Gene expression microarrays generate extremely high amounts of transcriptomic data. These datasets may account for thousands of genes from hundreds of different individuals and can be used to identify genes associated with a particular disease or to assess gene expression profiles in response to a given therapy. Transcriptomic datasets are usually uploaded to a larger database, such as Gene Expression Omnibus (GEO), where others can review the data and draw their own conclusions. In effect, these datasets don’t just shape the hypothesis of the paper from which it was published; they influence all other scientists using those datasets to guide their own research.
Most of us are familiar with the mainstream image of Santa Claus, with his reindeer, sleigh, and a country full of elves that love nothing more than to make presents for children. However, Saint Nicholas was not always the goofy bearded man we picture today, having gone through many facelifts since his humbler beginnings in the 4th century, a time when Saint Nicholas, otherwise known as Nicholas of Bari or Nicholas of Myra, lived.
Artificial intelligence (AI) has been a popular topic ever since it was introduced in 1956 by John McCarthy. It quickly captured the imagination of Hollywood, leading to many blockbuster movies being made using AI as a plot device, including the Terminator franchise. However, until now AI has remained as only science fiction, as it’s only recently that computers have become powerful enough to integrate AI into something appreciably functional, allowing some of the top companies in the world, such as Google, IBM, and Apple, to design systems that learn on their own. Gartner, a research and advisory company who publishes a yearly list of the most hyped technologies (termed the Gartner Hype Cycle), has placed AI-associated technologies at the top of their list.1 With companies like PathAI, Freenome, and Benevolent AI all entering the market, it hasn’t taken long for scientists to adapt AI to solving complex biological and medical problems as well.
As any lab will attest, organizing your bank of cell lines is key to ensuring that your research runs smoothly and efficiently. However, this is easier said than done. How often do students and post-docs go searching for a specific cell line or passage number, only to discover that they cannot find what they’re looking for or that they’ve run out of the cells they need? Here are 4 simple ways that proper labeling can safeguard your lab against mismanaged cell line banking.
The week of Black Friday is one of the few days of the year when you'll see people standing in lines that wrap-around the block just to buy a toaster. For those in the United States, just preparing and hosting Thanksgiving dinner can often be overwhelming. Many also venture out to shop the next day, Black Friday, and throughout the week as companies ratchet up their sales and aim to boost their profits. The name originated from the fact that the Friday after thanksgiving was the day retailer's accounts went from "in the red" (loss), to "in the black" (profit). The modern concept of Black Friday began in the 1950s to signal the start of holiday shopping season. It’s become a national phenomenon, with people crowding stores and consumers behaving in extreme ways. You would think people would prefer to shop with a bit more room to maneuver, but there’s some strong psychological factors at play, all working to drive consumers to buy more and more. Let's dive into the top 6 reasons.
Healthcare institutions tend to use diverse systems for labeling specimens, each incorporating fail-safes at different levels of collection and processing. Many hospitals practice the Swiss cheese model of error prevention, using multiple layers (or fail-safes) to cover up any possible holes, preventing errors from slipping through.1 When it comes to reducing labeling errors, researchers have identified several types of interventions that act as additional fail-safes, many of them incorporating modern technology, such as barcodes, radio frequency identification (RFID), and automated systems.
For Part 1 of the series, detailing the costs of labeling errors in the clinic, click here.
Canada has recently passed new legislation legalizing cannabis throughout the country. Lineups at government-regulated stores were seen all over the country, with people eagerly awaiting legally sold marijuana for the first time ever in Canada. The demand is high, with many shops around Canada quickly selling out of many popular products on day 1. As the market is set to explode, the Canadian government has begun to invest heavily towards cannabis research, with the Canadian Institutes of Health Research funding around $20 million worth of projects within the last 5 years and ready to dole out another $3 million in the next few months.
Studying the real-time effects of labeling errors in the lab is extremely difficult. Billions of patient specimens, including blood and urine samples, as well as biopsies taken from multiple tissues and organs, are continually processed on a daily basis in clinical labs. Fortunately, several large-scale studies throughout the last 20 years have attempted to shed light on the clinical consequences of labeling errors in an effort to improve patient care and reduce healthcare costs worldwide.