Distinguishing Kikuchi-Fujimoto disease from lymphoma in patients by clinical and PET/CT features.

To develop a scheme for distinguishing Kikuchi-Fujimoto disease (KFD) from lymphoma in patients presenting enlarged lymph nodes (LNs) predominantly on the upper side of the diaphragm. From November 2015 to August 2023, 32 KFD patients and 38 lymphoma patients were pathologically confirmed and enrolled in this retrospectively study. Clinical and 18F-fluorodeoxyglucose positron emission tomography (PET)/computed tomography (CT) features were collected. When comparing those PET/CT parameters, we set 5 models with different research objects: (1) all affected LNs; (2) the 5 largest affected LNs in terms of maximum diameter; (3) the 5 largest affected LNs in terms of maximum standard uptake values (SUVmax); (4) the largest affected LNs in terms of maximum diameter; (5) the largest affected LNs in terms of SUVmax. Compared to lymphoma patients, KFD patients were younger; and with higher incidence of fever, arthralgia, abnormal serum white blood cell, lactate dehydrogenase (LDH) and splenomegaly; lower incidence of affected LNs perinodal infiltration, necrosis and conglomeration; more affected LNs in Head and Neck nodes (particularly in level II) and Axillary in KFD (P ˂ .05). PET/CT parameters presented as various difference in each model. Finally, 11 clinical and PET/CT features (age ≤ 34, with fever, arthralgia, abnormal white blood cell, abnormal LDH, and without node necrosis and node conglomeration have a score of 2 each; splenomegaly, perinodal infiltration, median maximum diameter ≤ 20.5 and median SUVmax ≤ 7.1 of affected LNs in model 2 have score of 1 each) were selected as scheme items for distinguishing KFD from lymphoma. Individuals who have a total score > 8, meet the criteria for KFD. Sensitivity and specificity were high: 86.8% (95% CI: 71.9%, 95.5%) and 96.9% (95% CI: 83.7%, 99.5%), AUC = 0.975 (95% CI: 90.5%, 99.6%), respectively. It can effectively distinguish KFD from lymphoma by clinical and PET/CT parameters.

Paradoxical herniation associated with hyperbaric oxygen therapy after decompressive craniectomy: A case report.

BACKGROUND: Whether hyperbaric oxygen therapy (HBOT) can cause paradoxical herniation is still unclear. CASE SUMMARY: A 65-year-old patient who was comatose due to brain trauma underwent decompressive craniotomy and gradually regained consciousness after surgery. HBOT was administered 22 d after surgery due to speech impairment. Paradoxical herniation appeared on the second day after treatment, and the patient's condition worsened after receiving mannitol treatment at the rehabilitation hospital. After timely skull repair, the paradoxical herniation was resolved, and the patient regained consciousness and had a good recovery as observed at the follow-up visit. CONCLUSION: Paradoxical herniation is rare and may be caused by HBOT. However, the underlying mechanism is unknown, and the understanding of this phenomenon is insufficient. The use of mannitol may worsen this condition. Timely skull repair can treat paradoxical herniation and prevent serious complications.

Characteristics of intestinal microbiota in preterm infants and the effects of probiotic supplementation on the microbiota.

Objective: In this study, we investigated the characteristics of the intestinal microbiota of preterm infants, and then analyzed the effects of probiotics supplementation on intestinal microbiota in preterm infants. Methods: This study enrolled 64 infants born between 26 and 32 weeks gestational age (GA) and 22 full-term infants. 34 premature infants received oral probiotic supplementation for 28 days. Stool samples were obtained on the first day (D1) and the 28th day (D28) after birth for each infant. Total bacterial DNA was extracted and sequenced using the Illumina MiSeq Sequencing System, specifically targeting the V3-V4 hyper-variable regions of the 16S rDNA gene. The sequencing results were then used to compare and analyze the composition and diversity index of the intestinal microbiota. Results: There was no significant difference in meconium bacterial colonization rate between premature and full-term infants after birth (p > 0.05). At D1, the relative abundance of Bifidobacterium, Bacteroides, and Lactobacillus in the stool of preterm infants was lower than that of full-term infants, and the relative abundance of Acinetobacter was higher than that of full-term infants. The Shannon index and Chao1 index of intestinal microbiota in preterm infants are lower than those in full-term infants (p < 0.05). Supplementation of probiotics can increase the relative abundance of Enterococcus and Enterobacter, and reduce the relative abundance of Escherichia and Clostridium in premature infants. The Chao1 index of intestinal microbiota decreased in preterm infants after probiotic supplementation (p < 0.05). Conclusion: The characteristics of intestinal microbiota in preterm infants differ from those in full-term infants. Probiotic supplementation can reduce the relative abundance of potential pathogenic bacteria and increase the abundance of beneficial microbiota in premature infants.

Influence of angiotensin II on the gut microbiome: Modest effects in comparison to experimental factors.

INTRODUCTION: Animal models are regularly used to test the role of the gut microbiome in hypertension. Small-scale pre-clinical studies have investigated changes to the gut microbiome in the angiotensin II hypertensive model. However, the gut microbiome is influenced by internal and external experimental factors which are not regularly considered in the study design. Once these factors are accounted for, it is unclear if microbiome signatures are reproduceable. We aimed to determine the influence of angiotensin II treatment on the gut microbiome using a large and diverse cohort of mice and to quantify the magnitude by which other factors contribute to microbiome variations. METHODS AND RESULTS: We conducted a retrospective study to establish a diverse mouse cohort resembling large human studies. We sequenced the V4 region of the 16S rRNA gene from 538 samples across the gastrointestinal tract of 303 male and female C57BL/6J mice randomised into sham or angiotensin II treatment from different genotypes, diets, animal facilities, and age groups. Analysing over 17 million sequencing reads, we observed that angiotensin II treatment influenced α-diversity (P = 0.0137) and ß-diversity (i.e., composition of the microbiome, P < 0.001). Bacterial abundance analysis revealed patterns consistent with a reduction in short-chain fatty acid-producers, microbial metabolites that lower blood pressure. Furthermore, animal facility, genotype, diet, age, sex, intestinal sampling site, and sequencing batch had significant effects on both α- and ß-diversity (all P < 0.001). Sampling site (6.8%) and diet (6%) had the largest impact on the microbiome, while angiotensin II and sex had the smallest effect (each 0.4%). CONCLUSIONS: Our large-scale data confirmed findings from small-scale studies that angiotensin II impacted the gut microbiome. However, this effect was modest relative to most of the other factors studied. Accounting for these factors in future pre-clinical hypertensive studies will increase the likelihood that microbiome findings are replicable and translatable.

Heat and mass transfer simulation of the microwave-assisted toluene desorption for activated carbons regeneration.

The low cost and high efficiency of microwave-assisted regeneration render it a viable alternative to conventional regeneration methods. To enhance the regeneration performance, we developed a coupled electromagnetic, heat, and mass transfer model to investigate the heat and mass transfer mechanisms of activated carbon during microwave-assisted regeneration. Simulation results demonstrated that the toluene desorption process is governed by temperature distribution. Changing the input power and flow rate can promote the intensity of hot spots and adjust their distribution, respectively, thereby accelerating toluene desorption, inhibiting readsorption, and promoting regeneration efficiency. Ultimately, controlling the input power and flow rate can flexibly adjust toluene emissions to satisfy the processing demands of desorbed toluene. Taken together, this study provides a comprehensive understanding of the heat and mass transfer mechanisms of microwave-assisted regeneration and insights into adsorbent regeneration.

Sleep deprivation changes frequency-specific functional organization of the resting human brain.

Previous resting-state functional magnetic resonance imaging (rs-fMRI) studies have widely explored the temporal connection changes in the human brain following long-term sleep deprivation (SD). However, the frequency-specific topological properties of sleep-deprived functional networks remain virtually unclear. In this study, thirty-seven healthy male subjects underwent resting-state fMRI during rested wakefulness (RW) and after 36 hours of SD, and we examined frequency-specific spectral connection changes (0.01-0.08 Hz, interval = 0.01 Hz) caused by SD. First, we conducted a multivariate pattern analysis combining linear SVM classifiers with a robust feature selection algorithm, and the results revealed that accuracies of 74.29%-84.29% could be achieved in the classification between RW and SD states in leave-one-out cross-validation at different frequency bands, moreover, the spectral connection at the lowest and highest frequency bands exhibited higher discriminative power. Connection involving the cingulo-opercular network increased most, while connection involving the default-mode network decreased most following SD. Then we performed a graph-theoretic analysis and observed reduced low-frequency modularity and high-frequency global efficiency in the SD state. Moreover, hub regions, which were primarily situated in the cerebellum and the cingulo-opercular network after SD, exhibited high discriminative power in the aforementioned classification consistently. The findings may indicate the frequency-dependent effects of SD on the functional network topology and its efficiency of information exchange, providing new insights into the impact of SD on the human brain.

Loss of PA28γ exacerbates imbalanced differentiation of bone marrow stromal cells during bone formation and bone healing in mice.

Proteasome activator subunit 3 (PA28γ) is a member of the proteasome activator family, which mainly regulates the degradation and stability of proteins. Studies have shown that it plays crucial roles in lipid formation, stemness maintenance, and blood vessel formation. However, few studies have clarified the association between PA28γ and bone diseases. Herein, we identified PA28γ as a previously unknown regulator of bone homeostasis that coordinates bone formation and lipid accumulation. PA28γ-knockout mice presented with the characteristics of low bone mass and accumulation of lipids. Suppressed expression of PA28γ restrained the osteogenic differentiation and enhanced the adipogenic differentiation of bone marrow stromal cells (BMSCs). Overexpression of PA28γ promoted osteogenic differentiation and inhibited adipogenic differentiation of BMSCs. Mechanistically, PA28γ interacted with Wnt5α, and the two interactors appeared to be positively correlated. PA28γ mainly activated the downstream Wnt/ß-catenin signaling pathway, which affects BMSCs differentiation homeostasis. Deletion of Wnt5α significantly delayed the promotion of osteogenic differentiation and partially alleviated the inhibitory effect of adipogenic differentiation of BMSCs in the PA28γ-overexpressing group. Furthermore, we demonstrated that PA28γ-knockout mice had an inhibited rate of bone healing in a drill-hole femoral bone defect model in vivo. Therefore, our results confirm the effects of PA28γ on bone formation and bone defect repair, indicating that PA28γ mainly interacts with Wnt5α to activate the Wnt/ß-catenin signaling pathway regulating BMSCs differentiation homeostasis. Our results reveal the function of PA28γ in bone diseases and provide a new theoretical basis for expanding the treatment of bone diseases.

Developing a machine learning model for accurate nucleoside hydrogels prediction based on descriptors.

Supramolecular hydrogels derived from nucleosides have been gaining significant attention in the biomedical field due to their unique properties and excellent biocompatibility. However, a major challenge in this field is that there is no model for predicting whether nucleoside derivative will form a hydrogel. Here, we successfully develop a machine learning model to predict the hydrogel-forming ability of nucleoside derivatives. The optimal model with a 71% (95% Confidence Interval, 0.69-0.73) accuracy is established based on a dataset of 71 reported nucleoside derivatives. 24 molecules are selected via the optimal model external application and the hydrogel-forming ability is experimentally verified. Among these, two rarely reported cation-independent nucleoside hydrogels are found. Based on their self-assemble mechanisms, the cation-independent hydrogel is found to have potential applications in rapid visual detection of Ag+ and cysteine. Here, we show the machine learning model may provide a tool to predict nucleoside derivatives with hydrogel-forming ability.

DAHOS Study: Efficacy of dapagliflozin in treating heart failure with reduced ejection fraction and obstructive sleep apnea syndrome - A 3-month, multicenter, randomized controlled clinical trial.

BACKGROUND: The recent discovery of new therapeutic approaches to heart failure with reduced ejection fraction (HFrEF), including sodium-glucose cotransporter-2 (SGLT-2) inhibitors, as well as improved treatment of co-morbidities has provided much needed help to HFrEF. In addition, dapagliflozin, one of the SGLT-2 inhibitors, serves as a promising candidate in treating obstructive sleep apnea (OSA) of HFrEF patients due to its likely mechanism of countering the pathophysiology of OSA of HFrEF. METHODS: This 3-month multicenter, prospective, randomized controlled trial enrolled participants with left ventricular ejection fraction (LVEF) less than 40% and apnea-hypopnea index (AHI) greater than 15. Participants were randomized into two groups: the treatment group received optimized heart failure treatment and standard-dose dapagliflozin, while the control group only received optimized heart failure treatment. The primary endpoint was the difference in AHI before and after treatment between the two groups. Secondary endpoints included oxygen desaturation index (ODI), minimum oxygen saturation, longest apnea duration, inflammatory factors (CRP, IL-6), quality of life score, and LVEF. RESULTS: A total of 107 patients were included in the final analysis. AHI, LVEF and other baseline data were similar for the dapagliflozin and control groups. After 12 weeks of dapagliflozin treatment, the dapagliflozin group showed significant improvements in sleep parameters including AHI, HI, longest pause time, ODI, time spent with SpO2 < 90%, and average SpO2. Meanwhile, the control group showed no significant changes in sleep parameters, but did demonstrate significant improvements in left ventricular end-diastolic diameter, LVEF, and NT-proBNP levels at 12 weeks. In the experimental group, BMI was significantly reduced, and there were improvements in ESS score, MLHFQ score, and EQ-5D-3L score, as well as significant reductions in CRP and IL-6 levels, while the CRP and IL-6 levels were not improved in the control group. The decrease in LVEF was more significant in the experimental group compared to the control group. There were no significant differences in the magnitude of the decreases between the two groups. CONCLUSIONS: Dapagliflozin may be an effective treatment for heart failure complicated with OSA, and could be considered as a potential new treatment for OSA. (Trial registration  www.chictr.org.cn , ChiCTR2100049834. Registered 10 August 2021).

Cell-based versus enzyme-linked immunosorbent assay for detection of anti-Tribbles homolog 2 autoantibodies in Chinese patients with narcolepsy.

STUDY OBJECTIVES: Narcolepsy type 1 (NT1) is attributed to a deficiency in cerebrospinal fluid orexin and is considered linked to autoimmunity. The levels of anti-Tribbles homolog 2 (TRIB2) autoantibodies are elevated in the sera of some patients with narcolepsy with cataplexy. Additionally, injecting mice with serum immunoglobulin from patients with narcolepsy with positive anti-TRIB2 antibodies can induce hypothalamic neuron loss and alterations in sleep patterns. Consequently, we hypothesized the existence of a potential association between anti-TRIB2 antibodies and narcolepsy. To test this possibility, we used cell-based assays (CBAs) and enzyme-linked immunosorbent assays (ELISAs) to detect the presence of anti-TRIB2 antibodies in Chinese patients with narcolepsy. METHODS: We included 68 patients with NT1; 39 patients with other central disorders of hypersomnolence; and 43 healthy controls (HCs). A CBA and a conventional ELISA were used to detect anti-TRIB2 antibody levels in patient sera. RESULTS: CBA was used to detect serum anti-TRIB2 antibodies in Chinese patients with narcolepsy, and the results were negative. However, when the ELISA was used, only two NT1 patients had TRIB2 antibody titers higher than the mean titer plus 2 SD of the HCs. CONCLUSIONS: In our study, ELISA identified TRIB2 autoantibodies in sera of narcolepsy patients where CBA failed to demonstrate them. Contrary to our hypothesis, this intriguing finding deserves further research to elucidate the potential association between TRIB2 and NT1. Exploring the implications of TRIB2 autoantibodies in narcolepsy and disparate outcomes between ELISA and CBA could provide crucial insights.

Elucidating the impact of oxygen functional groups on the catalytic activity of M-N4-C catalysts for the oxygen reduction reaction: a density functional theory and machine learning approach.

Efforts to enhance the efficiency of electrocatalysts for the oxygen reduction reaction (ORR) in energy conversion and storage devices present formidable challenges. In this endeavor, M-N4-C single-atom catalysts (MN4) have emerged as promising candidates due to their precise atomic structure and adaptable electronic properties. However, MN4 catalysts inherently introduce oxygen functional groups (OGs), intricately influencing the catalytic process and complicating the identification of active sites. This study employs advanced density functional theory (DFT) calculations to investigate the profound influence of OGs on ORR catalysis within MN4 catalysts (referred to as OGs@MN4, where M represents Fe or Co). We established the following activity order for the 2eORR: for OGs@CoN4: OH@CoN4 > CoN4 > CHO@CoN4 > C-O-C@CoN4 > COC@CoN4 > COOH@CoN4 > CO@CoN4; for OGs@FeN4: COC@FeN4 > CO@FeN4 > OH@FeN4 > FeN4 > COOH@FeN4 > CHO@FeN4 > C-O-C@FeN4. Multiple oxygen combinations were constructed and found to be the true origin of MN4 activity (for instance, the overpotential of 2OH@CoN4 as low as 0.07 V). Furthermore, we explored the performance of the OGs@MN4 system through charge and d-band center analysis, revealing the limitations of previous electron-withdrawing/donating strategies. Machine learning analysis, including GBR, GPR, and LINER models, effectively guides the prediction of catalyst performance (with an R2 value of 0.93 for predicting ΔG*OOH_vac in the GBR model). The Eg descriptor was identified as the primary factor characterizing ΔG*OOH_vac (accounting for 62.8%; OGs@CoN4: R2 = 0.9077, OGs@FeN4: R2 = 0.7781). This study unveils the significant impact of OGs on MN4 catalysts and pioneers design and synthesis criteria rooted in Eg. These innovative findings provide valuable insights into understanding the origins of catalytic activity and guiding the design of carbon-based single-atom catalysts, appealing to a broad audience interested in energy conversion technologies and materials science.

Association between cytokines and fatigue in patients with type 1 narcolepsy.

BACKGROUND: Fatigue is a frequent complaint among patients with narcolepsy. Studies have shown that inflammatory cytokines are associated with fatigue in neurological disorders; however, this association has not been identified in patients with type 1 narcolepsy. The purpose of this study was to investigate the potential relationship between cytokines and fatigue in patients with type 1 narcolepsy. METHODS: We investigated the association between 12 inflammatory cytokines and fatigue in 49 patients with type 1 narcolepsy. The Multidimensional Fatigue Inventory-20 was used to assess the fatigue severity. The associations of fatigue were identified using Spearman and Pearson correlation analyses. A linear regression analysis model was used to adjust the confounding factors and evaluate the associations of fatigue. RESULTS: Correlation analysis showed that the plasma interleukin (IL)-2 level (r = 0.409, p = 0.004) was positively correlated with fatigue in patients with narcolepsy type 1. After adjusting for confounding factors, the linear regression model revealed a positive association between the IL-2 level (ß = 1.148, p = 0.04) and fatigue in individuals diagnosed with type 1 narcolepsy. CONCLUSION: IL-2 levels show a positive correlation with fatigue in type 1 narcolepsy, suggesting its potential role in the pathophysiology of fatigue.

Circ_0006640 transferred by bone marrow-mesenchymal stem cell-exosomes suppresses lipopolysaccharide-induced apoptotic, inflammatory and oxidative injury in spinal cord injury.

BACKGROUND: Emerging proofs have shown that differentially expressed circular RNAs (circRNAs) are closely associated with the pathophysiological process of spinal cord injury (SCI). Mesenchymal stem cell (MSC)-exosomes have been demonstrated to possess favorable therapeutic effects in diseases. Herein, this work aimed to investigate the action of circ_0006640 transferred by MSC-exosomes functional recovery after SCI. METHODS: SCI animal models were established by spinal cord contusion surgery in mice and lipopolysaccharide (LPS)-stimulated mouse microglial cell line BV2. Levels of genes and proteins were detected by qRT-PCR and Western blot. Properties of BV2 cells were characterized using CCK-8 assay, flow cytometry and ELISA analysis. The oxidative stress was evaluated. Dual-luciferase reporter assay was used for verifying the binding between miR-382-5p and circ_0006640 or IGF-1 (Insulin-like Growth Factor 1). Exosome separation was conducted by using the commercial kit. RESULTS: Circ_0006640 expression was lower in SCI mice and LPS-induced microglial cells. Circ_0006640 overexpression protected microglial cells from LPS-induced apoptotic, inflammatory and oxidative injury. Mechanistically, circ_0006640 directly sponged miR-382-5p, which targeted IGF-1. MiR-382-5p was increased, while IGF-1 was decreased in SCI mice and LPS-induced microglial cells. Knockdown of miR-382-5p suppressed apoptosis, inflammation and oxidative stress in LPS-induced microglial cells, which were reversed by IGF-1 deficiency. Moreover, miR-382-5p up-regulation abolished the protective functions of circ_0006640 in LPS-induced microglial cells. Additionally, circ_0006640 was packaged into MSC-exosomes and could be transferred by exosomes. Exosomal circ_0006640 also had protective effects on microglial cells via miR-382-5p/IGF-1 axis. CONCLUSION: Circ_0006640 transferred by BMSC-exosomes suppressed LPS-induced apoptotic, inflammatory and oxidative injury via miR-382-5p/IGF-1 axis, indicating a new insight into the clinical application of exosomal circRNA-based therapeutic in the function recovery after SCI.

Fusion of Multi-domain EEG Signatures Improves Emotion Recognition.

BACKGROUND: Affective computing has gained increasing attention in the area of the human-computer interface where electroencephalography (EEG)-based emotion recognition occupies an important position. Nevertheless, the diversity of emotions and the complexity of EEG signals result in unexplored relationships between emotion and multichannel EEG signal frequency, as well as spatial and temporal information. METHODS: Audio-video stimulus materials were used that elicited four types of emotions (sad, fearful, happy, neutral) in 32 male and female subjects (age 21-42 years) while collecting EEG signals. We developed a multidimensional analysis framework using a fusion of phase-locking value (PLV), microstates, and power spectral densities (PSDs) of EEG features to improve emotion recognition. RESULTS: An increasing trend of PSDs was observed as emotional valence increased, and connections in the prefrontal, temporal, and occipital lobes in high-frequency bands showed more differentiation between emotions. Transition probability between microstates was likely related to emotional valence. The average cross-subject classification accuracy of features fused by Discriminant Correlation Analysis achieved 64.69%, higher than that of single mode and direct-concatenated features, with an increase of more than 7%. CONCLUSIONS: Different types of EEG features have complementary properties in emotion recognition, and combining EEG data from three types of features in a correlated way, improves the performance of emotion classification.

Guanxin V alleviates ventricular remodeling after acute myocardial infarction with circadian disruption by regulating mitochondrial dynamics.

PURPOSE: Circadian disruption has been a common issue due to modern lifestyles. Ventricular remodeling (VR) is a pivotal progressive pathologic change after acute myocardial infarction (AMI) and circadian disruption may have a negative influence on VR according to the latest research. Whether or not Guanxin V (GXV) has a positive effect on VR after AMI with circadian disruption drew our interest. METHODS: Rats were randomly divided into a sham group, an AMI group, an AMI with circadian disruption group, and an AMI with circadian disruption treated with the GXV group according to a random number table. RNA sequencing (RNA-Seq) was utilized to confirm the different expressed genes regulated by circadian disruption. Cardiac function, inflammation factors, pathological evaluation, and mitochondrial dynamics after the intervention were conducted to reveal the mechanism by which GXV regulated VR after AMI with circadian disruption. RESULTS: RNA-Seq demonstrated that NF-κB was up-regulated by circadian disruption in rats with AMI. Functional and pathological evaluation indicated that compared with the AMI group, circadian disruption was associcataed with deteriorated cardiac function, expanded infarcted size, and exacerbated fibrosis and cardiomyocyte apoptosis. Further investigation demonstrated that mitochondrial dynamics imbalance was induced by circadian disruption. GXV intervention reversed the inflammatory status including down-regulation of NF-κB. Reserved cardiac function, limited infarct size, and ameliorated fibrosis and apoptosis were also observed in the GXV treated group. GXV maintained mitochondrial fission/fusion imbalance through suppressed expression of mitochondrial fission-associated proteins. CONCLUSION: The study findings suggest that identified mitochondrial dysfunctions may underlie the link between circadian disruption and VR. GXV may exert cardioprotection after AMI with circadian disruption through regulating mitochondrial dynamics.

Integrating scRNA-seq to explore novel macrophage infiltration-associated biomarkers for diagnosis of heart failure.

OBJECTIVE: Inflammation and immune cells are closely intertwined mechanisms that contribute to the progression of heart failure (HF). Nonetheless, there is a paucity of information regarding the distinct features of dysregulated immune cells and efficient diagnostic biomarkers linked with HF. This study aims to explore diagnostic biomarkers related to immune cells in HF to gain new insights into the underlying molecular mechanisms of HF and to provide novel perspectives for the detection and treatment of HF. METHOD: The CIBERSORT method was employed to quantify 22 types of immune cells in HF and normal subjects from publicly available GEO databases (GSE3586, GSE42955, GSE57338, and GSE79962). Machine learning methods were utilized to screen for important cell types. Single-cell RNA sequencing (GSE145154) was further utilized to identify important cell types and hub genes. WGCNA was employed to screen for immune cell-related genes and ultimately diagnostic models were constructed and evaluated. To validate these predictive results, blood samples were collected from 40 normal controls and 40 HF patients for RT-qPCR analysis. Lastly, key cell clusters were divided into high and low biomarker expression groups to identify transcription factors that may affect biomarkers. RESULTS: The study found a noticeable difference in immune environment between HF and normal subjects. Macrophages were identified as key immune cells by machine learning. Single-cell analysis further showed that macrophages differed dramatically between HF and normal subjects. This study revealed the existence of five subsets of macrophages that have different differentiation states. Based on module genes most relevant to macrophages, macrophage differentiation-related genes (MDRGs), and DEGs in HF and normal subjects from GEO datasets, four genes (CD163, RNASE2, LYVE1, and VSIG4) were identified as valid diagnostic markers for HF. Ultimately, a diagnostic model containing two hub genes was constructed and then validated with a validation dataset and clinical samples. In addition, key transcription factors driving or maintaining the biomarkers expression programs were identified. CONCLUSION: The analytical results and diagnostic model of this study can assist clinicians in identifying high-risk individuals, thereby aiding in guiding treatment decisions for patients with HF.

An Improved Canonical Correlation Analysis for EEG Inter-Band Correlation Extraction.

(1) Background: Emotion recognition based on EEG signals is a rapidly growing and promising research field in affective computing. However, traditional methods have focused on single-channel features that reflect time-domain or frequency-domain information of the EEG, as well as bi-channel features that reveal channel-wise relationships across brain regions. Despite these efforts, the mechanism of mutual interactions between EEG rhythms under different emotional expressions remains largely unexplored. Currently, the primary form of information interaction between EEG rhythms is phase-amplitude coupling (PAC), which results in computational complexity and high computational cost. (2) Methods: To address this issue, we proposed a method of extracting inter-bands correlation (IBC) features via canonical correlation analysis (CCA) based on differential entropy (DE) features. This approach eliminates the need for surrogate testing and reduces computational complexity. (3) Results: Our experiments verified the effectiveness of IBC features through several tests, demonstrating that the more correlated features between EEG frequency bands contribute more to emotion classification accuracy. We then fused IBC features and traditional DE features at the decision level, which significantly improved the accuracy of emotion recognition on the SEED dataset and the local CUMULATE dataset compared to using a single feature alone. (4) Conclusions: These findings suggest that IBC features are a promising approach to promoting emotion recognition accuracy. By exploring the mutual interactions between EEG rhythms under different emotional expressions, our method can provide valuable insights into the underlying mechanisms of emotion processing and improve the performance of emotion recognition systems.

The increasing instance of negative emotion reduce the performance of emotion recognition.

Introduction: Emotion recognition plays a crucial role in affective computing. Recent studies have demonstrated that the fuzzy boundaries among negative emotions make recognition difficult. However, to the best of our knowledge, no formal study has been conducted thus far to explore the effects of increased negative emotion categories on emotion recognition. Methods: A dataset of three sessions containing consistent non-negative emotions and increased types of negative emotions was designed and built which consisted the electroencephalogram (EEG) and the electrocardiogram (ECG) recording of 45 participants. Results: The results revealed that as negative emotion categories increased, the recognition rates decreased by more than 9%. Further analysis depicted that the discriminative features gradually reduced with an increase in the negative emotion types, particularly in the θ, α, and ß frequency bands. Discussion: This study provided new insight into the balance of emotion-inducing stimuli materials.

Multimodal Vigilance Estimation with Modality-Pairwise Contrastive Loss.

Nowadays, how to estimate vigilance with higher accuracy has become a hot field of research direction. Although the increasing available modalities opens the door for amazing new possibilities to achieve good performance, the uncertain cross-modal interaction still poses a real challenge to the multimodal fusion. In this paper, a cross-modality alignment method has been proposed based on the contrastive learning for extracting shared but not the same information among modalities. The contrastive learning is adopted to minimize the intermodal differences by maximizing the similarity of semantic representation of modalities. Applying our proposed modeling framework, we evaluated our approach on SEED-VIG dataset consisting of EEG and EOG signals. Experiments showed that our study achieved state-of-the-art multimodal vigilance estimation performance both in intra-subject and inter-subject situations, the average of RMSE/CORR were improved to 0.092/0.893 and 0.144/0.887, respectively. In addition, analysis on the frequency bands showed that theta and alpha activities contain valuable information for vigilance estimation, and the correlation between them and PERCLOS can be significantly improved by contrastive learning. We argue that the proposed method in the inter-subject case could offer the possibility of reducing the high-cost of data annotation, and further analysis may provide an idea for the application of multimodal vigilance regression.

Evaluation of combined pathological responses in primary tumor and lymph nodes following neoadjuvant chemoimmunotherapy in non-small cell lung cancer.

BACKGROUND: Inconsistent pathological responses of tumor and lymph nodes (LNs) were frequently observed in non-small cell lung cancer (NSCLC) receiving neoadjuvant chemoimmunotherapy. However, there is a lack of studies to report the prognostic significance and the relevant clinicopathological factors of tumor-nodal inconsistent responses after neoadjuvant immunotherapy or chemoimmunotherapy. Therefore, this study aimed to depict the inconsistent pathological combined tumor-nodal responses in NSCLC patients after neoadjuvant chemoimmunotherapy as well as the underlying clinical significance. METHODS: A total of 81 node-positive NSCLC patients who underwent neoadjuvant chemoimmunotherapy were eligible for inclusion. Demographic, radiologic, and pathological features of patients were recorded. Patients with pathological complete response of both tumor (ypT(pCR)) and LNs (ypN0) were classified into the combined good responder group and the relevant clinicopathological features were evaluated. The event-free survival (EFS) outcome was analyzed using Kaplan-Meier analysis. RESULTS: The ypN0 and ypT(pCR) rates were 74.1 % and 42.0 %, respectively. A significant correlation was observed between ypT(pCR) and ypN0 (P = 0.003), but inconsistent responses remained. The combined responses of the primary tumor and LNs demonstrated a significant association with the prognosis outcome (P = 0.005). Notably,patients who received at least twice of their infusions of immune checkpoint inhibitors after 15:30 had a worse prognosis (P = 0.015). CONCLUSION: A significant but not absolute correlation was observed between good tumor response and good nodal response in NSCLC patients after neoadjuvant chemoimmunotherapy, but inconsistent responses were also found. The combination of tumor and nodal responses is significantly associated with prognosis and combined good responder can be used as a reliable prognosis predictor.