Precise Size Determination of Supported Catalyst Nanoparticles via Generative AI and Scanning Transmission Electron Microscopy.

Transmission electron microscopy (TEM) plays a crucial role in heterogeneous catalysis for assessing the size distribution of supported metal nanoparticles. Typically, nanoparticle size is quantified by measuring the diameter under the assumption of spherical geometry, a simplification that limits the precision needed for advancing synthesis-structure-performance relationships. Currently, there is a lack of techniques that can reliably extract more meaningful information from atomically resolved TEM images, like nuclearity or geometry. Here, cycle-consistent generative adversarial networks (CycleGANs) are explored to bridge experimental and simulated images, directly linking experimental observations with information from their underlying atomic structure. Using the versatile Pt/CeO2 (Pt particles centered ≈2 nm) catalyst synthesized by impregnation, large datasets of experimental scanning transmission electron micrographs and physical image simulations are created to train a CycleGAN. A subsequent size-estimation network is developed to determine the nuclearity of imaged nanoparticles, providing plausible estimates for ≈70% of experimentally observed particles. This automatic approach enables precise size determination of supported nanoparticle-based catalysts overcoming crystal orientation limitations of conventional techniques, promising high accuracy with sufficient training data. Tools like this are envisioned to be of great use in designing and characterizing catalytic materials with improved atomic precision.

Assessment of transport phenomena in catalyst effectiveness for chemical polyolefin recycling.

Since the dawn of agitated brewing in the Paleolithic era, effective mixing has enabled efficient reactions. Emerging catalytic chemical polyolefin recycling processes present unique challenges, considering that the polymer melt has a viscosity three orders of magnitude higher than that of honey. The lack of protocols to achieve effective mixing may have resulted in suboptimal catalyst effectiveness. In this study, we have tackled the hydrogenolysis of commercial-grade high-density polyethylene and polypropylene to show how different stirring strategies can create differences of up to 85% and 40% in catalyst effectiveness and selectivity, respectively. The reaction develops near the H2-melt interface, with the extension of the interface and access to catalyst particles the main performance drivers. Leveraging computational fluid dynamics simulations, we have identified a power number of 15,000-40,000 to maximize the catalyst effectiveness factor and optimize stirring parameters. This temperature- and pressure-independent model holds across a viscosity range of 1-1,000 Pa s. Temperature gradients may quickly become relevant for reactor scale-up.

Environmental Benefits of Circular Ethylene Production from Polymer Waste.

The linear nature of the current plastics economy and increasing demand for polymers poses a pressing global problem. In this work, we explore the environmental and economic performance of a circular alternative for polymer production through chemical plastic recycling following the waste-to-methanol-to-olefins (WMO) route. We assess the life-cycle environmental impacts and techno-economic feasibility of this novel circular production route (CPR) in 2020 and 2050, and compare them to the existing linear production route (LPR), deploying naphtha steam cracking for olefin production, and a mix of landfill and incineration as end-of-life treatment. Our results showcase that CPR could enable significant impact reductions, notably in 2050 assuming a low-carbon electricity mix based on renewables. However, the shift from linear to circular comes with burden-shifting, increasing the impacts relative to LPR on five environmental indicators in 2020 (i.e., terrestrial and freshwater eutrophication, particulate matter formation, acidification, and metal/mineral resources use). From the techno-economic viewpoint, we found that ethylene from waste polymers could become competitive with fossil ethylene when deployed at large scale. Moreover, it is significantly cheaper than its green analogs, which deploy methanol-to-olefins with green methanol from captured CO2 and electrolytic H2, showcasing the potential of implementing high-readiness level technologies to close the loop for polymers.

Development and validation of prediction models for fetal growth restriction and birthweight: an individual participant data meta-analysis.

Background: Fetal growth restriction is associated with perinatal morbidity and mortality. Early identification of women having at-risk fetuses can reduce perinatal adverse outcomes. Objectives: To assess the predictive performance of existing models predicting fetal growth restriction and birthweight, and if needed, to develop and validate new multivariable models using individual participant data. Design: Individual participant data meta-analyses of cohorts in International Prediction of Pregnancy Complications network, decision curve analysis and health economics analysis. Participants: Pregnant women at booking. External validation of existing models (9 cohorts, 441,415 pregnancies); International Prediction of Pregnancy Complications model development and validation (4 cohorts, 237,228 pregnancies). Predictors: Maternal clinical characteristics, biochemical and ultrasound markers. Primary outcomes: fetal growth restriction defined as birthweight <10th centile adjusted for gestational age and with stillbirth, neonatal death or delivery before 32 weeks' gestation birthweight. Analysis: First, we externally validated existing models using individual participant data meta-analysis. If needed, we developed and validated new International Prediction of Pregnancy Complications models using random-intercept regression models with backward elimination for variable selection and undertook internal-external cross-validation. We estimated the study-specific performance (c-statistic, calibration slope, calibration-in-the-large) for each model and pooled using random-effects meta-analysis. Heterogeneity was quantified using τ2 and 95% prediction intervals. We assessed the clinical utility of the fetal growth restriction model using decision curve analysis, and health economics analysis based on National Institute for Health and Care Excellence 2008 model. Results: Of the 119 published models, one birthweight model (Poon) could be validated. None reported fetal growth restriction using our definition. Across all cohorts, the Poon model had good summary calibration slope of 0.93 (95% confidence interval 0.90 to 0.96) with slight overfitting, and underpredicted birthweight by 90.4 g on average (95% confidence interval 37.9 g to 142.9 g). The newly developed International Prediction of Pregnancy Complications-fetal growth restriction model included maternal age, height, parity, smoking status, ethnicity, and any history of hypertension, pre-eclampsia, previous stillbirth or small for gestational age baby and gestational age at delivery. This allowed predictions conditional on a range of assumed gestational ages at delivery. The pooled apparent c-statistic and calibration were 0.96 (95% confidence interval 0.51 to 1.0), and 0.95 (95% confidence interval 0.67 to 1.23), respectively. The model showed positive net benefit for predicted probability thresholds between 1% and 90%. In addition to the predictors in the International Prediction of Pregnancy Complications-fetal growth restriction model, the International Prediction of Pregnancy Complications-birthweight model included maternal weight, history of diabetes and mode of conception. Average calibration slope across cohorts in the internal-external cross-validation was 1.00 (95% confidence interval 0.78 to 1.23) with no evidence of overfitting. Birthweight was underestimated by 9.7 g on average (95% confidence interval -154.3 g to 173.8 g). Limitations: We could not externally validate most of the published models due to variations in the definitions of outcomes. Internal-external cross-validation of our International Prediction of Pregnancy Complications-fetal growth restriction model was limited by the paucity of events in the included cohorts. The economic evaluation using the published National Institute for Health and Care Excellence 2008 model may not reflect current practice, and full economic evaluation was not possible due to paucity of data. Future work: International Prediction of Pregnancy Complications models' performance needs to be assessed in routine practice, and their impact on decision-making and clinical outcomes needs evaluation. Conclusion: The International Prediction of Pregnancy Complications-fetal growth restriction and International Prediction of Pregnancy Complications-birthweight models accurately predict fetal growth restriction and birthweight for various assumed gestational ages at delivery. These can be used to stratify the risk status at booking, plan monitoring and management. Study registration: This study is registered as PROSPERO CRD42019135045. Funding: This award was funded by the National Institute for Health and Care Research (NIHR) Health Technology Assessment programme (NIHR award ref: 17/148/07) and is published in full in Health Technology Assessment; Vol. 28, No. 14. See the NIHR Funding and Awards website for further award information.

One in ten babies is born small for their age. A third of such small babies are considered to be 'growth-restricted' as they have complications such as dying in the womb (stillbirth) or after birth (newborn death), cerebral palsy, or needing long stays in hospital. When growth restriction is suspected in fetuses, they are closely monitored and often delivered early to avoid complications. Hence, it is important that we identify growth-restricted babies early to plan care. Our goal was to provide personalised and accurate estimates of the mother's chances of having a growth-restricted baby and predict the baby's weight if delivered at various time points in pregnancy. To do so, first we tested how accurate existing risk calculators ('prediction models') were in predicting growth restriction and birthweight. We then developed new risk-calculators and studied their clinical and economic benefits. We did so by accessing the data from individual pregnant women and their babies in our large database library (International Prediction of Pregnancy Complications). Published risk-calculators had various definitions of growth restriction and none predicted the chances of having a growth-restricted baby using our definition. One predicted baby's birthweight. This risk-calculator performed well, but underpredicted the birthweight by up to 143 g. We developed two new risk-calculators to predict growth-restricted babies (International Prediction of Pregnancy Complications-fetal growth restriction) and birthweight (International Prediction of Pregnancy Complications-birthweight). Both calculators accurately predicted the chances of the baby being born with growth restriction, and its birthweight. The birthweight was underpredicted by <9.7 g. The calculators performed well in both mothers predicted to be low and high risk. Further research is needed to determine the impact of using these calculators in practice, and challenges to implementing them in practice. Both International Prediction of Pregnancy Complications-fetal growth restriction and International Prediction of Pregnancy Complications-birthweight risk calculators will inform healthcare professionals and empower parents make informed decisions on monitoring and timing of delivery.

Design Principles of Catalytic Materials for CO2 Hydrogenation to Methanol.

Heterogeneous catalysts are essential for thermocatalytic CO2 hydrogenation to methanol, a key route for sustainable production of this vital platform chemical and energy carrier. The primary catalyst families studied include copper-based, indium oxide-based, and mixed zinc-zirconium oxides-based materials. Despite significant progress in their design, research is often compartmentalized, lacking a holistic overview needed to surpass current performance limits. This perspective introduces generalized design principles for catalytic materials in CO2-to-methanol conversion, illustrating how complex architectures with improved functionality can be assembled from simple components (e.g., active phases, supports, and promoters). After reviewing basic concepts in CO2-based methanol synthesis, engineering principles are explored, building in complexity from single to binary and ternary systems. As active nanostructures are complex and strongly depend on their reaction environment, recent progress in operando characterization techniques and machine learning approaches is examined. Finally, common design rules centered around symbiotic interfaces integrating acid-base and redox functions and their role in performance optimization are identified, pinpointing important future directions in catalyst design for CO2 hydrogenation to methanol.

Translational modeling-based evidence for enhanced efficacy of standard-of-care drugs in combination with anti-microRNA-155 in non-small-cell lung cancer.

BACKGROUND: Elevated microRNA-155 (miR-155) expression in non-small-cell lung cancer (NSCLC) promotes cisplatin resistance and negatively impacts treatment outcomes. However, miR-155 can also boost anti-tumor immunity by suppressing PD-L1 expression. Therapeutic targeting of miR-155 through its antagonist, anti-miR-155, has proven challenging due to its dual molecular effects. METHODS: We developed a multiscale mechanistic model, calibrated with in vivo data and then extrapolated to humans, to investigate the therapeutic effects of nanoparticle-delivered anti-miR-155 in NSCLC, alone or in combination with standard-of-care drugs. RESULTS: Model simulations and analyses of the clinical scenario revealed that monotherapy with anti-miR-155 at a dose of 2.5 mg/kg administered once every three weeks has substantial anti-cancer activity. It led to a median progression-free survival (PFS) of 6.7 months, which compared favorably to cisplatin and immune checkpoint inhibitors. Further, we explored the combinations of anti-miR-155 with standard-of-care drugs, and found strongly synergistic two- and three-drug combinations. A three-drug combination of anti-miR-155, cisplatin, and pembrolizumab resulted in a median PFS of 13.1 months, while a two-drug combination of anti-miR-155 and cisplatin resulted in a median PFS of 11.3 months, which emerged as a more practical option due to its simple design and cost-effectiveness. Our analyses also provided valuable insights into unfavorable dose ratios for drug combinations, highlighting the need for optimizing dose regimens to prevent antagonistic effects. CONCLUSIONS: This work bridges the gap between preclinical development and clinical translation of anti-miR-155 and unravels the potential of anti-miR-155 combination therapies in NSCLC.

TooManyCellsInteractive: A visualization tool for dynamic exploration of single-cell data.

BACKGROUND: As single-cell sequencing technologies continue to advance, the growing volume and complexity of the ensuing data present new analytical challenges. Large cellular populations from single-cell atlases are more difficult to visualize and require extensive processing to identify biologically relevant subpopulations. Managing these workflows is also laborious for technical users and unintuitive for nontechnical users. RESULTS: We present TooManyCellsInteractive (TMCI), a browser-based JavaScript application for interactive exploration of cell populations. TMCI provides an intuitive interface to visualize and manipulate a radial tree representation of hierarchical cell subpopulations and allows users to easily overlay, filter, and compare biological features at multiple resolutions. Here we describe the software architecture and demonstrate how we used TMCI in a pan-cancer analysis to identify unique survival pathways among drug-tolerant persister cells. CONCLUSIONS: TMCI will facilitate exploration and visualization of large-scale sequencing data in a user-friendly way. TMCI is freely available at https://github.com/schwartzlab-methods/too-many-cells-interactive. An example tree from data within this article is available at https://tmci.schwartzlab.ca/.

Expanding the repertoire of ceDAF-12 ligands for modulation of the steroid endocrine system in C. elegans.

Steroid hormones are essential for the biological processes of eukaryotic organisms. The steroid endocrine system of C. elegans, which includes dafachronic acids (DA) and the nuclear receptor ceDAF-12, provides a simple model for exploring the role of steroid hormone signaling pathways in animals. In this study, we show for the first time the feasibility of designing synthetic steroids that can modulate different physiological processes, such as development, reproduction and ageing, in relation to ceDAF-12. Our results not only confirm the conclusions derived from genetic studies linking these processes but also provide new chemical tools to selectively manipulate them, as we found that different compounds produce different phenotypic results. The structures of these compounds are much more diverse than those of endogenous hormones and analogues previously described by other researchers, allowing further development of the chemical modulation of the steroid endocrine system in C. elegans and related nematodes.

Mechanochemically-derived iron atoms on defective boron nitride for stable propylene production.

Single-atom catalysts (SACs), possessing a uniform metal site structure, are a promising class of materials for selective oxidations of hydrocarbons. However, their design for targeted applications requires careful choice of metal-host combinations and suitable synthetic techniques. Here, we report iron atoms stabilised on defective hexagonal boron nitride (h-BN) via mechanochemical activation in a ball mill as an effective catalyst for propylene production via N2O-mediated oxidative propane dehydrogenation (N2O-ODHP), reaching 95% selectivity at 6% propane conversion and maintaining stable performance for 40 h on stream. This solvent-free synthesis allows simultaneous carrier exfoliation and surface defect generation, creating anchoring sites for catalytically-active iron atoms. The incorporation of a small metal quantity (0.5 wt%) predominantly generates a mix of atomically-dispersed Fe2+ and Fe3+ species, as confirmed by combining advanced microscopy and electron paramagnetic resonance, UV-vis and X-ray photoelectron spectroscopy analyses. Single-atom iron favours selective propylene formation, while metal oxide nanoparticles yield large quantities of CO x and cracking by-products. The lack of acidic functionalities on h-BN, hindering coke formation, and firm stabilisation of Fe sites, preventing metal sintering, ensure stable operation. These findings showcase N2O-ODHP as a promising propylene production technology and foster wider adoption of mechanochemical activation as a viable method for SACs synthesis.

Understanding and Controlling Reactivity Patterns of Pd1@C3N4-Catalyzed Suzuki-Miyaura Couplings.

Using heterogeneous single-atom catalysts (SACs) in the Suzuki-Miyaura coupling (SMC) has promising economic and environmental benefits over traditionally applied palladium complexes. However, limited mechanistic understanding hinders progress in their design and implementation. Our study provides critical insights into the working principles of Pd1@C3N4, a promising SAC for the SMC. We demonstrate that the base, ligand, and solvent play pivotal roles in facilitating interface formation with reaction media, activating Pd centers, and modulating competing reaction pathways. Controlling the previously overlooked interplay between base strength, reagent solubility, and surface wetting is essential for mitigating mass transfer limitations in the triphasic reaction system and promoting catalyst reusability. Optimum conditions for Pd1@C3N4 require polar solvents, intermediate base strength, and increased water content. Our investigations reveal that high selectivity requires minimizing competitive coordination of bases and phosphine ligands to the Pd centers, to avoid homocoupling and alternative reductive elimination mechanisms giving rise to phosphonium side-products. Furthermore, in situ XAS investigations probing electronic structures and coordination environments of Pd sites further rationalize the base and ligand coordination, confirming and expanding upon previous computational hypotheses for Pd1@C3N4. This understanding allows for designing a more selective ligand-free reaction pathway using the solvent and base to modulate the chemical environment of the active sites. We highlight the importance of environment-compatible surface tension, the creation of coordinatively available active sites, and the stabilization of partially reduced Pd centers, emphasizing the importance of mechanistic studies to advance the design of SACs in organic liquid phase reactions.

Long noncoding RNA MALAT1 in dermatologic disorders: a comprehensive review.

Dermatologic disorders, affecting the integumentary system, involve diverse molecular mechanisms such as cell proliferation, apoptosis, inflammation and immune responses. Long noncoding RNAs, particularly Metastasis-Associated Lung Adenocarcinoma Transcript 1 (MALAT1), are crucial regulators of gene expression. MALAT1 influences inflammatory responses, immune cell function and signaling pathways, impacting various physiological and pathological processes, including dermatologic disorders. Dysregulation of MALAT1 is observed in skin conditions like psoriasis, atopic dermatitis and systemic lupus erythematosus. However, its precise role remains unclear. This review consolidates knowledge on MALAT1's impact on skin biology and pathology, emphasizing its potential diagnostic and therapeutic implications in dermatologic conditions.

[Box: see text].

NCCR Catalysis at a Glance: A National Research Program on Sustainable Chemistry.

Curious about how chemistry can contribute to sustainable development? In this overview, we explain the essence of NCCR funding, the research focus and structural goals of NCCR Catalysis, and how these align with the sustainable development goals (SDGs). Additionally, we highlight opportunities for getting involved with our program.

Active learning streamlines development of high performance catalysts for higher alcohol synthesis.

Developing efficient catalysts for syngas-based higher alcohol synthesis (HAS) remains a formidable research challenge. The chain growth and CO insertion requirements demand multicomponent materials, whose complex reaction dynamics and extensive chemical space defy catalyst design norms. We present an alternative strategy by integrating active learning into experimental workflows, exemplified via the FeCoCuZr catalyst family. Our data-aided framework streamlines navigation of the extensive composition and reaction condition space in 86 experiments, offering >90% reduction in environmental footprint and costs over traditional programs. It identifies the Fe65Co19Cu5Zr11 catalyst with optimized reaction conditions to attain higher alcohol productivities of 1.1 gHA h-1 gcat-1 under stable operation for 150 h on stream, a 5-fold improvement over typically reported yields. Characterization reveals catalytic properties linked to superior activities despite moderate higher alcohol selectivities. To better reflect catalyst demands, we devise multi-objective optimization to maximize higher alcohol productivity while minimizing undesired CO2 and CH4 selectivities. An intrinsic trade-off between these metrics is uncovered, identifying Pareto-optimal catalysts not readily discernible by human experts. Finally, based on feature-importance analysis, we formulate data-informed guidelines to develop performance-specific FeCoCuZr systems. This approach goes beyond existing HAS catalyst design strategies, is adaptable to broader catalytic transformations, and fosters laboratory sustainability.

Integrating climate policies in the sustainability analysis of green chemicals.

New and enhanced processes will not be the only drivers toward a sustainable chemical industry. Implementing climate policies will impact all components of the chemical supply chain over the following decades, making improvements in energy generation, material extraction, or transportation contribute to reducing the overall impacts of chemical technologies. Including this synergistic effect when comparing technologies offers a clearer vision of their future potential and may allow researchers to support their sustainability propositions more strongly. Ammonia and methanol production account for more than fifty percent of the CO2 emissions in this industry and are, therefore, excellent case studies. This work performs a prospective life cycle assessment until 2050 for fossil, blue, wind, and solar-based technologies under climate policies aiming to limit the global temperature rise to 1.5 °C, 2 °C, or 3.5 °C. The first finding is the inability of fossil-based routes to reduce their CO2 emissions beyond 10% by 2050 without tailored decarbonisation strategies, regardless of the chemical and climate policy considered. In contrast, green routes may produce chemicals with around 90% fewer emissions than today and even with net negative emissions (on a cradle-to-gate basis), as in the case of methanol (up to -1.4 kg CO2-eq per kg), mainly due to the contributions of technology development and increasing penetration of renewable energies. Overall, the combined production of these chemicals could be net-zero by 2050 despite their predicted two to fivefold increase in demand. Lastly, we propose a roadmap for progressive implementation by 2050 of green routes in 26 regions worldwide, applying the criterion of at least 80% reduction in climate change impacts when compared to their fossil alternatives. Furthermore, an exploratory prospective techno-economic assessment showed that by 2050, green routes could become more economically attractive. This work offers quantitative arguments to reinforce research, development, and policymaking efforts on green chemical routes reliant on renewable energies.

Technology Readiness and Emerging Prospects of Coupled Catalytic Reactions for Sustainable Chemical Value Chains.

Transitioning from both the direct and indirect use of fossil fuels to the renewable and sustainable resources of the near future demands a focal shift in catalysis research - from investigating catalytic reactions in isolation to developing coupled reactions for modern chemical value chains. In this Perspective, we discuss the status and emerging prospects of coupled catalytic reactions across various scales and provide key examples. Besides being a sustainable and essential alternative to current fossil-based processes, the coupling of catalytic reactions offers novel and scalable pathways to value-added chemicals. By emphasizing the specific requirements and challenges arising from coupled reactions, we aim to identify and underscore research needs that are critical to expedite their development and to fully unlock their potential for chemical and fuel production.

Body composition and metabolic consequences of antibiotics most frequently administered to newborns in intensive care units: an experimental study in healthy newborn rats.

Introduction: The increasing overuse of antibiotics in recent years has led to antibiotics being the most prescribed drugs for pediatric patients, and 72% of patients in the neonatal intensive care unit are treated with antibiotics. One effect of antibiotic use is the alteration of the microbiota, which is associated with metabolic disorders, including obesity. Methods: This experimental study in newborn rats compared the administration of ampicillin/meropenem (Access/Watch groups) at 100/10 µg/g every 12 h, cefotaxime 200 µg/g every 24 h (Watch group), and amikacin 15 µg/g every 24 h (Access group) versus saline solution as the control. Each antibiotic was adjusted to the required dosages based on weight, and the doses were administered intraperitoneally daily for 5 days to 10-14 newborn male rats per group. A comparison of the morphometric and biochemical parameters registered on day 28 was performed using ANOVA. Results: Amikacin had the largest effect on morphometric measurements, and low-density lipoprotein cholesterol, while cefotaxime had the largest effect on glucose and triglycerides, whereas ampicillin/meropenem produced the weakest effect on the measured parameters. Discussion: The administration of antibiotics in the neonatal stage can affect the body composition of rats as well as the lipid and carbohydrate serum levels. Future studies should evaluate the toxicity of antibiotics in immature neonatal organs and could help to improve therapeutic decisions and prevent the unjustified use of antibiotics in newborns, thereby reducing metabolic consequences.

Bridging Imaging and Clinical Scores in Parkinson's Progression via Multimodal Self-Supervised Deep Learning.

Neurodegenerative diseases pose a formidable challenge to medical research, demanding a nuanced understanding of their progressive nature. In this regard, latent generative models can effectively be used in a data-driven modeling of different dimensions of neurodegeneration, framed within the context of the manifold hypothesis. This paper proposes a joint framework for a multi-modal, common latent generative model to address the need for a more comprehensive understanding of the neurodegenerative landscape in the context of Parkinson's disease (PD). The proposed architecture uses coupled variational autoencoders (VAEs) to joint model a common latent space to both neuroimaging and clinical data from the Parkinson's Progression Markers Initiative (PPMI). Alternative loss functions, different normalization procedures, and the interpretability and explainability of latent generative models are addressed, leading to a model that was able to predict clinical symptomatology in the test set, as measured by the unified Parkinson's disease rating scale (UPDRS), with R2 up to 0.86 for same-modality and 0.441 cross-modality (using solely neuroimaging). The findings provide a foundation for further advancements in the field of clinical research and practice, with potential applications in decision-making processes for PD. The study also highlights the limitations and capabilities of the proposed model, emphasizing its direct interpretability and potential impact on understanding and interpreting neuroimaging patterns associated with PD symptomatology.

The Future of Chemical Sciences is Sustainable.

Chemistry, a vital tool for sustainable development, faces a challenge due to the lack of clear guidance on actionable steps, hindering the optimal adoption of sustainability practices across its diverse facets from discovery to implementation. This Scientific Perspective explores established frameworks and principles, proposing a conciliated set of triple E priorities anchored on Environmental, Economic, and Equity pillars for research and decision making. We outline associated metrics, crucial for quantifying impacts, classifying them according to their focus areas and scales tackled. Emphasizing catalysis as a key driver of sustainable synthesis of chemicals and materials, we exemplify how triple E priorities can practically guide the development and implementation of processes from renewables conversions to complex customized products. We summarize by proposing a roadmap for the community aimed at raising awareness, fostering academia-industry collaboration, and stimulating further advances in sustainable chemical technologies across their broad scope.

Translational modeling-based evidence for enhanced efficacy of standard-of-care drugs in combination with anti-microRNA-155 in non-small-cell lung cancer.

Elevated microRNA-155 (miR-155) expression in non-small-cell lung cancer (NSCLC) promotes cisplatin resistance and negatively impacts treatment outcomes. However, miR-155 can also boost anti-tumor immunity by suppressing PD-L1 expression. We developed a multiscale mechanistic model, calibrated with in vivo data and then extrapolated to humans, to investigate the therapeutic effects of nanoparticle-delivered anti-miR-155 in NSCLC, alone or in combination with standard-of-care drugs. Model simulations and analyses of the clinical scenario revealed that monotherapy with anti-miR-155 at a dose of 2.5 mg/kg administered once every three weeks has substantial anti-cancer activity. It led to a median progression-free survival (PFS) of 6.7 months, which compared favorably to cisplatin and immune checkpoint inhibitors. Further, we explored the combinations of anti-miR-155 with standard-of-care drugs, and found strongly synergistic two- and three-drug combinations. A three-drug combination of anti-miR-155, cisplatin, and pembrolizumab resulted in a median PFS of 13.1 months, while a two-drug combination of anti-miR-155 and cisplatin resulted in a median PFS of 11.3 months, which emerged as a more practical option due to its simple design and cost-effectiveness. Our analyses also provided valuable insights into unfavorable dose ratios for drug combinations, highlighting the need for optimizing dose regimen to prevent antagonistic effects. Thus, this work bridges the gap between preclinical development and clinical translation of anti-miR-155 and unravels the potential of anti-miR-155 combination therapies in NSCLC.