Human amniotic mesenchymal stem cells-derived conditioned medium and exosomes alleviate oxidative stress-induced retinal degeneration by activating PI3K/Akt/FoxO3 pathway.

Age-related macular degeneration (AMD) is the leading cause of vision loss among the elderly, which is primarily attributed to oxidative stress-induced damage to the retinal pigment epithelium (RPE). Human amniotic mesenchymal stem cells (hAMSC) were considered to be one of the most promising stem cells for clinical application due to their low immunogenicity, tissue repair ability, pluripotent potential and potent paracrine effects. The conditional medium (hAMSC-CM) and exosomes (hAMSC-exo) derived from hAMSC, as mediators of intercellular communication, play an important role in the treatment of retinal diseases, but their effect and mechanism on oxidative stress-induced retinal degeneration are not explored. Here, we reported that hAMSC-CM alleviated H2O2-induced ARPE-19 cell death through inhibiting mitochondrial-mediated apoptosis pathway in vitro. The overproduction of reactive oxygen species (ROS), alteration in mitochondrial morphology, loss of mitochondrial membrane potential and elevation of Bax/Bcl2 ratio in ARPE-19 cells under oxidative stress were efficiently reversed by hAMSC-CM. Moreover, it was found that hAMSC-CM protected cells against oxidative injury via PI3K/Akt/FoxO3 signaling. Intriguingly, exosome inhibitor GW4869 alleviated the inhibitory effect of hAMSC-CM on H2O2-induced decrease in cell viability of ARPE-19 cells. We further demonstrated that hAMSC-exo exerted the similar protective effect on ARPE-19 cells against oxidative damage as hAMSC-CM. Additionally, both hAMSC-CM and hAMSC-exo ameliorated sodium iodate-induced deterioration of RPE and retinal damage in vivo. These results first indicate that hAMSC-CM and hAMSC-exo protect RPE cells from oxidative damage by regulating PI3K/Akt/FoxO3 pathway, suggesting hAMSC-CM and hAMSC-exo will be a promising cell-free therapy for the treatment of AMD in the future.

Targeted inhibition of branched-chain amino acid metabolism drives apoptosis of glioblastoma by facilitating ubiquitin degradation of Mfn2 and oxidative stress.

Glioblastoma is one of the most challenging malignancies with high aggressiveness and invasiveness and its development and progression of glioblastoma highly depends on branched-chain amino acid (BCAA) metabolism. The study aimed to investigate effects of inhibition of BCAA metabolism with cytosolic branched-chain amino acid transaminase (BCATc) Inhibitor 2 on glioblastoma, elucidate its underlying mechanisms, and explore therapeutic potential of targeting BCAA metabolism. The expression of BCATc was upregulated in glioblastoma and BCATc Inhibitor 2 precipitated apoptosis both in vivo and in vitro with the activation of Bax/Bcl2/Caspase-3/Caspase-9 axis. In addition, BCATc Inhibitor 2 promoted K63-linkage ubiquitination of mitofusin 2 (Mfn2), which subsequently caused lysosomal degradation of Mfn2, and then oxidative stress, mitochondrial fission and loss of mitochondrial membrane potential. Furthermore, BCATc Inhibitor 2 treatment resulted in metabolic reprogramming, and significant inhibition of expression of ATP5A, UQCRC2, SDHB and COX II, indicative of suppressed oxidative phosphorylation. Moreover, Mfn2 overexpression or scavenging mitochondria-originated reactive oxygen species (ROS) with mito-TEMPO ameliorated BCATc Inhibitor 2-induced oxidative stress, mitochondrial membrane potential disruption and mitochondrial fission, and abrogated the inhibitory effect of BCATc Inhibitor 2 on glioblastoma cells through PI3K/AKT/mTOR signaling. All of these findings indicate suppression of BCAA metabolism promotes glioblastoma cell apoptosis via disruption of Mfn2-mediated mitochondrial dynamics and inhibition of PI3K/AKT/mTOR pathway, and suggest that BCAA metabolism can be targeted for developing therapeutic agents to treat glioblastoma.

Associations between patterns of blood heavy metal exposure and health outcomes: insights from NHANES 2011-2016.

BACKGROUND: Extensive research has explored the association between heavy metal exposure and various health outcomes, including malignant neoplasms, hypertension, diabetes, and heart diseases. This study aimed to investigate the relationship between patterns of exposure to a mixture of seven heavy metals and these health outcomes. METHODS: Blood samples from 7,236 adults in the NHANES 2011-2016 studies were analyzed for levels of cadmium, manganese, lead, mercury, selenium, copper, and zinc. Cluster analysis and logistic regression identified three distinct patterns of mixed heavy metal exposure, and their associations with health outcomes were evaluated. RESULTS: Pattern 1 exhibited higher odds ratios (ORs) for malignancy during NHANES 2011-2012 (OR = 1.33) and 2015-2016 (OR = 1.29) compared to pattern 2. Pattern 3 showed a lower OR for malignancy during NHANES 2013-2014 (OR = 0.62). For hypertension, pattern 1 displayed higher ORs than pattern 2 for NHANES 2011-2012 (OR = 1.26), 2013-2014 (OR = 1.31), and 2015-2016 (OR = 1.41). Pattern 3 had lower ORs for hypertension during NHANES 2013-2014 (OR = 0.72) and 2015-2016 (OR = 0.67). In terms of heart diseases, pattern 1 exhibited higher ORs than pattern 2 for NHANES 2011-2012 (OR = 1.34), 2013-2014 (OR = 1.76), and 2015-2016 (OR = 1.68). Pattern 3 had lower ORs for heart diseases during NHANES 2013-2014 (OR = 0.59) and 2015-2016 (OR = 0.52). However, no significant trend was observed for diabetes. All three patterns showed the strongest association with hypertension among the health outcomes studied. CONCLUSIONS: The identified patterns of seven-metal mixtures in NHANES 2011-2016 were robust. Pattern 1 exhibited higher correlations with hypertension, heart disease, and malignancy compared to pattern 2, suggesting an interaction between these metals. Particularly, the identified patterns could offer valuable insights into the management of hypertension in healthy populations.

Development and validation of a predictive model for depression risk in the U.S. adult population: Evidence from the 2007-2014 NHANES.

BACKGROUND: Depression is a prevalent mental health disorder with a complex etiology and substantial public health implications. Early identification of individuals at risk for depression is crucial for effective intervention and prevention efforts. This study aimed to develop a predictive model for depression by integrating demographic factors (age, race, marital status, income), lifestyle factors (sleep duration, physical activity), and physiological measures (hypertension, blood lead levels). A key objective was to explore the role of physical activity and blood lead levels as predictors of current depression risk. METHODS: Data were extracted from the 2007-2014 National Health and Nutrition Examination Survey (NHANES). We applied a logistic regression analysis to these data to assess the predictive value of the above eight factors for depression to create the predictive model. RESULTS: The predictive model had bootstrap-corrected c-indexes of 0.68 (95% CI, 0.67-0.70) and 0.66 (95% CI, 0.64-0.68) for the training and validation cohorts, respectively, and well-calibrated curves. As the risk of depression increased, the proportion of participants with 1.76 ~ 68.90 µg/L blood lead gradually increased, and the proportion of participants with 0.05 ~ 0.66 µg/L blood lead gradually decreased. In addition, the proportion of sedentary participants increased as the risk of depression increased. CONCLUSIONS: This study developed a depression risk assessment model that incorporates physical activity and blood lead factors. This model is a promising tool for screening, assessing, and treating depression in the general population. However, because the corrected c-indices of the predictive model have not yet reached an acceptable threshold of 0.70, caution should be exercised when drawing conclusions. Further research is required to improve the performance of this model.

Prognosis of colon cancer patients based on enhancer RNAs-related genes.

PURPOSE: Colon cancer (CC) is a cancer of the large intestine with high prevalence and poor prognosis. enhancer RNAs. Therefore, valuable tools or biomarkers for predicting patient status, directing clinical practice, and reducing overtreatment are needed. Enhancer RNAs (eRNAs), a class of noncoding RNAs transcribed from enhancers, have been shown to function as regulators of oncogene or tumor suppressor gene expression. The aim of our study was to explore the potential roles of eRNAs and their target enhancer-related genes (ERGs) in the prognosis of CC. METHODS: Selected CC cases (stage I-III) from The Cancer Genome Atlas database were used as a training set, and cases from the Gene Expression Omnibus were used as the validation set. ERGs associated with prognosis were screened through three steps: potential, candidate, and prognosis ERGs. Multivariate Cox proportional hazards analysis was used to identify independent prognostic factors, and a nomogram was created. Calibration curves were drawn by comparing predicted and observed survival probability. For validation, the calibration curves and ROC analysis were also applied to two external validation sets. The biological significance and clinical application of the genes obtained were investigated. RESULTS: Based on the multiple tiers of strict screening, 11 prognostic ERGs were obtained, which were combined to obtain a prognosis signature. A compound nomogram integrating age, TNM classification, and the prognostic signature was constructed. The model was reliable in distinguishing the risk of patients with stage I-III CC, with AUCs of 0.78 and 0.70 at 5 and 7 years, respectively. There was good reproducibility in calibration curves. The prognostic model also yielded good prediction capability in the validation sets. CONCLUSION: In this study, the usefulness and specificity of the ERGs in prognosis were described, which should be considered a key feature in the clinical guidance of CC patients with early stage. We concluded that the major implications of the eRNAs and ERGs should be valued, which would be an emerging hallmark in the prognosis of cancer.

Trends in care quality in China from 2011 to 2017: An analysis based on the National Specific (Single) Disease Monitoring System.

Background: The Ministry of Health of China conducted a study targeting in single-disease quality control in 2009, aimed to strengthen quality management and improve health care services. This study retrospectively investigated the trends of quality indicators for six monitored diseases 2011-2017 to evaluate the improvement of care quality for the first batch of single-disease. Methods: We extracted data from the National Specific (Single) Disease Monitoring System for 2011-2017. We focused on six conditions: acute myocardial infarction, heart failure, community-acquired pneumonia, coronary artery bypass graft, hip / knee replacement, and acute ischemic stroke. A total of 56 quality indicators (QIs) were adopted to monitor the quality change and determine the trends in care quality. We also calculated the hospital process composite performance (HPCP) using a denominator-based weighting method for each hospital per year. The estimated annual percentage changes (EAPC) 2011-2017 were calculated at national and regional levels. Results: The results showed that use of four QIs had significant downward trends, whereas 25 QIs (including reversed indicators) showed significant upward trends from 2011 to 2017. The greatest improvement was observed in CAP-4 (antibiotic treatment within four hours after admission to the hospital for critical pneumonia) in the central region (EAPC = 48.36, 95% CI = 15.92-89.87); while the largest decrease appeared in AIS-1 (thrombolytic therapy within 4.5 hours of symptom onset) in the western region (EAPC = -13.44, 95% CI = -24.98,-0.11). An increased HPCP was observed in four diseases nationwide, but not for acute myocardial infarction and heart failure. However, there were significant differences across regions in the process of care and outcomes, with the performance of Eastern and Western regions showing remarkable advantages compared with the Central region. Conclusions: We provide evidence for major advancement in care quality in China nationwide. However, the improvement of care in China was unbalanced geographically and should be carefully considered. Future challenges include expanding the coverage of quality monitoring, greater delivery efficiency, and region-balanced health care.

The significance of spread through air spaces in the prognostic assessment model of stage I lung adenocarcinoma and the exploration of its invasion mechanism.

PURPOSE: Spread through air spaces (STAS) is a crucial invasive mode of lung cancer and has been shown to be associated with early recurrence and metastasis. We aimed to develop a prognostic risk assessment model for stage I lung adenocarcinoma based on STAS and other pathological features and to explore the potential relationship between CXCL-8, Smad2, Snail, and STAS. METHODS: 312 patients who underwent surgery at Harbin Medical University Cancer Hospital with pathologically diagnosed stage I lung adenocarcinoma were reviewed in the study. STAS and other pathological features were identified by H&E staining, and a prognostic risk assessment model was established. The expression levels of CXCL8, Smad2, and Snail were determined by immunohistochemistry. RESULTS: The nomogram was established based on age, smoking history, STAS, tumor lymphocyte infiltration, tissue subtype, nuclear grade, and tumor size. The C-index for DFS was (training set 0.84 vs validation set 0.77) and for OS was (training set 0.83 vs validation set 0.78). Decision curve analysis showed that the model constructed has a better net benefit than traditional reporting. The prognostic risk score validated the risk stratification value for stage I lung adenocarcinoma. STAS was an important prognostic factor associated with stronger invasiveness and higher expression of CXCL8, Smad2, and Snail. CXCL8 was associated with poorer DFS and OS. CONCLUSIONS: We developed and validated a survival risk assessment model and the prognostic risk score formula for stage I lung adenocarcinoma. Additionally, we found that CXCL8 could be used as a potential biomarker for STAS and poor prognosis, and its mechanism may be related to EMT.

Associations between the compositional patterns of blood volatile organic compounds and chronic respiratory diseases and ages at onset in NHANES 2003-2012.

BACKGROUND: and Purpose Volatile organic compounds (VOCs) pose a serious respiratory hazard. This study evaluated the relationship between the compositional patterns of blood VOCs and the risk and age at onset of chronic respiratory diseases (CRDs), including asthma, emphysema and chronic bronchitis, with the objective of preventing or delaying CRDs. METHODS: Participants from five cycles of the NHANES survey were included. Blood VOCs were clustered using k-means clustering. Differences in VOCs and age at onset between multiple groups were compared with the Kruskal‒Wallis test. Logistic regression and a generalized linear model were applied to examine the associations between different compositional patterns of blood VOCs and risk and age at onset of CRDs. RESULTS: 12,386 participants were enrolled in this study. Three VOC compositional patterns were identified after clustering nine species of blood VOCs. The concentration of VOCs in pattern 2 was relatively low and stable. The concentrations of benzene, ethylbenzene, o-xylene, styrene, toluene and m-p-xylene in pattern 3 and the concentrations of 1,4-dichlorobenzene and MTBE in pattern 1 were significantly higher than those in pattern 2. After adjustment for covariates, the participants with VOC pattern 3 had an increased risk of asthma (OR = 1.23, 95% CI: 1.02, 1.49), emphysema (OR = 3.37, 95% CI: 2.24, 5.06) and chronic bronchitis (OR = 1.79, 95% CI: 1.30, 2.45). Meanwhile, VOC pattern 3 was negatively correlated with the age at onset of asthma (ß = -5.61, 95% CI: 9.69, -1.52) and chronic bronchitis (ß = -9.17, 95% CI: 13.96, -4.39). VOC pattern 1 was not associated with either risk or age at onset of the three CRDs after adjustment. CONCLUSIONS: Changing the compositional pattern of blood VOCs by reducing certain species of VOCs may be a new strategy to lengthen the ages at onset of CRDs and effectively prevent them.

Determining the spatial non-stationarity underlying social and natural environment in thyroid cancer in China.

BACKGROUND: Light at night (LAN) is a physiological environmental factor related to thyroid cancer (TC). The spatial relationship between the number of TC incident cases, LAN, air pollution and other macro social factors and stationarity needs to be further examined to provide evidence for regional control of TC. METHODS: Spatial econometrics methods for spatial nonstationarity were used to explore the impacts of LAN, air pollutants, economic factors, and population size on the number of TC incident cases in 182 Chinese prefecture-level cities and the local coefficients were further tested for nonstationarity. Temporally weighted regression (TWR), geographically weighted regression (GWR), and geographically and temporally weighted regression (GTWR) were compared in this study for model selection. RESULTS: Based on the ordinary least squares (OLS), LAN, air pollutants, and urbanization all significantly affected the number of TC incident cases. GWR had the best goodness of fit, and the coefficients of all the variables passed the nonstationarity test. The strong positive impact of LAN was mainly concentrated in North China, air pollutants in Central China and neighboring regions, and urbanization in the eastern coast of China. CONCLUSIONS: The locational factors of the prefecture-level city influence the spatial pattern of the number of TC incident cases. Governments should pay attention to this influence, adhere to the Health in All Policies principle, and formulate region-specific policies based on regional characteristics, which this study provides updated evidence for.

The quality disparity of stroke care over time: An analysis based on the national dataset from 2011 to 2017.

BACKGROUND: Adherence to evidence-based hospital stroke care is variable and may change over time. It is important to determine which process measures are associated with variation in outcome. In a large dataset, we analyzed the association between process and outcome and the fluctuations of indicators over time, and identified quality indicators (QIs) that should be prioritized for improving the quality of stroke care. METHODS: We analyzed data from 123,259 patients diagnosed with acute ischemic stroke (AIS) who were treated at 109 large tertiary hospitals in China between January 2011 and May 2017. In total, 12 stroke treatment indicators were selected to calculate the hospital process composite performance (HPCP). Hospitals were divided into subgroups according to the time trend of HPCP estimated by the Group-Based Model. We analyzed the influence of hospital subgroups on the patient outcomes using a multi-level model and explored the QIs that led to variation. RESULTS: The HPCP trends for stroke indicators of 109 hospitals over 7 years were divided into two groups (Group 1, low-HPCP; Group 2, high-HPCP). After adjusting for patient age, medical insurance, comorbidities, patterns of admission, and NIHSS-scores, patients in the high-HPCP group presented higher rate of independence and longer length of stay compared to the low-HPCP group. The multi-level model showed that there was a statistically significant difference in the utilization rate between the two groups, with most marked differences seen in emergency assessment and function evaluation indicators. CONCLUSION: Variation in the quality of stroke care exists across hospitals, and better adherence to guideline-based care is associated with improved outcomes. We found that QIs related to emergency examination and functional assessment were the main factors differing between good and poor adherers to stroke indicators, suggesting that quality improvement in stroke care could prioritize these QIs.

A Reversible Fluorescent Probe Based on a Redox-Switchable Excited-State Intramolecular Proton-Transfer Active Metal-Organic Framework for Detection and Imaging of Highly Reactive Oxygen Species in Live Cells.

Detection and imaging of highly reactive oxygen species (hROS) in biological systems using fluorescent probes are critical for the study of physiological and pathological processes induced by hROS. Herein, we report a redox-active luminescent metal-organic framework (MOF), which incorporates a hydroquinone moiety that can undergo a reversible transformation from the hydroquinone to the quinone by hROS like •OH and ClO-. Moreover, the intrinsic fluorescence originating from the excited-state intramolecular proton transfer (ESIPT) property of the organic linker can be finely regulated during this redox-switchable process. A reversible fluorescent probe for hROS is thus developed. The presented probe shows a sensitive, selective, and reversible response to hROS due to the integration of excellent structural characteristics and unique spectral properties of the MOF. The detection limits of •OH and ClO- are 0.22 and 0.18 µM, respectively. Furthermore, with good photostability and super biocompatibility, this simple yet efficient fluorescent probe has been successfully applied to dynamic monitoring of endogenous and exogenous •OH and ClO- in live cells.

Factors that dynamically affect provincial incidences of catastrophic health expenditure among middle-aged and elderly Chinese population-transition of disease financial risk protection from global to local.

BACKGROUND: The high incidence of catastrophic health expenditure (ICHE) among middle-aged and elderly population is a major deterrent for reducing the financial risk of disease. Current research is predominantly based on the assumption of spatial homogeneity of nationwide population characteristics, ignoring the differences in regional characteristics. Thus, our study aimed to explore the impact of various influencing factors on the ICHE from a spatiotemporal perspective. METHODS: We used data from the China Health and Retirement Longitudinal Study (waves 1 to 4), to conduct a retrospective cohort study across 28 provinces, from 2011 to 2018. We measured regional incidences of catastrophic health expenditure using methods recommended by the World Health Organization. Ordinary least squares (OLS) and geographical and temporal weighted regression (GTWR) were used as the global and local estimation models, respectively. The Fortheringham method was used to test the spatiotemporal non-stationarity. RESULTS: National ICHE showed a gradual increase from 2011 to 2015, but suddenly decreased from 2015 to 2018, also showing the spatial heterogeneity. And the southwest area and Hebei showed persistently high ICHE (Qinghai even reached the highest value of 27.5% in 2015). Out-of-pocket payment, gross domestic product, PM2.5, ageing, incidence of non-communicable diseases and disabilities, number of nurses, and health insurance coverage in the global estimation passed the significance test, and the GTWR model showed a better model fit (0.769) than the OLS model (0.388). Furthermore, except for health insurance coverage, all seven variables had spatiotemporal non-stationarity among their impacts on ICHE. CONCLUSION: In this longitudinal study, we found spatiotemporal non-stationarity among the variable relationships, supporting regional governments' adoption of regional-target policies. First, after the completion of universal health insurance coverage, the spatiotemporal non-stationarity of the prevalence of non-communicable diseases and disability and ageing should be the focus of the next phase of health insurance design, where improvements to compensation coverage and benefit packages are possible policy instruments. Second, the governance and causes of catastrophic health expenditure need to be laid out from a macro perspective rather than only from the individual/household perspective, especially for the potential impact of economic development, air pollution and nursing resources.

Assessing the depression risk in the U.S. adults using nomogram.

BACKGROUND: Depression has received a lot of attention as a common and serious illness. However, people are rarely aware of their current depression risk probabilities. We aimed to develop and validate a predictive model applicable to the risk of depression in US adults. METHODS: This study was conducted using the database of the National Health and Nutrition Examination Survey (NHANES, 2017-2012). In particular, NHANES (2007-2010) was used as the training cohort (n = 6015) for prediction model construction and NHANES (2011-2012) was used as the validation cohort (n = 2812) to test the model. Depression was assessed (defined as a binary variable) by the Patient Health Questionnaire (PHQ-9). Socio-demographic characteristics, sleep time, illicit drug use and anxious days were assessed using a self-report questionnaire. Logistic regression analysis was used to evaluate independent risk factors for depression. The nomogram has the advantage of being able to visualize complex statistical prediction models as risk estimates of individualized disease probabilities. Then, we developed two depression risk nomograms based on the results of logistic regression. Finally, several validation methods were used to evaluate the prediction performance of nomograms. RESULTS: The predictors of model 1 included gender, age, income, education, marital status, sleep time and illicit drug use, and model 2, furthermore, included anxious days. Both model 1 and model 2 showed good discrimination ability, with a bootstrap-corrected C index of 0.71 (95% CI, 0.69-0.73) and 0.85 (95% CI, 0.83-0.86), and an externally validated C index of 0.71 (95% CI, 0.68-0.74) and 0.83 (95% CI, 0.81-0.86), respectively, and had well-fitted calibration curves. The area under the receiver operating characteristic curve (AUC) values of the models with 1000 different weighted random sampling and depression scores of 10-17 threshold range were higher than 0.7 and 0.8, respectively. Calculated net reclassification improvement (NRI) and integrated discrimination improvement (IDI) showed the discrimination or accuracy of the prediction models. Decision curve analysis (DCA) demonstrated that the depression models were practically useful. The network calculators work for participants to make personalized predictions. CONCLUSIONS: This study presents two prediction models of depression, which can effectively and accurately predict the probability of depression as well as helping the U.S. civilian non-institutionalized population to make optimal treatment decisions.

Development and Validation of a Novel Prognostic Model for Lower-Grade Glioma Based on Enhancer RNA-Regulated Prognostic Genes.

Enhancer RNAs (eRNAs) are present specifically in tumors, where they affect the expression of eRNA-regulated genes (ERGs). Owing to this characteristic, ERGs were hypothesized to improve prognosis of overall survival in heterogeneous low-grade and intermediate-grade gliomas. This study aimed to construct and validate an ERG prognostic tool to facilitate clinical management, and offer more effective diagnostic and therapeutic biomarkers for glioma. Survival-related eRNAs were identified, and their ERGs were selected based on eRNA and target gene information. The ERG prognostic model was constructed and validated using internal and external validation cohorts. Finally, biological differences related to the ERG signature were analysed to explore the potential mechanisms influencing survival outcomes. Thirteen ERGs were identified and used to build an ERG risk signature, which included five super-enhancer RNA (seRNA)-regulated genes and five LGG-specific eRNA-regulated genes. The prognostic nomogram established based on combining the ERG score, age, and sex was evaluated by calibration curves, clinical utility, Harrell's concordance index (0.86; 95% CI: 0.83-0.90), and time-dependent receiver operator characteristic curves. We also explored potential immune-related mechanisms that might cause variation in survival. The established prognostic model displayed high validity and robustness. Several immune-related genes regulated by seRNAs or specific eRNAs were identified, indicating that these transcripts or their genes were potential targets for improving immunotherapeutic/therapeutic outcomes. The functions of an important specific eRNA-regulated gene (USP28) were validated in robust vitro experiments. In addition, the ERG risk signature was significantly associated with the immune microenvironment and other immune-related features.

A Novel Prognostic Tool for Glioma Based on Enhancer RNA-Regulated Immune Genes.

Background: Gliomas are the most malignant tumors of the nervous system. Even though their survival outcome is closely affected by immune-related genes (IRGs) in the tumor microenvironment (TME), the corresponding regulatory mechanism remains poorly characterized. Methods: Specific enhancer RNAs (eRNAs) can be found in tumors, where they control downstream genes. The present study aimed to identify eRNA-regulated IRGs, evaluate their influence on the TME, and use them to construct a novel prognostic model for gliomas. Results: Thirteen target genes (ADCYAP1R1, BMP2, BMPR1A, CD4, DDX17, ELN, FGF13, MAPT, PDIA2, PSMB8, PTPN6, SEMA6C, and SSTR5) were identified and integrated into a comprehensive risk signature, which distinguished two risk subclasses. Discrepancies between these subclasses were compared to explore potential mechanisms attributed to eRNA-regulated genes, including immune cell infiltration, clinicopathological features, survival outcomes, and chemotherapeutic drug sensitivity. Furthermore, the risk signature was used to construct a prognostic tool that was evaluated by calibration curve, clinical utility, Harrell's concordance index (0.87; 95% CI: 0.84-0.90), and time-dependent receiver operator characteristic curves (AUCs: 0.93 and 0.89 at 3 and 5 years, respectively). The strong reliability and robustness of the established prognostic tool were validated in another independent cohort. Finally, potential subtypes were explored in patients with grade III tumors. Conclusion: Overall, eRNAs were associated with immune-related dysfunctions in the TME. Targeting of IRGs regulated by eRNAs could improve immunotherapeutic/therapeutic outcomes.

[Enhancement of gene transfection efficiency and therapeutic effect of ultrasound-targeted microbubble destruction in vivo with cationic microbubble].

Objective: To construct a cationic microbubble (CMB), and investigate the enhancement of gene transfection efficiency and therapeutic effect of ultrasound-targeted microbubble destruction (UTMD) in vivo with CMB compared to definity MB (DMB). Methods: In vitro, the CMB was prepared by the method of thin film hydration. The morphology, size, zeta potential, and gene-carrying capacity of CMB were compared with the DMB. In vivo, the firefly luciferase gene which was used as a reporter gene was targeted transfected into myocardium of 16 rats with CMB and DMB, respectively. The gene transfection efficiency and targeting were observed dynamically. Then, ischemia-reperfusion (I/R) model was performed on 64 rats. The models of 60 rats were successfully confirmed by using ultrasonography at 5 days after I/R. The rats were divided into 3 groups ( n=20) randomly. The control group received DMB carrying empty plasmid for transfection; DMB group received DMB carrying AKT plasmid for transfection; and CMB group received CMB carrying AKT plasmid for transfection. The cardiac perfusion, cardiac function, infarct size, and infarct thickness were measured by ultrasonography and histological observations after treatment. In addition, the capillary and arteriolar densities were measured with immunohistochemical staining. The myocyte apoptosis was measured with TUNEL staining. The protein expressions of AKT, phospho-AKT (P-AKT), Survivin, and phospho-BAD (P-BAD) were measured by Western blot. Results: The size of CMB was uniformly. The zeta potential of CMB was significantly higher than that of DMB ( t=28.680, P=0.000). The CMB bound more plasmid DNA than the DMB ( P<0.05). The luciferase activity of myocardium were higher in CMB group than in DMB group both in vitro and in vivo measurements ( P<0.05). There was no significant difference between groups in the ratio of signal intensity in anterior wall to posterior wall, ejection fraction (EF), and fractional shortening (FS) at 5 days after I/R ( P>0.05), but the above indexes were significant higher in CMB and DMB groups than in control group at 21 days after I/R ( P<0.05). Besides, the above indexes were significant higher in CMB group than in DMB group at 21 days after I/R ( P<0.05). The infarct size was the smallest and infarct thickness was the thickest in the CMB group, followed by DMB group, control group at 21 days after I/R. The capillary and arteriolar densities of CMB and DMB groups were significant higher than those of control group at 21 days after I/R ( P<0.05). Besides, the capillary and arteriolar densities of CMB group were significant higher than those of DMB group ( P<0.05). The apoptotic cells were the most in the control group, followed by DMB group, CMB group at 3 days after gene transfection, showing significant differences between groups ( P<0.05). The protein expressions of AKT, P-AKT, Survivin, and P-BAD were significant higher in CMB and DMB groups than those in control group at 3 days after gene transfection ( P<0.05). Besides, these protein expressions were significant higher in CMB group than those in DMB group ( P<0.05). Conclusion: The DNA-carrying capacity and gene transfection efficiency are elevated by CMB, although its physicochemical property is the same as DMB. When ultrasound-targeted AKT gene transfection is used to treat myocardial I/R injury in rats, delivery of AKT with the CMB can result in higher transfection efficiency and greater cardiac functional improvements compared to the DMB.