Atractylone in the Atractylodes macrocephala Rhizoma Essential Oil and Its Anti-Inflammatory Activity.

The aim of this study was to conduct a screening of potential therapeutic compounds found in the Atractylodes macrocephala rhizoma essential oil (AO) and explore its mechanism of action in the treatment of ulcerative colitis (UC). An inflammation cell model was employed in conjunction with phospho-antibody array technology to explore potential therapeutic compounds of AO and their anti-inflammatory and antioxidant effects. Furthermore, we assessed their efficacy and mechanisms of action in treating dextran sulfate sodium (DSS)-induced colitis in mice. Via the screening process, we identified atractylone (ATR) as the primary active compound in AO. It has been demonstrated that ATR can both decrease the levels of tumor necrosis factor (TNF)-α and reactive oxygen species (ROS) and increase the expression of adhesion proteins such as claudin, ZO-1, and occludin in vitro. Moreover, ATR has been shown to improve UC symptoms in vivo. Via a non-targeted metabolomics analysis of colon tissue, we identified 57 distinct metabolites that responded to ATR treatment. Subsequent analysis of the metabolic pathways revealed that the action of ATR was primarily focused on the amino acid metabolism pathway. In summary, ATR may alleviate the symptoms of UC by regulating multiple signaling pathways. Additionally, ATR has a comprehensive function in anti-inflammation, antioxidative stress, and intestinal injury reduction.

Regulating lactate-related immunometabolism and EMT reversal for colorectal cancer liver metastases using shikonin targeted delivery.

BACKGROUND: There are few effective medications for treating colorectal cancer and liver metastases (CRLM). The interactions among glycolysis, epithelial-mesenchymal transition (EMT), and immune microenvironment contribute to the progression of CRLM. A main glycolytic enzyme pyruvate Kinase M2 (PKM2) is highly expressed in colorectal cancer and CRLM, and thus can be a potential therapeutic target. METHODS: A therapeutic strategy was proposed and the shikonin-loaded and hyaluronic acid-modified MPDA nanoparticles (SHK@HA-MPDA) were designed for CRLM therapy via PKM2 inhibition for immunometabolic reprogramming. The treatment efficacy was evaluated in various murine models with liver metastasis of colorectal tumor. RESULTS: SHK@HA-MPDA achieved tumor-targeted delivery via hyaluronic acid-mediated binding with the tumor-associated CD44, and efficiently arrested colorectal tumor growth. The inhibition of PKM2 by SHK@HA-MPDA led to the remodeling of the tumor immune microenvironment and reversing EMT by lactate abatement and the suppression of TGFß signaling; the amount of cytotoxic effector CD8+ T cells was increased while the immunosuppressive MDSCs decreased. CONCLUSION: The work provided a promising targeted delivery strategy for CRLM treatment by regulating glycolysis, EMT, and anticancer immunity. An immunometabolic strategy for treating colorectal cancer liver metastases using the shikonin-loaded, hyaluronic acid-modified mesoporous polydopamine nanoparticles (SHK@HA-MPDA) via glycolysis inhibition, anticancer immunity activation, and EMT reversal. SHK@HA-MPDA can inhibit cytoplasmic PKM2 and glycolysis of the tumor and reduce lactate flux, and then activate the DCs and remodel the tumor immune microenvironment. The reduced lactate flux can reduce MDSC migration and suppress EMT.

GPP-TSAIII nanocomposite hydrogel-based photothermal ablation facilitates melanoma therapy.

BACKGROUND: Photothermal therapy (PTT) is a promising cancer treatment, but its application is limited by low photoconversion efficiency. In this study, we aimed to develop a novel graphene oxide (GO)-based nanocomposite hydrogel to improve the bioavailability of timosaponin AIII (TSAIII) while maximizing PTT efficacy and enhancing the antitumor effect. METHODS: GO was modified via physical cross-linking with polyvinyl alcohol. The pore structure of the gel was adjusted by repeated freeze-thawing and the addition of polyethylene glycol 2000 to obtain a nanocomposite hydrogel (GPP). The GPP loaded with TSAIII constituted a GPP-TSAIII drug delivery system, and its efficacy was evaluated by in vitro cytotoxicity, apoptosis, migration, and uptake analyses, and in vivo antitumor studies. RESULTS: The encapsulation rate of GPP-TSAIII was 66.36 ± 3.97%, with slower in vitro release and higher tumor cell uptake (6.4-fold) compared to TSAIII. GPP-TSAIII in combination with PTT showed better bioavailability and antitumor effects in vivo than did TSAIII, with a 1.9-fold higher tumor suppression rate than the TSAIII group. CONCLUSIONS: GPP is a potential vehicle for delivery of TSAIII-like poor water-soluble anticancer drugs. The innovative PTT co-delivery system may serve as a safe and effective melanoma treatment platform for further anticancer translational purposes.

"Platelet-coated bullets" biomimetic nanoparticles to ameliorate experimental colitis by targeting endothelial cells.

Intestinal vascular impairment is critical to the recovery of inflammatory bowel disease (IBD), and targeting vascular endothelial cells is a promising emerging therapeutic option. Considering the natural homing properties of platelets to activated vascular endothelium, platelet membrane-mimetic nanoparticles are expected to achieve precise treatment of IBD. Patchouli alcohol (PA) has proven efficacy in experimental colitis, yet its pharmacochemical properties require improvement to enhance efficacy. The rationale for targeting vascular lesions in IBD was analyzed by network pharmacology, and PA-affecting pathways were predicted. PA-encapsulated bio-nanoparticles (PNPs) were constructed to investigate the efficacy of agents on mouse intestinal microvascular endothelial cells (MIMVEC) inflammation model and dextran sulfate sodium (DSS)-induced acute mouse colitis model. PNPs were endocytosed by MIMVEC in vitro and efficiently enriched in inflamed colon. PNPs significantly alleviated the symptoms of experimental colitis and improved neutrophil infiltration. PNPs down-regulated LPS-induced aberrant elevation of il1ß, tnfα and il6 mRNAs and reduced p65 phosphorylation in MIMVEC. Intracellular calcium expression, mitochondrial respiration and reactive oxygen species expression were also downregulated by PNPs. PNPs amplified the potency of PA as a calcium antagonist, restrained intracellular Ca2+ perturbations to prevent endothelial activation, which may block leukocyte recruitment in vivo to improve colitis.

Prediction of wavefront distortion for wavefront sensorless adaptive optics based on deep learning.

Aimed at the slow detection speed and low measurement accuracy of wavefront aberration in current wavefront sensorless adaptive optic technology, different convolution neural networks (CNNs) are established to detect the turbulence wavefront, including an ordinary convolutional neural network, a ResNet network, and an EfficientNet-B0 network. By using the nonlinear fitting ability of deep neural networks, the mapping relationship between Zernike coefficients and focal degraded image can be established. The simulation results show that the optimal network model after training can quickly and efficiently predict the Zernike coefficients directly from a single focal degraded image. The root-mean-square errors of the wavefront detection accuracy of the three networks are 0.075λ, 0.058λ, and 0.013λ, and the time consumed for predicting the wavefront from the single degraded image are 2.3, 4.6, and 3.4 ms, respectively. Among the three networks presented, the EfficientNet-B0 CNN has obvious advantages in wavefront detection accuracy and speed under different turbulence intensities than the ordinary CNN and ResNet networks. Compared with the traditional method, the deep learning method has the advantages of high precision and fast speed, without iteration and the local minimum problem, when solving wavefront aberration.

Piston Error Measurement for Segmented Telescopes with an Artificial Neural Network.

A piston error detection method is proposed based on the broadband intensity distribution on the image plane using a back-propagation (BP) artificial neural network. By setting a mask with a sparse circular clear multi-subaperture configuration in the exit pupil plane of a segmented telescope to fragment the pupil, the relation between the piston error of segments and amplitude of the modulation transfer function (MTF) sidelobes is strictly derived according to the Fourier optics principle. Then the BP artificial neural network is utilized to establish the mapping relation between them, where the amplitudes of the MTF sidelobes directly calculated from theoretical relationship and the introduced piston errors are used as inputs and outputs respectively to train the network. With the well trained-network, the piston errors are measured to a good precision using one in-focused broadband image without defocus division as input, and the capture range achieving the coherence length of the broadband light is available. Adequate simulations demonstrate the effectiveness and accuracy of the proposed method; the results show that the trained network has high measurement accuracy, wide detection range, quite good noise immunity and generalization ability. This method provides a feasible and easily implemented way to measure piston error and can simultaneously detect the multiple piston errors of the entire aperture of the segmented telescope.