Armando Alcázar Magaña, N. Kamimura, A. Soumyanath
et al.
Caffeoylquinic acids (CQAs) are specialized plant metabolites we encounter in our daily life. Humans consume CQAs in mg-to-gram quantities through dietary consumption of plant products. CQAs are considered beneficial for human health, mainly due to their anti-inflammatory and antioxidant properties. Recently, new biosynthetic pathways via a peroxidase-type p-coumaric acid 3-hydroxylase enzyme were discovered. More recently, a new GDSL lipase-like enzyme able to transform mono-caffeoylquinic acids into di-caffeoylquinic acid was identified in Ipomoea batatas. CQAs were recently linked to memory improvement; they seem to be strong indirect antioxidants via Nrf2 activation. However, there is a prevalent confusion in the designation and nomenclature of different CQAs isomers. Such inconsistencies are critical and complicate bioactivity assessment since different isomers differ in bioactivity and potency. A detailed explanation regarding the origin of such confusion is provided, and a recommendation to unify nomenclature is suggested. Furthermore, for studies on CQA bioactivity, plant-based laboratory animal diets contain CQAs, which makes it difficult to include proper control groups for comparison. Therefore, a synthetic diet free of CQAs is advised to avoid interferences since some CQAs may produce bioactivity even at nanomolar levels. Biotransformation of CQAs by gut microbiota, the discovery of new enzymatic biosynthetic and metabolic pathways, dietary assessment, and assessment of biological properties with potential for drug development are areas of active, ongoing research. This review is focused on the chemistry, biosynthesis, occurrence, analytical challenges, and bioactivity recently reported for mono, di, tri, and tetracaffeoylquinic acids.
Abstract Developing new green solvents is one of the key subjects in Green Chemistry. Ionic liquids and deep eutectic solvents were discovered as an option to replace organic solvents. However, ionic liquids and deep eutectic solvents (DES) have still some limitations to be applied to real chemical industry. In this sense, a new generation of designer solvents have emerged in the last decade as promising green media. When the compounds that constitute DES are primary metabolites, namely, aminoacids, organic acids, sugars, or choline derivatives, DES are called Natural Deep Eutectic Solvents (NADES). NADES fully represent green chemistry principles. These solvents offer many striking advantages including biodegradability, low toxicity, solute stabilization, sustainability, low costs and simple preparation. Thus, interesting applications in health-related areas can be proposed. This review presents an overview in order to up-to-date knowledge regarding NADES with special emphasis on their analytical applications and further perspectives as truly green solvents.
Computational chemistry has become an indispensable tool for generating data and insights, pervading all branches of experimental chemistry. Its most central concept is the potential energy hypersurface, key to all chemistry and materials science, as it assigns an energy to a molecular structure, the necessary ingredient for reaction mechanism elucidation and reaction rate calculation. Density functional theory (DFT) has been the most important method in practice for obtaining such energies, which is mirrored in the use of high-performance computing hardware. In the last two decades, a new class of surrogate potential energy functions has been evolving with remarkable properties: quantum accuracy combined with force-field speed. Until very recently, their application was hampered by the fact that they needed to be trained on truly large system-specific data sets, generated before a computational chemistry study could be started (in sharp contrast to DFT, which, as a first-principles method, works out of the box, but at a far higher price of computational cost). Very recently, this roadblock has been overcome by so-called foundation machine learning interatomic potentials, which are poised to completely change the way we do computational chemistry, likely prompting us to abandon DFT as the prime method of choice for this purpose in less than a decade.
Objective:
To evaluate and compare the retention strengths of prefabricated posts cemented using zinc phosphate (ZP), glass ionomer (GIC), and resin cements (RC).
Methodology:
About 60 extracted human mandibular premolars were decoronated to a standardized length and endodontically treated. Post spaces were prepared to a depth of 10 mm, were grouped into 20 each. Prefabricated metal posts were cemented according to the manufacturers’ instructions. Tensile forces until post dislodgment were measured after one day.
Results:
Mean retention strengths were: Group A (ZP)—124 N, Group B (GIC)—336.86 N, and Group C (RC)—1262.51 N. RC exhibited significantly higher retention than both GIC and ZP cements, and GIC cement showed significantly higher retention than ZP cement.
Conclusion:
The type of luting cement significantly affects the retention of prefabricated posts. RC demonstrated the highest retention strength, suggesting its superiority for clinical applications requiring enhanced post-retention.
Stable isotopes have frequently been used to study metabolic processes in live cells both in vitro and in vivo. Glutamine, the most abundant amino acid in human blood, plays multiple roles in cellular metabolism by contributing to the production of nucleotides, lipids, glutathione, and other amino acids. It also supports energy production via anaplerosis of tricarboxylic acid cycle intermediates. While 13C-glutamine has been extensively employed to study glutamine metabolism in various cell types, detailed analyses of specific lipids derived from 13C-glutamine via the reductive carboxylation pathway are limited. In this protocol, we present a detailed procedure to investigate glutamine metabolism in human glioblastoma (GBM) cells by conducting 13C-glutamine tracing coupled with untargeted metabolomics analysis using liquid chromatography–mass spectrometry (LC–MS/MS). The method includes step-by-step instructions for the extraction and detection of polar metabolites and long-chain fatty acids (LCFAs) derived from 13C-glutamine in GBM cells. Notably, this approach enables the distinction between isomers of two monounsaturated FAs with identical masses: palmitoleic acid (16:1n-7) (cis-9-hexadecenoic acid) and palmitelaidic acid (16:1n-7) (trans-9-hexadecenoic acid) derived from 13C-glutamine through the reductive carboxylation process. In addition, using this protocol, we also unveil previously unknown metabolic alterations in GBM cells following lysosome inhibition by the antipsychotic drug pimozide.
CHEMSMART (Chemistry Simulation and Modeling Automation Toolkit) is an open-source, Python-based framework designed to streamline quantum chemistry workflows for homogeneous catalysis and molecular modeling. By integrating job preparation, submission, execution, results analysis, and visualization, CHEMSMART addresses the inefficiencies of manual workflow management in computational chemistry by ensuring seamless interoperability with quantum chemistry packages and cheminformatics platforms. Its modular architecture supports automated job submission and execution tasks for geometry optimization, transition state searches, thermochemical analysis, and non-covalent interaction plotting, while auxiliary scripts facilitate file conversion, data organization, and electronic structure analysis. Future developments aim to expand compatibility with additional software, incorporate QM/MM and classical MD, and align with FAIR data principles for enhanced reproducibility and data reuse. Available on GitHub, CHEMSMART empowers researchers with a robust, user-friendly platform for efficient and reproducible computational chemistry.
Gas and dust in the Galactic Center are subjected to energetic processing by intense UV radiation fields, widespread shocks, enhanced rates of cosmic-rays and X-rays, and strong magnetic fields. The Giant Molecular Clouds in the Galactic Center present a rich chemistry in a wide variety of chemical compounds, some of which are prebiotic. We have conducted unbiased, ultrasensitive and broadband spectral surveys toward the G+0.693-0.027 molecular cloud located in the Galactic Center, which have yielded the discovery of new complex organic molecules proposed as precursors of the "building blocks" of life. I will review our current understanding of the chemistry in Galactic Center molecular clouds, and summarize the recent detections toward G+0.693-0.027 of key precursors of prebiotic chemistry. All this suggests that the ISM is an important source of prebiotic material that could have contributed to the process of the origin of life on Earth and elsewhere in the Universe.
Green analytical chemistry, although not being a new concept, does not have a greenness metrics system. Green chemistry metrics are not suitable for analytical procedure assessment because they often refer to the mass of the product. Some efforts have been made to develop suitable metrics for analytical chemistry. Some are simple to use but do not cover all aspects of analytical methods’ environmental impact. Others are more comprehensive but may be difficult to be applied. The analytical reagents were not assessed but some clues about their greenness can be obtained from assessments from other branches of chemistry. New reagents and solvents applied in analytical chemistry require their detailed assessment in terms of greenness. Environmental issues have to be taken into consideration during reagent and solvent selection, analytical waste disposal practices, the energetic requirements of analytical processes and the development or selection of analytical procedures, and, for that reason, metrics systems are required.
Saurabh RamBihariLal Shrivastava, Gandes Retno Rahayu, Titi Savitri Prihatiningsih
Background:
Participatory rural appraisal (PRA) methods have been acknowledged as important tools to involve members of the community in the process of identification of their problems, the factors contributing to the development of these problems, and ways by which these problems can be resolved.
Materials and Methods:
A quasi-experimental study will be conducted in two stages among first professional phase medical students. In the first stage, these students will be trained on PRA methods by the trained teachers, while in the second stage, these students will implement PRA methods in the local community. The entire training process and its different components will be evaluated using validated study tools (semi-structured questionnaire), which will be administered using Google Forms. The statistical analysis will be performed using frequency and percentages, and a paired t-test will be used to compare the change in knowledge before and after training.
Conclusion:
In conclusion, first professional phase medical students must be trained in PRA methods and subsequently supervised to monitor their change in behavior. However, as the success of the entire program will depend on the quality of training imparted to medical students, the training program must be evaluated from the perspective of students, community, and teachers.
Tapas K. Sarkar, Divya Pandya, Mohit Sharma
et al.
Objective:
To evaluate patient satisfaction and acceptance of acupuncture in dental practice.
Methods:
This randomized controlled study involved 30 participants (15 males, 15 females, mean age 38.2 years) requiring mandibular third molar extraction. Patients were randomly assigned to receive either real or placebo acupuncture. Pain intensity was assessed using a 4-point scale, and patients’ acceptance of and satisfaction with acupuncture were measured. The study was conducted at an oral and maxillofacial surgery clinic.
Results:
Toothache was the most common dental issue (16.7%). Participants showed moderate to high levels of acceptance toward acupuncture (mean score of 3.37 on a 4-point scale) and satisfaction with the treatment (mean score of 3.50).
Conclusion:
The study demonstrates a positive reception of acupuncture in dental settings, with moderate to high levels of patient acceptance and satisfaction. These findings suggest that acupuncture could be a valuable adjunctive therapy in comprehensive dental care, particularly for pain management and anxiety reduction. However, further research is needed to fully establish its efficacy across various dental procedures.
Background:
Afterpain is a common discomfort felt by most of the postpartum mothers which is occurred due to delayed involution. Kegel exercise and prone position enhance the involution.
Aim:
The aim of the study was to determine the effectiveness of Kegel exercise and prone position on afterpain among postpartum mothers.
Material and Methods:
A Quasi-experimental pretest post-test with control group research design was used. Using nonprobability purposive sampling technique recruited 60 postpartum mothers who met the criteria. The level of afterpain was assessed by using visual analog scale. Experimental group was educated to do Kegel exercise for 3 days and lie in prone position for 3–5 minutes, 3 times for 3 days. Control group follows routine postnatal care posttest was conducted.
Results:
The study results concluded that the afterpain mean difference was 0.53, 1.70, 1.80, and 4.03. The calculated paired t-test value of t = 5.757 between pretest and post-test 1, t = 12.420 between post-test 1 and post-test 2, t = 12.953 between post-test 2 and post-test 3, and t = 21.378 between pretest and post-test 3 was found to be statistically significant at P < 0.001 level.
Conclusion:
Therefore, Kegel exercise and prone position was effective in reducing afterpain during postpartum period.
Costanza Cucci, Tommaso Guidi, Marcello Picollo
et al.
Abstract The study aims at investigating the use of reflectance Hyperspectral Imaging (HSI) in the Visible (Vis) and Near Infrared (NIR) range in combination with Deep Convolutional Neural Networks (CNN) to address the tasks related to ancient Egyptian hieroglyphs recognition. Recently, well-established CNN architectures trained to address segmentation of objects within images have been successfully tested also for trial sets of hieroglyphs. In real conditions, however, the surfaces of the artefacts can be highly degraded, featuring corrupted and scarcely readable inscriptions which highly reduce the CNNs capabilities in automated recognition of symbols. In this study, the use of HSI technique in the extended Vis-NIR range is proposed to retrieve readability of degraded symbols by exploiting spectral images. Using different algorithmic chains, HSI data are processed to obtain enhanced images to be fed to the CNN architectures. In this pilot study, an ancient Egyptian coffin (XXV Dynasty), featuring a degraded hieroglyphic inscription, was used as a benchmark to test, in real conditions, the proposed methodological approaches. A set of Vis-NIR HSI data acquired on-site, in the framework of a non-invasive diagnostic campaign, was used in combination with CNN architectures to perform hieroglyphs segmentation. The outcomes of the different methodological approaches are presented and compared to each other and to the results obtained using standard RGB images.
Salma Mortada, Khalid Karrouchi, El Hadki Hamza
et al.
Abstract In this study, a two pyrazole derivatives; 2-(5-methyl-1H-pyrazole-3-carbonyl)-N-phenylhydrazine-1-carboxamide (Pyz-1) and 4-amino-5-(5-methyl-1H-pyrazol-3-yl)-4H-1,2,4-triazole-3-thiol (Pyz-2) were synthesized and characterized by 13C-NMR, 1H-NMR, FT-IR, and mass spectrometry. A complete molecular structures optimization, electronic and thermodynamic properties of Pyz-1 and Pyz-2 in gas phase and aqueous solution were predicted by using hybrid B3LYP method with the 6-311++G** basis sets. Pyz-1 and Pyz-2 were evaluated in vitro for their anti-diabetic, antioxidant and xanthine oxidase inhibition activities. For anti-diabetic activity, Pyz-1 and Pyz-2 showed a potent α-glucosidase and α-amylase inhibition with IC50 values of 75.62 ± 0.56, 95.85 ± 0.92 and 119.3 ± 0.75, 120.2 ± 0.68 µM, respectively, compared to Acarbose (IC50(α-glucosidase) = 72.58 ± 0.68 µM, IC50(α-amylase) = 115.6 ± 0.574 µM). In xanthine oxidase assay, Pyz-1 and Pyz-2 exhibited remarkable inhibitory ability with IC50 values 24.32 ± 0.78 and 10.75 ± 0.54 µM, respectively. The result of antioxidant activities showed that the title compounds have considerable antioxidant and radical scavenger abilities. In addition, molecular docking simulation was used to determine the binding modes and energies between the title compounds and α-glucosidase and α-amylase enzymes.
Paul J. Robinson, Adam Rettig, Hieu Q. Dinh
et al.
Molecular quantum chemistry has seen enormous progress in the last few decades thanks to the more advanced and sophisticated numerical techniques and computing power. Following the recent interest in extending these capabilities to condensed-phase problems, we summarize basic knowledge of condensed-phase quantum chemistry for ones with experience in molecular quantum chemistry. We highlight recent efforts in this direction, including solving the electron repulsion integrals bottleneck and implementing hybrid density functional theory and wavefunction methods, and lattice dynamics for periodic systems within atom-centered basis sets. Many computational techniques presented here are inspired by the extensive method developments rooted in quantum chemistry. In this Focus Article, we selectively focus on the computational techniques rooted in molecular quantum chemistry, emphasize some challenges, and point out open questions. We hope our perspectives will encourage researchers to pursue this exciting and promising research avenue.
Adrian Lutsch, Muhammad El-Hindi, Matthias Heinrich
et al.
Trusted Execution Environments (TEEs), such as Intel's Software Guard Extensions (SGX), are increasingly being adopted to address trust and compliance issues in the public cloud. Intel SGX's second generation (SGXv2) addresses many limitations of its predecessor (SGXv1), offering the potential for secure and efficient analytical cloud DBMSs. We assess this potential and conduct the first in-depth evaluation study of analytical query processing algorithms inside SGXv2. Our study reveals that, unlike SGXv1, state-of-the-art algorithms like radix joins and SIMD-based scans are a good starting point for achieving high-performance query processing inside SGXv2. However, subtle hardware and software differences still influence code execution inside SGX enclaves and cause substantial overheads. We investigate these differences and propose new optimizations to bring the performance inside enclaves on par with native code execution outside enclaves.
Understanding the combustion chemistry of acetaldehyde is crucial to developing robust and accurate combustion chemistry models for practical fuels, especially for biofuels. This study aims to reevaluate the important rate and thermodynamic parameters for acetaldehyde combustion chemistry. The rate parameters of 79 key reactions are reevaluated using more than 100,000 direct experiments and quantum chemistry computations from >900 studies, and the thermochemistry (Δhf(298K), s0(298K) and cp) of 24 key species are reevaluated based on the ATCT database, the NIST Chemistry WebBook, the TMTD database, and 35 published chemistry models. The updated parameters are incorporated into a recent acetaldehyde chemistry model, which is further assessed against available fundamental experiments (123 ignition delay times and 385 species concentrations) and existing chemistry models, with clearly better performance obtained in the high-temperature regime. Sensitivity and flux analyses further highlight the insufficiencies of previous models in representing the key pathways, particularly the branching ratios of acetaldehyde- and formaldehyde-consuming pathways. Temperature-dependent and temperature-independent uncertainties are statistically evaluated for kinetic and thermochemical parameters, respectively, where the large differences between the updated and the original model parameters reveal the necessity of reassessment of kinetic and thermochemical parameters completely based on direct experiments and theoretical calculations for rate and thermodynamic parameters.
Abstract 3D printing has revolutionized the concept of object manufacturing, making an enormous impact on industry and economy. The technology has found a niche in countless fields, including scientific research. It has rendered practical solutions to scientific problems by offering tailored-shaped devices with exquisite control in design and geometry and through the versatility of printable materials. Applications in analytical and bioanalytical chemistry have been on the rise, with microfluidics being one of the most represented areas of 3D printing towards this chemistry branch. Most stages of the analytical workflow comprising sample collection, pre-treatment and readout, have been enabled by 3D-printed components. Sensor fabrication for detecting explosives and nerve agents, the construction of microfluidic platforms for pharmacokinetic profiling, bacterial separation and genotoxicity screening, the assembly of parts for an on-site equipment for nucleic acid-based detection, the manufacturing of an online device for in vivo detection of metabolites, represent just a few examples of how additive manufacturing technologies have aided the field of (bio)analytical chemistry. In this review, we summarize the most relevant trends of 3D printing applications in this field.