Nomogram Analysis Research Articles

Postoperative fever following surgery for oral cancer: Incidence, risk factors, and the formulation of a machine learning-based predictive model.

Postoperative fever (POF) is a common occurrence in patients undergoing major surgery, presenting challenges and burdens for both patients and surgeons yet. This study endeavors to examine the incidence, identify risk factors, and establish a machine learning-based predictive model for POF following surgery of oral cancer. A total of seven hundred and twenty-seven consecutive patients undergoing radical resection of oral cancer were retrospectively investigated. The analysis encompassed 34 parameters, incorporating demographic and clinical characteristics, biochemical and hematological assay results, surgical-related data, hospitalization costs and stay in hospital. Six machine learning models were compared by the area under the receiver operating characteristic curve (AUC). The best-performing models were selected for further analyze, including feature importance evaluation and nomogram analysis, identifying key POF risk factors, and establish a comprehensive prediction model. A total of 466 patients with surgery for oral cancer met the criteria, with an average age of (54.2 ± 11.1) years, including an POF group (n = 197) and a non-POF group (n = 269). The fever group has greater hospitalization costs, longer lengths of stay, and higher infection biochemical indicators (leucocyte ratio and erythrocyte sedimentation rate). Furthermore, Among the 6 machine learning models, logistic regression models performed best, with the higher AUC and accuracy. In univariate and multivariate logistic analysis showed that age, sex, reoperation, Charlson Comorbidity Index score (CCI), leukocyte, bleeding and blood transfusion were independent risk factors for POF of patients following surgery in oral cancer. Then seven variables were selected to establish the nomogram for predict the probability of POF by nomogram algorithm. Postoperative fever patients following radical resection of oral cancer have greater burden. Machine learning algorithms can be effectively used to identify potential risk factors of POF, which may enhance individualized treatment plans in oral cancer patient during perioperative period.

Machine learning-based identification and validation of amino acid metabolism related genes as novel biomarkers in chronic kidney disease.

Chronic kidney disease (CKD) is a progressive illness with a high rate of morbidity and mortality with no proven therapy. Alterations of amino acid(AA) metabolism are associated with the incidence and progression of CKD. To characterize the potential value of AA metabolism related genes in the diagnosis and progression of CKD. We filtered the key genes associated with AA metabolism based on the least absolute shrinkage and selection operator (LASSO) and SVM algorithm. Then, we constructed logistic regression models and evaluated the accuracy and specificity by nomogram analysis and DCA. Also, we mapped the ROC curves.Meanwhile, in order to determine the underlying mechanism and relevant biological features of CKD, we conducted differential analysis between high and low risk subgroups in CKD. Moreover,we employed ssGSEA algorithm to evaluate the infiltration abundance of immune cells and calculated the correlation among the immune cells with the key genes. Finally,we validated the expression and clinical relevance of amino acid metabolism key genes via cultured cells and clinical data. A total of six key genes related to amino acid metabolism were identified, including ALDH18A1, CENPF, CSAD, CTH, CYP27B1, HBB. All six genes exhibited promising diagnostic capabilities (AUC:0.7 to 0.9). Immune cells such as Activated CD4+ T cells, Regulatory T cells, Immature B cells and MDSC,etc.infiltrated differentially in the high and low risk groups of CKD. There were correlations between immune cells abundance and the expression of key genes. All key genes correlated significantly with markers of kidney injury, such as eGFR and serum creatinine. The expression of ALDH18A1, CENPF were increased while CSAD, CTH and CYP27B1 were decreased in HK-2cells cultured with indole sulfate. Our study identified key genes involved in amino acid metabolism associated with immune cells infiltration and renal function in CKD, which may be potential biomarkers for the diagnosis and prognosis of CKD.

A New Risk Score Based on Lipid Indicators for Patients with Advanced Hepatocellular Carcinoma.

The prognosis is extremely troubling in advanced hepatocellular carcinoma (HCC). Prognostic scores have been developed. Yet, the positive predictive values might appear inadequate. This retrospective study aimed to develop a quick and efficient risk score to assess prognosis and clinical response. A total of 391hCC patients were enrolled and were divided into training and validation groups between 2015 and 2024. Patients were separated into high-risk and low-risk groups using X-tile software. Using the COX proportional risk model analysis method, we then created a risk score and examined them using Kaplan-Meier, time-dependent receiver operating characteristics (ROC) curve, and nomogram analysis. In predicting overall survival (OS), free fatty acid/high-density lipoprotein cholesterol (FFHL), tumor size, and BCLC stage were independent prognostic variables. A new risk score was developed just above and used as a prognostic factor (p < 0.001 in the training and validation groups) and had a high time-dependent ROC for progress-free survival (PFS) (area under the curve [AUC] 0.688-0.789 in the training group; AUC 0.592-0.741 in the validation group) and OS (AUC 0.812-0.918 in the training group; AUC 0.692-0.981 in the validation group). In comparison to the best overall response (BOR), the score offered a more accurate evaluation of durable clinical benefit (DCB) (p < 0.001 in the training and validation group; p = 0.061 vs 0.001 in the training and validation group). A new score based on lipid markers is a useful tool for evaluating prognosis and distinguishing patients with DCB.

Neuron stress-related genes serve as new biomarkers in hypothalamic tissue following high fat diet

ObjectiveEnergy homeostasis is modulated by the hypothalamic is essential for obesity progression, however, the gene expression profiling remains to be fully understood.MethodsGEO datasets were downloaded from the GEO website and analyzed by the R packages to obtain the DEGs. And, the WGCNA analysis and PPI networks of co-expressed DEGs were designed using STRING to get key genes. In addition, the single-cell sequencing datasets and GTEx database were utilized to receive the neuron-stress genes from the key genes. Further, high-fat diet (HFD)-induced hypothalamic tissue of mice was used as an animal model to validate the mRNA up-regulation of neuron-stress genes. In addition, the Bmi1 gene was identified as a hub gene through the LASSO model and nomogram analysis. Western blot confirmed the high expression of Bmi1 in hypothalamic tissue of HFD mice and PA-stimulated microglia. Immunofluorescence staining showed that HFD induced the activation of microglia and the expression of Bmi1 in hypothalamic tissue.ResultsWe found that six genes (Sacm1l, Junb, Bmi1, Erbb4, Dkc1, and Suv39h1) are neuron stress-related genes and increased in the HFD-induced mice obesity model, Bmi1gene was identified as a key genes that can reflect the pathophysiology of obesity.ConclusionsOur research depicted a comprehensive activation map of cell abnormality in the obese hypothalamus and Bim1 may be a diagnostic marker in the clinic, which provides a new perspective and basis for investigating the pathogenesis of obesity.

Analysis and Validation of a Diagnostic Nomogram for Predicting the Risk of Acute Respiratory Failure for Non-HIV Related Pneumocystis Jirovecii Pneumonia Patients.

Pneumocystis Pneumonia (PCP), primarily affecting individuals with weakened immune systems, is a severe respiratory infection caused by pneumocystis jirovecii and can lead to acute respiratory failure (ARF). In this article, we explore the risk factors of ARF and propose a prognostic model of ARF for PCP patients. In this multi-center, retrospective study in 6secondary or tertiary academic hospitals in China, 120 PCP patients were screened from the Dryad database for the development of a predictive model. A total of 49 patients from Peking University People's Hospital were collected for external validation. Crucial clinical features of these patients are selected applying univariate and multivariate logistic regression analysis. We established an intuitive nomogram. Receiver operating characteristic (ROC) curve, calibration curve, decision curve analysis (DCA) and clinical impact curve (CIC) were plotted to evaluate the model's performance. A cohort of 120 patients formed the training cohort for the development of the model, with 49 patients constituting the test cohort. Univariate and multivariate logistic regression analysis identified five risk factors associated with ARF, which are age, fever, dyspnea, high neutrophil count and use of antibiotics. A nomogram was then proposed based on these factors. The area under the ROC curve (AUROC) in the development group has reached 0.8576, while the validation group has an AUROC of 0.7372, indicating commendable ability for predicting ARF. In addition, results for Hosmer-Lemeshow test indicate the effectiveness of our model. Furthermore, DCA and CIC curves demonstrate excellent clinical benefit. We present a nomogram for predicting ARF in non-HIV related PCP patients. The prognostic model may provide references in clinical medicine, promote timely treatment and improve therapeutic outcomes of PCP patients.

Comparative Analysis of Nomogram and Machine Learning Models for Predicting Axillary Lymph Node Metastasis in Early-Stage Breast Cancer: A Study on Clinically and Ultrasound-Negative Axillary Cases Across Two Centers

ObjectiveEarly and accurate prediction of axillary lymph node metastasis (ALNM) is crucial in determining appropriate treatment strategies for patients with early-stage breast cancer. The aim of this study was to evaluate the efficacy of radiomic features extracted from ultrasound (US) images combined with machine learning (ML) methods in predicting ALNM to improve diagnostic accuracy and patient prognosis. MethodsIn this retrospective study, data of 282 early-stage breast cancer patients from two centers were analyzed. We considered clinicopathological characteristics, conventional US features, contrast-enhanced ultrasound (CEUS) characteristics, and radiomics features. Radiomics features were extracted from US images, and using least absolute shrinkage and selection operator (LASSO) regression, 12 key features were selected to compute a Radiomics score (Rad-score). A nomogram was developed based on these features, alongside five ML models: Logistic Regression (LR), Naive Bayes (NB), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Extreme Gradient Boosting (XGBoost). Model performance was evaluated using metrics such as the area under the curve (AUC), accuracy (ACC), sensitivity (SEN), specificity (SPE), negative predictive value (NPV), and positive predictive value (PPV). ResultsBoth the nomogram and ML models, including the Rad-score combined with histologic type, significantly predicted ALNM. Among all models, the XGBoost model showed the best performance with an AUC of 0.810 and an accuracy of 84.1% in the external test set, surpassing the nomogram and other ML models. SHapley Additive exPlanations (SHAP) analysis further provided insights into the influence of individual radiomics features on ALNM prediction. ConclusionsWhile the nomogram provides a useful traditional statistical approach, integrating radiomics features with ML, particularly the XGBoost model enhanced by SHAP interpretability, offers superior predictive accuracy for ALNM in early-stage breast cancer patients.

Identification and validation of lung adenocarcinoma (LUAD)-associated targets for monascin from the extracts of Monascus purpureus-fermented rice: Compound preparation, high-throughput genome sequencing and bioinformatics analysis

Despite the remarkable bioactivities exhibited by monascin against several tumors, its therapeutic targets remain unexplored. In this study, our objective is to identify therapeutic targets based on the antitumor activity of monascin against lung adenocarcinoma (LUAD) A549 cells. The compound monascin was derived from extracts of Monascus purpureus-fermented rice. Its chemical structure was determined using spectroscopic methods, while the physicochemical properties of monascin were investigated through thermal behavior analysis. Herein, antitumor studies indicated that monascin significantly inhibited the viability of A549 cells, with an IC50 value of 2.05 μM; RNA-sequencing revealed 12 up-regulated and 29 down-regulated genes as differentially expressed genes (DEGs) for A549 cells in response to monascin; RT-qPCR validation supported the plausibility of the RNA-sequencing results. Moreover, utilizing data from the Cancer Genome Atlas (TCGA) database, univariate and multivariate Cox regression analyses provided evidence supporting GPR37 and FAM83A as potential candidate genes for predicting disease progression in LUAD patients. Additionally, the LASSO Cox regression and nomogram analyses confirmed that FAM83A and GPR37 genes had the potential to predict overall survival for LUAD patients. Finally, molecular docking studies suggested that monascin could interact with both GPR37 and FAM83A proteins through the hydrogen bonding and hydrophobic interactions; Western blotting assays indicated that monascin inhibited the expression levels of both GPR37 and FAM83A proteins. Overall, the clinical prognosis of GPR37 and FAM83A highlights the rationale for targeting these specific genes with monascin. Additionally, there findings provide compelling evidence for the ploypharmacological properties of monascin against LUAD.

Role of CALCR expression in liver cancer: Implications for the immunotherapy response.

Liver hepatocellular carcinoma (LIHC) is a prevalent and lethal malignancy with a complex molecular landscape. Fibrosis and ferroptosis are implicated in LIHC progression, yet their roles remain to be elucidated. The present study investigated the expression and prognostic significance of calcitonin receptor (CALCR), a gene that intersects the pathways of fibrosis and ferroptosis, across LIHC and other types of cancer. Data were obtained from The Cancer Genome Atlas and the Molecular Signatures Database. LIHC patients were classified into two clusters based on fibrosis‑related gene expression using ConsensusClusterPlus. Single‑sample gene set enrichment analysis was employed to quantify fibrosis and ferroptosis levels. Correlation, survival and nomogram analyses were performed to assess the prognostic value of CALCR. Additionally, single‑cell RNA sequencing data from the Tumor Immune Single Cell Hub 2 (TISCH2) and pan‑cancer analyses of genomic heterogeneity features were incorporated. The present study also identified a putative regulatory role for CALCR in LIHC cell migration, proliferation and apoptosis. CALCR was identified as a significant prognostic marker for LIHC. Patients with high CALCR expression exhibited shortened overall survival (OS) and disease‑specific survival (DSS). Specifically, the hazard ratios (HRs) for OS and DSS were 1.76 [95% confidence interval (CI): 1.23=2.49) and 1.77 (95% CI: 1.13=2.78], respectively, with corresponding P‑values of 0.002 for OS and 0.013 for DSS. Analyses of immune cell infiltration revealed a more complex immune environment in patients with low CALCR expression, suggesting differential responses to immunotherapy. Furthermore, in HepG‑2 and HuH‑7 cells, small interfering (si)‑CALCR increased apoptosis while reducing proliferation and migration compared with si‑negative control. CALCR serves as a significant prognostic biomarker for LIHC, influencing both molecular pathways and the immune landscape. Its expression is associated with improved survival outcomes and distinct genomic features, positioning it as a potential therapeutic target and predictor of immunotherapy efficacy.

Plasma concentrations of venetoclax and Pharmacogenetics correlated with drug efficacy in treatment naive leukemia patients: a retrospective study

Venetoclax (VEN) was the only Bcl-2 inhibitor approved yet and showed large differences in clinical efficacy. The aim of the study was to explore the relationships between the plasma concentration and efficacy of VEN, and identify potential influencing factors. A retrospective cohort study was conducted and a total of 76 trough (C0h) and 91 6 h post-dose plasma concentration (C6h) blood concentrations of VEN were collected in 54 patients. C6h/D concentration of VEN was found to be significantly correlated with treatment efficacy (p = 0.006) in leukemia patients with good or intermediate prognosis stratification. A ROC curve was then established and the cut-off value was calculated as 0.2868 μg/ml (AUC = 0.7097, p = 0.1081). Besides, patients co-administered with triazoles or carrying CYP3A5 rs776746 AA/AG genotypes were prone to induce higher VEN plasma concentration regardless of whether VEN dosage was reduced or not. Through LASSO-logistic regression and nomogram analysis, chemotherapy regimens and neutrophil percentages were identified as the critical elements that may predict drug response. Above all, in addition to identify prognostic stratification, AML patients taken with VEN were suggested to test plasma concentration routinely so as to achieve desired efficacy, especially when co-administered with triazoles or carried with CYP3A5 rs776746 AA/AG.

Nomogram Analysis for Predicting Response to Androgen-Receptor-Axis-Targeted Therapies in Patients With Metastatic Castration-Resistant Prostate Cancer.

This study aimed to identify the clinical predictors for the response of patients with mCRPC to ARATs. We retrospectively collected data on consecutive patients who were diagnosed with mCRPC and underwent ARAT treatment during this stage of the disease. Clinical parameters were obtained through medical chart reviews. ARAT failure was defined as a continuous increase in the serum prostate-specific antigen (PSA) level above nadir to > 2 ng/mL, accompanied by radiographic progression. ARAT failure-free survival and overall survival were assessed through Kaplan-Meier survival analysis and Cox regression survival analysis. Nomogram analysis based on significant predictors of ARAT failure-free survival was performed. In total, 319 patients with mCRPC who underwent ARAT were included. Multivariate analysis revealed that age, International Society of Urological Pathology (ISUP) grading, and chemotherapy-naïve status were significant predictors of ARAT failure-free survival. For overall survival, age, ISUP grading, and nadir PSA level during androgen deprivation therapy (ADT) were significant predictors. Through nomogram analysis based on age, ISUP grading, and chemotherapy-naïve status, the likelihood of ARAT duration being more or less than 1 year could be predicted. For mCRPC patients, being older, having ISUP Grade 5 cancer, and having a history of chemotherapy were associated with a shorter duration of response to next-line ARATs. Therefore, other therapeutic agents should be prioritized for such patients. Notably, among the included patients, those who were older, had a higher ISUP grade and a higher nadir PSA level during ADT exhibited worse overall survival.

Residual popliteal vein thrombosis after endovascular treatment of mixed-type lower extremity deep vein thrombosis is relevant to post-thrombotic syndrome

ObjectivePatients with mixed-type lower extremity deep vein thrombosis (DVT) have a higher incidence of post-thrombotic syndrome (PTS) after endovascular treatment (EVT). This study aimed to identify risk factors associated with PTS in these patients after EVT. MethodsThis retrospective study included patients diagnosed with acute mixed-type lower extremity DVT who underwent EVT between January 2020 and December 2022. Patient assessments were conducted using ultrasound examination and the Villalta scale. Baseline characteristics, management details, and follow-up findings were compared between patients who developed PTS and those who did not at 6 months after EVT. Cox regression and nomogram analyses were performed to identify risk factors associated with the development of PTS. ResultsThe study enrolled 118 patients, of which 103 completed the follow-up. Among them, 24.3% developed post-thrombotic syndrome (PTS) within 6 months. Significant differences between the PTS and non-PTS groups were found concerning residual thrombosis in the popliteal, common femoral, and femoral veins. Multivariate Cox regression analysis indicated that residual popliteal vein thrombosis (RPVT) (hazard ratio [HR], 4.93; 95% confidence interval [CI], 1.61-15.11) and preoperative iliac vein stenosis (HR, 3.21; 95% CI, 1.11-9.33) were significant risk factors for PTS. Additionally, subgroup analysis for preoperative iliac vein stenosis and sensitivity analysis confirmed that RPVT remained a risk factor for PTS (HR, 4.48; 95% CI, 1.27-15.84). ConclusionsOur study demonstrated a positive association between RPVT and PTS in patients with extensive mixed-type lower extremity DVT after EVT. These findings suggest that intensive monitoring and aggressive therapeutic interventions may be required for patients with RPVT to decrease the risk of PTS.

Host Response in Critically Ill Patients Aged 65 Years or Older: A Prospective Study.

The host response plays a critical role in the progression of all critical illnesses, especially in the aging population. With aging becoming a global phenomenon, understanding changes in the host response among elderly patients can provide valuable insights for diagnosis and treatment in the ICU. This study included all patients aged 65 and older admitted to our geriatric intensive care unit (GICU). Demographic, clinical, and medication data were extracted from electronic medical records. The primary outcome was in-hospital mortality, while secondary outcomes included hospital length of stay (LOS) and ICU stay duration. We employed the generalized additive mixed model for analysis and utilized nomogram analysis to build a predictive mortality model. A total of 1204 patients, with a median age of 75 years and a maximum age of 110 years, were admitted to the GICU. Host response biomarkers were notably lower in patients over 85 years. White blood cell (WBC) count, lactate dehydrogenase (LDH), interleukin-10 (IL-10), and tumor necrosis factor-α (TNF-α) were positively associated with mortality, while a higher platelet-to-lymphocyte ratio (PLR) was inversely related to mortality. Lymphocyte count was identified as a significant risk factor for mortality (RR = 1.2181). Elevated host response biomarkers were inversely associated with both hospital and ICU LOS. The predictive model integrating these biomarkers exhibited strong predictive performance for mortality. Our findings underscore the significant impact of aging on host response in critically ill patients. Older patients, particularly those over 85, exhibited lower biomarker levels and higher mortality rates. The predictive model developed from inflammatory, immune, and coagulation markers demonstrated robust prognostic utility, aiding in the evaluation of critically ill elderly patients.

Investigation of chromatin remodeling-related biomarkers and associated molecular mechanism in pulpitis

The current study aimed to identify potential chromatin remodeling-related biomarkers and the associated molecular mechanisms in pulpitis. Differentially expressed genes associated with chromatin remodeling (DECRGs) were identified using datasets from an online database. Enrichment and protein–protein interaction (PPI) network analyses were performed based on the DECRGs to identify biomarkers for pulpitis, followed by GSEA (gene set enrichment analysis). The diagnostic value of these biomarkers were evaluated by ROC (Receiver operating characteristic) and nomogram investigation. Next, microRNA(miRNA)-mRNA-TF (transcription factor), ceRNA (competing endogenous RNA), and drug prediction networks were constructed based on the biomarkers. Finally, reverse transcription-real-time quantitative PCR analysis and western blot were performed to validate the results of the bioinformatic analysis. This study identified 87 DECRGs between pulpitis and normal dental pulp samples that were mainly enriched in chromatin remodeling functions and pathways in cancer. A PPI network identified five biomarkers: TNF, STAT3, MYC, ACTB, and MAPK8. ROC and nomogram analyses demonstrated the diagnostic value of these biomarkers. GSEA of biomarkers such as STAT3 was mainly enriched in functions such as the B cell receptor signaling pathway. A biomarker-disease network and miRNA-mRNA-TF interactions were constructed using these biomarkers. A ceRNA network was constructed with interactions including chr22-38_28785274-29006793.1-miR-125b-5p-STAT3. A drug-gene network was established using 170 drugs and five biomarkers. Finally, qRT-PCR was used to validate the expression of all five biomarkers identified by the bioinformatics analysis. TNF, STAT3, MYC, ACTB, and MAPK8 are potential chromatin remodeling-related diagnostic markers for pulpitis. Moreover, long non-coding RNA (lncRNA) chr22-38_28785274-29006793.1 might function as a ceRNA to regulate the expression of the chromatin remodeling gene STAT3 by sponging miR-125b-5p in pulpitis.

Nomogram based on burn characteristics and the national early warning score to predict survival in severely burned patients

BackgroundExtensive burns are associated with a high mortality rate. Early prediction and action can reduce mortality. The National Early Warning Score (NEWS) is considered the best early warning score for predicting mortality. However, there has been no assessment conducted on the clinical prognostic significance of NEWS in individuals suffering from severe burns. The objective of this research was to establish a nomogram based on burn characteristics and the NEWS to predict survival in severely burned patients. MethodsA retrospective analysis was performed on 335 patients diagnosed with extensive burns from 2005 to 2021 in the Department of Burn Surgery of XX Hospital. Univariate and multivariate analyses were used to determine independent prognostic factors. A nomogram was developed using these prognostic factors and its internal validity was assessed through bootstrap resampling. ResultsThe results of multivariate analysis showed that the independent factors affecting the prognosis of severe burn patients were age, full-thickness burn, creatinine, absorptive injury, tracheotomy, and the NEWS, all of which were identified to create the nomogram. The Akaike Information Criterion and Bayesian Information Criterion values of the nomogram demonstrated superior goodness-of-fit in predicting severe burns compared to NEWS, with lower scores (195.21 vs. 201.24; 221.91 vs. 224.12, respectively). The bootstrap-adjusted concordance index (C-index) of the nomogram yielded a higher value of 0.923 (95% CI 0.892–0.953), compared to NEWS which had a C-index of 0.699 (95% CI 0.628–0.770). The calibration curves demonstrated excellent agreement between predicted probabilities and observed outcomes in the nomogram analysis. Furthermore, decision curve analysis indicated promising clinical utility for the proposed nomogram model. By applying an appropriate cutoff value derived from receiver operating characteristics curve analysis, it was observed that the high-risk group identified by the nomogram exhibited a significantly higher mortality rate than the low-risk group. ConclusionThis study introduces an innovative nomogram that predicts the survival rate of individuals with severe burn injuries by combining clinical attributes and laboratory examinations, demonstrating superior efficacy compared to conventional NEWS systems.

A predictive model of computed tomography and clinical features of EGFR gene mutation in lung adenocarcinoma.

Purpose: This study aims to develop a predictive model for epidermal growth factor receptor (EGFR) mutations in lung adenocarcinoma by integrating computed tomography (CT) imaging features with clinical characteristics. Methods: A retrospective analysis was conducted using electronic medical records from 194 patients diagnosed with lung adenocarcinoma between January 2016 and December 2020, with approval from the institutional review board. Features were selected using LASSO regression, and predictive models were built using logistic regression, support vector machine, and random forest methods. Individual models were created for clinical features, CT imaging features, and a combined model to predict EGFR mutations. Results: The training set revealed that alcohol consumption, intrapulmonary metastasis, and pleural effusion were statistically significant in distinguishing between wild-type and mutation groups (p < 0.05). In the testing set, hilar and mediastinal lymphadenopathy showed statistical significance (p < 0.05). The combined model outperformed the individual clinical and CT imaging feature models. In the testing set, the logistic regression model achieved the highest AUC of 0.827, with sensitivity, specificity, and accuracy of 0.714, 0.712, and 0.712, respectively. Nomogram analysis identified lobulation as an important feature, with a predicted probability of up to 0.9. The decision curve analysis showed that the CT imaging feature model provided a higher net benefit compared to both the clinical feature model and the combined model. Conclusion: In summary, while the combined model outperformed the individual feature models in the testing set, the CT imaging feature model demonstrated the greatest clinical net benefit. Lobulation was identified as an important predictor of EGFR mutations in lung adenocarcinoma.

Development and validation of a clinical prediction model for osteonecrosis of the jaw in patients receiving zoledronic acid using FAERS and canadian databases.

Osteonecrosis of the jaw (ONJ) stands as a severe complication linked to the use of bisphosphonates, particularly zoledronic acid, which is widely prescribed for managing conditions like osteoporosis and bone metastasis. This study is geared towards the development and validation of a clinical prediction model for ONJ in patients undergoing zoledronic acid treatment. We harnessed data from the FDA Adverse Event Reporting System (FAERS) as our training dataset, while the Canada Vigilance Adverse Reaction (CVAR) database served as the testing dataset. The study encompassed patients treated with zoledronic acid and subsequently diagnosed with ONJ. We analysed a range of predictive factors, including breast cancer, bone metastasis, osteoporosis, vitamin D and calcium levels, comorbidities, the number of concomitant medications, dosage, age, weight, and gender. Logistic regression and nomogram analysis were the chosen methodologies for constructing the predictive model. To evaluate the model's performance, we utilized receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). The study encompassed a total of 2,126 patients in the training cohort, 911 patients in the internal test cohort from the FAERS database, and 121 patients in the external test cohort from the CVAR database. Notable predictors for ONJ included bone metastasis (OR: 1.65, 95% CI: 1.22-2.24), osteoporosis (OR: 0.33, 95% CI: 0.21-0.52), the number of concomitant medications (OR: 1.07, 95% CI: 1.05-1.09), and the dosage of zoledronic acid (OR: 1.24, 95% CI: 1.10-1.39). The nomogram exhibited robust discriminatory power, evidenced by an area under the curve (AUC) of 0.77 in the training cohort, 0.76 in the internal test cohort, and 0.90 in the external test cohort. Calibration plots demonstrated a strong alignment between observed and predicted probabilities. Furthermore, DCA highlighted the prediction model's significant net benefit across various threshold probabilities. By leveraging data from both the FAERS and Canadian databases, this study has successfully developed and validated a clinical prediction model for ONJ in patients receiving zoledronic acid. This model stands as a valuable tool for clinicians, enabling them to pinpoint high-risk patients and make evidence-based treatment decisions to minimize the risk of ONJ.

Clinical model to predict the risk of nonalcoholic fatty liver disease: A secondary analysis of data from a cross-sectional study.

This study aimed to develop and validate a clinical model for predicting the risk of nonalcoholic fatty liver disease (NAFLD) by using data from a cross-sectional study. This investigation utilized data from the Dryad database and employed multivariable logistic regression analysis, restricted cubic spline, and nomogram analysis to achieve comprehensive insights. The discrimination and calibration of the nomogram were evaluated using the receiver operating characteristic curve and calibration plot. A total of 1072 patients were included in the study, including 456 with non-NAFLD and 616 with NAFLD. Significant differences were observed in terms of sex, body mass index (BMI), tobacco, hypertension, diabetes, alanine aminotransferase (ALT), aspartate aminotransferase (AST), ALT/AST ratio, uric acid (UA), fasting blood glucose (FBG), triglyceride (TG), high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, systolic blood pressure, and diastolic blood pressure (P < .05 for all comparisons). Multivariable logistic regression analysis indicated that sex, BMI, diabetes, ALT/AST ratio, UA, FBG, and TG were associated with an increased risk of NAFLD. Restricted cubic spline indicated a nonlinear relationship between the risk of NAFLD and variables including ALT/AST ratio, FPG, TG, and UA (P for nonlinearity < .01). The variables in the nomogram included BMI, diabetes, ALT/AST ratio, UA, FBG, and TG. The value of area under the curve was 0.790, indicating that the nomogram prediction model exhibited significant discriminatory accuracy. A reliable clinical model for predicting the risk of NAFLD was developed using readily available clinical data. The model can assist clinicians in identifying individuals with an increased risk of NAFLD, enabling early interventions for preventing and managing this prevalent liver disease.

Identification of a novel necroptosis-related LncRNA signature for prognostic prediction and immune response in oral squamous cell carcinoma.

Necroptosis is a caspase-independent regulated necrotic cell death modality that elicits strong adaptive immune responses, and has the potential to activate antitumor immunity. Long non-coding RNAs (lncRNAs) have critical effects on oral squamous cell carcinoma (OSCC), which are closely associated with the prognosis and immune regulation of OSCC patients. This study aimed to identify a novel necroptosis-related lncRNAs signature to predict the prognosis and immune response of OSCC patients and provide patients with anti-tumor drug selection through bioinformatics analysis and in vitro experiments. A series of analyses, including differential lncRNA screening, survival analysis, Cox regression analysis, ROC analysis, nomogram prediction, enrichment analysis, tumor-infiltrating immune cells, drug sensitivity analysis, and consensus cluster analysis, were performed to determine and validate the prognostic value of necroptosis-associated lncRNAs signature in OSCC. And real-time quantitative polymerase chain reaction (RT-qPCR) was used to determine the expression levels of these lncRNAs. This signature including 5 lncRNAs (AC099850.3, StarD4-AS1, AC011978.1, LINC01503, CDKN2A-DT) in OSCC associated with necroptosis were established and verified by bioinformatics. Further, ROC, K-M, univariate/multivariate Cox regression, and nomogram analysis were used to evaluate the model's features for OSCC prognosis. Using multiple bioinformatics techniques, the levels of tumor-infiltrating immune cells, immune checkpoints and semi-inhibitory concentrations showed significant differences across risk subtypes. By consensus cluster analysis, there were significant differences between clusters in survival, immune checkpoint expression, clinicopathological correlation, and tumor immunity. RT-qPCR showed that AC099850.3, AC011978.1, LINC01503 were up-regulated, STARD4-AS1 and CDKN2A-DT were down-regulated in OSCC cell lines compared with human normal oral keratinoid cell line. We established 5-NRLs markers, which is useful for assessing OSCC immune response and prognosis, recommending personalized antitumor drugs. The expression level of 5-NRLs in OSCC was identified in vitro, and the results preliminarily verified this model. And this study would generate new insights for future experimental research.

Predictors of prolonged supratherapeutic serum lithium concentrations: a retrospective chart review

Introduction The Extracorporeal Treatments in Poisoning (EXTRIP) workgroup suggests hemodialysis in severe lithium poisoning if specific criteria are met. One criterion is if the expected time to obtain a lithium concentration <1.0 mEq/L with optimal management is >36 h. There are a lack of data regarding which patient characteristics are associated with the rate at which patients achieve a lithium concentration <1.0 mEq/L. Methods We conducted a retrospective chart review analyzing hospital electronic medical records. Inclusion criteria consisted of a lithium concentration >1.2 mEq/L during hospitalization. We excluded patients who received extracorporeal treatment before 36 h elapsed from time of initial lithium concentration >1.2 mEq/L. The primary analysis consisted of a Cox regression and a secondary analysis evaluated the nomogram method described by Buckley and colleagues for predicting prolonged supratherapeutic lithium concentration. Results One hundred and one patients were included in the study. The median time to reach a lithium concentration <1.0 mEq/L was 42.5 h (IQR: 33.8–51.1). Older patients, patients taking a thiazide, angiotensin-converting enzyme inhibitor or angiotensin receptor blocker, patients with a higher initial lithium concentration, and patients with higher sodium concentrations achieved a lithium concentration <1 mEq/L at a slower rate. For the nomogram analysis, sensitivity (61.5%) and specificity (54.5%) were moderate, the positive predictive value (16.7%) was poor, and the negative predictive value (90.6%) was excellent. Discussion The results from our primary analysis suggest that identifying higher serum sodium concentration and use of certain antihypertensives that decrease glomerular filtration rate as predictors of an increased time to reach a therapeutic lithium concentration may help identify patients who meet the Extracorporeal Treatments in Poisoning criteria for hemodialysis. The nomogram method performed similarly to prior validation studies. Conclusions In this retrospective chart review of patients with supratherapeutic lithium concentrations, we identified several risk factors for prolonged supratherapeutic lithium concentrations.

High expression of COPZ2 is associated with poor prognosis and cancer progression in glioma.

Coatomer protein complex zeta 2 (COPZ2) is a member of heptameric coatomer protein complex I and has been reported to be involved in various tumors. However, COPZ2's potential involvement in glioma remains to be explored. The COPZ2 expression and related clinical data were obtained from The Cancer Genome Atlas (TCGA). TIMER2.0 and the Ualcan database were utilized to assess the COPZ2 expression in various tumors. Univariable, multivariate Cox regression, Kaplan-Meier methods, nomogram analysis, and ROC curve analysis were carried out to assess the relationship of COPZ2 and other prognostic factors with glioma. The LinkedOmics database was used to predict the potential biological mechanism of COPZ2 in glioma. We also conducted in vitro experiments to evaluate the functional role and mechanism of COPZ2 in glioma cell lines. We found that COPZ2 was highly expressed in glioma and it was associated with age and WHO grades. Kaplan-Meier survival curves, Cox analysis, nomogram analysis, and ROC curve showed that COPZ2 was a disadvantageous factor in poor glioma prognosis. The functions of COPZ2 and co-expression genes were significantly associated with neutrophil-mediated immunity, granulocyte activation, and response to interferon-gamma. In addition, COPZ2 knockdown significantly inhibited the proliferation, migration, and invasion of glioblastoma cells. Mechanistically, COPZ2 suppressed tumor development by participating in the regulation of the PI3K-AKT signaling pathway. Our results demonstrated that the elevation of COPZ2 was associated with the prognosis and progression of glioma, and it might be a potential diagnostic and prognostic biomarker for glioma.