Document Type : Original Research


1 MD, Pediatric Surgeon, Hippokratio General Hospital, Thessaloniki, Greece

2 PhD Candidate, Histology and Embryology Laboratory, Medical School, Faculty of Health Sciences, Aristotle University of Thessaloniki, Greece

3 MD, Department of Molecular Biology and Genetics, Democritus University of Thrace, Greece

4 MD, Department of Biopathology and Microbiology, Hippokratio General Hospital, Thessaloniki, Greece

5 PhD, Department of Biopathology and Microbiology, Hippokratio General Hospital, Thessaloniki, Greece

6 PhD, Histology and Embryology Laboratory, Medical School, Faculty of Health Sciences, Aristotle University of Thessaloniki, Greece



Background: Electromagnetic non-ionizing radiation has both thermal and non-thermal outcomes on biological systems, such as humans, animals, and bacteria. 
Objective: This study aimed to investigate the effect of non-ionizing radiofrequency radiation, emitted by Wi-Fi routers, on bacterial strains and the modification of their susceptibility to modern antibiotics.
Material and Methods: In this case-control paired study, four bacteria were selected, and one colony from each bacterial strain was exposed to Wi-Fi radiation forming the exposure group. Another set of colonies was not exposed to Wi-Fi radiation, forming the control group. Eight different antibiotic disks were set on the bacterial plates, and the inhibition zone was measured every 3 h for each colony. 
Results: Electromagnetic radiation affects bacterial colonies and their susceptibility to antibiotics. Analysis revealed statistically significant differences, correlated with the bacterial strain, the antibiotic agent, and the time of the exposure, in the inhibition zones, mostly after 6 and 24 h (p-value < 0.05).  
Conclusion: A correlation was observed between antibiotic susceptibility and non-ionizing radiofrequency exposure. Studying the effects of radiofrequency radiation on prokaryotic organisms could clarify more complicated cell structures and organisms, such as eukaryotic. Further experiments, in vitro and in vivo, could provide more information about these outcomes and cause experts to discuss the current guidelines of exposure limits.



Antibiotic drug resistance is developed into a global phenomenon and a significant threat to public health. Prolonged hospitalization, higher pharmaceutical costs, and increased mortality, especially in intensive care units, are some of the most common consequences [ 1 , 2 ]. Modern antibiotics, designed and developed for the management of serious infections by multi-resistant bacteria, are widely used in common infections due to resistance to front-line medication. Global campaigns for the restriction of oral antibiotics overuse have been launched for communities to comply with the international guidelines [ 3 , 4 ].

The present experimental protocol investigates the effects of other factors, such as non-ionizing radiation, on antibiotic susceptibility [ 1 , 5 , 6 ]. Due to the tremendous technological development in recent years, exposure to such kinds of radiation has been dramatically increased, not only in adults but in young children. Wireless communications are a modern trend, leading to technological development. Several studies cause implications regarding the safety of such exposures [ 7 - 9 ].

Non-ionizing radiation, such as microwave and radiofrequency radiation, affects not only humans but other living organisms. In addition to the known thermal effects, significant non-thermal outcomes are on living organisms [ 10 ]. Accordingly, the scientific community has also focused on non-thermal outcomes, which are not as obvious as the thermal ones [ 11 - 18 ].

Wireless Fidelity (Wi-Fi) routers, also called “modems”, are small stations that provide an internet connection to the devices connected via Wi-Fi Protected Access (WPA-PSK) encryption. A connection can be either wireless or wired, and Wi-Fi technology consists of a “family” of wireless network protocols based on the Institute of Electrical and Electronics Engineers (IEEE 8211) standards. Broadcast frequencies are mainly 2.4 and 5 GHz [ 19 ].

Studies have emerged regarding the bacterial resistance to antibiotics, about the exposure to electromagnetic waves. Movahedi et al. revealed that exposure to mobile electromagnetic radiation (RF-EMFs) for a whole day increases the resistance in Staphylococcus aureus and Pseudomonas aeruginosam [ 1 ]. A case-control experiment revealed that the exposure to Wi-Fi radiation might increase the metabolic activity in Escherichia coli, Staphylococcus aureus, and Staphylococcus epidermis, implicating a possible augmentation of the resistance [ 5 ]. Escherichia Coli was also affected by the electromagnetic radiation, emitted from common Wi-Fi systems [ 6 ]. In addition, Taheri et al. showed that Wi-Fi radiation affects the sensitivity of Klebsiella pneumoniae. Another study revealed the nonthermal effects of microwaves (MW) on the antibiotic sensitivity of Pseudomonas aeruginosa [ 20 ].

Routers, installed in houses, public buildings, shops, restaurants, hospitals, open public areas, and public squares, have inundated the lives of people. Wi-Fi function is mostly turned on the whole day; therefore, an electromagnetic cloud is formed from the networks surrounding. Humans, but also microorganisms, are radiated during their abidance inside the “cloud”. Antibiotic susceptibility of bacteria exposed to non-ionizing radiation of Wi-fi routers was measured and statistically analyzed to study these effects.

Material and Methods

This case-control paired study was conducted with the laboratory of bio-pathology and microbiology of Hippokratio hospital in Thessaloniki, Greece. According to the literature [ 1 , 2 , 5 , 6 ], four microbial strains were selected according to their availability and the already existing data from other research so that the results could be comparable

The selected strains were three Gram (-) bacteria, Escherichia Coli, Klebsiella pneumoniae, Pseudomonas aeruginosa, and one Gram (+) coccus, Staphylococcus aureus. Antimicrobial disks were placed into the cultural plates to measure the antibiotic susceptibility, using the basic methodology elements of the Kirby-Bauer method. The plates were divided into two groups: the control and the exposure group (experimental). The control group was incubated in the central incubation chamber inside a Faraday bag at 37 °C to exclude any radiation exposure. The exposure group was incubated in a subsidiary chamber at 37 °C, in which the source of the radiofrequency radiation (RFR) was mounted 30 cm away.

For both control and exposure groups, standard turbidity of 0.5 McFarland was swabbed on Muller-Hinton agar to have 1.5×108 CFU/ml as the total count. For staphylococcus aureus blood agar was used as a nutrient medium. The selected source of RFR was a Wi-Fi router, configured properly for the experiment [ 1 , 2 , 5 , 6 ]. The router was designed and developed by Ubiquiti, broadcasting wireless at 5 GHz.

A laptop was connected to the router and exchanged data at 1.6 Mbps during the whole experimental process, which lasted 24 h. The selected method does not compromise with the standards, in which readings of the inhibition zones are done after 18 or 24 h, depending on the pathogen. The current research depends on continuous exposure and the simultaneous measurement of the experimental parameters. Bacterial growth and exposure to non-ionizing radiation increase in parallel time, which was the gold standard of this research. The selected method does not controvert the standard Kirby-Bauer method [ 21 , 22 ], following the same concept as literature; however, it uses a modified script to investigate the null hypothesis. Additionally, the current study aimed to observe and measure the differences produced by the effect of non-ionizing radiation on a biological factor, such as antibiotic susceptibility. Therefore, measurements were performed every 3 h (3rd h, 6th h, 9th h, 12th h, 15th h, and 18th h) except the last one, which was held 6 h after the 18th h (24th h). The antibacterial disks used in the experiment were selected based on the international literature, the bacterial characteristics, and their antibacterial range [ 1 , 2 , 5 , 6 ].

The eight antibacterial agents selected were as follows: Piperacillin-Tazobactam (110 μgr), Imipenem (10 μgr), Cefotaxime (30 μgr), Ciprofloxacin (5 μgr), Aztreonam (30 μgr), Cotrimoxazole (25 μgr), Levofloxacin (5 μgr), and Ceftriaxone (30 μgr). These agents were inserted into the culture plates in a set of four disks per plate. The diameter of each disk was standardized to 5mm by the manufacturer; a radial diffusion zone was developed around each disk. The diameter of the zone was proportional to each susceptibility of the pathogen to the agents and also measured with a Vernier caliper in millimeters. Every three hours, the plates were removed from the chambers for the measurements and were then reinserted to continue with the process of incubation.

The radial diffusion zone for each antibiotic disk for both two groups of the four bacteria was computed every three hours.

In this case-control paired study, controls consist of the non-radiated bacteria cultures while the cases consist of the Wi-Fi exposed bacteria. The statistical processing of the results was done by the method of analysis of variance of two factors without interaction and with statistically significant values of variable p <0.05. The t-test was used to determine whether or not two populations are statistically different from each other, by processing the difference in means and variances. The samples are independent of each other and present normal distribution. Statistical analysis was conducted using SPSS (version 24.0, IBM, SPSS Inc., Chicago, IL, USA). P-values under 0.05 were statistically significant.


The radial diffusion zone for each antibiotic disk was calculated in millimeters (Tables 1-5 and Figures 1 and 2). With sign R (resistant), zero measurements of the diffusion zone were recorded as 5 mm (millimeters) equal to the diameter of the disk (Table 1). In the first three hours, no bacterial development was seen for all four studied bacteria, and there were no statistically significant differences to declare (p-value > 0.05). Similar results were presented by other studies, in the first three hours, using different culturing techniques [ 1 , 2 , 5 , 6 ]. First signs of bacterial growth were observed at the next checkpoint, six hours from the beginning of the experiment, in which the first results were recorded (Figure 1).

Antibiotic Agent Escherichia Coli Klebsiella pneumoniae Pseudomonas aeruginosa Staphylococcus aureus
6 hours
TZP 22 20 19 16 26 R 20 14
LEV 29 26 18 16 15 14 R R
ATM 26 22 13 10 20 R R R
CIP 30 30 21 20 16 14 8 R
CTX 25 24 20 18 14 R 15 10
COT 24 22 18 14 R R R R
CRO 26 22 17 14 15 R 14 12
9 hours
TZP 23 23 19 18 26 26 25 13
IMI R R R R R R 12 R
LEV 33 32 18 18 22 23 9 R
ATM 28 28 13 13 22 23 R R
CIP 33 35 22 20 29 25 9 R
CTX 25 28 18 18 17 16 22 19
COT 24 25 15 15 R R 22 22
CRO 25 28 15 17 18 15 20 18
12 hours
TZP 24 25 19 17 30 30 25 27
IMI R R R R R R 9 10
LEV 29 35 20 20 21 25 9 7
ATM 30 30 13 12 26 25 R R
CIP 33 38 21 20 30 29 10 6
CTX 30 29 18 27 19 19 22 22
COT 24 26 15 14 R R 22 23
CRO 28 30 16 17 21 19 20 19
15 hours
TZP 24 25 20 18 29 28 27 27
IMI R R R R R R 12 10
LEV 31 35 20 20 22 24 8 7
ATM 29 30 12 13 27 26 R R
CIP 35 35 21 22 31 30 10 R
CTX 29 28 19 19 20 20 21 21
COT 24 25 13 13 R R 23 25
CRO 28 30 16 17 21 21 19 19
18 hours
TZP 24 25 20 18 30 30 29 27
IMI R 6 R R 7 6 8 10
LEV 30 31 20 21 23 24 7 8
ATM 30 29 13 12 27 27 R 6
CIP 30 31 22 22 30 32 10 7
CTX 29 30 19 18 20 19 20 21
COT 23 25 11 12 6 6 23 25
CRO 30 31 17 17 23 20 18 19
24 hours
TZP 26 25 20 18 30 30 25 26
IMI 6 6 6 6 6 6 10 9
LEV 31 35 20 20 23 25 8 7
ATM 30 31 14 12 27 26 6 6
CIP 35 32 22 22 30 35 10 6
CTX 30 30 19 17 20 18 20 21
COT 23 23 11 10 6 R 22 24
CRO 30 31 17 16 20 20 19 19
TZP: Piperacillin-Tazobactam (110 μgr), IMI: Imipenem (10 μgr), CTX: Cefotaxime (30 μgr), CIP: Ciprofloxacin (5 μgr), ATM: Aztreonam (30 μgr), COT: Cotrimoxazole (25 μgr), LEV: Levofloxacin (5 μgr), CRO: Ceftriaxone (30 μgr), CTRL: Control Group, EXP: Experimental Group
Table 1.Bacterial growth observed at each checkpoint from the beginning of the experiment. Experimental radiated group vs. control non-radiated group.
Paired Differences t df Sig. (2-tailed) P-value
Mean Std. Deviation Std. Error Mean 95% Confidence Interval of the Difference
Lower Upper
Pair1 *CTRL6 - EXP6 2.00000 1.60357 0.56695 0.65938 3.34062 3.528 7 0.010
Pair2 CTRL9 - EXP9 -1.00000 1.51186 0.53452 -2.26394 0.26394 -1.871 7 0.104
Pair3 CTRL12 - EXP12 -1.87500 2.47487 0.87500 -3.94405 0.19405 -2.143 7 0.069
Pair4 CTRL15 - EXP15 -1.00000 1.51186 0.53452 -2.26394 0.26394 -1.871 7 0.104
Pair5 CTRL18 - EXP18 -0.87500 0.83452 0.29505 -1.57268 -0.17732 -2.966 7 0.021
Pair 6 CTRL24 - EXP24 -0.25000 1.98206 0.70076 -1.90705 1.40705 -0.357 7 0.732
*CTRL#: Control Group #Hour, EXP#: Experimental Group #Hour
Table 2.Paired Samples Test Escherichia coli (with bold are highlighted the statistically significant differences).
Paired Differences t df Sig. (2-tailed) P-value
Mean Std. Deviation Std. Error Mean 95% Confidence Interval of the Difference
Lower Upper
Pair 1 *CTRL6 - EXP6 2.25000 1.28174 0.45316 1.17844 3.32156 4.965 7 0.002
Pair 2 CTRL9 - EXP9 0.12500 1.12599 0.39810 -0.81635 1.06635 0.314 7 0.763
Pair 3 CTRL12 - EXP12 -0.62500 3.50255 1.23834 -3.55321 2.30321 -0.505 7 0.629
Pair 4 CTRL15 - EXP15 -0.12500 0.99103 0.35038 -0.95352 0.70352 -0.357 7 0.732
Pair 5 CTRL18 - EXP18 0.25000 1.03510 0.36596 -0.61536 1.11536 0.683 7 0.516
Pair 6 CTRL24 - EXP24 1.00000 0.92582 0.32733 0.22600 1.77400 3.055 7 0.018
*CTRL#: Control Group #Hour, EXP#: Experimental Group #Hour
Table 3.Paired Samples Test Klebsiella pneumοniae (with bold are highlighted the statistically significant differences).
Paired Differences t df Sig. (2-tailed) P-value
Mean Std. Deviation Std. Error Mean 95% Confidence Interval of the Difference
Lower Upper
Pair 1 *CTRL6 - EXP6 7.25000 7.85130 2.77585 0.68615 13.81385 2.612 7 0.035
Pair 2 CTRL9 - EXP9 0.75000 1.83225 0.64780 -0.78180 2.28180 1.158 7 0.285
Pair 3 CTRL12 - EXP12 0.00000 1.77281 0.62678 -1.48211 1.48211 0.000 7 1.000
Pair 4 CTRL15 - EXP15 0.12500 0.99103 0.35038 -0.70352 0.95352 0.357 7 0.732
Pair 5 CTRL18 - EXP18 0.25000 1.48805 0.52610 -0.99404 1.49404 0.475 7 0.649
Pair 6 CTRL24 - EXP24 -0.37500 2.19984 0.77776 -2.21411 1.46411 -0.482 7 0.644
*CTRL#: Control Group #Hour, EXP#: Experimental Group #Hour
Table 4.Paired Samples Test Pseudomonas aeruginosa (with bold are highlighted the statistically significant differences).
Paired Differences t df Sig. (2-tailed) P-value
Mean Std. Deviation Std. Error Mean 95% Confidence Interval of the Difference
Lower Upper
Pair 1 *CTRL6 - EXP6 2.00000 2.44949 0.86603 -0.04782 4.04782 2.309 7 0.054
Pair 2 CTRL9 - EXP9 4.00000 3.96412 1.40153 0.68591 7.31409 2.854 7 0.025
Pair 3 CTRL12 - EXP12 0.37500 1.92261 0.67975 -1.23234 1.98234 0.552 7 0.598
Pair 4 CTRL15 - EXP15 0.75000 2.05287 0.72580 -0.96624 2.46624 1.033 7 0.336
Pair 5 CTRL18 - EXP18 -0.37500 1.84681 0.65295 -1.91897 1.16897 -0.574 7 0.584
Pair 6 CTRL24 - EXP24 0.25000 1.83225 0.64780 -1.28180 1.78180 0.386 7 0.711
*CTRL#: Control Group #Hour, EXP#: Experimental Group #Hour
Table 5.Paired Samples Test Staphylococcus aureus (with bold are highlighted the statistically significant differences).

Figure 1. Comparisons of the bacterial growth observed six (6) h from the beginning of the experiment. Exposed group vs. control group. The x-axis represents the 8 antibiotic agents selected and Y-axis stands for the mean inhibition zone of each pathogen.

Figure 2. Mean inhibition zone diameter in the studied strains against four selected antibiotics during 24 hours.

Analyzing the results, a common behavior for all four bacteria was observed related to the divergence of antibiotic susceptibility. The maximum disparity was observed at the same checkpoint for all bacteria, at the logarithmic phase of their growth, the 6th hour. A stabilization was observed in the recorded differences and, in some cases, an inversion of the results among the control and exposure groups (Table 1).

The paired difference is presented for each of the studied pathogens. The comparison was done among each pair (Control – Experimental) for all the antibiotic agents tested, at every checkpoint.

The variation of the susceptibility with notable differences of four selected antibiotic agents against each bacterial strain is presented below (Figure 2).


From the recorded results during the experiment, a reduction in antibiotic susceptibility was observed. The major difference was recorded mainly during the logarithmic phase of the bacterial growth [ 1 , 5 , 6 , 18 ]. More specifically, at the sixth hour, E. Coli presented a p-value of 0.010, Klebsiella pneumοniae0.002, Pseudomonas aeruginosa0.035, and Staphylococcus aureus marginally significant at 0.054. These findings support our hypothesis regarding the alterations in the susceptibility due to the Wi-Fi RFR. A significant decrease in the inhibition zone diameters was observed, showing an antibacterial resistance pattern due to the radiation. The next checkpoints presented fewer disparities, until the 24th h. These results were similar to other studies [ 2 , 6 , 20 ].

In this study, the checkpoints were set at 3, 6, 9, 12, 15, 18, and 24 h with the first recorded results at 6th h. A notable difference was observed at the beginning of the logarithmic phase (6 h), which constituted the first recorded result. Three hours after the beginning of the experiment, no growth was observed at any of the culture plates. In other studies, results are recorded much earlier in four or even two hours from the beginning of the experiment [ 1 , 2 ]. In the current study, based on a team of biopathology experts, the time needed for the first results to emerge, was similar to the usual time that a bacterial culture needs to reach the logarithmic phase.

The analysis revealed two facts as follows: 1) the strong correlation between time and antibiotic susceptibility [ 3 ] and 2) the recorded significant differences (p-values < 0.05) in our four pathogens, at the second checkpoint. Significant findings (p-values < 05) were recorded also in other checkpoints (E. Coli at the 18th hour, Klebsiella pneumοniae at the 24th h, and Staphylococcus aureus at the 9th h); however, the observations at the 6th h pertained to all pathogens (Table 1).

The variation of the antibiotic susceptibility under the effect of RFR is a common finding in several published studies [ 1 , 2 , 5 , 18 ]. This susceptibility variation is less for bacteria exposed to RFR. These mechanisms are related not only to the structure of the bacterial membrane and the bacterial wall but also to their biochemical properties. Electromagnetic waves may also affect the potential of the bacterial wall and the ions exchange. The molecules of water present in the bacterial cell are bipolar. Therefore, inside a magnetic field, these molecules are orientated towards the magnetic lines. Molecules and bigger substances are transferred in and out of the bacterial cell via bacterial membranous proteins. The structure of these proteins may be altered under the effect of an electromagnetic field [ 6 , 15 ]. Therefore, some researchers also examine the effect of these alterations on the structure of antibiotics.

An important role in all of these mechanisms plays the intensity and the type of radiation. However, many disparities are based on the sensitivity of the experimental conditions [ 6 , 15 , 17 ].

A similar investigation focused on Pseudomonas aeruginosa and Staphylococcus aureus, revealed that exposure to electromagnetic waves at a frequency of 2.4 GHz, leads to the augmentation of the bacterial resistance against them, especially after 24 h [ 23 ]. In the current study, Pseudomonas aeruginosa resistance was increased for the cases of Cefotaxime, Aztreonam, and Cotrimoxazole, 24 h after the exposure (Table 1), which is following the aforementioned study [ 23 ]. Moreover, S. aureus resistance was increased for the cases of Imipenem, Ciprofloxacin, and Levofloxacin, similarly to the above study [ 23 ].

In the present study, many of the given drugs led to resistance 24 h after the exposure, especially, for Klebsiella pneumοniae with a full-spectrum resistance. Future studies aimed to focus on the development of further research protocols and increase the number of statistical samples. The experience gained from this study indicates that the bigger the sample can lead the safer the conclusions. Therefore, future research should also study other types of RFR and compare the results to exclude the factor of randomness.

Numerous studies just justify the growing concerns regarding the non-ionizing EMF that correlated with the development of malignant tumors in rats [ 24 ]. Apart from the risk of cancer, EMF could be responsible for “Microwave Syndrome”, known as “electromagnetic hypersensitivity” (EHS), a phenomenon characterized by the appearance of symptoms after exposure of people to electromagnetic fields. These symptoms are generally non-specific multiple organ symptoms that manifest in the skin and nervous system, respiratory, cardiovascular, and musculoskeletal systems. Central nervous system symptoms are the most common as a consequence of the neural damage and the over-sensitized neural responses due to RF-EMFs [ 25 , 26 ]. The upcoming 5G mobile network might have adverse systemic outcomes due to the synergistic effects of other toxic stimuli [ 27 ]. The accumulative outcomes are directly linked to deoxyribonucleic acid (DNA) damage, leading to cancer, neurodegenerative diseases, and reproductive declines [ 28 ].

Investigation of the RFR effects on biological systems is vital. The present study focused on the drug-resistance phenomena due to RF-EMFs and showed a correlation between antibiotic susceptibility and RFR.

However, this study aimed to investigate the non-thermal effects of RFR, the temperature rise during the irradiation can be an important parameter. However, it was not feasible to measure the temperature rise due to the special conditions of our specimens’ storage inside the incubation chambers, the culture chambers were regulated to hold a specific temperature that the bacteria needed (37°C during the experiment in both exposure and control group).

Additionally, it was impossible to measure the specific absorption rate (SAR) due to the nature of the experimental specimens. The experiment was based on bacterial strains with very limited mass; therefore, the SAR could not be calculated.


Antibiotic susceptibility and non-ionizing radiofrequency exposure correlated. The effects of radiofrequency radiation on prokaryotic organisms could clarify more complicated cell structures and organisms, such as eukaryotic. Further experiments, in vitro and in vivo, could provide more information about these outcomes and arise experts for further discussion on the current guidelines of exposure limits.

Authors’ Contribution

A. Pegios and E. Vagdatli conceived the idea. The introduction of the paper was written by A. Pegios and D. Kavvadas. A. Pegios, D. Kavvadas, and K. Ζarras gathered the images and the related literature and also help with the writing of the related works. The method implementation was carried out A. Pegios, K. Mpani, P. Soukiouroglou, and S. Charalampidou. Results and analysis were conducted by K. Ζarras, A. Pegios, and D. Kavvadas. The research work was proofread and supervised by Th. Papamitsou. All the authors read, modified, and approved the final version of the manuscript.

Ethical Approval

Permission for conducting the experiment was granted from the Hippokratio General Hospital of Thessaloniki, Greece.

Conflict of Interest



  1. Movahedi MM, Nouri F, Tavakoli Golpaygani A, Ataee L, Amani S, Taheri M. Antibacterial Susceptibility Pattern of the Pseudomonas aeruginosa and Staphylococcus aureus after Exposure to Electromagnetic Waves Emitted from Mobile Phone Simulator. J Biomed Phys Eng. 2019; 9(6):637-46. Publisher Full Text | DOI | PubMed
  2. Taheri M, Mortazavi SMJ, Moradi M, Mansouri Sh, et al, Bahmanzadegan F. Klebsiella pneumonia, a Microorganism that Approves the Non-linear Responses to Antibiotics and Window Theory after Exposure to Wi-Fi 2.4 GHz Electromagnetic Radiofrequency Radiation. J Biomed Phys Eng. 2015; 5(3):115-20. Publisher Full Text | PubMed
  3. Llor C, Bjerrum L. Antimicrobial resistance: risk associated with antibiotic overuse and initiatives to reduce the problem. Ther Adv Drug Saf. 2014; 5(6):229-41. Publisher Full Text | DOI | PubMed
  4. Lee CR, Cho IH, Jeong BC, Lee SH. Strategies to minimize antibiotic resistance. Int J Environ Res Public Health. 2013; 10(9):4274-305. Publisher Full Text | DOI | PubMed
  5. Said-Salman IH, Jebaii FA, Yusef HH, Moustafa ME. Evaluation of Wi-Fi Radiation Effects on Antibiotic Susceptibility, Metabolic Activity and Biofilm Formation by Escherichia Coli 0157H7, Staphylococcus Aureus and Staphylococcus Epidermis. J Biomed Phys Eng. 2019; 9(5):579-86. Publisher Full Text | DOI | PubMed
  6. Taheri M, Mortazavi SMJ, Moradi M, Mansouri S, Hatam GR, Nouri F. Evaluation of the Effect of Radiofrequency Radiation Emitted From Wi-Fi Router and Mobile Phone Simulator on the Antibacterial Susceptibility of Pathogenic Bacteria Listeria monocytogenes and Escherichia coli. Dose Response. 2017; 15(1):1559325816688527. Publisher Full Text | DOI | PubMed
  7. Gupta S, Sharma RS, Singh R. Non-ionizing radiation as possible carcinogen. Int J Environ Health Res. 2022; 32(4):916-40. DOI | PubMed
  8. Lai H. Genetic effects of non-ionizing electromagnetic fields. Electromagn Biol Med. 2021; 40(2):264-73. DOI | PubMed
  9. Havas M. When theory and observation collide: Can non-ionizing radiation cause cancer?. Environ Pollut. 2017; 221:501-5. DOI | PubMed
  10. Belpomme D, Hardell L, Belyaev I, Burgio E, Carpenter DO. Thermal and non-thermal health effects of low intensity non-ionizing radiation: An international perspective. Environ Pollut. 2018; 242(Pt A):643-58. DOI | PubMed
  11. Morgan LL, Miller AB, Sasco A, Davis DL. Mobile phone radiation causes brain tumors and should be classified as a probable human carcinogen (2A) (review). Int J Oncol. 2015; 46(5):1865-71. DOI | PubMed
  12. Jaffar FHF, Osman K, Ismail NH, Chin KY, Ibrahim SF. Adverse Effects of Wi-Fi Radiation on Male Reproductive System: A Systematic Review. Tohoku J Exp Med. 2019; 248(3):169-179. DOI | PubMed
  13. Shekoohi-Shooli F, Mortazavi SM, Shojaei-Fard MB, Nematollahi S, Tayebi M. Evaluation of the Protective Role of Vitamin C on the Metabolic and Enzymatic Activities of the Liver in the Male Rats After Exposure to 2.45 GHz Of Wi-Fi Routers. J Biomed Phys Eng. 2016; 6(3):157-64. Publisher Full Text | PubMed
  14. Soran ML, Stan M, Niinemets Ü, Copolovici L. Influence of microwave frequency electromagnetic radiation on terpene emission and content in aromatic plants. J Plant Physiol. 2014; 171(15):1436-43. Publisher Full Text | DOI | PubMed
  15. Zeleke BM, Brzozek C, Bhatt CR, Abramson MJ, Croft RJ, et al. Personal Exposure to Radio Frequency Electromagnetic Fields among Australian Adults. Int J Environ Res Public Health. 2018; 15(10):2234. Publisher Full Text | DOI | PubMed
  16. Belyaev I. Nonthermal biological effects of microwaves: current knowledge, further perspective, and urgent needs. Electromagn Biol Med. 2005; 24(3):375-403. DOI
  17. Salmen S. Non-thermal biological effects of electromagnetic field on bacteria-a review. Am J Res Commun. 2016; 4(6):16-28.
  18. Mortazavi SMJ, Rahimi S, Talebi A, Soleimani A, Rafati A. Survey of the Effects of Exposure to 900 MHz Radiofrequency Radiation Emitted by a GSM Mobile Phone on the Pattern of Muscle Contractions in an Animal Model. J Biomed Phys Eng. 2015; 5(3):121-32. Publisher Full Text | PubMed
  19. Zentai N, Fiocchi S, Parazzini M, Trunk A, Juhász P, et al. Characterization and Evaluation of a Commercial WLAN System for Human Provocation Studies. Biomed Res Int. 2015; 2015:289152. Publisher Full Text | DOI | PubMed
  20. Nakouti I, Hobbs G, Teethaisong Y, Phipps D. A demonstration of athermal effects of continuous microwave irradiation on the growth and antibiotic sensitivity of Pseudomonas aeruginosa PAO1. Biotechnol Prog. 2017; 33(1):37-44. DOI | PubMed
  21. Kluge RM. Accuracy of Kirby-Bauer susceptibility tests read at 4, 8, and 12 hours of incubation: comparison with readings at 18 to 20 hours. Antimicrob Agents Chemother. 1975; 8(2):139-45. Publisher Full Text | DOI | PubMed
  22. Wasilauskas BL, Morrell RM Jr. An evaluation of the necessity of 24-hour incubation for oxacillin minimum inhibitory concentrations. Am J Clin Pathol. 1996; 105(4):380-3. DOI | PubMed
  23. Amani S, Taheri M, Movahedi MM, Mohebi M, Nouri F, Mehdizadeh AR. Evaluation of Short-Term Exposure to 2.4 GHz Radiofrequency Radiation Emitted from Wi-Fi Routers on the Antimicrobial Susceptibility of Pseudomonas aeruginosa and Staphylococcus aureus. Galen Med J. 2020; 9:e1580. Publisher Full Text | DOI | PubMed
  24. Miah T, Kamat D. Current Understanding of the Health Effects of Electromagnetic Fields. Pediatr Ann. 2017; 46(4):e172-4. DOI | PubMed
  25. Stein Y, Udasin IG. Electromagnetic hypersensitivity (EHS, microwave syndrome) - Review of mechanisms. Environ Res. 2020; 186:109445. DOI | PubMed
  26. Kaszuba-Zwoińska J, Gremba J, Gałdzińska-Calik B, Wójcik-Piotrowicz K, Thor PJ. Electromagnetic field induced biological effects in humans. Przegl Lek. 2015; 72(11):636-41. PubMed
  27. Kostoff RN, Heroux P, Aschner M, Tsatsakis A. Adverse health effects of 5G mobile networking technology under real-life conditions. Toxicol Lett. 2020; 323:35-40. DOI | PubMed
  28. Panagopoulos DJ. Comparing DNA damage induced by mobile telephony and other types of man-made electromagnetic fields. Mutat Res Rev Mutat Res. 2019; 781:53-62. DOI | PubMed