Evaluation Of Prognosis In Patients Research Articles

Oxidative stress-related genes score predicts prognosis and immune cell infiltration landscape characterization in breast cancer

BackgroundThe tumor microenvironment (TME) typically experiences oxidative stress (OS), marked by a high level of reactive oxygen species (ROS) that can impact tumor advancement and prognosis by modulating the behavior of tumor cells and various immune cells. Oxidative stress-related genes (OSRG) encompass a range of genes involved in ROS pathways, and their specific roles in breast cancer (BC) necessitate further investigation. MethodsUnivariate Cox analysis was performed on genes linked to the OS pathway in the Gene Set Enrichment Analysis (GSEA) database, leading to the identification of 29 significant OSRG in BC. OSRG was divided into three distinct clusters according to the expression and the OSRG score based on the differentially expressed genes (DEGs) was further calculated by principal component analysis (PCA). The correlation between OSRG score and BC clinical features, mutation characteristics, immune checkpoints and immune cell infiltration was analyzed. Establish a multiariable Cox regression model to predict OSRG score effects on clinical characteristics. ResultsSignificant differences were observed in survival analysis, enriched pathways, and immune infiltration among the three OSRG clusters based on 29 genes. Gene clusters were identified through the final selected 395 DEGs, revealing three distinct OSRG expression patterns. An OSRG score model was constructed using DEGs, demonstrating a significant association between high OSRG score and poor prognosis. Significantly, immune checkpoint-related genes exhibited a notable upregulation in the high OSRG score cohort. Additionally, a positive correlation was observed between the OSRG score and tumor mutation burden (TMB) in BC. The OSRG score holds potential implications for clinical immunotherapy in BC patients, and a nomogram was constructed with robust predictive capability for evaluating patient prognosis. ConclusionsThis study elucidated the features of OSRG within BC TME and their possible prognostic significance, offering valuable insights for the development of more targeted immunotherapy approaches for individuals with BC.

Construction and Validation of Nomograms for Predicting Overall Survival and Cancer-Specific Survival in Patients with Primary Anaplastic Oligodendroglioma

Anaplastic oligodendroglioma (AOD) is a rare high-grade central nervous system tumor. The current research on prognostic prediction of AOD remains limited. This study aimed to identify prognostic factors and establish the nomograms to predict overall survival (OS) and cancer-specific survival (CSS) for patients with AOD. Patients diagnosed with AOD between 1992 and 2020 were extracted from the Surveillance, Epidemiology, and End Result database. We performed univariate and multivariate Cox regression analyses to identify independent prognostic factors based on the training group. Kaplan-Meier survival curves were used to compare the impact of various independent factors on patient prognosis. For OS and CSS, the nomograms were constructed and verified by the validation group. Harrell''s concordance index, receiver operating characteristic curves, calibration curves, and decision curve analyses were used to assess the discrimination, consistency, and clinical value of the nomograms. A total of 1202 AOD patients were enrolled, being randomly divided into training (n= 841) and validation (n= 361) groups (7:3 ratio). Univariate and multivariate Cox analysis identified 4 significant independent factors (tumor site, age, surgery, and chemotherapy). For OS and CSS, Harrell''s concordance index were 0.731 (0.705-0.757) and 0.728 (0.701-0.754) in the training group, 0.688 (0.646-0.731) and 0.684 (0.639-0.729) in the validation group, respectively. Receiver operating characteristic curves and Calibration curves showed good discrimination and consistency, respectively. In addition, the decision curve analyses curves showed the nomograms have good clinical benefits. We successfully established the nomograms to predict the OS and CSS for AOD patients. The nomograms showed good performance in prognostic prediction, assisting clinicians in evaluating patient prognosis and personalizing treatment plans.

Evaluation of Prognosis in Patients with Lung Adenocarcinoma with Atypical Solid Nodules on Thin-Section CT Images.

Purpose To evaluate the clinicopathologic characteristics and prognosis of patients with clinical stage IA lung adenocarcinoma with atypical solid nodules (ASNs) on thin-section CT images. Materials and Methods Data from patients with clinical stage IA lung adenocarcinoma who underwent resection between January 2005 and December 2012 were retrospectively reviewed. According to their manifestations on thin-section CT images, nodules were classified as ASNs, subsolid nodules (SSNs), and typical solid nodules (TSNs). The clinicopathologic characteristics of the ASNs were investigated, and the differences across the three groups were analyzed. The Kaplan-Meier method and multivariable Cox analysis were used to evaluate survival differences among patients with ASNs, SSNs, and TSNs. Results Of the 254 patients (median age, 58 years [IQR, 53-66]; 152 women) evaluated, 49 had ASNs, 123 had SSNs, and 82 had TSNs. Compared with patients with SSNs, those with ASNs were more likely to have nonsmall adenocarcinoma (P < .001), advanced-stage adenocarcinoma (P = .004), nonlepidic growth adenocarcinoma (P < .001), and middle- or low-grade differentiation tumors (P < .001). Compared with patients with TSNs, those with ASNs were more likely to have no lymph node involvement (P = .009) and epidermal growth factor receptor mutation positivity (P = .018). Average disease-free survival in patients with ASNs was significantly longer than that in patients with TSNs (P < .001) but was not distinguishable from that in patients with SSNs (P = .051). Conclusion ASNs were associated with better clinical outcomes than TSNs in patients with clinical stage IA lung adenocarcinoma. Keywords: Adenocarcinoma, Atypical Solid Nodules, CT, Disease-free Survival, Lung, Prognosis, Pulmonary Supplemental material is available for this article. Published under a CC BY 4.0 license.

Research Advancements on the Correlation Between Spontaneous Intracerebral Hemorrhage of Different Etiologies and Imaging Markers of Cerebral Small Vessel Disease.

The purpose of this review is to identify the correlation between ICH and CSVD imaging markers under SMASH-U classification by searching and analyzing a large number of literatures in recent years, laying a theoretical foundation for future clinical research. At the same time, by collecting clinical data to evaluate patient prognosis, analyzing whether there are differences or supplements between clinical trial conclusions and previous theories, and ultimately guiding clinical diagnosis and treatment through the analysis of imaging biomarkers. In this review, by searching CNKI, Web of Science, PubMed, FMRS and other databases, the use of "spontaneous intracerebral hemorrhage", "hypertensive hemorrhagic cerebral small vessel disease", "cerebral small vessel disease imaging", "Based cerebral small vessel diseases", "SMASH the -u classification" and their Chinese equivalents for the main search term. We focused on reading and analyzing hundreds of relevant literatures in the last decade from August 2011 to April 2020, and also included some earlier literatures with conceptual data sources. After screening and ranking the degree of relevance to this study, sixty of them were cited for analysis and elaboration. In patients with ICH, the number of cerebral microbleeds in lobes, basal ganglia, and the deep brain is positively correlated with ICH volume and independently correlated with neurological functional outcomes; white matter hyperintensity severity is positively correlated with ICH recurrence risk; multiple lacunar infarction independently predict the risk of ICH; severe brain atrophy is an independent risk factor for a poor prognosis in the long term in patients diagnosed with ICH; and the number of enlarged perivascular spaces is correlated with ICH recurrence. However, small subcortical infarct and ICH are the subject of few studies. Higher CSVD scores are independently associated with functional outcomes at 90 days in patients diagnosed with ICH.

A Multi-Center Randomized Controlled Study Using ΔPCO2/Ca-vO2 as the Target to Guide Early Tissue Hypoperfusion in Sepsis in Plateau Areas

Objective: To explore the value of using the venous-arterial carbon dioxide partial pressure difference and the arterial-venous oxygen content difference ratio (ΔPCO2/Ca-vO2) as targets to guide early tissue hypoperfusion in sepsis in plateau areas. Methods: 90 sepsis patients admitted to the Third People’s Hospital of Xining and Golmud People’s Hospital from June 2017 to December 2022 were selected as the research subjects, and they were divided into the ScvO2 (central venous oxygen saturation) group and the ΔPCO2/Ca-vO2 group, with 45 cases in each group. The two groups were treated with early shock resuscitation according to different protocols. The hemodynamic characteristics of the two groups of patients before and after resuscitation were observed, and the volume responsiveness was evaluated. The ROC (receiver operating characteristic) curve was used to analyze the significance of ΔPCO2/Ca-vO2, ScvO2, lactate, lactate clearance, and urine output in evaluating patient prognosis and the correlation between ΔPCO2/Ca-vO2 and the above indicators was explored. Results: Compared with before resuscitation, after fluid resuscitation, the heart rate (HR), mean arterial pressure (MAP), central venous pressure (CVP), cardiac index (CI), lactate, lactate clearance rate, and urine output of the two groups of patients were significantly improved (P &lt; 0.05); in terms of therapeutic effect, the 28-day mortality rate, 6-hour fluid balance, and lactic acid clearance of the ΔPCO2/Ca-vO2 group were better than the ScvO2 group. The ROC characteristic curve showed that the ΔPCO2/Ca-vO2 value can effectively predict the prognosis of patients (AUC = 0.907, sensitivity was 97%, specificity was 72.4%, and critical value was 1.84). ΔPCO2/Ca-vO2 significantly correlated with ScvO2, lactic acid, and lactic acid clearance rate. Conclusion: The ΔPCO2/Ca-vO2 value can be used to guide fluid resuscitation in early hypoperfusion in sepsis in plateau areas, improve patients’ hemodynamics, reduce lactate indicators, and increase urine output. ΔPCO2/Ca-vO2 level &gt; 1.84 can effectively improve patient prognosis.&#x0D; Corrigendum available at: https://www.doi.org/10.26689/jcnr.v8i2.6467

Nomogram for predicting the overall survival and cancer-specific survival of patients with intraductal carcinoma of the prostate

PurposeIntraductal carcinoma of the prostate (IDC-P) is a histological subtype that differs from conventional acinar adenocarcinoma in terms of its origin, appearance, and pathological features. For IDC-P, there is currently no recognized best course of action, and its prognosis is unclear. The goal of this study is to analyze independent prognostic factors in IDC-P patients and to develop and validate a nomogram to predict overall survival (OS) and cancer-specific survival (CSS).MethodsClinical data for IDC-P patients were collected from the Surveillance, Epidemiology, and End Results database. To identify the independent variables influencing prognosis, multivariate Cox regression analysis was performed. A nomogram model was created utilizing these variables after comparing the variations in OS and CSS among various subgroups using Kaplan‒Meier curves. Internal validation of the nomograms was verified using the bootstrap resampling method.ResultsThe study included 280 IDC-P patients in total. Marital status, summary stage, grade, and the presence of lung metastases were significant factors impacting OS, and CSS was significantly influenced by marital status, summary stage, AJCC stage, the presence of lung metastases, the presence of bone metastases, and PSA according to univariate and multivariate Cox regression models (P < 0.05). Nomogram models were created to estimate OS and CSS using these parameters. The OS prediction model’s C-index was 0.744, whereas the CSS prediction model’s C-index was 0.831.ConclusionWe developed and verified nomogram models for the prediction of 1-, 3-, and 5-year OS and CSS in patients with IDC-P. These nomograms serve as a resource for evaluating patient prognosis, therapy, and diagnosis, ultimately improving clinical decision-making accuracy.

The integration of multi-omics analysis and machine learning for the identification of prognostic assessment and immunotherapy efficacy through aging-associated genes in lung cancer.

Recent years revealed key molecules in lung cancer research, yet their exact roles in disease onset and progression remain uncertain. Lung cancer's heterogeneity complicates prognosis prediction. This study integrates pivotal molecules to evaluate patient prognosis and immunotherapy efficacy. The WGCNA algorithm identified module genes linked to immunity. The Lasso-Cox method built a prognostic model for outcome prediction. GO and KEGG analyses explored gene pathways. ssGSEA quantified immune cell types and functions. The riskScore predicts the effectiveness of immunotherapy based on its correlation with DNA repair and immune checkpoint genes. Single-cell sequencing examined key gene expression across cell types. Using WGCNA, we identified the MEbrown module related to immunity. Lasso-Cox selected "BLK," "ITGB4," "PRKCH," and "SNAI1" for the prognostic model. MF analysis revealed enriched functions including antigen binding, GTPase regulator activity. In terms of BP, processes like immune signaling and mitotic division were enriched. CC enrichment included immunoglobulin complexes and chromosomal regions. Enriched pathways encompassed Cell cycle, Focal adhesion, Cellular senescence, and p53 signaling. ssGSEA evaluated immune cell abundance. RiskScore correlated with CTLA4 and PD1 through MMR and immune checkpoint analysis. Single-cell analysis indicated gene expression across cell types for BLK, ITGB4, PRKCH, and SNAI1. In summary, our developed prognostic model utilizing age-related genes effectively predicts lung cancer prognosis and the efficacy of immune therapy.

A practical online prediction platform to predict the survival status of laryngeal squamous cell carcinoma after 5 years

ObjectiveCurrently, there are few practical tools for predicting the prognosis of laryngeal squamous cell carcinoma (LSCC). This study aims to establish a model and a convenient online prediction platform to predict whether LSCC patients will survive 5 years after diagnosis, providing a reference for further evaluation of patient prognosis. MethodsThis is a retrospective study based on data collected from two centers. Center 1 included 117 LSCC patients with survival prognosis data, and center 2 included 33 patients, totaling 150 patients. All data were divided into independent training sets (60 %) and testing sets (40 %). Eight machine learning (ML) algorithms were used to establish models with 11 clinical parameters as input features. The accuracy, sensitivity, specificity, and receiver operating characteristic curve (ROC) of the testing set were used to evaluate the models, and the best model was selected. The model was then developed into a website-based 5-year survival status prediction platform for LSCC. In addition, we also used the SHapley Additive exPlanations (SHAP) tool to conduct interpretability analysis on the parameters of the model. ResultsThe LSCC 5-year survival status prediction model using the support vector machine (SVM) algorithm achieved the best results, with accuracy, sensitivity, specificity, and area under the ROC curve (AUC) of 85.0 %, 87.5 %, 75.0 %, and 81.2 % respectively. The online platform for predicting the 5-year survival status of LSCC based on this model was successfully established. The SHAP analysis shows that the clinical stage is the most important feature of the model. ConclusionThis study successfully established a ML model and a practical online prediction platform to predict the survival status of laryngeal cancer patients after 5 years, which may help clinicians to better evaluate the prognosis of LSCC. Level of evidence3.

Expression of PTP4A1 in circulating tumor cells and its efficacy evaluation in patients with early- and intermediate-stage esophageal cancer.

The objective of this study is to investigate the expression of protein tyrosine phosphatase type I (PTP4A1) in circulating tumor cells (CTCs) in patients with early- and intermediate-stage esophageal cancer and its clinical value in evaluating patient prognosis. Tissue and peripheral blood samples were collected from patients with esophageal cancer, as well as their clinical data. Follow-up was performed on all patients. PTP4A1 expression in the CTCs of patients were analyzed by regression analysis, and its correlation with the clinical characteristics of esophageal cancer was discussed. The numbers of mixed tumor cells and T-CTCs were significantly correlated with lymph node metastasis. Advanced tumor-node metastasis (TNM) stage (odds ratio = 12.063) and lymph node metastasis (odds ratio = 13.541) were influencing factors of PTP4A1+MCTC expression disorders in patients with esophageal cancer. The receiver operating characteristic curve showed that TNM stage and lymph node metastasis had a high predictive efficiency for PTP4A1+MCTCs, with an area under the ROC curve of 0.725. PTP4A1+mixed tumor cells had strong predictive value for the efficacy of neoadjuvant therapy, with a sensitivity of 94.7% and a specificity of 63.6%. Advanced TNM stage and lymph node metastasis are influencing factors for increased CTCs and poor expression of PTP4A1 in patients with esophageal cancer.

Histogram analysis of quantitative susceptibility mapping and apparent diffusion coefficient for identifying isocitrate dehydrogenase genotypes and tumor subtypes of adult-type diffuse gliomas.

Accurate preoperative identification of isocitrate dehydrogenase (IDH) genotypes and tumor subtypes is highly important for proper treatment planning and prognosis evaluation in patients with glioma. This study aimed to differentiate IDH genotypes and tumor subtypes of adult-type diffuse gliomas using histogram features of quantitative susceptibility mapping (QSM) and apparent diffusion coefficient (ADC). This prospective study enrolled patients with suspected gliomas between March 2019 and January 2022 in a random series. Histogram features of QSM and ADC were extracted from the tumor parenchyma. The Mann-Whitney U test was used to compare the difference in histogram features between different IDH genotypes and among tumor subtypes. Receiver operating characteristic (ROC) curves were constructed to assess the corresponding diagnostic performance. This study included 47 patients with histopathologically confirmed adult-type diffuse gliomas. Totals of seven QSM features including 10th percentile (P10), 90th percentile (P90), interquartile range (IQR), maximum, mean absolute deviation (MAD), root mean squared (RMS), and variance, and five ADC features including P10, mean, median, RMS, and skewness exhibited significant differences between different IDH genotypes (P<0.05 for all), with the IQR of QSM demonstrating the highest area under curve (AUC) of 0.774 [95% confidence interval (CI): 0.635-0.913]. For separating tumor subtypes, the IQR of QSM also showed the highest AUC of 0.745 (95% CI: 0.566-0.924) for glioblastoma (GBM) versus astrocytoma and 0.848 (95% CI: 0.706-0.989) for GBM versus oligodendroglioma, but none of the features could discriminate astrocytoma from oligodendroglioma. The combination of the IQR of QSM, P10 of ADC, and age achieved the highest AUC of 0.910 (95% CI: 0.826-0.994) for IDH genotypes, and 0.939 (95% CI: 0.859-1.000) and 0.967 (95% CI: 0.904-1.000) for GBM versus astrocytoma and GBM versus oligodendroglioma, respectively. QSM and ADC histogram features may serve as potential imaging markers for noninvasively assessing IDH genotypes and tumor subtypes of adult-type diffuse gliomas. Combining significant features may enhance the diagnostic performance substantially.

Effect of quantitative parameters of contrast-enhanced ultrasound on the long-term prognosis of patients with chronic coronary syndrome.

Ultrasound is one of the most commonly used examination methods in patients with coronary artery disease (CAD) and is valuable in evaluating patient prognosis. Although contrast-enhanced ultrasound (CEUS) can assess more in depth the vascular lesions of patients, there is still a lack of relevant research on the value of quantitative parameters of CEUS in predicting the long-term prognosis of patients with chronic coronary syndrome (CCS), thus, we designed this study. From January 2016 to December 2017, a total of 473 patients with CCS admitted to Yueyang People's Hospital were retrospectively enrolled. The patients were followed up for five years. According to whether the patients had major adverse cardiovascular events (MACE), patients were divided into the MACE group (n=113) and the control group (n=360). The CEUS was performed to detect the myocardial perfusion status. The value of quantitative parameters of CEUS in predicting the MACE in patients with CCS was analyzed using the receiver operating characteristic (ROC) curve. Peak intensity of contrast agent at platform stage, rising rate of microbubble reperfusion, and left ventricular ejection fraction (LVEF) were found to be valuable in predicting the risk of MACE in patients with CCS. Among them, the peak intensity of contrast agent at platform stage had the highest predictive value, and the area under the curve (AUC) was 0.860 [95% confidence interval (CI): 0.827-0.894, P<0.001]. Multivariate logistics regression analysis showed that the peak intensity of contrast agent at platform stage <4.54 dB and rising rate of microbubble reperfusion <0.275 s were independent risk factors of MACE in patients with CCS. The relative risks were 12.238 (95% CI: 6.632-22.585) and 5.724 (95% CI: 3.149-10.405), respectively. Quantitative parameters of CEUS can be used as predictors of MACE in patients with CCS, and strengthening the management of such high-risk patients may be beneficial to reduce the incidence of MACE.

Function and prognosis analysis of nucleolus protein DCAF13 in breast cancer.

Breast cancer is one of the main causes of death among women. RNA binding proteins (RBPs) play a crucial role in the progression of breast cancer, with increasingly detailed understanding of RBP functional molecular mechanisms in breast cancer, the functional research of RBPs may help elucidate the potential mechanisms of tumor occurrence, development, invasion, metastasis and prognosis. DDB1- and CUL4-associated factor 13 (DCAF13) is an RBPs has been identified as a substrate receptor for the CUL4-DDB1 E3 ligase complex. Its expression is related to the prognosis of certain cancer. We tried to explore both co-expressed network and biological functions of DCAF13 in breast cancer. The Cancer Genome Atlas (TCGA) database was used to analyze the different expression of DCAF13 messenger RNA (mRNA) between normal breast tissue and breast carcinoma tissue, and the clinical data about 960 samples were downloaded from the cBio Cancer Genomics Portal (cBioPortal). The expression level of DCAF13, co-expression network, and survival were analyzed. Those with a fold change ≥1 and FDR <0.05 were considered to have statistical significance. Unsupervised clustering of differentially expressed RBPs was performed based on log2-transformed FPKM values using the "pheatmap" package in R. Genes with a Spearman score >0.55 were regarded as moderately co-expressed genes. The Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database was used to construct a co-expression network. Meanwhile, the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases were used to identify the biological process cluster and pathway cluster, respectively. Compared with normal breast tissue, DCAF13 mRNA expression was significantly increased in breast cancer tissue (P<0.01). The Database for Annotation, Visualization and Integrated Discovery (DAVID) was used to identify the functions of the co-expressed network. These genes were mainly enriched in mitosis, nuclear division, metabolic process, recombination, replication and repair of DNA, double-strand break repair, posttranscriptional regulation of gene expression, regulation of cell cycle, division and proliferation, regulation of protein stability and also participation in in regulation of poly(A) RNA binding, mRNA binding, tRNA binding, adenosine triphosphate (ATP) binding. KEGG pathway analysis revealed that the genes were mainly enriched in cell cycle, oocyte meiosis and oxidative phosphorylation. According to survival analysis, upregulation of DCAF13 mRNA was significant for overall survival (OS) (P=0.0163). DCAF13 is up-regulated in breast cancer, the OS of patients with DCAF13 up-regulation was obviously reduced. DCAF13 was used as a diagnostic marker and therapeutic target for breast cancer. By building a co-expression network of DCAF13 and conducting bioinformatics analysis, it is possible to find the biomarker to evaluate patient prognosis. This finding provides a new target in mechanism and cell research of breast cancer.

SST2 and Big ET-1 as Alternatives of Multi-Biomarkers Strategies for Prognosis Evaluation in Patients Hospitalized with Heart Failure.

To identify biomarkers with independent prognostic value and investigate the prognostic value of multiple biomarkers in combination in patients hospitalized with heart failure. A total of 884 consecutive patients hospitalized with heart failure from 2015 to 2017 were enrolled. Twelve biomarkers were measured on admission, and the relationships between biomarkers and outcomes were assessed. During the median follow-up of 913 days, 291 patients (32.9%) suffered from primary endpoint events. Soluble suppression of tumorigenicity-2 (sST2) (per log [unit] increase, adjusted HR [95% CI]: 1.39 [1.13,1.72], P = 0.002) and big endothelin-1 (big ET-1) (per log [unit] increase, adjusted HR [95% CI]: 1.56 [1.23,1.97], P < 0.001) remained independent predictors of primary endpoint event after adjusting for other predictors including N-terminal pro-B-type natriuretic peptide (NT-proBNP) and high-sensitivity cardiac troponin T (hs-cTnT). Both sST2 (C-statistic: 0.810 vs 0.801, P = 0.005, and 0.832 vs 0.826, P = 0.024, respectively) and big ET-1 (C-statistic: 0.829 vs 0.801, P = 0.001, and 0.843 vs 0.826, P < 0.001, respectively) significantly improved the predictive value for primary endpoint event at 1 year and 3 years. However, only big ET-1 (C-statistic: 0.852 vs 0.846, P = 0.014) significantly improved the predictive value at 3 months when added to clinical predictors and known biomarkers. According to the number of elevated biomarkers (including NT-proBNP, hs-cTnT, sST2, and big ET-1), patients with three or more elevated biomarkers had a higher risk of primary endpoint event compared to those with two elevated biomarkers (P = 0.001), as well as in patients with two elevated biomarkers compared to those with one elevated biomarker (P = 0.004). However, the risk of primary endpoint event was comparable between patients with one elevated biomarker and those with no elevated biomarker (P = 0.582). Multiple biomarkers in combination could provide a better prognostic value than a single biomarker. sST2 and big ET-1 could act as alternatives of multi-biomarkers strategies for prognosis evaluation beyond NT-proBNP and hs-cTnT in patients hospitalized with heart failure.

Cyclin A1 affects the invasion, metastasis, and prognosis of hepatocellular carcinoma

Objective: To investigate the effect of cyclin A1 on the invasion, metastasis, and prognosis of hepatocellular carcinoma (HCC). Methods: Immunohistochemistry (IHC) was used to detect the expressional condition of cyclin A1 in HCC and paraffin-embedded non-tumor adjacent tissues. Kaplan-Meier method was used for the survival analysis of patients with HCC. Western blot (WB) was used to detect the expression of cyclin A1 in HCCLM3 and QGY-7703 cells. Scratch wound healing assay, transwell migration, and invasion assay were used to detect the effect of cyclin A1 overexpression on cell migration and invasion ability. WB was used to detect changes in the expression of matrix metalloproteinase (MMP) 2, MMP9, and vascular endothelial growth factor (VEGF) after overexpression of cyclin A1. Measurement data were compared using a t-test and analysis of variance. Count data was measured using χ (2) test and the Log-rank method was performed for survival analysis. Results: Cyclin A1 expression rates were higher in the tissues of HCC patients with recurrent metastasis than in the tissues of patients without recurrent metastasis (60.42% vs. 46.81%, χ (2) = 4.711, P < 0.05). The overall postoperative survival time (OS) and disease-free survival (DFS) were shorter in patients with high cyclin A1 expression than those with low cyclin A1 expression (45.9 months vs. 53.1 months; 42.9 months vs. 51.3 months, and P < 0.01). The postoperative OS and DFS were shorter in patients with high cyclin A1 expression and recurrent metastasis than those with low cyclin A1 expression without recurrent metastasis (31.7 months vs. 43.9 months; 18.0 months vs. 31.5 months, and P < 0.05). HCCLM3 and QGY-7703 cells were higher in the cyclin A1-pEX group than in the empty vector (vector) group (1.56 ± 0.06 vs. 0.18 ± 0.01, t = 18.75, P < 0.001; 1.31 ± 0.05 vs.0.37 ± 0.02, t = 15.17, P < 0.001). The migrated distances of HCCLM3 cells in the cyclin A1-pEX group and the vector group were (536.7 ± 14.5) μm and (327.3 ± 9.3) μm, t = 11.84, P < 0.05, respectively, while the migrated distances of QGY-7703 cells in the two groups were (916.7 ± 35.3) μm and (320.0 ± 20.8) μm, t = 13.54, P < 0.01. The migrated numbers of HCCLM3 cells in the cyclin A1-pEX group and vector group were (37.3 ± 2.4) and (7.0 ± 1.2), t = 12.67, P < 0.001, and the number of invasive cells was (73.7 ± 4.1) and (12.6 ± 1.5), t = 12.36, P < 0.001, respectively. The migrated numbers of QGY-7703 cells in the two groups were (153.3 ± 6.0) and (17.7 ± 3.7), t = 17.59, P < 0.001, and the number of invasive cells was (45.0 ± 2.9) and (9.3 ± 1.5), t = 10.66, P < 0.001, respectively. The expression levels of MMP2, MMP9, and VEGF in HCCLM3 and QGY-7703 cells were significantly higher in the cyclin A1-pEX group than those in the vector group (P < 0.05). Conclusion: Cyclin A1 plays an important role in HCC invasion and metastasis, but HCC patients with high cyclin A1 expression have a poor prognosis. Hence, cyclin A1 has high guiding significance for evaluating patient prognosis.

Improved Cochlear Implant Performance Estimation Using Tonotopic-Based Electrocochleography

Cochlear implantation produces remarkable results in postlingual deafness, although auditory outcomes vary. Electrocochleography (ECochG) has emerged as a valuable tool for assessing the cochlear-neural substrate and evaluating patient prognosis. To assess whether ECochG-total response (ECochG-TR) recorded at the best-frequency electrode (BF-ECochG-TR) correlates more strongly with speech perception performance than ECochG-TR measured at the round window (RW-ECochG-TR). This single-center cross-sectional study recruited 142 patients from July 1, 2021, to April 30, 2022, with 1-year follow-up. Exclusions included perilymph suctioning, crimped sound delivery tubes, non-native English speakers, inner ear malformations, nonpatent external auditory canals, or cochlear implantation revision surgery. Cochlear implantation. Speech perception testing, including the consonant-nucleus-consonant (CNC) words test, AzBio sentences in quiet, and AzBio sentences in noise plus 10-dB signal to noise ratio (with low scores indicating poor performance and high scores indicating excellent performance on all tests), at 6 months postoperatively; and RW-ECochG-TR and BF-ECochG-TR (measured for 250, 500, 1000, and 2000 Hz). A total of 109 of the 142 eligible postlingual adults (mean [SD] age, 68.7 [15.8] years; 67 [61.5%] male) were included in the study. Both BF-ECochG-TR and RW-ECochG-TR were correlated with 6-month CNC scores (BF-ECochG-TR: r = 0.74; 95% CI, 0.62-0.82; RW-ECochG-TR: r = 0.67; 95% CI, 0.54-0.76). A multivariate model incorporating age, duration of hearing loss, and angular insertion depth did not outperform BF-ECochG-TR or RW-ECochG-TR alone. The BF-ECochG-TR correlation with CNC scores was significantly stronger than the RW-ECochG-TR correlation (r difference = -0.18; 95% CI, -0.31 to -0.01; z = -2.02). More moderate correlations existed between 6-month AzBio scores in noise, Montreal Cognitive Assessment (MoCA) scores (r = 0.46; 95% CI, 0.29-0.60), and BF-ECochG-TR (r = 0.42; 95% CI, 0.22-0.58). MoCA and the interaction between BF-ECochG-TR and MoCA accounted for a substantial proportion of variability in AzBio scores in noise at 6 months (R2 = 0.50; 95% CI, 0.36-0.61). In this case series, BF-ECochG-TR was identified as having a stronger correlation with cochlear implantation performance than RW-ECochG-TR, although both measures highlight the critical role of the cochlear-neural substrate on outcomes. Demographic, audiologic, and surgical factors demonstrated weak correlations with cochlear implantation performance, and performance in noise was found to require a robust cochlear-neural substrate (BF-ECochG-TR) as well as sufficient cognitive capacity (MoCA). Future cochlear implantation studies should consider these variables when assessing performance and related interventions.

Preoperative Prediction of Meningioma Subtype by Constructing a Clinical-Radiomics Model Nomogram Based on Magnetic Resonance Imaging

We sought to investigate the value of a clinical-radiomics model based on magnetic resonance imaging in differentiating fibroblastic meningiomas from non-fibroblastic meningiomas. Clinical, imaging, and postoperative pathologic data of 423 patients (128 fibroblastic meningiomas and 295 non-fibroblastic meningiomas) were randomly categorized into training (n= 296) and validation (n= 127) groups at a 7:3 ratio. The Selectpercentile and LASSO were used to selected the highly correlated features from 3376 radiomics features. Different classifiers were used to train and verify the model. The receiver operating characteristic curves, accuracy (ACC), sensitivity (SEN), and specificity (SPE) were drawn to evaluate the performance. The optimal radiomics model was selected. Calibration curves and decision curve analysis were used to verify the clinical utility and consistency of the nomogram constructed from the radiomics features and clinical factors. Thirteen radiomics features were selected from contrast-enhanced T1-weighted imaging and T2-weighted imaging after dimensionality reduction. The prediction performance of random forest radiomics model is slightly lower than that of the clinical-radiomics model. The area under the curve, SEN, SPE, and ACC of the clinical-radiomics model training set were 0.836 (95% confidence interval, 0.795-0.878), 0.922, 0.583, and 0.686, respectively. The area under the curve, SEN, SPE, and ACC of the validation set were 0.756 (95% confidence interval, 0.660-0.846), 0.816, 0.596, and 0.661, respectively. The diagnostic efficacy of the clinical-radiomics model of fibroblastic meningioma and non-fibroblastic meningioma was better than that of the radiomics prediction model alone and can be used as a potential tool for clinical surgical planning and evaluation of patient prognosis.

Expression and Predictive Significance of FHL1 and SLIT3 in Surgically Resected Lung Adenocarcinoma.

Lung adenocarcinoma (LUAD) is the most common type of lung cancer. However, predictive biomarkers for early efficacy and prognosis evaluation in patients with surgically resected LUAD are not completely explained. Differentially expressed genes (DEGs), gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were identified by RNA sequencing (RNA-Seq) between thirteen LUAD tissues and five normal lung tissues. The expression of DEGs was confirmed by qRT-PCR and a validated cohort from GEPIA. Protein-protein interaction (PPI) network of the top 5% DEGs was constructed by STRING and visualized in Cytoscape. Immunofluorescence results were acquired from clinical specimens from LUAD patients. The expression of FHL1 was analyzed by ImageJ. Survival analysis was performed using the GEPIA dataset. Consistent with the RNA-Seq data, validation of DEGs expression by qRT-PCR and GEPIA cohort showed that FHL1 and SLIT3 were down-regulated in LUAD patient tissues compared with non-tumor tissues. Moreover, FHL1 was significantly reduced in LUAD cell lines compared to the bronchial epithelium cell line (P < 0.01). However, SLIT3 was elevated in A549 and H1299 cells (wide type EGFR) (P < 0.05) while decreased in HCC827 and PC9 cells (mutant EGFR) compared to BESA-2B cells (P < 0.01). PPI network revealed the most significant cluster with 10 nodes and 43 edges. Immunofluorescent staining also showed that the expression of FHL1 was lower in LUAD tissues compared with that in normal lung tissues (P < 0.01). The expressions of SLIT3 and FHL1 were positively correlated. Specifically, the higher expression level of SLIT3 and FHL1 independently predicted a better prognosis (P < 0.01 or P < 0.05). Our findings provide two novel candidates, FHL1 and SLIT3, for prognostic evaluation and treatments after surgery.

Deep-learning and conventional radiomics to predict IDH genotyping status based on magnetic resonance imaging data in adult diffuse glioma.

In adult diffuse glioma, preoperative detection of isocitrate dehydrogenase (IDH) status helps clinicians develop surgical strategies and evaluate patient prognosis. Here, we aim to identify an optimal machine-learning model for prediction of IDH genotyping by combining deep-learning (DL) signatures and conventional radiomics (CR) features as model predictors. In this study, a total of 486 patients with adult diffuse gliomas were retrospectively collected from our medical center (n=268) and the public database (TCGA, n=218). All included patients were randomly divided into the training and validation sets by using nested 10-fold cross-validation. A total of 6,736 CR features were extracted from four MRI modalities in each patient, namely T1WI, T1CE, T2WI, and FLAIR. The LASSO algorithm was performed for CR feature selection. In each MRI modality, we applied a CNN+LSTM-based neural network to extract DL features and integrate these features into a DL signature after the fully connected layer with sigmoid activation. Eight classic machine-learning models were analyzed and compared in terms of their prediction performance and stability in IDH genotyping by combining the LASSO-selected CR features and integrated DL signatures as model predictors. In the validation sets, the prediction performance was evaluated by using accuracy and the area under the curve (AUC) of the receiver operating characteristics, while the model stability was analyzed by using the relative standard deviation of the AUC (RSDAUC). Subgroup analyses of DL signatures and CR features were also individually conducted to explore their independent prediction values. Logistic regression (LR) achieved favorable prediction performance (AUC: 0.920 ± 0.043, accuracy: 0.843 ± 0.044), whereas support vector machine with the linear kernel (l-SVM) displayed low prediction performance (AUC: 0.812 ± 0.052, accuracy: 0.821 ± 0.050). With regard to stability, LR also showed high robustness against data perturbation (RSDAUC: 4.7%). Subgroup analyses showed that DL signatures outperformed CR features (DL, AUC: 0.915 ± 0.054, accuracy: 0.835 ± 0.061, RSDAUC: 5.9%; CR, AUC: 0.830 ± 0.066, accuracy: 0.771 ± 0.051, RSDAUC: 8.0%), while DL and DL+CR achieved similar prediction results. In IDH genotyping, LR is a promising machine-learning classification model. Compared with CR features, DL signatures exhibit markedly superior prediction values and discriminative capability.

Designing a Model to Predict the Mortality in Multiple Trauma Patients due to Traffic Accidents

Background: It is of prime importance to manage trauma patients in the early hours and use easy trauma severity scoring systems to make decisions and evaluate patient prognosis. Objectives: The present study aimed to design a predictive model of the mortality of multi-trauma patients due to traffic accidents. Methods: This cross-sectional analytical study was performed on 600 patients who suffered from multi-trauma caused by traffic accidents from December 2019 to September 2021. Collected data included age, sex, vital signs, trauma mechanism, involved vehicle in the accident, accident location, and hospital outcome. Results: In this study, 600 multi-trauma cases caused by traffic accidents were evaluated. Among the significant variables included in the regression model, age, Mean Arterial Pressure (MAP), Glasgow Coma Scale (GCS), AVPU (Alert, Verbal response, Pain response, Unresponsive), and vehicle versus fixed objects (in Vehicle 2) in the presence of other variables in the model, significantly predicted patient outcomes. Therefore, with the other variables being constant, one unit increase in the age variable increases the probability of death by 1.04 times, one unit increase in the score of the two variables of MAP and GCS, and also the transfer of trauma mechanism from the fixed object to the vehicle reduces death by 0.92, 0.62, and 0.10 times, respectively. In the AVPU variable, the transition from Alert to Verbal, the transition from Verbal to Pain, and the transition from Pain to Unresponsive increases the probability of death by 32, 104, and 567, respectively. Conclusion: In this study, AVPU, age, MAP, primary GCS, and trauma mechanism due to hitting a vehicle with a fixed object had significantly the highest predictive power of hospital mortality in patients with multiple trauma due to traffic accidents, respectively. It is suggested that further studies be performed to replace the AVPU variable with GCS in the newly designed formulas for calculating the severity of trauma to simplify these scores.

The significance of serum marker levels for assessing the status and prognosis of patients with inflammatory bowel disease

Purpose of the work: to analyze the research performed in the last decade on the study of serum markers used to diagnose inflammatory bowel diseases (IBD), differential diagnosis of nosological forms of IBD, to predict the course of the disease, response to ongoing therapy, as well as to predict the recurrence of the disease. Materials and methods. A search was carried out in the text databases “Scopus”, “Web of Science”, “PubMed” using keywords from the literature sources of the last 10 years about blood serum biomarkers used for diagnosis, evaluation of therapeutic efficacy, monitoring of disease activity and evaluation of prognosis in patients with IBD. Results. Information on serological biomarkers of IBD, which are well known and widely used in clinical practice (C-reactive protein), recently discovered biomarkers (cytokines, antibodies and non-coding RNA), as well as recent advances in serological biomarkers (metabolomics, proteomics, oncostatin M, galectins), were analyzed, which are used in various aspects of the diagnosis of IBD. Since intestinal fibrosis significantly affects the prognosis in patients with IBD, data on serum markers of intestinal fibrosis are presented separately. Conclusion. Recently, serum biomarkers in patients with IBD have become the object of close attention, since they are minimally invasive, convenient, and relatively inexpensive than markers in biopsy specimens, other biological fluids, fecal, and respiratory tests. Despite an extensive list of studies conducted over the past 10 years, there are still no ideal serum biomarkers for the diagnosis of IBD. Serum and non-coding RNA profiling techniques are just beginning to develop, but offer great promise for clinical practice. A combination of different biomarkers may be useful to improve the efficiency of IBD diagnosis.