Genetics causal analysis of oral microbiome on type 2 diabetes in East Asian populations: a bidirectional two-sample Mendelian randomized study.

Introduction: The dysbiosis of the oral microbiome is associated with the progression of various systemic diseases, including diabetes. However, the precise causal relationships remain elusive. This study aims to investigate the potential causal associations between oral microbiome and type 2 diabetes (T2D) using Mendelian randomization (MR) analyses. Methods: We conducted bidirectional two-sample MR analyses to investigate the impact of oral microbiome from saliva and the tongue T2D. This analysis was based on metagenome-genome-wide association studies (mgGWAS) summary statistics of the oral microbiome and a large meta-analysis of GWAS of T2D in East Asian populations. Additionally, we utilized the T2D GWAS summary statistics from the Biobank Japan (BBJ) project for replication. The MR methods employed included Wald ratio, inverse variance weighting (IVW), weighted median, MR-Egger, contamination mixture (ConMix), and robust adjusted profile score (RAPS). Results: Our MR analyses revealed genetic associations between specific bacterial species in the oral microbiome of saliva and tongue with T2D in East Asian populations. The MR results indicated that nine genera were shared by both saliva and tongue. Among these, the genera Aggregatibacter, Pauljensenia, and Prevotella were identified as risk factors for T2D. Conversely, the genera Granulicatella and Haemophilus D were found to be protective elements against T2D. However, different species within the genera Catonella, Lachnoanaerobaculum, Streptococcus, and Saccharimonadaceae TM7x exhibited multifaceted influences; some species were positively correlated with the risk of developing T2D, while others were negatively correlated. Discussion: This study utilized genetic variation tools to confirm the causal effect of specific oral microbiomes on T2D in East Asian populations. These findings provide valuable insights for the treatment and early screening of T2D, potentially informing more targeted and effective therapeutic strategies.

Embedded EEG Feature Selection for Multi-Dimension Emotion Recognition via Local and Global Label Relevance.

Due to the problem of a small amount of EEG samples and relatively high dimensionality of electroencephalogram (EEG) features, feature selection plays an essential role in EEG-based emotion recognition. However, current EEG-based emotion recognition studies utilize a problem transformation approach to transform multi-dimension emotional labels into single-dimension labels, and then implement commonly used single-label feature selection methods to search feature subsets, which ignores the relations between different emotional dimensions. To tackle the problem, we propose an efficient EEG feature selection method for multi-dimension emotion recognition (EFSMDER) via local and global label relevance. First, to capture the local label correlations, EFSMDER implements orthogonal regression to map the original EEG feature space into a low-dimension space. Then, it employs the global label correlations in the original multi-dimension emotional label space to effectively construct the label information in the low-dimension space. With the aid of local and global relevance information, EFSMDER can conduct representational EEG feature subset selection. Three EEG emotional databases with multi-dimension emotional labels were used for performance comparison between EFSMDER and fourteen state-of-the-art methods, and the EFSMDER method achieves the best multi-dimension classification accuracies of 86.43, 84.80, and 97.86 percent on the DREAMER, DEAP, and HDED datasets, respectively.

Hepatitis C virus infection is associated with high risk of breast cancer: a pooled analysis of 68,014 participants.

Introduction: Breast cancer is the most common malignancy among women. Previous studies had shown that hepatitis C virus (HCV) infection might serve as a risk factor for breast cancer, while some studies failed to find such an association. Methods: In this study, we presented a first attempt to capture and clarify this clinical debate via a cumulative analysis (registration ID: CRD42023445888). Results: After systematically searching and excluding the irrelevant publications, five case-control or cohort studies were finally included. The synthetic effect from the eligible studies showed that patients with HCV infection had a significantly higher prevalence of breast cancer than non-HCV infected general population (combined HR= 1.382, 95%CI: 1.129 to 1.692, P=0.002). There was no evidence of statistical heterogeneity during this pooled analysis (I2 = 13.2%, P=0.33). The sensitivity analyses confirmed the above findings. No significant publication bias was observed among the included studies. The underlying pathophysiological mechanisms for this relationship might be associated with persistent infection/inflammation, host immune response, and the modulation of HCV-associated gene expression. Discussion: Though the causal association between HCV infection and breast cancer did not seem quite as strong, screening for HCV might enable the early detection of breast cancer and help to prevent the progression of the disease. Since the topic of this study remains a matter of clinical debate, further studies are still warranted to validate this potential association. Systematic review registration: https://www.crd.york.ac.uk/PROSPERO/, identifier CRD42023445888.

Hsa_circ_0000585 promotes chemoresistance to cis-platin in epithelial cells of ovarian cancer by modulating autophagy.

BACKGROUND: Chemoresistance, i.e., resistance to cisplatin (DDP), has been a major obstacle to ovarian cancer treatment. It has been found that circular RNAs (circRNAs) play vital roles in the tumorigenesis various cancers by regulating autophagy, while few studies focusing on cisplatin-resistance ovarian cancer (CROC). METHODS: The expressions of the circRNAs were detected by qRT-PCR. Short hairpin RNA targeting circRNA was used to explore the biological functions of the circRNA. Cell viability, autophagic flux, immunofluorescence, and xenograft tumors experiments were performed to further illustrate the underlying mechanisms. RESULTS: Hsa_circ_0000585 was increased in cisplatin-resistant SKOV3/DDP cells. Stably knocking down hsa_circRNA_0000585 expression in SKOV3/DDP cells was established by RNA interference. We found that downregulation of hsa_circ_0000585 significantly enhanced the sensitivity of DDP/SkOV3 cells to DDP. In vivo study, hsa_circRNA_0000585 knockdown significantly decreased tumor volume in nude mice. Under the measurements of western blot and cellular immunofluorescence, hsa_circ_0000585 knockdown significantly inhibited the expression of Beclin1 and P62, indicating the autophagic flux was inhibited. Administrations with autophagic inhibitor "Chloroquine (CQ)" and autophagy activator "QX77" further confirmed that hsa_circ_0000585 knockdown resulted in autophagy inhibition. CONCLUSIONS: Overall, this study provided a new insight into the role of circRNAs in the mechanism of DDP-resistance in ovarian cancer. Hsa_circRNA_0000585 may be promising therapeutic targets for the enhancement of the sensitivity of ovarian cancer cells to cisplatin-mediated chemotherapy.

A Multi-Source Transfer Joint Matching Method for Inter-Subject Motor Imagery Decoding.

Individual differences among different subjects pose a great challenge to motor imagery (MI) decoding. Multi-source transfer learning (MSTL) is one of the most promising ways to reduce individual differences, which can utilize rich information and align the data distribution among different subjects. However, most MSTL methods in MI-BCI combine all data in the source subjects into a single mixed domain, which will ignore the effect of important samples and the large differences in multiple source subjects. To address these issues, we introduce transfer joint matching and improve it to multi-source transfer joint matching (MSTJM) and weighted MSTJM (wMSTJM). Different from previous MSTL methods in MI, our methods align the data distribution for each pair of subjects, and then integrate the results by decision fusion. Besides that, we design an inter-subject MI decoding framework to verify the effectiveness of these two MSTL algorithms. It mainly consists of three modules: covariance matrix centroid alignment in the Riemannian space, source selection in the Euclidean space after tangent space mapping to reduce negative transfer and computation overhead, and further distribution alignment by MSTJM or wMSTJM. The superiority of this framework is verified on two common public MI datasets from BCI competition IV. The average classification accuracy of the MSTJM and wMSTJ methods outperformed other state-of-the-art methods by at least 4.24% and 2.62% respectively. It's promising to advance the practical applications of MI-BCI.

Supervised Feature Selection With Orthogonal Regression and Feature Weighting.

Effective features can improve the performance of a model and help us understand the characteristics and underlying structure of complex data. Previously proposed feature selection methods usually cannot retain more discriminative information. To address this shortcoming, we propose a novel supervised orthogonal least square regression model with feature weighting for feature selection. The optimization problem of the objective function can be solved by employing generalized power iteration and augmented Lagrangian multiplier methods. Experimental results show that the proposed method can more effectively reduce feature dimensionality and obtain better classification results than traditional feature selection methods. The convergence of our iterative method is also proved. Consequently, the effectiveness and superiority of the proposed method are verified both theoretically and experimentally.

Functional Magnetic Resonance Imaging Study of Apathy in Alzheimer's Disease.

An fMRI study was launched to understand mechanisms of Alzheimer's disease (AD) patients with apathy. The authors reviewed 7 AD patients with apathy and 6 AD patients without apathy. The block method was adopted, and 24 pictures representing positive, negative, and neutral emotional stimuli were viewed when patients were given brain fMRI. Under "sad versus neutral" stimulation, non-apathetic AD patients had increased activity in bilateral amygdala and bilateral fusiform gyrus, whereas apathetic AD patients only had increased activities in bilateral fusiform gyrus. Thus, the amygdala was the possible anatomical structure related to apathy in AD patients.

Role of magnetic resonance diffusion-weighted imaging in evaluating response after chemoembolization of hepatocellular carcinoma.

OBJECTIVE: To investigate the value of hepatocellular carcinoma pretreatment apparent diffusion coefficients (ADCs) and its ADCs changes after treatment in predicting and early monitoring the response after chemoembolization. MATERIALS AND METHODS: Twenty-five responding and nine nonresponding hepatocellular carcinoma lesions were prospectively evaluated with magnetic resonance diffusion-weighted imaging in 24 h before and in 48 h after chemoembolization. Quantitative ADC maps were calculated with images with b values of 0 and 500 s/mm(2). RESULTS: Nonresponding lesions had a significantly higher pretreatment mean ADC than did responding lesions (1.726+/-0.323 x 10(-3) mm(2)/s vs.1.294+/-0.18510(-3) mm(2)/s, P< or =0.001). The results of receiver operator characteristic (ROC) analysis for identification of nonresponding lesions showed that threshold ADC value of 1.618 x 10(-3) mm(2)/s had 96.0% sensitivity and 77.8% specificity. After transarterial chemoembolization, responding lesions had a significant increase in %ADC values than did nonresponding lesions (32.63% vs. 5.24%, P=0.025). The results of ROC analysis for identification of responding lesions showed that threshold %ADC value of 16.21% had 72% sensitivity and 100% specificity. No significant change was observed in normal liver parenchyma (P=0.862) and spleen (P=0.052). CONCLUSION: High pretreatment mean ADC value of hepatocellular carcinoma was predictive of poor response to chemoembolization. A significant increase in %ADC value was observed in lesions that responded to chemoembolization.