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

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.

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.

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.

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.

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

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.

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.

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.

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).

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.

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)/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.

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 (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.

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).

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.

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.

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.

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.

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.

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.

Using random forest to classify T-cell epitopes based on amino acid properties and molecular features.

T-lymphocyte (T-cell) is a very important component in human immune system. T-cell epitopes can be used for the accurately monitoring the immune responses which activation by major histocompatibility complex (MHC), and rationally designing vaccines. Therefore, accurate prediction of T-cell epitopes is crucial for vaccine development and clinical immunology.

Selective of informative metabolites using random forests based on model population analysis.

One of the main goals of metabolomics studies is to discover informative metabolites or biomarkers, which may be used to diagnose diseases and to find out pathology. Sophisticated feature selection approaches are required to extract the information hidden in such complex 'omics' data. In this study, it is proposed a new and robust selective method by combining random forests (RF) with model population analysis (MPA), for selecting informative metabolites from three metabolomic datasets.

PyDPI: freely available python package for chemoinformatics, bioinformatics, and chemogenomics studies.

The rapidly increasing amount of publicly available data in biology and chemistry enables researchers to revisit interaction problems by systematic integration and analysis of heterogeneous data. Herein, we developed a comprehensive python package to emphasize the integration of chemoinformatics and bioinformatics into a molecular informatics platform for drug discovery. PyDPI (drug-protein interaction with Python) is a powerful python toolkit for computing commonly used structural and physicochemical features of proteins and peptides from amino acid sequences, molecular descriptors of drug molecules from their topology, and protein-protein interaction and protein-ligand interaction descriptors.

A perspective demonstration on the importance of variable selection in inverse calibration for complex analytical systems.

Classical calibration and inverse calibration are two kinds of multivariate calibration in chemical modeling. They use strategies of modeling in component spectral space and in measured variable space, respectively. However, the intrinsic difference between these two calibration models is not fully investigated.

Large-scale prediction of human kinase-inhibitor interactions using protein sequences and molecular topological structures.

The kinase family is one of the largest target families in the human genome. The family's key function in signal transduction for all organisms makes it a very attractive target class for the therapeutic interventions in many diseases states such as cancer, diabetes, inflammation and arthritis. A first step toward accelerating kinase drug discovery process is to fast identify whether a chemical and a kinase interact or not.