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- CorrectionAçık Erişim2022Corrigendum to “Forecasting of Oxidant/Antioxidant levels of COVID-19 patients by using Expert models with biomarkers used in the Diagnosis/Prognosis of COVID-19” [Int. Immunopharmacol. 100 (2021) 108127](S1567576921007633)(10.1016/j.intimp.2021.108127)
Huyut, Mehmet Tahir; Huyut, Zübeyir
In this article, we noticed some errors in the calculations for R2 and error metrics in Table 3. For this reason, these calculations were re-done in detail and new results are presented in the table below. We apologize for the inaccuracy in the previous calculations.
RDL Kalıcı Kaydıİncele - ArticleYalnız künye2022The Effect of Some Symptoms and Features During the Infection Period on the Level of Anxiety and Depression of Adults after Recovery from COVID-19
Huyut, Mehmet Tahir; Kocatürk, İdris
Background: Considering the previous evidence regarding SARS/MERS outbreaks, ıt is expected that emergent psychiatric conditions such as mood disorders, anxiety, and depression disorders may occur in survivors of COVID-19. Objectives: In this study, the effects of clinical/neurological symptoms and sociodemographic characteristics of COVID-19 patients during the infection period on the level of anxiety/depression after recovery were investigated. Methods: Firstly, the socio-demographic characteristics of the participants and their clinical/neurological symptoms during the infection period were determined. In addition, the Beck anxiety/depression scale was administered to the participants upon discharge. Then, the odds ratio of the variables was calculated by applying the Poisson regression method to determine the predictors affecting the level of anxiety/depression. Results: In this study, the clinical/neurological symptoms and the sociodemographic characteristics affecting the level of anxiety/depression were found to be associated with adults (mean age 45-years). The number of days of hospitalization was the only clinical feature that increased both anxiety and depression levels. In addition, post-discharge sleep disorder, headache, and alcohol use were found to be predictors of increased anxiety levels. In addition, smoking, the presence of a person with COVID-19 infection in the immediate vicinity, traveling in the last month before the illness, fever, and loss of smell during the infection period was found to increase the level of depression. Moreover, asthma was the only significant comorbidity that increased the anxiety level, while diabetes was the only comorbidity that increased the depression level. Conclusion: It is expected that this study will support social-psychological intervention and have some effects on reducing depressive/anxiety symptoms, which can vary widely in adults during stressful events.
RDL Kalıcı Kaydıİncele - ArticleAçık Erişim2022Diagnosis and Prognosis of COVID‐19 Disease Using Routine Blood Values and LogNNet Neural Network
Huyut, Mehmet Tahir; Velichko, Andrei
Since February 2020, the world has been engaged in an intense struggle with the COVID‐19 disease, and health systems have come under tragic pressure as the disease turned into a pandemic. The aim of this study is to obtain the most effective routine blood values (RBV) in the diagnosis and prognosis of COVID‐19 using a backward feature elimination algorithm for the LogNNet reservoir neural network. The first dataset in the study consists of a total of 5296 patients with the same number of negative and positive COVID‐19 tests. The LogNNet‐model achieved the accuracy rate of 99.5% in the diagnosis of the disease with 46 features and the accuracy of 99.17% with only mean corpuscular hemoglobin concentration, mean corpuscular hemoglobin, and acti-vated partial prothrombin time. The second dataset consists of a total of 3899 patients with a diagnosis of COVID‐19 who were treated in hospital, of which 203 were severe patients and 3696 were mild patients. The model reached the accuracy rate of 94.4% in determining the prognosis of the disease with 48 features and the accuracy of 82.7% with only erythrocyte sedimentation rate, neu-trophil count, and C reactive protein features. Our method will reduce the negative pressures on the health sector and help doctors to understand the pathogenesis of COVID‐19 using the key fea-tures. The method is promising to create mobile health monitoring systems in the Internet of Things.
RDL Kalıcı Kaydıİncele - ArticleAçık Erişim2022Detection of Risk Predictors of COVID-19 Mortality with Classifier Machine Learning Models Operated with Routine Laboratory Biomarkers
Huyut, Mehmet Tahir; Velichko, Andrei; Belyaev, Maksim
Early evaluation of patients who require special care and who have high death-expectancy in COVID-19, and the effective determination of relevant biomarkers on large sample-groups are important to reduce mortality. This study aimed to reveal the routine blood-value predictors of COVID-19 mortality and to determine the lethal-risk levels of these predictors during the disease process. The dataset of the study consists of 38 routine blood-values of 2597 patients who died (n = 233) and those who recovered (n = 2364) from COVID-19 in August–December, 2021. In this study, the histogram-based gradient-boosting (HGB) model was the most successful machine-learning classifier in detecting living and deceased COVID-19 patients (with squared F1 metrics F12 = 1). The most efficient binary combinations with procalcitonin were obtained with D-dimer, ESR, D-Bil and ferritin. The HGB model operated with these feature pairs correctly detected almost all of the patients who survived and those who died (precision > 0.98, recall > 0.98, F12 > 0.98). Furthermore, in the HGB model operated with a single feature, the most efficient features were procalcitonin (F12 = 0.96) and ferritin (F12 = 0.91). In addition, according to the two-threshold approach, ferritin values between 376.2 μg/L and 396.0 μg/L (F12 = 0.91) and procalcitonin values between 0.2 μg/L and 5.2 μg/L (F12 = 0.95) were found to be fatal risk levels for COVID-19. Considering all the results, we suggest that many features combined with these features, especially procalcitonin and ferritin, operated with the HGB model, can be used to achieve very successful results in the classification of those who live, and those who die from COVID-19. Moreover, we strongly recommend that clinicians consider the critical levels we have found for procalcitonin and ferritin properties, to reduce the lethality of the COVID-19 disease.
RDL Kalıcı Kaydıİncele - ArticleYalnız künye2022Can zaprinast and avanafil induce the levels of angiogenesis, bone morphogenic protein 2, 4 and 7 in kidney of ovariectomised rats?
Huyut, Zübeyir; Bakan, Nuri; Yıldırım, Serkan; Akbay, Halil İbrahim; Huyut, Mehmet Tahir; Ahlatçı, Adem; Uçar, Bünyamin
Objective: This study investigated effects of zaprinast and avanafil on angiogenesis, vascular endothelial growth factor (VEGF), bone morphogenic protein (BMP) 2, 4 and 7. Methods: Female rats were randomly divided into four groups (n = 6). Sham; abdomen was approximately 2 cm opened and closed. Ovariectomised (OVX); abdomen was opened 2 cm and the ovaries were cut. OVX + zaprinast and OVX + avanafil groups; after the same procedure with OVX, 10 mg/kg zaprinast and avanafil were orally administered for 2 month, respectively. Angiogenesis and the levels of VEGF, BMP2, 4 and 7 were determined. Results: VEGF, BMP2, 4 and 7 levels in OVX + zaprinast and especially OVX + avanafil groups were higher than the sham and OVX (p <.05). However, only VEGF and BMP2 levels in OVX + zaprinast group were significant according to sham (p <.05). Also, angiogenesis in OVX + zaprinast and OVX + avanafil groups was dominant according to sham and OVX (p <.05). Conclusions: Zaprinast and avanafil induced BMP2, 4 and 7 levels synergistically with increased VEGF and angiogenesis in renal tissue.
RDL Kalıcı Kaydıİncele - ArticleAçık Erişim2022Machine Learning Sensors for Diagnosis of COVID-19 Disease Using Routine Blood Values for Internet of Things Application
Velichko, Andrei; Huyut, Mehmet Tahir; Belyaev, Maksim; Izotov, Yuriy; Korzun, Dmitry
Healthcare digitalization requires effective applications of human sensors, when various parameters of the human body are instantly monitored in everyday life due to the Internet of Things (IoT). In particular, machine learning (ML) sensors for the prompt diagnosis of COVID-19 are an important option for IoT application in healthcare and ambient assisted living (AAL). Determining a COVID-19 infected status with various diagnostic tests and imaging results is costly and time-consuming. This study provides a fast, reliable and cost-effective alternative tool for the diagnosis of COVID-19 based on the routine blood values (RBVs) measured at admission. The dataset of the study consists of a total of 5296 patients with the same number of negative and positive COVID-19 test results and 51 routine blood values. In this study, 13 popular classifier machine learning models and the LogNNet neural network model were exanimated. The most successful classifier model in terms of time and accuracy in the detection of the disease was the histogram-based gradient boosting (HGB) (accuracy: 100%, time: 6.39 sec). The HGB classifier identified the 11 most important features (LDL, cholesterol, HDL-C, MCHC, triglyceride, amylase, UA, LDH, CK-MB, ALP and MCH) to detect the disease with 100% accuracy. In addition, the importance of single, double and triple combinations of these features in the diagnosis of the disease was discussed. We propose to use these 11 features and their binary combinations as important biomarkers for ML sensors in the diagnosis of the disease, supporting edge computing on Arduino and cloud IoT service.
RDL Kalıcı Kaydıİncele - ArticleAçık Erişim2022The Multi-Relationship Structure between Some Symptoms and Features Seen during the New Coronavirus 19 Infection and the Levels of Anxiety and Depression post-Covid
Huyut, Mehmet Tahir; Soygüder, Süleyman
The novel coronavirus (SARS-CoV-2 or COVID-19) epidemic and the measures taken to combat it have adver sely affected the psychological health of societies and individuals. In this study, it was aimed to examine the mult i-relationship structure between some symptoms, demographic characteristics during the infection period of individuals treated for COVID-19 infection and their anxiety/depression levels after discharge. One hundred individuals (n=100) who were treated with the diagnosis of COVID-19 participated in the study. Some symptoms and socio-demographic characteristics of the participants during the infection period were recorded and the Beck anxiety/depression scale was administered to the participants after discharge. Firstly, variables that create multicollinearity were removed from the data set via Explanatory Factor Analysis and the variable was reduced. Then, the independent principal components were determined and their attributes were found. The relationship structure between the features and the anxiety/depression levels of the patients was analyzed by reducing them to two dimensions via Multiple Correspondence Analysis. In this study, patients with chronic renal failure who received oxygen support during the infection process were positively associated with mild/moderate post-infection anxiety. Patients with chronic renal failure were found to have higher depression than those without. Patients with COPD who experienced loss of appetite and fever during the infection were positively associated with moderate to high levels of anxiety and moderate depression. In addition, these i ndividuals received more oxygen support during the treatment process and the depression level of these individual s was higher than the other patients. Our study revealed the multi-relationships structure between some symptoms and features seen during COVID-19 infection and the levels of anxiety/depression post-COVID.
RDL Kalıcı Kaydıİncele - ArticleAçık Erişim2021Farklı Ölçek Seviyelerindeki Değişkenlerin Analizinde Kısıtlı Ordinasyon Yöntemlerinin Başarısı
Keskin, Sıddık; Huyut, Mehmet Tahir
Doğadaki olaylar, birbiriyle ilişkili birçok değişkenin ayrı ayrı veya birlikte etkisiyle meydana gelir. Değişkenler arası doğrusal ve doğrusal olmayan ilişki yapısı ile farklı ölçek düzeylerindeki veri setlerinin analizi için kullanılan yöntemlerin tanıtılması ve kullanılması önemlidir. Bu tür verilerin analizinde kullanılan yöntemler arasında Gereksizlik Analizi ve Kanonik Uyum Analizi yer almaktadır. Yukarıda belirtilen tekniklerle ilgili çalışmaların genellikle ekolojistler tarafından yapıldığı ve sağlık alanında sınırlı sayıda çalışma olduğu görülmüştür. Bu nedenle çalışmada, belirtilen yöntemlerin uygulamaları farklı ölçek düzeylerindeki değişkenleri içeren bir Kardiyoloji veri seti üzerinde gerçekleştirilmiş ve performansları karşılaştırılmıştır. Performans kriteri olarak Belirleme Katsayısı (R2 ) ve MAPE (Ortalama Mutlak Yüzde Hata) değeri hesaplandı. Elde edilen sonuçlara göre, farklı ölçek türlerindeki (kardiyolojik veri seti) değişkenler arasındaki ilişki yapılarını inceleyen CCA ve RDA'nın, varyasyonu yeterince açıkladığı görülmüştür. Ayrıca her iki yöntemin de düşük MAPE değeri (% 10'dan az) ile iyi sınıflandırıldığı ve koordinasyon diyagramı gerçekleştirdiği vurgulanmıştır. Ayrıca, kısıtlı koordinasyon diyagram modellerinin koroner kalp hastalığı verileri arasındaki ilişkilerin belirlenmesinde tatmin edici sonuçlar verdiği ve benzeri sağlık alanlarında da kullanılabilirliği görülmüştür.
RDL Kalıcı Kaydıİncele - ArticleAçık Erişim2021The effectiveness of blood routine parameters and some biomarkers as a potential diagnostic tool in the diagnosis and prognosis of Covid-19 disease
Huyut, Mehmet Tahir; Ilkbahar, Fatih
Since February-2020, the world has been battling a tragic public-health crisis with the emergence and spread of 2019-nCoV. Due to the lack of information about the pathogenesis-specific treatment of Covid-19, early diagnosis and timely treatment are important. However, there is still a lack of information about routine-blood-parameteres (RBP) findings and effects in the disease process. Although the literature includes various interventions, existing studies need to be generalized and their reliability improved. In this study, the efficacy of routine blood values used in the diagnosis and prognosis of Covid-19 and independent biomarkers obtained from them were evaluated retrospectively in a large patient group. Low lymphocyte (LYM) and white-blood-cell (WBC), high CRP and Ferritin were effective in the diagnosis of the disease. The (d-CWL) = CRP/WBC*LYM and (d-CFL) = CRP*Ferritin/LYM biomarkers derived from them were the most important risk factors in diagnosing the disease and were more successful than direct RBP values. High d-CWL and d-CFL values largely confirmed the Covid-19 diagnosis. The most effective RBP in the prognosis of the disease was CRP. (d-CIT) = CRP*INR*Troponin; (d-CT) = CRP*Troponin; (d-PPT) = PT*Troponin*Procalcitonin biomarkers were found to be more successful than direct RBP values and biomarkers used in previous studies in the prognosis of the disease. In this study, biomarkers derived from RBP were found to be more successful in both diagnosis and prognosis of Covid-19 than previously used direct RBP and biomarkers.
RDL Kalıcı Kaydıİncele - ArticleYalnız künye2021How do routine laboratory tests change in coronavirus disease 2019?
Mertoglu, Cuma; Huyut, Mehmet Tahir; Arslan, Yusuf; Ceylan, Yasar; Coban, Taha Abdulkadir
How the routine laboratory tests change in terms of coronavirus disease 2019 (COVID-19) was retrospectively analyzed in a large group of patients. Biochemical, hematological and inflammatory variables of a totaly 555 (n = 532 in non-intensive care unit (non-ICU), n = 23 in ICU) patients diagnosed with COVID-19 were analyzed accessing them through the laboratory information system. White blood cell (WBC), neutrophil (NEU), platelet large cell ratio, neutrophil to lymphocyte ratio (NLR), derived NLR (d-NLR), aspartate aminotransferase, urea, creatine kinase (CK) myocardial band (CK-MB), procalcitonin (PCT) values were high whereas lymphocyte (LYM), eosinophil, red blood cells (RBC), hemoglobin, lymphocyte to monocyte ratio, estimated glomerular filtration rate values were low in the ICU group when compared with non-ICU. WBC, NEU, red cell distribution width, alanine transaminase, creatinine, urea, CK-MB, CK, direct bilirubin, lactate dehydrogenase, glucose, C-reactive protein, erythrocyte sedimentation rate, ferritin, D-dimer, PCT and international normalized ratio values increased while RBC, hemoglobin, hematocrit, mean corpuscular volume and total bilirubin values decreased in a significant proportion of patients in both groups based on the normal reference ranges. LYM count was found to be low in a significant number of patients (57.5%) especially in the ICU group and as an important risk factor and diagnostic parameter on admission to ICU (OR: 125, AUC: 0.74). Routine laboratory tests provide important information in terms of both diagnosis and severity of COVID-19. Lymphopenia is a condition that should be monitored which indicates the severity of the disease.
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