Cap Inspection Provides Criticalb Information for Lab Automation

| November 17, 2018

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Cost containment in the healthcare system has placed clinical laboratories under constant pressure to improve efficiency while addressing patient care and safety concerns. As part of the design process, today’s instrument manufacturers seek to ensure that automation errors do not occur. A cap inspection system is one solution to improve error prevention processes. By inspecting test tubes and caps to collect detailed information such as diameter, cap color, and cap type, instrument manufacturers no longer need to engineer for the worst case scenario.

Spotlight

Surface Oncology Inc

Surface Oncology was created to advance next‐generation approaches to cancer immunotherapy based on proprietary insights about novel immunotherapy targets and emerging areas of cancer immuno‐biology. The company’s scientific founders and SAB are comprised of world‐leading immunologists and cancer researchers, including co‐chairs Sasha Rudensky (Memorial Sloan Kettering) and Arlene Sharpe (Harvard/DFCI). They are joined on the SAB by Christopher Hunter and John Wherry (University of Pennsylvania), Carla Rothlin (Yale University), Elliott Sigal, and John Stagg (University of Montreal).

OTHER ARTICLES

Translating Pharmacomicrobiomics: Three Actionable Challenges/Prospects in 2020

Article | February 24, 2020

The year 2020 marks a decade since the term pharmacomicrobiomics was coined (Rizkallah et al., 2010) to crystallize a century-old concept of mutual interactions between humans, drugs, and the microbial world. The human microbiome, with its immense metabolic potential that exceeds and expands the human metabolic capacities, has the ability to modulate pharmacotherapy by affecting both pharmacokinetics and pharmacodynamics of drug molecules:

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Better Purification and Recovery in Bioprocessing

Article | August 2, 2021

In the downstream portion of any bioprocess, one must pick through the dross before one can seize the gold the biotherapeutic that the bioprocess was always meant to generate. Unfortunately, the dross is both voluminous and various. And the biotherapeutic gold, unlike real gold, is corruptible. That is, it can suffer structural damage and activity loss. When discarding the dross and collecting the gold, bioprocessors must be efficient and gentle. They must, to the extent possible, eliminate contaminants and organic debris while ensuring that biotherapeutics avoid aggregation-inducing stresses and retain their integrity during purification and recovery. Anything less compromises purity and reduces yield. To purify and recover biotherapeutics efficiently and gently, bioprocessors must avail themselves of the most appropriate tools and techniques. Here, we talk with several experts about which tools and techniques can help bioprocessors overcome persistent challenges. Some of these experts also touch on new approaches that can help bioprocessors address emerging challenges.

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Making Predictions by Digitizing Bioprocessing

Article | April 20, 2021

With advances in data analytics and machine learning, the move from descriptive and diagnostic analytics to predictive and prescriptive analytics and controls—allowing us to better forecast and understand what will happen and thus optimize process outcomes—is not only feasible but inevitable, according to Bonnie Shum, principal engineer, pharma technical innovation, technology & manufacturing sciences and technology at Genentech. “Well-trained artificial intelligence systems can help drive better decision making and how data is analyzed from drug discovery to process development and to manufacturing processes,” she says. Those advances, though, only really matter when they improve the lives of patients. That’s exactly what Shum expects. “The convergence of digital transformation and operational/processing changes will be critical for the facilities of the future and meeting the needs of our patients,” she continues. “Digital solutions may one day provide fully automated bioprocessing, eliminating manual intervention and enabling us to anticipate potential process deviations to prevent process failures, leading to real-time release and thus faster access for patients.” To turn Bioprocessing 4.0 into a production line for precision healthcare, real-time release and quickly manufacturing personalized medicines will be critical. Adding digitization and advanced analytics wherever possible will drive those improvements. In fact, many of these improvements, especially moving from descriptive to predictive bioprocessing, depend on more digitization.

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AI and Biotechnology: The Future of Healthcare Industry

Article | January 20, 2021

Artificial intelligence has grasped the foundation in biotech. It can have the most innovative impact on biotechnology. AI has already established its presence in our day-to-day life. AI has made the existence of self-driving cars possible. Likewise, the benefits and quality that it can contribute to biotech can also be felt. With AI, bio technicians will be able to enhance virtual screening, overlook preliminary datasets from clinics, and decipher an enormous amount of information. It can also help in improving the medication process by gathering and analyzing every bit of information. The Significance of AI in Biotechnology In the past few years, the application of artificial intelligence in the biotechnology industry has shifted from being sci-fi to sci-fact. A vast number of biotech companies like Deep Genomics are adopting AI for making data-driven decisions and use analytics tools to work efficiently. Unlike the AI robots in sci-fi that are ready to take over the world. AI designed for biotech has been designed to solve certain problems or complete a bunch of tasks by using automated algorithms. The aim of AI technology for biotech is to collect insights along with hidden patterns from large amounts of data. All the different industries of biotech including agriculture, animal, medical, industrial, and bioinformatics are gradually being affected by artificial intelligence. Moreover, the biotech industry is realizing that AI enables them some of the important strength to their business, including: Expanding accessibility Cost-effectiveness Critical predictions Efficient decision-making Research centers like PwC have also estimated output of $15.7 trillion by 2030 solely with AI contribution in industries. A survey revealed that about 44% of life science experts are using AI for R&D activities, as well. Use of AI in Biotechnology Altering Biomedical and Clinical Data So far the most developed use of AI is its ability to read voluminous data records and interpret them. It can prove to be a life-save for bio technicians who would have to examine that much data from research publications by themselves for the validation of their hypothesis. With the help of AI, clinical studies of patients will also become easier as all the examination reports and prescriptions will be stored in one place for cross-reference. Furthermore, it will also help in blending and fetching data into usable formats for analysis. Test Result Prediction Through trial and error, AI along with machine learning can help in predicting the response of the patient to certain drugs to provide more effective outcomes. Drug Design & Discovery AI plays a vital role whether it’s designing a new molecule or identifying new biological targets. It helps in identifying and validating drugs. It reduces the cost and time spent on the entire drug trial process and reaches the market. Personalized Medications for Rare Diseases With the combination of body scan results, patients’ body and analytics, AI can also help in detecting dangerous diseases at an early stage. Improving Process of Manufacturing To improve the process of manufacturing in biotechnology, AI offers a wide range of opportunities. It controls quality, reduces wastage, improves useability, and minimizes the designing time. Moving Towards AI-Enhanced Biotech Future Ever since the concept of artificial intelligence has arrived, being curious by nature, humans have started working towards achieving this goal. It has been growing at a fast pace while showing unbelievable growth and achievements at times. In comparison to the traditional methods used in the biotechnology industry, AI-based methods seem more reliable and accurate. In the upcoming years, it will show its success by improving the quality of health people have. You can also develop your AI-based application or know more about it by taking IT consultations.

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Spotlight

Surface Oncology Inc

Surface Oncology was created to advance next‐generation approaches to cancer immunotherapy based on proprietary insights about novel immunotherapy targets and emerging areas of cancer immuno‐biology. The company’s scientific founders and SAB are comprised of world‐leading immunologists and cancer researchers, including co‐chairs Sasha Rudensky (Memorial Sloan Kettering) and Arlene Sharpe (Harvard/DFCI). They are joined on the SAB by Christopher Hunter and John Wherry (University of Pennsylvania), Carla Rothlin (Yale University), Elliott Sigal, and John Stagg (University of Montreal).

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