scholarly journals Equivalence of spray-dried K2EDTA,spray-dried K3EDTA, and liquid K3EDTA anticoagulated blood samples for routine blood center or transfusion service testing

2020 ◽  
Vol 19 (4) ◽  
pp. 117-121
Author(s):  
Stacie Leathem ◽  
Nicole Dodge Zantek ◽  
Marti Kemper ◽  
Laura Korte ◽  
Al Langeberg ◽  
...  
Keyword(s):  
Blood Samples ◽  
Blood Center ◽  
Routine Blood ◽  
Service Testing ◽  
Spray Dried

RETRACTED: Eosinophilia in routine blood samples as a biomarker for solid tumor development – A study based on The Copenhagen Primary Care Differential Count (CopDiff) Database

2013 ◽  
pp. 1-7
Author(s):  
Christen Lykkegaard Andersen ◽  
Volkert Dirk Siersma ◽  
Hans Carl Hasselbalch ◽  
Hanne Lindegaard ◽  
Hanne Vestergaard ◽  
...  
Keyword(s):  
Primary Care ◽  
Solid Tumor ◽  
Tumor Development ◽  
Differential Count ◽  
Blood Samples ◽  
Routine Blood

scholarly journals An Artificial Intelligence Model to Predict the Mortality of COVID-19 Patients at Hospital Admission Time Using Routine Blood Samples: Development and Validation of an Ensemble Model (Preprint)

2020 ◽  
Author(s):  
Hoon Ko ◽  
Heewon Chung ◽  
Wu Seong Kang ◽  
Chul Park ◽  
Do Wan Kim ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Random Forest ◽  
Hospital Admission ◽  
Deep Neural Network ◽  
Care Providers ◽  
Blood Samples ◽  
Routine Blood ◽  
Forest Models ◽  
Random Forest Models

BACKGROUND COVID-19, which is accompanied by acute respiratory distress, multiple organ failure, and death, has spread worldwide much faster than previously thought. However, at present, it has limited treatments. OBJECTIVE To overcome this issue, we developed an artificial intelligence (AI) model of COVID-19, named EDRnet (ensemble learning model based on deep neural network and random forest models), to predict in-hospital mortality using a routine blood sample at the time of hospital admission. METHODS We selected 28 blood biomarkers and used the age and gender information of patients as model inputs. To improve the mortality prediction, we adopted an ensemble approach combining deep neural network and random forest models. We trained our model with a database of blood samples from 361 COVID-19 patients in Wuhan, China, and applied it to 106 COVID-19 patients in three Korean medical institutions. RESULTS In the testing data sets, EDRnet provided high sensitivity (100%), specificity (91%), and accuracy (92%). To extend the number of patient data points, we developed a web application (BeatCOVID19) where anyone can access the model to predict mortality and can register his or her own blood laboratory results. CONCLUSIONS Our new AI model, EDRnet, accurately predicts the mortality rate for COVID-19. It is publicly available and aims to help health care providers fight COVID-19 and improve patients’ outcomes.

scholarly journals Application of Immunohistochemistry to Monitor the CD3+T Cell Subset Counts in Patients with Sepsis

2021 ◽  
Author(s):  
Ronghua Deng ◽  
Lan Deng ◽  
Yamin Tian ◽  
Guopeng Deng ◽  
Jiannan Zeng ◽  
...  
Keyword(s):  
T Cell ◽  
Peripheral Blood ◽  
Cell Subset ◽  
Sepsis Group ◽  
Blood Samples ◽  
T Cell Subset ◽  
Blood Tests ◽  
Control Subjects ◽  
Routine Blood ◽  
Healthy Control

Abstract [Objective]: To evaluate the application significance of Immunohistochemistry for monitoring peripheral blood CD3 + T cell subset (CD3+/CD3 + CD4+/CD3 + CD8+) counts in patients with sepsis.[Methods]: Two peripheral blood samples of 117 patients with sepsis on the first day of admission (D1) and 20 healthy control subjects were collected, and two peripheral blood samples of 20 patients with sepsis on the fourth day of admission (D4) were randomly collected and used to detect the lymphocyte counts of routine blood tests and CD3 + T cell subset count by Immunohistochemistry; the lymphocyte count levels between the sepsis group and the healthy control group were compared, and the correlation between the two in the same group were analyzed.[Results]:lymphocyte counts by routine blood tests and CD3 + T cell subset counts of patients with sepsis were significantly lower than those in healthy control subjects (P < 0.01). In the surviving group, the mean values of D4 CD3 + T cell subset counts increased significantly compared with D1, while the nonsurviving group did not rebound significantly; There was a significant positive correlation between lymphocyte counts by routine blood tests and CD3 + T lymphocyte subset counts in patients with sepsis and the healthy control subjects. (P < 0.01).[Conclusion]: Detection of CD3 + T cell subset counts by immunohistochemical method can reflect the cellular immune status of patients at a given time, thus it can be used as one of the immune monitoring methods in patients with sepsis.

scholarly journals A Tree-based Mortality Prediction Model of COVID-19 from Routine Blood Samples

2021 ◽  
Author(s):  
Nunung Nurul Qomariyah ◽  
Ardimas Andi Purwita ◽  
Sri Dhuny Atas Asri ◽  
Dimitar Kazakov
Keyword(s):  
Prediction Model ◽  
Mortality Prediction ◽  
Blood Samples ◽  
Routine Blood ◽  
Mortality Prediction Model

scholarly journals Eosinophilia in routine blood samples as a biomarker for solid tumor development – A study based on The Copenhagen Primary Care Differential Count (CopDiff) Database

2014 ◽  
Vol 53 (9) ◽  
pp. 1245-1250 ◽  
Author(s):  
Christen Lykkegaard Andersen ◽  
Volkert Dirk Siersma ◽  
Hans Carl Hasselbalch ◽  
Hanne Lindegaard ◽  
Hanne Vestergaard ◽  
...  
Keyword(s):  
Primary Care ◽  
Solid Tumor ◽  
Tumor Development ◽  
Differential Count ◽  
Blood Samples ◽  
Routine Blood

scholarly journals An Artificial Intelligence Model to Predict the Mortality of COVID-19 Patients at Hospital Admission Time Using Routine Blood Samples: Development and Validation of an Ensemble Model

10.2196/25442 ◽  
2020 ◽  
Vol 22 (12) ◽  
pp. e25442
Author(s):  
Hoon Ko ◽  
Heewon Chung ◽  
Wu Seong Kang ◽  
Chul Park ◽  
Do Wan Kim ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Random Forest ◽  
Hospital Admission ◽  
Deep Neural Network ◽  
Care Providers ◽  
Blood Samples ◽  
Routine Blood ◽  
Forest Models ◽  
Random Forest Models

Background COVID-19, which is accompanied by acute respiratory distress, multiple organ failure, and death, has spread worldwide much faster than previously thought. However, at present, it has limited treatments. Objective To overcome this issue, we developed an artificial intelligence (AI) model of COVID-19, named EDRnet (ensemble learning model based on deep neural network and random forest models), to predict in-hospital mortality using a routine blood sample at the time of hospital admission. Methods We selected 28 blood biomarkers and used the age and gender information of patients as model inputs. To improve the mortality prediction, we adopted an ensemble approach combining deep neural network and random forest models. We trained our model with a database of blood samples from 361 COVID-19 patients in Wuhan, China, and applied it to 106 COVID-19 patients in three Korean medical institutions. Results In the testing data sets, EDRnet provided high sensitivity (100%), specificity (91%), and accuracy (92%). To extend the number of patient data points, we developed a web application (BeatCOVID19) where anyone can access the model to predict mortality and can register his or her own blood laboratory results. Conclusions Our new AI model, EDRnet, accurately predicts the mortality rate for COVID-19. It is publicly available and aims to help health care providers fight COVID-19 and improve patients’ outcomes.

scholarly journals Serum cadmium levels in a sample of blood donors in the Western Amazon, Brazil, 2010-2011

2014 ◽  
Vol 30 (2) ◽  
pp. 403-414 ◽  
Author(s):  
Andre Ricardo Maia da Costa de Faro ◽  
Wagner de Jesus Pinto ◽  
Aldo Pacheco Ferreira ◽  
Fernando Barbosa Junior ◽  
Vanessa Cristina de Oliveira Souza ◽  
...  
Keyword(s):  
Blood Donors ◽  
Reference Value ◽  
Positive Association ◽  
Cross Sectional Study ◽  
Sectional Study ◽  
Cross Sectional ◽  
Blood Samples ◽  
Blood Center ◽  
Years Of Schooling

A cross-sectional study was conducted to determine the distribution of serum cadmium (Cd) levels in blood donors in Rio Branco, Acre State, Brazil. Blood samples were obtained from 922 volunteer blood donors from 18 to 65 years of age at the Hemoacre blood center in 2010-2011. Mean serum Cd was 0.37µg/L (95%CI: 0.33-0.41). Increased serum Cd was associated with lower schooling; individuals with less than five years of schooling showed a mean Cd of 0.61µg/L (95%CI: 0.34-0.89), compared to 0.34µg/L (95%CI: 0.28-0.40) among those with more than nine years of schooling. Mean serum Cd was three times higher among smokers. Smoking showed a positive association with Cd level, with an OR of 12.36 (95%CI: 7.70-19.84). Meanwhile, serum Cd was lower among individuals that regularly drank tea, as compared to non-tea drinkers. Serum Cd levels were mostly below the reference value (88.3% of participants). Mean serum Cd in the current study indicates that in general the population studied here is not exposed to worrisome Cd levels.

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