NOTCH signaling in liver sinusoidal endothelial cells (LSECs) regulates liver fibrosis, a pathological feature of chronic liver diseases. POFUT1 is an essential regulator of NOTCH signaling. Here, we investigated the role of LSECs-expressed POFUT1 in liver fibrosis. Endothelial-specific Pofut1 knockout mice were generated and subjected to experimental liver fibrosis by chronic carbon tetrachloride exposure or by common bile duct ligation. Liver samples were assessed by ELISA, histology, electron microscopy, immunostaining and RNA in situ hybridization. LSECs and hepatic stellate cells (HSCs) were isolated for gene expression analysis by RNA-seq, qPCR, and Western blotting. Signaling crosstalk between LSECs and HSCs was investigated by treating HSCs with supernatant from LSECs cultures. Liver single-cell RNA-seq data sets from cirrhotic patients and healthy individuals were analyzed to evaluate the clinical relevance of gene expression changes observed in mouse studies. POFUT1 loss promoted injury-induced LSECs capillarization and HSC activation, leading to aggravated liver fibrosis. RNA-seq analysis revealed that POFUT1 deficiency upregulated fibrinogen expression in LSECs. Consistently, fibrinogen was elevated in LSECs of cirrhotic patients. HSCs treated with supernatant from LSECs of Pofut1 null mice showed exacerbated activation compared to treatment with supernatant from control LSECs, and this effect was attenuated by knockdown of fibrinogen or by pharmacological inhibition of fibrinogen receptor signaling, altogether suggesting that LSEC-derived fibrinogen induced the activation of HSCs. Mechanistically, POFUT1 loss augmented fibrinogen expression by enhancing NOTCH/HES1/STAT3 signaling. Endothelial POFUT1 prevents injury-induced liver fibrosis by repressing the expression of fibrinogen which function as a profibrotic paracrine signal to activate HSCs. Therapies targeting the POFUT1/NOTCH/HES1/STAT3/fibrinogen axis offer a promising strategy for the prevention and treatment of fibrotic liver diseases.

Background: Obstructive sleep apnea (OSA) is a globally prevalent disease with a complex diagnostic method. Severe OSA is associated with multi-system dysfunction. We aimed to develop an interpretable machine learning (ML) model for predicting the risk of severe OSA and analyzing the risk factors based on clinical characteristics and questionnaires. Methods: This was a retrospective study comprising 1656 subjects who presented and underwent polysomnography (PSG) between 2018 and 2021. A total of 23 variables were included, and after univariate analysis, 15 variables were selected for further preprocessing. Six types of classification models were used to evaluate the ability to predict severe OSA, namely logistic regression (LR), gradient boosting machine (GBM), extreme gradient boosting (XGBoost), adaptive boosting (AdaBoost), bootstrapped aggregating (Bagging), and multilayer perceptron (MLP). All models used the area under the receiver operating characteristic curve (AUC) was calculated as the performance metric. We also drew SHapley Additive exPlanations (SHAP) plots to interpret predictive results and to analyze the relative importance of risk factors. An online calculator was developed to estimate the risk of severe OSA in individuals. Results: Among the enrolled subjects, 61.47% (1018/1656) were diagnosed with severe OSA. Multivariate LR analysis showed that 10 of 23 variables were independent risk factors for severe OSA. The GBM model showed the best performance (AUC = 0.857, accuracy = 0.766, sensitivity = 0.798, specificity = 0.734). An online calculator was developed to estimate the risk of severe OSA based on the GBM model. Finally, waist circumference, neck circumference, the Epworth Sleepiness Scale, age, and the Berlin questionnaire were revealed by the SHAP plot as the top five critical variables contributing to the diagnosis of severe OSA. Additionally, two typical cases were analyzed to interpret the contribution of each variable to the outcome prediction in a single patient. Conclusions: We established six risk prediction models for severe OSA using ML algorithms. Among them, the GBM model performed best. The model facilitates individualized assessment and further clinical strategies for patients with suspected severe OSA. This will help to identify patients with severe OSA as early as possible and ensure their timely treatment. Trial registration: Retrospectively registered.

Purpose: To develop a deep learning (DL) model for obstructive sleep apnea (OSA) detection and severity assessment and provide a new approach for convenient, economical, and accurate disease detection. Methods: Considering medical reliability and acquisition simplicity, we used electrocardiogram (ECG) and oxygen saturation (SpO₂) signals to develop a multimodal signal fusion multiscale Transformer model for OSA detection and severity assessment. The proposed model comprises signal preprocessing, feature extraction, cross-modal interaction, and classification modules. A total of 510 patients who underwent polysomnography were included in the hospital dataset. The model was tested on hospital and public datasets. The hospital dataset was utilized to demonstrate the applicability and generalizability of the model. Two public datasets, Apnea-ECG dataset (consisting of 8 recordings) and UCD dataset (consisting of 21 recordings), were used to compare the results with those of previous studies. Results: In the hospital dataset, the accuracy (Acc) values of per-segment and per-recording detection were 91.38 and 96.08%, respectively. The Acc values for mild, moderate, and severe OSA were 90.20, 88.24, and 92.16%, respectively. The Bland‒Altman plots revealed the consistency of the true apnea–hypopnea index (AHI) and the predicted AHI. In the public datasets, the per-segment detection Acc values of the Apnea-ECG and UCD datasets were 95.04 and 90.56%, respectively. Conclusion: The experiments on hospital and public datasets have demonstrated that the proposed model is more advanced, accurate, and applicable in OSA detection and severity assessment than previous models.

Background: Obstructive sleep apnea (OSA) is a globally prevalent disease closely associated with hypertension. To date, no predictive model for OSA-related hypertension has been established. We aimed to use machine learning (ML) to construct a model to analyze risk factors and predict OSA-related hypertension.Materials and methods: We retrospectively collected the clinical data of OSA patients diagnosed by polysomnography from October 2019 to December 2021 and randomly divided them into training and validation sets. A total of 1,493 OSA patients with 27 variables were included. Independent risk factors for the risk of OSA-related hypertension were screened by the multifactorial logistic regression models. Six ML algorithms, including the logistic regression (LR), the gradient boosting machine (GBM), the extreme gradient boosting (XGBoost), adaptive boosting (AdaBoost), bootstrapped aggregating (Bagging), and the multilayer perceptron (MLP), were used to develop the model on the training set. The validation set was used to tune the model hyperparameters to determine the final prediction model. We compared the accuracy and discrimination of the models to identify the best machine learning algorithm for predicting OSA-related hypertension. In addition, a web-based tool was developed to promote its clinical application. We used permutation importance and Shapley additive explanations (SHAP) to determine the importance of the selected features and interpret the ML models.Results: A total of 18 variables were selected for the models. The GBM model achieved the most extraordinary discriminatory ability (area under the receiver operating characteristic curve = 0.873, accuracy = 0.885, sensitivity = 0.713), and on the basis of this model, an online tool was built to help clinicians optimize OSA-related hypertension patient diagnosis. Finally, age, family history of hypertension, minimum arterial oxygen saturation, body mass index, and percentage of time of SaO₂ < 90% were revealed by the SHAP method as the top five critical variables contributing to the diagnosis of OSA-related hypertension.Conclusion: We established a risk prediction model for OSA-related hypertension patients using the ML method and demonstrated that among the six ML models, the gradient boosting machine model performs best. This prediction model could help to identify high-risk OSA-related hypertension patients, provide early and individualized diagnoses and treatment plans, protect patients from the serious consequences of OSA-related hypertension, and minimize the burden on society.

Background: Obstructive sleep apnea (OSA) is mainly characterized by sleep fragmentation and chronic intermittent hypoxia (CIH), the latter one being associated with multiple organ injury. Recently, OSA-induced cognition dysfunction has received extensive attention from scholars. Astrocytes are essential in neurocognitive deficits via A1/A2 phenotypic changes. Nucleotide oligomerization domain (NOD)-like receptor protein 3 (NLRP3) inflammasome is considered the most important factor inducing and maintaining neuroinflammation. However, whether the NLRP3 regulates the A1/A2 transformation of astrocytes in CIH-related brain injury remains unclear. Methods: We constructed an OSA-related CIH animal model and assessed the rats' learning ability in the Morris water maze; the histopathological assessment was performed by HE and Nissl staining. The expression of GFAP (astrocyte marker), C3d (A1-type astrocyte marker), and S100a10 (A2-type astrocyte marker) were detected by immunohistochemistry and immunofluorescence. Western blotting and RT-qPCR were used to evaluate the changes of A1/A2 astrocyte-related protein and NLRP3/Caspase-1/ASC/IL-1β. Results: The learning ability of rats decreased under CIH. Further pathological examination revealed that the neurocyte in the hippocampus were damaged. The cell nuclei were fragmented and dissolved, and Nissl bodies were reduced. Immunohistochemistry showed that astrocytes were activated, and morphology and number of astrocytes changed. Immunofluorescence, Western blotting and RT-qPCR showed that the expression of C3d was increased while S100a10 was decreased. Also, the expression of the inflammasome (NLRP3/Caspase-1/ASC/IL-1β) was increased. After treatment of MCC950 (a small molecule inhibitor of NLRP3), the damage of nerve cells was alleviated, the Nissl bodies increased, the activation of astrocytes was reduced, and the expression of A2-type astrocytes was increased. In contrast, A1-type astrocytes decreased, and the expression of inflammasome NLRP3/Caspase-1/ASC/IL-1β pathway-related proteins decreased. Conclusion: The NLRP3 inflammasome could regulate the A1/A2 transformation of astrocytes in brain injury induced by CIH