Laboratory Automation Research Articles

Advancing the application of organoids, tissue-on-a-chip, and other emerging in vitro models to support cell and gene therapy

Jason Ekert PhD, MBA, is Senior Director, GSK Fellow and Head of the complex in vitro models (CIVM) group at GlaxoSmithKline. He is responsible for an integrated enterprise strategy for R&D applications of complex human-relevant and translatable complex in vitro models (e.g. Organoids, Microphysiological systems and bioprinting). Before joining GSK he worked at Janssen in biotherapeutic drug discovery. Jason received his PhD from Adelaide University. Post-doctoral training was performed at University of California, Davis and Coriell Institute for Medical Research. He’s currently the past chair for the IQ-MPS affiliate. He is a member of the society for lab automation and screening (SLAS) and society of toxicology (SOT).Marie Davies PhD, is a Senior Director in the Oncology Cell Therapy Research Unit in GSK and has more than 20 years of drug discovery experience in the biotechnology industry and large pharma. Dr Davies has extensive knowledge of drug discovery across biopharms, cell and gene therapies and vaccines with particular interest in the inter-relationships between safety, efficacy and manufacture. Throughout her career she has been accountable for delivering, reviewing and approving non-clinical safety and pharmacology packages to support non-regulatory project decisions and regulatory submissions. Dr Davies holds a PhD in Rheumatology and Immunology from University of Birmingham, UKPelin Candarlioglu PhD, is a tissue engineer by training having received her PhD in the field from Imperial College London but moved into oncology during her PostDoc position about circulating tumour cells at the UCL. Her introduction to organ on chip (OoC) was during the time when she was leading a Cell Biology/Microfluidics lab in Cambridge at Enplas Corporation where she was developing a microfluidic chip system specifically designed for immuno-oncology applications. Currently, as part of Complex In vitro Models (CIVM) group, she is leading a small team utilizing her expertise in microfluidics, tissue engineering and especially OoC to lead multiple initiatives both externally and internally to expand the complex in vitro model portfolio of GSK for immuno-oncology. Pelin is very active at the 3Rs initiative in GSK and supporting from reduce and replacement aspect. She is also representing GSK globally in relevant organizations such as NA3RsC MPS Initiative, IQ-MPS, NC3R, OOACT in UK and as chair of industry advisory board at EUROoCS.

Towards robotic laboratory automation Plug & Play: The "LAPP" framework.

Increasing the level of automation in pharmaceutical laboratories and production facilities plays a crucial role in delivering medicine to patients. However, the particular requirements of this field make it challenging to adapt cutting-edge technologies present in other industries. This article provides an overview of relevant approaches and how they can be utilized in the pharmaceutical industry, especially in development laboratories. Recent advancements include the application of flexible mobile manipulators capable of handling complex tasks. However, integrating devices from many different vendors into an end-to-end automation system is complicated due to the diversity of interfaces. Therefore, various approaches for standardization are considered in this article, and a concept is proposed for taking them a step further. This concept enables a mobile manipulator with a vision system to "learn" the pose of each device and - utilizing a barcode - fetch interface information from a universal cloud database. This information includes control and communication protocol definitions and a representation of robot actions needed to operate the device. In order to define the movements in relation to the device, devices have to feature - besides the barcode - a fiducial marker as standard. The concept will be elaborated following appropriate research activities in follow-up papers.

Digital Visual Sensing Design Teaching Using Digital Twins

Digital twin technology can support teachers in this major to complete monitoring related topics and application research and serve teaching and scientific research through the establishment of automatic monitoring teaching model laboratory and in-depth combination with the current students’ skill training needs in this major. This exploration aims to make the visual sensor industry have a steady stream of talents. Promoting the development of visual sensor technology is to promote the development of science and technology. Based on the teaching research of visual sensing design using digital technology, a set of teaching systems of visual sensing design is designed by using the methods of literature research and investigation and analysis. Special topics are set up from the aspects of professional subjects; regarding course content, various sensor principle research courses, visual sensor design courses, experimental courses, and example courses are set up; teaching methods are divided into online and offline synchronous classes; the evaluation method should focus on the distribution, examples, and practice. The results show that traditional classroom teaching is seriously separated from extracurricular learning. Most students are in a state of passive acceptance of knowledge and have few thinking activities. The established teaching system integrates brain and cognition, photoelectric foundation, sensory imaging, visual sensing imaging, visual sensing technology courses, computer technology, virtual experiment courses, and physical experiment courses. It can carry out more than 30 experiments of optical microscopic imaging and X-ray imaging based on the principle of visual sensing. Therefore, the teaching effect and teaching mode of the proposed digital visual sensing teaching system have been greatly improved.

A step towards optimal efficiency of HbA1c measurement as a first-line laboratory test: the TOP-HOLE (Towards OPtimal glycoHemOgLobin tEsting) project.

The TOP-HOLE (Towards OPtimal glycoHemOgLobin tEsting) project aimed to validate the HbA1c enzymatic method on the Abbott Alinity c platform and to implement the HbA1c testing process on the total laboratory automation (TLA) system of our institution. Three different measuring systems were employed: Architect c4000 stand-alone (s-a), Alinity c s-a, and Alinity c TLA. Eight frozen whole blood samples, IFCC value-assigned, were used for checking trueness. A comparison study testing transferability of HbA1c results from Architect to Alinity was also performed. The alignment of Alinity TLA vs. s-a was verified and the measurement uncertainty (MU) estimated according to ISO 20914:2019. Turnaround time (TAT) and full time equivalent (FTE) were used as efficiency indicators. For HbA1c concentrations covering cut-offs adopted in clinical setting, the bias for both Architect and Alinity s-a was negligible. When compared with Architect, Alinity showed a mean positive bias of 0.54mmol/mol, corresponding to a mean difference of 0.87%. A perfect alignment of Alinity TLA to the Alinity s-a was shown, and a MU of 1.58% was obtained, widely fulfilling the desirable 3.0% goal. After the full automation of HbA1c testing, 90% of results were released with a maximum TAT of 1h, 0.30 FTE resource was also saved. The traceability of Alinity HbA1c enzymatic assay to the IFCC reference system was correctly implemented. We successfully completed the integration of the HbA1c testing on our TLA system, without worsening the optimal analytical performance. The shift of HbA1c testing from s-a mode to TLA significantly decreased TAT.

From Platform to Knowledge Graph: Evolution of Laboratory Automation.

High-fidelity computer-aided experimentation is becoming more accessible with the development of computing power and artificial intelligence tools. The advancement of experimental hardware also empowers researchers to reach a level of accuracy that was not possible in the past. Marching toward the next generation of self-driving laboratories, the orchestration of both resources lies at the focal point of autonomous discovery in chemical science. To achieve such a goal, algorithmically accessible data representations and standardized communication protocols are indispensable. In this perspective, we recategorize the recently introduced approach based on Materials Acceleration Platforms into five functional components and discuss recent case studies that focus on the data representation and exchange scheme between different components. Emerging technologies for interoperable data representation and multi-agent systems are also discussed with their recent applications in chemical automation. We hypothesize that knowledge graph technology, orchestrating semantic web technologies and multi-agent systems, will be the driving force to bring data to knowledge, evolving our way of automating the laboratory.

S4 Features and Artificial Intelligence for Designing a Robot against COVID-19—Robocov

Since the COVID-19 Pandemic began, there have been several efforts to create new technology to mitigate the impact of the COVID-19 Pandemic around the world. One of those efforts is to design a new task force, robots, to deal with fundamental goals such as public safety, clinical care, and continuity of work. However, those characteristics need new products based on features that create them more innovatively and creatively. Those products could be designed using the S4 concept (sensing, smart, sustainable, and social features) presented as a concept able to create a new generation of products. This paper presents a low-cost robot, Robocov, designed as a rapid response against the COVID-19 Pandemic at Tecnologico de Monterrey, Mexico, with implementations of artificial intelligence and the S4 concept for the design. Robocov can achieve numerous tasks using the S4 concept that provides flexibility in hardware and software. Thus, Robocov can impact positivity public safety, clinical care, continuity of work, quality of life, laboratory and supply chain automation, and non-hospital care. The mechanical structure and software development allow Robocov to complete support tasks effectively so Robocov can be integrated as a technological tool for achieving the new normality’s required conditions according to government regulations. Besides, the reconfiguration of the robot for moving from one task (robot for disinfecting) to another one (robot for detecting face masks) is an easy endeavor that only one operator could do. Robocov is a teleoperated system that transmits information by cameras and an ultrasonic sensor to the operator. In addition, pre-recorded paths can be executed autonomously. In terms of communication channels, Robocov includes a speaker and microphone. Moreover, a machine learning algorithm for detecting face masks and social distance is incorporated using a pre-trained model for the classification process. One of the most important contributions of this paper is to show how a reconfigurable robot can be designed under the S3 concept and integrate AI methodologies. Besides, it is important that this paper does not show specific details about each subsystem in the robot.

Enabling Modular Autonomous Feedback‐Loops in Materials Science through Hierarchical Experimental Laboratory Automation and Orchestration

AbstractMaterials acceleration platforms (MAPs) operate on the paradigm of integrating combinatorial synthesis, high‐throughput characterization, automatic analysis, and machine learning. Within a MAP, one or multiple autonomous feedback loops may aim to optimize materials for certain functional properties or to 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, the authors present a method for integrating many actions within a hierarchical experimental laboratory automation and orchestration (HELAO) framework. They demonstrate the capability of orchestrating distributed research instruments that can incorporate data from experiments, simulations, and databases. HELAO interfaces laboratory hardware and software 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 the authors 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 experimental orchestration, which to the best of the authors’ knowledge, is demonstrated for the first time herein.

Economic Evaluation of Total Laboratory Automation in the Clinical Laboratory of a Tertiary Care Hospital.

BackgroundTotal laboratory automation (TLA) is an innovation in laboratory technology; however, the high up-front costs restrict its widespread adoption. To examine whether the capital investment for TLA is worthwhile, we analyzed its clinical- and cost-effectiveness for the expected payback period.MethodsClinical chemistry tests and immunoassays performed in the clinical laboratory of a tertiary care hospital were divided into a post-TLA group, including 1,182,419 tests performed during December 2019, and a pre-TLA group, including 1,151,501 tests performed during December 2018. Laboratory information system data were used to measure clinical effectiveness, and depreciation data were used to calculate TLA costs.ResultsLaboratory performance improved after TLA adoption in all four key performance indicators mean turn-around time (TAT), representing the timeliness of result reporting, decreased by 6.1%; the 99th percentile of TAT, representing the outlier rate, decreased by 13.3%; the TAT CV, representing predictability, decreased by 70.0%; and weighted tube touch moment (wTTM), representing staff safety, improved by 77.6%. Based on these effectiveness results, economic evaluation was performed using two approaches. First, the incremental cost-effectiveness ratio and wTTM were used as the most cost-effective performance indicators. Second, the expected payback period was calculated. Considering only staff cost reduction, it was anticipated that 4.75 yrs would be needed to payback the initial investment.ConclusionsTLA can significantly enhance laboratory performance, has a relatively quick payback period, and can reduce total hospital expenses in the long term. Therefore, the capital investment for TLA adoption is considered to be worthwhile.

Assessment of Total Laboratory Errors in Clinical Chemistry Laboratory: Experience at a Tertiary Care Hospital

Background: Efficient Laboratory services are the backbone of the modern healthcare facility. 70% of all medical decisions are based on laboratory data. We need to assess errors in the testing process to reduce their negative effect on patient outcomes and the entire healthcare sector. Objective: To assess Total Laboratory Errors (Pre-analytical, analytical, post-analytical) in the clinical chemistry laboratory. Materials and Methods: This cross-sectional study was conducted in Clinical Chemistry Laboratory, Sheikh Zayed Hospital, Rahim Yar Khan from 1st January to 30th June 2021. All blood samples received in Chemical Pathology Section for routine chemistry analysis in 6 months were included in the study using a consecutive sampling technique. 61891 blood samples were assessed for the following pre-analytical errors: Hemolysis, icterus, lipemia, incorrect order of draw, insufficient quantity, wrong tube, mislabeled specimen, missed patient information. Analytical errors were a violation of westgard rules while post-analytical errors included uninformed critical results, prolonged turnaround time and data transcription errors. Urine & other body fluids such as cerebrospinal fluid (CSF), ascitic fluid, pleural fluid, pericardial fluid received for chemical analysis were excluded. Data analysis was done using SPSS 16. Results: Out of the total laboratory errors 5574, pre-analytical errors were the most frequently occurring errors 4512 (80.95%) followed by post-analytical errors 1017 (18.25%) & analytical errors 45 (0.80%). The most frequently occurring pre-analytical error was hemolysis (64.38% of 80.95%). The most frequently occurring post-analytical error was uninformed critical results (70.79% of 18.25%). Conclusion: Pre-analytical errors have the highest percentage in total lab errors followed by post-analytical errors. Errors in the analytical phase are less due to Internal Quality Control & laboratory automation.

SiLA 2: The Next Generation Lab Automation Standard.

SiLA's mission is to establish international standards which create open connectivity in lab automation. SiLA's vision is to create interoperability, flexibility, and resource optimization for laboratory instrument integration and software services based on standardized communication protocols and data specifications. SiLA promotes open standards to allow integration and exchange of intelligent systems in a cost-effective and easy way.SiLA 2 is based on open, well-established communication protocols and defines a thin domain-specific layer on top of these, consisting of common concepts, vocabulary, and taxonomy.

Accelerating manufacturing for biomass conversion via integrated process and bench digitalization: a perspective

This article provides a vision on how to accelerate the production of chemicals and fuels from biomass feedstocks using an integrated framework of data mining, retrosynthesis, lab automation, and process systems engineering.

Process automation in a clinical laboratory of a Brazilian general hospital: co-creating value within the supply chain

Process automation in a clinical laboratory of a Brazilian general hospital: co-creating value within the supply chain

“A ROBUST METHOD FOR REDUCING SAMPLE RETRIEVAL TAT IN CLINICAL LABORATORY SETUP AT ZERO ADDED INVESTMENT.”

Background: The concept of Total Laboratory Automation (TLA) is shifting towards the automation in individual process i.e., Task Targeted automation (TTA). Sample storage is one of the key areas in the Total Testing Process (TTP), hence we developed a robust method to reduce the TAT for blood sample retrieval in clinical biochemistry laboratory with zero added investment. We compared the sample retrieval time (TAT) improvement in sample retrieval with the old conventional and the new method of sample storage. Material and method: study was conducted at a medium-large scale lab with average sample of 1000-1200 per day. The blood sample vacutainers are stored in the refrigerator at controlled temperature for the duration of 48hrs as the lab policy. We requested the technicians to retrieve 10 patient samples from the storage for the duration of 5 days, with different person allotted each day. The samples for day were stored and retrieved the requested sample by conventional & new method. Result: our new method has shown a good improvement in the TAT for the retrieval of the stored blood sample for the retesting or the added parameter for testing on request. The conventional method had an average time to identify and retrieve sample was 9.2mins, which was significantly decreased to 1.4mins with the new method of sample storage and retrieval system. Conclusion: Study emphasis this procedure / process will help the small and medium scale laboratories in having a robust method of sample storage and retrieval at a very short TAT with a minimal or no extra investment.

Current State of Laboratory Automation in Clinical Microbiology Laboratory.

Although it has been 30 years since the first automation systems were introduced in the microbiology laboratory, total laboratory automation (TLA) has only recently been recognized as a valuable component of the laboratory. A growing number of publications illustrate the potential impact of automation. TLA can improve standardization, increase laboratory efficiency, increase workplace safety, and reduce long-term costs. This review provides a preview of the current state of automation in clinical microbiology and covers the main developments during the last years. We describe the available hardware systems (that range from single function devices to multifunction workstations) and the challenging alterations on workflow and organization of the laboratory that have to be implemented to optimize automation. Despite the many advantages in efficiency, productivity, and timeliness that automation offers, it is not without new and unique challenges. For every advantage that laboratory automation provides, there are similar challenges that a laboratory must face. Change management strategies should be used to lead to a successful implementation. TLA represents, moreover, a substantial initial investment. Nevertheless, if properly approached, there are a number of important benefits that can be achieved through implementation of automation in the clinical microbiology laboratory. Future developments in the field of automation will likely focus on image analysis and artificial intelligence improvements. Patient care, however, should remain the epicenter of all future directions and there will always be a need for clinical microbiology expertise to interpret the complex clinical and laboratory information.

Matlab Based Potent Algorithm for WBc cancer Detection and classification

This paper aims to automate the detection of cancer using digital image processing techniques in MATLAB software. The analysis of white blood cells (WBC) is a powerful diagnostic tool for the prediction of Leukemia. The automatic detection of leukemia is a challenging task, which remains an unresolved problem in the medical imaging field. This Automation in Biological laboratories can be done by extracting the features of the blood film images taken from the digital microscopes and processed using MATLAB software. The aim of this approach is to discover the WBC cancer cells in an earlier stage and to reduce the discrepancies in diagnosis, by improving the system learning methodology. This paper presents the potent algorithm, which will eliminate the dubiety, in diagnosing the cancers with similar symptoms. This Algorithm concentrates on major WBC cancers, such as Acute Lymphocytic Leukemia, Acute Myeloid Leukemia, Chronic Lymphocytic Leukemia and Chronic Myeloid Leukemia. As they are life threatening diseases, rapid and precise differentiation is necessary in clinical settings. These cancers are categorized by segmentation and feature extraction, which will be further, classified using Random forest classification (RFC). RFC will classify the cancer using a decision tree learning method, which uses predictors at each node to make better decision.

AI-driven laboratory workflows enable operation in the age of social distancing.

The COVID-19 (Coronavirus disease 2019) global pandemic has upended the normal pace of society at multiple levels—from daily activities in personal and professional lives to the way the sciences operate. Many laboratories have reported shortage in vital supplies, change in standard operating protocols, suspension of operations because of social distancing and stay-at-home guidelines during the pandemic. This global crisis has opened opportunities to leverage internet of things, connectivity, and artificial intelligence (AI) to build a connected laboratory automation platform. However, laboratory operations involve complex, multicomponent systems. It is unrealistic to completely automate the entire diversity of laboratories and processes. Recently, AI technology, particularly, game simulation has made significant strides in modeling and learning complex, multicomponent systems. Here, we present a cloud-based laboratory management and automation platform which combines multilayer information on a simulation-driven inference engine to plan and optimize laboratory operations under various constraints of COVID-19 and risk scenarios. The platform was used to assess the execution of two cell-based assays with distinct parameters in a real-life high-content screening laboratory scenario. The results show that the platform can provide a systematic framework for assessing laboratory operation scenarios under different conditions, quantifying tradeoffs, and determining the performance impact of specific resources or constraints, thereby enabling decision-making in a cost-effective manner. We envisage the laboratory management and automation platform to be further expanded by connecting it with sensors, robotic equipment, and other components of scientific operations to provide an integrated, end-to-end platform for scientific laboratory automation.

Assessing the impact of automation in pharmaceutical quality control labs using a digital twin

Nowadays, pharmaceutical Quality Control (QC) laboratories have complex workflows where analysts test different samples simultaneously. Tests ensure the physical properties of drugs are expected and within guidelines. Each test follows an analytical procedure containing tasks. Cyber-Physical Production System (CPPS) improves tasks/operations; however, accurate cost analysis with reasoned data is challenging. Theoretical estimation of impacts requires a high level of abstraction and fails to capture the proper behavior of the workflow. This paper proposes a method for evaluating the introduction of automation in a pharmaceutical QC laboratory. In the proposed methodology, this paper developed a simulation model of the analytical workflow of the tests. The impact assessment compares the current As-Is and future To-Be workflows, reworking the affected tasks. The model of the new resource is a hybrid parallel process with an initial buffer. The paper analyses several scenarios on parameters such as throughput, resource occupation, and annual man-hours gained. The simulation model was validated against actual historical data and compared to theoretical projections on the impact of automation. From our results, we found the available equipment has a high impact. Using production data, we project an increase in the analyst availability of 4,7% and equipment availability of 1,2%.

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.

Drops join to make a stream: high-throughput nanoscale cultivation to grasp the lettuce root microbiome.

Root endospheres house complex and diverse bacterial communities, of which many strains have not been cultivated yet by means of the currently available isolation techniques. The Prospector® (General Automation Lab Technologies, San Carlos, CA, USA), an automated and high-throughput bacterial cultivation system, was applied to analyse the root endomicrobiome of lettuce (Lactuca sativa L.). By using deep sequencing, we compared the results obtained with the Prospector and the traditional solid medium culturing and extinction methods. We found that the species richness did not differ and that the amount of previously uncultured bacteria did not increase, but that the bacterial diversity isolated by the three methods varied. In addition, the tryptic soy broth and King's B media provided a lower, but different, diversity of bacteria than that of Reasoner's 2A (R2A) medium when used within the Prospector system and the number of unique bacterial strains did not weigh up against those isolated with the R2A medium. Thus, to cultivate as broad a variety of bacteria as possible, divergent isolation techniques should be used in parallel. Thanks to its speed and limited manual requirements, the Prospector is a valuable system to enlarge root microbiome culture collections.

Copan Walk Away Specimen Processor (WASP) Automated System for Pathogen Detection in Female Reproductive Tract Specimens

ObjectiveAutomation is increasingly being applied in clinical laboratories; however, preanalytical processing for microbiology tests and screening is still largely performed using manual methods owing to the complex procedures involved. To promote automation of clinical microbiology laboratories, it is important to assess the performance of automated systems for different specimen types separately. Therefore, the aim of this study was to explore the potential clinical application of the Copan Walk Away Specimen Processor (WASP) automated preanalytical microbiology processing system in the detection of pathogens in female reproductive tract specimens and its feasibility in optimizing diagnostic procedures.MethodsFemale reproductive tract specimens collected from pregnant women at their first obstetric check-up were inoculated into culture media using the Copan WASP automated specimen processing system and were also cultured using a conventional manual inoculation method. After 48 h of culture, the growth of colonies was observed, and the types of bacteria, number of colonies, and efficiency in isolating single colonies were compared between the automated and manual groups. The specimens collected from the WASP system using the Copan-ESwab sample collection tubes were further analyzed for the presence of Chlamydia trachomatis (CT), Neisseria gonorrhoeae (NG), and Ureaplasmaurealyticum (UU) via fluorescence quantitative polymerase chain reaction (qPCR) and an immunochromatographic assay to investigate the feasibility of this method in optimizing detection of these common pathogens of the female reproductive tract.ResultsCompared with the manual culture method, the Copan WASP microbiology automation system detected fewer bacterial types (P<0.001) and bacterial colonies (P<0.001) but had a higher detection rate of single colonies (P<0.001). There was no significant difference in the detection rates of common pathogens encountered in clinical obstetrics and gynecology, including group B Streptococcus (GBS) (P=0.575) and Candida (P=0.917), between the two methods. Specimens collected in the Copan-ESwab tubes could be used for screening of GBS and CT via fluorescence-based qPCR but not with immunochromatography. However, UU and NG were not detected in any sample with either method; thus, further validation is required to determine the feasibility of the Copan system for screening these pathogens.ConclusionThe Copan WASP microbiology automation system could facilitate the optimization of diagnostic procedures for detecting common pathogens of the female reproductive system, thereby reducing associated costs.