Animal models, particularly the chicken embryo (CE), remain crucial for advancing developmental biology and medicine. Liver embryogenesis, a complex and tightly regulated process, is particularly susceptible to developmental abnormalities. This study presents a comprehensive 2D and 3D µCT analysis of CE liver development (HH22–HH41) using a refined 1% phosphotungstic acid (PTA) staining protocol. Our methodology yielded high-resolution visualization of liver microstructures, including intricate vascular networks, with image quality and contrast comparable to histological analysis. Quantitative assessment revealed a critical period of rapid liver growth between incubation days 6 and 9 (HH29–HH35), followed by the stabilization of hepatic vascular volume by day 10 (HH36). The ease of structural orientation in µCT datasets, enhanced by 3D renderings, further underscores the technique’s utility. By establishing µCT as an effective tool for liver imaging, this study provides essential normative data on the liver’s spatial organization, morphology, and vascular architecture during embryogenesis, thereby opening new avenues for research in embryology, teratology, pharmacology, and toxicology.

Carbon nanotubes (CNTs) have emerged as non-viral gene delivery vectors, due to their physicochemical properties, high surface area, anisotropic needlelike geometry, tunable surface chemistry, and unique optical and thermal characteristics. This review synthesizes mechanistic understanding of CNT-mediated gene delivery: cellular uptake, receptor-mediated targeting through surface functionalization, and the challenge of endosomal escape. Understanding how CNTs enter cells, behave intracellularly, and what determines functional therapeutic outcomes is essential for advancing their clinical development. We detail four primary endosomal escape mechanisms: direct cell membrane translocation through intrinsic needlelike property or with cell-penetrating peptide (CPP) facilitation, the proton sponge effect from polyamine coatings, and photothermal/photochemical internalization. We also compare their efficiency against lipid nanoparticle (LNP) and polymeric vector. We further examine CNT's intracellular fate, covering protein corona formation, complement pathway activation and its immune consequences, macrophage recognition, and biodegradation. Intracellular fate trajectories are linked to measurable functional outcomes. Lysosomal sequestration governs their silencing potency, tumor tissue pharmacokinetics determine knockdown duration, and CNS biopersistence coincides with sustained microglial activation. An immunotoxicological evaluation framework adapted from established NCL and regulatory guidelines (ISO/TS 10993-20, ISO/TR 10993-22, ICH S8) is outlined, encompassing complement split product quantification, PBMC cytokine profiling, inflammasome activation assays, and in vivo immunophenotyping. Translational obstacles remain. Including biopersistence, unpredictable protein corona composition, cytotoxicity, and the absence of standardized manufacturing and characterization protocols. Emerging stimuli-responsive hybrid nanostructures and AI-assisted design offer promising pathways forward, contingent on coordinated progress in reproducible manufacturing, longitudinal fate monitoring, and rigorous immunotoxicological assessment.

The combined pretreatment of ultrasound irradiation, urea, and sodium hydroxide was explored to increase the extraction and delignification of cellulose from spent coffee grounds (SCGs), shortening the extraction time. The impacts of different experimental conditions, namely alkali concentration (5 vs. 10%), urea addition (0 vs. 3%), and ultrasound aid (30 min, 40 kHz, 150 W), on the physicochemical characteristics of the extracted cellulose were determined. The first characteristic of the raw biomass was its high lignin content (30.4%), necessitating a rigorous delignification process. To confirm this, samples were further analyzed by FTIR, XRD, TGA, and SEM. The successful elimination of non-cellulosic material, as indicated by the disappearance of the carbonyl stretch at 1730 cm⁻¹ and attributed to hemicellulose and pectin, was confirmed by FTIR. The XRD results revealed that while the individual application of ultrasound yielded the highest apparent crystallinity index (64.5% for sample B), the synergistic low-alkali combination with urea (sample F) resulted in a lower, controlled crystallinity index (56.2%). This indicated that the coexistence of urea and ultrasound induces significant fiber swelling and localized hydrogen-bond disruption, as well as highly efficient chemical purification. The thermal analysis revealed that all samples had high thermal stability with a maximum degradation temperature (T_max) between 316 and 322 °C, which can be used in composites. The process was visually confirmed by morphological analysis carried out under SEM, which revealed a progressive surface erosion of the compact lignocellulosic matrix into cellulose-rich samples treated with ultrasound.

Background . Healthcare evidence generation is shifting beyond randomized controlled trials (RCTs) to embrace real-world data (RWD). Collected from diverse routine care settings, RWD underpins real-world evidence (RWE), offering insights into patient outcomes, treatment effectiveness, and healthcare delivery. While RWE enhances generalizability and cost-effectiveness compared with RCTs, its inherent complexities such as data quality issues, missingness, confounding, and selection bias demand rigorous analytical approaches. Methods . This critical review synthesizes the spectrum of analytical methodologies applied to RWD, ranging from traditional statistical techniques and causal inference frameworks to machine learning and advanced methods including natural language processing (NLP), Bayesian modeling, and network analysis. Each approach is appraised in terms of strengths, limitations, and suitability for addressing the unique challenges of RWD. Results . Traditional statistical models provide interpretability and control for observed confounding but are limited by strong assumptions and vulnerability to unmeasured confounders. Causal inference methods, such as instrumental variables and target trial emulation, strengthen causal interpretation but require strong assumptions and specialized expertise. Machine learning approaches excel in prediction and high-dimensional data analysis but face interpretability and generalizability challenges. Advanced methods, including NLP and Bayesian models, extend analytical capacity but demand significant expertise and computational resources. Across categories, triangulation of multiple methods enhances robustness and credibility. Conclusions . RWE plays a critical role in drug development, post-market surveillance, comparative effectiveness research, health economics and outcomes research, and personalized medicine. Future progress hinges on emerging innovations such as federated learning, synthetic data generation, and explainable AI (XAI), alongside advances in data integration, harmonization, and reproducibility. Methodological transparency, rigorous validation, and interdisciplinary collaboration are essential to ensure that RWE delivers trustworthy, ethical, and clinically actionable insights capable of informing regulatory decisions and optimizing healthcare globally.

Kiryowa I. BIOTECHNOLOGICAL INNOVATIONS IN THE FIGHT AGAINST EMERGING INFECTIOUS DISEASES: A FOCUS ON RECOMBINANT DNA TECHNOLOGY // Universum: химия и биология : электрон. научн. журн. 2025. 3(129). URL: https://7universum.com/ru/nature/archive/item/19314