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Drug-based ionic derivatives of NSAIDs: influence of cation structure on physicochemical properties, skin permeation, and biocompatibility
Abstract Drug-based ionic systems represent a promising strategy to improve the physicochemical properties and delivery performance of poorly soluble active pharmaceutical ingredients. In this study, ionic derivatives of four nonsteroidal anti-inflammatory drugs (NSAIDs)—ibuprofen, ketoprofen, naproxen, and salicylic acid—were synthesized using three organic cations: didecyldimethylammonium (DDA), 1-ethyl-3-methylimidazolium (EMIM), and cholinium (Chol). The obtained systems were compared with the parent drugs and their sodium salts in terms of thermal stability, viscosity, solubility, lipophilicity, transdermal permeation, and cytotoxicity. Conversion of NSAIDs into ionic forms significantly improved aqueous solubility, increasing from 0.039 to 0.142 g·dm -3 for the parent drugs to 20.5–178.3 g·dm -3 for EMIM- and cholinium-based systems, while sodium salts exhibited extremely high solubility (151–232 g·dm -3 ). Rheological analysis revealed strong cation-dependent viscosity differences, ranging from approximately 0.2–8.0 Pa·s for EMIM systems to ~ 175 Pa·s for [Chol][KETO] at 20 °C. In vitro permeation studies demonstrated that ionic modification substantially affected transdermal transport. For ibuprofen, cumulative permeation after 24 h increased from 379 µg·cm⁻ 2 (parent drug) to 1629 µg·cm -2 for [EMIM][IBU] and 1709 µg·cm -2 for [Chol][IBU]. Naproxen derivatives also showed significant enhancement, reaching ~ 797 µg·cm -2 for [Na][NAP] and ~ 759 µg·cm -2 for [EMIM][NAP] compared with 235 µg·cm -2 for the parent compound. In contrast, DDA-based systems generally reduced permeation. Ketoprofen derivatives exhibited moderate enhancement (up to ~ 1.5-fold), while salicylic acid showed formulation-dependent behaviour. Overall, the results demonstrate that the structure of the organic cation significantly influences the physicochemical properties, skin permeation, and biocompatibility of NSAID-derived ionic systems.
Climate risk and corporate green innovation bubbles: evidence from Chinese listed enterprises
Abstract Against the backdrop of global climate change and China’s “dual carbon” goals, climate risk has become a critical external factor shaping corporate sustainable development strategies in emerging economies. Using a sample of China’s A-share listed companies from 2010 to 2023 and a two-way fixed-effects panel model, this paper empirically examines the impact of climate risk on corporate green innovation bubbles—a phenomenon characterized by a systematic divergence between green patent quantity and quality. Climate risk is found to significantly exacerbate green innovation bubbles, as firms facing greater climate uncertainty systematically prioritize patent quantity over substantive technological quality. Managerial myopia and financing constraints are two key transmission mechanisms through which climate risk distorts the allocation of green innovation resources. Digital transformation and ESG performance unexpectedly amplify rather than mitigate these distortionary effects. Heterogeneity analysis further reveals that the bubble-inducing effect of climate risk is more pronounced among non-state-owned enterprises, low-carbon-emission firms, and firms with higher managerial ownership. These findings extend research on the economic consequences of climate uncertainty and carry important implications for the design of green innovation evaluation systems, climate disclosure policy, and sustainable finance governance in emerging markets.
Research on the evolutionary trajectories of intelligent energy technology based on main path analysis and SAO semantic analysis
Efficient removal of regorafenib from aqueous solutions using KOH-activated hazelnut husk-derived activated carbon: adsorption mechanism, kinetics, isotherms and thermodynamics
Hierarchical reinforcement learning with low-level agents and active curriculum for torpedo countermeasures
Prevalence of colour vision deficiency among Schoolchildren at Repi Primary school in Addis Ababa, Ethiopia
Harmonic resonance analysis in PMSG based wind farms: comparison of simplified and detailed impedance models and passive and active damping
Design rule formalization from documented requirements for detailed 3D CAD model verification
Source limitation drives slow compositional recovery in tropical secondary forests
Tropical forests can recover diversity, structure, and function rapidly after disturbance. However, plant compositional recovery remains incomplete even after many decades, and the reasons for this lag are poorly understood. We investigated the roles of source, dispersal, and establishment limitation in affecting compositional recovery in Costa Rican forests using long-term data on species composition of trees, seeds, seedlings, and saplings, along with measurements of herbivory and leaf pathogen damage in seedlings and saplings from old-growth (OG) and second-growth (SG) forest plots. We classified tree species into successional groups, including: generalist (similar relative abundance in OG and SG), old-growth specialist (higher relative abundance in OG), old-growth exclusive (detected only in OG), and too rare to classify conclusively. Infrequent species drove community dissimilarity between old-growth and second-growth plots, and 40% of species in old-growth plots were absent as trees in second-growth forests up to 55 y old. Old-growth exclusive species (as trees and seedlings and saplings in OG plots and as seeds in seed traps in OG and SG) have extremely low abundance (11 trees/ha; 1 seedling or sapling/ha; 8 seeds/ha), indicating strong source limitation. Old-growth exclusive species shared seed dispersal traits with old-growth specialists established in SG, providing no evidence of dispersal limitation. Old-growth specialist seedlings and saplings had higher survival in SG than in OG, providing no evidence of establishment limitation. These findings suggest source limitation as the primary driver of delayed compositional recovery in mid-to-late stages of forest succession and underscore the potential for targeted enrichment plantings to accelerate full compositional recovery.
No gender differences in predictive processing
Abstract Statistical learning, defined as the implicit extraction of environmental regularities, is considered a fundamental cognitive mechanism that supports predictive processing across individuals, domains, and species. However, whether it is modulated by gender remains unclear. This study investigated potential gender-related differences in statistical learning using an age-matched sample of 129 women and 129 men ( N = 258) who completed a well-established visuomotor probabilistic learning task. Statistical learning was revealed in both reaction time and accuracy measures, with participants responding faster and more accurately to high-probability than to low-probability trials. Critically, neither the magnitude nor the trajectory of statistical learning differed between women and men. Furthermore, no baseline differences in visuomotor performance interacted with learning metrics. Overall, these findings suggest that implicit statistical learning is a robust cognitive mechanism that is highly resilient to gender-related variation.
Knowledge, attitudes and practice of pediatric nurses on child life therapeutic play: insights into clinical care
Identification and characterization of PldB domain-containing esterase from a freshwater pond metagenome
Retraction Note: Knockdown of CCNB2 inhibits the tumorigenesis of gastric cancer by regulation of the PI3K/Akt pathway
Plasma irradiation of rice seeds activates embryo physiology and promotes growth throughout all developmental stages
Density dependence and evolvability limit adaptive therapy in non-small cell lung cancer mouse model
Abstract Using NSG mice grafted with H3122 non-small cell lung cancer cells, we compared outcomes from (1) continuous therapy with alectinib, (2) adaptive therapy where alectinib was cycled on and off based on a mouse’s tumor burden, and (3) no treatment. Adaptive therapy has proven successful in mouse models and clinical trials. The success of adaptive therapy improves when either there is density-dependent feedbacks favoring competition by sensitive over resistant cancer cells when therapy is off, or when the rate at which the population of cancer cells becomes more sensitive to therapy during periods of no treatment is faster than the rate at which resistance increases when on therapy. We found tumor growth rates were lowest under continuous therapy and highest under no therapy with adaptive therapy in between. We fitted the data to three separate game-theory models of resistance evolution where we let resistance be a quantitative trait consistent with resistance mechanisms to alectinib. All models successfully identified why adaptive therapy proved less successful than continuous: there was no evidence for favorable density-dependent feedbacks; and there was no significant difference in the rate of evolution towards or away from resistance when therapy was on or off.