Search Results (293)

Bio-based and biodegradable polymers such as short-chain-length (scl) poly(3-hydroxybutyrate) (PHB) and poly(3-hydroxybutyrate-co-3-hydroxyvalerate) (PHBV) are widely adopted in diverse areas such as healthcare, manufacturing, and packaging. However, high production costs and the complexity of tailoring their thermal properties, such as glass transition temperature (Tg), melting temperature (Tm), and crystallization temperature (Tc), hinder further adoption. The current study reported on the development of a raw dataset of PHB and PHBV materials compiled from 572 instances collected from the literature (558 instances) and in-house experiments (14 instances). The dataset encompassed compositional physicochemical parameters, molecular features, and corresponding thermal characteristics. After assessing data quality and filtering for completeness and available features, curated datasets were created for machine learning (ML) analysis. Two ML models, Random Forest (RF) and eXtreme Gradient Boosting (XGBoost), were utilized to predict values of Tg, Tc, and Tm using feature engineering methods that integrated chemistry-based descriptors with polymer-specific and experimental variables. The predictive performance of the models was systematically investigated using different combinations of input features to identify the most informative descriptor sets for each target property. The best-performing models were obtained using 118 data points for Tg and Tm and 201 data points for Tc, achieving R² values of 0.77, 0.76, and 0.82 for Tg, Tc, and Tm, respectively. Despite the reliable prediction of the thermal properties of scl-PHAs, the main limitations of the study were the relatively small dataset size for certain targets and incomplete or missing reporting of experimental conditions in the literature sources, which may introduce variability in the compiled data. The findings implied that curated polymer datasets and interpretable ML models can support the rational design of sustainable polymers with tailored properties for specific applications. Full article

Recently, there has been great interest in AI-based approaches for de novo design of novel drug candidates. However, the generation of useful lead drug candidate compounds requires more than predicting engagement with the desired protein target. Candidate molecules must also be anchored in the real world of medicinal chemistry for their synthesis and modification as well as satisfying multiple drug development-related criteria. Here, we present Nevermore, an AI target-conditioned, database-grounded workflow for prioritizing candidate ligands from large compound libraries. Nevermore uses a geometry-aware protein–ligand affinity oracle to score target-specific binding and perform sparse integer edits in count-based Morgan fingerprint space. Nevermore then retrieves the most structurally similar molecules from public chemical databases. This design enables multi-objective search over predicted affinity and absorption, distribution, metabolism, excretion, and toxicity (ADMET) proxies while keeping all candidates anchored to valid database compounds. We evaluated Nevermore’s performance across three biologically distinct targets: Menin, a protein-interaction target relevant to leukemia; SARS-CoV-2 M^pro, a viral cysteine protease relevant to antiviral discovery; and epidermal growth factor receptor (EGFR), a kinase-superfamily oncology target with extensive experimentally tested compounds. Nevermore retrieved candidate sets with favorable predicted affinity–property trade-offs. These results support database-grounded fingerprint steering as a practical computational strategy for lead prioritization and for generating testable molecular hypotheses, although the prioritized candidates remain predictions, requiring follow-up experimental validation. Full article

While double-layer insulation structures are widely adopted, their thermal performance is critically dependent on the thermophysical behavior of the interstitial air cavity, a variable often oversimplified in current design practices. This article moves beyond generic material descriptions to investigate the specific mechanism of heat transfer transition within sealed air gaps sandwiched between graphite polystyrene boards. The innovation of this experiment lies in the rigorous isolation of air gap thickness as the primary independent variable within a 1 × 1 × 1 m closed building model, instrumented with high-precision GPRS temperature and humidity sensors to capture real-time thermal gradients under the authentic climate conditions of Anyang, Henan. The results demonstrate a non-monotonic relationship between gap thickness and effective thermal resistance, governed by the competition between molecular conduction and buoyancy-driven natural convection. Specifically, the data validates that a 20 mm air gap represents the statistically significant optimum, thereby maximizing insulation efficiency while minimizing radiative heat loss. Using this optimized structure reduces steady-state heat flux compared to monolithic equivalents and aligns with the energy conservation target. Unlike previous studies limited by simulation assumptions or short-term testing, this research provides empirically verified, long-term field data that bridges the gap between theoretical fluid dynamics and practical building envelope engineering. These findings offer a robust, physics-based reference for optimizing double-layer insulation systems in the Central Plains, directly supporting the low-carbon retrofitting of existing building stocks. Full article

The Yezo virus (YEZV) is a recently discovered tick-borne orthonairovirus with pathogenic potential, causing acute febrile illness in humans. Viral nucleoproteins (N) play a key role in genome packaging, replication, and modulation of host immune responses, making their structural characterization essential for understanding viral pathogenesis and developing targeted countermeasures. However, the absence of structural data for YEZV proteins significantly hinders these efforts. This study presents the first solution structure of the YEZV N domain 1 (D1). A highly purified, soluble, tag-free recombinant YEZV N D1 was produced from the native sequence of the clinical YEZV isolate. The native-state conformation was resolved through an integrated approach combining size-exclusion chromatography coupled with small-angle X-ray scattering (SEC-SAXS), AlphaFold 3 structure prediction, and all-atom molecular dynamics simulations. The YEZV N D1 structure adopts a stable, predominantly α-helical globular fold that remains monomeric under near-physiological conditions. SEC-SAXS data show excellent agreement with computational models, revealing moderate conformational flexibility. The characterized recombinant YEZV N D1 and its first solution structure reported here providing essential insights into understanding of YEZV molecular architecture. These findings lay a foundation for rational serological assay development and structure-guided therapeutic design against this and other emerging orthonairoviruses. Full article

Background/Objectives: The present study estimated the potential therapeutic effects of Borassus flabellifer immature endosperm extract (BFE) on the metabolic disorders of diabetes and hypothyroidism using a mixed research design. Methods: Characterization of phytochemicals via GC-MS demonstrated a highly abundant list of bioactive compounds, and it encompassed phenolic derivatives, methylxanthines, fatty acids, and inositol-related compounds. Molecular docking indicated that the major phytoconstituents showed positive binding affinities to the most vital metabolism and endocrine receptors, namely, TRβ1, PPARγ, and AMP-activated protein kinase (AMPK). Notably, both compounds C1 and C2 were highly affined towards TRβ1 (−7.8 and −7.6 kcal/mol), which is attributed to interactions in the active site through hydrogen bonding and hydrophobic responses, which means that the identified compounds were found to have good predicted interactions with some metabolic- and thyroid-associated targets and could be used to form preliminary hypotheses for further mechanistic studies. The in vivo data showed that the disease-induced groups were marked by hyperglycemia, imbalance in thyroid hormones, and dyslipidemia, as well as liver, kidney, and heart dysfunction. BFE caused significant decreases in these changes, which were also observed through improvements in fasting blood glucose, T3, T4, and TSH; partial restoration of lipid profiles; and dampening of liver and kidney injury signalers. The cardiac risk indices were also reduced significantly after BFE administration. Positive changes in body weight gain, feed ratio, and metabolic ratio further reflected better physiological stability. Results: These findings were corroborated by histopathological analysis, which showed that the tissue architecture of the pancreas, liver, kidney, and heart had significantly recovered in the study. BFE still showed constant therapeutic activity even though the magnitude of response was attenuated when combined disease conditions were used. Conclusions: Comprehensively, the results indicate that BFE potentially plays a role in the amelioration of metabolic and endocrine abnormalities of diabetic and hypothyroid conditions. These observations should be regarded as hypothesis-generating, as further mechanistic and translational studies are needed to substantiate their biological relevance. Full article

Background/Objectives: Age-related functional decline is increasingly linked to chronic low-grade inflammation (inflammaging) and sarcopenia, two interconnected processes contributing to frailty, metabolic dysregulation, and impaired physical function. These conditions share several underlying mechanisms, including immune dysregulation, mitochondrial dysfunction, oxidative stress, and impaired anabolic signaling. This narrative review critically evaluated the mechanistic and translational interactions between natural bioactive compounds and lifestyle interventions in modulating inflammaging and sarcopenia. Methods: Evidence from molecular, experimental, epidemiological, and clinical studies was synthesized to examine the effects of bioactive compounds—including polyphenols, flavonoids, carotenoids, and omega-3 fatty acids—as well as physical activity and dietary patterns. Particular emphasis was placed on inflammatory regulation, redox homeostasis, mitochondrial adaptation, and muscle metabolism, including NF-κB, AMPK–mTOR, and Nrf2 signaling pathways. Results: Observational studies and randomized controlled trials generally indicate that anti-inflammatory dietary patterns and regular physical activity are associated with improved muscle strength, physical performance, and inflammatory status in older adults. Mechanistically, nutritional bioactives and exercise appear to converge on several pathways involved in mitochondrial function, oxidative stress, anabolic signaling, and immune activation. Emerging evidence suggests potential convergence and interaction of biological pathways affected by nutritional and lifestyle interventions; however, formal evidence demonstrating true synergistic effects in humans remains limited. Nevertheless, substantial heterogeneity persists regarding intervention protocols, dosage strategies, bioavailability, and long-term clinical outcomes. Conclusions: Natural bioactive compounds and lifestyle-based interventions represent promising approaches for targeting biological processes implicated in inflammaging and sarcopenia. By integrating current evidence within a hormesis-oriented geroscience framework, this review highlights the importance of adaptive redox regulation, metabolic resilience, and evidence-based lifestyle strategies in healthy aging. Future well-designed longitudinal and intervention studies are needed to clarify the clinical relevance of these interactions and optimize translational implementation. Full article

Background/Objectives: Methylxanthines, such as caffeine, exhibit neuroprotective properties in neurodegenerative conditions, partly linked to modulation of monoamine oxidase B (MAO-B) and oxidative stress pathways. This work aimed to design, synthesize and functionally characterize new caffeine-8-methylthioglycolic acid derivatives as selective MAO-B inhibitors with neuroprotective potential. Methods: A QSAR model was built on 94 studies of xanthine derivatives to guide the design of ten new semi- and thiosemicarbazides (Jas1–Jas10), followed by molecular docking to human MAO-B (PDB: 2V5Z) using Glide, GOLD and MM-GBSA binding free energy calculations. The target compounds were synthesized in relatively high yields, structurally confirmed by spectroscopic methods and tested in vitro for hMAO-A/B inhibition, as well as for neurotoxicity and neuroprotection in isolated mouse brain synaptosomes, mitochondria and microsomes under 6-hydroxydopamine (6-OHDA), tert-butyl hydroperoxide (t-BuOOH) and Fe/ascorbate (Fe²⁺/AA)-induced oxidative stress. Results: Docking and MM-GBSA identified Jas6 and Jas7 as the most stable MAO-B binders, with binding free energies approaching those of safinamide. All derivatives inhibited hMAO-A and hMAO-B in the submicromolar range, with Jas2 and Jas6 showing the highest MAO-B selectivity indices. At 100 µM, the series produced mild but significant pro-oxidant and cytotoxic effects when applied alone, yet under oxidative stress all compounds, especially Jas2 and Jas6, markedly preserved synaptosomal and mitochondrial viability, maintained glutathione levels, and reduced malondialdehyde production. Conclusions: The caffeine-based semi- and thiosemicarbazides, particularly Jas2 and Jas6, emerge as promising selective MAO-B inhibitors with pronounced antioxidant and neuroprotective activity, supporting their further optimization as multitarget candidates for neurodegenerative disorders such as Parkinson’s disease. Full article

The wide application of liquid hydrogen as a key energy carrier is severely limited by the reliability of high-pressure and low-temperature pumps. The traditional research on liquid hydrogen pumps relies on empirical analysis of isolated components, but fails to reveal the fundamental failure mechanism of these pumps. This review argues for a paradigm shift in the understanding and design of liquid hydrogen pumps. We systematically decomposed the failure of the liquid hydrogen pump into a thermodynamic-driven, cross-scale cascading process rather than the failure of isolated components. At the molecular level, the extreme thermal physical properties of liquid hydrogen (ultra-low latent heat and surface tension) can lead to widespread nucleation under slight thermal disturbances. At the mesoscopic scale, the initial perturbation is significantly amplified through the nonlinear dynamics of bubble clusters. This amplification is characterized by intense collapse and strong energy concentration due to the low density and low viscosity of liquid hydrogen. At the component level, this enhanced destructive energy will cause faults similar to phase transitions; namely, the liquid lubrication in the bearings will disappear, the seals will shift from viscous blockage to gas diffusion, and at the same time, the damage caused by low-temperature hydrogen cavitation and corrosion to the materials will also occur simultaneously. At the system level, the strong dynamic coupling among the subsystems has led to a nonlinear performance collapse. This cross-scale failure chain reveals the flaws in the classical cavitation theory, which is based on the assumptions of isothermal and inertia dominance. We have expounded the thermodynamic-dominated cavitation state in liquid hydrogen. This state is quantified by the Σ parameter and governs the multimodal behavior of low-temperature cavitation phenomena. To address this complexity, we have proposed a comprehensive framework that integrates multi-scale collaborative simulation and digital twin, combining molecular dynamics, CFD, system dynamics, and targeted experiments. This review proposes a candidate physical framework for addressing the reliability challenges of liquid hydrogen pumps. It also provides a clear roadmap for the next generation of inherently robust cryogenic fluid machinery, and offers a reference for the design of energy systems under other extreme conditions. Full article

Life expectancy in high-income countries is increasing, leading to a higher incidence of age-related neurodegenerative diseases. To address this urgent medical need, several molecular targets have been identified, including advanced glycation end products (AGEs) and tyrosinase. Given the well-established role of diet in counteracting degenerative processes, this study aimed to identify a potential food ingredient with combined anti-tyrosinase and anti-glycative properties. Acanthus mollis L. was selected based on its inclusion in the BelFrIt list and its known content of tyrosinase inhibitors, such as benzoxazinones and verbascoside. Extraction of A. mollis leaves was optimized using a design of experiments approach, comparing microwave- and ultrasound-assisted techniques. Optimal conditions were achieved using microwave-assisted extraction with ethanol 80%, 80 °C, one cycle, drug-to-solvent ratio of 10 mL/g. The optimized extract (at 5 mg/mL) inhibited tyrosinase activity by approximately 47%, increasing to 58% after chlorophyll removal. Moreover, the extract reduces AGEs formation in presence of methylglyoxal, with an activity at 1 mg/mL comparable with that of a well-known anti-glycative agent. A similar trend was observed in the reduction in methylglyoxal and glyoxal levels. Overall, these results support the potential of the optimized A. mollis extract as a functional food ingredient to counteract aging-related neurodegeneration. Full article

Fibroblast Growth Factor 2 (FGF2) is a highly effective regulator of cell proliferation, differentiation, migration, and adhesion, suggesting a significant therapeutic potential as a tissue regeneration promoter both in acute and chronic tissue damage settings. Despite an extensive list of pathologies that lend themselves as viable targets for FGF2-based therapy (ranging from periodontics to burns to diabetic ulcers to coronary artery disease), the success record in the clinic remains modest, with no FDA approvals obtained so far. The inferior stability of this protein is frequently cited as the most significant factor behind its disappointing performance as a biotherapeutic. Multiple strategies have been designed and tested in an effort to ameliorate this problem, but the success remains elusive. We investigate the aggregation propensity of a recombinantly produced FGF2 using native mass spectrometry (MS) to identify conditions favoring formation of small soluble oligomers, which are considered precursors to larger aggregates. Tandem MS of proteolytic fragments produced by digestion of the oligomeric species allows the formation of external disulfide bonds to be identified as the process leading to oligomerization. Specifically, Cys-31 (one of the two unpaired cysteine residues in intact FGF2) appears to be a particularly active promoter of oligomerization by forming external disulfide bonds. As a high-pI protein, FGF2 readily associates with heparin, and molecular modeling identifies a positive charge basin proximal to Cys-31 as a potential heparin binding site, which can readily accommodate a synthetic heparin mimetic fondaparinux. Adding an equimolar amount of the latter to the FGF2 solution not only leads to formation of a stable protein/polyanion complex (as revealed by native MS), but also inhibits formation of FGF2 oligomers (presumably via a combination of steric hindrance and electrostatic repulsion). These findings advance our understanding of FGF2 stability, which will be invaluable for optimizing its formulation, storage, and administration. Full article

Two-dimensional-material-based FET biosensors have gained attention for being label-free and having ultra-sensitive detection capability. The high carrier mobility and large surface-to-volume ratio of 2D materials enable low detection limits under buffer conditions; however, practical detection still faces many challenges. Current reviews have largely summarized materials, functionalization routes, or target classes separately, but a clearer framework linking interface design, device architecture, and practical sensing performance is still needed. In this review, we examine how interfacial engineering and device architecture govern signal transduction and sensing behavior in 2D material FET biosensors. We also analyze the major barriers to real-sample detection, including Debye screening, nonspecific adsorption, and signal drift, together with commonly used mitigation strategies. On this basis, an “interface–device–performance” framework is discussed as a conceptual approach for understanding the relationship between molecular recognition, electrical response, and sensing performance. This review mainly focuses on the key challenges of 2D material FET biosensors in practical medical applications, discusses the differences between material and application perspectives, and examines the major factors limiting clinical translation. Full article

Antibiotic resistance is a growing global health threat. However, its evolution cannot be fully understood without considering antibiotic tolerance and persistence. Persister cells are phenotypic variants that survive lethal antibiotic exposure without heritable resistance, primarily through growth arrest, metabolic slowdown, and stress-adaptive states. Although persistence has been viewed as a transient survival phenomenon, increasing evidence suggests that it may also have a genetic basis by preserving populations during antibiotic-induced bottlenecks and enabling regrowth, mutation, and selection under certain conditions. This review examines the molecular mechanisms underlying persister formation, including toxin–antitoxin systems, stringent-response signaling, ATP depletion, translational arrest, and stress-response networks. We discuss how persistence contributes to antibiotic tolerance in biofilms, host environments, and recurrent infections, and how repeated antibiotic exposure may promote stepwise evolution from phenotypic survival to stable resistance in specific contexts. Evidence from experimental evolution, clinical observations, and system-level analyses supports a potential but context-dependent link between persistence and resistance. We also highlight therapeutic strategies targeting persister cells, including antipersister compounds, metabolic activation, combination therapies, bacteriophages, and alternative approaches. Finally, we outline future research directions, emphasizing single-cell technologies, systems biology, longitudinal clinical studies, and evolution-informed treatment design. A comprehensive understanding of persistence and its evolutionary implications is essential for improving treatment efficacy and limiting the emergence of long-term antibiotic resistance. Full article

Morphological disciplines, namely Human Anatomy, Histology, and Embryology, have traditionally provided the foundational knowledge for medical education, offering spatial, cellular, and temporal coordinates of the human body. However, reducing these disciplines to static and purely descriptive learning undermines their deeper purpose: interpreting morphology as the dynamic outcome of biological processes. This review emphasizes three interrelated pillars of morphological sciences—cell differentiation, tissue homeostasis, and organ remodeling—as essential frameworks for understanding both normal physiology and disease pathogenesis. Cell differentiation establishes functional identity, tissue homeostasis ensures structural stability, and organ remodeling enables adaptation to both physiological and pathological stimuli. Dysregulation of these programs underlies a wide range of conditions, from degenerative diseases and chronic inflammation to neoplasms. Integrating classical morphological knowledge with modern approaches—including stem cell biology, organoids, tissue engineering, and computational modeling—enables predictive and regenerative strategies in personalized medicine. Furthermore, recent advances in artificial intelligence applied to histopathology have enhanced our capacity to detect early deviations from homeostasis and guide targeted interventions. By combining spatial, cellular, and molecular perspectives, the morphological sciences can provide clinicians with tools to interpret disease as the result of altered biological programs, anticipate pathology, and design precise therapeutic strategies. This integrated approach highlights the renewed centrality of morphology in contemporary medicine, bridging foundational knowledge with predictive, regenerative, and personalized healthcare. Full article

Fe–carbon catalysts (FCCs) are extensively used for persulfate activation in advanced oxidation processes (PS-AOPs), an approach regarded as an efficient and cost-effective strategy for removing emerging contaminants (ECs). However, the quantitative structure–activity relationship between the degradation efficiency of ECs with diverse molecular characteristics and the microstructure of FCCs has not been clearly elucidated. This hinders the widespread practical implementation of FCCs. Herein, density functional theory (DFT)-derived molecular-descriptor-assisted machine learning models were employed to accurately predict the reaction rate constants for EC degradation in FCC-PS AOPs, mainly focusing on three aspects: performance prediction, operating condition optimization and mechanism interpretation. Additionally, DFT-derived descriptors are integrated with fabrication and operational parameters to facilitate the generative design of FCCs. The excellent fitting performance of the overall XGB model in predicting the reaction constants for EC degradation (Test R² = 0.813) highlights the notable advantages of customized hyperparameter tuning for improving predictive accuracy. Subsequently, the submodels trained using different EC clusters (derived from t-SNE and K-means clustering methods) can offer specific strategies for selecting optimal parameters of FCC-PS AOPs that target ECs with distinct properties. The interpretability of the model was improved by using SHAP values and partial-dependence plots to clarify the internal relationships of the ML “black box”. Overall, a feasible and generalizable ML model is proposed to facilitate a paradigm shift in the inverse design of FCCs for the degradation of specific ECs. Full article

Oxidative stress and inflammation are interconnected pathological processes involved in the progression of neurodegenerative, cardiovascular, and metabolic diseases, highlighting the need for multifunctional therapeutic agents targeting multiple pathways. In this study, two novel hybrid compounds were designed and synthesized in three steps by conjugating butylated phenolic moieties derived from butylated hydroxytoluene and p-coumaric acid with proline and γ-aminobutyric acid (GABA). The aim was the combination of antioxidant, anti-inflammatory, and cytoprotective properties within a single molecular framework. The compounds were evaluated using a comprehensive panel of in vitro and in vivo assays to assess antioxidant, metal-reducing, iron-chelating, antiglycation, anti-inflammatory, and acetylcholinesterase inhibitory activities. Both compounds exhibited significant antioxidant activity, with compound 2 demonstrating superior radical scavenging ability against DPPH, ABTS·⁺ and hydrogen peroxide (IC₅₀ 86 μM, 25 μM and 104 μM, respectively), enhanced ferric-reducing capacity (up to 91% of trolox activity), and strong iron-chelating activity (61.3%). Compound 2 also showed potent inhibition of lipid peroxidation (IC₅₀ 17.5 μM) and moderate antiglycation effects (44%), indicating substantial cytoprotective potential. Furthermore, both compounds selectively inhibited COX-2 over COX-1 and demonstrated moderate lipoxygenase inhibition, while compound 2 exhibited significant in vivo anti-inflammatory activity (53%), exceeding that of ibuprofen. Moderate acetylcholinesterase inhibition was also observed. In summary, the results confirm the design rationale, indicating that compound 2 could be further optimized as a multi-targeting molecule directed against oxidative stress- and inflammation-mediated conditions. Full article