Machine Learning Effectively Diagnoses Mandibular Deformity Using Three-Dimensional Landmarks.

BACKGROUND: Jaw deformity diagnosis requires objective tests. Current methods, like cephalometry, have limitations. However, recent studies have shown that machine learning can diagnose jaw deformities in two dimensions. Therefore, we hypothesized that a multilayer perceptron (MLP) could accurately diagnose jaw deformities in three dimensions (3D). PURPOSE: Examine the hypothesis by focusing on anomalous mandibular position. We aimed to: (1) create a machine learning model to diagnose mandibular retrognathism and prognathism; and (2) compare its performance with traditional cephalometric methods. STUDY DESIGN, SETTING, SAMPLE: An in-silico experiment on deidentified retrospective data. The study was conducted at the Houston Methodist Research Institute and Rensselaer Polytechnic Institute. Included were patient records with jaw deformities and preoperative 3D facial models. Patients with significant jaw asymmetry were excluded. PREDICTOR VARIABLES: The tests used to diagnose mandibular anteroposterior position are: (1) SNB angle; (2) facial angle; (3) mandibular unit length (MdUL); and (4) MLP model. MAIN OUTCOME VARIABLE: The resultant diagnoses: normal, prognathic, or retrognathic. COVARIATES: None. ANALYSES: A senior surgeon labeled the patients' mandibles as prognathic, normal, or retrognathic, creating a gold standard. Scientists at Rensselaer Polytechnic Institute developed an MLP model to diagnose mandibular prognathism and retrognathism using the 3D coordinates of 50 landmarks. The performance of the MLP model was compared with three traditional cephalometric measurements: (1) SNB, (2) facial angle, and (3) MdUL. The primary metric used to assess the performance was diagnostic accuracy. McNemar's exact test tested the difference between traditional cephalometric measurement and MLP. Cohen's Kappa measured inter-rater agreement between each method and the gold standard. RESULTS: The sample included 101 patients. The diagnostic accuracy of SNB, facial angle, MdUL, and MLP were 74.3, 74.3, 75.3, and 85.2%, respectively. McNemar's test shows that our MLP performs significantly better than the SNB (P = .027), facial angle (P = .019), and MdUL (P = .031). The agreement between the traditional cephalometric measurements and the surgeon's diagnosis was fair. In contrast, the agreement between the MLP and the surgeon was moderate. CONCLUSION AND RELEVANCE: The performance of the MLP is significantly better than that of the traditional cephalometric measurements.

Midsagittal Plane First: Building a Strong Facial Reference Frame for Computer-Aided Surgical Simulation.

PURPOSE: A facial reference frame is a 3-dimensional Cartesian coordinate system that includes 3 perpendicular planes: midsagittal, axial, and coronal. The order in which one defines the planes matters. The purposes of this study are to determine the following: 1) what sequence (axial-midsagittal-coronal vs midsagittal-axial-coronal) produced more appropriate reference frames and 2) whether orbital or auricular dystopia influenced the outcomes. METHODS: This study is an ambispective cross-sectional study. Fifty-four subjects with facial asymmetry were included. The facial reference frames of each subject (outcome variable) were constructed using 2 methods (independent variable): axial plane first and midsagittal plane first. Two board-certified orthodontists together blindly evaluated the results using a 3-point categorical scale based on their careful inspection and expert intuition. The covariant for stratification was the existence of orbital or auricular dystopia. Finally, Wilcoxon signed rank tests were performed. RESULTS: The facial reference frames defined by the midsagittal plane first method was statistically significantly different from ones defined by the axial plane first method (P = .001). Using the midsagittal plane first method, the reference frames were more appropriately defined in 22 (40.7%) subjects, equivalent in 26 (48.1%) and less appropriately defined in 6 (11.1%). After stratified by orbital or auricular dystopia, the results also showed that the reference frame computed using midsagittal plane first method was statistically significantly more appropriate in both subject groups regardless of the existence of orbital or auricular dystopia (27 with orbital or auricular dystopia and 27 without, both P < .05). CONCLUSIONS: The midsagittal plane first sequence improves the facial reference frames compared with the traditional axial plane first approach. However, regardless of the sequence used, clinicians need to judge the correctness of the reference frame before diagnosis or surgical planning.

Neutron grating interferometer with an analyzer grating based on a light blocker.

We study an analyzer grating based on a scintillation light blocker for a Talbot-Lau grating interferometer. This is an alternative way to analyze the Talbot self-image without the need for an often difficult to fabricate absorption grating for the incident radiation. The feasibility of this approach using a neutron beam has been evaluated and experiments have been conducted at the cold neutron imaging facility of the NIST center for Neutron Research. The neutron grating interferometer with the proposed analyzer grating successfully produced attenuation, differential phase, and dark-field contrast images. In addition, numerical simulations were performed to simulate the Talbot pattern and visibility using scintillation screens of different thicknesses and there is good agreement with the experimental measurements. The results show potential for reducing the difficulty of fabricating analyzer grating, and a possibility for the so-called shadow effect to be eliminated and large-area gratings to be produced, especially when applied to X-rays. We report the performance of the analyzer grating based on a light blocker and evaluate its feasibility for the grating interferometer.

Clinical Evaluation of Digital Dental Articulation for One-Piece Maxillary Surgery.

PURPOSE: Methods for digital dental alignment are not readily available to automatically articulate the upper and lower jaw models. The purpose of the present study was to assess the accuracy of our newly developed 3-stage automatic digital articulation approach by comparing it with the reference standard of orthodontist-articulated occlusion. MATERIALS AND METHODS: Thirty pairs of stone dental models from double-jaw orthognathic surgery patients who had undergone 1-piece Le Fort I osteotomy were used. Two experienced orthodontists manually articulated the models to their perceived final occlusion for surgery. Each pair of models was then scanned twice-while in the orthodontist-determined occlusion and again with the upper and lower models separated and positioned randomly. The separately scanned models were automatically articulated to the final occlusion using our 3-stage algorithm, resulting in an algorithm-articulated occlusion (experimental group). The models scanned together represented the manually articulated occlusion (control group). A qualitative evaluation was completed using a 3-point categorical scale by the same orthodontists, who were unaware of the methods used to articulate the models. A quantitative evaluation was also completed to determine whether any differences were present in the midline, canine, and molar relationships between the algorithm-determined and manually articulated occlusions using repeated measures analysis of variance (ANOVA). Finally, the mean ± standard deviation values were computed to determine the differences between the 2 methods. RESULTS: The results of the qualitative evaluation revealed that all the algorithm-articulated occlusions were as good as the manually articulated ones. The results of the repeated measures ANOVA found no statistically significant differences between the 2 methods [F(1,28) = 0.03; P = .87]. The mean differences between the 2 methods were all within 0.2 mm. CONCLUSIONS: The results of our study have demonstrated that dental models can be accurately, reliably, and automatically articulated using our 3-stage algorithm approach, meeting the reference standard of orthodontist-articulated occlusion.

TraRECo: a greedy approach based de novo transcriptome assembler with read error correction using consensus matrix.

BACKGROUND: The challenges when developing a good de novo transcriptome assembler include how to deal with read errors and sequence repeats. Almost all de novo assemblers utilize a de Bruijn graph, with which complexity grows linearly with data size while suffering from errors and repeats. Although one can correct the errors by inspecting the topological structure of the graph, this is not an easy task when there are too many branches. Two research directions are to improve either the graph reliability or the path search precision, and in this study, we focused on the former. RESULTS: We present TraRECo, a greedy approach to de novo assembly employing error-aware graph construction. In the proposed approach, we built contigs by direct read alignment within a distance margin and performed a junction search to construct splicing graphs. While doing so, a contig of length l was represented by a 4 × l matrix (called a consensus matrix), in which each element was the base count of the aligned reads so far. A representative sequence was obtained by taking the majority in each column of the consensus matrix to be used for further read alignment. Once the splicing graphs had been obtained, we used IsoLasso to find paths with a noticeable read depth. The experiments using real and simulated reads show that the method provided considerable improvement in sensitivity and moderately better performance when comparing sensitivity and precision. This was achieved by the error-aware graph construction using the consensus matrix, with which the reads having errors were made usable for the graph construction (otherwise, they might have been eventually discarded). This improved the quality of the coverage depth information used in the subsequent path search step and finally the reliability of the graph. CONCLUSIONS: De novo assembly is mainly used to explore undiscovered isoforms and must be able to represent as many reads as possible in an efficient way. In this sense, TraRECo provides us with a potential alternative for improving graph reliability even though the computational burden is much higher than the single k-mer in the de Bruijn graph approach.

Arctigenin Inhibits Adipogenesis by Inducing AMPK Activation and Reduces Weight Gain in High-Fat Diet-Induced Obese Mice.

Although arctigenin (ARC) has been reported to have some pharmacological effects such as anti-inflammation, anti-cancer, and antioxidant, there have been no reports on the anti-obesity effect of ARC. The aim of this study is to investigate whether ARC has an anti-obesity effect and mediates the AMP-activated protein kinase (AMPK) pathway. We investigated the anti-adipogenic effect of ARC using 3T3-L1 pre-adipocytes and human adipose tissue-derived mesenchymal stem cells (hAMSCs). In high-fat diet (HFD)-induced obese mice, whether ARC can inhibit weight gain was investigated. We found that ARC reduced weight gain, fat pad weight, and triglycerides in HFD-induced obese mice. ARC also inhibited the expression of peroxisome proliferator-activated receptor gamma (PPARγ) and CCAAT/enhancer-binding protein alpha (C/EBPα) in in vitro and in vivo. Furthermore, ARC induced the AMPK activation resulting in down-modulation of adipogenesis-related factors including PPARγ, C/EBPα, fatty acid synthase, adipocyte fatty acid-binding protein, and lipoprotein lipase. This study demonstrates that ARC can reduce key adipogenic factors by activating the AMPK in vitro and in vivo and suggests a therapeutic implication of ARC for obesity treatment. J. Cell. Biochem. 117: 2067-2077, 2016. © 2016 Wiley Periodicals, Inc.

Arctigenin Inhibits Lung Metastasis of Colorectal Cancer by Regulating Cell Viability and Metastatic Phenotypes.

Arctigenin (ARC) has been shown to have an anti-cancer effect in various cell types and tissues. However, there have been no studies concerning metastatic colorectal cancer (CRC). In this study, we investigated the anti-metastatic properties of ARC on colorectal metastasis and present a potential candidate drug. ARC induced cell cycle arrest and apoptosis in CT26 cells through the intrinsic apoptotic pathway via MAPKs signaling. In several metastatic phenotypes, ARC controlled epithelial-mesenchymal transition (EMT) through increasing the expression of epithelial marker E-cadherin and decreasing the expressions of mesenchymal markers; N-cadherin, vimentin, ß-catenin, and Snail. Moreover, ARC inhibited migration and invasion through reducing of matrix metalloproteinase-2 (MMP-2) and MMP-9 expressions. In an experimental metastasis model, ARC significantly inhibited lung metastasis of CT26 cells. Taken together, our study demonstrates the inhibitory effects of ARC on colorectal metastasis.

Effects of Ixeris dentata water extract and caffeic acid on allergic inflammation in vivo and in vitro.

BACKGROUND: Ixeris dentata Nakai has been used for the treatment of mithridatism, calculous, indigestion, pneumonia, hepatitis, and tumors in Korea, China, and Japan. However, the effect of a water extract of Ixeris dentata (ID) and its molecular mechanism on allergic inflammation has not been elucidated. In this study, we attempted to evaluate the effects of ID and its major compound caffeic acid on allergic inflammation in vivo and in vitro. METHODS: ID was applied to 2, 4-dinitrofluorobenzene (DNFB)-induced atopic dermatitis (AD)-like skin lesion mice and immune cell infiltration, cytokine production, and the activation of mitogen-activated protein kinases (MAPKs) were investigated. Moreover, the effect of ID on compound 48/80-induced anaphylactic shock was investigated in a mouse model. The human keratinocyte cell line (HaCaT cells) and human mast cells (HMC-1) were treated with ID or caffeic acid to investigate the effects on the production of chemokines and proinflammatory cytokines and on the activation of MAPKs. RESULTS: ID inhibited the serum levels of IgE and interleukin (IL)-1ß in DNFB-induced AD-like skin lesion mouse models and suppressed anaphylactic shock in the mouse models. ID and caffeic acid inhibited the production of chemokines and adhesion molecules in HaCaT cells. In addition, ID reduced the release of tumor necrosis factor-α and IL-8 via the inhibition of MAPKs phosphorylation in HMC-1 cells. CONCLUSIONS: These results suggest that ID is a potential therapeutic agent for allergic inflammatory diseases, including dermatitis.

The antiinflammatory mechanism of Igongsan in mouse peritoneal macrophages via suppression of NF-κB/Caspase-1 activation.

UNLABELLED: Igongsan (IGS), which is an herbal prescription composed of five different herbs, Ginseng Radix (root of Panax ginseng, Araliaceae), Atractylodis Rhizoma Alba (rhizome of Atractylodes Macrocephala, Compositae), Poria Sclerotium (sclerotium of Poria cocos, Polyporaceae), Glycyrrhizae Radix et Rhizoma (root and rhizome of Glycyrrhiza uralensis, Leguminosae), and Citri Unshius Pericarpium (Peel of Citrus unshiu, Rutaceae), has been traditionally used in Korea to treat a variety of inflammatory diseases. In this study, we investigated to elucidate the mechanism responsible for IGS's antiinflammatory effect in mouse peritoneal macrophages. The findings demonstrate that IGS inhibited the production of inflammatory cytokine and prostaglandins E2 . IGS inhibited the enhanced levels of cyclooxygenase-2 and inducible NO synthase caused by lipopolysaccharide (LPS). Additionally, it was shown that the antiinflammatory effect of IGS is through regulating the activation of nuclear factor-kappa B and caspase-1 in LPS-stimulated mouse peritoneal macrophages. These results provide novel insights into the pharmacological actions of IGS as a potential candidate for development of new drugs to treat inflammatory diseases. DISCUSSION AND CONCLUSION: These results provide novel insights into the pharmacological actions of IGS as a potential candidate for development of new drugs to treat inflammatory diseases.

Correspondence attention for facial appearance simulation.

In orthognathic surgical planning for patients with jaw deformities, it is crucial to accurately simulate the changes in facial appearance that follow the bony movement. Compared with the traditional biomechanics-based methods like the finite-element method (FEM), which are both labor-intensive and computationally inefficient, deep learning-based methods offer an efficient and robust modeling alternative. However, current methods do not account for the physical relationship between facial soft tissue and bony structure, causing them to fall short in accuracy compared to FEM. In this work, we propose an Attentive Correspondence assisted Movement Transformation network (ACMT-Net) to predict facial changes by correlating facial soft tissue changes with bony movement through a point-to-point attentive correspondence matrix. To ensure efficient training, we also introduce a contrastive loss for self-supervised pre-training of the ACMT-Net with a k-Nearest Neighbors (k-NN) based clustering. Experimental results on patients with jaw deformities show that our proposed solution can achieve significantly improved computational efficiency over the state-of-the-art FEM-based method with comparable facial change prediction accuracy.

Saengmaeksan inhibits inflammatory mediators by suppressing RIP-2/caspase-1 activation.

BACKGROUND AND OBJECTIVES: Saengmaeksan (SMS) is a Korean herbal prescription consisting of three different herbal drugs: Liriopis Tuber (tuber of Liriope platyphylla, Liliaceae), Ginseng Radix (root of Panax ginseng) and Schisandrae Fructus (fruit of Schisandra chinensis). SMS is commonly used in Korea to treat various diseases that involve the respiratory and cardiovascular systems. However, to date, the mechanism underlying the anti-inflammatory effects of SMS is not clearly understood. In this study, we attempt to determine the effects of SMS on lipopolysaccharide (LPS)-induced inflammatory responses in mouse peritoneal macrophages. METHODS: Cell viability was measured by using a 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide assay, and nitric oxide (NO) levels were measured by using Griess reagent. The tumor necrosis factor (TNF)-α and interleukin (IL)-6 levels secreted by the cells were measured using a modified enzyme-linked immunosorbent assay. Expression of cyclooxygenase (COX)-2 and nuclear factor-kappa B (NF-κB), respectively was investigated using a western blot analysis. A caspase colorimetric assay kit was used to assay enzymatic caspase-1 activity. RESULTS: The findings of this study showed that SMS reduced TNF-α and IL-6 production induced by LPS. During the inflammatory process, COX-2 and NO levels were increased in mouse peritoneal macrophages, but SMS decreased the enhanced levels of COX-2 and the production of NO. In addition, SMS suppressed the activation of NF-κB and receptor interacting protein-2/caspase-1. DISCUSSION AND CONCLUSION: Our results provide novel insights into the pharmacological actions of SMS, a molecule that can potentially be exploited in the treatment of inflammatory diseases.

Predicting Optimal Patient-Specific Postoperative Facial Landmarks for Patients with Craniomaxillofacial Deformities.

Orthognathic surgery traditionally focuses on correcting skeletal abnormalities and malocclusion, with the expectation that an optimal facial appearance will naturally follow. However, this skeletal-driven approach can lead to undesirable facial aesthetics and residual asymmetry. To address these issues, a soft-tissue-driven planning method has been proposed. This innovative method bases bone movement estimates on the targeted ideal facial appearance, thus increasing the surgical plan's accuracy and effectiveness. This study explores the initial phase of implementing a soft-tissue-driven approach, simulating the patient's optimal facial look by repositioning deformed facial landmarks to an ideal state. The algorithm incorporates symmetrization and weighted optimization strategies, aligning projected optimal landmarks with standard cephalometric values for both facial symmetry and form, which are integral to facial aesthetics in orthognathic surgery. It also includes regularization to preserve the patient's original facial characteristics. Validated using retrospective analysis of data from both preoperative patients and normal subjects, this approach effectively achieves not only facial symmetry, particularly in the lower face, but also a more natural and normalized facial form. This novel approach, aligning with soft-tissue-driven planning principles, shows promise in surpassing traditional methods, potentially leading to enhanced facial outcomes and patient satisfaction in orthognathic surgery.

Simulation of Postoperative Facial Appearances via Geometric Deep Learning for Efficient Orthognathic Surgical Planning.

Orthognathic surgery corrects jaw deformities to improve aesthetics and functions. Due to the complexity of the craniomaxillofacial (CMF) anatomy, orthognathic surgery requires precise surgical planning, which involves predicting postoperative changes in facial appearance. To this end, most conventional methods involve simulation with biomechanical modeling methods, which are labor intensive and computationally expensive. Here we introduce a learning-based framework to speed up the simulation of postoperative facial appearances. Specifically, we introduce a facial shape change prediction network (FSC-Net) to learn the nonlinear mapping from bony shape changes to facial shape changes. FSC-Net is a point transform network weakly-supervised by paired preoperative and postoperative data without point-wise correspondence. In FSC-Net, a distance-guided shape loss places more emphasis on the jaw region. A local point constraint loss restricts point displacements to preserve the topology and smoothness of the surface mesh after point transformation. Evaluation results indicate that FSC-Net achieves 15× speedup with accuracy comparable to a state-of-the-art (SOTA) finite-element modeling (FEM) method.

Bidirectional prediction of facial and bony shapes for orthognathic surgical planning.

This paper proposes a deep learning framework to encode subject-specific transformations between facial and bony shapes for orthognathic surgical planning. Our framework involves a bidirectional point-to-point convolutional network (P2P-Conv) to predict the transformations between facial and bony shapes. P2P-Conv is an extension of the state-of-the-art P2P-Net and leverages dynamic point-wise convolution (i.e., PointConv) to capture local-to-global spatial information. Data augmentation is carried out in the training of P2P-Conv with multiple point subsets from the facial and bony shapes. During inference, network outputs generated for multiple point subsets are combined into a dense transformation. Finally, non-rigid registration using the coherent point drift (CPD) algorithm is applied to generate surface meshes based on the predicted point sets. Experimental results on real-subject data demonstrate that our method substantially improves the prediction of facial and bony shapes over state-of-the-art methods.

The therapeutic effect of chelidonic acid on ulcerative colitis.

Chelidonic acid (CA), a constituent of Chelidonium majus L., has many pharmacological effects, including mild analgesic and antimicrobial effects. However, the effects of CA on intestinal inflammation and the molecular mechanisms responsible are poorly understood. The aim of this study was to investigate the protective effects of CA against dextran sulfate sodium (DSS)-induced ulcerative colitis (UC). Mice treated with DSS displayed obvious clinic signs, such as, body weight loss and a shortening of colon length, but the administration of CA attenuated both of these signs. Additionally, CA was found to regulate levels of interleukin-6 and tumor necrosis factor-α in serum. In colonic tissues, prostaglandin E(2) (PGE(2)) production levels and cyclooxygenase-2 (COX-2) and hypoxia induced factor-1α (HIF-1α) expression levels were increased by DSS, but CA attenuated increases in COX-2 and HIF-1α levels. These results provide novel insights into the pharmacological actions of CA and its potential use for the treatment of intestinal inflammation.

Phase retrieval based on deep learning in grating interferometer.

Grating interferometry is a promising technique to obtain differential phase contrast images with illumination source of low intrinsic transverse coherence. However, retrieving the phase contrast image from the differential phase contrast image is difficult due to the accumulated noise and artifacts from the differential phase contrast image (DPCI) reconstruction. In this paper, we implemented a deep learning-based phase retrieval method to suppress these artifacts. Conventional deep learning based denoising requires noise/clean image pair, but it is not feasible to obtain sufficient number of clean images for grating interferometry. In this paper, we apply a recently developed neural network called Noise2Noise (N2N) that uses noise/noise image pairs for training. We obtained many DPCIs through combination of phase stepping images, and these were used as input/target pairs for N2N training. The application of the N2N network to simulated and measured DPCI showed that the phase contrast images were retrieved with strongly suppressed phase retrieval artifacts. These results can be used in grating interferometer applications which uses phase stepping method.

Analysis of a silicon comb structure using an inverse Talbot-Lau neutron grating interferometer.

We describe an inverse Talbot-Lau neutron grating interferometer that provides an extended autocorrelation length range for quantitative dark-field imaging. To our knowledge, this is the first report of a Talbot-Lau neutron grating interferometer (nTLI) with inverse geometry. We demonstrate a range of autocorrelation lengths (ACL) starting at low tens of nanometers, which is significantly extended compared to the ranges of conventional and symmetric setups. ACLs from a minimum of 44 nm to the maximum of 3.5 µm were presented for the designed wavelength of 4.4 Å in experiments. Additionally, the inverse nTLI has neutron-absorbing gratings with an optically thick gadolinium oxysulfide (Gadox) structure, allowing it to provide a visibility of up to 52% while maintaining a large field of view of approximately 100 mm × 100 mm. We demonstrate the application of our interferometer to quantitative dark-field imaging by using diluted polystyrene particles in an aqueous solution and silicon comb structures. We obtain quantitative structural information of the sphere size and concentration of diluted polystyrene particles and the period, height, and duty cycle of the silicon comb structures. The optically thick Gadox structure of the analyzer grating also provides improved characteristics for the correction of incoherent neutron scattering in an aqueous solution compared to the symmetric nTLI.

Deep learning for biomechanical modeling of facial tissue deformation in orthognathic surgical planning.

PURPOSE: Orthognathic surgery requires an accurate surgical plan of how bony segments are moved and how the face passively responds to the bony movement. Currently, finite element method (FEM) is the standard for predicting facial deformation. Deep learning models have recently been used to approximate FEM because of their faster simulation speed. However, current solutions are not compatible with detailed facial meshes and often do not explicitly provide the network with known boundary type information. Therefore, the purpose of this proof-of-concept study is to develop a biomechanics-informed deep neural network that accepts point cloud data and explicit boundary types as inputs to the network for fast prediction of soft-tissue deformation. METHODS: A deep learning network was developed based on the PointNet++ architecture. The network accepts the starting facial mesh, input displacement, and explicit boundary type information and predicts the final facial mesh deformation. RESULTS: We trained and tested our deep learning model on datasets created from FEM simulations of facial meshes. Our model achieved a mean error between 0.159 and 0.642 mm on five subjects. Including explicit boundary types had mixed results, improving performance in simulations with large deformations but decreasing performance in simulations with small deformations. These results suggest that including explicit boundary types may not be necessary to improve network performance. CONCLUSION: Our deep learning method can approximate FEM for facial change prediction in orthognathic surgical planning by accepting geometrically detailed meshes and explicit boundary types while significantly reducing simulation time.

Identification and Characterization of Cancer-Associated Fibroblast Subpopulations in Lung Adenocarcinoma.

Cancer-associated fibroblasts (CAFs) reside within the tumor microenvironment, facilitating cancer progression and metastasis via direct and indirect interactions with cancer cells and other stromal cell types. CAFs are composed of heterogeneous subpopulations of activated fibroblasts, including myofibroblastic, inflammatory, and immunosuppressive CAFs. In this study, we sought to identify subpopulations of CAFs isolated from human lung adenocarcinomas and describe their transcriptomic and functional characteristics through single-cell RNA sequencing (scRNA-seq) and subsequent bioinformatics analyses. Cell trajectory analysis of combined total and THY1 + CAFs revealed two branching points with five distinct branches. Based on Gene Ontology analysis, we denoted Branch 1 as "immunosuppressive", Branch 2 as "neoantigen presenting", Branch 4 as "myofibroblastic", and Branch 5 as "proliferative" CAFs. We selected representative branch-specific markers and measured their expression levels in total and THY1 + CAFs. We also investigated the effects of these markers on CAF activity under coculture with lung cancer cells. This study describes novel subpopulations of CAFs in lung adenocarcinoma, highlighting their potential value as therapeutic targets.

Vanillic acid inhibits inflammatory mediators by suppressing NF-κB in lipopolysaccharide-stimulated mouse peritoneal macrophages.

Vanillic acid is a benzoic acid derivative that is used as a flavoring agent. It is an oxidized form of vanillin. At present, the mechanisms by which vanillic acid exerts its anti-inflammatory effects are incompletely understood. In this study, we attempted to determine the effects of vanillic acid on lipopolysaccharide (LPS)-induced inflammatory responses in mouse peritoneal macrophages. Our findings indicate that vanillic acid inhibits LPS-induced production of tumor necrosis factor (TNF)-α and interleukin (IL)-6. During the inflammatory process, the levels of cyclooxygenase (COX)-2 and nitric oxide (NO) increased in mouse peritoneal macrophages, but vanillic acid suppressed both the enhanced levels of COX-2 and the production of prostaglandin E(2) and NO. Moreover, vanillic acid suppressed the activation of nuclear factor-kappa B (NF-κB) and caspase-1. These results provide novel insights into the pharmacological actions of vanillic acid and are indicative of the potential use of this molecule in the treatment of inflammatory diseases.