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Purpose Metachromatic leukodystrophy (MLD) is a lysosomal storage disease caused by arylsulfatase A deficiency, which leads to progressive demyelination in both the central and peripheral nervous systems, resulting in significant gross motor deterioration. This study aimed to analyze data concerning neuroimaging and clinical phenotypes of MLD patients, categorized by disease subtype. Methods Patients diagnosed with MLD based on arylsulfatase A enzymatic activity, demyelination observed in brain magnetic resonance images, and/or pathogenic mutations were included in this study. The medical charts of 10 patients with confirmed MLD were retrospectively reviewed. We used a simplified magnetic resonance imaging (MRI) scoring system and clinical status, including survival. We analyzed the correlations between the scores of specific neuroimaging lesions and clinical status in two groups, categorized as late-infantile and juvenile types based on the age at symptom onset. Results We detected a positive relationship between clinical function deterioration and MRI score (rho=0.59, P=0.002) in patients with MLD. This correlation was stronger in the late-infantile type (rho=0.700, P=0.003) than in the juvenile type (rho=0.513, P=0.029). A strong relationship was also noted in patients with high signal intensities in the pons and basal ganglia, and cerebellar atrophy, but not in those with lesions in the midbrain. MLD with a high MRI score was associated with poor clinical function. Conclusion The identified correlations between modified MRI scores and clinical function scales may help predict the prognosis of patients with MLD, thereby aiding in the identification of treatment options and enhancing the quality of life for these patients.

Purpose The Omicron variant wave spread rapidly from February 2022 in South Korea following the initial management of the coronavirus disease 2019 (COVID-19) outbreak. This study examined electroencephalogram (EEG) findings and serological inflammatory markers in pediatric patients with COVID-19 (Omicron variant). Methods We retrospectively reviewed the medical records of 41 patients who presented at Gyeongsang National University Changwon Hospital between March and May 2022 and were diagnosed with COVID-19. All serological tests were performed within 24 hours of fever or seizure onset. Results The median patient age was 3.6 years (range, 0.08 to 14.00), and the average hospital stay was 3.7 days (range, 1.0 to 7.0). Interleukin-6 (IL-6) levels were elevated above the normal range in all patients (median, 43.18 pg/mL; range, 7.0 to 190.0) and were higher among those who experienced seizures. Of the 41 total patients, 17 (41.5%; mean age, 5.4 years) visited the clinic for seizure. Three patients experienced prolonged seizures (lasting longer than 30 minutes) and received intravenous lorazepam, while eight presented with complex febrile seizures. Nine patients underwent EEG, of whom five exhibited abnormal initial findings. Linear regression demonstrated correlations between prolonged seizure duration and both serum IL-6 level and blood lymphocyte count. Conclusion Numerous serological markers associated with the immune cascade were found to be elevated in children with COVID-19. Nevertheless, febrile seizures represent a relatively common neurological presentation among pediatric patients infected with Omicron variants. Consequently, COVID-19 infection exhibits both familiar and distinct characteristics regarding the mechanisms inducing seizures and fever in children.

Introduction: Drug response prediction, especially in terms of cell viability prediction, is a well-studied research problem with significant implications for personalized medicine. It enables the identification of the most effective drugs based on individual genetic profiles, aids in selecting potential drug candidates, and helps identify biomarkers that predict drug efficacy and toxicity.A deeper investigation on drug response prediction reveals that drugs exert their effects by targeting specific proteins, which in turn perturb related genes in cascading ways. This perturbation affects cellular pathways and regulatory networks, ultimately influencing the cellular response to the drug. Identifying which genes are perturbed and how they interact can provide critical insights into the mechanisms of drug action. Hence, the problem of predicting drug response can be framed as a dual problem involving both the prediction of drug efficacy and the selection of drug-specific genes. Identifying these drug-specific genes (biomarkers) is crucial because they serve as indicators of how the drug will affect the biological system, thereby facilitating both drug response prediction and biomarker discovery.Methods: In this study, we propose DGDRP (Drug-specific Gene selection for Drug Response Prediction), a graph neural network (GNN)-based model that uses a novel rank-and-re-rank process for drug-specific gene selection. DGDRP first ranks genes using a pathway knowledge-enhanced network propagation algorithm based on drug target information, ensuring biological relevance. It then re-ranks genes based on the similarity between gene and drug target embeddings learned from the GNN, incorporating semantic relationships. Thus, our model adaptively learns to select drug mechanism-associated genes that contribute to drug response prediction. This integrated approach not only improves drug response predictions compared to other gene selection methods but also allows for effective biomarker discovery.Discussion: As a result, our approach demonstrates improved drug response predictions compared to other gene selection methods and demonstrates comparability with state-of-the-art deep learning models. Case studies further support our method by showing alignment of selected gene sets with the mechanisms of action of input drugs.Conclusion: Overall, DGDRP represents a deep learning based re-ranking strategy, offering a robust gene selection framework for more accurate drug response prediction. The source code for DGDRP can be found at: https://github.com/minwoopak/heteronet.

Abstract Background Headaches are the most common neurologic symptoms in the pediatric population. Most primary headache in children and adolescents focuses on associated factors, including noise. Auditory discomfort is related to recognizing the pain. We aimed to analyze the headache profile of pediatric populations and the connection between noise exposure and head pain in children and adolescents. Methods We reviewed retrospectively medical records of the pediatric population with headaches in Gyeongsang National University Changwon Hospital from January 2022 to April 2023. Personal headache profiling from self-questionnaires and environmental noise data from the National Noise Information System (NNIS) were used to analyze each variable, and chi-square tests and linear regression models by SAS were used to analyze the statistical correlation. Results Of the 224 participants, 125 were clinically diagnosed with headaches. Of the 104 pubertal subjects, 56.7% were diagnosed with headaches, compared to 60% in the prepubertal group. Both daytime and nighttime noise was significantly higher in the diagnosed headache group than in the non-diagnosed group. Headache duration increased by daytime and nighttime noise with statistical significance in age-adjusted models. Conclusion We found that noise exposure is correlated to headaches in children and adolescents. Daytime and nighttime environmental noise exposure was significantly associated with the duration of headaches through our data. Therefore, we assume that noise exposure is vitally relevant to prolonged headaches in the pediatric population. Further research is needed to improve our data.

Abstract We, for the first time, report the nanoscopic imaging study of anomalous infrared (IR) phonon enhancement of bilayer graphene, originated from the charge imbalance between the top and bottom layers, resulting in the enhancement of E1u mode of bilayer graphene near 0.2 eV. We modified the multifrequency atomic force microscope platform to combine photo-induced force microscope with electrostatic/Kelvin probe force microscope constituting a novel hybrid nanoscale optical-electrical force imaging system. This enables to observe a correlation between the IR response, doping level, and topographic information of the graphene layers. Through the nanoscale spectroscopic image measurements, we demonstrate that the charge imbalance at the graphene interface can be controlled by chemical (doping effect via Redox mechanism) and mechanical (triboelectric effect by the doped cantilever) approaches. Moreover, we can also diagnosis the subsurface cracks on the stacked few-layer graphene at nanoscale, by monitoring the strain-induced IR phonon shift. Our approach provides new insights into the development of graphene-based electronic and photonic devices and their potential applications.

Abstract Mesenchymal stem cell (MSC)-derived small extracellular vesicles (MSC-sEVs) are known to exert immunosuppressive functions. This study showed that MSC-sEVs specifically convert T helper 17 (Th17) cells into IL-17 low-producer (ex-Th17) cells by degrading RAR-related orphan receptor γt (RORγt) at the protein level. In experimental autoimmune encephalomyelitis (EAE)-induced mice, treatment with MSC-sEVs was found to not only ameliorate clinical symptoms but also to reduce the number of Th17 cells in draining lymph nodes and the central nervous system. MSC-sEVs were found to destabilize RORγt by K63 deubiquitination and deacetylation, which was attributed to the EP300-interacting inhibitor of differentiation 3 (Eid3) contained in the MSC-sEVs. Small extracellular vesicles isolated from the Eid3 knockdown MSCs by Eid3-shRNA failed to downregulate RORγt. Moreover, forced expression of Eid3 by gene transfection was found to significantly decrease the protein level of RORγt in Th17 cells. Altogether, this study reveals the novel immunosuppressive mechanisms of MSC-sEVs, which suggests the feasibility of MSC-sEVs as an attractive therapeutic tool for curing Th17-mediated inflammatory diseases.

As the demand for battery technology with enhanced safety and high energy density increases, solid-state batteries are currently attracting attention as a solution to problems such as fire and explosion risks associated with lithium-ion batteries. In this study, experiments were conducted to synthesize and optimize Li7La3Zr2O12 (LLZO), a solid electrolyte that is a key component of lithium-ion batteries with stability and high energy density. Experimental results showed that sintering at a low temperature of 800 °C for 8 h was the optimal synthesis and sintering time. Additionally, the excess lithium-containing bed powder enabled the production of pure cubic-phase LLZO. Through a sintering process that creates a lithium atmosphere on the bottom surface and facilitates lithium replenishment, an additional tunnel was introduced between the specimen and the alumina powder, allowing the bottom surface of the specimen to be exposed to the lithium atmosphere. By manufacturing a uniform cubic electrolyte, the path to manufacturing all-solid-state batteries was opened. These findings provide a new approach to forming cubic-phase LLZO with much higher ionic conductivity than the tetragonal phase at low sintering temperatures.

Motivation: Lead identification is a fundamental step to prioritize candidate compounds for downstream drug discovery process. Machine learning (ML) and deep learning (DL) approaches are widely used to identify lead compounds using both chemical property and experimental information. However, ML or DL methods rarely consider compound similarity information directly since ML and DL models use abstract representation of molecules for model construction. Alternatively, data mining approaches are also used to explore chemical space with drug candidates by screening undesirable compounds. A major challenge for data mining approaches is to develop efficient data mining methods that search large chemical space for desirable lead compounds with low false positive rate. Results: In this work, we developed a network propagation (NP) based data mining method for lead identification that performs search on an ensemble of chemical similarity networks. We compiled 14 fingerprint-based similarity networks. Given a target protein of interest, we use a deep learning-based drug target interaction model to narrow down compound candidates and then we use network propagation to prioritize drug candidates that are highly correlated with drug activity score such as IC50. In an extensive experiment with BindingDB, we showed that our approach successfully discovered intentionally unlabeled compounds for given targets. To further demonstrate the prediction power of our approach, we identified 24 candidate leads for CLK1. Two out of five synthesizable candidates were experimentally validated in binding assays. In conclusion, our framework can be very useful for lead identification from very large compound databases such as ZINC.

Based on the paradigm shift in modern warfare, ground forces conduct pilot operations of wireless unmanned maneuvering systems, such as tactical drones, in the form of manned and unmanned cooperative tactics after deploying the relevant systems in the battlefield. However, security considerations for relevant systems are limited to the scope of using only the existing end-to-end encryption and public key authentication modules, and no defense strategy to actively respond to specialized cyber-electronic warfare threats has been officially established. To drastically reduce both the potential attack surface and security vulnerabilities of drones employed in network-centric-warfare, a proactive defense technology that expires the effectiveness of attacks by avoiding invasive action at the target is expected to be essential. Accordingly, this paper proposes the concept of active moving-target-defense (MTD), an element of cyber deception that minimizes the rate of success of cyber-attacks while conversely maximizing both defense predominance and attack complexity asymmetrically, exclusive according to partially observable Markov decision process (POMDP)-based threat modeling that considers both the internal and external operation sequences of target drones. To optimally design the proposed drone-type MTD based on the Pareto frontier, we additionally advanced and simulated a drone-based defensive deception game framework (D3GF), which represents a general-sum combat framework reflecting decision logics such as the perfect Bayesian Nash equilibrium, stochastic Stackelberg, and partial signal game. This study was conducted to compare and calculate the efficiencies of the drone-type MTD’s deceptive defense, which had not been considered in prior studies, by unique environmental features inside and outside the drone. Furthermore, we conducted a detailed performance evaluation considering game metrics based on sensitivity analysis. Hereafter, the drone-type MTD will be extended into an actual active drone protection technology combining cyber flare-type avoidance strategies and cyber camouflage-type disarrangement strategies by expanding its optimization domain as a hypergame, while integrating it with drone decoy elements.

Introduction Direct and indirect smoking exposure is highly related to mental health in children. This study aimed to identify the association between exposure to smoking or secondhand smoke (SHS) and attention deficit hyperactivity disorder (ADHD). Methods We used data from the Korean National Health and Nutrition Examination Survey (KNHANES) from 2007 to 2019, including variables such as diagnosed ADHD, smoking status, SHS, and urine cotinine levels among children and adolescents. We estimated the risk of ADHD according to smoking or SHS exposure in various exposure groups using adjusted logistic or linear regression models. Results Among 16434 participants, 133 children were diagnosed with ADHD (0.8%). Of these, 58 (43.6%) were aged

Abstract Biomarkers for treatment sensitivity or drug resistance used in precision medicine include prognostic and predictive molecules, critical factors in selecting appropriate treatment protocols and improving survival rates. However, identification of accurate biomarkers remain challenging due to the high risk of false-positive findings and lack of functional validation results for each biomarker. Here, we discovered a mechanical correlation between leucine proline-enriched proteoglycan 1 (LEPRE1) and pelitinib drug sensitivity using in silico statistical methods and confirmed the correlation in acute myeloid leukemia (AML) and A549 lung cancer cells. We determined that high LEPRE1 levels induce protein kinase B activation, overexpression of ATP-binding cassette superfamily G member 2 (ABCG2) and E-cadherin, and cell colonization, resulting in a cancer stem cell-like phenotype. Sensitivity to pelitinib increases in LEPRE1-overexpressing cells due to the reversing effect of ABCG2 upregulation. LEPRE1 silencing induces pelitinib resistance and promotes epithelial-to-mesenchymal transition through actin rearrangement via a series of Src/ERK/cofilin cascades. The in silico results identified a mechanistic relationship between LEPRE1 and pelitinib drug sensitivity, confirmed in two cancer types. This study demonstrates the potential of LEPRE1 as a biomarker in cancer through in-silico prediction and in vitro experiments supporting the clinical development of personalized medicine strategies based on bioinformatics findings.

Summary: Drug-induced liver injury (DILI) is the main cause of drug failure in clinical trials. The characterization of toxic compounds in terms of chemical structure is important because compounds can be metabolized to toxic substances in the liver. Traditional machine learning approaches have had limited success in predicting DILI, and emerging deep graph neural network (GNN) models are yet powerful enough to predict DILI. In this study, we developed a completely different approach, supervised subgraph mining (SSM), a strategy to mine explicit subgraph features by iteratively updating individual graph transitions to maximize DILI fidelity. Our method outperformed previous methods including state-of-the-art GNN tools in classifying DILI on two different datasets: DILIst and TDC-benchmark. We also combined the subgraph features by using SMARTS-based frequent structural pattern matching and associated them with drugs’ ATC code.

Abstract Background Diagnostic delay of superior mesenteric artery syndrome (SMAS) is common due to its rarity and lack of index of clinical suspicion. Early diagnosis under suspicion is pivotal for adequate treatment. Present study aims to explore the endoscopic features for early decision to evaluate SMAS in children. Methods In case controlled observation study, the recruitment was limited to patients who had endoscopic finding I or finding 1 plus more as follows: a pulsating vertical or oblique band or slit like luminal narrowing of the third part of the duodenum without no expansion over one third during air insufflation for at least 15 s (finding I), a marked dilation of the duodenal first and second part during air insufflation at the third part of the duodenum (finding II), a bile mixed fluid collection (bile lake) in the stomach (finding III). SMAS was confirmed with UGI series or hypotonic duodenography in enrolled patients. We analyzed positive endoscopic findings related with SMAS. Results The enrolled 29 patients consisted of 18 (62.1%) with SMAS and 11 (37.9%) without SMAS. The three most common presenting symptoms were abdominal pain, postprandial discomfort, and early satiety. The clinical impressions based on history and physical examination before endoscopy were functional dyspepsia (34.6%), gastritis or gastric ulcer (31.0%), and SMAS (17.3%). The constellation of three endoscopic findings (finding I + II + III, feature D) observed in 13 (72.2%) patients of SMAS group and 3 (27.3%) patients of non SMAS group (P = 0.027). Of 16 patients with features D, SMAS was diagnosed in 13 patients (81.2%) and not detected in 3 patients (18.8%) on UGI series or hypotonic duodenography. Conclusions Endoscopic examination to the third part of the duodenum can provide a clue making a decision to evaluate SMAS, which consists of features of three endoscopic findings as follows: a pulsating vertical or oblique band or slit like luminal narrowing of the third part of the duodenum without no expansion over one third during air insufflation for at least 15 s, a marked dilation of the first and second part of the duodenum, and a bile lake in the stomach.

Molecular and sequencing technologies have been successfully used in decoding biological mechanisms of various diseases. As revealed by many novel discoveries, the role of non-coding RNAs (ncRNAs) in understanding disease mechanisms is becoming increasingly important. Since ncRNAs primarily act as regulators of transcription, associating ncRNAs with diseases involves multiple inference steps. Leveraging the fast-accumulating high-throughput screening results, a number of computational models predicting ncRNA-disease associations have been developed. These tools suggest novel disease-related biomarkers or therapeutic targetable ncRNAs, contributing to the realization of precision medicine. In this survey, we first introduce the biological roles of different ncRNAs and summarize the databases containing ncRNA-disease associations. Then, we suggest a new trend in recent computational prediction of ncRNA-disease association, which is the mode of action (MoA) network perspective. This perspective includes integrating ncRNAs with mRNA, pathway and phenotype information. In the next section, we describe computational methodologies widely used in this research domain. Existing computational studies are then summarized in terms of their coverage of the MoA network. Lastly, we discuss the potential applications and future roles of the MoA network in terms of integrating biological mechanisms for ncRNA-disease associations.

Abstract Background The use of whole genome sequence has increased recently with rapid progression of next-generation sequencing (NGS) technologies. However, storing raw sequence reads to perform large-scale genome analysis pose hardware challenges. Despite advancement in genome analytic platforms, efficient approaches remain relevant especially as applied to the human genome. In this study, an Integrated Genome Sizing (IGS) approach is adopted to speed up multiple whole genome analysis in high-performance computing (HPC) environment. The approach splits a genome (GRCh37) into 630 chunks (fragments) wherein multiple chunks can simultaneously be parallelized for sequence analyses across cohorts. Results IGS was integrated on Maha-Fs (HPC) system, to provide the parallelization required to analyze 2504 whole genomes. Using a single reference pilot genome, NA12878, we compared the NGS process time between Maha-Fs (NFS SATA hard disk drive) and SGI-UV300 (solid state drive memory). It was observed that SGI-UV300 was faster, having 32.5 mins of process time, while that of the Maha-Fs was 55.2 mins. Conclusions The implementation of IGS can leverage the ability of HPC systems to analyze multiple genomes simultaneously. We believe this approach will accelerate research advancement in personalized genomic medicine. Our method is comparable to the fastest methods for sequence alignment.

Boiling histotripsy is a High Intensity Focused Ultrasound (HIFU) technique which uses a number of short pulses with high acoustic pressures at the HIFU focus to induce mechanical tissue fractionation. In boiling histotripsy, two different types of acoustic cavitation contribute towards mechanical tissue destruction: a boiling vapour bubble and cavitation clouds. An understanding of the mechanisms underpinning these phenomena and their dynamics is therefore paramount to predicting and controlling the overall size of a lesion produced for a given boiling histotripsy exposure condition. A number of studies have shown the effects of shockwave heating in generating a boiling bubble at the HIFU focus and have studied its dynamics under boiling histotripsy insonation. However, not much is known about the subsequent production of cavitation clouds that form between the HIFU transducer and the boiling bubble. The main objective of the present study is to examine what causes this bubble cluster formation after the generation of a boiling vapour bubble. A numerical simulation of 2D nonlinear wave propagation with the presence of a bubble at the focus of a HIFU field was performed using the k-Wave MATLAB toolbox for time domain ultrasound simulations, which numerically solves the generalised Westervelt equation. The numerical results clearly demonstrate the appearance of the constructive interference of a backscattered shockwave by a bubble with incoming incident shockwaves. This interaction (i.e., the reflected and inverted peak positive phase from the bubble with the incoming incident rarefactional phase) can eventually induce a greater peak negative pressure field compared to that without the bubble at the HIFU focus. In addition, the backscattered peak negative pressure magnitude gradually increased from 17.4 MPa to 31.6 MPa when increasing the bubble size from 0.2 mm to 1.5 mm. The latter value is above the intrinsic cavitation threshold of –28 MPa in soft tissue. Our results suggest that the formation of a cavitation cloud in boiling histotripsy is a threshold effect which primarily depends (a) the size and location of a boiling bubble, and (b) the sum of the incident field and that scattered by a bubble.

Abstract Background Several recent studies showed that next-generation sequencing (NGS)-based human leukocyte antigen (HLA) typing is a feasible and promising technique for variant calling of highly polymorphic regions. To date, however, no method with sufficient read depth has completely solved the allele phasing issue. In this study, we developed a new method (HLAscan) for HLA genotyping using NGS data. Results HLAscan performs alignment of reads to HLA sequences from the international ImMunoGeneTics project/human leukocyte antigen (IMGT/HLA) database. The distribution of aligned reads was used to calculate a score function to determine correctly phased alleles by progressively removing false-positive alleles. Comparative HLA typing tests using public datasets from the 1000 Genomes Project and the International HapMap Project demonstrated that HLAscan could perform HLA typing more accurately than previously reported NGS-based methods such as HLAreporter and PHLAT. In addition, the results of HLA-A, −B, and -DRB1 typing by HLAscan using data generated by NextGen were identical to those obtained using a Sanger sequencing–based method. We also applied HLAscan to a family dataset with various coverage depths generated on the Illumina HiSeq X-TEN platform. HLAscan identified allele types of HLA-A, −B, −C, −DQB1, and -DRB1 with 100% accuracy for sequences at ≥ 90× depth, and the overall accuracy was 96.9%. Conclusions HLAscan, an alignment-based program that takes read distribution into account to determine true allele types, outperformed previously developed HLA typing tools. Therefore, HLAscan can be reliably applied for determination of HLA type across the whole-genome, exome, and target sequences.

Background: Leigh syndrome is a mitochondrial cytopathy that presents as a neurodegenerative disease with apparent manifestation in the central nervous system. The aim of the present study was to describe its dominant neurological clinical features and analyze data related to epilepsy in Leigh syndrome accompanied by a mitochondrial DNA mutation.Methods: Whole mitochondrial sequencing was performed on 125 patients clinically suspected of Leigh syndrome. Among them, 25 patients were identified to have mitochondrial DNA associated Leigh syndrome. Electroencephalography (EEG) findings, semiology, brain imaging findings, and biochemical results, were evaluated. We also compared brain magnetic resonance imaging findings and biochemical features in patients with Leigh syndrome based on the presence of epilepsy.Results: Clinical seizures were observed in 14 out of 25 enrolled patients (56%), with focal seizures being the most common type (6/14, 42.8%). All patients were found to have slow and disorganized background neural activity while eight exhibited epileptic discharges on EEG. Mutations at base pairs 10,191 and 8,993 were revealed in a relatively larger number of patients of Leigh syndrome with epilepsy. The presence of gastrointestinal symptoms was significantly more frequent in the epilepsy group (P = 0.042). Diffuse cerebral atrophy was significantly increased (P = 0.042) and cortex signal abnormalities were also increased (P = 0.033) in the epilepsy group.Conclusions: Patients with Leigh syndrome and mitochondrial DNA mutations had a high proportion of central nervous system comorbidities, though the prevalence of epilepsy in this population was not particularly high. Various types of seizure and EEG findings are common in those with Leigh syndrome. Future imaging studies involving more patients and proper mitochondrial DNA mutation analyses are needed to further evaluate the natural course of Leigh syndrome with epilepsy.

Repeated exposure to traumatic experiences may put professional firefighters at increased risk of developing posttraumatic stress disorder (PTSD). To date, however, the rate of PTSD symptoms, unmet need for mental health treatment, and barriers to treatment have only been investigated in subsamples rather than the total population of firefighters. We conducted a nationwide, total population-based survey of all currently employed South Korean firefighters (n = 39,562). The overall response rate was 93.8% (n = 37,093), with 68.0% (n = 26,887) complete responses for all variables. The rate of current probable PTSD was estimated as 5.4%. Among those with current probable PTSD (n = 1,995), only a small proportion (9.7%) had received mental health treatment during the past month. For those who had not received treatment, perceived barriers of accessibility to treatment (29.3%) and concerns about potential stigma (33.8%) were reasons for not receiving treatment. Although those with higher PTSD symptom severity and functional impairment were more likely to seek treatment, greater symptom severity and functional impairment were most strongly associated with increased concerns about potential stigma. This nationwide study points to the need for new approaches to promote access to mental health treatment in professional firefighters.

Anti-inflammatory effects of tocopherol (TOL) analogs have been attributed to their potent antioxidant activities. However, we and others have separately reported that γTOL or α-tocopheryl succinate (αTOS), despite their lower antioxidant activities, inhibit lipopolysaccharide (LPS)-induced production of prostaglandin E2 (PGE2) in macrophages and lung epithelial cells more effectively than αTOL. In the present study, we sought to directly analyze the effect of three TOL analogs (αTOL, αTOS, and γTOL) on LPS-induced production of pro-inflammatory mediators in macrophages. Our data demonstrated that the inhibitory effects of all three TOL analogs on nitric oxide production were very limited. In contrast, αTOS dose-dependently and significantly inhibited LPS-induced PGE2 production in both RAW264.7 cells and peritoneal macrophages, whereas αTOL and γTOL were much less effective. Although αTOS had no effect on LPS-induced cyclooxygenase-2 expression, it did inhibit COX activity in intact cells. αTOS in combination with sulforaphane, a compound that blocked LPS-induced COX-2 expression, cooperatively and more significantly inhibited PGE2 production. These findings suggest that αTOS is a more potent inhibitor of the pro-inflammatory mediator PGE2. The inclusion of αTOS in vitamin supplements may further enhance the effectiveness of strategies for preventing diseases associated with inflammation. Keywords:: α-tocopheryl succinate, prostaglandin E2, cyclooxygenase, inflammation, vitamin