Nomogram predicting bullying victimization in adolescents

Exploring the interplay of parenting styles, basic empathy, domestic violence, and bystander behavior in adolescent school bullying: a moderated mediation analysis

Introduction

This study investigates how parental styles, basic empathy, and family violence influence adolescents' bystander behaviors in school bullying.

Methods

A survey was conducted with 1,067 students from three middle schools in southern China. Multifactor logistic regression and a moderated mediation model were employed to analyze the relationships between positive and negative parental styles, basic empathy, and bystander behaviors.

Results

The study found significant correlations and predictive relationships: Positive parental styles were strongly associated with increased basic empathy (r = 0.29, p < 0.01) and behaviors that protect victims (r = 0.29, p < 0.01). In contrast, negative parental styles correlated positively with behaviors that support bullying (r = 0.12, p < 0.01) and instances of family violence (r = 0.62, p < 0.01). Basic empathy negatively predicted behaviors that promote bullying (β = -0.098, p < 0.01) and positively predicted protective behaviors toward victims (β = 0.249, p < 0.001). Furthermore, family violence weakened the positive effects of positive parental styles on both empathy (β = -0.075, p < 0.001) and protective behaviors (β = -0.025, p < 0.01).

Conclusion

The findings indicate that positive parental styles indirectly promote adolescents' victim protector behaviors by enhancing their basic empathy, underscoring the importance of emotional cultivation. Meanwhile, family violence weakens the positive impact of these parental styles on basic empathy and protective behaviors, harming adolescents' emotional security and behavioral norms.

Development and validation of a nomogram to predict severe influenza

BACKGROUND

Influenza is an acute respiratory disease posing significant harm to human health. Early prediction and intervention in patients at risk of developing severe influenza can significantly decrease mortality.

METHOD

A comprehensive analysis of 146 patients with influenza was conducted using the Gene Expression Omnibus (GEO) database. We assessed the relationship between severe influenza and patients' clinical information and molecular characteristics. First, the variables of differentially expressed genes were selected using R software. Least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression analysis were performed to investigate the association between clinical information and molecular characteristics and severe influenza. A nomogram was developed to predict the presence of severe influenza. At the same time, the concordance index (C-index) is adopted area under the receiver operating characteristic (ROC), area under the curve (AUC), decision curve analysis (DCA), and calibration curve to evaluate the predictive ability of the model and its clinical application.

RESULTS

Severe influenza was identified in 47 of 146 patients (32.20%) and was significantly related to age and duration of illness. Multivariate logistic regression demonstrated significant correlations between severe influenza and myloperoxidase (MPO) level, haptoglobin (HP) level, and duration of illness. A nomogram was formulated based on MPO level, HP level, and duration of illness. This model produced a C-index of 0.904 and AUC of 0.904.

CONCLUSIONS

A nomogram based on the expression levels of MPO, HP, and duration of illness is an efficient model for the early identification of patients with severe influenza. These results will be useful in guiding prevention and treatment for severe influenza disease.

A study on the academic innovation ability and influencing factors of public health graduate students based on nomograms: a cross-sectional survey from Shandong, China

Background

In recent years, the impact of the COVID-19 pandemic and various public crises has highlighted the importance of cultivating high-quality public health talents, especially those with innovative capabilities. This study focuses on the academic innovation ability of public health postgraduate students, which can provide important theoretical support for the cultivation of more public health workers with high innovative capabilities.

Methods

From May to October 2022, a cluster sampling method was used to select 1,076 public health postgraduate students from five universities in Shandong Province. A self-designed questionnaire survey was conducted. A chi-square test and binary logistic regression analysis were used to analyze the influencing factors of students' academic innovation ability. Based on these factors, a nomogram was constructed to intuitively demonstrate the impact of these complex factors on students' innovation ability.

Results

The results showed that gender, whether serving as a student leader, teacher-student relationship, academic motivation, learning style, academic environment, and teaching mode were the influencing factors of postgraduate students' academic innovation ability. The column-line diagram (AUC = 0.892, 95% CI = 0.803 ~ 0.833) constructed based on the above influencing factors has good differentiation. The area under the ROC curve is 0.892 (95% CI = 0.803 ~ 0.833), and the calibration curve shows that the predicted value is the same as the measured value.

Conclusion

The nomogram constructed in this study can be used to predict the academic innovation level of public health graduate students, which is helpful for university education administrators to evaluate students' academic innovation ability based on nomogram scores and carry out accurate and efficient training.

Prognostic value analysis and survival model construction of different treatment methods for advanced intestinal type gastric adenocarcinoma

Background

Intestinal-type gastric adenocarcinoma, representing 95 % of gastric malignancies, originates from the malignant transformation of gastric gland cells. Despite its prevalence, existing methods for prognosis evaluation of this cancer subtype are inadequate. This study aims to enhance patient-specific prognosis evaluation by analyzing the clinicopathological characteristics and prognostic risk factors of intestinal-type gastric adenocarcinoma patients using data from the Surveillance, Epidemiology, and End Results (SEER) Program of the National Cancer Institute (NCI).

Methods

We extracted clinical data for patients diagnosed with intestinal-type gastric adenocarcinoma between 2010 and 2015 from the SEER database, selecting 257 cases based on predefined inclusion and exclusion criteria. Independent risk factors for overall survival (OS) and cancer-specific survival (CSS) were identified using a Cox regression model. A nomogram model for predicting OS or CSS was developed from the Cox risk regression analysis and validated through the consistency index (C-index), ROC curve, and calibration curve.

Results

Age, primary tumor resection, chemotherapy, lymph node metastasis, and tumor size were identified as independent prognostic factors for OS and CSS (P < 0.05). The nomogram model, constructed from these indicators, demonstrated superior predictive consistency for OS and CSS compared to the AJCC-TNM staging system. ROC curve analysis confirmed the model's higher accuracy, and calibration curve analysis indicated good agreement between the nomogram's predictions and actual observed outcomes.

Conclusion

The nomogram model derived from SEER database analyses accurately predicts OS and CSS for patients with intestinal-type gastric adenocarcinoma. This model promises to facilitate more tailored treatments in clinical practice.

Associations of vegetable and fruit intake, physical activity, and school bullying with depressive symptoms in secondary school students: the mediating role of internet addiction

BACKGROUND

Few studies have simultaneously focused on the associations of vegetable and fruit intake, physical activity, school bullying, and Internet addiction (IA) with depressive symptoms. This study aimed to explore the direct and indirect effects of the above factors on depressive symptoms in adolescents by constructing a structural equation model (SEM).

METHODS

This study was conducted in Qingdao from September to November 2021. A total of 6195 secondary school students aged 10-19 years were included in the analysis. Information on all variables was assessed using a self-administered questionnaire. An SEM was constructed with depressive symptoms as the endogenous latent variable, IA as the mediating variable, and vegetable and fruit intake, physical activity, and school bullying as the exogenous latent variables. The standardized path coefficients (β) were the direct effects between the latent variables, and the indirect effects were obtained by the product of direct effects between relevant latent variables.

RESULTS

The median value with the interquartile range of depressive symptom scores was 7 (3,12). Vegetable and fruit intake (β=-0.100, P<0.001) and physical activity (β=-0.140, P<0.001) were directly negatively related to depressive symptoms. While school bullying (β=0.138, P<0.001) and IA (β=0.452, P<0.001) were directly positively related to depressive symptoms. IA had the greatest impact on depressive symptoms. Vegetable and fruit intake, physical activity, and school bullying could not only directly affect depressive symptoms, but also indirectly affect depressive symptoms through the mediating effect of IA, the indirect effects and 95% confidence intervals (CIs) were -0.028 (-0.051, -0.007), -0.114 (-0.148, -0.089) and 0.095 (0.060, 0.157), respectively. The results of the multi-group analysis showed that the SEM we constructed still fit in boy and girl groups.

CONCLUSIONS

The results indicated that vegetable and fruit intake, physical activity, school bullying, and IA had a significant direct impact on depressive symptoms, among which IA had the greatest impact. In addition, both vegetable and fruit intake, school bullying, and physical activity indirectly affected depressive symptoms through the mediating effect of IA. The impact of IA on depressive symptoms should be given extra attention by schools and parents. This study provides a scientific and effective basis for the prevention and control of adolescent depressive symptoms.

Prediction of high-level fear of cancer recurrence in breast cancer survivors: An integrative approach utilizing random forest algorithm and visual nomogram

PURPOSE

This study is the first attempt to use a combination of regression analysis and random forest algorithm to predict the risk factors for high-level fear of cancer recurrence and develop a predictive nomogram to guide clinicians and nurses in identifying high-risk populations for high-level fear of cancer recurrence.

METHODS

After receiving various recruitment strategies, a total of 781 survivors who had undergone breast cancer resection within 5 years in four Grade-A hospitals in China were included. Besides demographic and clinical characteristics, variables were also selected from the perspectives of somatic, cognitive, psychological, social and economic factors, all of which were measured using a scale with high reliability and validity. This study established univariate regression analysis and random forest model to screen for risk factors for high-level fear of cancer recurrence. Based on the results of the multi-variable regression model, a nomogram was constructed to visualize risk prediction.

RESULTS

Fatigue, social constraints, maladaptive cognitive emotion regulation strategies, meta-cognition and age were identified as risk factors. Based on the predictive model, a nomogram was constructed, and the area under the curve was 0.949, indicating strong discrimination and calibration.

CONCLUSIONS

The integration of two models enhances the credibility of the prediction outcomes. The nomogram effectively transformed intricate regression equations into a visual representation, enhancing the readability and accessibility of the prediction model's results. It aids clinicians and nurses in swiftly and precisely identifying high-risk individuals for high-level fear of cancer recurrence, enabling the development of timely, predictable, and personalized intervention programs for high-risk patients.

Elaboration and validation of a novelty nomogram for the prognostication of anxiety susceptibility in individuals suffering from low back pain

Low back pain (LBP) constitutes a distressing emotional ordeal and serves as a potent catalyst for adverse emotional states, notably anxiety. We dedicated to discerning methodologies for identifying patients who are predisposed to heightened levels of anxiety and pain. A self-assessment questionnaire was administered to patients afflicted with LBP. The pain scores were subjected to analysis in conjunction with anxiety scores, and a clustering procedure was executed using the scientific k-means methodology. Subsequently, six machine learning algorithms, including Logistics Regression (LR), K-Nearest Neighbor (KNN), Decision Tree (DT), Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGB), were employed. Next, five pertinent variables were identified, namely Age, Course, Body Mass Index (BMI), Education, and Marital status. Furthermore, a LR model was utilized to construct a nomogram, which was subsequently subjected to assessment for discrimination, calibration, and evaluation of its clinical utility. As a result, 599 questionnaires were valid (effective rate: 99 %). The correlation analysis revealed a significant association between anxiety and pain scores (r = 0.31, P < 0.001). LBP patients could be divided into two clusters, Cluster1 had higher pain scores (P < 0.05) and SAS scores (P < 0.001). The proposed nomogram demonstrated an area under the receiver operating characteristics curve (ROC) of 0.841 (95 %CI: 0.804-0.878) and 0.800 (95 %CI: 0.733-0.867) in the training and test groups, respectively. Briefly, the established nomogram has demonstrated remarkable proficiency in discerning individuals afflicted with LBP who are at a heightened risk of experiencing anxiety.

A machine learning model to predict the risk of depression in US adults with obstructive sleep apnea hypopnea syndrome: a cross-sectional study

Objective

Depression is very common and harmful in patients with obstructive sleep apnea hypopnea syndrome (OSAHS). It is necessary to screen OSAHS patients for depression early. However, there are no validated tools to assess the likelihood of depression in patients with OSAHS. This study used data from the National Health and Nutrition Examination Survey (NHANES) database and machine learning (ML) methods to construct a risk prediction model for depression, aiming to predict the probability of depression in the OSAHS population. Relevant features were analyzed and a nomogram was drawn to visually predict and easily estimate the risk of depression according to the best performing model.

Study design

This is a cross-sectional study.

Methods

Data from three cycles (2005-2006, 2007-2008, and 2015-2016) were selected from the NHANES database, and 16 influencing factors were screened and included. Three prediction models were established by the logistic regression algorithm, least absolute shrinkage and selection operator (LASSO) algorithm, and random forest algorithm, respectively. The receiver operating characteristic (ROC) area under the curve (AUC), specificity, sensitivity, and decision curve analysis (DCA) were used to assess evaluate and compare the different ML models.

Results

The logistic regression model had lower sensitivity than the lasso model, while the specificity and AUC area were higher than the random forest and lasso models. Moreover, when the threshold probability range was 0.19-0.25 and 0.45-0.82, the net benefit of the logistic regression model was the largest. The logistic regression model clarified the factors contributing to depression, including gender, general health condition, body mass index (BMI), smoking, OSAHS severity, age, education level, ratio of family income to poverty (PIR), and asthma.

Conclusion

This study developed three machine learning (ML) models (logistic regression model, lasso model, and random forest model) using the NHANES database to predict depression and identify influencing factors among OSAHS patients. Among them, the logistic regression model was superior to the lasso and random forest models in overall prediction performance. By drawing the nomogram and applying it to the sleep testing center or sleep clinic, sleep technicians and medical staff can quickly and easily identify whether OSAHS patients have depression to carry out the necessary referral and psychological treatment.

Predicting prognosis outcomes of primary central nervous system lymphoma with high-dose methotrexate-based chemotherapeutic treatment using lipidomics

Background

Primary central nervous system lymphoma (PCNSL) is a rare extranodal lymphomatous malignancy which is commonly treated with high-dose methotrexate (HD-MTX)-based chemotherapy. However, the prognosis outcome of HD-MTX-based treatment cannot be accurately predicted using the current prognostic scoring systems, such as the Memorial Sloan-Kettering Cancer Center (MSKCC) score.

Methods

We studied 2 cohorts of patients with PCNSL and applied lipidomic analysis to their cerebrospinal fluid (CSF) samples. After removing the batch effects and features engineering, we applied and compared several classic machine-learning models based on lipidomic data of CSF to predict the relapse of PCNSL in patients who were treated with HD-MTX-based chemotherapy.

Results

We managed to remove the batch effects and get the optimum features of each model. Finally, we found that Cox regression had the best prediction performance (AUC = 0.711) on prognosis outcomes.

Conclusions

We developed a Cox regression model based on lipidomic data, which could effectively predict PCNSL patient prognosis before the HD-MTX-based chemotherapy treatments.

The prognostic and biological importance of chromatin regulation-related genes for lung cancer is examined using bioinformatics and experimentally confirmed

BACKGROUND

The pathogenesis and clinical diagnosis of lung adenocarcinoma (LUAD), a malignant illness with substantial morbidity and mortality, are still being investigated. Genes involved in chromatin regulation are crucial in the biological function of LUAD.

METHODS

The prognostic prediction model for LUAD was developed using multivariables and least absolute shrinkage and selection operator (LASSO) regression. It consisted of 10 chromatin regulators. The LUAD has been divided into two groups, high- and low-risk, using a predictive model. The model was shown to be accurate in predicting survival by the nomogram, receiver operating characteristic (ROC) curves, and principal component analysis (PCA). An analysis of differences in immune-cell infiltration, immunologicalfunction, and clinical traits between low- and high-risk populations was conducted. Protein-protein interaction (PPI) networks and Gene Ontology (GO) pathways of differentially expressed genes (DEGs) in the high versus low risk group were also examined to investigate the association between genes and biological pathways. The biological roles of chromatin regulators (CRs) in LUAD were finally estimated using colony formation and cell movement. The important genes' mRNA expression has been measured using real-time polymerase chain reaction (RT-PCR).

RESULTS AND CONCLUSION

Risk score and stage based on the model could be seen as separate prognostic indicators for patients with LUAD. The main signaling pathway difference across various risk groups was in cell cycle. The immunoinfiltration profile of the tumor microenvironment (TME) and individuals with different risk levels were correlated, suggesting that the interaction of immune cells with the tumor led to the creation of a favorable immunosuppressive microenvironment. These discoveries aid in the creation of individualized therapies for LUAD patients.

Development and validation of a nomogram for predicting metabolic-associated fatty liver disease in the Chinese physical examination population

AIM

We aim to develop and validate a nomogram including readily available clinical and laboratory indicators to predict the risk of metabolic-associated fatty liver disease (MAFLD) in the Chinese physical examination population.

METHODS

The annual physical examination data of Chinese adults from 2016 to 2020 were retrospectively analyzed. We extracted the clinical data of 138 664 subjects and randomized participants to the development and validation groups (7:3). Significant predictors associated with MAFLD were identified by using univariate and random forest analyses, and a nomogram was constructed to predict the risk of MAFLD based on a Lasso logistic model. Receiver operating characteristic curve analysis, calibration curves, and decision curve analysis were used to verify the discrimination, calibration, and clinical practicability of the nomogram, respectively.

RESULTS

Ten variables were selected to establish the nomogram for predicting MAFLD risk: sex, age, waist circumference (WC), uric acid (UA), body mass index (BMI), waist-to-hip ratio (WHR), systolic blood pressure (SBP), fasting plasma glucose (FPG), triglycerides (TG), and alanine aminotransferase (ALT). The nomogram built on the nonoverfitting multivariable model showed good prediction of discrimination (AUC 0.914, 95% CI: 0.911-0.917), calibration, and clinical utility.

CONCLUSIONS

This nomogram can be used as a quick screening tool to assess MAFLD risk and identify individuals at high risk of MAFLD, thus contributing to the improved management of MAFLD.

Genetic polymorphism of the Dab2 gene and its association with Type 2 Diabetes Mellitus in the Chinese Uyghur population

Objective

The human Disabled-2 (Dab2) protein is an endocytic adaptor protein, which plays an essential role in endocytosis of transmembrane cargo, including low-density lipoprotein cholesterol (LDL-C). As a candidate gene for dyslipidemia, Dab2 is also involved in the development of type 2 diabetes mellitus(T2DM). The aim of this study was to investigate the effects of genetic variants of the Dab2 gene on the related risk of T2DM in the Uygur and Han populations of Xinjiang, China.

Methods

A total of 2,157 age- and sex-matched individuals (528 T2DM patients and 1,629 controls) were included in this case-control study. Four high frequency SNPs (rs1050903, rs2255280, rs2855512 and rs11959928) of the Dab2 gene were genotyped using an improved multiplex ligation detection reaction (iMLDR) genotyping assay, and the forecast value of the SNP for T2DM was assessed by statistical analysis of clinical data profiles and gene frequencies.

Results

We found that in the Uygur population studied, for both rs2255280 and rs2855512, there were significant differences in the distribution of genotypes (AA/CA/CC), and the recessive model (CC vs. CA + AA) between T2DM patients and the controls (P < 0.05). After adjusting for confounders, the recessive model (CC vs. CA + AA) of both rs2255280 and rs2855512 remained significantly associated with T2DM in this population (rs2255280: OR = 5.303, 95% CI [1.236 to -22.755], P = 0.025; rs2855512: OR = 4.892, 95% CI [1.136 to -21.013], P = 0.033). The genotypes (AA/CA/CC) and recessive models (CC vs. CA + AA) of rs2855512 and rs2255280 were also associated with the plasma glucose and HbA1c levels (all P < 0.05) in this population. There were no significant differences in genotypes, all genetic models, or allele frequencies between the T2DM and control group in the Han population group (all P > 0.05).

Conclusions

The present study suggests that the variation of the Dab2 gene loci rs2255280 and rs2855512 is related to the incidence of T2DM in the Uygur population, but not in the Han population. In this study, these variations in Dab2 were an independent predictor for T2DM in the Uygur population of Xinjiang, China.

A nomogram for predicting lung-related diseases among construction workers in Wuhan, China

Objective

To develop a prediction nomogram for the risk of lung-related diseases (LRD) in construction workers.

Methods

Seven hundred and fifty-two construction workers were recruited. A self- designed questionnaire was performed to collected relevant information. Chest X-ray was taken to judge builders' lung health. The potential predictors subsets of the risk of LRD were screened by the least absolute shrinkage and selection operator regression and univariate analysis, and determined by using multivariate logistic regression analysis, then were used for developing a prediction nomogram for the risk of LRD. C-index, calibration curve, receiver operating characteristic curve, decision curve analysis (DCA) and clinical impact curve analysis (CICA) were used to evaluation the identification, calibration, predictive ability and clinical effectiveness of the nomogram.

Results

Five hundred and twenty-six construction workers were allocated to training group and 226 to validation group. The predictors included in the nomogram were symptoms, years of dust exposure, work in shifts and labor intensity. Our model showed good discrimination ability, with a bootstrap-corrected C index of 0.931 (95% CI = 0.906-0.956), and had well-fitted calibration curves. The area under the curve (AUC) of the nomogram were (95% CI = 0.906-0.956) and 0.945 (95% CI = 0.891-0.999) in the training and validation groups, respectively. The results of DCA and CICA indicated that the nomogram may have clinical usefulness.

Conclusion

We established and validated a novel nomogram that can provide individual prediction of LRD for construction workers. This practical prediction model may help occupational physicians in decision making and design of occupational health examination.