A phase I study to evaluate the safety, pharmacokinetics, and pharmacodynamics of PF-06939999 (PRMT5 inhibitor) in patients with selected advanced or metastatic tumors with high incidence of splicing factor gene mutations.

BACKGROUND: Protein arginine methyltransferase 5 (PRMT5) methylates multiple substrates dysregulated in cancer, including spliceosome machinery components. PF-06939999 is a selective small-molecule PRMT5 inhibitor. PATIENTS AND METHODS: This phase I dose-escalation and -expansion trial (NCT03854227) enrolled patients with selected solid tumors. PF-06939999 was administered orally once or twice a day (q.d./b.i.d.) in 28-day cycles. The objectives were to evaluate PF-06939999 safety and tolerability to identify maximum tolerated dose (MTD) and recommended part 2 dose (RP2D), and assess pharmacokinetics (PK), pharmacodynamics [changes in plasma symmetric dimethylarginine (SDMA) levels], and antitumor activities. RESULTS: In part 1 dose escalation, 28 patients received PF-06939999 (0.5 mg q.d. to 6 mg b.i.d.). Four of 24 (17%) patients reported dose-limiting toxicities: thrombocytopenia (n = 2, 6 mg b.i.d.), anemia (n = 1, 8 mg q.d.), and neutropenia (n = 1, 6 mg q.d.). PF-06939999 exposure increased with dose. Steady-state PK was achieved by day 15. Plasma SDMA was reduced at steady state (58%-88%). Modulation of plasma SDMA was dose dependent. No MTD was determined. In part 2 dose expansion, 26 patients received PF-06939999 6 mg q.d. (RP2D). Overall (part 1 + part 2), the most common grade ≥3 treatment-related adverse events included anemia (28%), thrombocytopenia/platelet count decreased (22%), fatigue (6%), and neutropenia (4%). Three patients (6.8%) had confirmed partial response (head and neck squamous cell carcinoma, n = 1; non-small-cell lung cancer, n = 2), and 19 (43.2%) had stable disease. No predictive biomarkers were identified. CONCLUSIONS: PF-06939999 demonstrated a tolerable safety profile and objective clinical responses in a subset of patients, suggesting that PRMT5 is an interesting cancer target with clinical validation. However, no predictive biomarker was identified. The role of PRMT5 in cancer biology is complex and requires further preclinical, mechanistic investigation to identify predictive biomarkers for patient selection.

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.

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.

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.

Understanding the role of imidazolium-based ionic liquids in the electrochemical CO2 reduction reaction.

The development of efficient CO2 capture and utilization technologies driven by renewable energy sources is mandatory to reduce the impact of climate change. Herein, seven imidazolium-based ionic liquids (ILs) with different anions and cations were tested as catholytes for the CO2 electrocatalytic reduction to CO over Ag electrode. Relevant activity and stability, but different selectivities for CO2 reduction or the side H2 evolution were observed. Density functional theory results show that depending on the IL anions the CO2 is captured or converted. Acetate anions (being strong Lewis bases) enhance CO2 capture and H2 evolution, while fluorinated anions (being weaker Lewis bases) favour the CO2 electroreduction. Differently from the hydrolytically unstable 1-butyl-3-methylimidazolium tetrafluoroborate, 1-Butyl-3-Methylimidazolium Triflate was the most promising IL, showing the highest Faradaic efficiency to CO (>95%), and up to 8 h of stable operation at high current rates (-20 mA & -60 mA), which opens the way for a prospective process scale-up.

Cybersecurity and Information Assurance for the Clinical Laboratory.

BACKGROUND: Network-connected medical devices have rapidly proliferated in the wake of recent global catalysts, leaving clinical laboratories and healthcare organizations vulnerable to malicious actors seeking to ransom sensitive healthcare information. As organizations become increasingly dependent on integrated systems and data-driven patient care operations, a sudden cyberattack and the associated downtime can have a devastating impact on patient care and the institution as a whole. Cybersecurity, information security, and information assurance principles are, therefore, vital for clinical laboratories to fully prepare for what has now become inevitable, future cyberattacks. CONTENT: This review aims to provide a basic understanding of cybersecurity, information security, and information assurance principles as they relate to healthcare and the clinical laboratories. Common cybersecurity risks and threats are defined in addition to current proactive and reactive cybersecurity controls. Information assurance strategies are reviewed, including traditional castle-and-moat and zero-trust security models. Finally, ways in which clinical laboratories can prepare for an eventual cyberattack with extended downtime are discussed. SUMMARY: The future of healthcare is intimately tied to technology, interoperability, and data to deliver the highest quality of patient care. Understanding cybersecurity and information assurance is just the first preparative step for clinical laboratories as they ensure the protection of patient data and the continuity of their operations.

A preliminary study of hydrocodone and hydromorphone to oxycodone ratios for distinguishing impurities from independent opioid use.

BACKGROUND: Urine drug testing (UDT) monitors prescription compliance and/or drug abuse. However, interpretation of UDT results obtained by liquid chromatography-tandem mass spectrometry (LC-MS-MS) can be complicated by the presence of drug impurities that are detected by highly sensitive methods. Hydrocodone is a drug impurity that can be found as high as 1% in oxycodone pills. OBJECTIVES: We evaluated the frequency and concentration of hydrocodone and its metabolite, hydromorphone, in patients taking oxycodone to check if the ratio of hydrocodone or hydromorphone to oxycodone could distinguish between oxycodone only use from those consuming additional opiates. DESIGN & METHODS: We correlated LC-MS/MS results with medication records of 319 patients with positive oxycodone results over 7 months (4/2021-11/2021). RESULTS: Fifteen of 319 patients with positive oxycodone results were taking oxycodone only. For these 15 patients, the mean ratio of hydrocodone to oxycodone was 0.57% (range 0.05%-3.35%), and the mean ratio of hydromorphone to oxycodone was 0.81% (range 0.18-3.51%). CONCLUSIONS: Hydrocodone and/or hydromorphone are detectable in patients taking only oxycodone and can likely be identified as an impurity if their calculated ratio to oxycodone is <1 %. Further validation of the ratios in a larger sample size is recommended.

Contamination of clinical blood samples with crystalloid solutions: An experimental approach to derive multianalyte delta checks.

BACKGROUND: Laboratorians are left unguided by a paucity of literature on how to configure rules for the detection of intravenous (IV) fluid contamination in blood samples. We designed a study to determine the in vitro effect of increasing blood sample contamination from commonly used crystalloid solutions and how these observations can guide the derivation of multianalyte delta checks to detect such pre-analytical error. METHODS: In this study, we spiked increasing volumes of commonly used IV fluids (normal saline (NS), lactated ringers (LR), and 5% dextrose) into blood samples that were collected from healthy donors. Routine chemistry analytes were measured and compared between neat and contrived samples. From these observations, we derived several permutations of multianalyte delta checks using the basic metabolic panel framework and evaluated rule performance using retrospective data. RESULTS: The wet chemistry experiments showed that increasing the volume of crystalloid solution contamination significantly changed several analytes. Subsequently derived multianalyte delta check procedures were applied to retrospective data. For all IV fluids tested, smaller magnitudes of analyte change resulted in more samples flagged. CONCLUSION: Multianalyte delta checks may be an effective method for the detection of IV fluid contamination.

Regulation at Drosophila's Malic Enzyme highlights the complexity of transvection and its sensitivity to genetic background.

Transvection, a type of trans-regulation of gene expression in which regulatory elements on one chromosome influence elements on a paired homologous chromosome, is itself a complex biological phenotype subject to modification by genetic background effects. However, relatively few studies have explored how transvection is affected by distal genetic variation, perhaps because it is strongly influenced by local regulatory elements and chromosomal architecture. With the emergence of the "hub" model of transvection and a series of studies showing variation in transvection effects, it is becoming clear that genetic background plays an important role in how transvection influences gene transcription. We explored the effects of genetic background on transvection by performing two independent genome wide association studies (GWASs) using the Drosophila genetic reference panel (DGRP) and a suite of Malic enzyme (Men) excision alleles. We found substantial variation in the amount of transvection in the 149 DGRP lines used, with broad-sense heritability of 0.89 and 0.84, depending on the excision allele used. The specific genetic variation identified was dependent on the excision allele used, highlighting the complex genetic interactions influencing transvection. We focussed primarily on genes identified as significant using a relaxed P-value cutoff in both GWASs. The most strongly associated genetic variant mapped to an intergenic single nucleotide polymorphism (SNP), located upstream of Tiggrin (Tig), a gene that codes for an extracellular matrix protein. Variants in other genes, such transcription factors (CG7368 and Sima), RNA binding proteins (CG10418, Rbp6, and Rig), enzymes (AdamTS-A, CG9743, and Pgant8), proteins influencing cell cycle progression (Dally and Eip63E) and signaling proteins (Atg-1, Axo, Egfr, and Path) also associated with transvection in Men. Although not intuitively obvious how many of these genes may influence transvection, some have been previously identified as promoting or antagonizing somatic homolog pairing. These results identify several candidate genes to further explore in the understanding of transvection in Men and in other genes regulated by transvection. Overall, these findings highlight the complexity of the interactions involved in gene regulation, even in phenotypes, such as transvection, that were traditionally considered to be primarily influenced by local genetic variation.

Advances in molecular infectious diseases testing in the time of COVID-19.

The Coronavirus Disease of 2019 (COVID-19) pandemic has been a challenging event for laboratory medicine and diagnostics manufacturers. We have had to confront numerous unique and previously unthinkable issues on a daily basis in order to continue offering diagnostic testing for not only Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2), but other testing that was significantly impacted by supply chain and staffing disruptions related to COVID-19. Out of this tremendously stressful and, at times, chaotic environment, decades of innovations and advances in testing methodologies and instrumentation became essential to handle the overwhelming volume of samples with clinically appropriate turn-around-time. Additionally, a number of novel testing approaches and technological innovations emerged to address laboratory and public health needs for widespread testing. In this review we consider both technological advances in infectious diseases testing and other innovations in sample collection, processing, automation, workflow, and testing that have embodied the laboratory response to the COVID-19 pandemic.

Investigation of a Fluorine-Free Phosphonium-Based Ionic Liquid Electrolyte and Its Compatibility with Lithium Metal.

A novel fluorine-free ionic liquid electrolyte comprising lithium dicyanamide (LiDCA) and trimethyl(isobutyl)phosphonium tricyanomethanide (P111i4TCM) in a 1:9 molar ratio is studied as an electrolyte for lithium metal batteries. At room temperature, it demonstrates high ionic conductivity and viscosity of about 4.5 mS cm-1 and 64.9 mPa s, respectively, as well as a 4 V electrochemical stability window (ESW). Li stripping/plating tests prove the excellent electrolyte compatibility with Li metal, evidenced by the remarkable cycling stability over 800 cycles. The evolution of the Li-electrolyte interface upon cycling was investigated via electrochemical impedance spectroscopy, displaying a relatively low impedance increase after the initial formation cycles. Finally, the solid electrolyte interphase (SEI) formed on Li metal appeared to have a bilayer structure mostly consisting of DCA and TCM reduction products. Additionally, decomposition products of the phosphonium cation were also detected, despite prior studies reporting its stability against Li metal.

Quality Management to Improve CSF Gram Stain Turnaround Time.

BACKGROUND: With appropriate turnaround time (TAT), cerebrospinal fluid (CSF) Gram stains can provide rapid, clinically actionable information in patients with meningitis. Monitoring CSF Gram stain TAT at our institution revealed workflow gaps that were causing result reporting delays. We then implemented a new quality management program to improve TAT. METHODS: We reviewed the TAT of all CSF specimens submitted for bacterial culture received between August 1, 2016, and July 31, 2020, and began prospectively monitoring CSF Gram stain TAT in January 2019. We then implemented the following changes in April 2019: (i) monthly reviews of TAT with staff, (ii) hand-off sheets for shift transitions, and (iii) pre- and post-shift walk throughs including centrifuge checks. RESULTS: A total of 6913 samples were included in the analysis. CSF samples with TAT > 60 min decreased from 27.3% to 9.89% (P < 0.0001), and median TAT decreased by 9 min (P < 0.0001) with significantly reduced variability. These changes were sustained throughout the follow-up period across all shifts and shift transitions. CONCLUSIONS: A new monthly quality metric allowed us to track CSF Gram stain TAT, identify barriers to TAT goals, and implement workflow changes that significantly improved TAT without the need for costly interventions.

Supervised machine learning in the mass spectrometry laboratory: A tutorial.

As the demand for laboratory testing by mass spectrometry increases, so does the need for automated methods for data analysis. Clinical mass spectrometry (MS) data is particularly well-suited for machine learning (ML) methods, which deal nicely with structured and discrete data elements. The alignment of these two fields offers a promising synergy that can be used to optimize workflows, improve result quality, and enhance our understanding of high-dimensional datasets and their inherent relationship with disease. In recent years, there has been an increasing number of publications that examine the capabilities of ML-based software in the context of chromatography and MS. However, given the historically distant nature between the fields of clinical chemistry and computer science, there is an opportunity to improve technological literacy of ML-based software within the clinical laboratory scientist community. To this end, we present a basic overview of ML and a tutorial of an ML-based experiment using a previously published MS dataset. The purpose of this paper is to describe the fundamental principles of supervised ML, outline the steps that are classically involved in an ML-based experiment, and discuss the purpose of good ML practice in the context of a binary MS classification problem.

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.

The Effects of Essential and Non-Essential Metal Toxicity in the Drosophila melanogaster Insect Model: A Review.

The biological effects of environmental metal contamination are important issues in an industrialized, resource-dependent world. Different metals have different roles in biology and can be classified as essential if they are required by a living organism (e.g., as cofactors), or as non-essential metals if they are not. While essential metal ions have been well studied in many eukaryotic species, less is known about the effects of non-essential metals, even though essential and non-essential metals are often chemically similar and can bind to the same biological ligands. Insects are often exposed to a variety of contaminated environments and associated essential and non-essential metal toxicity, but many questions regarding their response to toxicity remain unanswered. Drosophila melanogaster is an excellent insect model species in which to study the effects of toxic metal due to the extensive experimental and genetic resources available for this species. Here, we review the current understanding of the impact of a suite of essential and non-essential metals (Cu, Fe, Zn, Hg, Pb, Cd, and Ni) on the D. melanogaster metal response system, highlighting the knowledge gaps between essential and non-essential metals in D. melanogaster. This review emphasizes the need to use multiple metals, multiple genetic backgrounds, and both sexes in future studies to help guide future research towards better understanding the effects of metal contamination in general.

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.