Frontiers in Chemistry最新文献

Biopharmaceuticals are increasingly utilised in the treatment of oncological, inflammatory, and autoimmune diseases, largely due to their exceptional specificity in targeting antigens. However, their structural complexity, heterogeneity, and sensitivity pose crucial challenges in their production, purification, and delivery. Charge heterogeneity analysis, a Critical Quality Attribute of these biomolecules used in their Quality Control, is often performed using separative analytical techniques such as imaged capillary Isoelectric Focusing (icIEF). Recognized as a gold standard by the industry, icIEF leverages a pH gradient to provide high-resolution profiling of charge variants in biotherapeutics. In this review, critical experimental parameters for icIEF method development in the context of biotherapeutic drug development and QC will be discussed. Key aspects, including sample preparation, capillary properties, carrier ampholytes, stabilizers, and detection are examined, and supported by recent literature. Advances in icIEF technology and its expanding applications underline its robustness, reproducibility, and compliance with regulatory standards, affirming its pivotal role in ensuring the identity and consistency of biological products.

In this study, CuO-CoO_x/SBA-15 catalysts were successfully synthesized via ultrasonic impregnation, and their performance in degrading nitrobenzene within a Fenton-like system was investigated. The catalyst materials were characterized using X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), inductively coupled plasma mass spectrometry (ICP-MS), scanning electron microscopy (SEM), transmission electron microscope(TEM) and energy-dispersive X-ray spectroscopy (EDS). The CuO-CoO_x/SBA-15 catalysts featured well-distributed CuO-CoO_x nanoparticles within the mesoporous SBA-15 support. Compared to CuO/SBA-15 and Co₃O₄/SBA-15 catalysts with similar microstructures, the CuO-CoO_x/SBA-15 catalysts exhibited Cu-Co dual active centers and a higher abundance of redox-active sites. During catalytic degradation, H₂O₂ was continuously activated on the catalyst surface through efficient Cu⁺/Cu²⁺ and Co²⁺/Co³⁺ redox cycles. The experimental conditions (initial pH, catalyst dosage, and H₂O₂ dosage) were optimized, resulting in 99% nitrobenzene removal over a wide pH range (3.0-9.0). The primary mechanisms for the oxidation and subsequent removal of nitrobenzene in the CuO-CoO_x/SBA-15-H₂O₂ system were identified as reactions with hydroxyl radicals (·OH).

Infection is one of the leading causes of failure in titanium-based implant materials during clinical surgeries, often resulting in delayed or non-union of bone healing. Furthermore, the overuse of antibiotics can lead to bacterial resistance. Therefore, developing a novel titanium-based implant material with both antimicrobial and osteogenic properties is of great significance. In this study, chitosan (CS), polydopamine (PDA), and antimicrobial peptides (AMPs) HHC36 were applied to modify the surface of titanium, resulting in the successful preparation of the composite material Ti-PDA-CS/PDA@HHC36 (abbreviated as T-P-C/P@H). CS promotes osteogenesis and cell adhesion, providing an ideal microenvironment for bone repair. PDA enhances the material's biocompatibility and corrosion resistance, offering cell adhesion sites, while both components exhibit pH-responsive characteristics. The HHC36 effectively prevents infection, protecting the bone repair material from bacterial damage. Overall, the synergistic effects of these components in T-P-C/P@H not only confer excellent antimicrobial and osteogenic properties but also improve biocompatibility, offering a new strategy for applying titanium-based implants in clinical settings.

Introduction: Laurus nobilis (LN), has traditional medicinal uses, and this study investigates its therapeutic potential by focusing on its phenolic content and bioactivities such as antioxidant, antidiabetic, and anticholinergic properties. Phenolic compounds play key roles in reducing oxidative stress and modulating enzymatic activities, relevant to metabolic and neurodegenerative disorders.

Methods: LN leaf extracts were prepared via ethanol maceration, followed by filtration and concentration. Phenolic content was analyzed using LC-MS/MS. Antioxidant activity was assessed through ferric thiocyanate, DPPH, ABTS, and FRAP assays. Enzyme inhibition assays targeted AChE, BChE, and α-GLY, with IC50 values from dose-response curves. In silico analyses were conducted using molecular docking techniques to predict the binding mechanisms of identified phenolic compounds with the active sites of target enzymes, evaluating binding affinities and interaction profiles.

Results: Vanillic acid and catechin hydrate were the most abundant phenolics. LN extract showed strong lipid peroxidation inhibition (50.53%) compared to Trolox (28.33%) and α-tocopherol (37.79%). Moderate radical scavenging and metal reduction potentials were observed. IC50 values were 2.57 µg/L for AChE, 3.78 µg/L for BChE, and 4.65 µg/L for α-GLY, indicating notable bioactivity. In silico studies confirmed strong binding affinities of phenolics to target enzymes.

Discussion: LN extracts demonstrated promising antioxidant, antidiabetic, and anticholinergic activities, attributed to high phenolic content. Enzyme inhibition results suggest potential in managing metabolic and neurodegenerative disorders. In silico findings support these bioactivities, highlighting LN's therapeutic potential.

One way to combat the black pharmaceutical market is to exchange experience and knowledge among the laboratories involved in this fight. A beneficial approach is compiling application examples that demonstrate the development and growing potential of the two analytical techniques that are undoubtedly useful in investigating pharmacologically active ingredients found in products dangerous to consumers health and life. Attenuated total reflectance Fourier transform infrared spectroscopy and X-ray powder diffraction are nondestructive techniques substantial for examining evidence seized by the police, demanding minimal preparation of the sample. Importantly, they are among the few that do not negatively impact the environment because they do not require the production or disposal of chemical reagents or solvents, aligning with the principles of green chemistry. Both techniques provide consistent, reproducible results, essential for legal and scientific validity.

In this paper, we investigate square-hexagonal chains, a class of systems where the inner dual of a structure with a square-hexagon shape forms a path graph. The specific configuration of square and hexagonal polygons, and how they are concatenated, leads to different types of square-hexagonal chains. A square containing a vertex of degree 2 is classified as having a kink, and the resulting kink is referred to as a type ${}_2\top$ kink. This kink is further subdivided into three types: ${}_2\top _1$ , ₂ $\top 2$ , and ₂ $\top 3$ . We focus on the kink chain of type ₂ $\top 2$ and compute various topological descriptors for this configuration. By deriving analytical expressions, we determine the maximizing and minimizing values of these descriptors. Additionally, we provide a comprehensive analysis of the expected values for these descriptors and offer a comparison of their behaviors through analytical, numerical, and graphical methods. These results offer insights into the structural properties and behavior of square-hexagonal chains, particularly in relation to the optimization of topological descriptors.

Introduction: Plate culturing and visual inspection are the gold standard methods for bacterial identification. Despite the growing attention on molecular biology techniques, colony identification using agar plates remains manual, interpretative, and heavily reliant on human experience, making it prone to errors. Advanced imaging techniques, like hyperspectral imaging, offer potential alternatives. However, the use of hyperspectral imaging in the VIS-NIR region has been hindered by sensitivity to various components and culture medium changes, leading to inaccurate results. The application of hyperspectral imaging in the ultraviolet (UV) region has not been explored, despite the presence of specific absorption and emission peaks in bacterial components.

Methods: To address this gap, we developed a predictive model for bacterial colony detection and identification using UV hyperspectral imaging. The model utilizes hyperspectral images acquired in the UV wavelength range of 225-400 nm, processed with principal component analysis (PCA) and discriminant analysis (DA). The measurement setup includes a hyperspectral imager, a PC for automated data analysis, and a conveyor belt system to transport agar plates for automated analysis. Four bacterial species (Escherichia coli, Staphylococcus, Pseudomonas, and Shewanella) were cultured on two different media, Luria Bertani and Tryptic Soy, to train and validate the model.

Results: The PCA-DA-based model demonstrated high accuracy (90%) in differentiating bacterial species based on the first three principal components, highlighting the potential of UV hyperspectral imaging for bacterial identification.

Discussion: This study shows that UV hyperspectral imaging, coupled with advanced data analysis techniques, offers a robust and automated alternative to traditional methods for bacterial identification. The model's high accuracy emphasizes the untapped potential of UV hyperspectral imaging in microbiological analysis, reducing human error and improving reliability in bacterial species differentiation.

[This corrects the article DOI: 10.3389/fchem.2024.1473217.].

Cancer is a complex global health challenge that requires novel and holistic approaches to treatment and prevention. Polyherbal medicines, composed of multiple plants with historical use in traditional medicine, have gained popularity due to their safety, cost-effectiveness, and accessibility. However, selecting the right plants and determining optimal combinations for enhanced biological effects remains challenging. To address this, a molecular docking study was conducted, targeting proteins implicated in cancer pathogenesis. The study identified bioactive compounds with strong binding energies, guiding the selection of polyherbal formulations for further experimentation. Using response surface methodology, various combinations of plant extracts were screened for their antioxidant properties and phytochemical content. Among the formulations tested, PHEE (Polyherbal Ethanolic Extract), comprising 70% soursop leaf, 5% jackfruit leaf, 5% orange peel, 15% citrus juice, and 5% apple fruit ethanolic extracts, exhibited the most potent biological activities, followed by SLEE (Soursop Leaf Ethanolic Extract), a 100% soursop leaf ethanolic extract. Design Expert Software predicted soursop leaf extract as a key contributor to desirable outcomes, attributed to its rich phytochemical composition. Cell-based assays revealed varying cytotoxic effects of the extracts on leukemia cells, with PHEE showing the highest potency (IC50 = 2.50 μg/mL), followed closely by SLEE (IC50 = 2.90 μg/mL). These effects are potentially due to the abundant acetogenins and flavonoids present in the extracts. However, caution is warranted regarding their cytotoxicity to normal cells. Apoptotic studies confirmed the ability of both PHEE and SLEE to induce programmed cell death, further supporting their potential as anticancer agents. This research underscores the importance of strategic plant combinations in polyherbal formulations and highlights PHEE as a promising candidate for further investigation in cancer treatment.