Introduction

Millions of people globally are impacted by the global health issue known as chronic kidney disease (CKD). During time, there is a cumulative and permanent decrease of kidney function, which causes waste materials and fluid to build up in the body. In the ageing population, the increase in chronic diseases like diabetes and hypertension, as well as other variables including environmental pollution and lifestyle habits, are all contributing to an increase in the prevalence of CKD, which is correlated with substantial mortality and morbidity ^1,2.

Recent research has demonstrated that CKD, which affects an estimated 10% of adults worldwide, is a serious global health issue³. By 2040, it is anticipated that CKD, would rank as the 5^th leading cause of death globally³. Dialysis with CKD is a multifactorial disease with many risk factors, such as age, sex or race, genetic makeup, environmental exposures, and lifestyle choices. Also, it is a major contributor to coronary artery disease (CAD), heart failure, and stroke. It is also linked to a higher likelihood of other chronic conditions like diabetes, hypertension, and cancer⁴. Obesity, smoking, a poor diet, inactivity, and exposure to pollutants from the environment are other CKD risk factors^4,5.

Moreover, CKD can lead to mental decline, sadness, and a diminished standard of living⁴. Individuals with CKD frequently have impaired immune systems, making them more prone to bacterial infections. The rise in  (MDR) has, regrettably, made it more difficult to treat these infections, which has increased the mortality and morbidity rates^6,7.

The MDR bacteria are those that are challenging to eradicate with traditional antibiotic therapy because they are resistant to a minimum of three distinct groups of antimicrobial drugs. CKD patients who have previously compromised immune systems and/or have had kidney transplants are at an increased risk of developing infections, making these germs a serious hazard to them⁸. According to previous studies, CKD patients had higher rates of MDR bacteria than the general population, with infections of the urinary tract representing the most common infection type⁹.

A multidisciplinary strategy, including the utilisation of infection control procedures, antimicrobial stewardship programmes, and customised treatment plans, is necessary for the treatment of MDR bacteria in CKD patients. The MDR bacteria can spread among these patients, but the prudent use of antibiotics and the disease management to prevent infections, like hand cleanliness and correct catheter care, can assist^10,11. In this study, we aimed to screen and identify multidrug-resistant bacteria from (CKD) patients undergoing dialysis besides exploring the impact of MDR bacteria on CKD patients with urinary tract infections (UTI). The aim of this study was to examine how MDR bacteria affect chronic kidney disease patients and the difficulties in controlling these infections.

Materials and Methods 

Sampling and study setting

During the research period, urine samples from 2219 patients with (CDK) who had undergone dialysis were obtained from 7 different dialysis centres in Warangal, Telangana State, India. Demographic details are shown in Figure 1. The samples were obtained between March 2018 and February 2019. Clear-catch mid-stream urine samples were collected from patients, both men and women with ages ranging from 20-65. The samples were taken in 2 ml sterilised screw-cap bottles, kept at 4°C, and then brought right away to the lab for further examination.

Figure 1: Demographic details of the sample collection centres with positive cases.   Click here to view figure

Renal function estimation

This study used electronic health records (EHR) and was an experimental approach. The goal of this study was to ascertain the prevalence of MDR bacteria and the incidence of antibiotic resistance among CKD admitted patients in dialysis centres of Warangal.  Serum creatinine concentration and infections that were confirmed by microbial culturing at the time of hospitalization were taken into consideration for being included in the research. Patients receiving kidney replacement therapy who were also terminally sick were not included in the study. The estimated glomerular filtration rate (eGFR) of four major CKD Epidemiology Collaboration (KD-EPI) categories – eGFR 105, 60-104, 30-59, and 30 ml/min/1.73 m2 m² was taken into consideration and compared for the reason that this range had the lowest infection risk and highest eGFR in an earlier study¹².  

Isolation of bacteria from urine samples

On different media viz, blood agar, nutrient agar and McConkey agar, bacterial colonies were isolated using spread plate method. The samples collected were diluted by 10⁴-fold using sterilized water and 0.1ml of diluted sample was then spread quantitatively on petri plates having different media. The plates were finally incubated at 37⁰C for 24 for the growth of bacterial colonies.

For this study, standard reference strains viz., E. coli MTCC-443, K. pneumoniae MTCC–4031, and P. aeruginosa MTCC-1688, procured from the MTTC, IMTECH, Chandigarh were used in this present study.

Preparation of the test organisms for antibiogram profiling

Conforming to the guiding principles of the CLSI (Clinical and Laboratory Standards Institute), only 36 morphologically distinct bacterial colonies were evaluated for antibiotic sensitivity pattern by disc diffusion Kirby methodology on MH (Mueller-Hilton) agar¹³. Fresh bacterial culture in an aliquot of 0.1 ml and 0.5 McFarland was placed on MH agar and left to dry for 5 to 10 minutes. Following that, antibiotic discs were positioned in petri plate and incubated for 24 hours at 37⁰C¹⁴. The tested antibiotics like Ampicillin (2mcg), Ceftriaxone (30mcg), Levofloxacin (5mcg), Tazobactam (10mcg), Gentamycin (10mcg), Meropenem (10mcg), Imipenem (10mcg), Co-trimoxazole (25mcg), Tobramycin (30mcg), Norfloxacin (10mcg), Methicillin (10mcg), Azithromycin (15mcg), Ceftazidime (30mcg), Cefoxitin (30mcg), Cefazolin (30mcg), Ciprofloxacin (5mcg), Ticarcillin-Calvunate (15mcg), Tetracycline (30mcg), Fosfomycin (200 mcg) from Himedia, Mumbai, were procured. Following a 24 h incubation, the diameter (mm) of the inhibition was measured using the meter ruler and recorded in accordance with CLSI¹⁵. Only one of the isolates is subsequently studied if two or even more isolates collected from the same sample site showed identical results.

Determination of antibiotic resistance and multidrug resistance pattern

The Multi-Drug Resistant Bacteria (MDR) were those bacteria that have demonstrated resistance to more than 12 different antibiotics. The MAR index value for each bacterial isolate was calculated based on the formula.

MAR Index Value = M/n

Where, M is the number of drugs/antibiotics to which the bacterial isolate shows resistance; n is the total number of drugs/antibiotics employed¹⁶. Mostly, MAR index greater than 0.2 is an indication that the isolate is MDR¹⁷.

Cultural and biochemical characterization of MDR isolates

MDR bacterial isolates obtained from MAR index were further characterized by both cultural and biochemical tests according to the Bergey’s Manual of Systematic Bacteriology¹⁸. Cell morphology was microscopically observed using Gram staining. Biochemical characteristics viz., IMViC, oxidase, nitrate reductase, H₂S production, sugar fermentation, catalase, amylase and gelatin hydrolysis, etc. were carried out¹⁹.

Molecular identification of MDR isolates

DNA isolation and PCR amplification

DNA from 24 h old bacterial isolates was extracted using phenol-chloroform and stored at 16⁰C²⁰.  Using a set of forward primer, 27F (50′-AGAGTTTGATCCTGGTCAG-3′) and reverse primer, 1492 R, the 16S rRNA genes were amplified (5′- GGTTACCTTGTTACGACTT-3’) After PCR amplification, DNA was purified by QIAGEN QIAquick PCR Purification kit (Cat.No./ID:28104) and 15 ml of the amplicons were electrophoresed on 1% agarose gel for 1hr at 80 Volts. The DNA bands were then visualized under a UV-transilluminator and the purified product was sent for sequencing²¹.

16S rRNA gene sequencing and phylogenetical analysis

The sequencing of 16S rRNA gene of the bacterial isolates was performed by Sanger’s method using Applied Biosystems 3730xL analyser by Barcode Biosciences, Bangalore India. Using the BLAST programme, the FASTA sequences were further analysed for taxonomical identification by comparing them to a database of 16S rRNA sequences from NCBI²². With the use of the NJ (Neighborhood Joining) method, a phylogenetic tree was generated for the alignments of the eight closely related matches with the query sequences²³.

Results and Discussion

Initially, the urine samples were assessed for the amount of serum creatinine and eGFR and a different pattern was noted in the first positive culture, according to different eGFR categories. Gram positive MDR bacteria increased while Gram negative MDR bacteria decreased from eGFR between 60–104 ml/min/1.73 m². Enterococcus sp., were the predominant MDR bacteria, followed by Escherichia sp., Klebsiella sp., Enterobacter sp., and Providencia sp. Compared with eGFR between 60 -104 ml/min/1.73 m², the proportion of E. coli started to rise as the eGFR declined.

The crude MDR ratio increased with decreasing eGFR for E. coli, Klebsiella, Enterobacter sp., Enterococcus sp., but decreased for Providencia sp. These results are in relation with the studies conducted by Evans and his coworkers²⁴. Also, in a study by two different research teams lead by James and Dalrymple, it was found that eGFR ≥ 105 ml/min/1.73 m² might indicate malnutrition, which would be predisposed to high risk of infection^12,25.

The results from various hospitals over the course of a year indicate varying numbers of samples screened and tested positive for antimicrobial resistance (AMR). Hospital M.G.M screened 764 samples, with 151 testing positive for AMR. Similarly, S.V.R screened 582 samples, of which 119 were positive. Max Care screened 338 samples, yielding 96 positive cases. Hospital Jaya had 11 positive results out of 69 samples. Hospital Rohini, Relief, and Vishwas recorded 10, 10, and 48 positive cases out of 99, 51, and 316 screened samples respectively. In total, 445 samples (20.05%) tested positive for AMR, while 1774 samples (79.94%) tested negative, culminating in a cumulative screening of 2219 samples (Table 1, Figure 2).

Table 1: Screening of positive urine samples for Bacteria from CKD Patients undergoing Dialysis

Figure 2: Screening of positive samples from CKD patients undergoing dialysis.   Click here to view figure

36 colonies with unique morphology were picked up and tested for antibiotic sensitivity or resistance. The Multi Antibiotic Resistance (MAR) index is an indication of the bacterial isolates showing resistance to different antibiotics. If the MAR index value is more than 0.2 (>0.2), the bacteria is considered as multi-drug-resistant bacteria. From the MAR indices, it was found that 5 out of 36 isolates exhibited resistance to multiple antibiotics (Figure 3, Table 2).

Figure 3: Disc diffusion antibiotic testing of isolates   Click here to view figure

Table 2: Antibiogram and MAR Index of MDR bacterial Isolates

S = Sensitive; R = Resistant

Isolates viz., CKD_7_1, CKD_19, CKD_21, CKD_27_1 and CKD_31 was found to be resistant to most of the antibiotics. Almost all isolated showed resistance towards Ceftazidime and Levofloxacin. Highest MAR index (0.7) was shown by CKD_07_1 and the least by CKD_31_1 (0.3). CKD_07_1 showed resistance to all the antibiotics except Azithromycin, Norfloxacin, Meropenem, Imipenem, Tetracycline, Cephalexin and Fosfomycin; CKD_19 was found to be sensitive to the Ampicillin, cefazolin, Ticarcillin, Methicillin Gentamycin, Meropenem and imipenem. From the results tabulated in table 2, it is evident that patients with CKD are possibly more likely to have a resistant strain.

In a study conducted by Moges and his co-workers, multidrug resistance was also prevalent in Gram-negative bacteria like E. coli, Enterobacter sp. Citrobacter sp. and Klebsiella sp. and Gram-positive bacteria like Staphylococcus sp. which is in accordance with our study²⁶.

Research efforts have extensively delved into the antibiotic sensitivity profiles of Enterococcus faecalis. Numerous investigations have explored the susceptibility of Enterococcus faecalis to various antibiotics. In a recent study conducted by Khalil et al. (2022), the findings highlighted that a majority of E. faecalis strains exhibited resistance to tetracycline, erythromycin, levofloxacin, and quinupristin-dalfopristin²⁷. In parallel, Samani et al. (2021) conducted another study focused on the prevalence of virulence genes and the antibiotic resistance pattern of Enterococcus faecalis isolated from urinary tract infections in Shahrekord, Iran and found that it was resistant to Norfloxacin, Vancomycin, tetracycline, ciprofloxacin and erythromycin²⁸. This is in complete contradiction to the isolate (CKD_7_01) in our study which was sensitive to Norfloxacin and tetracycline.

Providencia stuartii, in a study conducted by Stock and Wiedemann (1998), showed resistance to polymyxin B, colistin and nitrofurantoin, some aminoglycosides and Penicillins, older cephalosporins, tetracyclines, gentamicin, tobramycin and chloramphenicol²⁹. The results were of our isolate (CKD_19) in accordance to the literature.

CKD_21 also showed resistance to many antibiotics similar to a study conducted by Rodriguez et al. (2019), researchers analyzed the antibiotic susceptibility of Klebsiella variicola isolates. They found variable resistance rates to commonly used antibiotics, including ampicillin, amoxicillin/clavulanate, ciprofloxacin, and gentamicin³⁰. Another study by Feng et al. (2020) investigated the genetic basis of antibiotic resistance in Klebsiella variicola. The research highlighted the presence of various resistance genes, including those conferring resistance to beta-lactams, aminoglycosides, and quinolones³¹.

In a study by Urbaniak et al (2018), it was found that E. bugandensis displayed resistance to cefazolin, cefoxitin, erythromycin, oxacillin, penicillin, and rifampin. Additionally, concerning ciprofloxacin and gentamicin, the strains exhibited either resistance or intermediate resistance. Notably, with tobramycin, a combination of resistant, intermediate resistant, and susceptible strains was observed which was contradictory to our isolate (CKD_27_01) being sensitive to tobramycin³².

In a study by McGregor et al. (2013), which investigated antibiotic resistance patterns of Escherichia coli urinary isolates from outpatients, it was found that E. coli demonstrated susceptibility to ampicillin, amoxicillin-clavulanate, ciprofloxacin, and nitrofurantoin³³. The results are in contract with the finding of McGregor.

The MDR bacterial isolates were further analysed by morphological, biochemical and molecular analysis. Biochemical and cultural characteristics of all isolates CKD_7_1, CKD_19, CKD_21, CKD_27_1 and CKD_31_1 was studied and Table 3 specifies the structural and biochemical characteristics of the isolated strains.

Table 3: Morphological and biochemical characterization of MDR bacterial isolates

The DNA from all the isolated strains was isolated, amplified and the PCR amplicons were analysed for molecular identification by 16S rRNA sequencing employing Sanger’s method. Nucleotide BLAST studies revealed that the 16S rRNA sequences of the isolates, CKD_07_1 (919 bp), CKD_19 (711 bp), CKD_21 (705 bp), CKD_27_1 (875 bp), CKD_31_1 (897 bp) was found to be Enterococcus faecium, Providencia stuartii, Klebsiella variicola, Enterobacter bugandensis and Escherichia coli respectively when checked with nearest homology. The partial sequences of 16s rRNA gene for the MDR isolates were submitted to the GenBank, NCBI and can be accessed under the given accession numbers (Table 4). The phylogenetic trees were constructed for few MDR bacterial isolates (Figure 4).

Table 4: MDR bacterial isolates and their accession numbers

Figure 4: Phylogenetic tree of (4.A) CKD_7_1 (4.B) CKD_19 (4.C) CKD_31_1 (4.D) CKD_27_1 (4.E) CKD_21   Click here to view figure

CKD appears to be a health risk for bacterial infection brought on by pathogens that are resistant to antibiotics, but the majority of published studies are concentrated on the risk of MDR bacterial infections in patients with end stage kidney disease (ESKD) who are receiving dialysis, and there are few studies that examine the association between early stages of CKD and antibiotic susceptibility³⁴.

Conclusions

The prevalence of antibiotic-resistant strains, including Enterococcus faecium, Providencia stuartii, Klebsiella variicola, Enterobacter bugandensis, and Escherichia coli, among CKD patients underscores the urgency of addressing antimicrobial resistance. On the other hand, Cephalosporins and the beta lactam class of antibiotics are frequently administered in case of any bacterial infections, which could be the reason for development of resistance in most of the bacteria. In order to prevent the overuse of antibiotics and shorten the treatment times, it is vital to develop efficient guidelines for the judicious usage of antibiotics for CKD patients. Research into plant extracts’ efficacy against MDR bacteria is on-going, where they can also be used to combat multidrug resistant bacteria. By leveraging in silico methods and data analysis, we can identify new drug targets and develop more effective antibiotics to combat MDR infections in CKD patients with UTIs also. Future challenges in this field include the need to improve the accuracy and reliability of in silico analysis, as well as to validate the findings of in silico studies with experimental data that can account for the complex and dynamic nature of multidrug resistance in CKD patients, including the impact of co-morbidities and drug interactions. 

Acknowledgment

The authors are grateful to the department of Microbiology, Kakatiya University and thankful to the Head, Department of Microbiology for their facilities and constant support.

Conflict of Interests

The authors declare that there are no conflicts of interest.

References

1. Bello, A. K., Nwankwo, E., El Nahas, A. M. Prevention of chronic kidney disease: a global challenge. Kidney International. 2005; 68, pp.S11-S17.
   CrossRef
2. Lv, J. C., & Zhang, L. X. Prevalence and disease burden of chronic kidney disease. Renal fibrosis: mechanisms and therapies. 2019; pp.3-15.
   CrossRef
3. Bikbov, B., Purcell, C. A., Levey, A. S., Smith, M., Abdoli, A., Abebe, M., Owolabi, M. O. Global, regional, and national burden of chronic kidney disease, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. The lancet. 2020; 395(10225), pp.709-733.
4. Chu, C. D., McCulloch, C. E., Banerjee, T., Pavkov, M. E., Burrows, N. R., Gillespie, B. W., Waller, L. CKD awareness among US adults by future risk of kidney failure. American Journal of Kidney Diseases. 2020; 76(2), pp.174-183.
   CrossRef
5. Saliba, R., Zahar, J. R., Dabar, G., Riachy, M., Karam-Sarkis, D., Husni, R. Limiting the Spread of Multidrug-Resistant Bacteria in Low-to-Middle-Income Countries: One Size Does Not Fit All. Pathogens. 2023; 12(1), pp.144.
   CrossRef
6. Morehead, M. S., Scarbrough, C. Emergence of global antibiotic resistance. Primary care: clinics in office practice. 2018; 45(3), pp. 467-484.
   CrossRef
7. World Health Organization. Antimicrobial resistance: global report on surveillance. WHO Press. 2019.
8. Molnar MZ, Alhourani HM, Wall BM, Lu JL, Streja E, Kalantar-Zadeh K, Kovesdy CP. Association of hepatitis C viral infection with incidence and progression of chronic kidney disease in a large cohort of US veterans. . 2015 May;61(5): pp.1495-502.
   CrossRef
9. McCullough, P. A., Uhlig, K., Neylan, J. F., Pergola, P. E., Fishbane, S. Usefulness of oral ferric citrate in patients with iron-deficiency anemia and chronic kidney disease with or without heart failure. The American journal of cardiology. 2018; 122(4), pp.683-688.
   CrossRef
10. Moradi, H., Vaziri, N. D. Lipid disorders associated with chronic kidney disease and nephrotic syndrome. Endocrine Disorders in Kidney Disease: Diagnosis and Treatment. 2019; pp.153-169. 
    CrossRef
11. Algammal, A., Hetta, H. F., Mabrok, M., Behzadi, P. Emerging multidrug-resistant bacterial pathogens “superbugs”: A rising public health threat. Frontiers in Microbiology, 2023; pp.14.
    CrossRef
12. Dalrymple, L. S., Mu, Y., Romano, P. S., Nguyen, D. V., Chertow, G. M., Delgado, C., Johansen, K. L. Outcomes of infection-related hospitalization in Medicare beneficiaries receiving in-center hemodialysis. American Journal of Kidney Diseases.2015; 65(5), pp.754-762.
    CrossRef
13. Bauer, A. W., Kirby, W. M., Sherris, J. C., Turck, M. Antibiotic susceptibility testing by a standardized single disk method. American journal of clinical pathology. 1966; 45(4), pp.493–496.
    CrossRef
14. Fekete, T., Tumah, H., Woodwell, J., Truant, A., Satischandran, V., Axelrod, P., Kreter, B. A comparison of serial plate agar dilution, bauer-kirby disk diffusion, and the vitek automicrobic system for the determination of susceptibilities of Klebsiella spp., Enterobacter spp., and Pseudomonas aeruginosa to ten antimicrobial agents. Diagnostic microbiology and infectious disease. 1994; 18(4), pp.251-258.
    CrossRef
15. Hsueh, P. R., Ko, W. C., Wu, J. J., Lu, J. J., Wang, F. D., Wu, H. Y., Teng, L. J. Consensus statement on the adherence to Clinical and Laboratory Standards Institute (CLSI) Antimicrobial Susceptibility Testing Guidelines (CLSI-2010 and CLSI-2010-update) for Enterobacteriaceae in clinical microbiology laboratories in Taiwan. Journal of Microbiology, Immunology and Infection. 2010; 43(5), pp.452-455.
    CrossRef
16. Krumperman, P. H. Multiple antibiotic resistances indexing of Escherichia coli to identify high-risk sources of fecal contamination of foods. Applied and environmental microbiology. 1983; 46(1), pp.165-170.
    CrossRef
17. Adefisoye MA, Okoh AI (2017). Ecological and public health implications of the discharge of multidrug-resistant bacteria and physicochemical contaminants from treated wastewater effluents in the Eastern Cape, South Africa. Water. 2017; 9(8): pp.562.
    CrossRef
18. Sharpe, M. Elisabeth, ed. Bergey’s manual of systematic bacteriology. Wiliams and Wilkins, 1986.
19. Cappuccino, J. G. S. Microbiology: a laboratory manual/James G., Cappuccino and Natalie Sherman. 1999; (No. 576 C3.).
20. Sambrook, J., Fritsch, E. F., Maniatis, T. Molecular Cloning. A Laboratory Manual, 2nd edn.(Cold Spring Harbor Laboratory Press: New York, USA.) 1989.
21. Satyanarayana, S. D., Krishna, M. S. R., Kumar, P. P. Optimization of highyielding protocol for DNA extraction from the forest rhizosphere microbes. 3 Biotech. 2017; 7, pp.1-9.
    CrossRef
22. Whitford M.F. Forster R.J. Beard C.E. Gong J Teather R.M. Phylogenetic analysis of rumen bacteria by comparative sequence analysis of cloned 16S rRNA genes. Anaerobe. 1998; 4, pp.153–163.
    CrossRef
23. Saitou, N., Nei, M. The neighbor-joining method: a new method for reconstructing phylogenetic trees. Molecular biology and evolution. 1987; 4(4), pp.406-425.
24. Evans, R., Caskey, F., Fluck, R., Crowley, L., Davies, J., Nsonwu, O., & Farrington, K. UK Renal Registry 18th annual report: chapter 12 epidemiology of reported infections amongst patients receiving dialysis for established renal failure in England 2013 to 2014: a joint report from Public Health England and the UK Renal Registry. Nephron Clinical Practice. (2016). 
    CrossRef
25. James, M. T., Quan, H., Tonelli, M., Manns, B. J., Faris, P., Laupland, K. B., Alberta Kidney Disease Network. CKD and risk of hospitalization and death with pneumonia. American Journal of Kidney Diseases. 2009; 54(1), pp.24-32.
    CrossRef
26. Moges, F., Endris, M., Belyhun, Y., Worku, W. Isolation and characterization of multiple drug resistance bacterial pathogens from waste water in hospital and non-hospital environments, Northwest Ethiopia. BMC research notes. 2014; 7, pp.1-6.
    CrossRef
27. Khalil, M. A., Alorabi, J. A., Al-Otaibi, L. M., Ali, S. S., Elsilk, S. E. Antibiotic resistance and biofilm formation in Enterococcus spp. isolated from urinary tract infections. Pathogens. 2022; 12(1), 34.
    CrossRef
28. Samani, R. J., Tajbakhsh, E., Momtaz, H., Samani, M. K. Prevalence of virulence genes and antibiotic resistance pattern in Enterococcus faecalis isolated from urinary tract infection in Shahrekord, Iran. Reports of Biochemistry & Molecular Biology. 2021; 10(1), 50.
    CrossRef
29. Rodríguez-Medina, N., Barrios-Camacho, H., Duran-Bedolla, J., Garza-Ramos, U. Klebsiella variicola: an emerging pathogen in humans. Emerging microbes & infections. 2019; 8(1), 973-988.
    CrossRef
30. Stock, I., and Wiedemann, B. Natural antibiotic susceptibility of Providencia stuartii, P. rettgeri, P. alcalifaciens and P. rustigianii strains. Journal of medical microbiology 1998; 47(7), 629-642.
    CrossRef
31. Li, Shuangshuang, Xudong Feng, Min Li, and Zhen Shen. “In vivo adaptive antimicrobial resistance in Klebsiella pneumoniae during antibiotic therapy.” Frontiers in Microbiology. 2023; 14, 1159912.
    CrossRef
32. Urbaniak, C., Sielaff, A. C., Frey, K. G., Allen, J. E., Singh, N., Jaing, C., … Venkateswaran, K. Detection of antimicrobial resistance genes associated with the International Space Station environmental surfaces. Scientific reports. 2018; 8(1), 814.
    CrossRef
33. McGregor, J. C., Elman, M. R., Bearden, D. T., Smith, D. H. Sex-and age-specific trends in antibiotic resistance patterns of Escherichia coli urinary isolates from outpatients. BMC family practice. 2013; 14(1), 1-5.
    CrossRef
34. Vacaroiu, I. A., Cuiban, E., Geavlete, B. F., Gheorghita, V., David, C., Ene, C. V., Radulescu, D. Chronic Kidney Disease-An Underestimated Risk Factor for Antimicrobial Resistance in Patients with Urinary Tract Infections. Biomedicine. 2022; 10(10), pp.2368.
    CrossRef