Tuesday 31 May 2022

Deep Learning is Changing Microscopy


Microscopy is a science that dates back to the late 1500s, though historians don’t know exactly who invented it. The introduction of the microscope enabled scientists and researchers to observe a whole new world that was previously hidden from the naked eye. Microscope technology has evolved dramatically in the intervening centuries, giving scientists and researchers the ability to observe some of the smallest particles in the universe. 


By Emily Newton

Improvements in microscope technology have led to a plethora of discoveries — and a massive collection of data, more than even the most skilled scientists could sort through in a lifetime. That’s where deep learning comes in. What is deep learning microscopy? How can researchers capitalize on this technology moving forward?


Improvements in Microscopic Imagery


According to a recent survey, microscopy appears in upwards of 90% of life science-related publications in one form or another. Since the inception of the technology, what’s changed is the reliance on digital imagery, capturing the information in pixels for further study instead of relying solely on the researcher’s observations. These digital images are an invaluable tool for further research and study. Still, researchers generate so many of them each day that turning this raw data into actionable insights becomes more challenging.


Imagine having to sort through and catalog a thousand images every single day. It seems like a manageable task until a thousand more appear each day — and the number of actual digital microscopy images generated daily is likely much higher.


Understanding Deep Learning


Understanding how deep learning is changing microscopy begins with understanding the technology itself. Experts define deep learning as “a type of machine learning and artificial intelligence (AI) that imitates the way humans gain certain types of knowledge.” These programs quickly become an integral part of the data science industry, contributing to fields like statistics and predictive modeling.


Humans learn through repetition and reinforcement. Think of a toddler learning the basics of their first language. They learn that each thing they encounter has a word assigned to it — dog or bottle or spoon — and how to differentiate those items from the rest of the world. As the child grows, it learns that there can be different breeds of dogs — german shepherd, golden retriever, chihuahua — and while they are all dogs, each one has a different name. This linear education is how deep learning systems collect and store data.


The difference between these systems and the human mind lies in recall and application. Deep learning systems don’t forget information like the human mind and are programmed for perfect recall. To stick with the example from above, a deep learning system programmed to identify dog breeds will be able to continue to identify specific dogs or breeds with the same efficacy, regardless of how long ago it learned the information.


Deep Learning Microscopy


What do dogs and deep learning have to do with microscopy?


While it isn’t the only tool necessary for modern microscopy, deep learning is quickly becoming invaluable. The more information a deep learning system has access to, the smarter it becomes and the more connections it can make. It’s a lot easier to see patterns if the whole picture is visible and that’s what deep learning microscopy provides.


Image segmentation is one practice that is helping researchers make the most of their digital microscopic images. Instead of focusing on the picture, image segmentation breaks it down into sets of pixels. This segmentation makes it easier to spot objects of interest in what might otherwise be a crowded image. Traditional image segmentation is both time and labor-intensive, making it more difficult for researchers to complete their tasks promptly.


Deep learning microscopy helps to streamline workflows and save researchers a lot of time, freeing them up to complete other tasks that may also be vital to their work. Training the deep learning algorithms is often as simple as feeding data into the system and ensuring that the output is what the scientists are looking for. Once they confirm the system’s accuracy, a deep learning system can become as much a team member as any human researcher.


Deep learning microscopy isn’t a perfect solution, nor is it a replacement for human researchers. These systems still require continual monitoring to ensure accuracy and need to be programmed before they can offer any actionable insights. With the sheer amount of digital microscopic images generated by the life sciences sectors every year, the implementation of deep learning can help fill in some of the gaps and make connections that even the most skilled researcher may miss.


Looking Forward


Deep learning microscopy might be in its infancy. Still, with the amount of digital microscopic imagery that happens every day, it has the potential to become one of the most invaluable tools in the life sciences toolbox. The data sciences sector is growing exponentially and finding a niche in nearly every field and industry. Researchers are beginning to understand all the potential applications of this technology. Deep learning and microscopy will continue to evolve, hand in digital hand, as research moves into the future.


Pharmaceutical Microbiology Resources (http://www.pharmamicroresources.com/)

Friday 27 May 2022

Bacteria and temperatures of growth


Microorganisms are adept at surviving extreme environments (both physical and geochemical), including ultracentrifugation, hypervelocity, shock pressure, high temperature variations, vacuums, and different ultraviolet and ionizing radiation intensities. On Earth, microorganisms are found to thrive at 6.7 kilometres depth inside the Earth’s crust, and more than 10 kilometres deep inside the ocean at pressures of up to 110 Megapascals (the piezophiles); from extreme acid, even at pH 0 (acidophiles) to extreme basic conditions, with up to pH 12.8 recorded (alkaliphiles); and from hydrothermal vents at 122 °C to frozen sea water at −20 °C (in the case of Psychrobacter cryopegella).

Understanding temperature variations is of importance of assessing microbial risk in pharmaceutical processing, in terms of whether microorganisms may be present.


(such as a cold room or hot water system); whether they might survive and pose a problem to a non-sterile product; and as part of the process of establishing their origins for root cause analysis.


This article assesses the core differences with bacterial growth and survival at different temperatures. The article also explains why organisms that are often generally associated with cold environments might survive, potentially elevating the risks to pharmaceutical facility cold rooms.


Sandle, T. (2022) Bacteria and temperatures of growth: Cold comfort?, Pharmig News, Issue 87, pp12-16 (see: here)


Posted by Dr. Tim Sandle, Pharmaceutical Microbiology Resources (http://www.pharmamicroresources.com/)

Monday 23 May 2022

Improving Microbiological Control Of Non-Sterile Pharmaceuticals: Unlocking The FDA Guidance


The document is the first time that the FDA has pieced together a compliance framework for non-sterile medicines, and, in some ways, the document stands as a companion piece to the Aseptic Processing Guidance (issued in 2004, although in clear need of an update). The trigger for the non-sterile document was based on the pattern of non-sterile product recalls involving microbial contamination, based on the period 2014 to 2017 when 197 bacterial and fungal contamination events, associated with either the manufacture, packaging, shipping, or storage of the drug, were recorded.



This paper critically assesses the regulatory text.


For details, see:


Sandle, T. (2021) Improving Microbiological Control Of Non-Sterile Pharmaceuticals: Unlocking The FDA Guidance, Journal of GxP Compliance, 25 (6). DOI: https://www.ivtnetwork.com/article/improving-microbiological-control-non-sterile-pharmaceuticals-unlocking-fda-guidance


Posted by Dr. Tim Sandle, Pharmaceutical Microbiology Resources (http://www.pharmamicroresources.com/)

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