A nomogram to predict the risk of venous thromboembolism in patients with colon cancer in China.

OBJECTIVE: To create a nomogram for predicting the likelihood of venous thromboembolism (VTE) in colon cancer patients from China. METHODS: The data of colon cancer patients from Chongqing University Cancer Hospital between 2019 and 2022 were analyzed. Patients were divided into training set and internal validation set by random split-sample method in a split ratio of 7:3. The univariable and multivariable logistic analysis gradually identified the independent risk factors for VTE. A nomogram was created using all the variables that had a significance level of p < 0.05 in the multivariable logistic analysis and those with clinical significance. Calibration curves and clinical decision curve analysis (DCA) were used to assess model's fitting performance and clinical value. Harrell's C-index (concordance statistic) and the area under the receiver operating characteristic curves (AUC) were used to evaluate the predictive effectiveness of models. RESULTS: A total of 1996 patients were ultimately included. There were 1398 patients in the training set and 598 patients in the internal validation set. The nomogram included age, chemotherapy, targeted therapy, hypertension, activated partial thromboplastin time, prothrombin time, platelet, absolute lymphocyte count, and D-dimer. The C-index of nomogram and Khorana score were 0.754 (95% CI 0.711-0.798), 0.520 (95% CI 0.477-0.563) in the training cohort and 0.713 (95% CI 0.643-0.784), 0.542 (95% CI 0.473-0.612) in the internal validation cohort. CONCLUSIONS: We have established and validated a nomogram to predict the VTE risk of colon cancer patients in China, which encompasses a diverse age range, a significant population size, and various clinical factors. It facilitates the identification of high-risk groups and may enable the implementation of targeted preventive measures.

Exploring the ferroptosis-related gene lipocalin 2 as a potential biomarker for sepsis-induced acute respiratory distress syndrome based on machine learning.

BACKGROUND: Sepsis is a major cause of mortality in patients, and ARDS is one of the most common outcomes. The pathophysiology of acute respiratory distress syndrome (ARDS) caused by sepsis is significantly impacted by genes related to ferroptosis. METHODS: In this study, Weighted gene co-expression network analysis (WGCNA), protein-protein interaction (PPI) networks, functional enrichment analysis, and machine learning were employed to identify characterized genes and to construct receiver operating characteristic (ROC) curves. Additionally, DNA methylation levels were quantified and single-cell analysis was conducted. To validate the alterations in the expression of Lipocalin-2 (LCN2) and ferroptosis-related proteins in the in vitro model, Western blotting was carried out, and the changes in intracellular ROS and Fe2+ levels were detected. RESULTS: A combination of eight machine learning algorithms, including RFE, LASSO, RandomForest, SVM-RFE, GBDT, Bagging, XGBoost, and Boruta, were used with a machine learning model to highlight the significance of LCN2 as a key gene in sepsis-induced ARDS. Analysis of immune cell infiltration showed a positive correlation between neutrophils and LCN2. In a cell model induced by LPS, it was found that Ferrostatin-1 (Fer-1), a ferroptosis inhibitor, was able to reverse the expression of LCN2. Knocking down LCN2 in BEAS-2B cells reversed the LPS-induced lipid peroxidation, Fe2+ levels, ACSL4, and GPX4 levels, indicating that LCN2, a ferroptosis-related gene (FRG), plays a crucial role in mediating ferroptosis. CONCLUSION: Upon establishing an FRG model for individuals with sepsis-induced ARDS, we determined that LCN2 could be a dependable marker for predicting survival in these patients. This finding provides a basis for more accurate ARDS diagnosis and the exploration of innovative treatment options.

The Role of Long Noncoding RNA Negative Regulator of Interferon Response in the Regulation of Hantaan Virus Infection.

Hantaan virus (HTNV) is prevalent in Eurasia. It causes hemorrhagic fever with renal syndrome (HFRS). Long noncoding RNAs (lncRNAs) play key roles in regulating innate immunity. Among these, lncRNA negative regulator of interferon response (NRIR) was reported as an inhibitor of several interferon (IFN)-stimulated genes. Our results showed that: NRIR expression was upregulated by HTNV infection in a type I IFN-dependent manner. The expression of NRIR in CD14+ monocytes from HFRS patients in acute phase was significantly higher than that in convalescent phase and healthy controls. HTNV infection in some HTNV-compatible cells was promoted by NRIR. NRIR negatively regulated innate immunity, especially IFITM3 expression. Localized in the nucleus, NRIR bound with HNRNPC, and knockdown of HNRNPC significantly weakened the effect of NRIR in promoting HTNV infection and restored IFITM3 expression. These results indicated that NRIR regulates the innate immune response against HTNV infection possibly through its interaction with HNRNPC and its influence on IFITM3.

Development and validation of a novel death risk stratification scale in patients with hemorrhagic fever with renal syndrome: a 14-year ambispective cohort study.

OBJECTIVES: To develop and validate a simple and effective death risk stratification scale for hemorrhagic fever with renal syndrome (HFRS). METHODS: In this ambispective cohort study, we investigated the epidemiological and clinical data of 2245 patients with HFRS (1873 enrolled retrospectively and constituting the training cohort, 372 prospectively recruited as the validation cohort) from September 2008 to December 2021, and identified independent risk factors for 30-day death of HFRS. Using logistic regression analysis, a nomogram prediction model was established and was further simplified into a novel scoring scale. Calibration plot, receiver operating characteristic curve, net reclassification index, integrated discrimination index, and decision curve analysis were used to assess the calibration, discrimination, precision, and clinical utility in both training and validation cohorts. RESULTS: Of 2245 patients with HFRS, 132 (5.9%) died during hospitalization. The nomogram prediction model and scoring scale were developed using six predictors: comorbid hypertension, hypotensive shock, hypoxemia, neutrophils, aspartate aminotransferase, and activated partial thromboplastin time. Both the scale and nomogram were well calibrated (near-diagonal calibration curves) and demonstrated significant predictive values (areas under receiver operating characteristic curves >0.9, sensitivity and specificity >90% in the training cohort and >84% in the validation cohort). The simplified scoring scale demonstrated equivalent discriminative ability to the nomogram, with net reclassification index and integrated discrimination index of 0.022 and 0.007 in the training cohort, 0.126 and 0.022 in the validation cohort. Decision curve analysis graphically represented significant clinical utility and comparable net benefits of the nomogram and scoring scale across a range of threshold probabilities. DISCUSSION: This evidence-based, factor-weighted, accurate score could help clinicians swiftly stratify HFRS mortality risk and facilitate the implementation of patient triage and tiered medical services during epidemic peaks.

A multiple-responsive self-healing supramolecular polymer gel network based on multiple orthogonal interactions.

Supramolecular polymer networks have attracted considerable attention not only due to their topological importance but also because they can show some fantastic properties such as stimuli-responsiveness and self-healing. Although various supramolecular networks are constructed by supramolecular chemists based on different non-covalent interactions, supramolecular polymer networks based on multiple orthogonal interactions are still rare. Here, a supramolecular polymer network is presented on the basis of the host-guest interactions between dibenzo-24-crown-8 (DB24C8) and dibenzylammonium salts (DBAS), the metal-ligand coordination interactions between terpyridine and Zn(OTf)2 , and between 1,2,3-triazole and PdCl2 (PhCN)2 . The topology of the networks can be easily tuned from monomer to main-chain supramolecular polymer and then to the supramolecular networks. This process is well studied by various characterization methods such as (1) H NMR, UV-vis, DOSY, viscosity, and rheological measurements. More importantly, a supramolecular gel is obtained at high concentrations of the supramolecular networks, which demonstrates both stimuli-responsiveness and self-healing properties.

A stimuli-responsive orthogonal supramolecular polymer network formed by metal-ligand and host-guest interactions.

A stimuli-responsive supramolecular polymer network was constructed by the orthogonal assembly of metal-ligand interactions and host-guest interactions.