Home Medicine Comparative evaluation of various disc elution methods for the detection of colistin-resistant gram-negative bacteria
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Comparative evaluation of various disc elution methods for the detection of colistin-resistant gram-negative bacteria

  • Shubham Chauhan ORCID logo , Pottathil Shinu ORCID logo EMAIL logo , Narinder Kaur ORCID logo , Adesh K. Saini ORCID logo , Harit Kumar ORCID logo , Abdulaziz K. Al Mouslem ORCID logo , Mahesh Attimarad ORCID logo and Anroop B. Nair ORCID logo
Published/Copyright: December 17, 2025

Abstract

Objectives

The current study was designed to determine the performance of colistin broth disc elution (CBDE), colistin broth micro elution (CBME), and microplate elution (MPE) methods with the broth microdilution (BMD) reference method.

Methods

For the study, multidrug-resistant Gram-negative bacilli (MDR GNB) isolates (n=715) obtained from various clinical specimens were tested for colistin sensitivity testing using CBDE, CBME, and MPE methods, and the results were compared with the BMD reference method.

Results

Of the total MDR-GNB isolates (n=715), 6.83 % (n=49) were colistin-resistant, while none yielded the mcr gene. The CBDE method demonstrated a sensitivity (95.91 %), specificity (100 %), positive predictive value (PPV) of 100 %, and negative predictive value (NPV) of 99.7 % when compared to the reference BMD method. The CBME method yielded 93.87 %, 93.33 %, 51.11 %, and 99.7 % of sensitivity, specificity, PPV, and NPV against the reference BMD method. However, the MPE method demonstrated sensitivity (91.83 %), specificity (92.64 %), PPV (47.36 %), and NPV (99.32 %), respectively, when compared to the reference BMD method.

Conclusions

The CBDE method has the potential to replace the BMD method for detecting colistin resistance among Gram-negative bacteria in laboratories. It offers a cost-effective and easy-to-learn alternative, while ensuring strong sensitivity and specificity compared to the BMD reference method.

Introduction

Overuse and misuse of antibiotics have led to an alarming rise in drug resistance within bacterial infections, resulting in prolonged illnesses, higher mortality rates, and increased healthcare expenses worldwide. This increase in multidrug resistance (MDR) is an inevitable outcome of bacterial evolution and presents a significant public health threat. Consequently, colistin has been reevaluated, once considered a last-resort treatment. However, the extensive use of colistin has increased concerns about developing further resistance. Colistin is a potent panta-cationic antimicrobial agent that targets Gram-negative pathogens by binding to lipopolysaccharides (LPS) in their outer membrane. This interaction disrupts the membrane’s integrity, resulting in bacterial cell death. Despite its efficacy, colistin was infrequently used due to its significant neurotoxic and nephrotoxic effects [1], 2]. Nonetheless, over the past decade, its use has surged threefold in combating infections caused by MDR bacteria. Concurrently, the unregulated use of colistin has resulted in the development of colistin-resistant strains [3], 4].

The spread or emergence of colistin resistance among bacteria already resistant to other antibiotics could cause untreatable infections. Gram-negative bacteria (GNB) develop colistin resistance as an adaptive mechanism following in vitro exposure to colistin. It emerges due to the dissemination of plasmid-mediated mcr genes or chromosomal mutation of genes associated with LPS synthesis. The most frequent resistance mechanism involves modifying the bacterial outer membrane by altering the structure of LPS [3], 5]. Other mechanisms may be caused by overexpression of efflux pumps or the excessive production of capsular polysaccharides. Clinical isolates have shown colistin resistance in GNB isolates such as Enterobacteriales, Pseudomonas species, and Acinetobacter species, whereas Proteus, Morganella, and Providencia species are naturally resistant to colistin [4]. It is important to assess colistin-resistant isolates to prevent their dissemination and treatment of MDR-GNBs.

Various culture media-based diagnostic tests, such as CHROMagar COL-APSE (Colistin-resistant Acinetobacter, Pseudomonas, Stenotrophomonas, and Enterobacteriaceae spp.), LBJMR Medium, Superpolymyxin, and other automated minimum-inhibitory concentration (MIC) based methods, such as Com ASP colistin, UMIC colistin kit, Vitek-2 Compact, Micronaut-s colistin broth, have been developed for the detection of colistin-resistant bacteria [6], [7], [8]. However, these methods are relatively expensive and may not be affordable in resource-limited settings. Further, colistin sensitivity testing using the disc diffusion method is not recommended because of the large molecular size of colistin, which prevents adequate diffusion through an agar medium. The European Committee on Antimicrobial Susceptibility Testing (EUCAST) and Clinical and Testing Standard Institute (CLSI) guidelines endorsed the micro broth dilution (BMD) test as the reference standard for the detection of colistin resistance [6], 9]. Further, various elution methods have been designed to identify colistin resistance [7], 8]. However, to our knowledge, there are no studies available to determine the performance of colistin broth disc elution (CBDE), colistin broth micro elution (CBME), and microplate elution (MPE) methods with the BMD for the detection of colistin resistance. Thus, this study aimed to analyze the accuracy of CBDE, CBME, and MPE methods with the reference BMD method in detecting colistin resistance in clinical bacterial isolates.

Materials and methods

Study settings

Clinical specimens and bacterial isolates

A cross-sectional study was conducted at the Department of Microbiology, M.M. Institute of Medical Sciences and Research, Ambala, India, between May 2021 and February 2022. In this study, MDR GNB (n=715) isolated from different clinical samples were subjected to CBDE, CBME, MPE, and BMD methods to assess colistin resistance in these isolates.

In the current study, MDR organisms were defined as bacteria resistant to a minimum of one antibiotic from three different classes of first-line antibiotics [10]. Additionally, the organisms with intrinsic colistin resistance, such as Morganella morganii, Proteus, Providencia, and Serratia species, were excluded. Further, we excluded duplicate samples, including repetitions or those collected from the same patients in the current study.

Bacterial identification, antimicrobial susceptibility testing, and MIC determination

The identification of GNBs was carried out using the Vitek-2 Compact system (bioMérieux, Marcy-l’Étoile, France). For all the GNBs, the MIC of various antibiotics was determined using the Vitek-2 Compact system with AST-N280 and AST-N281 cards, corresponding to lactose and non-lactose fermenters, respectively, according to the manufacturer’s instructions. All MDR-GNB strains were further confirmed using the Kirby-Bauer disc diffusion method, with the AST results interpreted as per CLSI 2020 guidelines [9], 11]. Further, all elution tests were evaluated by two trained, independent technicians to ensure consistency, with each technician interpreting the results in a blinded manner. If any inter-observer variability was observed, the results were reviewed and resolved through joint review by the study supervisor.

Phenotypic detection of colistin resistance

Broth microdilution method for colistin

The colistin MIC was determined using colistin sulphate salt (Hi Media, Mumbai, India), which was dissolved in cation-adjusted Mueller Hinton broth (CA-MHB, HiMedia, Mumbai, India) and distributed into untreated (50 µL) polystyrene microtiter plates with 96 wells. The standard BMD methodology (MIC range: 0.5–16 μg/mL) assessed the MIC values following CLSI standards [9], 11]. For Enterobacterales, Pseudomonas aeruginosa (P. aeruginosa), and Acinetobacter baumannii (A. baumannii), isolates with colistin MIC ≥4 μg/mL are considered resistant, while isolates with ≤2 μg/mL are considered intermediate [9], 11]. Proteus mirabilis (Colistin MIC >16 μg/mL) strain served as a positive control strain, and Escherichia coli (E.coli) ATCC 25922 was employed as the negative control.

Colistin broth disc elution method

For this test, four test tubes containing 10 mL of CA-MHB (HiMedia, Mumbai, India) were prepared and labeled 1 through 4, respectively. Colistin discs (10 µg colistin sulfate, Oxoid Ltd, UK) were added to the tubes to achieve final concentrations of 0 (growth control), 1, 2, and 4 μg/mL by adding 0, 1, 2, and 4 discs into the respective tubes. To allow colistin to elute from the discs into the broth, the tubes were incubated at room temperature for 30 min. A bacterial suspension was prepared in normal saline from the growth on blood agar and calibrated with 0.5 McFarland standard. From this suspension, 50 µL was added to each test tube and mixed thoroughly [7]. Afterward, the tubes were incubated at 37 °C for 24 h, after which they were visually examined for turbidity to determine the minimum inhibitory concentration [11].

Colistin broth micro elution method

The CBME method was performed by preparing four sterile test tubes containing 10 mL of CA-MHB (HiMedia, Mumbai, India) each. Colistin discs (10 µg colistin sulphate, Oxoid, UK) were added to the tubes in increasing numbers: 0 discs (growth control), 1, 2, and 4 discs, respectively. To allow the antibiotic to elute from the discs into the broth, the tubes were incubated at room temperature for 30 min, achieving final concentrations of 0, 1 μg/mL, 2 μg/mL, and 4 μg/mL, respectively. From each tube, 1 mL of the antibiotic-containing broth was transferred to new four sterile test tubes. A bacterial inoculum was prepared in normal saline using growth from a blood agar plate, and the inoculum was standardized with a 0.5 McFarland standard. Then, 5 µL of the standardized bacterial suspension was added to each tube, thoroughly mixed, and these tubes were incubated at 37 °C for 24 h [8]. After 24 h of incubation, the MICs were visually determined, and the results were interpreted using CLSI breakpoints [11].

Microplates elution method

The colistin-containing CA-MHB solution was distributed into microtiter plate wells, with 200 µL in each well, to establish concentrations of 0, 1, 2, and 4 μg/mL. To each well, including the growth control well (0 μg/mL), 3 µL of bacterial suspension, standardized to 0.5 McFarland, was added, followed by incubation at 37 °C for 24 h [8]. The MIC values were visually determined and interpreted according to CLSI breakpoints [11] (Table 1).

Table 1:

Targeted genes and their corresponding primers.

Sr. No. mcr genes Size of amplicon, bp Primer sequences (5′- 3′)
1. mcr 1 320bp fw AGTCCGTTTGTTCTTGTGGC

rev AGATCCTTGGTCTCGGCTTG
2. mcr 2 700bp fw CAAGTGTGTTGGTCGCAGTT

rev TCTAGCCCGACAAGCATACC
3. mcr 3 900bp fw AAATAAAAATTGTTCCGCTTATG

rev AATGGAGATCCCCGTTTTT
4. mcr 4 1100bp fw TCACTTTCATCACTGCGTTG

rev TTGGTCCATGACTACCAATG
5. mcr 5 1644bp fw ATGCGGTTGTCTGCATTTATC

rev TCATTGTGGTTGTCCTTTTCTG

Genotypic detection

Detection of mcr genes using PCR

DNA templates were prepared using thermal cell lysis from overnight agar cultures. PCR amplification was performed as described by Rebelo et al. [12], 13]. The amplified products were analyzed by electrophoresis on a 1.5 % agarose gel to visualize the results, and staining was done by ethidium bromide at 130 V [12].

Data collection and statistical analysis

Diagnostic accuracy performance metrics such as sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), categorical agreement (CA), major errors (ME), and very major errors (VME) of BMD (reference methods) were calculated and compared with CBDE, CBME, and MPE as well. Additionally, to minimize the increased risk of Type I error, multiple testing corrections using the Bonferroni adjustment were also performed. The statistical significance of differences in sensitivity rates was evaluated using McNemar’s test. The statistical analysis of differences in colistin resistance detection rates was performed using the Chi-square test. GraphPad Prism 6 software (Graph-Pad Software, Inc., La Jolla, CA, USA) was used to determine the significance.

Ethical details

The study received approval from the Institutional Ethical Committee via letter no. MMIMSR/IEC/1916.

Results

A total of 715 MDR Gram-negative bacterial isolates were obtained from various clinical specimens. The highest number of isolates were obtained from urine samples (26.0 %, n=186), followed by pus (21.6 %, n=154), blood (16.7 %, n=119), sputum (18.0 %, n=129), wound swabs (9.5 %, n=68), vaginal swabs (2.0 %, n=14) and other specimens (6.6 %, n=47), respectively. Of the 715 MDR Gram-negative bacterial isolates tested for colistin resistance, 6.85 % (49/715) isolates showed colistin resistance by the BMD method and 6.57 % (47/715) by the CBDE method. These isolates primarily consisted of four species, with Klebsiella pneumoniae being the most prevalent, comprising 379 isolates; 3.43 % (13/379) of these were resistant to colistin by both BMD and CBDE methods. E. coli accounted for 196 isolates, with 5.1 % (10/196) isolates demonstrating resistance by both BMD and CBDE methods, respectively. However, P. aeruginosa included 101 isolates, with 17.82 % (18/101) resistant by BMD and 16.83 % (17/101) by CBDE methods, respectively. Finally, A. baumannii had 39 isolates, with 20 % (8/39) resistant by BMD and 17.95 % (7/39) by CBDE methods, respectively. Figure 1 illustrates a flowchart showing the study profile. Figure 2 shows the BMD method used for detecting colistin resistance. The MIC values of all the isolates tested against colistin (ranging from ≤0.5 μg/mL to 16 μg/mL) were noted and illustrated in Table 2. Most of the colistin-resistant GNBs showed MIC values of 4 μg/mL, followed by 8 μg/mL and 16 μg/mL, respectively. Non-lactose-fermenting bacteria had a higher rate of colistin resistance compared to lactose-fermenting bacteria. Among non-lactose fermenters, P. aeruginosa (18/49) isolates were found to be predominant colistin-resistant bacteria, followed by A. baumannii (8/49). However, among lactose fermenters, K. pneumoniae demonstrated the highest rate of resistance to colistin (13/49), followed by E. coli (10/49).

Figure 1: 
Flowchart showing the study profile.
Figure 1:

Flowchart showing the study profile.

Figure 2: 
Broth microdilution (BMD) plate method for colistin susceptibility testing. Figure 2 Illustrates the BMD method in a 96-well microtiter plate. Row A (wells 1–9) serves as the negative control, while rows B–G (wells 1–8) are inoculated with the test organisms. Row H (wells 1–8) contains the positive control strain. Column 8 (wells A–B) is designated as the growth control, and column 9 (wells A–B) serves as the media control. Two-fold serial dilutions of colistin sulfate, ranging from 0.5 to 32 μg/mL (left to right), are prepared across the plate to determine the minimum inhibitory concentration (MIC). The MIC is defined as the lowest concentration of colistin at which no visible bacterial growth was observed.
Figure 2:

Broth microdilution (BMD) plate method for colistin susceptibility testing. Figure 2 Illustrates the BMD method in a 96-well microtiter plate. Row A (wells 1–9) serves as the negative control, while rows B–G (wells 1–8) are inoculated with the test organisms. Row H (wells 1–8) contains the positive control strain. Column 8 (wells A–B) is designated as the growth control, and column 9 (wells A–B) serves as the media control. Two-fold serial dilutions of colistin sulfate, ranging from 0.5 to 32 μg/mL (left to right), are prepared across the plate to determine the minimum inhibitory concentration (MIC). The MIC is defined as the lowest concentration of colistin at which no visible bacterial growth was observed.

Table 2:

Distribution of colistin minimum inhibitory concentrations in gram-negative bacteria by the broth microdilution method.

Total number of gram-negative bacterial isolates Minimum inhibitory concentration values of colistin
≤0.5 μg/mL 1 μg/mL 2 μg/mL 4 μg/mL 8 μg/mL 16 μg/mL
Klebsiella pneumoniae

(n=379)
354 (93.4 %) 7 (1.8 %) 5 (1.31 %) 9 (2.3 %) 3 (0.7 %) 1 (0.2 %)
Escherichia coli

(n=196)
172 (87.7 %) 8 (4.08 %) 6 (3.06 %) 6 (3.06 %) 4 (2.04 %) 00
Pseudomonas aeruginosa

(n=101)
56 (55.44 %) 21 (20.7 %) 6 (5.9 %) 17 (16.83 %) 1 (0.99 %) 00
Acinetobacter baumannii

(n=39)
15 (38 %) 14 (35.8 %) 2 (5.9 %) 7 (17.94 %) 1 (2.5 %) 00
Total

(n=715)
597 (83.5 %) 50 (7 %) 19 (2.6 %) 39 (5.45 %) 9 (1.25 %) 1 (0.13 %)

The antibiotic sensitivity results of all isolates tested using CBDE, CBME, and MPE methods were compared with the BMD method. The CBDE methods failed to detect two colistin-resistant isolates, while the CBME and MPE methods identified 41 and 45 additional isolates as colistin-resistant, respectively. Among lactose-fermenting Gram-negative bacteria, the CBDE method showed antibiotic susceptibility patterns comparable to the BMD (the reference method), while both the CBME and MPE methods showed minor variations in identifying colistin-sensitive bacteria, as shown in Table 3. However, the colistin susceptibility of the non-lactose fermenting bacteria tested showed variations in all the elution methods evaluated. Figure 3 shows the identification of colistin resistance by the CBDE method. The comparison of the susceptibility pattern of all the MDR-GNB tested against colistin is depicted in Table 3.

Table 3:

Comparison of colistin resistance rates among gram-negative bacterial isolates as determined by broth microdilution, colistin broth disc elution, colistin broth microelution, and microplate elution methods with multiplex PCR screening for mcr-1 to mcr-5 genes.

Total number of bacterial isolates (n=715) aBMD

(n=715)

eR/I, R %
bCBDE

(n=715)

eI/R, R %
cCBME

(n=715)

eI/R, R %
dMPE

(n=715)

eI/R, R %
Multiplex polymerase chain reaction for the detection of mcr (1–5) genes
Klebsiella pneumoniae sp.

(n=379)
13/366 (3.4 %) 13/366 (3.4 %) 32/347 (8.44 %) 35/344 (9.23 %) Not detected
Escherichia coli

(n=196)
10/186 (5.1 %) 10/186 (5.1 %) 22/174 (11.22 %) 22/174 (11.22 %) -do-
Pseudomonas aeruginosa

(n=101)
18/83 (17.82 %) 17/84 (16.83 %) 25/76 (21.75 %) 26/75 (16.83 %) -do-
Acinetobacter baumannii

(n=39)
8/31 (20.5 %) 7/32 (17.94 %) 11/28 (28.20 %) 11/28 (28.20 %) -do-
Total (715) 49/666 (6.83 %) 47/668 (6.57 %) 90/625 (12.58 %) 94/621 (13.14 %) Not detected
  1. aBroth microdilution method, bColistin broth disc elution method, cColistin broth micro elution method, dMicroplate elution method, eI-Intermediate, R-Resistant, R %-resistance percentage.

Figure 3: 
Colistin broth disc elution method for detection of colistin resistance in gram-negative bacilli. From left to right: (A) Negative control consisting of four tubes – first tube with cation-adjusted Mueller–Hinton broth (CAMHB) inoculated with the colistin-susceptible Escherichia coli (ATCC 25922) strain serving as the growth control, and three tubes containing CAMHB and same E. coli (ATCC 25922) strain with colistin at 1, 2, and 4 μg/mL, all showing no visible growth, indicating colistin susceptibility; (B) test isolate, with four tubes containing CAMHB and the test strain, yielding a minimum inhibitory concentration (MIC) of 4 μg/mL (visible growth at ≤ 2 μg/mL, no growth at 4 μg/mL defines the MIC); (C) positive control (colistin-resistant in-house Proteus species), showing visible growth in tubes containing 1, 2, and 4 μg/mL of colistin indicating colistin resistance.
Figure 3:

Colistin broth disc elution method for detection of colistin resistance in gram-negative bacilli. From left to right: (A) Negative control consisting of four tubes – first tube with cation-adjusted Mueller–Hinton broth (CAMHB) inoculated with the colistin-susceptible Escherichia coli (ATCC 25922) strain serving as the growth control, and three tubes containing CAMHB and same E. coli (ATCC 25922) strain with colistin at 1, 2, and 4 μg/mL, all showing no visible growth, indicating colistin susceptibility; (B) test isolate, with four tubes containing CAMHB and the test strain, yielding a minimum inhibitory concentration (MIC) of 4 μg/mL (visible growth at ≤ 2 μg/mL, no growth at 4 μg/mL defines the MIC); (C) positive control (colistin-resistant in-house Proteus species), showing visible growth in tubes containing 1, 2, and 4 μg/mL of colistin indicating colistin resistance.

Figure 4 illustrates the identification of colistin-resistant GNB by the CBME method. The distributions of colistin MICs for all MDR-GNB were analyzed and compared across all tested elution methods, as shown in Table 3. Figure 5 shows the identification of colistin-resistant GNB by the MPE method. The performance of the CBDE method demonstrated minor variations in MIC detection when compared with the BMD reference method, while both the CBME and MPE methods showed significant variations (p<0.0001), as presented in Table 4.

Figure 4: 
Colistin broth micro-elution method for detection of colistin resistance in gram-negative bacilli. From left to right: (A) Negative control with four tubes – first tube containing cation-adjusted Mueller–Hinton broth (CAMHB) inoculated with colistin-susceptible Escherichia coli (ATCC 25922) serving as the growth control, and three tubes containing the same strain with colistin at 1, 2, and 4 μg/mL, all showing no visible growth, thereby confirming susceptibility; (B) test isolate as represented by four tubes with CAMHB and the test strain, showing a minimum inhibitory concentration (MIC) of 4 μg/mL (growth present at ≤ 2 μg/mL but absent at 4 μg/mL); (C) positive control, an in-house colistin-resistant Proteus species with visible growth at 1, 2, and 4 μg/mL, indicating resistance to colistin.
Figure 4:

Colistin broth micro-elution method for detection of colistin resistance in gram-negative bacilli. From left to right: (A) Negative control with four tubes – first tube containing cation-adjusted Mueller–Hinton broth (CAMHB) inoculated with colistin-susceptible Escherichia coli (ATCC 25922) serving as the growth control, and three tubes containing the same strain with colistin at 1, 2, and 4 μg/mL, all showing no visible growth, thereby confirming susceptibility; (B) test isolate as represented by four tubes with CAMHB and the test strain, showing a minimum inhibitory concentration (MIC) of 4 μg/mL (growth present at ≤ 2 μg/mL but absent at 4 μg/mL); (C) positive control, an in-house colistin-resistant Proteus species with visible growth at 1, 2, and 4 μg/mL, indicating resistance to colistin.

Figure 5: 
Microplate elution plate method for detection of colistin resistance in gram-negative bacilli. From left to right, (row A): Negative control consisting of four wells – the first well containing cation-adjusted Mueller–Hinton broth (CAMHB) inoculated with colistin-susceptible Escherichia coli ATCC 25922 as the growth control, and three wells containing the same strain with colistin at 1, 2, and 4 μg/mL, all showing no visible growth, confirming susceptibility. (rows B-G): test isolates dispensed across the different concentration range and showing colistin susceptibility and resistance depending on the strain. (row H): positive control, an in-house colistin-resistant Proteus species, showing visible growth at 1, 2, and 4 μg/mL, consistent with colistin resistance. Column 1: growth control (0 μg/mL colistin). Columns 2–4: 1, 2, and 4 μg/mL colistin, respectively.
Figure 5:

Microplate elution plate method for detection of colistin resistance in gram-negative bacilli. From left to right, (row A): Negative control consisting of four wells – the first well containing cation-adjusted Mueller–Hinton broth (CAMHB) inoculated with colistin-susceptible Escherichia coli ATCC 25922 as the growth control, and three wells containing the same strain with colistin at 1, 2, and 4 μg/mL, all showing no visible growth, confirming susceptibility. (rows B-G): test isolates dispensed across the different concentration range and showing colistin susceptibility and resistance depending on the strain. (row H): positive control, an in-house colistin-resistant Proteus species, showing visible growth at 1, 2, and 4 μg/mL, consistent with colistin resistance. Column 1: growth control (0 μg/mL colistin). Columns 2–4: 1, 2, and 4 μg/mL colistin, respectively.

Table 4:

Comparison of minimum inhibitory concentrations as detected by various antimicrobial susceptibility test methods.

aMICs of colistin bBMD method

(n=715)
cCBDE method

(n=715)
dCBME method

(n=715)
eMPE method (n=715) p-Value
≤ 1 μg/mL 647 651 556 576 P<0.0001f
2 μg/mL 19 17 69 45
4 μg/mL 14 38 43 59
≥ 4 μg/mL 0 09 47 35
  1. aMinimum inhibitory concentrations, bBroth microdilution method, cColistin broth disc elution method, dColistin broth micro elution method, eMicroplate elution method, fChi-square test.

The diagnostic accuracy of all the elution methods was evaluated and compared with the BMD method on parameters such as sensitivity, specificity, PPV, NPV, M.E, VME, and CE, and the details are depicted in Table 5. A comparative bar chart showing sensitivity, specificity, PPV, and NPV for each method (with standard error) is presented in Figure 6. The major errors were noted in the CBM method (6.60 %) and MPE method (7.35 %), respectively. However, no major errors were noted in the CBDE method compared to BMD. The categorical agreement exceeded 90 % for all methods tested in the current study. The results demonstrated that among the tested methods, CBDE showed superior performance with a sensitivity (95.91 %) and specificity (100 %) compared to the reference BMD method (McNemar test, p<0.005) in detecting colistin resistance. Similarly, the CBME achieved a sensitivity (93.87 %) and specificity (93.33 %) when compared to the BMD method (McNemar test, p=0.0574). However, the MPE method showed slightly lower but still substantial performance, with a sensitivity (91.83 %) and specificity (92.64 %) compared to the BMD method (McNemar test, p=0.063). Additionally, molecular analysis (multiplex PCR) of all colistin-resistant GNBs yielded negative results for mcr genes (Table 3). Table 6 shows the comparison of the cost analysis of CBDE, CBME, and the MPE method with the BMD reference method.

Table 5:

Diagnostic performance of the various elution methods with the broth microdilution reference method.

Methods (n=715) aBMD method Sensitivity (%)

(95 % CI)
Specificity (%)

(95 % CI)
PPV (%)

(95 % CI)
NPV (%) (95 % CI) Major errors, % Very significant errors, % Categorical agreement, % McNemar test p-Value (bonferroni adjusted)
Positive Negative
bCBDE method Positive 47 0 95.91 (84.86–99.2) 100 (99.28–100) 100 (90.58–100) 99.7 (98.8–99.94) 00 4.08 99.72 0.015 a
Negative 2 666
cCBME method Positive 46 44 93.87 (82.13–98.4) 93.33 (91.16–95.1) 51.11 (40.1–61.7) 99.52 (98.48–99.87) 6.60 6.12 93.42 0.172
Negative 03 622
dMPE method Positive 45 49 91.83 (79.51–97.35) 92.64 (90.32–94.45) 47.36 (37.55–58.36) 99.32 (98.24–99.79) 7.35 8.16 92.58 0.189
negative 04 617
  1. aBroth microdilution method, bColistin broth disc elution method, cColistin broth micro elution method, dMicroplate elution method. Bold values indicate statistically highly significant.

Figure 6: 
Comparison of performance by various disc elution methods, such as colistin broth disc elution method (CBDE), colistin broth micro elution method (CBME), and microplate elution method (MPE) with broth microdilution method (BMD) for the detection of colistin resistance.
Figure 6:

Comparison of performance by various disc elution methods, such as colistin broth disc elution method (CBDE), colistin broth micro elution method (CBME), and microplate elution method (MPE) with broth microdilution method (BMD) for the detection of colistin resistance.

Table 6:

Comparison of cost analysis of colistin broth disc elution, colistin broth micro elution, and microplate elution method with the broth microdilution reference method (the amount is provided in both Indian rupees and US dollars).

Reagents and labor requirements Broth microdilution method Colistin broth disc elution method Colistin broth micro elution method Microplate elution method
$ $ $ $
Colistin disc or solution 15 0.20 30 0.40 10 0.13 10 0.13
Culture media (like cation-adjusted Mueller-Hinton broth) 30 0.40 10 0.13 5 0.07 10 0.13
Consumables (plates, tubes, cotton) 25 0.33 10 0.13 15 0.20 25 0.33
Labor requirements (technicians and others) 50 0.67 30 0.40 40 0.53 50 0.67
Total estimated cost per test 120 1.6 80 1.06 70 0.93 95 1.27

Discussion

In recent years, carbapenem-resistant and MDR-GNBs have been increasingly isolated from clinical samples. Colistin is increasingly used as a treatment option against MDR and carbapenem-resistant GNBs [6]. However, resistance to colistin has been detected in various GNBs across multiple countries, making the timely detection of colistin resistance critical for patient care. Different methods have been introduced for detecting colistin resistance, including quick assays, disc diffusion tests, E-test methods, and the BMD reference method. However, all of these techniques require significant resources, specialized equipment, and expertise; such limitations can affect their availability and broad implementation, particularly in low-resource settings. These challenges highlight the urgent need for a simple, reliable, cost-effective alternative method that can be implemented in resource-limited settings. This study evaluated the performance of various elution methods (CBDE, CBME, and MPE) with the BMD method. This study compared diagnostic accuracy parameters such as sensitivity, specificity, NPV, and PPV of various elution methods with the BMD method. Additionally, we assessed the ease of implementation and cost-effectiveness of these elution methods with the BMD method [14], 15].

Of the 715 MDR-GNB analyzed in this study, K. pneumoniae was the most prevalent GNB (53 %), with E. coli (27.4 %), P. aeruginosa (14.1 %), and A. baumannii (5.4 %) following. This high burden of MDR strains in the hospital setting underscores the need for proper infection control measures, targeted antibiotic policies, and structuring hospital-specific antimicrobial stewardship programs. According to the current EUCAST (2025) guidelines, colistin sensitivity is defined as MIC ≤ 2 μg/mL, with resistance classified as MIC >2 μg/mL for both Enterobacterales and Paeruginosa [16]. However, the CLSI guidelines define colistin resistance breakpoints for P. aeruginosa and Acinetobacter spp. are set at ≥ 4 μg/mL, with susceptibility defined as ≤ 2 μg/mL, but do not provide breakpoint interpretations for Enterobacterales [17]. It is evident from Table 1 that 83.5 % of highly susceptible isolates suggest colistin remains an effective option for most infections in this population. However, 7 and 2.6 % of isolates with MIC=1 μg/mL and 2 μg/mL indicate emerging resistance trends. The 6.83 % of resistant isolates (MIC ≥ 4 μg/mL) are clinically significant because colistin is often considered a last-line treatment option for MDR infections, and these patients require combination therapies with rifampin, carbapenems, or novel agents like cefiderocol [18]. Analysis of colistin resistance patterns across bacterial species (Table 1) revealed that only a single K. pneumoniae isolate (0.01 %) showed a high degree of resistance to colistin with a MIC value of 16 μg/mL. This degree of resistance makes colistin ineffective, significantly limiting available therapeutic options for this isolate. Further, among colistin-resistant isolates (MIC of > 4 μg/mL), P. aeruginosa was the most frequent (n=18), followed by other GNBs, respectively (Table 4). This potential cause of colistin resistance could be linked to the environmental persistence of colistin-resistant strains in ICUs and hospital settings [14], 18], 19].

Colistin resistance detection is a major concern worldwide, particularly in low-resource settings. The CBDE method to detect colistin resistance by Simner et al. was a breakthrough [7]. The other modification of the CBDE method was accomplished by Dalmolin et al. in 2020 [8]. As per the World Health Organization Global Antimicrobial Resistance and Use Surveillance System, it is advised that all carbapenem-resistant bacteria and MDR-GNB be tested for colistin resistance. In India, we are still in urgent need of a method to assess colistin resistance among MDR-GNB on a routine basis to replace the BMD method [20]. For this study, we evaluated available elution methods, and the results were evaluated with the reference BMD method. The current study demonstrated a zero percent discrepancy in the detection of colistin resistance in fermenters by the CBDE method compared to the BMD reference method. However, in the case of non-fermenting bacteria, the CBDE method failed to detect colistin-resistant strains in one P. aeruginosa and one A. baumannii isolate, respectively. This may be due to various reasons, such as the lower sensitivity of the CBDE method to detect colistin resistance in comparison with the BMD reference method. The other possible reason would be the alteration of LPS through chromosomal mutations, resulting in reduced colistin binding affinity [21]. However, it is evident from Table 2 that both CBME and MPE methods detected a few false-positive colistin-resistant GNBs and failed to identify some false-negative colistin-resistant bacteria as well. These false positive results may be due to certain bacterial strains’ inherent ability to modify their colistin binding sites, thereby affecting the accuracy of resistance detection [7], 8], 21]. Further, the failure of detection of false-negative colistin resistance may be attributed to the fact that if the proportion of resistant cells is too low compared to the total population, this type of colistin heteroresistance is largely undetected [22].

Colistin disc elution methods were developed to assess colistin resistance in bacteria and provide MICs comparable to the BMD method [7]. In the current study, the data of MICs as obtained by the BMD method were compared with the CBDE, CBME, and MPE methods, and variations were noted. MICs were categorized from ≤1 μg/mL to ≥4 μg/mL. The elution method could not be used to detect ≤0.5 μg/mL and ≥4 μg/ml MICs of colistin; therefore, we compared only ≤1 μg/mL to ≥4 μg/mL MICs. This narrow MIC range (1–4 μg/mL) in disc elution methods can further limit the detection of isolates with high-level resistance, particularly when the actual MIC exceeds the upper limit of the test range. This may further result in underestimation of resistance rates. Additionally, the visual interpretation of MIC endpoints involves a degree of subjectivity, mainly due to observer bias. To minimize this variability, two independent technicians performed all readings, and discrepancies were resolved through joint review by the study supervisor. However, the use of instrument-based readings or digital image analysis could minimize this observer bias. The CBDE method yielded consistent outcomes with the BMD reference method, whereas both the CBME and MPE methods showed varied MIC values, with many Gram-negative bacteria exhibiting higher MIC values. (Table 3). These variations in MIC values can be attributed to two main factors: either inconsistent colistin elution from the discs into the broth medium or inadequate mixing of colistin within the medium. Although we implemented quality control measures, including vortexing all tubes before sample processing, there are no currently available methods to validate the colistin concentration in these elution methods.

The diagnostic efficiency of the test is of utmost importance for its acceptance and implementation in routine laboratory analysis. We also determined their relative diagnostic performances, such as sensitivities, specificities, NPV, PPV, ME, VME, EA, and CA, and the details are summarized in Table 4. It is evident from Table 4 that the CBDE method showed higher sensitivity and specificity in comparison with the reference BMD method. Further, the McNemar test p-values for each method were reported as follows: CBDE (p<0.005), CBME (p=0.0574), and MPE (p=0.063), providing a clearer perspective on their performance relative to the reference BMD method. CBDE demonstrated statistically significant concordance with BMD, underscoring its reliability for detecting colistin resistance. While CBME and MPE showed slightly lower sensitivity and specificity and did not achieve conventional statistical significance, their performance remains robust, indicating that both methods are likely to be clinically meaningful and could serve as feasible alternatives in routine laboratory practice. Furthermore, the CBDE method showed no ME and a very low VME rate (Table 4), suggesting that CBDE is the most reliable method to substitute for the BMD method in laboratory settings. In contrast, CBME and MPE methods displayed higher rates of VME (6.12 and 8.16 %, respectively), indicating resistant strains were incorrectly reported as susceptible (false-negative), which could result in inappropriate therapies and treatment failure. This type of treatment failure occurs mainly because of the administration of an ineffective drug instead of a potentially active alternative (due to false susceptibility), eventually resulting in prolonged infections, increased transmission rates of MDR strains within healthcare settings, and higher mortality rates. In contrast, tests with high major errors (ME), where susceptible isolates are incorrectly reported as resistant, can lead to the unnecessary avoidance of colistin and the use of less effective alternative treatments. Further, the relatively low PPV of CBME (51.11 %) and MPE (47.36 %) suggests high false-positive results. These results would mislead the treatment options in resource-limited settings as well. The CBDE, CBME, and MPE only provide data for MIC concentrations ranging from 1 μg/mL to 4 μg/mL, suggesting these tests may not be suitable for detecting colistin resistance at MIC values beyond this range. The calculation of EA (Essential Agreement) for disc elution tests was unsatisfactory and was excluded from further analysis in the current study. This suggests that the MIC determination using elution methods may not align well with BMD, limiting their consistency in certain cases. This effect was most evident among isolates with MIC values around the clinical breakpoint of 2 μg/mL, where even minor variability in elution often resulted in borderline strains being categorized as resistant. The contributing factors likely include inconsistent or incomplete drug release, binding of colistin to plasticware, and pipetting inaccuracies in low-volume assays. Additionally, minor variations in the composition of cation-adjusted Mueller–Hinton broth or residual substances from antibiotic discs may influence apparent colistin activity, with the greatest impact observed for isolates near the susceptibility breakpoint. However, future use of these assays should prioritize the strict standardization of the elution process to improve reproducibility. The improvement strategies may include establishing uniform elution times, using low-binding plasticware to reduce adsorption rate, ensuring thorough mixing of solutions just before inoculation, and quantitative validation of eluted concentrations of colistin using validated analytical methods. It is also advisable that laboratories consistently incorporate both susceptible and borderline control strains to track performance across batches, while confirming results in the 2–4 μg/mL range with the BMD method. These adjustments would reduce the likelihood of false-positive results, improve consistency, and strengthen the overall reliability of CBME and MPE as a practical alternative to the BMD reference method.

In the current study, all the colistin-resistant Gram-negative bacteria were screened by multiplex PCR for all mcr genes yielded negative results. However, these findings have limited statistical significance because the resistance to colistin can develop through two mechanisms: the spread of mcr genes (plasmid-mediated) or chromosomal mutations in genes responsible for LPS synthesis [3], 5]. Therefore, the lack of mcr genes does not limit the significance of this study. We also noted the ease of performing tests, their associated cost, and training requirements for each test in this study (Table 6). In resource-limited settings, performing BMD can be challenging because it requires specialized glass-coated plates, calibrated micropipettes, and well-trained personnel [23]. In these settings, disc-based methods such as CBDE, CBME, and MPE provide feasible and practical alternatives. For example, our cost analysis demonstrated that CBDE and CBME can be completed for approximately 0.8–1.0 USD per test, compared with 1.5–2.0 USD for BMD. Moreover, the simplicity of the CBDE protocol reduces the burden of training and minimizes handling errors, making it particularly suitable for regional and peripheral laboratories. By contrast, in high-income settings, BMD remains the reference standard, with many laboratories relying on automated MIC detection and molecular techniques to detect colistin resistance. In these contexts, elution-based assays are best positioned as supplementary tools, for example, a useful method to detect uncertain MIC results or as a low-cost preliminary screening method in epidemiological surveillance studies for screening of a large collection of bacterial isolates. Further, this study possesses a few limitations, for instance, a) In this study, molecular screening was restricted to the detection of mcr-1 to mcr-5 genes. Although mcr-6 to mcr-10 have been reported only rarely in various settings, these additional mcr variants were not assessed in our analysis and may therefore have remained undetected. b) Chromosomal resistance mechanisms such as pmrAB and mgrB mutations were not assessed due to the limited resources. c) Colistin concentrations were not quantitatively tested due to resource restrictions and were instead assumed using standard disc elution methods. d) Other rapid procedures, such as rapid NP and col Agar, should be evaluated for all the strains tested in this study.

In summary, the CBDE method demonstrated higher sensitivity and specificity while requiring the least amount of training compared to the BMD for detecting colistin-resistant GNBs, suggesting the potential of the CBDE method to replace the BMD method for the routine identification of colistin-resistant GNBs in resource-limited laboratories.


Corresponding author: Pottathil Shinu, Department of Biomedical Sciences, College of Clinical Pharmacy, King Faisal University, Al-Ahsa 31982, Saudi Arabia, E-mail:

Acknowledgments

We are thankful to the Deanship of Scientific Research, Vice Presidency for Graduate Studies and Scientific Research, King Faisal University, Saudi Arabia, and Maharishi Markandeshwar (Deemed to be University), Mullana, Ambala, India, for their support.

  1. Funding information: This work was supported through the Ambitious Researcher-Research Articles (Annual Grants) by the Deanship of Scientific Research, Vice Presidency for Graduate Studies and Scientific Research, King Faisal University, Al-Ahsa, Saudi Arabia [Grant Number: KFU252977].

  2. Author contribution: Conceptualization and design, S.C. and P.S.; Methodology, S.C., P.S., N.K., A.K.S., H.K., A.K.M., M.A., and A.B.N.; Data curation and investigations, S.C., P.S., and N.K.; formal analysis, S.C., P.S., N.K., A.K.S., H.K., A.K.M., M.A., and A.B.N.; Resources, S.C., P.S.; Writing-original draft preparations, S.C., P.S. and N.K.; Writing-review and editing, S.C., P.S., N.K., A.K.S., H.K., A.K.M., M.A., and A.B.N.; Project administration, S.C. and P.S.; Funding acquisition, P.S. All authors have read and agreed to the published version of the manuscript.

  3. Conflict of interest: The authors declare no conflict of interest.

  4. Data Availability Statement: The data supporting this study’s findings are available from the corresponding author upon reasonable request.

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Received: 2025-02-19
Accepted: 2025-10-06
Published Online: 2025-12-17

© 2025 the author(s), published by De Gruyter, Berlin/Boston

This work is licensed under the Creative Commons Attribution 4.0 International License.

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  108. Electroacupuncture-induced reduction of myocardial ischemia–reperfusion injury via FTO-dependent m6A methylation modulation
  109. Hemorrhoids and cardiovascular disease: A bidirectional Mendelian randomization study
  110. Cell-free adipose extract inhibits hypertrophic scar formation through collagen remodeling and antiangiogenesis
  111. HALP score in Demodex blepharitis: A case–control study
  112. Assessment of SOX2 performance as a marker for circulating cancer stem-like cells (CCSCs) identification in advanced breast cancer patients using CytoTrack system
  113. Risk and prognosis for brain metastasis in primary metastatic cervical cancer patients: A population-based study
  114. Comparison of the two intestinal anastomosis methods in pediatric patients
  115. Factors influencing hematological toxicity and adverse effects of perioperative hyperthermic intraperitoneal vs intraperitoneal chemotherapy in gastrointestinal cancer
  116. Endotoxin tolerance inhibits NLRP3 inflammasome activation in macrophages of septic mice by restoring autophagic flux through TRIM26
  117. Lateral transperitoneal laparoscopic adrenalectomy: A single-centre experience of 21 procedures
  118. Petunidin attenuates lipopolysaccharide-induced retinal microglia inflammatory response in diabetic retinopathy by targeting OGT/NF-κB/LCN2 axis
  119. Procalcitonin and C-reactive protein as biomarkers for diagnosing and assessing the severity of acute cholecystitis
  120. Factors determining the number of sessions in successful extracorporeal shock wave lithotripsy patients
  121. Development of a nomogram for predicting cancer-specific survival in patients with renal pelvic cancer following surgery
  122. Inhibition of ATG7 promotes orthodontic tooth movement by regulating the RANKL/OPG ratio under compression force
  123. A machine learning-based prognostic model integrating mRNA stemness index, hypoxia, and glycolysis‑related biomarkers for colorectal cancer
  124. Glutathione attenuates sepsis-associated encephalopathy via dual modulation of NF-κB and PKA/CREB pathways
  125. FAHD1 prevents neuronal ferroptosis by modulating R-loop and the cGAS–STING pathway
  126. Association of placenta weight and morphology with term low birth weight: A case–control study
  127. Investigation of the pathogenic variants induced Sjogren’s syndrome in Turkish population
  128. Nucleotide metabolic abnormalities in post-COVID-19 condition and type 2 diabetes mellitus patients and their association with endocrine dysfunction
  129. TGF-β–Smad2/3 signaling in high-altitude pulmonary hypertension in rats: Role and mechanisms via macrophage M2 polarization
  130. Ultrasound-guided unilateral versus bilateral erector spinae plane block for postoperative analgesia of patients undergoing laparoscopic cholecystectomy
  131. Profiling gut microbiome dynamics in subacute thyroiditis: Implications for pathogenesis, diagnosis, and treatment
  132. Delta neutrophil index, CRP/albumin ratio, procalcitonin, immature granulocytes, and HALP score in acute appendicitis: Best performing biomarker?
  133. Anticancer activity mechanism of novelly synthesized and characterized benzofuran ring-linked 3-nitrophenyl chalcone derivative on colon cancer cells
  134. H2valdien3 arrests the cell cycle and induces apoptosis of gastric cancer
  135. Prognostic relevance of PRSS2 and its immune correlates in papillary thyroid carcinoma
  136. Association of SGLT2 inhibition with psychiatric disorders: A Mendelian randomization study
  137. Motivational interviewing for alcohol use reduction in Thai patients
  138. Luteolin alleviates oxygen-glucose deprivation/reoxygenation-induced neuron injury by regulating NLRP3/IL-1β signaling
  139. Polyphyllin II inhibits thyroid cancer cell growth by simultaneously inhibiting glycolysis and oxidative phosphorylation
  140. Relationship between the expression of copper death promoting factor SLC31A1 in papillary thyroid carcinoma and clinicopathological indicators and prognosis
  141. CSF2 polarized neutrophils and invaded renal cancer cells in vitro influence
  142. Proton pump inhibitors-induced thrombocytopenia: A systematic literature analysis of case reports
  143. The current status and influence factors of research ability among community nurses: A sequential qualitative–quantitative study
  144. OKAIN: A comprehensive oncology knowledge base for the interpretation of clinically actionable alterations
  145. The relationship between serum CA50, CA242, and SAA levels and clinical pathological characteristics and prognosis in patients with pancreatic cancer
  146. Identification and external validation of a prognostic signature based on hypoxia–glycolysis-related genes for kidney renal clear cell carcinoma
  147. Engineered RBC-derived nanovesicles functionalized with tumor-targeting ligands: A comparative study on breast cancer targeting efficiency and biocompatibility
  148. Relationship of resting echocardiography combined with serum micronutrients to the severity of low-gradient severe aortic stenosis
  149. Effect of vibration on pain during subcutaneous heparin injection: A randomized, single-blind, placebo-controlled trial
  150. The diagnostic performance of machine learning-based FFRCT for coronary artery disease: A meta-analysis
  151. Comparing biofeedback device vs diaphragmatic breathing for bloating relief: A randomized controlled trial
  152. Serum uric acid to albumin ratio and C-reactive protein as predictive biomarkers for chronic total occlusion and coronary collateral circulation quality
  153. Multiple organ scoring systems for predicting in-hospital mortality of sepsis patients in the intensive care unit
  154. Single-cell RNA sequencing data analysis of the inner ear in gentamicin-treated mice via intraperitoneal injection
  155. Suppression of cathepsin B attenuates myocardial injury via limiting cardiomyocyte apoptosis
  156. Influence of sevoflurane combined with propofol anesthesia on the anesthesia effect and adverse reactions in children with acute appendicitis
  157. Identification of hub genes related to acute kidney injury caused by sevoflurane anesthesia and endoplasmic reticulum stress
  158. Efficacy and safety of PD-1/PD-L1 inhibitors in pancreatic ductal adenocarcinoma: a systematic review and Meta-analysis of randomized controlled trials
  159. The value of diagnostic experience in O-RADS MRI score for ovarian-adnexal lesions
  160. Health education pathway for individuals with temporary enterostomies using patient journey mapping
  161. Serum TLR8 as a potential diagnostic biomarker of coronary heart disease
  162. Intraoperative temperature management and its effect on surgical outcomes in elderly patients undergoing lichtenstein unilateral inguinal hernia repair
  163. Immunohistochemical profiling and neuroepithelial heterogeneity in immature ovarian teratomas: a retrospective digital pathology-based study
  164. Associated risk factors and prevalence of human papillomavirus infection among females visiting tertiary care hospital: a cross-sectional study from Nepal
  165. Comparative evaluation of various disc elution methods for the detection of colistin-resistant gram-negative bacteria
  166. Effect of timing of cholecystectomy on weight loss after sleeve gastrectomy in morbidly obese individuals with cholelithiasis: a retrospective cohort study
  167. Causal association between ceramide levels and central precocious puberty: a mendelian randomization study
  168. Novel predictive model for colorectal liver metastases recurrence: a radiomics and clinical data approach
  169. Relationship between resident physicians’ perceived professional value and exposure to violence
  170. Multiple sclerosis and type 1 diabetes: a Mendelian randomization study of European ancestry
  171. Rapid pathogen identification in peritoneal dialysis effluent by MALDI-TOF MS following blood culture enrichment
  172. Comparison of open and percutaneous A1 pulley release in pediatric trigger thumb: a retrospective cohort study
  173. Impact of combined diaphragm-lung ultrasound assessment on postoperative respiratory function in patients under general anesthesia recovery
  174. Development and internal validation of a nomogram for predicting short-term prognosis in ICU patients with acute pyelonephritis
  175. The association between hypoxic burden and blood pressure in patients with obstructive sleep apnea
  176. Promotion of asthenozoospermia by C9orf72 through suppression of spermatogonia activity via fructose metabolism and mitophagy
  177. Review Articles
  178. The effects of enhanced external counter-pulsation on post-acute sequelae of COVID-19: A narrative review
  179. Diabetes-related cognitive impairment: Mechanisms, symptoms, and treatments
  180. Microscopic changes and gross morphology of placenta in women affected by gestational diabetes mellitus in dietary treatment: A systematic review
  181. Review of mechanisms and frontier applications in IL-17A-induced hypertension
  182. Research progress on the correlation between islet amyloid peptides and type 2 diabetes mellitus
  183. The safety and efficacy of BCG combined with mitomycin C compared with BCG monotherapy in patients with non-muscle-invasive bladder cancer: A systematic review and meta-analysis
  184. The application of augmented reality in robotic general surgery: A mini-review
  185. The effect of Greek mountain tea extract and wheat germ extract on peripheral blood flow and eicosanoid metabolism in mammals
  186. Neurogasobiology of migraine: Carbon monoxide, hydrogen sulfide, and nitric oxide as emerging pathophysiological trinacrium relevant to nociception regulation
  187. Plant polyphenols, terpenes, and terpenoids in oral health
  188. Laboratory medicine between technological innovation, rights safeguarding, and patient safety: A bioethical perspective
  189. End-of-life in cancer patients: Medicolegal implications and ethical challenges in Europe
  190. The maternal factors during pregnancy for intrauterine growth retardation: An umbrella review
  191. Intra-abdominal hypertension/abdominal compartment syndrome of pediatric patients in critical care settings
  192. PI3K/Akt pathway and neuroinflammation in sepsis-associated encephalopathy
  193. Screening of Group B Streptococcus in pregnancy: A systematic review for the laboratory detection
  194. Giant borderline ovarian tumours – review of the literature
  195. Leveraging artificial intelligence for collaborative care planning: Innovations and impacts in shared decision-making – A systematic review
  196. Cholera epidemiology analysis through the experience of the 1973 Naples epidemic
  197. Risk factors of frailty/sarcopenia in community older adults: Meta-analysis
  198. Supplement strategies for infertility in overweight women: Evidence and legal insights
  199. Scurvy, a not obsolete disorder: Clinical report in eight young children and literature review
  200. A meta-analysis of the effects of DBS on cognitive function in patients with advanced PD
  201. Protective role of selenium in sepsis: Mechanisms and potential therapeutic strategies
  202. Strategies for hyperkalemia management in dialysis patients: A systematic review
  203. C-reactive protein-to-albumin ratio in peripheral artery disease
  204. Research progress on autophagy and its roles in sepsis induced organ injury
  205. Neuronutrition in autism spectrum disorders
  206. Pumilio 2 in neural development, function, and specific neurological disorders
  207. Antibiotic prescribing patterns in general dental practice- a scoping review
  208. Clinical and medico-legal reflections on non-invasive prenatal testing
  209. Smartphone use and back pain: a narrative review of postural pathologies
  210. Targeting endothelial oxidative stress in hypertension
  211. Exploring links between acne and metabolic syndrome: a narrative review
  212. Case Reports
  213. Delayed graft function after renal transplantation
  214. Semaglutide treatment for type 2 diabetes in a patient with chronic myeloid leukemia: A case report and review of the literature
  215. Diverse electrophysiological demyelinating features in a late-onset glycogen storage disease type IIIa case
  216. Giant right atrial hemangioma presenting with ascites: A case report
  217. Laser excision of a large granular cell tumor of the vocal cord with subglottic extension: A case report
  218. EsoFLIP-assisted dilation for dysphagia in systemic sclerosis: Highlighting the role of multimodal esophageal evaluation
  219. Molecular hydrogen-rhodiola as an adjuvant therapy for ischemic stroke in internal carotid artery occlusion: A case report
  220. Coronary artery anomalies: A case of the “malignant” left coronary artery and its surgical management
  221. Combined VAT and retroperitoneoscopy for pleural empyema due to nephro-pleuric fistula in xanthogranulomatous pyelonephritis
  222. A rare case of Opalski syndrome with a suspected multiple sclerosis etiology
  223. Newly diagnosed B-cell acute lymphoblastic leukemia demonstrating localized bone marrow infiltration exclusively in the lower extremities
  224. Rapid Communication
  225. Biological properties of valve materials using RGD and EC
  226. A single oral administration of flavanols enhances short-term memory in mice along with increased brain-derived neurotrophic factor
  227. Repeat influenza incidence across two consecutive influenza seasons
  228. Letter to the Editor
  229. Role of enhanced external counterpulsation in long COVID
  230. Expression of Concern
  231. Expression of concern “A ceRNA network mediated by LINC00475 in papillary thyroid carcinoma”
  232. Expression of concern “Notoginsenoside R1 alleviates spinal cord injury through the miR-301a/KLF7 axis to activate Wnt/β-catenin pathway”
  233. Expression of concern “circ_0020123 promotes cell proliferation and migration in lung adenocarcinoma via PDZD8”
  234. Corrigendum
  235. Corrigendum to “Empagliflozin improves aortic injury in obese mice by regulating fatty acid metabolism”
  236. Corrigendum to “Comparing the therapeutic efficacy of endoscopic minimally invasive surgery and traditional surgery for early-stage breast cancer: A meta-analysis”
  237. Corrigendum to “The progress of autoimmune hepatitis research and future challenges”
  238. Retraction
  239. Retraction of “miR-654-5p promotes gastric cancer progression via the GPRIN1/NF-κB pathway”
  240. Retraction of: “LncRNA CASC15 inhibition relieves renal fibrosis in diabetic nephropathy through downregulating SP-A by sponging to miR-424”
  241. Retraction of: “SCARA5 inhibits oral squamous cell carcinoma via inactivating the STAT3 and PI3K/AKT signaling pathways”
  242. Special Issue Advancements in oncology: bridging clinical and experimental research - Part II
  243. Unveiling novel biomarkers for platinum chemoresistance in ovarian cancer
  244. Lathyrol affects the expression of AR and PSA and inhibits the malignant behavior of RCC cells
  245. The era of increasing cancer survivorship: Trends in fertility preservation, medico-legal implications, and ethical challenges
  246. Bone scintigraphy and positron emission tomography in the early diagnosis of MRONJ
  247. Meta-analysis of clinical efficacy and safety of immunotherapy combined with chemotherapy in non-small cell lung cancer
  248. Special Issue Computational Intelligence Methodologies Meets Recurrent Cancers - Part IV
  249. Exploration of mRNA-modifying METTL3 oncogene as momentous prognostic biomarker responsible for colorectal cancer development
  250. Special Issue The evolving saga of RNAs from bench to bedside - Part III
  251. Interaction and verification of ferroptosis-related RNAs Rela and Stat3 in promoting sepsis-associated acute kidney injury
  252. The mRNA MOXD1: Link to oxidative stress and prognostic significance in gastric cancer
  253. Special Issue Exploring the biological mechanism of human diseases based on MultiOmics Technology - Part II
  254. Dynamic changes in lactate-related genes in microglia and their role in immune cell interactions after ischemic stroke
  255. A prognostic model correlated with fatty acid metabolism in Ewing’s sarcoma based on bioinformatics analysis
  256. Red cell distribution width predicts early kidney injury: A NHANES cross-sectional study
  257. Special Issue Diabetes mellitus: pathophysiology, complications & treatment
  258. Nutritional risk assessment and nutritional support in children with congenital diabetes during surgery
  259. Correlation of the differential expressions of RANK, RANKL, and OPG with obesity in the elderly population in Xinjiang
  260. A discussion on the application of fluorescence micro-optical sectioning tomography in the research of cognitive dysfunction in diabetes
  261. A review of brain research on T2DM-related cognitive dysfunction
  262. Metformin and estrogen modulation in LABC with T2DM: A 36-month randomized trial
  263. Special Issue Innovative Biomarker Discovery and Precision Medicine in Cancer Diagnostics
  264. CircASH1L-mediated tumor progression in triple-negative breast cancer: PI3K/AKT pathway mechanisms
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