Topological measures of connectomics for low grades Glioma

Recent advancements in neuroimaging have allowed the use of network analysis to study the brain in a system-based approach. In fact, several neurological disorders have been investigated from a network perspective. These include Alzheimer’s disease, autism spectrum disorder, stroke, and traumatic brain injury. So far, few studies have been conducted on glioma by using connectome techniques. A connectomebased approach might be useful in quantifying the status of patients, in supporting surgical procedures, and ultimately shedding light on the underlying mechanisms and the recovery process. In this manuscript, by using graph theoretical methods of segregation and integration, topological structural connectivity is studied comparing patients with low grade glioma to healthy control. These measures suggest that it is possible to quantify the status of patients pre- and post-surgical intervention to evaluate the condition

MultiLink Analysis: Brain Network Comparison via Sparse Connectivity Analysis

Abstract The analysis of the brain from a connectivity perspective is revealing novel insights into brain structure and function. Discovery is, however, hindered by the lack of prior knowledge used to make hypotheses. Additionally, exploratory data analysis is made complex by the high dimensionality of data. Indeed, to assess the effect of pathological states on brain networks, neuroscientists are often required to evaluate experimental effects in case-control studies, with hundreds of thousands of connections. In this paper, we propose an approach to identify the multivariate relationships in brain connections that characterize two distinct groups, hence permitting the investigators to immediately discover the subnetworks that contain information about the differences between experimental groups. In particular, we are interested in data discovery related to connectomics, where the connections that characterize differences between two groups of subjects are found. Nevertheless, those connections do not necessarily maximize the accuracy in classification since this does not guarantee reliable interpretation of specific differences between groups. In practice, our method exploits recent machine learning techniques employing sparsity to deal with weighted networks describing the whole-brain macro connectivity. We evaluated our technique on functional and structural connectomes from human and murine brain data. In our experiments, we automatically identified disease-relevant connections in datasets with supervised and unsupervised anatomy-driven parcellation approaches and by using high-dimensional datasets

Establishing Trust in ChatGPT BioMedical Generated Text: An Ontology-Based Knowledge Graph to Validate Disease-Symptom Links

Methods: Through an innovative approach, we construct ontology-based knowledge graphs from authentic medical literature and AI-generated content. Our goal is to distinguish factual information from unverified data. We compiled two datasets: one from biomedical literature using a "human disease and symptoms" query, and another generated by ChatGPT, simulating articles. With these datasets (PubMed and ChatGPT), we curated 10 sets of 250 abstracts each, selected randomly with a specific seed. Our method focuses on utilizing disease ontology (DOID) and symptom ontology (SYMP) to build knowledge graphs, robust mathematical models that facilitate unbiased comparisons. By employing our fact-checking algorithms and network centrality metrics, we conducted GPT disease-symptoms link analysis to quantify the accuracy of factual knowledge amid noise, hypotheses, and significant findings. Results: The findings obtained from the comparison of diverse ChatGPT knowledge graphs with their PubMed counterparts revealed some interesting observations. While PubMed knowledge graphs exhibit a wealth of disease-symptom terms, it is surprising to observe that some ChatGPT graphs surpass them in the number of connections. Furthermore, some GPT graphs are demonstrating supremacy of the centrality scores, especially for the overlapping nodes. This striking contrast indicates the untapped potential of knowledge that can be derived from AI-generated content, awaiting verification. Out of all the graphs, the factual link ratio between any two graphs reached its peak at 60%. Conclusions: An intriguing insight from our findings was the striking number of links among terms in the knowledge graph generated from ChatGPT datasets, surpassing some of those in its PubMed counterpart. This early discovery has prompted further investigation using universal network metrics to unveil the new knowledge the links may hold.Comment: 7 Pages, 3 algorithms, 4 tables, and 7 figure

Mechanical correlates of dyspnea in bronchial asthma.

We hypothesized that dyspnea and its descriptors, that is, chest tightness, inspiratory effort, unrewarded inspiration, and expiratory difficulty in asthma reflect different mechanisms of airflow obstruction and their perception varies with the severity of bronchoconstriction. Eighty-three asthmatics were studied before and after inhalation of methacholine doses decreasing the 1-sec forced expiratory volume by ~15% (mild bronchoconstriction) and ~25% (moderate bronchoconstriction). Symptoms were examined as a function of changes in lung mechanics. Dyspnea increased with the severity of obstruction, mostly because of inspiratory effort and chest tightness. At mild bronchoconstriction, multivariate analysis showed that dyspnea was related to the increase in inspiratory resistance at 5 Hz (R 5) (r (2) = 0.10, P = 0.004), chest tightness to the decrease in maximal flow at 40% of control forced vital capacity, and the increase in R 5 at full lung inflation (r (2) = 0.15, P = 0.006), inspiratory effort to the temporal variability in R 5-19 (r (2) = 0.13, P = 0.003), and unrewarded inspiration to the recovery of R 5 after deep breath (r (2) = 0.07, P = 0.01). At moderate bronchoconstriction, multivariate analysis showed that dyspnea and inspiratory effort were related to the increase in temporal variability in inspiratory reactance at 5 Hz (X 5) (r (2) = 0.12, P = 0.04 and r (2) = 0.18, P < 0.001, respectively), and unrewarded inspiration to the decrease in X 5 at maximum lung inflation (r (2) = 0.07, P = 0.04). We conclude that symptom perception is partly explained by indexes of airway narrowing and loss of bronchodilatation with deep breath at low levels of bronchoconstriction, but by markers of ventilation heterogeneity and lung volume recruitment when bronchoconstriction becomes more severe

Real-Life effects of benralizumab on exacerbation number and lung hyperinflation in atopic patients with severe eosinophilic asthma.

Background: The humanized monoclonal antibody benralizumab targets the α subunit of the interleukin-5 (IL-5) receptor and the FcγRIIIa receptor expressed by natural killer cells. Through this dual mechanism of action, benralizumab neutralizes the pro-eosinophil functions of IL-5 and promotes eosinophil apoptosis. Objectives and methods: The present real-life study aimed to evaluate, in 22 allergic patients with severe eosinophilic asthma, the effects of benralizumab on asthma exacerbations and lung hyperinflation. Results: In this regard here we show that, after 24 weeks of add-on treatment, benralizumab completely depleted peripheral blood eosinophils (from 810 to 0 cells/μL; p < 0.0001), and significantly decreased both asthma exacerbation number (from 4 to 0; p < 0.0001) and residual volume (from 2720 to 2300 mL; p < 0.01). Moreover, at the same time point (24 weeks) benralizumab also increased pre-bronchodilator FEV1 (from 1295 to 1985 mL; p < 0.0001), FVC (from 2390 to 2974 mL; p < 0.0001), FEF25−75 (from 0.6 to 1.42 L/sec; p < 0.0001), IC (from 1940 to 2460 mL; not significant), and ACT score (from 14.73 to 22.95; p < 0.0001), as well as reduced prednisone intake (from 25 to 0 mg; p < 0.0001). Conclusion: In conclusion, our results suggest that via its anti-eosinophil actions benralizumab improved airflow limitation, lung hyperinflation, and respiratory symptoms, as well as lowered asthma exacerbation rate and abrogated OCS consumption in most patients

Relating Global and Local Connectome Changes to Dementia and Targeted Gene Expression in Alzheimer's Disease

Networks are present in many aspects of our lives, and networks in neuroscience have recently gained much attention leading to novel representations of brain connectivity. The integration of neuroimaging characteristics and genetics data allows a better understanding of the effects of the gene expression on brain structural and functional connections. The current work uses whole-brain tractography in a longitudinal setting, and by measuring the brain structural connectivity changes studies the neurodegeneration of Alzheimer's disease. This is accomplished by examining the effect of targeted genetic risk factors on the most common local and global brain connectivity measures. Furthermore, we examined the extent to which Clinical Dementia Rating relates to brain connections longitudinally, as well as to gene expression. For instance, here we show that the expression of PLAU gene increases the change over time in betweenness centrality related to the fusiform gyrus. We also show that the betweenness centrality metric impact dementia-related changes in distinct brain regions. Our findings provide insights into the complex longitudinal interplay between genetics and brain characteristics and highlight the role of Alzheimer's genetic risk factors in the estimation of regional brain connectivity alterations

Designing a web spatial decision support system based on analytic network process to locate a freight lorry parking

The relevant role of freight lorry parking facilities as a tool to reduce nuisances and impact of economic activities in densely populated urban areas is widely recognised in the literature. Nevertheless, the literature currently lacks specific contributions addressing the use of a complex Multiple Criteria Decision Analysis (MCDA) approach for coping with an optimal location of freight lorry parking facilities in the urban context. This paper contributes to filling this gap by analysing a real-world case study motivated by the problem of intense freight vehicles traffic around the city of Bradford, Yorkshire (UK). Since it is necessary to include diverse analysis perspectives, reflecting the different classes of involved stakeholders, this study proposes adopting the Analytic Network Process (ANP) approach as a tool to support the selection and evaluation of alternatives for a freight lorry parking facility, followed by the design of software based on this approach. The proposed web Spatial Decision Support System provides a valuable tool to foster extended discussions with experts and facilitate the decision process in this class of location problems

Drug eluting stents are superior to bare metal stents to reduce clinical outcome and stent-related complications in CKD patients, a systematic review, meta-analysis and network meta-analysis.

AimsTo compare clinical outcome in Chronic kidney disease (CKD) patients receiving coronary stents according to stent type BMS versus DES and 1st generation versus 2nd generation DES.Methods and ResultsPubMed, Cinhal, Cochrane, Embase, and Web of Science were searched for studies including CKD patients. CKD was defined as eGFR < 60 mL/min. We selected n = 35 articles leading to 376 169 patients, of which 76 557 CKD patients receiving BMS n = 35,807, 1st generation DES n = 37,650, or 2nd generation DES n = 3100. Patient receiving DES, compared to BMS, had a 18% lower all‐cause mortality (RR 0.82, 95%CI 0.71‐0.94). The composite of death or myocardial infarction (MI) was lower in DES patients (RR 0.78, 95%CI 0.67‐0.91), as was stent thrombosis (ST) (RR 0.57, 95%CI 0.34‐0.95), target vessel/lesion revascularization (TVR/TLR) (RR 0.69, 95%CI 0.57‐0.84) and death for cardiovascular cause (RR 0.43, 95%CI 0.25‐0.74). We also found a gradient between 1st and 2nd generation DES, through BMS. Second, compared to 1st generation DES, were associated with further relative risk (RR) reduction of −18% in of all‐cause death, and lower incidence of stent‐related clinical events: −39% RR of ST risk; −27 RR of TVR/TLR risk.ConclusionsDES in CKD patients undergoing PCI were superior to BMS in reducing major adverse clinical events. This was possibly explained, by a lower risk of stent‐related events as ST and TVR or TLR. Second, compared to 1st generation DES may furtherly reduce clinical events

Smoker's paradox in ST-elevation myocardial infarction: Role of inflammation and platelets.

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Evaluating glioma growth predictions as a forward ranking problem

The problem of tumor growth prediction is challenging, but promising results have been achieved with both model-driven and statistical methods. In this work, we present a framework for the evaluation of growth predictions that focuses on the spatial infiltration patterns, and specifically evaluating a prediction of future growth. We propose to frame the problem as a ranking problem rather than a segmentation problem. Using the average precision as a metric, we can evaluate the results with segmentations while using the full spatiotemporal prediction. Furthermore, by separating the model goodness-of-fit from future predictive performance, we show that in some cases, a better fit of model parameters does not guarantee a better the predictive power