Total Laboratory Automation and Three Shifts Reduce Turnaround Time of Cerebrospinal Fluid Culture Results in the Chinese Clinical Microbiology Laboratory

BackgroundTotal laboratory automation (TLA) has the potential to reduce specimen processing time, optimize workflow, and decrease turnaround time (TAT). The purpose of this research is to investigate whether the TAT of our laboratory has changed since the adoption of TLA, as well as to optimize laboratory workflow, improve laboratory testing efficiency, and provide better services of clinical diagnosis and treatment.Materials and MethodsLaboratory data was extracted from our laboratory information system in two 6-month periods: pre-TLA (July to December 2019) and post-TLA (July to December 2020), respectively.ResultsThe median TAT for positive cultures decreased significantly from pre-TLA to post-TLA (65.93 vs 63.53, P<0.001). For different types of cultures, The TAT of CSF changed the most (86.76 vs 64.30, P=0.007), followed by sputum (64.38 vs 61.41, P<0.001), urine (52.10 vs 49,57, P<0.001), blood (68.49 vs 66.60, P<0.001). For Ascites and Pleural fluid, there was no significant difference (P>0.05). Further analysis found that the incidence of broth growth only for pre-TLA was 12.4% (14/133), while for post-TLA, it was 3.4% (4/119). The difference was statistically significant (P=0.01). The common isolates from CSF samples were Cryptococcus neoformans, coagulase-negative Staphylococcus, Acinetobacter baumannii, and Klebsiella pneumonia.ConclusionUsing TLA and setting up three shifts shortened the TAT of our clinical microbiology laboratory, especially for CSF samples.

Enabling modular autonomous feedback-loops in materials science through hierarchical experimental laboratory automation and orchestration

Materials acceleration platforms (MAPs) operate on the paradigm of integrating combinatorial synthesis, high-throughput characterization, automatic analysis, and machine learning. Within these MAPs, one or multiple autonomous feedback loops may aim to optimize materials for certain functional properties or generate new insights. The scope of a given experiment campaign is defined by the range of experiment and analysis actions that are integrated into the experiment framework. Herein we present a method for integrating many actions within a hierarchical experimental laboratory automation and orchestration (HELAO) framework. We demonstrate the capability of orchestrating distributed research instruments that can incorporate data from experiments, simulations, and databases. HELAO interfaces laboratory hardware and software that are distributed across several computers and operating systems for executing experiments, data analysis, provenance tracking, and autonomous planning. Parallelization is an effective approach for accelerating knowledge generation provided that multiple instruments can be effectively coordinated, which we demonstrate with parallel electrochemistry experiments orchestrated by HELAO. Efficient implementation of autonomous research strategies requires device sharing, asynchronous multithreading, and full integration of data management in experiment orchestration, which to the best of our knowledge, is demonstrated for the first time herein.

Sample Preparation and Diagnostic Methods for a Variety of Settings: A Comprehensive Review

Sample preparation is an essential step for nearly every type of biochemical analysis in use today. Among the most important of these analyses is the diagnosis of diseases, since their treatment may rely greatly on time and, in the case of infectious diseases, containing their spread within a population to prevent outbreaks. To address this, many different methods have been developed for use in the wide variety of settings for which they are needed. In this work, we have reviewed the literature and report on a broad range of methods that have been developed in recent years and their applications to point-of-care (POC), high-throughput screening, and low-resource and traditional clinical settings for diagnosis, including some of those that were developed in response to the coronavirus disease 2019 (COVID-19) pandemic. In addition to covering alternative approaches and improvements to traditional sample preparation techniques such as extractions and separations, techniques that have been developed with focuses on integration with smart devices, laboratory automation, and biosensors are also discussed.

Automation in small labs

Objectives The following report describes the development and implementation of a small-lab automation solution for small hospitals. Methods It uses a new generation of collaborative robots instead of the traditional laboratory automation lines with their input and output units and connected analyzers. After the Proof of Concept during fall 2018, both a centrifuge and several routine analyzers were integrated. Results The run-up phase ended after successful test operations in continuous mode in 2019. Routine operations were launched in October 2020 in a MVZ routine lab after a delay caused by the pandemic. Conclusions Apart from the direct cost savings for night duty or compensation for the lack of personnel, the mentioned solution delivers a significant upgrading of the laboratory technicians’ activities and an improvement in their working conditions.