scholarly journals Robust Cell Selections and Depletions across Various Hematopoietic Cell Fractions on the Clinimacs Plus Instrument

Blood ◽  
2018 ◽  
Vol 132 (Supplement 1) ◽  
pp. 3818-3818
Author(s):  
Thejaswi Bikkani ◽  
Katherine Li ◽  
Anusha Rao ◽  
Angela Pickett ◽  
Xiaobai Li ◽  
...  
Keyword(s):  
Bone Marrow ◽  
Multivariate Regression ◽  
Selection Method ◽  
Cell Selection ◽  
Regression Analyses ◽  
Selection Methods ◽  
Cell Counts ◽  
Paramagnetic Beads ◽  
Cell Fractions ◽  
Graft Engineering

Abstract Introduction: Cell enrichment and/or depletion by selection is critical for graft engineering in cellular and gene therapies. The semi-automated CliniMACS Plus Instrument (Miltenyi Biotec) is used in clinical cell processing to enrich or deplete a variety of hematopoietic cells including T, B, monocytes, NK cells, and/or CD34+ hematopoietic stem/progenitor cells (HSPCs). Antibody-conjugated paramagnetic beads attach to the desired cells which are separated from the rest of the apheresis or bone marrow product in a closed-system magnetic separation column. Either the selected cells retained in the separation column (positive selection) or those in the "flow-through" fraction (negative selection) may be used for downstream processing. There is sparse data comparing cell selection efficiency and purity across the various selection methods on the CliniMACS Plus Instrument. Further, the effect of donor demographics and other upstream processing events on final viable cell recovery (VCR)/ cell purity (CP) are unknown. This study systematically compared these parameters across various selection methods. Methods: From 2008 to 2018, data were obtained from 45 clinical processing protocols involving 991 consecutive adult and pediatric donor/patient apheresis products or bone marrow harvests, which underwent cell selections on the Miltenyi CliniMACS Plus device. Cell selections included 1.CD34+ (n=669), 2.CD4+ (n=179), 3.CD3-/56+ (n=79), 4.CD3+/19- (n=6), 5.CD4+/8+ (n=21), and 6.CD25- (n=37) cells. Based on protocol design or logistical needs, apheresis or bone marrow collections were processed/selected on the same day or held overnight for next day processing. Products were also either washed to remove platelets (PW) or loaded onto the selection device directly. Data on donor age, BMI, donor type (autologous vs. allogeneic), sex, race, pre-apheresis peripheral blood cell counts and concentrations, as well as apheresis bag blood counts were evaluated. Outcome variables included post-selection VCR and CP. Correlations and multivariate regression analyses were performed for the largest selection type, i.e CD34+ cell selections. Results: Summary data (means ± 1SD) on method-specific enrichments and depletions are shown in the Table. Median post-selection VCR and CP varied by selection method. VCR was the highest for the CD34+ cell selections (Fig. a). CP was the highest and most consistent for CD4+ selections (Fig. b). Platelet and red cell depletions also varied by selection method and averaged 3.9 ± 1.63 and 2.06 ± 0.74 logs, respectively. Selection procedures used for cell depletion resulted in near complete removal of the undesired cell fractions (Table). In multivariate regression analyses, products processed on the same day (compared to those held overnight) (Fig. c), those that underwent a PW (Fig. d), and had higher pre-apheresis peripheral blood CD34+ absolute cell counts and concentrations (Fig. e) were associated significantly with higher CD34+ CP (adjusted r2=0.2; p<0.0001). Higher pre-apheresis CD34+ concentrations (Fig. f) were associated with a lower CD34+ VCR (15%, CI: 5.6-25.3). Conversely, PW (Fig. g) correlated with higher CD34+ VCR (3.5%; CI: 0.8-6.28). Trends across the study period demonstrated a significant improvement in CD34+ VCR from 2008 to 2017 (Fig. h). Conclusions: This is the largest study, to our knowledge, to systematically summarize and analyze Miltenyi CliniMACS Plus selections for clinical graft engineering. Cell selections on the Miltenyi CliniMACS Plus resulted in robust enrichment with near complete depletion of the undesired cell fractions. CD34+ CP was higher in patients with higher CD34+ cells counts following mobilization. Same day processing, as well as PW further improved CD34+ CP. PW also improved CD34+ VCR. This was likely due to a reduction in non-specific platelet binding to the paramagnetic beads. Higher pre-selection CD34+ cell concentrations in the product marginally worsened VCR. This was possibly due to bead saturation in each selection method. Improved CD34+ VCR over time was likely due to increased platelet washes in the latter years. Further studies are ongoing to assess impact of cell selection variations on downstream manufacturing steps (cell transduction, expansion) and clinical (cell engraftment, GVHD incidence) outcomes. Disclosures Larochelle: Stem Cell Technologies: Patents & Royalties: StemDiff Hematopoietic Differentiation Kit.

Abstract 12754: Infused CD34 Cell Dose, not Bone Marrow CD34+ Cell Content, Improves Clinical Outcomes and LVEF in Patients With Left Ventricular Dysfunction post STEMI: Results of the PreSERVE-AMI Trial

Circulation ◽  
2015 ◽  
Vol 132 (suppl_3) ◽  
Author(s):  
Arshed Quyyumi ◽  
Dean J Kereiakes ◽  
David M Shavelle ◽  
Timothy D Henry ◽  
Ahmed Abdel-Latif ◽  
...  
Keyword(s):  
Bone Marrow ◽  
Left Ventricular ◽  
Lv Function ◽  
Regression Analyses ◽  
Cell Content ◽  
Cell Dose ◽  
Cell Counts ◽  
Intracoronary Infusion ◽  
Cd34 Cells ◽  
Dose Dependent

Background: Prior studies suggest an association between naturally high circulating levels of CD34+ cells (CD34) and better outcomes following ischemic events. Pts in the treatment arm of the PreSERVE-AMI study were infused with all CD34 yielded from their bone marrow harvest, resulting in pts with naturally higher CD34 cell counts being infused with a higher cell dose. To determine whether selection bias may have influenced results, we performed an analysis of the relationship between BM CD34 content and outcomes in PreSERVE-AMI. Methods: PreSERVE-AMI is a Phase 2, randomized, double-blind, placebo-controlled trial of autologous, BM derived CD34 (CLBS10) in pts with LV dysfunction (LVEF ≤48% by MRI) ≥4 days post-STEMI. All pts underwent mini BM harvest and were randomized 1:1 to intracoronary infusion of CD34 (minimum dose 10M±20% cells) or placebo (cell diluent). Primary safety endpoints included AEs, SAEs and MACE (CV mortality, HF hospitalization, reinfarction, revascularization). Secondary endpoints included LVEF change from baseline. Regression analyses were performed to assess the influence of BM CD34 content on outcomes. Results: 161 patients were randomized and received intracoronary infusion (Jan 2013-Dec 2014). Mean age was 57±10 and 82% were men. Mean follow-up time was 18 mo, and safety data are accruing. BM CD34 content was not associated with differences in baseline characteristics with the exception of diabetes prevalence being higher in those with higher BM CD34 count (p=0.03). In controls, regression analyses showed no association of BM CD34 content with rates of MACE (p=0.2), SAEs (p=0.4), or change in LVEF (p=0.9). However, in CD34 treated pts, there was a dose-dependent improvement in LV function (p=0.045) and dose-dependent trends in MACE and SAE reduction compared to controls. As previously reported SPECT perfusion improved to a similar degree in control and treated pts. Conclusions: These data suggest that intracoronary infusion of CD34 cells, rather than BM CD34 cell content, is responsible for the benefit observed in the CD34 treatment group. The finding of a dose-response relationship is an important advance for defining the CD34 as an active agent for tissue repair. Future studies will further define CD34 dosimetry.

The Effectiveness of the Fused Weighted Filter Feature Selection Method to Improve Software Fault Prediction

2016 ◽  
Vol 8 ◽  
pp. 5 ◽  
Author(s):  
Fatemeh Alighardashi ◽  
Mohammad Ali Zare Chahooki
Keyword(s):  
Feature Selection ◽  
Feature Selection Method ◽  
Selection Method ◽  
Machine Learning Algorithms ◽  
Fault Prediction ◽  
Filter Method ◽  
Selection Methods ◽  
Software Projects ◽  
Software Fault Prediction ◽  
Software Fault

Improving the software product quality before releasing by periodic tests is one of the most expensive activities in software projects. Due to limited resources to modules test in software projects, it is important to identify fault-prone modules and use the test sources for fault prediction in these modules. Software fault predictors based on machine learning algorithms, are effective tools for identifying fault-prone modules. Extensive studies are being done in this field to find the connection between features of software modules, and their fault-prone. Some of features in predictive algorithms are ineffective and reduce the accuracy of prediction process. So, feature selection methods to increase performance of prediction models in fault-prone modules are widely used. In this study, we proposed a feature selection method for effective selection of features, by using combination of filter feature selection methods. In the proposed filter method, the combination of several filter feature selection methods presented as fused weighed filter method. Then, the proposed method caused convergence rate of feature selection as well as the accuracy improvement. The obtained results on NASA and PROMISE with ten datasets, indicates the effectiveness of proposed method in improvement of accuracy and convergence of software fault prediction.

Use of Damage Control and the Open Abdomen in Combat

2013 ◽  
Vol 79 (8) ◽  
pp. 747-753 ◽  
Author(s):  
Benjamin Bograd ◽  
Carlos Rodriguez ◽  
Richard Amdur ◽  
Fred Gage ◽  
Eric Elster ◽  
...  
Keyword(s):  
Length Of Stay ◽  
Multivariate Regression ◽  
Injury Severity ◽  
Open Abdomen ◽  
Damage Control ◽  
Unit Length ◽  
Significant Risk ◽  
Hospital Length Of Stay ◽  
Study Cohort ◽  
Regression Analyses

Despite the well-documented use of damage control laparotomy (DCL) in civilian trauma, its use has not been well described in the combat setting. Therefore, we sought to document the use of DCL and to investigate its effect on patient outcome. Prospective data were collected on 1603 combat casualties injured between April 2003 and January 2009. One hundred seventy patients (11%) underwent an exploratory laparotomy (ex lap) in theater and comprised the study cohort. DCL was defined as an abbreviated ex lap resulting in an open abdomen. Patients were stratified by age, Injury Severity Score (ISS), Glasgow Coma Score (GCS), mechanism of injury, and blood product administration. Multivariate regression analyses were used to determine risks factors for intensive care unit length of stay (ICU LOS), hospital length of stay (HLOS), and the need for DCL. Mean age of the cohort was 24 ± 5 years, ISS was 21 ± 11, and 94 per cent sustained penetrating injury. Patients with DCL comprised 50.6 per cent (n = 86) of the study cohort and had significant increases in ICU admission ( P < 0.001), ICU LOS ( P < 0.001), HLOS ( P < 0.05), ventilator days ( P < 0.001), abdominal complications ( P < 0.05), but not mortality ( P = 0.65) compared with patients without DCL. When compared with the non-DCL group, patients undergoing DCL required significantly more blood products (packed red blood cells, fresh-frozen plasma, platelets, and cryoprecipitate; P < 0.001). Multivariate regression analyses revealed blood transfusion and GCS as significant risk factors for DCL ( P < 0.05). Patients undergoing DCL had increased complications and resource use but not mortality compared with patients not undergoing DCL. The need for combat DCL may be different compared with civilian use. Prospective studies to evaluate outcomes of DCL are warranted.

A fuzzy gaussian rank aggregation ensemble feature selection method for microarray data

2021 ◽  
Vol 24 (4) ◽  
pp. 289-301
Author(s):  
B. Venkatesh ◽  
J. Anuradha
Keyword(s):  
Feature Selection ◽  
Microarray Data ◽  
Classification Accuracy ◽  
Performance Metrics ◽  
Feature Selection Method ◽  
Selection Method ◽  
Support Vector ◽  
Svm Classifier ◽  
Binary Particle Swarm Optimization ◽  
Selection Methods

In Microarray Data, it is complicated to achieve more classification accuracy due to the presence of high dimensions, irrelevant and noisy data. And also It had more gene expression data and fewer samples. To increase the classification accuracy and the processing speed of the model, an optimal number of features need to extract, this can be achieved by applying the feature selection method. In this paper, we propose a hybrid ensemble feature selection method. The proposed method has two phases, filter and wrapper phase in filter phase ensemble technique is used for aggregating the feature ranks of the Relief, minimum redundancy Maximum Relevance (mRMR), and Feature Correlation (FC) filter feature selection methods. This paper uses the Fuzzy Gaussian membership function ordering for aggregating the ranks. In wrapper phase, Improved Binary Particle Swarm Optimization (IBPSO) is used for selecting the optimal features, and the RBF Kernel-based Support Vector Machine (SVM) classifier is used as an evaluator. The performance of the proposed model are compared with state of art feature selection methods using five benchmark datasets. For evaluation various performance metrics such as Accuracy, Recall, Precision, and F1-Score are used. Furthermore, the experimental results show that the performance of the proposed method outperforms the other feature selection methods.

scholarly journals Positivity to Cocaine and/or Benzoylecgonine in Confirmation Analyses for On-Road Tests in Spain

2021 ◽  
Vol 18 (10) ◽  
pp. 5371
Author(s):  
Francisco Herrera-Gómez ◽  
Eduardo Gutiérrez-Abejón ◽  
Mercedes García-Mingo ◽  
F. Javier Álvarez
Keyword(s):  
Multivariate Regression ◽  
Real Life ◽  
Multivariate Logistic Regression Analysis ◽  
Regression Analyses ◽  
Multivariate Logistic Regression ◽  
Life Data ◽  
Polysubstance Use ◽  
Real Life Data ◽  
Other Substances ◽  
Positive Results

We are using real-life data in order to determine the prevalence of driving with the presence of cocaine and/or benzoylecgonine (BZE), their concentrations, and their use in combination with other drugs. This study assessed data on Spanish drivers with confirmed drug-positive results recorded by the Spanish National Traffic Agency from 2011–2016. Frequencies of positivity for cocaine and/or BZE and concentration of such substances were obtained. Comparisons and univariate and multivariate regression analyses were performed. Drivers who tested positive for cocaine and/or BZE accounted for 48.59% of the total positive results for drugs. In positive cases for both cocaine and BZE, other substances were detected in 81.74%: delta-9-tetrahydrocannabinol (THC) (68.19%), opioids (20.78%) and amphetamine-like substances (16.76%). In the multivariate logistic regression analysis, the frequency of cocaine and/or BZE positive cases decreased with age (OR:0.97) and were less likely among women (OR:0.63). Concentrations (ng/mL) of cocaine (249.30) and BZE (137.90) were higher when both substances were detected together than when detected alone. Positivity to cocaine represented an important proportion among Spanish drivers who tested positive for drugs, and polysubstance use was especially observed in more than 8 out of 10 positive cases for cocaine and/or BZE.

Retirement intentions: what is the role of push factors in predicting retirement intentions?

2012 ◽  
Vol 33 (6) ◽  
pp. 988-1008 ◽  
Author(s):  
JODI OAKMAN ◽  
YVONNE WELLS
Keyword(s):  
Job Satisfaction ◽  
Social Cohesion ◽  
Multivariate Regression ◽  
Regression Analyses ◽  
Length Of Service ◽  
Older Employees ◽  
Work Factors ◽  
Retirement Intentions ◽  
Insight Into

ABSTRACTPopulation ageing will significantly impact labour markets in most Organisation for Economic Cooperation and Development countries and as a result individuals will need to remain in paid employment for longer to fund their retirement years. This study examines the retirement intentions of employees of a large public-sector organisation located in Victoria, Australia that was interested in developing policies to assist with retention of their mature-age workforce. Multivariate regression analyses were used to identify the most important predictors of intention to retire. The dependent variable, Intended timing of retirement, was analysed in two forms, as continuous and dichotomised measures. Age and Length of service were strong independent predictors of Intention to retire soon (within five years). Of the work factors that were analysed (Job satisfaction, Job demands, Job control, and Social cohesion), low Job satisfaction and high Social cohesion scores indicated an increased likelihood of retiring soon. The results provide some insight into the development of organisational interventions that might assist with retaining older employees for longer.

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