OBJECTIVE: Temporomandibular disorders (TMDs) have a relatively high prevalence among university students. This study aimed to identify independent risk factors for TMD in university students and develop an effective risk prediction model.
METHODS: This study included 1,122 university students from four universities in Changchun City, Jilin Province, as subjects. Predictive factors were screened by using the least absolute shrinkage and selection operator (LASSO) regression and the machine learning Boruta algorithm in the training cohort. A multifactorial logistic regression analysis was used to construct a TMD risk prediction model. Internal validation of the model was conducted via bootstrap resampling, and an external validation cohort comprised 205 university students undergoing oral examinations at the Stomatological Hospital of Jilin University.
RESULTS: The prevalence of TMD among university students was 44.30%. Ten predictive factors were included in the model, comprising gender, facial cold stimulation, unilateral chewing, biting hard or resilient foods, clenching teeth, grinding teeth, excessive mouth opening, malocclusion, stress, and anxiety. The model demonstrated good predictive ability with area under the receiver operating characteristic curve (AUC) values of 0.853, 0.838, and 0.821 in the training cohort, internal validation cohort, and external validation cohort, respectively. The calibration curves demonstrated that the predicted results were consistent with the actual results, and the decision curve analysis (DCA) indicated the model\'s high clinical utility.
CONCLUSIONS: An online nomogram of TMD in university students with good predictive performance was constructed, which can effectively predict the risk of TMD in university students. The model provides a useful tool for the early identification and treatment of TMDs in university students, helping clinicians to predict the probability of TMDs in each patient, thus providing more personalized and accurate treatment decisions for patients.

This study aimed to develop a predictive tool for surgical site infections (SSI) following hysterectomy and propose strategies for their prevention and control. We conducted a retrospective analysis at a tertiary maternity and child specialist hospital in Zhejiang Province, focusing on patients who underwent hysterectomy between January 2018 and December 2023 for gynecological malignancies or benign reproductive system diseases resistant to medical treatment. Risk factors associated with surgical site infections (SSI) following hysterectomy were identified using LASSO regression analysis on data from 2018 to 2022 as the training set. Independent risk factors were then used to develop a nomogram. The model was validated using data from 2023 as the validation set. Model performance was assessed using the area under the receiver operating characteristic curve (ROC), while calibration curves were employed to gauge model accuracy. Furthermore, clinical utility was evaluated through clinical decision curve analysis (DCA) and clinical impact curve analysis (CIC), providing insights into the practical application of the nomogram. Multivariate analysis identified six independent risk factors associated with SSI development after hysterectomy: BMI ≥ 24 kg/m2 (OR: 2.58; 95% CI 1.14-6.19; P < 0.05), hypoproteinaemia diagnosis (OR: 4.99; 95% CI 1.95-13.02; P < 0.05), postoperative antibiotic use for ≥ 3 days (OR: 49.53; 95% CI 9.73-91.01; P < 0.05), history of previous abdominal surgery (OR: 7.46; 95% CI 2.93-20.01; P < 0.05), hospital stay ≥ 10 days (OR: 9.67; 95% CI 2.06-76.46; P < 0.05), and malignant pathological type (OR: 4.62; 95% CI 1.78-12.76; P < 0.05). A nomogram model was constructed using these variables. ROC and calibration curves demonstrated good model calibration and discrimination in both training and validation sets. Analysis with DCA and CIC confirmed the clinical utility of the nomogram. Personalized nomogram mapping for SSI after hysterectomy enables early identification of high-risk patients, facilitating timely interventions to reduce SSI incidence post-surgery.

BACKGROUND: Asthma, a prevalent chronic inflammatory disorder, is shaped by a multifaceted interplay between genetic susceptibilities and environmental exposures. Despite strides in deciphering its pathophysiological landscape, the intricate molecular underpinnings of asthma remain elusive. The focus has increasingly shifted toward the metabolic aberrations accompanying asthma, particularly within the domain of pyrimidine metabolism (PyM)-a critical pathway in nucleotide synthesis and degradation. While the therapeutic relevance of PyM has been recognized across various diseases, its specific contributions to asthma pathology are yet underexplored. This study employs sophisticated bioinformatics approaches to delineate and confirm the involvement of PyM genes (PyMGs) in asthma, aiming to bridge this significant gap in knowledge.
METHODS: Employing cutting-edge bioinformatics techniques, this research aimed to elucidate the role of PyMGs in asthma. We conducted a detailed examination of 31 PyMGs to assess their differential expression. Through Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA), we explored the biological functions and pathways linked to these genes. We utilized Lasso regression and Support Vector Machine-Recursive Feature Elimination (SVM-RFE) to pinpoint critical hub genes and to ascertain the diagnostic accuracy of eight PyMGs in distinguishing asthma, complemented by an extensive correlation study with the clinical features of the disease. Validation of the gene expressions was performed using datasets GSE76262 and GSE147878.
RESULTS: Our analyses revealed that eleven PyMGs-DHODH, UMPS, NME7, NME1, POLR2B, POLR3B, POLR1C, POLE, ENPP3, RRM2B, TK2-are significantly associated with asthma. These genes play crucial roles in essential biological processes such as RNA splicing, anatomical structure maintenance, and metabolic processes involving purine compounds.
CONCLUSIONS: This investigation identifies eleven PyMGs at the core of asthma\'s pathogenesis, establishing them as potential biomarkers for this disease. Our findings enhance the understanding of asthma\'s molecular mechanisms and open new avenues for improving diagnostics, monitoring, and progression evaluation. By providing new insights into non-cancerous pathologies, our work introduces a novel perspective and sets the stage for further studies in this field.

Idiopathic orbital inflammation, formerly known as NSOI (nonspecific orbital inflammation), is characterized as a spectrum disorder distinguished by the polymorphic infiltration of lymphoid tissue, presenting a complex and poorly understood etiology. Recent advancements have shed light on the HLF (Human lactoferrin), proposing its critical involvement in the regulation of hematopoiesis and the maintenance of innate mucosal immunity. This revelation has generated significant interest in exploring HLF\'s utility as a biomarker for NSOI, despite the existing gaps in our understanding of its biosynthetic pathways and operational mechanisms. Intersecting multi-omic datasets-specifically, common differentially expressed genes between GSE58331 and GSE105149 from the Gene Expression Omnibus and immune-related gene compendiums from the ImmPort database-we employed sophisticated analytical methodologies, including Lasso regression and support vector machine-recursive feature elimination, to identify HLF. Gene set enrichment analysis and gene set variation analysis disclosed significant immune pathway enrichment within gene sets linked to HLF. The intricate relationship between HLF expression and immunological processes was further dissected through the utilization of CIBERSORT and ESTIMATE algorithms, which assess characteristics of the immune microenvironment, highlighting a noteworthy association between increased HLF expression and enhanced immune cell infiltration. The expression levels of HLF were corroborated using data from the GSE58331 dataset, reinforcing the validity of our findings. Analysis of 218 HLF-related differentially expressed genes revealed statistically significant discrepancies. Fifteen hub genes were distilled using LASSO and SVM-RFE algorithms. Biological functions connected with HLF, such as leukocyte migration, ossification, and the negative regulation of immune processes, were illuminated. Immune cell analysis depicted a positive correlation between HLF and various cells, including resting mast cells, activated NK cells, plasma cells, and CD8 T cells. Conversely, a negative association was observed with gamma delta T cells, naive B cells, M0 and M1 macrophages, and activated mast cells. Diagnostic assessments of HLF in distinguishing NSOI showed promising accuracy. Our investigation delineates HLF as intricately associated with NSOI, casting light on novel biomarkers for diagnosis and progression monitoring of this perplexing condition.

This study aimed to develop and validate a predictive model of all-cause mortality risk in American adults aged ≥ 18 years with diabetes. 7918 participants with diabetes were enrolled from the National Health and Nutrition Examination Survey (NHANES) 1999-2016 and followed for a median of 96 months. The primary study endpoint was the all-cause mortality. Predictors of all-cause mortality included age, Monocytes, Erythrocyte, creatinine, Nutrition Risk Index (NRI), neutrophils/lymphocytes (NLR), smoking habits, alcohol consumption, cardiovascular disease (CVD), urinary albumin excretion rate (UAE), and insulin use. The c-index was 0.790 (95% CI 0.779-0.801, P < 0.001) and 0.792 (95% CI: 0.776-0.808, P < 0.001) for the training and validation sets, respectively. The area under the ROC curve was 0.815, 0.814, 0.827 and 0.812, 0.818 and 0.829 for the training and validation sets at 3, 5, and 10 years of follow-up, respectively. Both calibration plots and DCA curves performed well. The model provides accurate predictions of the risk of death for American persons with diabetes and its scores can effectively determine the risk of death in outpatients, providing guidance for clinical decision-making and predicting prognosis for patients.

Introduction: Lung cancer remains a significant global health burden, with non-small cell lung cancer (NSCLC) being the predominant subtype. Despite advancements in treatment, the prognosis for patients with advanced NSCLC remains unsatisfactory, underscoring the imperative for precise prognostic assessment models. This study aimed to develop and validate a survival prediction model specifically tailored for patients diagnosed with NSCLC.
METHODS: A total of 523 patients were randomly divided into a training dataset (n=313) and a validation dataset (n=210). We conducted initial variable selection using three analytical methods: univariate Cox regression, LASSO regression, and random survival forest (RSF) analysis. Multivariate Cox regression was then performed on the variables selected by each method to construct the final predictive models. The optimal model was selected based on the highest bootstrap C-index observed in the validation dataset. Additionally, the predictive performance of the model was evaluated using time-dependent receiver operating characteristic (Time-ROC) curves, calibration plots, and decision curve analysis (DCA).
RESULTS: The LASSO regression model, which included N stage, neutrophil-lymphocyte ratio (NLR), D-dimer, neuron-specific enolase (NSE), squamous cell carcinoma antigen (SCC), driver alterations, and first-line treatment, achieved a bootstrap C-index of 0.668 (95% CI: 0.626-0.722) in the validation dataset, the highest among the three models tested. The model demonstrated good discrimination in the validation dataset, with area under the ROC curve (AUC) values of 0.707 (95% CI: 0.633-0.781) for 1-year survival, 0.691 (95% CI: 0.616-0.765) for 2-year survival, and 0.696 (95% CI: 0.611-0.781) for 3-year survival predictions, respectively. Calibration plots indicated good agreement between predicted and observed survival probabilities. Decision curve analysis demonstrated that the model provides clinical benefit at a range of decision thresholds.
CONCLUSIONS: The LASSO regression model exhibited robust performance in the validation dataset, predicting survival outcomes for patients with advanced NSCLC effectively. This model can assist clinicians in making more informed treatment decisions and provide a valuable tool for patient risk stratification and personalized management.

UNASSIGNED: A considerable proportion of hypertensive patients may experience lacunar infarction. Therefore, early identification of the risk for lacunar infarction in hypertensive patients is particularly important. This study aimed to develop and validate a concise nomogram for predicting lacunar infarction in hypertensive patients.
UNASSIGNED: Retrospectively analyzed the clinical data of 314 patients with accurate history of hypertension in the Second Affiliated Hospital of Wannan Medical College from January 2021 to December 2022. All the patients were randomly assigned to the training set (n=220) and the validation set (n=94) with 7:3. The diagnosis of lacunar infarction in patients was confirmed using cranial CT or MRI. The independent risk factors of lacunar infarction were determined by Least absolute shrinkage and selection operator (LASSO) regression and multivariable logistic regression analysis. The nomogram was built based on the independent risk factors. The nomogram\'s discrimination, calibration, and clinical usefulness were evaluated by receiver operating characteristics (ROC) curve, calibration curve, and decision curve analysis (DCA) analysis, respectively.
UNASSIGNED: The incidence of lacunar infarction was 34.50% and 33.00% in the training and validation sets, respectively. Five independent predictors were made up of the nomogram, including age (OR=1.142, 95% CI: 1.089-1.198, P<0.001), diabetes mellitus (OR=3.058, 95% CI: 1.396-6.697, P=0.005), atrial fibrillation (OR=3.103, 95% CI: 1.328-7.250, P=0.009), duration of hypertension (OR=1.130, 95% CI: 1.045-1.222, P=0.002), and low-density lipoprotein (OR=2.147, 95% CI: 1.250-3.688, P=0.006). The discrimination with area under the curve (AUC) was 0.847 (95% CI: 0.789-0.905) in the training set and was a slight increase to 0.907 (95% CI: 0.838-0.976) in the validation set. The calibration curve showed high coherence between the predicted and actual probability of lacunar infarction. Moreover, the DCA analysis indicated that the nomogram had a higher overall net benefit of the threshold probability range in both two sets.
UNASSIGNED: Age, diabetes mellitus, atrial fibrillation, duration of hypertension, and low-density lipoprotein were significant predictors of lacunar infarction in hypertensive patients. The nomogram based on the clinical data was constructed, which was a useful visualized tool for clinicians to assess the risk of the lacunar infarction in hypertensive patients.

This study aimed to characterize PANoptosis-related genes with immunoregulatory features in osteoarthritis (OA) and investigate their potential diagnostic and therapeutic implications. Gene expression data from OA patients and healthy controls were obtained from the Gene Expression Omnibus (GEO) database. Differential expression analysis and functional enrichment analysis were conducted to identify PANoptosis-related genes (PRGs) associated with OA pathogenesis. A diagnostic model was developed using LASSO regression, and the diagnostic value of key PRGs was evaluated using Receiver Operating Characteristic Curve (ROC) analysis. The infiltration of immune cells and potential small molecule agents were also examined. A total of 39 differentially expressed PANoptosis-related genes (DE-PRGs) were identified, with functional enrichment analysis revealing their involvement in inflammatory response regulation and immune modulation pathways. Seven key PRGs, including CDKN1A, EZH2, MEG3, NR4A1, PIK3R2, S100A8, and SYVN1, were selected for diagnostic model construction, demonstrating high predictive performance in both training and validation datasets. The correlation between key PRGs and immune cell infiltration was explored. Additionally, molecular docking analysis identified APHA-compound-8 as a potential therapeutic agent targeting key PRGs. This study identified and analyzed PRGs in OA, uncovering their roles in immune regulation. Seven key PRGs were used to construct a diagnostic model with high predictive performance. The identified PRGs\' correlation with immune cell infiltration was elucidated, and APHA-compound-8 was highlighted as a potential therapeutic agent. These findings offer novel diagnostic markers and therapeutic targets for OA, warranting further in vivo validation and exploration of clinical applications.

BACKGROUND: Long noncoding RNAs (lncRNAs) and N6-methyladenosine (m6A) modification of RNA play pivotal roles in tumorigenesis and cancer progression. However, knowledge regarding the expression patterns of m6A-related lncRNAs and their corresponding m6A regulators in prostate cancer (PCa) is limited. This study aimed to delineate the landscape of m6A-related lncRNAs, develop a predictive model, and identify the critical m6A regulators of prognostic lncRNAs in PCa.
METHODS: Clinical and transcriptome data of PCa patients were downloaded from The Cancer Genome Atlas (TCGA) database. Prognostic m6A-related lncRNAs were subsequently identified through Pearson correlation and univariate Cox regression analyses. The prognostic lncRNAs were clustered into two groups by consensus clustering analysis, and a risk signature model was constructed using least absolute shrinkage and selection operator (LASSO) regression analysis of the lncRNAs. This model was evaluated using survival, clinicopathological, and immunological analyses. Furthermore, based on the constructed lncRNA-m6A regulatory network and RT-qPCR results, RBM15 was identified as a critical regulator of m6A-related lncRNAs. The biological roles of RBM15 in PCa were explored through bioinformatics analysis and biological experiments.
RESULTS: Thirty-four prognostic m6A-related lncRNAs were identified and categorized into two clusters with different expression patterns and survival outcomes in PCa patients. Seven m6A lncRNAs (AC105345.1, AL354989.1, AC138028.4, AC022211.1, AC020558.2, AC004076.2, and LINC02666) were selected to construct a risk signature with robust predictive ability for overall survival and were correlated with clinicopathological characteristics and the immune microenvironment of PCa patients. Among them, LINC02666 and AC022211.1 were regulated by RBM15. In addition, RBM15 expression correlated with PCa progression, survival, and the immune response. Patients with elevated RBM15 expression were more susceptible to the drug AMG-232. Moreover, silencing RBM15 decreased the viability of PCa cells and promoted apoptosis.
CONCLUSIONS: RBM15 is involved in the regulation of prognostic lncRNAs in the risk signature and has a robust predictive ability for PCa, making it a promising biomarker in PCa.

BACKGROUND: PDAC, also known as pancreatic ductal adenocarcinoma, is often diagnosed at a late stage due to nonspecific symptoms and a distinct lack of reliable biomarkers for timely diagnosis. Ferroptosis, a novel non-apoptotic cell death mode discovered in recent years, is strongly linked to the progression of PDAC and the evasion of the immune system. The objective of this study is to discover a novel ceRNA biomarker associated with ferroptosis and investigate its possible molecular mechanisms and therapeutic potential in PDAC.
METHODS: Based on the FerrDb and TCGA databases, the R survival package was used to screen for ferroptosis-related mRNAs associated with PDAC prognosis. The ferroptosis-related ceRNA network was identified by miRTarBase, miRNet, and starBase and visualized using Cytoscape. The LASSO regression analysis was used to build a risk model associated with ceRNA. Additionally, we investigated the correlation between the ceRNA axis and the infiltration of immune cells in PDAC by employing the ssGSEA algorithm. Spearman correlation analysis was used to investigate the association between the ceRNA network and the expression levels of immune checkpoint genes in PDAC. The prediction of potential medications for PAAD patients with high risk scores was conducted using the R package oncoPredict and the Genomics of Drug Sensitivity in Cancer (GDSC) repository. Expression levels of LINC02535 in clinical specimens and PDAC cell lines were determined using qRT-PCR. CCK-8, colony formation, EdU, wound healing, and transwell assays were performed to assess the impact of reducing LINC02535 on the growth, migration, and invasion of PDAC cell lines BxPC3 and PANC1.
RESULTS: We first discovered a new LINC02535/miR-30c-5p/EIF2S1 axis associated with ferroptosis and created a prognostic nomogram for predicting overall survival. Meanwhile, the risk scores of the LINC02535/miR-30c-5p/EIF2S1 axis associated with ferroptosis were linked to immune subtypes in PDAC. The high immune infiltration subtype exhibited elevated ceRNA risk scores and EIF2S1 expression. The correlation analysis revealed a positive correlation between ceRNA risk scores and four immune cells, namely Activated CD4 T cell, Memory B cell, Neutrophil, and Type 2 T helper cell, as well as four immune checkpoint genes, namely CD274, HAVCR2, PDCD1LG2, and TIGIT. The analysis of drug sensitivity indicated that individuals with a high-risk score may exhibit greater sensitivity to inhibitors targeting MEK1/2 compared to those with a low-risk score. In our validation experiments, it was observed that the expression of LINC02535 was increased in both PDAC tissues and cell lines. Additionally, the inhibition of LINC02535 resulted in decreased proliferation, migration, and invasion of PDAC cells. Rescue experiments demonstrated that LINC02535 promoted PDAC cell growth and metastasis by upregulating EIF2S1 expression.
CONCLUSIONS: To summarize, a novel ferroptosis-associated LINC02535/miR-30c-5p/EIF2S1 ceRNA network for PDAC patients was established. The analysis of this network\'s functionality offers potential insights for clinical decision-making and the advancement of precision medicine.