Biological
monitoring of genotoxicity to organophosphate pesticide exposure among rice
farmers: Exposure-effect continuum study
Vivien H, BSc1,
Hashim Z, PhD2*, Ismail P, PhD3, Md Said S, MD4,
Omar D, PhD5, Bahri Mohd Tamrin SH, PhD6
1- Tutor, Dept., of Environmental and Occupational Health, Faculty
of Medicine and Health Sciences, University Putra Malaysia, Serdang, Malaysia.
2- Professor, Dept., of Environmental and Occupational Health, Faculty of
Medicine and Health Sciences, University Putra Malaysia, Serdang,
Malaysia.3-Professor, Dept., of Biomedical Sciences, Faculty of Medicine and
Health Sciences, University Putra Malaysia, Serdang, Malaysia. 4- Medical
Lecturer, Dept., of Community Health, Faculty of Medicine and Health
Sciences, University Putra Malaysia, Serdang, Malaysia. 5- Professor, Dept., of
Plant Protection, Faculty of Agriculture, University Putra Malaysia, Serdang,
Malaysia. 6-Associate Prof., Dept., of Environmental and Occupational Health,
Faculty of Medicine and Health Sciences, University Putra Malaysia, Serdang,
Malaysia.
Abstract
Received: October 2013, Accepted: February 2014
Background: This study has used biomarker of exposure-effect
continuum to examine the biological characteristics of organophosphate (OP)
toxicity and its genotoxic effect among rice farmers. Materials and Methods: A cross-sectional study was conducted among 160 pesticide
exposed rice farmers and 160 adults from the fishing village as the unexposed
group. They share the common socio-economical background for inter-individual
comparison in human toxicology assessment. In this study depression of blood
cholinesterase is used as a biomarker of exposure to OP toxicity. Two
genotoxic assays (micronuclei and comet assay) were conducted as a biomarker
of genotoxic effect among the adult population. In this context, micronuclei
assay is used to indicate the chromosome breakage and comet assay to estimate
the possible DNA damage. Results: The study showed a significant difference of
blood cholinesterase level (p=0.001)
between the exposed-unexposed groups. Besides, the results showed that farmers had at least 2-2.5 folds of significant increase (p=0.001)
in MN frequency (in 1000 cells) and comet tail length (µm) compared to the
unexposed group. In addition, regression analysis among farmers showed that
blood cholinesterase level decreased with the genotoxic effects. A small
variation (R2=0.148) of
MN frequency could be explained by the depression of blood cholinesterase
level; however, a significant reduction (p=0.001),
with strong changes (R2=0.712)
in comet tail length was attributed to the depression of blood cholinesterase
levels. Risk factors like
age, body mass index, smoking status and years of working showed the different
strength of the relationship with these genotoxic effects. Conclusions: This
study suggests that chronic exposure to OP shows an inhibition to blood cholinesterase level,
which is associated with the potential DNA breakage as indicated by comet assay.
Age, smoking and years of working are the contributing factors influencing
the biomarker of effects. |
Keywords: Organophosphate,
cholinesterase, depression, chromosomal breakage, DNA damage.
Introduction
Over the past 60 years, various organophosphates (OP) insecticides have
been introduced as the replacement for legally banned organochlorine (OC) used
in the markets. In a
global rice production area in Southeastern Asia, at least 40% of the total cost was spent to
subsidize OP insecticides used by rice farmers [1]. Unfortunately, by the year*
2005, a report from Pesticide
Action Network Asia and the Pacific (PANAP) highlighted that at least 39.5% of the insects resistant to OP, with
approximately 542 species showed resistance to the total of 316 different
insecticide compounds [2].
In view of this, the frequency and dosage of insecticides used by
farmers is expected to increase in order to cope with the insects’ rapid evolution
process of its genetic adaptation. In this study, the principle of human
biological monitoring is used to identify and quantify the level of exposure
and health effect to the OP contaminants [3]. In order to characterize the risk
of potential health effect, dose monitoring (=exposure biomonitoring) is
complemented by studying together with biological effects (=effect
biomonitoring). Pathway for biological measurements as suggested in this context
is adapted as shown in Figure 1.
Figure 1: Pathways for biological measurements of
mixture of OP (Source: Adapted from [3])
As shown in Figure 1, the primary mechanism of OP toxicity involves the
inhibition of the acetyl cholinesterase (AChE) in the central and peripheral
nervous system. This has been widely accepted by the occupational practitioner
as an effective biomarker of OP exposure [4-6]. In fact, mixture of OP as the
esters of phosphoric acid shares a common biochemical mechanism for additive
chronic and/or acute health effects [7-8].
Over the years, monitoring of the OP induced chronic effect at the
cellular level is of interest. Past studies suggested that the reduction in
activity of AChE demonstrates a significant association with the increase of oxidative
stress and lipid per oxidation among farmers who were chronically exposed to OP
[9-12]. In fact, the mechanisms of AChE inhibition from OP exposure
to increase the potential oxidative stress have been studied by Banerjee [13].
Theoretically, increased formation of reactive oxygen species (ROS) during
metabolism may cause oxidative damage to the cellular DNA [14-15]. The
background of this principle further suggested that the elevated ROS level and reduction
of ROS scavenger and antioxidant enzymes are associated to cause damage to
nucleic acids, proteins and lipids. This might partly exert their effect to
induce chromosomal instability, mutations, loss of organelle functions, cause membrane
damage and undergo a multistage of carcinogenesis process (16-18).
Currently, there is a growing concern on OP for its
possible chronic effects to exert harmful genotoxic risk among farmers due to
their long-term occupational exposure [19-22]. However, the potential association
between the depressions of AChE to the potential genotoxic effect of chronic OP
exposure has not been well investigated. Therefore, the question arises whether
the evidence association between oxidative stress and depression of AChE level
will also mark the AChE reduction and induce genotoxic risks [23]. The aim of
this study is to fill the knowledge gap by biomonitoring the potential cholinesterase
inhibition and genotoxic effects from OP exposure among the study population.
Materials
and Methods
The study was approved by the Ethics Committee of
the University Research Involving Human Putra Malaysia (UPM/FPSK/100-9/2-JKEUPM).
A total of 160 rice farmers (40.13±10.56) who reported to have at least 2 years
of farming experience were exposed to a mixture of OPs activity were recruited
to participate in this study. They were considered as the rural farming
community living in proximity to the paddy farmland. Therefore, they were
presumed to have cumulative exposure to low level of pesticide through
pesticide drift, deposition, sedimentation, leaching and drainage. An unexposed
group consisting of 160 villagers (40.22±9.75) from fishing village was
selected as an inter-individual comparison of toxicological effect.
Biomarker of exposure
The primary mechanism
of toxicity of OP pesticide is by phosphorylation of the acetyl cholinesterase
enzyme (AChE) at the nerve endings [5-6, 24-25]. In this study, monitoring of blood
cholinesterase level is a useful tool which was used as a biomarker of exposure
to OP pesticide.
Invasive manner of collecting biological samples has
been the obstacles in human health study, particularly among rural villagers.
Therefore, blood cholinesterase test kit (Lovibond, AF267; Tintometer Ltd., UK)
is used to determine the exposure level with only 10µl of capillary blood from the
finger tips. This test kit which works based on the colorimetric principle is
helpful to ease the laboratory analysis with an on-site blood cholinesterase
level estimation.
Finger pricked blood is pipetted to round test tube
which contained of 0.5ml of indicator solution (Bromothymol blue solution)
followed by 0.5ml of substrate solution (Acetylcholine Percholarate). The test
tube was then mixed thoroughly and transferred to 2.5mm cuvettes. Next, the
2.5mm of cuvettes was placed in the colour compartment to view the indicator
colour through a prism. The result is read based on the acid-base blood
cholinesterase level (%) obtained through the pH colour indicator. This
colorimetric principle is based on the normal breakdown of acetylcholine as
shown in Equation 1. The presence of acetic acid determines the normal workout
between acetylcholine and acetyl cholinesterase; otherwise, it forms the bases
due to the accumulation of acetylcholine.
Acetylcholine ŕ
Acetic acid + choline (Equation 1)
Biomarker of effect (Genotoxicity assessment)
Biomarkers of effect
are biological indicators of the body’s response to exposure. The effect of OP
exposure is determined by monitoring the genotoxicity consequences of
sub-clinical changes. The genotoxic effect is measured through the micronuclei
and comet assay. To ensure biological samples are collected in a convenient and
less invasive manner, exfoliated buccal mucosa cells were collected as a
sensitive biomarker of genotoxic damage in the target tissues [26 -27].
Genotoxicity test
(Micronuclei Assay)
Micronuclei (MN) assay is used as an internal
dosimeter to monitor the presence of MN for chromosome [28]. This is to
estimate the possibility of early cancer risk experienced by the study
population. This assay was conducted based on the standard protocol from Thomas
and Fenech [29].
Buccal mucosa cell specimens collected were first centrifuged
at 1500rpm to wash the cells in the buffer solution (80% methanol, absolute
ethanol). The supernatant is aspirated off and the buccal cells were smeared on
the slide by using a pulling technique and keep air-drying. The cell was fixed
with methanol: acetic acid (3:1) solution in a 0.1% phosphate buffer (pH 7.5)
for 20 minutes. The slide was then stained by Feulgen reaction, followed by
counterstaining of the slide with 0.1% of fast green for 30 seconds and rinsed
well with deionized water (dH2O). The slide was then placed
face-down to blot away any residual moisture and allowed drying for about 10-15
minutes before analyzing under light microscope with 100x magnification.
The end point is to measure the cells in the
presence of MN (s), which is scored based on the cells presented with a main
nucleus and smaller nuclei called MN. The MN was usually round or oval in shape,
and their diameter ranged between1/3 to 1/10, the diameter of the main nucleus.
Genotoxicity test
(Comet Assay)
The Comet Assay, also known as
Single-Cell Gel Electrophoresis (SCGE) technique, is a fast and effective way
to measure DNA damage by estimating the comet tail length (µm). This assay was
based on the standard procedure from Comet Assay Kit (Trevigen, USA).
Collected buccal mucosa cell specimens were first
centrifuged for 1 minute at 2500 rpm. Next, Low Melting-point Agarose (LMA)
melted in a beaker of boiling water, with the cap loose in 5 minutes and then
kept cool at 37ºC water bath for at least 20 minutes. This was followed by
pipetted 75µL of 1:10 (v/v) aliquot onto comet slide. Lysis process initiated
by placing the slide at 4ºC in the dark for 10 minutes, then immersed in
pre-chilled lysis solution for 60 minutes. Cells were further denatured by
immersion in freshly prepared alkaline solution, pH>13 for 45 minutes at
room temperature in the dark. The cells were now ready for electrophoresis
system and set the power supply was set at 1volt/cm. After 1 hour, the slide
was rinsed by dipping in deionized water (dH2O), and immersing slide in 70%
ethanol for 5 minutes. The slides then were stained with 50 µL of dilute SYBR
green before viewing under fluorescent microscope (DM2500, LEICA) with
magnification 100x and the images were captured. The cells were then analyzed
by using the commercially available TriTek Comet Score (version 1.5) software
(TriTek Corp., Sumerduck, VA, USA). The tail length was measured (µm) to
indicate the distance of DNA migration from the body of nuclear core and it was
used to evaluate the extent of DNA damage.
Results
Table 1 summarizes the socio-demographic characteristic
of the study population. The 320 participates were all male and Muslims, who
did not drink alcohol. Both exposed and unexposed groups had similar
socio-economical background.
Table
1: Demographic characteristics of study population (N=320)
Demographic
characteristics |
Rice
farmer (N=160) |
Unexposed
(N=160) |
||
N
(%) |
Mean
(SD) |
N
(%) |
Mean
(SD) |
|
Male gender |
160
(100) |
- |
160
(100) |
- |
Age (year) |
- |
40.13
(10.56) |
- |
40.22(
9.75) |
Body Mass Index (BMI) |
- |
24.05
(3.37) |
- |
23.06
(2.59) |
Smoker |
109
(68.1) |
- |
92
(57.5) |
- |
Farming experience
(year) |
- |
17.63
(11.35) |
- |
- |
The level of blood cholinesterase is to reflect the biomarker
of OP exposure during the farm activity. Table 2 shows there is a significant
difference (p=0.001) of blood
cholinesterase level between exposed and unexposed groups. When an acid-base
(%) of blood cholinesterase level is estimated, with the average farmers
indicated an “over-exposed” (41.02%) level and unexposed group show an average
of “normal” (75%) level.
Table 2: Comparison biomarkers of
exposure (blood cholinesterase level) among study population (N=320)
Biomarker of exposure |
Rice farmer (N=160) |
Unexposed (N=160) |
t-statistic a |
p-value |
Mean (SD) |
Mean (SD) |
|||
Blood
Cholinesterase levelb |
41.02 (24.57) |
74.84 (15.42) |
-14.751 |
<0.001** |
** p-value
is significant at level 0.001
a Independent t-test
b Analysis of
result based on blood cholinesterase test kit(Lovibond AF267, Tintometer Ltd.,
UK),
100.0-75.0 (%): Normal;
62.5-50.0 (%): Over-exposure;
37.5-25.0 (%): Serious over exposure;
0.0 (%): Very serious and dangerous over exposure
The study utilized
two genotoxic assays, e.g. MN assay and comet assay to evaluate the genotoxic
effect. Table 3 suggested that farmers had at least an increase of 2-2.5 folds
in genotoxic effects as compared to the unexposed group. The significant
difference of MN frequency (per 1000 cells) and comet tail length(µm) are
crucial to highlight that farmers are at high risk of the genotoxic effects due
to the nature of their work as a pesticide applicator as compared to the unexposed
group.
Table
3: Comparison of biomarker of effect (genotoxic risk) among study population
(N=320)
Biomarker of effect |
Paddy farmer (N=160) |
Unexposed (N=160) |
t-statistic a |
p-value |
Mean (SD) |
Mean (SD) |
|||
Micronuclei (per 1000
cells) |
14.48 (4.20) |
5.46 (1.67) |
25.2 |
<0.001** |
Comet tail length (µm) |
24.35 (8.20) |
12.85 (3.10) |
16.6 |
<0.001** |
** P-value
is significant at level 0.001
a Independent t-test
The difference
between the genotoxic effects examined by MN assay and comet assay is due to
the variations in the type of DNA alterations in the test system. The MN assay
is commonly used to detect fixed mutations which persist for at least one
mitotic cycle; whereas, comet assay is a biomarker for the repairable DNA
lesions or single and double stranded DNA that breaks at a single cell. In other
words, MN assay is widely used to indicate early genotoxic risk due to
chromosomal breakage; in contrast, comet assay is used to estimate the
potential of DNA strand breaks in a cell.
Table 4: Relationship between blood
cholinesterase level and genotoxic effects among rice farmers (N=160)
Variables
a |
Micronuclei (per 1000 cells) |
Comet tail length (µm) |
|||||
β-coefficient |
r, correlation coefficient |
β-coefficient |
r, correlation coefficient |
||||
Block 1 |
|||||||
Age |
0.063 |
0.264 |
0.766** |
0.830 |
|||
BMI |
0.180 |
0.209 |
0.049 |
0.165 |
|||
Smoker b |
-1.323* |
-0.155 |
-1.745* |
-0.102 |
|||
Year of
employment |
0.162** |
0.335 |
-0.126 |
0.697 |
|||
Adjusted
R2 |
0.136 |
|
0.699 |
|
|||
F-statistics |
7.248** |
|
93.113** |
|
|||
Block 2c |
|||||||
Blood
cholinesterase level |
-0.023 |
-0.146 |
-0.042* |
-0.166 |
|||
Adjusted
R2 |
0.148 |
|
0.712 |
|
|||
F-statistics |
6.519** |
|
79.662** |
|
|||
a Simple linear Regression (SLR) by block
b For coding for categorical variables (1=smoker, 0=non-smoker)
cIncluded predictor: Blood cholinesterase level and covariate (Block 1)
**
P-value is significant at level 0.01
* P-value is significant at level 0.05
Table 4 shows the relationship between blood
cholinesterase and the genotoxic effects. Risk factors, such as, age, body mass
index (BMI), smoking and year of employment (as farmer) are introduced as
covariate which may influence the genotoxic risks in the study population. Both
genotoxic effects indicated an inverse relationship with the level of blood
cholinesterase. The result indicated that the blood cholinesterase
significantly decreased with MN frequency (p=0.001)
and comet tail length (p=0.001).
Regression models for adult farmers suggested that 14.8% of MN frequency (per
1000 cells) were explained by the blood cholinesterase level; however, 71.2% of
comet tail length (µm) changes were attributed by the depression of blood
cholinesterase levels among adult farmers.
Overall, there is a significant increase in adjusted
R2 from block 1(MN assay and
covariate:R2=0.136;
Comet assay and in a way that covariate:R2=0.699)
to block 2 that the
combination of the predictor and the covariates seems to contribute to the
variance in the outcome genotoxic effect.
Discussion
The present study used biomarker of exposure-effect continuum to examine
the biological characteristics of OP toxicity and its genotoxic risk among rice
farmers. The result is in agreement with other studies which showed a significant
reduction in blood cholinesterase level among farmers [2-25, 30]. Besides,
genotoxic effects shown in the current study are consistent with previous
studies. Both the effect of chromosomal breakage (MNformation) and DNA
strand breaks (comet tail length) showed a significant increase in farmers as
compared with the unexposed group [20, 31-33].
As shown in Figure 1, there are factors which influence the biomarker
effects’ output, such as the role of lifestyle, individual parameters and
occupational factors. In this study, we examine the changes in blood
cholinesterase level with the genotoxic effects by evaluating the selected risk
factors, such as, age, BMI, smoking and year of employment (as a farmer).
Among these, occupational exposure such as the year of working
experience is of particular importance due to farmers’ work nature as a
pesticide applicator. This is considered as a crucial factor contributing to
the genotoxic effects [34-35]. Besides, it is known that individual factors
such as age, BMI, and smoking are also the predisposing factors contributing
the cancer development [28]. This finding showed that age, smoking and obesity
contributed to the bodyfunction impairments over time. In other words, dynamic
interaction between individual predispositions and genotype were associated
with the production of reactive oxygen species (ROS) under a sustained stress
exogenous and endogenous environment, which characterize the increase in
intracellular oxidative stress modulating the multistage carcinogenic process
over an extended period of time [14,18,36].
The different response presented by MN assay and comet assay to
cholinesterase inhibition in this study is in agreement with previous studies [22,
38]. These studies suggested that the spontaneous hydrolysis of OP from the
active site (serine) of acetyl cholinesterase enzyme is very slow, and may
cause irreversible impairment due to cumulative and long-term toxic effects
among farmers. Under
this continuous environmental stress, ROS are more inclined to accumulate and
produce over a long period of time after chronic and low level of OP exposure [11,
22, 38-39].
Furthermore, the association between blood
cholinesterase activities with ROS suggest a relevant gene-pesticide
interaction which could further lead to genotoxic risk and carcinogenicity.
Since the increases in stress of ROS and reducing of ROS scavengers and
antioxidant enzymes may lead to a significant damage to cell structures [15, 18],
a significant damage may occur to the cell structure and induce somatic
mutation and neoplastic transformation over time. Indeed, cancer initiation and
progression have been associated with oxidative stress through increased DNA
mutations or induced DNA damage and genomic instability [36, 40].
Conclusion
This study suggests that the chronic exposure to OP marks an inhibition
to blood cholinesterase level, which is associated with the potential DNA
breakage as indicated by comet assay. However, the biomarker for short-term
acute exposure showed no chromosomal breakage from MN frequency.
Nevertheless, further study is needed to further quantify the potential
body burden perceived from OP exposure and estimate the adverse effect from
chronic exposure at the cellular level of the organism by considering the
weight of evidence.
Acknowledgements
This work was supported by the Research University
Grant Scheme (RUGS) Initiative-6 [grant number: 9337400] under Research
Management Centre (RMC), University Putra Malaysia from 2012-2014.
Conflict of
Interest: Non declared.
References
1.
Magallona ED.
(1989). Effects of insecticides in
rice ecosystems in Southeast Asia. In: Bourdeau P, Haines JA, Klein W, Krishna
Murti CR, Ecotoxicology and Climate. New York: John Wiley & Sons Ltd.
P265-97.
2.
Watts M (2010).
Pesticides: Sowing poison, growing
hunger, reaping sorrow. In Rengam SV, editors. 2nd ed.
Malaysia: Pesticide Action Network (PAN) Asia and the Pacific. P 1-104.
3.
Kapka-Skrzypczak
L, Cyranka M, Skrzypczak M, Kruszewski M. Biomonitoring and biomarkers of
organophosphate pesticides exposure - state of the art. Ann Agric Environ Med
2011; 18(2): 294-303.
4.
Bhalli JA, Khan
QM, Nasim A. DNA damage in Pakistani pesticide-manufacturing workers assayed
using the Comet assay. Environ Mol Mutagen 2006; 47(8):587-93.
5.
Mohebbi GH,
Jahangiri A, Hajeb P. Inhibition of acetyl cholinesterase activity farmers
exposed to organophosphate pesticides in Bushehr, Iran. American-Eurasian
Journal of Toxicological Sciences 2011; 3(3):127-29.
6.
Wilson BW,
Arrieta DE, Henderson JD. Monitoring cholinesterases to detect pesticide
exposure. Chem Biol Interact 2005; 157-158:253-6.
7.
Abdullah AR, Bajet
CM, Matin MA, Nhan DD, Sulaiman AH. Ecotoxicology of pesticides in the tropical
paddy field ecosystem. Environ Toxicol
Chem 1997; 16(1):59-70.
8.
Dubois KP. The
toxicity of organophosphorus compounds to mammals. Bull World Health Organ 1971; 44(1-3):233-40.
9.
Haque QS, Jamal
F, Rastogi SK. Effect of organophosphorus on biochemical parameters on
agricultural workers of mango orchards. Asian Journal Biochemistry 2012;
7(1):37-45.
10. Khan
DA, Bhatti MM, Khan FA, Naqvi ST. Evaluation of pesticides induced toxicity by
oxidative stress and inflammatory biomarkers. Pakistan Armed Forces Medical
Journal 2008; (4). Available from:
http://www.pafmj.org/showdetails.php?id=208&t=o
11. Ogut
S, Gultekin F, Kisioglu AN, Kucukoner E.
Oxidative stress in the blood of farm workers following intensive pesticide
exposure. Toxicol Ind Health 2011; 27(9):820-5.
12. Santi
A, Menezes C, Duarte MM, Leitemperger J, Lopes T, Loro VL. Oxidative stress
biomarkers and acetylcholinesterase activity in human erythrocytes exposed to
clomazone (in vitro). Interdiscip Toxicol 2011; 4(3):149-53.
13. Banerjee
BD, Seth V, Bhattacharya A, Pasha ST, Chakraborty AK. Biochemical effects of
some pesticides on lipid peroxidation and free-radical scavengers. Toxicol Lett
1999; 107(1-3):33-47.
14. Bozina
N, Bradamante V, Lovric M. Genetic polymorphism of metabolic enzymes P450 (CYP)
as a susceptibility factor for drug response, toxicity, and cancer risk. Arh Hig Rada Toksikol 2009; 60(2):217-42.
15. Waris
G, Ahsan H. Reactive oxygen species: role in the development of cancer and
various chronic conditions. J Carcinog
2006; 5:14.
16. Alavanja
MC, Ross MK, Bonner MR. Increased cancer burden among pesticide applicators and
others due to pesticide exposure. CA Cancer J Clin 2013; 63(2):120-42.
17.
Halliwell B.
Oxidative stress and cancer: Have we moved forward? Biochem J 2007; 401(1):1-11.
18.
Reuter S, Gupta
SC, Chaturvedi MM, Aggarwal BB. Oxidative stress, inflammation, and cancer: how
are they linked? Free Radic Biol Med
2010; 49(11):1603-16.
19.
Vivien H, Zailina
H, Patimah I, Dzolkhifli O, Salmiah MS,
Shamsul BMT. Characterization of risk factors for DNA damage among paddy
farmworker exposed to mixture of organophosphate. Arch Environ Occup Health
2013; (doi: 10.1080/19338244.2013.823905).
20. Yadav
AS, Sehrawat G. Evaluation of genetic damage in farmers exposed to pesticide
mixtures. Int J Hum Genet 2011; 11(2):105-9.
21. Acquavella
J, Doe J, Tomenson J, Chester G.
Cowell J, Bloemen L. Epidemiologic studies of occupational pesticide
exposure and cancer: regulatory risk assessments and biologic plausibility. Ann Epidemiol 2003; 13(1):1-7.
22. How V, Hashim Z, Ismail P, Md Said S, Omar
D, Bahri Mohd Tamrin S. Exploring cancer development in adulthood:
cholinesterase depression and genotoxic effect from chronic exposure to
organophosphate pesticides among rural farm children. J Agromedicine 2014;
19(1): 35-43
23. Elersek
T, Filipic M. (2011). Organophosphorous
Pesticides-Mechanisms of Their Toxicity. In: Margarita Stoytcheva,
Pesticides-The Impacts of Pesticides Exposure. [Internet]. Croatia: In Tech:
2011. Chapter 12. P243-60. Available from:
http://www.intechopen.com/books/pesticides-the-impacts-of-pesticidesexposure/
organophosphorous-pesticides-mechanisms-of-their-toxicity
24. Cocker
J, Mason HJ, Garfitt SJ, Jones K. Biological monitoring of exposure to
organophosphate pesticides. Toxicol Lett 2002; 134(1-3):97-103.
25. Bhalli
JA, Khan QM, Haq MA, Khalid AM, Nasim A. Cytogenetic
analysis of Pakistani individuals occupationally exposed to pesticides in a
pesticide production industry. Mutagenesis
2006; 21(2):143-8.
26. Benedetti
D, Nunes E, Sarmento M, Porto C, Dos Santos CE, Dias JF, et al. Genetic damage
in soybean workers exposed to pesticides: evaluation with the comet and buccal
micronucleus cytome assays. Mutat Res 2013; 752(1-2):28-33.
27. Szeto
YT, Benzie IF, Collins AR, Choi SW, Cheng CY, Yow CM, et al. A buccal cell
model comet assay: development and evaluation for human biomonitoring and nutritional
studies. Mutat Res 2005; 578(1-2):371-81.
28. Battershill
JM, Burnett K, Bull S. Factors affecting the incidence of genotoxicity
biomarkers in peripheral blood lymphocytes: impact on design of biomonitoring
studies. Mutagenesis 2008;
23(6):423-37.
29.
Thomas P, Fenech
M. (2011). Chapter 17 In DNA Damage
Detection In Situ, Ex Vivo, and In Vivo: Methods and Protocols. In:
Didenko V, editor. New York: Humana
Press; p235-248. (Methods in Molecular Biology Vol.682).
30. Husin
LS, Uttaman A, Hisham HJ, Hussain IH, Jamil MR. The effect of pesticide on the
activity of serum cholinesterase and current perception threshold on the paddy
farmers in the Muda agriultural development area, MADA, Kedah, Malaysia. Med J Malaysia 1999; 54(3):320-4.
31. Bhalli
JA, Ali T, Asi MR, Khalid ZM, Ceppi M,
Khan QM. DNA damage in Pakistani agricultural workers exposed to mixture
of pesticides. Environ Mol Mutagen
2009; 50(1):37-45.
32. Costa
C, Silva S, Coelho P, Roma-Torres J,
Teixeira JP, Mayan O. Micronucleus analysis in a Portuguese population
exposed to pesticides: preliminary survey. Int J Hyg Environ Health 2007; 210(3-4):415-8.
33. Undeger
U, Basaran N. Assessment of DNA damage in workers occupationally exposed to
pesticide mixtures by the alkaline comet assay. Arch Toxicol 2002; 76(7):430-6.
34. Garaj-Vrhovac
V, Zeljezic D. Assessment of genome damage in a population of Croatian workers
employed in pesticide production by chromosomal aberration analysis,
micronucleus assay and Comet assay. J Appl Toxicol 2002; 22(4):249-55.
35. Hashmi
I, Khan AD. (2012). Adverse Health Effects of Pesticides Exposure in
Agricultural and Industrial Workers of Developing Country. In: Margarita
Stoytcheva. Pesticides-The Impacts of Pesticides Exposure. Croatia: In Tech.
P155-78.
36. Devasagayam
TP, Tilak JC, Boloor KK, Sane KS,
Ghaskadbi SS, Lele RD. Free radicals and antioxidants in human health:
current status and future prospects. J
Assoc Physicians India 2004; 52:794-804.
37. Lukaszewicz-Hussain
A. Role of oxidative stress in organophosphate insecticide toxicity- short
review. Pestic Biochem Physiol 2010; 98(2):145-50.
38.
Bayrami M,
Hashemi T, Malekirad AA, Ashayeri H, Faraji F, Abdollahi M.
Electroencephalogram, cognitive state, psychological disorders, clinical
symptom, and oxidative stress in horticulture farmers exposed to
organophosphate pesticides. Toxicol Ind Health 2012; 28(1):90-6.
39. Hernandez
AF, Lacasana M, Gil F, Rodríguez-Barranco
M,
Pla A,
López-Guarnido
O.
Evaluation of pesticide-induced oxidative stress from a gene-environment
interaction perspective. Toxicology 2013; 307:95-102.
40.
Visconti R,
Grieco D. New insights on oxidative stress in cancer. Curr Opin Drug Discov Devel 2009; 12(2):240-5.
* Corresponding
author: Zailina Hashim, Dept., of Environmental and Occupational Health,
Faculty of Medicine and Health Sciences, University Putra Malaysia, Serdang,
Malaysia.
Email: vivienhw1022@gmail.com