SON is an essential RNA splicing factor promoting ErbB2 and ErbB3 expression in breast cancer.

BACKGROUND: In breast cancer, ErbB receptors play a critical role, and overcoming drug resistance remains a major challenge in the clinic. However, intricate regulatory mechanisms of ErbB family genes are poorly understood. Here, we demonstrate SON as an ErbB-regulatory splicing factor and a novel therapeutic target for ErbB-positive breast cancer. METHODS: SON and ErbB expression analyses using public database, patient tissue microarray, and cell lines were performed. SON knockdown assessed its impact on cell proliferation, apoptosis, kinase phosphorylation, RNA splicing, and in vivo tumour growth. RNA immunoprecipitation was performed to measure SON binding. RESULTS: SON is highly expressed in ErbB2-positive breast cancer patient samples, inversely correlating with patient survival. SON knockdown induced intron retention in selective splice sites within ErbB2 and ErbB3 transcripts, impairing effective RNA splicing and reducing protein expression. SON disruption suppressed downstream kinase signalling of ErbB2/3, including the Akt, p38, and JNK pathways, with increased vulnerability in ErbB2-positive breast cancer cells compared to ErbB2-negative cells. SON silencing in ErbB2-positive breast cancer xenografts led to tumour regression in vivo. CONCLUSION: We identified SON as a novel RNA splicing factor that plays a critical role in regulating ErbB2/3 expression, suggesting SON is an ideal therapeutic target in ErbB2-positive breast cancers.

Improving Text-to-SQL with a Hybrid Decoding Method.

Text-to-SQL is a task that converts natural language questions into SQL queries. Recent text-to-SQL models employ two decoding methods: sketch-based and generation-based, but each has its own shortcomings. The sketch-based method has limitations in performance as it does not reflect the relevance between SQL elements, while the generation-based method may increase inference time and cause syntactic errors. Therefore, we propose a novel decoding method, Hybrid decoder, which combines both methods. This reflects inter-SQL element information and defines elements that can be generated, enabling the generation of syntactically accurate SQL queries. Additionally, we introduce a Value prediction module for predicting values in the WHERE clause. It simplifies the decoding process and reduces the size of vocabulary by predicting values at once, regardless of the number of conditions. The results of evaluating the significance of Hybrid decoder indicate that it improves performance by effectively incorporating mutual information among SQL elements, compared to the sketch-based method. It also efficiently generates SQL queries by simplifying the decoding process in the generation-based method. In addition, we design a new evaluation measure to evaluate if it generates syntactically correct SQL queries. The result demonstrates that the proposed model generates syntactically accurate SQL queries.

Sex differences in the association between self-rated health and high-sensitivity C-reactive protein levels in Koreans: a cross-sectional study using data from the Korea National Health and Nutrition Examination Survey.

BACKGROUND: No studies have investigated the association between self-rated health (SRH) and high-sensitivity C-reactive protein (hs-CRP) levels in South Koreans. We explored this association and analyzed differences between sexes. METHODS: Using cross-sectional data from the 2015-2017 Korea National Health and Nutrition Examination Survey, we analyzed the association between SRH and high hs-CRP levels (> 1.0 mg/L) in 14,544 Koreans aged ≥ 19 years who responded to the SRH survey and had available hs-CRP test results. Differences in sociodemographic factors were analyzed using the Pearson's chi-square test for categorical variables or the Mann-Whitney U test for continuous variables. Multiple logistic regression analysis was used to measure the association between hs-CRP levels and SRH according to sex while adjusting for other possible confounders. RESULTS: The percentage of very poor to poor SRH was higher in the high hs-CRP group (22.4%) than in the low hs-CRP group (17.66%). Among men, the risk of a high hs-CRP level increased with worse SRH (adjusted for confounders; P for trend < 0.001). After adjusting for all confounders, including chronic diseases, men with very poor SRH showed a higher odds ratio (OR) for high hs-CRP levels than those with very good SRH (fully adjusted OR, 1.74; 95% CI, 1.04-2.90). Significant correlations were absent among women. CONCLUSIONS: Poor SRH was correlated with low-grade inflammation (high hs-CRP levels) among Korean male adults. These findings could be useful for developing health improvement programs and in goal setting at a national scale.

Effective sentence-level relation extraction model using entity-centric dependency tree.

The syntactic information of a dependency tree is an essential feature in relation extraction studies. Traditional dependency-based relation extraction methods can be categorized into hard pruning methods, which aim to remove unnecessary information, and soft pruning methods, which aim to utilize all lexical information. However, hard pruning has the potential to overlook important lexical information, while soft pruning can weaken the syntactic information between entities. As a result, recent studies in relation extraction have been shifting from dependency-based methods to pre-trained language model (LM) based methods. Nonetheless, LM-based methods increasingly demand larger language models and additional data. This trend leads to higher resource consumption, longer training times, and increased computational costs, yet often results in only marginal performance improvements. To address this problem, we propose a relation extraction model based on an entity-centric dependency tree: a dependency tree that is reconstructed by considering entities as root nodes. Using the entity-centric dependency tree, the proposed method can capture the syntactic information of an input sentence without losing lexical information. Additionally, we propose a novel model that utilizes entity-centric dependency trees in conjunction with language models, enabling efficient relation extraction without the need for additional data or larger models. In experiments with representative sentence-level relation extraction datasets such as TACRED, Re-TACRED, and SemEval 2010 Task 8, the proposed method achieves F1-scores of 74.9%, 91.2%, and 90.5%, respectively, which are state-of-the-art performances.

Machine learning-based prediction of seasonal hypoxia in eutrophic estuary using capacitive potentiometric sensor.

A hypoxia occurred in eutrophic estuary was predicted using long short-term memory (LSTM) model with prediction time steps (PTSs) of 0, 1, 12, and 24 h. A capacitive potential (CP), which provides quantitative information on dissolved oxygen (DO) concentration, was used as a predictor along with precipitation, tide level, salinity, and water temperature. First, annual changes in DO concentration were clustered in three phases of annual DO trends (oversaturation, depletion, and stable) using k-means clustering. CP was the most influential variable in clustering the DO phases. The LSTM was implemented to predict the DO phases and hypoxia occurrences. In the simultaneous prediction of the depletion phase and hypoxia occurrence with a 12 h PTS, the accuracy was 92.1% using CP along with other variables; it was 3.3% higher than that achieved using variables other than CP. In the case of predicting the depletion phase and hypoxia non-occurrence using CP along with other variables, the accuracy was 61.1%, which was 5.5% higher than that when CP was not used. When using CP along with other variables, the total accuracy was highest for all PTS. Overall, the utilization of CP and machine learning techniques enables accurate predictions of both short-term and long-term hypoxia occurrences, providing us with the opportunity to proactively respond to disasters in aquaculture and environmental management due to hypoxia.

Artificial Multimodal Neuron with Associative Learning Capabilities: Acquisition, Extinction, and Spontaneous Recovery.

Associative multimodal artificial intelligence (AMAI) has gained significant attention across various fields, yet its implementation poses challenges due to the burden on computing and memory resources. To address these challenges, researchers have paid increasing attention to neuromorphic devices based on novel materials and structures, which can implement classical conditioning behaviors with simplified circuitry. Herein, we introduce an artificial multimodal neuron device that shows not only the acquisition behavior but also the extinction and the spontaneous recovery behaviors for the first time. Being composed of an ovonic threshold switch (OTS)-based neuron device, a conductive bridge memristor (CBM)-based synapse device, and a few passive electrical elements, such observed behaviors of this neuron device are explained in terms of the electroforming and the diffusion of metallic ions in the CBM. We believe that the proposed associative learning neuron device will shed light on the way of developing large-scale AMAI systems by providing inspiration to devise an associative learning network with improved energy efficiency.

Demonstration of an energy-efficient Ising solver composed of Ovonic threshold switch (OTS)-based nano-oscillators (OTSNOs).

As there is an increasing need for an efficient solver of combinatorial optimization problems, much interest is paid to the Ising machine, which is a novel physics-driven computing system composed of coupled oscillators mimicking the dynamics of the system of coupled electronic spins. In this work, we propose an energy-efficient nano-oscillator, called OTSNO, which is composed of an Ovonic Threshold Switch (OTS) and an electrical resistor. We demonstrate that the OTSNO shows the synchronization behavior, an essential property for the realization of an Ising machine. Furthermore, we have discovered that the capacitive coupling is advantageous over the resistive coupling for the hardware implementation of an Ising solver by providing a larger margin of the variations of components. Finally, we implement an Ising machine composed of capacitively-coupled OTSNOs to demonstrate that the solution to a 14-node MaxCut problem can be obtained in 40 µs while consuming no more than 2.3 µJ of energy. Compared to a previous hardware implementation of the phase-transition nano-oscillator (PTNO)-based Ising machine, the OTSNO-based Ising machine in this work shows the performance of the increased speed by more than one order while consuming less energy by about an order.

Hybrid CMOS-Memristor synapse circuits for implementing Ca ion-based plasticity model.

Neuromorphic computing research is being actively pursued to address the challenges posed by the need for energy-efficient processing of big data. One of the promising approaches to tackle the challenges is the hardware implementation of spiking neural networks (SNNs) with bio-plausible learning rules. Numerous research works have been done to implement the SNN hardware with different synaptic plasticity rules to emulate human brain operations. While a standard spike-timing-dependent-plasticity (STDP) rule is emulated in many SNN hardware, the various STDP rules found in the biological brain have rarely been implemented in hardware. This study proposes a CMOS-memristor hybrid synapse circuit for the hardware implementation of a Ca ion-based plasticity model to emulate the various STDP curves. The memristor was adopted as a memory device in the CMOS synapse circuit because memristors have been identified as promising candidates for analog non-volatile memory devices in terms of energy efficiency and scalability. The circuit design was divided into four sub-blocks based on biological behavior, exploiting the non-volatile and analog state properties of memristors. The circuit was designed to vary weights using an H-bridge circuit structure and PWM modulation. The various STDP curves have been emulated in one CMOS-memristor hybrid circuit, and furthermore a simple neural network operation was demonstrated for associative learning such as Pavlovian conditioning. The proposed circuit is expected to facilitate large-scale operations for neuromorphic computing through its scale-up.

Robust visual detection of brake-lights in front for commercialized dashboard camera.

The collision avoidance system (CAS) is an essential system for safe driving that alerts the driver or automatically applies the brakes in an expected situation of a vehicle collision. To realize this, an autonomous system that can quickly and precisely detect brake-lights of preceding vehicle is essential and this should works well in various environments for safety reason. Our proposed vision algorithm solves these objectives focusing on simple color features rather than a learning algorithm with a high computational cost, since our target system is a real-time embedded device, i.e., forward-facing dashboard camera. However, the existing feature-based algorithms are vulnerable to the ambient noise (noise problem), and cannot be flexibly applied to various environments (applicability problem). Therefore, our method is divided into two stages: rear-lights region detection using gamma correction for noise problem, and brake-lights detection using HSV color space for applicability problem, respectively. (i) Rear-lights region detection: we confirm the presence of the vehicle in front and derive the rear-lights region, and used non-linear mapping of gamma correction to make the detected region robust to noise. (ii) Brake-lights detection: from the detected rear-lights region, we extract color features using the HSV color range so that we can classify brake on and off in various conditions. Experimental results show that our algorithm overcomes the noise problem and applicability problem in various environments.

sEMG-Based Gesture Recognition Using Temporal History.

Surface electromyography (sEMG) patterns have been decoded using learning-based methods that determine complicated nonlinear decision boundaries. However, overlapping classes in sEMG pattern recognition still degrade the classification accuracy because they cannot be separated by the decision boundaries. We hypothesized that certain overlapping classes can be separated while tracing the temporal history of sEMG patterns. Therefore, a novel post-processing method is proposed to adjust classification errors using the separated patterns from the temporal history of overlapping classes. The proposed method confirms the confidence of the prediction result by calculating the instantaneous pattern separability for the sequential sEMG input. The prediction result with high separability pattern is considered to have a high confidence of being correct (reliable). This result is stored for adjusting the next sEMG input. When the subsequent prediction is identified as having low confidence (unreliable), the predicted result is adjusted using the stored reliable predicted results. The proposed method adds an adjustment step to an existing classifier (maximum likelihood classifier (MLC), k-nearest neighbor (KNN), and support vector machine (SVM)), such that it can be attached to the back-end regardless of the type of classifier. Ten subjects performed 13 types of hand gestures, including overlapping patterns. The overall classification accuracy was enhanced to 88.93%(+8.12%p, MLC), 91.31%(+7.68%p, KNN), and 99.65%(+11.63%p, SVM) after the implementation of the proposed post-processing. Additionally, a faster and more accurate gesture classification was achieved with accuracy enhancement before gesture completion as 85.62%(+4.23%p, MLC), 89.77%(+4.23%p, KNN), and 97.62%(+11.12%p, SVM).

The Effectiveness and Safety of Cupping Therapy on CV8 Shenque for Urticaria; a Protocol for Systematic Review and Meta-Analysis.

Purpose: Urticaria is a common mast-cell-driven disease that poses a great burden on patients and society. Suggested therapeutic methods include avoidance of triggers and the use of medications, such as H1-antihistamines; however, limitations remain regarding efficacy, dealing with comorbidities, and adverse events. Cupping therapy (CT) at CV8 Shenque has been used in traditional Chinese medicine for the treatment of various dermatological diseases, including urticaria. The efficacy of the treatment has been revealed by previous clinical trials and case reports. This study was performed to provide a protocol for a systematic review and meta-analysis to analyze the effectiveness and safety of CT at CV8 Shenque for urticaria patients. Patients and Methods: Searches of electronic databases using manual searches and contact with the corresponding authors will be performed using predefined criteria for all randomized controlled trials on CT at CV8 Shenque for urticaria patients. Every part of the process will be conducted by two independent researchers, with conflicts being solved by a third author. The primary outcomes will be symptom scores, quality of life, and effective rate. Secondary outcomes will be adverse events and diagnostic test results. RevMan 5.4 software will be used to perform the meta-analysis. The Cochrane Collaboration "Risk of bias" tool will be used for risk of bias judgments. Results: Our study will evaluate the effectiveness and safety of CT at CV8 Shenque as a treatment option for urticaria. Conclusion: This systematic review is the first to investigate the effect of CT at CV8 Shenque for urticaria patients. Our study will provide objective evidence of an alternative approach to urticaria for clinicians and patients. Study Registration: PROSPERO (Registration number: CRD42023434913).

Elimination of alpha-gal xenoreactive epitope: alpha-galactosidase treatment of porcine heart valves.

BACKGROUND AND AIM OF THE STUDY: Porcine heart valves are among the most widely used tissue valves in clinical heart valve implantation. However, immunologic responses have been implicated as potential causes of the limited durability of xenograft heart valves. The study aim was to determine the effectiveness of alpha-galactosidase treatment used to degrade the major xenoreactive antigens found in xenograft heart valves. METHODS: Fresh porcine heart valves and pericardium treated with alpha-galactosidase were studied to evaluate the xenoreactive galactose (alpha1,3) galactose (alpha-gal) antigen. Removal of the alpha-gal epitope from the porcine heart valve was monitored via 3,3'-diaminobenzidine staining intensity, while the removal of alpha-gal from N-glycans on porcine heart valves treated with recombinant alpha-galactosidase was determined either qualitatively or quantitatively by mass fingerprinting using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS). The porcine pericardium was used for monitoring the change in mechanical properties after alpha-galactosidase treatment. In addition, the biomechanical modification property of collagen fiber rearrangement on tissue was assessed using transmission electron microscopy (TEM). RESULTS: Following a 24-h incubation at pH 7.2, 4 degrees C, employing 0.1 U/ml of Bacteroides thetaiotaomicron-derived recombinant alpha-galactosidase, the enzyme effectively removed the alpha-gal epitopes expressed on porcine heart valves. The identification type of alpha-gal N-glycan on fresh aortic valve, aortic wall, pulmonary valve, and pulmonary wall was 7.1%, 10.3%, 6% and 8%, respectively. In the presence of alpha-galactosidase treatment, alpha-gal-containing N-glycans were converted into alpha-gal-negative N-glycans. Likewise, alpha-gal-containing N-glycans were not detected when MALDI-TOF MS quantitative analysis was used. Furthermore, no significant difference was observed in the mechanical properties and findings from TEM in alpha-galactosidase-treated porcine pericardial tissue when compared to fresh porcine pericardium. CONCLUSION: Alpha-galactosidase can effectively remove the alpha-gal epitope from porcine heart valves and pericardium. This may possibly alleviate harmful xenoreactive immunologic responses by alpha-gal, without adversely affecting the biomechanical properties of the alpha-galactosidase-processed tissue.

Temsirolimus Enhances Anti-Cancer Immunity by Inducing Autophagy-Mediated Degradation of the Secretion of Small Extracellular Vesicle PD-L1.

Tumor-derived small extracellular vesicle (sEV) programmed death-ligand 1 (PD-L1) contributes to the low reactivity of cells to immune checkpoint blockade therapy (ICBT), because sEV PD-L1 binds to programmed death 1 (PD-1) in immune cells. However, there are no commercially available anti-cancer drugs that activate immune cells by inhibiting tumor-derived sEV PD-L1 secretion and cellular PD-L1. Here, we aimed to investigate if temsirolimus (TEM) inhibits both sEV PD-L1 and cellular PD-L1 levels in MDA-MB-231 cells. In cancer cell autophagy activated by TEM, multivesicular bodies (MVBs) associated with the secretion of sEV are degraded through colocalization with autophagosomes or lysosomes. TEM promotes CD8+ T cell-mediated anti-cancer immunity in co-cultures of CD8+ T cells and tumor cells. Furthermore, the combination therapy of TEM and anti-PD-L1 antibodies enhanced anti-cancer immunity by increasing both the number and activity of CD4+ and CD8+ T cells in the tumor and draining lymph nodes (DLNs) of breast cancer-bearing immunocompetent mice. In contrast, the anti-cancer effect of the combination therapy with TEM and anti-PD-L1 antibodies was reversed by the injection of exogenous sEV PD-L1. These findings suggest that TEM, previously known as a targeted anti-cancer drug, can overcome the low reactivity of ICBT by inhibiting sEV PD-L1 and cellular PD-L1 levels.

The effectiveness and safety of Simiao Xiaobi decoction on rheumatoid arthritis: A study protocol for systematic review and meta-analysis.

BACKGROUND: Rheumatoid arthritis (RA) is a common chronic autoimmune disease that contributes to progressive disability, systemic complications, higher mortality, and societal burden. Typical symptoms of RA include symmetrical pain and swelling in multiple joints, morning stiffness, and elevated levels of erythrocyte sedimentation rate, C-reactive protein, and rheumatoid factor. The representative treatment for RA is medication, including disease-modifying antirheumatic drugs, glucocorticoids, and nonsteroidal anti-inflammatory drugs. However, these medications are not yet curative nor preventative and are associated with several adverse effects, leading to their discontinuation. Recent articles reported that Simiao Xiaobi decoction (SXD) could relieve the symptoms of RA by clinical trial and experimental study, but an evidence-based review on the effectiveness and safety of SXD on RA has not yet been provided. METHODS: Searching for randomized controlled trials on the use of SXD for RA will be performed by using multiple electronic databases, manual search, and contacting the authors by e-mail if needed. Studies will be selected according to the predefined criteria and the data collected on study participants, interventions, control groups, outcome measurements, their results, adverse events, and risk of bias will be summarized. The primary outcome will be the disease activity score (including effective rate, swollen joint count, tender joint count, and morning stiffness), and the secondary outcomes will be blood tests (including erythrocyte sedimentation rate, C-reactive protein, and rheumatoid factor) and adverse events. We will use Review Manager software to perform a meta-analysis, the Cochrane Collaboration "risk of bias" tool for assessing the risk of bias, and grades of recommendation, assessment, development and evaluation for the determination of the quality of evidence. TRIAL REGISTRATION NUMBER: https://inplasy.com; INPLASY202230026. RESULTS: We are going to investigate the effectiveness and safety of SXD for RA. CONCLUSION: This study will provide reliable evidence on whether SXD is effective on RA.

Atorvastatin Enhances the Efficacy of Immune Checkpoint Therapy and Suppresses the Cellular and Extracellular Vesicle PD-L1.

According to clinical studies, statins improve the efficacy of programmed death-1/programmed death-ligand 1 (PD-1/PD-L1) blockade therapy for breast cancer; however, the underlying mechanisms are unclear. Herein, we showed that atorvastatin (ATO) decreased the content of PD-L1 in extracellular vesicles (EVs) by reducing cellular PD-L1 expression and inhibiting EV secretion in breast cancer cells, thereby enhancing the efficacy of anti-PD-L1 therapy. ATO reduced EV secretion by regulating the Rab proteins involved in EV biogenesis and secretion. ATO-mediated inhibition of the Ras-activated MAPK signaling pathway downregulated PD-L1 expression. In addition, ATO strongly promoted antitumor efficacy by inducing T cell-mediated tumor destruction when combined with an anti-PD-L1 antibody. Moreover, suppression of EV PD-L1 by ATO improved the reactivity of anti-PD-L1 therapy by enhancing T-cell activity in draining lymph nodes of EMT6-bearing immunocompetent mice. Therefore, ATO is a potential therapeutic drug that improves antitumor immunity by inhibiting EV PD-L1, particularly in response to immune escape during cancer.

Macitentan improves antitumor immune responses by inhibiting the secretion of tumor-derived extracellular vesicle PD-L1.

Extracellular vesicles (EVs) carrying tumor cell-derived programmed death-ligand 1 (PD-L1) interact with programmed death 1 (PD-1)-producing T cells, thus significantly lowering a patient's response to immune checkpoint blockade drugs. No drug that reinvigorates CD8+ T cells by suppressing EV PD-L1 has been approved for clinical usage. Here we have identified macitentan (MAC), an FDA-approved oral drug, as a robust booster of antitumor responses in CD8+ T cells by suppressing tumor cell-derived EV PD-L1. Methods: EV was analyzed by the data from nanoparticle tracking, immunoblotting analyses, and nano-flow cytometry. Antitumor immunity was evaluated by luciferase assay and immune phenotyping using flow cytometry. Clinical relevance was analyzed using the cancer genome atlas database. Results: MAC inhibited secretion of tumor-derived EV PD-L1 by targeting the endothelin receptor A (ETA) in breast cancer cells and xenograft models. MAC enhanced CD8+ T cell-mediated tumor killing by decreasing the binding of PD-1 to the EV PD-L1 and thus synergizing the effects of the anti-PD-L1 antibody. MAC also showed an anticancer effect in triple-negative breast cancer (TNBC)-bearing immunocompetent mice but not in nude mice. The combination therapy of MAC and anti-PD-L1 antibody significantly improved antitumor efficacy by increasing CD8+ T cell number and activity with decreasing Treg number in the tumors and draining lymph nodes in TNBC, colon, and lung syngeneic tumor models. The antitumor effect of MAC was reversed by injecting exogenous EV PD-L1. Notably, ETA level was strongly associated with the innate anti-PD-1 resistance gene signature and the low response to the PD-1/PD-L1 blockade. Conclusion: These findings strongly demonstrate that MAC, already approved for clinical applications, can be used to improve and/or overcome the inadequate response to PD-1/PD-L1 blockade therapy.

Sulfisoxazole Elicits Robust Antitumour Immune Response Along with Immune Checkpoint Therapy by Inhibiting Exosomal PD-L1.

Despite their potent antitumor activity, clinical application of immune checkpoint inhibitors has been significantly limited by their poor response rates (<30%) in cancer patients, primarily due to immunosuppressive tumor microenvironments. As a representative immune escape mechanism, cancer-derived exosomes have recently been demonstrated to exhaust CD8+ cytotoxic T cells. Here, it is reported that sulfisoxazole, a sulfonamide antibacterial, significantly decreases the exosomal PD-L1 level in blood when orally administered to the tumor-bearing mice. Consequently, sulfisoxazole effectively reinvigorates exhausted T cells, thereby eliciting robust antitumor effects in combination with anti-PD-1 antibody. Overall, sulfisoxazole regulates immunosuppression through the inhibition of exosomal PD-L1, implying its potential to improve the response rate of anti-PD-1 antibodies.

Seasonal Sampling of a Microbial Community in the Sediment of Geoje-Hansan Bay, Republic of Korea.

Several oyster farms are concentrated in Geoje-Hansan Bay, Republic of Korea, and there is concern about marine pollution. Hence, we monitored the sediment at this site for a year using 16S rRNA gene sequencing. The predominant phyla were Proteobacteria (69.9 to 79.1%) and Bacteroidetes (8.2 to 10.6%) in all seasons.

Co-Administration of Gagam-Sipjeondaebo-Tang and Ibuprofen Alleviates the Inflammatory Response in MPTP-Induced Parkinson's Disease Mouse Model and RAW264.7 Macrophages.

Parkinson's disease (PD), a common neurodegenerative disease, is characterized by degeneration of dopaminergic neurons with neuroinflammation. Gagam-Sipjeondaebo-Tang (GST), a traditional herbal formula made of twelve medicinal herbs, is known to be effective in PD, and the use of ibuprofen has been associated with a lower risk of PD. The aim of this study was to evaluate whether the combined administration of GST and ibuprofen affects the inflammatory response of Parkinson's disease. MPTP-induced parkinsonian mouse models were treated with GST or ibuprofen using oral gavage once a day for 5 days. The effects of GST were examined by measuring the TH level and expression of CD68 in the mice brain in addition to behavioral tests. The anti-inflammatory effect of GST on the LPS-treated RAW264.7 murine macrophages was examined using the NO assay. Inflammatory cytokines were analyzed using quantitative-PCR and flow cytometry. In the results, GST significantly improved the loss of dopaminergic neurons and alleviated PD-induced behavioral deficits. GST also decreased macrophage activation in the MPTP-induced PD mouse model. Interestingly, co-administration of GST and ibuprofen showed a synergistic effect in improving the loss of dopaminergic neurons and decreasing the activation of macrophages. Moreover, the NO level decreased in LPS-stimulated macrophages with this combined treatment. GST reduced iNOS, COX-2, IL-1ß, and IL-6 levels, and co-administration with ibuprofen showed a synergistic effect. Furthermore, pretreatment of GST reduced the expression levels of MCP-1 and IL-12 p70 in LPS-stimulated RAW264.7 cells. These results can possibly suggest a future therapeutic approach for PD patients.

The complete chloroplast genome of Bupleurum euphorbioides, a traditional medicinal plant.

Bupleurum euphorbioides is a rare native plant attributed with analgesic, gallbladder-supportive, and other functions in China and the Republic of Korea. However, the complete chloroplast genome sequence of the native plant B. euphorbioides has not been determined. In this study, we sequenced the complete chloroplast genome sequence, and examined the molecular phylogeny and genetic information of B. euphorbioides. The total chloroplast genome of B. euphorbioides was 154,871 bp in length with a large single-copy region (85,089 bp), small single-copy region (17,714 bp), and pair of inverted repeats regions (26,034 bp). The chloroplast genome encoded a total of 176 genes, including 131 protein-coding genes, 37 tRNA genes, and eight rRNA genes. The phylogenetic tree indicated that B. euphorbioides was the most closely related to B. latissimum.