White blood cells (WBC) or leukocytes are a vital component ofthe blood which forms the immune system, which is accountable to fightforeign elements. The WBC images can be exposed to different data analysisapproaches ...White blood cells (WBC) or leukocytes are a vital component ofthe blood which forms the immune system, which is accountable to fightforeign elements. The WBC images can be exposed to different data analysisapproaches which categorize different kinds of WBC. Conventionally, laboratorytests are carried out to determine the kind of WBC which is erroneousand time consuming. Recently, deep learning (DL) models can be employedfor automated investigation of WBC images in short duration. Therefore,this paper introduces an Aquila Optimizer with Transfer Learning basedAutomated White Blood Cells Classification (AOTL-WBCC) technique. Thepresented AOTL-WBCC model executes data normalization and data augmentationprocess (rotation and zooming) at the initial stage. In addition,the residual network (ResNet) approach was used for feature extraction inwhich the initial hyperparameter values of the ResNet model are tuned by theuse of AO algorithm. Finally, Bayesian neural network (BNN) classificationtechnique has been implied for the identification of WBC images into distinctclasses. The experimental validation of the AOTL-WBCC methodology isperformed with the help of Kaggle dataset. The experimental results foundthat the AOTL-WBCC model has outperformed other techniques which arebased on image processing and manual feature engineering approaches underdifferent dimensions.展开更多
Background and Objective The white blood cell count to mean platelet volume ratio(WMR)has recently been described as a predictor of cardiovascular events in patients who undergo percutaneous coronary intervention(PCI)...Background and Objective The white blood cell count to mean platelet volume ratio(WMR)has recently been described as a predictor of cardiovascular events in patients who undergo percutaneous coronary intervention(PCI).The aim of this study was to investigate the usefulness of admission WMR in predicting outcomes in patients with acute coronary syndrome(ACS).展开更多
White blood cells(WBC)are immune system cells,which is why they are also known as immune cells.They protect the human body from a variety of dangerous diseases and outside invaders.The majority of WBCs come from red b...White blood cells(WBC)are immune system cells,which is why they are also known as immune cells.They protect the human body from a variety of dangerous diseases and outside invaders.The majority of WBCs come from red bone marrow,although some come from other important organs in the body.Because manual diagnosis of blood disorders is difficult,it is necessary to design a computerized technique.Researchers have introduced various automated strategies in recent years,but they still face several obstacles,such as imbalanced datasets,incorrect feature selection,and incorrect deep model selection.We proposed an automated deep learning approach for classifying white blood disorders in this paper.The data augmentation approach is initially used to increase the size of a dataset.Then,a Darknet-53 pre-trained deep learning model is used and finetuned according to the nature of the chosen dataset.On the fine-tuned model,transfer learning is used,and features engineering is done on the global average pooling layer.The retrieved characteristics are subsequently improved with a specified number of iterations using a hybrid reformed binary grey wolf optimization technique.Following that,machine learning classifiers are used to classify the selected best features for final classification.The experiment was carried out using a dataset of increased blood diseases imaging and resulted in an improved accuracy of over 99%.展开更多
AIM: To examine characteristics of patients with blood urea nitrogen(BUN) levels higher and lower than the normal limit.METHODS: Patient records between April 2011 and March 2014 were analyzed retrospectively. During ...AIM: To examine characteristics of patients with blood urea nitrogen(BUN) levels higher and lower than the normal limit.METHODS: Patient records between April 2011 and March 2014 were analyzed retrospectively. During this time, 3296 patients underwent upper endoscopy. In total, 50 male(69.2 ± 13.2 years) and 26 female(72.3 ± 10.2 years) patients were assessed. Patients were divided into two groups based on BUN levels: higher than the normal limit(21.0 mg/d L)(H) and lower thanthe normal limit(L). One-way analysis of variance was performed to reveal differences in the variables between the H and L groups. Fisher's exact test was used to compare the percentage of patients with gastric ulcer or gastric cancer in the H and L groups.RESULTS: White blood cell count was higher in the H group than in the L group(P = 0.0047). Hemoglobin level was lower in the H group than in the L group(P = 0.0307). Glycated hemoglobin was higher in the H group than in the L group(P = 0.0264). The percentage of patients with gastric ulcer was higher in the H group(P = 0.0002). The H group contained no patients with gastric cancer.CONCLUSION: Patients with BUN ≥ 21 mg/d L might have more severe upper gastrointestinal bleeding.展开更多
Hyperuricemia(HUA)is a risk factor for chronic kidney disease(CKD).The relationship between HUA and white blood cell(WBC)count remains unknown.A sampling survey for CKD was conducted in Sanlin community in 2012 and 20...Hyperuricemia(HUA)is a risk factor for chronic kidney disease(CKD).The relationship between HUA and white blood cell(WBC)count remains unknown.A sampling survey for CKD was conducted in Sanlin community in 2012 and 2014.CKD was defined as proteinuria in at least the microalbuminuric stage or an estimated GFR of 60 mL/(min·1.73 m2).HUA was defined as serum uric acid>420µmol/L in men and>360µmol/L in women.This study included 1024 participants.The prevalence of HUA was 17.77%.Patients with HUA were more likely to have higher levels of WBC count,which was positively associated with HUA prevalence.This association was also observed in participants without CKD,diabetes mellitus,hyperlipidemia,or obesity.Multivariate logistic regression analysis showed that WBC count was independently associated with the risk for HUA in male and female participants.Compared with participants without HUA,inflammatory factors such as high-sensitivity C-reactive protein,tumor necrosis factor-α,and interleukin 6 increased in participants with HUA.Hence,WBC count is positively associated with HUA,and this association is independent of conventional risk factors for CKD.展开更多
基金The Deanship of Scientific Research (DSR)at King Abdulaziz University (KAU),Jeddah,Saudi Arabia has funded this project,under Grant No.KEP-1–120–42.
文摘White blood cells (WBC) or leukocytes are a vital component ofthe blood which forms the immune system, which is accountable to fightforeign elements. The WBC images can be exposed to different data analysisapproaches which categorize different kinds of WBC. Conventionally, laboratorytests are carried out to determine the kind of WBC which is erroneousand time consuming. Recently, deep learning (DL) models can be employedfor automated investigation of WBC images in short duration. Therefore,this paper introduces an Aquila Optimizer with Transfer Learning basedAutomated White Blood Cells Classification (AOTL-WBCC) technique. Thepresented AOTL-WBCC model executes data normalization and data augmentationprocess (rotation and zooming) at the initial stage. In addition,the residual network (ResNet) approach was used for feature extraction inwhich the initial hyperparameter values of the ResNet model are tuned by theuse of AO algorithm. Finally, Bayesian neural network (BNN) classificationtechnique has been implied for the identification of WBC images into distinctclasses. The experimental validation of the AOTL-WBCC methodology isperformed with the help of Kaggle dataset. The experimental results foundthat the AOTL-WBCC model has outperformed other techniques which arebased on image processing and manual feature engineering approaches underdifferent dimensions.
文摘Background and Objective The white blood cell count to mean platelet volume ratio(WMR)has recently been described as a predictor of cardiovascular events in patients who undergo percutaneous coronary intervention(PCI).The aim of this study was to investigate the usefulness of admission WMR in predicting outcomes in patients with acute coronary syndrome(ACS).
基金This research project was supported by the Deanship of Scientific Research,Prince Sattam Bin Abdulaziz University,KSA,Project Grant No.2021/01/18613.
文摘White blood cells(WBC)are immune system cells,which is why they are also known as immune cells.They protect the human body from a variety of dangerous diseases and outside invaders.The majority of WBCs come from red bone marrow,although some come from other important organs in the body.Because manual diagnosis of blood disorders is difficult,it is necessary to design a computerized technique.Researchers have introduced various automated strategies in recent years,but they still face several obstacles,such as imbalanced datasets,incorrect feature selection,and incorrect deep model selection.We proposed an automated deep learning approach for classifying white blood disorders in this paper.The data augmentation approach is initially used to increase the size of a dataset.Then,a Darknet-53 pre-trained deep learning model is used and finetuned according to the nature of the chosen dataset.On the fine-tuned model,transfer learning is used,and features engineering is done on the global average pooling layer.The retrieved characteristics are subsequently improved with a specified number of iterations using a hybrid reformed binary grey wolf optimization technique.Following that,machine learning classifiers are used to classify the selected best features for final classification.The experiment was carried out using a dataset of increased blood diseases imaging and resulted in an improved accuracy of over 99%.
文摘AIM: To examine characteristics of patients with blood urea nitrogen(BUN) levels higher and lower than the normal limit.METHODS: Patient records between April 2011 and March 2014 were analyzed retrospectively. During this time, 3296 patients underwent upper endoscopy. In total, 50 male(69.2 ± 13.2 years) and 26 female(72.3 ± 10.2 years) patients were assessed. Patients were divided into two groups based on BUN levels: higher than the normal limit(21.0 mg/d L)(H) and lower thanthe normal limit(L). One-way analysis of variance was performed to reveal differences in the variables between the H and L groups. Fisher's exact test was used to compare the percentage of patients with gastric ulcer or gastric cancer in the H and L groups.RESULTS: White blood cell count was higher in the H group than in the L group(P = 0.0047). Hemoglobin level was lower in the H group than in the L group(P = 0.0307). Glycated hemoglobin was higher in the H group than in the L group(P = 0.0264). The percentage of patients with gastric ulcer was higher in the H group(P = 0.0002). The H group contained no patients with gastric cancer.CONCLUSION: Patients with BUN ≥ 21 mg/d L might have more severe upper gastrointestinal bleeding.
基金This study was supported by the National Project for the Construction of Clinical Key Specialty,Project of Special Fund for Health-Scientific Research(No.201002010)National Key Research and Development Program of China(No.2016YFC 1305402)+4 种基金National Key Technology R&D Program(No.2011BAI10B00)Experimental Animal Project of Shanghai Science and Technology Committee(No.15140902800)Key Projects of National Basic Research Program of China(973 Program,Nos.2012CB517700 and 2012CB517604)National Natural Science Foundation of China(Nos.81700647,81270782,and 30771000)Key Discipline Construction Projects approved by the Health Development Planning Commission of Shanghai.
文摘Hyperuricemia(HUA)is a risk factor for chronic kidney disease(CKD).The relationship between HUA and white blood cell(WBC)count remains unknown.A sampling survey for CKD was conducted in Sanlin community in 2012 and 2014.CKD was defined as proteinuria in at least the microalbuminuric stage or an estimated GFR of 60 mL/(min·1.73 m2).HUA was defined as serum uric acid>420µmol/L in men and>360µmol/L in women.This study included 1024 participants.The prevalence of HUA was 17.77%.Patients with HUA were more likely to have higher levels of WBC count,which was positively associated with HUA prevalence.This association was also observed in participants without CKD,diabetes mellitus,hyperlipidemia,or obesity.Multivariate logistic regression analysis showed that WBC count was independently associated with the risk for HUA in male and female participants.Compared with participants without HUA,inflammatory factors such as high-sensitivity C-reactive protein,tumor necrosis factor-α,and interleukin 6 increased in participants with HUA.Hence,WBC count is positively associated with HUA,and this association is independent of conventional risk factors for CKD.
