Investigate the metabolic changes in intestinal diseases by employing a 1H-NMR-based metabolomics approach on Caco-2 cells treated with cedrol.

Mitochondrial dysfunction may precipitate intestinal dysfunction, while inflammatory bowel disease manifests as a chronic inflammatory ailment affecting the gastrointestinal tract. This condition disrupts the barrier function of the intestinal epithelium and alters metabolic products. Increasing mitochondrial adenosine triphosphate (ATP) synthesis in intestinal epithelial cells presents a promising avenue for colitis treatments. Nevertheless, the impact of cedrol on ATP and the intestinal barrier remains unexplored. Hence, this study is dedicated to examining the cedrol's protective effect on an inflammatory cocktail (IC)-induced intestinal epithelial barrier dysfunction in Caco-2 cells. The finding reveals that cedrol enhances ATP content and the transepithelial electrical resistance value in the intestinal epithelial barrier. Moreover, cedrol mitigates the IC-induced decrease in the messenger ribonucleic acid (mRNA) expression of tight junction proteins (ZO-1, Occludin, and Claudin-1), thereby ameliorating intestinal epithelial barrier dysfunction. Furthermore, nuclear magnetic resonance (NMR)-based metabolomic analysis indicated that IC-exposed Caco-2 cells are restored by cedrol treatments. Notably, cedrol elevates metabolites such as amino acids, thereby enhancing the intestinal barrier. In conclusion, cedrol alleviates IC-induced intestinal epithelial barrier dysfunction by promoting ATP-dependent proliferation of Caco-2 cells and bolstering amino acid levels to sustain tight junction messenger ribonucleic acid expression.

Composition Characterization of Crossostephium chinense Leaf Essential Oil and Its Anti-Inflammatory Activity Mechanisms.

This study investigates the composition characteristics and anti-inflammatory activity mechanisms of the essential oil from the leaves of Crossostephium chinense. C. chinense is a perennial herb commonly found in East Asia, traditionally used to treat various ailments. The essential oil extracted through water distillation, primarily contains 1,8-cineole (13.73%), santolina triene (13.53%), and germacrene D (10.67%). Three compounds were identified from the essential oil, namely 1-acetoxy-2-(2-hydroxypropyl)-5-methylhex-3,5-diene, 1-acetoxy-isopyliden-hex-5-en-4-one, and chrysanthemyl acetate, with the first two being newly discovered compounds. Then, the essential oil of C. chinense exhibits significant anti-inflammatory effects on RAW264.7 macrophages, effectively inhibiting the production of NO and ROS, with the IC50 value of 10.3 µg/mL. Furthermore, the essential oil reduces the expression of pro-inflammatory cytokines such as TNF-α, IL-6, and IL-1ß. Mechanistic studies indicate that the essential oil affects the inflammatory response by inhibiting the expression of iNOS but has no significant impact on COX-2. Further analysis suggests that the essential oil may regulate the inflammatory response through the ERK protein in the MAPK pathway and IκBα in the NF-κB pathway, while also promoting the activity of the NRF2/HO-1 antioxidant pathway, enhancing the cell's antioxidant capacity, thereby achieving an effect of inhibiting the inflammatory response. These results highlight the potential application value of C. chinense leaf essential oil in the medical and healthcare fields.

From computational screening to the synthesis of a promising OER catalyst.

The search for new materials can be laborious and expensive. Given the challenges that mankind faces today concerning the climate change crisis, the need to accelerate materials discovery for applications like water-splitting could be very relevant for a renewable economy. In this work, we introduce a computational framework to predict the activity of oxygen evolution reaction (OER) catalysts, in order to accelerate the discovery of materials that can facilitate water splitting. We use this framework to screen 6155 ternary-phase spinel oxides and have isolated 33 candidates which are predicted to have potentially high OER activity. We have also trained a machine learning model to predict the binding energies of the *O, *OH and *OOH intermediates calculated within this workflow to gain a deeper understanding of the relationship between electronic structure descriptors and OER activity. Out of the 33 candidates predicted to have high OER activity, we have synthesized three compounds and characterized them using linear sweep voltammetry to gauge their performance in OER. From these three catalyst materials, we have identified a new material, Co2.5Ga0.5O4, that is competitive with benchmark OER catalysts in the literature with a low overpotential of 220 mV at 10 mA cm-2 and a Tafel slope at 56.0 mV dec-1. Given the vast size of chemical space as well as the success of this technique to date, we believe that further application of this computational framework based on the high-throughput virtual screening of materials can lead to the discovery of additional novel, high-performing OER catalysts.

Label-Free Three-Dimensional Morphological Characterization of Cell Death Using Holographic Tomography.

This study presents a novel label-free approach for characterizing cell death states, eliminating the need for complex molecular labeling that may yield artificial or ambiguous results due to technical limitations in microscope resolution. The proposed holographic tomography technique offers a label-free avenue for capturing precise three-dimensional (3D) refractive index morphologies of cells and directly analyzing cellular parameters like area, height, volume, and nucleus/cytoplasm ratio within the 3D cellular model. We showcase holographic tomography results illustrating various cell death types and elucidate distinctive refractive index correlations with specific cell morphologies complemented by biochemical assays to verify cell death states. These findings hold promise for advancing in situ single cell state identification and diagnosis applications.

Composition Characterization of Cinnamomum osmophloeum Kanehira Hydrosol and Its Enhanced Effects on Erectile Function.

Cinnamomum osmophloeum Kanehira (CO) is an endemic species of Taiwan. This study elucidated the composition of CO hydrosol, revealing trans-cinnamaldehyde (65.03%), trans-cinnamyl acetate (7.57%), and coumarin (4.31%) as the main volatile compounds. Seven compounds were identified in the water fraction of hydrosol, including a novel compound, 2-(2-hydroxyphenyl)oxetan-3-ol. This marks the first investigation into high-polarity compounds in hydrosol, extending beyond the volatile components. Notably, two compounds, trans-phenyloxetan-3-ol and cis-phenyloxetan-3-ol, demonstrated significant inhibition activity against phosphodiesterase type five (PDE5), with IC50 values of 4.37 µM and 3.40 µM, respectively, indicating their potential as novel PDE5 inhibitors. Furthermore, CO hydrosol was evaluated against enzymes associated with erectile dysfunction, namely acetylcholinesterase (AChE), angiotensin-I converting enzyme (ACE), and arginase type 2 (ARG2). These findings underscore the potential of CO hydrosol to modulate erectile function through diverse physiological pathways, hinting at its prospects for future development in a beverage or additive with enhanced effects on erectile function.

Advancing mass spectrometry-based chemical imaging: A noncontact continuous flow surface probe in mass spectrometry for enhanced signal detection and spatial resolution.

A new method has been developed for mass spectrometric imaging of small molecules and proteins on tissue or in thinly sliced materials. A laser desorption Venturi electrospray ionization-mass spectrometer was developed for molecular imaging. This method combines laser desorption (LD) and electrospray ionization (ESI) systems before a mass spectrometer (MS). To carry out laser desorption, samples are excited with a laser from the back side of a glass substrate. The desorbed molecules or particles are then captured by a solvent flow. In the ESI system, these desorbed particles and molecules are ionized. The spray part of the solvent system consists of two capillaries: one delivers solvent to the sample plate sides to capture desorbed molecules and particles, and the other carries the solution to the mass spectrometry side using the Venturi effect. A 2D stage facilitates sampling. The system is designed to minimize the sample size after desorption using a 355 nm diode laser, and it is optimized for molecules of various sizes, including organic molecules, amino acids, and proteins. Despite challenging atmospheric conditions for protein desorption, this specialized design enables the collection of protein spectra. The amino acids and other small molecules showed high sensitivity in the MSI measurements. This innovative MS imaging system can be directly applied to real tissue systems and other plant samples to visualize the molecular level distributions.

Corrigendum to "Advancing mass spectrometry-based chemical imaging: A noncontact continuous flow surface probe in mass spectrometry for enhanced signal detection and spatial resolution" [Talanta 273 (2024) 125858].

In the statement "This innovative MS imaging system can be directly applied to real tissue systems and other plant samples to visualize the molecular level distributions." "innovative" should be read as "important".

An FM-Index Based High-Throughput Memory-Efficient FPGA Accelerator for Paired-End Short-Read Mapping.

This article presents an Ferragina-Manzini index (FM-index) based paired-end short-read mapping hardware accelerator. Four techniques are proposed to significantly reduce the number of memory accesses and operations to improve the throughput. First, an interleaved data structure is proposed to reduce the processing time by 51.8% by leveraging the data locality. Second, the boundaries of possible mapping location candidates can be retrieved within only one memory access by constructing a lookup table along with the FM-index. This reduces the number of DRAM accesses by 60% with only a 64 MB memory overhead. Third, an additional step is added to skip the time-consuming repetitive location candidates filtering conditionally, avoiding unnecessary operations. Lastly, an early termination method is proposed to terminate the mapping process if any location candidate with a high enough alignment score is detected, greatly decreasing the execution time. Overall, the computation time is reduced by 92.6% with only a 2% memory overhead in DRAM. The proposed methods are realized on a Xilinx Alveo U250 FPGA. The proposed FPGA accelerator processes 1,085,812,766 short-reads from the U.S. Food and Drug Administration (FDA) dataset within 35.4 minutes at 200 MHz. It achieves a 1.7-to-18.6× higher throughput and the highest 99.3% accuracy by exploiting the paired-end short-read mapping, compared to state-of-the-art FPGA-based designs.

Intrinsic and external active sites of single-atom catalysts.

Active components with suitable supports are the common paradigm for industrial catalysis, and the catalytic activity usually increases with minimizing the active component size, generating a new frontier in catalysis, single-atom catalysts (SACs). However, further improvement of SACs activity is limited by the relatively low loading of single atoms (SAs, which are heteroatoms for most SACs, i.e., external active sites) because of the highly favorable aggregation of single heteroatoms during preparation. Research interest should be shifted to investigate SACs with intrinsic SAs, which could circumvent the aggregation of external SAs and consequently increase the SAs loading while maintaining them individual to further improve the activity. In this review, SACs with external or intrinsic SAs are discussed and, at last, the perspectives and challenges for obtaining high-loading SACs with intrinsic SAs are outlined.

Aptamer against Aflatoxin B1 Obtained by SELEX and Applied in Detection.

Aflatoxins, especially aflatoxin B1 (AFB1), are the most prevalent mycotoxins in nature. They contaminate various crops and cause global food and feed safety concerns. Therefore, a simple, rapid, sensitive, and specific AFB1 detection tool is urgently needed. Aptamers generated by SELEX technology can specifically bind the desired targets with high affinity. The broad range of targets expands the scope of applications for aptamers. We used an AFB1-immobilized magnetic nanoparticle for SELEX to select AFB1-specific aptamers. One aptamer, fl-2CS1, revealed a dissociation constant (Kd = 2.5 µM) with AFB1 determined by isothermal titration calorimetry. Furthermore, no interaction was shown with other toxins (AFB2, AFG1, AFG2, OTA, and FB1). According to structural prediction and analysis, we identified a short version of the AFB1-specific aptamer, fl-2CS1/core, with a minimum length of 39-mer used in the AFB1-aptasensor system by real-time qPCR. The aptasensor showed a broad range of detection from 50 ppt to 50 ppb with an accuracy of 90% in the spiked peanut extract samples. With the application of the AFB1-aptasensor we have constructed, a wide range detection tool with high accuracy might be developed as a point-of-care testing tool in agriculture.

Simultaneous detection of positively and negatively charged microparticles by ion trap mass spectrometry.

This study describes the simultaneous detection of positively and negatively charged microparticles by ion trap mass spectrometry (IT-MS) as a novel analytical measurement technique. The instrument was configured with a feeding capillary for particle introduction, an ion trap, and a charge detector that responds to both ions simultaneously. Positively and negatively charged particles are generated by the triboelectric effect inside the capillary entrance of the instrument. The particles were fed in dry form with a cotton tip to provide the best dispersion. No potential was applied to the lenses on the path of particles and end caps on the ion trap. Particle size calibration has been done using well-defined polystyrene spheres in different sizes. For this study, 2 µm standard polystyrene (PS) spheres were used and checked by different particle sizes. A charge detector detected the ejected positive and negative ions, and the results were evaluated by a program that works under the Labview. The positive and negative ions reached the detector sequentially with respect to their m/z amount. The masses of particles were determined depending on their arrival time at the detector. The IT-MS system and charge detector simultaneously allow positively and negatively charged particles to be detected. This is the first study in the literature that simultaneously shows the trapping and detection of oppositely charged particles.

A booster dose of Delta × Omicron hybrid mRNA vaccine produced broadly neutralizing antibody against Omicron and other SARS-CoV-2 variants.

BACKGROUND: With the continuous emergence of new SARS-CoV-2 variants that feature increased transmission and immune escape, there is an urgent demand for a better vaccine design that will provide broader neutralizing efficacy. METHODS: We report an mRNA-based vaccine using an engineered "hybrid" receptor binding domain (RBD) that contains all 16 point-mutations shown in the currently prevailing Omicron and Delta variants. RESULTS: A booster dose of hybrid vaccine in mice previously immunized with wild-type RBD vaccine induced high titers of broadly neutralizing antibodies against all tested SARS-CoV-2 variants of concern (VOCs). In naïve mice, hybrid vaccine generated strong Omicron-specific neutralizing antibodies as well as low but significant titers against other VOCs. Hybrid vaccine also elicited CD8+/IFN-γ+ T cell responses against a conserved T cell epitope present in wild type and all VOCs. CONCLUSIONS: These results demonstrate that inclusion of different antigenic mutations from various SARS-CoV-2 variants is a feasible approach to develop cross-protective vaccines.

Artificial intelligence in food science and nutrition: a narrative review.

In the late 2010s, artificial intelligence (AI) technologies became complementary to the research areas of food science and nutrition. This review aims to summarize these technological advances by systematically describing the following: the use of AI in other fields (eg, engineering, pharmacy, and medicine); the history of AI in relation to food science and nutrition; the AI technologies currently used in the agricultural and food industries; and some of the important applications of AI in areas such as immunity-boosting foods, dietary assessment, gut microbiome profile analysis, and toxicity prediction of food ingredients. These applications are likely to be in great demand in the near future. This review can provide a starting point for brainstorming and for generating new AI applications in food science and nutrition that have yet to be imagined.

Portable particle mass spectrometer.

Particle pollutants in air have been confirmed to damage human health. The PM10 concentration is an important parameter for air quality determination. In this study, a portable quadrupole ion trap mass spectrometer (QIT-MS) was developed and used to quantitate microparticles and particulate standards. The instrument can be used to perform online analysis of various microsized particles. The instrument can be used to analyze various sizes of disperse particles with accurate mass by a histogram profile. The overall detection efficiencies of particles in the sample for polystyrene were obtained. PM10-like reference materials were used for calibration to analyze the size and mass distribution of an environmental sample. The instrument shows the potential for quantitation of different particles of an unknown sample.

Investigation of solvent microparticle formation in spray ionization-quadrupole ion trap-mass spectrometry.

In the present study, a new method has been developed for the real-time analysis of insource created solvent particles based on spray ionization-quadrupole ion trap-mass spectrometry (SI-QIT-MS). This is the first work in the literature reporting the formation of different solvent particles during solvent spray in mass spectrometry. The solvent particles formed from the solvent droplets are detected by a charge detector. Our ion trap system allows the measurement of a wide range particle masses. Various solvents and solvent mixtures such as water, methanol, acetone, toluene, n-butanol, water-methanol, and water-ethanol were sprayed through a cone system, and the mass of the particles was monitored by different trap frequencies and voltages. While polar molecules produce larger and more diverse particles due to their strong intermolecular forces, apolar solvents generally do not produce a significant number of particles. We obtained results using a homemade ion trap mass spectrometer capable of determining the mass of micro-sized solvent and solvent mixture particles weighing up to 1015 (Da). The instrument uses a charge detector connected to the exit of the ion trap. Simultaneous acquisition of particle mass spectra and measurement of the amount of charge in each particle allow mass assignment of each particle. Sprayed solvent particles were examined at various trap frequencies and voltages to find the best instrumental parameters for the highest trapping efficiency. The custom SI-QIT-MS instrument allows the measurement of the mass distribution of charged particles from the solvent spray.

Development of a focused high-energy macromolecular ion beam.

In this work, we report the development of a focused macromolecular ion beam with kinetic energy of up to 110 keV. The system consists of a quadrupole ion trap (QIT), einzel lens and linear accelerator (LINAC). Based on the combination of matrix-assisted laser desorption ionization (MALDI) and quadrupole ion trapping (QIT), ions were desorbed from the surface and trapped with an ion trap to form biomolecular ion packets. Positive- and negative-pulsed voltages were applied on each end-cap electrode of the QIT to extract the ion packets and form an ion beam that was subsequently focused via an einzel lens and accelerated by stepwise pulsed voltages. The tabletop instrument was designed and successfully demonstrated via measurements of molecular ions of insulin, cytochrome c and bovine serum albumin (BSA) with mass-to-charge ratios (m/z) ranging from ∼5.8 to 66.5 k. This is the first report of both a focused and high-kinetic-energy protein ion beam. In addition, both secondary ions and electrons were observed from the surface by hypervelocity ion beam bombardment. This focused macromolecular ion beam has demonstrated its potential in the study of interactions between large molecular ions with other molecules either in the gas phase or upon a surface.

Monitoring Silver(I)-Insulin Complexes with Electrospray Ionization Quadrupole Ion Trap Mass Spectrometry.

Ag(I)-insulin complex formation was investigated using electrospray quadrupole ion trap mass spectrometry (ESI-QIT-MS), and Ag(I) ion binding to an insulin molecule was evaluated. The Ag(I) binding ratios were measured in the range of pH 3-8. The highest binding ratio of the Ag(I) ions was obtained at pH 7. Spectrometric titration was carried out at varied molar ratios of Ag(I) ions to insulin from 20/1 to 250/1. It was observed that four Ag(I) ions were bound effectively to an insulin molecule to form Ag(I)1-4-insulin complexes. The formation equilibrium constants of Ag(I)1-4-insulin complexes were calculated from the ESI-QIT-MS peak intensities. The equilibrium constants were found as Kf1 = (2.92 ± 0.18) × 104 M-1, Kf2 = (1.03 ± 0.07) × 104 M-1, Kf3 = (6.67 ± 0.46) × 103 M-1, and Kf4 = (2.00 ± 0.16) × 103 M-1. The tandem MS/MS spectroscopies were studied to evaluate the stability of the Ag(I) complexes. The different flow rates with nano-ESI were performed to determine the binding of Ag(I) ions in solution or gas phase. In conclusion, it was observed that the Ag(I) ion forms stable Ag(I)1-4-complexes with high formation equilibrium constants.

Influence of noise-reduction techniques in sparse-data sample rotation tomographic imaging.

Data acquisition and processing is a critical issue for high-speed applications, especially in three-dimensional live cell imaging and analysis. This paper focuses on sparse-data sample rotation tomographic reconstruction and analysis with several noise-reduction techniques. For the sample rotation experiments, a live Candida rugosa sample is used and controlled by holographic optical tweezers, and the transmitted complex wavefronts of the sample are recorded with digital holographic microscopy. Three different cases of sample rotation tomography were reconstructed for dense angle with a step rotation at every 2°, and for sparse angles with step rotation at every 5° and 10°. The three cases of tomographic reconstruction performance are analyzed with consideration for data processing using four noise-reduction techniques. The experimental results demonstrate potential capability in retaining the tomographic image quality, even at the sparse angle reconstructions, with the help of noise-reduction techniques.

Deep learning and ensemble stacking technique for differentiating polypoidal choroidal vasculopathy from neovascular age-related macular degeneration.

Polypoidal choroidal vasculopathy (PCV) and neovascular age-related macular degeneration (nAMD) share some similarity in clinical imaging manifestations. However, their disease entity and treatment strategy as well as visual outcomes are very different. To distinguish these two vision-threatening diseases is somewhat challenging but necessary. In this study, we propose a new artificial intelligence model using an ensemble stacking technique, which combines a color fundus photograph-based deep learning (DL) model and optical coherence tomography-based biomarkers, for differentiation of PCV from nAMD. Furthermore, we introduced multiple correspondence analysis, a method of transforming categorical data into principal components, to handle the dichotomous data for combining with another image DL system. This model achieved a robust performance with an accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve of 83.67%, 80.76%, 84.72%, and 88.57%, respectively, by training nearly 700 active cases with suitable imaging quality and transfer learning architecture. This work could offer an alternative method of developing a multimodal DL model, improve its efficiency for distinguishing different diseases, and facilitate the broad application of medical engineering in a DL model design.