A novel machine learning model for predicting stroke associated pneumonia after spontaneous intracerebral hemorrhage.

BACKGROUND: Pneumonia is one of the most common complications after spontaneous intracerebral hemorrhage (sICH), namely stroke associated pneumonia (SAP). Timely identification of targeted patients is beneficial to reduce poor prognosis. So far, there is no consensus on SAP prediction, and application of existing predictors is limited. The aim of the study is to develop a machine learning model to predict SAP after sICH. METHODS: We retrospectively reviewed 748 patients diagnosed with sICH and collected their data from four dimensions including demographic features, clinical features, medical history, and laboratory tests. Five machine learning algorithms including logistic regression, gradient boosting decision tree, random forest, extreme gradient boosting, and category boosting were used to build and validate the predictive model. And we applied recursive feature elimination with cross-validation to obtain the best feature combination for each model. The predictive performance was evaluated by the areas under the receiver operating characteristic curves (AUC). RESULTS: A total of 237 patients were diagnosed as SAP. The model developed by category boosting yielded the most satisfied outcomes overall with its AUC in training set and test set were 0.8307 and 0.8178, respectively. CONCLUSIONS: The incidence of SAP after sICH in our center was 31.68%. Machine learning could provide assistance potentially in the prediction of SAP after sICH.

DDX39B protects against sorafenib-induced ferroptosis by facilitating the splicing and cytoplasmic export of GPX4 pre-mRNA in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is the main histological subtype of primary liver cancer and remains one of the most common solid malignancies globally. Ferroptosis was recently defined as an iron-catalyzed form of regulated necrosis. Because cancer cells exhibit higher iron requirements than noncancer cells, treatment with ferroptosis-inducing compounds may be a feasible strategy for cancer therapy. However, cancer cells develop acquired resistance to evade ferroptosis, and the mechanisms responsible for ferroptosis resistance are not fully clarified. In the current study, we reported that DDX39B was downregulated during sorafenib-induced ferroptosis in a dose- and time-dependent manner. Exogenous introduction of DDX39B ensured the survival of HCC cells upon exposure to sorafenib, while the opposite phenomenon was observed in DDX39B-silenced HCC cells. Mechanistically, we demonstrated that DDX39B increased GPX4 levels by promoting the splicing and cytoplasmic translocation of GPX4 pre-mRNA, which was sufficient to detoxify sorafenib-triggered excess lipid ROS production, lipid peroxidation accumulation, ferrous iron levels, and mitochondrial damage. Inhibition of DDX39B ATPase activity by CCT018159 repressed the splicing and cytoplasmic export of GPX4 pre-mRNA and synergistically assisted sorafenib-induced ferroptotic cell death in HCC cells. Taken together, our data uncover a novel role for DDX39B in ferroptosis resistance by modulating the maturation of GPX4 mRNA via a posttranscriptional approach and suggest that DDX39B inhibition may be a promising therapeutic strategy to enhance the sensitivity and vulnerability of HCC cells to sorafenib.

A MOGA and extended RPN based approach for heuristic key reliability characteristics analysis in manufacturing.

The manufacturing process is the last opportunity to build an ideal design reliability index into a product. With the advancement of intelligent manufacturing technology, the concept of quality evolves from conformance to fitness for use, which emphasizes that reliability should be built into product with quality control. To effectively implement reliability assurance in the manufacturing process, it is necessary to accurately identify the vital few characteristics that are critical to reliability. Thus, a heuristic key reliability characteristic (KRC) analysis in manufacturing model fusing big quality data is proposed. First, on the basis of the fusion big quality data in manufacturing-by-manufacturing system Reliability-operational process Quality- output product Reliability (RQR) chain, a data driven KRC analysis model is proposed, and a reliability proactive control framework in manufacturing driven by KRC is expounded. Second, considering mass quality and reliability data, an effective KRC identification method based on data mining using multi-objectives genetic algorithm (MOGA) is established. Third, considering manufacturing data and product failure risk, an extended risk priority number (RPN) for KRC ranking is proposed. Finally, an example of an insulating base of subway locomotive is provided to verify the proposed approach.

Discovery of metal-binding proteins by thermal proteome profiling.

Metal-binding proteins (MBPs) have various and important biological roles in all living species and many human diseases are intricately linked to dysfunctional MBPs. Here, we report a chemoproteomic method named 'metal extraction-triggered agitation logged by thermal proteome profiling' (METAL-TPP) to globally profile MBPs in proteomes. The method involves the extraction of metals from MBPs using chelators and monitoring the resulting protein stability changes through thermal proteome profiling. Applying METAL-TPP to the human proteome with a broad-spectrum chelator, EDTA, revealed a group of proteins with reduced thermal stability that contained both previously known MBPs and currently unannotated MBP candidates. Biochemical characterization of one potential target, glutamine-fructose-6-phosphate transaminase 2 (GFPT2), showed that zinc bound the protein, inhibited its enzymatic activity and modulated the hexosamine biosynthesis pathway. METAL-TPP profiling with another chelator, TPEN, uncovered additional MBPs in proteomes. Collectively, this study developed a robust tool for proteomic discovery of MBPs and provides a rich resource for functional studies of metals in cell biology.

Enhancer Recognition: A Transformer Encoder-Based Method with WGAN-GP for Data Augmentation.

Enhancers are located upstream or downstream of key deoxyribonucleic acid (DNA) sequences in genes and can adjust the transcription activity of neighboring genes. Identifying enhancers and determining their functions are important for understanding gene regulatory networks and expression regulatory mechanisms. However, traditional enhancer recognition relies on manual feature engineering, which is time-consuming and labor-intensive, making it difficult to perform large-scale recognition analysis. In addition, if the original dataset is too small, there is a risk of overfitting. In recent years, emerging methods, such as deep learning, have provided new insights for enhancing identification. However, these methods also present certain challenges. Deep learning models typically require a large amount of high-quality data, and data acquisition demands considerable time and resources. To address these challenges, in this paper, we propose a data-augmentation method based on generative adversarial networks to solve the problem of small datasets. Moreover, we used regularization methods such as weight decay to improve the generalizability of the model and alleviate overfitting. The Transformer encoder was used as the main component to capture the complex relationships and dependencies in enhancer sequences. The encoding layer was designed based on the principle of k-mers to preserve more information from the original DNA sequence. Compared with existing methods, the proposed approach made significant progress in enhancing the accuracy and strength of enhancer identification and prediction, demonstrating the effectiveness of the proposed method. This paper provides valuable insights for enhancer analysis and is of great significance for understanding gene regulatory mechanisms and studying disease correlations.

Discovery of Rhodanine Inhibitors Targeting Of ChtI Based on the π-Stacking Effect and Aqueous Solubility.

The application of some reported inhibitors against the chitinolytic enzyme Of ChtI was limited due to their unsatisfactory insecticidal activities. Hence, we first performed a synergetic design strategy combining the π-stacking effect with aqueous solubility to find novel rhodanine analogues with inhibitory activities against Of ChtI. Novel rhodanine compounds IAa-f and IBa-f have weak aqueous solubility, but they (IAd: Ki = 4.0 µM; IBd: Ki = 2.2 µM) showed better inhibitory activities against Of ChtI and comparable insecticidal efficiency toward Ostrinia furnacalis compared to the high aqueous solubility compounds IIAa-f and IIBa-f (IIAd: Ki = 21.6 µM; IIBd: Ki = 14.3 µM) without a large conjugate plane. Further optimized compounds IIIAa-j with a conjugate plane as well as a higher aqueous solubility exhibited similar good inhibitory activities against Of ChtI (IIIAe: Ki = 2.4 µM) and better insecticidal potency (IIIAe: mortality rate of 63.33%) compared to compounds IAa-f and IBa-f, respectively. Molecular docking studies indicated that the conjugate planarity with the π-stacking effect for rhodanine analogues is responsible for their enzyme inhibitory activity against Of ChtI. This study provides a new strategy for designing insect chitinolytic enzyme inhibitors as insect growth regulators for pest control.

[Effects of Long-term Combined Application of Organic and Inorganic Fertilizers on Soil Carbon Pool and Greenhouse Gas Emissions in Orchards].

Applying organic fertilizer can increase the contents of soil organic carbon (SOC) and active organic carbon, which are crucial for strengthening soil quality and fertility. Four treatments were established:no fertilization (CK), single application of organic fertilizer (M), single application of chemical fertilizer (NPK), and combined application of organic and inorganic fertilizers (MNPK). The changes in SOC and active components under long-term combined application of organic and inorganic fertilizers were investigated, as were the effects of various fertilization measures on greenhouse gas emissions. Moreover, we evaluated the variation in the soil carbon pool management index (CPMI). Total organic carbon (TOC), microbial biomass carbon (MBC), dissolved organic carbon (DOC), easily oxidized organic carbon (EOC), and particulate organic carbon (POC) increased by 82.84%, 66.30%, 21.12%, 93.28%, and 145.80%, respectively, when compared to those in the CK treatment. The NPK treatment had no discernible effect on SOC and organic carbon components. The combined application of organic and inorganic materials could enhance LI, CPI, and the soil carbon pool management index, with the increase in LI and CPI being the primary reason for the increase in CPMI. Correlation analyses revealed that soil organic carbon components and CPMI were significantly positively correlated with greenhouse gas emissions. The combined application of organic and inorganic materials enhanced cumulative CO2 emissions and warming potential (GWP) but decreased GHGI and yielded a maximum of 56365 kg·hm-2. Compared with that in the CK treatment (29073 kg·hm-2), apple yield in MNPK increased by 93.87%. Therefore, applying organic and inorganic fertilizers in dryland apple orchards can improve the accumulation of organic carbon and stabilize the soil carbon pool, which is more beneficial to the sustainable development of orchards.

Discovery of piperonyl-tethered sulfoximines as novel low bee-toxicity aphicides targeting Amelα1/ratß2 complex.

Nicotinic acetylcholine receptor (nAChR) is recognized as a significant insecticide target for neonicotinoids and some agonists. In this study, the nAChR α1 subunit from Apis mellifera was first found to be narrowly tuned to different bee toxicity insecticides, namely, sulfoxaflor (SFX) and flupyradifurone (FPF). Hence, novel sulfoximine derivatives 7a-h were rationally designed and synthesized by introducing a benzo[d][1,3]dioxole moiety into a unique sulfoximine skeleton based on the binding cavity characteristics of Amelα1/ratß2. The two electrode voltage clamp responses of 7a-h were obviously lower than that of SFX, indicating their potentially low bee toxicity. Besides, representative compounds 7b and 7g exhibited low bee toxicity (LD50 > 11.0 µg/bee at 48 h) revealed by acute contact toxicity bioassays. Molecular modelling results indicated that Ile152, Ala151, and Val160 from honeybee subunit Amelα1 and Lys144 and Trp80 from aphid subunit Mpα1 may be crucial for bee toxicity and aphicidal activity, respectively. These results clarify the toxic mechanism of agonist insecticides on nontargeted pollinators and reveal novel scaffold sulfoximine aphicidal candidates with low bee toxicity. These results will provide a new perspective on the rational design and highly effective development of novel eco-friendly insecticides based on the structure of the nAChR subunit.

DDX39B facilitates the malignant progression of hepatocellular carcinoma via activation of SREBP1-mediated de novo lipid synthesis.

PURPOSE: The detailed molecular mechanisms of aberrant lipid metabolism in HCC remain unclear. Herein, we focused on the potential role of DDX39B in aberrant lipogenesis and malignant development in HCC. METHODS: DDX39B expression in HCC and para-cancer tissues was measured by immunohistochemistry. CCK-8, colony formation and Transwell assays were utilized to detect HCC cell proliferation, migration and invasion in vitro. Oil red O and Nile red staining and triglyceride and cholesterol detection were used to measure lipogenesis. Coimmunoprecipitation was used to detect interactions between DDX39B and SREBP1. Immunofluorescence assays were performed to investigate the impact of DDX39B on SREBP1 nuclear translocation. A luciferase assay was used to explore the transcriptional activity of SREBP1. The subcutaneous and orthotopic xenograft models in nude mice were generated to verify the contribution of the DDX39B/SREBP1 axis to tumor growth, lung metastasis and lipid synthesis in vivo. RESULTS: DDX39B is upregulated in HCC tissues and predicts a worse prognosis. Upregulated DDX39B contributes to the proliferation, metastasis and lipogenesis of HCC cells. Mechanistically, DDX39B directly interacts with SREBP1, and silencing DDX39B impairs the stabilization of the SREBP1 protein through FBXW7-mediated ubiquitination and degradation of SREBP1. Furthermore, DDX39B deficiency decreases the nuclear translocation and activation of SREBP1 and transcription of SREBP1 downstream genes, resulting in reduced lipid accumulation. CONCLUSIONS: Our study reveals a novel mechanism by which DDX39B facilitates the malignant progression of HCC via activation of SREBP1-mediated de novo lipogenesis, implicating DDX39B as both a potential predictor of recurrence and prognosis and a promising therapeutic target.

ZNF32 prevents the activation of cancer-associated fibroblasts through negative regulation of TGFB1 transcription in breast cancer.

Breast cancer is the most frequently diagnosed malignancy and the leading cause of cancer-related deaths in women worldwide. Cancer-associated fibroblasts (CAFs) are one of the fundamental cellular components of the tumor microenvironment and play a critical role in the initiation, progression, and therapy resistance of breast cancer. However, the detailed molecular mechanisms of CAFs activation from normal fibroblasts (NFs) are still not well understood. In the present study, we reported that ZNF32 expression in breast cancer cells was negatively correlated with CAF-related markers (FSP1, α-SMA, and FAP) in stromal fibroblasts, and loss of ZNF32 promoted the activation of CAFs, as evidenced by the enhanced proliferation and contractility of CAFs. ZNF32 deficiency-mediated fibroblast activation promoted the growth and metastasis of breast cancer cells in vitro and in vivo. Mechanistically, we demonstrated that ZNF32 inhibited TGFB1 transcription by directly binding to the -1968/-1962 region of the TGFB1 promoter, leading to the prevention of fibroblast activation. Altogether, our findings reveal an important mechanism by which ZNF32 suppression increases the transcription of the TGFB1 gene in breast cancer cells, and subsequently, elevated levels of secretory TGF-ß stimulate NFs transformation into CAFs, which in turn facilitates the malignant progression of breast cancer. Our data implicated ZNF32 as a potential therapeutic strategy against breast cancer.

DDX17 induces epithelial-mesenchymal transition and metastasis through the miR-149-3p/CYBRD1 pathway in colorectal cancer.

DEAD box helicase 17 (DDX17) has been reported to be involved in the initiation and development of several cancers. However, the functional role and mechanisms of DDX17 in colorectal cancer (CRC) malignant progression and metastasis remain unclear. Here, we reported that DDX17 expression was increased in CRC tissues compared with noncancerous mucosa tissues and further upregulated in CRC liver metastasis compared with patient-paired primary tumors. High levels of DDX17 were significantly correlated with aggressive phenotypes and worse clinical outcomes in CRC patients. Ectopic expression of DDX17 promoted cell migration and invasion in vitro and in vivo, while the opposite results were obtained in DDX17-deficient CRC cells. We identified miR-149-3p as a potential downstream miRNA of DDX17 through RNA sequencing analysis, and miR-149-3p displayed a suppressive effect on the metastatic potential of CRC cells. We demonstrated that CYBRD1 (a ferric reductase that contributes to dietary iron absorption) was a direct target of miR-149-3p and that miR-149-3p was required for DDX17-mediated regulation of CYBRD1 expression. Moreover, DDX17 contributed to the metastasis and epithelial to mesenchymal transition (EMT) of CRC cells via downregulation of miR-149-3p, which resulted in increased CYBRD1 expression. In conclusion, our findings not only highlight the significance of DDX17 in the aggressive development and prognosis of CRC patients, but also reveal a novel mechanism underlying DDX17-mediated CRC cell metastasis and EMT progression through manipulation of the miR-149-3p/CYBRD1 pathway.

Coronatine promotes maize water uptake by directly binding to the aquaporin ZmPIP2;5 and enhancing its activity.

Water uptake is crucial for crop growth and development and drought stress tolerance. The water channel aquaporins (AQP) play important roles in plant water uptake. Here, we discovered that a jasmonic acid analog, coronatine (COR), enhanced maize (Zea mays) root water uptake capacity under artificial water deficiency conditions. COR treatment induced the expression of the AQP gene Plasma membrane intrinsic protein 2;5 (ZmPIP2;5). In vivo and in vitro experiments indicated that COR also directly acts on ZmPIP2;5 to improve water uptake in maize and Xenopus oocytes. The leaf water potential and hydraulic conductivity of roots growing under hyperosmotic conditions were higher in ZmPIP2;5-overexpression lines and lower in the zmpip2;5 knockout mutant, compared to wild-type plants. Based on a comparison between ZmPIP2;5 and other PIP2s, we predicted that COR may bind to the functional site in loop E of ZmPIP2;5. We confirmed this prediction by surface plasmon resonance technology and a microscale thermophoresis assay, and showed that deleting the binding motif greatly reduced COR binding. We identified the N241 residue as the COR-specific binding site, which may activate the channel of the AQP tetramer and increase water transport activity, which may facilitate water uptake under hyperosmotic stress.

Ambient air pollution, temperature and hospital admissions due to respiratory diseases in a cold, industrial city.

Background: The influences of air pollution exposure and temperature on respiratory diseases have become major global health concerns. This study investigated the relationship between ambient air pollutant concentrations and temperature in cold industrial cities that have the risk of hospitalization for respiratory diseases. Methods: A time-series study was conducted in Changchun, China, from 2015 to 2019 to analyse the number of daily admissions for respiratory diseases, air pollutant concentrations, and meteorological factors. Time-series decomposition was applied to analyse the trend and characteristics of the number of admissions. Generalized additive models and distributed lag nonlinear models were constructed to explore the effects of air pollutant concentrations and temperature on the number of admissions. Results: The number of daily admissions showed an increasing trend, and the seasonal fluctuation was obvious, with more daily admissions in winter and spring than in summer and autumn. There were positive and gradually decreasing lag effects of PM10, PM2.5, NO2, and CO concentrations on the number of admissions, whereas O3 showed a J-shaped trend. The results showed that within the 7-day lag period, 0.5°C was the temperature associated with the lowest relative risk of admission due to respiratory disease, and extremely low and high temperatures (<-18°C, >27°C, respectively) increased the risk of hospitalization for respiratory diseases by 8.3% and 12.1%, respectively. Conclusions: From 2015 to 2019, respiratory diseases in Changchun showed an increasing trend with obvious seasonality. The increased concentrations of SO2, NO2, CO, PM2.5, O3 and PM10 lead to an increased risk of hospitalization for respiratory diseases, with a significant lag effect. Both extreme heat and cold could lead to increases in the risk of admission due to respiratory disease.

The comparative analysis of SARIMA, Facebook Prophet, and LSTM for road traffic injury prediction in Northeast China.

Objective: This cross-sectional research aims to develop reliable predictive short-term prediction models to predict the number of RTIs in Northeast China through comparative studies. Methodology: Seasonal auto-regressive integrated moving average (SARIMA), Long Short-Term Memory (LSTM), and Facebook Prophet (Prophet) models were used for time series prediction of the number of RTIs inpatients. The three models were trained using data from 2015 to 2019, and their prediction accuracy was compared using data from 2020 as a test set. The parameters of the SARIMA model were determined using the autocorrelation function (ACF) and the partial autocorrelation function (PACF). The LSTM uses linear as the activation function, the mean square error (MSE) as the loss function and the Adam optimizer to construct the model, while the Prophet model is built on the Python platform. The root mean squared error (RMSE), mean absolute error (MAE) and Mean Absolute Percentage Error (MAPE) are used to measure the predictive performance of the model. Findings: In this research, the LSTM model had the highest prediction accuracy, followed by the Prophet model, and the SARIMA model had the lowest prediction accuracy. The trend in medical expenditure of RTIs inpatients overlapped highly with the number of RTIs inpatients. Conclusion: By adjusting the activation function and optimizer, the LSTM predicts the number of RTIs inpatients more accurately and robustly than other models. Compared with other models, LSTM models still show excellent prediction performance in the face of data with seasonal and drastic changes. The LSTM can provide a better basis for planning and management in healthcare administration. Implication: The results of this research show that it is feasible to accurately forecast the demand for healthcare resources with seasonal distribution using a suitable forecasting model. The prediction of specific medical service volumes will be an important basis for medical management to allocate medical and health resources.

DDX39B drives colorectal cancer progression by promoting the stability and nuclear translocation of PKM2.

Metastasis is a major cause of colorectal cancer (CRC) mortality, but its molecular mechanisms are still not fully understood. Here, we show that upregulated DDX39B correlates with liver metastases and aggressive phenotypes in CRC. DDX39B is an independent prognostic factor associated with poor clinical outcome in CRC patients. We demonstrate that Sp1 potently activates DDX39B transcription by directly binding to the GC box of the DDX39B promoter in CRC cells. DDX39B overexpression augments the proliferation, migration, and invasion of CRC cells, while the opposite results are obtained in DDX39B-deficient CRC cells. Mechanistically, DDX39B interacts directly with and stabilizes PKM2 by competitively suppressing STUB1-mediated PKM2 ubiquitination and degradation. Importantly, DDX39B recruits importin α5 to accelerate the nuclear translocation of PKM2 independent of ERK1/2-mediated phosphorylation of PKM2, leading to the transactivation of oncogenes and glycolysis-related genes. Consequently, DDX39B enhances glucose uptake and lactate production to activate Warburg effect in CRC. We identify that Arg319 of DDX39B is required for PKM2 binding as well as PKM2 nuclear accumulation and for DDX39B to promote CRC growth and metastasis. In addition, blocking PKM2 nuclear translocation or treatment with glycolytic inhibitor 2-deoxy-D-glucose efficiently abolishes DDX39B-triggered malignant development in CRC. Taken together, our findings uncover a key role for DDX39B in modulating glycolytic reprogramming and aggressive progression, and implicate DDX39B as a potential therapeutic target in CRC.

Cost-Effectiveness Analysis of the Helicobacter Pylori Screening Programme in an Asymptomatic Population in China.

Objective The aim of this study was to investigate the cost-effectiveness of Helicobacter pylori (H. pylori) screening and eradication treatment in an asymptomatic population in China and to explore the most cost-effective screening protocol for H. pylori. Method We used TreeAge 2019 to construct Markov models to assess the direct healthcare costs and quality-adjusted life years (QALYs) and the cost per year of life saved (YoLS) of three therapies, i.e., annual, triennial and five-yearly H. pylori screening. Excess probabilities were derived from published high quality studies and Meta-analyses, and costs and utilities were derived from the Chinese Yearbook of Health Care Statistics and published studies. Incremental cost-effectiveness ratios (ICERs) were used to describe the results. The willingness-to-pay threshold was set at China's Gross National Product per capita. Result In the asymptomatic population, the ICER per QALYs gained was US$1238.47 and US$1163.71 for every three and five years of screening compared to the annual screening group; the ICER per YoLS gained was US$3067.91 and US$1602.78, respectively. Conclusion Screening for H. pylori in asymptomatic populations in China and eradicating treatment for those who test positive is cost-effective. Increasing screening participation in asymptomatic populations is more effective than increasing the frequency of screening. From a national payer perspective, it is cost-effective to screen the general asymptomatic population in China for H. pylori and to eradicate those who test positive. Individuals need to choose a screening programme that they can afford according to their financial situation.

Cost-Effectiveness Analysis of Rivaroxaban in Chinese Patients With Stable Cardiovascular Disease.

Objective: This study aimed to investigate the cost-effectiveness of low-dose rivaroxaban plus aspirin versus aspirin alone for patients with stable cardiovascular diseases in the China. Methods: We used TreeAge 2019 to construct a Markov model to assess the direct healthcare costs and quality-adjusted life years for three therapies, namely low-does rivaroxaban plus aspirin, rivaroxaban alone, and aspirin alone. Transitional probabilities were derived from the COMPASS trial, and the costs and utilities were obtained from the Chinese Health Care Statistical Yearbook and published studies. Use the Incremental cost-effectiveness ratio to describe the results. The willingness-to-pay threshold is set at US$11,000 (China's 2020 Gross National Product per capita). Result: In patients with stable cardiovascular disease, the increased cost per quality-adjusted life year gained in the low-dose rivaroxaban combined with aspirin group compared to the aspirin alone group was US$7937.30. The increased cost per quality-adjusted life year gained in the rivaroxaban alone group versus the aspirin alone group was US$15,045.78. Conclusion: A low-does rivaroxaban plus aspirin therapy may be cost-effective in the secondary prevention of stable cardiovascular disease in patients.

[Correlation analysis of Poor Prognosis and Immunotherapy of lncRNAs Related with m 6A Modification in Cervical Cancer].

Objective: To study the correlation between N 6-methyladenosine (m 6A)-modification-associated long non-coding RNAs (lncRNAs) and poor prognosis and immunotherapy in cervical cancer based on data mining of The Cancer Genome Atlas (TCGA) cervical cancer dataset, so as to assess effectively the prognosis of cervical cancer patients and the feasibility of immunotherapy. Methods: We identified m 6A-modification-associated lncRNAs correlated to the prognosis of cervical cancer by conducting bioinformatics analysis of cervical cancer samples from the TCGA datasets and constructed a prognostic risk model of cervical cancer accordingly. Results: A total of 343 m 6A-modification-associated lncRNAs were identified from the samples of 304 cervical cancer patients. Univariate Cox regression analysis showed that 26 out of the 343 m 6A-modification-associated lncRNAs were significantly associated with the prognosis of cervical cancer patients. We identified 7 m 6A-modification-associated lncRNAs, including DLEU1, AC099850.4, DDN-AS1, EP300-AS1, AC131159.1, AL441992.2, and AL021707.6 through Lasso regression analysis and then developed a prognostic risk model based on them. According to the Kaplan-Meier survival analysis, cervical cancer patients in the low-risk group exhibited significantly improved overall survival (OS) in comparison with those in the high-risk group ( P<0.001). The area under the curve ( AUC) of receiver operating characteristic (ROC) curve analysis demonstrated the high sensitivity and credibility of the risk model. Multivariate Cox analysis showed that the risk score was an independent prognostic factor of cervical cancer patients. Tumor immune dysfunction and exclusion (TIDE) analysis predicted that the high-risk group would benefit more from immunotherapy. In addition, we found that immune checkpoint PD1 was associated with the expression of m6A-modification-related lncRNAs such as DDN-AS1, and the expression was higher in the high-risk group ( P<0.05). Conclusion: The prognostic risk model constructed on the basis of the aforementioned 7 m 6A-modification-associated lncRNAs can be used to effectively predict the prognosis of cervical cancer patients and assess the efficacy of immunotherapy targeting PD1.

Comprehensive Analysis of the COVID-19: Based on the Social-Related Indexes From NUMBEO.

Background: The COVID-19 has been spreading globally since 2019 and causes serious damage to the whole society. A macro perspective study to explore the changes of some social-related indexes of different countries is meaningful. Methods: We collected nine social-related indexes and the score of COVID-safety-assessment. Data analysis is carried out using three time series models. In particular, a prediction-correction procedure was employed to explore the impact of the pandemic on the indexes of developed and developing countries. Results: It shows that COVID-19 epidemic has an impact on the life of residents in various aspects, specifically in quality of life, purchasing power, and safety. Cluster analysis and bivariate statistical analysis further indicate that indexes affected by the pandemic in developed and developing countries are different. Conclusion: This pandemic has altered the lives of residents in many ways. Our further research shows that the impacts of social-related indexes in developed and developing countries are different, which is bounded up with their epidemic severity and control measures. On the other hand, the climate is crucial for the control of COVID-19. Consequently, exploring the changes of social-related indexes is significative, and it is conducive to provide targeted governance strategies for various countries. Our article will contribute to countries with different levels of development pay more attention to social changes and take timely and effective measures to adjust social changes while trying to control this pandemic.

Identified lncRNAs functional modules and genes in prediabetes with hypertriglyceridemia by weighted gene co-expression network analysis.

BACKGROUND: Hypertriglyceridemia (HTG) is one of the most important comorbidities in abnormal glucose patients. The aim of this study was to identify lncRNAs functional modules and hub genes related to triglyceride (TG) in prediabetes. METHODS: The study included 12 prediabetic patients: 6 participants with HTG and 6 participants with normal triglyceride (NTG). Whole peripheral blood RNA sequencing was performed for these samples to establish a lncRNA library. WGCNA, KEGG pathways analysis and the PPI network were used to construct co-expression network, to obtain modules related to blood glucose, and to detect key lncRNAs. Meanwhile, GEO database and qRT-PCR were used to validate above key lncRNAs. RESULTS: We found out that the TCONS_00334653 and PVT1, whose target mRNA are MYC and HIST1H2BM, were downregulating in the prediabetes with HTG. Moreover, both of TCONS_00334653 and PVT1 were validated in the GEO database and qRT-PCR. CONCLUSIONS: Therefore, the TCONS_00334653 and PVT1 were detected the key lncRNAs for the prediabetes with HTG, which might be a potential therapeutic or diagnostic target for the treatment of prediabetes with HTG according to the results of validation in the GEO database, qRT-PCR and ROC curves.