Objective: To characterize perioperative dynamic changes in immune-cell phenotypes and inflammatory cytokines in patients undergoing CPB (cardiopulmonary bypass) cardiac surgery, and to explore their associations with postoperative outcomes. Methods: In this prospective cohort study, 120 adult patients who underwent elective cardiac surgery under CPB at West China Hospital from May 2022 to March 2023 were enrolled. Perioperative immune-cell phenotypes and concentrations of 40 inflammation-related cytokines were measured. The primary outcomes were the sequential organ failure assessment (SOFA) score at 24 h after surgery and ΔSOFA (the peak SOFA score within 48 h after surgery minus the preoperative SOFA score). Secondary outcomes included major adverse cardiovascular events (MACE), acute kidney injury (AKI), respiratory failure, severe liver injury, and infection. Results: The mean age of enrolled patients was 57±10 years. Of these, 52% (62/120) were male and 90% (108/120) underwent valve surgery. During the rewarming to the end of CPB, neutrophil counts rapidly increased (7.39×10 /L vs preoperative 3.07×10 /L, P<0.001), with significant upregulation of CD11b (7.30×10 /L vs preoperative 3.05×10 /L, P<0.001) and CD54 (7.15×10 /L vs preoperative 2.99×10 /L, P<0.001). Lymphocyte counts increased at the end of CPB (1.75×10 /L vs preoperative 1.12×10 /L, P<0.001) but decreased significantly at 24 h after surgery (0.59×10 /L vs preoperative 1.12×10 /L, P<0.001). Plasma analysis showed that multiple pro-inflammatory cytokines increased during CPB and remained elevated up to 24 h after surgery; five chemokines and the anti-inflammatory cytokine IL-10 peaked at the end of CPB. The SOFA score increased from 1 (1, 2) preoperatively to 7 (5, 10) at 24 h after surgery, with a ΔSOFA of 6 (4, 8). Within 30 days after surgery, 48 patients (40.0%) developed AKI, 17 (14.2%) developed infection, 4 (3.3%) developed severe liver injury, 3 (2.5%) developed respiratory failure, and 3 (2.5%) experienced MACE. During the 2-year follow-up, 8 patients (6.7%) experienced MACE and 5 (4.2%) died. Conclusion: Multi-organ dysfunction is common after cardiac surgery under CPB (median ΔSOFA, 6), accompanied by perioperative activation of multiple immune-cell subsets and upregulation of pro-inflammatory, anti-inflammatory, and chemotactic mediators. This study provides data-driven evidence and research clues for further investigation of the associations between CPB-related immune perturbations and postoperative organ dysfunction and clinical outcomes.

[Objective] To explore the relationship between neutrophil activation under cardiopulmonary bypass (CPB) and the incidence of cardiac surgery-associated acute kidney injury (CS-AKI). [Methods] This prospective cohort study enrolled adult patients who scheduled for cardiac surgery under CPB at West China Hospital between May 1, 2022 and March 31, 2023. The primary outcome was acute kidney injury (AKI). Blood samples (5 mL) were obtained from the central vein before surgery, at rewarming, at the end of CPB, and 24 hours after surgery. Neutrophils were labeled with CD11b, CD54 and other markers. To assess the effect of neutrophils activation on AKI, propensity score matching (PSM) was employed to equilibrate covariates between the groups. [Results] A total of 120 patients included into the study, and 17 (14.2%) developed AKI. Both CD11b⁺ and CD54⁺ neutrophils significantly increased during the rewarming phase and the increases were kept until 24 hours after surgery. During rewarming, the numbers of CD11b⁺ neutrophils were significantly higher in AKI compared to non-AKI (4.71×10⁹/L vs 3.31×10⁹/L, Z=-2.14, P<0.05). Similarly, the CD54⁺ neutrophils counts were also significantly higher in AKI than in non-AKI before surgery (2.75×10⁹/L vs 1.79×10⁹/L, Z=-2.99, P<0.05), during rewarming (3.12×10⁹/L vs 1.62×10⁹/L, Z=-4.34, P<0.05), and at the end of CPB (4.28×10⁹/L vs 2.14×10⁹/L, Z=-3.91, P<0.05). An analysis of 32 matched patients (16 in each group) revealed that CD11b⁺ and CD54⁺ neutrophil levels of AKI were 1.74 folds (4.83×10⁹/L vs 2.77×10⁹/L, Z=-2.72, P<0.05) and 2.34 folds (3.32×10⁹/L vs 1.42×10⁹/L, Z=-4.12, P<0.05), respectively, of non-AKI at rewarming phase. [Conclusion] Neutrophils are activated during CPB, and they can be identified by CD11b/CD54 markers. The activated neutrophils of AKI patients are approximately 2 folds of non-AKI during the rewarming phase, with disparity reached peak between groups during rewarming. These findings suggest the removal of 50% of activated neutrophils during the rewarming phase may be effective to reduce the risk of AKI.

Objective:To construct a diagnostic prediction nomogram for antibody-mediated rejection (AMR) after kidney transplantation (KT) based upon peripheral blood gene expression profiling and preliminarily screening potential drugs for AMR.Methods:Seven large kidney transplant cohort datasets related to AMR were retrieved from the database of GEO. Differential expression analysis was utilized for identifying differentially expressed genes between AMR and normal recipients. Multiple machine learning algorithms of random forest (RF), extreme gradient boosting (XGB), support vector machine (SVM) and generalized linear model (GLM) were employed for constructing diagnostic models for AMR after kidney transplantation. Receiver operating characteristic (ROC) curve was plotted for comparing the accuracy of each model. The key genes of optimal model were integrated for creating a diagnostic prediction nomogram for AMR. Calibration curve and decision curve analyses were employed for evaluating the accuracy of nomogram. The differentially expressed genes from biopsy tissues of AMR recipients were uploaded to the database of CMap for identifying potential therapeutic drugs through screening Top 5 compounds with opposite expression patterns to AMR.Results:Seven genes of CXCL10, FCGR1B, GBP5, CD69, LY96, BCL2A1 and EVI2A were over-expressed in both peripheral blood and biopsy tissues of AMR recipients. There were statistically significant differences with recipients without AMR (FDR<0.05). The AMR diagnostic model based upon RF algorithm demonstrated the highest AUC value (0.904) among various machine learning algorithms. Its AUC values were 0.876 and 0.824 in external datasets of GSE50084 and GSE175718. As for the diagnostic prediction nomogram constructed through integrating five key genes of BCL2A1, CXCL10, FCGR1BP, CD69 & EVI2A from RF model, calibration curve indicated that the predicted outcomes of nomogram approximated actual outcomes. Decision curve indicated that net benefit rate of nomogram was higher than that of extreme curves over a wide range of horizontal axis. The predicted results of CMap suggested that Top 5 compounds were raltegravir, rilmenidine, hydrastine, metyrapone and valproic acid.Conclusions:The nomogram constructed based upon peripheral blood gene expression profiling demonstrates high accuracy and generalizability in the diagnosis of AMR. As predicted by CMap, raltegravir, rilmenidine, hydrastine, metyrapone and valproic acid may be potential therapeutic drugs for AMR.

Fibrosis, which is a manifestation of the physiological response to injury characterized by excessive accumulation of extracellular matrix components, is a ubiquitous outcome of the repair process. However, in cases of repetitive or severe injury, fibrosis may become dysregulated, leading to a pathological state and organ failure. In recent years, a novel form of regulated cell death, referred to as ferroptosis, has been identified as a possible contributor to fibrosis; it is characterized by iron-mediated lipid peroxidation. It has garnered attention due to the growing body of evidence linking ferroptosis and fibrogenesis, which is believed to be driven by underlying inflammation and immune responses. Despite the increasing interest in the relationship between ferroptosis and fibrosis, a comprehensive understanding of the precise role that ferroptosis plays in the formation of fibrotic tissue remains limited. This review seeks to synthesize previous research related to the topic. We categorized the different direct and indirect mechanisms by which ferroptosis may contribute to fibrosis into three categories: (1) iron overload toxicity; (2) ferroptosis-evoked necroinflammation, with a focus on ferroptosis and macrophage interplay; and (3) ferroptosis-associated pro-fibrotic factors and pathways. Furthermore, the review considers the potential implications of these findings and highlights the utilization of ferroptosis-targeted therapies as a promising strategy for mitigating the progression of fibrosis. In conclusion, novel anti-fibrotic treatments targeting ferroptosis could be an effective treatment for fibrosis.

Chromosome karyotyping is a critical way to diagnose various hematological malignancies and genetic diseases, of which chromosome detection in raw metaphase cell images is the most critical and challenging step. In this work, focusing on the joint optimization of chromosome localization and classification, we propose ChromTR to accurately detect and classify 24 classes of chromosomes in raw metaphase cell images. ChromTR incorporates semantic feature learning and class distribution learning into a unified DETR-based detection framework. Specifically, we first propose a Semantic Feature Learning Network (SFLN) for semantic feature extraction and chromosome foreground region segmentation with object-wise supervision. Next, we construct a Semantic-Aware Transformer (SAT) with two parallel encoders and a Semantic-Aware decoder to integrate global visual and semantic features. To provide a prediction with a precise chromosome number and category distribution, a Category Distribution Reasoning Module (CDRM) is built for foreground-background objects and chromosome class distribution reasoning. We evaluate ChromTR on 1404 newly collected R-band metaphase images and the public G-band dataset AutoKary2022. Our proposed ChromTR outperforms all previous chromosome detection methods with an average precision of 92.56% in R-band chromosome detection, surpassing the baseline method by 3.02%. In a clinical test, ChromTR is also confident in tackling normal and numerically abnormal karyotypes. When extended to the chromosome enumeration task, ChromTR also demonstrates state-of-the-art performances on R-band and G-band two metaphase image datasets. Given these superior performances to other methods, our proposed method has been applied to assist clinical karyotype diagnosis.
Humans ; Metaphase ; Karyotyping/methods* ; Image Processing, Computer-Assisted/methods* ; Algorithms ; Chromosomes, Human/genetics*

Objective:To explore the effects of mouth opening breathing for different reasons on children's maxillofacial development. Methods:One hundred and fifty-one children were selected as the research objects of this experiment. They were divided into 49 cases of adenoid hypertrophy group（group A）, 52 cases of tonsillar hypertrophy group（group B） and 50 cases of adenoid with tonsillar hypertrophy group（Group C）. Healthy children in the same period were selected as the control group, a total of 45 cases. The reflex nasopharyngeal measurement parameters, facial development indexes and cephalometric parameters of group A, group B, group C and control group were analyzed, and the incidence of Angle ClassⅡand Angle Class Ⅲ in group A, group B and group C were studied. Results:Compared with the control group, the reflex nasopharyngeal measurement parameters in group A, group B and group C was significantly different（P<0.05）, and the cephalometric parameters changed with variation in groups（P<0.05）. The incidence of Angle Class Ⅱ facial pattern in group A and group C was higher, but the incidence of Angle Class Ⅲ facial pattern in group B and group C was higher（P<0.05）. Conclusion:Adenoid hypertrophy leads to mandibular retraction; tonsil hypertrophy leads to anterior mandibular arch; adenoid hypertrophy and tonsil hypertrophy are easy to lead to clockwise rotation of the mandible. In clinical practice, to avoid children's uncoordinated maxillofacial development, we should correct the maxillofacial situation of children as soon as possible.
Child ; Humans ; Maxillofacial Development ; Malocclusion, Angle Class III/complications* ; Nasopharynx ; Adenoids ; Palatine Tonsil ; Mouth Breathing/etiology* ; Hypertrophy/complications* ; Mouth

Objective To create risk predictive models of healthcare-seeking delay among imported malaria patients in Jiangsu Province based on machine learning algorithms, so as to provide insights into early identification of imported malaria cases in Jiangsu Province. Methods Case investigation, first symptoms and time of initial diagnosis of imported malaria patients in Jiangsu Province in 2019 were captured from Infectious Disease Report Information Management System and Parasitic Disease Prevention and Control Information Management System of Chinese Center for Disease Control and Prevention. The risk predictive models of healthcare-seeking delay among imported malaria patients were created with the back propagation (BP) neural network model, logistic regression model, random forest model and Bayesian model using thirteen factors as independent variables, including occupation, species of malaria parasite, main clinical manifestations, presence of complications, severity of disease, age, duration of residing abroad, frequency of malaria parasite infections abroad, incubation period, level of institution at initial diagnosis, country of origin, number of individuals travelling with patients and way to go abroad, and time of healthcare-seeking delay as a dependent variable. Logistic regression model was visualized using a nomogram, and the nomogram was evaluated using calibration curves. In addition, the efficiency of the four models for prediction of risk of healthcare-seeking delay among imported malaria patients was evaluated using the area under curve (AUC) of receiver operating characteristic curve (ROC). The importance of each characteristic was quantified and attributed by using SHAP to examine the positive and negative effects of the value of each characteristic on the predictive efficiency. Results A total of 244 imported malaria patients were enrolled, including 100 cases (40.98%) with the duration from onset of first symptoms to time of initial diagnosis that exceeded 24 hours. Logistic regression analysis identified a history of malaria parasite infection [odds ratio (OR) = 3.075, 95% confidential interval (CI): (1.597, 5.923)], long incubation period [OR = 1.010, 95% CI: (1.001, 1.018)] and seeking healthcare in provincial or municipal medical facilities [OR = 12.550, 95% CI: (1.158, 135.963)] as risk factors for delay in seeking healthcare among imported malaria cases. BP neural network modeling showed that duration of residing abroad, incubation period and age posed great impacts on delay in healthcare-seek among imported malaria patients. Random forest modeling showed that the top five factors with the greatest impact on healthcare-seeking delay included main clinical manifestations, the way to go abroad, incubation period, duration of residing abroad and age among imported malaria patients, and Bayesian modeling revealed that the top five factors affecting healthcare-seeking delay among imported malaria patients included level of institutions at initial diagnosis, age, country of origin, history of malaria parasite infection and individuals travelling with imported malaria patients. ROC curve analysis showed higher overall performance of the BP neural network model and the logistic regression model for prediction of the risk of healthcare-seeking delay among imported malaria patients (Z = 2.700 to 4.641, all P values < 0.01), with no statistically significant difference in the AUC among four models (Z = 1.209, P > 0.05). The sensitivity (71.00%) and Youden index (43.92%) of the logistic regression model was higher than those of the BP neural network (63.00% and 36.61%, respectively), and the specificity of the BP neural network model (73.61%) was higher than that of the logistic regression model (72.92%). Conclusions Imported malaria cases with long duration of residing abroad, a history of malaria parasite infection, long incubation period, advanced age and seeking healthcare in provincial or municipal medical institutions have a high likelihood of delay in healthcare-seeking in Jiangsu Province. The models created based on the logistic regression and BP neural network show a high efficiency for prediction of the risk of healthcare-seeking among imported malaria patients in Jiangsu Province, which may provide insights into health management of imported malaria patients.

With the rapid aging of the global population posing a serious problem, frailty, a non-specific state that reflects physiological senescence rather than aging in time, has become more widely addressed by researchers in various medical fields. A high prevalence of frailty is found among kidney transplant (KT) candidates and recipients. Therefore, their frailty has become a research hotspot in the field of transplantation. However, current studies mainly focus on the cross-sectional survey of the incidence of frailty among KT candidates and recipients and the relationship between frailty and transplantation. Research on the pathogenesis and intervention is scattered, and relevant review literature is scarce. Exploring the pathogenesis of frailty in KT candidates and recipients and determining effective intervention measures may reduce waiting list mortality and improve the long-term quality of life of KT recipients. Therefore, this review explains the pathogenesis and intervention measures for frailty in KT candidates and recipients to provide a reference for the formulation of effective intervention strategies.
Humans ; Frailty/epidemiology* ; Risk Factors ; Quality of Life ; Kidney Failure, Chronic ; Kidney Transplantation/adverse effects* ; Cross-Sectional Studies ; Transplant Recipients

OBJECTIVE: To explore the biomarkers of tinnitus in vestibular schwannoma patients using electroencephalographic (EEG) microstate technology. METHODS: The EEG and clinical data of 41 patients with vestibular schwannoma were collected. All the patients were evaluated by SAS, SDS, THI and VAS scales. The EEG acquisition time was 10-15 min, and the EEG data were preprocessed and analyzed using MATLAB and EEGLAB software package. RESULTS: Of the 41 patients with vestibular schwannoma, 29 patients had tinnitus and 12 did not have tinnitus, and their clinical parameters were comparable. The average global explanation variances of the non-tinnitus and tinnitus groups were 78.8% and 80.1%, respectively. The results of EEG microstate analysis showed that compared with those without tinnitus, the patients with tinnitus had an increased frequency (P=0.033) and contribution (P=0.028) of microstate C. Correlation analysis showed that THI scale scores of the patients were negatively correlated with the duration of microstate A (R=-0.435, P=0.018) and positively with the frequencies of microstate B (R=0.456, P=0.013) and microstate C (R=0.412, P=0.026). Syntax analysis showed that the probability of transition from microstate C to microstate B increased significantly in vestibular schwannoma patients with tinnitus (P=0.031). CONCLUSION EEG microstate features differ significantly between vestibular schwannoma patients with and without tinnitus. This abnormality in patients with tinnitus may reflect the potential abnormality in the allocation of neural resources and the transition of brain functional activity.
Humans ; Neuroma, Acoustic/complications* ; Electroencephalography ; Patients ; Probability

Objective:To investigate risk factors for decompressive craniectomy (DC) after endovascular therapy (EVT) in patients with acute anterior circulation ischemic stroke.Methods:Patients underwent EVT due to acute anterior circulation large vessel occlusion in Liaocheng Brain Hospital from January 2018 to January 2020 were retrospectively included. They were divided into DC group and non-DC group. Univariate and multivariate logistic regression analyses were used to determine risk factors for DC after EVT. Results:A total of 207 patients were enrolled, 126 were male (60.87%), and their age was 66.22±11.24 years old. The baseline National Institutes of Health Stroke Scale (NIHSS) score was 19.84±9.20, and the Alberta Stroke Program Early CT Score (ASPECTS) was 7.98±0.66. The immediate postoperative modified Treatment In Cerebral Ischemia (mTICI) blood flow grade in seven patients (5.80%) was ≤2a, 30 (14.49%) experienced hemorrhagic transformation (HT) after procedure, and 28 (13.5%) received DC. There were statistically significant differences between the DC group and the non-DC group in terms of past stroke history, preoperative NIHSS score and ASPECTS, vascular occlusion site, EVT time, immediate postoperative mTICI ≤2a, and HT (all P<0.05). Multivariate logistic regression analysis showed that atrial fibrillation (odds ratio [ OR] 3.202, 95% confidence interval [ CI] 1.335-9.796; P=0.011), previous stroke history ( OR 2.655, 95% CI 1.016-6.938; P=0.046), high preoperative NIHSS score ( OR 1.074, 95% CI 1.026-1.124; P=0.002), internal carotid artery occlusion ( OR 4.268, 95% CI 1.399-13.024; P=0.011), longer EVT time ( OR 1.010, 95% CI 1.003-1.016; P=0.003), mTICI grade ≤2a ( OR 5.342, 95% CI 1.565-18.227; P=0.007) and postoperative HT ( OR 3.036, 95% CI 1.024-9.004; P=0.045) were independent risk factors for DC. Conclusions:It is not uncommon for patients with acute anterior circulation ischemic stroke to need DC after EVT. Previous stroke history, atrial fibrillation, high baseline NIHSS score, internal carotid artery occlusion, prolonged blood EVT time, mTICI grade ≤2a and postoperative HT are independent predictors of needing DC after EVT.