Transarterial embolization with hepatectomy for ruptured hepatocellular carcinoma: a meta-analysis.

PURPOSE: To compare the clinical effectiveness between transarterial embolization (TAE) with staged hepatectomy (SH) and emergency hepatectomy (EH) for ruptured hepatocellular carcinoma (HCC). MATERIAL AND METHODS: Pubmed, Embase, and Cochrane Library databases were screened for eligible publications from the inception of the databases till February 2021. RESULTS: This meta-analysis included seven studies comprising 162 patients who underwent TAE with SH and 266 patients who underwent EH. The pooled intraoperative blood loss was less in the TAE with SH cohort, as compared to the EH cohort without significant difference (p = .20). The pooled blood transfer rate (p<.00001), blood transfer volume (p = .002), and 30-day patient death (p = .04) were all markedly reduced in the TAE with SH cohort versus the EH cohort. No significant differences in surgery duration (p = .27), hospital stay period (p = .81), complication rate (p = 0.92), disease-free survival (DFS) (p = .79), and overall survival (OS) (p = 0.28) were found between the two groups. CONCLUSIONS: Compared with EH for ruptured HCC, TAE with SH could effectively decrease intraoperative blood loss and 30-day mortality. However, the long-term DFS and OS might not be beneficial to preoperative TAE.

Coil localization assisted wedge resection for pulmonary nodules in patients with malignant history.

ABSTRACT: We describe the clinical efficacy of coil localization (CL) assisted video-assisted thoracoscopic surgery (VATS) wedge resection (WR) for pulmonary nodules (PNs) in patients having a history of malignancy.In a total of 16 patients having PNs and malignant history, treatment was carried out using computed tomography (CT)-guided CL and subsequent VATS-guided WR procedures from November 2015 to December 2019. Technical success of CL, WR, and long-term outcomes was analyzed.A total of 21 PNs were localized (1.3 PNs per patient). A 100% technical success rate was achieved in this study for CT-guided CL. Each PN was localized with 1 coil. Two and 2 patients experienced pneumothorax and hemoptysis, respectively. VATS-guided WR also achieved a 100% technical success rate. Additional lobectomy was performed in 2 patients due to the invasive adenocarcinoma. The final diagnoses of these 21 PNs were adenocarcinoma (T1N0M0, n = 8), adenocarcinoma in situ (n = 2), pre-cancerosis (n = 1), metastasis (n = 2), and benign (n = 8). All patients underwent CT follow-up for 6 to 48 months. All patients were alive during the follow-up. The cumulative 6-, 12, and 24-month disease-free survival rates were 100%, 92.9%, and 47.3%, respectively. The median disease-free survival was 27.9 months.Pre-operative CT-guided CL can be safely and conveniently used to facilitate a high success rate of VATS-guided WR for PNs in patients with a malignant history. Among the PNs in patients with malignant history, primary lung cancer also occupied approximately half of the PNs.

Clinical-radiological predictive model in differential diagnosis of small (≤ 20 mm) solitary pulmonary nodules.

BACKGROUND: There is a lack of clinical-radiological predictive models for the small (≤ 20 mm) solitary pulmonary nodules (SPNs). We aim to establish a clinical-radiological predictive model for differentiating malignant and benign small SPNs. MATERIALS AND METHODS: Between January 2013 and December 2018, a retrospective cohort of 250 patients with small SPNs was used to construct the predictive model. A second retrospective cohort of 101 patients treated between January 2019 and December 2020 was used to independently test the model. The model was also compared to two other models that had previously been identified. RESULTS: In the training group, 250 patients with small SPNs including 156 (62.4%) malignant SPNs and 94 (37.6%) benign SPNs patients were included. Multivariate logistic regression analysis indicated that older age, pleural retraction sign, CT bronchus sign, and higher CEA level were the risk factors of malignant small SPNs. The predictive model was established as: X = - 10.111 + [0.129 × age (y)] + [1.214 × pleural retraction sign (present = 1; no present = 0)] + [0.985 × CT bronchus sign (present = 1; no present = 0)] + [0.21 × CEA level (ug/L)]. Our model had a significantly higher region under the receiver operating characteristic (ROC) curve (0.870; 50% CI: 0.828-0.913) than the other two models. CONCLUSIONS: We established and validated a predictive model for estimating the pre-test probability of malignant small SPNs, that can help physicians to choose and interpret the outcomes of subsequent diagnostic tests.

REBET: a method to determine the number of cell clusters based on batch effect removal.

In single-cell RNA-seq (scRNA-seq) data analysis, a fundamental problem is to determine the number of cell clusters based on the gene expression profiles. However, the performance of current methods is still far from satisfactory, presumably due to their limitations in capturing the expression variability among cell clusters. Batch effects represent the undesired variability between data measured in different batches. When data are obtained from different labs or protocols batch effects occur. Motivated by the practice of batch effect removal, we considered cell clusters as batches. We hypothesized that the number of cell clusters (i.e. batches) could be correctly determined if the variances among clusters (i.e. batch effects) were removed. We developed a new method, namely, removal of batch effect and testing (REBET), for determining the number of cell clusters. In this method, cells are first partitioned into k clusters. Second, the batch effects among these k clusters are then removed. Third, the quality of batch effect removal is evaluated with the average range of normalized mutual information (ARNMI), which measures how uniformly the cells with batch-effects-removal are mixed. By testing a range of k values, the k value that corresponds to the lowest ARNMI is determined to be the optimal number of clusters. We compared REBET with state-of-the-art methods on 32 simulated datasets and 14 published scRNA-seq datasets. The results show that REBET can accurately and robustly estimate the number of cell clusters and outperform existing methods. Contact: H.D.L. (hongdong@csu.edu.cn) or Q.S.X. (qsxu@csu.edu.cn).

Computed tomography-guided coil localization for scapula-blocked pulmonary nodules: A trans-scapular approach.

ABSTRACT: To evaluate the clinical efficiency, feasibility, and safety of computed tomography (CT)-guided trans-scapular coil localization (TSCL) approach to treating scapula-blocked pulmonary nodules (SBPNs).In total, 105 patients with pulmonary nodules underwent CT-guided CL and subsequent video-assisted thoracoscopic surgery (VATS)-guided wedge resection (WR) between January 2016 and July 2020. Six of these patients (5.7%) had SBPNs that led them to undergo CT-guided TSCL. Rates of technical success and localization-related complications were then recorded and analyzed.CT-guided TSCL was associated with a 100% technical success rate, with one coil being placed per patient. The median CT-guided TSCL duration was 15 min. No patients experienced any complications associated with this procedure, and subsequent VATS-guided WR of SBPNs was 100% technically successful. In two patients with invasive adenocarcinoma, additional lobectomy was performed. Median VATS duration and intraoperative blood loss were 120 min and 150 mL, respectively.In summary, these results indicate that CT-guided TSCL could be easily and safely implemented to achieve high success rate when performing the VATS-guided WR of SBPNs.

Kernel density-based likelihood ratio tests for linear regression models.

In this article, we develop a so-called profile likelihood ratio test (PLRT) based on the estimated error density for the multiple linear regression model. Unlike the existing likelihood ratio test (LRT), our proposed PLRT does not require any specification on the error distribution. The asymptotic properties are developed and the Wilks phenomenon is studied. Simulation studies are conducted to examine the performance of the PLRT. It is observed that our proposed PLRT generally outperforms the existing LRT, empirical likelihood ratio test and the weighted profile likelihood ratio test in sense that (i) its type I error rates are closer to the prespecified nominal level; (ii) it generally has higher powers; (iii) it performs satisfactorily when moments of the error do not exist (eg, Cauchy distribution); and (iv) it has higher probability of correctly selecting the correct model in the multiple testing problem. A mammalian eye gene expression dataset and a concrete compressive strength dataset are analyzed to illustrate our methodologies.

Computed tomography-guided lung biopsy: a randomized controlled trial of low-dose versus standard-dose protocol.

OBJECTIVES: To assess the relative diagnostic utility of low- and standard-dose computed tomography (CT)-guided lung biopsy. METHODS: In this single-center, single-blind, prospective, randomized controlled trial, patients were enrolled between November 2016 and June 2017. Enrolled study participants were randomly selected to undergo either low- or standard-dose CT-guided lung biopsy. Diagnostic accuracy was the primary study endpoint, whereas technical success, radiation dose, and associated complications were secondary study endpoints. RESULTS: In total, 280 patients underwent study enrollment and randomization, with 271 (low-dose group, 135; standard-dose group, 136) receiving the assigned interventions. Both groups had a 100% technical success rate for CT-guided lung biopsy, and complication rates were similar between groups (p > 0.05). The mean dose-length product (36.0 ± 14.1 mGy cm vs. 361.8 ± 108.0 mGy cm, p < 0.001) and effective dose (0.5 ± 0.2 mSv vs. 5.1 ± 1.5 mSv, p < 0.001) were significantly reduced in the low-dose group participants. Sensitivity, specificity, and overall diagnostic accuracy rates in the low-dose group were 91.8%, 100%, and 94.6%, respectively, whereas in the standard-dose group, the corresponding values were 89.6%, 100%, and 92.4%, respectively. These results indicated that diagnostic performance did not differ significantly between the 2 groups. Using univariate and multivariate analyses, we found larger lesion size (p = 0.038) and procedure-related pneumothorax (p = 0.033) to both be independent predictors of diagnostic failure. CONCLUSIONS: Our results demonstrate that low-dose CT-guided lung biopsy can yield comparable diagnostic accuracy to standard-dose CT guidance, while significantly reducing the radiation dose delivered to patients. TRIAL REGISTRATION: ClinicalTrials.gov NCT02971176 KEY POINTS: • Low-dose CT-guided lung biopsy is a safe and simple method for diagnosis of lung lesions. • Low-dose CT-guided lung biopsy can yield comparable diagnostic accuracy to standard-dose CT guidance. • Low-dose CT-guided lung biopsy can achieve a 90% reduction in radiation exposure when compared with standard-dose CT guidance.

A hybrid variable selection strategy based on continuous shrinkage of variable space in multivariate calibration.

When analyzing high-dimensional near-infrared (NIR) spectral datasets, variable selection is critical to improving models' predictive abilities. However, some methods have many limitations, such as a high risk of overfitting, time-intensiveness, or large computation demands, when dealing with a high number of variables. In this study, we propose a hybrid variable selection strategy based on the continuous shrinkage of variable space which is the core idea of variable combination population analysis (VCPA). The VCPA-based hybrid strategy continuously shrinks the variable space from big to small and optimizes it based on modified VCPA in the first step. It then employs iteratively retaining informative variables (IRIV) and a genetic algorithm (GA) to carry out further optimization in the second step. It takes full advantage of VCPA, GA, and IRIV, and makes up for their drawbacks in the face of high numbers of variables. Three NIR datasets and three variable selection methods including two widely-used methods (competitive adaptive reweighted sampling, CARS and genetic algorithm-interval partial least squares, GA-iPLS) and one hybrid method (variable importance in projection coupled with genetic algorithm, VIP-GA) were used to investigate the improvement of VCPA-based hybrid strategy. The results show that VCPA-GA and VCPA-IRIV significantly improve model's prediction performance when compared with other methods, indicating that the modified VCPA step is a very efficient way to filter the uninformative variables and VCPA-based hybrid strategy is a good and promising strategy for variable selection in NIR. The MATLAB source codes of VCPA-GA and VCPA-IRIV can be freely downloaded in the website: https://cn.mathworks.com/matlabcentral/profile/authors/5526470-yonghuan-yun.

Intramolecular Imino-ene Reaction of 2H-azirines with Alkenes: Rapid Construction of Spiro NH Aziridines from Vinyl Azides.

A range of novel (poly)cyclic alkaloids incorporating an unprecedented 1,5-diazaspiro[2.4]heptane core that carry a spiro NH aziridine moiety and a 7-vinyl group are constructed from the thermal reaction of vinyl azides with tethered alkenes. Vinyl azides are converted to 2H-azirines in situ, which serve as enophiles for intramolecular imino-ene reactions with suitable alkenes. High stereoselectivity and specificity have been achieved for this novel intramolecular imino-ene reaction of azirines.

Copper-Catalyzed Borylative Cyclization of Substituted N-(2-Vinylaryl)benzaldimines.

A t-BuOCu-initiated reaction sequence of styrene borometalation and intramolecular imine addition has been achieved using a Cu(OTf)2/dppf combination as catalyst. The product of this reaction cascade is a useful 2,3-disubstituted indoline bearing a versatile boryl moiety and is formed with sole cis-selectivity. To account for the observation of the exclusive formation of cis-stereoisomers, a transition state featuring copper-imine coordination is suggested. The application to the synthesis of antioxidant tetrahydroindenoindoles is described.

Prediction of Radix Astragali Immunomodulatory Effect of CD80 Expression from Chromatograms by Quantitative Pattern-Activity Relationship.

The current use of a single chemical component as the representative quality control marker of herbal food supplement is inadequate. In this CD80-Quantitative-Pattern-Activity-Relationship (QPAR) study, we built a bioactivity predictive model that can be applicable for complex mixtures. Through integrating the chemical fingerprinting profiles of the immunomodulating herb Radix Astragali (RA) extracts, and their related biological data of immunological marker CD80 expression on dendritic cells, a chemometric model using the Elastic Net Partial Least Square (EN-PLS) algorithm was established. The EN-PLS algorithm increased the biological predictive capability with lower value of RMSEP (11.66) and higher values of Rp2 (0.55) when compared to the standard PLS model. This CD80-QPAR platform provides a useful predictive model for unknown RA extract's bioactivities using the chemical fingerprint inputs. Furthermore, this bioactivity prediction platform facilitates identification of key bioactivity-related chemical components within complex mixtures for future drug discovery and understanding of the batch-to-batch consistency for quality clinical trials.

A new strategy to prevent over-fitting in partial least squares models based on model population analysis.

Partial least squares (PLS) is one of the most widely used methods for chemical modeling. However, like many other parameter tunable methods, it has strong tendency of over-fitting. Thus, a crucial step in PLS model building is to select the optimal number of latent variables (nLVs). Cross-validation (CV) is the most popular method for PLS model selection because it selects a model from the perspective of prediction ability. However, a clear minimum of prediction errors may not be obtained in CV which makes the model selection difficult. To solve the problem, we proposed a new strategy for PLS model selection which combines the cross-validated coefficient of determination (Qcv(2)) and model stability (S). S is defined as the stability of PLS regression vectors which is obtained using model population analysis (MPA). The results show that, when a clear maximum of Qcv(2) is not obtained, S can provide additional information of over-fitting and it helps in finding the optimal nLVs. Compared with other regression vector based indictors such as the Euclidean 2-norm (B2), the Durbin Watson statistic (DW) and the jaggedness (J), S is more sensitive to over-fitting. The model selected by our method has both good prediction ability and stability.

Identifying bioactive components in natural products through chromatographic fingerprint.

Bioactive component identification is a crucial issue in search for new drug leads. We provide a new strategy to search for bioactive components based on Sure Independence Screening (SIS) and interval PLS (iPLS). The method, which is termed as SIS-iPLS, is not only able to find out the chief bioactive components, but also able to judge how many components should be there responsible for the total bioactivity. The method is totally "data-driven" with no need for prior knowledge about the unknown mixture analyzed, therefore especially suitable for effect-directed work like bioassay-guided fractionation. Two data sets, a synthetic mixture system of twelve components and a suite of Radix Puerariae Lobatae extracts samples, are used to test the identification ability of the SIS-iPLS method.

Using variable combination population analysis for variable selection in multivariate calibration.

Variable (wavelength or feature) selection techniques have become a critical step for the analysis of datasets with high number of variables and relatively few samples. In this study, a novel variable selection strategy, variable combination population analysis (VCPA), was proposed. This strategy consists of two crucial procedures. First, the exponentially decreasing function (EDF), which is the simple and effective principle of 'survival of the fittest' from Darwin's natural evolution theory, is employed to determine the number of variables to keep and continuously shrink the variable space. Second, in each EDF run, binary matrix sampling (BMS) strategy that gives each variable the same chance to be selected and generates different variable combinations, is used to produce a population of subsets to construct a population of sub-models. Then, model population analysis (MPA) is employed to find the variable subsets with the lower root mean squares error of cross validation (RMSECV). The frequency of each variable appearing in the best 10% sub-models is computed. The higher the frequency is, the more important the variable is. The performance of the proposed procedure was investigated using three real NIR datasets. The results indicate that VCPA is a good variable selection strategy when compared with four high performing variable selection methods: genetic algorithm-partial least squares (GA-PLS), Monte Carlo uninformative variable elimination by PLS (MC-UVE-PLS), competitive adaptive reweighted sampling (CARS) and iteratively retains informative variables (IRIV). The MATLAB source code of VCPA is available for academic research on the website: http://www.mathworks.com/matlabcentral/fileexchange/authors/498750.

protr/ProtrWeb: R package and web server for generating various numerical representation schemes of protein sequences.

UNLABELLED: Amino acid sequence-derived structural and physiochemical descriptors are extensively utilized for the research of structural, functional, expression and interaction profiles of proteins and peptides. We developed protr, a comprehensive R package for generating various numerical representation schemes of proteins and peptides from amino acid sequence. The package calculates eight descriptor groups composed of 22 types of commonly used descriptors that include about 22 700 descriptor values. It allows users to select amino acid properties from the AAindex database, and use self-defined properties to construct customized descriptors. For proteochemometric modeling, it calculates six types of scales-based descriptors derived by various dimensionality reduction methods. The protr package also integrates the functionality of similarity score computation derived by protein sequence alignment and Gene Ontology semantic similarity measures within a list of proteins, and calculates profile-based protein features based on position-specific scoring matrix. We also developed ProtrWeb, a user-friendly web server for calculating descriptors presented in the protr package. AVAILABILITY AND IMPLEMENTATION: The protr package is freely available from CRAN: http://cran.r-project.org/package=protr, ProtrWeb, is freely available at http://protrweb.scbdd.com/.

Rcpi: R/Bioconductor package to generate various descriptors of proteins, compounds and their interactions.

UNLABELLED: In chemoinformatics and bioinformatics fields, one of the main computational challenges in various predictive modeling is to find a suitable way to effectively represent the molecules under investigation, such as small molecules, proteins and even complex interactions. To solve this problem, we developed a freely available R/Bioconductor package, called Compound-Protein Interaction with R (Rcpi), for complex molecular representation from drugs, proteins and more complex interactions, including protein-protein and compound-protein interactions. Rcpi could calculate a large number of structural and physicochemical features of proteins and peptides from amino acid sequences, molecular descriptors of small molecules from their topology and protein-protein interaction and compound-protein interaction descriptors. In addition to main functionalities, Rcpi could also provide a number of useful auxiliary utilities to facilitate the user's need. With the descriptors calculated by this package, the users could conveniently apply various statistical machine learning methods in R to solve various biological and drug research questions in computational biology and drug discovery. AVAILABILITY AND IMPLEMENTATION: Rcpi is freely available from the Bioconductor site (http://bioconductor.org/packages/release/bioc/html/Rcpi.html).

Prediction of peptide fragment ion mass spectra by data mining techniques.

Accurate prediction of peptide fragment ion mass spectra is one of the critical factors to guarantee confident peptide identification by protein sequence database search in bottom-up proteomics. In an attempt to accurately and comprehensively predict this type of mass spectra, a framework named MS(2)PBPI is proposed. MS(2)PBPI first extracts fragment ions from large-scale MS/MS spectra data sets according to the peptide fragmentation pathways and uses binary trees to divide the obtained bulky data into tens to more than 1000 regions. For each adequate region, stochastic gradient boosting tree regression model is constructed. By constructing hundreds of these models, MS(2)PBPI is able to predict MS/MS spectra for unmodified and modified peptides with reasonable accuracy. Moreover, high consistency between predicted and experimental MS/MS spectra derived from different ion trap instruments with low and high resolving power is achieved. MS(2)PBPI outperforms existing algorithms MassAnalyzer and PeptideART.

A strategy that iteratively retains informative variables for selecting optimal variable subset in multivariate calibration.

Nowadays, with a high dimensionality of dataset, it faces a great challenge in the creation of effective methods which can select an optimal variables subset. In this study, a strategy that considers the possible interaction effect among variables through random combinations was proposed, called iteratively retaining informative variables (IRIV). Moreover, the variables are classified into four categories as strongly informative, weakly informative, uninformative and interfering variables. On this basis, IRIV retains both the strongly and weakly informative variables in every iterative round until no uninformative and interfering variables exist. Three datasets were employed to investigate the performance of IRIV coupled with partial least squares (PLS). The results show that IRIV is a good alternative for variable selection strategy when compared with three outstanding and frequently used variable selection methods such as genetic algorithm-PLS, Monte Carlo uninformative variable elimination by PLS (MC-UVE-PLS) and competitive adaptive reweighted sampling (CARS). The MATLAB source code of IRIV can be freely downloaded for academy research at the website: http://code.google.com/p/multivariate-calibration/downloads/list.

Computational Prediction of DrugTarget Interactions Using Chemical, Biological, and Network Features.

Drugtarget interactions (DTIs) are central to current drug discovery processes. Efforts have been devoted to the development of methodology for predicting DTIs and drugtarget interaction networks. Most existing methods mainly focus on the application of information about drug or protein structure features. In the present work, we proposed a computational method for DTI prediction by combining the information from chemical, biological and network properties. The method was developed based on a learning algorithm-random forest (RF) combined with integrated features for predicting DTIs. Four classes of drugtarget interaction networks in humans involving enzymes, ion channels, G-protein-coupled receptors (GPCRs) and nuclear receptors, are independently used for establishing predictive models. The RF models gave prediction accuracy of 93.52 %, 94.84 %, 89.68 % and 84.72 % for four pharmaceutically useful datasets, respectively. The prediction ability of our approach is comparative to or even better than that of other DTI prediction methods. These comparative results demonstrated the relevance of the network topology as source of information for predicting DTIs. Further analysis confirmed that among our top ranked predictions of DTIs, several DTIs are supported by databases, while the others represent novel potential DTIs. We believe that our proposed approach can help to limit the search space of DTIs and provide a new way towards repositioning old drugs and identifying targets.

Chitosan oligosaccharides block LPS-induced O-GlcNAcylation of NF-κB and endothelial inflammatory response.

It is known that chitosan oligosaccharides (COS) suppress LPS-induced vascular endothelial inflammatory response by mechanism involving NF-κB blockade. It remains unknown how COS inhibit NF-κB. We provided evidence both in cultured endothelial cells and mouse model supporting a new mechanism. Regardless of the endothelial cell types, the LPS-induced NF-κB-dependent inflammatory gene expression was suppressed by COS, which was associated with reduced NF-κB nucleus translocation. LPS enhanced O-GlcNAc modification of NF-κB/p65 and activated NF-κB pathway, which could be prevented either by siRNA knockdown of O-GlcNAc transferase (OGT) or pretreatment with COS. Inhibition of either mitogen-activated protein kinase or superoxide generation abolishes LPS-induced NF-κB O-GlcNAcylation. Consistently, aortic tissues from LPS-treated mice presented enhanced NF-κB/p65 O-GlcNAcylation in association with upregulated gene expression of inflammatory cytokines in vascular tissues; however, pre-administration of COS prevented these responses. In conclusion, COS decreased OGT-dependent O-GlcNAcylation of NF-κB and thereby attenuated LPS-induced vascular endothelial inflammatory response.