Hasil untuk "Organic chemistry"

Angelo Jamerlan, John Hulme

The cytoplasmic accumulation of TDP-43 aggregates remains a persistent pathological hallmark of neurodegenerative diseases, including amyotrophic lateral sclerosis (ALS), frontotemporal dementia (FTD), and limbic-predominant age-related TDP-43 encephalopathy (LATE). The cell’s natural clearance mechanisms, the Ubiquitin-Proteasome System (UPS) and the autophagy-lysosome pathway (ALP), are hypothesized to fail, at least in part, due to the sequestration of key components of these pathways by pathological TDP-43 species, thereby impairing autophagosome-lysosome fusion and lysosomal competence. Classical autophagic activators (e.g., rapamycin) can initiate upstream steps in the pathway but cannot address downstream flux bottlenecks, limiting their ability to restore effective TDP-43 clearance. This review revisits classical strategies and discusses newer approaches to modulate TDP-43 clearance, including transcription factor EB (TFEB) activators, proteolysis-targeting chimeras (PROTACs), and antisense oligonucleotides (ASOs). We propose that adopting multi-targeting strategies and developing better biomarkers are vital for clinical success.

Jaron Cohen, Alexander G. Hasson, Sara Tanovic

Since the advent of machine learning, interpretability has remained a persistent challenge, becoming increasingly urgent as generative models support high-stakes applications in drug and material discovery. Recent advances in large language model (LLM) architectures have yielded chemistry language models (CLMs) with impressive capabilities in molecular property prediction and molecular generation. However, how these models internally represent chemical knowledge remains poorly understood. In this work, we extend sparse autoencoder techniques to uncover and examine interpretable features within CLMs. Applying our methodology to the Foundation Models for Materials (FM4M) SMI-TED chemistry foundation model, we extract semantically meaningful latent features and analyse their activation patterns across diverse molecular datasets. Our findings reveal that these models encode a rich landscape of chemical concepts. We identify correlations between specific latent features and distinct domains of chemical knowledge, including structural motifs, physicochemical properties, and pharmacological drug classes. Our approach provides a generalisable framework for uncovering latent knowledge in chemistry-focused AI systems. This work has implications for both foundational understanding and practical deployment; with the potential to accelerate computational chemistry research.

A. K. Pandey, A. K. Pandey, D. S. Stevenson et al.

<p>We compare tropospheric column nitrogen dioxide (<span class="inline-formula">NO₂</span>) in the United Kingdom Chemistry and Aerosol (UKCA) model version 11.0 with satellite measurements from NASA's Earth Observing System (EOS) Aura satellite Ozone Monitoring Instrument (OMI) to investigate the seasonality and trends of tropospheric <span class="inline-formula">NO₂</span> over south and east Asia (S and E Asia). UKCA is the atmospheric composition component of the UK Earth System Model (UKESM). UKCA was run with nudged meteorology, producing hourly output over S and E Asia for 2005–2015. OMI averaging kernels have been applied to the model hourly data sampled at Aura's local overpass time of 13:45 LT <span class="inline-formula">±</span> 15 min to allow for consistent model–data comparison. Background UKCA and OMI tropospheric column <span class="inline-formula">NO₂</span> typically ranges between <span class="inline-formula">0×10¹⁵</span> and <span class="inline-formula">2×10¹⁵</span> <span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M8" display="inline" overflow="scroll" dspmath="mathml"><mrow class="unit"><mi mathvariant="normal">molec</mi><mo>.</mo><mspace linebreak="nobreak" width="0.125em"/><msup><mi mathvariant="normal">cm</mi><mrow><mo>-</mo><mn mathvariant="normal">2</mn></mrow></msup></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="60pt" height="13pt" class="svg-formula" dspmath="mathimg" md5hash="2754044258d9cb5ecfc3c4229425567e"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="acp-25-4785-2025-ie00001.svg" width="60pt" height="13pt" src="acp-25-4785-2025-ie00001.png"/></svg:svg></span></span>. Diurnal cycles and vertical profiles of the tropospheric <span class="inline-formula">NO₂</span> column in UKCA show that the daily minimum tropospheric column <span class="inline-formula">NO₂</span> occurs around the satellite overpass time. UKCA captures the seasonality but overestimates <span class="inline-formula">NO₂</span> by a factor of <span class="inline-formula">∼</span> 2.5, especially during winter over eastern China and north India, at times and locations with high aerosol loadings. Heterogeneous chemistry is represented in the version of UKCA used here as uptake of <span class="inline-formula">N₂O₅</span> on internally generated sulfate aerosol. However, aerosol surface area may be underestimated in polluted locations, contributing to overestimation of <span class="inline-formula">NO₂</span>. In addition, the model may underestimate emissions of volatile organic compounds (VOCs) and associated peroxy acetyl nitrate (PAN) formation, leading to insufficient long-range transport of oxidised nitrogen and also contributing to overestimation of <span class="inline-formula">NO₂</span> over polluted regions and underestimation over remote regions. Quantifying and understanding discrepancies in modelled <span class="inline-formula">NO₂</span> warrant further investigation as they propagate into modelling of multiple environmental issues.</p>

Andrew D. McNaughton, Gautham Ramalaxmi, Agustin Kruel et al.

Large language models (LLMs) have shown remarkable potential in various domains, but they often lack the ability to access and reason over domain-specific knowledge and tools. In this paper, we introduced CACTUS (Chemistry Agent Connecting Tool-Usage to Science), an LLM-based agent that integrates cheminformatics tools to enable advanced reasoning and problem-solving in chemistry and molecular discovery. We evaluate the performance of CACTUS using a diverse set of open-source LLMs, including Gemma-7b, Falcon-7b, MPT-7b, Llama2-7b, and Mistral-7b, on a benchmark of thousands of chemistry questions. Our results demonstrate that CACTUS significantly outperforms baseline LLMs, with the Gemma-7b and Mistral-7b models achieving the highest accuracy regardless of the prompting strategy used. Moreover, we explore the impact of domain-specific prompting and hardware configurations on model performance, highlighting the importance of prompt engineering and the potential for deploying smaller models on consumer-grade hardware without significant loss in accuracy. By combining the cognitive capabilities of open-source LLMs with domain-specific tools, CACTUS can assist researchers in tasks such as molecular property prediction, similarity searching, and drug-likeness assessment. Furthermore, CACTUS represents a significant milestone in the field of cheminformatics, offering an adaptable tool for researchers engaged in chemistry and molecular discovery. By integrating the strengths of open-source LLMs with domain-specific tools, CACTUS has the potential to accelerate scientific advancement and unlock new frontiers in the exploration of novel, effective, and safe therapeutic candidates, catalysts, and materials. Moreover, CACTUS's ability to integrate with automated experimentation platforms and make data-driven decisions in real time opens up new possibilities for autonomous discovery.

Debarshi Banerjee, Khatereh Azizi, Colin K. Egan et al.

The use of computer simulations to study the properties of aqueous systems is, today more than ever, an active area of research. In this context, during the last decade there has been a tremendous growth in the use of data-driven approaches to develop more accurate potentials for water as well as to characterize its complexity in chemical and biological contexts. We highlight the progress, giving a historical context, on the path to the development of many-body and reactive potentials to model aqueous chemistry, including the role of machine learning strategies. We focus specifically on conceptual and methodological challenges along the way in performing simulations that seek to tackle problems in modeling the chemistry of aqueous solutions. In conclusion, we summarize our perspectives on the use and integration of advanced data-science techniques to provide chemical insights in physical chemistry and how this will influence computer simulations of aqueous systems in the future.

Junyi Li, Ye Liu, Rui Zhang et al.

Abstract With the development of the economy and the increasing prevalence of skin problems, cutaneous medical aesthetics are gaining more and more attention. Skin disorders like poor wound healing, aging, and pigmentation have an impact not only on appearance but also on patients with physical and psychological issues, and even impose a significant financial burden on families and society. However, due to the complexities of its occurrence, present treatment options cannot produce optimal outcomes, indicating a dire need for new and effective treatments. Mesenchymal stem cells (MSCs) and their secretomics treatment is a new regenerative medicine therapy that promotes and regulates endogenous stem cell populations and/or replenishes cell pools to achieve tissue homeostasis and regeneration. It has demonstrated remarkable advantages in several skin-related in vivo and in vitro investigations, aiding in the improvement of skin conditions and the promotion of skin aesthetics. As a result, this review gives a complete description of recent scientific breakthroughs in MSCs for skin aesthetics and the limitations of their clinical applications, aiming to provide new ideas for future research and clinical transformation.

Harrison Nicholls, Eric Hébrard, Olivia Venot et al.

The effect of enhanced UV irradiation associated with stellar flares on the atmospheric composition and temperature of gas giant exoplanets was investigated. This was done using a 1D radiative-convective-chemical model with self-consistent feedback between the temperature and the non-equilibrium chemistry. It was found that flare-driven changes to chemical composition and temperature give rise to prolonged trends in evolution across a broad range of pressure levels and species. Allowing feedback between chemistry and temperature plays an important role in establishing the quiescent structure of these atmospheres, and determines their evolution due to flares. It was found that cooler planets are more susceptible to flares than warmer ones, seeing larger changes in composition and temperature, and that temperature-chemistry feedback modifies their evolution. Long-term exposure to flares changes the transmission spectra of gas giant atmospheres; these changes differed when the temperature structure was allowed to evolve self-consistently with the chemistry. Changes in spectral features due to the effects of flares on these atmospheres can be associated with changes in composition. The effects of flares on the atmospheres of sufficiently cool planets will impact observations made with JWST. It is necessary to use self-consistent models of temperature and chemistry in order to accurately capture the effects of flares on features in the transmission spectra of cooler gas giants, but this depends heavily on the radiation environment of the planet.

Emilie Panek, Jean-Philippe Beaulieu, Pierre Drossart et al.

Studying chemistry and chemical composition is fundamental to go back to formation history of planetary systems. We propose here to have another look at five targets to better determine their composition and the chemical mechanisms that take place in their atmospheres. We present a re-analysis of five Hot Jupiters, combining multiple instruments and using Bayesian retrieval methods. We compare different combinations of molecules present in the simulated atmosphere, different chemistry types as well as different clouds parametrization. As a consequence of recent studies questioning the detection of Na and K in the atmosphere of HD 209458b as being potentially contaminated by stellar lines when present, we study the impact on other retrieval parameters of misinterpreting the presence of these alkali species. We use spatially scanned observations from the grisms G102 and G141 of the WFC3 on HST, with a wavelength coverage of $\sim$0.8 to $\sim$1.7 microns. We analyse these data with the publicly available Iraclis pipeline. We added to our datasets STIS observations to increase our wavelength coverage from $\sim$0.4 to $\sim$1.7 microns. We then performed a Bayesian retrieval analysis with the open-source TauREx using a nested sampling algorithm. We explore the influence of including Na and K on the retrieval of the molecules from the atmosphere. Our data re-analysis and Bayesian retrieval are consistent with previous studies but we find small differences in the retrieved parameters. After all, Na and K has no significant impact on the properties of the planet atmospheres. Therefore, we present here our new best-fit models, taking into account molecular abundances varying freely and equilibrium chemistry. This work is a preparation for a future addition of more sophisticated representation of chemistry taking into account disequilibrium effects such as vertical mixing and photochemistry.

NAYAB RAZA, Mehreen Raza, ZARYAB RAZA et al.

United Nations Sustainable Goals. 06 emphasis on unbiased and even access of water and basic water sanitation and health sciences (WASH) facilities but, relentless reality is polar opposite where a stellar portion especially children are devoid of necessity facilities, especially in Pakistan. The most vulnerable group i-e children deprived of WASH facilities. The main objective of the study was, a WASH survey was conducted in two school settings i.e., Mehran and Sindh primary schools, using 100 forms comprising 21 Closed-ended questions directly linked with WASH facilities. The response was gathered from both boys and girls to maintain equity. Survey questionnaires are drafted as per international guild lines. The survey focused mainly on four dimensions: Handwashing, toilet, drinking water facilities, and Hygiene practice showing average (%) responses of boys to girls as 17.4:20.3, 29:14.6, 33.6:21, and 20.6:25.8, respectively showed the condition of mentioned dimensions improved in Mehran School rather than that of Sindh School. Bacteria were observed in water samples under a Fluorescence microscope that confirmed the presence of various bacteria species namely: Shigella, Escherichia coli, Vibrio, Salmonella, Cryptosporidium, Staphylococcus spp. Conclusively, there is a dire need to upgrade the WASH policy parallel to current scenario and need of society to lessen the severity of the problems, especially children facing in developing country like Pakistan.

José Escurra, Francisco P. Ferreira, Tomás R. López et al.

Lactic acid is a compound used industrially due to its properties. There are two methods for its production: chemical synthesis and microbial fermentation. In microbial fermentation, food industry waste can be used as a substrate, providing a route towards achieving a circular economy. Thus, this study evaluated different substrates for <i>Lactobacillus plantarum</i> growth, a lactic acid producer, such as molasses, whey, glucose, and saccharose, either alone or supplemented with additional nutrients. Bacterial growth parameters were assessed using OD₆₂₀ measurement. It was shown that whey supplemented with yeast extract supported the best growth, allowing a μ_max = 0.63 h⁻¹.

Neelam Iqbal, Tejal Pant, Nanda Rohra et al.

Bone regeneration and repair are complex processes with the potential of added complications, like delayed repair, fracture non-union, and post-surgical infections. These conditions remain a challenge globally, pressurizing the economy and patients suffering from these conditions. Applications of nanotechnology (NBT) in the field of medicine have provided a medium for several approaches to support these global challenges. Tissue engineering is one such field that has been on the rise in the last three decades through the utilization of NBT for addressing the challenges related to bone regeneration. First, NBT enables the formation of scaffolds at the nanoscale needed for bone tissue engineering (BTE) using natural and synthetic polymers, as well as with minerals and metals. Then, it aids the development of the nano-formulation strategized to deliver antimicrobial drugs and/or growth factors through various ways to enhance bone repair through the scaffold. Third, NBT facilitates the use of specialized nanoparticles to image and track cellular events in vitro as well as in vivo. This review is an effort to bring together the current knowledge in the field of BTE and present the scope of ever-evolving NBT, a contribution towards precision medicine.

Ioanna Aggeletopoulou, Efthymios P. Tsounis, Athanasia Mouzaki et al.

The microbiome has a major impact on human physiology and plays a critical role in enhancing or impairing various physiological functions such as regulation of the immune system, metabolic activities, and biosynthesis of vitamins and hormones. Variations in the gut microbial community play a critical role in both health and disease. Regulation of calcium and bone metabolism, as well as cellular functions such as proliferation, apoptosis, differentiation, and immune modulation, are among the known effects of vitamin D. These biological functions are primarily carried out through the binding of vitamin D to the vitamin D receptor (VDR), a member of the nuclear receptor superfamily. The immunomodulatory properties of vitamin D suggest that this molecule plays an important role in various diseases. Maintenance of immune homeostasis appears to occur in part through the interaction of the gut microbiota with vitamin D. Increasing evidence points to the central role of vitamin D in maintaining mucosal barrier function, as vitamin D deficiency has been associated with disruption of gut barrier integrity, translocation of bacteria into the bloodstream, and systemic inflammation. In parallel, a bidirectional interaction between vitamin D and the gut microbiota has been demonstrated as data show upregulation of intestinal VDR expression and downregulation of inflammatory markers in response to fermentation products. The aim of this review is to provide an overview of the evidence of a link between the gut microbiome and vitamin D, with a focus on data from experimental models and translational data from human studies related to vitamin D-induced changes in gut microbiota composition.

Joonho Lee, Hung Q. Pham, David R. Reichman

In this work, we present an overview of the phaseless auxiliary-field quantum Monte Carlo (ph- AFQMC) approach from a computational quantum chemistry perspective, and present a numerical assessment of its performance on main group chemistry and bond-breaking problems with a total of 1004 relative energies. While our benchmark study is somewhat limited, we make recommendations for the use of ph-AFQMC for general main-group chemistry applications. For systems where single determinant wave functions are qualitatively accurate, we expect the accuracy of ph-AFQMC in conjunction with a single determinant trial wave function to be between that of coupled-cluster with singles and doubles (CCSD) and CCSD with perturbative triples (CCSD(T)). For these applications, ph-AFQMC should be a method of choice when canonical CCSD(T) is too expensive to run. For systems where multi-reference (MR) wave functions are needed for qualitative accuracy, ph-AFQMC is far more accurate than MR perturbation theory methods and competitive with MR configuration interaction (MRCI) methods. Due to the computational efficiency of ph-AFQMC compared to MRCI, we recommended ph-AFQMC as a method of choice for handling dynamic correlation in MR problems. We conclude with a discussion of important directions for future development of the ph-AFQMC approach.

Jan Hermann, James Spencer, Kenny Choo et al.

Machine learning and specifically deep-learning methods have outperformed human capabilities in many pattern recognition and data processing problems, in game playing, and now also play an increasingly important role in scientific discovery. A key application of machine learning in the molecular sciences is to learn potential energy surfaces or force fields from ab-initio solutions of the electronic Schrödinger equation using datasets obtained with density functional theory, coupled cluster, or other quantum chemistry methods. Here we review a recent and complementary approach: using machine learning to aid the direct solution of quantum chemistry problems from first principles. Specifically, we focus on quantum Monte Carlo (QMC) methods that use neural network ansatz functions in order to solve the electronic Schrödinger equation, both in first and second quantization, computing ground and excited states, and generalizing over multiple nuclear configurations. Compared to existing quantum chemistry methods, these new deep QMC methods have the potential to generate highly accurate solutions of the Schrödinger equation at relatively modest computational cost.

Shaojun Guo, Jinzhao Sun, Haoran Qian et al.

Quantum computational chemistry has emerged as an important application of quantum computing. Hybrid quantum-classical computing methods, such as variational quantum eigensolvers (VQE), have been designed as promising solutions to quantum chemistry problems, yet challenges due to theoretical complexity and experimental imperfections hinder progress in achieving reliable and accurate results. Experimental works for solving electronic structures are consequently still restricted to nonscalable (hardware efficient) or classically simulable (Hartree-Fock) ansatz, or limited to a few qubits with large errors. The experimental realisation of scalable and high-precision quantum chemistry simulation remains elusive. Here, we address the critical challenges {associated with} solving molecular electronic structures using noisy quantum processors. Our protocol presents significant improvements in the circuit depth and running time, key metrics for chemistry simulation. Through systematic hardware enhancements and the integration of error mitigation techniques, we push forward the limit of experimental quantum computational chemistry and successfully scale up the implementation of VQE with an optimised unitary coupled-cluster ansatz to 12 qubits. We produce high-precision results of the ground-state energy for molecules with error suppression by around two orders of magnitude. We achieve chemical accuracy for H$_2$ at all bond distances and LiH at small bond distances in the experiment, even beyond the two recent concurrent works. Our work demonstrates a feasible path towards a scalable solution to electronic structure calculation, validating the key technological features and identifying future challenges for this goal.

Michael Ruggenthaler, Dominik Sidler, Angel Rubio

In this review we present the theoretical foundations and first principles frameworks to describe quantum matter within quantum electrodynamics (QED) in the low-energy regime. Having a rigorous and fully quantized description of interacting photons, electrons and nuclei/ions, from weak to strong light-matter coupling regimes, is pivotal for a detailed understanding of the emerging fields of polaritonic chemistry and cavity materials engineering. The use of rigorous first principles avoids ambiguities and problems stemming from using approximate models based on phenomenological descriptions of light, matter and their interactions. By starting from fundamental physical and mathematical principles, we first review in great detail non-relativistic QED, which allows to study polaritonic systems non-perturbatively by solving a Schrödinger-type equation. The resulting Pauli-Fierz quantum field theory serves as a cornerstone for the development of computational methods, such as quantum-electrodynamical density functional theory, QED coupled cluster or cavity Born-Oppenheimer molecular dynamics. These methods treat light and matter on equal footing and have the same level of accuracy and reliability as established methods of computational chemistry and electronic structure theory. After an overview of the key-ideas behind those novel ab initio QED methods, we explain their benefits for a better understanding of photon-induced changes of chemical properties and reactions. Based on results obtained by ab initio QED methods we identify the open theoretical questions and how a so far missing mechanistic understanding of polaritonic chemistry can be established. We finally give an outlook on future directions within polaritonic chemistry and first principles QED and address the open questions that need to be solved in the next years both from a theoretical as well as experimental viewpoint.

Tomasz Ligor, Przemysław Adamczyk, Tomasz Kowalkowski et al.

Bladder cancer is one of most common types of cancer diagnosed in the genitourinary tract. Typical tests are costly and characterized by low sensitivity, which contributes to a growing interest in volatile biomarkers. Head space solid phase microextraction (SPME) was applied for the extraction of volatile organic compounds from urine samples, and gas chromatography time of flight mass spectrometry (GC×GC TOF MS) was used for the separation and detection of urinary volatiles. A cohort of 40 adult patients with bladder cancer and 57 healthy persons was recruited. Different VOC profiles were obtained for urine samples taken from each group. Twelvecompounds were found only in the samples from theBC group.The proposed candidate biomarkers are butyrolactone; 2-methoxyphenol; 3-methoxy-5-methylphenol; 1-(2,6,6-trimethylcyclohexa-1,3-dien-1-yl)-2-buten-1-one; nootkatone and 1-(2,6,6-trimethyl-1-cyclohexenyl)-2-buten-1-one.Since most of the studies published in the field are proving the potential of VOCs detected in urine samples for the screening and discrimination of patients with bladder cancer from healthy, but rarely presenting the identity of proposed biomarkers, our study represents a novel approach.

Daniel B. Straus, Cherie R. Kagan

2D organic-inorganic hybrid perovskites (2DHPs) consist of alternating anionic metal-halide and cationic organic layers. They have widely tunable structural and optical properties. We review the role of the organic cation in defining the structural and optical properties of 2DHPs through example lead iodide 2DHPs. Even though excitons reside in the metal halide layers, the organic and inorganic frameworks cannot be separated-they must be considered as a single unit to fully understand the photophysics of 2DHPs. We correlate cation-induced distortion and disorder in the inorganic lattice with the resulting optical properties. We also discuss the role of the cation in creating and altering the discrete excitonic structure that appears at cryogenic temperatures in some 2DHPs, including the cation-dependent presence of hot exciton photoluminescence. We conclude our review with an outlook for 2DHPs, highlighting existing gaps in fundamental knowledge as well as potential future applications.

Leszek Potocki, Bernadetta Oklejewicz, Ewelina Kuna et al.

As microalgae are producers of proteins, lipids, polysaccharides, pigments, vitamins and unique secondary metabolites, microalgal biotechnology has gained attention in recent decades. Microalgae can be used for biomass production and to obtain biotechnologically important products. Here, we present the application of a method of producing a natural, biologically active composite obtained from unicellular microalgae of the genus <i>Planktochlorella</i> sp. as a modulator of the growth of microorganisms that can be used in the cosmetics and pharmaceutical industries by exploiting the phenomenon of photo-reprogramming of metabolism. The combination of red and blue light allows the collection of biomass with unique biochemical profiles, especially fatty acid composition (Patent Application P.429620). The ethanolic and water extracts of algae biomass inhibited the growth of a number of pathogenic bacteria, namely <i>Enterococcus faecalis</i>, <i>Staphylococcus aureus</i> PCM 458, <i>Streptococcus pyogenes</i> PCM 2318, <i>Pseudomonas aeruginosa</i>, <i>Escherichia coli</i> PCM 2209 and <i>Candida albicans</i> ATCC 14053. The algal biocomposite obtained according to our procedure can be used also as a prebiotic supplement. The presented technology may allow the limitation of the use of antibiotics and environmentally harmful chemicals commonly used in preparations against <i>Enterococcus faecalis</i>, <i>Staphylococcus aureus</i>, <i>Streptococcus pyogenes</i>, <i>Pseudomonas aeruginosa</i>, <i>Escherichia coli</i> or <i>Candida</i> spp.