Using Fuzzy Sets in Surgical Treatment Selection and Homogenizing Stratification of Patients with Significant Chronic Ischemic Mitral Regurgitation

Nikolova, Natalia; Panayotov, Plamen; Panayotova, Daniela; Ivanova, Snejana D.; Tenekedjiev, Kiril

doi:10.2991/ijcis.d.190923.002

Cited by 3 publications

(1 citation statement)

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“…Because our immunofluorescence measurements were performed on arterial samples from 11 donors, in each of which we evaluated the platelet and fibrin content from different number of collected data (in the range 111-225), we used a fuzzy sample approach [18] to evaluate the data. This approach allows for the achievement of parity of the arterial samples from different donors, as applied previously [19][20][21]. Here, we implemented a novel explicit fuzzy estimation method and a novel fuzzy version of the invertible cumulative distribution function estimator with maximum count of nodes [22] for the implicit estimation of the median, lower quartile and upper quartiles of a random variable.…”

Section: Discussionmentioning

confidence: 99%

Cryopreservation moderates the thrombogenicity of arterial allografts during storage

et al. 2021

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Introduction Management of vascular infections represents a major challenge in vascular surgery. The use of cryopreserved vascular allografts could be a feasible therapeutic option, but the optimal conditions for their production and use are not precisely defined. Aims To evaluate the effects of cryopreservation and the duration of storage on the thrombogenicity of femoral artery allografts. Methods In our prospective study, eleven multi-organ-donation-harvested human femoral arteries were examined at five time points during storage at -80°C: before cryopreservation as a fresh native sample and immediately, one, twelve and twenty-four weeks after the cryopreservation. Cross-sections of allografts were perfused with heparin-anticoagulated blood at shear-rates relevant to medium-sized arteries. The deposited platelets and fibrin were immunostained. The thrombogenicity of the intima, media and adventitia layers of the artery grafts was assessed quantitatively from the relative area covered by fibrin- and platelet-related fluorescent signal in the confocal micrographs. Results Regression analysis of the fibrin and platelet coverage in the course of the 24-week storage excluded the possibility for increase in the graft thrombogenicity in the course of time and supported the hypothesis for a descending trend in fibrin generation and platelet deposition on the arterial wall. The fibrin deposition in the cryopreserved samples did not exceed the level detected in any of the three layers of the native graft. However, an early (up to week 12) shift above the native sample level was observed in the platelet adhesion to the media. Conclusions The hemostatic potential of cryopreserved arterial allografts was retained, whereas their thrombogenic potential declined during the 6-month storage. The only transient prothrombotic change was observed in the media layer, where the platelet deposition exceeded that of the fresh native grafts in the initial twelve weeks after cryopreservation, suggesting a potential clinical benefit from antiplatelet therapy in this time-window.

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Section: Discussionmentioning

confidence: 99%

Cryopreservation moderates the thrombogenicity of arterial allografts during storage

et al. 2021

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Outlier Detection Algorithms Over Fuzzy Data with Weighted Least Squares

Nikolova

Rodríguez

Symes

et al. 2021

Int. J. Fuzzy Syst.

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In the classical leave-one-out procedure for outlier detection in regression analysis, we exclude an observation, then construct a model on the remaining data. If the difference between predicted and observed value is high we declare this value an outlier. As a rule, those procedures utilize single comparison testing. The problem becomes much harder when the observations can be associated with a given degree of membership to an underlying population and the outlier detection should be generalized to operate over fuzzy data. We present a new approach for outlier that operates over fuzzy data using two inter-related algorithms. Due to the way outliers enter the observation sample, they may be of various order of magnitude. To account for this, we divided the outlier detection procedure into cycles. Furthermore, each cycle consists of two phases. In Phase 1 we apply a leave-one-out procedure for each nonoutlier in the data set. In Phase 2, all previously declared outliers are subjected to Benjamini-Hochberg step-up multiple testing procedure controlling the false discovery rate, and the non-confirmed outliers can return to the data set. Finally, we construct a regression model over the resulting set of non-outliers. In that way we ensure that a reliable and high-quality regression model is obtained in Phase 1 because the leaveone-out procedure comparatively easily purges the dubious observations due to the single comparison testing. In the same time, the confirmation of the outlier status in relation to the newly obtained high-quality regression model is much harder due to the multiple testing procedure applied hence only the true outliers remain outside the data sample. The two phases in each cycle are a good trade-off between the desire to construct a high-quality model (i.e. over informative data points) and the desire to use as much data points as possible (thus leaving as much observations as possible in the data sample). The number of cycles is user-defined, but the procedures can finalize the analysis in case a cycle with no new outliers is detected. We offer one illustrative example and two other practical case studies (from real-life thrombosis studies) that demonstrate the application and strengths of our algorithms. In the concluding section, we discuss several limitations of our approach and also offer directions for future research. Keywords: regression analysis, leave-one-out method, degree of membership, multiple testing, Benjamini-Hochberg step-up multiple testing, false-discovery rate Highlights: -We develop algorithms for outlier rejection over fuzzy samples using weighted least squares that operate in a given number of cycles -Each cycle has two phases -use single testing leave-one-out procedure for initial purging of data, then confirm the previous outlier status with multiple testing -We offer one illustrative example and two examples from a case study in thrombosis research to show the strength of our cycle-based approach

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