The Pharmacokinetics in Mice and Cell Uptake of Thymus Immunosuppressive Pentapeptide Using LC-MS/MS Analysis

Thymus immunosuppressive pentapeptide (TIPP) is a novel anti-inflammatory peptide with high efficacy and low toxicity. This study aims to establish a selective LC-MS/MS method for analyzing the analyte TIPP in biological samples, laying the foundation for further PK and PD studies of TIPP. Protein precipitation was conducted in acetonitrile supplemented with 2% formic acid and 25 mg/mL dithiothreitol as a stabilizer, which was followed by backwashing the organic phase using dichloromethane. The chromatographic separation of TIPP was achieved on a C18 column with a gradient elution method. During positive electrospray ionization, TIPP was analyzed via multiple-reaction monitoring. The linear relationships between the concentration of TIPP and peak area in murine plasma cell lysates, supernatants, and the final cell rinse PBS were established within the ranges of 20–5000 ng/mL, 1–200 ng/mL, 10–200 μg/mL, and 0.1–20 ng/mL, respectively (r² > 0.99). Validated according to U.S. FDA guidelines, the proposed method was proved to be acceptable. Such a method had been successfully applied to investigate the pharmacokinetics of TIPP in mice via subcutaneous injection. The plasma half-life in mice was 5.987 ± 1.824 min, suggesting that TIPP is swiftly eliminated in vivo. The amount of TIPP uptake by RBL-2H3 cells was determined using this method, which was also visually verified by confocal. Furthermore, the effective intracellular concentration of TIPP was deduced by comparing the intracellular concentration of TIPP and degrees of inflammation, enlightening further investigation on the intracellular target and mechanism of TIPP.     

Role of Natural Compounds and Target Enzymes in the Treatment of Alzheimer’s Disease

Alzheimer’s disease (AD) is a progressive neurological condition. The rising prevalence of AD necessitates the rapid development of efficient therapy options. Despite substantial study, only a few medications are capable of delaying the disease. Several substances with pharmacological activity, derived from plants, have been shown to have positive benefits for the treatment of AD by targeting various enzymes, such as acetylcholinesterase (AChE), butyrylcholinesterase (BuChE), β-secretase, γ-secretase, and monoamine oxidases (MAOs), which are discussed as potential targets. Medicinal plants have already contributed a number of lead molecules to medicine development, with many of them currently undergoing clinical trials. A variety of medicinal plants have been shown to diminish the degenerative symptoms associated with AD, either in their raw form or as isolated compounds. The aim of this review was to provide a brief summary of AD and its current therapies, followed by a discussion of the natural compounds examined as therapeutic agents and the processes underlying the positive effects, particularly the management of AD.     

The Classification of Rice Blast Resistant Seed Based on Ranman Spectroscopy and SVM

Rice blast is a serious threat to rice yield. Breeding disease-resistant varieties is one of the most economical and effective ways to prevent damage from rice blast. The traditional identification of resistant rice seeds has some shortcoming, such as long possession time, high cost and complex operation. The purpose of this study was to develop an optimal prediction model for determining resistant rice seeds using Ranman spectroscopy. First, the support vector machine (SVM), BP neural network (BP) and probabilistic neural network (PNN) models were initially established on the original spectral data. Second, due to the recognition accuracy of the Raw-SVM model, the running time was fast. The support vector machine model was selected for optimization, and four improved support vector machine models (ABC-SVM (artificial bee colony algorithm, ABC), IABC-SVM (improving the artificial bee colony algorithm, IABC), GSA-SVM (gravity search algorithm, GSA) and GWO-SVM (gray wolf algorithm, GWO)) were used to identify resistant rice seeds. The difference in modeling accuracy and running time between the improved support vector machine model established in feature wavelengths and full wavelengths (200–3202 cm⁻¹) was compared. Finally, five spectral preproccessing algorithms, Savitzky–Golay 1-Der (SGD), Savitzky–Golay Smoothing (SGS), baseline (Base), multivariate scatter correction (MSC) and standard normal variable (SNV), were used to preprocess the original spectra. The random forest algorithm (RF) was used to extract the characteristic wavelengths. After different spectral preproccessing algorithms and the RF feature extraction, the improved support vector machine models were established. The results show that the recognition accuracy of the optimal IABC-SVM model based on the original data was 71%. Among the five spectral preproccessing algorithms, the SNV algorithm’s accuracy was the best. The accuracy of the test set in the IABC-SVM model was 100%, and the running time was 13 s. After SNV algorithms and the RF feature extraction, the classification accuracy of the IABC-SVM model did not decrease, and the running time was shortened to 9 s. This demonstrates the feasibility and effectiveness of IABC in SVM parameter optimization, with higher prediction accuracy and better stability. Therefore, the improved support vector machine model based on Ranman spectroscopy can be applied to the fast and non-destructive identification of resistant rice seeds.     

Investigation Driven by Network Pharmacology on Potential Components and Mechanism of DGS,a Natural Vasoprotective Combination,for the Phytotherapy of Coronary Artery Disease

Phytotherapy offers obvious advantages in the intervention of Coronary Artery Disease (CAD), but it is difficult to clarify the working mechanisms of the medicinal materials it uses. DGS is a natural vasoprotective combination that was screened out in our previous research, yet its potential components and mechanisms are unknown. Therefore, in this study, HPLC-MS and network pharmacology were employed to identify the active components and key signaling pathways of DGS. Transgenic zebrafish and HUVECs cell assays were used to evaluate the effectiveness of DGS. A total of 37 potentially active compounds were identified that interacted with 112 potential targets of CAD. Furthermore, PI3K-Akt, MAPK, relaxin, VEGF, and other signal pathways were determined to be the most promising DGS-mediated pathways. NO kit, ELISA, and Western blot results showed that DGS significantly promoted NO and VEGFA secretion via the upregulation of VEGFR2 expression and the phosphorylation of Akt, Erk1/2, and eNOS to cause angiogenesis and vasodilation. The result of dynamics molecular docking indicated that Salvianolic acid C may be a key active component of DGS in the treatment of CAD. In conclusion, this study has shed light on the network molecular mechanism of DGS for the intervention of CAD using a network pharmacology-driven strategy for the first time to aid in the intervention of CAD.     

The Antibacterial Activity Mode of Action of Plantaricin YKX against Staphylococcus aureus

We aimed to evaluate the inhibitory effect and mechanism of plantaricin YKX on S. aureus. The mode of action of plantaricin YKX against the cells of S. aureus indicated that plantaricin YKX was able to cause the leakage of cellular content and damage the structure of the cell membranes. Additionally, plantaricin YKX was also able to inhibit the formation of S. aureus biofilms. As the concentration of plantaricin YKX reached 3/4 MIC, the percentage of biofilm formation inhibition was over 50%. Fluorescent dye labeling combined with fluorescence microscopy confirmed the results. Finally, the effect of plantaricin YKX on the AI-2/LuxS QS system was investigated. Molecular docking predicted that the binding energy of AI-2 and plantaricin YKX was −4.7 kcal/mol and the binding energy of bacteriocin and luxS protein was −183.701 kcal/mol. The expression of the luxS gene increased significantly after being cocultured with plantaricin YKX, suggesting that plantaricin YKX can affect the QS system of S. aureus.     

Investigation on the Peroxidase-like Activity of Vitamin B6 and Its Applications in Colorimetric Detection of Hydrogen Peroxide and Total Antioxidant Capacity Evaluation

The peroxidase-like activity of vitamin B6 (VB6) was firstly demonstrated by catalyzing the peroxidase chromogenic substrate 3,3′,5,5′-tetramethylbenzidine (TMB) at the existence of H₂O₂. The influence of different factors on the catalytic property of VB6, including pH, temperature, VB6 concentration, and incubation time, were investigated. The steady-state kinetic study results indicate that VB6 possesses higher affinity to H₂O₂ than natural horseradish peroxidase and some other peroxidase mimics. Besides, the radical quenching experiment results confirm that hydroxyl radical (•OH) accounts for the catalytic process. Based on the excellent peroxidase-like catalytic activity of VB6, the colorimetric methods for H₂O₂ and gallic acid (GA) detection were developed by measuring the absorbance variance of the catalytic system. Under the optimal conditions, the linear ranges of the methods for H₂O₂ and GA determination with good selectivity are 50.0–600.0 μM and 10.0–50.0 μM, respectively. In addition, the developed method was applied in the detection of H₂O₂ in milk samples and evaluation of total antioxidant capacity of different tea infusions. This study may broaden the application prospect of VB6 in environmental and biomedical analysis fields, contribute to profound insight of the physiological functions of VB6, as well as lay foundation for further excavation of small-molecule peroxidase mimics.     

Electronic structure of strongly correlated systems: recent developments in multiconfiguration pair-density functional theory and multiconfiguration nonclassical-energy functional theory

Strong electron correlation plays an important role in transition-metal and heavy-metal chemistry, magnetic molecules, bond breaking, biradicals, excited states, and many functional materials, but it provides a significant challenge for modern electronic structure theory. The treatment of strongly correlated systems usually requires a multireference method to adequately describe spin densities and near-degeneracy correlation. However, quantitative computation of dynamic correlation with multireference wave functions is often difficult or impractical. Multiconfiguration pair-density functional theory (MC-PDFT) provides a way to blend multiconfiguration wave function theory and density functional theory to quantitatively treat both near-degeneracy correlation and dynamic correlation in strongly correlated systems; it is more affordable than multireference perturbation theory, multireference configuration interaction, or multireference coupled cluster theory and more accurate for many properties than Kohn–Sham density functional theory. This perspective article provides a brief introduction to strongly correlated systems and previously reviewed progress on MC-PDFT followed by a discussion of several recent developments and applications of MC-PDFT and related methods, including localized-active-space MC-PDFT, generalized active-space MC-PDFT, density-matrix-renormalization-group MC-PDFT, hybrid MC-PDFT, multistate MC-PDFT, spin–orbit coupling, analytic gradients, and dipole moments. We also review the more recently introduced multiconfiguration nonclassical-energy functional theory (MC-NEFT), which is like MC-PDFT but allows for other ingredients in the nonclassical-energy functional. We discuss two new kinds of MC-NEFT methods, namely multiconfiguration density coherence functional theory and machine-learned functionals.

This feature article overviews recent work on active spaces, matrix product reference states, treatment of quasidegeneracy, hybrid theory, density-coherence functionals, machine-learned functionals, spin–orbit coupling, gradients, and dipole moments.     

Three-dimensional architectures based on carbon nanotube bridged Ti2C MXene nanosheets for Li-S batteries

Lithium-sulfur(Li-S)batteries are promising candidates for high density electrochemical energy storage systems.However,the poor conductivity of S and the shuttl...     

Structural and Bonding Properties of AlnC4-/0 (n=2-4) Clusters:Anion Photoelectron Spectroscopy and Theoretical Calculations

We measured the photoelectron spectra of AlnC4-(n=2-4) clusters by using size-selected anion photoelectron spectroscopy.The structures of AlnC4-/0 (n=2-4) clust...     

Steam-assisted crystallization of highly dispersed nanosized hierarchical zeolites from solid raw materials and their catalytic performance in lactide production

A solvent-free route based on solid raw materials affords higher product yield and lower waste production compared to the traditional hydrothermal synthesis. However, the as-made zeolites usually present blocky aggregation states, limiting their mass transfer and exposure of active sites in catalytic applications. Herein, highly dispersed nanosized hierarchical Beta zeolites with varied Si/Al ratios were prepared via steam-assisted crystallization from ball-milled solid raw materials. Thanks to the sufficient mixing of solid raw materials and favorable migration of solid mixture, nanosized Beta zeolites are obtained that are assembled from nanoparticles (∼15 nm) and possess abundant interconnected intraparticle mesopores. The strategy can also be extended to synthesize nanosized hierarchical ZSM-5 zeolites. The as-prepared Beta zeolite (Si/Al = 10) exhibits outstanding catalytic performance in conversion of lactic acid to lactide (as high as 77.5% in yield). This work provides avenues for simple and cost-efficient synthesis of highly dispersed nanosized hierarchical zeolites, promising their important catalytic applications.

A cost-effective synthesis strategy based on steam-assisted crystallization from ball-milled solid raw materials is developed to prepare a highly dispersed nanosized hierarchical Beta zeolite for conversion of lactic acid (LA) to lactide (LT).