Abstract Background Papillary thyroid cancer (PTC) is the most common endocrine malignancy, despite marked achieves in recent decades, the mechanisms underlying the pathogenesis and progression for PTC are incomplete. Accumulating evidence shows that γ-glutamylcyclotransferase (GGCT), an enzyme participated in glutathione homeostasis that is elevated in multiple types of tumors, represents an attractive therapeutic target. Methods Bioinformatics, immunohistochemistry (IHC), qRT-PCR and western blot (WB) assays were used to determine the elevation of GGCT in PTC. The biological functions of GGCT were examined using CCK8, wound healing and transwell assays. Subcutaneous xenograft and tail vein pulmonary metastatic mouse models were constructed to determine the effect of GGCT on tumorigenicity and metastasis in vivo. The effect of miR-205-5p on GGCT and the relationship between these two molecules were examined by dual luciferase reporter assay, RNA-RNA pull down assay as well as the rescue experiments both in vitro and in vivo. The interaction between GGCT and CD44 was assessed by co-immunoprecipitation (Co-IP) and IHC assays. Results Our results showed that GGCT expression is upregulated in PTC, correlates with more aggressive clinicopathological characteristics and worse prognosis. GGCT knockdown inhibited the cell proliferation, migratory and invasion ability of PTC cells and reduced the expression of mesenchymal markers (N-cadherin, CD44, MMP-2 and MMP9) while increased epithelial marker (E-cadherin) in PTC cells. We confirmed binding of miR-205-5p on the 3’-UTR regions of GGCT and delivery of miR-205-5p reversed the pro-malignant capacity of GGCT both in vitro and in vivo. Lastly, we found GGCT interacted with and stabilized CD44 in PTC cells. Conclusions Our findings illustrate a novel signaling pathway, miR-205-5p/GGCT/CD44, that involves in the carcinogenesis and progression of PTC. Development of miR-205-mimics or GGCT inhibitors as potential therapeutics for PTC may have remarkable applications.
Automatic sleep staging offers a quick and objective assessment for quantitatively interpreting sleep stages in neonates. However, most of the existing studies either do not encompass any temporal information, or simply apply neural networks to exploit temporal information at the expense of high computational overhead and modeling ambiguity. This limits the application of these methods to multiple scenarios. In this paper, a sequential end-to-end sleep staging model, SeqEESleepNet, which is competent for parallelly processing sequential epochs and has a fast training rate to adapt to different scenarios, is proposed. SeqEESleepNet consists of a sequence epoch generation (SEG) module, a sequential multi-scale convolution neural network (SMSCNN) and squeeze and excitation (SE) blocks. The SEG module expands independent epochs into sequential signals, enabling the model to learn the temporal information between sleep stages. SMSCNN is a multi-scale convolution neural network that can extract both multi-scale features and temporal information from the signal. Subsequently, the followed SE block can reassign the weights of features through mapping and pooling. Experimental results exhibit that in a clinical dataset, the proposed method outperforms the state-of-the-art approaches, achieving an overall accuracy, F1-score, and Kappa coefficient of 71.8%, 71.8%, and 0.684 on a three-class classification task with a single channel EEG signal. Based on our overall results, we believe the proposed method could pave the way for convenient multi-scenario neonatal sleep staging methods.
Abstract Pyroptosis, an inflammatory form of cell death, promotes the release of immunogenic substances and stimulates immune cell recruitment, a process, which could turn cold tumors into hot ones. Thus, instigating pyroptosis in triple‐negative breast cancer (TNBC) serves as a viable method for restoring antitumor immunity. We analyzed the effects of Histone Deacetylase Inhibitors (HDACi) on TNBC cells using the Cell Counting Kit‐8 and colony formation assay. Apoptosis and lactate dehydrogenase (LDH) release assays were utilized to determine the form of cell death. The pyroptotic executor was validated by quantitative real‐time polymerase chain reaction and western blot. Transcriptome was analyzed to investigate pyroptosis‐inducing mechanisms. A subcutaneously transplanted tumor model was generated in BALB/c mice to evaluate infiltration of immune cells. HDACi significantly diminished cell proliferation, and pyroptotic “balloon”‐like cells became apparent. HDACi led to an intra and extracellular material exchange, signified by the release of LDH and the uptake of propidium iodide. Among the gasdermin family, TNBC cells expressed maximum quantities of GSDME, and expression of GSDMA, GSDMB, and GSDME were augmented post HDACi treatment. Pyroptosis was instigated via the activation of the caspase 3‐GSDME pathway with the potential mechanisms being cell cycle arrest and altered intracellular REDOX balance due to aberrant glutathione metabolism. In vivo experiments demonstrated that HDACi can activate pyroptosis, limit tumor growth, and escalate CD8+ lymphocyte and CD11b+ cell infiltration along with an increased presence of granzyme B in tumors. HDACi can instigate pyroptosis in TNBC, promoting infiltration of immune cells and consequently intensifying the efficacy of anticancer immunity.
Despite the recent advances in automatic sleep staging, few studies have focused on real-time sleep staging to promote the regulation of sleep or the intervention of sleep disorders. In this paper, a novel network named SwSleepNet, that can handle both precisely offline sleep staging, and online sleep stages prediction and calibration is proposed. For offline analysis, the proposed network coordinates sequence broadening module (SBM), sequential CNN (SCNN), squeeze and excitation (SE) block, and sequence consolidation module (SCM) to balance the operational efficiency of the network and the comprehensive feature extraction. For online analysis, only SCNN and SE are involved in predicting the sleep stage within a short-time segment of the recordings. Once more than two successive segments have disparate predictions, the calibration mechanism will be triggered, and contextual information will be involved. In addition, to investigate the appropriate time of the segment that is suitable to predict a sleep stage, segments with five-second, three-second, and two-second data are analyzed. The performance of SwSleepNet is validated on two publicly available datasets Sleep-EDF Expanded and Montreal Archive of Sleep Studies (MASS), and one clinical dataset Huashan Hospital Fudan University (HSFU), with the offline accuracy of 84.5%, 86.7%, and 81.8%, respectively, which outperforms the state-of-the-art methods. Additionally, for the online sleep staging, the dedicated calibration mechanism allows SwSleepNet to achieve high accuracy over 80% on three datasets with the short-time segments, demonstrating the robustness and stability of SwSleepNet. This study presents a real-time sleep staging architecture, which is expected to pave the way for accurate sleep regulation and intervention.
Additional file 1: Table S1. Baseline characteristics on death and MACE risks. Table S2. Baseline characteristics on LVEF and LVMI. Table S3. Relationship between metabolites and death risk. Table S4. Relationship between metabolites and MACE risk. Table S5. Multivariate Cox proportional hazards model for clinical outcomes. Table S6. Spearman correlation analysis between clinical factors and those metabolites associated with death risk. Table S7. Spearman correlation analysis between clinical factors and those metabolites associated with death risk. Table S8. Relationship between metabolites and LVEF. Table S9. Relationship between metabolites and LVMI. Table S10. Estimation of the potential causal relationships between the metabolites and outcomes and LV remodeling traits by MR analyses. Table S11. Mediation effect of metabolites-LVEF-outcomes association. Table S12. The ionisation modes and ion pairs of the metabolites. Table S13. The coefficient of deviation of the metabolites between QC samples. Table S14. Associations between SNP and metabolites in the metabolome-based genome-wide association study.
Przewalski's Gazelle (Procapra przewalskii) is endemic to China and was classified as Critically Endangered by the International Union for Conservation of Nature (IUCN) in 1996. Once widespread, it has declined to 8 populations near Qinghai Lake. Subjected to the dual pressures of human activities and natural enemies, the gazelle is faced with the probability of extinction. By analyzing past years' statistical data, a Population Viability Model of Przewalski's Gazelle (PVMPG) was established. STELLA, a graphical-interface software, was used, first to establish the PVMPG model, and then to simulate environmental conditions and predict the future population viability. Finally, VORTEX software was used to forecast the probability of extinction of Przewalski's Gazelle in 100 years, to verify the reliability and rationality of the PVMPG model that was developed using STELLA. The results will provide the theoretical basis for the Przewalski's Gazelle population's protection.
Abstract Background The kidney is an important organ for maintaining homeostasis. Kidney-specific senescence plays an important role in aging; however, the mechanism underlying this process is still unclear. We conducted an analysis of renal function impairment in a group of healthy volunteers with a wide range of ages. The aims of this study were to investigate the age profile of kidney-specific aging, the characteristics of symbiotic microorganisms in urine and the aging mechanism related to these microorganisms. Results A total of 3342 adults aged 20 to 104 were included in the study. Serum creatinine, urea nitrogen, and glomerular filtration rate were evaluated by blood biochemical analysis to determine a typical age range of 50–65 years for abrupt changes in renal function. Urinary microbiota 16S rDNA sequencing was performed on 74 subjects from a distribution based on quartiles of renal function among cohort members in the age range of 50–65 years. Analysis of the sequencing results revealed significant differences between the flora in males and females, and significant changes in urinary flora with renal aging. Correlation analysis combined with clinical indicators revealed that the dominant microbiota in female subjects in the quartile of poorest renal function was closely associated with altered electrolyte metabolism, inflammatory activation and positive balance of energy metabolism during renal aging. In particular, our analysis suggests that Streptococcus plays an important role. Conclusions The age at which early impairment of renal function occurs was confirmed and a multidimensional analysis revealed that changes in urinary commensal bacteria, especially Streptococcus , are closely associated with renal aging.
Surface electromyography (sEMG)-based gesture recognition can achieve high intra-session performance. However, the inter-session performance of gesture recognition decreases sharply due to the shift in data distribution. Therefore, developing a robust model to minimize the data distribution difference is crucial to improving the user experience. In this work, based on the inter-session gesture recognition task, we propose a novel algorithm called locality preserving and maximum margin criterion (LPMM). The LPMM algorithm integrates three main modules, including domain alignment, pseudo-label selection, and iteration result selection. Domain alignment is designed to preserve the neighborhood structure of the feature and minimize the overlap of different classes. The pseudo-label selection and iteration result selection can avoid the decrease in accuracy caused by mislabeled samples. The proposed algorithm was evaluated on two of the most widely used EMG databases. It achieves a mean accuracy of 98.46% and 71.64%, respectively, which is superior to state-of-the-art domain adaptation methods.
Textile capacitively-coupled (CC) electrodes have the potential of being integrated into home textiles to realize long-term cardiac surveillance, since they are more comfortable and convenient than conventional contact electrodes. Previous study mainly focused on the measurement performance of CC-based electrocardiogram (ECG) devices, but the diagnostic value of these novel devices, such as capability of supporting preliminary arrhythmia diagnosis, has not been investigated comprehensively. In this study, we proposed a CC-based ECG cushion and assessed measurement performance and diagnostic value by a clinical test on 4 healthy people and 7 arrhythmia patients. In addition to precise measurement of critical ECG structures (true positive rate of R waves: 0.994±0.006, mean absolute error of RR intervals: 1.690±1.331 ms), the proposed cushion can provide valuable information, comparable with the Holter, for cardiologists to distinguish the normal beats, ventricular beats and premature supraventricular contractions during long-term monitoring. In conclusion, the proposed cushion is verified to provide enough signal details for preliminary diagnosis of arrhythmia, as a promising system for disease screen and danger alarm of arrhythmia in certain scenario of long-term cardiac surveillance at home.
Background Inflammation serves as a key pathologic mediator in the progression of infections and various diseases, involving significant alterations in the gut microbiome and metabolism. This study aims to probe into the potential causal relationships between gut microbial taxa and human blood metabolites with various serum inflammatory markers (CRP, SAA1, IL-6, TNF-α, WBC, and GlycA) and the risks of seven common infections (gastrointestinal infections, dysentery, pneumonia, bacterial pneumonia, bronchopneumonia and lung abscess, pneumococcal pneumonia, and urinary tract infections). Methods Two-sample Mendelian randomization (MR) analysis was performed using inverse variance weighted (IVW), maximum likelihood, MR-Egger, weighted median, and MR-PRESSO. Results After adding other MR models and sensitivity analyses, genus Roseburia was simultaneously associated adversely with CRP (Beta IVW = −0.040) and SAA1 (Beta IVW = −0.280), and family Bifidobacteriaceae was negatively associated with both CRP (Beta IVW = −0.034) and pneumonia risk (Beta IVW = −0.391). After correction by FDR , only glutaroyl carnitine remained significantly associated with elevated CRP levels (Beta IVW = 0.112). Additionally, threonine (Beta IVW = 0.200) and 1-heptadecanoylglycerophosphocholine (Beta IVW = −0.246) were found to be significantly associated with WBC levels. Three metabolites showed similar causal effects on different inflammatory markers or infectious phenotypes, stearidonate (18:4n3) was negatively related to SAA1 and urinary tract infections, and 5-oxoproline contributed to elevated IL-6 and SAA1 levels. In addition, 7-methylguanine showed a positive correlation with dysentery and bacterial pneumonia. Conclusion This study provides novel evidence confirming the causal effects of the gut microbiome and the plasma metabolite profile on inflammation and the risk of infection. These potential molecular alterations may aid in the development of new targets for the intervention and management of disorders associated with inflammation and infections.
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