Aberrant splicing of CaV1.2 calcium channel induced by decreased Rbfox1 enhances arterial constriction during diabetic hyperglycemia.

Diabetic hyperglycemia induces dysfunctions of arterial smooth muscle, leading to diabetic vascular complications. The CaV1.2 calcium channel is one primary pathway for Ca2+ influx, which initiates vasoconstriction. However, the long-term regulation mechanism(s) for vascular CaV1.2 functions under hyperglycemic condition remains unknown. Here, Sprague-Dawley rats fed with high-fat diet in combination with low dose streptozotocin and Goto-Kakizaki (GK) rats were used as diabetic models. Isolated mesenteric arteries (MAs) and vascular smooth muscle cells (VSMCs) from rat models were used to assess K+-induced arterial constriction and CaV1.2 channel functions using vascular myograph and whole-cell patch clamp, respectively. K+-induced vasoconstriction is persistently enhanced in the MAs from diabetic rats, and CaV1.2 alternative spliced exon 9* is increased, while exon 33 is decreased in rat diabetic arteries. Furthermore, CaV1.2 channels exhibit hyperpolarized current-voltage and activation curve in VSMCs from diabetic rats, which facilitates the channel function. Unexpectedly, the application of glycated serum (GS), mimicking advanced glycation end-products (AGEs), but not glucose, downregulates the expression of the splicing factor Rbfox1 in VSMCs. Moreover, GS application or Rbfox1 knockdown dynamically regulates alternative exons 9* and 33, leading to facilitated functions of CaV1.2 channels in VSMCs and MAs. Notably, GS increases K+-induced intracellular calcium concentration of VSMCs and the vasoconstriction of MAs. These results reveal that AGEs, not glucose, long-termly regulates CaV1.2 alternative splicing events by decreasing Rbfox1 expression, thereby enhancing channel functions and increasing vasoconstriction under diabetic hyperglycemia. This study identifies the specific molecular mechanism for enhanced vasoconstriction under hyperglycemia, providing a potential target for managing diabetic vascular complications.

A Machine Learning Model for Predicting In-Hospital Mortality in Chinese Patients With ST-Segment Elevation Myocardial Infarction: Findings From the China Myocardial Infarction Registry.

BACKGROUND: Machine learning (ML) risk prediction models, although much more accurate than traditional statistical methods, are inconvenient to use in clinical practice due to their nontransparency and requirement of a large number of input variables. OBJECTIVE: We aimed to develop a precise, explainable, and flexible ML model to predict the risk of in-hospital mortality in patients with ST-segment elevation myocardial infarction (STEMI). METHODS: This study recruited 18,744 patients enrolled in the 2013 China Acute Myocardial Infarction (CAMI) registry and 12,018 patients from the China Patient-Centered Evaluative Assessment of Cardiac Events (PEACE)-Retrospective Acute Myocardial Infarction Study. The Extreme Gradient Boosting (XGBoost) model was derived from 9616 patients in the CAMI registry (2014, 89 variables) with 5-fold cross-validation and validated on both the 9125 patients in the CAMI registry (89 variables) and the independent China PEACE cohort (10 variables). The Shapley Additive Explanations (SHAP) approach was employed to interpret the complex relationships embedded in the proposed model. RESULTS: In the XGBoost model for predicting all-cause in-hospital mortality, the variables with the top 8 most important scores were age, left ventricular ejection fraction, Killip class, heart rate, creatinine, blood glucose, white blood cell count, and use of angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin II receptor blockers (ARBs). The area under the curve (AUC) on the CAMI validation set was 0.896 (95% CI 0.884-0.909), significantly higher than the previous models. The AUC for the Global Registry of Acute Coronary Events (GRACE) model was 0.809 (95% CI 0.790-0.828), and for the TIMI model, it was 0.782 (95% CI 0.763-0.800). Despite the China PEACE validation set only having 10 available variables, the AUC reached 0.840 (0.829-0.852), showing a substantial improvement to the GRACE (0.762, 95% CI 0.748-0.776) and TIMI (0.789, 95% CI 0.776-0.803) scores. Several novel and nonlinear relationships were discovered between patients' characteristics and in-hospital mortality, including a U-shape pattern of high-density lipoprotein cholesterol (HDL-C). CONCLUSIONS: The proposed ML risk prediction model was highly accurate in predicting in-hospital mortality. Its flexible and explainable characteristics make the model convenient to use in clinical practice and could help guide patient management. TRIAL REGISTRATION: ClinicalTrials.gov NCT01874691; https://clinicaltrials.gov/study/NCT01874691.

Dysregulated Rbfox2 produces aberrant splicing of CaV1.2 calcium channel in diabetes-induced cardiac hypertrophy.

BACKGROUND: L-type Ca2+ channel CaV1.2 is essential for cardiomyocyte excitation, contraction and gene transcription in the heart, and abnormal functions of cardiac CaV1.2 channels are presented in diabetic cardiomyopathy. However, the underlying mechanisms are largely unclear. The functions of CaV1.2 channels are subtly modulated by splicing factor-mediated alternative splicing (AS), but whether and how CaV1.2 channels are alternatively spliced in diabetic heart remains unknown. METHODS: Diabetic rat models were established by using high-fat diet in combination with low dose streptozotocin. Cardiac function and morphology were assessed by echocardiography and HE staining, respectively. Isolated neonatal rat ventricular myocytes (NRVMs) were used as a cell-based model. Cardiac CaV1.2 channel functions were measured by whole-cell patch clamp, and intracellular Ca2+ concentration was monitored by using Fluo-4 AM. RESULTS: We find that diabetic rats develop diastolic dysfunction and cardiac hypertrophy accompanied by an increased CaV1.2 channel with alternative exon 9* (CaV1.2E9*), but unchanged that with alternative exon 8/8a or exon 33. The splicing factor Rbfox2 expression is also increased in diabetic heart, presumably because of dominate-negative (DN) isoform. Unexpectedly, high glucose cannot induce the aberrant expressions of CaV1.2 exon 9* and Rbfox2. But glycated serum (GS), the mimic of advanced glycation end-products (AGEs), upregulates CaV1.2E9* channels proportion and downregulates Rbfox2 expression in NRVMs. By whole-cell patch clamp, we find GS application hyperpolarizes the current-voltage curve and window currents of cardiac CaV1.2 channels. Moreover, GS treatment raises K+-triggered intracellular Ca2+ concentration ([Ca2+]i), enlarges cell surface area of NRVMs and induces hypertrophic genes transcription. Consistently, siRNA-mediated knockdown of Rbfox2 in NRVMs upregulates CaV1.2E9* channel, shifts CaV1.2 window currents to hyperpolarization, increases [Ca2+]i and induces cardiomyocyte hypertrophy. CONCLUSIONS: AGEs, not glucose, dysregulates Rbfox2 which thereby increases CaV1.2E9* channels and hyperpolarizes channel window currents. These make the channels open at greater negative potentials and lead to increased [Ca2+]i in cardiomyocytes, and finally induce cardiomyocyte hypertrophy in diabetes. Our work elucidates the underlying mechanisms for CaV1.2 channel regulation in diabetic heart, and targeting Rbfox2 to reset the aberrantly spliced CaV1.2 channel might be a promising therapeutic approach in diabetes-induced cardiac hypertrophy.

Identification of atrial fibrillation phenotypes at low risk of stroke in patients with CHA2DS2-VASc ≥2: Insight from the China-AF study.

OBJECTIVE: Patients with atrial fibrillation (AF) are highly heterogeneous, and current risk stratification scores are only modestly good at predicting an individual's stroke risk. We aim to identify distinct AF clinical phenotypes with cluster analysis to optimize stroke prevention practices. METHODS: From the prospective Chinese Atrial Fibrillation Registry cohort study, we included 4337 AF patients with CHA2 DS2 -VASc≥2 for males and 3 for females who were not treated with oral anticoagulation. We randomly split the patients into derivation and validation sets by a ratio of 7:3. In the derivation set, we used outcome-driven patient clustering with metric learning to group patients into clusters with different risk levels of ischemic stroke and systemic embolism, and identify clusters of patients with low risks. Then we tested the results in the validation set, using the clustering rules generated from the derivation set. Finally, the survival decision tree was applied as a sensitivity analysis to confirm the results. RESULTS: Up to the follow-up of 1 year, 140 thromboembolic events (ischemic stroke or systemic embolism) occurred. After supervised metric learning from six variables involved in CHA2 DS2 -VASc scheme, we identified a cluster of patients (255/3035, 8.4%) at an annual thromboembolism risk of 0.8% in the derivation set. None of the patients in the low-risk cluster had prior thromboembolism, heart failure, diabetes, or age older than 70 years. After applying the regularities from metric learning on the validation set, we also identified a cluster of patients (137/1302, 10.5%) with an incident thromboembolism rate of 0.7%. Sensitivity analysis based on the survival decision tree approach selected a subgroup of patients with the same phenotypes as the metric-learning algorithm. CONCLUSIONS: Cluster analysis identified a distinct clinical phenotype at low risk of stroke among high-risk [CHA2 DS2 -VASc≥2 (3 for females)] patients with AF. The use of the novel analytic approach has the potential to prevent a subset of AF patients from unnecessary anticoagulation and avoid the associated risk of major bleeding.

A predictive model for postoperative progressive haemorrhagic injury in traumatic brain injuries.

BACKGROUND: Progressive haemorrhagic injury after surgery in patients with traumatic brain injury often results in poor patient outcomes. This study aimed to develop and validate a practical predictive tool that can reliably estimate the risk of postoperative progressive haemorrhagic injury (PHI) in patients with traumatic brain injury (TBI). METHODS: Data from 645 patients who underwent surgery for TBI between March 2018 and December 2020 were collected. The outcome was postoperative intracranial PHI, which was assessed on postoperative computed tomography. The least absolute shrinkage and selection operator (LASSO) regression model, univariate analysis, and Delphi method were applied to select the most relevant prognostic predictors. We combined conventional coagulation test (CCT) data, thromboelastography (TEG) variables, and several predictors to develop a predictive model using binary logistic regression and then presented the results as a nomogram. The predictive performance of the model was assessed with calibration and discrimination. Internal validation was assessed. RESULTS: The signature, which consisted of 11 selected features, was significantly associated with intracranial PHI (p < 0.05, for both primary and validation cohorts). Predictors in the prediction nomogram included age, S-pressure, D-pressure, pulse, temperature, reaction time, PLT, prothrombin time, activated partial thromboplastin time, FIB, and kinetics values. The model showed good discrimination, with an area under the curve of 0.8694 (95% CI, 0.8083-0.9304), and good calibration. CONCLUSION: This model is based on a nomogram incorporating CCT and TEG variables, which can be conveniently derived at hospital admission. It allows determination of this individual risk for postoperative intracranial PHI and will facilitate a timely intervention to improve outcomes.

Machine Learning for Prediction and Risk Stratification of Lupus Nephritis Renal Flare.

BACKGROUND: Renal flare of lupus nephritis (LN) is strongly associated with poor kidney outcomes, and predicting renal flare and stratifying its risk are important for clinical decision-making and individualized management to reduce LN flare. METHODS: We randomly divided 1,694 patients with biopsy-proven LN, who had achieved remission after treatment, into a derivation cohort (n = 1,186) and an internal validation cohort (n = 508), at a ratio of 7:3. The risk of renal flare 5 years after remission was predicted using an eXtreme Gradient Boosting (XGBoost) method model, developed from 59 variables, including demographic, clinical, immunological, pathological, and therapeutic characteristics. A simplified risk score prediction model (SRSPM) was developed from important variables selected by XGBoost model using stepwise Cox regression for practical convenience. RESULTS: The 5-year relapse rates were 39.5% and 38.2% in the derivation and internal validation cohorts, respectively. Both the XGBoost model and the SRSPM had good predictive performance, with a C-index of 0.819 (95% confidence interval [CI]: 0.774-0.857) and 0.746 (95% CI: 0.697-0.795), respectively, in the validation cohort. The SRSPM comprised 6 variables, including partial remission and endocapillary hypercellularity at baseline, age, serum Alb, anti-dsDNA, and serum complement C3 at the point of remission. Using Kaplan-Meier analysis, the SRSPM identified significant risk stratification for renal flares (p < 0.001). CONCLUSIONS: Renal flare of LN can be readily predicted using the XGBoost model and the SRSPM, and the SRSPM can also stratify flare risk. Both models are useful for clinical decision-making and individualized management in LN.

An integrated framework for modelling quantitative effects of entry restrictions and travel quarantine on importation risk of COVID-19.

OBJECTIVE: As the potential spread of COVID-19 sparked by imported cases from overseas will pose continuous challenges, it is essential to estimate the effects of control measures on reducing the importation risk of COVID-19. Our objective is to provide a framework of methodology for quantifying the combined effects of entry restrictions and travel quarantine on managing the importation risk of COVID-19 and other pandemics by leveraging different sets of parameters. METHODS: Three major categories of control measures on controlling importation risk were parameterized and modelled by the framework: 1) entry restrictions, 2) travel quarantine, and 3) domestic containment measures. Integrating the parameterized intensity of control measures, a modified SEIR model was developed to simulate the case importation and local epidemic under different scenarios of global epidemic dynamics. A web-based tool was also provided to enable interactive visualization of epidemic simulation. RESULTS: The simulated number of case importation and local spread modelled by the proposed framework of methods fitted well to the historical epidemic curve of China and Singapore. Based on the simulation results, the total numbers of infected cases when reducing 30% of visitor arrivals would be 88·4 (IQR 87·5-89·6) and 58·8 (IQR 58·3-59·5) times more than those when reducing 99% of visitor arrivals in mainland China and Singapore respectively, assuming actual time-varying Rt and travel quarantine policy. If the number of global daily new infections reached 100,000, 85%-91% of inbound travels should be reduced to keep the daily new infected number below 100 for a country with a similar travel volume as Singapore (daily 52,000 tourist arrivals in 2019). Whereas if the number was lower than 10,000, the daily new infected case would be less than 100 even with no entry restrictions. DISCUSSIONS: We proposed a framework that first estimated the intensity of travel restrictions and local containment measures for countries since the first overseas imported case. Our approach then quantified the combined effects of entry restrictions and travel quarantine using a modified SEIR model to simulate the potential epidemic spread under hypothetical intensities of these control measures. We also developed a web-based system that enables interactive simulation, which could serve as a valuable tool for health system administrators to assess policy effects on managing the importation risk. By leveraging different sets of parameters, it could adapt to any specific country and specific type of epidemic. CONCLUSIONS: This framework has provided a valuable tool to parameterize the intensity of control measures, simulate both the case importation and local epidemic, and quantify the combined effects of entry restrictions and travel quarantine on managing the importation risk.

Trans-base and trans-vault low-velocity penetrating brain injury: A retrospective comparative study of characteristics, treatment, and outcomes.

PURPOSE: Low-velocity penetrating brain injury (LVPBI) caused by foreign bodies can pose life-threatening emergencies. Their complexity and lack of validated classification data have prevented standardization of clinical management. We aimed to compare the trans-base and trans-vault phenotypes of LVPBI to help provide guidance for clinical decision-making of such injury type. METHODS: A retrospective study on LVPBI patients managed at our institution from November 2013 to March 2020 was conducted. We included LVPBI patients admitted for the first time for surgery, and excluded those with multiple injuries, gunshot wounds, pregnancy, severe blunt head trauma, etc. Patients were categorized into trans-base and trans-vault LVPBI groups based on the penetration pathway. Discharged patients were followed up by outpatient visit or telephone. The data were entered into the Electronic Medical Record system by clinicians, and subsequently derived by researchers. The demography and injury characteristics, treatment protocols, complications, and outcomes were analyzed and compared between the two groups. A t-test was used for analysis of normally distributed data, and a Mann-Whitney U test for non-parametric data. A generalized linear model was further established to determine whether the factors length of stay and performance scale score were influenced by each factor. RESULTS: A total of 27 LVPBI patients were included in this analysis, comprised of 13 (48.1%) trans-base cases and 14 (51.9%) trans-vault cases. Statistical analyses suggested that trans-base LVPBI was correlated with deeper wounds; while the trans-vault phenotype was correlated with injury by metal foreign bodies. There was no difference in Glasgow Coma Scale score and the risk of intracranial hemorrhage between the two groups. Surgical approaches in the trans-base LVPBI group included subfrontal (n = 5, 38.5%), subtemporal (n = 5, 38.5%), lateral fissure (n = 2, 15.4%), and distal lateral (n = 1, 7.7%). All patients in the trans-vault group underwent a brain convex approach using the foreign body as reference (n = 14, 100%). Moreover, the two groups differed in application prerequisites for intracranial pressure monitoring and vessel-related treatment. Trans-base LVPBI was associated with higher rates of cranial nerve and major vessel injuries; in contrast, trans-vault LVPBI was associated with lower functional outcome scores. CONCLUSION: Our findings suggest that trans-base and trans-vault LVPBIs differ in terms of characteristics, treatment, and outcomes. Further understanding of these differences may help guide clinical decisions and contribute to a better management of LVPBIs.

Incorporating Tumor Characteristics to Maximize 21-Gene Assay Utility: A Cost-Effectiveness Analysis.

Background: Literature suggests that Oncotype DX (ODX) is cost-effective. These studies, however, tend to ignore clinical characteristics and have not incorporated population-based data regarding the distribution of ODX results across different clinical risk groups. Accordingly, this study assessed the cost-effectiveness of ODX across strata of clinical risk groups using population-based ODX data. Methods: We created state-transition models to calculate costs and quality-adjusted life years (QALYs) gained over the lifetime for women with estrogen receptor (ER)-positive, HER2-negative, lymph node-negative breast cancer from a US payer perspective. Using the Connecticut Tumor Registry, we classified the 2,245 patients diagnosed in 2011 through 2013 into 3 clinical risk groups according to the PREDICT model, a risk calculator developed by the National Health Service in the United Kingdom. Within each risk group, we then determined the recurrence score (RS) distributions (<18, 18-30, and ≥31). Other input parameters were derived from the literature. Uncertainty was assessed using deterministic and probabilistic sensitivity analyses. Results: Approximately 82.5%, 11.9%, and 5.6% of our sample were in the PREDICT low-, intermediate-, and high-risk groups, respectively. When combining these 3 groups, ODX had an incremental cost-effectiveness ratio (ICER) of $62,200 per QALY for patients aged 60 years. The ICERs, however, differed across clinical risk groups, ranging from $124,600 per QALY in the low-risk group, to $28,700 per QALY in the intermediate-risk group, to $15,700 per QALY in the high-risk group. Results were sensitive to patient age: the ICER for patients aged 45 to 75 years ranged from $77,100 to $344,600 per QALY in the PREDICT low-risk group, and was lower than $100,000 per QALY in the intermediate- and high-risk groups. Conclusions: ODX is not cost-effective for women with clinical low-risk breast cancer, which constitutes most patients with ER-positive disease.

Chromatin Licensing and DNA Replication Factor 1 (CDT1) Is a Potential Prognostic Biomarker Involved in the Malignant Biological Behavior of Glioma.

Glioma is the primary malignant tumor with the highest incidence rate in the adult central nervous system. The application of bioinformatics methods to analyze the RNA sequences of multiple gliomas revealed that the CDT1 gene has a significant impact on the cell cycle of glioma cells. Subsequently, we comprehensively and systematically investigated the expression of CDT1 in gliomas through bioinformatics analysis, clinical tissue specimens, and in vitro functional experiments. Our study is the first to report the expression of CDT1 in glioma. Our findings demonstrate that CDT1 plays a crucial role in the proliferation and invasion of glioma. Additionally, our bioinformatics analysis identified several other genes and signaling pathways that are dysregulated in multifocal gliomas, providing potential targets for further research and drug development.

Under Water Superelastic Porous Nanofibrous Sponge for Efficient RNA Separation and Purification.

Developing monolithic materials for chromatography columns with a novel interconnected porous structure is vital for the enhancement of the separation efficiency of RNA purification processes. Herein, a porous nanofibrous sponge (PNFS) is constructed by freeze molding and freeze-drying a nanofiber dispersion with ethylene vinyl alcohol copolymer nanofibers as the skeleton, chitosan (CS) and polyethylenimine (PEI) as the binders, and glutaraldehyde (GA) as the crosslinking agent. The results show that when the CS content of the dispersion is 1.5 wt %, PNFS demonstrates a high static adsorption capacity of 406.5 mg/g (30.7 mg/m2) and a dynamic adsorption capacity of 382.6 mg/g (28.9 mg/m2) at a flow rate of 1 mm/min. Moreover, PNFS shows a high specific adsorption performance toward RNA in the presence of bovine serum albumin, lecithin, or DNA by adjusting the solution pH value and the method of gradient elution. Besides, PNFS presents exceptional performance in the rapid separation of RNA from HT22 cells without degradation. This result can be attributed to optimized morphology, pore structure, and comprehensive performance of PNFS, benefiting from the synergistic effect of the highly oriented porous structure and CS-PEI interaction derived from the high-density adsorption ligands on the channel walls of PNFS. This work provided an efficient strategy to handle the permeability/adsorptivity trade-off for ion-exchange chromatographic materials.

Restoration of ARA metabolic disorders in vascular smooth muscle cells alleviates intimal hyperplasia.

Intimal hyperplasia (IH) is an innegligible issue for patients undergoing interventional therapy. The proliferation and migration of vascular smooth muscle cells (VSMCs) induced by platelet-derived growth factor-BB (PDGF-BB) are critical events in the development of IH. While the exact mechanism and effective target for IH needs further investigation. Metabolic disorders of arachidonic acid (ARA) are involved in the occurrence and progression of various diseases. In this study, we found that the expressions of soluble epoxide hydrolase (sEH) and cyclooxygenase-2 (COX-2) were significantly increased in the VSMCs during balloon injury-induced IH. Then, we employed a COX-2/sEH dual inhibitor PTUPB to increase the concentration of epoxyeicosatrienoic acids (EETs) while prevent the release of pro-inflammatory prostaglandins. Results showed that PTUPB treatment significantly reduced neointimal thickening induced by balloon injury in rats in vivo and inhibited PDGF-BB-induced proliferation and migration of VSMCs in vitro. Our results showed that PTUPB may reverse the phenotypic transition of VSMCs by inhibiting Pttg1 expression. In conclusion, we found that the dysfunction of ARA metabolism in VSMCs contributes to IH, and the COX-2/sEH dual inhibitor PTUPB attenuates IH progression by reversing the phenotypic switch in VSMC through the Sirt1/Pttg1 pathway.

Deep learning model to predict lupus nephritis renal flare based on dynamic multivariable time-series data.

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.

Proposed novel grading system for stage I invasive lung adenocarcinoma and a comparison with the 2020 IASLC grading system.

BACKGROUND: Several studies have proposed grading systems for risk stratification of early-stage lung adenocarcinoma based on histological patterns. However, the reproducibility of these systems is poor in clinical practice, indicating the need to develop a new grading system which is easy to apply and has high accuracy in prognostic stratification of patients. METHODS: Patients with stage I invasive nonmucinous lung adenocarcinoma were retrospectively collected from pathology archives between 2009 and 2016. The patients were divided into a training and validation set at a 6:4 ratio. Histological features associated with patient outcomes (overall survival [OS] and progression-free survival [PFS]) identified in the training set were used to construct a new grading system. The newly proposed system was validated using the validation set. Survival differences between subgroups were assessed using the log-rank test. The prognostic performance of the novel grading system was compared with two previously proposed systems using the concordance index. RESULTS: A total of 539 patients were included in this study. Using a multioutcome decision tree model, four pathological factors, including the presence of tumor spread through air space (STAS) and the percentage of lepidic, micropapillary and solid subtype components, were selected for the proposed grading system. Patients were accordingly classified into three groups: low, medium, and high risk. The high-risk group showed a 5-year OS of 52.4% compared to 89.9% and 97.5% in the medium and low-risk groups, respectively. The 5-year PFS of patients in the high-risk group was 38.1% compared to 61.7% and 90.9% in the medium and low-risk groups, respectively. Similar results were observed in the subgroup analysis. Additionally, our proposed grading system provided superior prognostic stratification compared to the other two systems with a higher concordance index. CONCLUSION: The newly proposed grading system based on four pathological factors (presence of STAS, and percentage of lepidic, micropapillary, and solid subtypes) exhibits high accuracy and good reproducibility in the prognostic stratification of stage I lung adenocarcinoma patients.

A New Risk Model based on the Machine Learning Approach for Prediction of Mortality in the Respiratory Intensive Care Unit.

BACKGROUND: Intensive care unit (ICU) resources are inadequate for the large population in China, so it is essential for physicians to evaluate the condition of patients at admission. In this study, our objective was to construct a machine-learning risk prediction model for mortality in respiratory intensive care units (RICUs). METHODS: This study involved 817 patients who made 1,063 visits and who were admitted to the RICU from 2012 to 2017. Potential predictors such as demographic information, laboratory results, vital signs and clinical characteristics were considered. We constructed eXtreme Gradient Boosting (XGBoost) models and compared performances with random forest models, logistic regression models and clinical scores such as Acute Physiology and Chronic Health Evaluation II (APACHE II) and the sequential organ failure assessment (SOFA) system. The model was externally validated using data from Medical Information Mart for Intensive Care (MIMIC-III) database. A web-based calculator was developed for practical use. RESULTS: Among the 1,063 visits, the RICU mortality rate was 13.5%. The XGBoost model achieved the best performance with the area under the receiver operating characteristics curve (AUROC) of 0.860 (95% confidence interval (CI): 0.808 - 0.909) in the test set, which was significantly greater than APACHE II (0.749, 95% CI: 0.674 - 0.820; P = 0.015) and SOFA (0.751, 95% CI: 0.669 - 0.818; P = 0.018). The Hosmer-Lemeshow test indicated a good calibration of our predictive model in the test set with a P-value of 0.176. In the external validation dataset, the AUROC of XGBoost model was 0.779 (95% CI: 0.714 - 0.813). The final model contained variables that were previously known to be associated with mortality, but it also included some features absent from the clinical scores. The mean N-terminal pro-B-type natriuretic peptide (NTproBNP) of survivors was significantly lower than that of the non-survival group (2066.43 pg/mL vs. 8232.81 pg/mL; P < 0.001). CONCLUSIONS: Our results showed that the XGBoost model could be a suitable model for predicting RICU mortality with easy-to-collect variables at admission and help intensivists improve clinical decision-making for RICU patients. We found that higher NT-proBNP can be a good indicator of poor prognosis.

Machine learning for prediction of bleeding in acute myocardial infarction patients after percutaneous coronary intervention.

Background: Prediction of bleeding is critical for acute myocardial infarction (AMI) patients after percutaneous coronary intervention (PCI). Machine learning methods can automatically select the combination of the important features and learn their underlying relationship with the outcome. Objectives: We aimed to evaluate the predictive value of machine learning methods to predict in-hospital bleeding for AMI patients. Design: We used data from the multicenter China Acute Myocardial Infarction (CAMI) registry. The cohort was randomly partitioned into derivation set (50%) and validation set (50%). We applied a state-of-art machine learning algorithm, eXtreme Gradient Boosting (XGBoost), to automatically select features from 98 candidate variables and developed a risk prediction model to predict in-hospital bleeding (Bleeding Academic Research Consortium [BARC] 3 or 5 definition). Results: A total of 16,736 AMI patients who underwent PCI were finally enrolled. 45 features were automatically selected and were used to construct the prediction model. The developed XGBoost model showed ideal prediction results. The area under the receiver-operating characteristic curve (AUROC) on the derivation data set was 0.941 (95% CI = 0.909-0.973, p < 0.001); the AUROC on the validation set was 0.837 (95% CI = 0.772-0.903, p < 0.001), which was better than the CRUSADE score (AUROC: 0.741; 95% CI = 0.654-0.828, p < 0.001) and ACUITY-HORIZONS score (AUROC: 0.731; 95% CI = 0.641-0.820, p < 0.001). We also developed an online calculator with 12 most important variables (http://101.89.95.81:8260/), and AUROC still reached 0.809 on the validation set. Conclusion: For the first time, we developed the CAMI bleeding model using machine learning methods for AMI patients after PCI. Trial registration: NCT01874691. Registered 11 Jun 2013.

HOXD9 is a potential prognostic biomarker involved in immune microenvironment of glioma.

BACKGROUND: Glioma is the prevailing malignant tumor affecting the brain and central nervous system, constituting over 80% of all malignant brain tumors. HOXD9 has been implicated in the development of glioma, but the specific mechanism of its influence on glioma pathogenesis remains incompletely understood. The purpose of this study was to investigate the role of HOXD9 in glioma and examine the changes in HOXD9 expression during the progression of glioma, thus contributing new insights into the pathogenesis of glioma. METHODS: Glioma samples from the Cancer Gene Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) datasets were included in this study. Variations in HOXD9 expression in gliomas between different subgroups of multiple clinical characteristics were explored, and the expression was validated in glioma samples using qRT-PCR and western blotting. Next, the impact of HOXD9 on the prognosis of gliomas was explored by survival analysis, receiver operating characteristic curve, and nomogram plots. Subsequently, the association between HOXD9 and the tumor immune microenvironment was explored using the ssGSEA algorithm and the ESTIMATE algorithm. Then, immune-related pathways associated with HOXD9 were determined by differential express analysis and GSEA. Finally, HOXD9-related genomic alterations were identified. RESULTS: HOXD9 expression is upregulated and correlated with malignant properties in glioma. Similarly, our validation results showed significantly upregulated protein and mRNA levels of HOXD9 in glioma brain tissues. In addition, high HOXD9 expression was indicative of a poor prognosis for glioma patients. Additionally, elevated HOXD9 levels were associated with reduced tumor purity and higher levels of immune invasion. Finally, HOXD9 was significantly associated with genomic alterations. CONCLUSION: Overall, this study has unveiled a significant association between HOXD9 and the prognosis and survival of glioma patients. Our findings highlight the potential of HOXD9 as a prognostic biomarker, implicating its role in influencing the glioma immune microenvironment.

Loading neural stem cells on hydrogel scaffold improves cell retention rate and promotes functional recovery in traumatic brain injury.

Neural stem cell (NSC) has gained considerable attention in traumatic brain injury (TBI) treatment because of their ability to replenish dysfunctional neurons and stimulate endogenous neurorestorative processes. However, their therapeutic effects are hindered by the low cell retention rate after transplantation into the dynamic brain. In this study, we found cerebrospinal fluid (CSF) flow after TBI is an important factor associated with cell loss following NSC transplantation. Recently, several studies have shown that hydrogels could serve as a beneficial carrier for stem cell transplantation, which provides a solution to prevent CSF flow-induced cell loss after TBI. For this purpose, we evaluated three different hydrogel scaffolds and found the gelatin methacrylate (GelMA)/sodium alginate (Alg) (GelMA/Alg) hydrogel scaffold showed the best capabilities for NSC adherence, growth, and differentiation. Additionally, we detected that pre-differentiated NSCs, which were loaded on the GelMA/Alg hydrogel and cultured for 7 days in neuronal differentiation medium (NSC [7d]), had the highest cell retention rate after CSF impact. Next, the neuroprotective effects of the NSC-loaded GelMA/Alg hydrogel scaffold were evaluated in a rat model of TBI. NSC [7d]-loaded GelMA/Alg markedly decreased microglial activation and neuronal death in the acute phase, reduced tissue loss, alleviated astrogliosis, promoted neurogenesis, and improved neurological recovery in the chronic phase. In summary, we demonstrated that the integration with the GelMA/Alg and modification of NSC differentiation could inhibit the influence of CSF flow on transplanted NSCs, leading to increased number of retained NSCs and improved neuroprotective effects, providing a promising alternative for TBI treatment.

Thiocarbonyl photosensitizer, a feasible way to eliminate the photosensitizer residues in photodynamic therapy.

Photodynamic therapy (PDT) has been successfully applied in clinical treatment several years. However, after finished treatment process the residual photosensitizer will spread throughout body, which forces patients stay in the dark room to avoid exposure in sunlight several weeks. Therefore, develop degradable photosensitizer could effectively eliminate this inconvenience. In the past, researchers have developed degradable photosensitizers based on supramolecular structure. In this study, we achieved the same effect in small molecule level. Three thiocarbonyl photosensitizers (PS) have high photogenerated 1O2 quantum yield and can be photodegraded by laser irradiation within 15 min. And due to its high phototoxicity and low toxicity, thiocarbonyl PS still maintains its high phototoxicity. Especially, mitochondrial targeting PS 1a has better properties than many BODIPY or cyanine heavy-atom-free photosensitizers. It only needs 1 µM to reduce HeLa cell activity to 30%. Finally the thiocarbonyl PS provided a convenient way to solve the PS residue problem without sacrificing PDT efficiency.

Intracranial Pressure after Closure of Dura Predicts Decompressive Craniectomy in Patients with Head Trauma.

This study aimed to address the risk factors of second decompressive craniectomy (DC) in patients with traumatic brain injury (TBI) who initially underwent mass lesion evacuation, but no primary DC. Patients were enrolled if they had had a hospital visit to Xiangya Hospital, Central South University with acute closed TBI from January 1, 2017 to December 31, 2019 and had undergone craniotomic mass lesion evacuation. Sociodemographic information, computed tomography (CT) information, clinical profiles, and surgical information were obtained from an electronic database. Twenty-four patients who had undergone a second decompressive craniectomy (SDC) and 39 patients who had not (NSO) were included in the analysis. The prevailing lesions differed between the groups (p = 0.010). The SDC group had more compressed/obliterated basal cisterns than the NSO group (p = 0.028). After closure of the dura, the SDC group also had higher intracranial pressure (ICP) than the NSO group (10.9 mm Hg vs. 6.5 mm Hg, p = 0.005). Binary logistical regression indicated that ICP after dura closure was an independent predictor of second DC (odds ratio [OR] = 1.317, p = 0.011). A model using ICP after dura closure alone had an area under the curve value of 0.757 in its receiver operating characteristic curve. An ICP >10.5 mm Hg after closure of dura for the prediction of a second DC had a sensitivity of 56.3% and a specificity of 92.6%.