Estimating actual SARS-CoV-2 infections from secondary data

Eminent in pandemic management is accurate information on infection dynamics to plan for timely installation of control measures and vaccination campaigns. Despite huge efforts in diagnostic testing of individuals, the underestimation of the actual number of SARS-CoV-2 infections remains significant due to the large number of undocumented cases. In this paper we demonstrate and compare three methods to estimate the dynamics of true infections based on secondary data i.e., (a) test positivity, (b) infection fatality and (c) wastewater monitoring. The concept is tested with Austrian data on a national basis for the period of April 2020 to December 2022. Further, we use the results of prevalence studies from the same period to generate (upper and lower bounds of) credible intervals for true infections for four data points. Model parameters are subsequently estimated by applying Approximate Bayesian Computation-rejection sampling and Genetic Algorithms. The method is then validated for the case study Vienna. We find that all three methods yield fairly similar results for estimating the true number of infections, which supports the idea that all three datasets contain similar baseline information. None of them is considered superior, as their advantages and shortcomings depend on the specific case study at hand.

High prevalence group testing in epidemiology with geometrically inspired algorithms

Demand for mass surveillance during peak times of the SARS-CoV-2 pandemic caused high workload for clinical laboratories. Efficient and cost conserving testing designs by means of group testing can substantially reduce resources during possible future emergency situations. The novel hypercube algorithm proposed by Mutesa et al. 2021 published in Nature provides methodological proof of concept and points out the applicability to epidemiological testing. In this work, the algorithm is explored and expanded for settings with high group prevalence. Numerical studies investigate the limits of the adapted hypercube methodology, allowing to optimize pooling designs for specific requirements (i.e. number of samples and group prevalence). Hyperparameter optimization is performed to maximize test-reduction. Standard deviation is examined to investigate resilience and precision. Moreover, empirical validation was performed by elaborately pooling SARS-CoV-2 virus samples according to numerically optimized pooling designs. Laboratory experiments with SARS-CoV-2 sample groups, ranging from 50 to 200 items, characterized by group prevalence up to 10%, are successfully processed and analysed. Test-reductions from 50 to 72.5% were achieved in the experimental setups when compared to individual testing. Higher theoretical test-reduction is possible, depending on the number of samples and the group prevalence, indicated by simulation results.

Prediction of hospitalisations based on wastewater-based SARS-CoV-2 epidemiology

Wastewater-based epidemiology is widely applied in Austria since April 2020 to monitor the SARS-CoV-2 pandemic. With a steadily increasing number of monitored wastewater facilities, 123 plants covering roughly 70 % of the 9 million population were monitored as of August 2022. In this study, the SARS-CoV-2 viral concentrations in raw sewage were analysed to infer short-term hospitalisation occupancy. The temporal lead of wastewater-based epidemiological time series over hospitalisation occupancy levels facilitates the construction of forecast models. Data pre-processing techniques are presented, including the approach of comparing multiple decentralised wastewater signals with aggregated and centralised clinical data. Time‑lead quantification was performed using cross-correlation analysis and coefficient of determination optimisation approaches. Multivariate regression models were successfully applied to infer hospitalisation bed occupancy. The results show a predictive potential of viral loads in sewage towards Covid-19 hospitalisation occupancy, with an average lead time towards ICU and non-ICU bed occupancy between 14.8-17.7 days and 8.6-11.6 days, respectively. The presented procedure provides access to the trend and tipping point behaviour of pandemic dynamics and allows the prediction of short-term demand for public health services. The results showed an increase in forecast accuracy with an increase in the number of monitored wastewater treatment plants. Trained models are sensitive to changing variant types and require recalibration of model parameters, likely caused by immunity by vaccination and/or infection. The utilised approach displays a practical and rapidly implementable application of wastewater-based epidemiology to infer hospitalisation occupancy.

Data modelling recipes for SARS-CoV-2 wastewater-based epidemiology

Wastewater based epidemiology is recognized as one of the monitoring pillars, providing essential information for pandemic management. Central in the methodology are data modelling concepts for both communicating the monitoring results but also for analysis of the signal. It is due to the fast development of the field that a range of modelling concepts are used but without a coherent framework. This paper provides for such a framework, focusing on robust and simple concepts readily applicable, rather than applying latest findings from e.g., machine learning. It is demonstrated that data preprocessing, most important normalization by means of biomarkers and equal temporal spacing of the scattered data, is crucial. In terms of the latter, downsampling to a weekly spaced series is sufficient. Also, data smoothing turned out to be essential, not only for communication of the signal dynamics but likewise for regressions, nowcasting and forecasting. Correlation of the signal with epidemic indicators requires multivariate regression as the signal alone cannot explain the dynamics but - for this case study - multiple linear regression proofed to be a suitable tool when the focus is on understanding and interpretation. It was also demonstrated that short term prediction (7 days) is accurate with simple models (exponential smoothing or autoregressive models) but forecast accuracy deteriorates fast for longer periods.

Characterizing renal involvement in Hermansky-Pudlak Syndrome in a zebrafish model

Hermansky-Pudlak Syndrome (HPS) is a rare disease caused by mutations in the genes coding for various HPS proteins. HPS proteins are part of multi-subunit complexes involved in the biogenesis of organelles from the lysosomal-endosomal-system. In humans, this syndrome is characterized by the presence of albinism, platelet dysfunction and pulmonary fibrosis. The renal component to the disease remains unstudied and untreated in patients with HPS. Here we demonstrate that in humans, HPS proteins have a high renal expression with active transcription of HPS1, 3, 4 and 5 in human podocyte cell culture, suggesting that impaired function of HPS proteins could directly impact renal function. Therefore, we developed a zebrafish model to study the renal involvement of HPS proteins in proteinuric kidney disease. Remarkably, knockdown of HPS genes in zebrafish causes glomerular injury with edema, proteinuria and structural changes of the glomerular filtration barrier. Moreover, reduced expression of HPS proteins in zebrafish recapitulates other important disease hallmarks, like hypopigmentation and accumulation of intracellular debris characteristic of lysosomal disorders. In conclusion, we present a valid zebrafish model that highlights the previously underestimated relevance of renal disease in HPS. This draws attention to the therapeutic options available to manage this component of the syndrome.

[Acute on chronic renal failure in a 62-year-old man with ANCA-associated vasculitis]

We describe a patient with ANCA (antineutrophil cytoplasmic antibodies) associated vasculitis and acute-on-chronic renal failure. He had initially presented with severe pulmonary hemorrhage and anuric renal failure and improved rapidly with immunosuppressive therapy. Repeat renal biopsy revealed candida interstitial nephritis. Candida was also detected in bronchoalveolar lavage. Kidney function improved with long-term antifungal therapy. This report adds induction therapy for ANCA vasculitis to the conditions where invasive candidal infections including nephritis need to be considered.

Circulating factors cause proteinuria in parabiotic zebrafish

Proteinuria can be induced by impairment of any component of the glomerular filtration barrier (GFB). To determine the role of circulating permeability factors on glomerular damage, we developed a parabiosis-based zebrafish model to generate a common circulation between zebrafish larvae. A morpholino-mediated knockdown of a podocyte specific gene (nephronectin) was induced in one zebrafish larva which was then fused to an un-manipulated fish. Notably, proteinuria and glomerular damage were present in the manipulated fish and in the parabiotically-fused partner. Thus, circulating permeability factors may be induced by proteinuria even when an induced podocyte gene dysregulation is the initiating cause.

Signal transducer and activator of transcription 5b (STAT5b) as a novel target for anti-neoplastic therapy in human pancreatic cancer

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Background: Activation of signal transducer and activator of transcription 5b (STAT5b) has been associated with tumor growth and metastases in various tumor entities. A number of cytokines, growth factors, and oncogenes that can induce STAT5b activity are also implicated in pancreatic cancer growth and metastases. Hence, we sought to determine STAT5b expression in human pancreatic cancer specimen and effects of selective STAT5b inhibition on pancreatic cancer cells.

Methods: Expression of STAT5b in human pancreatic adenocarcinomas was determined by immunohistochemistry. For in vitro experiments, human pancreatic cancer cell lines (BxPC-3, HPAF-II, L3.6pl) were used. Cancer cells were transfected with STAT5b shRNA plasmid to create stable STAT5b knock-down. Effects of STAT5b inhibition on growth and motility of tumor cells was investigated by MTT and modified Boyden chamber assays. In vivo effects of STAT5b blockade were determined in subcutaneous mouse model.

Results: Nuclear expression of STAT5b was detected in 42/80 human pancreatic adenocarcinomas. In human cancer cell lines, stable knock-down of STAT5b had no effect on growth of tumor cells in vitro. However, tumor cell motility was significantly reduced upon STAT5b blockade (p<0.05). Moreover, expression of various signaling intermediates and transcription factors including c-myc was impaired upon STAT5b knock-down. In a subcutaneous tumor model, inhibition of STAT5b led to significantly reduced tumor growth (p<0.05) which was also reflected by final tumor weights (p<0.05). Furthermore, as revealed by immunohistochemistry, blockade of STAT5b significantly reduced tumor vascularization in vivo (p<0.05).

Conclusions: STAT5b is expressed in human pancreatic adenocarcinomas. Blockade of STAT5b impairs cancer cell motility in vitro, suggesting antimetastatic potential. Moreover, inhibition of STAT5b significantly reduces tumor growth and tumor vascularization in vivo. Hence, STAT5b might be an interesting target for antineoplastic therapy in human pancreatic cancer.

No significant financial relationships to disclose.

Crystal structure of new Ni(II) complex with non-symmetrical bis-enaminone from powder diffraction data

The crystal structure of C22H32BrN2NiO2 was ab initio solved from conventional X-ray powder data by combination of few powder diffraction techniques. After the intensity estimating procedure based on texture method, the orientation and approximate position of the molecule was found by the Patterson methods. Next, Patterson and direct method search program PATSEE was used to locate the molecule more precisely. Missing atoms of flexible groups and final refinement was performed by Rietveld method. The structure consists of flat molecules connected by van der Waals forces. The compound crystallises in the monoclinic space group P21/c (No. 14) with a=10.362(3) Å, b=18.468(3) Å, c=12.066(3) Å, β = 124.53(1)°, Z=4, and contains 28 atoms in asymmetric unit.

Crystal structure determination of 2-oxo-3-benzoyloxazolo[3,2-a]pyridine from X-ray powder data

The crystal structure of the mesoionic compound 2-oxo-3-benzoyloxazolo[3,2-a]pyridine (C14NO3H9) has been determined from X-ray powder data using the grid search procedure.

The crystal symmetry is orthorhombic (space group Pna21, a=7,940(1) Å, b=24,562(3) Å, c=5,402(1) Å). The results of Rietveld refinement (Rwp = 11,9%; Rexp = 3,7%) with gradually relaxed bond restrains show that the delocalization of positive charge does not involve the entire pyridine fragment and occurs along the triad N-C-O, whereas the negative charge is localized on the carbon atom.

Application of simulated annealing approach for structure solution of molecular crystals from X-ray laboratory powder data

Simulated annealing approach was successfully applied to solve three unknown molecular structures from X-ray laboratory powder data using a priory known structural fragments. Some possible developments of the method are discussed.

Crystal structure of 5′-phenyl-1,1′:3′,1″-terphenyl-4-carboxylic acid, a 27 atoms organic compound by powder method

The crystal structure of a complex organic compound containing 27 independent non-hydrogen atoms in asymmetric unit has been solved from powder diffraction data collected at ESRF Grenoble. Structure model was found using PATSEE program. By Rietveld method the structure was completed and refined to final values of discrepancy factors RF and Rwp equal to 14.6 and 13.2%, respectively. The space group is P21/c, unit cell parameters a = 11.0769(5), b = 22.387(1), c = 7.3885(3) Å, β = 91.923(4)°.