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  • Original Article
  • Open Access

Blood biomarkers of nicotine-induced toxicity in healthy males

  • 1,
  • 1,
  • 1,
  • 1Email author and
  • 2
Egyptian Journal of Forensic Sciences20199:28

https://doi.org/10.1186/s41935-019-0135-5

  • Received: 22 January 2019
  • Accepted: 21 May 2019
  • Published:

Abstract

Background

Smoking enhances oxidative stress by causing an oxidant/antioxidant imbalance in the body leading to deleterious effects on various tissues and organs. 8-hydroxy-2′-deoxyguanosine (8-OHdG) is a predominant form of free radical-induced oxidative lesions, and is widely used as a biomarker of oxidative stress.

Objectives

This study aimed to estimate blood 8-OHdG level in active, passive and non-smokers and its relation with some lifestyle determinants as well as to assess smoking effects on oxidant/antioxidant status.

Participants and methods

The current work included 90 male participants who were classified into 3 groups; 20 non-smokers, 30 passive smokers, and 40 active smokers. Data were collected by answering a premade questionnaire. Peripheral venous blood samples were withdrawn then 8-OHdG, cotinine, superoxide dismutase (SOD), and total antioxidant capacity (TAC) levels were estimated in plasma using ELISA kits, while malondialdehyde (MDA) was estimated by colorimetry.

Results

There were highly statistically significant higher levels of 8-OHdG, cotinine and MDA and lower levels of SOD and TAC in active smokers compared to both passive and non-smokers (p <  0.001) whereas, no statistically significant difference between passive and non-smokers was detected (p > 0.05). There were no significant differences between 8-OHdG level and smoking habits, age, exercise, tea and coffee consumption, and body mass index (BMI) among the 3 studied groups.

Conclusion

Smoking induces oxidative stress not only through the production of reactive oxygen radicals and increasing blood 8-OHdG levels but also through weakening of the antioxidant defense systems. Further studies are required to reach a consensus on the background level of 8-OHdG and understand which factors determine it and to further differentiate between passive and non-smokers.

Keywords

  • Smoking
  • Oxidative
  • Stress
  • Antioxidant
  • 8-OHdG

Background

Cigarette smoking is one of the most common habits of the modern world. It is reported by the World Health Organization (WHO) that 30% of the world’s population over the age of 15 smoked cigarettes (Kulikowska-Karpińska and Czerw 2015).

Oxidative stress caused by smoking reflects an imbalance between the systemic manifestation of reactive oxygen species (ROS) and a biological system’s ability to promptly detoxify the reactive intermediates or to repair the subsequent harm. Disturbances in the typical redox condition of cells can cause lethal impacts through the creation of peroxides and free radicals that harm all segments of the cell, including lipids, proteins, and DNA (Andersson 2017). The potential damage caused by free radicals found in tobacco smoke is minimized by biological antioxidant mechanisms including enzymatic and non-enzymatic reactions (Mahapatra et al. 2008). Antioxidant enzymes such as superoxide dismutase are an important line of defense against oxidative cell damage preventing lipid peroxidation and overproduction of malondialdehyde (Kamceva et al. 2016). Total antioxidant capacity is frequently used to assess the cumulative effects of antioxidants in a biological sample and can evaluate the antioxidant response against the free radicals produced (Rubio et al. 2016).

Damage to DNA is indicated by increased levels of 8-OHdG, a repair product of the oxidation of guanine in DNA. It can be detected in human tissues, blood or urine, and is considered a reliable and pivotal biomarker of generalized and cellular oxidative stress (Liu et al. 2018). Also, it is an important biomarker for various pathological conditions such as aging, carcinogenesis, neurodegenerative, and cardiovascular diseases as estimated by quantitative analytical techniques in blood and urine (Valavanidis et al. 2009).

Cotinine, a metabolite of nicotine, is a specific biomarker of tobacco smoking exposure. Using this biomarker increases the measured precision for the association between 8-OHdG and smoking (Lu et al. 2014).

Therefore, the current study aimed to investigate nicotine-induced oxidative stress by estimating blood 8-OHdG level and assessing its relation with some lifestyle determinants. Oxidant stress marker (MDA) and antioxidant defense enzyme (SOD) as well as cotinine and TAC levels were also measured.

Participants and methods

The current study is a case control study in which healthy male participants were randomly selected and interviewed after taking their informed consent as well as obtaining the approval of the study design from the Research Ethics Committee at KasrAlainy, Faculty of Medicine.

The study represented collaboration between Forensic Medicine and Clinical Toxicology Department and Medical Biochemistry Department, Faculty of Medicine, Cairo University, Egypt. The study was conducted according to the Declaration of Helsinki (World Medical Association 2013).

A premade Arabic questionnaire was answered; it included questions about socio-demographic data, smoking status, space ventilation, exercise, tea, coffee, energy drinks, and alcohol consumption, present, past, and family history of diseases.

Inclusion criteria

Ninety male participants, aged 20–60 years, were enrolled in the study. According to Lodovici et al. (2005), participants were classified according to their smoking history into three groups:
  • Non-smokers (20 cases) ➔ never smoked and not exposed to environmental tobacco smoking (ETS).

  • Passive smokers (30 cases) ➔ non-smokers but exposed to ETS in poorly ventilated areas for > 3 h/day in the year.

  • Active smokers (40 cases) ➔ > 3 cigarettes/day for over 1 year.

Exclusion criteria

  • Medical disorders ➔ diabetes, autoimmune diseases, cardiovascular and neurodegenerative diseases, hepatic and renal diseases, infections, cancer, chronic skin ulcerations, and endocrine and hematologic disorders (Chen et al. 2017).

  • Medications ➔ steroids, non-steroidal anti-inflammatory, diuretics, anticonvulsants, antidepressants, antibiotics, and vitamin supplementation.

  • Participants receiving mutagenic and carcinogenic drugs, chemotherapy, and/or radiotherapy.

  • Participants living near or working in industrial areas.

Study measurements and estimations

Body weight and height were measured for all participants. Peripheral venous blood samples were withdrawn (3 ml) into EDTA tubes. The collected whole blood was centrifuged for approximately 20 min at 1000–3000 rpm (rpm) within 30 min after collection. The supernatant was collected carefully and stored at − 80 °C.8-OHdG, cotinine, SOD, and TAC levels were estimated in plasma using ELISA kits, while MDA was estimated by colorimetry.

ELISA kits

Human 8-OHdG ELISA kit (Cat No. MBS267161) was provided by My Biosource Company, USA. According to manual instructions provided by the applied kit, double-sandwich ELISA technique was used where the pre-coated antibody was human 8-OHdG monoclonal antibody and the detecting antibody was polyclonal antibody which was biotin-labeled. Samples and biotin labeling antibody were added into ELISA plate wells and washed out with phosphate-buffered saline (PBS) followed by avidin-peroxidase conjugates which were added in order.

The reactant was washed out thoroughly by PBS, tetramethylbenzidine (TMB) substrate was then used for coloring. It turned blue in peroxidase and finally yellow under the action of acid. Human cotinine ELISA kit (Cat No. MBS019457) was provided by My Biosource Company, USA, while Human SOD and TAC ELISA kits (Cat No. BYEK1111) were provided by Chongqing Biospes Company, China. According to manual instructions provided by the applied kits, purified anti-substance antibody was pre-coated onto well plates and the horseradish peroxidase (HRP) conjugated anti-substance antibody was used as detection antibodies. Standards, test samples, and HRP conjugated detection antibody were added to the wells subsequently, mixed and incubated, then, the unbound conjugates were washed away with wash buffer. Tetramethylbenzidine substrates (A and B) were used to visualize HRP enzymatic reaction. It is catalyzed by HRP to produce a blue color product that changes into yellow after adding acidic stop solution. The density of yellow was proportional to the substance amount of sample captured in plate (Mamsen et al. 2010; Gao et al. 2018; Antunes-Lopes et al. 2018).

Calculation: Absorbance of the optical density (O.D.) was read at 450 nm in a microplate reader and then the concentration of substance was calculated.
$$ \mathrm{Relative}\ \mathrm{O}.\mathrm{D}.450=O.D.450\ \mathrm{of}\ \mathrm{each}\ \mathrm{well}-\mathrm{O}.\mathrm{D}.450\ \mathrm{of}\ \mathrm{blank}/\mathrm{control}\ \mathrm{well} $$
  • The standard curve can be plotted as the relative O.D. 450 of each standard solution (Y) versus the respective concentration of the standard solution (X). A line was drawn to connect each coordinate point of standard solution. Sample concentrations were found by checking sample O.D. reading. It is recommended to employ the professional curve software (e.g., curve expert 1.3) to analyze and compute the results.

Colorimetric method

Malondialdehydecolorimetric kit (Cat No. MD 25 29) was provided by Biodiagnostic Company, Egypt. According to manual instructions provided by the applied kit, thiobarbituric acid (TBA) reacted with MDA in acidic medium at 95 °C for 30 min to form TBA-reactive product, and this is a sensitive assay method for lipid peroxidation (Aboutaleb et al. 2019).
  • Calculation: The absorbance of sample (A sample) was read against blank and standard against water at 534 nm, the resultant pink color was stable for 6 h and linearity up to 100 nmol/ml. Malondialdehyde in sample was calculated as follows:

$$ \mathrm{Plasma}=\kern0.5em \frac{\mathrm{A}\ \mathrm{sample}}{\mathrm{A}\ \mathrm{standard}}\times 10\ \mathrm{nmol}/\mathrm{ml} $$

Statistical analysis of data

Microsoft excel 2013 was used for data entry after coding and the statistical package for social science (SPSS) version 21 (SPSS, Armonk, NY: International Business Machines Corporation) was used for data analysis. Simple descriptive statistics (arithmetic mean and standard deviation) were used for summary of quantitative data and frequencies were used for qualitative data. Bivariate relationship was displayed in cross tabulations and comparison of proportions was performed using the chi-square test. Independent t test, one-way analysis of variance (ANOVA), and post hoc tests were used to compare normally distributed quantitative data. The level of significance was set at probability (p) value ≤ 0.05 and p value <  0.001 was considered as highly significant. Correlations between variables were done using Pearson correlation coefficient (r) which was able to describe both correlation direction (positive or negative according to the sign) and power (weak correlation if < 0.5, moderate correlation from 0.5 and 0.7 and strong correlation if > 0.7) (Dawson and Trapp 2004).

Results

The current study included 90 male participants, aged 20–60 years, who were interviewed and classified into 3 groups (20 non-smokers, 30 passive smokers, and 40 active smokers). Demographic characteristics of the studied groups were shown in Table 1.There were no statistically significant differences between the studied groups regarding their age and residence whereas, statistically significant differences were detected between the groups concerning their occupation and educational level.
Table 1

Baseline demographic characteristics of the studied groups

Variable

Non-smokers (n = 20)

Passive smokers (n = 30)

Active smokers (n = 40)

p value

Age (years) (mean ± SD)

31.7 ± 14.12

34.57 ± 12.47

32.58 ± 9.34

0.655

Residence

n

%

n

%

n

%

0.069

 Urban

15

75

13

43.3

25

62.5

 Rural

5

25

17

56.7

15

37.5

Occupation

n

%

n

%

n

%

0.016*

 Physical

11

55

24

80

35

87.5

 Mental

9

45

6

20

5

12.5

Educational level

n

%

n

%

n

%

0.016*

 Low

3

15

10

33.3

16

40

 Moderate

8

40

13

43.3

21

52.5

 High

9

45

7

23.3

3

7.5

*Significant p ≤ 0.05

SD standard deviation

Lifestyle factors of the studied groups were demonstrated in Table 2. No significant difference between exercise-practicing and non-practicing participants was detected. There were increased tea and coffee consumption rates in active smokers with highly significant (p <  0.001) and significant (p = 0.052) values respectively. Active smokers had the lowest BMI while non-smokers had the highest ones with a highly significant difference between the studied groups (p <  0.001).
Table 2

Lifestyle factors of the studied groups

Variable

Non-smokers (n = 20)

Passive smokers (n = 30)

Active smokers (n = 40)

p value

Exercise

n

%

n

%

n

%

0.319

 No

19

95

29

96.7

35

87.5

 Yes

1

5

1

3.3

5

12.5

Tea (cups/day) (mean ± SD)

Min.

Max.

Min.

Max.

Min.

Max.

< 0.001**

0

7

0

10

0

20

2.06 ± 1.66

2.75 ± 2.16

5.13 ± 3.82

Coffee (cups/day) (mean ± SD)

Min.

Max.

Min.

Max.

Min.

Max.

0.052*

0

1

0

1

0

5

0.16 ± 0.37

0.08 ± 0.21

0.61 ± 1.4

BMI (kg/m2) (mean ± SD)

29.19 ± 4.79

26.81 ± 4.49

24.43 ± 4.34

< 0.001**

*Significant p ≤ 0.05

**Highly significant p <  0.001

Min. minimum; Max. maximum; SD standard deviation

As shown in Table 3, there were higher levels of 8-OHdG, cotinine, and MDA and lower levels of SOD and TAC in active smokers compared to passive and non-smokers with a highly significant difference between the studied groups (p <  0.001).
Table 3

Mean levels of the measured biochemical parameters among the studied groups

Variable

Non-smokers (n = 20)

Passive smokers (n = 30)

Active smokers (n = 40)

Total (n = 90)

p value

8-OHdG (ng/ml) (mean ± SD)

0.77 ± 0.39

0.84 ± 1.18

3.59 ± 1.98

2.04 ± 2.03

< 0.001**

Cotinine (ng/ml) (mean ± SD)

1.47 ± 1.06

1.67 ± 1.77

13.9 ± 5.26

7.06 ± 7.16

< 0.001**

MDA (nmol/ml) (mean ± SD)

28.24 ± 24.82

24.33 ± 20.09

96.69 ± 25.22

57.36 ± 42.37

< 0.001**

SOD (U/ml) (mean ± SD)

1.79 ± 0.65

1.88 ± 0.63

0.62 ± 0.28

1.3 ± 0.79

< 0.001**

TAC (U/ml) (mean ± SD)

18.99 ± 5.47

18.41 ± 5.97

7.41 ± 2.2

13.65 ± 7.19

<0.001**

**Highly significant p < 0.001

8-OHdG 8-hydroxy-2′-deoxyguanosine; MDA malondialdehyde; SOD superoxide dismutase; TAC total antioxidant capacity

When pairwise comparison (post hoc test) was performed, a highly significant difference was detected between active and both passive and non-smokers (p <  0.001) whereas, no significant difference between passive and non-smokers was observed (p > 0.05) (Table 4).
Table 4

Pairwise comparison between the studied groups concerning levels of the measured biochemical parameters

Dependent variable

Group

Group

p value

8-OHdG (ng/ml)

Non-smokers

Passive smokers

0.985

Active smokers

Non-smokers

0.000**

Passive smokers

0.000**

Cotinine (ng/ml)

Non-smokers

Passive smokers

0.983

Active smokers

Non-smokers

0.000**

Passive smokers

0.000**

MDA (nmol/ml)

Non-smokers

Passive smokers

0.834

Active smokers

Non-smokers

0.000**

Passive smokers

0.000**

SOD (U/ml)

Non-smokers

Passive smokers

0.802

Active smokers

Non-smokers

0.000**

Passive smokers

0.000**

TAC (U/ml)

Non-smokers

Passive smokers

0.895

Active smokers

Non-smokers

0.000**

Passive smokers

0.000**

**Highly significant p < 0.001

8-OHdG 8-hydroxy-2′-deoxyguanosine; MDA malondialdehyde; SOD superoxide dismutase; TAC total antioxidant capacity

Concerning smoking habits in active smokers, the mean values were provided in Fig. 1.
Fig. 1
Fig. 1

Mean values of smoking habits in active smokers

No significant correlations were observed between 8-OHdG level and smoking habits as shown in Table 5.
Table 5

Correlation between 8-OHdG level and smoking habits in active smokers

 

Active smokers (n = 40)

8-OHdG (ng/ml)

Number of cigarettes smoked/day

Duration of smoking/years

Smoking index (pack-year)

Correlation Coefficient (r)

0.219

0.135

0.058

p value

0.174

0.405

0.720

8-OHdG 8-hydroxy-2′-deoxyguanosine

As shown in Table 6, there were no significant differences between 8-OHdG level and age, exercise, tea and coffee consumption, and BMI among the 3 studied groups (p > 0.05).
Table 6

Comparison between 8-OHdG level and age as well as lifestyle factors among the studied groups

 

Non-smokers (n = 20)

Passive smokers (n = 30)

Active smokers (n = 40)

8-OHdG (ng/ml)

Age (years)

Exercise

Tea (cups/day)

Coffee (cups/day)

BMI (kg/m2)

Age (years)

Exercise

Tea (cups/day)

Coffee (cups/day)

BMI (kg/m2)

Age (years)

Exercise

Tea (cups/day)

Coffee (cups/day)

BMI (kg/m2)

Correlation Coefficient (r)

0.033

0.252

0.249

0.061

− 0.200

− 0.070

− 0.167

0.346

0.164

0.233

0.179

0.042

p value

0.889

0.536

0.285

0.290

0.797

0.288

0.621

0.713

0.377

0.061

0.312

0.192

0.148

0.269

0.796

8-OHdG 8-hydroxy-2′-deoxyguanosine; BMI body mass index

Discussion

Oxidative stress caused by tobacco smoke can result in damage to lipids, proteins, and DNA leading to cellular dysfunction or ultimately cell death (Black et al. 2016). Sensitive and specific biomarkers for antioxidant status/oxidative stress are essential to better understand the role of antioxidants and oxidative stress in human health and diseases (Yeum et al. 2011).

In the current study, active smokers represented 62.5% and 37.5% in urban and rural areas respectively. This finding was in agreement with Völzke et al. (2006) who reported the presence of a higher percent of smokers among urban compared to rural communities. This was explained by the more stressful life in urban areas. In contrary, researchers in Canada stated that an increased number of smokers in rural areas may be present due to fewer restrictions concerning smoking (Li et al. 2009).

In the current work, while passing from non- to active smokers, the physical work increased while mental work decreased. These results were in agreement with Kim et al. (2015) who investigated smoking and possible related factors among Korean male workers and found that smoking rate in manual was higher than in non-manual workers due to less effective antismoking policies in manual workers.

In the present study, the percent of active smokers with high educational level was the lowest (7.5%). These results were in accordance with Han et al. (2010) and may be explained by the fact that high educational level increased the awareness of smoking dangers and reduced environmental and occupational exposure.

In the current work, there was significant increase in tea and coffee consumption rates in active smokers. These results were in agreement with Chao (2015) who stated that non-smokers metabolize caffeine at a lower rate than smokers.

In the current study, active smokers had the lowest BMI. This was attributed to the fact that nicotine is associated with reduced appetite, less efficient absorption and storage of calories and increased thermogenesis (Chiolero et al. 2008).

In the present work, there were highly significant higher levels of 8-OHdG, cotinine, and MDA in active smokers compared to both passive and non-smokers whereas, there was no significant difference between passive and non-smokers. These results were in accordance with Lodovici et al. (2005) who conducted a study on lifestyle determinants of 8-OHdG levels in human leukocyte DNA in Italy and stated that 8-OHdG and cotinine levels were higher in active smokers compared to non-smokers; while the difference between passive and non-smokers was not significant. Also, Chávez et al. (2007) measured serum levels of MDA and found that they were higher in smokers than in non-smokers.

Concerning the levels of SOD and TAC, there were highly significant lower levels in active smokers compared to both passive and non-smokers. These results were in accordance with Reddy et al. (2012) where there was a significant decrease in SOD levels of smokers with increased duration and frequency of smoking. However, according to Jenifer et al. (2015), SOD levels were higher in smokers than in non-smokers. Also, the results of this study agreed with Bakhtiari et al. (2015) who conducted a cross-sectional study to investigate the influence of cigarette smoke on salivary TAC. The mean salivary TAC in smokers (0.529 ± 0.167 U/ml) was statistically significantly lower than in non-smokers (0.741 ± 0.123 U/ml) (p <  0.001). In contrary, Nagler (2007) showed that salivary TAC was significantly higher in smokers compared to non-smokers.

Discrepancy between the results of the above-mentioned studies was explained as follows: increased oxidative stress leads to an induction in the activities of antioxidants as a part of compensatory and defense mechanisms to protect the organism. These mechanisms prevent the accumulation of free radicals present in cigarette smoke and the lipid peroxidation products, such as MDA. At a point, this compensatory effect is overcome and toxic effects are initiated resulting in a decrease in antioxidants (Chávez et al. 2007; Mizrak et al. 2015).

In the current study, there were no significant correlations between 8-OHdG level and smoking habits in active smokers. Similarly, Lodovici et al. (2005) observed no correlation between 8-OHdG levels and the number of cigarettes smoked/day. However, Kulikowska-Karpińska and Czerw (2015) estimated urinary 8-OHdG concentration in 63 smokers and 20 non-smokers and found that the increase in 8-OHdG was depended on the amount of smoked cigarettes/day and the duration of smoking.

In the current work, there were no significant correlations between 8-OHdG level and age among the studied groups. These results were in accordance with Lodovici et al. (2005) and Tamae et al. (2009) who studied the influence of age, smoking, and other lifestyle factors on 8-OHdG where data from male subjects, 18–60 years were analyzed. In contrary, Yao et al. (2004) found a significant difference between 8-OHdG level and age (p = 0.032) and concluded that oxidative DNA damage increases with age.

In the present study, there was no significant difference between 8-OHdG and exercise among the studied groups. Similarly, Black et al. (2016) conducted a study on socio-demographic and lifestyle factors of 8-OHdG in the Netherlands and found no significant difference between 8-OHdG level and physical activity.

In the current study, there were no significant correlations between 8-OHdG level and tea and coffee consumption rates among the studied groups. These results were in accordance with Han et al. (2010) where they showed that tea and coffee consumption did not affect urinary 8-OHdG levels. In contrary, Raza and John (2008) demonstrated the effects of tea polyphenols on oxidative stress and concluded that they have protective effect and strong inhibitory activity on the free radical-induced oxidation.

In the current study, there were no significant correlations between 8-OHdG level and BMI among the studied groups. Similarly, Lodovici et al. (2005), Tamae et al. (2009), Han et al. (2010), and Black et al. (2016) found no significant correlation between 8-OHdGlevel and BMI. However, Kasai et al. (2001) studied the influence of smoking and BMI on urinary 8-OHdG and found a negative correlation between them and explained it by the fact that lean persons have a higher metabolic rate than obese and therefore have higher oxidative stress.

Conclusion

The results of the current study seem to support the hypothesis that active smoking is associated with increased oxidative stress and decreased antioxidant defense. It elevates blood 8-OHdG, a reliable biomarker for oxidative DNA damage. No statistically significant difference between passive and non-smokers was detected in all the biomarkers. There were no significant differences between 8-OHdG level and lifestyle factors among the three studied groups. Hence, it is recommended to strengthen tobacco control measures, promote healthy lifestyle, and conduct further work to further understand which lifestyle factors determine basal oxidative stress and 8-OHdG level as well as to carry out individual exposure assessments instead of relying on the reports of the participants in relation to smoking to further differentiate between passive and non-smokers taking into consideration gender and individual variations as well as methodological and analytical differences. It is also important to investigate the effect of dietary patterns.

Abbreviations

8-OHdG: 

8-hydroxy-2′-deoxyguanosine

ANOVA: 

One-way Analysis of Variance

BMI: 

Body mass index

DNA: 

Deoxyribonucleic acid

ELISA: 

Enzyme-linked immunosorbent assay

ETS: 

Environmental tobacco smoking

HRP: 

Horseradish peroxidase

Max.: 

Maximum

MDA: 

Malondialdehyde

Min.: 

Minimum

O.D.: 

Optical density

P: 

Probability

PBS: 

Phosphate-buffered saline

ROS: 

Reactive oxygen species

SD: 

Standard deviation

SOD: 

Superoxide dismutase

SPSS: 

Statistical Package for Social Science

TAC: 

Total antioxidant capacity

TBA: 

Thiobarbituric acid

TMB: 

Tetramethylbenzidine

WHO: 

World Health Organization

Declarations

Acknowledgements

None.

Funding

Cairo University.

Authors’ contributions

MM contributed to the experimental work and writing the paper. EU gave assistance in writing and final revision of the paper. IM helped in the design of the research, starting from the idea, interpretation of results, and revision of the paper. GI helped in the final revision of the paper. RL contributed to the laboratory part of the research. All authors read and approved the final manuscript.

Ethics approval and consent to participate

The study work was conducted after the approval of the Research Ethics Committee, Faculty of Medicine, Cairo University.

Consent for publication

Consent forms were given and signed by all subjects prior to participation.

Competing interests

The authors declare that they have no competing interests.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors’ Affiliations

(1)
Department of Forensic Medicine and Clinical Toxicology, Faculty of Medicine, Cairo University, Cairo, Egypt
(2)
Department of Medical Biochemistry and Molecular Biology, Faculty of Medicine, Cairo University, Cairo, Egypt

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