A nomogram for predicting depression in patients with hepatocellular carcinoma: an observational cross-sectional study

A nomogram and online calculator for predicting depression risk in obese Americans

Background

Obese patients with depression face higher risks of adverse events. However, depression is often misdiagnosed and undertreated in this group. This study aimed to identify predictors of depression and create a nomogram and calculator to assess depression risk in obese Americans.

Methods

This cross-sectional study included 2674 patients from the National Health and Nutrition Examination Survey database (NHANES). These participants were randomly classified into the training and validation groups in a 7:3 ratio. Predictors were selected by LASSO and multivariate logistic regression analysis to create the nomogram. C-statistics, calibration plots, and decision curve analysis (DCA) were used to test the nomogram's discriminative ability, calibration quality, and clinical value. Internal validation with bootstrap resampling and external validation with the validation group were also conducted.

Results

The training and validation group consists of 1871 and 803 participants. Depression was presented in 11.4 % (203/2674) of these participants. Seven predictors were found, including gender, hypertension, weekday sleep duration, poverty to income ratio, history of seeing mental health doctor, diabetes, and feeling sleepy during the day. The nomogram showed good discrimination, with the area under the receiver operating characteristic curve (AUC) of 0.817 (95 % CI: 0.786-0.848) (0.806 through internal validation and 0.772 through external validation) and good calibration (P = 0.536). The DCA further confirmed the nomogram's clinical usefulness.

Conclusion

The nomogram and calculator effectively predict depression risk in obese Americans and can be used as auxiliary tools for early screening in primary care.

Development and validation of a nomogram for predicting depressive symptoms in dentistry patients: A cross-sectional study

Depressive symptoms are frequently occur among dentistry patients, many of whom struggle with dental anxiety and poor oral conditions. Identifying the factors that influence these symptoms can enable dentists to recognize and address mental health concerns more effectively. This study aimed to investigate the factors associated with depressive symptoms in dentistry patients and develop a clinical tool, a nomogram, to assist dentists in predicting these symptoms. Methods: After exclusion of ineligible participants, a total of 1355 patients from the dentistry department were included. The patients were randomly assigned to training and validation sets at a 2:1 ratio. The LASSO regression method was initially employed to select highly influrtial features. This was followed by the application of a multi-factor logistic regression to determine independent factors and construct a nomogram. And it was evaluated by 4 methods and 2 indicators. The nomograms were formulated based on questionnaire data collected from dentistry patients. Nomogram2 incorporated factors such as medical burden, personality traits (extraversion, conscientiousness, and emotional stability), life purpose, and life satisfaction. In the training set, Nomogram2 exhibited a Concordance index (C-index) of 0.805 and an Area Under the Receiver Operating Characteristic Curve (AUC) of 0.805 (95% CI: 0.775-0.835). In the validation set, Nomogram2 demonstrated an Area Under the Receiver Operating Characteristic Curve (AUC) of 0.810 (0.768-0.851) and a Concordance index (C-index) of 0.810. Similarly, Nomogram1 achieved an Area Under the Receiver Operating Characteristic Curve (AUC) of 0.816 (0.788-0.845) and a Concordance index (C-index) of 0.816 in the training set, and an Area Under the Receiver Operating Characteristic Curve (AUC) of 0.824 (95% CI: 0.784-0.864) and a Concordance index (C-index) of 0.824 in the validation set. Net Reclassification Improvement (NRI) and Integrated Discrimination Improvement (IDI) indicated that Nomogram1, which included oral-related factors (oral health and dental anxiety), outperformed Nomogram2. We developed a nomogram to predict depressive symptoms in dentistry patients. Importantly, this nomogram can serve as a valuable psychometric tool for dentists, facilitating the assessment of their patients' mental health and enabling more tailored treatment plans.

Depression and hepatobiliary diseases: a bidirectional Mendelian randomization study

Background

More and more evidence suggests a close association between depression and hepatobiliary diseases, but its causal relationship is not yet clear.

Method

Using genome-wide association studies (GWAS) to summarize data, independent genetic variations associated with depression were selected as instrumental variables. Firstly, we designed a univariate Mendelian randomization (UVMR) analysis with two samples and simultaneously conducted reverse validation to evaluate the potential bidirectional causal relationship between depression and various hepatobiliary diseases. Secondly, we conducted a multivariate Mendelian randomization (MVMR) analysis on diseases closely related to depression, exploring the mediating effects of waist to hip ratio, hypertension, and daytime nap. The mediating effects were obtained through MVMR. For UVMR and MVMR, inverse variance weighted method (IVW) is considered the most important analytical method. Sensitivity analysis was conducted using Cochran'Q, MR Egger, and Leave-one-out methods.

Results

UVMR analysis showed that depression may increase the risk of non-alcoholic fatty liver disease (OR, 1.22; 95% CI, 1.03-1.46; p=0.0248) in liver diseases, while depression does not increase the risk of other liver diseases; In biliary and pancreatic related diseases, depression may increase the risk of cholelithiasis (OR, 1.26; 95% CI, 1.05-1.50; p=0.0120), chronic pancreatitis (OR, 1.61; 95% CI, 1.10-2.35; p=0.0140), and cholecystitis (OR, 1.23; 95% CI, 1.03-1.48; p=0.0250). In addition, through reverse validation, we found that non-alcoholic fatty liver disease, cholelithiasis, chronic pancreatitis, cholecystitis, or the inability to increase the risk of depression (p>0.05). The waist to hip ratio, hypertension, and daytime nap play a certain role in the process of depression leading to non-alcoholic fatty liver disease, with a mediating effect of 35.8%.

Conclusion

Depression is a susceptibility factor for non-alcoholic fatty liver disease, and the causal effect of genetic susceptibility to depression on non-alcoholic fatty liver disease is mediated by waist-hip ratio, hypertension, and daytime nap.

Relationship of Hepatocellular Carcinoma Stage and Hepatic Function to Health-Related Quality of Life: A Single Center Analysis

Health-related quality of life (HRQoL) is known to be an important prognostic indicator and clinical endpoint for patients with hepatocellular carcinoma (HCC). However, the correlation of the Barcelona Clinic Liver Cancer (BCLC) stage with HRQoL in HCC has not been previously studied. We examined the relationship between BCLC stage, Child-Pugh (CP) score, and Eastern Cooperative Oncology Group (ECOG) performance status on HRQoL for patients who presented at a multidisciplinary liver cancer clinic. HRQoL was assessed using the Functional Assessment of Cancer Therapy-Hepatobiliary (FACT-Hep) questionnaire. Fifty-one patients met our inclusion criteria. The FACT-Hep total and subscales showed no significant association with BCLC stages (p = 0.224). Patients with CP B had significantly more impairment in FACT-Hep than patients with CP A. These data indicate that in patients with HCC, impaired liver function is associated with reduced quality of life, whereas the BCLC stage poorly correlates with quality of life metrics. Impairment of quality of life is common in HCC patients and further studies are warranted to determine the impact of early supportive interventions on HRQoL and survival outcomes.

Machine learning-based algorithms to predict severe psychological distress among cancer patients with spinal metastatic disease

BACKGROUND CONTEXT

Metastatic spinal disease is an advanced stage of cancer patients and often suffer from terrible psychological health status; however, the ability to estimate the risk probability of this adverse outcome using current available data is very limited.

PURPOSE

The goal of this study was to propose a precise model based on machine learning techniques to predict psychological status among cancer patients with spinal metastatic disease.

STUDY DESIGN/SETTING

A prospective cohort study.

PATIENT SAMPLE

A total of 1043 cancer patients with spinal metastatic disease were included.

OUTCOME MEASURES

The main outcome was severe psychological distress.

METHODS

The total of patients was randomly divided into a training dataset and a testing dataset on a ratio of 9:1. Patients' demographics, lifestyle choices, cancer-related features, clinical manifestations, and treatments were collected as potential model predictors in the study. Five machine learning algorithms, including XGBoosting machine, random forest, gradient boosting machine, support vector machine, and ensemble prediction model, as well as a logistic regression model were employed to train and optimize models in the training set, and their predictive performance was assessed in the testing set.

RESULTS

Up to 21.48% of all patients who were recruited had severe psychological distress. Elderly patients (p<0.001), female (p =0.045), current smoking (p=0.002) or drinking (p=0.003), a lower level of education (p<0.001), a stronger spiritual desire (p<0.001), visceral metastasis (p=0.005), and a higher Eastern Cooperative Oncology Group (ECOG) score (p<0.001) were significantly associated with worse psychological health. With an area under the curve (AUC) of 0.865 (95% CI: 0.788-0.941) and an accuracy of up to 0.843, the gradient boosting machine algorithm performed best in the prediction of the outcome, followed by the XGBooting machine algorithm (AUC: 0.851, 95% CI: 0.768-0.934; Accuracy: 0.826) and ensemble prediction (AUC: 0.851, 95% CI: 0.770-0.932; Accuracy: 0.809) in the testing set. In contrast, the AUC of the logistic regression model was only 0.836 (95% CI: 0.756-0.916; Accuracy: 0.783).

CONCLUSIONS

Machine learning models have greater predictive power and can offer useful tools to identify individuals with spinal metastatic disease who are experiencing severe psychological distress.

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

BACKGROUND

Depression is a prevalent mental health disorder with a complex etiology and substantial public health implications. Early identification of individuals at risk for depression is crucial for effective intervention and prevention efforts. This study aimed to develop a predictive model for depression by integrating demographic factors (age, race, marital status, income), lifestyle factors (sleep duration, physical activity), and physiological measures (hypertension, blood lead levels). A key objective was to explore the role of physical activity and blood lead levels as predictors of current depression risk.

METHODS

Data were extracted from the 2007-2014 National Health and Nutrition Examination Survey (NHANES). We applied a logistic regression analysis to these data to assess the predictive value of the above eight factors for depression to create the predictive model.

RESULTS

The predictive model had bootstrap-corrected c-indexes of 0.68 (95% CI, 0.67-0.70) and 0.66 (95% CI, 0.64-0.68) for the training and validation cohorts, respectively, and well-calibrated curves. As the risk of depression increased, the proportion of participants with 1.76 ~ 68.90 µg/L blood lead gradually increased, and the proportion of participants with 0.05 ~ 0.66 µg/L blood lead gradually decreased. In addition, the proportion of sedentary participants increased as the risk of depression increased.

CONCLUSIONS

This study developed a depression risk assessment model that incorporates physical activity and blood lead factors. This model is a promising tool for screening, assessing, and treating depression in the general population. However, because the corrected c-indices of the predictive model have not yet reached an acceptable threshold of 0.70, caution should be exercised when drawing conclusions. Further research is required to improve the performance of this model.

Development of a web-based calculator to predict three-month mortality among patients with bone metastases from cancer of unknown primary: An internally and externally validated study using machine-learning techniques

Background

Individualized therapeutic strategies can be carried out under the guidance of expected lifespan, hence survival prediction is important. Nonetheless, reliable survival estimation in individuals with bone metastases from cancer of unknown primary (CUP) is still scarce. The objective of the study is to construct a model as well as a web-based calculator to predict three-month mortality among bone metastasis patients with CUP using machine learning-based techniques.

Methods

This study enrolled 1010 patients from a large oncological database, the Surveillance, Epidemiology, and End Results (SEER) database, in the United States between 2010 and 2018. The entire patient population was classified into two cohorts at random: a training cohort (n=600, 60%) and a validation cohort (410, 40%). Patients from the validation cohort were used to validate models after they had been developed using the four machine learning approaches of random forest, gradient boosting machine, decision tree, and eXGBoosting machine on patients from the training cohort. In addition, 101 patients from two large teaching hospital were served as an external validation cohort. To evaluate each model's ability to predict the outcome, prediction measures such as area under the receiver operating characteristic (AUROC) curves, accuracy, and Youden index were generated. The study's risk stratification was done using the best cut-off value. The Streamlit software was used to establish a web-based calculator.

Results

The three-month mortality was 72.38% (731/1010) in the entire cohort. The multivariate analysis revealed that older age (P=0.031), lung metastasis (P=0.012), and liver metastasis (P=0.008) were risk contributors for three-month mortality, while radiation (P=0.002) and chemotherapy (P<0.001) were protective factors. The random forest model showed the highest area under curve (AUC) value (0.796, 95% CI: 0.746-0.847), the second-highest precision (0.876) and accuracy (0.778), and the highest Youden index (1.486), in comparison to the other three machine learning approaches. The AUC value was 0.748 (95% CI: 0.653-0.843) and the accuracy was 0.745, according to the external validation cohort. Based on the random forest model, a web calculator was established: https://starxueshu-codeok-main-8jv2ws.streamlitapp.com/. When compared to patients in the low-risk groups, patients in the high-risk groups had a 1.99 times higher chance of dying within three months in the internal validation cohort and a 2.37 times higher chance in the external validation cohort (Both P<0.001).

Conclusions

The random forest model has promising performance with favorable discrimination and calibration. This study suggests a web-based calculator based on the random forest model to estimate the three-month mortality among bone metastases from CUP, and it may be a helpful tool to direct clinical decision-making, inform patients about their prognosis, and facilitate therapeutic communication between patients and physicians.

Global prevalence of depression and anxiety in patients with hepatocellular carcinoma: Systematic review and meta-analysis

BACKGROUND/AIMS

Depression and anxiety are associated with poorer outcomes in patients with hepatocellular carcinoma (HCC). However, the prevalence of depression and anxiety in HCC are unclear. We aimed to establish the prevalence of depression and anxiety in patients with HCC.

METHODS

MEDLINE and Embase were searched and original articles reporting prevalence of anxiety or depression in patients with HCC were included. A generalized linear mixed model with Clopper-Pearson intervals was used to obtain the pooled prevalence of depression and anxiety in patients with HCC. Risk factors were analyzed via a fractional-logistic regression model.

RESULTS

Seventeen articles involving 64,247 patients with HCC were included. The pooled prevalence of depression and anxiety in patients with HCC was 24.04% (95% confidence interval [CI], 13.99-38.11%) and 22.20% (95% CI, 10.07-42.09%) respectively. Subgroup analysis determined that the prevalence of depression was lowest in studies where depression was diagnosed via clinician-administered scales (16.07%;95% CI, 4.42-44.20%) and highest in self-reported scales (30.03%; 95% CI, 17.19-47.01%). Depression in patients with HCC was lowest in the Americas (16.44%; 95% CI, 6.37-36.27%) and highest in South-East Asia (66.67%; 95% CI, 56.68-75.35%). Alcohol consumption, cirrhosis, and college education significantly increased risk of depression in patients with HCC.

CONCLUSION

One in four patients with HCC have depression, while one in five have anxiety. Further studies are required to validate these findings, as seen from the wide CIs in certain subgroup analyses. Screening strategies for depression and anxiety should also be developed for patients with HCC.

Development of a nomogram prediction model for depression in patients with systemic lupus erythematosus

Systemic lupus erythematosus (SLE) is an inflammatory autoimmune disease with depression as one of its most common symptoms. The aim of this study is to establish a nomogram prediction model to assess the occurrence of depression in patients with SLE. Based on the Hospital Anxiety and Depression Scale cutoff of 8, 341 patients with SLE, recruited between June 2017 and December 2019, were divided into depressive and non-depressive groups. Data on socio-demographic characteristics, medical history, sociopsychological factors, and other risk factors were collected. Between-group differences in clinical characteristics were assessed with depression as the dependent variable and the variables selected by logistic multiple regression as predictors. The model was established using R language. Marital status, education, social support, coping, and anxiety predicted depression (p < 0.05). The nomogram prediction model showed that the risk rate was from 0.01 to 0.80, and the receiver operating characteristic curve analysis showed that the area under the curve was 0.891 (p < 0.001). The calibration curve can intuitively show that the probability of depression predicted by the nomogram model is consistent with the actual comparison. The designed nomogram provides a highly predictive assessment of depression in patients with SLE, facilitating more comprehensive depression evaluation in usual clinical care.

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

BACKGROUND

Depression has received a lot of attention as a common and serious illness. However, people are rarely aware of their current depression risk probabilities. We aimed to develop and validate a predictive model applicable to the risk of depression in US adults.

METHODS

This study was conducted using the database of the National Health and Nutrition Examination Survey (NHANES, 2017-2012). In particular, NHANES (2007-2010) was used as the training cohort (n = 6015) for prediction model construction and NHANES (2011-2012) was used as the validation cohort (n = 2812) to test the model. Depression was assessed (defined as a binary variable) by the Patient Health Questionnaire (PHQ-9). Socio-demographic characteristics, sleep time, illicit drug use and anxious days were assessed using a self-report questionnaire. Logistic regression analysis was used to evaluate independent risk factors for depression. The nomogram has the advantage of being able to visualize complex statistical prediction models as risk estimates of individualized disease probabilities. Then, we developed two depression risk nomograms based on the results of logistic regression. Finally, several validation methods were used to evaluate the prediction performance of nomograms.

RESULTS

The predictors of model 1 included gender, age, income, education, marital status, sleep time and illicit drug use, and model 2, furthermore, included anxious days. Both model 1 and model 2 showed good discrimination ability, with a bootstrap-corrected C index of 0.71 (95% CI, 0.69-0.73) and 0.85 (95% CI, 0.83-0.86), and an externally validated C index of 0.71 (95% CI, 0.68-0.74) and 0.83 (95% CI, 0.81-0.86), respectively, and had well-fitted calibration curves. The area under the receiver operating characteristic curve (AUC) values of the models with 1000 different weighted random sampling and depression scores of 10-17 threshold range were higher than 0.7 and 0.8, respectively. Calculated net reclassification improvement (NRI) and integrated discrimination improvement (IDI) showed the discrimination or accuracy of the prediction models. Decision curve analysis (DCA) demonstrated that the depression models were practically useful. The network calculators work for participants to make personalized predictions.

CONCLUSIONS

This study presents two prediction models of depression, which can effectively and accurately predict the probability of depression as well as helping the U.S. civilian non-institutionalized population to make optimal treatment decisions.

A randomized clinical trial of guided self-help intervention based on mindfulness for patients with hepatocellular carcinoma: effects and mechanisms

BACKGROUND

Compared with face-to-face mindfulness-based interventions (MBIs), online mindfulness interventions may be more convenient for patients with limited resources and can provide self-help mindfulness methods to improve the quality of life of cancer patients. This study investigated the effects of guided self-help mindfulness-based interventions (GSH-MBIs) on psychological distress, quality of life and sleep quality in patients with hepatocellular carcinoma and explored the underlying mechanisms.

METHODS

A total of 122 patients with hepatocellular carcinoma were randomly divided into the intervention group or the conventional treatment group. Psychological distress, quality of life, sleep quality, psychological flexibility and perceived stress were evaluated in the groups before the intervention at baseline, after the intervention, at 1-month follow-up and 3-month follow-up. The intervention's effects over time and the potential mediating effects were analysed using generalized estimating equations (GEE).

RESULTS

GEE results indicated significant time-group interaction effects on psychological distress (P < 0.001) and sleep quality (P < 0.001). The intervention significantly improved psychological flexibility (β, -2.066; 95% CI, -3.631, -0.500) and reduced perceived stress (β, -2.639; 95% CI, -4.110, -1.169). Psychological flexibility and perceived stress played a mediating role in the observed results.

CONCLUSION

GSH-MBIs can improve psychological distress and sleep quality via changing the psychological flexibility and perceived stress in hepatocellular carcinoma patients.

Relationship between Mindfulness and Psychological Distress in Patients with Hepatocellular Carcinoma: The Mediation Effect of Self-regulation

OBJECTIVES

In this study, we examine the relationship among mindfulness, psychological distress, and self-regulation, to determine whether self-regulation plays a mediating role in the relationship between mindfulness and psychological distress among patients with hepatocellular carcinoma (HCC).

METHODS

Participants completed questionnaires including the Hospital Anxiety and Depression Scale (HADS), the Five-facet Mindfulness Questionnaire (FFMQ), and the Self-regulation Scale (SRS). We used structural equation modeling to analyze the relationships among psychological distress, mindfulness, and self-regulation, with self-regulation as a mediator.

RESULTS

We found that psychological distress is negatively associated with both mindfulness (r = -0.687, p < .001) and self-regulation (r = -0.629, p < .001), and mindfulness is positively associated with self-regulation (r = 0.534, p < .001). The model indicates that mindfulness has direct impact on self-regulation (β = 0.570, p < .001) and psychological distress (β = -0.685, p < .001). Self-regulation asserts a certain mediation effect on the relationship between mindfulness and psychological distress. A bootstrap test suggests perceived stress has a mediation effect on mindfulness and psychological distress (95% CI: -0.299, -0.134, p < .001), accounting for 23.6% of total effect.

CONCLUSIONS

Psychological distress is common in HCC patients. The mediation effect of self-regulation provides a reference for discussing possible correlations between mindfulness and psychological distress.

Constructing a Predictive Model of Depression in Chemotherapy Patients with Non-Hodgkin's Lymphoma to Improve Medical Staffs' Psychiatric Care

Objectives

Depression is highly prevalent in non-Hodgkin's lymphoma (NHL) patients undergoing chemotherapy. The social stress associated with malignancy induces neurovascular pathology promoting clinical levels of depressive symptomatology. The purpose of this study was to establish an effective depressive symptomatology risk prediction model to those patients.

Methods

This study included 238 NHL patients receiving chemotherapy, 80 of whom developed depressive symptomatology. Different types of variables (sociodemographic, medical, and psychosocial) were entered in the models. Three prediction models (support vector machine-recursive feature elimination model, random forest model, and nomogram prediction model based on logistic regression analysis) were compared in order to select the one with the best predictive power. The selected model was then evaluated using calibration plots, ROC curves, and C-index. The clinical utility of the nomogram was assessed by the decision curve analysis (DCA).

Results

The nomogram prediction has the most efficient predictive ability when 10 predictors are included (AUC = 0.938). A nomogram prediction model was constructed based on the logistic regression analysis with the best predictive accuracy. Sex, age, medical insurance, marital status, education level, per capita monthly household income, pathological stage, SSRS, PSQI, and QLQ-C30 were included in the nomogram. The C-index was 0.944, the AUC value was 0.972, and the calibration curve also showed the good predictive ability of the nomogram. The DCA curve suggested that the nomogram had a strong clinical utility.

Conclusions

We constructed a depressive symptomatology risk prediction model for NHL chemotherapy patients with good predictive power and clinical utility.

Psychological Burden and Psycho-Oncological Interventions for Patients With Hepatobiliary Cancers-A Systematic Review

Background

Worldwide, hepatobiliary cancers are frequent diseases and often accompanied by a poor prognosis. These cancers, with hepatocellular carcinoma (HCC) and cholangiocarcinoma (CHC) being the most frequent, are often associated with a considerable amount of psychological burden such as anxiety, depressiveness, and reduced health-related quality of life (HRQOL) which may lead to psychiatric comorbidities. This systematic review gives an overview on psychological burden and on the effectiveness of psycho-oncological interventions for patients with HCC and CHC.

Methods

The databases PubMed, PubPsych, and PsycINFO were used and searched using the following combination of terms: (Neoplasm OR Cancer OR Tumor OR Carcinoma) AND (Psycho-Oncology OR Psychotherapy OR Psychiatr^∗) AND (Liver OR Hepatic OR Hepatocellular OR Gallbladder OR Bile^∗). Studies were eligible for inclusion if investigating patients affected with tumors of the liver (HCC/CHC) and using diagnostic instruments to assess mental health symptoms and research concerning specific psycho-oncological interventions. In total, 1027 studies were screened by one author with regard to title and abstracts. Afterward, the two authors of the paper discussed inclusion of possible articles.

Results

Twelve studies focusing on distress, anxiety, and depression symptoms as well as quality of life among patients with HCC/CHC and three studies on psycho-oncological interventions were included. Patients suffering from hepatobiliary cancers often experience considerable psychological burden. A quarter of patients suffer from depressive symptoms; anxiety is even more common among these patients with almost 40%. The HRQOL of those affected is reduced in almost all areas, suicide rates increased and the level of distress is considerably increased in one third of patients even in comparison to those with other kinds of cancer. By psycho-oncological intervention the prevalence of depressive symptoms and anxiety can be reduced, while the quality of life and also the survival rate of patients with hepatobiliary cancer can be increased.

Discussion and Conclusion

Psychological burden is high in patients with hepatobiliary cancers as reflected in high levels of depressiveness and anxiety as well as reduced quality of life. The use of psycho-oncological interventions can reduce psychological burden and increase quality of life compared to patients receiving standard support only.

Systematic Review Registration

(prospero), identifier (CRD42021243192).

A Prediction Model for Cognitive Impairment Risk in Colorectal Cancer after Chemotherapy Treatment

Background

A prediction model can be developed to predict the risk of cancer-related cognitive impairment in colorectal cancer patients after chemotherapy.

Methods

A regression analysis was performed on 386 colorectal cancer patients who had undergone chemotherapy. Three prediction models (random forest, logistic regression, and support vector machine models) were constructed using collected clinical and pathological data of the patients. Calibration and ROC curves and C-indexes were used to evaluate the selected models. A decision curve analysis (DCA) was used to determine the clinical utility of the line graph.

Results

Three prediction models including a random forest, a logistic regression, and a support vector machine were constructed. The logistic regression model had the strongest predictive power with an area under the curve (AUC) of 0.799. Age, BMI, colostomy, complications, CRA, depression, diabetes, QLQ-C30 score, exercise, hypercholesterolemia, diet, marital status, education level, and pathological stage were included in the nomogram. The C-index (0.826) and calibration curve showed that the nomogram had good predictive ability and the DCA curves indicated that the model had strong clinical utility.

Conclusions

A prediction model with good predictive ability and practical clinical value can be developed for predicting the risk of cognitive impairment in colorectal cancer after chemotherapy.

Role of lncRNA NR2F1-AS1 and lncRNA H19 Genes in Hepatocellular Carcinoma and Their Effects on Biological Function of Huh-7

Objective

This research was designed to probe into the expression and related mechanism of lncRNA NR2F1-AS1 and H19 in hepatocellular carcinoma (HCC).

Methods

Forty-two HCC patients who came to our hospital from February 2018 to August 2019 were included into a research group (RG). Meanwhile, 46 healthy controls were regarded as a control group (CG). BEL-7402, Huh-7 human hepatoma cells and HL-7702 human normal liver cells were purchased, and the NR2F1-AS1 and H19 levels in serum and tissues of HCC patients were detected. PcDNA3.1-NR2F1-AS1, si-NR2F1-AS1, NC, pcDNA3.1-H19 and si-H19 were transfected into BEL-7402 and Huh-7 cells. The NR2F1-AS1 and H19 levels in samples were detected via qRT-PCR, and the expression of apoptosis-related proteins in cells was tested through WB. Cell proliferation, invasion, or apoptosis was detected by CCK8, Transwell or flow cytometry, respectively.

Results

The NR2F1-AS1 and H19 levels were high in human hepatoma cells, and AUCs of lncRNA NR2F1-AS1 and lncRNA H19 were both >0.8. The lncRNA NR2F1-AS1 and lncRNA H19 were associated with HCC staging. After transfection of pcDNA3.1-NR2F1-AS1, si-NR2F1-AS1, NC, pcDNA3.1-H19, si-H19 BEL-7402 and Huh-7 cells, silencing NR2F1-AS1 and H19 expression can promote apoptosis and inhibit cell growth, while silencing their over-expression can inhibit the EMT process of Huh-7 cells.

Conclusion

lncRNA NR2F1-AS1 and lncRNA H19 genes are abnormally expressed in HCC. Furthermore, the two can suppress the EMT process of Huh-7 cells and promote apoptosis effectively.

Construction of a nomogram for predicting the risk of allergic rhinitis among employees of long-distance bus stations in China

This study examined indoor air pollutants and their health effects on allergic rhinitis in 3194 employees of 226 bus station halls and then constructed a nomogram model to predict allergic rhinitis risk in those employees. Indoor air temperature, relative humidity, PM₁₀ , PM_2.5 , total bacteria, and total fungi were measured, and questionnaires were used to collect basic station information and employee health information. The results revealed that the over-standard rates of PM₁₀ , PM_2.5 , total bacteria, and total fungi were 18.16%, 31.13%, 2.22%, and 55.89%, respectively. Seasonal variations were found in temperature, relative humidity, and PM_2.5 . Passenger flow could affect temperature, and total bacteria. Central air conditioning could affect total bacteria. A total of 15.90% of the employees were diagnosed as allergic rhinitis by physicians. Relative humidity, fungi, self-reported allergic rhinitis symptoms, and exposure to smoking were the influencing factors for allergic rhinitis. These four variables were incorporated to construct a nomogram. The concordance index of the nomogram was 0.775 (95% CI: 0.745-0.806) and 0.749 (95% CI: 0.715-0.783) for the training cohort and test cohort, respectively. The calibration plot revealed that the nomogram model exhibited good discrimination and consistency. This nomogram model may help predict the occurrence of allergic rhinitis.