Prediction of Renal Function 1 Year After Transplantation Using Machine Learning Methods Based on Ultrasound Radiomics Combined With Clinical and Imaging Features.

Kidney transplantation is the most effective treatment for advanced chronic kidney disease (CKD). If the prognosis of transplantation can be predicted early after transplantation, it might improve the long-term survival of patients with transplanted kidneys. Currently, studies on the assessment and prediction of renal function by radiomics are limited. Therefore, the present study aimed to explore the value of ultrasound (US)-based imaging and radiomics features, combined with clinical features to develop and validate the models for predicting transplanted kidney function after 1 year (TKF-1Y) using different machine learning algorithms. A total of 189 patients were included and classified into the abnormal TKF-1Y group, and the normal TKF-1Y group based on their estimated glomerular filtration rate (eGFR) levels 1 year after transplantation. The radiomics features were derived from the US images of each case. Three machine learning methods were employed to establish different models for predicting TKF-1Y using selected clinical and US imaging as well as radiomics features from the training set. Two US imaging, four clinical, and six radiomics features were selected. Then, the clinical (including clinical and US image features), radiomics, and combined models were developed. The area under the curves (AUCs) of the models was 0.62 to 0.82 within the test set. Combined models showed statistically higher AUCs than the radiomics models (all p-values <.05). The prediction performance of different models was not significantly affected by the different machine learning algorithms (all p-values >.05). In conclusion, US imaging features combined with clinical features could predict TKF-1Y and yield an incremental value over radiomics features. A model integrating all available features may further improve the predictive efficacy. Different machine learning algorithms may not have a significant impact on the predictive performance of the model.

One-Dimensional CoO@C Core­Shell Nanostructures for Improved Lithium Storage Properties.

Exploring conversion-type anode materials with large and stable lithium storage properties as well as good rate performance still remains a great challenge. This work presents one-dimensional core­shell CoO@C nanostructures as high-performance anode materials for lithium ion batteries. TEM measurements show that interior voids and carbon shell can be observed in the core­shell nanostructures. Electrochemical impedance spectroscopy reveals that the coated carbon layer can remarkably reduce the charge transfer resistance of the electrochemical conversion reaction. Endowed with the combined advantages mentioned above, the present sample exhibits good electrochemical properties when tested as anode materials, such as high and stable lithium storage properties (877.2 mAh g⁻¹ after 100 cycles at 100 mA g⁻¹ and 661.5 mAh g⁻¹ after 500 cycles at 1 A g⁻¹), superior rate performance, and long cycling life (over 500 cycles at high current density of 1 A g⁻¹). These good performances make it a promising candidate for high-performance anode materials.

Neck circumference and future cardiovascular events in a high-risk population--A prospective cohort study.

BACKGROUND: The distribution of adipose tissue has been evaluated in relation to cardiovascular risk factors and biochemical components of the metabolic syndrome. Neck circumference (NC) has been shown to have a strong relationship with cardiovascular disease (CVD) and may be a novel indicator of CVD. The aim of this study was to compare the incidence of CVD events in cohorts with different NC distributions, and to correlate NC with future CVD events and relative mortality. METHODS: A prospective cohort study was performed on 12,151 high-risk cardiology outpatients from 2004 until 2014. Anthropometric parameters like body mass index, NC, waist circumference, and hip circumference were measured at baseline and follow-up and compared in different cohorts with high, medium, and low NC. Fatal and non-fatal CVD events were compared in the follow-up study, and survival analysis was conducted. Independent Chi-square tests were performed to compare the incidence of CVD events and mortality among the cohorts and analyze the interactions. RESULTS: The subjects comprised of 6696 women and 5819 men who completed a mean 8.8-year follow-up. All of the participants had two or more CVD risk factors at baseline. At the end of the study, 4049 CVD events had occurred in 2304 participants. The incidence of non-fatal CVD events was 14.08, 16,65, and 25.21 % in the low-NC, medium-NC, and high-NC cohorts, respectively (p < 0.001). The all-cause mortality was 9.77, 11.93, and 19.31 %, and CVD mortality, 4.00, 6.29, and 8.01 %, respectively (p < 0.001). Compared with baseline, the number of CVD risk factors in participants had increased from 2.6, 3.0, and 3.4 to 3.5, 4.1, and 4.7 in the low-, medium-, and high-NC cohorts (34, 36, and 38 %), respectively. The event-free survival rate was 95.32, 80.15, and 75.47 %, respectively. CONCLUSIONS: A higher NC indicated a higher incidence of future fatal and non-fatal CVD events and all-cause mortality in both male and female high-risk participants. CVD risk factors increased more in the higher NC group. NC as a novel indicator of CVD showed good predictive ability for CVD events and mortality in a high-risk population.

Pharmacokinetics, mass balance, and metabolism of [14C]envonalkib (TQ-B3139), a novel ALK tyrosine kinase inhibitor, in healthy Chinese subjects.

PURPOSE: Envonalkib (TQ-B3139) is a novel, potent anaplastic lymphoma kinase (ALK) tyrosine kinase inhibitor used to treat ALK-positive non-small cell lung cancer. This phase I mass balance study investigated the pharmacokinetics, metabolism, and excretion of 14C-radiolabeled envonalkib in healthy Chinese male subjects. METHODS: A single oral dose of 600 mg (150 µCi) [14C]envonalkib was administered to healthy male subjects under fasted state. Samples of blood, urine and feces were collected for quantitative determination of total radioactivity and unchanged envonalkib, and the metabolites identification. RESULTS: After dosing, the median Tmax of radioactivity was 4 h and the mean t1/2 was 65.2 h in plasma. The exposure of total radioactivity was much higher than that of unchanged envonalkib in plasma. The mean total recovery of the radiolabeled dose was 93.93% over 504 h post-dose, with 15.23% in urine and 78.71% in feces. Envonalkib underwent extensive metabolism and a total of 15 metabolites were identified in plasma, urine, and feces. Unchanged envonalkib and its major metabolite M315 were the main components in plasma, accounting for 20.37% and 33.33% of total plasma radioactivity. In urine, O-dealkylation metabolite M315 was the major component accounted for 7.98% of dose. In feces, 16.01% of dose was excreted as cysteine conjugate M434-1. Envonalkib was well tolerated and there were no serious adverse events observed in the study. CONCLUSION: Envonalkib was extensively metabolized prior to excretion and eliminated primarily as metabolites via feces.

Exploration of the molecular mechanism of insulin resistance in adipose tissue of patients with type 2 diabetes mellitus through a bioinformatic analysis.

BACKGROUND: We aimed to determine the cis-expression Quantitative Trait Loci (cis-eQTL) and trans-eQTL of differentially expressed genes (DEGs) in insulin resistance (IR) related pathways. METHODS: The expression profile data for insulin sensitivity (IS) and IR in the adipose tissue of patients with type 2 diabetes mellitus (T2DM) were acquired from the Gene Expression Omnibus databases. Then, the Gene set enrichment analysis (GSEA) and Gene set variation analysis (GSVA) methods were performed to identify the significant enrichment of potential Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways between IS and IR groups, and the Wilcoxon rank sum test was carried out to identify the DEGs related to KEGG pathways. Finally, the cis-eQTLs and trans-eQTLs that can affect the expression of DEGs were screened from the eQTLGen database. RESULTS: The GSEA and GSVA analysis indicated that the mTOR signaling pathway, insulin signaling pathway and T2DM had a strong correlation with the pathological process of T2DM. Furthermore, six genes (ACACA, GYS2, PCK1, PRKAR1A, SLC2A4, and VEGFA) were found to be significantly differentially expressed in IR-related pathways. Finally, we have identified a total of 1073 cis-eQTLs and 24 trans-eQTLs. CONCLUSIONS: We screened out six genes that were significantly differentially expressed in IR-related pathways, including ACACA, GYS2, PCK1, PRKAR1A, SLC2A4, and VEGFA. Moreover, we discovered that these six genes were affected by 1073 cis-eQTLs and 24 trans-eQTLs.

Identification of candidate biomarkers associated with gastric cancer prognosis based on an integrated bioinformatics analysis.

Background: This study sought to identify candidate biomarkers associated with gastric cancer (GC) prognosis based on an integrated bioinformatics analysis. Methods: First, the GSE54129 and GSE79973 data sets were downloaded from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) identified between the 2 data sets were screened using the limma software package in R, and the intersection DEGs were obtained by a Venn analysis. Subsequently, gene clustering and a functional analysis were performed to explore the roles of the DEGs. The protein-protein interaction (PPI) network of the genes in clusters was constructed using the Search Tool for the Retrieval of Interacting Genes/Proteins. A survival analysis evaluated the associations between the candidate genes and the overall survival of GC patients. A drug-gene interaction analysis and an external data set analysis were conducted using The Cancer Genome Atlas-Stomach Adenocarcinoma (TCGA-STAD) data set to validate the prognostic genes. Results: We extracted 421 intersection DEGs from the 2 GEO data sets. There were 5 gene clusters, and the functional analysis revealed that they were mainly associated with the extracellular matrix-receptor interaction pathway. The PPI interaction analysis identified the top 36 hub genes. The survival analysis revealed that 7 upregulated genes [i.e., platelet-derived growth factor receptor beta (PDGFRB), angiopoietin 2 (ANGPT2), vascular endothelial growth factor C (VEGFC), collagen type IV alpha 2 chain (COL4A2), collagen type IV alpha 1 chain (COL4A1), thrombospondin 1 (THBS1), and fibronectin 1 (FN1)] were associated with the survival prognosis of GC patients. The 20 drug-gene interaction pairs among the 4 genes and 18 drugs were obtained. Finally, TCGA-STAD data set was used to validate the expression levels of COL4A1, PDGFRB, and FN1. Conclusions: We found that 7 upregulated genes (i.e., PDGFRB, ANGPT2, VEGFC, COL4A2, COL4A1, THBS1, and FN1) were promising markers of prognosis in GC patients.

First-in-human phase I study of TQ-B3139 (CT-711) in advanced non-small cell lung cancer patients with ALK and ROS1 rearrangements.

BACKGROUND: TQ-B3139 is a novel ALK tyrosine kinase inhibitor (TKI) against a broad range of ALK mutations. The aim of this first-in-human phase I trial was to investigate the safety, tolerability, pharmacokinetics, and clinical efficacy of TQ-B3139 in ALK or ROS1 positive advanced NSCLC patients. METHODS: Following a 3 + 3 design, patients received escalating daily dose of TQ-B3139 (50-800 mg) continuously in 28-day cycles. Expansion stage started at dose of 200 mg twice daily (BID). The primary objectives were the safety, dose-limited toxicities (DLT) and recommended phase II dose (RP2D); secondary objectives included pharmacokinetics and antitumor activity. Non-obligatory tumor samples at baseline were collected and sequenced. RESULTS: The study enrolled 63 patients. Fifty-nine (93.4%) patients experienced treatment-related adverse events (TRAEs), mostly grade 1-2 vomiting (79.3%), diarrhea (76.1%) or nausea (68.2%). 1 (1/6) DLT occurred at 600 mg BID and 1 (1/3) at 800 mg BID. Based on safety and pharmacokinetics data, the RP2D was selected as 600 mg BID. At a dose level ≥200 mg BID, the overall response rate (ORR) was 76.7% (33/43), and the median progression free survival (mPFS) was 25.2 months (95%CI 11.9-NR) for TKI-naive patients. For TKI-treated patients, the ORR was 37.5% (6/16), and the mPFS was 5.4 months (95%CI 3.6-9.1). The ORR was 66.7% (2/3) in patients with ROS1 fusion at dose level ≥200 mg BID. In patients with measurable brain metastases, the intracranial ORR was 70% (7/10), with median intracranial PFS of 15.9 months. In TKI-treated patients, variant 3 and TP53 alteration were associated with poor PFS. CONCLUSIONS: TQ-B3139 was well-tolerated and exhibited promising anti-tumor activities in patients with ALK and ROS1 positive advanced NSCLC. CLINICAL TRIAL NUMBER: NCT03099330.

Identification of Key Genes Involved in Pancreatic Ductal Adenocarcinoma with Diabetes Mellitus Based on Gene Expression Profiling Analysis.

This study aimed to identify key genes involved in the progression of diabetic pancreatic ductal adenocarcinoma (PDAC). Two gene expression datasets (GSE74629 and GSE15932) were obtained from Gene Expression Omnibus. Then, differentially expressed genes (DEGs) between diabetic PDAC and non-diabetic PDAC were identified, followed by a functional analysis. Subsequently, gene modules related to DM were extracted by weighed gene co-expression network analysis. The protein-protein interaction (PPI) network for genes in significant modules was constructed and functional analyses were also performed. After that, the optimal feature genes were screened by support vector machine (SVM) recursive feature elimination and SVM classification model was built. Finally, survival analysis was conducted to identify prognostic genes. The correlations between prognostic genes and other clinical factors were also analyzed. Totally, 1546 DEGs with consistent change tendencies were identified and functional analyses showed they were strongly correlated with metabolic pathways. Furthermore, there were two significant gene modules, in which RPS27A and UBA52 were key genes. Functional analysis of genes in two gene modules revealed that these genes primarily participated in oxidative phosphorylation pathway. Additionally, 21 feature genes were closely related with diabetic PDAC and the corresponding SVM classifier markedly distinguished diabetic PDAC from non-diabetic PDAC patients. Finally, decreased KIF22 and PYGL levels had good survival outcomes for PDAC. Four genes (RPS27A, UBA52, KIF22 and PYGL) might be involved in the pathogenesis of diabetic PDAC. Furthermore, KIF22 and PYGL acted as prognostic biomarkers for diabetic PDAC.

Feasibility and Accuracy of a Fully Automated Right Ventricular Quantification Software With Three-Dimensional Echocardiography: Comparison With Cardiac Magnetic Resonance.

Background: A novel, fully automated right ventricular (RV) software for three-dimensional quantification of RV volumes and function was developed. The direct comparison of the software performance with cardiac magnetic resonance (CMR) was limited. Therefore, the aim of this study was to test the feasibility, accuracy, and reproducibility of a fully automated RV quantification software against CMR imaging as a reference. Methods: A total of 170 patients who underwent both CMR and three-dimensional echocardiography were enrolled. RV end-diastolic volume (RVEDV), RV end-systolic volume (RVESV), and RV ejection fraction (RVEF) were obtained using fully automated three-dimensional RV quantification software and compared with a CMR reference. For inter-technical agreement, Spearman correlation and Bland-Altman analysis were used. Results: The fully automated RV quantification software was feasible in 149 patients. RVEDV and RVESV were underestimated, and RVEF was overestimated compared with CMR values. RV measurements obtained from the manual editing method correlated better with CMR values than that without manual editing (RVEDV, 0.924 vs. 0.794: RVESV, 0.955 vs. 0.854; RVEF, 0.941 vs. 0.781 respectively, all p < 0.0001) with less bias and narrower limit of agreement (LOA). The bias and LOA for RV volumes and EF using the automated software without and with manual editing were greater in patients with severely impaired RV function or low frame rate than those with normal and mild impaired RV function, or high frame rate. The fully automated RV three-dimensional measurements were highly reproducible. Conclusion: The novel fully automated RV software shows good feasibility and reproducibility, and the measurements had a high correlation with CMR values. These findings support the routine application of the novel 3D automated RV software in clinical practice.

Comprehensive Assessment of Right Ventricular Function by Three-Dimensional Speckle-Tracking Echocardiography: Comparisons with Cardiac Magnetic Resonance Imaging.

BACKGROUND: Three-dimensional speckle-tracking echocardiography (3D-STE) has been increasingly used to quantify right ventricular (RV) function. However, direct comparisons of 3D-STE with cardiac magnetic resonance (CMR) imaging for evaluation of RV function are limited. This study aimed to test the feasibility and accuracy of 3D-STE for the quantification of RV volumes, ejection fraction (EF), and longitudinal strain in comparison with CMR imaging and to determine whether 3D-STE for RV strain is superior to two-dimensional (2D) STE in comparison with CMR imaging. METHODS: A total of 195 consecutive patients referred for both CMR imaging and echocardiography were studied. Right ventricular end-diastolic volume (RVEDV), RV end-systolic volume (RVESV), RVEF, and 3D RV longitudinal strain (3D-RVLS) of the free wall by 3D-STE and 2D-RVLS of the free wall by 2D-STE, were compared with CMR measurements. Pearson correlation and Bland-Altman analyses were used to assess the intertechnique agreement. RESULTS: Right ventricular 3D-STE was feasible in 174 patients (89%). Right ventricular volumes and EF determined by 3D-STE strongly correlated with CMR values (RVEDV, r = 0.94; RVESV, r = 0.96; RVEF, r = 0.91; all P < .001). Three-dimensional STE slightly underestimated the RV volumes and longitudinal strain and overestimated the RVEF. The 3D-RVLS values correlated better than 2D-RVLS values with CMR values (0.85 vs 0.64, P < .001) with smaller bias and narrower limits of agreement (bias: 2.0 and 2.6; limits of agreement: 8.5 and 12.5, respectively). The bias and limits of agreement for 3D-STE-obtained RVLS were increased in patients with RV dilation, RVEF < 45%, or lower frame rate compared with those with normal RV size, RVEF ≥ 45%, or higher frame rate, respectively. Right ventricular 3D-STE measurements were highly reproducible. CONCLUSIONS: The 3D-STE measurements of RV volumes, EF, and longitudinal strain are highly feasible and reproducible, and data measured by 3D-STE correlate strongly with those determined using CMR imaging. Thus, 3D-STE may be a valid alternative to CMR imaging for the quantification of RV function in everyday clinical practice.

Histochemical evaluation of alkaloids in rhizome of Coptis chinensis using laser microdissection and liquid chromatography/mass spectrometry.

Traditional macroscopic and microscopic identification methods of medicinal materials are economical and practical, but usually experience-based due to few chemical supports. Here histochemical evaluation on bioactive components of Coptidis Rhizoma (CR) in anatomic sections using laser microdissection and liquid chromatography-mass spectrometry (LMD-LC-MS) was developed to correlate the inner quality and outer features of materials from different growing areas. Results of a total 33 peaks representing potential different alkaloids were detected and 8 common peaks were identified as the major alkaloids, namely magnoflorine, thalifendine, columbamine, epiberberine, jatrorrhizine, coptisine, palmatine, and berberine. Six major alkaloids were quantified in the top and middle sections of raw materials and in their tissues and cells at the same time. Histochemical analyses showed consistent results with direct determination in raw materials and explained the reason why top sections of all samples contained higher contents of alkaloids by giving out attributions of each alkaloid in different anatomic sections. Besides, results manifested the distribution and accumulation rules of alkaloids in diverse tissues and cells of CR. This study demonstrates an effective and scientific way to correlate bioactive components and morphological features of medicinal materials, which is beneficial to future research, agriculture and application.

Visualization of network target crosstalk optimizes drug synergism in myocardial ischemia.

Numerous drugs and compounds have been validated as protecting against myocardial ischemia (MI), a leading cause of heart failure; however, synergistic possibilities among them have not been systematically explored. Thus, there appears to be significant room for optimization in the field of drug combination therapy for MI. Here, we propose an easy approach for the identification and optimization of MI-related synergistic drug combinations via visualization of the crosstalk between networks of drug targets corresponding to different drugs (each drug has a unique network of targets). As an example, in the present study, 28 target crosstalk networks (TCNs) of random pairwise combinations of 8 MI-related drugs (curcumin, capsaicin, celecoxib, raloxifene, silibinin, sulforaphane, tacrolimus, and tamoxifen) were established to illustrate the proposed method. The TCNs revealed a high likelihood of synergy between curcumin and the other drugs, which was confirmed by in vitro experiments. Further drug combination optimization showed a synergistic protective effect of curcumin, celecoxib, and sililinin in combination against H2O2-induced ischemic injury of cardiomyocytes at a relatively low concentration of 500 nM. This result is in agreement with the earlier finding of a denser and modular functional crosstalk between their networks of targets in the regulation of cell apoptosis. Our study offers a simple approach to rapidly search for and optimize potent synergistic drug combinations, which can be used for identifying better MI therapeutic strategies. Some new light was also shed on the characteristic features of drug synergy, suggesting that it is possible to apply this method to other complex human diseases.