Erianin alleviates LPS-induced acute lung injury via antagonizing P-selectin-mediated neutrophil adhesion function.

ETHNOPHARMACOLOGICAL RELEVANCE: Dendrobium officinale Kimura et Migo, known as "Tiepi Shihu" in traditional Chinese medicine, boasts an extensive history of medicinal use documented in the Chinese Pharmacopoeia. "Shen Nong Ben Cao Jing" records D. officinale as a superior herbal medicine for fortifying "Yin" and invigorating the five viscera. Erianin, a benzidine compound, emerges as a prominent active constituent derived from D. officinale, with the pharmacological efficacy of D. officinale closely linked to the anti-inflammatory properties of erianin. AIM OF THE STUDY: Acute lung injury (ALI) is a substantial threat to global public health, while P-selectin stands out as a promising novel target for treating acute inflammatory conditions. This investigation aims to explore the therapeutic potential of erianin in ALI treatment and elucidate the underlying mechanisms. EXPERIMENTAL DESIGN: The effectiveness of erianin in conferring protection against ALI was investigated through comprehensive histopathological and biochemical analyses of lung tissues and bronchoalveolar lavage fluid (BALF) in an in vivo model of LPS-induced ALI in mice. The impact of erianin on fMLP-induced neutrophil chemotaxis was quantitatively assessed using the Transwell and Zigmond chamber, respectively. To determine the therapeutic target of erianin and elucidate their binding capability, a series of sophisticated assays were employed, including drug affinity responsive target stability (DARTS) assay, cellular thermal shift assay (CETSA), and molecular docking analyses. RESULTS: Erianin demonstrated a significant alleviation of LPS-induced acute lung injury, characterized by reduced total cell and neutrophil counts and diminished total protein contents in BALF. Moreover, erianin exhibited a capacity to decrease proinflammatory cytokine production in both lung tissues and BALF. Notably, erianin effectively suppressed the activation of NF-κB signaling in the lung tissues of LPS- challenged mice; however, it did not exhibit in vitro inhibitory effects on inflammation in LPS-induced human pulmonary microvascular endothelial cells (HPMECs). Additionally, erianin blocked the adhesion and rolling of neutrophils on HPMECs. While erianin did not influence endothelial P-selectin expression or cytomembrane translocation, it significantly reduced the ligand affinity between P-selectin and P-selectin glycoprotein ligand-1 (PSGL-1). CONCLUSIONS: Erianin inhibits P-selectin-mediated neutrophil adhesion to activated endothelium, thereby alleviating ALI. The present study highlights the potential of erianin as a promising lead for ALI treatment.

Iodine-doped TiO2 nanotube coatings: a technique for enhancing the antimicrobial properties of titanium surfaces against Staphylococcus aureus.

BACKGROUND: Implant-related infections are a challenging complication of orthopedic surgery, primarily due to the formation of bacterial biofilms on the implant surface. An antibacterial coating for titanium implants was developed to provide novel insights into the prevention and treatment of implant-related infections. METHODS: Titanium plates were coated with TiO2 nanotubes by anodization, and iodine was doped onto the coating via electrophoretic deposition. The obtained plates were characterized using a range of analytical techniques. Subsequently, Staphylococcus aureus was inoculated onto the surfaces of untreated titanium plates (control group), TiO2-nanocoated titanium plates (TiO2 group), and iodine-doped TiO2-nanocoated titanium plates (I-TiO2 group) to compare their antibacterial properties. RESULTS: Twenty-four hour in vitro antimicrobial activity test of the I-TiO2 group against Staphylococcus aureus was superior to those of the other groups, and this difference was statistically significant (P < 0.05). CONCLUSIONS: This coating technology provides a new theoretical basis for the development of anti-infective implants against Staphylococcus aureus in orthopedics.

Lightweight Machine-Learning Model for Efficient Design of Graphene-Based Microwave Metasurfaces for Versatile Absorption Performance.

Graphene, as a widely used nanomaterial, has shown great flexibility in designing optically transparent microwave metasurfaces with broadband absorption. However, the design of graphene-based microwave metasurfaces relies on cumbersome parameter sweeping as well as the expertise of researchers. In this paper, we propose a machine-learning network which enables the forward prediction of reflection spectra and inverse design of versatile microwave absorbers. Techniques such as the normalization of input and transposed convolution layers are introduced in the machine-learning network to make the model lightweight and efficient. Particularly, the tunable conductivity of graphene enables a new degree in the intelligent design of metasurfaces. The inverse design system based on the optimization method is proposed for the versatile design of microwave absorbers. Representative cases are demonstrated, showing very promising performances on satisfying various absorption requirements. The proposed machine-learning network has significant potential for the intelligent design of graphene-based metasurfaces for various microwave applications.