Investigating the individual and combined effects of coenzyme Q10 and vitamin C on CLP-induced cardiac injury in rats.

Sepsis-induced cardiac injury represents a major clinical challenge, amplifying the urgency for effective therapeutic interventions. This study aimed to delve into the individual and combined prophylactic effects of Vitamin C (Vit C) and Coenzyme Q10 (CoQ10) against inflammatory heart injury in a cecal ligation and puncture (CLP) induced polymicrobial sepsis rat model. Thirty adult female Sprague-Dawley rats were randomly divided into five groups: Control, CLP, Vitamin C, CoQ10, and Vit C + CoQ10, each consisting of six rats. Treatments were administered orally via gavage for 10 days prior to the operation. Eighteen hours post-sepsis induction, the animals were euthanized, and specimens were collected for analysis. The study examined variations in oxidative (TOS, OSI, MDA, MPO) and antioxidative markers (TAS, SOD, CAT, GSH), histopathological changes, inflammatory cytokine concentrations (TNF-α, IL-1ß), nitric oxide (NO) dynamics, and cardiac indicators such as CK-MB. Impressively, the combined regimen markedly diminished oxidative stress, and antioxidative parameters reflected notable enhancements. Elevated NO levels, a central player in sepsis-driven inflammatory cascades, were effectively tempered by our intervention. Histological examinations corroborated the biochemical data, revealing diminished cardiac tissue damage in treated subjects. Furthermore, a marked suppression in pro-inflammatory cytokines was discerned, solidifying the therapeutic potential of our intervention. Interestingly, in certain evaluations, CoQ10 exhibited superior benefits over Vit C. Collectively, these findings underscore the potential therapeutic promise of Vit C and CoQ10 combination against septic cardiac injuries in rats.

Effects of mask use on cardiopulmonary functions and coronaphobia in the COVID-19 pandemic: an observational study among university students.

Mask use during the coronavirus disease 2019 (COVID-19) pandemic has been widely recommended and mandated worldwide. However, there is a lack of comprehensive research on the potential adverse health effects of mask usage. This study aimed to investigate and evaluate the negative effects of surgical mask use on scientifically proven cardiopulmonary functions in undergraduate and associate degree students, as well as its impact on coronaphobia. A total of 145 volunteer university students (49 males, 96 females, with a mean age of 20 years) were enrolled in the study, which consisted of two 120-minute sessions. Blood oxygen saturation, heart rate, and blood pressure were assessed before and immediately after each session. The Coronavirus-19 Phobia Scale was utilized to measure levels of COVID-19 phobia. While a time-dependent decrease in oxygen saturation level, blood pressure, and heart rate was measured when vital signs were evaluated at 1 and 120 minutes, none of the values fell outside the reference range. The study also investigated the effects of mask use on various symptoms including headaches, visual impairment, facial discomfort, earaches, shortness of breath, and anxiety. Significantly increased occurrences of all these symptoms were observed at the 60th and 120th minute compared with the baseline. The participants enrolled in the study demonstrated a moderate level of COVID-19 phobia based on the mean total score. Furthermore, high scores were recorded in the psychological and social sub-dimensions, while lower scores were recorded in the economic and psychosomatic sub-dimensions. In the post-COVID-19 normalization phase, the use of a surgical mask during a 120-minute course was found to have no significant impact on cardiopulmonary functions, but moderately affected coronaphobia scores.

ENHANCED EFFICACY OF RESVERATROL-LOADED SILVER NANOPARTICLE IN ATTENUATING SEPSIS-INDUCED ACUTE LIVER INJURY: MODULATION OF INFLAMMATION, OXIDATIVE STRESS, AND SIRT1 ACTIVATION.

ABSTRACT: Sepsis-induced acute liver injury is a life-threatening condition involving inflammation, oxidative stress, and endothelial dysfunction. In the present study, the preventive effects of resveratrol (RV) alone and RV-loaded silver nanoparticles (AgNPs + RV) against sepsis-induced damage were investigated and compared in a rat model of polymicrobial sepsis induced by cecal ligation and puncture (CLP). Rats were divided into four groups: Sham, CLP, RV, and AgNPs + RV. Pro-inflammatory cytokines (TNF-α, IL-1ß, IL-6), nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) activation, presepsin, procalcitonin (PCT), 8-hydroxy-2'-deoxyguanosine (8-OHDG), vascular endothelial growth factor (VEGF), and sirtuin-1 (SIRT1) levels were assessed to determine the treatments' effects. AgNPs + RV treatment significantly reduced pro-inflammatory cytokines, NF-κB activation, presepsin, PCT, 8-OHDG, and VEGF levels compared with the CLP group, indicating attenuation of sepsis-induced liver injury. Both RV and AgNPs + RV treatments increased SIRT1 levels, suggesting a potential role of SIRT1 activation in mediating the protective effects. In conclusion, AgNPs + RV treatment demonstrated extremely enhanced efficacy in alleviating sepsis-induced liver injury by modulating inflammation, oxidative stress, and endothelial dysfunction, potentially mediated through SIRT1 activation. In this study, the effect of AgNPs + RV on sepsis was evaluated for the first time, and these findings highlight AgNPs + RV as a promising therapeutic strategy for managing sepsis-induced liver injury, warranting further investigation.

A new treatment approach: Melatonin and ascorbic acid synergy shields against sepsis-induced heart and kidney damage in male rats.

AIM: To investigate the combined therapeutic potential of melatonin and ascorbic acid in mitigating sepsis-induced heart and kidney injury in male rats and assess the combination therapy's effects on inflammation, cellular damage, oxidative stress, and vascular function-related markers. MATERIALS AND METHODS: Cecal ligation and puncture (CLP) induced sepsis in male rats, which were divided into five groups: Sham, CLP, MEL (melatonin), ASA (ascorbic acid), and MEL+ASA (melatonin and ascorbic acid). Rats were treated, and heart and kidney tissues were collected for biochemical and histopathological analyses. Inflammatory markers (presepsin, procalcitonin, NF-κB, IL-1ß, IL-6, TNF-α), cellular damage marker (8-OHDG), oxidative status, nitric oxide (NO), vascular endothelial growth factor (VEGF), and sirtuin 1 (SIRT1) levels were assessed. KEY FINDINGS: Melatonin and ascorbic acid treatment reduced inflammatory and cellular damage markers compared to the CLP group. Combined treatment improved NO, VEGF levels, and increased SIRT1 expression, suggesting a synergistic effect in mitigating sepsis-induced inflammation, cellular damage, and oxidative stress. Histopathological analyses supported these findings, revealing reduced heart and kidney injury in the MEL+ASA group. SIGNIFICANCE: Our study highlights potential benefits of combining melatonin and ascorbic acid as a therapeutic strategy for alleviating sepsis-induced heart and kidney injury. The synergistic effects of these agents may provide stronger protection against inflammation, oxidative stress, and tissue damage, opening new avenues for future research and clinical applications in sepsis management.

LogNNet model as a fast, simple and economical AI instrument in the diagnosis and prognosis of COVID-19.

Rapid and effective detection of the diagnosis and prognosis of COVID-19 disease is important in terms of reducing the mortality of the disease and reducing the pressure on health systems. Methods such as PCR testing and computed tomography used for this purpose in current health systems are costly, require an expert team and take time. This study offers a fast, economical and reliable approach for the early diagnosis and prognosis of infectious diseases, especially COVID-19. For this purpose, characteristics of a large population of COVID-19 patients were determined (51 different routine blood values) and calibrated. In order to determine the diagnosis and prognosis of the disease, the calibrated features were run with the LogNNet model. LogNNet has a simple algorithm and performance indicators comparable to the most efficient algorithms available.This approach pointed out that routine blood values contain important information, especially in the detection of COVID-19, and showed that the LogNNet model can be used as an economical, safe and fast alternative tool in the diagnosis of this disease.-In the LogNNet feedforward neural network, a feature vector is passed through a specially designed reservoir matrix and transformed into a new feature vector of a different size, increasing the classification accuracy.-The presented network architecture can achieve 80%-99% classification accuracy using a range of weightings on devices with a total memory size of 1 to 29 kB constrained.-Due to the chaotic mapping procedures, the RAM usage in the LogNNet neural network processing process is greatly reduced. Hence, optimization of chaotic map parameters has an important function in LogNNet neural network application.

Protective effect of melatonin and ascorbic acid combination on sepsis-induced lung injury: An Experimental study.

This study investigated the synergistic protective effects of melatonin (MEL) and ascorbic acid (vitamin C, ASA) in treating sepsis-induced lung injury in rats. Rats were divided into five groups: control, cecal ligation and puncture (CLP), CLP + MEL, CLP + ASA and CLP + MEL + ASA. The effects of MEL (10 mg/kg), ASA (100 mg/kg) and their combination on oxidative stress, inflammation and histopathology were evaluated in septic rats' lung tissues. Sepsis-induced oxidative stress and inflammation were evident through increased levels of malondialdehyde (MDA), myeloperoxidase (MPO), total oxidant status (TOS) and oxidative stress index (OSI); decreased levels of superoxide dismutase (SOD), glutathione (GSH), catalase (CAT) and glutathione peroxidase (GPx); and elevated levels of tumour necrosis factor-α (TNF-α) and interleukin-1 ß (IL-1ß) in the lung tissue. Treatment with MEL, ASA and their combination significantly improved antioxidant capacity and reduced oxidative stress, with the combination treatment being more effective. The combination treatment also significantly reduced TNF-α and IL-1ß levels and improved peroxisome proliferator-activated receptor (PPAR), arylesterase (ARE) and paraoxonase (PON) levels in the lung tissue. Histopathological examination showed reduced oedema and lymphocyte infiltration with a lung tissue appearance similar to the control group. Immunohistochemical staining for caspase 3 demonstrated reduced immune positivity in the treatment groups. In conclusion, this study supports the potential synergistic protective effects of MEL and ASA in treating sepsis-induced lung injury. The combination therapy could effectively reduce oxidative stress, inflammation and improve antioxidant capacity in septic rats, suggesting a promising strategy for treating sepsis-induced lung injury.

Effect of ferritin, INR, and D-dimer immunological parameters levels as predictors of COVID-19 mortality: A strong prediction with the decision trees.

Background and objective: A hyperinflammatory environment is thought to be the distinctive characteristic of COVID-19 infection and an important mediator of morbidity. This study aimed to determine the effect of other immunological parameter levels, especially ferritin, as a predictor of COVID-19 mortality via decision-trees analysis. Material and method: This is a retrospective study evaluating a total of 2568 patients who died (n = 232) and recovered (n = 2336) from COVID-19 in August and December 2021. Immunological laboratory data were compared between two groups that died and recovered from patients with COVID-19. In addition, decision trees from machine learning models were used to evaluate the performance of immunological parameters in the mortality of the COVID-19 disease. Results: Non-surviving from COVID-19 had 1.75 times higher ferritin, 10.7 times higher CRP, 2.4 times higher D-dimer, 1.14 times higher international-normalized-ratio (INR), 1.1 times higher Fibrinogen, 22.9 times higher procalcitonin, 3.35 times higher troponin, 2.77 mm/h times higher erythrocyte-sedimentation-rate (ESR), 1.13sec times longer prothrombin time (PT) when compared surviving patients. In addition, our interpretable decision tree, which was constructed with only the cut-off values of ferritin, INR, and D-dimer, correctly predicted 99.7% of surviving patients and 92.7% of non-surviving patients. Conclusions: This study perfectly predicted the mortality of COVID-19 with our interpretable decision tree constructed with INR and D-dimer, especially ferritin. For this reason, we think that it may be important to include ferritin, INR, and D-dimer parameters and their cut-off values in the scoring systems to be planned for COVID-19 mortality.

Effect of curcumin on lipid profile, fibrosis, and apoptosis in liver tissue in abemaciclib-administered rats.

Abemaciclib (ABEM) is an important antitumor agent for breast cancer treatment. However, the side-effects of ABEM are unclear in the liver. This study investigated the protective effect of curcumin (CURC) on liver damage caused by ABEM. The rats were divided into five groups with eight animals in each group; Control, DMSO (150 µL for per rats), CURC, 30 mg/kg/day), ABE (26 mg/kg/day), and ABE + CURC (26 mg/kg/day ABE, 30 mg/kg/day) groups. Injections were administered daily for 28 days. The levels of AST, LDH, HDL, LDL, triglyceride, and total cholesterol in serum, and hepatic tissue fibrosis, caspase-3, Bax, and TNF-α expression were higher in the ABE group compared to the control group (p < 0.05). Also, these parameters in the ABEM + CURC group were lower than in the ABE group (p < 0.05). The results showed that ABE administration could cause liver damage and increase fibrosis in the liver. In addition, it was shown that co-administration of CURC with ABE could suppress the levels of AST, LDH, HDL, LDL, triglyceride, and total cholesterol in serum, and fibrosis, caspase-3, Bax, and TNF-α expressions in the liver. These data are the first in the literature. Therefore, the administration of CURC following ABE may be a therapeutic agent in preventing liver damage.

Machine Learning Sensors for Diagnosis of COVID-19 Disease Using Routine Blood Values for Internet of Things Application.

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.

Diagnosis and Prognosis of COVID-19 Disease Using Routine Blood Values and LogNNet Neural Network.

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 activated 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, neutrophil 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 features. The method is promising to create mobile health monitoring systems in the Internet of Things.

Can zaprinast and avanafil induce the levels of angiogenesis, bone morphogenic protein 2, 4 and 7 in kidney of ovariectomised rats?

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.

COVID-19 is more dangerous for older people and its severity is increasing: a case-control study.

Coronavirus disease 2019 (COVID-19) triggers important changes in routine blood tests. In this retrospective case-control study, biochemical, hematological and inflammatory biomarkers between March 10, 2020, and November 30, 2020 from 3969 COVID-19 patients (3746 in the non-intensive care unit (non-ICU) group and 223 in the ICU group) were analyzed by dividing into three groups as spring, summer and autumn. In the non-ICU group, lymphocyte to monocyte ratio was lower in autumn than the other two seasons and neutrophil to lymphocyte ratio was higher in autumn than the other two seasons. Also, monocyte and platelet were higher in spring than autumn; and eosinophil, hematocrit, hemoglobin, lymphocyte, and red blood cells decreased from spring to autumn. In the non-ICU group, alanine aminotransferase and gamma-glutamyltransferase gradually increased from spring to autumn, while albumin, alkaline phosphatase, calcium, total bilirubin and total protein gradually decreased. Additionally, C-reactive protein was higher in autumn than the other seasons, erythrocyte sedimentation rate was higher in autumn than summer. The changes in routine blood biomarkers in COVID-19 varied from the emergence of the disease until now. Also, the timely changes of blood biomarkers were mostly more negative, indicating that the disease progresses severely. The study was approved by the Erzincan Binali Yildirim University Non-interventional Clinical Trials Ethic Committee (approval No. 86041) on June 21, 2021.

Prediction of diagnosis and prognosis of COVID-19 disease by blood gas parameters using decision trees machine learning model: a retrospective observational study.

The coronavirus disease 2019 (COVID-19) epidemic went down in history as a pandemic caused by corona-viruses that emerged in 2019 and spread rapidly around the world. The different symptoms of COVID-19 made it difficult to understand which variables were more influential on the diagnosis, course and mortality of the disease. Machine learning models can accurately assess hidden patterns among risk factors by analyzing large-datasets to quickly predict diagnosis, prognosis and mortality of diseases. Because of this advantage, the use of machine learning models as decision support systems in health services is increasing. The aim of this study is to determine the diagnosis and prognosis of COVID-19 disease with blood-gas data using the Chi-squared Automatic Interaction Detector (CHAID) decision-tree-model, one of the machine learning methods, which is a subfield of artificial intelligence. This study was carried out on a total of 686 patients with COVID-19 (n = 343) and non-COVID-19 (n = 343) treated at Erzincan-Mengücek-Gazi-Training and Research-Hospital between April 1, 2020 and March 1, 2021. Arterial blood gas values of all patients were obtained from the hospital registry system. While the total-accuracyratio of the decision-tree-model was 65.0% in predicting the prognosis of the disease, it was 68.2% in the diagnosis of the disease. According to the results obtained, the low ionized-calcium value (< 1.10 mM) significantly predicted the need for intensive care of COVID-19 patients. At admission, low-carboxyhemoglobin (< 1.00%), high-pH (> 7.43), low-sodium (< 135.0 mM), hematocrit (< 40.0%), and methemoglobin (< 1.30%) values are important biomarkers in the diagnosis of COVID-19 and the results were promising. The findings in the study may aid in the early-diagnosis of the disease and the intensive-care treatment of patients who are severe. The study was approved by the Ministry of Health and Erzincan University Faculty of Medicine Clinical Research Ethics Committee.

Forecasting of Oxidant/Antioxidant levels of COVID-19 patients by using Expert models with biomarkers used in the Diagnosis/Prognosis of COVID-19.

BACKGROUND: Early detection of oxidant-antioxidant levels and special care in severe patients are important in combating the COVID-19 epidemic. However, this process is costly and time consuming. Therefore, there is a need for faster, reliable and economical methods. METHODS: In this study, antioxidant/oxidant levels of patients were estimated by Expert-models using biomarkers, which are effective in the diagnosis/prognosis of COVID-19 disease. For this purpose, Expert-models were trained and created between the white-blood-cell-count (WBC), lymphocyte-count (LYM), C-reactive-protein (CRP), D-dimer, ferritin values of 35 patients with COVID-19 and antioxidant/oxidant parameter values of the same patients. Error criteria and R2 ratio were taken into account for the performance of the models. The validity of the all models was checked by the Box-Jenkis-method. RESULTS: Antioxidant/Oxidant levels were estimated with 95% confidence-coefficient using the values of WBC, LYM, CRP, D-dimer, ferritin of different 500 patients diagnosed with COVID-19 with the trained models. The error rate of all models was low and the coefficients of determination were sufficient. In the first data set, there was no significant difference between measured antioxidant/oxidant levels and predicted antioxidant/oxidant levels. This result showed that the models are accurate and reliable. In determining antioxidant/oxidant levels, LYM and ferritin biomarkers had the most effect on models, while WBC and CRP biomarkers had the least effect. The antioxidant/oxidant parameter estimated with the highest accuracy was Native-Thiol divided by Total-Thiol. CONCLUSIONS: The results showed that the antioxidant/oxidant levels of infected patients can be estimated accurately and reliably with LYM, ferritin, D-dimer, WBC, CRP biomarkers in the COVID-19 outbreak.

The effectiveness of blood routine parameters and some biomarkers as a potential diagnostic tool in the diagnosis and prognosis of Covid-19 disease.

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) = CRPWBC∗LYM and (d-CFL) = CRP∗FerritinLYM 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.

How do routine laboratory tests change in coronavirus disease 2019?

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.