The causal relationship between gut microbiota and lymphoma: a two-sample Mendelian randomization study.

Background: Previous studies have indicated a potential link between the gut microbiota and lymphoma. However, the exact causal interplay between the two remains an area of ambiguity. Methods: We performed a two-sample Mendelian randomization (MR) analysis to elucidate the causal relationship between gut microbiota and five types of lymphoma. The research drew upon microbiome data from a research project of 14,306 participants and lymphoma data encompassing 324,650 cases. Single-nucleotide polymorphisms were meticulously chosen as instrumental variables according to multiple stringent criteria. Five MR methodologies, including the inverse variance weighted approach, were utilized to assess the direct causal impact between the microbial exposures and lymphoma outcomes. Moreover, sensitivity analyses were carried out to robustly scrutinize and validate the potential presence of heterogeneity and pleiotropy, thereby ensuring the reliability and accuracy. Results: We discerned 38 potential causal associations linking genetic predispositions within the gut microbiome to the development of lymphoma. A few of the more significant results are as follows: Genus Coprobacter (OR = 0.619, 95% CI 0.438-0.873, P = 0.006) demonstrated a potentially protective effect against Hodgkin's lymphoma (HL). Genus Alistipes (OR = 0.473, 95% CI 0.278-0.807, P = 0.006) was a protective factor for diffuse large B-cell lymphoma. Genus Ruminococcaceae (OR = 0.541, 95% CI 0.341-0.857, P = 0.009) exhibited suggestive protective effects against follicular lymphoma. Genus LachnospiraceaeUCG001 (OR = 0.354, 95% CI 0.198-0.631, P = 0.0004) showed protective properties against T/NK cell lymphoma. The Q test indicated an absence of heterogeneity, and the MR-Egger test did not show significant horizontal polytropy. Furthermore, the leave-one-out analysis failed to identify any SNP that exerted a substantial influence on the overall results. Conclusion: Our study elucidates a definitive causal link between gut microbiota and lymphoma development, pinpointing specific microbial taxa with potential causative roles in lymphomagenesis, as well as identifying probiotic candidates that may impact disease progression, which provide new ideas for possible therapeutic approaches to lymphoma and clues to the pathogenesis of lymphoma.

Sustainable treatment of aquaculture water employing fungi-microalgae consortium: Nutrients removal enhancement, bacterial communities optimization, emerging contaminants elimination, and mechanism analysis.

Fungi-microalgae consortium (FMC) has emerged as a promising system for advanced wastewater treatment due to its high biomass yield and environmental sustainability. This study aimed to investigate the nutrients removal, bacterial community shift, emerging contaminants elimination, and treatment mechanism of a FMC composed of Cordyceps militaris and Navicula seminulum for aquaculture pond water treatment. The fungi and microalgae were cultured and employed either alone or in combination to evaluate the treatment performance. The results demonstrated that the FMC could improve water quality more significantly by reducing nutrient pollutants and optimizing the bacterial community structures. Furthermore, it exhibited stronger positive correlation between the enrichment of functional bacteria for water quality improvement and pollutants removal performance than the single-species treatments. Moreover, the FMC outperformed other groups in eliminating emerging contaminants such as heavy metals, antibiotics, and pathogenic Vibrios. Superiorly, the FMC also showed excellent symbiotic interactions and cooperative mechanisms for pollutants removal. The results collectively corroborated the feasibility and sustainability of using C. militaris and N. seminulum for treating aquaculture water, and the FMC would produce more mutualistic benefits and synergistic effects than single-species treatments.

PMF-CNN: parallel multi-band fusion convolutional neural network for SSVEP-EEG decoding.

Steady-state visual evoked potential (SSVEP) is a key technique of electroencephalography (EEG)-based brain-computer interfaces (BCI), which has been widely applied to neurological function assessment and postoperative rehabilitation. However, accurate decoding of the user's intended based on the SSVEP-EEG signals is challenging due to the low signal-to-noise ratio and large individual variability of the signals. To address these issues, we proposed a parallel multi-band fusion convolutional neural network (PMF-CNN). Multi frequency band signals were served as the input of PMF-CNN to fully utilize the time-frequency information of EEG. Three parallel modules, spatial self-attention (SAM), temporal self-attention (TAM), and squeeze-excitation (SEM), were proposed to automatically extract multi-dimensional features from spatial, temporal, and frequency domains, respectively. A novel spatial-temporal-frequency representation were designed to capture the correlation of electrode channels, time intervals, and different sub-harmonics by using SAM, TAM, and SEM, respectively. The three parallel modules operate independently and simultaneously. A four layers CNN classification module was designed to fuse parallel multi-dimensional features and achieve the accurate classification of SSVEP-EEG signals. The PMF-CNN was further interpreted by using brain functional connectivity analysis. The proposed method was validated using two large publicly available datasets. After trained using our proposed dual-stage training pattern, the classification accuracies were 99.37% and 93.96%, respectively, which are superior to the current state-of-the-art SSVEP-EEG classification algorithms. The algorithm exhibits high classification accuracy and good robustness, which has the potential to be applied to postoperative rehabilitation.

Cross-subject classification of depression by using multiparadigm EEG feature fusion.

BACKGROUND AND OBJECTIVE: The aim of this study is to solve the non-stationarity and complexity characteristics and huge individual differences in the electroencephalogram (EEG) signals for depression classification. METHODS: To address those problems, the Lempel-Ziv complexity feature matrices were extracted from the EEG signals under the two paradigms of eyes open and eyes closed in the resting state. Topographical map of brain and statistical analysis were introduced to investigate the significance of eyes open and eyes closed EEG for depression classification. To promote the classification accuracy, feature matrices from the two paradigms were fused. And linear combination and concatenation fusion methods were proposed to further reveal the underlying mechanism of improving classification accuracy. Support vector machine (SVM), K-nearest neighbor, and decision tree classifiers were employed and compared to classify depression under the eyes open, eyes closed and fused paradigm. RESULTS: The classification results of 10-fold cross-validation showed that the highest average accuracy (86.58%) under a single paradigm was achieved in the eyes-open state. The multiparadigm fusion method of concatenation was better than the linear combination. The best classification result was obtained using multiparadigm feature concatenation under the SVM classifier, yielding an accuracy of 94.03%. CONCLUSION: The multiparadigm feature fusion method proposed in this paper can effectively improve the accuracy of depression classification. It was proved that eyes open and eyes closed EEG have complementary information, which was benefit for the cross-subject classification of depression. It provides new ideas for depression classification in clinics.

Molecular subtype identification and predictive power of N6-methyladenosine regulator in unexplained recurrent pregnancy loss.

The etiology of recurrent pregnancy loss (RPL) is complicated and effective clinical preventive measures are lacking. Identifying biomarkers for RPL has been challenging, and to date, little is known about the role of N6-methyladenosine (m6A) regulators in RPL. Expression data for m6A regulators in 29 patients with RPL and 29 healthy controls were downloaded from the Gene Expression Omnibus (GEO) database. To establish a diagnostic model for unexplained RPL, differential gene expression analysis was conducting for 36 m6A regulators using least absolute shrinkage and selection operator (LASSO) regression. Unsupervised cluster analysis was conducted on hub genes, and probable mechanisms were explored using gene set enrichment analysis (GSEA) and gene ontology (GO) analysis. Correlations between m6A-related differentially expressed genes and immune infiltration were analyzed using single-sample GSEA. A total of 18 m6A regulators showed significant differences in expression in RPL: 10 were upregulated and eight were downregulated. Fifteen m6A regulators were integrated and used to construct a diagnostic model for RPL that had good predictive efficiency and robustness in differentiating RPL from control samples, with an overall area under the curve (AUC) value of 0.994. Crosstalk was identified between 10 hub genes, miRNAs, and transcription factors (TFs). For example, YTHDF2 was targeted by mir-1-3p and interacted with embryonic development-related TFs such as FOXA1 and GATA2. YTHDF2 was also positively correlated with METTL14 (r = 0.5983, p < 0.001). Two RPL subtypes (Cluster-1 and Cluster-2) with distinct hub gene signatures were identified. GSEA and GO analysis revealed that the differentially expressed genes were mainly associated with immune processes and cell cycle signaling pathway (normalized enrichment score, NES = -1.626, p < 0.001). Immune infiltration was significantly higher in Cluster-1 than in Cluster-2 (p < 0.01). In conclusion, we demonstrated that m6A modification plays a critical role in RPL. We also developed and validated a diagnostic model for RPL prediction based on m6A regulators. Finally, we identified two distinct RPL subtypes with different biological processes and immune statuses.

Exploiting the gut microbiota to predict the origins and quality traits of cultured sea cucumbers.

Nowadays, the true economic and nutritional value of food is underpinned by both origin and quality traits, more often expressed as increased quality benefits derived from the origin source. Gut microbiota contribute to food metabolism and host health, therefore, it may be suitable as a qualifying indicator of origin and quality of economic species. Here, we investigated relationships between the gut microbiota of the sea cucumber (Apostichopus japonicus), a valuable aquaculture species in Asia, with their origins and quality metrics. Based on data from 287 intestinal samples, we generated the first biogeographical patterns for A. japonicus gut microbiota from origins across China. Importantly, A. japonicus origins were predicted using the random forest model that was constructed using 20 key gut bacterial genera, with 97.6% accuracy. Furthermore, quality traits such as saponin, fat and taurine were also successfully predicted by random forest models based on gut microbiota, with approximately 80% consistency between predicted and true values. We showed that substantial variations existed in the gut microbiota and quality variables in A. japonicus across different origins, and we also demonstrated the great potential of gut microbiota to track A. japonicus origins and predict their quality traits.