Lab Automation

In this paper, a lab automation drone notional concept is introduced. Here, a robotic limb is attached to a robotic rotorcraft. The limb's gripper allows the unmanned aerial vehicle to dexterously manipulate objects such as micro-arrays and test tubes often used in high throughput systems (HTS). The resulting drone could augment existing HTS operations. The 6 degree-of-freedom (DOF) arm and gripper design are presented. Test-and-evaluation approach and results are also given.

Is lab automation right for your lab? Robin A. Felder, PhD On pages 46–57 is CAP TODAY’s roundup of laboratory automation technology. There you’ll find information, provided by automation vendors, on functionality and installed sites. The variety of clinical laboratory automation technologies on the market is greater than ever, but the divergence in technologies presented challenges in hardware and software interconnectivity. Thus, in-vitro diagnostics manufacturers, automation vendors, and laboratorians pooled their efforts to enhance and transform technology by supporting the NCCLS laboratory automation standards effort. NCCLS recently published the last document in its five-part automation standard (Auto 1-5, see below). CAP TODAY included in its survey of the vendors questions about NCCLS conformance for various system components. Your justification for the purchase of laboratory automation must be based on a sound forecast of cost savings, turnaround and worker safety improvement, and reduction in errors. But how can you predict that automation will yield beneficial outcome for your laboratory? To suggest functional systems for their customers’ laboratories, and to implement those systems properly, vendors will request operations data and use various tools. You need data that compare the function of each automation system (see the CAP TODAY survey), and you will want to have on hand data that summarize the hourly arrival rate and distribution of specimens (not tests) delivered to the laboratory. You will also have to know the types and models of instruments and analyzers you have in the laboratory, and the types of laboratory tests and number requested for each specimen each hour of the day. Also required will be labor utilization rates in the laboratory, including job duties, number of full-time-equivalents assigned to each task each hour, total hours worked, and skill level of each technologist. Designing an automation process and selecting automation technology are not necessarily intuitive. For example, if you purchase an automation system sized to your lab’s peak demands, the lowvolume periods during the day will become more noticeable. There are numerous systems on the market from which to choose. (For tips on selecting automated systems, log on to http://marc.med.virginia.edu/.) Performance evaluations of individual systems have been published (Dadoun R. Clinical Laboratory Management Review. 1998;12: 248–255; Seaborg RC, et al. MLO. 1999:31: 46–54; Markin RS, et al. AJCC. 46:5;764-771), but published comparisons of competing clinical laboratory automation systems (with the exception of CAP TODAY’s side-by-side look at laboratory automation technology) are absent from the literature. Furthermore, the determination of ROI is still an inexact science, because, with test mix and labor needs differing so widely, efficiency improvements in one laboratory cannot be predicted on the basis of published reports from other labs. However, with computer methods you can attempt to model the many processes within a clinical laboratory. Indeed, computer simulation is gaining in popularity as a method to gather and analyze data regarding the prediction of productivity of clinical processes (Rosetti MD, Kumar A, Felder RA. Mobile robot simulation of mid-sized hospital delivery processes. Health Care Man Sci. 2000;3: 201–213). A trained simulation specialist will enter data and provide interpretations about implementing automation technologies. Make sure the simulation data are validated against the actual laboratory operation. One of the most important tasks of a simulation model is to ensure the laboratory that the automation technology is configured to avoid specimen bottlenecks. One way to validate the performance of laboratory automation is to perform a clinical trial. For example, to measure the performance of a recently released commercial preanalytical processor, we performed a two-site comparison of a preanalytical processor that can accommodate a variety of commercial specimens (Abbott-Tecan Partnership’s Genesis FE500). Our studies focused on testing more than 3,000 bar-code-labeled specimens according to a protocol designed to test a breadth of capabilities of preanalytical processors (for example, aliquot number, fraction centrifuged, and platelet depletion studies). Mean system output performance varied between 93 and 502 total tubes per hour depending on the batch size, aliquot number requested, and percentage of tubes that required centrifugation. The preanalytical processor was operated by one fulltime-equivalent compared with the three FTEs required to perform the same tasks manually during peak hours, which yields a calculated return on investment of less than three years. Furthermore, there was a significant reduction in laboratory errors. The costs of clinical lab automation technology have begun to decline as the production of technology has increased. However, only recently have automation tools become available for the smaller laboratory (sample throughput of less than 250 to 300 specimens per hour). Preanalytical workstations are now part of the clinical laboratory automation lineup and available from most of the in vitro diagnostics manufacturers. The data on pages 46–57 serve as a useful tool to compare the features of various automation systems for an initial assessment of automation compatibility with your laboratory. Armed with information about each system’s performance features, you can employ the services of a consultant who can develop a customized solution for your lab using specialized tools such as simulation modeling. Actual automation system performance must be evaluated, of course, following comprehensive clinical trials. Ultimately, laboratories with automation will be able to demonstrate remarkable increases in efficiency, quality, and safety.

In this paper, the author presents a lab automation drone concept design for high throughput systems(HTS). A 6 degree-of-freedom (6-DOF) parallel manipulator and parallel sensorized gripper are affixed together to a rotorcraft. The manipulator-gripper allows dexterously delivery of micro-plates broadly used for sample-testing in high throughput systems. For testing and evaluation, the concept design is deployed in two pick-and-place experiments using micro-plates. First the manipulator-gripper system is attached to a gantry crane system, then it is tested on a quadcopter under manual flight within motion capture space. The results demonstrate the viability of the design and point towards future work on stability controls for autonomous flight.

Copyright: © 2014 Rogatsky E. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. This week in San Diego, USA the Society for Lab Automation and Screening (SLAS) annual meeting and expo is taking place. This conference features 132 word class podium presentations and 400 poster presentations attracting experts from industry and academia, students, engineers and business leaders from around the World. This is only the third annual meeting, representing a relatively young and rapidly growing and developing field of automation for different laboratory workflows and processes, which is steadily replacing semi-automatic or manual methods. Initially, the complete automation of manufacturing processes was achieved in industry for various production lines. Next, automation has brought benefits to sterile environments, e.g. vaccine production and blood banks in order to minimize employee exposure to biological hazards, minimizing human error, and ensuring product purity and integrity. The next breakthrough of automation was in routine laboratory operations, such as immunoassays, crystallography, DNA screening/sequencing, cell culture and stem cells assays. Automation of immunoassays has allowed multiplexing (multiple parallel assays) using single source serum samples significantly speeding up analysis while enhancing sample integrity by eliminating multiple freeze thaw cycles, and/or reducing sample requirements by reformatting from a single source sample. ELISA and other plate based immunoassay are the most frequently used assays in clinical diagnostics. They allow fast, relatively specific and sensitive assays of many analytes or metabolites. A workflow bottleneck for immunoassays is a typical incubation time of several hours, that makes the processing throughput of manual processing relatively slow only a few plates per day per lab technician. Automation of plate based assays, which has matured over the past few years has increased the productivity of clinical laboratories, decreased operational costs, minimized (human) errors, while enhancing workplace safety for laboratory personnel.

In this paper, the author presents micro-plate grasping work using a re-sized lab automation drone in high throughput system. Here, a robotic arm is affixed to a rotorcraft. The arm's gripper allows the unmanned aerial vehicle to dexterously manipulate objects such as micro-arrays and test samples often used in high throughput systems (HTS). The result shows that drone could improve existing HTS operations. The 6 degree-of-freedom (DOF) arm and gripper design are deployed to pick and place microplae with different samples. Test-and-evaluation approach and results are also given.

In this paper we presented the a Lab Automation System (LAS) using Nodemcu esp8266 that employs the integration of cloud networking, wireless communication, which provide the user with remote control of lights, fans, and appliances within their lab and storing the data in the cloud. The system will automatically change on the basis of sensors’ data. This system is designed with low cost and expanded in lab to control variety of devices.

by RobinFelder Lab Automation, now in its third year, is the premier meeting for exploring breaking developments in the expanding world of laboratory automation. Routine laboratory tests previously performed manually are being converted to fully automated procedures. However, there are many challenges in the interface between chemistry, biochemistry and mechanics. At LabAutomation leading scientists, vendors, and end users meet and develop collaborations related to laboratory robotics and automation.

Novartis is a founding Core Member of the SiLA Consortium, and supports SiLA in making lab automation through standardization more effective and therefore enabling scientists to advance science through new technologies. The NIBR Basel engineering group in screening has led multiple SiLA-implementations in screening and compound management automation at NIBR. NIBR automation engineers are very supportive of the SiLA effort in looking to establish also standards in data communication and building an open source approach towards the development of the standards.

There is a rapid increase in usage and dependence on the vibrant features of smart devices, the need for IOT in them is valid. Many existing systems have project into the globe of Lab Automation but have actually failed to provide costeffective solutions for the same. We illustrate a methodology to provide a low cost Lab Automation System (LAS) using Bluetooth Module. A Bluetooth based Model is designed for the purpose of monitoring and controlling environmental, safety and electrical parameters of a smart organized home. The user can exercise flawless control over the devices in a smart Lab via the Android application based Graphical User Interface (GUI) on a Smart-phone. KeywordsInternet of things, smart governance, smart education, smart agriculture, smart health care, smart homes, Lab Automation System, Wireless Fidelity.

Abstract Objectives Automated storage and retrieval modules (SRM), as part of total lab automation (TLA) systems, offer tremendous practical and economic benefits. In contrast to manual storage systems, SRMs indicate continuous motion of samples and may leave samples prone to temperature fluctuations. This study investigates analyte stability in serum and heparin plasma within an automated storage module. Methods The stability of 28 common biochemistry analytes was investigated using 57 freshly obtained routine serum samples and 42 lithium-heparin plasma samples. Following baseline measurement, samples were stored at 2–8 °C in the automated SRM of the Accelerator a3600 TLA and reanalyzed at fixed time points (2, 4, 8, 12, 24, 48 and 72 h) on the Abbott Architect c16000 chemistry analyzer. The concentration at each time point was expressed as %-difference to the baseline value and mean results were compared to the criteria for desirable bias derived from the biological variation database. Results Nine of the analytes exceeded the bias criterion within 72 h after initial measurement in either serum samples, plasma samples or both. Lithium-heparin plasma samples showed increasing values for phosphor, potassium and lactate dehydrogenase (LDH), which were only considered stable for respectively 24, 12 and 4 h, glucose was considered stable for 8 h. Electrolyte concentrations and LDH activity significantly increased in serum samples beyond 48 h. Bicarbonate should not be performed as add-on test at all. Conclusions The presented data indicate that the conditions within an SRM have no clinical impact on sample stability and allow stable measurement of routine analytes within 72 h, comparable to manual storage facilities.