An acoustically-driven biochip - Impact of flow on the cell-association of targeted drug carriers

The interaction of targeted drug carriers with epithelial and endothelial barriers in vivo is largely determined by the dynamics of the body fluids. To simulate these conditions in binding assays, a fully biocompatible in vitro model was developed which can accurately mimic a wide range of physiological flow conditions on a thumbnail-format cell-chip. This acoustically-driven microfluidic system was used to study the interaction characteristics of protein-coated particles with cells. Poly(D,L-lactide-co-glycolide) (PLGA) microparticles (2.86 {\pm} 0.95 {\mu}m) were conjugated with wheat germ agglutinin (WGA-MP, cytoadhesive protein) or bovine serum albumin (BSA-MP, nonspecific protein) and their binding to epithelial cell monolayers was investigated under stationary and flow conditions. While mean numbers of 1500 {\pm} 307 mm-2 WGA-MP and 94 {\pm} 64 mm-2 BSA-MP respectively were detected to be cell-bound in the stationary setup, incubation at increasing flow velocities increasingly antagonized the attachment of both types of surface-modified particles. However, while binding of BSA-MP was totally inhibited by flow, grafting with WGA resulted in a pronounced anchoring effect. This was indicated by a mean number of 747 {\pm} 241 mm-2 and 104 {\pm} 44 mm-2 attached particles at shear rates of 0.2 s-1 and 1 s-1 respectively. Due to the compactness of the fluidic chip which favours parallelization, this setup represents a highly promising approach towards a screening platform for the performance of drug delivery vehicles under physiological flow conditions. In this regard, the flow-chip is expected to provide substantial information for the successful design and development of targeted micro- and nanoparticulate drug carrier systems.Comment: 19 page

Testing lupus anticoagulants in a real-life scenario - a retrospective cohort study

Introduction: Lupus anticoagulant (LAC) testing is challenging. Most data are derived from a well-controlled study environment with potential alterations to daily routines. The aim of this retrospective cohort study was to assess the capacity of various LAC screening tests and derived mixing tests to predict a positive result in subsequent confirmation tests in a large cohort of patients. Materials and methods: In 5832 individuals, we retrospectively evaluated the accuracy of the aPTT-A, aPTT-LAscreen, aPTT-FS and dRVVTscreen and of their derived mixing tests in detecting a positive confirmation test result within the same blood specimen. The group differences, degree of correlation and the predictive accuracy of LAC coagulation tests were analysed using the Mann-Whitney U test, the Spearman-rank-correlation and by area under the receiver operating characteristic curve (ROC-AUC) analysis. ROC-AUCs were compared with the Venkatraman´s permutation test. Results: The pre-test probability of patients with clinically suspected LAC was 36% in patients without factor deficiency or anticoagulation therapy. The aPTT-LAscreen showed the best diagnostic accuracy with a ROC-AUC of 0.84 (95% CI: 0.82 – 0.86). No clear advantage of the dRVVT-derived mixing test was detectable when compared to the dRVVTscreen (P = 0.829). Usage of the index of circulating anticoagulant (ICA) did not improve the diagnostic power of respective mixing tests. Conclusions: Among the parameters evaluated, aPTT-LAscreen and derived mixing test parameters were the most accurate tests. In our study cohort, neither other mixing test nor the ICA presented any further advantage in LAC diagnostics

Rapid Diagnostic Algorithms as a Screening Tool for Tuberculosis: An Assessor Blinded Cross-Sectional Study

Background: A major obstacle to effectively treat and control tuberculosis is the absence of an accurate, rapid, and low-cost diagnostic tool. A new approach for the screening of patients for tuberculosis is the use of rapid diagnostic classification algorithms. Methods: We tested a previously published diagnostic algorithm based on four biomarkers as a screening tool for tuberculosis in a Central European patient population using an assessor-blinded cross-sectional study design. In addition, we developed an improved diagnostic classification algorithm based on a study population at a tertiary hospital in Vienna, Austria, by supervised computational statistics. Results: The diagnostic accuracy of the previously published diagnostic algorithm for our patient population consisting of 206 patients was 54% (CI: 47%–61%). An improved model was constructed using inflammation parameters and clinical information. A diagnostic accuracy of 86% (CI: 80%–90%) was demonstrated by 10-fold cross validation. An alternative model relying solely on clinical parameters exhibited a diagnostic accuracy of 85% (CI: 79%–89%). Conclusion: Here we show that a rapid diagnostic algorithm based on clinical parameters is only slightly improved by inclusion of inflammation markers in our cohort. Our results also emphasize the need for validation of new diagnostic algorithms in different settings and patient populations

Machine learning strategies for identification of bacteraemia

Schwere Infektionen sind lebensbedrohliche Zustände, die schnelles diagnostisches und therapeutisches Handeln erfordern. Entscheidungsunterstützungssysteme für eine schnellere Erkennung von Patienten mit schweren Infektionen könnten das Überleben der Patienten verbessern oder Patienten vor nicht-notwendigen Medikationen bewahren. Im Rahmen dieser Doktorarbeit wurden verschiedene Ziele von Entscheidungsunterstützungssystemen mit Patienten zweier unterschiedlicher Kohorten evaluiert. Eine retrospektive Kohorte inkludierte Patienten mit klinischem Bakteriämie-Verdacht, die zwischen 2006 und 2010 am Allgemeinen Krankenhaus Wien behandelt wurden. Prospektiv wurden Patienten mit einem Systemischen Inflammatorischen Reaktionssyndrom (SIRS), welche am Allgemeinen Krankenhaus Wien an der Normalstation zwischen Juli 2011 und September 2012 behandelt wurden, rekrutiert. Die Vorhersagekraft von Routineparametern sowie von experimentellen Biomarkern für die Erkennung von Bakteriämie sowie des Bakterientyps wurde erhoben. Machine-Learning-Modelle (ML-Modelle) wurden erstellt, um die Vorhersagekraft der Einzelparameter zu erhöhen. Bei 298 SIRS-Patienten auf der Normalstation zeigte Prokalzitonin (PCT) 0,78 ROC-AUC (95%CI: 0,720,83) für die Vorhersage von Bakteriämie. Bei 466 SIRS-Patienten konnte jedoch diese Vorhersagekraft durch lineare oder nicht-lineare ML Strategien nicht verbessert werden. Eine Random-Forest-Strategie zeigte die beste Vorhersagekraft, welche die diagnostische Vorhersagekraft von PCT nicht verbesserte. In der retrospektiven krankenhausweiten Kohorte mit 15.985 Patienten mit Bakteriämieverdacht wurden Routineparameter für die Bakteriämievorhersage evaluiert. Das beste Modell zeigte 0,80 ROC-AUC (95%CI: 0,760,84), welches in 98,8% negativen Vorhersagewert resultierte. Daten von 1.180 bakteriämischen Patienten wurden verwendet, um Modelle zu erstellen, die zwischen Gram-positiver und Gram-negativer Bakteriämie unterscheiden können. Jedoch schlug dieser Ansatz fehl. Zusammenfassend gesagt würden ML-Strategien sich eignen, um in unselektierten Kohorten hoch- oder niedrig-Risikopatienten für Bakteriämie zu erkennen. Bei selektierten Patienten erfordert es weitere Studien, welche evaluieren, ob zelluläre oder genetische Marker die Vorhersagekraft von PCT erhöhen.Severe infections are life-threating conditions, requiring fast diagnostic and therapeutic actions. Decision support systems for faster detection of patients with severe infections might improve patients survival rate or prevent patients from receiving unnecessary therapies. Within this thesis, several goals for decision support systems were evaluated using data of two different patient cohorts. A retrospective cohort included patients with clinically suspected bacteraemia treated at Vienna General Hospital between 2006 and 2010. Further, a prospective cohort of patients with systemic inflammatory response syndrome (SIRS) treated at the Vienna General Hospital standard care wards between July 2011 and September 2012 was recruited. The capacity of standard parameters and experimental biomarkers used to identify bacteraemia or the type of bacteria (Gram staining) was evaluated. Machine learning (ML) models were trained to enhance the predictive performance of individual parameters. Among 298 evaluated standard care patients with SIRS, procalcitonin (PCT) presented 0.78 ROC-AUC (95%CI: 0.720.83) for identifying bacteraemia. However, this discriminatory potency could not be improved by applying linear or non-linear ML strategies, in 466 SIRS patients treated on standard care wards. A random forest strategy showed the best results but failed to improve the diagnostic accuracy of PCT. In a retrospective hospital-wide cohort study including 15,985 patients with suspected bacteraemia, routinely available laboratory parameters were evaluated regarding their predictive capacity to identify bacteraemia. The best model resulted in 0.80 ROC-AUC (95%CI: 0.760.84), which yielded a 98.8% negative predictive value for bacteraemia. Data from 1,180 bactaeremic patients were utilized to establish models for predicting the presence of Gram-positive or Gram-negative bacteraemia, which failed. In conclusion, ML strategies might be applied in unselected patients to identify high-risk or low-risk patients. For application in selected patients, further studies are required to evaluate the potency of cellular or genetic markers to enhance the capacity of PCT.submitted by Franz RatzingerAbweichender Titel laut Übersetzung der Verfasserin/des VerfassersMedizinische Universität Wien, Diss., 2018(VLID)287817

Evaluation of the Septifast MGrade Test on Standard Care Wards--A Cohort Study.

BACKGROUND:The immediate need for appropriate antimicrobial therapy in septic patients requires the detection of the causative pathogen in a timely and reliable manner. In this study, the real-time PCR Septifast MGrade test was evaluated in adult patients meeting the systemic inflammatory response syndrome (SIRS) criteria that were treated at standard care wards. METHODS:Patients with clinical suspected infection, drawn blood cultures (BC), the Septifast M(Grade) test (SF) and sepsis biomarkers were prospectively screened for fulfillment of SIRS criteria and evaluated using the criteria of the European Centre of Disease Control (ECDC) for infection point prevalence studies. RESULTS:In total, 220 patients with SIRS were prospectively enrolled, including 56 patients with detection of bacteria in the blood (incidence: 25.5%). BC analysis resulted in 75.0% sensitivity (95% confidence interval, CI: 61.6%- 85.6%) with 97.6% specificity (CI: 93.9%- 99.3%) for detecting bacteria in the blood. In comparison to BC, SF presented with 80.4% sensitivity (CI: 67.6%- 89.8%) and with 97.6% specificity (CI: 93.9%- 99.3%). BC and SF analysis yielded comparable ROC-AUCs (0.86, 0.89), which did not differ significantly (p = 0.558). A trend of a shorter time-to-positivity of BC analysis was not seen in bacteremic patients with a positive SF test than those with a negative test result. Sepsis biomarkers, including PCT, IL-6 or CRP, did not help to explain discordant test results for BC and SF. CONCLUSION:Since negative results do not exclude bacteremia, the Septifast M(Grade) test is not suited to replacing BC, but it is a valuable tool with which to complement BC for faster detection of pathogens

Substantial diagnostic impact of blood culture independent molecular methods in bloodstream infections : Superior performance of PCR/ESI-MS

This study analyzed the performance of different molecular technologies along with blood culture (BC) in the diagnosis of bloodstream infections (BSI) in patients from internal medicine wards - including intensive care units (ICUs) - and the emergency room. Patients with systemic inflammatory response syndrome were prospectively included. BCs and EDTA whole blood were obtained simultaneously. The latter was analyzed by PCR combined with electrospray ionization mass spectrometry (PCR/ESI-MS; IRIDICA BAC BSI assay, Abbott) and by SeptiFast (Roche). Cases were classified as BSI according to adapted European Centre for Disease Prevention and Control criteria. Out of 462 analyzed episodes, 193 with valid test results fulfilled the inclusion criteria and were further evaluated. Sixty-nine (35.8%) were classified as BSI. PCR/ESI-MS showed a significantly better overall performance than BC (p=0.004) or SeptiFast (p=0.034). Only in patients from the ICU the performance of SeptiFast was comparable to that of PCR/ESI-MS. Mainly due to the negative effect of antimicrobial pre-treatment on BC results, the cumulative performance of each of the molecular tests with BC was significantly higher than that of BC alone (p<0.001). SeptiFast and in particular the broad-range pathogen detection system PCR/ESI-MS proved to be an essential addition to BC-based diagnostics in BSI.(VLID)472612

Scientific Reports / Machine learning for fast identification of bacteraemia in SIRS patients treated on standard care wards : a cohort study

Bacteraemia is a life-threating condition requiring immediate diagnostic and therapeutic actions. Blood culture (BC) analyses often result in a low true positive result rate, indicating its improper usage. A predictive model might assist clinicians in deciding for whom to conduct or to avoid BC analysis in patients having a relevant bacteraemia risk. Predictive models were established by using linear and non-linear machine learning methods. To obtain proper data, a unique data set was collected prior to model estimation in a prospective cohort study, screening 3,370 standard care patients with suspected bacteraemia. Data from 466 patients fulfilling two or more systemic inflammatory response syndrome criteria (bacteraemia rate: 28.8%) were finally used. A 29 parameter panel of clinical data, cytokine expression levels and standard laboratory markers was used for model training. Model tuning was performed in a ten-fold cross validation and tuned models were validated in a test set (80:20 random split). The random forest strategy presented the best result in the test set validation (ROC-AUC: 0.729, 95%CI: 0.6790.779). However, procalcitonin (PCT), as the best individual variable, yielded a similar ROC-AUC (0.729, 95%CI: 0.6790.779). Thus, machine learning methods failed to improve the moderate diagnostic accuracy of PCT.(VLID)464409

Prevalence and Outcome of Secondary Hemophagocytic Lymphohistiocytosis Among SIRS Patients: Results from a Prospective Cohort Study

Secondary hemophagocytic lymphohistiocytosis (sHLH) is a life-threatening condition clinically presenting as SIRS (Systemic Inflammatory Response Syndrome). However, there is no comprehensive data concerning diagnostic algorithms, prevalence, outcome and biomarker performance in SIRS patients. We conducted a prospective observational cohort study on 451 consecutive patients fulfilling &#8805;2 SIRS criteria. The Hscore and the HLH-2004 criteria were used to determine the presence of sHLH, and the correlation of the screening-biomarkers ferritin, sCD25, and sCD163 with both scores was assessed. Out of 451 standard-care SIRS patients, five patients had high Hscores (&#8805;169), suggesting incipient or HLH-like disease, and these patients were in urgent need for intensified therapy. However, none of these patients fulfilled five HLH-2004 criteria required for formal diagnosis. From the studied biomarkers, ferritin correlated strongest to both the HLH-2004 criteria and the Hscore (rs = 0.72, 0.41, respectively), and was the best predictor of 30-day survival (HR:1.012 per 100 &#956;g/L, 95% CI: 1.004&#8211;1.021), when adjusted for patient&#8217;s age, sex, bacteremia and malignant underlying-disease. Also, the HLH-2004 (HR per point increase: 1.435, 95% CI: 1.1012&#8211;2.086) and the Hscore (HR per point increase:1.011, 95% CI: 1.002&#8211;1.020) were independent predictors of 30-day-survival. The Hscore detected patients in hyperinflammatory states requiring urgent therapy escalation. Degrees of hyperinflammation, as assessed by ferritin and both HLH scores, are associated with worse outcomes

Surface modification of PLGA nanospheres with Gd-DTPA and Gd-DOTA for high-relaxivity MRI contrast agents

The preparation of particulate contrast agents for magnetic resonance imaging (MRI) based on biodegradable poly(D,L-lactide-co-glycolide) (PLGA) nanocarriers is reported. By spacer-aided covalent surface-grafting of the prominent chelating ligands diethylenetriaminepentaacetic acid (DTPA) and 1,4,7,10-tetraazacyclododecane-1,4,7,10-tetraacetic acid (DOTA), respectively, up to 236 μg gadolinium per mg PLGA can be immobilized in a stable manner. Due to the localisation at the particle surface, water protons may effectively interact with the gadolinium chelates and the modified particles exhibit high proton relaxivities as confirmed by T1 relaxivities of up to 17.5 mm(-1)s(-1) (25 °C, 1.41 T) in case of Gd-DOTA-functionalized carriers and also supported by NMRD profiles. The obtained values compare favorably with marketed low-molecular weight contrast agents and thus suggest suitability for in vivo us