Transient Receptor Vanilloid Subtype 4-Mediated Ca2+ Influx Promotes Glomerular Endothelial Inflammation in Sepsis-Associated Acute Kidney Injury.

Sepsis-associated acute kidney injury (S-AKI) is a frequent complication in patients who are critically ill, which is often initiated by glomerular endothelial cell dysfunction. Although transient receptor vanilloid subtype 4 (TRPV4) ion channels are known to be permeable to Ca2+ and are widely expressed in the kidneys, the role of TRPV4 on glomerular endothelial inflammation in sepsis remains elusive. In the present study, we found that TRPV4 expression in mouse glomerular endothelial cells (MGECs) increased after lipopolysaccharide (LPS) stimulation or cecal ligation and puncture challenge, which increased intracellular Ca2+ in MGECs. Furthermore, the inhibition or knockdown of TRPV4 suppressed LPS-induced phosphorylation and translocation of inflammatory transcription factors NF-κB and IRF-3 in MGECs. Clamping intracellular Ca2+ mimicked LPS-induced responses observed in the absence of TRPV4. In vivo experiments showed that the pharmacologic blockade or knockdown of TRPV4 reduced glomerular endothelial inflammatory responses, increased survival rate, and improved renal function in cecal ligation and puncture-induced sepsis without altering renal cortical blood perfusion. Taken together, our results suggest that TRPV4 promotes glomerular endothelial inflammation in S-AKI and that its inhibition or knockdown alleviates glomerular endothelial inflammation by reducing Ca2+ overload and NF-κB/IRF-3 activation. These findings provide insights that may aid in the development of novel pharmacologic strategies for the treatment of S-AKI.

Particle filter-based parameter estimation algorithm for prognostic risk assessment of progression in non-small cell lung cancer.

Non-small cell lung cancer (NSCLC) is a malignant tumor that threatens human life and health. The development of a new NSCLC risk assessment model based on electronic medical records has great potential for reducing the risk of cancer recurrence. In this process, machine learning is a powerful method for automatically extracting risk factors and indicating impact weights for NSCLC deaths. However, when the number of samples reaches a certain value, it is difficult for machine learning to improve the prediction accuracy, and it is also challenging to use the characteristic data of subsequent patients effectively. Therefore, this study aimed to build a postoperative survival risk assessment model for patients with NSCLC that updates the model parameters and improves model accuracy based on new patient data. The model perspective was a combination of particle filtering and parameter estimation. To demonstrate the feasibility and further evaluate the performance of our approach, we performed an empirical analysis experiment. The study showed that our method achieved an overall accuracy of 92% and a recall of 71% for deceased patients. Compared with traditional machine learning models, the accuracy of the model estimated by particle filter parameters has been improved by 2%, and the recall rate for dead patients has been improved by 11%. Additionally, this study outcome shows that this method can better utilize subsequent patients' characteristic data, be more relevant to different patients, and help achieve precision medicine.

Machine learning-based risk models for procedural complications of radiofrequency ablation for atrial fibrillation.

BACKGROUND: Radiofrequency ablation (RFA) for atrial fibrillation (AF) is associated with a risk of complications. This study aimed to develop and validate risk models for predicting complications after radiofrequency ablation of atrial fibrillation patients. METHODS: This retrospective cohort study included 3365 procedures on 3187 patients with atrial fibrillation at a single medical center from 2018 to 2021. The outcome was the occurrence of postoperative procedural complications during hospitalization. Logistic regression, decision tree, random forest, gradient boosting machine, and extreme gradient boosting were used to develop risk models for any postoperative complications, cardiac effusion/tamponade, and hemorrhage, respectively. Patients' demographic characteristics, medical history, signs, symptoms at presentation, electrocardiographic features, procedural characteristics, laboratory values, and postoperative complications were collected from the medical record. The prediction results were evaluated by performance metrics (i.e., the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, F score, and Brier score) with repeated fivefold cross-validation. RESULTS: Of the 3365 RFA procedures, there were 62 procedural complications with a rate of 1.84% in the entire cohort. The most common complications were cardiac effusion/tamponade (28 cases, 0.83%), and hemorrhage (21 cases, 0.80%). There was no procedure-related mortality. The machine learning algorithms of random forest (RF) outperformed other models for any complication (AUC 0.721 vs 0.627 to 0.707), and hemorrhage (AUC 0.839 vs 0.649 to 0.794). The extreme gradient boosting (XGBoost) model outperformed other models for cardiac effusion/tamponade (AUC 0.696 vs 0.606 to 0.662). CONCLUSIONS: The developed risk models using machine learning algorithms showed good performance in predicting complications after RFA of AF patients. These models help identify patients at high risk of complications and guiding clinical decision-making.

Clinical characterization and immunosuppressive regulation of CD161 (KLRB1) in glioma through 916 samples.

BACKGROUND: Glioblastoma is a paradigm of cancer-associated immunosuppression, limiting the effects of immunotherapeutic strategies. Thus, identifying the molecular mechanisms underlying immune surveillance evasion is critical. Recently, the preferential expression of inhibitory natural killer (NK) cell receptor CD161 on glioma-infiltrating cytotoxic T cells was identified. Focusing on the molecularly annotated, large-scale clinical samples from different ethnic origins, the data presented here provide evidence of this immune modulator's essential roles in brain tumor biology. METHODS: Retrospective RNA-seq data analysis was conducted in a cohort of 313 patients with glioma in the Chinese Glioma Genome Atlas (CGGA) database and 603 patients in The Cancer Genome Atlas (TCGA) database. In addition, single-cell sequencing data from seven surgical specimens of glioblastoma patients and a model in which patient-derived glioma stem cells were cocultured with peripheral lymphocytes, were used to analyze the molecular evolution process during gliomagenesis. RESULTS: CD161 was enriched in high-grade gliomas and isocitrate dehydrogenase (IDH)-wildtype glioma. CD161 acted as a potential biomarker for the mesenchymal subtype of glioma and an independent prognostic factor for the overall survival (OS) of patients with glioma. In addition, CD161 played an essential role in inhibiting the cytotoxicity of T cells in glioma patients. During the process of gliomagenesis, the expression of CD161 on different lymphocytes dynamically evolved. CONCLUSION: The expression of CD161 was closely related to the pathology and molecular pathology of glioma. Meanwhile, CD161 promoted the progression and evolution of gliomas through its unique effect on T cell dysfunction. Thus, CD161 is a promising novel target for immunotherapeutic strategies in glioma treatment.

Bladder Backfilling versus Standard Catheter Removal for Trial of Void after Outpatient Laparoscopic Gynecologic Surgery: A Systematic Review and Meta-Analysis.

OBJECTIVE: To compare the rate of postoperative urinary retention and time to discharge between bladder backfilling and standard catheter removal for trial of void (TOV) after outpatient laparoscopic gynecologic surgery. Our secondary objectives were to compare the time to void, postoperative complications, and patient satisfaction. DATA SOURCES: We searched the PubMed, Ovid MEDLINE, Embase, Cochrane Library databases, and relevant reference lists of eligible articles up to March of 2021. METHODS OF STUDY SELECTION: This review included randomized controlled trials (RCTs) of TOV after outpatient laparoscopic gynecologic surgery. Odds ratios (ORs) with 95% confidence interval (CI) and weighted mean differences (WMDs) were reported. The quality of the studies was assessed according to the Cochrane Handbook for Systematic Reviews of Interventions. Data were analyzed with Review Manager 5.4 software (RevMan 5.4.1; Cochrane Collaboration, London, United Kingdom). TABULATION, INTEGRATION, AND RESULTS: Five RCTs (N = 488) were included. The bladder backfilling group had a significantly shorter time to void than the standard TOV group (WMD, -25.19 minutes; 95% CI, -44.60 to -5.77; p = .01). Successful TOV was not significantly different between the 2 (OR, 0.92; 95% CI, 0.51 to -1.65; p = .77), without significant heterogeneity (I2 = 24%). There was also no significant difference in the time to discharge between the 2 TOV techniques (WMD, -25.19 minutes; 95% CI, -44.60 to -5.77; p = .01). There was no significant difference in complication rates or patient satisfaction between the 2 groups. CONCLUSION: The bladder backfilling technique of TOV after outpatient laparoscopic gynecologic surgery may reduce the time to first spontaneous void without affecting patient satisfaction or postoperative complications, but it does not significantly affect the time to discharge or urinary retention.

Mechanism of Impact of Big Data Resources on Medical Collaborative Networks From the Perspective of Transaction Efficiency of Medical Services: Survey Study.

BACKGROUND: The application of big data resources and the development of medical collaborative networks (MCNs) boost each other. However, MCNs are often assumed to be exogenous. How big data resources affect the emergence, development, and evolution of endogenous MCNs has not been well explained. OBJECTIVE: This study aimed to explore and understand the influence of the mechanism of a wide range of shared and private big data resources on the transaction efficiency of medical services to reveal the impact of big data resources on the emergence and development of endogenous MCNs. METHODS: This study was conducted by administering a survey questionnaire to information technology staff and medical staff from 132 medical institutions in China. Data from information technology staff and medical staff were integrated. Structural equation modeling was used to test the direct impact of big data resources on transaction efficiency of medical services. For those big data resources that had no direct impact, we analyzed their indirect impact. RESULTS: Sharing of diagnosis and treatment data (ß=.222; P=.03) and sharing of medical research data (ß=.289; P=.04) at the network level (as big data itself) positively directly affected the transaction efficiency of medical services. Network protection of the external link systems (ß=.271; P=.008) at the level of medical institutions (as big data technology) positively directly affected the transaction efficiency of medical services. Encryption security of web-based data (as big data technology) at the level of medical institutions, medical service capacity available for external use, real-time data of diagnosis and treatment services (as big data itself) at the level of medical institutions, and policies and regulations at the network level indirectly affected the transaction efficiency through network protection of the external link systems at the level of medical institutions. CONCLUSIONS: This study found that big data technology, big data itself, and policy at the network and organizational levels interact with, and influence, each other to form the transaction efficiency of medical services. On the basis of the theory of neoclassical economics, the study highlighted the implications of big data resources for the emergence and development of endogenous MCNs.

Reversible Insertion of Mg-Cl Superhalides in Graphite as a Cathode for Aqueous Dual-Ion Batteries.

Oxidative anion insertion into graphite in an aqueous environment represents a significant challenge in the construction of aqueous dual-ion batteries. In dilute aqueous electrolytes, the oxygen evolution reaction (OER) dominates the anodic current before anions can be inserted into the graphite gallery. Herein, we report that the reversible insertion of Mg-Cl superhalides in graphite delivers a record-high reversible capacity of 150 mAh g-1 from an aqueous deep eutectic solvent comprising magnesium chloride and choline chloride. The insertion of Mg-Cl superhalides in graphite does not form staged graphite intercalation compounds; instead, the insertion of Mg-Cl superhalides makes the graphite partially turbostratic.

The Advent of Ultra-high Volume Thoracic Surgical Centers in Shanghai.

BACKGROUND: A core principle in surgery is that high surgical volumes are conducive toward better outcomes. Ultra-high volume centers (UHVCs) have now emerged in thoracic surgery in China that now perform a volume of thoracic operations far greater than even traditional international centers of excellence. METHODS: In 2016, two hospitals in Shanghai performed over 10,000 major pulmonary, esophageal and mediastinal resections each. A qualitative analysis of the lessons learned in achieving such large operation volumes was undertaken. RESULTS: The advent of these UHVCs gives important insights for not only thoracic surgeons, but for surgical oncologists and surgeons globally. First, these ultra-high volumes were achieved to a large degree by cancer screening-but the success of the screening programs relies on reaching wider patient groups and allowing for affordable 'self-screening.' Second, the ultra-high clinical volumes at UHVCs offer unique opportunities for surgical training and research, potentially changing paradigms for academic surgery. Third, these ultra-high volumes may place new stresses on existing healthcare resources and prompt novel management strategies in response. CONCLUSIONS: The UHVCs represent a revolutionary development in modern surgery, and it behooves surgeons to both accept the challenges and harness the advantages they may bring.

Poliovirus receptor-based chimeric antigen receptor T cells combined with NK-92 cells exert potent activity against glioblastoma.

BACKGROUND: Poliovirus receptor interacts with 3 receptors: T-cell immunoglobulin immunoreceptor tyrosine-based inhibitory motif, CD96, and DNAX accessory molecule 1, which are predominantly expressed on T cells and natural killer (NK) cells. Many solid tumors, including IDH wild-type glioblastoma, have been reported to overexpress poliovirus receptor, and this overexpression is associated with poor prognosis. However, there are no preclinical or clinical trials investigating the use of cell-based immunotherapies targeting poliovirus receptor in IDH wild-type glioblastoma. METHODS: We analyzed poliovirus receptor expression in transcriptome sequencing databases and specimens from IDH wild-type glioblastoma patients. We developed poliovirus receptor targeting chimeric antigen receptor T cells using lentivirus. The antitumor activity of chimeric antigen receptor T cells was demonstrated in patient-derived glioma stem cells, intracranial and subcutaneous mouse xenograft models. RESULTS: We verified poliovirus receptor expression in primary glioma stem cells, surgical specimens from IDH wild-type glioblastoma patients, and organoids. Accordingly, we developed poliovirus receptor-based second-generation chimeric antigen receptor T cells. The antitumor activity of chimeric antigen receptor T cells was demonstrated in glioma stem cells and xenograft models. Tumor recurrence occurred in intracranial xenograft models because of antigen loss. The combinational therapy of tyrosine-based inhibitory motif extracellular domain-based chimeric antigen receptor T cells and NK-92 cells markedly suppressed tumor recurrence and prolonged survival. CONCLUSIONS: Poliovirus receptor-based chimeric antigen receptor T cells were capable of killing glioma stem cells and suppressing tumor recurrence when combined with NK-92 cells.

SUMOylation of TEAD1 Modulates the Mechanism of Pathological Cardiac Hypertrophy.

Pathological cardiac hypertrophy is the leading cause of heart failure and has an extremely complicated pathogenesis. TEA domain transcription factor 1 (TEAD1) is recognized as an important transcription factor that plays a key regulatory role in cardiovascular disease. This study aimed to explore the role of TEAD1 in cardiac hypertrophy and to clarify the regulatory role of small ubiquitin-like modifier (SUMO)-mediated modifications. First, the expression level of TEAD1 in patients with heart failure, mice, and cardiomyocytes is investigated. It is discovered that TEAD1 is modified by SUMO1 during cardiac hypertrophy and that the process of deSUMOylation is regulated by SUMO-specific protease 1 (SENP1). Lysine 173 is an essential site for TEAD1 SUMOylation, which affects the protein stability, nuclear localization, and DNA-binding ability of TEAD1 and enhances the interaction between TEAD1 and its transcriptional co-activator yes-associated protein 1 in the Hippo pathway. Finally, adeno-associated virus serotype 9 is used to construct TEAD1 wild-type and KR mutant mice and demonstrated that the deSUMOylation of TEAD1 markedly exacerbated cardiomyocyte enlargement in vitro and in a mouse model of cardiac hypertrophy. The results provide novel evidence that the SUMOylation of TEAD1 is a promising therapeutic strategy for hypertrophy-related heart failure.

Resynchronization effects and clinical outcomes during left bundle branch area pacing with and without conduction system capture.

BACKGROUND: Left bundle branch area pacing (LBBAP) includes left bundle branch pacing (LBBP) and left ventricular (LV) septal myocardial pacing (LVSP). HYPOTHESIS: The study aimed to assess resynchronization effects and clinical outcomes by LBBAP in heart failure (HF) patients with cardiac resynchronization therapy (CRT) indications. METHODS: LBBAP was successfully performed in 29 consecutive patients and further classified as the LBBP-group (N = 15) and LVSP-group (N = 14) based on the LBBP criteria and novel LV conduction time measurement (LV CT, between LBBAP site and LV pacing (LVP) site). AV-interval optimized LBBP or LVSP, or LVSP combined with LVP (LVSP-LVP) was applied. LV electrical and mechanical synchrony and clinical outcomes were assessed. RESULTS: All 15 patients in the LBBP-group received optimized LBBP while 14 patients in the LVSP-group received either optimized LVSP (5) or LVSP-LVP (9). The LV CT during LBBP was significantly faster than that during LVP (p < .001), while LV CT during LVSP were similar to LVP (p = .226). The stimulus to peak LV activation time (Stim-LVAT, 71.2 ± 8.3 ms) and LV mechanical synchrony (TSI-SD, 35.3 ± 9.5 ms) during LBBP were significantly shorter than those during LVSP (Stim-LVAT 89.1 ± 19.5 ms, TSI-SD 49.8 ± 14.4 ms, both p < .05). Following 17(IQR 8) months of follow-up, the improvement of LVEF (26.0%(IQR 16.0)) in the LBBP-group was significantly greater than that in the LVSP-group (6.0%(IQR 20.8), p = .001). CONCLUSIONS: LV activation in LBBP propagated significantly faster than that of LVSP. LBBP generated superior electrical and mechanical resynchronization and better LVEF improvement over LVSP in HF patients with CRT indications.

Use of machine learning models to predict in-hospital mortality in patients with acute coronary syndrome.

BACKGROUND: Cardiovascular diseases are a significant health burden with the prevalence increasing worldwide. Thus, a highly accurate assessment and prediction of death risk are crucial to meet the clinical demand. This study sought to develop and validate a model to predict in-hospital mortality among patients with the acute coronary syndrome (ACS) using nonlinear algorithms. METHODS: A total of 2414 ACS patients were enrolled in this study. All samples were divided into five groups for cross-validation. The logistic regression (LR) model and XGboost model were applied to predict in-hospital mortality. The results of two models were compared between the variable set by the global registry of acute coronary events (GRACE) score and the selected variable set. RESULTS: The in-hospital mortality rate was 3.5% in the dataset. Model performance on the selected variable set was better than that on GRACE variables: a 3% increase in area under the receiver operating characteristic (ROC) curve (AUC) for LR and 1.3% for XGBoost. The AUC of XGBoost is 0.913 (95% confidence interval [CI]: 0.910-0.916), demonstrating a better discrimination ability than LR (AUC = 0.904, 95% CI: 0.902-0.905) on the selected variable set. Almost perfect calibration was found in XGBoost (slope of predicted to observed events, 1.08; intercept, -0.103; p < .001). CONCLUSIONS: XGboost modeling, an advanced machine learning algorithm, identifies new variables and provides high accuracy for the prediction of in-hospital mortality in ACS patients.

EGFRvIII Promotes the Proneural-Mesenchymal Transition of Glioblastoma Multiforme and Reduces Its Sensitivity to Temozolomide by Regulating the NF-κB/ALDH1A3 Axis.

(1) Background: Glioblastoma multiforme (GBM) is the most common and malignant intracranial tumor in adults. At present, temozolomide (TMZ) is recognized as the preferred chemotherapeutic drug for GBM, but some patients have low sensitivity to TMZ or chemotherapy resistance to TMZ. Our previous study found that GBM patients with EGFRvIII (+) have low sensitivity to TMZ. However, the reasons and possible mechanisms of the chemoradiotherapy resistance in GBM patients with EGFRvIII (+) are not clear. (2) Methods: In this study, tissue samples of patients with GBM, GBM cell lines, glioma stem cell lines, and NSG mice were used to explore the causes and possible mechanisms of low sensitivity to TMZ in patients with EGFRvIII (+)-GBM. (3) Results: The study found that EGFRvIII promoted the proneural-mesenchymal transition of GBM and reduced its sensitivity to TMZ, and EGFRvIII regulated of the expression of ALDH1A3. (4) Conclusions: EGFRvIII activated the NF-κB pathway and further regulated the expression of ALDH1A3 to promote the proneural-mesenchymal transition of GBM and reduce its sensitivity to TMZ, which will provide an experimental basis for the selection of clinical drugs for GBM patients with EGFRvIII (+).

Trogocytosis of CAR molecule regulates CAR-T cell dysfunction and tumor antigen escape.

Chimeric antigen receptor (CAR) T-cell therapy has demonstrated clinical response in treating both hematologic malignancies and solid tumors. Although instances of rapid tumor remissions have been observed in animal models and clinical trials, tumor relapses occur with multiple therapeutic resistance mechanisms. Furthermore, while the mechanisms underlying the long-term therapeutic resistance are well-known, short-term adaptation remains less understood. However, more views shed light on short-term adaptation and hold that it provides an opportunity window for long-term resistance. In this study, we explore a previously unreported mechanism in which tumor cells employ trogocytosis to acquire CAR molecules from CAR-T cells, a reversal of previously documented processes. This mechanism results in the depletion of CAR molecules and subsequent CAR-T cell dysfunction, also leading to short-term antigen loss and antigen masking. Such type of intercellular communication is independent of CAR downstream signaling, CAR-T cell condition, target antigen, and tumor cell type. However, it is mainly dependent on antigen density and CAR sensitivity, and is associated with tumor cell cholesterol metabolism. Partial mitigation of this trogocytosis-induced CAR molecule transfer can be achieved by adaptively administering CAR-T cells with antigen density-individualized CAR sensitivities. Together, our study reveals a dynamic process of CAR molecule transfer and refining the framework of clinical CAR-T therapy for solid tumors.

Immunological profiles of human oligodendrogliomas define two distinct molecular subtypes.

BACKGROUND: Human oligodendroglioma presents as a heterogeneous disease, primarily characterized by the isocitrate dehydrogenase (IDH) mutation and 1p/19q co-deletion. Therapy development for this tumor is hindered by incomplete knowledge of somatic driving alterations and suboptimal disease classification. We herein aim to identify intrinsic molecular subtypes through integrated analysis of transcriptome, genome and methylome. METHODS: 137 oligodendroglioma patients from the Cancer Genome Atlas (TCGA) dataset were collected for unsupervised clustering analysis of immune gene expression profiles and comparative analysis of genome and methylome. Two independent datasets containing 218 patients were used for validation. FINDINGS: We identified and independently validated two reproducible subtypes associated with distinct molecular characteristics and clinical outcomes. The proliferative subtype, named Oligo1, was characterized by more tumors of CNS WHO grade 3, as well as worse prognosis compared to the Oligo2 subtype. Besides the clinicopathologic features, Oligo1 exhibited enrichment of cell proliferation, regulation of cell cycle and Wnt signaling pathways, and significantly altered genes, such as EGFR, NOTCH1 and MET. In contrast, Oligo2, with favorable outcome, presented increased activation of immune response and metabolic process. Higher T cell/APC co-inhibition and inhibitory checkpoint levels were observed in Oligo2 tumors. Finally, multivariable analysis revealed our classification was an independent prognostic factor in oligodendrogliomas, and the robustness of these molecular subgroups was verified in the validation cohorts. INTERPRETATION: This study provides further insights into patient stratification as well as presents opportunities for therapeutic development in human oligodendrogliomas. FUNDING: The funders are listed in the Acknowledgement.

Diffuse Isocitrate Dehydrogenase-Mutant Gliomas With Histone H3 Alterations Are Distinguished by Unique Clinical Characteristics, Molecular Expression Profile, and Survival Prognosis.

BACKGROUND AND OBJECTIVES: Histopathological features and molecular biomarkers have been studied as potential prognostic factors. This study aimed to investigate the clinical features, molecular phenotypes, and survival prognosis of isocitrate dehydrogenase (IDH)-mutant (IDHmt) gliomas with histone H3 alterations (H3-alterations). METHODS: A total of 236 and 657 patients with whole-exome sequencing data were separately collected from the Chinese Glioma Genome Atlas and The Cancer Genome Atlas databases. Survival analysis of patients with glioma was performed using Kaplan-Meier survival curves stratified by histone H3 status. Univariate and multivariate analyses were used to identify the associations between histone H3 status and other clinicopathological factors with survival in patients with IDH-mutant gliomas. RESULTS: Diffuse gliomas with H3 alterations are more likely to be high grade in 2 cohorts ( P = .025 and P = .021, respectively). IDHmt glioma patients with H3-alteration had significantly less life expectancy than histone H3 wild-type ( P = .041 and P = .008, respectively). In the Chinese Glioma Genome Atlas cohort, Karnofsky performance scores ≤ 80 (HR 2.394, 95% CI 1.257-4.559, P = .008), extent of resection (HR 0.971, 95% CI 0.957-0.986, P < .001), high WHO grade (HR 6.938, 95% CI 2.787-17.269, P < .001), H3-alteration (HR 2.482, 95% CI 1.183-4.981, P = .016), and 1p/19q codeletion (HR 0.169, 95% CI 0.073-0.390, P < .001) were independently associated with IDHmt gliomas. In the The Cancer Genome Atlas cohort, age (HR 1.034, 95% CI 1.008-1.061, P = .010), high WHO grade (HR 2.365, 95% CI 1.263-4.427, P = .007), and H3-alteration (HR 2.501, 95% CI 1.312-4.766, P = .005) were independently associated with IDHmt gliomas. CONCLUSION: Identification and assessment of histone H3 status in clinical practice might help improve prognostic prediction and develop therapeutic strategies for these patient subgroups.

Explainable and efficient deep early warning system for cardiac arrest prediction from electronic health records.

Cardiac arrest (CA) is a fatal acute event. The development of new CA early warning system based on time series of vital signs from electronic health records (EHR) has great potential to reduce CA damage. In this process, recursive architecture-based deep learning, as a powerful tool for time series data processing, enables automatically extract features from various monitoring clinical parameters and to further improve the performance for acute critical illness prediction. However, the unexplainable nature and excessive time caused by black box structure with poor parallelism are the limitations of its development, especially in the CA clinical application with strict requirement of emergency treatment and low hidden dangers. In this study, we present an explainable and efficient deep early warning system for CA prediction, which features are captured by an efficient temporal convolutional network (TCN) on EHR clinical parameters sequence and explained by deep Taylor decomposition (DTD) theoretical framework. To demonstrate the feasibility of our method and further evaluate its performance, prediction and explanation experiments were performed. Experimental results show that our method achieves superior CA prediction accuracy compared with standard national early warning score (NEWS), in terms of overall AUROC (0.850 Vs. 0.476) and F1-Score (0.750 Vs. 0.450). Furthermore, our method improves the interpretability and efficiency of deep learning-based CA early warning system. It provides the relevance of prediction results for each clinical parameter and about 1.7 times speed enhancement for system calculation compared with the long short-term memory network.

Impact of big data resources on clinicians' activation of prior medical knowledge.

Background: Activating prior medical knowledge in diagnosis and treatment is an important basis for clinicians to improve their care ability. However, it has not been systematically explained whether and how various big data resources affect the activation of prior knowledge in the big data environment faced by clinicians. Objective: The aim of this study is to contribute to a better understanding on how the activation of prior knowledge of clinicians is affected by a wide range of shared and private big data resources, to reveal the impact of big data resources on clinical competence and professional development of clinicians. Method: Through the comprehensive analysis of extant research results, big data resources are classified as big data itself, big data technology and big data services at the public and institutional levels. A survey was conducted on clinicians and IT personnel in Chinese hospitals. A total of 616 surveys are completed, involving 308 medical institutions. Each medical institution includes a clinician and an IT personnel. SmartPLS version 2.0 software package was used to test the direct impact of big data resources on the activation of prior knowledge. We further analyze their indirect impact of those big data resources without direct impact. Results: (1) Big data quality environment at the institutional level and the big data sharing environment at the public level directly affect activation of prior medical knowledge; (2) Big data service environment at the institutional level directly affects activation of prior medical knowledge; (3) Big data deployment environment at the institutional level and big data service environment at the public level have no direct impact on activation of prior knowledge of clinicians, but they have an indirect impact through big data quality environment and service environment at the institutional level and the big data sharing environment at the public level. Conclusions: Big data technology, big data itself and big data service at the public level and institutional level interact and influence each other to activate prior medical knowledge. This study highlights the implications of big data resources on improvement of clinicians' diagnosis and treatment ability.

Mobile Cardiac Acoustic Monitoring System to Evaluate Left Ventricular Systolic Function in Pacemaker Patients.

The mobile cardiac acoustic monitoring system is a promising tool to enable detection and assist the diagnosis of left ventricular systolic dysfunction (LVSD). The objective of the study was to evaluate the diagnostic value of electromechanical activation time (EMAT), an important cardiac acoustic biomarker, in quantifying LVSD among left bundle branch pacing (LBBP) and right ventricular apical pacing (RVAP) patients using a mobile acoustic cardiography monitoring system. In this prospective single-center observational study, pacemaker-dependent patients were consecutively enrolled. EMAT, the time from the start of the pacing QRS wave to first heart sound (S1) peak; left ventricular systolic time (LVST), the time from S1 peak to S2 peak; and ECG were recorded simultaneously by the mobile cardiac acoustic monitoring system. LVEF was measured by echocardiography. A logistic regression model was applied to evaluate the association between EMAT and reduced EF (LVEF < 50%). A total of 105 pacemaker-dependent patients participated. The RVAP group (n = 58) displayed a significantly higher EMAT than the LBBP group (n = 47) (150.95 ± 19.46 vs. 108.23 ± 12.26 ms, p < 0.001). Pearson correlation analysis revealed a statistically significant negative correlation between EMAT and LVEF (p < 0.001). Survival analysis showed the sensitivity and specificity of detecting LVEF to be < 50% when EMAT ≥ 151 ms were 96.00% and 96.97% in the RVAP group. In LBBP patients, the sensitivity and specificity of using EMAT ≥ 110 ms as the cutoff value for the detection of LVEF < 50% were 75.00% and 100.00%. There was no significant difference in LVST with or without LVSD in the RVAP group (p = 0.823) and LBBP group (p = 0.086). Compared to LVST, EMAT was more helpful to identify LVSD in pacemaker-dependent patients. The cutoff point of EMAT for diagnosing LVEF < 50% differed regarding the pacing type. Therefore, the mobile cardiac acoustic monitoring system can be used to identify the progress of LVSD in pacemaker patients.