Severity of coronary artery disease is associated with diminished circANRIL expression: A possible blood based transcriptional biomarker in East Africa.

Antisense Noncoding RNA in the INK4 Locus (ANRIL) is the prime candidate gene at Chr9p21, the well-defined genetic risk locus associated with coronary artery disease (CAD). ANRIL and its transcript variants were investigated for the susceptibility to CAD in adipose tissues (AT) and peripheral blood mononuclear cells (PBMCs) of the study group and the impact of 9p21.3 locus mutations was further analysed. Expressions of ANRIL, circANRIL (hsa_circ_0008574), NR003529, EU741058 and DQ485454 were detected in epicardial AT (EAT) mediastinal AT (MAT), subcutaneous AT (SAT) and PBMCs of CAD patients undergoing coronary artery bypass grafting and non-CAD patients undergoing heart valve surgery. ANRIL expression was significantly upregulated, while the expression of circANRIL was significantly downregulated in CAD patients. Decreased circANRIL levels were significantly associated with the severity of CAD and correlated with aggressive clinical characteristics. rs10757278 and rs10811656 were significantly associated with ANRIL and circANRIL expressions in AT and PBMCs. The ROC-curve analysis suggested that circANRIL has high diagnostic accuracy (AUC: 0.9808, cut-off: 0.33, sensitivity: 1.0, specificity: 0.88). circANRIL has high diagnostic accuracy (AUC: 0.9808, cut-off: 0.33, sensitivity: 1.0, specificity: 0.88). We report the first data demonstrating the presence of ANRIL and its transcript variants expressions in the AT and PBMCs of CAD patients. circANRIL having a synergetic effect with ANRIL plays a protective role in CAD pathogenesis. Therefore, altered circANRIL expression may become a potential diagnostic transcriptional biomarker for early CAD diagnosis.

Common SNP-based haplotype analysis of the 9p21.3 gene locus as predictor coronary artery disease in Tanzanian population.

Genome-wide association studies (GWAS) have recently confirmed a strong association of the 9p21.3 locus with Coronary Artery Disease (CAD) in different populations but no data has been reported for the Tanzanian population. This study aimed to investigate the 9p21.3 locus harboring the disease-causing hotspot variations in Tanzanian CAD patients and their associations with the risk factors. 135 patients with CAD and 140 non-CAD patients were enrolled into the study. Further the biochemical analysis, the genotyping assays were performed by the use of qRT-PCR. The genotype and allele frequencies of rs1333049, rs2383207, rs2383206, rs10757274, rs10757278, and rs10811656 were significantly different between the groups (p<0.005). The genotype distribution of rs1333049, rs10757278 and rs10811656 polymorphisms were significantly different among patients with one, two, three stenotic vessels (p<0.05). For rs10757274 and rs10757278, the GG genotype indicated a significant 3-fold and 4-fold increased risk of CAD (p<0.0001,respectively). Additionally, haplotype analysis revealed that AAGCAG, AAACAG, GGGTGC haplotypes of 9p21.3 locus polymorphisms are associated with CAD risk. The GGGTGC haplotype was over-represented while the other two underrepresented in patients as compared to controls (p<0.00001,respectively) suggesting the first one a high-risk and the other two low-risk haplotypes for Tanzanian population. The AUC of a risk model based on non-genetic risk factors was 0.954 (95% CI: 0.930-0.977) and the combination with genetic risk factors improved the AUC to 0.982 (95% CI: 0.954-0.985) (p<0.012), indicating good diagnostic accuracy. Our results are the first data reporting statistically significant associations between 9p21.3 polymorphisms and CAD, and the very first haplotype block harboring the disease-causing variations in Tanzanian population.

Surgical Phase Recognition in Inguinal Hernia Repair-AI-Based Confirmatory Baseline and Exploration of Competitive Models.

Video-recorded robotic-assisted surgeries allow the use of automated computer vision and artificial intelligence/deep learning methods for quality assessment and workflow analysis in surgical phase recognition. We considered a dataset of 209 videos of robotic-assisted laparoscopic inguinal hernia repair (RALIHR) collected from 8 surgeons, defined rigorous ground-truth annotation rules, then pre-processed and annotated the videos. We deployed seven deep learning models to establish the baseline accuracy for surgical phase recognition and explored four advanced architectures. For rapid execution of the studies, we initially engaged three dozen MS-level engineering students in a competitive classroom setting, followed by focused research. We unified the data processing pipeline in a confirmatory study, and explored a number of scenarios which differ in how the DL networks were trained and evaluated. For the scenario with 21 validation videos of all surgeons, the Video Swin Transformer model achieved ~0.85 validation accuracy, and the Perceiver IO model achieved ~0.84. Our studies affirm the necessity of close collaborative research between medical experts and engineers for developing automated surgical phase recognition models deployable in clinical settings.

A Programmable Ontology Encompassing the Functional Logic of the Drosophila Brain.

The Drosophila brain has only a fraction of the number of neurons of higher organisms such as mice and humans. Yet the sheer complexity of its neural circuits recently revealed by large connectomics datasets suggests that computationally modeling the function of fruit fly brain circuits at this scale poses significant challenges. To address these challenges, we present here a programmable ontology that expands the scope of the current Drosophila brain anatomy ontologies to encompass the functional logic of the fly brain. The programmable ontology provides a language not only for modeling circuit motifs but also for programmatically exploring their functional logic. To achieve this goal, we tightly integrated the programmable ontology with the workflow of the interactive FlyBrainLab computing platform. As part of the programmable ontology, we developed NeuroNLP++, a web application that supports free-form English queries for constructing functional brain circuits fully anchored on the available connectome/synaptome datasets, and the published worldwide literature. In addition, we present a methodology for including a model of the space of odorants into the programmable ontology, and for modeling olfactory sensory circuits of the antenna of the fruit fly brain that detect odorant sources. Furthermore, we describe a methodology for modeling the functional logic of the antennal lobe circuit consisting of a massive number of local feedback loops, a characteristic feature observed across Drosophila brain regions. Finally, using a circuit library, we demonstrate the power of our methodology for interactively exploring the functional logic of the massive number of feedback loops in the antennal lobe.

Accelerating with FlyBrainLab the discovery of the functional logic of the Drosophila brain in the connectomic and synaptomic era.

In recent years, a wealth of Drosophila neuroscience data have become available including cell type and connectome/synaptome datasets for both the larva and adult fly. To facilitate integration across data modalities and to accelerate the understanding of the functional logic of the fruit fly brain, we have developed FlyBrainLab, a unique open-source computing platform that integrates 3D exploration and visualization of diverse datasets with interactive exploration of the functional logic of modeled executable brain circuits. FlyBrainLab's User Interface, Utilities Libraries and Circuit Libraries bring together neuroanatomical, neurogenetic and electrophysiological datasets with computational models of different researchers for validation and comparison within the same platform. Seeking to transcend the limitations of the connectome/synaptome, FlyBrainLab also provides libraries for molecular transduction arising in sensory coding in vision/olfaction. Together with sensory neuron activity data, these libraries serve as entry points for the exploration, analysis, comparison, and evaluation of circuit functions of the fruit fly brain.