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Blood tests are necessary, easy-to-perform and low-cost alternatives for monitoring of oncolytic virotherapy and other biological therapies in translational research. Here we assessed three candidate proteins with the potential to be used as biomarkers in biological fluids: two glucuronidases from E. coli (GusA) and Staphylococcus sp. RLH1 (GusPlus), and the luciferase from Gaussia princeps (GLuc). The three genes encoding these proteins were inserted individually into vaccinia virus GLV-1h68 genome under the control of an identical promoter. The three resulting recombinant viruses were used to infect tumor cells in cultures and human tumor xenografts in nude mice. In contrast to the actively secreted GLuc, the cytoplasmic glucuronidases GusA and GusPlus were released into the supernatants only as a result of virus-mediated oncolysis. GusPlus resulted in the most sensitive detection of enzyme activity under controlled assay conditions in samples containing as little as 1 pg/ml of GusPlus, followed by GusA (25 pg/ml) and GLuc (≥375 pg/ml). Unexpectedly, even though GusA had a lower specific activity compared to GusPlus, the substrate conversion in the serum of tumor-bearing mice injected with the GusA-encoding virus strains was substantially higher than that of GusPlus. This was attributed to a 3.2 fold and 16.2 fold longer half-life of GusA in the blood stream compared to GusPlus and GLuc respectively, thus a more sensitive monitor of virus replication than the other two enzymes. Due to the good correlation between enzymatic activity of expressed marker gene and virus titer, we conclude that the amount of the biomarker protein in the body fluid semiquantitatively represents the amount of virus in the infected tumors which was confirmed by low light imaging. We found GusA to be the most reliable biomarker for monitoring oncolytic virotherapy among the three tested markers.
There is a debate on the optimal way of monitoring training loads in elite endurance athletes especially during altitude training camps. In this case report, including nine members of the German national middle distance running team, we describe a practical approach to monitor the psychobiological stress markers during 21 days of altitude training (~2100 m above sea‐level) to estimate the training load and to control muscle damage, fatigue, and/or chronic overreaching. Daily examination included: oxygen saturation of hemoglobin, resting heart rate, body mass, body and sleep perception, capillary blood concentration of creatine kinase. Every other day, venous serum concentration of blood urea nitrogen, venous blood concentration of hemoglobin, hematocrit, red and white blood cell were measured. If two or more of the above‐mentioned stress markers were beyond or beneath the athlete's normal individual range, the training load of the subsequent training session was reduced. Running speed at 3 mmol L\(^{−1}\) blood lactate (V\(_{3}\)) improved and no athlete showed any signs of underperformance, chronic muscle damage, decrease body and sleep perception as well as activated inflammatory process during the 21 days. The dense screening of biomarkers in the present case study may stimulate further research to identify candidate markers for load monitoring in elite middle‐ and long‐distance runners during a training camp at altitude.
Patients with chronic kidney disease (CKD) exhibit an increased cancer risk compared to a healthy control population. To be able to estimate the cancer risk of the patients and to assess the impact of interventional therapies thereon, it is of particular interest to measure the patients’ burden of genomic damage. Chromosomal abnormalities, reduced DNA repair, and DNA lesions were found indeed in cells of patients with CKD. Biomarkers for DNA damage measurable in easily accessible cells like peripheral blood lymphocytes are chromosomal aberrations, structural DNA lesions, and oxidatively modified DNA bases. In this review the most common methods quantifying the three parameters mentioned above, the cytokinesis-block micronucleus assay, the comet assay, and the quantification of 8-oxo-7,8-dihydro-2′-deoxyguanosine, are evaluated concerning the feasibility of the analysis and regarding the marker’s potential to predict clinical outcomes.
Background
The identification of additional prognostic markers to improve risk stratification and to avoid overtreatment is one of the most urgent clinical needs in prostate cancer (PCa). MicroRNAs, being important regulators of gene expression, are promising biomarkers in various cancer entities, though the impact as prognostic predictors in PCa is poorly understood. The aim of this study was to identify specific miRNAs as potential prognostic markers in high-risk PCa and to validate their clinical impact.
Methodology and Principal Findings
We performed miRNA-microarray analysis in a high-risk PCa study group selected by their clinical outcome (clinical progression free survival (CPFS) vs. clinical failure (CF)). We identified seven candidate miRNAs (let-7a/b/c, miR-515-3p/5p, -181b, -146b, and -361) that showed differential expression between both groups. Further qRT-PCR analysis revealed down-regulation of members of the let-7 family in the majority of a large, well-characterized high-risk PCa cohort (n = 98). Expression of let-7a/b/and -c was correlated to clinical outcome parameters of this group. While let-7a showed no association or correlation with clinical relevant data, let-7b and let-7c were associated with CF in PCa patients and functioned partially as independent prognostic marker. Validation of the data using an independent high-risk study cohort revealed that let-7b, but not let-7c, has impact as an independent prognostic marker for BCR and CF. Furthermore, we identified HMGA1, a non-histone protein, as a new target of let-7b and found correlation of let-7b down-regulation with HMGA1 over-expression in primary PCa samples.
Conclusion
Our findings define a distinct miRNA expression profile in PCa cases with early CF and identified let-7b as prognostic biomarker in high-risk PCa. This study highlights the importance of let-7b as tumor suppressor miRNA in high-risk PCa and presents a basis to improve individual therapy for high-risk PCa patients.
Predicting hypertension subtypes with machine learning using targeted metabolites and their ratios
(2022)
Hypertension is a major global health problem with high prevalence and complex associated health risks. Primary hypertension (PHT) is most common and the reasons behind primary hypertension are largely unknown. Endocrine hypertension (EHT) is another complex form of hypertension with an estimated prevalence varying from 3 to 20% depending on the population studied. It occurs due to underlying conditions associated with hormonal excess mainly related to adrenal tumours and sub-categorised: primary aldosteronism (PA), Cushing’s syndrome (CS), pheochromocytoma or functional paraganglioma (PPGL). Endocrine hypertension is often misdiagnosed as primary hypertension, causing delays in treatment for the underlying condition, reduced quality of life, and costly antihypertensive treatment that is often ineffective. This study systematically used targeted metabolomics and high-throughput machine learning methods to predict the key biomarkers in classifying and distinguishing the various subtypes of endocrine and primary hypertension. The trained models successfully classified CS from PHT and EHT from PHT with 92% specificity on the test set. The most prominent targeted metabolites and metabolite ratios for hypertension identification for different disease comparisons were C18:1, C18:2, and Orn/Arg. Sex was identified as an important feature in CS vs. PHT classification.
Background:
We assessed the diagnostic value of standard clinical methods and combined biomarker testing (galactomannan assay and polymerase chain reaction screening) in a prospective case-control study to detect invasive pulmonary aspergillosis in patients with hematological malignancies and prolonged neutropenia.
Methods:
In this observational study 162 biomarker analyses were performed on samples from 27 febrile neutropenic episodes. Sera were successively screened for galactomannan antigen and for Aspergillus fumigatus specific nucleic acid targets. Furthermore thoracic computed tomography scanning was performed along with bronchoscopy with lavage when clinically indicated. Patients were retrospectively stratified to define a case-group with "proven" or "probable" invasive pulmonary aspergillosis (25.93 %) and a control-group of patients with no evidence for of invasive pulmonary aspergillosis (74.07 %). In 44.44 % of episodes fever ceased in response to antibiotic treatment (group II). Empirical antifungal therapy was administered for episodes with persistent or relapsing fever (group I). 48.15 % of patients died during the study period. Postmortem histology was pursued in 53.85 % of fatalities.
Results:
Concordant negative galactomannan and computed tomography supported by a polymerase chain reaction assay were shown to have the highest discriminatory power to exclude invasive pulmonary aspergillosis. Bronchoalveolar lavage was performed in 6 cases of invasive pulmonary aspergillosis and in 15 controls. Although bronchoalveolar lavage proved negative in 93 % of controls it did not detect IPA in 86 % of the cases. Remarkably post mortem histology convincingly supported the presence of Aspergillus hyphae in lung tissue from a single case which had consecutive positive polymerase chain reaction assay results but was misdiagnosed by both computed tomography and consistently negative galactomannan assay results. For the galactomannan enzyme-immunoassay the diagnostic odds ratio was 15.33 and for the polymerase chain reaction assay it was 28.67. According to Cohen's kappa our in-house polymerase chain reaction method showed a fair agreement with the galactomannan immunoassay. Combined analysis of the results from the Aspergillus galactomannan enzyme immunoassay together with those generated by our polymerase chain reaction assay led to no misdiagnoses in the control group.
Conclusion:
The data from this pilot-study demonstrate that the consideration of standard clinical methods combined with biomarker testing improves the capacity to make early and more accurate diagnostic decisions.
Stem cell therapy holds great promise for tissue regeneration and cancer treatment, although its efficacy is still inconclusive and requires further understanding and optimization of the procedures. Non-invasive cell tracking can provide an important opportunity to monitor in vivo cell distribution in living subjects. Here, using a combination of positron emission tomography (PET) and in vitro 2-deoxy-2-[18F]fluoro-D-glucose ([18F]FDG) direct cell labelling, the feasibility of engrafted stem cell monitoring was tested in multiple animal species. Human mesenchymal stem cells (MSCs) were incubated with phosphate-buffered saline containing [18F]FDG for in vitro cell radiolabelling. The pre-labelled MSCs were administrated via peripheral vein in a mouse (n=1), rats (n=4), rabbits (n=4) and non-human primates (n=3), via carotid artery in rats (n=4) and non-human primates (n=3), and via intra-myocardial injection in rats (n=5). PET imaging was started 10 min after cell administration using a dedicated small animal PET system for a mouse and rats. A clinical PET system was used for the imaging of rabbits and non-human primates. After MSC administration via peripheral vein, PET imaging revealed intense radiotracer signal from the lung in all tested animal species including mouse, rat, rabbit, and non-human primate, suggesting administrated MSCs were trapped in the lung tissue. Furthermore, the distribution of the PET signal significantly differed based on the route of cell administration. Administration via carotid artery showed the highest activity in the head, and intra-myocardial injection increased signal from the heart. In vitro [18F]FDG MSC pre-labelling for PET imaging is feasible and allows non-invasive visualization of initial cell distribution after different routes of cell administration in multiple animal models. Those results highlight the potential use of that imaging approach for the understanding and optimization of stem cell therapy in translational research.
Background
Troponin elevation is common in ischemic stroke (IS) patients. The pathomechanisms involved are incompletely understood and comprise coronary and non-coronary causes, e.g. autonomic dysfunction. We investigated determinants of troponin elevation in acute IS patients including markers of autonomic dysfunction, assessed by heart rate variability (HRV) time domain variables.
Methods
Data were collected within the Stroke Induced Cardiac FAILure (SICFAIL) cohort study. IS patients admitted to the Department of Neurology, Würzburg University Hospital, underwent baseline investigation including cardiac history, physical examination, echocardiography, and blood sampling. Four HRV time domain variables were calculated in patients undergoing electrocardiographic Holter monitoring. Multivariable logistic regression with corresponding odds ratios (OR) and 95% confidence intervals (CI) was used to investigate the determinants of high-sensitive troponin T (hs-TnT) levels ≥14 ng/L.
Results
We report results from 543 IS patients recruited between 01/2014–02/2017. Of those, 203 (37%) had hs-TnT ≥14 ng/L, which was independently associated with older age (OR per year 1.05; 95% CI 1.02–1.08), male sex (OR 2.65; 95% CI 1.54–4.58), decreasing estimated glomerular filtration rate (OR per 10 mL/min/1.73 m2 0.71; 95% CI 0.61–0.84), systolic dysfunction (OR 2.79; 95% CI 1.22–6.37), diastolic dysfunction (OR 2.29; 95% CI 1.29–4.02), atrial fibrillation (OR 2.30; 95% CI 1.25–4.23), and increasing levels of C-reactive protein (OR 1.48 per log unit; 95% CI 1.22–1.79). We did not identify an independent association of troponin elevation with the investigated HRV variables.
Conclusion
Cardiac dysfunction and elevated C-reactive protein, but not a reduced HRV as surrogate of autonomic dysfunction, were associated with increased hs-TnT levels in IS patients independent of established cardiovascular risk factors.
Background: The search for biomarkers in Parkinson's disease (PD) is crucial to identify the disease early and monitor the effectiveness of neuroprotective therapies. We aim to assess whether a gene signature could be detected in blood from early/mild PD patients that could support the diagnosis of early PD, focusing on genes found particularly altered in the substantia nigra of sporadic PD.
Results: The transcriptional expression of seven selected genes was examined in blood samples from 62 early stage PD patients and 64 healthy age-matched controls. Stepwise multivariate logistic regression analysis identified five genes as optimal predictors of PD: p19 S-phase kinase-associated protein 1A (odds ratio [OR] 0.73; 95% confidence interval [CI] 0.60-0.90), huntingtin interacting protein-2 (OR 1.32; CI 1.08-1.61), aldehyde dehydrogenase family 1 subfamily A1 (OR 0.86; 95% CI 0.75-0.99), 19 S proteasomal protein PSMC4 (OR 0.73; 95% CI 0.60-0.89) and heat shock 70-kDa protein 8 (OR 1.39; 95% CI 1.14-1.70). At a 0.5 cut-off the gene panel yielded a sensitivity and specificity in detecting PD of 90.3 and 89.1 respectively and the area under the receiving operating curve (ROC AUC) was 0.96. The performance of the five-gene classifier on the de novo PD individuals alone composing the early PD cohort (n = 38), resulted in a similar ROC with an AUC of 0.95, indicating the stability of the model and also, that patient medication had no significant effect on the predictive probability (PP) of the classifier for PD risk. The predictive ability of the model was validated in an independent cohort of 30 patients at advanced stage of PD, classifying correctly all cases as PD (100% sensitivity). Notably, the nominal average value of the PP for PD (0.95 (SD = 0.09)) in this cohort was higher than that of the early PD group (0.83 (SD = 0.22)), suggesting a potential for the model to assess disease severity. Lastly, the gene panel fully discriminated between PD and Alzheimer's disease (n = 29).
Conclusions: The findings provide evidence on the ability of a five-gene panel to diagnose early/mild PD, with a possible diagnostic value for detection of asymptomatic PD before overt expression of the disorder.
Background
To characterise the longitudinal dynamics of C-reactive protein (CRP) and Procalcitonin (PCT) in a cohort of hospitalised patients with COVID-19 and support antimicrobial decision-making.
Methods
Longitudinal CRP and PCT concentrations and trajectories of 237 hospitalised patients with COVID-19 were modelled. The dataset comprised of 2,021 data points for CRP and 284 points for PCT. Pairwise comparisons were performed between: (i) those with or without significant bacterial growth from cultures, and (ii) those who survived or died in hospital.
Results
CRP concentrations were higher over time in COVID-19 patients with positive microbiology (day 9: 236 vs 123 mg/L, p < 0.0001) and in those who died (day 8: 226 vs 152 mg/L, p < 0.0001) but only after day 7 of COVID-related symptom onset. Failure for CRP to reduce in the first week of hospital admission was associated with significantly higher odds of death. PCT concentrations were higher in patients with COVID-19 and positive microbiology or in those who died, although these differences were not statistically significant.
Conclusions
Both the absolute CRP concentration and the trajectory during the first week of hospital admission are important factors predicting microbiology culture positivity and outcome in patients hospitalised with COVID-19. Further work is needed to describe the role of PCT for co-infection. Understanding relationships of these biomarkers can support development of risk models and inform optimal antimicrobial strategies.