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Alzheimer’s disease (AD) is a complex neurodegenerative disorder and the most common form of dementia. There are two main types of AD: familial and sporadic. Familial AD is linked to mutations in amyloid precursor protein (APP), presenilin-1 (PSEN1), and presenilin-2 (PSEN2). On the other hand, sporadic AD is the more common form of the disease and has genetic, epigenetic, and environmental components that influence disease onset and progression. Investigating the epigenetic mechanisms associated with AD is essential for increasing understanding of pathology and identifying biomarkers for diagnosis and treatment. Chemical covalent modifications on DNA and RNA can epigenetically regulate gene expression at transcriptional and post-transcriptional levels and play protective or pathological roles in AD and other neurodegenerative diseases.

Introduction: Atrial fibrillation (AF) is the most common cardiac arrhythmia, which is clinically identified with irregular and rapid heartbeat rhythm. AF puts a patient at risk of forming blood clots, which can eventually lead to heart failure, stroke, or even sudden death. Electrocardiography (ECG), which involves acquiring bioelectrical signals from the body surface to reflect heart activity, is a standard procedure for detecting AF. However, the occurrence of AF is often intermittent, costing a significant amount of time and effort from medical doctors to identify AF episodes. Moreover, human error is inevitable, as even experienced medical professionals can overlook or misinterpret subtle signs of AF. As such, it is of critical importance to develop an advanced analytical model that can automatically interpret ECG signals and provide decision support for AF diagnostics.Methods: In this paper, we propose an innovative deep-learning method for automated AF identification using single-lead ECGs. We first extract time-frequency features from ECG signals using continuous wavelet transform (CWT). Second, the convolutional neural networks enhanced with residual learning (ReNet) are employed as the functional approximator to interpret the time-frequency features extracted by CWT. Third, we propose to incorporate a multi-branching structure into the ResNet to address the issue of class imbalance, where normal ECGs significantly outnumber instances of AF in ECG datasets.Results and Discussion: We evaluate the proposed Multi-branching Resnet with CWT (CWT-MB-Resnet) with two ECG datasets, i.e., PhysioNet/CinC challenge 2017 and ECGs obtained from the University of Oklahoma Health Sciences Center (OUHSC). The proposed CWT-MB-Resnet demonstrates robust prediction performance, achieving an F1 score of 0.8865 for the PhysioNet dataset and 0.7369 for the OUHSC dataset. The experimental results signify the model’s superior capability in balancing precision and recall, which is a desired attribute for ensuring reliable medical diagnoses.

Objective: This study aimed to evaluate the intervention effect of curcumin in myocardial infarction rodent models.Methods: A systematic retrieval of relevant studies on curcumin intervention in rats or mice myocardial infarction models was conducted, and the data were extracted. The outcome indicators included biochemical blood indicators, such as creatine kinase (CK), creatine kinase isoenzyme (CK-MB), malondialdehyde (MDA), lactate dehydrogenase (LDH) and superoxide dismutase (SOD), as well as cardiac tissue structure indicators, such as left ventricular weight to body weight ratio (LVW/BW), apoptosis index, left ventricular end-diastolic dimension (LVEDD), left ventricular end-systolic diameter (LVESD), and myocardial infarction area, and hemodynamic indexes, such as systolic blood pressure (SBP), diastolic blood pressure (DBP), left ventricular end-diastolic pressure (LVEDP), left ventricular ejection fraction (LVEF), left ventricular fractional shortening (LVFS), maximum rate of left ventricular pressure rise (+dp/dtmax), and maximum rate of left ventricular pressure decline (–dp/dtmax). These results were then analyzed by meta-analysis. Studies were evaluated for methodological quality using the syrcle’s bias risk tool.Results: A total of 24 studies were included in the meta-analysis. The quality assessment of included studies revealed that the evidence was low quality and none of studies was judged as having a low risk of bias across all domains. The results revealed that curcumin could reduce CK-MB, CK, LDH, and MDA levels. They also revealed that it could lower SBP, DBP, LVEDP, LVW/BW, apoptosis index, LVEDD, LVESD, and myocardial infarction area and increase LVEF, LVFS, +dp/dtmax, and–dp/dtmax. However, it had no significant impact on the heart rate and the levels of SOD in the models.Conclusion: Curcumin alleviates myocardial injury and oxidative stress in myocardial infarction rodent models in terms of blood biochemistry indicators, improves the diastolic and systolic capacity of the ventricle in terms of hemodynamic indexes, and reduces the necrosis and apoptosis of cardiomyocytes in terms of tissue structure. The methodological quality of the studies was low and additional research is warranted.

BackgroundThere is a lack of in-depth analysis regarding the disease burden of childhood cancer in China. Indeed, this is the first time the topic has been addressed in detail. Drawing on population-based data for the past 30 years, this study systematically analyzes the composition and long-term trend of this disease burden in China.MethodsGBD 2019 contained population-based data from 1990 to 2019 and was prepared using Microsoft Excel 2016. We used AAPC and ARIMA models for trend analysis and prediction formulation.ResultsIn 2019, there were 45,601 new cases, 9,156 cancer deaths, and 782,530 DALYs in China. From 1990 to 2019, leukemia, together with brain and CNS cancer, invariably ranked highest in terms of new cases, cancer deaths, and DALYs. Leukemia accounted for more than 50%, but decreased over time. By contrast, the proportions for brain and CNS cancer increased. There were significant decreases in the overall incidence, mortality, and DALY rates in China, but these were still higher than the corresponding global average levels. Considering all types of childhood cancer, the incidence rate of testicular cancer showed the biggest increase, and the mortality and DALY rates of leukemia showed the largest decrease. In terms of different age groups, the overall incidence rate of childhood cancers increased in 0 to 4 age group, but it decreased in 5 to 14 age groups. The overall mortality and DALY rates of childhood cancers decreased in all four age groups. Over the next 10 years, the overall incidence rate of childhood cancer will increase, but the overall mortality and DALY rates will decrease. The increase in malignant skin melanoma will comprise the largest rise in the incidence, while the decrease for leukemia will be the largest fall in the incidence, cancer deaths, and DALYs.ConclusionThe disease burden of all childhood cancers in China remains highly serious, especially for certain types of cancer and certain age groups. China should focus more emphatically on the incidence of childhood cancer in future, and it must consistently strengthen investment in the relevant research and medical resources to reduce the disease burden in this field.

Alzheimer's disease (AD) is a complex neurodegenerative disorder and the most common form of dementia. There are two main types of AD: familial and sporadic. Familial AD is linked to mutations in amyloid precursor protein (APP), presenilin-1 (PSEN1), and presenilin-2 (PSEN2). On the other hand, sporadic AD is the more common form of the disease and has genetic, epigenetic, and environmental components that influence disease onset and progression. Investigating the epigenetic mechanisms associated with AD is essential for increasing understanding of pathology and identifying biomarkers for diagnosis and treatment. Chemical covalent modifications on DNA and RNA can epigenetically regulate gene expression at transcriptional and posttranscriptional levels and play protective or pathological roles in AD and other neurodegenerative diseases. [ABSTRACT FROM AUTHOR]

Pulsed signal trains comprising clicks, buzzes, and burst-pulses play important roles in the life activities of odontocetes, but they have not been distinguished in Indo-Pacific humpback dolphins. Underwater vocalizations of this species were recorded from 27 September to 2 October 2019 in the Beibu Gulf, South China Sea. Pulsed signal trains were detected with variations in the pulsed signal number (range of 6–76), mean inter-pulse interval (IPIs_m: 0.1–315 ms), and mean duration (D ranged from tens to thousands of milliseconds). Principal component analysis and hierarchical cluster analysis based on six acoustic parameters in the pulsed signal trains identified three categories of trains designated as clicks, burst-pulses, and buzzes. Buzzes and burst-pulses (different from those described in previous research) were detected for the first time in Indo-Pacific humpback dolphins in China. The results indicated that the IPIs_m was longest for clicks but shortest for buzzes, and the D values were longer for both clicks and burst-pulses than buzzes. The three train types could be identified based on the IPIs_m, with threshold values of 4.9 and 15.5 ms. The significant variations in the three vocalization types were related to surface behaviors, and buzzes could have a special function in foraging by this species, thereby requiring further research. These findings may facilitate future quantitative evaluations of the echolocation performance in wild Indo-Pacific humpback dolphins and provide important guidance regarding acoustic observations and the identification of this species.

Fatigue, including cognitive fatigue, is one of the most debilitating symptoms reported by persons with multiple sclerosis (pwMS). Cognitive fatigue has been associated with disruptions in striato-thalamo-cortical and frontal networks, but what remains unknown is how the rate at which pwMS become fatigued over time relates to microstructural properties within the brain. The current study aims to fill this gap in knowledge by investigating how cognitive fatigue rate relates to white matter and basal ganglia microstructure in a sample of 62 persons with relapsing-remitting MS. Participants rated their level of cognitive fatigue at baseline and after each block (x7) of a within-scanner cognitive fatigue inducing task. The slope of the regression line of all eight fatigue ratings was designated as “cognitive fatigue rate.” Diffusional kurtosis imaging maps were processed using tract-based spatial statistics and regional analyses (i.e., basal ganglia) and associated with cognitive fatigue rate. Results showed cognitive fatigue rate to be related to several white matter tracts, with many having been associated with basal ganglia connectivity or the previously proposed “fatigue network.” In addition, cognitive fatigue rate was associated with the microstructure within the putamen, though this did not survive multiple comparisons correction. Our approach of using cognitive fatigue rate, rather than trait fatigue, brings us closer to understanding how brain pathology may be impacting the experience of fatigue in the moment, which is crucial for developing interventions. These results hold promise for continuing to unpack the complex construct that is cognitive fatigue.

Controlling the trapping process is one of the important themes in the study of random walk in real complex systems. We studied two types of random walks that are different from the traditional random walk on a directed weighted network. The first type of random walk is the weighted random walk controlled by the weight θ, and the other is the delayed-weighted random walk affected by both delay probability p and weight θ. Furthermore, we derived analytically the average trapping time (ATT) measuring the efficiency of the two types of trapping processes; the result shows that the ATT grows sub-linearly, linearly, and super-linearly with the network order when the weight satisfies θ32 , respectively. The weight θ of the directed network can be adjusted by direction, the delay parameter p only changes the pre-factor of the ATT, and the weight θ modifies both the pre-factor and scaling of the ATT.

Background: The emergence of castration resistance is fatal for patients with prostate cancer (PCa); however, there is still a lack of effective means to detect the early progression. In this study, a novel combined nomogram was established to predict the risk of progression related to castration resistance.Methods: The castration-resistant prostate cancer (CRPC)-related differentially expressed genes (DEGs) were identified by R packages “limma” and “WGCNA” in GSE35988-GPL6480 and GSE70768-GPL10558, respectively. Relationships between DEGs and progression-free interval (PFI) were analyzed using the Kaplan–Meier method in TCGA PCa patients. A multigene signature was built by lasso-penalized Cox regression analysis, and assessed by the receiver operator characteristic (ROC) curve and Kaplan–Meier curve. Finally, the univariate and multivariate Cox regression analyses were used to establish a combined nomogram. The prognostic value of the nomogram was validated by concordance index (C-index), calibration plots, ROC curve, and decision curve analysis (DCA).Results: 15 CRPC-related DEGs were identified finally, of which 13 genes were significantly associated with PFI and used as the candidate genes for modeling. A two-gene (KIFC2 and BCAS1) signature was built to predict the risk of progression. The ROC curve indicated that 5-year area under curve (AUC) in the training, testing, and whole TCGA dataset was 0.722, 0.739, and 0.731, respectively. Patients with high-risk scores were significantly associated with poorer PFI (p < 0.0001). A novel combined nomogram was successfully established for individualized prediction integrating with T stage, Gleason score, and risk score. While the 1-year, 3-year, and 5-year AUC were 0.76, 0.761, and 0.762, respectively, the good prognostic value of the nomogram was also validated by the C-index (0.734), calibration plots, and DCA.Conclusion: The combined nomogram can be used to predict the individualized risk of progression related to castration resistance for PCa patients and has been preliminarily verified to have good predictive ability.

Multiple Sclerosis (MS) affects 2.8 million persons worldwide. One of the most persistent, pervasive, and debilitating symptoms of MS is cognitive fatigue. While this has been known for over a century, cognitive fatigue has been difficult to study because patients’ subjective (self-reported) cognitive fatigue has consistently failed to correlate with more objective measures, such as reaction time (RT) and accuracy. Here, we investigated whether more nuanced metrics of performance, specifically the metrics of Signal Detection Theory (SDT), would show a relationship to cognitive fatigue even if RT and accuracy did not. We also measured brain activation to see whether SDT metrics were related to activation in brain areas that have been shown to be sensitive to cognitive fatigue. Fifty participants (30 MS, 20 controls) took part in this study and cognitive fatigue was induced using four blocks of a demanding working memory paradigm. Participants reported their fatigue before and after each block, and their performance was used to calculate SDT metrics (Perceptual Certainty and Criterion) and RT and accuracy. The results showed that the SDT metric of Criterion (i.e., response bias) was positively correlated with subjective cognitive fatigue. Moreover, the activation in brain areas previously shown to be related to cognitive fatigue, such as the striatum, was also related to Criterion. These results suggest that the metrics of SDT may represent a novel tool with which to study cognitive fatigue in MS and other neurological populations. These results hold promise for characterizing cognitive fatigue in MS and developing effective interventions in the future.

Background: Long non-coding RNAs (lncRNAs) are now under discussion as novel promising biomarkers for clear cell renal cell carcinoma (ccRCC). However, the role of genomic instability-associated lncRNA signatures in tumors has not been thoroughly uncovered. The purpose of our study is to probe the role of genomic instability-derived lncRNA signature (GILncSig) and to further investigate the mechanism of genomic instability-mediated ccRCC progression.Methods: The transcriptome data and somatic mutation profiles of ccRCC as well as clinical characteristics used in this study were obtained from The Cancer Genome Atlas database and Gene Expression Omnibus database. Lasso regression analysis was performed to construct the GILncSig. Gene set enrichment analysis (GSEA) was performed to elucidate the biological functions and relative pathways. CIBERSORT and EPIC algorithm were applied to calculate the proportion of immune cells in ccRCC. ESTIMATE algorithm was utilized to compute the immune microenvironment scores.Results: In total, 148 novel genomic instability-derived lncRNAs in ccRCC were identified. Immediately, on the basis of univariate cox analysis and lasso analysis, a GILncSig was appraised, through which the patients were allocated into High-Risk and Low-Risk groups with significantly different characteristics and prognoses. In addition, we confirmed that the somatic mutation count, tumor mutation burden, and the expression of UBQLN4, which were ascertainably associated with genomic instability, were significantly correlated with the GILncSig, indicating its reliability as a measurement of the genomic instability. Furthermore, the efficiency of GILncSig in prognostic aspects was better than the single mutation gene in ccRCC. In addition, MNX1-AS1 was defined to be a potential biomarker characterized by strong correlation with clinical features. Moreover, GSEA results indicated that the IL6/JAK/STAT3/SIGNALING pathway could be considered as a potential mechanism of genomic instability to influence tumor progression. Besides, the immune microenvironment showed significant differences between the GS-like group and the GU-like group, which was specifically manifested as high expression of CTLA4, GITR, TNFSF14, and regulatory T cells (Tregs) as well as low expression of endothelial cells (ECs) in the GU-like group. Finally, the prognostic value and clinical relevance of GILncSig were verified in GEO datasets and other urinary tumors in TCGA dataset.Conclusion: In conclusion, our study provided a new perspective for the role of lncRNAs in genomic instability and revealed that genomic instability may mediate tumor progression by affecting immunity. Besides, MNX1-AS1 played critical roles in promoting the progression of ccRCC, which may be a potential therapeutic target. What is more, the immune atlas of genomic instability was characterized by high expression of CTLA4, GITR, TNFSF14, and Tregs, and low expression of ECs.

Seminal plasma (SP), particularly SP exosomes (sExos), alters with age and can affect female mouse uterine immune microenvironment. However, the relationship between fertility decline in reproductively older males, and SP and sExos age-related changes, which may compromise the uterine immune microenvironment, remains unclear. The present study demonstrated that the implantation rate of female mice treated with SP from reproductively older male mice (aged-SP group) was lower than that of those treated with SP from younger male mice (young-SP group). RNA-sequencing analysis revealed altered levels of dendritic cell (DC)-related cytokines and chemokines in the uteri of the former group compared with those of the latter group. In vivo and in vitro experiments demonstrated a weaker inhibitory effect of aged SP on DC maturation than of young SP upon stimulation. After isolating and characterizing sExos from young and advanced-age male mice, we discovered that insemination of a subset of the aged-SP group with sExos from young male mice partially recovered the implantation rate decline. Additional in vivo and in vitro experiments revealed that sExos extracted from age male mice exerted a similar effect on DC maturation as SP of aged mice, indicating an age-related sExos inhibitory effect. In conclusion, our study demonstrated that age-related alterations of sExos may be partially responsible for lower implantation rates in the aged-SP group compared with those in the young-SP group, which were mediated by uterine immunomodulation. These findings provide new insights for clinical seminal adjuvant therapy.

Preimplantation genetic testing for aneuploidy (PGT-A) is widely used to select embryos having normal ploidy for transfer, but they require an invasive embryo biopsy procedure that may cause harm to the embryos and offspring. Therefore, a non-invasive approach to select embryos with normal ploidy for implantation is highly demanded. Non-invasive chromosome screening (NICS) methods have been proposed and applied in clinical practices, but a large-scale validation versus invasive preimplantation genetic testing (PGT) and the whole embryo ploidy has not yet been reported. In this study, by using the whole embryo as a gold standard, we validated NICS assay in a total of 265 donated human embryos and compared its performance with conventional trophectoderm (TE) biopsy PGT. The NICS assay showed promising performance, which is comparable to PGT-TE [sensitivity: 87.36 versus 89.66%; specificity: 80.28 versus 82.39%; negative predictive value (NPV): 91.2 versus 92.86%; positive predictive value (PPV): 73.08 versus 75.73%]. Additionally, NICS provides a scoring system for prioritizing embryo: embryos can be categorized into three groups with euploid prediction probabilities of 90.0, 27.8, and 72.2% for group euploid (A), aneuploid (B), and multiple abnormal chromosomes (MAC) (C), respectively. When an addition of TE assay is provided as a secondary validation, the accuracy significantly increases from 72.2 to 84.3% for group B and from 27.8 to 83.3% for group C. Our results suggest that NICS is a good rule in assay for identifying chromosomal normal embryos for transfer and might serve as a non-invasive approach for prioritizing embryos instead of preventing transfer of aneuploid and MAC embryos. It will help to ensure the safety of offspring and efficient utilization of embryos.

The mean first-passage time of random walks on a network has been extensively applied in the theory and practice of statistical physics, and its application effects depend on the behavior of first-passage time. Here, we firstly define a graphic operation, namely, rectangle operation, for generating a scale-free network. In this paper, we study the topological structures of our network obtained from the rectangle operation, including degree distribution, clustering coefficient, and diameter. And then, we also consider the characteristic quantities related to the network, including Kirchhoff index and mean first-passage time, where these characteristic quantities can not only be used to evaluate the properties of our network, but also have remarkable applications in science and engineering.

Complex networks have become a powerful tool to describe the structure and evolution in a large quantity of real networks in the past few years, such as friendship networks, metabolic networks, protein–protein interaction networks, and software networks. While a variety of complex networks have been published, dense networks sharing remarkable structural properties, such as the scale-free feature, are seldom reported. Here, our goal is to construct a class of dense networks. Then, we discover that our networks follow the mixture degree distribution; that is, there is a critical point above which the cumulative degree distribution has a power-law form and below which the exponential distribution is observed. Next, we also prove the networks proposed to show the small-world property. Finally, we study random walks on our networks with a trap fixed at a vertex with the highest degree and find that the closed form for the mean first-passage time increases logarithmically with the number of vertices of our networks.

Ovarian hyperstimulation syndrome (OHSS) is a common complication caused by ovulatory stimulation therapy, which manifests as an increase in ovarian volume, an increase in the number of oocytes retrieved, and increased vascular permeability throughout the body and especially in ovarian tissue. In our previous study, we found that electroacupuncture (EA) could prevent the progression of OHSS, by mainly affecting ovary. However, the specific molecules and the mechanism of this process were still unknown. In order to explore the underlying mechanism, OHSS rat model was established and EA treatment was performed, which was followed by proteomic analysis of ovaries. Results showed a significant increase in the expression level of CD200 in the ovaries of OHSS group treated with EA than those of OHSS group. Clinical data showed that the level of CD200 in follicular fluid was negatively correlated with the number of oocytes retrieved and serum E2 level. Further in vitro experiments showed a concentration-dependent role of human chorionic gonadotropin (hCG) in reducing CD200 and CD200R levels, and increasing inflammatory cytokine levels in cultured KGN cells. In human umbilical vein endothelial cells (HUVECs), the vascular barrier function was improved by CM (cultural medium from KGN cell) which treated with CD200Fc (CD200R agonist). Meanwhile, the results of in vivo experiments indicated that EA reduced the number of ovarian corpora lutea, decreased inflammatory response, and improved the vascular barrier function by increasing the expression of CD200 and CD200R in rat ovaries. These findings suggest that EA treatment may reduce oocyte number and maintain vascular barrier against OHSS through ovarian anti-inflammatory response mediated by CD200. Therefore, this study is the first to identify CD200 as a main of EA in the ovary and elucidate the possible mechanism of EA on preventing and treating OHSS, which provide a scientific basis for CD200 as an effector and indicator in EA treatment.

When we are fatigued, we feel that our performance is worse than when we are fresh. Yet, for over 100 years, researchers have been unable to identify an objective, behavioral measure that covaries with the subjective experience of fatigue. Previous work suggests that the metrics of signal detection theory (SDT)—response bias (criterion) and perceptual certainty (d’)—may change as a function of fatigue, but no work has yet been done to examine whether these metrics covary with fatigue. Here, we investigated cognitive fatigue using SDT. We induced fatigue through repetitive performance of the n-back working memory task, while functional magnetic resonance imaging (fMRI) data was acquired. We also assessed cognitive fatigue at intervals throughout. This enabled us to assess not only whether criterion and d’ covary with cognitive fatigue but also whether similar patterns of brain activation underlie cognitive fatigue and SDT measures. Our results show that both criterion and d’ were correlated with changes in cognitive fatigue: as fatigue increased, subjects became more conservative in their response bias and their perceptual certainty declined. Furthermore, activation in the striatum of the basal ganglia was also related to cognitive fatigue, criterion, and d’. These results suggest that SDT measures represent an objective measure of cognitive fatigue. Additionally, the overlap and difference in the fMRI results between cognitive fatigue and SDT measures indicate that these measures are related while also separate. In sum, we show the relevance of SDT measures in the understanding of fatigue, thus providing researchers with a new set of tools with which to better understand the nature and consequences of cognitive fatigue.

Precise genetic and epigenetic spatiotemporal regulation of gene expression is critical for proper brain development, function and circuitry formation in the mammalian central nervous system. Neuronal differentiation processes are tightly regulated by epigenetic mechanisms including DNA methylation, histone modifications, chromatin remodelers and non-coding RNAs. Dysregulation of any of these pathways is detrimental to normal neuronal development and functions, which can result in devastating neuropsychiatric disorders, such as depression, schizophrenia and autism spectrum disorders. In this review, we focus on the current understanding of epigenetic regulations in brain development and functions, as well as their implications in neuropsychiatric disorders.

Pancreatic stellate cells (PSCs) are a subset of pancreatic cancer-associated fibroblasts, which play a critical role in pancreatic fibrosis, a characteristic feature of pancreatic cancer. The interplay between PSCs and pancreatic cancer cells is vital for promotion of tumor progression and metastasis. BAG3 is correlated with poor prognostics in patients with pancreatic ductal adenocarcinoma (PDAC), however, the exact mechanisms remain largely unknown. In this study, we demonstrated that BAG3 downregulation decreased IL6 release by PDACs, and IL6 reduction was, at least partially, responsible for suppression of PSCs activation by PDACs with BAG3 downmodulation. Importantly, BAG3 expression positively correlated with fibrosis in pancreatic cancer tissue. With regard to the underlying mechanism, we demonstrated that BAG3 knockdown facilitated recruitment of Agonaute 2 (Ago2) to IL6 mRNA, resulting in destabilization of IL6 mRNA. In addition, the current study demonstrated that phosphorylation at Serine (Ser) 387 site was required for recruitment of Ago2-containing miRISC to IL6 mRNA and BAG3 knockdown facilitated Ago2 loading to IL6 mRNA via increasing its phosphorylation at Ser 387. This study shed new light on the tumor-promoting role of BAG3 in PDAC tumors, suggesting BAG3 might represent an interesting therapeutic opportunity to PDAC patients.

Fatigue, including cognitive fatigue, is one of the most debilitating symptoms reported by persons with multiple sclerosis (pwMS). Cognitive fatigue has been associated with disruptions in striato-thalamo-cortical and frontal networks, but what remains unknown is how the rate at which pwMS become fatigued over time relates to microstructural properties within the brain. The current study aims to fill this gap in knowledge by investigating how cognitive fatigue rate relates to white matter and basal ganglia microstructure in a sample of 62 persons with relapsing-remitting MS. Participants rated their level of cognitive fatigue at baseline and after each block (x7) of a within-scanner cognitive fatigue inducing task. The slope of the regression line of all eight fatigue ratings was designated as "cognitive fatigue rate." Diffusional kurtosis imaging maps were processed using tract-based spatial statistics and regional analyses (i.e., basal ganglia) and associated with cognitive fatigue rate. Results showed cognitive fatigue rate to be related to several white matter tracts, with many having been associated with basal ganglia connectivity or the previously proposed "fatigue network." In addition, cognitive fatigue rate was associated with the microstructure within the putamen, though this did not survive multiple comparisons correction. Our approach of using cognitive fatigue rate, rather than trait fatigue, brings us closer to understanding how brain pathology may be impacting the experience of fatigue in the moment, which is crucial for developing interventions. These results hold promise for continuing to unpack the complex construct that is cognitive fatigue. [ABSTRACT FROM AUTHOR]

Introduction: Digital twins of patients are virtual models that can create a digital patient replica to test clinical interventions in silico without exposing real patients to risk. With the increasing availability of electronic health records and sensorderived patient data, digital twins offer significant potential for applications in the healthcare sector. Methods: This article presents a scalable full-stack architecture for a patient simulation application driven by graph-based models. This patient simulation application enables medical practitioners and trainees to simulate the trajectory of critically ill patients with sepsis. Directed acyclic graphs are utilized to model the complex underlying causal pathways that focus on the physiological interactions and medication effects relevant to the first 6 h of critical illness. To realize the sepsis patient simulation at scale, we propose an application architecture with three core components, a cross-platform frontend application that clinicians and trainees use to run the simulation, a simulation engine hosted in the cloud on a serverless function that performs all of the computations, and a graph database that hosts the graph model utilized by the simulation engine to determine the progression of each simulation. Results: A short case study is presented to demonstrate the viability of the proposed simulation architecture. Discussion: The proposed patient simulation application could help train future generations of healthcare professionals and could be used to facilitate clinicians' bedside decision-making. [ABSTRACT FROM AUTHOR]

Objective: Recognizing emotions from electroencephalography (EEG) signals is a challenging task due to the complex, nonlinear, and nonstationary characteristics of brain activity. Traditional methods often fail to capture these subtle dynamics, while deep learning approaches lack explainability. In this research, we introduce a novel three-phase methodology integrating manifold embedding, multilevel heterogeneous recurrence analysis (MHRA), and ensemble learning to address these limitations in EEG-based emotion recognition. Approach: The proposed methodology was evaluated using the SJTU-SEED IV database. We first applied uniform manifold approximation and projection (UMAP) for manifold embedding of the 62-lead EEG signals into a lowerdimensional space. We then developed MHRA to characterize the complex recurrence dynamics of brain activity across multiple transition levels. Finally, we employed tree-based ensemble learning methods to classify four emotions (neutral, sad, fear, happy) based on the extracted MHRA features. Main results: Our approach achieved high performance, with an accuracy of 0.7885 and an AUC of 0.7552, outperforming existing methods on the same dataset. Additionally, our methodology provided the most consistent recognition performance across different emotions. Sensitivity analysis revealed specific MHRA metrics that were strongly associated with each emotion, offering valuable insights into the underlying neural dynamics. Significance: This study presents a novel framework for EEG-based emotion recognition that effectively captures the complex nonlinear and nonstationary dynamics of brain activity while maintaining explainability. The proposed methodology offers significant potential for advancing our understanding of emotional processing and developing more reliable emotion recognition systems with broad applications in healthcare and beyond. [ABSTRACT FROM AUTHOR]

Background: Triple-negative breast cancer (TNBC) is a heterogeneous disease that is characterized by metabolic disruption. Metabolic reprogramming and tumor cell immune escape play indispensable roles in the tumorigenesis that leads to TNBC. Methods: In this study, we constructed and validated two prognostic glutamine metabolic gene models, Clusters A and B, to better discriminate between groups of TNBC patients based on risk. Compared with the risk Cluster A patients, the Cluster B patients tended to exhibit better survival outcomes and higher immune cell infiltration. In addition, we established a scoring system, the glutamine metabolism score (GMS), to assess the pattern of glutamine metabolic modification. Results: We found that solute carrier family 7 member 5 (SLC7A5), an amino acid transporter, was the most important gene and plays a vital role in glutamine metabolism reprogramming in TNBC cells. Knocking down SLC7A5 significantly inhibited human and mouse TNBC cell proliferation, migration, and invasion. In addition, downregulation of SLC7A5 increased CD8+ T-cell infiltration. The combination of a SLC7A5 blockade mediated via JPH203 treatment and an anti-programmed cell death 1 (PD-1) antibody synergistically increased the immune cell infiltration rate and inhibited tumor progression. Conclusions: Hence, our results highlight the molecular mechanisms underlying SLC7A5 effects and lead to a better understanding of the potential benefit of targeting glutamine metabolism in combination with immunotherapy as a new therapy for TNBC. [ABSTRACT FROM AUTHOR]

Regulated cell death (RCD) contributes to reshaping the tumor immune microenvironment and participating in the progression of non-small cell lung cancer (NSCLC); however, related mechanisms have not been fully disclosed. Here, we identified 5 subclusters of NSCLC based on consensus clustering of 3429 RCD-associated genes in the TCGA database and depicted the genomic features and immune landscape of these clusters. Importantly, the clusters provided insights into recognizing tumor microenvironment (TME) and tumor responses to immunotherapy and chemotherapy. Further, we established and validated an RCD-Risk model based on RCD-associated genes, which strongly predicted the prognosis, TME, and immunotherapy outcomes in NSCLC patients. Notably, tissue microarray staining confirmed that the expression of LDLRAD3, a core gene in RCD-Risk model, correlated with poor survival. In conclusion, we developed a novel RCD classification system and RCD-Risk model of NSCLC, serving as a robust and promising predictor for prognosis and immunotherapy benefit of individual NSCLC patients. [ABSTRACT FROM AUTHOR]

Background: The emergence of castration resistance is fatal for patients with prostate cancer (PCa); however, there is still a lack of effective means to detect the early progression. In this study, a novel combined nomogram was established to predict the risk of progression related to castration resistance. Methods: The castration-resistant prostate cancer (CRPC)-related differentially expressed genes (DEGs) were identified by R packages "limma" and "WGCNA" in GSE35988-GPL6480 and GSE70768-GPL10558, respectively. Relationships between DEGs and progression-free interval (PFI) were analyzed using the Kaplan–Meier method in TCGA PCa patients. A multigene signature was built by lasso-penalized Cox regression analysis, and assessed by the receiver operator characteristic (ROC) curve and Kaplan–Meier curve. Finally, the univariate and multivariate Cox regression analyses were used to establish a combined nomogram. The prognostic value of the nomogram was validated by concordance index (C-index), calibration plots, ROC curve, and decision curve analysis (DCA). Results: 15 CRPC-related DEGs were identified finally, of which 13 genes were significantly associated with PFI and used as the candidate genes for modeling. A two-gene (KIFC2 and BCAS1) signature was built to predict the risk of progression. The ROC curve indicated that 5-year area under curve (AUC) in the training, testing, and whole TCGA dataset was 0.722, 0.739, and 0.731, respectively. Patients with high-risk scores were significantly associated with poorer PFI (p < 0.0001). A novel combined nomogram was successfully established for individualized prediction integrating with T stage, Gleason score, and risk score. While the 1-year, 3-year, and 5-year AUC were 0.76, 0.761, and 0.762, respectively, the good prognostic value of the nomogram was also validated by the C-index (0.734), calibration plots, and DCA. Conclusion: The combined nomogram can be used to predict the individualized risk of progression related to castration resistance for PCa patients and has been preliminarily verified to have good predictive ability. [ABSTRACT FROM AUTHOR]

Multiple Sclerosis (MS) affects 2.8 million persons worldwide. One of the most persistent, pervasive, and debilitating symptoms of MS is cognitive fatigue. While this has been known for over a century, cognitive fatigue has been difficult to study because patients' subjective (self-reported) cognitive fatigue has consistently failed to correlate with more objective measures, such as reaction time (RT) and accuracy. Here, we investigated whether more nuanced metrics of performance, specifically the metrics of Signal Detection Theory (SDT), would show a relationship to cognitive fatigue even if RT and accuracy did not. We also measured brain activation to see whether SDT metrics were related to activation in brain areas that have been shown to be sensitive to cognitive fatigue. Fifty participants (30 MS, 20 controls) took part in this study and cognitive fatigue was induced using four blocks of a demanding working memory paradigm. Participants reported their fatigue before and after each block, and their performance was used to calculate SDT metrics (Perceptual Certainty and Criterion) and RT and accuracy. The results showed that the SDT metric of Criterion (i.e., response bias) was positively correlated with subjective cognitive fatigue. Moreover, the activation in brain areas previously shown to be related to cognitive fatigue, such as the striatum, was also related to Criterion. These results suggest that the metrics of SDT may represent a novel tool with which to study cognitive fatigue in MS and other neurological populations. These results hold promise for characterizing cognitive fatigue in MS and developing effective interventions in the future. [ABSTRACT FROM AUTHOR]

Purpose: Myocardial infarction (MI) is one of the most common cardiovascular diseases, frequently resulting in death. Early and accurate diagnosis is therefore important, and the electrocardiogram (ECG) is a simple and effective method for achieving this. However, it requires assessment by a specialist; so many recent works have focused on the automatic assessment of ECG signals. Methods: For the detection and localization of MI, deep learning models have been proposed, but the diagnostic accuracy of this approaches still need to be improved. Moreover, with deep learning methods the way in which a given result was achieved lacks interpretability. In this study, ECG data was obtained from the PhysioBank open access database, and was analyzed as follows. Firstly, the 12-lead ECG signal was preprocessed to identify each beat and obtain each heart interval. Secondly, a multi-scale deep learning model combined with a residual network and attention mechanism was proposed, where the input was the 12-lead ECG recording. Through the SENet model and the Grad-CAM algorithm, the weighting of each lead was calculated and visualized. Using existing knowledge of the way in which different types of MI gave characteristic patterns in specific ECG leads, the model was used to provisionally diagnose the type of MI according to the characteristics of each of the 12 ECG leads. Results: Ten types of MI anterior, anterior lateral, anterior septal, inferior, inferior lateral, inferior posterior, inferior posterior lateral, lateral, posterior, and posterior lateral were diagnosed. The average accuracy, sensitivity, and specificity for MI detection of all lesion types was 99.98, 99.94, and 99.98%, respectively; and the average accuracy, sensitivity, and specificity for MI localization was 99.79, 99.88, and 99.98%, respectively. Conclusion: When compared to existing models based on traditional machine learning methods, convolutional neural networks and recurrent neural networks, the results showed that the proposed model had better diagnostic performance, being superior in accuracy, sensitivity, and specificity. [ABSTRACT FROM AUTHOR]

Seminal plasma (SP), particularly SP exosomes (sExos), alters with age and can affect female mouse uterine immune microenvironment. However, the relationship between fertility decline in reproductively older males, and SP and sExos age-related changes, which may compromise the uterine immune microenvironment, remains unclear. The present study demonstrated that the implantation rate of female mice treated with SP from reproductively older male mice (aged-SP group) was lower than that of those treated with SP from younger male mice (young-SP group). RNA-sequencing analysis revealed altered levels of dendritic cell (DC)-related cytokines and chemokines in the uteri of the former group compared with those of the latter group. In vivo and in vitro experiments demonstrated a weaker inhibitory effect of aged SP on DC maturation than of young SP upon stimulation. After isolating and characterizing sExos from young and advanced-age male mice, we discovered that insemination of a subset of the aged-SP group with sExos from young male mice partially recovered the implantation rate decline. Additional in vivo and in vitro experiments revealed that sExos extracted from age male mice exerted a similar effect on DC maturation as SP of aged mice, indicating an age-related sExos inhibitory effect. In conclusion, our study demonstrated that age-related alterations of sExos may be partially responsible for lower implantation rates in the aged-SP group compared with those in the young-SP group, which were mediated by uterine immunomodulation. These findings provide new insights for clinical seminal adjuvant therapy. [ABSTRACT FROM AUTHOR]

Background: Long non-coding RNAs (lncRNAs) are now under discussion as novel promising biomarkers for clear cell renal cell carcinoma (ccRCC). However, the role of genomic instability-associated lncRNA signatures in tumors has not been thoroughly uncovered. The purpose of our study is to probe the role of genomic instability-derived lncRNA signature (GILncSig) and to further investigate the mechanism of genomic instability-mediated ccRCC progression. Methods: The transcriptome data and somatic mutation profiles of ccRCC as well as clinical characteristics used in this study were obtained from The Cancer Genome Atlas database and Gene Expression Omnibus database. Lasso regression analysis was performed to construct the GILncSig. Gene set enrichment analysis (GSEA) was performed to elucidate the biological functions and relative pathways. CIBERSORT and EPIC algorithm were applied to calculate the proportion of immune cells in ccRCC. ESTIMATE algorithm was utilized to compute the immune microenvironment scores. Results: In total, 148 novel genomic instability-derived lncRNAs in ccRCC were identified. Immediately, on the basis of univariate cox analysis and lasso analysis, a GILncSig was appraised, through which the patients were allocated into High-Risk and Low-Risk groups with significantly different characteristics and prognoses. In addition, we confirmed that the somatic mutation count, tumor mutation burden, and the expression of UBQLN4, which were ascertainably associated with genomic instability, were significantly correlated with the GILncSig, indicating its reliability as a measurement of the genomic instability. Furthermore, the efficiency of GILncSig in prognostic aspects was better than the single mutation gene in ccRCC. In addition, MNX1-AS1 was defined to be a potential biomarker characterized by strong correlation with clinical features. Moreover, GSEA results indicated that the IL6/JAK/STAT3/SIGNALING pathway could be considered as a potential mechanism of genomic instability to influence tumor progression. Besides, the immune microenvironment showed significant differences between the GS-like group and the GU-like group, which was specifically manifested as high expression of CTLA4, GITR, TNFSF14, and regulatory T cells (Tregs) as well as low expression of endothelial cells (ECs) in the GU-like group. Finally, the prognostic value and clinical relevance of GILncSig were verified in GEO datasets and other urinary tumors in TCGA dataset. Conclusion: In conclusion, our study provided a new perspective for the role of lncRNAs in genomic instability and revealed that genomic instability may mediate tumor progression by affecting immunity. Besides, MNX1-AS1 played critical roles in promoting the progression of ccRCC, which may be a potential therapeutic target. What is more, the immune atlas of genomic instability was characterized by high expression of CTLA4, GITR, TNFSF14, and Tregs, and low expression of ECs. [ABSTRACT FROM AUTHOR]

Precise genetic and epigenetic spatiotemporal regulation of gene expression is critical for proper brain development, function and circuitry formation in the mammalian central nervous system. Neuronal differentiation processes are tightly regulated by epigenetic mechanisms including DNA methylation, histone modifications, chromatin remodelers and non-coding RNAs. Dysregulation of any of these pathways is detrimental to normal neuronal development and functions, which can result in devastating neuropsychiatric disorders, such as depression, schizophrenia and autism spectrum disorders. In this review, we focus on the current understanding of epigenetic regulations in brain development and functions, as well as their implications in neuropsychiatric disorders. [ABSTRACT FROM AUTHOR]

Pancreatic stellate cells (PSCs) are a subset of pancreatic cancer-associated fibroblasts, which play a critical role in pancreatic fibrosis, a characteristic feature of pancreatic cancer. The interplay between PSCs and pancreatic cancer cells is vital for promotion of tumor progression and metastasis. BAG3 is correlated with poor prognostics in patients with pancreatic ductal adenocarcinoma (PDAC), however, the exact mechanisms remain largely unknown. In this study, we demonstrated that BAG3 downregulation decreased IL6 release by PDACs, and IL6 reduction was, at least partially, responsible for suppression of PSCs activation by PDACs with BAG3 downmodulation. Importantly, BAG3 expression positively correlated with fibrosis in pancreatic cancer tissue. With regard to the underlying mechanism, we demonstrated that BAG3 knockdown facilitated recruitment of Agonaute 2 (Ago2) to IL6 mRNA, resulting in destabilization of IL6 mRNA. In addition, the current study demonstrated that phosphorylation at Serine (Ser) 387 site was required for recruitment of Ago2-containing miRISC to IL6 mRNA and BAG3 knockdown facilitated Ago2 loading to IL6 mRNA via increasing its phosphorylation at Ser 387. This study shed new light on the tumor-promoting role of BAG3 in PDAC tumors, suggesting BAG3 might represent an interesting therapeutic opportunity to PDAC patients. [ABSTRACT FROM AUTHOR]