Hyperdense Artery Sign in Patients With Acute Ischemic Stroke-Automated Detection With Artificial Intelligence-Driven Software.

Background: Hyperdense artery sign (HAS) on non-contrast CT (NCCT) can indicate a large vessel occlusion (LVO) in patients with acute ischemic stroke. HAS detection belongs to routine reporting in patients with acute stroke and can help to identify patients in whom LVO is not initially suspected. We sought to evaluate automated HAS detection by commercial software and compared its performance to that of trained physicians against a reference standard. Methods: Non-contrast CT scans from 154 patients with and without LVO proven by CT angiography (CTA) were independently rated for HAS by two blinded neuroradiologists and an AI-driven algorithm (Brainomix®). Sensitivity and specificity were analyzed for the clinicians and the software. As a secondary analysis, the clot length was automatically calculated by the software and compared with the length manually outlined on CTA images as the reference standard. Results: Among 154 patients, 84 (54.5%) had CTA-proven LVO. HAS on the correct side was detected with a sensitivity and specificity of 0.77 (CI:0.66-0.85) and 0.87 (0.77-0.94), 0.8 (0.69-0.88) and 0.97 (0.89-0.99), and 0.93 (0.84-0.97) and 0.71 (0.59-0.81) by the software and readers 1 and 2, respectively. The automated estimation of the thrombus length was in moderate agreement with the CTA-based reference standard [intraclass correlation coefficient (ICC) 0.73]. Conclusion: Automated detection of HAS and estimation of thrombus length on NCCT by the tested software is feasible with a sensitivity and specificity comparable to that of trained neuroradiologists.

Textbook outcome among patients undergoing enhanced recovery after liver transplantation stratified by risk. A single-center retrospective observational cohort study.

BACKGROUND & AIMS: Liver transplantation (LT) is one of the most complex surgical procedures. Enhanced recovery after surgery (ERAS) aims to reduce the risk of postoperative complications. When patients achieve all desirable outcomes after a procedure, they are considered to have experienced a textbook outcome (TO). METHODS: Two cohorts of patients undergoing low (n = 101) or medium risk (n = 15) LT were identified. The remaining patients (n = 65) were grouped separately. The ERAS protocol included pre-, intra-, and post-operative steps. TO was defined as the absence of complications, prolonged length of hospital stays, readmission and mortality during the first 90 days. RESULTS: One third of patients who underwent ERAS after LT experienced a TO. On multivariable analysis, age (OR, 1.05 [95% CI, 1.01-1.09]; P = .02), and having hepatocellular carcinoma (OR, 2.83 [95% CI, 1.37-6.03]; P = .005) were individually associated with a greater probability of achieving a TO. Belonging to the cohorts of medium risk or outside the selection criteria was associated with a lower probability of achieving a TO (OR, 0.46 [96% CI, 0.22-0.93]; P = .03). Patients less likely to experience TO required more hospital resources. Patients who achieved TO were more likely to remain free of chronic kidney disease (achieved TO, 83.8% [82.7-85.6]; failed TO, 67.9% [66.9-70.2]; P < .05). Tacrolimus dose and trough levels were similar. CONCLUSIONS: A novel finding of our study is that short and medium-term kidney function is better preserved in patients who experience a TO. Better kidney function of patients who achieve TO is not due to lower tacrolimus dosage.

Enhanced recovery after low- and medium-risk liver transplantation. A single-center prospective observational cohort study.

BACKGROUND & AIMS: Few studies have fully applied an enhanced recovery after surgery (ERAS) protocol to liver transplantation (LT). Our aim was to assess the effects of a comprehensive ERAS protocol in our cohort of low- and medium-risk LT patients. METHODS: The ERAS protocol included pre-, intra-, and post-operative steps. During the five-year study period, 181 LT were performed in our institution. Two cohorts were identified: low risk patients (n = 101) had a laboratory model for end-stage liver disease (MELD) score of 20 points or less at the time of LT, received a liver from a donor after brain death, and had a balance of risk score of 9 points or less; medium-risk patients (n = 15) had identical characteristics except for a higher MELD score (21-30 points). In addition, we analyzed the remaining patients (n = 65) who were transplanted over the same study period separately using the ERAS protocol. RESULTS: The low-risk cohort showed a low need for packed red blood cells transfusion (median: 0 units) and renal replacement therapy (1%), as well as a short length of stay both in the intensive care unit (13 h) and in the hospital (4 days); morbidity during one-year follow-up, and probability of surviving to one year (89.30%) and five years (76.99%) were in line with well-established reference data. Similar findings were observed in the medium-risk cohort. CONCLUSIONS: This single-center prospective observational cohort study provides evidence that ERAS is feasible and safe for low- and medium-risk LT.

Identification of rumen microbial biomarkers linked to methane emission in Holstein dairy cows.

Mitigation of greenhouse gas emissions is relevant for reducing the environmental impact of ruminant production. In this study, the rumen microbiome from Holstein cows was characterized through a combination of 16S rRNA gene and shotgun metagenomic sequencing. Methane production (CH4 ) and dry matter intake (DMI) were individually measured over 4-6 weeks to calculate the CH4 yield (CH4 y = CH4 /DMI) per cow. We implemented a combination of clustering, multivariate and mixed model analyses to identify a set of operational taxonomic unit (OTU) jointly associated with CH4 y and the structure of ruminal microbial communities. Three ruminotype clusters (R1, R2 and R3) were identified, and R2 was associated with higher CH4 y. The taxonomic composition on R2 had lower abundance of Succinivibrionaceae and Methanosphaera, and higher abundance of Ruminococcaceae, Christensenellaceae and Lachnospiraceae. Metagenomic data confirmed the lower abundance of Succinivibrionaceae and Methanosphaera in R2 and identified genera (Fibrobacter and unclassified Bacteroidales) not highlighted by metataxonomic analysis. In addition, the functional metagenomic analysis revealed that samples classified in cluster R2 were overrepresented by genes coding for KEGG modules associated with methanogenesis, including a significant relative abundance of the methyl-coenzyme M reductase enzyme. Based on the cluster assignment, we applied a sparse partial least-squares discriminant analysis at the taxonomic and functional levels. In addition, we implemented a sPLS regression model using the phenotypic variation of CH4 y. By combining these two approaches, we identified 86 discriminant bacterial OTUs, notably including families linked to CH4 emission such as Succinivibrionaceae, Ruminococcaceae, Christensenellaceae, Lachnospiraceae and Rikenellaceae. These selected OTUs explained 24% of the CH4 y phenotypic variance, whereas the host genome contribution was ~14%. In summary, we identified rumen microbial biomarkers associated with the methane production of dairy cows; these biomarkers could be used for targeted methane-reduction selection programmes in the dairy cattle industry provided they are heritable.

Unsupervised GRN Ensemble.

Inferring gene regulatory networks from expression data is a very challenging problem that has raised the interest of the scientific community. Different algorithms have been proposed to try to solve this issue, but it has been shown that different methods have some particular biases and strengths, and none of them is the best across all types of data and datasets. As a result, the idea of aggregating various network inferences through a consensus mechanism naturally arises. In this chapter, a common framework to standardize already proposed consensus methods is presented, and based on this framework different proposals are introduced and analyzed in two different scenarios: Homogeneous and Heterogeneous. The first scenario reflects situations where the networks to be aggregated are rather similar because they are obtained with inference algorithms working on the same data, whereas the second scenario deals with very diverse networks because various sources of data are used to generate the individual networks. A procedure for combining multiple network inference algorithms is analyzed in a systematic way. The results show that there is a very significant difference between these two scenarios, and that the best way to combine networks in the Heterogeneous scenario is not the most commonly used. We show in particular that aggregation in the Heterogeneous scenario can be very beneficial if the individual networks are combined with our new proposed method ScaleLSum.

Differential expression of long non-coding RNAs are related to proliferation and histological diversity in follicular lymphomas.

Long non-coding RNAs (lncRNAs) comprise a family of non-coding transcripts that are emerging as relevant gene expression regulators of different processes, including tumour development. To determine the possible contribution of lncRNA to the pathogenesis of follicular lymphoma (FL) we performed RNA-sequencing at high depth sequencing in primary FL samples ranging from grade 1-3A to aggressive grade 3B variants using unpurified (n = 16) and purified (n = 12) tumour cell suspensions from nodal samples. FL grade 3B had a significantly higher number of differentially expressed lncRNAs (dif-lncRNAs) with potential target coding genes related to cell cycle regulation. Nine out of the 18 selected dif-lncRNAs were validated by quantitative real time polymerase chain reaction in an independent series (n = 43) of FL. RP4-694A7.2 was identified as the top deregulated lncRNA potentially involved in cell proliferation. RP4-694A7.2 silencing in the WSU-FSCCL FL cell line reduced cell proliferation due to a block in the G1/S phase. The relationship between RP4-694A7.2 and proliferation was confirmed in primary samples as its expression levels positively related to the Ki-67 proliferation index. In summary, lncRNAs are differentially expressed across the clinico-biological spectrum of FL and a subset of them, related to cell cycle, may participate in cell proliferation regulation in these tumours.

Can Deep Learning Improve Genomic Prediction of Complex Human Traits?

The genetic analysis of complex traits does not escape the current excitement around artificial intelligence, including a renewed interest in "deep learning" (DL) techniques such as Multilayer Perceptrons (MLPs) and Convolutional Neural Networks (CNNs). However, the performance of DL for genomic prediction of complex human traits has not been comprehensively tested. To provide an evaluation of MLPs and CNNs, we used data from distantly related white Caucasian individuals (n ∼100k individuals, m ∼500k SNPs, and k = 1000) of the interim release of the UK Biobank. We analyzed a total of five phenotypes: height, bone heel mineral density, body mass index, systolic blood pressure, and waist-hip ratio, with genomic heritabilities ranging from ∼0.20 to 0.70. After hyperparameter optimization using a genetic algorithm, we considered several configurations, from shallow to deep learners, and compared the predictive performance of MLPs and CNNs with that of Bayesian linear regressions across sets of SNPs (from 10k to 50k) that were preselected using single-marker regression analyses. For height, a highly heritable phenotype, all methods performed similarly, although CNNs were slightly but consistently worse. For the rest of the phenotypes, the performance of some CNNs was comparable or slightly better than linear methods. Performance of MLPs was highly dependent on SNP set and phenotype. In all, over the range of traits evaluated in this study, CNN performance was competitive to linear models, but we did not find any case where DL outperformed the linear model by a sizable margin. We suggest that more research is needed to adapt CNN methodology, originally motivated by image analysis, to genetic-based problems in order for CNNs to be competitive with linear models.

Study of Meta-analysis strategies for network inference using information-theoretic approaches.

BACKGROUND: Reverse engineering of gene regulatory networks (GRNs) from gene expression data is a classical challenge in systems biology. Thanks to high-throughput technologies, a massive amount of gene-expression data has been accumulated in the public repositories. Modelling GRNs from multiple experiments (also called integrative analysis) has; therefore, naturally become a standard procedure in modern computational biology. Indeed, such analysis is usually more robust than the traditional approaches, which suffer from experimental biases and the low number of samples by analysing individual datasets. To date, there are mainly two strategies for the problem of interest: the first one ("data merging") merges all datasets together and then infers a GRN whereas the other ("networks ensemble") infers GRNs from every dataset separately and then aggregates them using some ensemble rules (such as ranksum or weightsum). Unfortunately, a thorough comparison of these two approaches is lacking. RESULTS: In this work, we are going to present another meta-analysis approach for inferring GRNs from multiple studies. Our proposed meta-analysis approach, adapted to methods based on pairwise measures such as correlation or mutual information, consists of two steps: aggregating matrices of the pairwise measures from every dataset followed by extracting the network from the meta-matrix. Afterwards, we evaluate the performance of the two commonly used approaches mentioned above and our presented approach with a systematic set of experiments based on in silico benchmarks. CONCLUSIONS: We proposed a first systematic evaluation of different strategies for reverse engineering GRNs from multiple datasets. Experiment results strongly suggest that assembling matrices of pairwise dependencies is a better strategy for network inference than the two commonly used ones.

NetBenchmark: a bioconductor package for reproducible benchmarks of gene regulatory network inference.

BACKGROUND: In the last decade, a great number of methods for reconstructing gene regulatory networks from expression data have been proposed. However, very few tools and datasets allow to evaluate accurately and reproducibly those methods. Hence, we propose here a new tool, able to perform a systematic, yet fully reproducible, evaluation of transcriptional network inference methods. RESULTS: Our open-source and freely available Bioconductor package aggregates a large set of tools to assess the robustness of network inference algorithms against different simulators, topologies, sample sizes and noise intensities. CONCLUSIONS: The benchmarking framework that uses various datasets highlights the specialization of some methods toward network types and data. As a result, it is possible to identify the techniques that have broad overall performances.

Hierarchical clustering combining numerical and biological similarities for gene expression data classification.

High throughput data analysis is a challenging problem due to the vast amount of available data. A major concern is to develop algorithms that provide accurate numerical predictions and biologically relevant results. A wide variety of tools exist in the literature using biological knowledge to evaluate analysis results. Only recently, some works have included biological knowledge inside the analysis process improving the prediction results.

NCX-1000, a nitric oxide-releasing derivative of UDCA, does not decrease portal pressure in patients with cirrhosis: results of a randomized, double-blind, dose-escalating study.

OBJECTIVES: NCX-1000 (2(acetyloxy) benzoic acid-3(nitrooxymethyl)phenyl ester) is an nitric oxide (NO)-releasing derivative of ursodeoxycholic acid (UDCA), which showed selective vasodilatory effect on intrahepatic circulation in animal models of cirrhosis. This study was aimed at testing the efficacy and tolerability of this compound in patients with cirrhosis and portal hypertension. METHODS: This was a single-center, phase-2a, randomized (4:1), double-blind, parallel-group, dose-escalating study. Patients received progressive oral doses of NCX-1000 or placebo up to 2 g t.i.d. or maximum tolerated doses for 16 days. Efficacy on fasting and postprandial hepatic venous pressure gradient (HVPG) at baseline and after treatment was assessed. Hepatic blood flow (HBF) and arterial blood pressure were also measured. RESULTS: Eleven patients (nine NCX-1000 and two placebo) were enrolled and completed the trial. After NCX-1000 treatment, HVPG did not change (16.7+/-3.8 vs. 17.1+/-3.8 mm Hg; P=0.596), and HBF decreased significantly (904+/-310 vs. 1,129+/-506 ml/min; P=0.043). The postprandial increase in portal pressure and HBF was not modified by NCX-1000. There was no significant effect on diastolic blood pressure, but systolic blood pressure was reduced by the treatment in a dose-dependent manner (121+/-11 mm Hg after NCX-1000 vs. 136+/-7 mm Hg at baseline; P=0.003). Seven non-serious adverse events were experienced by four patients (one on placebo). CONCLUSIONS: In patients with cirrhosis and portal hypertension, NCX-1000 administration was safe, but it was not able to reduce portal pressure. A significant reduction of systolic blood pressure and HBF was observed in the treatment arm, suggesting that the drug had systemic effects and lacked selective release of NO at the intrahepatic circulation.

Influence of beta-2 adrenergic receptor gene polymorphism on the hemodynamic response to propranolol in patients with cirrhosis.

The beta-2-adrenergic receptor (beta(2-)-AR) has several single-nucleotide polymorphisms. These influence the functional response to adrenergic stimulation; genotypes homozygous for Gly16-Glu27 or Gly16-Gln27 alleles (Gly16-Glu/Gln27 haplotypes) are associated with enhanced response, whereas genotypes homozygous for Arg16-Gln27 alleles (Arg16-Gln27) show a decreased response. We hypothesized that gene polymorphisms at the beta2-AR may influence the hemodynamic response to propranolol in patients with cirrhosis. The beta2-AR gene polymorphisms were determined by direct sequencing of the polymerase chain reaction (PCR) products in 48 patients with cirrhosis. All patients also had hepatic and systemic hemodynamic studies before and after propranolol administration. Prevalence of Gly16-Glu/Gln27 haplotypes was 29.1%, Arg16-Gln27 haplotype was 16.7%, and 54.2% were compound heterozygotes. Patients with cirrhosis with Gly16-Glu/Gln27 haplotypes had a greater decrease in heart rate, cardiac index, and hepatic blood flow after propranolol administration than those with Arg16-Gln27 haplotype. However, the HVPG response to propranolol was similar in both groups, whereas estimated hepatic sinusoidal resistance increased significantly in Gly16-Glu/Gln27 haplotypes but not in Arg16-Gln27 (+27.1 +/- 17.8% vs -17.9 +/- 13.9%, P = .042), suggesting that unopposed vasoconstrictive activity at the intrahepatic circulation hinders the fall in HVPG despite enhanced hemodynamic response to propranolol in Gly16-Glu/Gln27 haplotypes. In conclusion, beta2-AR gene polymorphisms influence the response to beta-blockade. However, HVPG reduction cannot be predicted from polymorphism analysis. Patients with the Gly16-Glu/Gln27 haplotypes may benefit from the association of hepatic vasodilators to propranolol therapy.

[Usefulness of tomographic computer colonography for colorectal polyp detection]. / Utilidad de la colonografía por tomografía computarizada en la detección de pólipos colorrectales.

BACKGROUND AND OBJECTIVE: Colonoscopy is the procedure of choice for the diagnosis of colorectal neoplasms. CT colonography (CTC), a recently developed minimal invasive radiological technique, permits the identification of colorectal tumors. The aim of the present study was to evaluate the efficacy of CTC in the detection of colorectal polyps, and to establish the factors determining a diagnostic accuracy. PATIENTS AND METHOD: Patients with colorectal polyps admitted for endoscopic polypectomy were included. CTC was performed prior to colonoscopy in all patients. Demographic and clinical data were registered, as well as the polyp characteristics. Efficacy of CTC was analyzed with respect to each individual polyp and each patient. RESULTS: Colonoscopy identified 87 colorectal polyps in 30 patients. CTC had a sensitivity of 70% for the detection of polyps of any size, being 92%, 73% and 55% for polyps > or = 10 mm, 5-9 mm and < or = 4 mm, respectively. On the other hand, the sensitivity of CTC for the detection of pedunculated, semipedunculated and sessile polyps was 85%, 92% and 56%, respectively. Accuracy of CTC was associated with polyp size (p = 0.007) and shape (p = 0.007). Sensitivity and specificity of CTC for the identification of patients with polyps > or = 10 mm were 88% and 100%, respectively. CONCLUSIONS: CTC is a highly accurate technique for the identification of colorectal polyps. Its diagnostic accuracy depends on lesion's size and shape.