. Full article

Background/Objectives: The most common soft tissue sarcomas in adults are liposarcomas, a heterogeneous group of malignant tumors. A structured literature search was conducted to better understand the clinical-imaging aspects and molecular behavior underlying the therapeutic approach. Methods: A scoping review was performed according to the PRISMA-ScR guidelines. Searches were conducted in MEDLINE, Web of Science Core Collection, and Google Scholar for the period of 1 January 2016–27 March 2026. Studies that addressed liposarcomas and their subtypes were included. Data were extracted descriptively and synthesized narratively. Results: We identified 113 studies that met the inclusion and exclusion criteria. Most studies have focused on a subtype of liposarcomas or on aspects such as clinical, imaging, histopathological, molecular, therapeutic, or prognostic features. The collected data identify, in each case, the need to develop new techniques useful for their identification and deeper molecular analysis. These needs arise from the need to improve treatment and to provide better prognosis, especially in rare or high-grade subtypes. Conclusions: The heterogeneity of these tumors requires the provision of a diagnosis that takes into account all possible instruments: clinical, imaging, and histo-molecular. Therefore, further research and translational medicine are needed to discover new pathogenic mechanisms in order to develop individualized therapies that increase quality of life. Full article

Fungal contamination and the buildup of mycotoxins are ongoing threats to global food safety, especially in tropical areas where environmental conditions favor the growth of toxigenic fungi such as Aspergillus spp., Fusarium spp., and Penicillium spp. These toxins contaminate various food products and are linked to serious health problems, including liver toxicity, nerve toxicity, immune suppression, and cancer. Traditional methods to reduce these risks, such as chemical preservatives, heat treatments, and irradiation, have limited success in fully eliminating mycotoxins due to their stability, safety concerns, and declining consumer acceptance of synthetic additives. As a result, there is increasing interest in biological options that are safer and more sustainable. This review critically examines the potential of probiotic lactic acid bacteria (LAB) isolated from local fermented foods as multifunctional biocontrol agents that inhibit toxin-producing fungi, detoxify mycotoxins, and reduce cellular toxicity caused by these toxins. Scientific studies were retrieved from PubMed, ScienceDirect, Scopus, Web of Science, and Google Scholar, focusing on research published from 2011 to 2025 on antifungal activity, detoxification mechanisms, and cellular toxicology. The evidence shows that probiotic LAB employ various strategies, including producing organic acids, secreting bacteriocins, competing with fungi, adsorbing toxins onto their cell walls, and enzymatically transforming mycotoxins into less harmful substances. Recent findings also indicate that metabolites from LAB may influence oxidative stress, inflammation, and cell death in mammalian cells exposed to mycotoxins. Overall, probiotic LAB from native fermented foods offer promising biological approaches to improve food safety and reduce health risks associated with toxins. Future studies should focus on omics-based analysis of detoxification pathways, testing in real food systems, and translational research to support regulatory approval and large-scale use of probiotic-based strategies for mycotoxin control. Full article

Background/Objectives: Metabolic dysfunction-associated fatty liver disease (MAFLD), previously known as non-alcoholic fatty liver disease (NAFLD), is the most prevalent chronic liver disease worldwide, affecting approximately 25% of the global population. MAFLD represents a broad disease spectrum ranging from simple steatosis to metabolic dysfunction-associated steatohepatitis (MASH), fibrosis, cirrhosis, and hepatocellular carcinoma (HCC). The availability of experimental models that faithfully reproduce human metabolic and hepatic pathology is essential for elucidating disease mechanisms and advancing therapeutic development. This review aims to critically evaluate commonly used rodent models of MAFLD and provide guidance for model selection based on specific research objectives. Methods: A narrative, semi-systematic literature search was performed using PubMed Central, Ovid MEDLINE, and Google Scholar. Rodent models were classified according to their mode of disease induction, including diet-induced, genetically engineered, chemically or pharmacologically induced, and combination models. Models were assessed based on frequency of use, relevance to different stages of MAFLD progression, metabolic fidelity, and suitability for mechanistic studies and preclinical therapeutic evaluation. Results: Diet-induced models incorporating high fat, fructose, and cholesterol most closely recapitulate human metabolic dysfunction and are highly relevant for translational research and drug screening. Nutrient-deficient diets induce rapid steatohepatitis and fibrosis but lack key features of metabolic syndrome. Genetic models enable the targeted interrogation of specific metabolic and inflammatory pathways, whereas chemical and combination models accelerate fibrosis and HCC development. No single rodent model fully reproduces the entire spectrum of human MAFLD. Conclusions: Rodent models remain indispensable tools for MAFLD research; however, their applicability depends on alignment with the defined experimental goals. Careful selection of models based on disease stage, dominant pathogenic mechanisms, and translational intent is essential for improving reproducibility and clinical relevance. This review provides a practical framework to guide investigators in choosing appropriate preclinical models for mechanistic studies and therapeutic development in MAFLD. Full article

Caffeic acid (CA) is a phenolic compound commonly found in fruits, vegetables, and coffee, with preclinical evidence demonstrating its important role in disease management through different mechanisms of action. This review aimed to explore CA’s pharmacological effects in different pathological conditions, and sources were retrieved by using databases like PubMed, Scopus, Google Scholar, and Web of Science and based on preclinical studies. CA notably protects cells and tissues from oxidative stress and inflammation, highlighting its therapeutic role in the management of pathogenesis. The neuroprotective, cardioprotective, hepatoprotective, anti-microbial, and anti-obesity effects are reported through in vitro and in vivo studies. Moreover, its anticancer effects are linked to modulation of cell signaling pathways, together with angiogenesis, cell cycle, apoptosis, and the PI3K/Akt pathway. This article explores how caffeic acid influences health conditions, providing a comprehensive overview of its effects on disease processes. Reviewing the literature aims to enhance the understanding of caffeic acid’s role in disease management and as a natural therapeutic agent. Although several studies demonstrate the anticancer effects and its role in the management of various pathological conditions, most of the existing evidence is based on in vitro, in vivo, and xenograft models. Moreover, many natural compounds, including CA, that exhibit activity in preclinical settings fail to translate into clinical applications, due to restrictions of poor bioavailability, toxicity, rapid metabolism, and differences in the tumor microenvironment. Thus, future studies should emphasize well-designed in vivo studies as well as controlled clinical trials to better describe CA’s safety, efficacy, mechanism of action, and therapeutic application in humans. Further investigation of its interactions with other therapeutic agents may offer insights into synergistic effects that enhance treatment efficacy. Overall, a more comprehensive understanding of this compound will be indispensable for its development as a therapeutic agent in the treatment of chronic disease. Full article

Introduction: Accurate detection and characterization of intracardiac masses remain a major challenge in cardiovascular imaging due to overlapping morphological features between tumors, thrombi, and vegetations, as well as the inherent limitations of echocardiography, including operator dependency and variable image quality. Although echocardiography is the first-line imaging modality for evaluating cardiac masses, diagnostic uncertainty frequently necessitates additional multimodality imaging. Artificial intelligence (AI), including machine learning and deep learning approaches, has emerged as a promising strategy to improve image interpretation, automate feature extraction, and enhance diagnostic consistency. Objective: This narrative review aims to examine current advances in AI-enhanced echocardiography for cardiac tumor detection, with a particular focus on detection, segmentation, classification, multimodal integration, and clinical translation. Methods: A narrative literature review was conducted using PubMed, Scopus, and Google Scholar databases. Relevant English-language studies published between 2016 and 2026 were identified using keywords including “artificial intelligence”, “machine learning”, “deep learning”, “echocardiography”, “cardiac tumors”, “intracardiac masses”, “multimodal imaging”, and “ultrasomics”. Original studies, reviews, and methodological papers related to AI-assisted cardiovascular imaging were evaluated. Discussion: Current evidence suggests that AI-driven techniques, including radiomics (ultrasomics), convolutional neural networks, and multimodal learning frameworks, can improve the detection, segmentation, and classification of intracardiac masses. Experimental studies have reported high diagnostic performance, with some deep learning models achieving diagnostic accuracies exceeding 95% under controlled conditions. AI-assisted systems may also reduce interobserver variability and improve workflow efficiency. Multimodal AI approaches integrating echocardiography with cardiac magnetic resonance imaging, computed tomography, electrocardiography, and clinical data appear particularly promising for improving diagnostic discrimination. However, current models remain limited by small and imbalanced datasets, insufficient external validation, data heterogeneity, and limited generalizability across institutions and imaging protocols. Additional barriers to clinical implementation include annotation variability, limited interpretability of deep learning models, and regulatory considerations. Conclusions: AI-enhanced echocardiography has substantial potential to improve the detection and characterization of intracardiac masses by augmenting diagnostic consistency and supporting clinical decision-making. Nevertheless, current evidence remains largely based on retrospective and experimental studies. Future progress will depend on large multicenter collaborations, standardized imaging datasets, explainable AI frameworks, and prospective clinical validation to enable safe and effective integration into routine cardiovascular practice. Full article

Background: Experimental animal models remain central to burn research and soft-tissue reconstruction/repair, but method heterogeneity compromises reproducibility, comparability, and translation for depth/area endpoints. Objective: We aimed to map burn-induction methods and examine reproducibility, intentional depth modulation, wound-area stability, validation, and operator-dependent variability. Methods: A PRISMA-ScR review, informed by JBI guidance, was conducted without registration but with predefined questions, criteria, and charting domains. PubMed/MEDLINE, Scopus, Web of Science, Embase, and Google Scholar were searched from inception to 30 January 2026. Eligible studies were English peer-reviewed full-text original in vivo animal studies. Two reviewers independently screened records; one charted data, another checked it. Evidence was mapped by modality, exposure-control architecture, validation, and operator-sensitive steps. Results: Studies varied by species, modality, device design, exposure settings, and severity verification. Modalities were contact, scald, steam, and radiant/infrared. Wound area was more reproducible than depth, which depended on temperature, duration, force/pressure, geometry, equilibration, anatomical site, and assessment timing. Histopathology was the main standard, sometimes complemented by morphometry, optical, or perfusion techniques. Operator-sensitive variability involved force, alignment, contact stability, template integrity, exposure geometry, source stability/environmental control. Conclusions: Burn induction is a measurement-system problem; constraining operator-sensitive variables, predefined validation timing, and quantitative variability reporting may improve validity, comparability, and translation. Full article

This review summarizes mechanisms regulating mRNA translation under cellular stress and highlights design strategies to improve translation efficiency and stability in the gene therapy of human diseases. mRNA-based therapeutics are emerging as a versatile gene therapy platform enabling transient and controllable expression of therapeutic proteins for the treatment of cancer, genetic disorders, and inflammatory diseases. The efficacy of mRNA-based gene therapy is strongly influenced by sequence design, chemical modifications, and structural features. Evidence shows that rational mRNA engineering can significantly enhance translation efficiency even under stress conditions that impair canonical protein synthesis, as observed in many pathological states. Cellular stress activates regulatory pathways that suppress global translation; however, optimized mRNA constructs can partially bypass these inhibitory mechanisms, enabling sustained protein expression. By improving mRNA stability and resistance to stress-responsive translational control, robust therapeutic protein production can be achieved even in challenging cellular environments. This article was prepared as a narrative review focused on translational regulation mechanisms relevant to therapeutic mRNA design under cellular stress conditions. Literature was collected from PubMed, Google Scholar, and Web of Science using keywords including “mRNA therapeutics,” “cellular stress,” “translation regulation,” “UTR engineering,” and “cap-independent translation.” Studies published mainly between 2010 and 2025 were considered. Original articles and reviews related to stress-responsive translation and therapeutic mRNA optimization were included, while studies outside the scope of translational control and mRNA engineering were excluded. Priority was given to recent and mechanistically relevant publications. Full article