Computer-Aided Medical Microbiology Monitoring Tool: A Strategy to Adapt to the SARS-CoV-2 Epidemic and That Highlights RT-PCR Consistency

Since the beginning of the COVID-19 pandemic, important health and regulatory decisions relied on SARS-CoV-2 reverse transcription polymerase chain reaction (RT-PCR) results. Our diagnostic laboratory faced a rapid increase in the number of SARS-CoV-2 RT-PCR. To maintain a rapid turnaround time, we moved from a case-by-case validation of RT-PCR results to an automated validation and immediate results transmission to clinicians. A quality-monitoring tool based on a homemade algorithm coded in R was developed, to preserve high quality and to track aberrant results. We present the results of this quality-monitoring tool applied to 35,137 RT-PCR results. Patients tested several times led to 4,939 pairwise comparisons: 88% concordant and 12% discrepant. The algorithm automatically solved 428 out of 573 discrepancies. The most likely explanation for these 573 discrepancies was related for 44.9% of the situations to the clinical evolution of the disease, 27.9% to preanalytical factors, and 25.3% to stochasticity of the assay. Finally, 11 discrepant results could not be explained, including 8 for which clinical data was not available. For patients repeatedly tested on the same day, the second result confirmed a first negative or positive result in 99.2% or 88.9% of cases, respectively. The implemented quality-monitoring strategy allowed to: i) assist the investigation of discrepant results ii) focus the attention of medical microbiologists onto results requiring a specific expertise and iii) maintain an acceptable turnaround time. This work highlights the high RT-PCR consistency for the detection of SARS-CoV-2 and the necessity for automated processes to handle a huge number of microbiological results while preserving quality.

MALBoost: a web-based application for gene regulatory network analysis in Plasmodium falciparum

Background Gene Regulatory Networks (GRN) produce powerful insights into transcriptional regulation in cells. The power of GRNs has been underutilized in malaria research. The Arboreto library was incorporated into a user-friendly web-based application for malaria researchers (http://malboost.bi.up.ac.za). This application will assist researchers with gaining an in depth understanding of transcriptomic datasets. Methods The web application for MALBoost was built in Python-Flask with Redis and Celery workers for queue submission handling, which execute the Arboreto suite algorithms. A submission of 5–50 regulators and total expression set of 5200 genes is permitted. The program runs in a point-and-click web user interface built using Bootstrap4 templates. Post-analysis submission, users are redirected to a status page with run time estimates and ultimately a download button upon completion. Result updates or failure updates will be emailed to the users. Results A web-based application with an easy-to-use interface is presented with a use case validation of AP2-G and AP2-I. The validation set incorporates cross-referencing with ChIP-seq and transcriptome datasets. For AP2-G, 5 ChIP-seq targets were significantly enriched with seven more targets presenting with strong evidence of validated targets. Conclusion The MALBoost application provides the first tool for easy interfacing and efficiently allows gene regulatory network construction for Plasmodium. Additionally, access is provided to a pre-compiled network for use as reference framework. Validation for sexually committed ring-stage parasite targets of AP2-G, suggests the algorithm was effective in resolving “traditionally” low-level signatures even in bulk RNA datasets.

Efficient framework for predicting MiRNA-disease associations based on improved hybrid collaborative filtering

Background Accumulating studies indicates that microRNAs (miRNAs) play vital roles in the process of development and progression of many human complex diseases. However, traditional biochemical experimental methods for identifying disease-related miRNAs cost large amount of time, manpower, material and financial resources. Methods In this study, we developed a framework named hybrid collaborative filtering for miRNA-disease association prediction (HCFMDA) by integrating heterogeneous data, e.g., miRNA functional similarity, disease semantic similarity, known miRNA-disease association networks, and Gaussian kernel similarity of miRNAs and diseases. To capture the intrinsic interaction patterns embedded in the sparse association matrix, we prioritized the predictive score by fusing three types of information: similar disease associations, similar miRNA associations, and similar disease-miRNA associations. Meanwhile, singular value decomposition was adopted to reduce the impact of noise and accelerate predictive speed. Results We then validated HCFMDA with leave-one-out cross-validation (LOOCV) and two types of case studies. In the LOOCV, we achieved 0.8379 of AUC (area under the curve). To evaluate the performance of HCFMDA on real diseases, we further implemented the first type of case validation over three important human diseases: Colon Neoplasms, Esophageal Neoplasms and Prostate Neoplasms. As a result, 44, 46 and 44 out of the top 50 predicted disease-related miRNAs were confirmed by experimental evidence. Moreover, the second type of case validation on Breast Neoplasms indicates that HCFMDA could also be applied to predict potential miRNAs towards those diseases without any known associated miRNA. Conclusions The satisfactory prediction performance demonstrates that our model could serve as a reliable tool to guide the following research for identifying candidate miRNAs associated with human diseases.

A variational method for calculating the longitudinal deformation of a shield tunnel in soft soil caused by grouting under tunnel

Purpose The paper aims to predict longitudinal deformation of a tunnel caused by grouting under the tunnel bottom in advance according to the grouting parameters, which can ensure the safety of the tunnel structure during the grouting process and also help to design the grouting parameters. Design/methodology/approach The paper adopted the analytical approach for calculating the longitudinal deformation of a shield tunnel caused by grouting under a tunnel, including usage of the Mindlin’s solution, the minimum potential energy principle and case validation. Findings The paper provides a variational method for calculating the longitudinal deformation of a shield tunnel in soft soil caused by grouting under the tunnel, which has high computational efficiency and accuracy. Originality/value This paper fulfils an identified need to study how the longitudinal deformation of a shield tunnel in soft soil caused by grouting under the tunnel can be calculated.

Diverse pathological lesions of primary aldosteronism and their clinical significance

Primary aldosteronism (PA) is mainly clinically classified as unilateral aldosterone-producing adenoma (APA) or bilateral idiopathic hyperaldosteronism. Immunohistochemistry for aldosterone synthase reveals a diverse PA pathology, including pathological APA and aldosterone-producing cell clusters. The relationship between PA pathology and adrenalectomy outcomes was examined herein. Data from 219 unilaterally adrenalectomized PA cases were analyzed. Pathological analyses revealed diverse putative aldosterone-producing lesions. Postoperative biochemical outcomes in 114 cases (test cohort) were classified as complete success (n = 85), partial success (n = 19), and absent success (n = 10). Outcomes in the large and small PA lesion groups, rather than between PA lesion types, were compared at five threshold values for PA lesion sizes (2–6 mm with 1-mm increments) to streamline the results. The proportion of complete success was significantly higher in the large PA lesion group than in the small PA lesion group at the 5-mm threshold only. The proportion of absent success was significantly higher in the small PA lesion group than in the large PA lesion group at all thresholds. Univariate and multivariate analyses of the test cohort identified serum K as an independent predictive factor for the small PA lesion group, which was confirmed in the 105-case validation cohort. Chi-squared automatic interaction detector analysis revealed that the best threshold of serum K for predicting large PA lesions was 2.82 mEq/L. These results will be beneficial for treating PA in clinical settings because patients with low serum K levels and apparent adrenal masses on CT may be subjected to adrenalectomy even if the adrenal venous sampling test is unavailable.

A Numerical Approach to Estimate Natural Frequency of Trees with Variable Properties

Free vibration analysis of a Euler-Bernoulli tapered column was conducted using the finite element method to identify the vibration modes of an equivalent tree structure under a specified set of conditions. A non-prismatic elastic circular column of height L was analysed, taking distributed self-weight into account. Various scenarios were considered: column taper, base fixity, radial and longitudinal stiffness (E) and density (ρ) and crown mass. The effect on the first natural frequency was assessed in each case. Validation against closed form solutions of benchmark problems was conducted satisfactorily. The results show that column taper, base fixity and E/ρ ratio are particularly important for this problem. Comparison of predictions with field observations of natural sway frequency for almost 700 coniferous and broadleaved trees from the published literature showed that the model worked well for coniferous trees, but less well for broadleaved trees with their more complicated crown architecture. Overall, the current study provides an in-depth numerical investigation of material properties, geometric properties and boundary conditions to create further understanding of vibration behaviour in trees.

Computer-aided medical microbiology monitoring tool: a strategy to adapt to the SARS-CoV-2 epidemic and that highlights RT-PCR consistency

Since the beginning of the COVID-19 pandemic, important health and regulatory decisions relied on the SARS-CoV-2 reverse transcription polymerase chain reaction (RT-PCR) results. Our diagnostic laboratory faced a rapid increase in the number of SARS-CoV-2 RT-PCR, with up to 1,007 tests per day. To maintain a rapid turnaround time to support patient management and public health authorities' decisions, we moved from a case-by-case validation of RT-PCR to an automated validation and immediate transmission of the results to clinicians. To maintain high quality and to track possible aberrant results, we developed a quality-monitoring tool based on a homemade algorithm coded in R. We present the results of this quality-monitoring tool applied to 35,137 RT-PCR results corresponding to 30,198 patients. Patients tested several times led to 4,939 pairwise comparisons; 88% concordant and 12% discrepant. Among the 573 discrepancies, 428 were automatically solved by the algorithm. The most likely explanation for these 573 discrepancies was related for 44.9% of the situations to "Clinical evolution", 27.9% to "Preanalytical" problems, and 25.3% to "Stochastic". Finally, 11 discrepant results could not be explained, including 8 received from external partners for which clinical data were not available. The implemented quality-monitoring strategy allowed to: i) assist the investigation of discrepant results ii) focus the attention of medical microbiologists onto results requiring a specific expertise and iii) maintain an acceptable TAT. This work highlighted the high RT-PCR consistency for the detection of SARS-CoV-2 and the importance of automated processes to handle a huge number of samples while preserving quality.

Genomic profiling reveals heterogeneous populations of ductal carcinoma in situ of the breast

A substantial number of cases of ductal carcinoma in situ (DCIS) of the breast will never progress to invasive ductal carcinoma (IDC), indicating they are overtreated under the current criteria. Although various candidate markers are available, the relevant markers for delineating the risk categories have not been established. In this study, we analyzed of the integrated clinical features of 431 cases of DCIS followed by deep sequence analyses in a 21-case discovery cohort and a 72-case validation cohort. We identified the five most critical markers of the aggressiveness of DCIS: age <45 years, HER2 amplification, GATA3 mutation positivity, PIK3CA mutation negativity, and PgR protein negativity. Spatial transcriptome and single-cell DNA sequencing further revealed that GATA3 dysfunction, but not PIK3CA mutation, upregulates EMT, invasion, and angiogenic pathways followed by PgR downregulation. These results reveal the existence of heterogeneous populations of DCIS and provide predictive markers for classifying DCIS and optimizing treatment.

Efficiency Prediction of Centrifugal Pump Using the Modified Affinity Laws

This research is a continuation of efforts aimed at establishing the modified affinity laws for viscosity to predict the pump performance directly from a plot in terms of dimensionless numbers, i.e., flow coefficient, Reynolds number, head coefficient, and efficiency. The group has earlier proposed modified head coefficient affinity law. This work proposes and validates a similar efficiency plot that completes the set of modified affinity laws that include all the input and output parameters for a specific pump design and type. A wide range of viscosities and flow rates are considered for CFD analysis to have a comprehensive set of data that includes enough data points to comment on both the laminar and turbulent flow cases categorized based on the hydraulic Reynolds number (2300). Initial analysis shows some inconsistency based on laminar versus turbulent simulation model selection which is addressed in the latter part of this work. In general, two curves can be constructed for laminar and turbulent flow cases. These curves have different axes parameters (exponents of the dimensionless numbers) depending on the plot being for a laminar or a turbulent flow case. Validation with established experimental data shows good agreement in terms of the variation of axes parameters (their exponents) depending on the pump type for a single suction impeller and a double suction impeller pump. The distinction between laminar and turbulent flow cases is found to be applicable to established experimental data as well.