Prognostic implication of residual inflammatory trajectories in acute type I aortic dissection: dual-center prospective cohort study.

BACKGROUND: Peripheral platelet-white blood cell ratio (PWR) integrating systemic inflammatory and coagulopathic pathways is a key residual inflammatory measurement in the management of acute DeBakey type I aortic dissection (AAD); however, trajectories of PWR in AAD is poorly defined. METHODS: Two AAD cohorts were included in two cardiovascular centers (2020-2022) if patients underwent emergency total arch replacement with frozen elephant trunk implantation. PWR data were collected over time at baseline and five consecutive days after surgery. Trajectory patterns of PWR were determined using the latent class mixed modelling (LCMM). Cox regression was used to determine independent risk factors. By adding PWR Trajectory, a user-friendly nomogram was developed for predicting mortality after surgery. RESULTS: 246 patients with AAD were included with a median follow-up of 26 (IRQ 20-37) months. Three trajectories of PWR were identified (cluster α 45[18.3%], ß105 [42.7%], and γ 96 [39.0%]). Cluster γ was associated with higher risk of mortality at follow-up (crude HR, 3.763; 95% CI, 1.126, 12.574; P=0.031) than cluster α. By the addition of PWR trajectories, an inflammatory nomogram, composed of age, hemoglobin, estimated glomerular filtration rate, and cardiopulmonary time was developed and internally validated, with adequate discrimination (the area under the receiver-operating characteristic curve 0.765, 95% CI [0.660-0.869]), calibration, and clinical utility. CONCLUSION: Based on PWR trajectories, three distinct clusters were identified with short-term outcomes, and longitudinal residual inflammatory shed some light to individualize treatment strategies for AAD.

Diagnosis of Early Bacterial Pneumonia and Sepsis After Cardiovascular Surgery: A Diagnostic Prediction Model Based on LASSO Logistic Regression.

Background: Early postoperative bacterial pneumonia and sepsis (ePOPS), which occurs within the first 48 hours after cardiovascular surgery, is a serious life-threatening complication. Diagnosis of ePOPS is extremely challenging, and the existing diagnostic tools are insufficient. The purpose of this study was to construct a novel diagnostic prediction model for ePOPS. Methods: Least Absolute Shrinkage and Selection Operator (LASSO) with logistic regression was used to construct a model to diagnose ePOPS based on patients' comorbidities, medical history, and laboratory findings. The area under the receiver operating characteristic curve (AUC) was used to evaluate the model discrimination. Results: A total of 1203 patients were recruited and randomly split into a training and validation set in a 7:3 ratio. By early morning on the 3rd postoperative day (POD3), 103 patients had experienced 133 episodes of bacterial pneumonia or sepsis (15 patients had both). LASSO logistic regression model showed that duration of mechanical ventilation (P=0.015), NYHA class ≥ III (P=0.001), diabetes (P<0.001), exudation on chest radiograph (P=0.011) and IL-6 on POD3 (P<0.001) were independent risk factors. Based on these factors, we created a nomogram named DICS-I with an AUC of 0.787 in the training set and 0.739 in the validation set. Conclusion: The DICS-I model may be used to predict the risk of ePOPS after cardiovascular surgery, and is also especially suitable for predicting the risk of IRAO. The DICS-I model could help clinicians to adjust antibiotics on the POD3.

A Novel Inflammation-Based Risk Score Predicts Mortality in Acute Type A Aortic Dissection Surgery: The Additive Anti-inflammatory Action for Aortopathy and Arteriopathy Score.

Objective: To develop an inflammation-based risk stratification tool for operative mortality in patients with acute type A aortic dissection. Methods: Between January 1, 2016 and December 31, 2021, 3124 patients from Beijing Anzhen Hospital were included for derivation, 571 patients from the same hospital were included for internal validation, and 1319 patients from other 12 hospitals were included for external validation. The primary outcome was operative mortality according to the Society of Thoracic Surgeons criteria. Least absolute shrinkage and selection operator regression were used to identify clinical risk factors. A model was developed using different machine learning algorithms. The performance of the model was determined using the area under the receiver operating characteristic curve (AUC) for discrimination, calibration curves, and Brier score for calibration. The final model (5A score) was tested with respect to the existing clinical scores. Results: Extreme gradient boosting was selected for model training (5A score) using 12 variables for prediction-the ratio of platelet to leukocyte count, creatinine level, age, hemoglobin level, prior cardiac surgery, extent of dissection extension, cerebral perfusion, aortic regurgitation, sex, pericardial effusion, shock, and coronary perfusion-which yields the highest AUC (0.873 [95% confidence interval (CI) 0.845-0.901]). The AUC of 5A score was 0.875 (95% CI 0.814-0.936), 0.845 (95% CI 0.811-0.878), and 0.852 (95% CI 0.821-0.883) in the internal, external, and total cohort, respectively, which outperformed the best existing risk score (German Registry for Acute Type A Aortic Dissection score AUC 0.709 [95% CI 0.669-0.749]). Conclusion: The 5A score is a novel, internally and externally validated inflammation-based tool for risk stratification of patients before surgical repair, potentially advancing individualized treatment. Trial Registration: clinicaltrials.gov Identifier: NCT04918108.

Circulating biomarker-based risk stratifications individualize arch repair strategy of acute Type A aortic dissection via the XGBoosting algorithm.

Aims: The incremental usefulness of circulating biomarkers from different pathological pathways for predicting mortality has not been evaluated in acute Type A aortic dissection (ATAAD) patients. We aim to develop a risk prediction model and investigate the impact of arch repair strategy on mortality based on distinct risk stratifications. Methods and results: A total of 3771 ATAAD patients who underwent aortic surgery retrospectively included were randomly divided into training and testing cohorts at a ratio of 7:3 for the development and validation of the risk model based on multiple circulating biomarkers and conventional clinical factors. Extreme gradient boosting was used to generate the risk models. Subgroup analyses were performed by risk stratifications (low vs. middle-high risk) and arch repair strategies (proximal vs. extensive arch repair). Addition of multiple biomarkers to a model with conventional factors fitted an ABC risk model consisting of platelet-leucocyte ratio, mean arterial pressure, albumin, age, creatinine, creatine kinase-MB, haemoglobin, lactate, left ventricular end-diastolic dimension, urea nitrogen, and aspartate aminotransferase, with adequate discrimination ability {area under the receiver operating characteristic curve (AUROC): 0.930 [95% confidence interval (CI) 0.906-0.954] and 0.954, 95% CI (0.930-0.977) in the derivation and validation cohort, respectively}. Compared with proximal arch repair, the extensive repair was associated with similar mortality risk among patients at low risk [odds ratio (OR) 1.838, 95% CI (0.559-6.038); P = 0.316], but associated with higher mortality risk among patients at middle-high risk [OR 2.007, 95% CI (1.460-2.757); P < 0.0001]. Conclusion: In ATAAD patients, the simultaneous addition of circulating biomarkers of inflammatory, cardiac, hepatic, renal, and metabolic abnormalities substantially improved risk stratification and individualized arch repair strategy.

The impact of gene-body H3K36me3 patterns on gene expression level changes in chronic myelogenous leukemia.

Chronic myelogenous leukemia (CML) is a malignant clonal disease of hematopoietic stem cells. Researches have exhibited that the progression of CML is related to histone modifications. Here, we perform the systematic analyses of H3K36me3 patterns and gene expression level changes. We observe that the genes with higher gene-body H3K36me3 levels in normal cells show fewer expression changes during leukemogenesis, while the genes with lower gene-body H3K36me3 levels in normal cells yield obvious expression changes during leukemogenesis (ρ = -0.98, P = 9.30 × 10-8). These findings are conserved in human lung/breast cancers and mouse CML, regardless of gene expression levels and gene lengths. Regulatory element analysis and Random Forest regression display that Hoxd13, Rara, Scl, Smad3, Smad4 and Tgif1 induce the up-regulation of genes with lower H3K36me3 levels (ρ = 0.97, P = 2.35 × 10-56). Enrichment analysis shows that the differentially expressed genes with lower H3K36me3 levels are involved in leukemia-related pathways, such as leukocyte migration and regulation of leukocyte activation. Finally, six driver genes (Tp53, Wt1, Dnmt3a, Cacna1b, Phactr1 and Gbp4) with lower H3K36me3 levels are identified. Our analyses indicate that lower gene-body H3K36me3 levels may serve as a biomarker for the progression of CML.

Identification of Key Histone Modifications and Their Regulatory Regions on Gene Expression Level Changes in Chronic Myelogenous Leukemia.

Chronic myelogenous leukemia (CML) is a type of cancer with a series of characteristics that make it particularly suitable for observations on leukemogenesis. Research have exhibited that the occurrence and progression of CML are associated with the dynamic alterations of histone modification (HM) patterns. In this study, we analyze the distribution patterns of 11 HM signals and calculate the signal changes of these HMs in CML cell lines as compared with that in normal cell lines. Meanwhile, the impacts of HM signal changes on expression level changes of CML-related genes are investigated. Based on the alterations of HM signals between CML and normal cell lines, the up- and down-regulated genes are predicted by the random forest algorithm to identify the key HMs and their regulatory regions. Research show that H3K79me2, H3K36me3, and H3K27ac are key HMs to expression level changes of CML-related genes in leukemogenesis. Especially H3K79me2 and H3K36me3 perform their important functions in all 100 bins studied. Our research reveals that H3K79me2 and H3K36me3 may be the core HMs for the clinical treatment of CML.

Gene expression classification using epigenetic features and DNA sequence composition in the human embryonic stem cell line H1.

Epigenetic factors are known to correlate with gene expression in the existing studies. However, quantitative models that accurately classify the highly and lowly expressed genes based on epigenetic factors are currently lacking. In this study, a new machine learning method combines histone modifications, DNA methylation, DNA accessibility, transcription factors, and trinucleotide composition with support vector machines (SVM) is developed in the context of human embryonic stem cell line (H1). The results indicate that the predictive accuracy will be markedly improved when the epigenetic features are considered. The predictive accuracy and Matthews correlation coefficient of the best model are as high as 95.96% and 0.92 for 10-fold cross-validation test, and 95.58% and 0.92 for independent dataset test, respectively. Our model provides a good way to judge a gene is either highly or lowly expressed gene by using genetic and epigenetic data, when the expression data of the gene is lacking. And a web-server GECES for our analysis method is established at http://202.207.14.87:8032/fuwu/GECES/index.asp, so that other scientists can easily get their desired results by our web-server, without going through the mathematical details.

Identification of thermophilic proteins by incorporating evolutionary and acid dissociation information into Chou's general pseudo amino acid composition.

Thermophilic proteins can thrive stalely at the high temperatures. Identification of thermophilic protein could be helpful to learn the function of protein. Automated prediction of thermophilic protein is an important tool for genome annotation. In this work, a powerful predictor is proposed by combining amino acid composition, evolutionary information, and acid dissociation constant. The overall prediction accuracy of 93.53% was obtained for using the algorithm of support vector machine. In order to check the performance of our method, two low-similarity independent testing datasets are used to test the proposed method. Comparisons with other methods show that the prediction results were better than other existing methods in literature. This indicates that our approach was effective to predict thermophilic proteins.

Sodium glycididazole enhances the radiosensitivity of laryngeal cancer cells through downregulation of ATM signaling pathway.

The purpose of this study was to evaluate the radiation-enhancing effect of sodium glycididazole, and the corresponding mechanisms of action on laryngeal cancer cells. Two laryngeal cancer cell lines (Hep-2 and UT-SCC-19A) were irradiated with X-rays in the presence or absence of sodium glycididazole. Cell survival, DNA damage and repair, cell apoptosis, cell cycle distribution, expression of proteins related to cell cycle checkpoint, and apoptosis were measured. Significantly increased DNA damages, decreased cells in the G1 phase, arrested cells at G2/M phase, decreased DNA repair protein XRCC1 foci formation, and enhanced cell apoptosis were observed in laryngeal cell lines treated by sodium glycididazole combined with irradiation compared with the irradiation alone. The combined treatment downregulated the protein expressions of ataxia-telangiectasia mutated (ATM), p-ATM, CHK2, and P53 but upregulated the protein expressions of MDM2 and Cdk2. This study indicates that sodium glycididazole enhances the radiosensitivity of laryngeal cancer cells through downregulation of ATM signaling pathway in vitro and in vivo.

DSPMP: Discriminating secretory proteins of malaria parasite by hybridizing different descriptors of Chou's pseudo amino acid patterns.

Identification of the proteins secreted by the malaria parasite is important for developing effective drugs and vaccines against infection. Therefore, we developed an improved predictor called "DSPMP" (Discriminating Secretory Proteins of Malaria Parasite) to identify the secretory proteins of the malaria parasite by integrating several vector features using support vector machine-based methods. DSPMP achieved an overall predictive accuracy of 98.61%, which is superior to that of the existing predictors in this field. We show that our method is capable of identifying the secretory proteins of the malaria parasite and found that the amino acid composition for buried and exposed sequences, denoted by AAC(b/e), was the most important feature for constructing the predictor. This article not only introduces a novel method for detecting the important features of sample proteins related to the malaria parasite but also provides a useful tool for tackling general protein-related problems. The DSPMP webserver is freely available at http://202.207.14.87:8032/fuwu/DSPMP/index.asp.

acACS: improving the prediction accuracy of protein subcellular locations and protein classification by incorporating the average chemical shifts composition.

The chemical shift is sensitive to changes in the local environments and can report the structural changes. The structure information of a protein can be represented by the average chemical shifts (ACS) composition, which has been broadly applied for enhancing the prediction accuracy in protein subcellular locations and protein classification. However, different kinds of ACS composition can solve different problems. We established an online web server named acACS, which can convert secondary structure into average chemical shift and then compose the vector for representing a protein by using the algorithm of auto covariance. Our solution is easy to use and can meet the needs of users.

Serum CA125 level predicts prognosis in patients with multiple brain metastases from non-small cell lung cancer before and after treatment of whole-brain radiotherapy.

This study was to evaluate the effect of serum CA125 level on the prognosis of patients with multiple brain metastases from non-small cell lung cancer before and after treatment of whole-brain radiotherapy. Sixty-six patients with multiple brain metastases from non-small cell lung cancer before and after treatment of radiotherapy were reviewed retrospectively. Radiotherapy was given to the whole brain using opposed 6MV lateral beams with a dose of 30 Gy in 15 fractions in 3 weeks. Elevated CA125 was defined as >35 U/mL. The survival rate was calculated using the Kaplan-Meier method, and the univariate and multivariate analyses were used to identify significant factors associated with prognosis, using a Cox proportional hazards model. During the median (range) follow-up of 1.25 (0.25-2.50) years, 62 patients died from non-small cell lung cancer; the 1-year cancer-specific survival (CSS) rate was 43.08 %. Thirty patients had a high CA125 level before chemoradiotherapy (>35U/mL), and their CSS rate was significantly worse than that in the remaining patients (P = 0.024). Multivariate analysis showed that CA125 level, number of metastases and total tumor volume were independent prognostic indicators for CSS, with a hazard ratio of 1.99, 1.67 and 2.02, respectively. The elevation of CA125 before treatment predicts a poor prognosis in patients with multiple brain metastases from non-small cell lung cancer before and after treatment of whole-brain radiotherapy.

Predicting peroxidase subcellular location by hybridizing different descriptors of Chou' pseudo amino acid patterns.

Peroxidases as universal enzymes are essential for the regulation of reactive oxygen species levels and play major roles in both disease prevention and human pathologies. Automated prediction of functional protein localization is rarely reported and also is important for designing new drugs and drug targets. In this study, we first propose a support vector machine (SVM)-based method to predict peroxidase subcellular localization. Various Chou' pseudo amino acid descriptors and gene ontology (GO)-homology patterns were selected as input features to multiclass SVM. Prediction results showed that the smoothed PSSM encoding pattern performed better than the other approaches. The best overall prediction accuracy was 87.0% in a jackknife test using a PSSM profile of pattern with width=5. We also demonstrate that the present GO annotation is far from complete or deep enough for annotating proteins with a specific function.

Discriminating bioluminescent proteins by incorporating average chemical shift and evolutionary information into the general form of Chou's pseudo amino acid composition.

Bioluminescent proteins are highly sensitive optical reporters for imaging in live animals; they have been extensively used in analytical applications in intracellular monitoring, genetic regulation and detection, and immune and binding assays. In this work, we systematically analyzed the sequence and structure information of 199 bioluminescent and nonbioluminescent proteins, respectively. Based on the results, we presented a novel method called auto covariance of averaged chemical shift (acACS) for extracting structure features from a sequence. A classifier of support vector machine (SVM) fusing increment of diversity (ID) was used to distinguish bioluminescent proteins from nonbioluminescent proteins by combining dipeptide composition, reduced amino acid composition, evolutionary information, and acACS. The overall prediction accuracy evaluated by jackknife validation reached 82.16%. This result was better than that obtained by other existing methods. Improvement of the overall prediction accuracy reached up to 5.33% higher than those of the SVM and auto covariance of sequential evolution information by 10-fold cross-validation. The acACS algorithm also outperformed other feature extraction methods, indicating that our approach is better than other existing methods in the literature.

PreDNA: accurate prediction of DNA-binding sites in proteins by integrating sequence and geometric structure information.

MOTIVATION: Protein-DNA interactions often take part in various crucial processes, which are essential for cellular function. The identification of DNA-binding sites in proteins is important for understanding the molecular mechanisms of protein-DNA interaction. Thus, we have developed an improved method to predict DNA-binding sites by integrating structural alignment algorithm and support vector machine-based methods. RESULTS: Evaluated on a new non-redundant protein set with 224 chains, the method has 80.7% sensitivity and 82.9% specificity in the 5-fold cross-validation test. In addition, it predicts DNA-binding sites with 85.1% sensitivity and 85.3% specificity when tested on a dataset with 62 protein-DNA complexes. Compared with a recently published method, BindN+, our method predicts DNA-binding sites with a 7% better area under the receiver operating characteristic curve value when tested on the same dataset. Many important problems in cell biology require the dense non-linear interactions between functional modules be considered. Thus, our prediction method will be useful in detecting such complex interactions.

A similarity distance of diversity measure for discriminating mesophilic and thermophilic proteins.

The successful prediction of thermophilic proteins is useful for designing stable enzymes that are functional at high temperature. We have used the increment of diversity (ID), a novel amino acid composition-based similarity distance, in a 2-class K-nearest neighbor classifier to classify thermophilic and mesophilic proteins. And the KNN-ID classifier was successfully developed to predict the thermophilic proteins. Instead of extracting features from protein sequences as done previously, our approach was based on a diversity measure of symbol sequences. The similarity distance between each pair of protein sequences was first calculated to quantitatively measure the similarity level of one given sequence and the other. The query protein is then determined using the K-nearest neighbor algorithm. Comparisons with multiple recently published methods showed that the KNN-ID proposed in this study outperforms the other methods. The improved predictive performance indicated it is a simple and effective classifier for discriminating thermophilic and mesophilic proteins. At last, the influence of protein length and protein identity on prediction accuracy was discussed further. The prediction model and dataset used in this article can be freely downloaded from http://wlxy.imu.edu.cn/college/biostation/fuwu/KNN-ID/index.htm .

Predict mycobacterial proteins subcellular locations by incorporating pseudo-average chemical shift into the general form of Chou's pseudo amino acid composition.

Mycobacterium tuberculosis (MTB) is a pathogenic bacterial species in the genus Mycobacterium and the causative agent of most cases of tuberculosis (Berman et al., 2000). Knowledge of the localization of Mycobacterial protein may help unravel the normal function of this protein. Automated prediction of Mycobacterial protein subcellular localization is an important tool for genome annotation and drug discovery. In this work, a benchmark data set with 638 non-redundant mycobacterial proteins is constructed and an approach for predicting Mycobacterium subcellular localization is proposed by combining amino acid composition, dipeptide composition, reduced physicochemical property, evolutionary information, pseudo-average chemical shift. The overall prediction accuracy is 87.77% for Mycobacterial subcellular localizations and 85.03% for three membrane protein types in Integral membranes using the algorithm of increment of diversity combined with support vector machine. The performance of pseudo-average chemical shift is excellent. In order to check the performance of our method, the data set constructed by Rashid was also predicted and the accuracy of 98.12% was obtained. This indicates that our approach was better than other existing methods in literature.

C-reactive protein level predicts prognosis in patients with locoregionally advanced laryngeal carcinoma treated with chemoradiotherapy.

The aim of this study was to investigate the effect of C-reactive protein (CRP) level on the prognosis of patients with locoregionally advanced laryngeal carcinoma treated with chemoradiotherapy. Fifty-seven patients with locoregionally advanced laryngeal carcinoma (cT3-4, N0-3, M0) treated with chemoradiotherapy were reviewed retrospectively. Chemoradiotherapy comprised external beam radiotherapy to the larynx (70 Gy) with three cycles of cisplatin at 3-week intervals. Elevated CRP was defined as >8 mg/L. The survival rate was calculated using the Kaplan-Meier method, and a multivariate analysis was used to identify significant factors associated with prognosis, using a Cox proportional hazards model. During the median (range) follow-up of 5 years (1.3-5), 29 patients died from laryngeal cancer; the 5-year cancer-specific survival (CSS) rate was 49.12%. Fifteen patients had a high CRP level before chemoradiotherapy (>8 mg/L), and their CSS rate was significantly worse than that in the remaining patients (P = 0.003). Multivariate analysis showed that CRP and tumor site were independent prognostic indicators for CSS, with a hazard ratio of 2.66 (95% confidence interval (CI), 1.22-5.82; P = 0.014) and a hazard ratio of 1.67 (95% CI, 1.01-2.77; P = 0.045), respectively. Of those with elevated CRP, the CRP levels of ten patients became normal after chemoradiotherapy, of whom four were alive with no evidence of recurrence or metastasis during the follow-up. By contrast, all six with no CRP normalization after chemoradiotherapy died within 3.8 years. The elevation of CRP before treatment predicts a poor prognosis in patients with locoregionally advanced laryngeal carcinoma treated with chemoradiotherapy.

Predicting protein submitochondria locations by combining different descriptors into the general form of Chou's pseudo amino acid composition.

Knowledge of the submitochondria location of protein is integral to understanding its function and a necessity in the proteomics era. In this work, a new submitochondria data set is constructed, and an approach for predicting protein submitochondria locations is proposed by combining the amino acid composition, dipeptide composition, reduced physicochemical properties, gene ontology, evolutionary information, and pseudo-average chemical shift. The overall prediction accuracy is 93.57% for the submitochondria location and 97.79% for the three membrane protein types in the mitochondria inner membrane using the algorithm of the increment of diversity combined with the support vector machine. The performance of the pseudo-average chemical shift is excellent. For contrast, the method is also used to predict submitochondria locations in the data set constructed by Du and Li; an accuracy of 94.95% is obtained by our method, which is better than that of other existing methods.

[Study on the genotoxicity of exhausts of diesel engine with ethanol-diesel blending fuel].

OBJECTIVE: To study the genotoxicity of components of diesel engine exhausts with ethanol-diesel blending fuel. To provide scientific arguments to find more economical and less polluted fuels. METHODS: Ames test, comet assay and GC-MS technique were used to test the genotoxicity and 16 kinds of PAHs on diesel engine exhausts with different proportions of ethanol (E0, E5, E10, E20). RESULTS: Both Ames test and comet assay were positive. It shows that diesel engine exhausts can lead to mutation and DNA damage, especially in pure diesel oil. But the content of 16 kinds of PAHs and DNA damage level decreased in exhausts of E5. With the increase of ethanol proportion in diesel oil, the content of 16 kinds of PAHs and DNA damage level increased. CONCLUSION: Compared with pure diesel oil and high proportion of ethanol fuel, E5 can reduce the genotoxicity and the brake specific exhausts of PAHs.