Theoretical approaches in the development of interventions to promote breastfeeding: A scoping review.

PROBLEM AND BACKGROUND: There is a low world rate of exclusive breastfeeding and a short duration of breastfeeding. More studies have constructed interventions to improve breastfeeding behavior, but the actual effect is not significant. AIM: The purpose of this review is identifying the ways that various theories have an influence on theory-based breastfeeding intervention studies. METHODS: A scoping review using Arksey and O'Malley's framework explored breastfeeding promotion practices. PubMed, The Cochrane Library, Web of Science, Embase, and CINAHL databases were searched from database creation to March 9, 2024. Building on previous research, key terms were used to search the literature. Data analysis involved descriptive and interpretive summaries of theories used and the proposed interventions. FINDINGS: An online search yielded 906 articles, with 28 meeting the inclusion criteria for the scoping review, including 5 reviews and 23 articles. Reviews demonstrated that interventions based on theories were more effective. Articles promoting breastfeeding used theories of self-efficacy (n = 9), theory of planned behavior (n = 8), social cognitive theory (n = 5) and individual and family self-management theory (n = 1). These theories were used in developing specific content of the intervention program (n = 20, 86.9%), constructing the framework of the program (n = 10, 43.5%), and evaluating outcomes (n = 19, 82.6%). Most interventions focused on education, professional support, and/or peer support for breastfeeding. CONCLUSION AND DISCUSSION: Theory can guide decisions and play a role in selecting a methodology or lens. Researchers should make deliberate choices in the use of a theory that relates to aspects of breastfeeding behavior. Future interventions based on theories should be more varied and effective and need to consider families' and social factors.

A nomogram for predicting risk factors of testicular salvage after testicular torsion in children.

OBJECTIVES: This study aimed to establish a nomogram for predicting the probability of testicular salvage after testicular torsion in children. METHODS: We retrospectively collected data of children with testicular torsion who were treated at Shenzhen Children's Hospital between September 2005 and August 2022. Of the training cohort, 113 patients who underwent orchiectomy and five with testicular atrophy after orchiopexy were included in the failed testicular salvage group. Additionally, 37 patients who underwent orchiopexy without postoperative testicular atrophy were included in the successful testicular salvage group. The predictive factors affecting testicular salvage were determined using univariate and multivariate logistic regression analyses; a nomogram was constructed. The nomogram was verified using data from the validation group. RESULTS: Using multivariate logistic regression analysis, the independent risk factors of testicular salvage after testicular torsion were symptom duration (p = 0.034), intratesticular blood flow (p = 0.003), spermatic cord torsion degree (p = 0.037), and monocyte count (odds ratio: 0.012, p = 0.036). A nomogram was established based on these four risk factors. In the training cohort, the area under the receiver operating characteristic curve was 0.969. The area under the receiver operating characteristic curve of the verification cohort was 0.965, indicating good discrimination ability of the nomogram. Increased symptom duration without intratesticular blood flow increased the monocyte count and spermatic cord torsion degree and decreased the success rate of testicular salvage. CONCLUSION: This prediction model could obtain the corresponding probability of testicular salvage according to the clinical characteristics of different patients with testicular torsion, providing reference for clinicians and parents.

Development of a prediction model for neonatal hypoglycemia risk factors: a retrospective study.

Background: It's challenging for healthcare workers to detect neonatal hypoglycemia due to its rapid progression and lack of aura symptoms. This may lead to brain function impairment for the newborn, placing a significant care burden on the family and creating an economic burden for society. Tools for early diagnosis of neonatal hypoglycemia are lacking. This study aimed to identify newborns at high risk of developing neonatal hypoglycemia early by developing a risk prediction model. Methods: Using a retrospective design, pairs (470) of women and their newborns in a tertiary hospital from December 2021 to September 2022 were included in this study. Socio-demographic data and clinical data of mothers and newborns were collected. Univariate and multivariate logistic regression were used to screen optimized factors. A neonatal hypoglycemia risk nomogram was constructed using R software, and the calibration curve and receiver operator characteristic curve (ROC) was utilized to evaluate model performance. Results: Factors integrated into the prediction risk nomogram were maternal age (odds ratio [OR] =1.10, 95% CI: 1.04, 1.17), fasting period (OR=1.07, 95% CI: 1.03, 1.12), ritodrine use (OR=2.00, 95% CI: 1.05, 3.88), gestational diabetes mellitus (OR=2.13, 95% CI: 1.30, 3.50), gestational week (OR=0.80, 95% CI: 0.66, 0.96), fetal distress (OR=1.76, 95% CI: 1.11, 2.79) and neonatal body mass index (OR=1.50, 95% CI: 1.24, 1.84). The area under the curve (AUC) was 0.79 (95% confidence interval [CI]: 0.75, 0.82), specificity was 0.82, and sensitivity was 0.62. Conclusion: The prediction model of this study demonstrated good predictive performance. The development of the model identifies advancing maternal age, an extended fasting period before delivery, ritodrine use, gestational diabetes mellitus diagnosis, fetal distress diagnosis and an increase in neonatal body mass index increase the probability of developing neonatal hypoglycemia, while an extended gestational week reduces the probability of developing neonatal hypoglycemia.

Co-Parenting Impact on Breastfeeding: Systematic Review and Meta-Analysis.

Background: Although the beneficial effects of exclusive breastfeeding (EBF) on infants and mothers have been identified, EBF rates remain unsatisfactory. Co-parenting interventions for perinatal couples have not been systematically evaluated and analyzed for their effects on breastfeeding outcomes. Aims and Objectives: To systematically evaluate the effects of co-parenting interventions on the rate of EBF, breastfeeding knowledge, breastfeeding attitude, breastfeeding self-efficacy, parental relationship, and partner support. Methods: Randomized controlled trials and quasi-experimental studies were systematically screened in eight online databases from inception to November 2022. Trials included in this review were assessed using the Cochrane Risk of Bias Assessment Tool. Eligible trials were used to conduct a meta-analysis using Review Manager software. The I2 statistic was used to assess heterogeneity between studies. When it was not possible to conduct a meta-analysis, a descriptive analysis was used to present the findings due to insufficient data from the included studies. Results: Fifteen of the 1,869 articles reviewed met the inclusion criteria. Co-parenting interventions significantly improved the EBF rate at 16 weeks (odds ratio [OR] = 3.85, 95% confidence interval, CI [1.84 to 8.03], p < 0.001, I2 = 69%) and 6 months (OR = 2.82, 95% CI [1.47 to 5.41], p = 0.002, I2 = 85%). This study revealed that co-parenting interventions made statistically significant improvements in parental relationship (standardized mean difference [SMD] = 0.26, 95% CI [0.13 to 0.38], p < 0.001, I2 = 80%). There was no evidence of the effectiveness of interventions in terms of overall parental support (SMD = 0.75, 95% CI [-0.46 to 1.97], p < 0.001, I2 = 96%). Given the discrepant and limited research data, findings on breastfeeding knowledge, breastfeeding attitudes, and breastfeeding self-efficacy were presented descriptively. Conclusion: Co-parenting interventions effectively increase EBF rates at 16 weeks and 6 months postpartum, and improve breastfeeding knowledge, breastfeeding attitude, and parental relationships.

Deep Into the Domain Shift: Transfer Learning Through Dependence Regularization.

Classical domain adaptation methods acquire transferability by regularizing the overall distributional discrepancies between features in the source domain (labeled) and features in the target domain (unlabeled). They often do not differentiate whether the domain differences come from the marginals or the dependence structures. In many business and financial applications, the labeling function usually has different sensitivities to the changes in the marginals versus changes in the dependence structures. Measuring the overall distributional differences will not be discriminative enough in acquiring transferability. Without the needed structural resolution, the learned transfer is less optimal. This article proposes a new domain adaptation approach in which one can measure the differences in the internal dependence structure separately from those in the marginals. By optimizing the relative weights among them, the new regularization strategy greatly relaxes the rigidness of the existing approaches. It allows a learning machine to pay special attention to places where the differences matter the most. Experiments on three real-world datasets show that the improvements are quite notable and robust compared to various benchmark domain adaptation models.

Breastfeeding Practices of Women Returning to Full-Time Employment in China: Prevalence and Determinants.

BACKGROUND: Returning to work after childbirth is a common reason for women to stop breastfeeding. This study aimed to assess breastfeeding practices and breastfeeding support available to employed women in China, and factors affecting breastfeeding duration. METHODS: A cross-sectional survey of 1,243 breastfeeding women employed full-time was conducted. Participants completed a sociodemographic questionnaire and the Workplace Breastfeeding Support Scale (WBSS). Multiple linear regression analysis was used to explore the factors that are associated with breastfeeding. RESULTS: The mean exclusive breastfeeding duration and any breastfeeding duration of full-time employed women were 5.7 ± 0.5 months and 9.8 ± 1.5 months, respectively. The total WBSS score was 46.6 ± 5.3 (M ± SD). Generally, women perceived coworkers (M [SD] = 4.8 [1.0]) and supervisors (M [SD] = 5.7 [1.2]) to be supportive of breastfeeding. Lower scores on the WBSS were related to lack of technical and facility support, indicating no access to a refrigerator to store breast milk (M [SD] = 2.0 [1.5]) or to a breast pump (M [SD] = 1.7 [1.7]). Similarly, a private area for expressing breast milk (M [SD] = 1.4 [1.0]) was unavailable. Maternity leave, residential province, ethnicity, education level, average monthly household income, main reason for stopping exclusive breastfeeding, commute time greater than 1 hour, and the total WBSS score were also factors influencing breastfeeding duration of the full-time employed women. CONCLUSIONS: There were gaps in breastfeeding practices and workplace breastfeeding support of Chinese full-time employed women when compared with the World Health Organization recommendations. Occupational health providers should consider these findings when developing programs to support breastfeeding in the workplace.

Development of a breastfeeding duration risk nomogram for use in postpartum Chinese women.

Previous studies have investigated influencing factors of early discontinuation of breastfeeding, but few studies have developed an easy-to-use tool to identify risk of breastfeeding cessation at 6 months after birth. This research team aimed to develop and validate an exclusive breastfeeding duration risk nomogram in Chinese mothers. A longitudinal cohort survey was conducted. Data were collected from 394 postpartum women in three hospitals in Hubei Province, China from December 2017 to December 2018. The LASSO regression model was used to screen for optimized factors in an exclusive breastfeeding duration model. Multivariable logistic regression was applied to construct a prediction model. Discrimination and calibration were assessed using a C-index and calibration curve, and internal validity was established using bootstrapping validation. Factors integrated in the prediction risk nomogram were monthly household income (odds ratio [OR] = 1.31, 95% confidence interval [CI]: [0.95, 1.80]), experiences of breastfeeding (OR = 1.23, 95% CI: [0.92, 1.63]), attitude (OR = 1.72, 95% CI: [0.94, 3.16]), self-efficacy (OR = 2.45, 95% CI: [1.40, 4.29]), perceived insufficient milk supply (OR = 0.12, 95% CI: [0.06, 0.25]) and postpartum depression (OR = 0.06, 95% CI: [0.02, 0.17]). The model displayed good discrimination with a C-index of 0.87 (95% CI: [0.84, 0.91]) and good calibration. The C-index interval validation was confirmed to be 0.86. This study resulted in the development of a novel nomogram with good accuracy to aid healthcare professionals in assessing the probability of a mother discontinuing exclusive breastfeeding at the breast before 6 months.

COVID-19 related fear and depression of pregnant women and new mothers.

OBJECTIVE: To explore factors associated with depression and COVID-19 related fear among pregnant women and new mothers. DESIGN: A cross-sectional survey was conducted in China from July 2020 to July 2021. SAMPLE: A total of 3027 pregnant and new mothers were recruited. MEASUREMENT: Sociodemographic characteristics and the perceptions of the COVID-19 pandemic were collected. The Patient Health Questionnaire-9 (PHQ-9) and the Fear Scale was used to assess the depressive and fear level towards the COVID-19 pandemic, respectively. RESULTS: Approximately 17.2% of the participants had depression (PHQ-9 ≥10). In Hong Kong, participants who perceived that they have increased knowledge to prevent infection were less likely to have depression (adjusted odds ratio [aOR] = 0.83; 95% confidence interval [CI] = 0.74-0.94). There was no association between perceived severity if infected and severity of spread and the depression level in our sample. An inverse relationship was found between the COVID-19 related fear level and perceived knowledge to prevent infection (Beta-coefficient [ß] = -0.20; 95% CI = -0.38 to -0.02). CONCLUSION: Public health nurses need to promote accurate and up to date COVID-19 related information at clinical and community settings and implement effective screening for depression and fear symptoms to identify these high-risk groups to improve women's psychological well-being.

International survey on fear and childbirth experience in pregnancy and the postpartum period during the COVID-19 pandemic: study protocol.

INTRODUCTION: The COVID-19 pandemic has caused unprecedented disruptions around the world. Adding to the existing stress surrounding pregnancy and childbirth, the threat of infection and social isolation policies may negatively impact pregnant women and new mothers. Literature on the effect of COVID-19 on fear during pregnancy and childbirth experience is limited. As the COVID-19 pandemic continues to affect the global population, it is important to understand how it has impacted pregnant women and new mothers' experiences worldwide to inform perinatal care and interventions. METHODS AND ANALYSIS: This multicountry study involving China and Canada targets to recruit 1000 pregnant women and new mothers who gave birth since 2020 in each participating country. Participants will be recruited online in the local language through mothers' groups, antenatal and postnatal clinics and hospital wards. All questionnaires will be completed online. Participants' level of fear, depression and childbirth experience will be assessed along with other sociodemographic, medical and COVID-related measures. Regression models will be used to compare the outcomes among the participating countries. ETHICS AND DISSEMINATION: The study has been reviewed and approved by the institutional review boards of the participating countries. Findings will be disseminated in peer-reviewed journals and academic conferences. Results from this study may guide the formulation of future health guidelines and policies in the face of a pandemic.

Individual mortality risk predictive system of patients with acute-on-chronic liver failure based on a random survival forest model.

BACKGROUND: The basis of individualized treatment should be individualized mortality risk predictive information. The present study aimed to develop an online individual mortality risk predictive tool for acute-on-chronic liver failure (ACLF) patients based on a random survival forest (RSF) algorithm. METHODS: The current study retrospectively enrolled ACLF patients from the Department of Infectious Diseases of The First People's Hospital of Foshan, Shunde Hospital of Southern Medical University, and Jiangmen Central Hospital. Two hundred seventy-six consecutive ACLF patients were included in the present study as a model cohort (n = 276). Then the current study constructed a validation cohort by drawing patients from the model dataset based on the resampling method (n = 276). The RSF algorithm was used to develop an individual prognostic model for ACLF patients. The Brier score was used to evaluate the diagnostic accuracy of prognostic models. The weighted mean rank estimation method was used to compare the differences between the areas under the time-dependent ROC curves (AUROCs) of prognostic models. RESULTS: Multivariate Cox regression identified hepatic encephalopathy (HE), age, serum sodium level, acute kidney injury (AKI), red cell distribution width (RDW), and international normalization index (INR) as independent risk factors for ACLF patients. A simplified RSF model was developed based on these previous risk factors. The AUROCs for predicting 3-, 6-, and 12-month mortality were 0.916, 0.916, and 0.905 for the RSF model and 0.872, 0.866, and 0.848 for the Cox model in the model cohort, respectively. The Brier scores were 0.119, 0.119, and 0.128 for the RSF model and 0.138, 0.146, and 0.156 for the Cox model, respectively. The nonparametric comparison suggested that the RSF model was superior to the Cox model for predicting the prognosis of ACLF patients. CONCLUSIONS: The current study developed a novel online individual mortality risk predictive tool that could predict individual mortality risk predictive curves for individual patients. Additionally, the current online individual mortality risk predictive tool could further provide predicted mortality percentages and 95% confidence intervals at user-defined time points.

Experience predicts the duration of exclusive breastfeeding: The serial mediating roles of attitude and self-efficacy.

BACKGROUND AND AIMS: The relationship between prior breastfeeding experience and the duration of exclusive breastfeeding is of significant interest, but few studies have explored the mechanisms underlying this relationship. The purpose of this study is to address two hypotheses: (a) that attitude and self-efficacy mediate the relationship between previous breastfeeding experience and the duration of exclusive breastfeeding; and (b) that attitude and self-efficacy have serial mediation functions in this relationship. METHODS: The data collection process was divided into two stages. The original stage included 394 women hospitalized after delivery who completed socio-demographic questionnaires (including previous breastfeeding experience), the Iowa Infant Feeding Attitude Scale (IIFAS), and the Breastfeeding Self-efficacy Short-Form Scale (BSES-SF). Follow-up data about duration of exclusive breastfeeding were obtained at six months postpartum. RESULTS: Mediation analysis indicated that previous breastfeeding experience directly affected the duration of exclusive breastfeeding and that the rate of the total indirect effect was 22.878%. Self-efficacy mediated previous breastfeeding experience and duration of exclusive breastfeeding, whereas attitude and self-efficacy played the series mediational role between previous breastfeeding experience and duration of exclusive breastfeeding. Cluster analysis supported these results. CONCLUSIONS: The duration of exclusive breastfeeding can be promoted by improving breastfeeding attitude and self-efficacy in women without breastfeeding experience.

Hindrances of peripherally inserted central catheter care of leukemia patients: a qualitative study.

OBJECTIVE: A peripherally inserted central catheter (PICC) needs regular care. However, clinical observations found that some discharged leukemia patients in mainland China had not complied with the requirement of regular care. Our study aims to explore the facilitators and hindrances of regular cares of PICC in leukemia patients with the Colaizzi phenomenon analysis. METHODS: This qualitative report used the descriptive phenomenological method to collect information and was conducted in accordance with the COREQ checklist. By purposive sampling, 11 leukemia patients with PICC were selected and interviewed in the Department of Hematology of a first-class hospital in Wuhan (central China). The interviews were conducted from March 2016 to May 2017. RESULTS: Two facilitators for PICC care were extracted through interviews, including fear of nosocomial infection and convenience for treatment. Eleven hindrances were summarized, including high costs, unavailability of local services, worries about affecting family members, a lack of health awareness, inconvenient transportations, fluke minds, physical discomfort, fears of leukemia and chemotherapy, short chemotherapy intervals, damage to appearance, and no insurance coverage of costs. CONCLUSION: Leukemia patients' compliance with PICC care was hindered by several factors. The improvement of PICC care may need joint efforts of patients, nursing professionals, hospitals' managerial staff, and governments.

Multifunctional cellulose nanocrystal structural colored film with good flexibility and water-resistance.

Chiral nematic cellulose nanocrystal (CNC) structural colored materials are the focus of extensive research in recent years on account of their unique helical structure and excellent optical properties. However, the general weak mechanical properties and poor water-resistance of CNC limit its utility. Here, we fabricated highly flexible, water-resistant chiral nematic CNC/Poly(ethylene glycol) diacrylate(PEGDA) composite films, which was realized by co-assembling CNC and PEGDA precursor then via UV curing to construct a three-dimensional crosslinking network. Due to the water stimulus-responsive feature of the CNC and PEGDA, the CNC/PEGDA (8/2) composite film could be used as a water-content detector, photonic paper. In addition, it could produce different crosslinking degrees by coating the PEGMA solution or not, which could be applied to an anti-counterfeit label. The composite film with excellent stability of cycles is expected to be a promising candidate in optical sensors, display, and security fields.

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.

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/.

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/.

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/.

Development and internal validation of a nine-lncRNA prognostic signature for prediction of overall survival in colorectal cancer patients.

BACKGROUND: Colorectal cancer remains a serious public health problem due to the poor prognosis. In the present study, we attempted to develop and validate a prognostic signature to predict the individual mortality risk in colorectal cancer patients. MATERIALS AND METHODS: The original study datasets were downloaded from The Cancer Genome Atlas database. The present study finally included 424 colorectal cancer patients with wholly gene expression information and overall survival information. RESULTS: A nine-lncRNA prognostic signature was built through univariate and multivariate Cox proportional regression model. Time-dependent receiver operating characteristic curves in model cohort demonstrated that the Harrell's concordance indexes of nine-lncRNA prognostic signature were 0.768 (95% CI [0.717-0.819]), 0.778 (95% CI [0.727-0.829]) and 0.870 (95% CI [0.819-0.921]) for 1-year, 3-year and 5-year overall survival respectively. In validation cohort, the Harrell's concordance indexes of nine-lncRNA prognostic signature were 0.761 (95% CI [0.710-0.812]), 0.801 (95% CI [0.750-0.852]) and 0.883 (95% CI [0.832-0.934]) for 1-year, 3-year and 5-year overall survival respectively. According to the median of nine-lncRNA prognostic signature score in model cohort, 424 CRC patients could be stratified into high risk group (n = 212) and low risk group (n = 212). Kaplan-Meier survival curves showed that the overall survival rate of high risk group was significantly lower than that of low risk group (P < 0.001). DISCUSSION: The present study developed and validated a nine-lncRNA prognostic signature for individual mortality risk assessment in colorectal cancer patients. This nine-lncRNA prognostic signature is helpful to evaluate the individual mortality risk and to improve the decision making of individualized treatments in colorectal cancer patients.

Nomogram for cirrhosis in patients with chronic hepatitis B: A simple self-assessed scale for individual risk of cirrhosis.

The aim of this retrospective study was to establish a simple self-assessed scale for individual risk of cirrhosis in patients with chronic hepatitis B. A total of 1808 consecutive patients were enrolled and analyzed. According to the results of multivariate logistic regression analysis, a simple nomogram was calculated for cirrhosis. The area under receiver operating characteristic curves (AUROCs) were calculated to compare the diagnostic accuracy of nomogram with aspartate aminotransferase to platelet ratio index (APRI), fibrosis index based on the four factors (FIB-4), and S index. The AUROCs of nomogram for cirrhosis were 0.807 (adjusted AUROC 0.876) in model group and 0.794 (adjusted AUROC0.866) in validation group. DeLong's test and Brier Score further demonstrated that nomogram was superior to APRI, FIB-4 and S index in both model group and validation group. The patients with nomogram <0.07 could be defined as low risk group with cirrhosis prevalence lower than 4.3% (17/397). The patients with nomogram >0.52 could be defined as high risk group with cirrhosis prevalence higher than 73.0% (119/163). In conclusion, as a self-assessed style, simple, non-invasive, economical, convenient, and repeatable scale, nomogram is suitable to serve as a massive health screening tool for cirrhosis in CHB patients and further external validation is needed.