Just a couple years ago, I was a research associate working at McGill University in the Meakins-Christie Laboratories, studying a rare disease called lymphangioleiomyomatosis, or LAM. LAM is a progressive, cystic disease afflicting young women with noncancerous lung tumors that can destroy lung function, making the disease potentially fatal. My job was to understand where these tumors came from and what made them propagate throughout the lungs. There was one unfortunate caveat: no one had been able to grow LAM tumor cells outside of the body. As anyone who has ever worked with cancer biology can attest to, there are a multitude of immortalized cancer cell lines, grown from the cells of a patient’s tumor, that can be studied to perform pre-clinical translational research. And yet, not a single representative cell line was available for LAM. Thankfully, my supervisor set me up with just the right project to help solve this puzzle, which centered around induced pluripotent stem cells (iPSCs).
When you hear 3D printing, what do you think of? Perhaps you imagine creating inanimate objects like chairs, wrenches, or toys out of construction materials (e.g. plastic, ceramic, or metal). The uses of additive printing have evolved way past that and now serve an important role in medicine and research.
The main purpose of any vaccine is to stop the spread of communicable diseases from one person to another and, where possible, to abolish the disease outright from the general population. There are many commercially available vaccines for a variety of viral and bacterial diseases, including diphtheria, tetanus, whooping cough, measles, polio, tuberculosis, hepatitis, human papillomavirus, and influenza. To develop these and other vaccines, three things are required: research to find an antigen (usually a protein produced by the pathogen) that produces a protective immune response against the disease, a platform in which to produce the vaccine, and clinical testing.
Barcodes are used worldwide as one of the most efficient means of tracking packages and containers. However, the use of barcodes is not solely limited to labels. Living organisms can also be barcoded genetically, allowing individual cells to be monitored and tracked.
Syringes are one of the most integral tools of any medical institution. Primarily used for injectable medications, they are critical to proper patient care, as anesthesiologists depend on them to sedate and anesthetize. With that in mind, it’s important to consider that syringes, which may hold any number of different classes of drugs, need to be properly labeled with pertinent information, such as the name of the drug and its concentration, as mislabeled syringes could yield potentially dire consequences for those who are injected with the wrong substance or dose.
Errors occur every day in healthcare institutions and research facilities. Medical lab errors can be very costly, setting hospitals back hundreds (sometimes thousands) of dollars for every mislabeled sample, causing irreparable harm to the physical and mental health of the patient. Errors in research also have a broad impact, skewing results and wasting precious materials—which are often irreplaceable—and years of effort.
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.
In both clinical and research labs, it is often necessary to label material or equipment with sensitive information. This can include patient information or confidential experimental data. Once the patient is discharged, or the samples are either processed or no longer needed, these labels must be discarded; however, the information on the labels must still remain private. In certain cases, this may prove to be more difficult than anticipated. Here are 3 unique label options to help ensure that discarded material can’t be used to procure sensitive information.
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.
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.