Bioinformatics analysis reveals immune prognostic markers for overall survival of colorectal cancer patients: a novel machine learning survival predictive system.

OBJECTIVES: Immune microenvironment was closely related to the occurrence and progression of colorectal cancer (CRC). The objective of the current research was to develop and verify a Machine learning survival predictive system for CRC based on immune gene expression data and machine learning algorithms. METHODS: The current study performed differentially expressed analyses between normal tissues and tumor tissues. Univariate Cox regression was used to screen prognostic markers for CRC. Prognostic immune genes and transcription factors were used to construct an immune-related regulatory network. Three machine learning algorithms were used to create an Machine learning survival predictive system for CRC. Concordance indexes, calibration curves, and Brier scores were used to evaluate the performance of prognostic model. RESULTS: Twenty immune genes (BCL2L12, FKBP10, XKRX, WFS1, TESC, CCR7, SPACA3, LY6G6C, L1CAM, OSM, EXTL1, LY6D, FCRL5, MYEOV, FOXD1, REG3G, HAPLN1, MAOB, TNFSF11, and AMIGO3) were recognized as independent risk factors for CRC. A prognostic nomogram was developed based on the previous immune genes. Concordance indexes were 0.852, 0.778, and 0.818 for 1-, 3- and 5-year survival. This prognostic model could discriminate high risk patients with poor prognosis from low risk patients with favorable prognosis. CONCLUSIONS: The current study identified twenty prognostic immune genes for CRC patients and constructed an immune-related regulatory network. Based on three machine learning algorithms, the current research provided three individual mortality predictive curves. The Machine learning survival predictive system was available at: https://zhangzhiqiao8.shinyapps.io/Artificial_Intelligence_Survival_Prediction_for_CRC_B1005_1/ , which was valuable for individualized treatment decision before surgery.

Improve individual treatment by comparing treatment benefits: cancer artificial intelligence survival analysis system for cervical carcinoma.

PURPOSE: The current study aimed to construct a novel cancer artificial intelligence survival analysis system for predicting the individual mortality risk curves for cervical carcinoma patients receiving different treatments. METHODS: Study dataset (n = 14,946) was downloaded from Surveillance Epidemiology and End Results database. Accelerated failure time algorithm, multi-task logistic regression algorithm, and Cox proportional hazard regression algorithm were used to develop prognostic models for cancer specific survival of cervical carcinoma patients. RESULTS: Multivariate Cox regression identified stage, PM, chemotherapy, Age, PT, and radiation_surgery as independent influence factors for cervical carcinoma patients. The concordance indexes of Cox model were 0.860, 0.849, and 0.848 for 12-month, 36-month, and 60-month in model dataset, whereas it were 0.881, 0.845, and 0.841 in validation dataset. The concordance indexes of accelerated failure time model were 0.861, 0.852, and 0.851 for 12-month, 36-month, and 60-month in model dataset, whereas it were 0.882, 0.847, and 0.846 in validation dataset. The concordance indexes of multi-task logistic regression model were 0.860, 0.863, and 0.861 for 12-month, 36-month, and 60-month in model dataset, whereas it were 0.880, 0.860, and 0.861 in validation dataset. Brier score indicated that these three prognostic models have good diagnostic accuracy for cervical carcinoma patients. The current research lacked independent external validation study. CONCLUSION: The current study developed a novel cancer artificial intelligence survival analysis system to provide individual mortality risk predictive curves for cervical carcinoma patients based on three different artificial intelligence algorithms. Cancer artificial intelligence survival analysis system could provide mortality percentage at specific time points and explore the actual treatment benefits under different treatments in four stages, which could help patient determine the best individualized treatment. Cancer artificial intelligence survival analysis system was available at: https://zhangzhiqiao15.shinyapps.io/Tumor_Artificial_Intelligence_Survival_Analysis_System/ .

Alert for non-respiratory symptoms of coronavirus disease 2019 patients in epidemic period: A case report of familial cluster with three asymptomatic COVID-19 patients.

At present, coronavirus disease 2019 (COVID-19) is rampaging around the world. However, asymptomatic carriers intensified the difficulty of prevention and management. Here we reported the screening, clinical features, and treatment process of a family cluster involving three COVID-19 patients. The discovery of the first asymptomatic carrier in this family cluster depends on the repeated and comprehensive epidemiological investigation by disease control experts. In addition, the combination of multiple detection methods can help clinicians find asymptomatic carriers as early as possible. In conclusion, the prevention and control experience of this family cluster showed that comprehensive rigorous epidemiological investigation and combination of multiple detection methods were of great value for the detection of hidden asymptomatic carriers.

Clinical features and optical coherence tomography findings of retinal astrocytic hamartomas in Chinese patients with tuberous sclerosis complex.

PURPOSE: To investigate the clinical features and spectral-domain optical coherence tomography (SD-OCT) findings of retinal astrocytic hamartoma (RAH) in Chinese patients with tuberous sclerosis complex (TSC). METHODS: The medical records of 91 consecutive patients with established TSC diagnosis were retrospectively reviewed. Fundus findings regarding RAH documented by fundus photography and SD-OCT at presentation were collected and analyzed. RESULTS: RAHs were seen in 69 of the 91 patients (75.8%); 50.7% of these patients showed bilateral retinal involvement. Type 1 RAH was found the most common type with a prevalence of 94.2%, while type 2 and type 3 RAH with 7.2% and 18.8% respectively. A significant correlation between age and RAH types was shown by Fisher's exact test (p < 0.001). By SD-OCT, non-calcified RAHs featured in hyperreflective thickening of the retinal nerve fiber layer with some degree of retinal disorganization, while multinodular calcified RAHs characterized with moth-eaten appearances representing intraretinal calcification with posterior dense optical shadowing. CONCLUSION: A higher prevalence of TSC-associated RAH but an unexpected lower prevalence of calcified RAHs was shown in Chinese compared with that of Caucasians. SD-OCT can be used to facilitate the detection and follow-up of RAHs.

The competitive endogenous RNA regulatory network reveals potential prognostic biomarkers for overall survival in hepatocellular carcinoma.

The aim of the present study is to construct a competitive endogenous RNA (ceRNA) regulatory network by using differentially expressed long noncoding RNAs (lncRNAs), microRNAs (miRNAs), and mRNAs in patients with hepatocellular carcinoma (HCC), and to construct a prognostic model for predicting overall survival (OS) of HCC patients. Differentially expressed lncRNAs, miRNAs, and mRNAs were explored between HCC tissues and normal liver tissues. A prognostic model was built for predicting OS of HCC patients and receiver operating characteristic curves were used to evaluate the performance of the prognostic model. There were 455 differentially expressed lncRNAs, 181 differentially expressed miRNAs, and 5035 differentially expressed mRNAs. A ceRNA regulatory network was constructed based on 43 lncRNAs, 37 miRNAs, and 105 mRNAs. Eight mRNA biomarkers (H2AFX, SQSTM1, ITM2A, PFKP, TPD52L1, ACSL4, STRN3, and CPEB3) were identified as independent risk factors by multivariate Cox regression and were used to develop a prognostic model for OS. The C-indexes in the model group were 0.776 (95% confidence interval [CI], 0.730-0.822), 0.745 (95% CI, 0.699-0.791), and 0.789 (95% CI, 0.743-0.835) for 1-, 3-, and 5-year OS, respectively. The current study revealed potential molecular biological regulation pathways and prognostic biomarkers by the ceRNA regulatory network. A prognostic model based on prognostic mRNAs in the ceRNA network might be helpful to predict the individual mortality risk for HCC patients. The individual mortality risk calculator can be used by visiting the following URL: https://zhangzhiqiao.shinyapps.io/Smart_cancer_predictive_system_HCC/.

Two precision medicine predictive tools for six malignant solid tumors: from gene-based research to clinical application.

BACKGROUND: The current study aimed to construct competing endogenous RNA (ceRNA) regulation network and develop two precision medicine predictive tools for colorectal cancer (CRC). METHODS: Differentially expressed (DE) analyses were performed between CRC tissues and normal tissues. A ceRNA regulation network was constructed based on DElncRNAs, DEmiRNAs, and DEmRNAs. RESULTS: Fifteen mRNAs (ENDOU, MFN2, FASLG, SHOC2, VEGFA, ZFPM2, HOXC6, KLK10, DDIT4, LPGAT1, BEX4, DENND5B, PHF20L1, HSP90B1, and PSPC1) were identified as prognostic biomarkers for CRC by multivariate Cox regression. Then a Fifteen-mRNA signature was developed to predict overall survival for CRC patients. Concordance indexes were 0.817, 0.838, and 0.825 for 1-, 2- and 3-year overall survival. Patients with high risk scores have worse OS compared with patients with low risk scores. CONCLUSION: The current study provided deeper understanding of prognosis-related ceRNA regulatory network for CRC. Two precision medicine predictive tools named Smart Cancer Survival Predictive System and Gene Survival Analysis Screen System were constructed for CRC. These two precision medicine predictive tools can provide valuable precious individual mortality risk prediction before surgery and improve the individualized treatment decision-making.

Integrated analysis reveals potential long non-coding RNA biomarkers and their potential biological functions for disease free survival in gastric cancer patients.

BACKGROUND: Increasing evidences supported the association between long non-coding RNA (lncRNA) and disease free survival in gastric cancer (GC) patients. The purpose of the current study was to construct and verify a noninvasive preoperative predictive tool for disease free survival in GC patients. METHODS: There were 265 and 300 GC patients in model dataset and validation dataset respectively. The associations between the lncRNA biomarkers and disease free survival were evaluated by univariate and multivariate Cox regression. RESULTS: Thirteen lncRNA biomarkers (GAS5-AS1, AL109615.3, KDM7A-DT, AP000866.2, KCNJ2-AS1, LINC00656, LINC01777, AC046185.3, TTTY14, LINC01526, LINC02523, LINC00592, and C5orf66) were identified as prognostic biomarkers with disease free survival. These thirteen lncRNA biomarkers were combined to construct a prognostic signature for disease free survival. The C-indexes of the current predictive signature in model cohort were 0.849 (95% CI 0.803-0.895), 0.859 (95% CI 0.813-0.905) and 0.888 (95% CI 0.842-0.934) for 1-year, 3-year and 5-year disease free survival respectively. Based on thirteen-lncRNA prognostic signature, patients in model cohort could be stratified into high risk group and low risk group with significant different disease free survival rate (hazard ratio [HR] = 7.355, 95% confidence interval [CI] 4.378-12.356). Good reproducibility of thirteen-lncRNA prognostic signature was confirmed in an external validation cohort (GSE62254) with HR 3.919 and 95% CI 2.817-5.453. Further analysis demonstrated that the prognostic significance of thirteen-lncRNA prognostic signature was independent of other clinical characteristics. CONCLUSIONS: In conclusion, a simple noninvasive prognostic signature was established for preoperative prediction of disease free survival in GC patients. This prognostic signature might predict the individual mortality risk of disease free survival without pathological information and facilitate individual treatment decision-making.

Comprehensive bioinformatics analysis reveals potential lncRNA biomarkers for overall survival in patients with hepatocellular carcinoma: an on-line individual risk calculator based on TCGA cohort.

BACKGROUND: Accumulated evidences have demonstrated that long non-coding RNAs (lncRNAs) are correlated with prognosis of patients with hepatocellular carcinoma. The current study aimed to develop and validate a prognostic lncRNA signature to improve the prediction of overall survival in hepatocellular carcinoma patients. METHODS: The study cohort involved 348 hepatocellular carcinoma patients with lncRNA expression information and overall survival information. Through gene mining approach, the current study established a prognostic lncRNA signature (named LncRNA risk prediction score) for predicting the overall survival of hepatocellular carcinoma patients. RESULTS: The current study built a predictive nomogram based on ten prognostic lncRNA predictors through Cox regression analysis. In model group, the Harrell's concordance indexes of LncRNA risk prediction score were 0.811 (95% CI 0.769-0.853) for 1-year overall survival, 0.814 (95% CI 0.772-0.856) for 3-year overall survival and 0.796 (95% CI 0.754-0.838) for 5-year overall survival respectively. In validation cohort, the Harrell's concordance indexes of LncRNA risk prediction score were 0.779 (95% CI 0.737-0.821), 0.828 (95% CI 0.786-0.870) and 0.796 (95%CI 0.754-0.838) for 1-year survival, 3-year survival and 5-year survival respectively. LncRNA risk prediction score could stratify hepatocellular carcinoma patients into low risk group and high risk group. Further survival curve analysis demonstrated that the overall survival rate of high risk patients was significantly poorer than that of low risk patients (P < 0.001). CONCLUSIONS: In conclusion, the current study developed and validated a prognostic signature to predict the individual mortality risk for hepatocellular carcinoma patients. LncRNA risk prediction score is helpful to identify the patients with high mortality risk and optimize the individualized treatment decision. The web calculator can be used by click the following URL: https://zhangzhiqiao2.shinyapps.io/Smart_cancer_predictive_system_HCC_3/.

Two predictive precision medicine tools for hepatocellular carcinoma.

BACKGROUND: Hepatocellular carcinoma (HCC) is a serious threat to public health due to its poor prognosis. The current study aimed to develop and validate a prognostic nomogram to predict the overall survival of HCC patients. METHODS: The model cohort consisted of 24,991 mRNA expression data points from 348 HCC patients. The least absolute shrinkage and selection operator method (LASSO) Cox regression model was used to evaluate the prognostic mRNA biomarkers for the overall survival of HCC patients. RESULTS: Using multivariate Cox proportional regression analyses, a prognostic nomogram (named Eight-mRNA prognostic nomogram) was constructed based on the expression data of N4BP3, -ADRA2B, E2F8, MAPT, PZP, HOXD9, COL15A1, and -NDST3. The C-index of the Eight-mRNA prognostic nomogram was 0.765 (95% CI 0.724-0.806) for the overall survival in the model cohort. The Harrell's concordance-index of the Eight-mRNA prognostic nomogram was 0.715 (95% CI 0.658-0.772) in the validation cohort. The survival curves demonstrated that the HCC patients in the high risk group had a significantly poorer overall survival than the patients in the low risk group. CONCLUSION: In the current study, we have developed two convenient and efficient predictive precision medicine tools for hepatocellular carcinoma. These two predictive precision medicine tools are helpful for predicting the individual mortality risk probability and improving the personalized comprehensive treatments for HCC patients. The Smart Cancer Predictive System can be used by clicking the following URL: https://zhangzhiqiao2.shinyapps.io/Smart_cancer_predictive_system_HCC_2/. The Gene Survival Analysis Screen System is available at the following URL: https://zhangzhiqiao5.shinyapps.io/Gene_Survival_Analysis_A1001/.

Increased expression of Ki-67 is a poor prognostic marker for colorectal cancer patients: a meta analysis.

BACKGROUND: The prognostic value of Ki-67 expression in colorectal cancer patients was controversial. Therefore, this meta analysis was conducted to ascertain the prognostic value of Ki-67 expression in colorectal cancer patients. METHODS: The electronic databases, including EMBASE, PubMed, Cochrane Library and Web of Knowledge database, were searched from January 1970 to July 2017. The pooled hazard ratios and 95% confidence intervals were calculated to evaluate the prognostic value of Ki-67 expression for colorectal cancer patients. RESULTS: Totally 34 eligible studies and 6180 colorectal cancer patients were included in the present meta analysis. The pooled hazard ratios were 1.54(95% CI 1.17-2.02, P = 0.005) for overall survival and 1.43(1.12-1.83, P = 0.008) for disease free survival in univariate analysis. After adjustment of other prognostic factors, the pooled HR was 1.50(95% CI 1.02-2.22, P = 0.03) for overall survival in multivariate analysis. CONCLUSION: The present meta analysis demonstrated that high Ki-67 expression is significantly correlated with poor overall survival and disease free survival, indicating that high Ki-67 expression may serve as a valuable predictive method for poor prognosis of colorectal cancer patients.

High Ki-67 expression is significantly associated with poor prognosis of ovarian cancer patients: evidence from a meta-analysis.

OBJECTIVE: The prognostic significance of Ki-67 expression in patients with ovarian cancer was controversial in various studies. Therefore, we carried out a meta-analysis to determine the prognostic significance of Ki-67 in ovarian cancer patients. METHODS: We searched PubMed, Cochrane Library, EMBASE, Web of Knowledge, China National Knowledge Infrastructure database and WanFang digital database for eligible studies from January 1, 1990 to June 1, 2017. The pooled hazard ratios and 95% confidence intervals were calculated to assess the prognostic significance of Ki-67 expression for overall survival in ovarian cancer patients. RESULTS: Finally, 38 eligible studies and 5004 ovarian cancer patients were included in the current study. The pooled hazard ratio was 1.35 (95% confidence interval 1.24-1.46, P = 0.001) for overall survival in ovarian cancer patients. The funnel plot bias was obviously asymmetrical and Egger's test also detected significant publication bias (P = 0.001). The Contour-enhanced funnel plot with trim-and-fill method supplemented 11 dummy studies to balance the funnel plot and nine new supplementary studies were in area with statistical significance. Sensitivity analysis and cumulative meta-analysis further demonstrated that the association between high Ki-67 expression and poor overall survival of ovarian cancer patients was stable and reliable. CONCLUSIONS: High Ki-67 expression is significantly related to poor overall survival and may serve as a prognostic biomarker for ovarian cancer patients.

Differences of survival benefits brought by various treatments in ovarian cancer patients with different tumor stages.

PURPOSE: The current study aimed to explore the prognosis of ovarian cancer patients in different subgroup using three prognostic research indexes. The current study aimed to build a prognostic model for ovarian cancer patients. METHODS: The study dataset was downloaded from Surveillance Epidemiology and End Results database. Accelerated Failure Time algorithm was used to construct a prognostic model for ovary cancer. RESULTS: The mortality rate in the model group was 51.6% (9,314/18,056), while the mortality rate in the validation group was 52.1% (6,358/12,199). The current study constructed a prognostic model for ovarian cancer patients. The C indexes were 0.741 (95% confidence interval: 0.731-0.751) in model dataset and 0.738 (95% confidence interval: 0.726-0.750) in validation dataset. Brier score was 0.179 for model dataset and validation dataset. The C indexes were 0.741 (95% confidence interval: 0.733-0.749) in bootstrap internal validation dataset. Brier score was 0.178 for bootstrap internal validation dataset. CONCLUSION: The current research indicated that there were significant differences in the survival benefits of treatments among ovarian cancer patients with different stages. The current research developed an individual mortality risk predictive system that could provide valuable predictive information for ovarian cancer patients.

Spectral-domain optical coherence tomography characteristics of macular contusion trauma.

PURPOSE: To describe the characteristics of macular lesions after eye trauma using spectral-domain optical coherence tomography (OCT). METHODS: We retrospectively reviewed and described 2-dimensional (2D) and 3-dimensional (3D) spectral-domain OCT characteristics of 24 consecutive eyes of 24 cases that were identified with macular lesions after eye trauma. RESULTS: Spectral-domain OCT clearly demonstrated a variety of lesions: hemorrhage, epiretinal membrane formation, macular hole, retinal pigment epithelium (RPE) rupture combined with choroidal neovascularization formation, photoreceptor inner/outer surface changes, RPE detachment, scar formation with e.g. diffuse macular edema, macular distortion or macular atrophy. Main lesions were located in the fovea area in 11 eyes (45.8%), the parafovea in 3 eyes (12.5%) and the whole macular area in 10 eyes (41.7%). CONCLUSION: Spectral-domain OCT is a useful investigation providing refined and credible 2D/3D images, precisely locating macular lesions after contusion trauma.

Spectral domain optical coherence tomography of Vogt-Koyanagi-Harada disease: novel findings and new insights into the pathogenesis.

OBJECTIVE: To provide novel spectral domain optical coherence tomography (SD OCT) findings of Vogt-Koyanagi-Harada (VKH) disease as well as new insights into the pathogenesis of this disease. METHODS: Detailed SD OCT and fluorescein angiography (FA) findings of 18 consecutive VKH patients (11 women and 7 men) from December 2007 to April 2009 who were in acute uveitic stage at presentation were reviewed. All the patients had been followed up for at least 6 months with reevaluation(s) of SD OCT performed in 10 patients. RESULTS: Intraretinal cysts were found to be located in various layers of the outer retina. In addition to the photoreceptor layer, they could also be found between the outer plexiform layer and the outer nuclear layer, or spanning the external limiting membrane. On FA, intraretinal cysts could be hypofluorescent, normofluorescent, or hyperfluorescent. Some intraretinal cysts had a characteristic FA pattern, in which a small round hypofluorescent area was surrounded by a ring of hyperfluorescence (donut-shaped dye pooling). Subretinal fibrinoid deposit appeared in acute uveitic stage in two severe VKH patients and seemed to develop from subretinal exudates and evolved into typical subretinal fibrosis. Gradual transfiguration/migration and progressive proliferation/pigmentation of the subretinal fibrinoid deposit/subretinal fibrosis was observed in one patient. CONCLUSIONS: Intraretinal cysts could form in various layers of the outer retina and may result from extension of choroidal inflammation. Subretinal fibrosis may develop from subretinal exudates in VKH patients and may cause substantial visual impairment.

[Ocular features of multiple endocrine neoplasia type 2b].

OBJECTIVE: To analyze the ocular features of multiple endocrine neoplasia type 2b (MEN 2b). METHODS: Three cases with MEN 2b were reviewed and their ocular features analyzed. RESULTS: All patients had medullary thyroid carcinoma and thickened corneal nerve fibers. Adrenal pheochromocytoma, oral mucosal neuroma, marfanoid body habitus, eyelid nodule and conjunctival nodule were found in two patients. And iris nodule was found in one patient. CONCLUSIONS: The most common ocular manifestations of MEN 2b is thickened corneal nerve fibers.

Artificial intelligence predictive system of individual survival rate for lung adenocarcinoma.

Background: The current research aimed to develop an artificial intelligence predictive system for individual survival rate of lung adenocarcinoma (LUAD). Methods: Independent risk variables were identified by multivariate Cox regression. Artificial intelligence predictive system was constructed using three different data mining algorithms. Results: Stage, PM, chemotherapy, PN, age, PT, sex, and radiation_surgery were determined as risk factors for LUAD patients. For 12-month survival rate in model cohort, concordance indexes of RFS, MTLR, and Cox models were 0.852, 0.821, and 0.835, respectively. For 36-month survival rate in model cohort, concordance indexes of RFS, MTLR, and Cox models were 0.901, 0.864, and 0.862, respectively. For 60-month survival rate in model cohort, concordance indexes of RFS, MTLR, and Cox models were 0.899, 0.874, and 0.866, respectively. The concordance indexes in validation dataset were similar to those in model dataset. Conclusions: The current study designed an individualized survival predictive system, which could provide individual survival curves using three different artificial intelligence algorithms. This artificial intelligence predictive system could directly convey treatment benefits by comparing individual mortality risk curves under different treatments. This artificial intelligence predictive tool is available at https://zhangzhiqiao11.shinyapps.io/Artificial_Intelligence_Survival_Prediction_System_AI_E1001/.

MicroRNA-122-5p Inhibition Improves Inflammation and Oxidative Stress Damage in Dietary-Induced Non-alcoholic Fatty Liver Disease Through Targeting FOXO3.

Misregulated microRNA network has been emerging as the main regulator in non-alcoholic fatty liver disease (NAFLD). The deregulation of miR-122-5p is associated with the liver disease. However, the specific role and molecular mechanism of miR-122-5p in NAFLD remain unclear. In this study, we have reported that the high-fat diet (HFD) or palmitic acid (PA) significantly upregulated the hepatic miR-122-5p expression in vivo and in vitro. Inhibition of miR-122-5p suppressed accumulation-induced inflammation of lipids and oxidative stress damage in PA-treated L02 cells and HFD-induced fatty liver. The effect of the miR-122-5p inhibitor on NAFLD did not depend on insulin resistance-mediated PI3K/AKT/mammalian target of rapamycin (mTOR) signaling pathway but rather on the upregulation of its downstream FOXO3. Subsequently, we validated that miR-122-5p directly binds to the predicted 3'-UTR of FOXO3 to inhibit its gene expression. Conversely, silencing FOXO3 abolished the hepatic benefits of miR-122-5p inhibition to obese mice by decreasing the activity of antioxidant enzymes of superoxide dismutase (SOD). This study provides a novel finding that FOXO3 was the target gene of miR-122-5p to attenuate inflammatory response and oxidative stress damage in dietary-induced NAFLD. Our study provided evidence to reveal the physiological role of miR-122-5p in dietary-induced NAFLD.

Prediction of individual mortality risk among patients with chronic obstructive pulmonary disease: a convenient, online, individualized, predictive mortality risk tool based on a retrospective cohort study.

Background: Chronic obstructive pulmonary disease (COPD) is a serious condition with a poor prognosis. No clinical study has reported an individual-level mortality risk curve for patients with COPD. As such, the present study aimed to construct a prognostic model for predicting individual mortality risk among patients with COPD, and to provide an online predictive tool to more easily predict individual mortality risk in this patient population. Patients and methods: The current study retrospectively included data from 1,255 patients with COPD. Random survival forest plots and Cox proportional hazards regression were used to screen for independent risk factors in patients with COPD. A prognostic model for predicting mortality risk was constructed using eight risk factors. Results: Cox proportional hazards regression analysis identified eight independent risk factors among COPD patients: B-type natriuretic peptide (hazard ratio [HR] 1.248 [95% confidence interval (CI) 1.155-1.348]); albumin (HR 0.952 [95% CI 0.931-0.974); age (HR 1.033 [95% CI 1.022-1.044]); globulin (HR 1.057 [95% CI 1.038-1.077]); smoking years (HR 1.011 [95% CI 1.006-1.015]); partial pressure of arterial carbon dioxide (HR 1.012 [95% CI 1.007-1.017]); granulocyte ratio (HR 1.018 [95% CI 1.010-1.026]); and blood urea nitrogen (HR 1.041 [95% CI 1.017-1.066]). A prognostic model for predicting risk for death was constructed using these eight risk factors. The areas under the time-dependent receiver operating characteristic curves for 1, 3, and 5 years were 0.784, 0.801, and 0.806 in the model cohort, respectively. Furthermore, an online predictive tool, the "Survival Curve Prediction System for COPD patients", was developed, providing an individual mortality risk predictive curve, and predicted mortality rate and 95% CI at a specific time. Conclusion: The current study constructed a prognostic model for predicting an individual mortality risk curve for COPD patients after discharge and provides a convenient online predictive tool for this patient population. This predictive tool may provide valuable prognostic information for clinical treatment decision making during hospitalization and health management after discharge (https://zhangzhiqiao15.shinyapps.io/Smart_survival_predictive_system_for_COPD/).

The miR528-D3 Module Regulates Plant Height in Rice by Modulating the Gibberellin and Abscisic Acid Metabolisms.

Plant height, as one of the important agronomic traits of rice, is closely related to yield. In recent years, plant height-related genes have been characterized and identified, among which the DWARF3 (D3) gene is one of the target genes of miR528, and regulates rice plant height and tillering mainly by affecting strigolactone (SL) signal transduction. However, it remains unknown whether the miR528 and D3 interaction functions in controlling plant height, and the underlying regulatory mechanism in rice. In this study, we found that the plant height, internode length, and cell length of internodes of d3 mutants and miR528-overexpressing (OE-miR528) lines were greatly shorter than WT, D3-overexpressing (OE-D3), and miR528 target mimicry (OE-MIM528) transgenic plants. Knockout of D3 gene (d3 mutants) or miR528-overexpressing (OE-miR528) triggers a substantial reduction of gibberellin (GA) content, but a significant increase of abscisic acid (ABA) accumulation than in WT. The d3 and OE-miR528 transgenic plants were much more sensitive to GA, but less sensitive to ABA than WT. Moreover, the expression level of GA biosynthesis-related key genes, including OsCPS1, OsCPS2, OsKO2 and OsKAO was remarkably higher in OE-D3 plants, while the NECD2 expression, a key gene involved in ABA biosynthesis, was significantly higher in d3 mutants than in WT and OE-D3 plants. The results indicate that the miR528-D3 module negatively regulates plant height in rice by modulating the GA and ABA homeostasis, thereby further affecting the elongation of internodes, and resulting in lower plant height, which adds a new regulatory role to the D3-mediated plant height controlling in rice.

Long-term efficacy and safety of sirolimus for retinal astrocytic hamartoma associated with tuberous sclerosis complex.

Mammalian target of rapamycin (mTOR) inhibitors (sirolimus or everolimus) have been demonstrated effective in reducing the size of tuberous sclerosis complex (TSC)-associated retinal astrocytic hamartoma (RAH) in short term. To investigate the long-term efficacy and safety of sirolimus on TSC-associated RAH, 13 TSC-associated RAH patients (59 RAH lesions) who received sirolimus therapy for at least 2 years were retrospectively enrolled in this study. Changes in the maximal thickness (MT) of RAH on optical coherence tomography and the longest base diameter (LBD) of RAH on color fundus photography were assessed. The results showed that for a mean follow-up of 39 months, sirolimus was associated with a mean reduction of 14.6% in MT and 6.8% in LBD of RAHs. The main impacts of sirolimus occurred within the first 6-12 months, with 14.8% reduction in MT and 4.7% reduction in LBD. Mouth ulceration (10 [76.9%]) and acne (9 [69.2%]) were the most common adverse events. These follow-up data support the long-term use of sirolimus in TSC-associated RAH patients, and persistent use of sirolimus possibly prevents tumor regrowth.