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In order to establish criteria for the Serodiagnosis of foamy virus infections we investigated the extent to which sera from iofected individuals of human and primate origin react with structural and non-structural virus proteins in immunoblot assays. Using lysates from infected cells as the source of virus antigen, antibodies were preferentially detected against the Gag proteins and the non-structural Bet protein. Both the Gag precursor molecules of 70 and 74K apparent M\(_r\) and the cytoplasmic 60K M\(_r\) Bet protein were found to be phosphorylated, the latter being synthesized in large amounts in infected cells. Rahbit antiserum raised against recombinant human foamy virus (HFV) Gag major capsid protein cross-reacted with foamy viruses of chimpanzee, gorilla, orang-utan, rhesus monkey and Mrican green monkey origin. This was reßected by a broad cross-reactivity of the respective monkey sera to the Gag proteins of the various foamy virus isolates. Cross-reactivity of antisera against the Bet protein was restricted to viruses from man and the great apes. Recombinant Gag and Bet proteins expressed in prokaryotes or in insect cells were readily recognized by foamy virus-positive primate sera. Screening serum samples from chimpanzees with HFV Gag and Bet proteins expressed by recombinant baculoviruses revealed that 18 out of 35 (52%) were positive for Gag antibodies. Of these, 13 (72 o/o) showed antiborlies against the Bet protein, indicating that Bet antigen is of value in sero1ogical screening for foamy virus infections.
Magnetic Resonance Imaging (MRI) is a non-invasive medical imaging technique, that is rou- tinely used in clinical practice for detection and diagnosis of a wide range of different diseases. In MRI, no ionizing radiation is used, making even repeated application unproblematic. This is an important advantage over other common imaging methods such as X-rays and Computer To- mography. One major drawback of MRI, however, are long acquisition times and associated high costs of experiments. Since the introduction of MRI, several important technical developments have been made to successfully reduce acquisition times. In this work, novel approaches were developed to increase the efficiency of MRI acquisitions.
In Chapter 4, an improved radial turbo spin-echo (TSE) combined acquisition and reconstruction strategy was introduced. Cartesian turbo spin-echo sequences [3] are widely used especially for the detection and diagnosis of neurological pathologies, as they provide high SNR images with both clinically important proton density and T2 contrasts. TSE acquisitions combined with radial sampling are very efficient, since it is possible to obtain a number of ETL images with different contrasts from a single radial TSE measurement [56–58]. Conventionally, images with a particular contrast are obtained from both radial and Cartesian TSE acquisitions by combining data from different echo times into a single image. In the radial case, this can be achieved by employing k-space weighted image contrast (KWIC) reconstruction. In KWIC, the center region of k-space is filled exclusively with data belonging to the desired contrast while outer regions also are assembled with data acquired at other echo times. However, this data sharing leads to mixed contrast contributions to both Cartesian and radial TSE images. This is true especially for proton density weighted images and therefore may reduce their diagnostic value.
In the proposed method, an adapted golden angle reordering scheme is introduced for radial TSE acquisitions, that allows a free choice of the echo train length and provides high flexibility in image reconstruction. Unwanted contrast contaminations are greatly reduced by employing a narrow-band KWIC filter, that restricts data sharing to a small temporal window around the de- sired echo time. This corresponds to using fewer data than required for fully sampled images and consequently leads to images exhibiting aliasing artifacts. In a second step, aliasing-free images are obtained using parallel imaging. In the neurological examples presented, the CG-SENSE algorithm [42] was chosen due to its stable convergence properties and its ability to reconstruct arbitrarily sampled data. In simulations as well as in different in vivo neurological applications, no unwanted contrast contributions could be observed in radial TSE images reconstructed with the proposed method. Since this novel approach is easy to implement on today’s scanners and requires low computational power, it might be valuable for the clinical breakthrough of radial TSE acquisitions.
In Chapter 5, an auto-calibrating method was introduced to correct for stimulated echo contribu- tions to T2 estimates from a mono-exponential fit of multi spin-echo (MSE) data. Quantification of T2 is a useful tool in clinical routine for the detection and diagnosis of diseases as well as for tis- sue characterization. Due to technical imperfections, refocusing flip angles in a MSE acquisition deviate from the ideal value of 180○. This gives rise to significant stimulated echo contributions to the overall signal evolution. Therefore, T2 estimates obtained from MSE acquisitions typically are notably higher than the reference. To obtain accurate T2 estimates from MSE acquisitions, MSE signal amplitudes can be predicted using the extended phase graph (EPG, [23, 24]) algo- rithm. Subsequently, a correction factor can be obtained from the simulated EPG T2 value and applied to the MSE T2 estimates. However, EPG calculations require knowledge about refocus- ing pulse amplitudes, T2 and T1 values and the temporal spacing of subsequent echoes. While the echo spacing is known and, as shown in simulations, an approximate T1 value can be assumed for high ratios of T1/T2 without compromising accuracy of the results, the remaining two parameters are estimated from the data themselves. An estimate for the refocusing flip angle can be obtained from the signal intensity ratio of the second to the first echo using EPG. A conventional mono- exponential fit of the MSE data yields a first estimate for T2. The T2 correction is then obtained iteratively by updating the T2 value used for EPG calculations in each step. For all examples pre- sented, two iterations proved to be sufficient for convergence. In the proposed method, a mean flip angle is extracted across the slice. As shown in simulations, this assumption leads to greatly reduced deviations even for more inhomogeneous slice profiles. The accuracy of corrected T2 values was shown in experiments using a phantom consisting of bottles filled with liquids with a wide range of different T2 values. While T2 MSE estimates were shown to deviate significantly from the spin-echo reference values, this is not the case for corrected T2 values. Furthermore, applicability was demonstrated for in vivo neurological experiments.
In Chapter 6, a new auto-calibrating parallel imaging method called iterative GROG was pre- sented for the reconstruction of non-Cartesian data. A wide range of different non-Cartesian schemes have been proposed for data acquisition in MRI, that present various advantages over conventional Cartesian sampling such as faster acquisitions, improved dynamic imaging and in- trinsic motion correction. However, one drawback of non-Cartesian data is the more complicated reconstruction, which is ever more problematic for non-Cartesian parallel imaging techniques. Iterative GROG uses Calibrationless Parallel Imaging by Structured Low-Rank Matrix Completion (CPI) for data reconstruction. Since CPI requires points on a Cartesian grid, it cannot be used to directly reconstruct non-Cartesian data. Instead, Grappa Operator Gridding (GROG) is employed in a first step to move the non-Cartesian points to the nearest Cartesian grid locations. However, GROG requires a fully sampled center region of k-space for calibration. Combining both methods in an iterative scheme, accurate GROG weights can be obtained even from highly undersampled non-Cartesian data. Subsequently, CPI can be used to reconstruct either full k- space or a calibration area of arbitrary size, which can then be employed for data reconstruction with conventional parallel imaging methods.
In Chapter 7, a new 2D sampling scheme was introduced consisting of multiple oscillating effi- cient trajectories (MOET), that is optimized for Compressed Sensing (CS) reconstructions. For successful CS reconstruction of a particular data set, some requirements have to be met. First, ev- ery data sample has to carry information about the whole object, which is automatically fulfilled for the Fourier sampling employed in MRI. Additionally, the image to be reconstructed has to be sparse in an arbitrary domain, which is true for a number of different applications. Last, data sam- pling has to be performed in an incoherent fashion. For 2D imaging, this important requirement of CS is difficult to achieve with conventional Cartesian and non-Cartesian sampling schemes. Ra- dial sampling is often used for CS reconstructions of dynamic data despite the streaking present in undersampled images. To obtain incoherent aliasing artifacts in undersampled images while at the same time preserving the advantages of radial sampling for dynamic imaging, MOET com- bines radial spokes with oscillating gradients of varying amplitude and alternating orientation orthogonal to the readout direction. The advantage of MOET over radial sampling in CS re- constructions was demonstrated in simulations and in in vivo cardiac imaging. MOET provides superior results especially when used in CS reconstructions with a sparsity constraint directly in image space. Here, accurate results could be obtained even from few MOET projections, while the coherent streaking artifacts present in the case of radial sampling prevent image recovery even for smaller acceleration factors. For CS reconstructions of dynamic data with sparsity constraint in xf-space, the advantage of MOET is smaller since the temporal reordering is responsible for an important part of incoherency. However, as was shown in simulations of a moving phantom and in the reconstruction of ungated cardiac data, the additional spatial incoherency provided by MOET still leads to improved results with higher accuracy and may allow reconstructions with higher acceleration factors.
Hintergrund
Im Rahmen der Pandemie des SARS-CoV-2-Virus erlangte das Patientenkollektiv der Schwangeren früh Aufmerksamkeit. Initial wurde angesichts sich früh abzeichnender Krankheitsfälle bei jüngeren Patienten mit einem erheblichen Aufkommen peripartal zu betreuender, COVID-19-positiver Schwangerer gerechnet.
Ziel der Arbeit
Diese Arbeit vermittelt einen Einblick in die SARS-CoV-2-Infektionszahlen im Rahmen der geburtshilflichen Anästhesie zu Beginn der Pandemie sowie während der zweiten Infektionswelle in Deutschland.
Methoden
Über das COALA-Register (COVID-19 related Obstetric Anaesthesia Longitudinal Assessment-Registry) wurden sowohl von März bis Mai 2020 als auch von Oktober 2020 bis Februar 2021 in Deutschland und der Schweiz wöchentlich prospektiv Daten zu Verdachts- und bestätigten SARS-CoV-2-Fällen bei Schwangeren zum Zeitpunkt der Geburt erhoben. Betrachtet wurden die Verteilung dieser auf die Anzahl der Geburten, Zentren und Erhebungswochen sowie mütterliche Charakteristika und Krankheitsverläufe.
Ergebnisse
Neun Zentren haben im Verlauf 44 SARS-CoV-2-positive Schwangere zum Zeitpunkt der Geburt bei 7167 Geburten (0,6 %) gemeldet (3 Fälle auf 2270 Geburten (0,4 %) und 41 Fälle auf 4897 Geburten (0,8 %)). Berichtet wurden 2 schwere COVID-19-Verläufe (n = 1 mit Todesfolge nach ECMO, n = 1 mit ECMO überlebt). Bei 28 (68 %) Patientinnen verlief die Infektion asymptomatisch. Ein Neugeborenes wurde im Verlauf positiv auf SARS-CoV‑2 getestet.
Schlussfolgerung
Mithilfe des Registers konnte das Auftreten von Fällen zu Beginn der Pandemie zeitnah eingeschätzt werden. Es traten sporadisch Verdachtsfälle bzw. bestätigte Fälle auf. Aufgrund fehlender flächendeckender Testung muss aber von einer Dunkelziffer asymptomatischer Fälle ausgegangen werden. Während der zweiten Infektionswelle wurden 68 % asymptomatische Fälle gemeldet. Jedoch kann es bei jungen, gesunden Patientinnen ohne das Vorliegen typischer Risikofaktoren zu schwerwiegenden Verläufen kommen.
Tuberculosis (TB) is one of the leading causes of death by an infectious disease. It remains a major health burden worldwide, in part due to misdiagnosis. Therefore, improved diagnostic tests allowing the faster and more reliable diagnosis of patients with active TB are urgently needed. This prospective study examined the performance of the new molecular whole-blood test T-Track\(^®\) TB, which relies on the combined evaluation of IFNG and CXCL10 mRNA levels, and compared it to that of the QuantiFERON\(^®\)-TB Gold Plus (QFT-Plus) enzyme-linked immunosorbent assay (ELISA). Diagnostic accuracy and agreement analyses were conducted on the whole blood of 181 active TB patients and 163 non-TB controls. T-Track\(^®\) TB presented sensitivity of 94.9% and specificity of 93.8% for the detection of active TB vs. non-TB controls. In comparison, the QFT-Plus ELISA showed sensitivity of 84.3%. The sensitivity of T-Track\(^®\) TB was significantly higher (p < 0.001) than that of QFT-Plus. The overall agreement of T-Track\(^®\) TB with QFT-Plus to diagnose active TB was 87.9%. Out of 21 samples with discordant results, 19 were correctly classified by T-Track\(^®\) TB while misclassified by QFT-Plus (T-Track\(^®\) TB-positive/QFT-Plus-negative), and two samples were misclassified by T-Track\(^®\) TB while correctly classified by QFT-Plus (T-Track\(^®\) TB-negative/QFT-Plus-positive). Our results demonstrate the excellent performance of the T-Track\(^®\) TB molecular assay and its suitability to accurately detect TB infection and discriminate active TB patients from non-infected controls.