INTRODUCTION: Staphylococcus capitis naturally colonizes the human skin but as an opportunistic pathogen, it can also cause biofilm-associated infections and bloodstream infections in newborns. Previously, we found that two strains from the subspecies S. capitis subsp. capitis produce yellow carotenoids despite the initial species description, reporting this subspecies as non-pigmented. In Staphylococcus aureus, the golden pigment staphyloxanthin is an important virulence factor, protecting cells against reactive oxygen species and modulating membrane fluidity. METHODS: In this study, we used two pigmented (DSM 111179 and DSM 113836) and two non-pigmented S. capitis subsp. capitis strains (DSM 20326T and DSM 31028) to identify the pigment, determine conditions under which pigment-production occurs and investigate whether pigmented strains show increased resistance to ROS and temperature stress. RESULTS: We found that the non-pigmented strains remained colorless regardless of the type of medium, whereas intensity of pigmentation in the two pigmented strains increased under low nutrient conditions and with longer incubation times. We were able to detect and identify staphyloxanthin and its derivates in the two pigmented strains but found that methanol cell extracts from all four strains showed ROS scavenging activity regardless of staphyloxanthin production. Increased survival to cold temperatures (-20°C) was detected in the two pigmented strains only after long-term storage compared to the non-pigmented strains. CONCLUSION: The identification of staphyloxanthin in S. capitis is of clinical relevance and could be used, in the same way as in S. aureus, as a possible target for anti-virulence drug design.
- Publikační typ
- časopisecké články MeSH
Melanins are complex pigments with various biological functions and potential applications in space exploration and biomedicine due to their radioprotective properties. Aspergillus niger, a fungus known for its high radiation resistance, is widely used in biotechnology and a candidate for melanin production. In this study, we investigated the production of fungal pyomelanin (PyoFun) in A. niger by inducing overproduction of the pigment using L-tyrosine in a recombinant ΔhmgA mutant strain (OS4.3). The PyoFun pigment was characterized using three spectroscopic methods, and its antioxidant properties were assessed using a DPPH-assay. Additionally, we evaluated the protective effect of PyoFun against non-ionizing radiation (monochromatic UV-C) and compared its efficacy to a synthetically produced control pyomelanin (PyoSyn). The results confirmed successful production of PyoFun in A. niger through inducible overproduction. Characterization using spectroscopic methods confirmed the presence of PyoFun, and the DPPH-assay demonstrated its strong antioxidant properties. Moreover, PyoFun exhibited a highly protective effect against radiation-induced stress, surpassing the protection provided by PyoSyn. The findings of this study suggest that PyoFun has significant potential as a biological shield against harmful radiation. Notably, PyoFun is synthesized extracellularly, differing it from other fungal melanins (such as L-DOPA- or DHN-melanin) that require cell lysis for pigment purification. This characteristic makes PyoFun a valuable resource for biotechnology, biomedicine, and the space industry. However, further research is needed to evaluate its protective effect in a dried form and against ionizing radiation.
- Publikační typ
- časopisecké články MeSH
Rapid and accurate identification of pathogens causing infections is one of the biggest challenges in medicine. Timely identification of causative agents and their antimicrobial resistance profile can significantly improve the management of infection, lower costs for healthcare, mitigate ever-growing antimicrobial resistance and in many cases, save lives. Raman spectroscopy was shown to be a useful-quick, non-invasive, and non-destructive -tool for identifying microbes from solid and liquid media. Modifications of Raman spectroscopy and/or pretreatment of samples allow single-cell analyses and identification of microbes from various samples. It was shown that those non-culture-based approaches could also detect antimicrobial resistance. Moreover, recent studies suggest that a combination of Raman spectroscopy with optical tweezers has the potential to identify microbes directly from human body fluids. This review aims to summarize recent advances in non-culture-based approaches of identification of microbes and their virulence factors, including antimicrobial resistance, using methods based on Raman spectroscopy in the context of possible use in the future point-of-care diagnostic process.
- MeSH
- analýza jednotlivých buněk MeSH
- antiinfekční látky * MeSH
- faktory virulence MeSH
- lidé MeSH
- Ramanova spektroskopie * metody MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- přehledy MeSH
Background: Vagal nerve stimulation (VNS) can be indicated in patients with drug-resistant epilepsy, who are not eligible for resective epilepsy surgery. In VNS therapy, the responder rate (i.e., percentage of subjects experiencing ≥50% seizure reduction) is ~50%. At the moment, there is no widely-accepted possibility to predict VNS efficacy in a particular patient based on pre-implantation data, which can lead to unnecessary surgery and improper allocation of financial resources. The principal aim of PRediction of vagal nerve stimulation EfficaCy In drug-reSistant Epilepsy (PRECISE) study is to verify the predictability of VNS efficacy by analysis of pre-implantation routine electroencephalogram (EEG). Methods: PRECISE is designed as a prospective multicentric study in which patients indicated to VNS therapy will be recruited. Patients will be classified as predicted responders vs. predicted non-responders using pre-implantation EEG analyses. After the first and second year of the study, the real-life outcome (responder vs. non-responder) will be determined. The real-life outcome and predicted outcome will be compared in terms of accuracy, specificity, and sensitivity. In the meantime, the patients will be managed according to the best clinical practice to obtain the best therapeutic response. The primary endpoint will be the accuracy of the statistical model for prediction of response to VNS therapy in terms of responders and non-responders. The secondary endpoint will be the quantification of differences in EEG power spectra (Relative Mean Power, %) between real-life responders and real-life non-responders to VNS therapy in drug-resistant epilepsy and the sensitivity and specificity of the model. Discussion: PRECISE relies on the results of our previous work, through which we developed a statistical classifier for VNS response (responders vs. non-responders) based on differences in EEG power spectra dynamics (Pre-X-Stim). Trial Registration: www.ClinicalTrials.gov, identifier: NCT04935567.
- Publikační typ
- časopisecké články MeSH
Background: Identifying patients with intractable epilepsy who would benefit from therapeutic chronic vagal nerve stimulation (VNS) preoperatively remains a major clinical challenge. We have developed a statistical model for predicting VNS efficacy using only routine preimplantation electroencephalogram (EEG) recorded with the TruScan EEG device (Brazdil et al., 2019). It remains to be seen, however, if this model can be applied in different clinical settings. Objective: To validate our model using EEG data acquired with a different recording system. Methods: We identified a validation cohort of eight patients implanted with VNS, whose preimplantation EEG was recorded on the BrainScope device and who underwent the EEG recording according to the protocol. The classifier developed in our earlier work, named Pre-X-Stim, was then employed to classify these patients as predicted responders or non-responders based on the dynamics in EEG power spectra. Predicted and real-world outcomes were compared to establish the applicability of this classifier. In total, two validation experiments were performed using two different validation approaches (single classifier or classifier voting). Results: The classifier achieved 75% accuracy, 67% sensitivity, and 100% specificity. Only two patients, both real-life responders, were classified incorrectly in both validation experiments. Conclusion: We have validated the Pre-X-Stim model on EEGs from a different recording system, which indicates its application under different technical conditions. Our approach, based on preoperative EEG, is easily applied and financially undemanding and presents great potential for real-world clinical use.
- Publikační typ
- časopisecké články MeSH
The temporomandibular joint (TMJ) is typically involved in 45-87% of children with Juvenile Idiopathic Arthritis (JIA). Accurate diagnosis of JIA is difficult as various clinical tests, including MRI, disagree. The purpose of this study is to optimize the methodological aspects of Dynamic Contrast Enhanced (DCE) MRI of the TMJ in children. In this cross-sectional study, including data from 73 JIA affected children, aged 6-15 years, effects of motion correction, sampling rate and parametric modelling on DCE-MRI data is investigated. Consensus among three radiologists determined the regions of interest. Quantitative perfusion parameters were estimated using four perfusion models; the Adiabatic Approximation to Tissue Homogeneity (AATH), Distributed Capillary Adiabatic Tissue Homogeneity (DCATH), Gamma Capillary Transit Time (GCTT) and Two Compartment Exchange (2CXM) models. Effects of motion correction were evaluated by a sum of least squares between corrected raw data and the GCTT model. The effect of systematically down sampling the raw data was tested. The sum of least squares was computed across all pharmacokinetic models. Relative difference perfusion parameters between the left and right TMJ were used for an unsupervised k-means based stratification of the data based on a principal component analysis, as well as for a supervised random forest classification. Diagnostic sensitivity and specificity were computed relative to structural image scorings. Paired sample t-tests, as well as ANOVA tests, were used (significant threshold: p < 0.05) with Tukeys post hoc test. High-level elastic motion correction provides the best least square fit to the GCTT model (percental improvement: 72-84%). A 4 s sampling rate captures more of the potentially disease relevant signal variations. The various parametric models all leave comparable residues (relative standard deviation: 3.4%). In further evaluation of DCE-MRI as a potential diagnostic tool for JIA a high-level elastic motion correction scheme should be adopted, with a sampling rate of at least 4 s. Results suggest that DCE-MRI data can be a valuable part in JIA diagnostics in the TMJ.
- MeSH
- artefakty MeSH
- dítě MeSH
- juvenilní artritida diagnostické zobrazování MeSH
- lidé MeSH
- magnetická rezonanční tomografie * MeSH
- mladiství MeSH
- počítačové zpracování obrazu * MeSH
- pohyb * MeSH
- předškolní dítě MeSH
- průřezové studie MeSH
- senzitivita a specificita MeSH
- statistické modely * MeSH
- temporomandibulární kloub diagnostické zobrazování MeSH
- Check Tag
- dítě MeSH
- lidé MeSH
- mladiství MeSH
- mužské pohlaví MeSH
- předškolní dítě MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
Introduction: Pulsed field ablation (PFA) exploits the delivery of short high-voltage shocks to induce cells death via irreversible electroporation. The therapy offers a potential paradigm shift for catheter ablation of cardiac arrhythmia. We designed an AC-burst generator and therapeutic strategy, based on the existing knowledge between efficacy and safety among different pulses. We performed a proof-of-concept chronic animal trial to test the feasibility and safety of our method and technology. Methods: We employed 6 female swine - weight 53.75 ± 4.77 kg - in this study. With fluoroscopic and electroanatomical mapping assistance, we performed ECG-gated AC-PFA in the following settings: in the left atrium with a decapolar loop catheter with electrodes connected in bipolar fashion; across the interventricular septum applying energy between the distal electrodes of two tip catheters. After procedure and 4-week follow-up, the animals were euthanized, and the hearts were inspected for tissue changes and characterized. We perform finite element method simulation of our AC-PFA scenarios to corroborate our method and better interpret our findings. Results: We applied square, 50% duty cycle, AC bursts of 100 μs duration, 100 kHz internal frequency, 900 V for 60 pulses in the atrium and 1500 V for 120 pulses in the septum. The inter-burst interval was determined by the native heart rhythm - 69 ± 9 bpm. Acute changes in the atrial and ventricular electrograms were immediately visible at the sites of AC-PFA - signals were elongated and reduced in amplitude (p < 0.0001) and tissue impedance dropped (p = 0.011). No adverse event (e.g., esophageal temperature rises or gas bubble streams) was observed - while twitching was avoided by addition of electrosurgical return electrodes. The implemented numerical simulations confirmed the non-thermal nature of our AC-PFA and provided specific information on the estimated treated area and need of pulse trains. The postmortem chest inspection showed no peripheral damage, but epicardial and endocardial discolorations at sites of ablation. T1-weighted scans revealed specific tissue changes in atria and ventricles, confirmed to be fibrotic scars via trichrome staining. We found isolated, transmural and continuous scars. A surviving cardiomyocyte core was visible in basal ventricular lesions. Conclusion: We proved that our method and technology of AC-PFA is feasible and safe for atrial and ventricular myocardial ablation, supporting their systematic investigation into effectiveness evaluation for the treatment of cardiac arrhythmia. Further optimization, with energy titration or longer follow-up, is required for a robust atrial and ventricular AC-PFA.
- Publikační typ
- časopisecké články MeSH
The automated detection of arrhythmia in a Holter ECG signal is a challenging task due to its complex clinical content and data quantity. It is also challenging due to the fact that Holter ECG is usually affected by noise. Such noise may be the result of the regular activity of patients using the Holter ECG-partially unplugged electrodes, short-time disconnections due to movement, or disturbances caused by electric devices or infrastructure. Furthermore, regular patient activities such as movement also affect the ECG signals and, in connection with artificial noise, may render the ECG non-readable or may lead to misinterpretation of the ECG. OBJECTIVE: In accordance with the PhysioNet/CinC Challenge 2017, we propose a method for automated classification of 1-lead Holter ECG recordings. APPROACH: The proposed method classifies a tested record into one of four classes-'normal', 'atrial fibrillation', 'other arrhythmia' or 'too noisy to classify'. It uses two machine learning methods in parallel. The first-a bagged tree ensemble (BTE)-processes a set of 43 features based on QRS detection and PQRS morphology. The second-a convolutional neural network connected to a shallow neural network (CNN/NN)-uses ECG filtered by nine different filters (8× envelograms, 1× band-pass). If the output of CNN/NN reaches a specific level of certainty, its output is used. Otherwise, the BTE output is preferred. MAIN RESULTS: The proposed method was trained using a reduced version of the public PhysioNet/CinC Challenge 2017 dataset (8183 records) and remotely tested on the hidden dataset on PhysioNet servers (3658 records). The method achieved F1 test scores of 0.92, 0.82 and 0.74 for normal recordings, atrial fibrillation and recordings containing other arrhythmias, respectively. The overall F1 score measured on the hidden test-set was 0.83. SIGNIFICANCE: This F1 score led to shared rank #2 in the follow-up PhysioNet/CinC Challenge 2017 ranking.
