Not Carfentanil-A Case of Unexpected Xylazine Detection.

Historically, xylazine has been utilized in veterinary medicine for decades as an anesthetic and analgesic sedative to facilitate safe handling, diagnostic testing, and surgical procedures in large animals. Currently, xylazine is an emerging threat to human health. It has been detected in the illicit drug supply chain, often as an adulterant. It has been more commonly added to illicit substances, most notably fentanyl, by drugmakers to enhance drug effect. End users are often unaware of its presence. This is alarming given the large number of xylazine-involved overdose deaths while laboratory detections are deficient and reversal agents are absent. Herein, we present the first documented case of xylazine identified via gas chromatography-tandem mass spectrometry at University of California Davis Health despite a peculiarly mild clinical presentation. We hope to increase awareness of this potentially fatal adulterant that is often missed in evaluation and engender further opportunities to study this ongoing issue.

Evolving Applications of Artificial Intelligence and Machine Learning in Infectious Diseases Testing.

BACKGROUND: Artificial intelligence (AI) and machine learning (ML) are poised to transform infectious disease testing. Uniquely, infectious disease testing is technologically diverse spaces in laboratory medicine, where multiple platforms and approaches may be required to support clinical decision-making. Despite advances in laboratory informatics, the vast array of infectious disease data is constrained by human analytical limitations. Machine learning can exploit multiple data streams, including but not limited to laboratory information and overcome human limitations to provide physicians with predictive and actionable results. As a quickly evolving area of computer science, laboratory professionals should become aware of AI/ML applications for infectious disease testing as more platforms are become commercially available. CONTENT: In this review we: (a) define both AI/ML, (b) provide an overview of common ML approaches used in laboratory medicine, (c) describe the current AI/ML landscape as it relates infectious disease testing, and (d) discuss the future evolution AI/ML for infectious disease testing in both laboratory and point-of-care applications. SUMMARY: The review provides an important educational overview of AI/ML technique in the context of infectious disease testing. This includes supervised ML approaches, which are frequently used in laboratory medicine applications including infectious diseases, such as COVID-19, sepsis, hepatitis, malaria, meningitis, Lyme disease, and tuberculosis. We also apply the concept of "data fusion" describing the future of laboratory testing where multiple data streams are integrated by AI/ML to provide actionable clinical knowledge.

Investigation into the interference of the monoclonal antibody daratumumab on the free light chain assay.

OBJECTIVES: Recently monoclonal antibody therapy has been introduced in the treatment of multiple Myeloma (MM). One such efficacious therapy is the anti-CD38 monoclonal antibody, daratumumab (Dara). Since it is an Ig-G-kappa it can interfere with both the serum protein electrophoresis and immunofixation electrophoresis (IFE). The free light chain (FLC) assay is also useful in the diagnosis and therapeutic monitoring of MM. Hence we tested the effect of Dara on the FLC assay. METHODS: 30 serum samples from patients with known IgG-kappa (n=20) and non-IgG-kappa M -proteins (n=10) were spiked with Dara at a final concentration of 1.0mg/mL and the FLC performed on samples. On a further 20 samples we performed IFE to determine the migration of Dara. RESULTS: On IFE, Dara migrated in the same area of the gamma zone. In the 30 samples in which we assayed FLC there was no significant differences in levels of kappa, lambda and the ratio of kappa to lambda between untreated and Dara-spiked samples. CONCLUSION: Whilst Dara can interfere with the IFE to determine clinical responses the FLC assay can be useful in patients who have abnormal FLC ratios prior to Dara therapy to determine responses especially in IgG-kappa Myeloma.

The use of climatologies and Bayesian models to link observations to outcomes; an example from the Torres Strait.

After widespread coral bleaching in the Torres Strait in 2009-2010 a monitoring program was established under the National Environmental Research Program and run by the Torres Strait Regional Authority to identify ocean conditions that may lead to future bleaching. One component of this program was a real time ocean monitoring station located between Thursday and Horn Islands in the south-western part of the Torres Strait. A key outcome of the project was to make the scientific data and knowledge available to the local communities in a form that they could engage with and with which they could act to instigate outcomes relevant to their needs. The project developed climatologies to give context to the temperature data allowing for historical limits to define the significance of the real time data as related to the longer term mean. This allowed the identification of 'normal', 'significant' and 'extreme' temperature events which could be linked into appropriate responses. Bayesian models were used to encapsulate the current scientific knowledge about the drivers and responses involved in coral bleaching. These models were used to convert the environmental parameters to an output index reflecting the current and future likelihood of coral bleaching occurring. Two web sites were used to integrate the real time data, climatology data and the bleaching indices generated from the Bayesian models. The first was a more technical site developed for the local environmental managers within the Torres Strait Regional Authority, the second was targeted at the general public with a display located within the local radio station and broadcast on a daily basis. Engagement with the project has been high to the point where additional monitoring stations and data display kiosks are to be installed in the near future. The combination of climatologies to give context and conceptual models to embody system knowledge has allowed the project to go from delivering simple measurements to being able to deliver knowledge about the system in a format that engages the local community and that can be used to facilitate environmental management outcomes.

Lessons from the field--two years of deploying operational wireless sensor networks on the Great Barrier Reef.

Wireless Sensor Networks promised to do for observation systems what consumer electronics have done for areas like photography--drive down the price per observation (photograph), introduce new functionality and capabilities, and make, what had been a relatively exclusive set of technologies and capabilities, ubiquitous. While this may have been true for some terrestrial sensor networks there are issues in the marine environment that have limited the realization of ubiquitous cheap sensing. This paper reports on the lessons learned from two years of operation of wireless sensor networks deployed at seven coral reefs along the Great Barrier Reef in north-eastern Australia.