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Sandwich-structured GaIn(Zn)P/ZnSeS@ZnS quantum dots-ag@Fe3O4@SiO2 magnetoplasmonic nanosensor with simulation-driven design for influenza a(H1N1) virus biosensing
Get PDFDeveloping next-generation ultrasensitive bioanalytical sensing systems requires multifunctional nanoarchitectures that integrate engineered photophysics with highly selective biorecognition interfaces. We report on a multifunctional, simulation-guided design of a fluorescence nanosensor for ultrasensitive detection of Influenza A (H1N1) virus in human saliva, integrating heavy-metal-free GaIn(Zn)P/ZnSeS@ZnS quantum dots (QDs) with magnetoplasmonic molecularly imprinted silica shell (Ag@Fe3O4@SiO₂-MIBs) interface. The QDs, engineered with a compositionally graded ZnSeS inner shell and ZnS outer shell, exhibit strong red emission (λemi = 652 nm) and high photoluminescence quantum yield (QY = 78 ± 1.4 %) in aqueous media following ligand exchange with thioglycolic acid (TGA). Self-consistent field (SCF) simulations revealed that TGA capping significantly stabilised the QDs surface and induced distinct magnetic properties, confirming favourable surface energetics for biosensing applications. The TGA-GaIn(Zn)P/ZnSeS@ZnS QDs were conjugated to H1N1-specific DNA aptamers and incorporated with graphene oxide (GO), forming a Förster resonance energy transfer (FRET)-based nanoprobe that switches from an “off” to “on” state upon viral recognition. Target-induced aptamer folding disrupted the QD-GO interaction, thereby restoring the QDs fluorescence. To amplify the fluorescence signal and enable selective enrichment, virus-imprinted Ag@Fe3O4@SiO₂-MIBs were employed. Finite-difference time-domain (FDTD) simulations demonstrated strong plasmon-exciton coupling between QDs and the Ag core, yielding approximately an 18-fold local field enhancement at a 5 nm spacing. The combined effect of molecular imprinting, magnetic separation, and plasmonic amplification enabled a detection limit of 0.15 pg/mL with high specificity against non-target viruses. This study presented a computationally guided design of hybrid nanomaterials for next-generation, point-of-care viral diagnostics with enhanced optical and molecular recognition performance
Response to the Discussion of "does the Cost of Borrowing Increase for Firms that are Socially and Environmentally Irresponsible?"
No full textOur study aimed to examine the relationship between irresponsible social, environmental, and governance (IESG) activities and the cost of debt (CoD), focusing on three main aspects. First, we explored the direct relationship between IESG practices and the CoD to understand the financial repercussions for firms engaged in these activities. Second, we assessed how country-level characteristics, measured by the corruption perception index (CPI), moderate the relationship between IESG practices and the CoD, shedding light on the influence of national governance contexts. Finally, we investigated whether operating in traditionally "sinful"industries impacts the association between IESG practices and the CoD, exploring whether industry-specific norms and public perceptions of these industries exacerbate these impacts. Our sample consists of 50,281 firm-year observations for non-financial listed firms across 44 countries, spanning the period from 2002 to 2022. Pooled regression, with clustered standard errors at the firm level and a two-stage instrumental variable method, was employed. We find that firms engaging in IESG practices incur a higher CoD. Notably, this effect is more pronounced in countries with lower levels of corruption. Further analysis focused on the impact within sinful industries - such as tobacco, alcohol, and gambling - revealed no significant differences in the CoD associated with IESG practices compared to non-sinful industries. The study offers valuable insights for lending institutions, firms, and credit rating agencies about the financial implications of irresponsible corporate practices.</p
Plasmodium falciparum protein kinase 6 and hemozoin formation are inhibited by a type II human kinase inhibitor exhibiting antimalarial activity
No full textKinase inhibitors are potent therapeutics, but most essential Plasmodium kinases remain unexploited as antimalarial targets. We identified compound 12, a type II kinase inhibitor based on aminopyridine and 2,6-benzimidazole scaffolds, as a lead compound with nanomolar potency, fast action, and in vivo activity in the Plasmodium berghei rodent malaria model. Three-hybrid luciferase fragment complementation, enzymatic studies, and cellular thermal shift assays implicated Plasmodium protein kinase 6 (PfPK6) as the target. However, conditional knockdown of PfPK6 did not alter 12 potency, suggesting complex mechanisms of action. In vitro selection for compound 12 resistance revealed mutations in three transporters: multidrug-resistance protein 1, chloroquine resistance transporter and V-type ATPase, indicating a digestive vacuole site of action. Compound 12 inhibited β-hematin and hemozoin formation while increasing free heme levels, suggesting antimalarial activity via blockade of heme detoxification. Our studies repurpose a safe human kinase inhibitor as a potent, fast-acting antimalarial with established in vivo efficacy.</p
Artificial Intelligence Transformations in Geotechnics:Progress, Challenges and Future Enablers
Get PDFOur reliance on the underground space to deliver critical civil engineering infrastructure is growing: to accommodate utility and transport infrastructure in urban environments, to provide innovative housing and commercial solutions, and to support proliferating renewable energy infrastructure, particularly offshore. Artificial intelligence (AI) is arguably the most promising enabler to transform geotechnical engineering by extracting knowledge from data to achieve step-change increases in efficiency, sustainability, reliability and safety. This paper seeks to develop a shared understanding of the state of the art of AI in geotechnics and to explore future developments. By way of example, specific popular use cases in geotechnics are considered to highlight current progress in AI applications including intelligent site investigation, predictive modelling for soil behaviour, and optimisation of design and construction processes. The paper then addresses key research challenges, such as data scarcity and interpretability, and discusses the opportunities that lie ahead in the integration of AI with geotechnical engineering. Finally, priority technological enablers are identified for future transformations
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