Objective To explore the change of serum levels of neutrophil gelatinase-associated lipocalin (NGAL), tissue inhibitor of metalloproteinases-2 (TIMP-2), and insulin-like growth factor-binding protein 7 (IGFBP-7) in the early stage of multiple trauma, and their predictive efficacy for acute kidney injury (AKI). Methods The multiple trauma patients admitted between February 2020 and July 2021 were prospectively selected, and they were divided into AKI group and non-AKI group according to whether they developed AKI within 72 h after injury. The serum levels of NGAL, TIMP-2, and IGFBP-7 measured at admission and 12, 24, and 48 h after injury, the Acute Pathophysiology and Chronic Health Evaluation Ⅱ(APACHE Ⅱ) score, intensive care unit duration, rate of renal replacement therapy, and 28-day mortality rate were compared between the two groups. Results A total of 51 patients were included, including 20 in the AKI group and 31 in the non-AKI group. The APACHE Ⅱ at admission (20.60±3.57 vs. 11.61±3.44), intensive care unit duration [(16.75±2.71) vs. (11.13±3.41) d], rate of renal replacement therapy (35.0% vs. 0.0%), and 28-day mortality rate (25.0% vs. 3.2%) in the AKI group were higher than those in the non-AKI group (P<0.05). The serum levels of NGAL and IGFBP-7 at admission and 12, 24, and 48 h after injury in the AKI group were all higher than those in the non-AKI group (P<0.05). For the prediction of AKI, the areas under receiver operating characteristic curves and 95% confidence intervals of serum NGAL, TIMP-2 and IGFBP-7 12 h after injury were 0.98 (0.96, 1.00), 0.92 (0.83, 1.00), and 0.87 (0.78, 0.97), respectively. Conclusion Serum NGAL, TIMP-2, and IGFBP-7 have high predictive efficacy for AKI secondary to multiple trauma, and continuous monitoring of serum NGAL can be used for early prediction of AKI secondary to multiple trauma.
Objective The management of pulmonary nodules is a common clinical problem, and this study constructed a nomogram model based on FUT7 methylation combined with CT imaging features to predict the risk of adenocarcinoma in patients with pulmonary nodules. Methods The clinical data of 219 patients with pulmonary nodules diagnosed by histopathology at the First Affiliated Hospital of Zhengzhou University from 2021 to 2022 were retrospectively analyzed. The FUT7 methylation level in peripheral blood were detected, and the patients were randomly divided into training set (n=154) and validation set (n=65) according to proportion of 7:3. They were divided into a lung adenocarcinoma group and a benign nodule group according to pathological results. Single-factor analysis and multi-factor logistic regression analysis were used to construct a prediction model in the training set and verified in the validation set. The receiver operating characteristic (ROC) curve was used to evaluate the discrimination of the model, the calibration curve was used to evaluate the consistency of the model, and the clinical decision curve analysis (DCA) was used to evaluate the clinical application value of the model. The applicability of the model was further evaluated in the subgroup of high-risk CT signs (located in the upper lobe, vascular sign, and pleural sign). Results Multivariate logistic regression analysis showed that female, age, FUT7_CpG_4, FUT7_CpG_6, sub-solid nodules, lobular sign and burr sign were independent risk factors for lung adenocarcinoma (P<0.05). A column-line graph prediction model was constructed based on the results of the multifactorial analysis, and the area under the ROC curve was 0.925 (95%CI 0.877 - 0.972 ), and the maximum approximate entry index corresponded to a critical value of 0.562, at which time the sensitivity was 89.25%, the specificity was 86.89%, the positive predictive value was 91.21%, and the negative predictive value was 84.13%. The calibration plot predicted the risk of adenocarcinoma of pulmonary nodules was highly consistent with the risk of actual occurrence. The DCA curve showed a good clinical net benefit value when the threshold probability of the model was 0.02 - 0.80, which showed a good clinical net benefit value. In the upper lobe, vascular sign and pleural sign groups, the area under the ROC curve was 0.903 (95%CI 0.847 - 0.959), 0.897 (95%CI 0.848 - 0.945), and 0.894 (95%CI 0.831 - 0.956). Conclusions This study developed a nomogram model to predict the risk of lung adenocarcinoma in patients with pulmonary nodules. The nomogram has high predictive performance and clinical application value, and can provide a theoretical basis for the diagnosis and subsequent clinical management of pulmonary nodules.
ObjectiveTo summarize the clinical application and future application prospects of organoid model in pancreatic cancer. MethodThe domestic and foreign literature related on the application of organoid model in pancreatic cancer was reviewed. ResultsIn recent years, the organoid model of pancreatic cancer was constructed mainly using patient-derived tissues, fine-needle aspiration samples, and human pluripotent stem cells. The biomarkers of pancreatic cancer were screened according to the histological and structural heterogeneities of the primary tumor retained in organoid model, such as microRNA, glypican-1, annexin A6 and protein biomarkers cytokeratin 7 and 20, cell tumor antigen p53, Claudin-4, carbohydrate antigen 19-9, etc.in the extracellular vesicles. The results of organoid model could maintain the original tumor characteristics and the higher correlation between the organoid model drug sensitivity data and the clinical results of pancreatic cancer patients suggested that, the drug sensitivity data of organoid model could be used to avoid ineffective chemotherapy, so as to improve the treatment response rate and reduce the toxicity of chemical drug treatment, and reasonably select individualized treatment plans for pancreatic cancer patients in future. ConclusionsOrganoid model has many research in screening biomarkers of pancreatic cancer, individualized drug screening, and drug sensitivity test. It can simulate the complex pathophysiological characteristics of pancreatic cancer in vitro, and retain the physiological characteristics and gene phenotype of original tumor cells. It is expected to become a new platform for selecting biomarkers of pancreatic cancer, testing drug sensitivity, and formulating individualized treatment methods for pancreatic cancer, which might further accelerate the research progress of pancreatic cancer.
【Abstract】 Objective To evaluate the relationship between multiple tumor biomarkers and idiopathic pulmonary fibrosis ( IPF) , and analyze the prognostic value of these biomarkers in IPF. Methods Clinical data of 43 confirmed IPF patients with no evidence of malignant disaeses, admitted in Peking Union Medical College Hospital between January 2000 and June 2010, were retrospectively analyzed. All IPF patients had detected serum alpha fetoprotein ( AFP) , cancer antigen 50 ( CA50) , cancer antigen 24-2( CA24-2) , carcinoembryonic antigen ( CEA) , carbohydrate antigen 19-9 ( CA19-9) , cancer antigen 125( CA125) , cancer antigen 15-3 ( CA15-3) , tissue polypeptide antigen ( TPA) , neuron specific enolase( NSE) , and cytokeratin-19-fragment ( Cyfra211) . Results The serum levels of CEA, CA19-9, CA125,CA15-3, and TPA were obviously higher than normal range, while the serum levels of AFP, CA50, CA24-2,NSE, and Cyfra211 were within normal range. Neither tumor biomarkers had correlation with 6-minute walk distance, FVC% pred, TLC% pred, DLCO/VA, PaO2 , PaO2 /FiO2 , P( A-a) O2 , BALF cell differentiation counting,or CD4 /CD8. The patients with increased CA19-9 level had shorter survival time than those with normal CA19-9 level ( P lt; 0. 05) . There was no significant difference in survival time between the patients with increased CEA/TPA levels and those with normal CEA/TPA levels( P gt;0. 05) , neither between the patients with glucocorticoid treatment and those with non-glucocorticoid treatment ( P gt; 0. 05) . Conclusions Multiple tumor biomarkers, especially CA19-9, increase in IPF patients. The degrees of those increases arenot associated with the severity of disease, but closely relate to prognosis, and may also indicate the progression. The increases of multiple tumor biomarkers may be a sign of poor prognosis of IPF with no evidence of malignant disaeses.
Breast cancer is a malignant tumor with the highest morbidity and mortality in female in recent years, and it is a complex disease that affects human health. Studies have shown that dynamic network biomarkers (DNB) can effectively identify critical states at which complex diseases such as breast cancer change from a normal state to a disease state. However, the traditional DNB method requires data from multiple samples in the same disease state, which is usually unachievable in clinical diagnosis. This paper quantitatively analyzes the time series data of MCF-7 breast cancer cells and finds the DNB module of a single sample in the time series based on landscape DNB (L-DNB) method. Then, a comprehensive index is constructed to detect its early warning signals to determine the critical state of breast cancer cell differentiation. The results of this study may be of great significance for the prevention and early diagnosis of breast cancer. It is expected that this paper can provide references for the related research of breast cancer.
Objective To explore, identify, and develop novel blood-based indicators using machine learning algorithms for accurate preoperative assessment and effective prediction of postoperative complication risks in patients with rheumatoid arthritis (RA) undergoing total knee arthroplasty (TKA). Methods A retrospective cohort study was conducted including RA patients who underwent unilateral TKA between January 2019 and December 2024. Inpatient and 30-day postoperative outpatient follow-up data were collected. Six machine learning algorithms, including decision tree, random forest, logistic regression, support vector machine, extreme gradient boosting, and light gradient boosting machine, were used to construct predictive models. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), F1-score, accuracy, precision, and recall. SHapley Additive exPlanations (SHAP) values were employed to interpret and rank the importance of individual variables. Results According to the inclusion criteria, a total of 1 548 patients were enrolled. Ultimately, 18 preoperative indicators were identified as effective predictive features, and 8 postoperative complications were defined as prediction labels for inclusion in the study. Within 30 days after surgery, 453 patients (29.2%) developed one or more complications. Considering overall accuracy, precision, recall, and F1-score, the random forest model [AUC=0.930, 95%CI (0.910, 0.950)] and the extreme gradient boosting model [AUC=0.909, 95%CI (0.880, 0.938)] demonstrated the best predictive performance. SHAP analysis revealed that anti-cyclic citrullinated peptide antibody, C-reactive protein, rheumatoid factor, interleukin-6, body mass index, age, and smoking status made significant contributions to the overall prediction of postoperative complications. Conclusion Machine learning-based models enable accurate prediction of postoperative complication risks among RA patients undergoing TKA. Inflammatory and immune-related blood biomarkers, such as anti-cyclic citrullinated peptide antibody, C-reactive protein, and rheumatoid factor, interleukin-6, play key predictive roles, highlighting their potential value in perioperative risk stratification and individualized management.
ObjectiveTo evaluate the monitoring value of brain injury biomarkers in the patients during extracorporeal membrane oxygenation (ECMO). MethodsWe searched PubMed, EMbase, the Cochrane Library, CNKI, and CBM from inception of each database to May 2015 to identify randomized controlled trials, or case-control trials, or cohort trials of brain injury biomarkers predict brain injury during ECMO. Data were extracted independently by two reviewers. Meta-analysis was conducted using STATA 12.0 software. ResultsFour retrospective trials were included. The results showed that compared with patients without brain injury, the patients with brain injury had a higher level of S100B protein (P < 0.05). The incidence of major neurological events was higher for high neuron-specific enolase level patients than mild-to-moderate neuron-specific enolase level patients (85% vs. 29%, P=0.01). The incidence of brain injury was higher for normal glial fibrillary acidic protein level than patients with glial fibrillary acidic protein > 0.436 ng/ml (OR=11.5, 95%CI 1.3-98.3). ConclusionsBrain injury biomarkers may be used as an indicator for earlier diagnosis of brain injury in patients during ECMO.
ObjectiveTo analyze the current development of researches on biomarkers for predicting the efficacy of immunotherapy in non-small cell lung cancer and to provide reference for subsequent studies. MethodsStudies on biomarkers for predicting the efficacy of immunotherapy for non-small cell lung cancer indexed in the Web of Science Core Collection from 2017 to 2021 were searched by computer. The annual distribution, journals, authors, countries, institutions, and keywords of studies were visualized and analyzed by CiteSpace. ResultsA total of 426 studies were collected, including 298 articles and 128 reviews. The average number of published studies was about 85, and increased year by year. PD-L1 expression, tumor mutational burden, tumor microenvironment and liquid biopsy were hot keywords in this field. ConclusionIn the future, combination of biomarkers in the liquid biopsy and tumor microenvironment with radiomics analysis will be the research hotspot and frontier in this field for more accurate assessment with tumor-related signatures such as lymphocytic immune status and characteristics of tumor lesions in non-small cell lung cancer patients.
Non-invasive biomarkers, due to their non-invasive and safe characteristics, hold significant potential for the diagnosis and prognosis of epilepsy. This review summarizes the research progress and future directions of non-invasive biomarkers for epilepsy, encompassing electrophysiological, imaging, biochemical, and genetic markers, and discusses biomarkers for specific types of epilepsy, such as structural lesion-related epilepsy, infection and inflammation-related epilepsy, autoimmune epilepsy, endocrine hormone-related epilepsy, and metabolic epilepsy, to facilitate their clinical application.
Transthyretin cardiac amyloidosis (ATTR-CA) is a form of restrictive cardiomyopathy characterized by the abnormal deposition of transthyretin in the myocardial interstitium, presenting with clinical manifestations such as heart failure, atrial fibrillation, and cardiac conduction system disorders. The significant individual variability in the symptomatology of ATTR-CA patients poses considerable challenges for precise diagnosis. This study provides a review of biomarkers associated with ATTR-CA and highlights the TTR tetramer as a potential novel indicator. The amyloid deposition in ATTR-CA is closely related to the dissociation of TTR tetramers; however, the TTR tetramer is not included among the biomarkers currently used in clinical practice. Investigating the concentration, profile, and dissociation rate of TTR tetramers holds profound significance for the early detection and prognostic assessment of ATTR-CA. This article synthesizes the research on traditional biomarkers of ATTR-CA and emphasizes the potential application value of TTR tetramers in disease diagnosis and prognostic evaluation.