Regulation of T helper cell differentiation by the interplay between histone modification and chromatin interaction.

The development and function of the immune system are controlled by temporospatial gene expression programs, which are regulated by cis-regulatory elements, chromatin structure, and trans-acting factors. In this study, we cataloged the dynamic histone modifications and chromatin interactions at regulatory regions during T helper (Th) cell differentiation. Our data revealed that the H3K4me1 landscape established by MLL4 in naive CD4+ T cells is critical for restructuring the regulatory interaction network and orchestrating gene expression during the early phase of Th differentiation. GATA3 plays a crucial role in further configuring H3K4me1 modification and the chromatin interaction network during Th2 differentiation. Furthermore, we demonstrated that HSS3-anchored chromatin loops function to restrict the activity of the Th2 locus control region (LCR), thus coordinating the expression of Th2 cytokines. Our results provide insights into the mechanisms of how the interplay between histone modifications, chromatin looping, and trans-acting factors contributes to the differentiation of Th cells.

NAD activates olfactory receptor 1386 to regulate type I interferon responses in Plasmodium yoelii YM infection.

Olfactory receptors (Olfr) are G protein-coupled receptors that are normally expressed on olfactory sensory neurons to detect volatile chemicals or odorants. Interestingly, many Olfrs are also expressed in diverse tissues and function in cell-cell recognition, migration, and proliferation as well as immune responses and disease processes. Here, we showed that many Olfr genes were expressed in the mouse spleen, linked to Plasmodium yoelii genetic loci significantly, and/or had genome-wide patterns of LOD scores (GPLSs) similar to those of host Toll-like receptor genes. Expression of specific Olfr genes such as Olfr1386 in HEK293T cells significantly increased luciferase signals driven by IFN-ß and NF-κB promoters, with elevated levels of phosphorylated TBK1, IRF3, P38, and JNK. Mice without Olfr1386 were generated using the CRISPR/Cas9 method, and the Olfr1386-/- mice showed significantly lower IFN-α/ß levels and longer survival than wild-type (WT) littermates after infection with P. yoelii YM parasites. Inhibition of G protein signaling and P38 activity could affect cyclic AMP-responsive element promoter-driven luciferase signals and IFN-ß mRNA levels in HEK293T cells expressing the Olfr1386 gene, respectively. Screening of malaria parasite metabolites identified nicotinamide adenine dinucleotide (NAD) as a potential ligand for Olfr1386, and NAD could stimulate IFN-ß responses and phosphorylation of TBK1 and STAT1/2 in RAW264.7 cells. Additionally, parasite RNA (pRNA) could significantly increase Olfr1386 mRNA levels. This study links multiple Olfrs to host immune response pathways, identifies a candidate ligand for Olfr1386, and demonstrates the important roles of Olfr1386 in regulating type I interferon (IFN-I) responses during malaria parasite infections.

Correction: Magnaporthe oryzae fimbrin organizes actin networks in the hyphal tip during polar growth and pathogenesis.

[This corrects the article DOI: 10.1371/journal.ppat.1008437.].

RGS4 controls airway hyperresponsiveness through GAP-independent mechanisms.

Regulators of G protein signaling (RGS) proteins constrain G protein-coupled receptor (GPCR)-mediated and other responses throughout the body primarily, but not exclusively, through their GTPase-activating protein activity. Asthma is a highly prevalent condition characterized by airway hyper-responsiveness (AHR) to environmental stimuli resulting in part from amplified GPCR-mediated airway smooth muscle contraction. Rgs2 or Rgs5 gene deletion in mice enhances AHR and airway smooth muscle contraction, whereas RGS4 KO mice unexpectedly have decreased AHR because of increased production of the bronchodilator prostaglandin E2 (PGE2) by lung epithelial cells. Here, we found that knockin mice harboring Rgs4 alleles encoding a point mutation (N128A) that sharply curtails RGS4 GTPase-activating protein activity had increased AHR, reduced airway PGE2 levels, and augmented GPCR-induced bronchoconstriction compared with either RGS4 KO mice or WT controls. RGS4 interacted with the p85α subunit of PI3K and inhibited PI3K-dependent PGE2 secretion elicited by transforming growth factor beta in airway epithelial cells. Together, these findings suggest that RGS4 affects asthma severity in part by regulating the airway inflammatory milieu in a G protein-independent manner.

Fecal Microbiota Transplantation for Sleep Disturbance in Post-acute COVID-19 Syndrome.

BACKGROUND & AIMS: Post-acute COVID-19 syndrome (PACS) is associated with sleep disturbance, but treatment options are limited. The etiology of PACS may be secondary to alterations in the gut microbiome. Here, we report the efficacy of fecal microbiota transplantation (FMT) in alleviating post-COVID insomnia symptoms in a nonrandomized, open-label prospective interventional study. METHODS: Between September 22, 2022, and May 22, 2023, we recruited 60 PACS patients with insomnia defined as Insomnia Severity Index (ISI) ≥8 and assigned them to the FMT group (FMT at weeks 0, 2, 4, and 8; n = 30) or the control group (n = 30). The primary outcome was clinical remission defined by an ISI of <8 at 12 weeks. Secondary outcomes included changes in the Pittsburgh Sleep Quality Index, Generalized Anxiety Disorder-7 scale, Epworth Sleepiness Scale, Multidimensional Fatigue Inventory, blood cortisol and melatonin, and gut microbiome analysis on metagenomic sequencing. RESULTS: At week 12, more patients in the FMT than the control group had insomnia remission (37.9% vs 10.0%; P = .018). The FMT group showed a decrease in ISI score (P < .0001), Pittsburgh Sleep Quality Index (P < .0001), Generalized Anxiety Disorder-7 scale (P = .0019), Epworth Sleepiness Scale (P = .0057), and blood cortisol concentration (P = .035) from baseline to week 12, but there was no significant change in the control group. There was enrichment of bacteria such as Gemmiger formicilis and depletion of microbial pathways producing menaquinol derivatives after FMT. The gut microbiome profile resembled that of the donor in FMT responders but not in nonresponders at week 12. There was no serious adverse event. CONCLUSIONS: This pilot study showed that FMT could be effective and safe in alleviating post-COVID insomnia, and further clinical trials are warranted. CLINICALTRIALS: gov, Number: NCT05556733.

Prediction of survival and analysis of prognostic factors for patients with AFP negative hepatocellular carcinoma: a population-based study.

PURPOSE: Hepatocellular carcinoma (HCC) has a poor prognosis, and alpha-fetoprotein (AFP) is widely used to evaluate HCC. However, the proportion of AFP-negative individuals cannot be disregarded. This study aimed to establish a nomogram of risk factors affecting the prognosis of patients with AFP-negative HCC and to evaluate its diagnostic efficiency. PATIENTS AND METHODS: Data from patients with AFP-negative initial diagnosis of HCC (ANHC) between 2004 and 2015 were collected from the Surveillance, Epidemiology, and End Results database for model establishment and validation. We randomly divided overall cohort into the training or validation cohort (7:3). Univariate and multivariate Cox regression analysis were used to identify the risk factors. We constructed nomograms with overall survival (OS) and cancer-specific survival (CSS) as clinical endpoint events and constructed survival analysis by using Kaplan-Meier curve. Also, we conducted internal validation with Receiver Operating Characteristic (ROC) analysis and Decision curve analysis (DCA) to validate the clinical value of the model. RESULTS: This study included 1811 patients (1409 men; 64.7% were Caucasian; the average age was 64 years; 60.7% were married). In the multivariate analysis, the independent risk factors affecting prognosis were age, ethnicity, year of diagnosis, tumor size, tumor grade, surgery, chemotherapy, and radiotherapy. The nomogram-based model related C-indexes were 0.762 (95% confidence interval (CI): 0.752-0.772) and 0.752 (95% CI: 0.740-0.769) for predicting OS, and 0.785 (95% CI: 0.774-0.795) and 0.779 (95% CI: 0.762-0.795) for predicting CSS. The nomogram model showed that the predicted death was consistent with the actual value. The ROC analysis and DCA showed that the nomogram had good clinical value compared with TNM staging. CONCLUSION: The age(HR:1.012, 95% CI: 1.006-1.018, P-value < 0.001), ethnicity(African-American: HR:0.946, 95% CI: 0.783-1.212, P-value: 0.66; Others: HR:0.737, 95% CI: 0.613-0.887, P-value: 0.001), tumor diameter(HR:1.006, 95% CI: 1.004-1.008, P-value < 0.001), year of diagnosis (HR:0.852, 95% CI: 0.729-0.997, P-value: 0.046), tumor grade(Grade 2: HR:1.124, 95% CI: 0.953-1.326, P-value: 0.164; Grade 3: HR:1.984, 95% CI: 1.574-2.501, P-value < 0.001; Grade 4: HR:2.119, 95% CI: 1.115-4.027, P-value: 0.022), surgery(Liver Resection: HR:0.193, 95% CI: 0.160-0.234, P-value < 0.001; Liver Transplant: HR:0.102, 95% CI: 0.072-0.145, P-value < 0.001), chemotherapy(HR:0.561, 95% CI: 0.471-0.668, P-value < 0.001), and radiotherapy(HR:0.641, 95% CI: 0.463-0.887, P-value:0.007) were independent prognostic factors for patients with ANHC. We developed a nomogram model for predicting the OS and CSS of patients with ANHC, with a good predictive performance.

Development and Validation of an Interpretable Conformal Predictor to Predict Sepsis Mortality Risk: Retrospective Cohort Study.

BACKGROUND: Early and reliable identification of patients with sepsis who are at high risk of mortality is important to improve clinical outcomes. However, 3 major barriers to artificial intelligence (AI) models, including the lack of interpretability, the difficulty in generalizability, and the risk of automation bias, hinder the widespread adoption of AI models for use in clinical practice. OBJECTIVE: This study aimed to develop and validate (internally and externally) a conformal predictor of sepsis mortality risk in patients who are critically ill, leveraging AI-assisted prediction modeling. The proposed approach enables explaining the model output and assessing its confidence level. METHODS: We retrospectively extracted data on adult patients with sepsis from a database collected in a teaching hospital at Beth Israel Deaconess Medical Center for model training and internal validation. A large multicenter critical care database from the Philips eICU Research Institute was used for external validation. A total of 103 clinical features were extracted from the first day after admission. We developed an AI model using gradient-boosting machines to predict the mortality risk of sepsis and used Mondrian conformal prediction to estimate the prediction uncertainty. The Shapley additive explanation method was used to explain the model. RESULTS: A total of 16,746 (80%) patients from Beth Israel Deaconess Medical Center were used to train the model. When tested on the internal validation population of 4187 (20%) patients, the model achieved an area under the receiver operating characteristic curve of 0.858 (95% CI 0.845-0.871), which was reduced to 0.800 (95% CI 0.789-0.811) when externally validated on 10,362 patients from the Philips eICU database. At a specified confidence level of 90% for the internal validation cohort the percentage of error predictions (n=438) out of all predictions (n=4187) was 10.5%, with 1229 (29.4%) predictions requiring clinician review. In contrast, the AI model without conformal prediction made 1449 (34.6%) errors. When externally validated, more predictions (n=4004, 38.6%) were flagged for clinician review due to interdatabase heterogeneity. Nevertheless, the model still produced significantly lower error rates compared to the point predictions by AI (n=1221, 11.8% vs n=4540, 43.8%). The most important predictors identified in this predictive model were Acute Physiology Score III, age, urine output, vasopressors, and pulmonary infection. Clinically relevant risk factors contributing to a single patient were also examined to show how the risk arose. CONCLUSIONS: By combining model explanation and conformal prediction, AI-based systems can be better translated into medical practice for clinical decision-making.

The Microtubule End Binding Protein Mal3 Is Essential for the Dynamic Assembly of Microtubules during Magnaporthe oryzae Growth and Pathogenesis.

Cytoskeletal microtubules (MTs) play crucial roles in many aspects of life processes in eukaryotic organisms. They dynamically assemble physiologically important MT arrays under different cell conditions. Currently, aspects of MT assembly underlying the development and pathogenesis of the model plant pathogenic fungus Magnaporthe oryzae (M. oryzae) are unclear. In this study, we characterized the MT plus end binding protein MoMal3 in M. oryzae. We found that knockout of MoMal3 results in defects in hyphal polar growth, appressorium-mediated host penetration and nucleus division. Using high-resolution live-cell imaging, we further found that the MoMal3 mutant assembled a rigid MT in parallel with the MT during hyphal polar growth, the cage-like network in the appressorium and the stick-like spindle in nuclear division. These aberrant MT organization patterns in the MoMal3 mutant impaired actin-based cell growth and host infection. Taken together, these findings showed that M. oryzae relies on MoMal3 to assemble elaborate MT arrays for growth and infection. The results also revealed the assembly mode of MTs in M. oryzae, indicating that MTs are pivotal for M. oryzae growth and host infection and may be new targets for devastating fungus control.

The actin motor protein OsMYA1 associates with OsExo70H1 and contributes to rice secretory defense by modulating OsSyp121 distribution.

Magnaporthe oryzae (M. oryzae) is a devastating hemibiotrophic pathogen. Its biotrophic invasive hyphae (IH) are enclosed in the extrainvasive hyphal membrane produced by plant cells, thus generating a front line of the battlefield between the pathogen and the host plants. In plants, defense-related complexes such as proteins, callose-rich materials and vesicles, are directionally secreted to this interface to confer defense responses, but the underlying molecular mechanism is poorly understood. In this study, we found that a Myosin gene, Myosin A1 (OsMYA1), contributed to rice defense. The OsMYA1 knockout mutant exhibited decreased resistance to M. oryzae infection. OsMYA1 localizes to the actin cytoskeleton and surrounds the IH of M. oryzae. OsMYA1 interacts with an exocyst subunit, OsExo70H1, and regulates its accumulation at the plasma membrane (PM) and pathogen-plant interface. Furthermore, OsExo70H1 interacted with the rice syntaxin of the plants121 protein (OsSyp121), and the distribution of OsSyp121 to the PM or the pathogen-plant interface was disrupted in both the OsMYA1 and OsExo70H1 mutants. Overall, these results not only reveal a new function of OsMYA1 in rice blast resistance, but also uncover a molecular mechanism by which plants regulate defense against M. oryzae by OsMYA1-initiated vesicle secretory pathway, which originates from the actin cytoskeleton to the PM.

[Artificial intelligence in wearable electrocardiogram monitoring].

Electrocardiogram (ECG) monitoring owns important clinical value in diagnosis, prevention and rehabilitation of cardiovascular disease (CVD). With the rapid development of Internet of Things (IoT), big data, cloud computing, artificial intelligence (AI) and other advanced technologies, wearable ECG is playing an increasingly important role. With the aging process of the population, it is more and more urgent to upgrade the diagnostic mode of CVD. Using AI technology to assist the clinical analysis of long-term ECGs, and thus to improve the ability of early detection and prediction of CVD has become an important direction. Intelligent wearable ECG monitoring needs the collaboration between edge and cloud computing. Meanwhile, the clarity of medical scene is conducive for the precise implementation of wearable ECG monitoring. This paper first summarized the progress of AI-related ECG studies and the current technical orientation. Then three cases were depicted to illustrate how the AI in wearable ECG cooperate with the clinic. Finally, we demonstrated the two core issues-the reliability and worth of AI-related ECG technology and prospected the future opportunities and challenges.

Evolution of blockchain accounting literature from the perspective of CiteSpace (2013-2023).

Against the backdrop of the Industrial Revolution 4.0, the advantages of blockchain technology in traceability, transparency, safety improvement, and efficiency improvement have made it possible to reduce the work of accounting personnel by 50 %, thus saving billions of dollars for global companies by combining this technology with accounting. However, the blockchain technology associated with accounting is in the experimental stage and has several problems to be solved including limited data processing capacity, information confidentiality, and regulatory difficulties. This innovation and progress in science and technology has provided more abundant, efficient, and professional technical support for the research of blockchain accounting documents. Among these advances, CiteSpace software has promoted the development of blockchain and accounting in the direction of visualization, comprehensiveness, security, and relevance. In this study, we used the knowledge map drawn by CiteSpace to search the core Blockchain Accounting database from 2013 to 2023 on the Web of Science (WoS). We obtained 1414 documents measured according to co-citation analysis, log-likelihood ratio (LLR) network clustering, co-occurrence keywords, and emergent time zone diagram method. We analyzed and summarized the important documents, research keywords, key research fields, and knowledge evolution related to "blockchain accounting" by network, literature integration, and popular research topics. We found that adopting blockchain technology in accounting information systems is expected to improve recordkeeping and reporting. Blockchain, as an innovative technology, provides a tamper-proof, traceable, and shareable platform for accounting information by using a distributed ledger system. By implementing blockchain, artificial intelligence can improve safety, transparency, and accuracy, and also may completely change the way we manage financial records. With its ability to improve overall efficiency and reduce errors, blockchain technology may change our familiar accounting methods. In addition, blockchain technology, intelligent contract, artificial intelligence, the Internet, information systems, and supply chain are the most important keywords, while blockchain technology, intelligent contract, and artificial intelligence are important components of blockchain accounting knowledge system. This research provided an important opportunity to advance the understanding of the crucial contribution of blockchain to the accounting field.

STAT5B SH2 variants disrupt mammary enhancers and the stability of genetic programs during pregnancy.

During pregnancy, mammary tissue undergoes expansion and differentiation, leading to lactation, a process regulated by the hormone prolactin through the JAK2-STAT5 pathway. STAT5 activation is key to successful lactation making the mammary gland an ideal experimental system to investigate the impact of human missense mutations on mammary tissue homeostasis. Here, we investigated the effects of two human variants in the STAT5B SH2 domain, which convert tyrosine 665 to either phenylalanine (Y665F) or histidine (Y665H), both shown to activate STAT5B in cell culture. We ported these mutations into the mouse genome and found distinct and divergent functions. Homozygous Stat5bY665H mice failed to form functional mammary tissue, leading to lactation failure, with impaired alveolar development and greatly reduced expression of key differentiation genes. STAT5BY665H failed to recognize mammary enhancers and impeded STAT5A binding. In contrast, mice carrying the Stat5bY665F mutation exhibited abnormal precocious development, accompanied by an early activation of the mammary transcription program and the induction of otherwise silent genetic programs. Physiological adaptation was observed in Stat5bY665H mice as continued exposure to pregnancy hormones led to lactation. In summary, our findings highlight that human STAT5B variants can modulate their response to cytokines and thereby impact mammary homeostasis and lactation.

A pretrain-finetune approach for improving model generalizability in outcome prediction of acute respiratory distress syndrome patients.

BACKGROUND: Early prediction of acute respiratory distress syndrome (ARDS) of critically ill patients in intensive care units (ICUs) has been intensively studied in the past years. Yet a prediction model trained on data from one hospital might not be well generalized to other hospitals. It is therefore essential to develop an accurate and generalizable ARDS prediction model adaptive to different hospital or medical centers. METHODS: We analyzed electronic medical records of 200,859 and 50,920 hospitalized patients within 24 h after being diagnosed with ARDS from the Philips eICU Institute (eICU-CRD) and the Medical Information Mart for Intensive Care (MIMIC-IV) dataset, respectively. Patients were sorted into three groups, including rapid death, long stay, and recovery, based on their condition or outcome between 24 and 72 h after ARDS diagnosis. To improve prediction performance and generalizability, a "pretrain-finetune" approach was applied, where we pretrained models on the eICU-CRD dataset and performed model finetuning using only a part (35%) of the MIMIC-IV dataset, and then tested the finetuned models on the remaining data from the MIMIC-IV dataset. Well-known machine-learning algorithms, including logistic regression, random forest, extreme gradient boosting, and multilayer perceptron neural networks, were employed to predict ARDS outcomes. Prediction performance was evaluated using the area under the receiver-operating characteristic curve (AUC). RESULTS: Results show that, in general, multilayer perceptron neural networks outperformed the other models. The use of pretrain-finetune yielded improved performance in predicting ARDS outcomes achieving a micro-AUC of 0.870 for the MIMIC-IV dataset, an improvement of 0.046 over the pretrain model. CONCLUSIONS: The proposed pretrain-finetune approach can effectively improve model generalizability from one to another dataset in ARDS prediction.

Sexually dimorphic renal expression of Klotho is directed by a kidney-specific distal enhancer responsive to HNF1b.

Transcription enhancers are genomic sequences regulating common and tissue-specific genes and their disruption can contribute to human disease development and progression. Klotho, a sexually dimorphic gene specifically expressed in kidney, is well-linked to kidney dysfunction and its deletion from the mouse genome leads to premature aging and death. However, the sexually dimorphic regulation of Klotho is not understood. Here, we characterize two candidate Klotho enhancers using H3K27ac epigenetic marks and transcription factor binding and investigate their functions, individually and combined, through CRISPR-Cas9 genome engineering. We discovered that only the distal (E1), but not the proximal (E2) candidate region constitutes a functional enhancer, with the double deletion not causing Klotho expression to further decrease. E1 activity is dependent on HNF1b transcription factor binding site within the enhancer. Further, E1 controls the sexual dimorphism of Klotho as evidenced by qPCR and RNA-seq. Despite the sharp reduction of Klotho mRNA, unlike germline Klotho knockouts, mutant mice presented normal phenotype, including weight, lifespan, and serum biochemistry. Lastly, only males lacking E1 display more prominent acute, but not chronic kidney injury responses, indicating a remarkable range of potential adaptation to isolated Klotho loss, especially in female E1 knockouts, retaining renoprotection despite over 80% Klotho reduction.

Statin therapy: a potential adjuvant to immunotherapies in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is one of the most prevalent cancers worldwide and accounts for more than 90% of primary liver cancer. The advent of immune checkpoint inhibitor (ICI)-related therapies combined with angiogenesis inhibition has revolutionized the treatment of HCC in late-stage and unresectable HCC, as ICIs alone were disappointing in treating HCC. In addition to the altered immune microenvironment, abnormal lipid metabolism in the liver has been extensively characterized in various types of HCC. Stains are known for their cholesterol-lowering properties and their long history of treating hypercholesterolemia and reducing cardiovascular disease risk. Apart from ICI and other conventional therapies, statins are frequently used by advanced HCC patients with dyslipidemia, which is often marked by the abnormal accumulation of cholesterol and fatty acids in the liver. Supported by a body of preclinical and clinical studies, statins may unexpectedly enhance the efficacy of ICI therapy in HCC patients through the regulation of inflammatory responses and the immune microenvironment. This review discusses the abnormal changes in lipid metabolism in HCC, summarizes the clinical evidence and benefits of stain use in HCC, and prospects the possible mechanistic actions of statins in transforming the immune microenvironment in HCC when combined with immunotherapies. Consequently, the use of statin therapy may emerge as a novel and valuable adjuvant for immunotherapies in HCC.

Sexually dimorphic renal expression of Klotho is directed by a kidney-specific distal enhancer responsive to HNF1b.

Transcription enhancers are genomic sequences regulating common and tissue-specific genes and their disruption can contribute to human disease development and progression. Klotho, a sexually dimorphic gene specifically expressed in kidney, is well-linked to kidney dysfunction and its deletion from the mouse genome leads to premature aging and death. However, the sexually dimorphic regulation of Klotho is not understood. Here, we characterize two candidate Klotho enhancers using H3K27ac epigenetic marks and transcription factor binding and investigate their functions, individually and combined, through CRISPR-Cas9 genome engineering. We discovered that only the distal (E1), but not the proximal (E2) candidate region constitutes a functional enhancer, with the double deletion not causing Klotho expression to further decrease. E1 activity is dependent on HNF1b transcription factor binding site within the enhancer. Further, E1 controls the sexual dimorphism of Klotho as evidenced by qPCR and RNA-seq. Despite the sharp reduction of Klotho mRNA, unlike germline Klotho knockouts, mutant mice presented normal phenotype, including weight, lifespan, and serum biochemistry. Lastly, only males lacking E1 display more prominent acute, but not chronic kidney injury responses, indicating a remarkable range of potential adaptation to isolated Klotho loss, especially in female E1 knockouts, retaining renoprotection despite over 80% Klotho reduction.

Physiologic Network-Based Brain-Heart Interaction Quantification During Visual Emotional Elicitation.

In recent years, there has been a surge in interest regarding the intricate physiological interplay between the brain and the heart, particularly during emotional processing. This has led to the development of various signal processing techniques aimed at investigating Brain-Heart Interactions (BHI), reflecting a growing appreciation for their bidirectional communication and influence on each other. Our study contributes to this burgeoning field by adopting a network physiology approach, employing time-delay stability as a quantifiable metric to discern and measure the coupling strength between the brain and the heart, specifically during visual emotional elicitation. We extract and transform features from EEG and ECG signals into a 1 Hz format, facilitating the calculation of BHI coupling strength through stability analysis on their maximal cross-correlation. Notably, our investigation sheds light on the critical role played by low-frequency components in EEG, particularly in the δ , θ , and α bands, as essential mediators of information transmission during the complex processing of emotion-related stimuli by the brain. Furthermore, our analysis highlights the pivotal involvement of frontal pole regions, emphasizing the significance of δ - θ coupling in mediating emotional responses. Additionally, we observe significant arousal-dependent changes in the θ frequency band across different emotional states, particularly evident in the prefrontal cortex. By offering novel insights into the synchronized dynamics of cortical and heartbeat activities during emotional elicitation, our research enriches the expanding knowledge base in the field of neurophysiology and emotion research.

Poor sleep quality and overweight/obesity in healthcare professionals: a cross-sectional study.

Objective: This study aimed to analyze the relationship between the sleep quality of healthcare professionals and the incidence of overweight and obesity, exploring the potential impact of sleep quality on the onset of overweight and obesity in order to provide a scientific basis for formulating effective health intervention measures. Methods: A convenience sampling method was used to conduct a survey on the sleep characteristics and obesity status among healthcare professionals at Peking Union Medical College Hospital and Tianjin Dongli District Traditional Chinese Medicine Hospital. The survey was conducted via online questionnaires, which included demographic data, the Pittsburgh Sleep Quality Index (PSQI), height, weight, and related sleep, exercise, and dietary habits. Univariate and multivariate logistic regression analyses were applied to study the relationship between sleep quality and overweight/obesity among healthcare professionals. Results: A total of 402 questionnaires were distributed, with a 100% retrieval rate, yielding 402 valid questionnaires. The average body mass index of the 402 participants was 23.22 ± 3.87 kg/m^2. Among them, 144 cases were overweight or obese, accounting for 35.8% (144/402) of the total. The prevalence of poor sleep quality among healthcare professionals was 27.4% (110/402), with an average PSQI score of 8.37 ± 3.624. The rate of poor sleep quality was significantly higher in the overweight and obese group compared to the normal weight group (36.1% vs. 22.5%, p = 0.003). The multivariate analysis indicated that gender, marital status, lower education level, sleep duration (odds ratio [OR] =1.411, 95% confidence interval [CI] 1.043-1.910, p = 0.026), and sleep disturbances (OR = 1.574, 95%CI 1.123-2.206, p = 0.008) were significant risk factors for overweight and obesity among healthcare professionals. Conclusion: Overweight or obese healthcare professionals had poorer sleep quality compared to those with a normal weight. Sleep duration and sleep disorders were identified as independent risk factors for overweight or obesity in healthcare professionals. Increasing sleep duration and improving sleep disorders may play a positive role in controlling overweight and obesity among healthcare professionals.

Association between nutritional risk and fatigue in frailty conditions for older adult patients: a multicentre cross-sectional survey study.

BACKGROUND AND AIMS: Frailty is widespread in the elderly, while there is a bi-directional relationship between frailty and malnutrition. The objectives of this study were to investigate the prevalence and correlation of frailty and nutritional risk in older adult patients and to analyse the factors associated with fatigue which is one indicator of frailty. METHODS: This cross-sectional multicentre survey study was conducted in five hospitals in the same city from 01 January 2021 to 01 December 2021. We collected information on gender, age, diseases, medication and dietary status. Frailty status was diagnosed using the FRAIL scale, and Nutritional Risk Screening-2002 was used to screen the nutritional risk. Spearman rank correlation was used to analyse the correlation between frailty and nutritional risk. Univariate and multivariate logistic regression analyses were used to analyse the risk factors related to fatigue in all patients and inpatients. RESULTS: Among 2016 older adult patients, the prevalence of frailty was 15.1% (305/2016), the prevalence of nutritional risk was 16.2% (327/2016) and the overlap prevalence of frailty and nutritional risk was 7.3% (147/2016). Multivariate analysis showed that nutritional risk (OR 3.109, 95% CI 2.384 to 4.056, p<0.001) was an independent risk factor for fatigue in all patients; similar results were found for nutritional risk (OR 2.717, 95% CI 2.068 to 3.571, p<0.001) in hospitalised patients. CONCLUSIONS: Frailty and nutritional risk are prevalent among older adult patients, and nutritional risk is associated with the occurrence of fatigue in older adult patients and older adult inpatients. TRIAL REGISTRATION NUMBER: China Clinical Trial Registry (Registered No. ChiCTR-EPC-14005253).