Occurrence, composition and risk assessment of antibiotics in soilsfrom Kenya, Africa

Yuyi Yang1,2 • Anita Awino Owino1,2,3 • Yan Gao4 • Xue Yan1,2 • Chen Xu1,2,3 •

Jun Wang1,2

Accepted: 11 May 2016

� Springer Science+Business Media New York 2016

Abstract Antibiotics can accumulate in soils via different

ways, which may pose serious threat to ecological envi-

ronment of soil and quality of agricultural products. In this

study, the occurrence of 12 antibiotics including four sul-

fonamides (SAs), four tetracyclines (TETs) and four fluo-

roquinolones (FQs) was investigated in soils from four

sampling sites of Kenya (Mai Mahiu, Narok, Mount Suswa

Conservancy, and Juja), Africa. The soils in suburban area

of Narok had the highest average concentrations of total 12

antibiotics with an average value of 43.64 lg kg-1 dw (dry

weight), followed by Mai Mahiu (26.70 lg kg-1 dw), Juja

(24.41 lg kg-1 dw) and Mount Suswa Conservancy

(12.21 lg kg-1 dw). Sulfamethoxazole, sulfamethazine,

oxytetracycline, and enrofloxacin were identified as the

main antibiotics polluted in soils. Total organic carbon may

influence the distribution of SAs in Narok and FQs in Juja.

Ecological risk analysis based on the risk quotient showed

that SAs detected in soils have higher risk compared to

TETs and FQs.

Keywords Kenya � Soils � Sulfonamides � Tetracyclines �Fluoroquinolones � Risk assessment

Introduction

Antibiotics are not only widely used in medicine to save

human lives or to control disease in factory farming (Durso

and Cook 2014; Gothwal and Shashidhar 2015), resulting

in lots of antibiotics in wastewater treatment plant (Michael

et al. 2013; Zhou et al. 2013) and livestock manure (Ho

et al. 2014, 2015; Prosser and Sibley 2015). However,

conventional sewage treatment facilities were never

designed to deal with pharmaceutical compounds, so the

removal efficiency of antibiotics was not stable and lots of

antibiotics were discharged into the environment (Michael

et al. 2013). Soil is also regarded as one of the important

media for existence and transformation of antibiotics (Tolls

2001; Zhang et al. 2011). Antibiotics entered into the soil

via sewage irrigation and manure fertilizer, and so on.

Antibiotics in soils has showed toxic effect on soil

microorganism (Liu et al. 2015; Reichel et al. 2015) and

plant (Du and Liu 2012; Jin et al. 2009). So, it is critical to

assess the levels of antibiotics in soils to avoid potential

health risk via plant (Li et al. 2014; Prosser and Sibley

2015). Degradation and adsorption were the two main

environmental behaviors of antibiotics in soil. Adsorption

mechanism of antibiotics in soil was mainly due to charge

transfer and ion interactions, and was significantly influ-

enced by the pH of the soil (Thiele-Bruhn 2003; Tolls

2001). Antibiotics were susceptible to microbial degrada-

tion under aerobic conditions (Pan and Chu 2016).

In Africa, no adequately enforced legislations regarding

antibiotic use in food-producing animals as well as moni-

toring and control of their residues are carried out (Nonga

et al. 2010). Hence, lots of antibiotics in Africa are not only

discharged into the environment, but also found in the food

which may threat the human health. For example, all

analyzed chicken eggs contained sulfadiazine (SD) and

& Jun Wang

wangjun@wbgcas.cn

1 Key Laboratory of Aquatic Botany and Watershed Ecology,

Wuhan Botanical Garden, Chinese Academy of Sciences,

Wuhan 430074, China

2 Sino-Africa Joint Research Center, Chinese Academy of

Sciences, Wuhan 430074, China

3 University of Chinese Academy of Sciences, Beijing 100049,

China

4 Wuhan Environmental Protection Bureau, Wuhan 430022,

China

123

Ecotoxicology

DOI 10.1007/s10646-016-1673-3

59.4 % contained sulfamethazine (SMZ) residues (Mubito

et al. 2014). In Kenya, antibiotics were also widespread and

intensively used in food producing, in which tetracyclines

(TETs) and sulfonamides (SAs) accounted for more than

70 % of the total consumption (Mitema et al. 2001). TETs,

SAs and trimethoprim, nitrofurans, aminoglycosides, b-lactams, and quinolones were the most commonly used

drugs in food-producing animals in Kenya (Darwish et al.

2013). Antibiotic residues were found to be prevalent in

milk within the Nakuru district of Kenya (Shitandi and

SternesjO 2001). To the best of our knowledge, less

information is about the antibiotics in soils of Kenya and

other countries in Africa. The analysis of antibiotics found

in agricultural soils of China and Malaysia indicated fer-

tilization with animal feces might be the primary source of

antibiotics (Ho et al. 2014; Wu et al. 2014). Due to the

intensive use of antibiotics and low treatment rate of

sewage water in Kenya, it is necessary to assess the levels

of antibiotics in soils. In this study, the occurrence, dis-

tribution and risk assessment of 12 antibiotics in soils from

Kenya were investigated. The information will expand our

knowledge of antibiotic pollution in African soils and

propose useful strategies for soils management in Kenya.

Method and materials

Reagents

Four SAs including SD, SMZ, sulfameter (SME) and sul-

famethoxazole (SMX), four TETs including oxytetracy-

cline (OTC), tetracycline (TC), chlortetracycline (CTC)

and doxycycline (DC), and four fluoroquinolones including

norfloxacin (NOR), ciprofloxacin (CIP), ofloxacin (OFL),

and enrofloxacin (ENR) were purchased from Sigma-

Aldrich Co. (St. Louis, USA). Organic solvents used for

antibiotics extraction were at chromatographical grade.

Sampling sites and sample collection

The total of 58 soil samples were obtained from Kenya,

including 18 samples in the rural area of Mai Mahiu, 14

Fig. 1 The map of studied sites in Kenya, Africa

Y. Yang et al.

123

samples in the suburban area of Narok, 16 samples in the

Mount Suswa Conservancy, and ten samples in the rural

area of Juja (Fig. 1). The soil samples (0–5 cm) were

collected with a grab sampler and packed in sterile con-

tainers. Three subsamples were collected at each site, and

combined as one sample before analysis. All the sites were

in the scope of highland area of Central Kenya. The pH and

cation exchange capacity of soil in this region were 6.2–6.5

and 20.5–24.4 cmol kg-1, respectively.

Sample preparation and analysis of antibiotics

Five grams of lyophilized and ground soil samples were

extracted successively in an ultrasonic bath for 15 min

using solution mixture (15 ml of methanol, 5 ml of Na2-EDTA and 10 ml of citrate buffer at pH 5.0). The process

was repeated at three times. The mixture was collected and

centrifuged at 40009g for 5 min. The supernatants were

combined and diluted into 500 ml using deionized sterile

water. The crude extract was cleaned up and concentrated

by solid phase extraction (SPE) using Strata strong anion

exchanger (SAX) cartridges (3 ml/200 mg, Thermo, USA)

and oasis hydrophilic–lipophilic balance (HLB) cartridges

(6 ml/500 mg, Waters, UK) in series. Detail information

for extraction of antibiotics could be found in the literature

(Luo et al. 2010). High-performance liquid chromatogra-

phy-tandem mass spectrometry (HPLC/MS/MS) operated

in positive mode with electrospray ionization (ESI) was

used to separate and detect the antibiotics as literature

previously published (Kim and Carlson 2007).

Quality assurance and quality control (QA/QC)

Average recoveries were monitored under a strict quality

assurance and quality control to test the availability of the

method before the sample analysis. Ten replicate spiked

soil samples with a concentration of 1.0 lg kg-1 were

extracted and analyzed in the same way as all the samples.

Method blanks were analyzed routinely with field samples

to check for interference and cross contamination. The

limits of detection (LOD) based on a signal-to-noise ratio

of three (S/N = 3) ranged from 0.10 to 0.50 lg kg-1 for

soil samples. The average recoveries of the target antibi-

otics ranged from 65.2 to 124.6 %. The calibration curves

obtained for the antibiotics presented good linear rela-

tionship (R2[ 0.99) for all individual standards. All results

were corrected with the recovery and concentrations of

antibiotics in soils were reported on dry-weight.

Risk assessment

Risk quotient (RQ) values was applied to assess the eco-

logical risk of antibiotics in soils, which are calculated as the

ratio of the measured environmental concentrations (MEC;

or predicted environmental concentrations, PEC) to the

predicted no-effect concentrations (PNEC) for the specific

Table 1 Concentrations of 12 antibiotics in soils from Kenya (lg kg-1 dw)

Antibiotic Mai Mahiu (n = 18) Narok (n = 14) Mount Suswa conservancy

(n = 16)

Juja (n = 10)

Freq (%) Range Mean Freq (%) Range Mean Freq (%) Range Mean Freq (%) Range Mean

SD 5.56 nd-3.24 0.18 28.57 nd-3.83 0.54 0 nd nd 10.00 nd-3.85 0.38

SMZ 77.78 nd-24.23 5.34 85.71 nd-6.83 4.29 81.25 nd-6.79 1.37 20.00 nd-7.68 1.01

SME 5.56 nd-1.50 0.08 28.57 nd-5.79 1.13 0 nd nd 0 nd nd

SMX 77.78 nd-10.71 4.78 78.57 nd-12.47 5.10 75.00 nd-14.47 3.09 80.00 nd-3.42 1.43P

SAs 100 2.48–34.94 10.39 100 2.86–22.59 11.06 81.25 nd-17.28 4.46 80.00 nd-10.24 2.83

OTC 83.33 nd-12.93 3.62 92.86 nd-29.38 9.17 68.75 nd-5.02 1.31 90.00 nd-12.91 5.02

TC 66.67 nd-8.54 2.22 71.43 nd-14.78 3.23 31.25 nd-3.82 0.64 20.00 nd-16.02 2.79

CTC 66.67 nd-15.71 1.86 71.43 nd-37.89 6.49 43.75 nd-6.66 0.86 90.00 nd-2.86 1.51

DC 5.56 nd-0.89 0.05 21.43 nd-3.85 0.57 12.50 nd-2.59 0.29 0 nd ndP

TETs 94.44 nd-31.82 7.74 100 2.93–61.28 19.47 68.75 nd-10.56 3.11 100 0.80–30.02 9.32

NOR 50.00 nd-7.47 1.17 42.86 nd-10.34 2.26 12.50 nd-8.80 0.76 10.00 nd-3.60 0.36

CIP 38.89 nd-6.98 1.75 14.29 nd-9.88 0.83 12.50 nd-3.82 0.43 20.00 nd-4.06 0.55

OFL 66.67 nd-9.19 1.60 35.71 nd-20.71 4.00 31.25 nd-8.55 0.77 30.00 nd-1.30 0.36

ENR 72.22 nd-16.84 4.05 92.86 nd-16.91 6.02 75.00 nd-8.78 2.68 100 2.00–24.50 10.98P

FQs 100 1.05–19.75 8.57 100 3.01–28.16 13.11 81.25 nd-29.96 4.65 100 2.00–24.50 12.25P

Total 100 5.41–76.92 26.70 100 14.18–98.81 43.64 87.50 nd-47.24 12.21 100 2.80–54.95 24.41

Freq Frequency

Occurrence, composition and risk assessment of antibiotics in soils from Kenya, Africa

123

pollutants (European Commission 2003). In general,

RQ\ 0.1 indicates low risk; 0.1 B RQ\ 1 means medium

risk, and RQ C 1 symbolizes high risk (European Com-

mission 2003; Verlicchi et al. 2012). The studies about the

direct toxicity of antibiotics to the terrestrial compartment

(particularly to the soil) are few (Gao et al. 2008), leading to

the challenge in estimating the PNEC in soil. Nevertheless,

the PNECsoil values estimated from PNECwater values

through the equilibrium partition approach were recom-

mended (European Commission 2003; Martın et al. 2012;

Wu et al. 2014). In this study, the PNECsoil values of OTC,

CTC, TC, NOR, CIP, ENR, SD, and SMX were 50, 270, 30,

29.68, 25.64, 24, 0.92 and 1.19 lg kg-1, which could be

found in literatures (Halling-Sorensen et al. 2000; Robinson

et al. 2005; Thiele-Bruhn and Beck 2005; Vaclavik et al.

2004; Zhang et al. 2015). The PNECsoil value of SMZ was

0.62, which was multiplied by the soil–water partition

coefficient of SMZ (3.1 L kg-1) (Thiele-Bruhn 2003) and

PNECwater values derived from toxicity of SMZ to Daphnia

magna (202 lg L-1) (De Liguoro et al. 2009).

Statistical analysis

Statistical analysis was performed using Microsoft Excel

2007 and SPSS software (Version 19.0, IBM, USA). One-

way analyses of variance (ANOVA) and Duncan (D) test

was performed to test the difference between the antibiotics

in different sampling sites. Pearson correlation analysis

was applied to investigate the relationship between TOC

and concentration of antibiotics. Statistical tests were

considered significant at p\ 0.05.

Results and discussion

Profile of antibiotics in soils from Kenya

Table 1 summarizes the concentrations of 12 antibiotics in

soils from Kenya. The soils in suburban area of Narok had the

highest mean concentrations of total 12 antibiotics with mean

value of 43.64 lg kg-1 dw (dry weight), followed by soils in

Fig. 2 Composition of antibiotics in soils of Kenya, Africa. (1 Mai Mahiu, 2 Narok, 3 Mount Suswa Conservancy, 4 Juja)

Y. Yang et al.

123

Mai Mahiu (26.70 lg kg-1 dw), Juja (24.41 lg kg-1 dw)

and Mount Suswa Conservancy (12.21 lg kg-1 dw).

ANOVA analysis indicated statistical difference existed

between concentrations of total 12 antibiotics in the four sites

at p\0.05 level. For SAs, soils in Narok had highest mean

concentration of total SAs among the tested four sites, fol-

lowed by Mai Mahiu, Mount Suswa Conservancy, and Juja.

For TETs, highest mean concentration of total TETs was also

observed in Narok (19.47 lg kg-1 dw), followed by Juja

(9.32 lg kg-1 dw), Mai Mahiu (7.74 lg kg-1 dw) and

Mount Suswa Conservancy (3.11 lg kg-1 dw). The trend for

the mean concentration of total FQs was Narok

(13.11 lg kg-1 dw)[ Juja (12.25 lg kg-1 dw)[Mai

Mahiu (8.57 lg kg-1 dw)[Mount Suswa Conservancy

(4.65 lg kg-1 dw). The soils in Narok experienced serious

pollution of antibiotics compared to Mai Mahiu, Mount

Suswa Conservancy and Juja. In Narok, application of

organic residues has been recommended as a more feasible

and sustainable alternative as fertilizers (Vincent et al. 2011).

The organic residues may play an important role in the pol-

lution of antibiotics in Narok.

Composition of antibiotics in soils from Kenya

For individual antibiotic in Mai Mahiu, OTC had the

highest detect frequency (83.33 %), followed by SMZ

(77.78 %), SMX (77.78 %) and ENR (72.22 %). The other

eight antibiotics had low frequency less than 70 %

(Table 1). SMZ had the highest average concentration

(5.34 lg kg-1 dw) among the tested 12 antibiotics, fol-

lowed by SMX (4.78 lg kg-1 dw), ENR (4.05 lg kg-1

dw) and OTC (3.62 lg kg-1 dw). In suburban soils from

Narok, SMZ, SMX, TC, OTC, CTC, and ENR had the

detect frequency more than 70 %, while the detect fre-

quency of other 6 antibiotics was less than 50 %. OTC had

the highest mean concentration (9.17 lg kg-1 dw), fol-

lowed by CTC (6.49 lg kg-1 dw), ENR (6.02 lg kg-1

dw) and SMX (5.10 lg kg-1 dw). In soils of Mount Suswa

Conservancy, only three antibiotics (SMZ, SMX and ENR)

had detected frequency more than 70 %. SMX had the

highest average concentration (3.09 lg kg-1 dw), followed

by ENR (2.68 lg kg-1 dw) and SMZ (1.37 lg kg-1 dw).

In soils of Juja, the detected frequency of ENR, DC, OTC

and SMX was more than 80 %, while the detected fre-

quency of the other 8 tested antibiotics was no more than

30 %. ENR had the highest average concentration

(10.98 lg kg-1 dw), followed by OTC (5.02 lg kg-1 dw),

and TC (2.79 lg kg-1 dw). Figure 2 shows the composi-

tion of SAs, TETs and FQs in the four sites. In group of

SAs (Fig. 2a), SMX and SMZ were the main pollutants

accounting for more than 80 % of total SAs in soils of the

four sites. SMZ also found to be an important antibiotic

pollutant in chicken eggs in Africa (Mubito et al. 2014). In

group of TETs, OTC was the priority pollutant which is

responsible for more than 40 % of total TETs. DC had the

lowest percentage in the composition of total TETs

(Fig. 2b). OTC also had the most detection rate among

TETs in the beef samples collected in and around the city

of Nairobi (Darwish et al. 2013). For the group of FQs,

Table 2 Concentrations of antibiotics in soils worldwide (lg kg-1 dw)

Compounds Antibiotic Concentration and sites

SAs SD 0.11 (Beijing, China)a, 13.4 (Guangdong, China)b, nd (Malaysia)c, nd (Austria)d, nd-3.85 (Kenya, this study)

SMZ 0.37 (Beijing, China)a, 5.5 (Guangdong, China)b, nd-24.23 (Kenya, this study)

SMX 0.06 (Beijing, China)a, 23.5 (Guangdong, China)b, nd-14.47 (Kenya, this study)

SME 51.4 (Guangdong, China)b, nd-5.79 (Kenya, this study)

SAs 400 (Turkey)e

TETs OTC 80 (Beijing, China)a, 9.6 (Guangdong, China)b, nd (Austria)d, nd-29.38 (Kenya, this study)

TC 5.2 (Beijing, China)a, 44.1 (Guangdong, China)b, nd (Austria)d, nd-16.02 (Kenya, this study)

CTC 17 (Beijing, China)a, 31.1 (Guangdong, China)b,10–15 (Denmark)f, nd-38.79 (Kenya, this study)

DC 63–728 (Malaysia)c, nd-3.85 (Kenya, this study)

FQs NOR 13 (Beijing, China)a, 61.9 (Guangdong, China)b, nd-96 (Malaysia)c, 55.7 (Shandong, China)g, nd-10.34 (Kenya, this

study)

CIP 23 (Beijing, China)a, 26.9 (Guangdong, China)b, nd (Turkey)e, 104.4 (Shandong, China)g, nd-9.88 (Kenya, this study)

ENR 47 (Beijing, China)a, 99.4 (Guangdong, China)b, 36–378 (Malaysia)c, 50 (Austria)d, 50 (Turkey)e, 18.6 (Shandong,

China)g, nd-16.91 (Kenya, this study)

SAs sulfadiazine ? sulfathiazole ? sulfamethoxazole, nd not detecteda–g Noted the following references: Li et al. (2011), (2014), (2015), Ho et al. (2014), Martınez-Carballo et al. (2007), Karcı and Balcıoglu (2009)and Jacobsen et al. (2004), respectively

Occurrence, composition and risk assessment of antibiotics in soils from Kenya, Africa

123

ENR was the main pollutant accounting for 45.95–89.61 %

of the total TETs in the four sites. CIP had the lowest mean

percentage in the composition of total FQs (Fig. 2c). There

is a steady increase in consumption of quinolones since

1998 in Kenya (Mitema et al. 2001) and the concentration

of ENR in Juja even exceeded 10 lg kg-1 dw. So, more

studies on the trend and transformation of FQs pollution in

soils from Africa should be done. The composition pattern

of SAs, TETs and FQs in Mai Mahiu and Mount Suswa

Conservancy was similar (Fig. 2d). In Juja, the TETs and

FQs had higher percentage compared to SAs (Fig. 2d). In

conclusion, SMX, SMZ, OTC and ENR should be paid

more attention in soils of Kenya.

To understand the status of antibiotics in soils of Kenya,

the concentrations of antibiotics in soils from other sites

worldwide were summarized in Table 2. The concentra-

tions of SD, SMZ, SME and SMX in soils of Kenya were

lower than those in vegetable farmland soil in the Pearl

River Delta, Southern China (Li et al. 2011), and higher

than soils from greenhouse vegetable production in Beijing

(Li et al. 2015) and soils in Austria (Martınez-Carballo

et al. 2007). OTC, TC, CTC concentrations in this study

were lower than those in soils in Beijing (Li et al. 2015)

and Guangdong (Li et al. 2011) of China and Denmark

(Jacobsen et al. 2004), but higher than those in Austria

(Martınez-Carballo et al. 2007). ENR, NOR and CIP con-

centrations in soils of this study were lower than those in

soils of China (Li et al. 2011, 2014, 2015) and Malaysia

(Ho et al. 2014), but higher than those in soils from Turkey

(Karcı and Balcıoglu 2009).

The adsorption process of antibiotics onto soils was

influenced by many factors, such as pH, salinity, TOC and

metals (Tolls 2001; Zhang et al. 2011). The correlation

relationship between TOC and concentrations of SAs,

TETs, and FQs was studied using Pearson correlation

analysis. The results showed that no significant correlation

relationship was found between TOC and concentrations of

SAs, TETs, and FQs in soils from Mai Mahiu and Mount

Suswa Conservancy. In Narok, TOC only had significantly

positive correlation relationship with concentrations of SAs

(r = 0.744, p = 0.022). In Juja, only the concentrations of

FQs were found to be significantly positive with the TOC

(r = 0.789, p = 0.011). So, TOC may influence the dis-

tribution of SAs in Narok and FQs in Juja.

Risk assessment of antibiotics in the suburban soils

of Kenya

The RQ values of antibiotics are shown in Fig. 3. From

Fig. 3a, RQ values of SMX and SMZ were more than 1.0

in more than 75 % soils samples from Mai Mahiu, Narok

and Mount Suswa Conservancy, indicated a high ecologi-

cal risk of SMX and SMZ in soils from these studied areas.

RQ values of SMX and SMZ were more than 1.0 in only 40

and 20 % soils samples from the tested four sites of Juja,

respectively, indicating less ecological risk in these

regions. RQ values of SD in most 72.43 % of samples were

less than 0.1, suggesting that SD pose a low risk to soils

from all sampling sites. For TETs, all RQ values were less

than 1.0 (Fig. 3b). RQ values of TC and OTC were less

Fig. 3 RQ values of antibiotics in soils of Kenya, Africa. (1 Mai

Mahiu, 2 Narok, 3 Mount Suswa Conservancy, 4 Juja)

Y. Yang et al.

123

than 0.1 in more than 60 % soil samples of Mai Mahiu,

Mount Suswa Conservancy and Juja, while RQ values of

TC and OTC were more than 0.1 in more than 50 % soil

samples from Narok. These results indicated TC and CTC

pose a higher risk in Narok compared to the other three

sites. RQ values of CTC were less than 0.1 in most samples

in Narok, indicating that the risk of CTC was low in soils

from Narok. For groups of FQs (Fig. 3c), all the RQ values

were less than 1.0 except one sample in Juja, indicated

medium risk of FQs exist in the studied areas. More than

65 % samples in all sites had low RQ values (\0.1) of

NOR and CIP, but the percentage of samples for RQ values

of ENR exceeding 0.1 ranged from 44.4 to 90.00 %. This

suggested that ENR had a higher ecological risk compared

to NOR and CIP. In summary, the SAs detected in soils

have a higher ecological risk compared to TETs and FQs in

all sampling sites.

Conclusion

The pollution levels of antibiotics in soils of Mai Mahiu,

Narok, Mount Suswa Conservancy, and Juja from Kenya

were assessed in this study. Statistical difference existed

between concentrations of total 12 antibiotics. Narok had

the highest levels of antibiotics with a mean concentration

of 43.64 lg kg-1 dw. Compared to soils from other sites

worldwide, the pollution levels of TETs and FQs were

relatively low in Kenya. Risk analysis showed more than

75 % soils samples of Mai Mahiu, Narok and Mount Suswa

Conservancy had higher RQ values of SMX and SMZ,

indicating these soils may have a high risk of SMX and

SMZ. While all TETs and most FQs had low RQ values,

indicating the ecological risk of TETs and FQs were lower

than SAs. So, SAs should be given more attention to reduce

the misuse of SAs in livestock and increase the treatment

efficiency of SAs in the wastewater plant.

Funding This study was funded by Funding Project of Sino-Africa

Joint Research Center, Chinese Academy of Sciences (Y623321K01),

Youth Innovation Promotion Association of Chinese Academy of

Sciences (NO. 2015282) and the Hundred Talents Program of the

Chinese Academy of Sciences (Y329671K01).

Compliance with ethical standards

Conflict of interest Yuyi Yang declares that he has no conflict of

interest, Anita Awino Owino declares that he has no conflict of

interest, Yan Gao declares that he has no conflict of interest, Xue Yan

declares that he has no conflict of interest, Chen Xu declares that she

has no conflict of interest, Jun Wang declares that he has no conflict

of interest.

Ethical approval This article does not contain any studies with

human participants or animals performed by any of the authors.

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