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Hierarchical networks are frequently encountered in animal groups, gene networks, and artificial engineering systems such as multiple robots, unmanned vehicle systems, smart grids, wind farm networks, and so forth. The structure of a large directed hierarchical network is often strongly influenced by reverse edges from lower- to higher-level nodes, such as lagging birds’ howl in a flock or the opinions of lower-level individuals feeding back to higher-level ones in a social group. This study reveals that, for most large-scale real hierarchical networks, the majority of the reverse edges do not affect the synchronization process of the entire network; the synchronization process is influenced only by a small part of these reverse edges along specific paths. More surprisingly, a single effective reverse edge can slow down the synchronization of a huge hierarchical network by over 60%. The effect of such edges depends not on the network size but only on the average in-degree of the involved subnetwork. The overwhelming majority of active reverse edges turn out to have some kind of “bunching” effect on the information flows of hierarchical networks, which slows down synchronization processes. This finding refines the current understanding of the role of reverse edges in many natural, social, and engineering hierarchical networks, which might be beneficial for precisely tuning the synchronization rhythms of these networks. Our study also proposes an effective way to attack a hierarchical network by adding a malicious reverse edge to it and provides some guidance for protecting a network by screening out the specific small proportion of vulnerable nodes.

Abstract Background Postoperative pneumonia (POP) is the most prevalent of all nosocomial infections in patients who underwent cardiac surgery. The aim of this study was to identify independent risk factors for pneumonia after cardiac surgery, from which we constructed a nomogram for prediction. Methods The clinical data of patients admitted to the Department of Cardiothoracic Surgery of Nanjing Drum Tower Hospital from October 2020 to September 2021 who underwent cardiac surgery were retrospectively analyzed, and the patients were divided into two groups according to whether they had POP: POP group (n=105) and non-POP group (n=1083). Preoperative, intraoperative, and postoperative indicators were collected and analyzed. Logistic regression was used to identify independent risk factors for POP in patients who underwent cardiac surgery. We constructed a nomogram based on these independent risk factors. Model discrimination was assessed via area under the receiver operating characteristic curve (AUC), and calibration was assessed via calibration plot. Results A total of 105 events occurred in the 1188 cases. Age (>55 years) (OR: 1.83, P=0.0225), preoperative malnutrition (OR: 3.71, P 135 min (OR: 2.80, P 20 hours (OR: 3.11, P

Acute aortic dissection is one of the most life-threatening cardiovascular diseases, with a high mortality rate. Its prevalence ranges from 0.2% to 0.8% in humans, resulting in a significant number of deaths due to being missed in manual examinations. More importantly, the aortic diameter—a critical indicator for surgical selection—significantly influences the outcomes of surgeries post-diagnosis. Therefore, it is an urgent yet challenging mission to develop an automatic aortic dissection diagnostic system that can recognize and classify the aortic dissection type and measure the aortic diameter. This paper offers a dual-functional deep learning system called aortic dissections diagnosis-aiding system (DDAsys) that enables both accurate classification of aortic dissection and precise diameter measurement of the aorta. To this end, we created a dataset containing 61 190 computed tomography angiography (CTA) images from 279 patients from the Division of Cardiovascular Surgery at Tongji Hospital, Wuhan, China. The dataset provides a slice-level summary of difficult-to-identify features, which helps to improve the accuracy of both recognition and classification. Our system achieves a recognition F1 score of 0.984, an average classification F1 score of 0.935, and the respective measurement precisions for ascending and descending aortic diameters are 0.994 mm and 0.767 mm root mean square error (RMSE). The high consistency (88.6%) between the recommended surgical treatments and the actual corresponding surgeries verifies the capability of our system to aid clinicians in developing a more prompt, precise, and consistent treatment strategy.

Abstract Background Postoperative hyper-inflammation is a frequent event in patients with acute Stanford type A aortic dissection (ATAAD) after surgical repair. This study's objective was to determine which inflammatory biomarkers could be used to make a better formula for identifying postoperative hyper-inflammation, and which risk factors were associated with hyper-inflammation. Methods A total of 405 patients were enrolled in this study from October 1, 2020 to April 1, 2023. Of these patients, 124 exhibited poor outcomes. In order to investigate the optimal cut-off values for poor outcomes, logistic and receiver operating characteristic analyses were performed on the following parameters on the first postoperative day: procalcitonin (PCT), C-reactive protein (CRP), interleukin-6 (IL-6), and systemic immune-inflammation index (SII). These cut-off points were used to separate the patients into hyper-inflammatory (n = 52) and control (n = 353) groups. Finally, the logistic were used to find the risk factors of hyper-inflammatory. Results PCT, CRP, IL-6, and SII were independent risk factors of poor outcomes in the multivariate logistic model. Cut-off points of these biomarkers were 2.18 ng/ml, 49.76 mg/L, 301.88 pg/ml, 2509.96 × 109/L respectively. These points were used to define postoperative hyper-inflammation (OR 2.97, 95% CI 1.35–6.53, P 180 min, and deep hypothermia circulatory arrest (DHCA) > 40 min were the independent risk factors for hyper-inflammation. Conclusions PCT > 2.18, CRP > 49.76, IL-6 > 301.88, and SII 180 min and DHCA > 40 min were separate risk factors for postoperative hyper-inflammation.

Abstract Due to a rapidly aging global population, osteoporosis and the associated risk of bone fractures have become a wide-spread public health problem. However, osteoporosis is very heterogeneous, and the existing standard diagnostic measure is not sufficient to accurately identify all patients at risk of osteoporotic fractures and to guide therapy. Here, we constructed the first prospective multi-omics atlas of the largest osteoporosis cohort to date (longitudinal data from 366 participants at three time points), and also implemented an explainable data-intensive analysis framework (DLSF: Deep Latent Space Fusion) for an omnigenic model based on a multi-modal approach that can capture the multi-modal molecular signatures (M3S) as explicit functional representations of hidden genotypes. Accordingly, through DLSF, we identified two subtypes of the osteoporosis population in Chinese individuals with corresponding molecular phenotypes, i.e., clinical intervention relevant subtypes (CISs), in which bone mineral density benefits response to calcium supplements in 2-year follow-up samples. Many snpGenes associated with these molecular phenotypes reveal diverse candidate biological mechanisms underlying osteoporosis, with xQTL preferences of osteoporosis and its subtypes indicating an omnigenic effect on different biological domains. Finally, these two subtypes were found to have different relevance to prior fracture and different fracture risk according to 4-year follow-up data. Thus, in clinical application, M3S could help us further develop improved diagnostic and treatment strategies for osteoporosis and identify a new composite index for fracture prediction, which were remarkably validated in an independent cohort (166 participants).

Objectives To develop an interpretable deep learning model of lupus nephritis (LN) relapse prediction based on dynamic multivariable time-series data.Design A single-centre, retrospective cohort study in China.Setting A Chinese central tertiary hospital.Participants The cohort study consisted of 1694 LN patients who had been registered in the Nanjing Glomerulonephritis Registry at the National Clinical Research Center of Kidney Diseases, Jinling Hospital from January 1985 to December 2010.Methods We developed a deep learning algorithm to predict LN relapse that consists of 59 features, including demographic, clinical, immunological, pathological and therapeutic characteristics that were collected for baseline analysis. A total of 32 227 data points were collected by the sliding window method and randomly divided into training (80%), validation (10%) and testing sets (10%). We developed a deep learning algorithm-based interpretable multivariable long short-term memory model for LN relapse risk prediction considering censored time-series data based on a cohort of 1694 LN patients. A mixture attention mechanism was deployed to capture variable interactions at different time points for estimating the temporal importance of the variables. Model performance was assessed according to C-index (concordance index).Results The median follow-up time since remission was 4.1 (IQR, 1.7–6.7) years. The interpretable deep learning model based on dynamic multivariable time-series data achieved the best performance, with a C-index of 0.897, among models using only variables at the point of remission or time-variant variables. The importance of urinary protein, serum albumin and serum C3 showed time dependency in the model, that is, their contributions to the risk prediction increased over time.Conclusions Deep learning algorithms can effectively learn through time-series data to develop a predictive model for LN relapse. The model provides accurate predictions of LN relapse for different renal disease stages, which could be used in clinical practice to guide physicians on the management of LN patients.

Abstract Background We sought to explore the relationship between dexmedetomidine as an anesthetic adjuvant in cardiac surgery and postoperative complications and length of stay (LOS) in the cardiac intensive care unit (CICU). Methods We conducted a retrospective study of patients aged 18 years and older who underwent heart valve surgery between October 2020 and June 2022. The primary endpoint of the study was major postoperative complications (cardiac arrest, atrial fibrillation, myocardial injury/infarction, heart failure) and the secondary endpoint was prolonged CICU LOS (defined as LOS > 90th percentile). Multivariate logistic regression analysis was performed for variables that were significant in the univariate analysis. Results A total of 856 patients entered our study. The 283 patients who experienced the primary and secondary endpoints were included in the adverse outcomes group, and the remaining 573 were included in the prognostic control group. Multivariate logistic regression analysis revealed that age > 60 years (odds ratio [OR], 1.68; 95% confidence interval [CI], 1.23–2.31; p 180 min (OR, 1.62; 95% CI, 1.03–2.55; p = 0.04) and postoperative mechanical ventilation time > 10 h (OR, 1.84; 95% CI, 1.35–2.52; p 60 years (OR, 3.20; 95% CI, 1.65–6.20; p 650 ml (OR, 2.04; 95% CI, 1.13–3.66; p = 0.02), Intraoperative bleeding > 1200 ml (OR, 2.69; 95% CI, 1.42–5.12; p

Abstract Background Myocardial injury-related cardiogenic shock (MICS) is significantly associated with poor outcomes in patients after cardiac surgery. Herein, we aimed to investigate the risk factor for postoperative MICS. Methods We performed a case-control study on 792 patients undergoing cardiac surgery from 2016 to 2019, including 172 patients with postoperative MICS and 620 age- and sex-matched controls. MICS was defined as composite criteria: a cardiac index of 5 mmol/L at the end of the surgery, a vasoactive-inotropic score of > 40 at the end of the surgery, and a cardiac troponin T (cTnT) level of > 0.8 µg/L on postoperative day 1 (POD1) with an increase of > 10% on POD 2. Results A total of 4671 patients who underwent cardiac surgery in our hospital between 2016 and 2019 were included; of these, 172 (3.68%) had MICS and the remaining 4499 did not. For investigating the risk factors, we selected 620 age- and sex-matched controls. In the univariate analysis, MICS was significantly associated with death (P 2 h (OR: 3.16, 95% CI: 1.94–5.15, P

Fluoride has strong electronegativity and exposes diversely in nature. Water fluoridation is the most pervasive form of occurrence, representing a significant threat to human health. In this study, we investigate the morphometric and physiological alterations triggered by fluoride stimulation during the embryogenesis of zebrafish and reveal its putative effects of stage- and/or dose-dependent. Fluoride exhibits potent biological activity and can be extensively absorbed by the yolk sac, exerting significant effects on the development of multiple organs. This is primarily manifested as restricted nutrient utilization and elevated levels of lipid peroxidation, further leading to the accumulation of superoxide in the yolk sac, liver, and intestines. Moreover, pericardial edema exerts pressure on the brain and eye development, resulting in spinal curvature and reduced body length. Besides, acute fluoride exposure with varying concentrations has led to diverse teratogenic outcomes. A low dose of water fluoridation tends to induce abnormal development of the embryonic yolk sac, while vascular malformation is widely observed in all fluoride-treated groups. The effect of fluoride exposure on blood circulation is universally present, even in zebrafish larvae that do not exhibit obvious deformities. Their swimming behavior is also affected by water fluoridation, resulting in reduced activity and delayed reactions. In conclusion, this study provides valuable insights into the monitoring of environmental quality related to water fluoridation and disease prevention.

Abstract The main manifestations of Takotsubo syndrome (TTS) are a spherical expansion of the left ventricle or near the apex and decreased systolic function. TTS is mostly thought to be induced by emotional stress, and the induction of TTS by severe infection is not often reported. A 72-year-old female patient with liver abscess reported herein was admitted due to repeated fever with a history of hypertension and impaired glucose tolerance. Her severe infection caused TTS, and her blood pressure dropped to 80/40 mmHg. IABP treatment was performed immediately and continued for 10 days, and comprehensive medication was administered. Based on her disease course and her smooth recovery, general insights and learnings may be: Adding to mental and other pathological stress reaction, serious infections from pathogenic microorganism could be of great important causation of stress reaction leading to TTS, while basic diseases such as coronary heart disease, hypertension, and diabetes were be of promoting factors; In addition to effective drug therapies for TTS, the importance of the timely using of IABP should be emphasized.

Background: For the patients with acute coronary syndrome (ACS) undergoing percutaneous coronary intervention (PCI), dual antiplatelet therapy (DAPT) for at least 1 year is recommended in the guidelines to minimize the risk of stent thrombosis. Persistently uncovered stent strut means delayed neointima formation and extend the window of time in which the stent is prone to thrombosis. Previous studies showed that statins could improve post-stenting strut endothelial coverage for patients undergoing PCI. However, there are lack of evidences on whether early initiation of proprotein convertase subtilisin/Kexin type 9 monoclonal antibody (PCSK9mAb) after PCI in ACS patients can further improve the rate of stent strut coverage on the background of oral lipid-lowering therapy (LLT). Methods: This is a single-center, randomized trial to enroll 36 patients undergoing PCI with a clinical diagnosis of non-ST-segment elevation ACS. The baseline level of low-density lipoprotein cholesterol (LDL-C) of these patients are between 1.4 mmol/L and 3.4 mmol/L. Patients will be assigned to intensive lipid-lowering therapy (LLT) with PCSK9mAb group and conventional LLT without PCSK9mAb group for 12 weeks in a clinical follow-up setting according to 1: 1 randomization. the rate of stent strut endothelial coverage by optical coherence tomography (OCT) examination at 12 weeks after enrollment between the groups will be compared. Conclusion: This will be the first study to investigate changes in the rate of stent strut endothelial coverage under intensive LLT with PCSK9mAb by OCT examination in ACS patients undergoing PCI. The finding of this study will provide clinical evidence for future research about the hypothesis of a novel strategy of “intensive LLT (PCSK9mAb + statin ± ezetimibe) combined with shortened DAPT duration” for ACS patients undergoing PCI.Clinical Trial Registration: URL: https://www.clinicaltrials.gov. Unique identifier: ChiCTR2200063395.

Résumé Contexte L’épidémie de maladie à coronavirus 2019 (COVID-19) a eu un impact étendu et profond sur la santé mentale des gens. Les facteurs associés aux symptômes mentaux chez les hommes diagnostiqués comme infertiles, une maladie étroitement liée aux conditions psychologiques, restent flous. L’objectif de cette étude est d’étudier les facteurs de risque associés aux symptômes mentaux chez les hommes chinois infertiles pendant la pandémie. Résultats Au total, 4 098 participants admissibles ont été recrutés dans cette étude transversale à l’échelle nationale, dont 2 034 (49,6%) présentaient une infertilité primaire et 2 064 (50,4%) une infertilité secondaire. La prévalence des problèmes de santé mentale était respectivement de 36,3 %, 39,6 % et 6,7 % pour l’anxiété, la dépression, et le stress postpandémique. La dysfonction sexuelle est associée à un risque plus élevé avec des odds ratios ajustés (OR) de 1,40 pour l’anxiété, 1,38 pour la dépression et 2,32 pour le stress. Les hommes recevant un traitement médicamenteux contre l’infertilité présentaient un risque plus élevé de symptômes d’anxiété (OR ajusté, 1,31) et de dépression (OR ajusté, 1,28), alors que ceux dont le traitement consistait à faire des inséminations intra-utérines présentaient un risque plus faible de symptômes d’anxiété (OR ajusté, 0,56) et de dépression (OR ajusté, 0,55). Conclusions La pandémie de COVID-19 a eu un impact psychologique important sur les hommes infertiles. Plusieurs populations psychologiquement vulnérables ont été identifiées, notamment les personnes souffrant de dysfonction sexuelle, les hommes recevant un traitement médicamenteux contre l’infertilité, et ceux subissant des mesures de contrôle de la COVID-19. Les résultats fournissent un profil complet de l’état de santé mentale des hommes Chinois infertiles pendant l’épidémie de COVID-19 et fournissent des stratégies potentielles d’intervention psychologique.

Abstract Background Recent high-quality trials have shown that the anti-inflammatory effects of colchicine reduce the risk of cardiovascular events in patients suffering post-myocardial infarction and chronic coronary disease. The effect of colchicine in patients undergoing non-coronary artery bypass grafting (non-CABG) with cardiopulmonary bypass remains unclear. We aim to evaluate the effect of colchicine on myocardial protection in patients who underwent non-CABG cardiac surgery. Method Patients were randomly assigned to colchicine or placebo groups starting 72 h before scheduled cardiac surgery and for 5 days thereafter (0.5 mg daily).The primary outcome was the level of cardiac troponin T (cTnT) at postoperative 48 h. The secondary outcomes included troponin I (cTnI) and creatine kinase-MB (CK-MB), inflammatory biomarkers (procalcitonin and interleukin-6, etc.), and adverse events (30-day mortality, stroke, ECMO and IABP use, etc.). Results A total of 132 patients underwent non-CAGB cardiac surgery, 11were excluded because of diarrhea (n = 6) and long aortic cross-clamp time > 2 h (n = 5), 59 were assigned to the colchicine group and 62 to the placebo group. Compared with the placebo group, cTnT (median: 0.3 μg/L, IQR 0.2–0.4 μg/L vs. median: 0.4 μg/L, IQR 0.3–0.6 μg/L, P

Microdissection testicular sperm extraction (micro-TESE) is widely used to treat nonobstructive azoospermia. However, a good prediction model is required to anticipate a successful sperm retrieval rate before performing micro-TESE. This retrospective study analyzed the clinical records of 200 nonobstructive azoospermia patients between January 2021 and December 2021. The backward method was used to perform binary logistic regression analysis and identify factors that predicted a successful micro-TESE sperm retrieval. The prediction model was constructed using acquired regression coefficients, and its predictive performance was assessed using the receiver operating characteristic curve. In all, 67 patients (sperm retrieval rate: 33.5%) underwent successful micro-TESE. Follicle-stimulating hormone, anti-Müllerian hormone, and inhibin B levels varied significantly between patients who underwent successful and unsuccessful micro-TESE. Binary logistic regression analysis yielded the following six predictors: anti-Müllerian hormone (odds ratio [OR] = 0.902, 95% confidence interval [CI]: 0.821–0.990), inhibin B (OR = 1.012, 95% CI: 1.001–1.024), Klinefelter's syndrome (OR = 0.022, 95% CI: 0.002–0.243), Y chromosome microdeletion (OR = 0.050, 95% CI: 0.005–0.504), cryptorchidism with orchiopexy (OR = 0.085, 95% CI: 0.008–0.929), and idiopathic nonobstructive azoospermia (OR = 0.031, 95% CI: 0.003–0.277). The prediction model had an area under the curve of 0.720 (95% CI: 0.645–0.794), sensitivity of 65.7%, specificity of 72.2%, Youden index of 0.379, and cut-off value of 0.305 overall, indicating good predictive value and accuracy. This model can assist clinicians and nonobstructive azoospermia patients in decision-making and avoiding negative micro-TESE results.

To design a treatment plan for patients with epididymal obstruction, we explored the potential impact of factors such as body mass index (BMI) and age on the surgical outcomes of vasoepididymostomy (VE). In this retrospective study, 181 patients diagnosed with obstructive azoospermia (OA) due to epididymal obstruction between September 2014 and September 2017 were reviewed. All patients underwent single-armed microsurgical intussusception VEs with longitudinal two-suture placement performed by a single surgeon (KH) in a single hospital (Peking University Third Hospital, Beijing, China). Six factors that could possibly influence the patency rates were analyzed, including BMI, age, mode of anastomosis, site of anastomosis, and sperm motility and quantity in the intraoperative epididymal fluid. Single-factor outcome analysis was performed via Chi-square test and multivariable analysis was performed using logistic regression. A total of 159 (87.8%, 159/181) patients were followed up. The follow-up time (mean ± standard deviation [s.d.]) was 27.7 ± 9.3 months, ranging from 12 months to 48 months. The overall patency rate was 73.0% (116/159). The multivariable analysis revealed that BMI and age significantly influenced the patency rate (P = 0.008 and 0.028, respectively). Younger age (≤28 years; odds ratio [OR] = 3.531, 95% confidence interval [95% CI]: 1.397–8.924) and lower BMI score (

Stochastic differential equations (SDEs) are mathematical models that are widely used to describe complex processes or phenomena perturbed by random noise from different sources. The identification of SDEs governing a system is often a challenge because of the inherent strong stochasticity of data and the complexity of the system’s dynamics. The practical utility of existing parametric approaches for identifying SDEs is usually limited by insufficient data resources. This study presents a novel framework for identifying SDEs by leveraging the sparse Bayesian learning (SBL) technique to search for a parsimonious, yet physically necessary representation from the space of candidate basis functions. More importantly, we use the analytical tractability of SBL to develop an efficient way to formulate the linear regression problem for the discovery of SDEs that requires considerably less time-series data. The effectiveness of the proposed framework is demonstrated using real data on stock and oil prices, bearing variation, and wind speed, as well as simulated data on well-known stochastic dynamical systems, including the generalized Wiener process and Langevin equation. This framework aims to assist specialists in extracting stochastic mathematical models from random phenomena in the natural sciences, economics, and engineering fields for analysis, prediction, and decision making.

Abstract Background Postoperative pneumonia (PP) is one of the most common complications after cardiac surgery. This study was designed to access the diagnostic value of interleukin-6 (IL-6) for pneumonia within the first 5 days after cardiac surgery in adults. Method This prospective observational study enrolled 694 patients who admitted to our center from 10 October 2020 to 30 June 2021. Blood samples were collected after admission and on five consecutive days after surgery to measure IL-6, procalcitonin (PCT), C-reactive protein (CRP) and white blood cells (WBC) respectively. Combined with clinical data, we assessed the diagnostic performance of different biomarkers using univariate and multifactorial analyses as well as receiver operating characteristic curves (ROC) and the area under the curve (AUC). Result Finally, 68 patients were diagnosed with PP (PP Group). In addition, 626 cases were assigned to the control group (Non-PP Group). From postoperative day 1 (POD1) to day 5, IL-6 and PCT levels showed higher diagnostic value (P