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.

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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.

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.

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.

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.

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.

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.

Low-nuclearity CuZn ensembles on ZnZrOx catalyze methanol synthesis from CO2.

Metal promotion could unlock high performance in zinc-zirconium catalysts, ZnZrOx, for CO2 hydrogenation to methanol. Still, with most efforts devoted to costly palladium, the optimal metal choice and necessary atomic-level architecture remain unclear. Herein, we investigate the promotion of ZnZrOx catalysts with small amounts (0.5 mol%) of diverse hydrogenation metals (Re, Co, Au, Ni, Rh, Ag, Ir, Ru, Pt, Pd, and Cu) prepared via a standardized flame spray pyrolysis approach. Cu emerges as the most effective promoter, doubling methanol productivity. Operando X-ray absorption, infrared, and electron paramagnetic resonance spectroscopic analyses and density functional theory simulations reveal that Cu0 species form Zn-rich low-nuclearity CuZn clusters on the ZrO2 surface during reaction, which correlates with the generation of oxygen vacancies in their vicinity. Mechanistic studies demonstrate that this catalytic ensemble promotes the rapid hydrogenation of intermediate formate into methanol while effectively suppressing CO production, showcasing the potential of low-nuclearity metal ensembles in CO2-based methanol synthesis.

Machine learning in small sample neuroimaging studies: Novel measures for schizophrenia analysis.

Novel features derived from imaging and artificial intelligence systems are commonly coupled to construct computer-aided diagnosis (CAD) systems that are intended as clinical support tools or for investigation of complex biological patterns. This study used sulcal patterns from structural images of the brain as the basis for classifying patients with schizophrenia from unaffected controls. Statistical, machine learning and deep learning techniques were sequentially applied as a demonstration of how a CAD system might be comprehensively evaluated in the absence of prior empirical work or extant literature to guide development, and the availability of only small sample datasets. Sulcal features of the entire cerebral cortex were derived from 58 schizophrenia patients and 56 healthy controls. No similar CAD systems has been reported that uses sulcal features from the entire cortex. We considered all the stages in a CAD system workflow: preprocessing, feature selection and extraction, and classification. The explainable AI techniques Local Interpretable Model-agnostic Explanations and SHapley Additive exPlanations were applied to detect the relevance of features to classification. At each stage, alternatives were compared in terms of their performance in the context of a small sample. Differentiating sulcal patterns were located in temporal and precentral areas, as well as the collateral fissure. We also verified the benefits of applying dimensionality reduction techniques and validation methods, such as resubstitution with upper bound correction, to optimize performance.

CO Cofeeding Affects Product Distribution in CH3Cl Coupling over ZSM-5 Zeolite: Pressure Twists the Plot.

C1 coupling reactions over zeolite catalysts are central to sustainable chemical production strategies. However, questions persist regarding the involvement of CO in ketene formation, and the impact of this elusive oxygenate intermediate on reactivity patterns. Using operando photoelectron photoion coincidence spectroscopy (PEPICO), we investigate the role of CO in methyl chloride conversion to hydrocarbons (MCTH), a prospective process for methane valorization with a reaction network akin to methanol to hydrocarbons (MTH) but without oxygenate intermediates. Our findings reveal the transformative role of CO in MCTH at the low pressures, inducing ketene formation in MCTH and boosting olefin production, confirming the Koch carbonylation step in the initial stages of C1 coupling. We uncover pressure-dependent product distributions driven by CO-induced ketene formation, and its subsequent desorption from the zeolite surface, which is enhanced at low pressure. Inspired by the above results, extension of the co-feeding approach to CH3OH as another simple oxygenate showcases the additional potential for improved catalyst stability in MCTH at ambient pressure.

Modulation of myeloid-derived suppressor cell functions by oral inflammatory diseases and important oral pathogens.

The oral cavity presents a diverse microbiota in a dynamic balance with the host. Disruption of the microbial community can promote dysregulation of local immune response which could generate oral diseases. Additionally, alterations in host immune system can result in inflammatory disorders. Different microorganisms have been associated with establishment and progression of the oral diseases. Oral cavity pathogens/diseases can modulate components of the inflammatory response. Myeloid-derived suppressor cells (MDSCs) own immunoregulatory functions and have been involved in different inflammatory conditions such as infectious processes, autoimmune diseases, and cancer. The aim of this review is to provide a comprehensive overview of generation, phenotypes, and biological functions of the MDSCs in oral inflammatory diseases. Also, it is addressed the biological aspects of MDSCs in presence of major oral pathogens. MDSCs have been mainly analyzed in periodontal disease and Sjögren's syndrome and could be involved in the outcome of these diseases. Studies including the participation of MDSCs in other important oral diseases are very scarce. Major oral bacterial and fungal pathogens can modulate expansion, subpopulations, recruitment, metabolism, immunosuppressive activity and osteoclastogenic potential of MDSCs. Moreover, MDSC plasticity is exhibited in presence of oral inflammatory diseases/oral pathogens and appears to be relevant in the disease progression and potentially useful in the searching of possible treatments. Further analyses of MDSCs in oral cavity context could allow to understand the contribution of these cells in the fine-tuned balance between host immune system and microorganism of the oral biofilm, as well as their involvement in the development of oral diseases when this balance is altered.

LncRNA MALAT1 in Keratinocyte function: A review of recent advances.

Keratinocytes, the principal epidermal cells, play a vital role in maintaining the structural integrity and functionality of the skin. Beyond their protective role, keratinocytes are key contributors to the process of wound healing, as they migrate to injury sites, proliferate, and generate new layers of epidermis, facilitating tissue repair and remodeling. Moreover, keratinocytes actively participate in the skin's immune responses, expressing pattern recognition receptors (PRRs) to detect microbial components and interact with immune cells to influence adaptive immunity. Keratinocytes express a diverse repertoire of signaling pathways, transcription factors, and epigenetic regulators to regulate their growth, differentiation, and response to environmental cues. Among these regulatory elements, long non-coding RNAs (lncRNAs) have emerged as essential players in keratinocyte biology. LncRNAs, including MALAT1, play diverse roles in gene regulation and cellular processes, influencing keratinocyte proliferation, differentiation, migration, and response to environmental stimuli. Dysregulation of specific lncRNAs such as MALAT1 can disrupt keratinocyte homeostasis, leading to impaired differentiation, compromised barrier integrity, and contributing to the pathogenesis of various skin disorders. Understanding the intricate interplay between lncRNAs and keratinocytes offers promising insights into the molecular underpinnings of skin health and disease, with potential implications for targeted therapies and advancements in dermatological research. Hence, our objective is to provide a comprehensive summary of the available knowledge concerning keratinocytes and their intricate relationship with MALAT1.

Active Learning-Based Guided Synthesis of Engineered Biochar for CO2 Capture.

Biomass waste-derived engineered biochar for CO2 capture presents a viable route for climate change mitigation and sustainable waste management. However, optimally synthesizing them for enhanced performance is time- and labor-intensive. To address these issues, we devise an active learning strategy to guide and expedite their synthesis with improved CO2 adsorption capacities. Our framework learns from experimental data and recommends optimal synthesis parameters, aiming to maximize the narrow micropore volume of engineered biochar, which exhibits a linear correlation with its CO2 adsorption capacity. We experimentally validate the active learning predictions, and these data are iteratively leveraged for subsequent model training and revalidation, thereby establishing a closed loop. Over three active learning cycles, we synthesized 16 property-specific engineered biochar samples such that the CO2 uptake nearly doubled by the final round. We demonstrate a data-driven workflow to accelerate the development of high-performance engineered biochar with enhanced CO2 uptake and broader applications as a functional material.

Droplet-Based Microfluidics Reveals Insights into Cross-Coupling Mechanisms over Single-Atom Heterogeneous Catalysts.

Single-atom heterogeneous catalysts (SACs) hold promise as sustainable alternatives to metal complexes in organic transformations. However, their working structure and dynamics remain poorly understood, hindering advances in their design. Exploiting the unique features of droplet-based microfluidics, we present the first in-situ assessment of a palladium SAC based on exfoliated carbon nitride in Suzuki-Miyaura cross-coupling using X-ray absorption spectroscopy. Our results confirm a surface-catalyzed mechanism, revealing the distinct electronic structure of active Pd centers compared to homogeneous systems, and providing insights into the stabilizing role of ligands and bases. This study establishes a valuable framework for advancing mechanistic understanding of organic syntheses catalyzed by SACs.

CO2 Electroreduction To Syngas With Tunable Composition In An Artificial Leaf.

Invited for this month's cover is the group of Javier Pérez-Ramírez at ETH Zürich, which collaborated with the group of Tsvetelina Merdzhanova at Forschungszentrum Jülich. The image shows how artificial leaves, able to recycle carbon dioxide into syngas of variable composition, could be integrated with chemical plants. The Research Article itself is available at 10.1002/cssc.202301398.

CO2 Electroreduction To Syngas With Tunable Composition In An Artificial Leaf.

Artificial leaves (a-leaves) can reduce carbon dioxide into syngas using solar power and could be combined with thermo- and biocatalytic technologies to decentralize the production of valuable products. By providing variable CO : H2 ratios on demand, a-leaves could facilitate optimal combinations and control the distribution of products in most of these hybrid systems. However, the current design procedures of a-leaves concentrate on achieving high performance for a predetermined syngas composition. This study demonstrates that incorporating the electrolyte flow as a design variable enables flexible production without compromising performance. The concept was tested on an a-leaf using a commercial cell, a Cu2 O:Inx cathodic catalyst, and an inexpensive amorphous silicon thin-film photovoltaic module. Syngas with CO : H2 ratio in the range of 1.8-2.3 could be attained with only 2 % deviation from the optimal cell voltage and controllable solely by catholyte flow. These features could be beneficial for downstream technologies such as Fischer-Tropsch synthesis and anaerobic fermentation.