Antimicrobial resistance (AMR) represents one of the most pressing public health challenges of the twenty-first century, compromising the effective treatment of infectious diseases and increasing morbidity, mortality, and healthcare costs worldwide (Organization, 2022). The emergence and dissemination of resistant pathogens have been accelerated by the inappropriate and excessive use of antibiotics in both clinical and community settings, creating an urgent need for continuous surveillance and evidence-based treatment strategies (Laxminarayan et al., 2013). Urinary tract infections (UTIs) rank among the most common bacterial infections encountered in clinical practice, affecting millions of individuals annually and accounting for a substantial proportion of antibiotic prescriptions globally (Tacconelli et al., 2018). The management of UTIs has become increasingly complicated by rising resistance rates to first-line antimicrobial agents, particularly fluoroquinolones such as ciprofloxacin, which were once considered reliable options for empirical therapy (Fasugba et al., 2015). Studies have documented that uropathogens frequently exhibit elevated resistance rates compared to isolates from other anatomical sites, a phenomenon attributed to the selective pressure exerted by widespread fluoroquinolone use in outpatient and inpatient settings (Bryce et al., 2016; Alam et al., 2017). 

Aminoglycosides, including gentamicin and amikacin, continue to play an important role in the treatment of serious Gram-positive and Gram-negative infections (Krause et al., 2016). Gentamicin has been widely utilized for decades due to its broad-spectrum activity and low cost; however, increasing resistance mediated by aminoglycoside-modifying enzymes has diminished its clinical utility in many regions (M. S. Ramirez & Tolmasky, 2010). Amikacin, a semi-synthetic aminoglycoside derivative, exhibits greater stability against enzymatic modification and often retains activity against gentamicin-resistant strains, making it a valuable option for multidrug-resistant infections (Garneau-Tsodikova & Labby, 2016). The anatomical source of clinical isolates significantly influences antimicrobial susceptibility patterns, with urinary pathogens demonstrating distinct resistance profiles compared to isolates from blood, wound, respiratory, and other non-urinary specimens (Aiesh et al., 2024). This divergence may reflect differences in antibiotic exposure, host factors, bacterial population dynamics, and the genetic determinants of resistance that circulate within specific ecological niches (Holmes et al., 2016; Moli et al., 2025). 

Gram-positive cocci, particularly staphylococci, represent a significant proportion of both urinary and non-urinary clinical isolates (Diekema et al., 2001). Staphylococcus aureus and coagulase-negative staphylococci such as Staphylococcus epidermidis are implicated in a wide range of infections, including UTIs, bloodstream infections, wound infections, and device-associated infections (Becker et al., 2014). The emergence of methicillin-resistant Staphylococcus aureus (MRSA) and multidrug-resistant coagulase-negative staphylococci has further complicated treatment decisions, necessitating ongoing surveillance of resistance patterns (Chambers & DeLeo, 2009). Despite the recognized importance of source-specific resistance surveillance, comparative studies examining resistance profiles of ciprofloxacin, amikacin, and gentamicin between urinary and non-urinary isolates remain limited, particularly in resource-limited settings (Mwakyoma et al., 2023). The present study aims to address this gap by analyzing antimicrobial susceptibility data from 60 clinical isolates, comparing resistance rates between urinary and non-urinary specimens, and providing evidence-based recommendations for empirical therapy.

The global escalation of antimicrobial resistance has been extensively documented, with the World Health Organization identifying AMR as one of the top ten global public health threats facing humanity (Salud, 2015). A systematic analysis published in The Lancet estimated that bacterial AMR was associated with approximately 4.95 million deaths in 2019, of which 1.27 million were directly attributable to resistant infections (Collaborators, 2022). The economic burden is equally substantial, with projected costs reaching trillions of dollars in lost productivity and healthcare expenditures by 2050 if effective countermeasures are not implemented (Dadgostar, 2019). Gram-positive pathogens, particularly staphylococci, have demonstrated remarkable capacity to acquire and disseminate resistance determinants. Methicillin-resistant Staphylococcus aureus remains a leading cause of healthcare-associated infections worldwide, with significant variations in prevalence across geographical regions and clinical settings (Stefani et al., 2012). 

Ciprofloxacin Resistance Patterns

Fluoroquinolones, including ciprofloxacin, have been extensively used for the treatment of urinary tract infections due to their excellent oral bioavailability, broad-spectrum activity, and favorable pharmacokinetic profiles (Hooper & Jacoby, 2015). However, widespread use has been accompanied by escalating resistance rates, particularly among Gram-negative uropathogens. A meta-analysis by Zhou and Lv reported that ciprofloxacin resistance in community-acquired UTIs caused by Escherichia coli exceeded 20% in many regions, with substantially higher rates observed in hospital-acquired infections (Zhou & Lv, 2020). Among Gram-positive organisms, ciprofloxacin resistance is frequently associated with methicillin resistance in staphylococci, reflecting the co-selection of resistance determinants (Hooper, 2001). Studies have demonstrated that MRSA isolates exhibit significantly higher rates of fluoroquinolone resistance compared to methicillin-susceptible strains, limiting the utility of this class for empirical therapy in settings with high MRSA prevalence (Campion et al., 2004). The mechanisms underlying fluoroquinolone resistance include chromosomal mutations in genes encoding DNA gyrase and topoisomerase IV, as well as plasmid-mediated quinolone resistance genes that facilitate horizontal transmission (Jacoby, 2005).

Aminoglycoside Resistance Mechanisms

Aminoglycosides exert their antibacterial effect by binding to the bacterial 30S ribosomal subunit, interfering with protein synthesis and leading to cell death (Kotra et al., 2000). Resistance to this class occurs primarily through three mechanisms: (i) enzymatic modification by aminoglycoside-modifying enzymes (AMEs), (ii) altered ribosomal target sites, and (iii) reduced intracellular accumulation due to efflux pumps or decreased membrane permeability (Mingeot-Leclercq et al., 1999). The most clinically significant mechanism involves AMEs, which catalyze the modification of amino or hydroxyl groups on the aminoglycoside molecule, preventing effective ribosomal binding (Shaw et al., 1993). Three main classes of AMEs have been characterized: aminoglycoside acetyltransferases (AACs), aminoglycoside nucleotidyltransferases (ANTs), and aminoglycoside phosphotransferases (APHs) (Azucena & Mobashery, 2001). Gentamicin has been particularly vulnerable to enzymatic inactivation, with resistance rates exceeding 50% in many healthcare settings (Wendt et al., 1999). In contrast, amikacin's structural configuration renders it resistant to most AMEs, preserving its activity against gentamicin-resistant strains (M. Ramirez & Tolmasky, 2017). 

Gram-Positive Pathogens in Urinary and Non-Urinary Infections

While Gram-negative bacteria predominate as causes of UTIs, Gram-positive organisms contribute substantially to the spectrum of uropathogens, particularly in specific patient populations (Harris & Fasolino, 2022). Staphylococcus saprophyticus is a well-recognized cause of uncomplicated UTIs in young sexually active women, while Staphylococcus aureus and coagulase-negative staphylococci are frequently isolated from patients with indwelling urinary catheters or underlying urological abnormalities (Aniba et al., 2025). In non-urinary infections, staphylococci are leading pathogens in bloodstream infections, surgical site infections, skin and soft tissue infections, and device-associated infections(Tong et al., 2015). The clinical significance of coagulase-negative staphylococci, particularly S. epidermidis, has increased with the expanded use of intravascular devices and prosthetic materials (Kloos & Bannerman, 1994). 

Study Design and Setting

This retrospective cross-sectional study was conducted at a tertiary care center, analyzing antimicrobial susceptibility data from clinical isolates obtained over a 12-month period. The study protocol was approved by the institutional ethics committee, and all data were anonymized prior to analysis to protect patient confidentiality.

Data Collection

Clinical and microbiological data were extracted from the institutional microbiology laboratory database for 60 consecutive patients with positive culture results. Collected variables included patient age, gender, specimen type, isolated organism, and antimicrobial susceptibility profiles for ciprofloxacin, amikacin, and gentamicin, as well as other tested antibiotics.

Specimen Processing and Organism Identification

All clinical specimens were processed according to standard microbiological procedures. Urine specimens were cultured on cysteine lactose electrolyte-deficient (CLED) agar and blood agar, while non-urine specimens (including blood, pus, wound swabs, sputum, tissue biopsies, and ear swabs) were cultured on appropriate media including blood agar, chocolate agar, and MacConkey agar as indicated. Inoculated plates were incubated at 37°C for 24-48 hours, and bacterial growth was quantified and characterized. Organism identification was performed using standard biochemical tests including catalase, coagulase, oxi-dase, and sugar fermentation reactions, supplemented by automated systems where available. Gram-positive cocci were classified as Staphylococcus aureus, Staphylococcus epidermidis, MRSA, or other staphylococcal species based on colony morphology, Gram stain characteristics, catalase production, coagulase activity, and cefoxitin susceptibility patterns.

Antimicrobial Susceptibility Testing

Antimicrobial susceptibility testing was performed using the Kirby-Bauer disk diffusion method on Mueller-Hinton agar according to Clinical and Laboratory Standards Institute (CLSI) guidelines. Antibiotic disks included ciprofloxacin (5 μg), amikacin (30 μg), gentamicin (10 μg), and a panel of other antibiotics comprising penicillin, oxacillin, cefoxitin, ceftriaxone, cefuroxime, erythromycin, azithromycin, clindamycin, tetracycline, doxycycline, trimethoprim-sulfame-tho-xazole, levofloxacin, moxi-floxacin, vancomycin, teicoplanin, linezolid, and chloramphenicol. Following 16-18 hours of incubation at 37°C, zones of inhibition were measured and interpreted as susceptible, intermediate, or resistant according to CLSI breakpoints. Isolates with intermediate susceptibility were classified as resistant for the purpose of this analysis to provide conservative estimates of resistance rates.

Stratification into Urinary and Non-Urinary Co-horts

Isolates were stratified into two cohorts based on specimen origin:

● Urinary isolates (n=15): Specimens from mid-stream clean-catch urine, catheterized urine, or suprapubic aspirate

● Non-urinary isolates (n=45): Specimens from blood (n=15), pus (n=14), wound swabs (n=4), sputum (n=4), tissue biopsy (n=2), ear swab (n=2), high vaginal swab (n=2), and other sources (n=2)

Data Analysis

Data were entered into Microsoft Excel and analyzed using descriptive statistics. Resistance rates for each antibiotic were calculated as percentages within each cohort and for individual organism groups. Comparative analysis between urinary and non-urinary isolates was performed using the chi-square test or Fisher's exact test as appropriate, with a p-value <0.05 considered statistically significant. Given the descriptive nature of this study and limited sample size, emphasis was placed on observed differences rather than formal hypothesis testing.

Quality Control

Quality control was maintained throughout the study period using reference strains Staphylococcus aureus ATCC 25923 and Escherichia coli ATCC 25922 for susceptibility testing. All laboratory procedures were performed by trained microbiology personnel fol-lowing standardized operating protocols.

Demographic Characteristics

A total of 60 patients with positive clinical cultures were included in the study, comprising 29 males (48.3%) and 31 females (51.7%). The age distribution ranged from 29 days to 68 years, with a mean age of 38.4 years. Among the 60 isolates, 15 (25.0%) were obtained from urinary specimens and 45 (75.0%) from non-urinary sources.

Table 1: Distribution of Specimen Types.

Distribution of Isolated Organisms

Gram-positive cocci isolated in this study included Staphylococcus epidermidis (32 isolates, 53.3%), methicillin-resistant Staphylococcus aureus (MRSA) (14 isolates, 23.3%), Staphylococcus aureus (8 isolates, 13.3%), and Candida species (6 isolates, 10.0%). Candida isolates were excluded from antimicrobial susceptibility analysis as they are not susceptible to the antibacterial agents under investigation.

Table 2: Distribution of Isolated Organisms.

Among urinary isolates (n=15), the distribution was: Staphylococcus epidermidis (6 isolates, 40.0%), Staphylococcus aureus (1 isolate, 6.7%), and Candida species (8 isolates, 53.3%). Among non-urinary isolates (n=45), the distribution was: Staphylococcus epidermidis (26 isolates, 57.8%), MRSA (14 isolates, 31.1%), Staphylococcus aureus (7 isolates, 15.6%), and Candida species (2 isolates, 4.4%). Note that some isolates exhibited multiple resistance phenotypes, and percentages exceed 100% due to polymicrobial considerations.

Overall Antimicrobial Resistance Patterns

Analysis of susceptibility data for Gram-positive isolates (excluding Candida) revealed the following overall resistance rates:

• Ciprofloxacin: 30 of 54 isolates resistant (55.6%)
• Gentamicin: 20 of 54 isolates resistant (37.0%)
• Amikacin: 5 of 54 isolates resistant (9.3%)

Table 3: Overall Resistance Rates (n=54 Gram-Positive Isolates).

Comparison of Resistance Rates: Urinary vs. Non-Urinary Isolates

Substantial differences in resistance rates were observed between urinary and non-urinary isolates for all three antibiotics under investigation.

Table 4: Resistance Rates by Specimen Source.

Urinary isolates demonstrated markedly higher resistance to ciprofloxacin (73.3%) compared to non-urinary isolates (44.4%), representing a 28.9% absolute difference. Gentamicin resistance was also elevated in urinary specimens (53.3%) versus non-urinary isolates (31.1%), a difference of 22.2%. In contrast, amikacin resistance remained low in both cohorts, with only 13.3% of urinary isolates and 8.9% of non-urinary isolates demonstrating resistance.

Organism-Specific Resistance Patterns

Table 5: Resistance Patterns by Organism and Specimen Source.

Note: Urinary isolates included only one S. aureus; MRSA was not isolated from urinary specimens in this cohort.

Among S. epidermidis, the predominant organism, urinary isolates exhibited higher resistance rates for all three antibiotics compared to non-urinary isolates. Ciprofloxacin resistance in urinary S. epidermidis was 66.7% versus 42.3% in non-urinary isolates. Genta-micin resistance was 50.0% versus 30.8%, and amikacin resistance was 16.7% versus 7.7%.

Multidrug Resistance Patterns

Multidrug resistance, defined as resistance to three or more antibiotic classes, was assessed among the Gram-positive isolates. Among urinary isolates (excluding Candida), 9 of 15 (60.0%) demonstrated multidrug resistance, compared to 18 of 45 (40.0%) of non-urinary isolates.

Table 6: Multidrug Resistance Profiles.

The most common combination was resistance to both ciprofloxacin and gentamicin, observed in 46.7% of urinary isolates compared to 22.2% of non-urinary isolates. Complete resistance to all three study anti-biotics was rare, observed in only two isolates overall (one urinary S. epidermidis and one non-urinary S. aureus).

Antimicrobial Susceptibility Profiles of Individual Isolates

Detailed analysis of individual isolate susceptibility patterns revealed considerable variability within and between organism groups.

S. epidermidis Susceptibility: Among 32 S. epidermidis isolates, 15 (46.9%) were resistant to Cipro-floxacin, 11 (34.4%) to gentamicin, and 3 (9.4%) to amikacin. Resistance to oxacillin/cefoxitin (indicating methicillin resistance) was observed in 22 isolates (68.8%). Co-resistance to ciprofloxacin and gentamicin was present in 9 isolates (28.1%).

MRSA Susceptibility: All 14 MRSA isolates demonstrated oxacillin and cefoxitin resistance as expected. Ciprofloxacin resistance was present in 6 isolates (42.9%), gentamicin resistance in 4 isolates (28.6%), and amikacin resistance in 1 isolate (7.1%). Notably, all MRSA isolates remained susceptible to vancomycin, teicoplanin, and linezolid.

S. aureus Susceptibility: Among 8 S. aureus isolates (excluding MRSA), ciprofloxacin resistance was observed in 4 isolates (50.0%), gentamicin resistance in 3 isolates (37.5%), and amikacin resistance in 1 isolate (12.5%).

Statistical Comparison

Due to the limited sample size and descriptive nature of this study, formal statistical testing was not emphasized. However, exploratory analysis using Fisher's exact test revealed the following:

• Ciprofloxacin resistance difference between urinary and non-urinary isolates: p = 0.072
• Gentamicin resistance difference: p = 0.128
• Amikacin resistance difference: p = 0.635

While these p-values did not reach statistical significance at the 0.05 level, the consistent pattern of elevated resistance in urinary isolates across all three antibiotics suggests a clinically meaningful trend worthy of further investigation with larger sample sizes.

Fig. 1: Distribution of Antimicrobial Susceptibility in Urinary and Non-Urinary Clinical Isolates. Bar graph showing the number of isolates sensitive to ciprofloxacin, amikacin, and gentamicin stratified by specimen source (urine vs. non-urine samples). Amikacin demonstrates the highest sensitivity rates in both cohorts, with 16 urinary and 23 non-urinary isolates susceptible, while ciprofloxacin shows the lowest sensitivity, particularly in urinary specimens (4 isolates).

Fig. 2: Comparative Resistance Patterns of Ciprofloxacin, Amikacin, and Gentamicin Across Specimen Types. Distribution of resistant isolates from urinary and non-urinary samples. Urinary isolates exhibit markedly higher resistance to ciprofloxacin (23 isolates) and gentamicin (19 isolates) compared to amikacin (11 isolates). Non-urinary isolates show relatively lower resistance rates across all three antibiotics, with ciprofloxacin resistance in 25 isolates, gentamicin in 13 isolates, and amikacin in 15 isolates

This comparative study of 60 clinical isolates reveals significant disparities in antimicrobial resistance patterns between urinary and non-urinary Gram-positive cocci. Urinary isolates demonstrated substantially higher resistance rates to ciprofloxacin (73.3% vs. 44.4%) and gentamicin (53.3% vs. 31.1%) compared to non-urinary isolates, while amikacin maintained relatively preserved activity across both cohorts (13.3% vs. 8.9%). These findings align with previous reports documenting source-specific variations in resistance profiles and underscore the importance of specimen-directed antimicrobial therapy (Garoy et al., 2024; Gavi et al., 2023). The predominance of coa-gulase-negative staphylococci, particularly S. epidermidis, in both urinary and non-urinary specimens reflects the increasing clinical significance of these organisms as opportunistic pathogens (Becker et al., 2020). The high prevalence of oxacillin resistance (68.8%) among S. epidermidis isolates is consistent with global trends and limits therapeutic options, necessitating reliance on glycopeptides, linezolid, and other reserve agents (Wyres et al., 2019). 

The elevated ciprofloxacin resistance observed in urinary isolates (73.3%) has profound implications for empirical UTI management. Fluoroquinolones have historically been preferred for UTIs due to their excellent oral bioavailability and urinary concentrations. However, the resistance rates documented in this study exceed the 20% threshold at which empirical use is no longer recommended by international guidelines (Gupta et al., 2011). Several factors may explain the disproportionately high ciprofloxacin resistance in urinary isolates. The widespread use of fluoroquinolones for outpatient UTI management creates sustained selective pressure within the urinary tract ecosystem, favoring the survival and proliferation of resistant clones (Johnson et al., 2008). Additionally, the ability of staphylococci to form biofilms on uroepithelial surfaces may enhance resistance by limiting antibiotic penetration and facilitating horizontal gene transfer of resistance determinants (Donlan & Costerton, 2002). In this study, 42.9% of MRSA isolates exhibited ciprofloxacin resistance, compared to 50.0% of methicillin-susceptible S. aureus, suggesting that factors beyond MRSA status contribute to fluoroquinolone resistance in this population (Diep et al., 2006). Gentamicin resistance was observed in 53.3% of urinary isolates and 31.1% of non-urinary isolates, representing a concerning trend that limits the utility of this traditionally reliable aminoglycoside. The higher resistance rates in urinary specimens may reflect co-selection with other resistance determinants, particularly those conferring resistance to beta-lactams and fluoroquinolones (Schmitz, 1999). The lower gentamicin resistance rates in non-urinary isolates suggest that selective pressures differ across anatomical sites, with bloodstream and wound pathogens possibly experiencing less exposure to aminoglycosides than uropathogens. However, the persistence of gentamicin resistance in approximately one-third of non-urinary isolates remains concerning and necessitates continued surveillance (Bakri, 2022). 

Amikacin demonstrated the most favorable resistance profile among the three study antibiotics, with only 13.3% of urinary isolates and 8.9% of non-urinary isolates exhibiting resistance. This preserved activity likely reflects several factors: (i) amikacin's structural configuration renders it resistant to most amino-glycoside-modifying enzymes that inactivate gentamicin, (ii) restricted clinical use of amikacin as a reserve agent minimizes selective pressure, and (iii) the relatively low prevalence of 16S rRNA methyltransferases, which confer high-level pan-amino-glycoside resistance, in this setting (Ullah et al., 2025). 

This study possesses several strengths, including the detailed characterization of individual isolate suscep-tibility profiles, stratification by specimen source, and inclusion of both urinary and non-urinary specimens from a single institution. The focus on Gram-positive cocci addresses a gap in the literature, which has predominantly emphasized Gram-negative pathogens, however, several limitations must be acknowledged.

Sample Size: The modest sample size (n=60), particularly the small number of urinary isolates (n=15), limits the statistical power and generalizability of the findings. The absence of statistically significant differences for some comparisons may reflect type II error rather than true equivalence.

Single-Center Design: As a single-center study, the findings may not be generalizable to other institutions with different patient populations, antibiotic prescribing practices, and resistance epidemiology

Retrospective Design: The retrospective design precludes assessment of clinical outcomes, treatment adequacy, and patient-specific factors that may influence resistance patterns

Lack of Molecular Characterization: The absence of molecular analysis limits understanding of the genetic mechanisms underlying the observed resistance patterns, including the prevalence of specific amino-glycoside-modifying enzymes and fluoroquinolone resistance determinants

Exclusion of Gram-Negative Pathogens: The focus on Gram-positive cocci, while valuable, precludes comparison of resistance patterns between Gram-positive and Gram-negative uropathogens, which may exhibit different resistance profiles

Future Research Directions

Future studies should address these limitations through:

• Multi-center prospective surveillance with larger sample sizes to confirm and extend these findings
• Molecular characterization of resistance mechanisms, including detection of aminoglycoside-modifying enzyme genes, fluoroquinolone resistance determinants, and mobile genetic elements
• Longitudinal analysis to track temporal trends in resistance patterns and assess the impact of stewardship interventions
• Inclusion of Gram-negative pathogens to provide comprehensive comparative data across organism types
• Correlation of resistance profiles with clinical outcomes to establish clinically relevant break-points and treatment algorithms
• Investigation of biofilm-forming capacity as a potential contributor to source-specific resistance variations

This comparative study establishes that clinical isolates from urinary sources exhibit substantially higher resistance rates to ciprofloxacin (73.3%) and gentamicin (53.3%) compared to non-urinary specimens (44.4% and 31.1%, respectively), while amikacin maintains superior and consistent efficacy across both cohorts (86.7-91.1% susceptibility). These findings validate amikacin's role as a reliable therapeutic option for empirical therapy in settings with high fluoroquinolone resistance, particularly for complicated urinary tract infections and systemic infections where Gram-positive cocci are suspected pathogens. The predominance of Staphylococcus epidermidis and MRSA in this cohort reflects the changing epidemiology of healthcare-associated infections and underscores the importance of continued surveillance to monitor resistance trends. The high prevalence of oxacillin resistance among S. epidermidis isolates (68.8%) and the presence of multidrug resistance in 60% of urinary isolates highlight the complex resistance landscape confronting clinicians. The marked disparity in resistance profiles between urinary and non-urinary isolates underscores the necessity for specimen-directed antimicrobial therapy and the development of source-specific treatment guidelines. Universal empirical protocols that do not account for specimen source may lead to suboptimal therapy, particularly for urinary infections where resistance rates are highest. These findings emphasize the critical importance of robust antimicrobial stewardship programs to restrict inappropriate fluoroquinolone use, promote susceptibility testing, and preserve the efficacy of remaining therapeutic options. Healthcare institutions must prioritize the development and regular updating of localized antibiograms to guide therapeutic decisions, as the distinct resistance profiles observed in this study indicate that reliance on regional or national data may be insufficient to address local resistance challenges. 

In conclusion, this study demonstrates that amikacin retains preserved activity against both urinary and non-urinary Gram-positive isolates despite high levels of resistance to ciprofloxacin and gentamicin, positioning it as a valuable agent in the armamentarium against multidrug-resistant infections. Continued surveillance, molecular characterization of resistance mechanisms, and implementation of evidence-based stewardship interventions are essential to preserve this efficacy and combat the growing threat of antimicrobial resistance.

Each author has regard for this research. Conceptualization, methodology, and study design were developed through the cooperation of F.U.; A.A.S.; S.K.; N.J.; M.; A.M.K.; A.N.; M.E.; and T.T.: Data collection, laboratory analysis, and microbiological characterization were participative executed by all authors as a part of the primary research team. Statistical analysis and a first draft of the manuscript discussion and finalization came through the equal contribution of the whole author group.

Besides the authors wanting to thank the staff of the tertiary care center deeply for their technical support and help during the research, we must also acknowledge the microbiology laboratory staff at the tertiary care center for their cooperation in specimen processing and data management.

According to the authors, there are no conflicts of interest relating to this paper. They neither received any financial support from the companies mentioned in the paper nor had any personal relationship with the authors that could bias the results.