Integrated Stress Response Potentiates Ponatinib-Induced Cardiotoxicity.

BACKGROUND: Mitochondrial dysfunction is a primary driver of cardiac contractile failure; yet, the cross talk between mitochondrial energetics and signaling regulation remains obscure. Ponatinib, a tyrosine kinase inhibitor used to treat chronic myeloid leukemia, is among the most cardiotoxic tyrosine kinase inhibitors and causes mitochondrial dysfunction. Whether ponatinib-induced mitochondrial dysfunction triggers the integrated stress response (ISR) to induce ponatinib-induced cardiotoxicity remains to be determined. METHODS: Using human induced pluripotent stem cells-derived cardiomyocytes and a recently developed mouse model of ponatinib-induced cardiotoxicity, we performed proteomic analysis, molecular and biochemical assays to investigate the relationship between ponatinib-induced mitochondrial stress and ISR and their role in promoting ponatinib-induced cardiotoxicity. RESULTS: Proteomic analysis revealed that ponatinib activated the ISR in cardiac cells. We identified GCN2 (general control nonderepressible 2) as the eIF2α (eukaryotic translation initiation factor 2α) kinase responsible for relaying mitochondrial stress signals to trigger the primary ISR effector-ATF4 (activating transcription factor 4), upon ponatinib exposure. Mechanistically, ponatinib treatment exerted inhibitory effects on ATP synthase activity and reduced its expression levels resulting in ATP deficits. Perturbed mitochondrial function resulting in ATP deficits then acts as a trigger of GCN2-mediated ISR activation, effects that were negated by nicotinamide mononucleotide, an NAD+ precursor, supplementation. Genetic inhibition of ATP synthase also activated GCN2. Interestingly, we showed that the decreased abundance of ATP also facilitated direct binding of ponatinib to GCN2, unexpectedly causing its activation most likely because of a conformational change in its structure. Importantly, administering an ISR inhibitor protected human induced pluripotent stem cell-derived cardiomyocytes against ponatinib. Ponatinib-treated mice also exhibited reduced cardiac function, effects that were attenuated upon systemic ISRIB administration. Importantly, ISRIB does not affect the antitumor effects of ponatinib in vitro. CONCLUSIONS: Neutralizing ISR hyperactivation could prevent or reverse ponatinib-induced cardiotoxicity. The findings that compromised ATP production potentiates GCN2-mediated ISR activation have broad implications across various cardiac diseases. Our results also highlight an unanticipated role of ponatinib in causing direct activation of a kinase target despite its role as an ATP-competitive kinase inhibitor.

E-cigarettes Induce Dysregulation of Autophagy Leading to Endothelial Dysfunction in Pulmonary Arterial Hypertension.

Given the increasing popularity of electronic cigarettes (e-cigs), it is imperative to evaluate the potential health risks of e-cigs, especially in users with preexisting health concerns such as pulmonary arterial hypertension (PAH). The aim of the present study was to investigate whether differential susceptibility exists between healthy and patients with PAH to e-cig exposure and the molecular mechanisms contributing to it. Patient-specific induced pluripotent stem cell-derived endothelial cells (iPSC-ECs) from healthy individuals and patients with PAH were used to investigate whether e-cig contributes to the pathophysiology of PAH and affects EC homeostasis in PAH. Our results showed that PAH iPSC-ECs showed a greater amount of damage than healthy iPSC-ECs upon e-cig exposure. Transcriptomic analyses revealed that differential expression of Akt3 may be responsible for increased autophagic flux impairment in PAH iPSC-ECs, which underlies increased susceptibility upon e-cig exposure. Moreover, knockdown of Akt3 in healthy iPSC-ECs significantly induced autophagic flux impairment and endothelial dysfunction, which further increased with e-cig treatment, thus mimicking the PAH cell phenotype after e-cig exposure. In addition, functional disruption of mTORC2 by knocking down Rictor in PAH iPSC-ECs caused autophagic flux impairment, which was mediated by downregulation of Akt3. Finally, pharmacological induction of autophagy via direct inhibition of mTORC1 and indirect activation of mTORC2 with rapamycin reverses e-cig-induced decreased Akt3 expression, endothelial dysfunction, autophagic flux impairment, and decreased cell viability, and migration in PAH iPSC-ECs. Taken together, these data suggest a potential link between autophagy and Akt3-mediated increased susceptibility to e-cig in PAH.

Structural variability of the cerebral cortex in schizophrenia and its association with clinical symptoms.

BACKGROUND: Substantial evidence indicates structural abnormalities in the cerebral cortex of patients with schizophrenia (SCZ), although their clinical implications remain unclear. Previous case-control studies have investigated group-level differences in structural abnormalities, although the study design cannot account for interindividual differences. Recent research has focused on the association between the heterogeneity of the cerebral cortex morphometric features and clinical heterogeneity. METHODS: We used neuroimaging data from 420 healthy controls and 695 patients with SCZ from seven studies. Four cerebral cortex measures were obtained: surface area, gray matter volume, thickness, and local gyrification index. We calculated the coefficient of variation (CV) and person-based similarity index (PBSI) scores and performed group comparisons. Associations between the PBSI scores and cognitive functions were evaluated using Spearman's rho test and normative modeling. RESULTS: Patients with SCZ had a greater CV of surface area and cortical thickness than those of healthy controls. All PBSI scores across cortical measures were lower in patients with SCZ than in HCs. In the patient group, the PBSI scores for gray matter volume and all cortical measures taken together positively correlated with the full-scale IQ scores. Patients with deviant PBSI scores for gray matter volume and all cortical measures taken together had lower full-scale IQ scores than those of other patients. CONCLUSIONS: The cerebral cortex in patients with SCZ showed greater regional and global structural variability than that in healthy controls. Patients with deviant similarity of cortical structural profiles exhibited a lower general intelligence than those exhibited by the other patients.

Alterations in the multilayer network in patients with rapid eye movement sleep behaviour disorder.

This study aimed to reveal the pathophysiology of isolated rapid eye movement sleep behaviour disorder (RBD) in patients using multilayer network analysis. Participants eligible for isolated RBD were included and verified via polysomnography. Both iRBD patients and healthy controls underwent brain MRI, including T1-weighted imaging and diffusion tensor imaging. Grey matter matrix was derived from T1-weighted images using a morphometric similarity network. White matter matrix was formed from diffusion tensor imaging-based structural connectivity. Multilayer network analysis of grey and white matter was performed using graph theory. We studied 29 isolated RBD patients and 30 healthy controls. Patients exhibited a higher average overlap degree (27.921 vs. 23.734, p = 0.002) and average multilayer clustering coefficient (0.474 vs. 0.413, p = 0.002) compared with controls. Additionally, several regions showed significant differences in the degree of overlap and multilayer clustering coefficient between patients with isolated RBD and healthy controls at the nodal level. The degree of overlap in the left medial orbitofrontal, left posterior cingulate, and right paracentral nodes and the multilayer clustering coefficients in the left lateral occipital, left rostral middle frontal, right fusiform, right inferior posterior parietal, and right parahippocampal nodes were higher in patients with isolated RBD than in healthy controls. We found alterations in the multilayer network at the global and nodal levels in patients with isolated RBD, and these changes may be associated with the pathophysiology of isolated RBD. Multilayer network analysis can be used widely to explore the mechanisms underlying various neurological disorders.

Multilayer network analysis in patients with juvenile myoclonic epilepsy.

INTRODUCTION: We conducted a multilayer network analysis in patients with juvenile myoclonic epilepsy (JME) and healthy controls, to investigate the gray matter layer using a morphometric similarity network and analyze the white matter layer using structural connectivity. METHODS: We enrolled 42 patients with newly diagnosed JME and 53 healthy controls. Brain magnetic resonance imaging (MRI) using a three-tesla MRI scanner, including T1-weighted imaging and diffusion tensor imaging (DTI) were performed. We created a gray matter layer matrix with a morphometric similarity network using T1-weighted imaging, and a white matter layer matrix with structural connectivity using the DTI. Subsequently, we performed a multilayer network analysis by applying graph theory. RESULTS: There were significant differences in network at the global level in the multilayer network analysis between the groups. The average multiplex participation of patients with JME was lower than that of healthy controls (0.858 vs. 0.878, p = 0.007). In addition, several regions showed significant differences in multiplex participation at the nodal level in the multilayer network analysis. Multiplex participation in the right entorhinal cortex was lower, whereas multiplex participation in the right supramarginal gyrus was higher at the nodal level in the multilayer network analysis of patients with JME compared to healthy controls. CONCLUSION: We demonstrated differences in network at the global and nodal levels in the multilayer network analysis between patients with JME and healthy controls. These features may be associated with the pathophysiology of JME and could help us understand the complex brain network in patients with JME.

Development and validation of a deep learning model to diagnose COVID-19 using time-series heart rate values before the onset of symptoms.

One of the effective ways to minimize the spread of COVID-19 infection is to diagnose it as early as possible before the onset of symptoms. In addition, if the infection can be simply diagnosed using a smartwatch, the effectiveness of preventing the spread will be greatly increased. In this study, we aimed to develop a deep learning model to diagnose COVID-19 before the onset of symptoms using heart rate (HR) data obtained from a smartwatch. In the deep learning model for the diagnosis, we proposed a transformer model that learns HR variability patterns in presymptom by tracking relationships in sequential HR data. In the cross-validation (CV) results from the COVID-19 unvaccinated patients, our proposed deep learning model exhibited high accuracy metrics: sensitivity of 84.38%, specificity of 85.25%, accuracy of 84.85%, balanced accuracy of 84.81%, and area under the receiver operating characteristics (AUROC) of 0.8778. Furthermore, we validated our model using external multiple datasets including healthy subjects, COVID-19 patients, as well as vaccinated patients. In the external healthy subject group, our model also achieved high specificity of 77.80%. In the external COVID-19 unvaccinated patient group, our model also provided similar accuracy metrics to those from the CV: balanced accuracy of 87.23% and AUROC of 0.8897. In the COVID-19 vaccinated patients, the balanced accuracy and AUROC dropped by 66.67% and 0.8072, respectively. The first finding in this study is that our proposed deep learning model can simply and accurately diagnose COVID-19 patients using HRs obtained from a smartwatch before the onset of symptoms. The second finding is that the model trained from unvaccinated patients may provide less accurate diagnosis performance compared with the vaccinated patients. The last finding is that the model trained in a certain period of time may provide degraded diagnosis performances as the virus continues to mutate.

MRI radiomics may predict early tumor recurrence in patients with sinonasal squamous cell carcinoma.

OBJECTIVES: Sinonasal squamous cell carcinoma (SCC) follows a poor prognosis with high tendency for local recurrence. We aimed to evaluate whether MRI radiomics can predict early local failure in sinonasal SCC. METHODS: Sixty-eight consecutive patients with node-negative sinonasal SCC (January 2005-December 2020) were enrolled, allocated to the training (n = 47) and test sets (n = 21). Early local failure, which occurred within 12 months of completion of initial treatment, was the primary endpoint. For clinical features (age, location, treatment modality, and clinical T stage), binary logistic regression analysis was performed. For 186 extracted radiomic features, different feature selections and classifiers were combined to create two prediction models: (1) a pure radiomics model; and (2) a combined model with clinical features and radiomics. The areas under the receiver operating characteristic curves (AUCs) were calculated and compared using DeLong's method. RESULTS: Early local failure occurred in 38.3% (18/47) and 23.8% (5/21) in the training and test sets, respectively. We identified several radiomic features which were strongly associated with early local failure. In the test set, both the best-performing radiomics model and the combined model (clinical + radiomic features) yielded higher AUCs compared to the clinical model (AUC, 0.838 vs. 0.438, p = 0.020; 0.850 vs. 0.438, p = 0.016, respectively). The performances of the best-performing radiomics model and the combined model did not differ significantly (AUC, 0.838 vs. 0.850, p = 0.904). CONCLUSION: MRI radiomics integrated with a machine learning classifier may predict early local failure in patients with sinonasal SCC. CLINICAL RELEVANCE STATEMENT: MRI radiomics intergrated with machine learning classifiers may predict early local failure in sinonasal squamous cell carcinomas more accurately than the clinical model. KEY POINTS: • A subset of radiomic features which showed significant association with early local failure in patients with sinonasal squamous cell carcinomas was identified. • MRI radiomics integrated with machine learning classifiers can predict early local failure with high accuracy, which was validated in the test set (area under the curve = 0.838). • The combined clinical and radiomics model yielded superior performance for early local failure prediction compared to that of the radiomics (area under the curve 0.850 vs. 0.838 in the test set), without a statistically significant difference.

Human Activity Prediction Based on Forecasted IMU Activity Signals by Sequence-to-Sequence Deep Neural Networks.

Human Activity Recognition (HAR) has gained significant attention due to its broad range of applications, such as healthcare, industrial work safety, activity assistance, and driver monitoring. Most prior HAR systems are based on recorded sensor data (i.e., past information) recognizing human activities. In fact, HAR works based on future sensor data to predict human activities are rare. Human Activity Prediction (HAP) can benefit in multiple applications, such as fall detection or exercise routines, to prevent injuries. This work presents a novel HAP system based on forecasted activity data of Inertial Measurement Units (IMU). Our HAP system consists of a deep learning forecaster of IMU activity signals and a deep learning classifier to recognize future activities. Our deep learning forecaster model is based on a Sequence-to-Sequence structure with attention and positional encoding layers. Then, a pre-trained deep learning Bi-LSTM classifier is used to classify future activities based on the forecasted IMU data. We have tested our HAP system for five daily activities with two tri-axial IMU sensors. The forecasted signals show an average correlation of 91.6% to the actual measured signals of the five activities. The proposed HAP system achieves an average accuracy of 97.96% in predicting future activities.

Accelerating 3D Convolutional Neural Network with Channel Bottleneck Module for EEG-Based Emotion Recognition.

Deep learning-based emotion recognition using EEG has received increasing attention in recent years. The existing studies on emotion recognition show great variability in their employed methods including the choice of deep learning approaches and the type of input features. Although deep learning models for EEG-based emotion recognition can deliver superior accuracy, it comes at the cost of high computational complexity. Here, we propose a novel 3D convolutional neural network with a channel bottleneck module (CNN-BN) model for EEG-based emotion recognition, with the aim of accelerating the CNN computation without a significant loss in classification accuracy. To this end, we constructed a 3D spatiotemporal representation of EEG signals as the input of our proposed model. Our CNN-BN model extracts spatiotemporal EEG features, which effectively utilize the spatial and temporal information in EEG. We evaluated the performance of the CNN-BN model in the valence and arousal classification tasks. Our proposed CNN-BN model achieved an average accuracy of 99.1% and 99.5% for valence and arousal, respectively, on the DEAP dataset, while significantly reducing the number of parameters by 93.08% and FLOPs by 94.94%. The CNN-BN model with fewer parameters based on 3D EEG spatiotemporal representation outperforms the state-of-the-art models. Our proposed CNN-BN model with a better parameter efficiency has excellent potential for accelerating CNN-based emotion recognition without losing classification performance.

Brain Age Prediction: A Comparison between Machine Learning Models Using Brain Morphometric Data.

Brain structural morphology varies over the aging trajectory, and the prediction of a person's age using brain morphological features can help the detection of an abnormal aging process. Neuroimaging-based brain age is widely used to quantify an individual's brain health as deviation from a normative brain aging trajectory. Machine learning approaches are expanding the potential for accurate brain age prediction but are challenging due to the great variety of machine learning algorithms. Here, we aimed to compare the performance of the machine learning models used to estimate brain age using brain morphological measures derived from structural magnetic resonance imaging scans. We evaluated 27 machine learning models, applied to three independent datasets from the Human Connectome Project (HCP, n = 1113, age range 22-37), the Cambridge Centre for Ageing and Neuroscience (Cam-CAN, n = 601, age range 18-88), and the Information eXtraction from Images (IXI, n = 567, age range 19-86). Performance was assessed within each sample using cross-validation and an unseen test set. The models achieved mean absolute errors of 2.75-3.12, 7.08-10.50, and 8.04-9.86 years, as well as Pearson's correlation coefficients of 0.11-0.42, 0.64-0.85, and 0.63-0.79 between predicted brain age and chronological age for the HCP, Cam-CAN, and IXI samples, respectively. We found a substantial difference in performance between models trained on the same data type, indicating that the choice of model yields considerable variation in brain-predicted age. Furthermore, in three datasets, regularized linear regression algorithms achieved similar performance to nonlinear and ensemble algorithms. Our results suggest that regularized linear algorithms are as effective as nonlinear and ensemble algorithms for brain age prediction, while significantly reducing computational costs. Our findings can serve as a starting point and quantitative reference for future efforts at improving brain age prediction using machine learning models applied to brain morphometric data.

Real-Time Human Activity Recognition with IMU and Encoder Sensors in Wearable Exoskeleton Robot via Deep Learning Networks.

Wearable exoskeleton robots have become a promising technology for supporting human motions in multiple tasks. Activity recognition in real-time provides useful information to enhance the robot's control assistance for daily tasks. This work implements a real-time activity recognition system based on the activity signals of an inertial measurement unit (IMU) and a pair of rotary encoders integrated into the exoskeleton robot. Five deep learning models have been trained and evaluated for activity recognition. As a result, a subset of optimized deep learning models was transferred to an edge device for real-time evaluation in a continuous action environment using eight common human tasks: stand, bend, crouch, walk, sit-down, sit-up, and ascend and descend stairs. These eight robot wearer's activities are recognized with an average accuracy of 97.35% in real-time tests, with an inference time under 10 ms and an overall latency of 0.506 s per recognition using the selected edge device.

Heritability and Cognitive Relevance of Structural Brain Controllability.

Cognition and behavior are thought to emerge from the connections and interactions among brain regions. The precise nature of these relationships remains elusive. Here we use tools provided by network control theory to determine how the structural connectivity profile of brain regions may shape individual variation in cognition. In a cohort of healthy young adults (n = 1066), we computed two fundamental brain regional control patterns, average and modal controllability, which index the degree of influence of a region over others. We first established that regional brain controllability measures were both reproducible and heritable. Regions with controllability profiles theoretically conducive to facilitating multiple cognitive operations were over-represented in higher-order resting-state networks. Finally, variation in regional controllability accounted for about 50% of interindividual variability in multiple cognitive domains. We conclude that controllability is a biologically plausible property of the structural connectome and provides a mechanistic explanation for how brain structural architecture may influence cognitive functions.

Schizophrenia Detection Using Machine Learning Approach from Social Media Content.

Schizophrenia is a severe mental disorder that ranks among the leading causes of disability worldwide. However, many cases of schizophrenia remain untreated due to failure to diagnose, self-denial, and social stigma. With the advent of social media, individuals suffering from schizophrenia share their mental health problems and seek support and treatment options. Machine learning approaches are increasingly used for detecting schizophrenia from social media posts. This study aims to determine whether machine learning could be effectively used to detect signs of schizophrenia in social media users by analyzing their social media texts. To this end, we collected posts from the social media platform Reddit focusing on schizophrenia, along with non-mental health related posts (fitness, jokes, meditation, parenting, relationships, and teaching) for the control group. We extracted linguistic features and content topics from the posts. Using supervised machine learning, we classified posts belonging to schizophrenia and interpreted important features to identify linguistic markers of schizophrenia. We applied unsupervised clustering to the features to uncover a coherent semantic representation of words in schizophrenia. We identified significant differences in linguistic features and topics including increased use of third person plural pronouns and negative emotion words and symptom-related topics. We distinguished schizophrenic from control posts with an accuracy of 96%. Finally, we found that coherent semantic groups of words were the key to detecting schizophrenia. Our findings suggest that machine learning approaches could help us understand the linguistic characteristics of schizophrenia and identify schizophrenia or otherwise at-risk individuals using social media texts.

Microbiota changes with fermented kimchi contributed to either the amelioration or rejuvenation of Helicobacter pylori-associated chronic atrophic gastritis.

Korean fermented kimchi is probiotic food preventing Helicobacter pylori (H. pylori)-associated atrophic gastritis in both animal and human trial. In order to reveal the effect of fermented kimchi against H. pylori infection, we performed clinical trial to document the changes of fecal microbiota in 32 volunteers (H. pylori (-) chronic superficial gastritis (CSG), H. pylori (+) CSG, and H. pylori (+) chronic atrophic gastritis (CAG) with 10 weeks kimchi. Each amplicon is sequenced on MiSeq of Illumina and the sequence reads were clustered into operational taxonomic units using VSEARCH and the Chao, Simpson, and Shannon Indices. Though significant difference in α- or ß-diversity was not seen in three groups, kimchi intake led to significant diversity of fecal microbiome. As results, Klebsiella, Enterococcus, Ruminococcaceae, Streptococcus, Roseburia, and Clostirdiumsensu were significantly increased in H. pylori (+) CAG, while Akkermansia, Citrobacter, and Lactobacillus were significantly decreased in H. pylori (+) CAG. With 10 weeks of kimchi administration, Bifidobacterium, Lactobacillus, and Ruminococcus were significantly increased in H. pylori (+) CAG, whereas Bacteroides, Subdoligranulum, and Eubacterium coprostanolines were significantly decreased in H. pylori (-) CAG. 10 weeks of kimchi intake significantly improved pepsinogen I/II ratio (p<0.01) with significant decreases in interleukin-1ß. Conclusively, fermented kimchi significantly changed fecal microbiota to mitigate H. pylori-associated atrophic gastritis.

Fecal microbiota changes with fermented kimchi intake regulated either formation or advancement of colon adenoma.

Gut bacteria might contribute in early stage of colorectal cancer through the development and advancement of colon adenoma, by which exploring either beneficial bacteria, which are decreased in formation or advancement of colon adenoma and harmful bacteria, which are increased in advancement of colon adenoma may result in implementation of dietary interventions or probiotic therapies to functional means for prevention. Korean fermented kimchi is one of representative probiotic food providing beneficiary microbiota and exerting significant inhibitory outcomes in both APC/Min+ polyposis model and colitis-associated cancer. Based on these backgrounds, we performed clinical trial to document the changes of fecal microbiota in 32 volunteers with normal colon, simple adenoma, and advanced colon adenoma with 10 weeks of fermented kimchi intake. Each amplicon is sequenced on MiSeq of Illumina and the sequence reads were clustered into Operational Taxonomic Units using VSEARCH and the Chao Indices, an estimator of richness of taxa per individual, were estimated to measure the diversity of each sample. Though significant difference in α or ß diversity was not seen between three groups, kimchi intake significantly led to significant diversity of fecal microbiome. After genus analysis, Acinobacteria, Cyanobacteria, Clostridium sensu, Turicibacter, Gastronaeophillales, H. pittma were proven to be increased in patients with advanced colon adenoma, whereas Enterococcua Roseburia, Coryobacteriaceau, Bifidobacterium spp., and Akkermansia were proven to be significantly decreased in feces from patients with advanced colon adenoma after kimchi intake. Conclusively, fermented kimchi plentiful of beneficiary microbiota can afford significant inhibition of either formation or advancement of colon adenoma.

Poly(Alkyl-Terphenyl Piperidinium) Ionomers and Membranes with an Outstanding Alkaline-Membrane Fuel-Cell Performance of 2.58†W cm-2.

Aryl-ether-free anion-exchange ionomers (AEIs) and membranes (AEMs) have become an important benchmark to address the insufficient durability and power-density issues associated with AEM fuel cells (AEMFCs). Here, we present aliphatic chain-containing poly(diphenyl-terphenyl piperidinium) (PDTP) copolymers to reduce the phenyl content and adsorption of AEIs and to increase the mechanical properties of AEMs. Specifically, PDTP AEMs possess excellent mechanical properties (storage modulus>1800 MPa, tensile strength>70 MPa), H2 fuel-barrier properties (<10 Barrer), good ion conductivity, and ex-situ stability. Meanwhile, PDTP AEIs with low phenyl content and high-water permeability display excellent peak power densities (PPDs). The present AEMFCs reach outstanding PPDs of 2.58 W cm-2 (>7.6 A cm-2 current density) and 1.38 W cm-2 at 80 °C in H2 /O2 and H2 /air, respectively, along with a specific power (PPD/catalyst loading) over 8 W mg-1 , which is the highest record for Pt-based AEMFCs so far.

Extracellular vesicle-associated antigens as a new vaccine platform against scrub typhus.

Scrub typhus is an acute vector-borne disease caused by infection with the intracellular gram-negative bacterium Orientia tsutsugamushi (Ot). The rapid production of an efficient vaccine against Ot using novel strategies is required because of the global increase in mortality caused by these infections; however, no commercial vaccine is currently available. Ot induces T-cell-mediated immunogenic responses upon infection; therefore, a new rapidly producible vaccine that maximizes T-cell responses against Ot is required. In this study, we sought to develop a model vaccine platform for T-cell-mediated Ot infection using T-cell-immunity associated Salmonella-derived extracellular vesicles (EVs). For this purpose, we optimized DNA sequences encoding the full-length Ot proteins, TSA56, ScaA, ScaC, ScaD, and ScaE, and their expression in Salmonella. The sequences were incorporated into a new platform vector, pKST, which ectopically and concurrently produces Ot proteins and EVs. Expression analysis using pKST-antigen plasmids showed that TSA56 and ScaC produced antigen-associated EVs and showed strong T-cell immunogenic responses. We found that mice vaccinated with EVs derived from TSA56-expressing cells were protected from Salmonella-induced mortality. Therefore, our findings showed that Salmonella EV-associated antigen is a model platform for T-cell immune response infections. Our system could help prepare EV-antigen vaccines against scrub typhus in an easy and rapid manner.

Sortase A-Inhibitory Coumarins from the Folk Medicinal Plant Poncirus trifoliata.

Thirteen coumarins (1-13), including five new compounds (1-5), were isolated from the folk medicinal plant Poncirus trifoliata. Combined spectroscopic analyses revealed that coumarins 1-4 are bis-isoprenylated coumarins with diverse oxidation patterns, while 5 is an enantiomeric di-isoprenylated coumarin. The absolute configurations of the stereogenic centers in the isoprenyl chains were assigned through MTPA and MPA methods, and those of the known compounds triphasiol (6) and ponciol (7) were also assigned using similar methods. These coumarins inhibited significantly Staphylococcus aureus-derived sortase A (SrtA), a transpeptidase responsible for anchoring surface proteins to the peptidoglycan cell wall in Gram-positive bacteria. The present results obtained indicated that the bioactivity and underlying mechanism of action of these coumarins are associated with the inhibition of SrtA-mediated S. aureus adhesion to eukaryotic cell matrix proteins including fibrinogen and fibronectin, thus potentially serving as SrtA inhibitors.

16S rDNA microbiome composition pattern analysis as a diagnostic biomarker for biliary tract cancer.

BACKGROUND: The aim of this study is to investigate the composition of microbiota in biliary tract cancer patients and healthy adults by metagenome analysis and evaluate its potential values as biomarkers for biliary tract cancer. METHODS: Patients who were diagnosed with biliary tract cancer or benign inflammation were enrolled in this study. The control group consisted of healthy adults who presented with no history of significant medical issues. We isolated bacteria-derived extracellular vesicles in the plasma. The microbiome composition was investigated with 16S rDNA metagenome analysis. We evaluated each microbiome to ensure suitability for the biliary tract cancer prediction model. RESULTS: A total of 155 patients were included in this study: 24 patients with diagnosed biliary tract cancers, 43 diagnosed with cholecystitis or cholangitis, and 88 healthy adults. The microbiome composition pattern of the biliary tract cancer differed from the microbiome composition pattern seen in healthy adult group in beta diversity analysis. The percent composition of microbiota was found to be different from the phylum to genus level. Differences in the composition of the Bifidobacteriaceae and Pseudomonaceae families and Corynebacteriaceae Corynebacterium, Oxalobacteraceae Ralstonia and Comamonadaceae Comamonas species may be used to develop predictive models for biliary tract cancer. CONCLUSION: Biliary tract cancer patients have altered microbiome composition, which represents a promising biomarker to differentiate malignant biliary tract disease from normal control group.

Fruit and vegetable discards preserved with sodium metabisulfite as a high-moisture ingredient in total mixed ration for ruminants: effect on in vitro ruminal fermentation and in vivo metabolism.

OBJECTIVE: Our recent series of laboratory- and large-scale experiments confirmed that under aerobic and anaerobic conditions, sodium metabisulfite (SMB) was effective in preserving nutrients and antioxidant capacity of highly perishable fruit and vegetable discards (FVD). Hence, the purpose of this study was to examine how partial inclusion of SMB-treated FVD in total mixed ration (TMR) influences in vitro ruminal fermentation, whole-tract digestibility, nitrogen metabolism, blood metabolites, and voluntary feed intake of sheep. METHODS: The FVD were mixed thoroughly with 6 g SMB/kg wet biomass and kept outdoors under aerobic conditions for 7 days. Four TMRs including four levels of SMB-treated FVD (as-fed basis) at 0%, 10%, 20%, and 30% (equaling to 0%, 1.9%, 3.8%, and 5.7% on dry matter basis, respectively), were prepared as replacement for corn grain. The ruminal fermentation metabolites were studied using an in vitro gas production test. Four mature male Corriedale sheep were assigned at random to the 4 diets for two separate sub-experiments; i) digestibility trial with four 21-d periods, and ii) voluntary feed intake trial with four 28-d periods. RESULTS: Inclusion of SMB-treated FVD in the TMR tended to quadratically increase partitioning factor. No effect was seen on total-tract digestibility of organic matter, ether extract, crude protein, and acid detergent fiber, except for neutral detergent fiber digestibility that tended to linearly increase with increasing SMB-treated FVD in the TMR. The progressive increase of FVD preserved with SMB in the diet had no effect on nitrogen metabolism. Treatment had no effect on serum antioxidant capacity and blood metabolites assayed. Voluntary feed intake was not impaired by inclusion of SMB-treated FVD in the TMR. CONCLUSION: It appears that FVD preserved with SMB can be safely incorporated into TMR as replacement of corn grain without impairment of nutrient metabolism and feed intake.