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Marine Natural Products for Human Health Care
Published in Hafiz Ansar Rasul Suleria, Megh R. Goyal, Health Benefits of Secondary Phytocompounds from Plant and Marine Sources, 2021
High throughput screening (HTS) is the process of assaying huge numbers of crude extracts or fractions against selected targets in a relatively short span of time. To conduct bioactivity screening in a high throughput manner, validated drug targets and assays suitable for detecting the bioactivity of a compound or an extract need to be developed. In addition to this, necessary equipment, like microtiter plates and laboratory automation techniques, are needed to make HTS executable [190]. In HTS of crude extracts or fractions, the assays need to detect desired bioactivity properties of constituents of complex samples. The assays are designed to possess a high efficiency to deliver rapid results at relatively low cost. In addition, they should be convenient, reliable, and sensitive and require little material. HTS is typically performed at a single concentration, and a positive hit is followed by additional testing to confirm the potency and target/phenotypic specificity. Additional testing ensures elimination of false positives caused by nonspecific activities of constituents of the assayed crude extracts or fractions.
Product Development
Published in Gary M. Matoren, The Clinical Research Process in the Pharmaceutical Industry, 2020
Lester Chafetz, Theodore I. Fand
Swarbrick and Stoll [1], in their survey of organization in 17 pharmaceutical companies, divided these into small, medium, and large firms on the basis of sales. Interestingly, they reported that all of them had total staffs of about 100 people in the production of clinical supplies, although there were qualitative differences in distribution of scientists and technicians and of B.S., M.S., and Ph.D. degrees among them. Some proportion of any staff of that size has to be devoted to administration, as well as secretarial and documentation services. Office automation has trailed laboratory automation; however, a new era of office word processors with electronic filing, with simplified revision of reports and manuscripts and rapid telecommunication of data, bids to make changes as revolutionary in the life of the Product Development manager as have occurred in the laboratories.
The Illumination
Published in William C. Beck, Ralph H. Meyer, The Health Care Environment: The User’s Viewpoint, 2019
In the recent years, there has been a tremendous change in the visual requirements in the hospital brought about by the technical advances, particularly in laboratory medicine. No longer are the physician’s and nurse’s clinical observations of as much importance as they were only a decade ago. The color of the lips and fingernails are no longer needed as indicators of cyanosis (too often they are disguised by cosmetics). This is now accurately established by blood gas studies. No longer does the physician rely upon the color of the eye sclera to diagnose the depth of jaundice. He now orders and reviews the laboratory reports of the serum bilirubin. This does not mean that the clinical examination is no longer practiced, nor that it is invalid. But reliance is not placed upon clinical estimations; often they are shown to be unreliable.8 Furthermore, in the laboratory, automation has even removed the need for fine color discrimination by the technician. Visual color estimations, when they are used, are now so gross that fine hue discrimination is no longer needed. The tests which were used in defining the preferable lamp quality by Wellwood Fergusen and productive of the deservedly famous report are no longer applicable.
Lean and Six Sigma as continuous quality improvement frameworks in the clinical diagnostic laboratory
Published in Critical Reviews in Clinical Laboratory Sciences, 2023
Vinita Thakur, Olatunji Anthony Akerele, Edward Randell
A case for total laboratory automation often includes such targets as reduced variability, and improved workflow and process efficiency, including reducing TATs and staffing requirements. While automation can have a significant impact on TAT, many studies that have achieved gains have focused on much less costly OFIs. For example, the use of Lean strategies [67] was successfully applied in Iowa City, USA to redesign pre-analytical processes, reducing steps and implementing a one-piece flow to move blood samples through accessioning, centrifugation, and aliquoting processes, and improving TATs for clinical chemistry tests while using existing resources. However, implementing automation under a Lean [68–73], Lean-Six Sigma [43,74–76], Six Sigma DMAIC [77], or PDC/SA [78–80] frameworks all brought improved TATs in molecular diagnostics [43,68,69,71,75,80] and hematology and clinical chemistry or core laboratories [70,72–74,76–79]. Improved testing capacity [69–71,75,76], financial savings [70,71,79], freed up space [70], improved specimen quality [68], reduced personnel requirements [71], improved service quality by reducing errors/defects [43,74,75,77,79] and biological hazards [74] and improved workplace experience for staff [69] have all been attributed to the use of process improvement schemes coupled with automation and changes to other resource usages.
Automation and artificial intelligence in the clinical laboratory
Published in Critical Reviews in Clinical Laboratory Sciences, 2019
Christopher Naugler, Deirdre L. Church
Major advances have recently been made in IT connectivity, artificial intelligence, and robotics to enable implementation of large automated core laboratory systems. These systems are comprised of several instruments run in parallel using a robotic track system and artificial intelligence software to determine where samples should load for testing [42–46]. Several examples of these highly integrated total laboratory automation platforms are already used in clinical laboratories. Cobas® (Roche) instruments provide highly automated core laboratory platforms that handle sample processing, analysis and storage [47]. In combination with one or more connection modules, a high-volume chemistry operation can be completely robotized within a single track that can perform sorting, decapping, extensive sample quality inspection, aliquotting, and re-capping of in vitro diagnostic (IVD) test tubes. This single track can also perform complex analysis of multiple tests and store samples under refrigeration upon test completion. However, Roche’s reagents, tests and consumables must be used with their automated core laboratory instruments. Abbott has recentely introduced a highly automated Accelerator instrument for performing pre-analytic tasks that has connectivity to a multi-instrument core laboratory platform. However, they have developed it to be flexible and “open” to use other manufacturer’s reagents and tests [48]. Other technological advances in whole cell and digital image analyses that are outlined below include enabling other laboratory areas that historically used “whole-cell” methods (i.e. pathology and microbiology) to automate many labor-intensive processes and procedures.
Recent evolutions of machine learning applications in clinical laboratory medicine
Published in Critical Reviews in Clinical Laboratory Sciences, 2021
Sander De Bruyne, Marijn M. Speeckaert, Wim Van Biesen, Joris R. Delanghe
Clinical microbiology laboratories are often confronted with problems of understaffing and an imbalance in workload. ML-based laboratory automation can offer an answer to repetitive high-volume tasks, thereby leaving more high-skilled work to the laboratory technicians [80]. Generally, the highest workload is caused by the need for confirmation of urinary tract infections in urine samples. Approximately, two-thirds of urine specimens show a negative culture result, and significant improvements in laboratory workflow can thus be obtained by minimizing the number of samples to be cultured. Burton et al. [81] proposed the application of supervised ML models to assess whether or not it is useful to culture a given urine specimen. The authors reported a decrease in workload associated with such cultures of approximately 41% while still correctly identifying 95.2% of culture-positive samples by using XGBoost. The system was trained on independent variables such as clinical information, demographics, urine microscopy, and historical urine culture results. Another interesting alternative is the use of ML for digital image analysis. Faron et al. [82] recently evaluated WASPLab colony segregation software designed by Copan (Brescia, Italy) to automatically detect significant growth of urine cultures plated out on standard blood and MacConkey agars. The authors concluded that the software was highly sensitive (sensitivity of 99.8%) and can be employed by microbiology labs to batch-review negative cultures, thereby reducing the diagnostic workload. In another study, an RBF-SVM based method for automatic hemolysis detection and classification on aligned dual-lightning images of cultured blood agars has been reported [83]. The model aligned more than 98.1% images with a residual error of less than 0.13 mm and classified hemolysis types (Alpha, Beta, or Gamma) with an 88.3% precision and a recall of 98.6%. ML-based digital image analysis has also been reported for the microscopic interpretation of stained smears, which can be regarded as one of the most time-intensive and operator-dependent tasks in the microbiology lab. Smith et al. [84] developed a system based on automated image acquisition and a deep CNN to automate the process of Gram stain classification. The model reached an overall accuracy of 94.9% in the classification of Gram-negative rods, Gram-positive cocci in chains/pairs, Gram-positive cocci in clusters, and background (no cells).