Fig. 1. Practical molecular diagnostic algorithm for macrolide-resistant Mycoplasma pneumoniae in the routine clinical microbiology laboratory. PCR, polymerase chain reaction.
Ann Clin Microbiol 2025;29(3):10. Macrolide-resistant Mycoplasma pneumoniae: laboratory diagnosis and epidemiology Download image
Table 1. Trend of macrolide-resistant Mycoplasma pneumoniae prevalence and dominant sequence types in Korea
Ann Clin Microbiol 2025;29(3):10. Macrolide-resistant Mycoplasma pneumoniae: laboratory diagnosis and epidemiology Download table Period Sample (n) MRMP prevalence (%) Dominant ST / clone Notes Reference 2000–2011(longitudinal) 2,089 specimens / 255 MP+ / 80 with mutation 2.9 (2003) → 62.9 (2011) Not typed National longitudinal trend [10] 2010(Oct–Nov) 195 / 17 MP+ 17.6 (3/17, A2063G) Not typed […]
Macrolide-resistant Mycoplasma pneumoniae: laboratory diagnosis and epidemiology
Review Kuenyoul Park1, Heungsup Sung2 1Department of Laboratory Medicine, Hanyang University Seoul Hospital, Hanyang University College of Medicine, Seoul, Korea2Department of Laboratory Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea Correspondence to Heungsup Sung E-mail: sung@amc.seoul.kr Ann Clin Microbiol 2026;29(3):10. https://doi.org/10.5145/ACM.2026.29.3.10Received on 28 April 2026, Revised on 21 May 2026, Accepted […]
Fig. 2. Performance of the two-stage laser scattering signal amplification (LSSA) deep-learning classifier on hold-out reference-strain and preliminary clinical specimens. (A) Confusion matrix for Model 1, a three-class classifier (Negative, <10³ colony-forming units [CFU] /mL; Positive—C. albicans, ≥10⁴ CFU/mL; Positive—Non-albicans, ≥10⁴ CFU/mL), evaluated on the 25% hold-out test partition of the Model 1 reference-strain dataset (n = 80 vials; reference strains: C. albicans ATCC 14053, N. glabratus KCTC 7219, C. tropicalis KCTC 7212, C. parapsilosis ATCC 22019, P. kudriavzevii ATCC 6258). Rows indicate culture-confirmed true class; columns indicate model prediction. Overall accuracy: 73.8% (95% confidence interval [CI] 62.7–83.0%). (B) Confusion matrix for Model 2, a binary classifier (C. albicans vs. C. tropicalis), evaluated in a cascade manner on reference-strain samples predicted as Positive by Model 1 (n = 36; cascade evaluation). Overall accuracy: 77.8% (95% CI 60.8–89.9%). (C) Confusion matrix for the preliminary clinical evaluation, comparing patient-level LSSA prediction against the Candida culture result for 20 vaginal discharge specimens (5 Candida culture-positive, 15 Candida culture-negative). Absolute counts are printed inside each cell. Cell shading intensity is proportional to the cell count within each sub-panel. All 95% CIs were calculated by the exact ClopperPearson binomial method.
Ann Clin Microbiol 2025;29(2):9. Laser scattering signal amplification and deep learning for detection of Candida culture positivity in women with vaginal symptoms: a preliminary analytical and pilot clinical evaluation Download image
Fig. 1. Schematic of the laser scattering signal amplification (LSSA) platform and two-stream convolutional neural network (CNN) architecture for vulvovaginal candidiasis classification. (A) LSSA signal amplification principle. At low fungal cell concentration, incident light undergoes weak scattering through the sample vial. At high fungal cell concentration, the same light undergoes strong multiple scattering. The Bacometer’s scattering chamber (CSMS™) provides an amplification surface so that even samples with low fungal cell counts produce strong multiple scattering equivalent to samples with high fungal cell counts without the chamber. (B) Clinical specimen measurement workflow. Vaginal discharge specimens are filtered through a 0.45-µm membrane filter; the filtrate is transferred to a glass vial, serially diluted (undiluted, 1:10, 1:100, 1:1000), and inserted into the Bacometer (The Wavetalk Co., Ltd.). The Bacometer captures 300 consecutive speckle images (256 × 256 pixels) over a 10-s window at 30 fps following 30 min thermal stabilization at 30°C. (C) Two-stage deep-learning classifier. Speckle image sequences are converted into 2-channel optical-flow feature maps (256×256×2; 100 three-frame chunks per vial) and processed by a two-stream CNN: a time-distributed spatial DenseNet (Dense Blocks 1–3, each followed by batch normalization [BN]+ReLU+Conv+Pooling) and a temporal DenseNet-based 1D CNN (Conv-BN-ReLU layers with global average pooling). Concatenated features are classified by Model 1 (3-class: Negative / Positive C. albicans / Positive Non-albicans) and, for Positive samples, by Model 2 (binary: C. albicans / C. tropicalis). Both models share the same network topology but were trained on entirely non-overlapping datasets (Model 1: 320 vials; Model 2: 80 vials). Layer-by-layer architecture specifications are provided in Supplementary Methods. CFU, colony-forming units.
Ann Clin Microbiol 2025;29(2):9. Laser scattering signal amplification and deep learning for detection of Candida culture positivity in women with vaginal symptoms: a preliminary analytical and pilot clinical evaluation Download image
Table 2. Class-specific performance of the two-stage LSSA classifier on hold-out reference-strain test sets (exact Clopper–Pearson 95% CI)
Ann Clin Microbiol 2025;29(2):9. Laser scattering signal amplification and deep learning for detection of Candida culture positivity in women with vaginal symptoms: a preliminary analytical and pilot clinical evaluation Download table Model Class Sensitivity (95% CI) Specificity (95% CI) PPV (95% CI) NPV (95% CI) F1 score 1a) Negative (<103 CFU/mL) 85.0% (70.2–94.3%) 90.0% (76.3–97.2%) 89.5% […]
Table 1. Summary of patient characteristics and pilot clinical evaluation results (n = 20)
Ann Clin Microbiol 2025;29(2):9. Laser scattering signal amplification and deep learning for detection of Candida culture positivity in women with vaginal symptoms: a preliminary analytical and pilot clinical evaluation Download table Characteristic Value Age, years — median (IQR) 49.5 (44–55); range 29–63 Premenopausal 13 (65.0%) Postmenopausal 6 (30.0%) Unknown menopausal statusa) 1 (5.0%) Diabetes mellitus […]
Table 1. Characteristics of two azithromycin-resistant Neisseria gonorrhoeae isolates
Ann Clin Microbiol 2025;29(2):8. Whole-genome characterization of two azithromycin-resistant Neisseria gonorrhoeae ST1600 isolates from Busan, South Korea Download table Characteristics Year of isolation 2018 2019 Area of isolation (city) Busan Busan Antimicrobial susceptibility (MIC (µg/mL) and interpretation) Azithromycin 32, R 32, R Ciprofloxacin 32, R 16, R Spectinomycin 32, S 16, S Tetracycline 2, R […]
Fig. 1. Proportion (%) of the (A) CNRR, (B) EMI, and (C) CNRR+EMI rates of the tested antibiotics for “successive” isolates (a successive isolate comprises two microorganisms with the same species identification obtained at two different time points from the same patient). CNRR, change from nonresistant to resistant; EMI, essential MIC increase; SAU, Staphylococcus aureus; EFA, Enterococcus faecalis; EFM, Enterococcus faecium; ECO, Escherichia coli; KPN, Klebsiella pneumoniae; ABA, Acinetobacter baumannii complex; PAE, Pseudomonas aeruginosa.
Ann Clin Microbiol 2025;29(2):7. Evaluating the adequacy of intervals for repeat antimicrobial susceptibility testing Download image
Table 3. Proportion (%) of “successive” isolates with categorical change from nonresistant to resistant when the MIC change leading to this category change was >1 doubling dilution (CNRR + EMI) specified for each antibiotic a) b)
Ann Clin Microbiol 2025;29(2):7. Evaluating the adequacy of intervals for repeat antimicrobial susceptibility testing Download table Microorganism(no. of tested antibiotics) Antibiotics No. of days between isolates 1 2 3 4 5 6 7 A. baumannii(n= 47,274) Ampicillin-sulbactam 2.0 (0.4, 3.5) 0.3 (0.0, 0.8) 0.9 (0.1, 1.8) 1.6 (0.0, 3.2) 2.0 (0.1, 4.0) 2.2 (0.7, 3.8) […]