State-Dependent tACS Effects Reveal the Potential Causal Role of Prestimulus Alpha Traveling Waves in Visual Contrast Detection.

The intricate relationship between prestimulus alpha oscillations and visual contrast detection variability has been the focus of numerous studies. However, the causal impact of prestimulus alpha traveling waves on visual contrast detection remains largely unexplored. In our research, we sought to discern the causal link between prestimulus alpha traveling waves and visual contrast detection across different levels of mental fatigue. Using electroencephalography alongside a visual detection task with 30 healthy adults (13 females; 17 males), we identified a robust negative correlation between prestimulus alpha forward traveling waves (FTWs) and visual contrast threshold (VCT). Inspired by this correlation, we utilized 45/-45° phase-shifted transcranial alternating current stimulation (tACS) in a sham-controlled, double-blind, within-subject experiment with 33 healthy adults (23 females; 10 males) to directly modulate these alpha traveling waves. After the application of 45° phase-shifted tACS, we observed a substantial decrease in FTW and an increase in backward traveling waves, along with a concurrent increase in VCT, compared with the sham condition. These changes were particularly pronounced under a low fatigue state. The findings of state-dependent tACS effects reveal the potential causal role of prestimulus alpha traveling waves in visual contrast detection. Moreover, our study highlights the potential of 45/-45° phase-shifted tACS in cognitive modulation and therapeutic applications.

Plasma-derived exosomal miRNA profiles associated with type 1 diabetes.

AIMS: Recently, exosomal miRNAs have been shown to play important roles in multiple diseases, including type 1 diabetes (T1D). To assess the biomarker potential of exosomal miRNAs for T1D, we measured the expression profiles of plasma-derived exosomal miRNAs in T1D and explored their potential functions by bioinformatic analysis. MATERIALS AND METHODS: In the discovery phase, exosome samples were isolated from plasma by size exclusion chromatography from 10 T1D patients and 10 sex- (p = 0.36), age- (p = 0.97), and body mass index-matched (p = 0.47) healthy control subjects. Exosomal miRNA expression profiles were measured using the Illumina NovaSeq 6000 platform. With verification by quantitative real-time PCR (qRT-PCR), we used multiple bioinformatics approaches to explore the potential biological functions of the identified differentially expressed miRNAs. The diagnostic signature of exosomal miRNAs was evaluated by least absolute shrinkage and selection operator (LASSO) regression and evaluated based on the area under the receiver operating characteristic curve (AUC). RESULTS: In total, 43 differentially expressed miRNAs, among which 34 were upregulated and 9 were downregulated, were identified in T1D. After correcting for multiple testing using false discovery rate, 11 identified exosomal miRNAs still showed statistical significance. Among the 5 selected miRNAs, 3 miRNAs (miR-103a-3p, miR-144-5p and miR-454-3p) were successfully validated by qRT-PCR. The biological analysis-enriched terms included protein autophosphorylation and the Hedgehog signalling pathway. The highest AUC of exosomal miRNA was 0.889 under the LASSO model. The expression levels of 5 selected exosomal miRNAs were correlated with multiple clinical characteristics such as fasting C-peptide and postprandial C-peptide. CONCLUSIONS: Our results indicated that plasma-derived exosomal miRNAs could serve as promising diagnostic biomarkers of T1D.

2-[18F]FDG PET-based quantification of lymph node metabolic heterogeneity for predicting lymph node metastasis in patients with colorectal cancer.

BACKGROUND AND PURPOSE: The pre-surgical estimation of lymph node (LN) metastasis in colorectal cancer (CRC) poses a significant diagnostic predicament. The associations between LN morphology, density, and metabolic heterogeneity and LN metastasis status in CRCs have been seldomly examined through the lens of radiomics. This research aimed to assess 2-[18F]FDG PET-based quantification of intratumoral metabolic heterogeneity for predicting lymph node metastasis in patients with colorectal cancer. MATERIALS AND METHODS: The construction of the model utilized data from 264 CRC patients, all of whom underwent preoperative 2-[18F]FDG PET/CT. Radiomic features were extracted from PET and CT images of LNs. Least absolute shrinkage and selection operator (LASSO) regression was implemented for selecting pertinent imaging features with a tenfold cross-validation. The predictive accuracy for LN metastasis status was juxtaposed against traditional methodologies (comprising CT-reported LN status and PET/CT-reported LN status) by deploying the receiver operating characteristic (ROC) curve analysis. The radiomics signature was evaluated based on discrimination, calibration, and clinical utility parameters. The model was further subjected to validation using an independent cohort of 132 patients from the period of January 2012 to June 2020. RESULTS: The radiomics model was composed of eight significant radiomic features (five from PET and three from CT), encapsulating metabolic and density heterogeneity. The radiomics signature (area under the curve (AUC), 0.908) showcased a significantly superior performance compared to CT-reported LN status (AUC, 0.563, P < 0.001) and PET/CT-reported LN status (AUC, 0.64, P < 0.001) for predicting LN-positive or LN-negative status. The radiomics signature (AUC, 0.885) also showcased a significantly superior performance compared to CT-reported LN status (AUC, 0.587, P < 0.001) and PET/CT-reported LN status (AUC, 0.621, P < 0.001) to identify N1 and N2. This signature maintained its independence from clinical risk factors and exhibited robustness in the validation test set. Decision curve analysis attested to the clinical utility of the radiomics signature. CONCLUSIONS: The radiomics signature based on 2-[18F]FDG PET/CT, which derived image features directly from LNs irrespective of clinical risk factors, displayed enhanced diagnostic performance compared to conventional CT or PET/CT-reported LN status. This allows for the identification of pre-surgical LN metastasis status and facilitates a patient-specific prediction of LN metastasis status in CRC patients.

[68Ga]Ga-FAPI-04 PET/MR imaging strategy in management of Krukenberg tumors (KTs) from gastric signet-ring-cell carcinoma: to overcome limitation of [68Ga]Ga-FAPI-04 PET imaging in KTs.

PURPOSE: To compare performance of whole-body [68Ga]Ga-FAPI-04 and [18F]FDG PET imaging in the detection of Krukenberg tumors (KTs), primary site and extra-ovarian metastases of gastric signet-ring-cell carcinoma (GSRCC), and evaluate the value of [68Ga]Ga-FAPI-04 PET/MR imaging strategy and its potential impact on the management of KTs from GSRCC. METHODS: Twelve patients with twenty-three KTs from GSRCC, who underwent both [68Ga]Ga-FAPI-04 pelvic PET/MR and whole-body [68Ga]Ga-FAPI-04 and [18F]FDG PET imaging were retrospectively analyzed. [68Ga]Ga-FAPI-04 and [18F]FDG uptakes were compared by using Wilcoxon signed-rank test or paired t test. McNemar's test was used to compare lesion detectability between two modalities. Two-tailed P<0.05 was considered statistically significant. Immunohistochemistry staining was utilized to analyze the fibroblast activation protein (FAP) expression in KTs. RESULTS: A total of 12 patients with 23 KTs from GSRCC (8 synchronous and 4 metachronous) were evaluated. [68Ga]Ga-FAPI-04 was superior to [18F]FDG PET in detecting primary sites of GSRCC (100% [11/11] vs. 18.2% [2/11], p = 0.002), involved lymph nodes (90.9% [10/11] vs. 54.5% [6/11], p = 0.046) and peritoneal metastases (100% [12/12] vs. 41.7% [5/12], p = 0.008), with higher SUVmax and TBR (all p < 0.005). Both tracers had limited value in identifying KTs, with 100% false negative rate on [68Ga]Ga-FAPI-04 PET and a low detection rate of 8.7% on [18F]FDG PET. Fap immunohistochemistry showed negative or slight FAP expression in neoplastic signet ring cells and ovarian stroma. [68Ga]Ga-FAPI-04 PET/MR imaging strategy greatly improved the detection rate of Krukenberg tumors (87%, 20/23). After adding diffusion-weighted imaging (DWI), the detection rate was further improved (87.5% vs. 100%, p = 0.083). [68Ga]Ga-FAPI-04 PET/MR imaging strategy either upgraded TNM staging or changed treatment management in twelve patients. CONCLUSIONS: [68Ga]Ga-FAPI-04 PET outperformed [18F]FDG PET in detecting primary site and most extra-ovarian metastases of GSRCC, but both tracers had limited value in identifying Krukenberg tumors. Pelvis MRI should be applied to compensate the limitation of [68Ga]Ga-FAPI-04 PET imaging to identify Krukenberg tumours. The [68Ga]Ga-FAPI-04 PET/MR imaging strategy has the potential to impact treatment decisions for GSRCC patients with KTs.

EEG microstate correlates of emotion dynamics and stimulation content during video watching.

INTRODUCTION: EEG microstates have been widely adopted to understand the complex and dynamic-changing process in dynamic brain systems, but how microstates are temporally modulated by emotion dynamics is still unclear. An investigation of EEG microstates under video-evoking emotion dynamics modulation would provide a novel insight into the understanding of temporal dynamics of functional brain networks. METHODS: In the present study, we postulate that emotional states dynamically modulate the microstate patterns, and perform an in-depth investigation between EEG microstates and emotion dynamics under a video-watching task. By mapping from subjective-experienced emotion states and objective-presented stimulation content to EEG microstates, we gauge the comprehensive associations among microstates, emotions, and multimedia stimulation. RESULTS: The results show that emotion dynamics could be well revealed by four EEG microstates (MS1, MS2, MS3, and MS4), where MS3 and MS4 are found to be highly correlated to different emotion states (emotion task effect and level effect) and the affective information involved in the multimedia content (visual and audio). CONCLUSION: In this work, we reveal the microstate patterns related to emotion dynamics from sensory and stimulation dimensions, which deepens the understanding of the neural representation under emotion dynamics modulation and will be beneficial for the future study of brain dynamic systems.

A brain-wide genome-wide association study of candidate quantitative trait loci associated with structural and functional phenotypes of pain sensitivity.

Individual pain sensitivity is modulated by the brain's structural and functional features, but its heritability remains unclear. This paper conducted a brain-wide genome-wide association study (GWAS) to explore the genetic bases of neuroimage phenotypes of pain sensitivity. In total, 432 normal participants were divided into high and low pain sensitivity groups according to the laser quantitative test threshold. Then, the brain's gray matter density (GMD) features correlated with pain sensitivity were identified. Next, GWAS was performed on each GMD phenotype using quality-controlled genotypes. Based on the heatmap and hierarchical clustering results, the right insula was identified for further refined analysis in terms of subregions GMD and resting-state functional connectivity (rs-FC) phenotypes. The results indicate that the right insula GMD in the high sensitivity group is significantly lower than that in the low sensitivity group. Also, the TT/TC group at locus rs187974 has lower right insula GMD than the CC group. Further, loci at gene CYP2D6 may lead to a variation of rs-FC between the right insula and left putamen. In conclusion, our study suggests that the right insula and multiple candidate loci may be importantly involved in pain sensitivity modulation, which may guide the future development of precision pain therapeutics.

Multimodal Sensing for Depression Risk Detection: Integrating Audio, Video, and Text Data.

Depression is a major psychological disorder with a growing impact worldwide. Traditional methods for detecting the risk of depression, predominantly reliant on psychiatric evaluations and self-assessment questionnaires, are often criticized for their inefficiency and lack of objectivity. Advancements in deep learning have paved the way for innovations in depression risk detection methods that fuse multimodal data. This paper introduces a novel framework, the Audio, Video, and Text Fusion-Three Branch Network (AVTF-TBN), designed to amalgamate auditory, visual, and textual cues for a comprehensive analysis of depression risk. Our approach encompasses three dedicated branches-Audio Branch, Video Branch, and Text Branch-each responsible for extracting salient features from the corresponding modality. These features are subsequently fused through a multimodal fusion (MMF) module, yielding a robust feature vector that feeds into a predictive modeling layer. To further our research, we devised an emotion elicitation paradigm based on two distinct tasks-reading and interviewing-implemented to gather a rich, sensor-based depression risk detection dataset. The sensory equipment, such as cameras, captures subtle facial expressions and vocal characteristics essential for our analysis. The research thoroughly investigates the data generated by varying emotional stimuli and evaluates the contribution of different tasks to emotion evocation. During the experiment, the AVTF-TBN model has the best performance when the data from the two tasks are simultaneously used for detection, where the F1 Score is 0.78, Precision is 0.76, and Recall is 0.81. Our experimental results confirm the validity of the paradigm and demonstrate the efficacy of the AVTF-TBN model in detecting depression risk, showcasing the crucial role of sensor-based data in mental health detection.

Functional connectivity profiles of the default mode and visual networks reflect temporal accumulative effects of sustained naturalistic emotional experience.

Determining and decoding emotional brain processes under ecologically valid conditions remains a key challenge in affective neuroscience. The current functional Magnetic Resonance Imaging (fMRI) based emotion decoding studies are mainly based on brief and isolated episodes of emotion induction, while sustained emotional experience in naturalistic environments that mirror daily life experiences are scarce. Here we used 12 different 10-minute movie clips as ecologically valid emotion-evoking procedures in n = 52 individuals to explore emotion-specific fMRI functional connectivity (FC) profiles on the whole-brain level at high spatial resolution (432 parcellations including cortical and subcortical structures). Employing machine-learning based decoding and cross validation procedures allowed to investigate FC profiles contributing to classification that can accurately distinguish sustained happiness and sadness and that generalize across subjects, movie clips, and parcellations. Both functional brain network-based and subnetwork-based emotion classification results suggested that emotion manifests as distributed representation of multiple networks, rather than a single functional network or subnetwork. Further, the results showed that the Visual Network (VN) and Default Mode Network (DMN) associated functional networks, especially VN-DMN, exhibited a strong contribution to emotion classification. To further estimate the temporal accumulative effect of naturalistic long-term movie-based video-evoking emotions, we divided the 10-min episode into three stages: early stimulation (1∼200 s), middle stimulation (201∼400 s), and late stimulation (401∼600 s) and examined the emotion classification performance at different stimulation stages. We found that the late stimulation contributes most to the classification (accuracy=85.32%, F1-score=85.62%) compared to early and middle stimulation stages, implying that continuous exposure to emotional stimulation can lead to more intense emotions and further enhance emotion-specific distinguishable representations. The present work demonstrated that sustained happiness and sadness under naturalistic conditions are presented in emotion-specific network profiles and these expressions may play different roles in the generation and modulation of emotions. These findings elucidated the importance of network level adaptations for sustained emotional experiences during naturalistic contexts and open new venues for imaging network level contributions under naturalistic conditions.

Elevated glucose metabolism driving pro-inflammatory response in B cells contributes to the progression of type 1 diabetes.

Type 1 diabetes (T1D) is an autoimmune disease characterized by the immune system's failure to maintain self-tolerance, resulting in the autoimmune destruction of pancreatic beta cells. Although T1D has conventionally been viewed as a T-cell-dominant disease, recent research has emphasized the contribution of B cells in the onset of the disease. However, the mechanism underlying aberrant B cell responses remains unknown. B cell metabolism is a crucial prerequisite for B cell function and the development of adaptive immune responses. Here, we investigated the metabolic features of B cells, first in a cross-sectional cohort and subsequently in non-obese diabetic (NOD) mice, and revealed that there is an increased frequency of high-glucose-avidity (2-NBDGhigh) B cell population that may contribute to T1D progression. Further characterization of the metabolic, transcriptional and functional phenotype of B cells in NOD mice found that elevated glucose avidity is associated with a greater capacity for co-stimulation, proliferation and inflammatory cytokine production. Mechanistically, elevated Myc signaling orchestrated the glucose metabolism and the pro-inflammatory response of B cells in T1D. In vitro experiments demonstrated that pharmacological inhibition of glucose metabolism using metformin and 2-DG reduced pro-inflammatory cytokine production and B cell proliferation. Moreover, the combination of these inhibitors successfully delayed insulitis development, onset of diabetes, and improved high blood glucose levels in streptozotocin (STZ)-induced diabetic mice model. Taken together, our work has uncovered these high-glucose-avidity B cells as novel adjuvant diagnostic and therapeutic targets for T1D.

Potential pathways and genes expressed in Chrysanthemum in response to early fusarium oxysporum infection.

BACKGROUND: Chrysanthemum Fusarium wilt is a common fungal disease caused by Fusarium oxysporum, which causes continuous cropping obstacles and huge losses to the chrysanthemum industry. The defense mechanism of chrysanthemum against F. oxysporum remains unclear, especially during the early stages of the disease. Therefore, in the present study, we analyzed chrysanthemum 'Jinba' samples inoculated with F. oxysporum at 0, 3, and 72 h using RNA-seq. RESULTS: The results revealed that 7985 differentially expressed genes (DEGs) were co-expressed at 3 and 72 h after F. oxysporum infection. We analyzed the identified DEGs using Kyoto Encyclopedia of Genes and Genomes and Gene Ontology. The DEGs were primarily enriched in "Plant pathogen interaction", "MAPK signaling pathway", "Starch and sucrose metabolism", and "Biosynthesis of secondary metabolites". Genes related to the synthesis of secondary metabolites were upregulated in chrysanthemum early during the inoculation period. Furthermore, peroxidase, polyphenol oxidase, and phenylalanine ammonia-lyase enzymes were consistently produced to accumulate large amounts of phenolic compounds to resist F. oxysporum infection. Additionally, genes related to the proline metabolic pathway were upregulated, and proline levels accumulated within 72 h, regulating osmotic balance in chrysanthemum. Notably, the soluble sugar content in chrysanthemum decreased early during the inoculation period; we speculate that this is a self-protective mechanism of chrysanthemums for inhibiting fungal reproduction by reducing the sugar content in vivo. In the meantime, we screened for transcription factors that respond to F. oxysporum at an early stage and analyzed the relationship between WRKY and DEGs in the "Plant-pathogen interaction" pathway. We screened a key WRKY as a research target for subsequent experiments. CONCLUSION: This study revealed the relevant physiological responses and gene expression changes in chrysanthemum in response to F. oxysporum infection, and provided a relevant candidate gene pool for subsequent studies on chrysanthemum Fusarium wilt.

OTUD1 ameliorates cerebral ischemic injury through inhibiting inflammation by disrupting K63-linked deubiquitination of RIP2.

BACKGROUND: Inflammatory response triggered by innate immunity plays a pivotal element in the progress of ischemic stroke. Receptor-interacting kinase 2 (RIP2) is implicated in maintaining immunity homeostasis and regulating inflammatory response. However, the underlying mechanism of RIP2 in ischemic stroke is still not well understood. Hence, the study investigated the role and the ubiquitination regulatory mechanism of RIP2 in ischemic stroke. METHODS: Focal cerebral ischemia was introduced by middle cerebral artery occlusion (MCAO) in wild-type (WT) and OTUD1-deficient (OTUD1-/-) mice, oxygen glucose deprivation and reoxygenation (OGD/R) models in BV2 cells and primary cultured astrocytes were performed for monitoring of experimental stroke. GSK2983559 (GSK559), a RIP2 inhibitor was intraventricularly administered 30 min before MCAO. Mice brain tissues were collected for TTC staining and histopathology. Protein expression of RIP2, OTUD1, p-NF-κB-p65 and IκBα was determined by western blot. Localization of RIP2 and OTUD1 was examined by immunofluorescence. The change of IL-1ß, IL-6 and TNF-α was detected by ELISA assay and quantitative real-time polymerase chain reaction. Immunoprecipitation and confocal microscopy were used to study the interaction of RIP2 and OTUD1. The activity of NF-κB was examined by dual-luciferase assay. RESULTS: Our results showed upregulated protein levels of RIP2 and OTUD1 in microglia and astrocytes in mice subjected to focal cerebral ischemia. Inhibition of RIP2 by GSK559 ameliorated the cerebral ischemic outcome by repressing the NF-κB activity and the inflammatory response. Mechanistically, OTUD1 interacted with RIP2 and sequentially removed the K63-linked polyubiquitin chains of RIP2, thereby inhibiting NF-κB activation. Furthermore, OTUD1 deficiency exacerbated cerebral ischemic injury in response to inflammation induced by RIP2 ubiquitination. CONCLUSIONS: These findings suggested that RIP2 mediated cerebral ischemic lesion via stimulating inflammatory response, and OTUD1 ameliorated brain injury after ischemia through inhibiting RIP2-induced NF-κB activation by specifically cleaving K63-linked ubiquitination of RIP2.

Prevalence, clinical characteristics and HLA genotypes of idiopathic type 1 diabetes: A cross-sectional study.

AIMS: Idiopathic type 1 diabetes (T1D) is a neglected subtype of T1D. Our aim was to investigate the frequency, clinical characteristics, and human leucocyte antigen (HLA) genotypes of idiopathic T1D. METHODS: We enrolled 1205 newly diagnosed T1D patients in our analysis. To exclude monogenic diabetes in autoantibody-negative patients, we utilised a custom monogenic diabetes gene panel. Individuals negative for autoantibodies and subsequently excluded for monogenic diabetes were diagnosed with idiopathic T1D. We collected clinical characteristics, measured islet autoantibodies by radioligand assay and obtained HLA data. RESULTS: After excluding 11 patients with monogenic diabetes, 284 cases were diagnosed with idiopathic T1D, accounting for 23.8% (284/1194) of all newly diagnosed T1D cases. When compared with autoimmune T1D, idiopathic T1D patients showed an older onset age, higher body mass index among adults, lower haemoglobin A1c, higher levels of fasting C-peptide and 2-h postprandial C-peptide, and were likely to have type 2 diabetes (T2D) family history and carry 0 susceptible HLA haplotype (all p < 0.01). A lower proportion of individuals carrying 2 susceptible HLA haplotypes in idiopathic T1D was observed in the adult-onset subgroup (15.7% vs. 38.0% in child-onset subgroup, p < 0.001) and in subgroup with preserved beta-cell function (11.0% vs. 30.1% in subgroup with poor beta-cell function, p < 0.001). Multivariable correlation analyses indicated that being overweight, having T2D family history and lacking susceptible HLA haplotypes were associated with negative autoantibodies. CONCLUSIONS: Idiopathic T1D represents about 1/4 of newly diagnosed T1D, with adult-onset and preserved beta-cell function patients showing lower HLA susceptibility and more insulin resistance.

Distribution of autoantibodies to insulinoma-associated antigen-2 and zinc transporter 8 in type 1 diabetes and latent autoimmune diabetes: A nationwide, multicentre, cross-sectional study.

AIMS: This study investigated insulinoma-associated-2 autoantibody (IA-2A) and zinc transporter 8 autoantibody (ZnT8A) distribution in patients with type 1 diabetes (T1D) and latent autoimmune diabetes (LAD) and the autoantibodies' association with clinical characteristics and HLA-DR-DQ genes. MATERIALS AND METHODS: This cross-sectional study recruited 17,536 patients with diabetes from 46 hospitals across China. A total of 189 patients with T1D and 58 patients with LAD with IA-2A positivity, 126 patients with T1D and 86 patients with LAD with ZnT8A positivity, and 231 patients with type 2 diabetes (T2D) were selected to evaluate islet autoantibodies, clinical phenotypes, and HLA-DR-DQ gene frequency. RESULTS: IA-2A was bimodally distributed in patients with T1D and LAD. Patients with low IA-2A titre LAD had lower fasting C-peptide (FCP) (p < 0.01), lower postprandial C-peptide (PCP) (p < 0.001), and higher haemoglobin A1c (HbA1c) levels (p < 0.05) than patients with T2D. Patients with high IA-2A titre LAD were younger than patients with low IA-2A titre LAD (p < 0.05). Patients with low IA-2A titre T1D had lower FCP (p < 0.01), lower PCP (p < 0.01), and higher HbA1c levels (p < 0.05) than patients with high IA-2A titre LAD. HLA-DR-DQ genetic analysis demonstrated that the frequency of susceptible HLA haplotypes was higher in IA-2A-positive patients (p < 0.001) than in patients with T2D. Patients with high ZnT8A titre LAD had lower FCP (p = 0.045), lower PCP (p = 0.023), and higher HbA1c levels (p = 0.009) and a higher frequency of total susceptible haplotypes (p < 0.001) than patients with low ZnT8A titre LAD. CONCLUSIONS: IA-2A in patients with T1D and LAD was bimodally distributed, and the presence of IA-2A could demonstrate partial LAD clinical characteristics. ZnT8A titre had a certain predictive value for islet functions in patients with LAD.

The impact of family history of type 2 diabetes on clinical heterogeneity in idiopathic type 1 diabetes.

AIM: To investigate the impact of family history of type 2 diabetes (T2D) on the clinical phenotypes of patients with idiopathic type 1 diabetes (T1D). METHODS: In clinically diagnosed T1D cases, a total of 335 idopathic T1D patients were included in the study, after excluding autoimmune T1D using islet autoantibody testing and monogenic diabetes using a custom monogenic diabetes gene panel obtained from clinically diagnosed T1D cases. A semi-structured questionnaire was used to collect information on the presence of T2D in first-degree relatives. The demographic and metabolic markers of idiopathic T1D patients were analysed. Subgroup analysis was performed to investigate potential interactions between T2D family history and human leukocyte antigen (HLA) genotypes. RESULTS: A total of 18.2% of individuals with idiopathic T1D had a T2D family history, and these individuals were more likely to have features associated with T2D, such as older age of onset, higher body mass index at diagnosis, lower insulin dosage and better beta-cell function, as indicated by higher levels of fasting C-peptide and 2-hour postprandial C-peptide (all P < 0.05). Additionally, regardless of HLA susceptible genotypes, the impact of family history of T2D was consistently observed in idiopathic T1D patients. Multivariable analyses showed that T2D family history was negatively correlated with the risk of beta-cell function failure in idiopathic T1D patients (P < 0.05). CONCLUSIONS: Family history of T2D may be implicated in the heterogeneity of idiopathic T1D patients.

Successful lexical tone production of Mandarin Chinese autistic children with intellectual impairment.

BACKGROUND: Atypical speech prosody has been commonly found among autistic children. Yet it remains unknown whether prosody impairment originates from poor pitch ability in general or whether it is the result of the difficulty in understanding and using prosody for communicative purposes. AIMS: To investigate whether native Mandarin Chinese-speaking autistic children with intellectual impairment were able to accurately produce native lexical tones, which are pitch patterns that distinguish word meaning lexically and serve little social purpose. METHODS & PROCEDURES: Using a picture-naming task, thirteen 8-13-year-old Mandarin Chinese-speaking autistic children with intellectual impairment were tested on their production of Chinese lexical tones. Chronical age-matched typically developing (TD) children were included as the control group. Perceptual assessment and phonetic analyses were conducted with the produced lexical tones. OUTCOMES & RESULTS: The majority of the lexical tones produced by the autistic children were perceived as accurate by adult judges. Phonetic analysis of the pitch contours found no significant difference between the two groups, and the autistic children and TD children used the phonetic features in comparable ways when differentiating the lexical tones. However, the lexical tone accuracy rate was lower among the autistic children than among the TDs, and the larger individual difference was observed among the autistic children than the TD children. CONCLUSIONS & IMPLICATIONS: These results indicate that autistic children are able to produce the global contours of the lexical tones, and pitch deficits do not seem to qualify as a core feature of autism. WHAT THIS PAPER ADDS: What is already known on the subject Atypical prosody has been considered a maker of the speech of autistic children, and meta-analysis found a significant difference in mean pitch and pitch range between TD children and autistic children. Yet it remains unknown whether the pitch deficits are the result of impaired perceptual-motoric ability or if they reflect failure in learning sentential prosody, which requires an understanding of the interlocutors' mind. In addition, research on pitch ability of autistic children with intellectual disabilities has been scarce, and whether these children are able to produce pitch variation is largely unknown. What this paper adds to existing knowledge We tested native Mandarin Chinese autistic children with intellectual impairment on their production of native lexical tones. The lexical tones in Chinese are pitch variations realized on individual syllables that distinguish lexical meaning, but they do not serve social pragmatic purposes. We found that although these autistic children had only developed limited spoken language, the majority of their lexical tones were perceived as accurate. They were able to use the phonetic features in comparable ways with the TD children when distinguishing the lexical tones. What are the potential or actual clinical implications of this work? It seems unlikely that pitch processing at the lexical level is fundamentally impaired in autistic children, and pitch deficits do not seem to qualify for a core feature of their speech. Practitioners should be cautious when using pitch production as a clinical marker for autistic children.

A novel classification model based on cerebral 18F-FDG uptake pattern facilitates the diagnosis of acute/subacute seropositive autoimmune encephalitis.

PURPOSE: To explore the intrinsic alteration of cerebral 18F-FDG metabolism in acute/subacute seropositive autoimmune encephalitis (AE) and to propose a universal classification model based on 18F-FDG metabolic patterns to predict AE. METHODS: Cerebral 18F-FDG PET images of 42 acute/subacute seropositive AE patients and 45 healthy controls (HCs) were compared using voxelwise and region of interest (ROI)-based schemes. The mean standardized uptake value ratios (SUVRs) of 59 subregions according to a modified Automated Anatomical Labeling (AAL) atlas were compared using a t-test. Subjects were randomly divided into a training set (70%) and a testing set (30%). Logistic regression models were built based on the SUVRs and the models were evaluated by determining their predictive value in the training and testing sets. RESULTS: The 18F-FDG uptake pattern in the AE group was characterized by increased SUVRs in the brainstem, cerebellum, basal ganglia, and temporal lobe, and decreased SUVRs in the occipital, and frontal regions with voxelwise analysis (false discovery rate [FDR] p<0.05). Utilizing ROI-based analysis, we identified 15 subareas that exhibited statistically significant changes in SUVRs among AE patients compared to HC (FDR p<0.05). Further, a logistic regression model incorporating SUVRs from the calcarine cortex, putamen, supramarginal gyrus, cerebelum_10, and hippocampus successfully enhanced the positive predictive value from 0.76 to 0.86 when compared to visual assessments. This model also demonstrated potent predictive ability, with AUC values of 0.94 and 0.91 observed for the training and testing sets, respectively. CONCLUSIONS: During the acute/subacute stages of seropositive AE, alterations in SUVRs appear to be concentrated within physiologically significant regions, ultimately defining the general cerebral metabolic pattern. By incorporating these key regions into a new classification model, we have improved the overall diagnostic efficiency of AE.

A new perspective on individual reliability beyond group effect for event-related potentials: A multisensory investigation and computational modeling.

The dominant approach in investigating the individual reliability for event-related potentials (ERPs) is to extract peak-related features at electrodes showing the strongest group effects. Such a peak-based approach implicitly assumes ERP components showing a stronger group effect are also more reliable, but this assumption has not been substantially validated and few studies have investigated the reliability of ERPs beyond peaks. In this study, we performed a rigorous evaluation of the test-retest reliability of ERPs collected in a multisensory and cognitive experiment from 82 healthy adolescents, each having two sessions. By comparing group effects and individual reliability, we found that a stronger group-level response in ERPs did not guarantee higher reliability. A perspective of neural oscillation should be adopted for the analysis of reliability. Further, by simulating ERPs with an oscillation-based computational model, we found that the consistency between group-level ERP responses and individual reliability was modulated by inter-subject latency jitter and inter-trial variability. The current findings suggest that the conventional peak-based approach may underestimate the individual reliability in ERPs and a neural oscillation perspective on ERP reliability should be considered. Hence, a comprehensive evaluation of the reliability of ERP measurements should be considered in individual-level neurophysiological trait evaluation and psychiatric disorder diagnosis.

M3CV: A multi-subject, multi-session, and multi-task database for EEG-based biometrics challenge.

EEG signals exhibit commonality and variability across subjects, sessions, and tasks. But most existing EEG studies focus on mean group effects (commonality) by averaging signals over trials and subjects. The substantial intra- and inter-subject variability of EEG have often been overlooked. The recently significant technological advances in machine learning, especially deep learning, have brought technological innovations to EEG signal application in many aspects, but there are still great challenges in cross-session, cross-task, and cross-subject EEG decoding. In this work, an EEG-based biometric competition based on a large-scale M3CV (A Multi-subject, Multi-session, and Multi-task Database for investigation of EEG Commonality and Variability) database was launched to better characterize and harness the intra- and inter-subject variability and promote the development of machine learning algorithm in this field. In the M3CV database, EEG signals were recorded from 106 subjects, of which 95 subjects repeated two sessions of the experiments on different days. The whole experiment consisted of 6 paradigms, including resting-state, transient-state sensory, steady-state sensory, cognitive oddball, motor execution, and steady-state sensory with selective attention with 14 types of EEG signals, 120000 epochs. Two learning tasks (identification and verification), performance metrics, and baseline methods were introduced in the competition. In general, the proposed M3CV dataset and the EEG-based biometric competition aim to provide the opportunity to develop advanced machine learning algorithms for achieving an in-depth understanding of the commonality and variability of EEG signals across subjects, sessions, and tasks.

Linolenic Acid-Metronidazole: a Compound Relieving Drug Resistance and Inhibiting Helicobacter pylori.

Metronidazole (Met) is the first choice for treating Helicobacter pylori (Hp). However, Hp is easy to resistant, making Met unable to be widely used. How to overcome Hp's Met resistance is still an issue. In this study, Met was used as the primary raw material with linolenic acid to prepare a novel compound-linolenic acid-metronidazole (Lla-Met). The MIC, minimum bactericidal concentration (MBC), colonization amount of Hp in gastric mucosa, etc., were evaluated, respectively. Lla-Met was successfully prepared by the detection of nuclear magnetic resonance, etc., and its MIC and MBC to Hp were 2~4 µg/mL, 8~16 µg/mL. Moreover, in vivo experiments, Lla-Met significantly reduced the colonization of drug-resistant Hp in gastric mucosa. In the toxicity test, Lla-Met inhibited rate to GES-1 and BGC823 cells were 15% at 128 µg/mL; the mice were administered 10 times treatment Lla-Met treatment (240 mg/kg), have no difference significant injuries were found in their stomach, liver, spleen, kidney, and weight. In addition, Hp G27 continued for 18 days in vitro with sub-Lla-Met concentration, G27 did not show drug resistance to Lla-Met; Lla-Met did not exert an effect on non-Hp species with 128 µg/mL; Compared with a neutral environment, when the acid concentration is 3.0, Lla-Met is not decomposed and has better stability. Conclusion: Lla-Met, a newly prepared compound, has relatively well antibacterial of Met-resistant and sensitive Hp, with a capability of overcoming the metronidazole resistance of Hp.

Characterization of whole-brain task-modulated functional connectivity in response to nociceptive pain: A multisensory comparison study.

Previous functional magnetic resonance imaging (fMRI) studies have shown that brain responses to nociceptive pain, non-nociceptive somatosensory, visual, and auditory stimuli are extremely similar. Actually, perception of external sensory stimulation requires complex interactions among distributed cortical and subcortical brain regions. However, the interactions among these regions elicited by nociceptive pain remain unclear, which limits our understanding of mechanisms of pain from a brain network perspective. Task fMRI data were collected with a random sequence of intermixed stimuli of four sensory modalities in 80 healthy subjects. Whole-brain psychophysiological interaction analysis was performed to identify task-modulated functional connectivity (FC) patterns for each modality. Task-modulated FC strength and graph-theoretical-based network properties were compared among the four modalities. Lastly, we performed across-sensory-modality prediction analysis based on the whole-brain task-modulated FC patterns to confirm the specific relationship between brain patterns and sensory modalities. For each sensory modality, task-modulated FC patterns were distributed over widespread brain regions beyond those typically activated or deactivated during the stimulation. As compared with the other three sensory modalities, nociceptive stimulation exhibited significantly different patterns (more widespread and stronger FC within the cingulo-opercular network, between cingulo-opercular and sensorimotor networks, between cingulo-opercular and emotional networks, and between default mode and emotional networks) and global property (smaller modularity). Further, a cross-sensory-modality prediction analysis found that task-modulated FC patterns could predict sensory modality at the subject level successfully. Collectively, these results demonstrated that the whole-brain task-modulated FC is preferentially modulated by pain, thus providing new insights into the neural mechanisms of pain processing.