2-(4-Iodo­phen­yl)-1,2,3,4-tetra­hydro­isoquinoline-1-carbonitrile

In the title compound, C16H13IN2, the benzene ring of the tetra­hydro­isoquinoline moiety makes a dihedral angle of 45.02 (9)° with the benzene ring of the 4-iodo­phenyl fragment. The N atom and the adjacent unsubstituted C atom of the tetra­hydro­isoquinoline unit are displaced by 0.294 (2) and 0.441 (3) Å, respectively, from the plane through the remaining eight C atoms. In the crystal, pairs of adjacent mol­ecules are linked into dimers by weak inter­molecular C—H⋯π inter­actions

Atherosclerosis T1-weighted characterization (CATCH): evaluation of the accuracy for identifying intraplaque hemorrhage with histological validation in carotid and coronary artery specimens

Background: Coronary high intensity plaques (CHIPs) detected using cardiovascular magnetic resonance (CMR) coronary atherosclerosis T1-weighted characterization with integrated anatomical reference (CATCH) have been shown to be positively associated with high-risk morphology observed on intracoronary optical coherence tomography (OCT). This study sought to validate whether CHIPs detected on CATCH indicate the presence of intraplaque hemorrhage (IPH) through ex vivo imaging of carotid and coronary plaque specimens, with histopathology as the standard reference. Methods: Ten patients scheduled to undergo carotid endarterectomy underwent CMR with the conventional T1-weighted (T1w) sequence. Eleven carotid atherosclerotic plaques removed at carotid endarterectomy and six coronary artery endarterectomy specimens removed from patients undergoing coronary artery bypass grafting (CABG) were scanned ex vivo using both the conventional T1w sequence and CATCH. Both in vivo and ex vivo images were examined for the presence of IPH. The sensitivity, specificity, and Cohen Kappa (k) value of each scan were calculated using matched histological sections as the reference. k value between each scan in the discrimination of IPH was also computed. Results: A total of 236 in vivo locations, 328 ex vivo and matching histology locations were included for the analysis. Sensitivity, specificity, and k value were 76.7%, 95.3%, and 0.75 for in vivo T1w imaging, 77.2%, 97.4%, and 0.78 for ex vivo T1w imaging, and 95.0%, 92.1%, and 0.84 for ex vivo CATCH, respectively. Moderate agreement was reached between in vivo T1w imaging, ex vivo T1w imaging, and ex vivo CATCH for the detection of IPH: between in vivo T1w imaging and ex vivo CATCH (k = 0.68), between ex vivo T1w imaging and ex vivo CATCH (k = 0.74), between in vivo T1w imaging and ex vivo T1w imaging (k = 0.83). None of the coronary artery plaque locations showed IPH. Conclusion: This study demonstrated that carotid CHIPs detected by CATCH can be used to assess for IPH, a high-risk plaque feature

Evaluation of appendicitis risk prediction models in adults with suspected appendicitis

Background Appendicitis is the most common general surgical emergency worldwide, but its diagnosis remains challenging. The aim of this study was to determine whether existing risk prediction models can reliably identify patients presenting to hospital in the UK with acute right iliac fossa (RIF) pain who are at low risk of appendicitis. Methods A systematic search was completed to identify all existing appendicitis risk prediction models. Models were validated using UK data from an international prospective cohort study that captured consecutive patients aged 16–45 years presenting to hospital with acute RIF in March to June 2017. The main outcome was best achievable model specificity (proportion of patients who did not have appendicitis correctly classified as low risk) whilst maintaining a failure rate below 5 per cent (proportion of patients identified as low risk who actually had appendicitis). Results Some 5345 patients across 154 UK hospitals were identified, of which two‐thirds (3613 of 5345, 67·6 per cent) were women. Women were more than twice as likely to undergo surgery with removal of a histologically normal appendix (272 of 964, 28·2 per cent) than men (120 of 993, 12·1 per cent) (relative risk 2·33, 95 per cent c.i. 1·92 to 2·84; P < 0·001). Of 15 validated risk prediction models, the Adult Appendicitis Score performed best (cut‐off score 8 or less, specificity 63·1 per cent, failure rate 3·7 per cent). The Appendicitis Inflammatory Response Score performed best for men (cut‐off score 2 or less, specificity 24·7 per cent, failure rate 2·4 per cent). Conclusion Women in the UK had a disproportionate risk of admission without surgical intervention and had high rates of normal appendicectomy. Risk prediction models to support shared decision‐making by identifying adults in the UK at low risk of appendicitis were identified

Improving the sensitivity of long read overlap detection using grouped short k-mer matches

Abstract Background Single-molecule, real-time sequencing (SMRT) developed by Pacific BioSciences produces longer reads than second-generation sequencing technologies such as Illumina. The increased read length enables PacBio sequencing to close gaps in genome assembly, reveal structural variations, and characterize the intra-species variations. It also holds the promise to decipher the community structure in complex microbial communities because long reads help metagenomic assembly. One key step in genome assembly using long reads is to quickly identify reads forming overlaps. Because PacBio data has higher sequencing error rate and lower coverage than popular short read sequencing technologies (such as Illumina), efficient detection of true overlaps requires specially designed algorithms. In particular, there is still a need to improve the sensitivity of detecting small overlaps or overlaps with high error rates in both reads. Addressing this need will enable better assembly for metagenomic data produced by third-generation sequencing technologies. Results In this work, we designed and implemented an overlap detection program named GroupK, for third-generation sequencing reads based on grouped k-mer hits. While using k-mer hits for detecting reads’ overlaps has been adopted by several existing programs, our method uses a group of short k-mer hits satisfying statistically derived distance constraints to increase the sensitivity of small overlap detection. Grouped k-mer hit was originally designed for homology search. We are the first to apply group hit for long read overlap detection. The experimental results of applying our pipeline to both simulated and real third-generation sequencing data showed that GroupK enables more sensitive overlap detection, especially for datasets of low sequencing coverage. Conclusions GroupK is best used for detecting small overlaps for third-generation sequencing data. It provides a useful supplementary tool to existing ones for more sensitive and accurate overlap detection. The source code is freely available at https://github.com/Strideradu/GroupK

Detection of MET Gene Copy Number in Cancer Samples Using the Droplet Digital PCR Method.

The analysis of MET gene copy number (CN) has been considered to be a potential biomarker to predict the response to MET-targeted therapies in various cancers. However, the current standard methods to determine MET CN are SNP 6.0 in the genomic DNA of cancer cell lines and fluorescence in situ hybridization (FISH) in tumor models, respectively, which are costly and require advanced technical skills and result in relatively subjective judgments. Therefore, we employed a novel method, droplet digital PCR (ddPCR), to determine the MET gene copy number with high accuracy and precision.The genomic DNA of cancer cell lines or tumor models were tested and compared with the MET gene CN and MET/CEN-7 ratio determined by SNP 6.0 and FISH, respectively.In cell lines, the linear association of the MET CN detected by ddPCR and SNP 6.0 is strong (Pearson correlation = 0.867). In tumor models, the MET CN detected by ddPCR was significantly different between the MET gene amplification and non-amplification groups according to FISH (mean: 15.4 vs 2.1; P = 0.044). Given that MET gene amplification is defined as MET CN >5.5 by ddPCR, the concordance rate between ddPCR and FISH was 98.0%, and Cohen's kappa coefficient was 0.760 (95% CI, 0.498-1.000; P <0.001).The results demonstrated that the ddPCR method has the potential to quantify the MET gene copy number with high precision and accuracy as compared with the results from SNP 6.0 and FISH in cancer cell lines and tumor samples, respectively

Hyperspectral Target Detection via Adaptive Information—Theoretic Metric Learning with Local Constraints

By using the high spectral resolution, hyperspectral images (HSIs) provide significant information for target detection, which is of great interest in HSI processing. However, most classical target detection methods may only perform well based on certain assumptions. Simultaneously, using limited numbers of target samples and preserving the discriminative information is also a challenging problem in hyperspectral target detection. To overcome these shortcomings, this paper proposes a novel adaptive information-theoretic metric learning with local constraints (ITML-ALC) for hyperspectral target detection. The proposed method firstly uses the information-theoretic metric learning (ITML) method as the objective function for learning a Mahalanobis distance to separate similar and dissimilar point-pairs without certain assumptions, needing fewer adjusted parameters. Then, adaptively local constraints are applied to shrink the distances between samples of similar pairs and expand the distances between samples of dissimilar pairs. Finally, target detection decision can be made by considering both the threshold and the changes between the distances before and after metric learning. Experimental results demonstrate that the proposed method can obviously separate target samples from background ones and outperform both the state-of-the-art target detection algorithms and the other classical metric learning methods

The Impact of Educational Investment on Sustainable Economic Growth in Guangdong, China: A Cointegration and Causality Analysis

Education, as an investment in human capital, is regarded as an important determinant of sustainable economic growth [1,2]. The purpose of this study is to explore the cointegration and causality between the investment in education and sustainable economic growth in Guangdong province by using the panel data of 21 cities from 2000 to 2016. We construct a variable intercept panel data model with an individual fixed effect based on the Cobb-Douglas production function, estimating the contribution of the investment in education to economic growth by introducing lags. The findings show the existence of the feedback causality between education and sustainable economic growth. Also, the results reveal that the local financial investment in education plays a positive and statistically significant role in promoting sustainable economic growth. However, the contribution of the local financial investment in education to economic growth varies in different areas. The investment in education in the Pearl River Delta region have the most obvious pull effects on its regional economy, whereas the Western region takes the second place. Meanwhile, the local financial investment in education for its role in promoting economic growth obviously has a two-year hysteresis effect. These findings have important implications for Guangdong&#8217;s solution to the imbalance between regional educational investment and sustainable economic growth