Journal of Clinical Microbiology最新文献

Intestinal parasitic infections (IPIs) have a worldwide distribution and have a major impact on health, work capacity, and economy in many countries. Light microscopy is still considered the reference method for IPI diagnosis but is labor-intensive. KU-F40, an automated feces analyzer, combines automated microscopic examination of stool samples and deep learning artificial intelligence. The aim of this study is to evaluate the performance of KU-F40 for the diagnosis of IPI. A random collection of stool samples prescribed for IPI investigation was retrospectively collected from six clinical laboratories in Belgium along with external quality controls. All samples were analyzed in our laboratory by wet mount preparation using classic light microscopy as reference. We assessed the sensitivity and specificity for parasite detection/identification. Finally, we studied the improvement in parasite detection rate when increasing the number of pictures taken to 150% and 200% of the standard settings. A total of 267 clinical stool samples were included. Using standard settings, overall sensitivity and specificity were 86% and 45%, respectively. When considering only clinically relevant parasites, sensitivity was 95%. Increasing the number of pictures allowed to improve detection rate, but it remained under 90% for several targets. KU-F40 offers an innovative approach and provides welcome automation in the diagnosis of IPI. Currently, its performance does not allow it to be used as a screening tool with automatic validation of negative results. Critical missing features could enhance its performance, including the addition of a 10x magnification objective and additional parasites currently absent from the database.IMPORTANCEIntestinal parasitic infections have a worldwide distribution and are a global health concern in many countries. Light microscopy is still considered the reference method for diagnosis but is labor-intensive, time-consuming, and requires highly skilled and motivated technologists. In this paper, we evaluate the KU-F40, an automated feces analyzer designed to diagnose intestinal parasitic infections by combining automated light microscopy and deep learning artificial intelligence for detection and presumptive identification of several protozoans and helminths. As it relies on microscopy, this method enables the detection and identification of a predefined panel of parasites, whose morphology is known to the system and included in the database, without requiring prior diagnostic suspicion, similarly to multiplex PCR assays. The automation could improve the quality, standardization, and turnaround time of stool parasitology. This study is the first to evaluate the performance of the KU-F40 on a wide range of parasites, collected from six Belgian hospitals, including our two national reference centers.

Mycobacterium xenopi causes non-tuberculous mycobacterial pulmonary disease (NTM-PD) that is difficult to treat. However, data on the genomic population structure, antimicrobial susceptibility, and the clinical significance of this pathogen remain scarce. We analyzed 76 clinical M. xenopi isolates from 70 patients collected between 1995 and 2020 in Frankfurt am Main, Germany. All isolates underwent phenotypic drug susceptibility testing and whole-genome sequencing. Cluster analysis, including isolates from this study and all hitherto available high-quality M. xenopi genome data sets in the Sequence Read Archive (n = 11), was performed by core genome multilocus sequence typing. In our cohort, only 26.5% of patients met criteria for clinically relevant NTM-PD. Phylogenetic analysis identified three large hospital-associated clusters (≤10 allelic difference), each involving between 7 and 20 patients and persisting for over 18 years, suggesting prolonged transmission chains or a common environmental source. We also defined three major clades (≤50 allelic difference), two of which contained isolates from the United Kingdom. Clofazimine and guideline-recommended antimycobacterial agents showed good in vitro efficacy, except rifampicin, with 23.6% resistance. This study represents a major expansion of M. xenopi genomic resources and provides insights into the genomic population structure, phenotypic susceptibility, and clinical characteristics of M. xenopi. Guideline-recommended antimycobacterials show good in vitro activity, while clofazimine may be a valuable addition to M. xenopi therapy. The identified clusters underscore the need for further investigation into transmission dynamics and globally successful clones.IMPORTANCEMycobacterium xenopi is an increasingly recognized opportunistic lung pathogen that is difficult to treat. Infections often occur in patients with pre-existing health conditions and can present substantial diagnostic and therapeutic challenges. A deeper understanding of its genetic diversity and resistance mechanisms is essential for optimal patient management and for clarifying potential transmission routes. By analyzing 76 whole-genome sequences together with detailed clinical information and phenotypic drug-susceptibility data, this study substantially expands the available genomic repertoire for M. xenopi. While clinical relevance was limited in our cohort, most guideline-recommended antimicrobial agents showed good efficacy in vitro. The detection of closely related strains might point toward a common environmental source of infection. These findings highlight the need for continued surveillance and provide a comprehensive foundation that supports more accurate monitoring, improved understanding of disease behavior, and future investigations into M. xenopi pathogenicity.

To evaluate the clinical performance of Hybribio's 14-type HPV real-time PCR with 16/18 genotyping (HBRT-H14) and its risk stratification utility among women with normal cytology (NILM). From 2017 to 2020, a multicenter cohort enrolled 8,401 women aged 30-64 years with NILM cytology. Baseline HPV testing used HBRT-H14. Women positive for HPV 16/18 were referred for colposcopy; follow-up was annual for 3 years or until the detection of cervical intraepithelial neoplasia grade 2 or worse (CIN2+). Analyses included 6,679 women who completed follow-up. Overall HPV positivity was 11.4%, including 2.3% HPV 16/18. Over 3 years, sensitivity and specificity of HPV positivity for CIN2+ were 92.3% (95% confidence interval [CI]: 84.2-96.4) and 89.6% (88.8-90.3). For HPV 16/18 positivity, sensitivity and specificity were 41.0% (30.8-52.1) and 98.2% (97.8-98.5). Three-year cumulative CIN2+ risk was 20.9% (15.2-28.1) for HPV 16/18-positive women, 6.6% (4.9-8.9) for other types, and 0.1% (0.04-0.2) for HPV-negative women. HBRT-H14 shows strong clinical performance for detecting CIN2+, and HPV 16/18 genotyping provides effective risk stratification among women with NILM cytology. Findings support integration of HBRT-H14 into HPV-based screening pathways with HPV 16/18 genotyping and cytology triage of other types.

Importance: This multicenter prospective study evaluated the Hybribio 14 high-risk HPV real-time PCR assay (HBRT-H14) in 8,401 women with normal (NILM) cytology under guideline-based follow-up. The assay showed high clinical sensitivity and a very low risk among HPV-negative women, and HPV 16/18 genotyping provided clear risk stratification. These findings deliver large-scale, practice-oriented evidence supporting integration of HBRT-H14 into HPV-based screening pathways that use HPV 16/18 genotyping with cytology triage of other types.

Molecular methods are increasingly used to diagnose visceral leishmaniasis (VL), a life-threatening disease caused by Leishmania infantum in the Mediterranean basin. In this study, we compared the analytical and clinical performances of three real-time polymerase chain reaction (PCR) assays used for VL diagnosis at the University Hospital of Bologna, Northern Italy. The first test, which is commercially available, targets the small subunit of the 18S ribosomal RNA gene of Leishmania (rDNA). The second and third methods, developed in-house, target the rDNA and the kinetoplast minicircle conserved region (kDNA). The three PCR assays were performed on standard samples prepared using a L. infantum reference strain, as well as on 90 peripheral blood samples from patients with clinical suspicion of VL. Among these, 33 were confirmed as VL cases. The in-house kDNA PCR exhibited the highest observed analytical and clinical sensitivity. Conversely, kDNA PCR showed lower specificity (95%) on clinical samples when compared to rDNA amplification assays (100%). The three methods exhibited excellent concordance in VL identification (Cohen's Kappa ≥ 0.9). The higher sensitivity of the kDNA target compared to the rDNA target was confirmed by analyzing the cycle threshold values obtained from parasitic DNA amplification using the three real-time PCR assays in VL-positive samples. In summary, all three molecular assays exhibited good analytical and clinical performance, with the kDNA-based PCR showing the highest sensitivity. These findings support kDNA as the most suitable target for detecting low levels of Leishmania DNA in peripheral blood samples from VL patients.

Importance: Visceral leishmaniasis is a life-threatening disease, rendering early and accurate diagnosis essential for patient survival. However, highly sensitive diagnostic tools are lacking. In this study, we compared the analytical and clinical performances of three real-time PCR assays routinely used for diagnosis of visceral leishmaniasis in a referral center in Northern Italy. All three assays exhibited robust diagnostic performance, with the PCR targeting the conserved region of the kinetoplast minicircle DNA exhibiting higher sensitivity than the assays targeting the small subunit of the 18S ribosomal RNA gene. This enhanced sensitivity is crucial for detecting visceral leishmaniasis in patients with low concentration of parasitic DNA in peripheral blood, as misdiagnosis in these patients can lead to severe consequences. Our findings highlight the need for the development of commercial, automated assays targeting the kinetoplast minicircle DNA to enhance the accuracy of the diagnosis of this potentially lethal disease.

Invasive fungal diseases (IFDs) are common and often fatal in severe burn patients due to skin barrier loss and immune dysfunction. However, current definitions of invasive mold infections are poorly adapted to this population. This study evaluated the characteristics of various diagnostic criteria and their combinations in relation to clinical outcomes in burn patients. We conducted a retrospective cohort study of all patients admitted to the Burn ICU from 2014 to 2023 with ≥15% total burn surface area and at least one sample sent to the mycology lab. Criteria included direct microscopy, culture (respiratory, skin, or tissue), species-specific quantitative PCR (qPCR) (Aspergillus, Mucorales, and Fusarium) on plasma/tissue/bronchoalveolar lavage fluid, and serum galactomannan. Among 276 patients, 489/6,184 samples were positive, including 281 skin biopsies (direct examination and conventional culture) and 132 plasma specimens (qPCR). Positive diagnostic criteria ≥1 was found in 93 patients (33.7%): Aspergillus (25.7%), Mucorales (10.9%), and Fusarium (9.8%). Twenty-seven patients (9.8%) had ≥2 criteria involving ≥2 mold types. Mortality rose with the number of positive criteria: 12.7% (0), 10.7% (1, 2), 27.3% (3, 4), and 46.7% (≥5) (P < 0.001). Plasma qPCR was positive in 81.3% of Mucorales, 40% of Aspergillus, and 15.4% of Fusarium cases with skin involvement. Skin biopsies (direct examination and conventional culture) combined with species-specific plasma qPCR enhance timely and reliable IFD diagnosis in burn patients. Mortality correlated with the number of positive criteria and coexistence of multiple mold species, underscoring the need for broad antifungal coverage and the value of multi-criteria diagnostics to guide treatment.IMPORTANCEInvasive mold infections are frequent and often fatal complications in patients with severe burns, occurring in up to 20% of cases with a total burn surface area exceeding 15%. Despite their severity, no standardized case definition currently exists to guide research or clinical management in this population. The performance of existing mycological diagnostic criteria remains unknown in burn patients. In this 10-year retrospective study, we evaluated the diagnostic performance of individual and combined mold-related criteria in relation to patient outcomes, analyzing more than 6,000 clinical samples. These findings provide a first comprehensive assessment of mold diagnostic markers in the burn population.

Rapid and accurate identification of Aspergillus species in clinical microbiology laboratories is crucial for aspergillosis diagnosis and antifungal therapy. However, traditional methods still face challenges in distinguishing phylogenetically related species due to their morphological similarities. This study presents FungalNet, a deep learning model integrating ResNet-50 architecture with Focal Loss algorithm, specifically designed to enhance feature extraction for Aspergillus identification. A total of 12,000 high-resolution images were obtained from lactophenol cotton blue-stained slide preparations under a 100× oil immersion objective, among which 311 images were excluded through a novel quality control approach combining fivefold cross-validation and expert manual review. The performance of four deep learning models (FungalNet and three established models) for identifying Aspergillus species and sections was evaluated using the remaining 11,689 qualified images. FungalNet demonstrated superior classification performance, achieving overall accuracies of 98.45% and 97.85% at the section and species levels, respectively. These results indicate that FungalNet shows significant promise for rapid and accurate identification of Aspergillus species. With further optimization and multicenter validation, this tool could potentially be integrated into routine diagnostic workflows to enhance the efficiency and reliability of fungal identification in clinical settings.IMPORTANCEThis study integrates microscopic morphology identification with deep learning to address the challenge of accurate Aspergillus species identification. Twelve clinically isolated Aspergillus species belonging to eight different sections were included. From touch-tape slide preparations with lactophenol cotton blue staining under a 100× oil immersion objective, 11,689 qualified images were collected and analyzed using FungalNet (our proposed model) along with three established models (GoogLeNet, ResNet-50, and Xception). The results showed that FungalNet demonstrated superior performance in Aspergillus identification, achieving the highest classification accuracy at both section (98.45%) and species (97.85%) levels. Given its rapid turnaround time and cost-effectiveness, this AI-based image analysis approach shows promising potential for the rapid and accurate identification of Aspergillus species in clinical microbiology laboratories.