Urine soluble TLR4 levels may contribute to predict urinary tract infection in children: the UTILISE Study.

BACKGROUND: One of the most common bacterial infections in childhood is urinary tract infection (UTI). Toll-like receptors (TLRs) contribute to immune response against UTI recognizing specific pathogenic agents. Our aim was to determine whether soluble TLR4 (sTLR4), soluble TLR5 (sTLR5) and interleukin 8 (IL-8) can be used as biomarkers to diagnose UTI. We also aimed to reveal the relationship between urine Heat Shock Protein 70 (uHSP70) and those biomarkers investigated in this study. METHODS: A total of 802 children from 37 centers participated in the study. The participants (n = 282) who did not meet the inclusion criteria were excluded from the study. The remaining 520 children, including 191 patients with UTI, 178 patients with non-UTI infections, 50 children with contaminated urine samples, 26 participants with asymptomatic bacteriuria and 75 healthy controls were included in the study. Urine and serum levels of sTLR4, sTLR5 and IL-8 were measured at presentation in all patients and after antibiotic treatment in patients with UTI. RESULTS: Urine sTLR4 was higher in the UTI group than in the other groups. UTI may be predicted using 1.28 ng/mL as cut-off for urine sTLR4 with 68% sensitivity and 65% specificity (AUC = 0.682). In the UTI group, urine sTLR4 levels were significantly higher in pyelonephritis than in cystitis (p < 0.0001). Post-treatment urine sTLR4 levels in the UTI group were significantly lower than pre-treatment values (p < 0.0001). CONCLUSIONS: Urine sTLR4 may be used as a useful biomarker in predicting UTI and subsequent pyelonephritis in children with UTI. A higher resolution version of the Graphical abstract is available as Supplementary information.

[Investigation of Biofilm Formation, Anti-Quorum Sensing Activity and Antimicrobial Resistance in Corynebacterium Species Isolated from Clinical Samples]. / Klinik Örneklerden Izole Edilen Corynebacterium Türlerinde Biyofilm Olusumunun, Anti-Quorum Sensing Aktivitesinin ve Antimikrobiyal Direnç Durumunun Arastirilmasi.

An increasing number of different clinical infections caused by Corynebacteria have been reported in the last decade. The aim of this study was to evaluate the antibiotic resistance rates, biofilm formation capacities and to investigate the ''anti-quorum-sensing (anti-QS)'' activities of corynebacteria, which were divided into three groups according to the type of growth in culture (pure, with another pathogenic bacterium and polymicrobial growth). In total 240 Corynebacterium spp. isolates from different clinical specimens sent to the medical microbiology laboratories of Düzce University Faculty of Medicine Hospital and Basaksehir Çam and Sakura City Hospital between June 2021 and June 2022 were classified into three groups: pure, isolated with another pathogen and polymicrobial, according to their growth patterns in culture. Bacteria were identified by matrix assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) Biotyper (Bruker, Germany) at an external centre. Antibiotic susceptibilities were determined by disc diffusion method and for vancomycin broth microdilution method was used. Results were interpreted according to EUCAST recommendations. The biofilm-forming properties of the isolates were determined quantitatively. Bioactive components of 17 isolates with strong biofilm formation were extracted and anti-QS activity was determined by agar diffusion method using Chromobacterium violaceum ATCC 12472 strain and then violacein pigment production was measured quantitatively. Of the 240 Corynebacterium spp. isolates, 138 (58%) were pure, 52 (22%) were isolated with another pathogen and 50 (20%) were part of a polymicrobial infection. Of the isolates, 140 were identified as C.striatum, 34 as C.amycolatum and 24 as Corynebacterium afermentans. When the antibiotic resistance rates of the Corynebacterium isolates were analysed according to the groups, the resistance rates to rifampicin and tetracycline antibiotics were found to be statistically significantly lower in the polymicrobial group than in the other groups. The resistance rates to penicillin, clindamycin, ciprofloxacin, moxifloxacin, rifampicin, tetracycline and linezolid were 96.7%, 88.3%, 86.3%, 73.8%, 62.5%, 59.2% and 0.8%, respectively. While all isolates were susceptible to vancomycin, linezolid resistance was detected in two C.afermentans isolates. When the biofilm formation ability was analysed, it was observed that 87 (36.3%) isolates formed biofilm. The biofilm formation rate of the isolates in the polymicrobial growth group was lower than the other two groups. The anti-QS activity of 17 isolates with strong biofilm formation was investigated and none of the Corynebacterium extracts tested were found to have anti-QS activity (inhibition of violacein pigment production without inhibiting bacterial growth) in the QS study with C.violaceum, whereas five isolate extracts had antibacterial activity (inhibition of bacterial growth). Four of the bacterial extracts with antimicrobial activity belonged to C.amycolatum and one to C.afermentans. In conclusion, when both antibiotic resistance rates and biofilm formation rates were analysed, the corynebacteria growing in culture with another pathogen showed similar characteristics to the corynebacteria growing as a pure culture. Therefore, it was thought that corynebacteria growing with another pathogen should not be ignored. In addition, the antimicrobial effects of some corynebacterial extracts suggested that more QS studies should be carried out with microbiota bacteria.

Urinary HSP70 improves diagnostic accuracy for urinary tract infection in children: UTILISE study.

BACKGROUND: The accuracy of conventional urinalysis in diagnosing urinary tract infection (UTI) in children is limited, leading to unnecessary antibiotic exposure in a large fraction of patients. Urinary heat shock protein 70 (uHSP70) is a novel marker of acute urinary tract inflammation. We explored the added value of uHSP70 in discriminating UTI from other infections and conditions confused with UTI. METHODS: A total of 802 children from 37 pediatric centers in seven countries participated in the study. Patients diagnosed with UTI (n = 191), non-UTI infections (n = 178), contaminated urine samples (n = 50), asymptomatic bacteriuria (n = 26), and healthy controls (n = 75) were enrolled. Urine and serum levels of HSP70 were measured at presentation in all patients and after resolution of the infection in patients with confirmed UTI. RESULTS: Urinary (u)HSP70 was selectively elevated in children with UTI as compared to all other conditions (p < 0.0001). uHSP70 predicted UTI with 89% sensitivity and 82% specificity (AUC = 0.934). Among the 265 patients with suspected UTI, the uHSP70 > 48 ng/mL criterion identified the 172 children with subsequently confirmed UTI with 90% sensitivity and 82% specificity (AUC = 0.862), exceeding the individual diagnostic accuracy of leukocyturia, nitrite, and leukocyte esterase positivity. uHSP70 had completely normalized by the end of antibiotic therapy in the UTI patients. Serum HSP70 was not predictive. CONCLUSIONS: Urine HSP70 is a novel non-invasive marker of UTI that improves the diagnostic accuracy of conventional urinalysis. We estimate that rapid urine HSP70 screening could spare empiric antibiotic administration in up to 80% of children with suspected UTI. A higher resolution version of the Graphical abstract is available as Supplementary information.

Artificial intelligence applications for immunology laboratory: image analysis and classification study of IIF photos.

Artificial intelligence (AI) is increasingly being used in medicine to enhance the speed and accuracy of disease diagnosis and treatment. AI-based image analysis is expected to play a crucial role in future healthcare facilities and laboratories, offering improved precision and cost-effectiveness. As technology advances, the requirement for specialized software knowledge to utilize AI applications is diminishing. Our study will examine the advantages and challenges of employing AI-based image analysis in the field of immunology and will investigate whether physicians without software expertise can use MS Azure Portal for ANA IIF test classification and image analysis. This is the first study to perform Hep-2 image analysis using MS Azure Portal. We will also assess the potential for AI applications to aid physicians in interpreting ANA IIF results in immunology laboratories. The study was designed in four stages by two specialists. Stage 1: creation of an image library, Stage 2: finding an artificial intelligence application, Stage 3: uploading images and training artificial intelligence, Stage 4: performance analysis of the artificial intelligence application. In the first training, the average pattern identification accuracy for 72 testing images was 81.94%. After the second training, this accuracy increased to 87.5%. Patterns Precision improved from 71.42 to 79.96% after the second training. As a result, the number of correctly identified patterns and their accuracy increased with the second training process. Artificial intelligence-based image analysis shows promising potential. This technology is expected to become essential in healthcare facility laboratories, offering higher accuracy rates and lower costs.