BackgroundPatients with advanced age and coronary heart disease (CHD) are at significantly increased risk for postoperative delirium (POD). However, there is no method to predict POD in elderly patients with CHD.MethodsDate from elderly patients with CHD who underwent non-cardiac surgery was collected. The dataset is subdivided into training and validation sets at a ratio of 7:3. Boruta algorithm, least absolute shrinkage and selection operator (LASSO) regression and multiple logistic regression analysis were used to select features. Machine learning method was used to construct a model for predicting the occurrence of POD. Receiver operating characteristic (ROC) curve, decision curve, calibration curve, specificity, sensitivity, accuracy, F1 score and Brier score were used to compare the predictive performance of these machine learning models, and the interpretability of the models was evaluated by Shapley additive interpretation (SHAP).ResultsA total of 861 patients were included in the study. The incidence of POD was 16.6% (143/861). Seven key features were identified. Ten machine learning models were constructed. Among the models, gradient boosting model (GBM) performed better. The area under the ROC curve (AUC) is 0.856 (95% confidence interval [CI]: 0.796-0.916). The decision curve, calibration curve, specificity, sensitivity, accuracy, F1 score and Brier score were also relatively good. SHAP plots of GBM showed that Clinical Frailty Scale (CFS) grade, Mini-mental State Examination (MMSE) score, and Athens Insomnia Scale (AIS) score were significant predictors of POD in elderly CHD patients, and an easy-to-use calculator for predicting the risk of POD was developed based on the GBM model.ConclusionThis study developed a reliable GBM model for predicting the occurrence of POD in elderly patients with CHD. Higher CFS grade, lower MMSE score and higher AIS score significantly enhanced the predictive ability of the model. External validation of our model is needed before it can be applied in a clinical setting.Trial registrationRegistration number of the Chinese Clinical Trial Registry: ChiCTR2500097325, Registration Date: 17/02/2025.

BackgroundPostoperative delirium (POD) is a severe complication in elderly hypertensive patients, associated with poor long-term outcomes. Existing models often rely on intraoperative data, limiting preoperative risk stratification. This study aimed to develop a non-invasive machine learning model to predict POD and investigate its preoperative markers’ impact on three-year mortality.MethodsPreoperative variables were selected using LASSO regression from a cohort of 1,782 patients. Ten machine learning models were trained and validated (7:3 ratio). Model performance was evaluated via AUC-ROC and decision curve analysis (DCA). The optimal model was interpreted using SHAP values. Long-term prognosis within the POD cohort was assessed using Kaplan-Meier curves and multivariable Cox proportional hazards regression.ResultsThe POD incidence was 10.9%. The Gradient Boosting Machine (GBM) demonstrated optimal performance (AUC = 0.868, 95% CI: 0.819–0.917). SHAP analysis identified MMSE score as the most influential predictor, followed by HADS score, age, CFS, frailty, and PSQI score. Multivariable Cox analysis revealed that lower MMSE, alongside elevated HADS, CFS, frailty, and PSQI scores—but not chronological age—were independent predictors of increased three-year mortality in POD patients (all P < 0.05).ConclusionWe developed a robust machine learning tool for individualized POD prediction. Cognitive impairment, psychological distress, frailty, and poor sleep quality serve as critical dual-prognostic markers for both acute POD occurrence and long-term survival. These findings underscore the necessity of routine multidimensional preoperative assessment to facilitate personalized interventions for vulnerable hypertensive populations.

Major Innate Disordered Notch2-Associated Receptor 1 (MINAR1) is known to suppress angiogenesis and breast cancer cell growth and is associated with neurological disorders such as epilepsy. However, its neurobiological function remains unclear. Herein, we reveal the specific expression of MINAR1 in somatostatin (SST)- and parvalbumin (PV)-positive interneurons in the mouse forebrain. To explore its functional significance, MINAR1 conditional knockout (CKO) mice were generated from Nestin-Cre mice. During postnatal growth, gross brain morphology and cytoarchitecture were comparable between MINAR1 CKO mice and littermate controls; adult CKO mice exhibited increased vulnerability to pentylenetetrazole (PTZ)-induced seizures, and this phenotype was also present in SST-Cre-mediated CKO mice. Mechanistically, MINAR1 deficiency selectively impaired SST+ (but not PV+) interneuron excitability, reducing the inhibitory drive toward pyramidal neurons. This defect correlated with decreased G protein alpha S (Gαs) levels and disrupted Gαs-cAMP signaling. Notably, pharmacological activation of adenylate cyclase with forskolin rescued this inhibitory defect. Collectively, our results establish MINAR1 as a key regulator of seizure susceptibility, likely via Gαs-cAMP-dependent modulation of SST+ interneurons, offering a molecular framework for developing targeted epilepsy therapies.

IntroductionPostoperative delirium (POD) is a commonly occurring condition in the postoperative period. Therefore, the study intends to investigate the relationship between B2M and POD and the effect of B2M levels on three-year postoperative mortality in patients with POD.MethodsPostoperatively, the Confusion Assessment Method (CAM) and the Monumental Delirium Assessment Scale (MDAS) were used to assess the incidence and severity of POD. Preoperative plasma B2M levels were measured utilizing a latex-enhanced immunoturbidimetric assay. Total tau protein (T-tau), phosphorylated tau protein (P-tau), and amyloid β plaque 42 (Aβ42) were detected in preoperative cerebrospinal fluid (CSF) by enzyme-linked immunosorbent assay. Logistic regression equations were applied to examine the risk factors linked to POD. Patients presenting with POD were grouped according to B2M level and followed up for 3 years postoperatively for their survival and Kaplan–Meier survival curves were plotted.ResultsThe prevalence of POD was 7.23%. Serum B2M levels were higher in POD patients compared to non-POD (NPOD) patients (p = 0.01). The results of the logistic regression analysis indicated that B2M (OR = 1.394, 95% CI = 1.017–1.910, p = 0.002) and T-tau (OR = 1.006, 95% CI = 1.002–1.011, p = 0.007) posed a risk for POD. B2M and POD were partially associated through the mediation of CSF T-tau (10.0%). The K-M survival curves showed that patients with high B2M who developed POD had a higher mortality rate 3 years after surgery (p = 0.031).ConclusionIn summary, B2M may be a risk factor for POD, which might be mediated in part by CSF T-tau.

Diabetes-Associated Cognitive Impairment (DACI) is a significant neurological complication of Type 2 Diabetes Mellitus (T2DM). This study investigates the role of the hippocampal adiponectin (APN) system in DACI and the therapeutic potential of AdipoRon, an oral APN receptor agonist. Using a high-fat diet/streptozotocin-induced T2DM mouse model, we found that cognitive deficits were associated with a significant downregulation of hippocampal adiponectin receptor 2 (AdipoR2), which was predominantly localized to neurons. Oral AdipoRon administration (50 and 100 mg/kg/day for 2 weeks) reversed these cognitive impairments and improved metabolic parameters. Mechanistically, these benefits were linked to the upregulation of hippocampal AdipoR2, restoration of postsynaptic proteins (PSD95, GluA1), and attenuation of neuroinflammation, as evidenced by reduced microglial and astrocyte activation. Furthermore, AdipoRon activated the hippocampal PPARα/CREB signaling pathway. In vitro experiments using high-glucose-treated HT22 hippocampal neurons confirmed that AdipoRon’s neuroprotective effects, including improved CREB phosphorylation and reduced oxidative stress, were mediated via a PPARα-dependent mechanism. In conclusion, our findings highlight hippocampal AdipoR2 dysregulation as a key factor in DACI and establish AdipoRon as a promising therapeutic agent that acts through the AdipoR2/PPARα/CREB pathway. This positions AdipoRon as a candidate for further investigation in the prevention and treatment of DACI.

The hippocampal dorsal CA2 subregion (dCA2) is critical for social memory; however, its contribution to other types of hippocampus-dependent memories is not well understood. Here, we performed dCA2-specific circuit tracing, single-neuron projectome analysis, photometric Ca2+ imaging, and optogenetic manipulations to study the physiological roles of dCA2 neurons and their axon projections in behavioral paradigms for novel object recognition, novel location recognition, and contextual fear memory. We found that dCA2 neurons sent their strongest axon projections to the dorsal portion of ventral CA1 (vCA1d) and showed object and location-specific Ca2+ responses. Notably, the dCA2-vCA1d projection contributed to the memory formation of object location but not identity. Furthermore, optogenetic inhibition of the dCA2-vCA1d axon terminals reduced fear responses to foot shocks and impaired contextual memory formation. Collectively, our study reveals critical roles of the dCA2-vCA1d circuit in spatial and context-dependent memories, providing new insights into the function of CA2 neurons.

Objective:Postoperative delirium (POD) is a prevalent neurological complication linked to adverse clinical outcomes. The underlying mechanisms of POD remain unclear. This study aimed to investigate the association between POD and frailty and determine whether frailty influences POD incidence. Furthermore, machine learning algorithms were utilized to identify key predictors of POD in patients undergoing hip or knee replacement. Methods:A total of 625 Han Chinese patients were recruited between September 2021 and May 2023. Preoperative frailty was assessed using the Frailty Scale and Frailty Phenotype criteria. The Mini-Mental State Examination (MMSE) evaluated preoperative cognitive function, while the Confusion Assessment Method (CAM) diagnosed POD. The severity of POD was additionally quantified using the Memorial Delirium Assessment Scale (MDAS). Receiver Operating Characteristic (ROC) curve analysis explored the association between preoperative frailty and POD, and the mediating effect of cerebrospinal fluid (CSF) biomarkers was analyzed. Ten machine learning algorithms-including Logistic Regression (LR), Support Vector Machine (SVM), Gradient Boosting Machine (GBM), Artificial Neural Network (ANN), Random Forest (RF), XGBoost, K-Nearest Neighbors (KNN), AdaBoost, LightGBM, and CatBoost-were implemented to develop predictive models. The dataset was randomly split into training (70%) and testing (30%) subsets. Ten-fold cross-validation was incorporated during model training and validation to mitigate overfitting and enhance generalizability. Model performance was evaluated using multiple metrics, such as accuracy, sensitivity, specificity, precision, Brier score, area under the ROC curve (AUC), and F1 score. Furthermore, graphical analyses-including calibration curves, decision diagrams, clinical impact curves, and confusion matrices-were applied to assess model robustness and clinical utility. Finally, SHAP (Shapley Additive Explanations) analysis elucidated the model's decision-making process, emphasizing the pivotal role of preoperative frailty in POD prediction. Results:The incidence of POD was 14.7%. The study identified frailty, Tau, and P-tau as significant risk factors for POD (OR = 67.229, 95% CI: 34.649-130.444, p < 0.001; OR = 1.020, 95% CI: 1.016-1.024, p < 0.001; OR = 1.018, 95% CI: 1.010-1.027, p < 0.001). ROC curve analysis (AUC = 0.983) demonstrated that combining frailty with CSF biomarkers had strong predictive power for distinguishing POD. The direct effect of frailty on POD was 0.504878, the total effect was 0.6547619, and the mediating effect of Tau accounted for 22.89%. Using Lasso regression for variable selection, we subsequently identified eight predictors-frailty, Tau, Aβ42/Tau, Aβ40, age, Aβ42, P-tau, and drinking history-from the training set via logistic regression. Based on these factors, we constructed 10 machine learning models. Among all machine learning algorithms, GBM performed the best, achieving an AUC of 0.973 (95% CI, 0.973-1.000) in the test set. Furthermore, SHAP analysis confirmed that frailty and Tau were the key determinants influencing the machine learning model's predictions. Conclusion:Preoperative frailty is an independent risk factor for POD. A machine learning model for predicting POD in patients undergoing hip or knee replacement was developed, with GBM demonstrating superior performance among all models. The GBM-based model enabled early identification of patients at high risk of delirium.

Attention-deficit hyperactivity disorder (ADHD) is a prevalent psychiatric disorder with high heritability, while its etiology and pathophysiology remain unclear. Med23 is a subunit of the Mediator complex, a key regulator of gene expression by linking transcription factors to RNA polymerase II. The mutations of Med23 are associated with several brain diseases including microcephaly, epilepsy and intellectual disability, but its biological roles in brain development and possible behavioral consequence have not been explored in the animal model. In this study, Emx1-Cre mice were used to generate Med23 conditional knockout (Med23 CKO) mice that showed severe hypoplasia of the dentate gyrus (DG) with malformation of the dendritic tree and spines along with impaired short-term synaptic plasticity. Interestingly, Med23 CKO mice exhibited ADHD-like behaviors as shown by hyperactivity, inattention and impulsivity, as well as impaired sensory gating and working memory. Importantly, methylphenidate (MPH), a common drug for ADHD ameliorated these deficits in the CKO mice. Furthermore, we also revealed that the impaired synaptic plasticity was partially restored by MPH in an N-methyl-d-aspartate (NMDA) receptor-dependent way. Collectively, our data demonstrate Med23 deficiency causes DG malformation and ADHD-like behaviors, suggesting a novel mechanism underlying relevant brain diseases.

Acute ischemic stroke (AIS) is a significant brain disease with a high mortality and disability rate. Additional therapies for AIS are urgently needed, and neuroplasticity mechanisms by agents are expected to be neuroprotective for AIS. As a major active component of Salvia miltiorrhiza, salvianolic acid A (SAA) has shown potential for preventing cardiovascular diseases. However, there is no evidence of the long-term effect of SAA on ischemic injury or its mechanism. Therefore, using rats and mice, we systematically investigated the impact of SAA on AIS from the perspective of neuroprotective and neuroplasticity. Here, we report that SAA induces a long-term depression (LTD)-like process in synapses. This antiexcitotoxicity action supports the SAA effect, including alleviating infarction and promoting blood circulation in photothrombosis and middle cerebral artery occlusion (MCAO) models. Furthermore, repeated positron emission tomography/computed tomography (PET/CT) imaging and behavioral assessments two months after AIS induction reveal that acute treatment of SAA promotes recovery from disrupted whole-brain glucose metabolism and impaired spatial memory. These data suggest that acute treatment of SAA is neuroprotective by improving long-term functional outcomes through a synaptic LTD-like process, providing a promising adjunct to current therapies to enable better recovery for AIS.

Social memory can undergo rapid forgetting at first according to the Ebbinghaus forgetting curve, for which the underlying mechanism remains entirely unknown. Here, we reported that rapid forgetting of social memory did not occur as indicated by social preference on stranger 2 (S2) over stranger 1 (S1) mouse, tested shortly after social interaction with S1. However, rapid forgetting of both social and object memories occurred as indicated by no social or object preference, respectively, when the constitutive active (CA) variant of Rac1 was knocked-in parvalbumin (PV) but not somatostatin (SST) neurons of the brain. Furthermore, rapid forgetting of only social memory occurred if this CA variant was knocked-in PV but not SST neurons of the medial prefrontal cortex (mPFC). By contrast, rapid forgetting of social memory was prevented by the dominant negative (DN) variant of Rac1 knocked-in PV neurons of the mPFC. Moreover, fiber photometry revealed that PV but not SST neurons of the mPFC generated dual calcium peaks to delineate each social interaction event. Thus, PV-specific Rac1 activity of the mPFC is both necessary and sufficient for controlling social behavior via rapid forgetting of social memory, providing a novel understanding of social behaviors under health and disease conditions.

INTRODUCTION:Postoperative delirium (POD) is a severe and common complication. This study aimed to investigate the association of cardiometabolic multimorbidity (CMM) and their different subgroups with POD. METHODS:This prospective cohort study ultimately included 875 patient samples from the Perioperative Neurocognitive Disorder and Lifestyle Biomarkers (PNDABLE) database, collected between July 2020 and September 2021. In this study, patients were first categorized into a POD group and a non-POD group, and the demographic characteristics of the two groups were compared. Next, logistic regression models were used to analyze the association between CMM and POD, as well as between cerebrospinal fluid (CSF) biomarkers and POD. Additionally, the models examined the relationship between different CMM subtypes and the incidence of POD. Subsequently, the robustness of the results was verified by sensitivity analysis and post hoc analysis. Further, the role of CSF biomarkers in the relationship between CMM and POD was assessed using mediation analysis. Finally, CMM patients with POD were followed up for three years, and Kaplan-Meier (K-M) survival analysis was used to compare the mortality rates of different CMM subgroups in patients with POD. RESULTS:Logistic regression analysis showed that CMM [odds ratio: 5.062; 95% CI: 3.279-7.661; P < 0.001], T-tau, and P-tau were risk factors for POD, while Aβ42 was a protective factor. Associations between different CMM subgroups and POD varied. Sensitivity and post hoc analyses supported these findings. Mediation analysis indicated that CMM could increase the incidence of POD through the CSF T-tau (proportion: 11%, P < 0.050). A follow-up of 50 patients showed that K-M survival analysis revealed that the POD patients in the diabetes combined with coronary heart disease group had a significantly higher three-year mortality compared to other CMM subgroups ( P = 0.004). CONCLUSIONS:CMM may be a risk factor for POD, with CSF T-tau potentially playing a mediating role. These findings underscore the importance of preoperative cognitive assessment for risk stratification and suggest CSF T-tau as a potential intervention target. Future studies may further explore intervention strategies targeting CMM and CSF T-tau.

OBJECTIVE:To investigate the association between the preoperative serum aspartate aminotransferase to alanine aminotransferase (AST/ALT) ratio and postoperative delirium (POD) and 3-year mortality in POD patients. METHODS:A total of 538 clinical participants were enrolled from the Perioperative Neurocognitive Disorder and Biomarkers Lifestyle (PNDABLE) study. In this study, patients were first categorized into a POD group and a non-POD group, and the demographic characteristics of the two groups were compared. Preoperative serum AST and ALT levels were measured to calculate the AST/ALT ratio. Cerebrospinal fluid (CSF) Alzheimer's disease (AD)-related biomarkers were analyzed. Logistic regression and sensitivity analyses were performed to identify protective and risk factors for POD. Mediation effect models were applied to evaluate the potential mediating roles of CSF biomarkers. Receiver operating characteristic curves and decision curve analysis were used to validate predictive performance. Restricted cubic spline (RCS) regression was employed to explore the dose-response relationship between AST/ALT levels and POD risk. Finally, patients with POD were followed up for 3 years, and Kaplan-Meier (K-M) survival analysis was used to compare the mortality rates of the AST/ALT ratio in patients with POD. RESULTS:The incidence of POD was 17.53%. Logistic regression revealed that an elevated preoperative AST/ALT ratio was an independent risk factor for POD [odds ratio (OR) = 4.305, 95% confidence interval (CI) 2.404-7.706, P < 0.001]. Reduced CSF amyloid-beta 42 (Aβ42) levels (OR = 0.992, 95% CI 0.990-0.994, P < 0.001) were inversely associated with POD risk, whereas elevated total tau (OR = 1.016, 95% CI 1.012-1.019, P < 0.001) and phosphorylated tau (P-tau) (OR = 1.097, 95% CI 1.069-1.127, P < 0.001) were identified as risk factors. Sensitivity and post hoc analyses supported these findings. Mediation analysis demonstrated that the effect of AST/ALT on POD was partially mediated by CSF Aβ42 (12.9%) and the Aβ42/P-tau ratio (14.8%). The predictive model combining AST/ALT with CSF biomarkers achieved optimal performance (area under the curve = 0.92). A follow-up of 88 patients showed that K-M survival analysis revealed that although mortality rates were higher in patients with POD, there were no significant differences in 3-year survival rates between the high AST/ALT ratio group and the low AST/ALT ratio group. CONCLUSION:Preoperative elevation of the serum AST/ALT ratio is a potential risk factor for POD, and its effect may be partially mediated by AD-related CSF biomarkers. However, an increase in the AST/ALT ratio did not have a significant effect on the three-year survival rate of POD patients.

AIMS:Ferroptosis plays a critical role in stroke pathophysiology, yet its dynamics during recovery remain unclear. This study aimed to investigate the evolution of ferroptosis throughout post-stroke recovery and evaluate auricular transcutaneous vagus nerve stimulation (atVNS) as a therapeutic intervention, focusing on the involvement of α7 nicotinic acetylcholine receptor (α7nAChR)-mediated mechanisms. METHODS:Using a middle cerebral artery occlusion (MCAO) mouse model, we examined ferroptosis-related protein expression (GPX4, ACSL4, TfR) and iron levels across acute to chronic recovery phases. The therapeutic effects of atVNS were evaluated through the assessment of ferroptosis markers, neurogenesis, angiogenesis, cognitive function, and neuroinflammation. α7nAChR knockout mice were used to investigate the receptor's role in atVNS-mediated recovery. RESULTS:We observed sustained alterations in ferroptosis markers and iron levels throughout post-stroke recovery. atVNS treatment reduced ferroptosis progression by modulating GPX4 and ACSL4 expression, enhanced neurogenesis and angiogenesis, improved cognitive recovery, and reduced neuroinflammation. These beneficial effects were absent in α7nAChR knockout mice, while atVNS increased neuronal α7nAChR expression in wild-type mice. CONCLUSIONS:This study reveals the persistent involvement of ferroptosis in stroke recovery and demonstrates that atVNS provides comprehensive neuroprotection through α7nAChR-dependent mechanisms. These findings establish atVNS as a promising noninvasive therapeutic approach for stroke recovery and highlight α7nAChR signaling as a potential therapeutic target.

Background:Postoperative delirium (POD) is one of the common central nervous system complications in elderly patients after non-cardiac surgery. Therefore, it is necessary to develop and validate a preoperative model for POD risk prediction. Methods:This study selected 663 elderly patients undergoing non-cardiac elective surgery under general anesthesia for tracheal intubation in general surgery, from September 1st, 2020 to June 1st, 2022. Simple random sampling method was used according to 7: 3. The occurrence of POD within 1 to 7 days after the operation (or before discharge) was followed up by the confusion assessment method (CAM). This study innovatively included the pittsburgh sleep quality index (PSQI) and the numerical pain score (NRS) for clinical work, to explore the relationship between sleep quality and postoperative pain and POD. Univariate and Multivariable Logistic regression analysis was used to analyze stepwise regression to screen independent risk factors for POD. The creation of prediction models involved the integration of outcomes through the implementation of logistic regression analysis. In addition, internal validation is employed to ensure the reproducibility of the model. Results:A total of 663 elderly patients were enrolled in this study, and 131 (19.76%) patients developed POD. The incidence of POD in each department was not statistically significant. The predictors in the POD column line graph included age, Mini Mental State Examination (MMSE) score, history of diabetes, years of education, sleep quality index, ASA classification, duration of anesthesia and NRS score. The formula Z= 8.293 + 0.102 × age - 1.214 × MMSE + 1.285 × diabetesHistory - 0.304 × yearsOfEducation + 0.602 × PSQI + 1.893 × ASA + 0.027 × anesthesiaTime + 1.297 × NRS. Conducive to the validation group to evaluate the prediction model, the validation group AUC is 0.939 (95% CI 0.894-0.969), the sensitivity is 94.44%, and the specificity is 85.09%. The calibration curves show a good fit between the clinically predicted situation and the actual situation. Conclusion:The clinical prediction model constructed based on these independent risk factors has a good predictive performance, which can provide reference for the early screening and prevention of POD in clinical work. Trial registration:ChiCTR2000033639 Retrospectively registered (date of registration: 06/07/2020).

Significant efforts have harvested a sophisticated understanding of Alzheimer's disease (AD) including amyloid beta (Aβ) cascade mechanisms, although effective treatment for reversing or stopping AD progression is not available. This study reports that ferul enanthate (SL), a novel derivative of active agents targeting brain microvessels, oxidative phosphorylation, and ATP generation can reverse the hippocampus‐dependent spatial memory defects and reduce Aβ plaques in AD model mice (APP/PS1) at advanced stages. Spatial transcriptomics discovers that SL endows a cluster of genes expressing in Aging‐AD‐Rescue (AAR) pattern, which is prominent in hippocampal dendritic region where Aβ plaques are densely deposited. Furthermore, this AAR rule covers hippocampal Glut1 (glucose transporter 1) expression and ATP generation, which are further confirmed by immunoblotting or immunofluorescence studies. Our data demonstrate that SL can still reverse memory defects at advanced stages of AD mice by modifying aging‐dependent multiple pathologies of AD, particularly promoting Glut1 expression and ATP generation.

Depression is a common psychiatric comorbidity in individuals with end-stage renal disease (ESRD). However, the underlying biological mechanisms and the precise relationship between depression and renal failure remain unclear. While interventions such as cognitive behavioral therapy and exercise have been shown to alleviate symptoms, the interplay between these conditions and their molecular pathways is poorly understood. An integrated analysis was conducted combining bioinformatics approaches and data from the UK Biobank (UKB) cohort. The UKB study revealed a significant association between renal failure and depression. Gene expression data from the Gene Expression Omnibus (GEO) database were analyzed to identify key co-expression modules using Weighted Gene Co-expression Network Analysis (WGCNA). Protein-protein interaction (PPI) networks were constructed using the STRING database, and immune cell infiltration was assessed with the CIBERSORT tool. UKB data confirmed a robust association between renal failure and depression. Bioinformatics analyses highlighted significant enrichment in pathways related to the acute inflammatory response, specific granule lumen, and immune receptor activity. PPI network analysis identified 23 hub genes, including CYP4F2, KCNA3, KISS1R, LILRA5, and ZC3H12D, as key players in the shared pathophysiology of ESRD and depression. Validation studies further emphasized the roles of LILRA5, CYP4F2, and KISS1R in these mechanisms. This study reveals novel insights into the molecular and immune interactions underlying the comorbidity of renal failure and depression. By combining cohort and bioinformatics analyses, we identify potential therapeutic targets and pathways that may inform innovative treatment strategies.

BACKGROUND:As a common postoperative neurological complication, postoperative delirium (POD) can lead to poor postoperative recovery in patients, prolonged hospitalization, and even increased mortality. However, POD's mechanism remains undefined and there are no reliable molecular markers of POD to date. The present work examined the associations of cerebrospinal fluid (CSF) soluble triggering receptor expressed on myeloid cells 2 (sTREM2) with CSF POD biomarkers, and investigated whether the effects of CSF sTREM2 on POD were modulated by the core pathological indexes of POD (Aβ 42 , tau, and ptau). The association of presurgical CSF sTREM2 with 3-year mortality in POD cases administered total knee or hip arthroplasty was assessed. METHODS:We enrolled 545 Chinese Han patients undergoing total knee or hip arthroplasty (aged 50-95 years, weighing 50-80 kg, and using ASA II-III) combined with epidural anesthesia between October 2020 and March 2022. In these participants, POD was identified using the Confusion Assessment Method (CAM) and the severity of POD was evaluated using the Memorial Delirium Assessment Scale (MDAS) at 1-7 days postoperatively (or before discharge) by an anesthesiologist. The levels of CSF POD biomarkers were measured by ELISA. Next, logistic regression models were used to analyze the association between sTREM2and POD, as well as between cerebrospinal fluid (CSF) biomarkers and POD. We used Stata MP16.0. to examine whether the association between sTREM2 and POD was mediated by CSF POD biomarkers. We also used potential predictive factors to built 5 models, including Logistic Regression (LR), Support Vector Machine (SVM), K Nearest Neighbours (KNN), AdaBoost and CatBoost to assess the predictive abilities of sTREM2. After that, we verified the performance of the 5 models in the set, plotting receiver operating characteristic (ROC) curve analysis and precision recall curve (PRC) were used to further evaluate whether the machine learning (ML) models were effective in supporting clinical decision-making. All POD patients were followed up for 3 years, and Kaplan-Meier (K-M) survival analysis was used to compare the 3-year mortality rates of high sTREM2 group and low sTREM2 group in patients with POD. RESULTS:Finally, a total of 545 patients (122patients in POD group and 423in NPOD group) were included in our study. sTREM2 and CSF levels of tau and ptau in the POD group were higher than those in the NPOD group. CSF Aβ 42 , Aβ 42 / tau, and Aβ 42 / ptau in the POD group were lower than those in the NPOD group. CSF sTREM2 was negatively associated with Aβ 42 ( r = -0.445, P < 0.001), Aβ 42 / tau ( r = -0.350, P < 0.001) and Aβ 42 / ptau ( r = -0.429, P < 0.001), CSF sTREM2 was positively associated with tau ( r = 0.179, P = 0.048) and ptau ( r = 0.311, P < 0.001). The relationship between sTREM2 and POD was partially mediated by tau and ptau, with the mediation proportion of 17.91% and 22.09%, respectively. The following five variables (sTREM2, age, tau, ptau, and Aβ42/ptau) were significant predictive factors via Lasso regression. Meanwhile, univariable analysis demonstrated CSF Aβ 42 /ptau levels was the protective factor of POD and sTREM2, age, tau, ptau were the risk factors of POD. Upon adjusting for possible confounders, including education level, sex, MMSE score, as well as history of diabetes, smoking, drinking, and hypertension, multivariable analysis showed consistent results. Following two rounds of sensitivity analysis, our results remained robust.The ROC(AUC = 0.999, 95% CI:0.999-1.000) and PRC(AUC = 0.998, 95% CI: 0.995-1.000) for CatBoost were significantly better than the other models. The dynamic online calculator can accurately predict the occurrence of POD by selecting POD patients for the internal validation study. The Kaplan-Meier curve showed no significant difference in survival probability between the low sTREM2 group and high sTREM2 group (log-rank P = 0.53), but age subgroup analysis revealed significantly between age≥80 plus sTREM2 ≥ 20 000 pg/ml subgroup and the other subgroups on mortality in patients with POD (log-rank P = 0.017). CONCLUSION:Elevated CSF sTREM2 is a preoperative risk factor for POD, which is partially mediated by tau and ptau. The CatBoost model can accurately predict the occurrence of POD. Age≥80 plus sTREM2 ≥ 20 000 pg/ml could increase 3-year mortality in POD cases.

The relationship between sleep quality and Alzheimer's disease (AD), and its interaction with genetic susceptibility, remains unclear. Our study explores the complex association between sleep quality and AD risk, focusing on the moderating role of the APOE ε4 allele. Linear regression models, linear mixed-effects models, and Cox proportional hazard models were conducted in 321,905 non-demented participants from UK Biobank (UKB, mean age = 56.49, mean follow-up: 12.3 years) and 1,598 non-demented participants (mean age = 73.19, mean follow-up: 3.90 years) from Alzheimer's Disease Neuroimaging Initiative (ADNI). The interaction terms of sleep by APOE ε4 status were added in all analyses and stratified analyses were further performed. Proteomic and bioinformatic analyses were conducted to explore the biological mechanisms by which sleep and its interaction with APOE ε4 influence the development of AD. Poor sleep quality was significantly associated with worse cognition, faster hippocampal atrophy, and increased AD risk (HR = 1.05 in UKB and HR = 1.37 in ADNI). Notably, these associations were intensified in APOE ε4 carriers. Proteomic analyses identified eleven proteins linked to both poor sleep and AD risk (P < 1.72 × 10–5). These proteins were enriched mainly in inflammatory and metabolic pathways. Growth differentiation factor 15 was identified as the bridge linking poor sleep and AD risk specifically in APOE ε4 carriers. Poor sleep is associated with increased risk of AD, possibly by dysregulating peripheral inflammatory responses and metabolic pathways. The interaction between poor sleep and APOE ε4 may further enhance AD risk. Future studies are warranted to test whether this interaction was driven by neuroinflammation.

With the aging demographic on the rise, we’re seeing a spike in the occurrence of postoperative delirium (POD). Our research aims to delve into the connection between plasma bilirubin levels and postoperative delirium, with the goal of crafting ten machine learning (ML) models capable of predicting POD instances. This study enrolled 621 elderly patients after knee/hip surgery. We used the Confusion Assessment Method (CAM) to assess whether participants had POD. Univariate binary logistic regression analysis and restricted cubic spline (RCS) analysis were used to evaluate the association between plasma total bilirubin and POD. This study further investigated whether cerebrospinal fluid plays some role in the relationship between bilirubin and POD using mediated causal analysis. Subsequently, we employed ten machine learning algorithms to train and develop the predictive models: Logistic Regression (LR), Support Vector Machine (SVM), Gradient Boosting Model (GBM), Neural Network (NN), Random Forest (RF), Xgboost, K-Nearest Neighbors (KNN), AdaBoost, LightGBM, and CatBoost. The performance of the models was evaluated by the area under the receiver operating characteristic curve (AUROC), Brier score, accuracy, sensitivity, specificity, precision, F1 score, calibration curve, decision curve, clinical impact curve, and confusion matrix. In addition, the model was interpreted through Shapley additive interpretation (SHAP) analysis to clarify the importance of bilirubin in the model and its decision-making basis. Univariate binary logistic regression analysis revealed that plasma total bilirubin was associated with POD. Furthermore, the RCS analysis illustrated there was no nonlinear relationship between total bilirubin and POD. Mediation analysis indicted that T-tau mediated the effect of total bilirubin on POD. Total bilirubin and other features(age, educational level, BMI, history of diabetes, ASA, albumin, Aβ42, T-tau and P-tau) were used to construct ML models. Compared with other ML algorithms, NN showed better performance, with an AUC of 0.973 (95