Retrospective evaluation of clinical decision support for within-laboratory optimization of SARS-CoV-2 NAAT workflow.

The unprecedented demand for severe acute respiratory syndrome coronavirus 2 (SARS­CoV­2) testing led to challenges in prioritizing and processing specimens efficiently. We describe and evaluate a novel workflow using provider- and patient-facing ask at order entry (AOE) questions to generate distinctive icons on specimen labels for within-laboratory clinical decision support (CDS) for specimen triaging. A multidisciplinary committee established target turnaround times (TATs) for SARS-CoV-2 nucleic acid amplification test (NAAT) based on common clinical scenarios. A set of AOE questions was used to collect relevant clinical information that prompted icon generation for triaging SARS-CoV-2 NAAT specimens. We assessed the collect-to-verify TATs among relevant clinical scenarios. Our study included a total of 1,385,813 SARS-CoV-2 NAAT conducted from March 2020 to June 2022. Most testing met the TAT targets established by institutional committees, but deviations from target TATs occurred during periods of high demand and supply shortages. Median TATs for emergency department (ED) and inpatient specimens and ambulatory pre-procedure populations were stable over the pandemic. However, healthcare worker and other ambulatory test TATs varied substantially, depending on testing volume and community transmission rates. Median TAT significantly differed throughout the pandemic for ED and inpatient clinical scenarios, and there were significant differences in TAT among label icon-signified ambulatory clinical scenarios. We describe a novel approach to CDS for triaging specimens within the laboratory. The use of CDS tools could help clinical laboratories prioritize and process specimens efficiently, especially during times of high demand. Further studies are needed to evaluate the impact of our CDS tool on overall laboratory efficiency and patient outcomes. IMPORTANCE We describe a novel approach to clinical decision support (CDS) for triaging specimens within the clinical laboratory for severe acute respiratory syndrome coronavirus 2 (SARS­CoV­2) nucleic acid amplification tests (NAAT). The use of our CDS tool could help clinical laboratories prioritize and process specimens efficiently, especially during times of high demand. There were significant differences in the turnaround time for specimens differentiated by icons on specimen labels. Further studies are needed to evaluate the impact of our CDS tool on overall laboratory efficiency and patient outcomes.

Biomarkers vs Machines: The Race to Predict Acute Kidney Injury.

BACKGROUND: Acute kidney injury (AKI) is a serious complication affecting up to 15% of hospitalized patients. Early diagnosis is critical to prevent irreversible kidney damage that could otherwise lead to significant morbidity and mortality. However, AKI is a clinically silent syndrome, and current detection primarily relies on measuring a rise in serum creatinine, an imperfect marker that can be slow to react to developing AKI. Over the past decade, new innovations have emerged in the form of biomarkers and artificial intelligence tools to aid in the early diagnosis and prediction of imminent AKI. CONTENT: This review summarizes and critically evaluates the latest developments in AKI detection and prediction by emerging biomarkers and artificial intelligence. Main guidelines and studies discussed herein include those evaluating clinical utilitiy of alternate filtration markers such as cystatin C and structural injury markers such as neutrophil gelatinase-associated lipocalin and tissue inhibitor of metalloprotease 2 with insulin-like growth factor binding protein 7 and machine learning algorithms for the detection and prediction of AKI in adult and pediatric populations. Recommendations for clinical practices considering the adoption of these new tools are also provided. SUMMARY: The race to detect AKI is heating up. Regulatory approval of select biomarkers for clinical use and the emergence of machine learning algorithms that can predict imminent AKI with high accuracy are all promising developments. But the race is far from being won. Future research focusing on clinical outcome studies that demonstrate the utility and validity of implementing these new tools into clinical practice is needed.

From Data to Wisdom: Biomedical Knowledge Graphs for Real-World Data Insights.

Graph data models are an emerging approach to structure clinical and biomedical information. These models offer intriguing opportunities for novel approaches in healthcare, such as disease phenotyping, risk prediction, and personalized precision care. The combination of data and information in a graph model to create knowledge graphs has rapidly expanded in biomedical research, but the integration of real-world data from the electronic health record has been limited. To broadly apply knowledge graphs to EHR and other real-world data, a deeper understanding of how to represent these data in a standardized graph model is needed. We provide an overview of the state-of-the-art research for clinical and biomedical data integration and summarize the potential to accelerate healthcare and precision medicine research through insight generation from integrated knowledge graphs.

Drosophila Evolution over Space and Time (DEST): A New Population Genomics Resource.

Drosophila melanogaster is a leading model in population genetics and genomics, and a growing number of whole-genome data sets from natural populations of this species have been published over the last years. A major challenge is the integration of disparate data sets, often generated using different sequencing technologies and bioinformatic pipelines, which hampers our ability to address questions about the evolution of this species. Here we address these issues by developing a bioinformatics pipeline that maps pooled sequencing (Pool-Seq) reads from D. melanogaster to a hologenome consisting of fly and symbiont genomes and estimates allele frequencies using either a heuristic (PoolSNP) or a probabilistic variant caller (SNAPE-pooled). We use this pipeline to generate the largest data repository of genomic data available for D. melanogaster to date, encompassing 271 previously published and unpublished population samples from over 100 locations in >20 countries on four continents. Several of these locations have been sampled at different seasons across multiple years. This data set, which we call Drosophila Evolution over Space and Time (DEST), is coupled with sampling and environmental metadata. A web-based genome browser and web portal provide easy access to the SNP data set. We further provide guidelines on how to use Pool-Seq data for model-based demographic inference. Our aim is to provide this scalable platform as a community resource which can be easily extended via future efforts for an even more extensive cosmopolitan data set. Our resource will enable population geneticists to analyze spatiotemporal genetic patterns and evolutionary dynamics of D. melanogaster populations in unprecedented detail.

Artificial Intelligence and Mapping a New Direction in Laboratory Medicine: A Review.

BACKGROUND: Modern artificial intelligence (AI) and machine learning (ML) methods are now capable of completing tasks with performance characteristics that are comparable to those of expert human operators. As a result, many areas throughout healthcare are incorporating these technologies, including in vitro diagnostics and, more broadly, laboratory medicine. However, there are limited literature reviews of the landscape, likely future, and challenges of the application of AI/ML in laboratory medicine. CONTENT: In this review, we begin with a brief introduction to AI and its subfield of ML. The ensuing sections describe ML systems that are currently in clinical laboratory practice or are being proposed for such use in recent literature, ML systems that use laboratory data outside the clinical laboratory, challenges to the adoption of ML, and future opportunities for ML in laboratory medicine. SUMMARY: AI and ML have and will continue to influence the practice and scope of laboratory medicine dramatically. This has been made possible by advancements in modern computing and the widespread digitization of health information. These technologies are being rapidly developed and described, but in comparison, their implementation thus far has been modest. To spur the implementation of reliable and sophisticated ML-based technologies, we need to establish best practices further and improve our information system and communication infrastructure. The participation of the clinical laboratory community is essential to ensure that laboratory data are sufficiently available and incorporated conscientiously into robust, safe, and clinically effective ML-supported clinical diagnostics.

Applications of Digital Microscopy and Densely Connected Convolutional Neural Networks for Automated Quantification of Babesia-Infected Erythrocytes.

BACKGROUND: Clinical babesiosis is diagnosed, and parasite burden is determined, by microscopic inspection of a thick or thin Giemsa-stained peripheral blood smear. However, quantitative analysis by manual microscopy is subject to error. As such, methods for the automated measurement of percent parasitemia in digital microscopic images of peripheral blood smears could improve clinical accuracy, relative to the predicate method. METHODS: Individual erythrocyte images were manually labeled as "parasite" or "normal" and were used to train a model for binary image classification. The best model was then used to calculate percent parasitemia from a clinical validation dataset, and values were compared to a clinical reference value. Lastly, model interpretability was examined using an integrated gradient to identify pixels most likely to influence classification decisions. RESULTS: The precision and recall of the model during development testing were 0.92 and 1.00, respectively. In clinical validation, the model returned increasing positive signal with increasing mean reference value. However, there were 2 highly erroneous false positive values returned by the model. Further, the model incorrectly assessed 3 cases well above the clinical threshold of 10%. The integrated gradient suggested potential sources of false positives including rouleaux formations, cell boundaries, and precipitate as deterministic factors in negative erythrocyte images. CONCLUSIONS: While the model demonstrated highly accurate single cell classification and correctly assessed most slides, several false positives were highly incorrect. This project highlights the need for integrated testing of machine learning-based models, even when models in the development phase perform well.

Biological Effects of Single-Nucleotide Polymorphisms in the Drosophila melanogaster Malic Enzyme Locus.

A pair of amino acid polymorphisms within the Drosophila melanogaster Malic enzyme (Men) locus presents an interesting case of genetic variation that appears to be under selection. The two alleles at each site are biochemically distinct, but their biological effects are unknown. One polymorphic site is near the active site and the other is buried within the protein. Strikingly, in twelve different populations, the first polymorphism is always found at approximately a 50:50 allelic frequency, whereas the second polymorphism is always found at approximately 90:10. The consistency of the frequencies between populations suggests that the polymorphisms are under selection and it is possible that balancing selection is at play. We used 16 lines of flies to create the nine genotypes needed to quantify both effects of the polymorphic sites and possible genetic background effects, which we found to be widespread. The alleles at each site differ, but in different biochemical characteristics. The first site significantly influences MEN Km and Vmax, whereas the second site affects the Km and the Vmax/Km ratio (relative activity). Interestingly, the rarest allele is the most biochemically distinct. We also assayed three more distal phenotypes, triglyceride concentration, carbohydrate concentration, and longevity. In all cases, the phenotypes of the heterozygous genotypes are intermediate between those of the respective homozygotes suggesting that if balancing selection is maintaining the observed allele frequencies it is not through non-linear combinations of the biochemical phenotypes.

Agile Health Care Analytics: Enabling Real-Time Disease Surveillance With a Computational Health Platform.

The ongoing coronavirus disease outbreak demonstrates the need for novel applications of real-time data to produce timely information about incident cases. Using health information technology (HIT) and real-world data, we sought to produce an interface that could, in near real time, identify patients presenting with suspected respiratory tract infection and enable monitoring of test results related to specific pathogens, including severe acute respiratory syndrome coronavirus 2. This tool was built upon our computational health platform, which provides access to near real-time data from disparate HIT sources across our health system. This combination of technology allowed us to rapidly prototype, iterate, and deploy a platform to support a cohesive organizational response to a rapidly evolving outbreak. Platforms that allow for agile analytics are needed to keep pace with evolving needs within the health care system.

Optimization of Turnaround Time for Group A Streptococcus PCR.

The use of some nucleic acid amplification tests (NAATs) for the diagnosis of group A Streptococcus (GAS) pharyngitis allows laboratories to adopt single-tiered testing without reflex culture. However, centralization may delay the delivery of actionable information to the bedside, particularly in the outpatient setting. We describe two novel workflows at our institution and their effect on in-lab turnaround time (TAT) at a tertiary care microbiology lab. Laboratory records were extracted, and relevant data were analyzed after the implementation of qualitative in vitro diagnostic testing for GAS with the Xpert Xpress Strep A assay, performed using the GeneXpert Infinity-48s. Workflow optimization steps studied included: (i) direct specimen submission to the microbiology laboratory via the pneumatic tube system and (ii) autoverification of GAS NAAT results in the laboratory information system. Between April 2018 and October 2018, 2,595 unique specimens were tested for GAS by PCR. Of these, 2,523 were included in the final analysis. Linear regression established that the total in-lab TAT was significantly reduced by direct specimen submission to the microbiology laboratory, autoverification, and processing during the night shift. We describe two workflow optimization methods that reduced the in-lab TAT for GAS NAAT. Although microbiology labs historically use manual processes, the advent of total laboratory automation and the adoption of on-demand NAATs will allow for more streamlined processing of microbiology specimens. It may be beneficial to consider instrument interfacing and specimen processing optimization during the early phases of implementation planning for NAATs in the microbiology laboratory.

Cystathionine gamma-lyase/hydrogen sulfide system is essential for adipogenesis and fat mass accumulation in mice.

Hydrogen sulfide (H2S) has been recognized as an important gasotransmitter analogous to nitric oxide and carbon monoxide. Cystathionine gamma-lyase (CSE)-derived H2S is implicated in the regulation of insulin resistance and glucose metabolism, but the involvement of CSE/H2S system in energy homeostasis and fat mass has not been extensively explored. In this study, a potential functional role of the CSE/H2S system in in vitro adipocyte differentiation and in vivo adipogenesis and the underlying mechanism was investigated. CSE expression and H2S production were increased during adipocyte differentiation, and that the pattern of CSE mRNA expression was similar to that of CCAAT/enhancer-binding protein (C/EBP) ß and δ, two key regulators for adipogenesis. C/EBPß and γ bind to the CCAAT box in CSE promoter and stimulate CSE gene transcription. H2S induced PPARγ transactivation activity by S-sulfhydrating all the cysteine residues in the DNA binding domain and stimulated adipogenesis. High fat diet-induced fat mass was lost in CSE deficient mice, and exogenously applied H2S promoted fat mass accumulation in fruit flies. In conclusion, CSE/H2S system is essential for adipogenesis and fat mass accumulation through enhancement of PPARγ function in adipocytes. This study suggests that the CSE/H2S system is involved in the pathogenesis of obesity in mice.

Iodospirocyclization of Tryptamine-Derived Isocyanides: Formal Total Synthesis of Aspidofractinine.

The N-iodosuccinimide-mediated spirocyclization of tryptamine-derived isocyanides to generate spiroindolenines is reported. The products contain both an imine and an imidoyl iodide as flexible handles for follow-up chemistry. Nucleophilic addition typically occurs chemoselectively on the imine moiety with complete diastereoselectivity, providing opportunities for the construction of complex molecular frameworks. The synthetic potential of the method was showcased in the formal total synthesis of (±)-aspidofractinine.

The BIG 2.04 MRC/EORTC SUPREMO Trial: pathology quality assurance of a large phase 3 randomised international clinical trial of postmastectomy radiotherapy in intermediate-risk breast cancer.

INTRODUCTION: SUPREMO is a phase 3 randomised trial evaluating radiotherapy post-mastectomy for intermediate-risk breast cancer. 1688 patients were enrolled from 16 countries between 2006 and 2013. We report the results of central pathology review carried out for quality assurance. PATIENTS AND METHODS: A single recut haematoxylin and eosin (H&E) tumour section was assessed by one of two reviewing pathologists, blinded to the originally reported pathology and patient data. Tumour type, grade and lymphovascular invasion were reviewed to assess if they met the inclusion criteria. Slides from potentially ineligible patients on central review were scanned and reviewed online together by the two pathologists and a consensus reached. A subset of 25 of these cases was double-reported independently by the pathologists prior to the online assessment. RESULTS: The major contributors to the trial were the UK (75%) and the Netherlands (10%). There is a striking difference in lymphovascular invasion (LVi) rates (41.6 vs. 15.1% (UK); p = <0.0001) and proportions of grade 3 carcinomas (54.0 vs. 42.0% (UK); p = <0.0001) on comparing local reporting with central review. There was no difference in the locally reported frequency of LVi rates in node-positive (N+) and node-negative (N-) subgroups (40.3 vs. 38.0%; p = 0.40) but a significant difference in the reviewed frequency (16.9 vs. 9.9%; p = 0.004). Of the N- cases, 104 (25.1%) would have been ineligible by initial central review by virtue of grade and/or lymphovascular invasion status. Following online consensus review, this fell to 70 cases (16.3% of N- cases, 4.1% of all cases). CONCLUSIONS: These data have important implications for the design, powering and interpretation of outcomes from this and future clinical trials. If critical pathology criteria are determinants for trial entry, serious consideration should be given to up-front central pathology review.

Very Deep Convolutional Neural Networks for Morphologic Classification of Erythrocytes.

BACKGROUND: Morphologic profiling of the erythrocyte population is a widely used and clinically valuable diagnostic modality, but one that relies on a slow manual process associated with significant labor cost and limited reproducibility. Automated profiling of erythrocytes from digital images by capable machine learning approaches would augment the throughput and value of morphologic analysis. To this end, we sought to evaluate the performance of leading implementation strategies for convolutional neural networks (CNNs) when applied to classification of erythrocytes based on morphology. METHODS: Erythrocytes were manually classified into 1 of 10 classes using a custom-developed Web application. Using recent literature to guide architectural considerations for neural network design, we implemented a "very deep" CNN, consisting of >150 layers, with dense shortcut connections. RESULTS: The final database comprised 3737 labeled cells. Ensemble model predictions on unseen data demonstrated a harmonic mean of recall and precision metrics of 92.70% and 89.39%, respectively. Of the 748 cells in the test set, 23 misclassification errors were made, with a correct classification frequency of 90.60%, represented as a harmonic mean across the 10 morphologic classes. CONCLUSIONS: These findings indicate that erythrocyte morphology profiles could be measured with a high degree of accuracy with "very deep" CNNs. Further, these data support future efforts to expand classes and optimize practical performance in a clinical environment as a prelude to full implementation as a clinical tool.

Seasonal variation in an acid mine drainage microbial community.

Environmental oxidation and microbial metabolism drive production of acid mine drainage (AMD). Understanding changes in the microbial community, due to geochemical and seasonal characteristics, is fundamental to AMD monitoring and remediation. Using direct sequencing of the 16S and 18S rRNA genes to identify bacterial, archaeal, and eukaryotic members of the microbial community at an AMD site in Northern Ontario, Canada, we found a dynamic community varying significantly across winter and summer sampling times. Community composition was correlated with physical and chemical properties, including water temperature, pH, conductivity, winter ice thickness, and metal concentrations. Within Bacteria, Acidithiobacillus was the dominant genus during winter (11%-57% of sequences) but Acidiphilium was dominant during summer (47%-87%). Within Eukarya, Chrysophyceae (1.5%-94%) and Microbotrymycetes (8%-92%) dominated the winter community, and LKM11 (4%-62%) and Chrysophyceae (25%-87%) the summer. There was less diversity and variability within the Archaea, with similar summer and winter communities mainly comprising Thermoplasmata (33%-64%) and Thermoprotei (5%-20%) classes but also including a large portion of unclassified reads (∼40%). Overall, the active AMD community varied significantly between winter and summer, with changing community profiles closely correlated to specific differences in AMD geochemical and physical properties, including pH, water temperature, ice thickness, and sulfate and metal concentrations.

Knee contact forces and lower extremity support moments during running in young individuals post-partial meniscectomy.

PURPOSE: While partial meniscectomy results in a compromised tibiofemoral joint, little is known regarding tibiofemoral joint loading during running in individuals who are post-partial meniscectomy. It was hypothesized that individuals post-partial meniscectomy would run with a greater hip support moment, yielding reduced peak knee extension moments and reduced tibiofemoral joint contact forces. METHODS: 3-D Treadmill running mechanics were evaluated in 23 athletic individuals post-partial meniscectomy (37.5 ± 19.0 months post-partial meniscectomy) and 23 matched controls. Bilateral hip, knee and ankle contributions to the total support moment and the peak knee extension moment were calculated. A musculoskeletal model estimated peak and impulse tibiofemoral joint contact forces. Knee function was quantified with the Knee injury and Osteoarthritis Outcome Score (KOOS). RESULTS: During running, the partial meniscectomy group had a greater hip support moment (p = 0.002) and a reduced knee support moment (p < 0.001) relative to the total support moment. This movement pattern was associated with a 14.5 % reduction (p = 0.019) in the peak knee extension moment. Despite these differences, there were no significant group differences in peak or impulse tibiofemoral joint contact forces. Lower KOOS Quality of Life scores were associated with greater hip support moment (p = 0.004, r = -0.58), reduced knee support moment (p = 0.006, r = 0.55) and reduced peak knee extension moment (p = 0.01, r = 0.52). CONCLUSIONS: Disordered running mechanics are present long term post-partial meniscectomy. A coordination strategy that shifts a proportion of the total support moment away from the knee to the hip reduces the peak knee extension moment, but does not equate to reduced tibiofemoral joint contact forces during running in individuals post-partial meniscectomy. LEVEL OF EVIDENCE: III.