Next Generation Sequencing (NGS) Target Approach for Undiagnosed Dysglycaemia

Next-generation sequencing (NGS) has revolutionized the field of genomics and created new opportunities for basic research. We described the strategy for the NGS validation of the "dysglycaemia panel" composed by 44 genes related to glucose metabolism disorders (MODY, Wolfram syndrome) and familial renal glycosuria using Ion AmpliSeq technology combined with Ion-PGM. Anonymized DNA of 32 previously genotyped cases with 33 different variants were used to optimize the methodology. Standard protocol was used to generate the primer design, library, template preparation, and sequencing. Ion Reporter tool was used for data analysis. In all the runs, the mean coverage was over 200×. Twenty-nine out of thirty three variants (96.5%) were detected; four frameshift variants were missed. All point mutations were detected with high sensitivity. We identified three further variants of unknown significance in addition to pathogenic mutations previously identified by Sanger sequencing. The NGS panel allowed us to identify pathogenic variants in multiple genes in a short time. This could help to identify several defects in children and young adults that have to receive the genetic diagnosis necessary for optimal treatment. In order not to lose any pathogenic variants, Sanger sequencing is included in our analytical protocol to avoid missing frameshift variants.

Systematic Evaluation of Two Classical Receptor Models in Source Apportionment of Soil Heavy Metal(loid) Pollution Using Synthetic and Real-World Datasets

Due to the lack of a priori knowledge on true source makeup and contributions, whether the source apportionment results of Unmix and positive matrix factorization (PMF) are accurate cannot be easily assessed, despite the availability of built-in indicators for their goodness of fit and robustness. This study systematically evaluated, for the first time, the applicability and reliability of these models in source apportionment of soil heavy metal(loid)s with synthetic datasets generated using known source profiles and contributions and a real-world dataset as well. For eight synthetic datasets with different pollution source characteristics, feasible Unmix solutions were close to the true source component compositions (R² > 0.936; total mean squared errors (MSEs) < 0.04), while those of PMF had significant deviations (R² of 0.484-0.998; total MSEs of 0.04-0.16). Nonetheless, both models failed to accurately apportion the sources with collinearity or non-normal distribution. Unmix generally outperformed PMF, and its solutions showed much less dependence on sample size than those of PMF. While the built-in indicators provided little hint on the reliability of both models for the real-world dataset, their sample-size dependence indicated that Unmix probably yielded more accurate solutions. These insights could help avoid the potential misuse of Unmix and PMF in source apportionment of soil heavy metal(loid) pollution.

On the usefulness of animals as a model system (part II): Considering benefits within distinct use domains

In many countries, animal experiments can only be performed when their necessity has been demonstrated in a legal document. As the usefulness of animals in research is also a significant societal and political issue, criteria to structure debates and evaluations are needed. Here, background information is given on laboratory animal studies. Moreover, parameters that may be considered in judging their usefulness are suggested. The discussion is strictly focused on animals used as tools/test systems/models to provide information on humans. In this context, general features and performance characteristics of models are discussed. Examples are given for well-recognized criteria (e.g., robustness, relevance, predictivity) to judge the usefulness of predictive models. The main hypothesis put forward here is that a benefits evaluation (usefulness metrics) is only possible within sharply circumscribed "use domains". Examples are given for the research fields of drug and vaccine research, toxicology, disease pathogenesis, and basic biological research. Efficacy, safety, and quality studies are highlighted as "use domains" within the field of drug discovery and production. A further separation into individual diseases, drug targets or symptoms is suggested for, e.g., efficacy studies or pathophysiology. Finally, an outlook is given on the evaluation of model advantages and disadvantages to arrive at their "net benefit". Moreover, the need to compare the net benefits of animal models versus that of their alternatives is highlighted.

Performance Validation of COVID-19 Self-Conduct Buccal and Nasal Swabs RTK-Antigen Diagnostic Kit

The antigen rapid diagnostic test (Ag-RDT) is an immunodiagnostic test that detects the presence of viral proteins (antigens) expressed by the COVID-19 virus in a sample from a patient's respiratory tract. This study focused on evaluating the performance of self-conduct buccal and nasal swabs RTK-antigen test compared to nasopharyngeal swab RTK-based COVID-19 diagnostic assays, Panbio™ COVID-19 Ag Rapid Test Device (Nasopharyngeal) (Abbott Rapid Diagnostics Jena GmbH, Jena, Germany) used in hospitals for first-line screening. The sensitivity and specificity of the paired RTK-Ag test in detecting the an-tigen were calculated at 96.4% and 100%, respectively. Fisher exact tests showed the association between nasopharyngeal swabs RTK-Ag assay and buccal-nasal swabs RTK-Ag from ProdetectTM is significant (p-values < 0.001). The result showed that a self-conducted buccal and nasal RTK-antigen rapid test by the patients is comparable to the results obtained from a rapid test device conducted by trained medical personnel using a nasopharyngeal swab.

Validation of a New Contactless and Continuous Respiratory Rate Monitoring Device Based on Ultra-Wideband Radar Technology

Respiratory rate (RR) is typically the first vital sign to change when a patient decompensates. Despite this, RR is often monitored infrequently and inaccurately. The Circadia Contactless Breathing Monitor™ (model C100) is a novel device that uses ultra-wideband radar to monitor RR continuously and un-obtrusively. Performance of the Circadia Monitor was assessed by direct comparison to manually scored reference data. Data were collected across a range of clinical and non-clinical settings, considering a broad range of user characteristics and use cases, in a total of 50 subjects. Bland-Altman analysis showed high agreement with the gold standard reference for all study data, and agreement fell within the predefined acceptance criteria of ±5 breaths per minute (BrPM). The 95% limits of agreement were -3.0 to 1.3 BrPM for a nonprobability sample of subjects while awake, -2.3 to 1.7 BrPM for a clinical sample of subjects while asleep, and -1.2 to 0.7 BrPM for a sample of healthy subjects while asleep. Accuracy rate, using an error margin of ±2 BrPM, was found to be 90% or higher. Results demonstrate that the Circadia Monitor can effectively and efficiently be used for accurate spot measurements and continuous bedside monitoring of RR in low acuity settings, such as the nursing home or hospital ward, or for remote patient monitoring.

DEEPGENTM-A Novel Variant Calling Assay for Low Frequency Variants

Detection of genetic variants in clinically relevant genomic hot-spot regions has become a promising application of next-generation sequencing technology in precision oncology. Effective personalized diagnostics requires the detection of variants with often very low frequencies. This can be achieved by targeted, short-read sequencing that provides high sequencing depths. However, rare genetic variants can contain crucial information for early cancer detection and subsequent treatment success, an inevitable level of background noise usually limits the accuracy of low frequency variant calling assays. To address this challenge, we developed DEEPGEN^TM, a variant calling assay intended for the detection of low frequency variants within liquid biopsy samples. We processed reference samples with validated mutations of known frequencies (0%-0.5%) to determine DEEPGEN^TM's performance and minimal input requirements. Our findings confirm DEEPGEN^TM's effectiveness in discriminating between signal and noise down to 0.09% variant allele frequency and an LOD(90) at 0.18%. A superior sensitivity was also confirmed by orthogonal comparison to a commercially available liquid biopsy-based assay for cancer detection.

A Critical Review on Ultraviolet Disinfection Systems against COVID-19 Outbreak: Applicability, Validation, and Safety Considerations

The global health-threatening crisis from the COVID-19 pandemic, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), highlights the scientific and engineering potentials of applying ultraviolet (UV) disinfection technologies for biocontaminated air and surfaces as the major media for disease transmission. Nowadays, various environmental public settings worldwide, from hospitals and health care facilities to shopping malls and airports, are considering implementation of UV disinfection devices for disinfection of frequently touched surfaces and circulating air streams. Moreover, the general public utilizes UV sterilization devices for various surfaces, from doorknobs and keypads to personal protective equipment, or air purification devices with an integrated UV disinfection technology. However, limited understanding of critical UV disinfection aspects can lead to improper use of this promising technology. In this work, fundamentals of UV disinfection phenomena are addressed; furthermore, the essential parameters and protocols to guarantee the efficacy of the UV sterilization process in a human-safe manner are systematically elaborated. In addition, the latest updates from the open literature on UV dose requirements for incremental log removal of SARS-CoV-2 are reviewed remarking the advancements and existing knowledge gaps. This study, along with the provided illustrations, will play an essential role in the design and fabrication of effective, reliable, and safe UV disinfection systems applicable to preventing viral contagion in the current COVID-19 pandemic, as well as potential future epidemics.

Empirical Comparison and Analysis of Web-Based DNA N 4-Methylcytosine Site Prediction Tools

DNA N⁴-methylcytosine (4mC) is a crucial epigenetic modification involved in various biological processes. Accurate genome-wide identification of these sites is critical for improving our understanding of their biological functions and mechanisms. As experimental methods for 4mC identification are tedious, expensive, and labor-intensive, several machine learning-based approaches have been developed for genome-wide detection of such sites in multiple species. However, the predictions projected by these tools are difficult to quantify and compare. To date, no systematic performance comparison of 4mC tools has been reported. The aim of this study was to compare and critically evaluate 12 publicly available 4mC site prediction tools according to species specificity, based on a huge independent validation dataset. The tools 4mCCNN (Escherichia coli), DNA4mC-LIP (Arabidopsis thaliana), iDNA-MS (Fragaria vesca), DNA4mC-LIP and 4mCCNN (Drosophila melanogaster), and four tools for Caenorhabditis elegans achieved excellent overall performance compared with their counterparts. However, none of the existing methods was suitable for Geoalkalibacter subterraneus, Geobacter pickeringii, and Mus musculus, thereby limiting their practical applicability. Model transferability to five species and non-transferability to three species are also discussed. The presented evaluation will assist researchers in selecting appropriate prediction tools that best suit their purpose and provide useful guidelines for the development of improved 4mC predictors in the future.

Workflow for defining reference chemicals for assessing performance of in vitro assays

Instilling confidence in use of in vitro assays for predictive toxicology requires evaluation of assay performance. Performance is typically assessed using reference chemicals--compounds with defined activity against the test system target. However, developing reference chemical lists has historically been very resource-intensive. We developed a semi-automated process for selecting and annotating reference chemicals across many targets in a standardized format and demonstrate the workflow here. A series of required fields defines the potential reference chemical: the in vitro molecular target, pathway, or phenotype affected; and the chemical's mode (e.g. agonist, antagonist, inhibitor). Activity information was computationally extracted into a database from multiple public sources including non-curated scientific literature and curated chemical-biological databases, resulting in the identification of chemical activity in 2995 biological targets. Sample data from literature sources covering 54 molecular targets ranging from data-poor to data-rich was manually checked for accuracy. Precision rates were 82.7% from curated data sources and 39.5% from automated literature extraction. We applied the final reference chemical lists to evaluating performance of EPA's ToxCast program in vitro bioassays. The level of support, i.e. the number of independent reports in the database linking a chemical to a target, was found to strongly correlate with likelihood of positive results in the ToxCast assays, although individual assay performance had considerable variation. This overall approach allows rapid development of candidate reference chemical lists for a wide variety of targets that can facilitate performance evaluation of in vitro assays as a critical step in imparting confidence in alternative approaches.

Smart Collaborative Caching for Information-Centric IoT in Fog Computing

The significant changes enabled by the fog computing had demonstrated that Internet of Things (IoT) urgently needs more evolutional reforms. Limited by the inflexible design philosophy; the traditional structure of a network is hard to meet the latest demands. However, Information-Centric Networking (ICN) is a promising option to bridge and cover these enormous gaps. In this paper, a Smart Collaborative Caching (SCC) scheme is established by leveraging high-level ICN principles for IoT within fog computing paradigm. The proposed solution is supposed to be utilized in resource pooling, content storing, node locating and other related situations. By investigating the available characteristics of ICN, some challenges of such combination are reviewed in depth. The details of building SCC, including basic model and advanced algorithms, are presented based on theoretical analysis and simplified examples. The validation focuses on two typical scenarios: simple status inquiry and complex content sharing. The number of clusters, packet loss probability and other parameters are also considered. The analytical results demonstrate that the performance of our scheme, regarding total packet number and average transmission latency, can outperform that of the original ones. We expect that the SCC will contribute an efficient solution to the related studies.

Performance analysis of newly developed point-of-care hemoglobinometer (TrueHb) against an automated hematology analyzer (Sysmex XT 1800i) in terms of precision in hemoglobin measurement

INTRODUCTION

The aim of this study was to evaluate the performance of the newly developed handheld hemoglobinmeter (TrueHb) by comparing its performance against and an automated five-part hematology analyzer, Sysmex counter XT 1800i (Sysmex).

METHODS

Two hundred venous blood samples were subjected through their total hemoglobin evaluation on each device three times. The average of the three readings on each device was considered as their respective device values, that is, TrueHb values and Sysmex values. The two set of values were comparatively analyzed. The repeatability of the performance of TrueHb was also evaluated against Sysmex values.

RESULTS

The scatter plot of TrueHb values and Sysmex values showed linear distribution with positive correlations (r = 0.99). The intraclass correlation (ICC) values between the two set of values was found to be 0.995. Regression coefficients through origin, β, was found to be 0.995, with 95% confidence intervals (CI) ranging between 0.9900 and 1.0000. The mean difference in Bland-Altman plots of TrueHb values against the Sysmex values was found to be -0.02, with limits of agreement between -0.777 and 0.732 g/dL. Statistical analysis suggested good repeatability in results of TrueHb, having a low mean CV of 2.22, against 4.44, that of Sysmex values, and 95% confidence interval of 1.99-2.44, against 3.85-5.03, that of Sysmex values.

CONCLUSION

These results suggested a strong positive correlation between the two measurements devices. It is thus concluded that TrueHb is a good point-of-care testing tool for estimating hemoglobin.