Validation of the Meditech ABPM-06 24-hour blood pressure monitoring system in a pediatric population according to International Organization for Standardization Protocol 81060-2:2018.

OBJECTIVE: Ambulatory blood pressure monitoring (ABPM) devices play a crucial role in diagnosing hypertension, not only in adults but also in pediatric patients. ABPM-06, the latest oscillometric device from Meditech Ltd. (Budapest, Hungary), is the focus of this study. The objective was to validate the ABPM-06 device using the International Organization for Standardization (ISO) 81060-2 : 2018 standard. METHODS: A total of 86 healthy patients, consisting of 34 males and 52 females, aged between 3 and 17 years, were enrolled in this study. During the ambulatory phase, a total of 50 patients were enrolled, with 35 patients falling within the 3- to 12-year-old age range and 15 patients aged between 12 and 17 years. Additionally, for the dynamic test, 36 patients were selected, comprising of 10 individuals aged 3-12 years and 26 patients aged 12-17 years. These patients were recruited from the outpatient clinics of the Department of Pediatrics at Albert Szent-Györgyi University in Szeged, Hungary. The validation process involved utilizing the same-arm sequence protocol, both in resting positions and during stress testing. RESULTS: The ABPM-06 performed well in both clinical and ambulatory validations. In terms of validation criterion 1, the mean ± SD of the differences between the test device and reference blood pressure readings was -1.3 ±â€…3.5  mmHg for systolic and -0.1  ±â€…2.3 mmHg for diastolic, in children under the age of 12 years. For those over the age of 12 years, the mean ± SD of the differences was -2.8 ± 4.6 mmHg for systolic and -0.5 ± 2.7 mmHg for diastolic. Regarding the ambulatory validation, for children under 12 years old, the mean ± SD of the differences was -1.3  ±â€…3.5  mmHg for systolic and -0.1  ±â€…2.3  mmHg for diastolic. In the age group above 12 years, the mean ± SD of the differences was -2.8 ± 4.6 mmHg for systolic and -0.5 ± 2.7 mmHg for diastolic. Both tests successfully met the established criteria regarding the mean and SD values of the differences between the device readings and the observed SBP and DBP measurements. CONCLUSION: The ABPM-06 oscillometric device fully adheres to the ISO 81060-2 : 2018 standard requirements for ABPM determination in the pediatric population (ages 3-17 years). Consequently, this ABPM device proves to be suitable for effectively managing hypertension in children and adolescents.

Tumour regression predicts better response to interferon therapy in melanoma patients: a retrospective single centre study.

We hypothesise that regression may have an impact on the effectiveness of adjuvant IFN therapy, based on its role in the host immune response. Our purpose is to investigate regression and ulceration as prognostic factors in case of interferon-alpha (IFN)-treated melanoma patients. We followed 357 IFN-treated melanoma patients retrospectively, investigating progression-free survival (PFS) and overall survival (OS) depending on the presence of ulceration and regression. A Kaplan-Meier analysis was performed, and we used a Cox regression analysis to relate risk factors. The survival function of the Cox regression was used to measure the effect of regression and ulceration on PFS and OS depending on the Breslow thickness (T1-T4) of the primary tumour. Regression was significantly positively related to PFS ( P  = 0.0018, HR = 0.352) and OS ( P  = 0.0112, HR = 0.380), while ulceration showed a negative effect (PFS: P  = 0.0001, HR = 2.629; OS: P  = 0.0003, HR = 2.388). They influence survival independently. The most favourable outcome was measured in the regressed/non-ulcerated group, whereas the worse was in the non-regressed/ulcerated one. Of risk factors, Breslow thickness is the most significant predictor. The efficacy of regression is regardless of Breslow thickness, though the more favourable the impact of regression was in the thicker primary lesions. Our results indicate that regression is associated with a more favourable outcome for IFN-treated melanoma patients, whereas ulceration shows an inverse relation. Further studies are needed to analyse the survival benefit of regression in relation to innovative immune checkpoint inhibitors.

Machine learning-based analysis of cancer cell-derived vesicular proteins revealed significant tumor-specificity and predictive potential of extracellular vesicles for cell invasion and proliferation - A meta-analysis.

BACKGROUND: Although interest in the role of extracellular vesicles (EV) in oncology is growing, not all potential aspects have been investigated. In this meta-analysis, data regarding (i) the EV proteome and (ii) the invasion and proliferation capacity of the NCI-60 tumor cell lines (60 cell lines from nine different tumor types) were analyzed using machine learning methods. METHODS: On the basis of the entire proteome or the proteins shared by all EV samples, 60 cell lines were classified into the nine tumor types using multiple logistic regression. Then, utilizing the Least Absolute Shrinkage and Selection Operator, we constructed a discriminative protein panel, upon which the samples were reclassified and pathway analyses were performed. These panels were validated using clinical data (n = 4,665) from Human Protein Atlas. RESULTS: Classification models based on the entire proteome, shared proteins, and discriminative protein panel were able to distinguish the nine tumor types with 49.15%, 69.10%, and 91.68% accuracy, respectively. Invasion and proliferation capacity of the 60 cell lines were predicted with R2 = 0.68 and R2 = 0.62 (p < 0.0001). The results of the Reactome pathway analysis of the discriminative protein panel suggest that the molecular content of EVs might be indicative of tumor-specific biological processes. CONCLUSION: Integrating in vitro EV proteomic data, cell physiological characteristics, and clinical data of various tumor types illuminates the diagnostic, prognostic, and therapeutic potential of EVs. Video Abstract.

Diagnosis of Epstein-Barr and cytomegalovirus infections using decision trees: an effective way to avoid antibiotic overuse in paediatric tonsillopharyngitis.

BACKGROUND: The incidence of tonsillopharyngitis is especially prevalent in children. Despite the fact that viruses cause the majority of infections, antibiotics are frequently used as a treatment, contrary to international guidelines. This is not only an inappropriate method of treatment for viral infections, but it also significantly contributes to the emergence of antibiotic-resistant strains. In this study, EBV and CMV-related tonsillopharyngitis were distinguished from other pathogens by using machine learning techniques to construct a classification tree based on clinical characteristics. MATERIALS AND METHODS: In 2016 and 2017, we assessed information regarding 242 children with tonsillopharyngitis. Patients were categorized according to whether acute cytomegalovirus or Epstein-Barr virus infections were confirmed (n = 91) or not (n = 151). Based on symptoms and blood test parameters, we constructed decision trees to discriminate the two groups. The classification efficiency of the model was characterized by its sensitivity, specificity, positive predictive value, and negative predictive value. Fisher's exact and Welch's tests were used to perform univariable statistical analyses. RESULTS: The best decision tree distinguished EBV/CMV infection from non-EBV/CMV group with 83.33% positive predictive value, 88.90% sensitivity and 90.30% specificity. GPT (U/l) was found to be the most discriminatory variable (p < 0.0001). Using the model, unnecessary antibiotic treatment could be reduced by 66.66% (p = 0.0002). DISCUSSION: Our classification model can be used as a diagnostic decision support tool to distinguish EBC/CMV infection from non EBV/CMV tonsillopharyngitis, thereby significantly reducing the overuse of antibiotics. It is hoped that the model may become a tool worth considering in routine clinical practice and may be developed to differentiate between viral and bacterial infections.

MMP-9 as Prognostic Marker for Brain Tumours: A Comparative Study on Serum-Derived Small Extracellular Vesicles.

Matrix metalloproteinase-9 (MMP-9) degrades the extracellular matrix, contributes to tumour cell invasion and metastasis, and its elevated level in brain tumour tissues indicates poor prognosis. High-risk tissue biopsy can be replaced by liquid biopsy; however, the blood-brain barrier (BBB) prevents tumour-associated components from entering the peripheral blood, making the development of blood-based biomarkers challenging. Therefore, we examined the MMP-9 content of small extracellular vesicles (sEVs)-which can cross the BBB and are stable in body fluids-to characterise tumours with different invasion capacity. From four patient groups (glioblastoma multiforme, brain metastases of lung cancer, meningioma, and lumbar disc herniation as controls), 222 serum-derived sEV samples were evaluated. After isolating and characterising sEVs, their MMP-9 content was measured by ELISA and assessed statistically (correlation, paired t-test, Welch's test, ANOVA, ROC). We found that the MMP-9 content of sEVs is independent of gender and age, but is affected by surgical intervention, treatment, and recurrence. We found a relation between low MMP-9 level in sEVs (<28 ppm) and improved survival (8-month advantage) of glioblastoma patients, and MMP-9 levels showed a positive correlation with aggressiveness. These findings suggest that vesicular MMP-9 level might be a useful prognostic marker for brain tumours.

Impact of Experimental Conditions on Extracellular Vesicles' Proteome: A Comparative Study.

Extracellular vesicle (EV) research is a rapidly developing field, mainly due to the key role of EVs in intercellular communication and pathophysiological processes. However, the heterogeneity of EVs challenges their exploration and the establishment of gold-standard methods. Here, we aimed to reveal the influence of technical changes on EV biology and the reliability of experimental data. We used B16F1 melanoma cells as a model and applied nanoparticle tracking analysis, mass spectrometry (LC-MS/MS) and pathway enrichment analysis to analyze the quantity, size distribution, proteome and function of their small EVs (sEVs) produced in sEV-depleted fetal bovine serum (FBS)-containing medium or serum-free medium. Additionally, we investigated the effects of minor technical variances on the quality of sEV preparations. We found that storage of the isolates at -80 °C has no adverse effect on LC-MS/MS analysis, and an additional washing step after differential ultracentrifugation has a minor influence on the sEV proteome. In contrast, FBS starvation affects the production and proteome of sEVs; moreover, these vesicles may have a greater impact on protein metabolism, but a smaller impact on cell adhesion and membrane raft assembly, than the control sEVs. As we demonstrated that FBS starvation has a strong influence on sEV biology, applying serum-free conditions might be considered in in vitro sEV studies.

Epidemiology and clinical features of SARS-CoV-2 infection in hospitalized children across four waves in Hungary: A retrospective, comparative study from March 2020 to December 2021.

Background and Aims: From 2019 till the present, infections induced by the novel coronavirus and its mutations have posed a new challenge for healthcare. However, comparative studies on pediatric infections throughout waves are few. During four different pandemic waves, we intended to investigate the clinical and epidemiological characteristic of the pediatric population hospitalized for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus infection. Methods: Between March 2020 and December 2021, we performed our retrospective research on children infected with the SARS-CoV-2 virus at the University of Szeged. We analyzed the data of all patients who required hospitalization due to positive results of SARS-CoV-2 tests (Nucleic Acid Amplification Test or rapid antigen test). Data analysis included demographic data, medical history, clinical findings, length of hospitalization, and complications, using medical records. Results: In this study, data from 358 coronavirus-infected children were analyzed. The most affected age group was children over 1 month and under 1 year (30.2%). The highest number of cases was recorded in the fourth wave (53.6%). Fever (65.6%), cough (51.4%), nasal discharge (35.3%), nausea and vomiting (31.3%), and decreased oral intake (28.9%) were the most common symptoms. The most common complications were dehydration (50.5%), pneumonia (14.9%), and bronchitis/bronchiolitis (14.5%). Based on RR values, there are considerable differences in the prevalence of the symptoms and complications between the different age groups and waves. Cox proportional hazard model analyzes showed that fever and tachypnoea had a relevant effect on days to recovery. Conclusions: We found trends similar to those previously published, overall statistics. The proportion of children requiring hospitalization varied from wave to wave, with the fourth wave affecting the Hungarian child population the most. Our findings suggest that hospitalization time is unrelated to age, but that certain symptoms (fever and tachypnoea) are associated with longer hospitalization. The onset of certain symptoms may differ by age group.

Characteristics of hepatitis E viral infections in Hungary.

BACKGROUND: Hepatitis E virus (HEV) is one of the most important causes of hepatitis worldwide. Despite this, limited data published more than ten years ago are only available about HEV epidemiology in Hungary. OBJECTIVES: We aimed to determine the epidemiological features of HEV infections among patients submitted to various departments of our university hospital in Hungary with signs and symptoms referring to acute hepatitis. STUDY DESIGN: One thousand four hundred thirty-one sera samples from 1,383 patients were analyzed by enzyme-linked immunosorbent assays (ELISA). In some patients, HEV RNA was detected by broad-range nested polymerase chain reaction (PCR) if acute hepatitis was confirmed. PCR products were sequenced and compared with other available sequences in GenBank. RESULTS: Five hundred eighteen sera from 429 patients proved positive (31.0%) for HEV-specific immunoglobulin G (IgG) with a mean age of 60.0 years. Most sera with anti-HEV IgG antibodies were collected from adults and elderly patients. Anti-HEV IgM positive results were found in the case of 95 sera samples from 70 patients (5.1%). Acute HEV infections were confirmed mostly over 40 (n = 67, 95.7%). The number of males (n = 47, 67.1%) was higher than females (n = 23, 32.9%). We detected HEV-specific PCR products in seven patients (10.9%). Genotyping was successful for 5 out of 7 PCR-positive samples. All sequences belonged to genotype 3 (subgenotypes: e, f). CONCLUSIONS: In our survey, we confirmed the constant presence of acute HEV infections in Hungary and an increased seroprevalence of anti-HEV IgG antibodies compared to a previous study.

The role of the metabolite cargo of extracellular vesicles in tumor progression.

Metabolomic reprogramming in tumor and stroma cells is a hallmark of cancer but understanding its effects on the metabolite composition and function of tumor-derived extracellular vesicles (EVs) is still in its infancy. EVs are membrane-bound sacs with a complex molecular composition secreted by all living cells. They are key mediators of intercellular communication both in normal and pathological conditions and play a crucial role in tumor development. Although lipids are major components of EVs, most of the EV cargo studies have targeted proteins and nucleic acids. The potential of the EV metabolome as a source for biomarker discovery has gained recognition recently, but knowledge on the biological activity of tumor EV metabolites still remains limited. Therefore, we aimed (i) to compile the list of metabolites identified in tumor EVs isolated from either clinical specimens or in vitro samples and (ii) describe their role in tumor progression through literature search and pathway analysis.

Raman Spectral Signatures of Serum-Derived Extracellular Vesicle-Enriched Isolates May Support the Diagnosis of CNS Tumors.

Investigating the molecular composition of small extracellular vesicles (sEVs) for tumor diagnostic purposes is becoming increasingly popular, especially for diseases for which diagnosis is challenging, such as central nervous system (CNS) malignancies. Thorough examination of the molecular content of sEVs by Raman spectroscopy is a promising but hitherto barely explored approach for these tumor types. We attempt to reveal the potential role of serum-derived sEVs in diagnosing CNS tumors through Raman spectroscopic analyses using a relevant number of clinical samples. A total of 138 serum samples were obtained from four patient groups (glioblastoma multiforme, non-small-cell lung cancer brain metastasis, meningioma and lumbar disc herniation as control). After isolation, characterization and Raman spectroscopic assessment of sEVs, the Principal Component Analysis-Support Vector Machine (PCA-SVM) algorithm was performed on the Raman spectra for pairwise classifications. Classification accuracy (CA), sensitivity, specificity and the Area Under the Curve (AUC) value derived from Receiver Operating Characteristic (ROC) analyses were used to evaluate the performance of classification. The groups compared were distinguishable with 82.9-92.5% CA, 80-95% sensitivity and 80-90% specificity. AUC scores in the range of 0.82-0.9 suggest excellent and outstanding classification performance. Our results support that Raman spectroscopic analysis of sEV-enriched isolates from serum is a promising method that could be further developed in order to be applicable in the diagnosis of CNS tumors.

Small Extracellular Vesicles Isolated from Serum May Serve as Signal-Enhancers for the Monitoring of CNS Tumors.

Liquid biopsy-based methods to test biomarkers (e.g., serum proteins and extracellular vesicles) may help to monitor brain tumors. In this proteomics-based study, we aimed to identify a characteristic protein fingerprint associated with central nervous system (CNS) tumors. Overall, 96 human serum samples were obtained from four patient groups, namely glioblastoma multiforme (GBM), non-small-cell lung cancer brain metastasis (BM), meningioma (M) and lumbar disc hernia patients (CTRL). After the isolation and characterization of small extracellular vesicles (sEVs) by nanoparticle tracking analysis (NTA) and atomic force microscopy (AFM), liquid chromatography -mass spectrometry (LC-MS) was performed on two different sample types (whole serum and serum sEVs). Statistical analyses (ratio, Cohen's d, receiver operating characteristic; ROC) were carried out to compare patient groups. To recognize differences between the two sample types, pairwise comparisons (Welch's test) and ingenuity pathway analysis (IPA) were performed. According to our knowledge, this is the first study that compares the proteome of whole serum and serum-derived sEVs. From the 311 proteins identified, 10 whole serum proteins and 17 sEV proteins showed the highest intergroup differences. Sixty-five proteins were significantly enriched in sEV samples, while 129 proteins were significantly depleted compared to whole serum. Based on principal component analysis (PCA) analyses, sEVs are more suitable to discriminate between the patient groups. Our results support that sEVs have greater potential to monitor CNS tumors, than whole serum.