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Benefits of Purchasing Used Lab Equipment

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In the laboratory industry, there can be a lot of pressure to always have the newest and latest technology. Due to concerns about having outdated or inefficient equipment, many professionals don’t even consider purchasing used equipment. However, there are numerous advantages to doing so. Here are some of the key benefits of purchasing used lab equipment to keep in mind next time you need to purchase equipment.


Financial Benefits

One of the most significant and obvious benefits of purchasing used lab equipment is that doing so can save you a lot of money. Used laboratory equipment is significantly less expensive than brand new models. Often laboratories can save up to 50 percent or more by purchasing quality second-hand equipment.

Because laboratory equipment is often one of the top expenses that laboratories incur, purchasing used can substantially reduce laboratory costs and free up some much-needed room in tight budgets.

 

Environmental Benefits

Large laboratory equipment can take up a lot of room in landfills and often contain toxic components such as lead or mercury. Such toxins can seep into the earth and contaminate groundwater over time. By purchasing used laboratory equipment rather than new, you can reduce the amount of equipment that ends up in landfills. Plus, you will also reduce the number of raw materials used to manufacture new equipment which will decrease your lab’s negative impact on the environment.

Increased Insight

Purchasing a new type of equipment right after it is released comes with some risks. When a piece of equipment is fresh on the market, there aren’t many reviews from past customers attesting to how well or poorly it operates.\

Used equipment, however, is far less risky. Because the equipment has been on the market for a while, there are likely plenty of reviews that you can reference and any potential issues have likely been well-documented. Just make sure to purchase from a reliable and trustworthy reseller that took the proper measures to ensure the equipment is in optimal operating condition.

Reduced Wait Time

If you need a piece of equipment in a short period of time, buying used is often the best option. Many manufacturers have long wait times that can require you to wait for several weeks or even months before the equipment will arrive. If you have deadlines you need to meet, that simply won’t do. In cases when you need equipment in a timelier manner, used equipment is already built and ready to go so you often only have to wait for shipping.


 

Source: Christina Duron is a freelance writer for multiple online publications where she can showcase her affinity for all things digital. She has focused her career around digital marketing and writes to explore topics that spark her interest.

 

Biotechnology

StemVacs Cellular Immunotherapy: Positive Animal Data Reported in the 4T1 Breast Cancer Model

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January 19, 2021

StemVacs cellular immunotherapy update: Therapeutics Solutions has reported positive animal data in the 4T1 breast cancer model using StemVacs cellular immunotherapy.

In a series of experiments, it was demonstrated that StemVacs™ administration resulted in a) regression of established breast cancer in mice; b) that regression was dependent on natural killer cells, and c) that immunity to breast cancer could be transferred by CD4 T cells to naïve mice.

Previously the Company announced positive safety data in 10 patients treated with StemVacs™1.  Additionally, the Company has filed an Investigational New Drug Application (IND) which is currently pending based on the Company responding to questions posed by the FDA.

“The current data, which conclusively demonstrates the induction of immunological memory to cancer, as well as the natural killer cell as a mechanism of action, will position us to provide the FDA responses to their questions from our submitted IND,” said Dr. James Veltmeyer, Chief Medical Officer of the Company and voted Top Doctor of San Diego. “Given the fact that we have human safety data from outside of the United States, combined with these new mechanistic findings, we are confident in a smooth interaction with the FDA as we get closer to clearing of our IND application.”

“It is known that numerous immunotherapies such as Herceptin® are dependent on the ability of natural killer cells to function properly,” said Famela Ramos, Vice President of Business Development. “The current data provides a scientific basis for us to collaborate with other immuno-oncology companies to identify and leverage possible synergies with other drugs that work via the natural killer pathway of the innate immune system.”

“Therapeutic Solutions International is a ‘science-driven’ organization,” said Timothy Dixon, President and CEO. “Having key opinion leaders such as Drs. Santosh Kesari and Francesco Marincola as advisors allows us to design and execute experiments with the highest level of academic rigor. We believe that the FDA and our scientific and medical peers will appreciate our philosophy.”

Source: https://infomeddnews.com/stemvacs-cellular-immunotherapy/

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Bioclinica Announces Breakthrough AI Image Redaction Technology for Clinical Trials

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January 19, 2021

Bioclinica, an integrated solutions provider of clinical life science and technology expertise, today announced a new product to support redaction of sensitive patient information from videos, photos, and PDFs in clinical trials.

Bioclinica notes the addition of Image Redact AI to the company’s portfolio of specialty software solutions makes it the first and only company that can redact sensitive patient identifiers from videos in addition to photos and PDFs.

Historically, it has been very difficult to ensure videos fully comply with patient privacy regulations in clinical trials, increasing the risk of costly penalties, downstream issues, and reputation damage for trial sponsors. With Bioclinica’s new AI application, sponsors benefit from peace of mind as well as processing capabilities that reduce time to redact patient identifiers within videos from days to hours.

The launch of Image Redact AI closely follows Bioclinica’s acquisition of Saliency, a Silicon Valley-based AI company with technology capable of dramatic acceleration of image QC and interpretation to support rapid development of digital diagnostics and therapeutics.

“Image Redact AI is the latest demonstration of how we are delivering value to our customers using cutting-edge technology,” said Dan Gebow, PhD, Chief Innovation Officer at Bioclinica. “Today, we can include videos in the long list of file types that our image de-identification system can handle with ease.”

Bioclinica Image Redact AI safeguards sensitive clinical trial data by automatically redacting sensitive patient identifiers from videos, photos, and PDFs. It is the only solution that pairs a high level of AI-driven de-identification with the human oversight of an experienced quality control team to help ensure videos and photos comply with 21 CFR Part 11, EU GDPR, and other privacy regulations.

Research that utilizes video or other image formats for studies on vision loss, gait or other movement analysis, dermatology, dental, or behavioral research are especially prone to issues with current products that blur a patient’s entire face.

These solutions do not work for this kind of research where it is necessary to see parts of the face, such as the upper lip, forehead, or skin color. In these instances, a sponsor’s only option was to do the redaction themselves. This manual process is error-prone, time-intensive, and can consume valuable research dollars.

Prior to submitting a video, photo, or PDF to our secure, 100% browser-based Global Cloud Network, Image Redact AI pre-screens to ensure accurate file type and size. The technology then performs the de-identification process to remove all sensitive patient information, followed by a 100% human visual quality control review by experienced Bioclinica technicians to ensure proper de-identification before the data is made available.

Source: https://infomeddnews.com/bioclinica-ai-image-redaction-technology/

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New approach emerges to better classify, treat brain tumors

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AUGUSTA, Ga. (Jan. 19, 2021) – A look at RNA tells us what our genes are telling our cells to do, and scientists say looking directly at the RNA of brain tumor cells appears to provide objective, efficient evidence to better classify a tumor and the most effective treatments.

Gliomas are the most common brain tumor type in adults, they have a wide range of possible outcomes and three subtypes, from the generally more treatable astrocytomas and oligodendrogliomas to the typically more lethal glioblastomas.

Medical College of Georgia scientists report in the journal Scientific Reports that their method, which produces what is termed a transcriptomic profile of the tumor is particularly adept at recognizing some of the most serious of these tumors, says Paul M.H. Tran, MD/PhD student.

Gliomas are currently classified through histology, primarily the shape, or morphology, pathologists see when they look at the cancerous cells under a microscope, as well as identification of known cancer-causing gene mutations present.

“We are adding a third method,” says Dr. Jin-Xiong She, director of the MCG Center for Biotechnology and Genomic Medicine, Georgia Research Alliance Eminent Scholar in Genomic Medicine and the study’s corresponding author. Tran, who is doing his PhD work in She’s lab, is first author.

While most patients have both the current classification methods performed, there are sometimes inconsistent findings between the two groups, like traditional pathology finding a cancer is a glioblastoma when the mutation study did not and vice versa, and even when two pathologists look at the same brain tumor cells under a microscope, the scientists say.

To more directly look at what a cancer cell is up to, they opted to look at relatively unexplored gene expression, more specifically the one-step downstream RNA, which indicates where the cell is headed. DNA expression equals RNA since DNA makes RNA, which makes proteins, which determine cell function. One way cancer thrives is by altering gene expression, turning some up and others way down or off.

They suspected the new approach would provide additional insight about the tumor, continue to assess the efficacy of existing classification methods and likely identify new treatment targets.

“RNA would be a snapshot of what is high and what is low currently in those glial cells as they are taken out of the body,” Tran says. “They are actually looking at how many copies of RNA relevant genes are making. Normally that gene expression determines everything from your hair color to how much you weigh,” She says. “The transcriptomic profile counts the number of copies of each gene you have in the cell.”

The glial cells, whose job is to support neurons, have a tightly regulated gene expression that enables them to do just that. With cancer, one of the first things that happens is how many RNA copies of each gene the cells are making changes and the important cell function changes with it. “You change gene expression to become something different,” She says.

Transcriptomic profiling starts like the other methods with a tumor sample from the surgeon, but then it goes through an automated process to extract RNA, which is put into an instrument that can read gene expression levels for the different genes. The massive amounts of data generated then is fed into a machine learning algorithm Tran developed, which computes the most likely glioma subtype and a prognosis associated with it.

They started with The Cancer Genome Atlas (TCGA) program and the Repository of Molecular Brain Neoplasia Data (REMBRANDT), two datasets that had already done the work of looking at RNA and also provided related clinical information, including outcomes on more than 1,400 patients with gliomas. Tran, She and their colleagues used their algorithm to discover patterns of gene expression and used those patterns to classify all glioma patients without any other input. They then compared the three major glioma subtypes that emerged with standard classification methods.

Their transcriptomic classification had about 90% agreement with the traditional approach looking at cells under a microscope and about 93% agreement with looking at genetic mutations, She says. They found about a 16% discrepancy between the two standard measures.

“All three methods don’t agree on about 10-15% of patients,” She says, but notes the most accurate analysis among the three should be theirs because their method is better than the others at predicting survival.

And the discrepancies they found between classification methods could be significant for some patients despite close percentages.

“We found our method may have some advantages because we found some patients actually had a worse prognosis that could be identified by our method, but not by the other approaches,” Tran says.

As an example, patients with a mutation in a gene called IDH, or isocitrate dehydrogenase, most typically have an astrocytoma or oligodendroglioma, which are generally more responsive to treatment and have better survival rates than glioblastomas. However they also found that even some lower-grade gliomas with this IDH mutation can progress to what’s called a secondary glioblastoma, something which may not be found by the other two methods. The IDH mutation is rare in primary glioblastomas, Tran notes.

Using the standard techniques, which look at a snapshot in time, these astrocytomas that progress to more lethal glioblastomas were classified as a less serious tumor in 27 patients. “That progression phenomenon is known but our technique is better at identifying those cases,” Tran says.

Further analysis also found that about 20% of the worse-prognosis patients had mutations in the promoter region of the TERT gene. The TERT gene is best known for making telomerases, enzymes that enable our chromosomes to stay a healthy length, a length known to decrease with age. TERT function is known to be hijacked by cancer to enable the endless cell proliferation that is a cancer hallmark. This mutation is not usually present in a glioma that starts out as a more aggressive glioblastoma, and implicates a mutation in the TERT promoter is important in glioma progression, they say.

“The implication would be that if we have inhibitors or something else that target the TERT gene, then you may be able to prevent some of those cases from having a worse prognosis,” Tran says.

These findings also point to strengths of the different classification methods, in this case suggesting that classification by mutation may not pick up these most aggressive brain tumors rather their new transcriptomic method, as well as the older approach of looking at the cancer cells under a microscope, are better at making this important distinction.

“It is known that a certain proportion of your lower-grade gliomas can progress to become a glioblastoma and those are some of the ones that can sometimes be misidentified by the original techniques,” Tran says. “Using our gene expression method, we found them even though some of them have the IDH mutation.”

All these variations have groups like the World Health Organization asking for better ways to determine poor prognosis IDH patients, they write. Other variations include some glioblastomas with the normal IDH gene carry one of the worse prognoses for gliomas, but there is a subgroup of glioblastomas that act more like astrocytes and tend to carry a better prognosis.

Now that the MCG team has a better indication of which patients will have a worse prognosis, next steps including finding out why and maybe what can be done.

In addition to accuracy of prognosis, a second way to assess a tumor classification method is whether it points you toward better treatment options, She says, which they are now moving toward. He notes that most drugs and many of our actions, like exercise and what we eat, alter RNA expression.

“Right now, if anyone gives us RNA expression data from patients anywhere in the world, we can quickly tell them which glioma subtype it most likely is,” Tran says. The fact that equipment that can examine RNA expression is becoming more widely available, should make transcriptomic profiling more widely available, they say.

Gliomas are tumors of glial cells — which include astrocytes, oligodendrocytes and microglial cells — brain cells which outnumber neurons and whose normal job is to surround and support neurons.

Identification of IDH gene mutations in the cells has already made standard glioma classification more systematic, the scientists say. The mutation can be identified by either staining the biopsy slide or by sequencing for it.

Much progress also has been made in using machine learning to automate and objectify cancer diagnosis and subtyping they write, including glioblastomas. Glioblastomas have been characterized using transcriptome-based analysis but not all gliomas, like the current study.

Like most genes, the IDH gene normally has many jobs in the body, including processing glucose and other metabolites for a variety of cell types. But when mutated, it can become destructive to cells, producing factors like reactive oxygen species, which damage the DNA and contribute to cancer and other diseases. These mutations can result with age and/or environmental exposures. IDH inhibitors are in clinical trials for a variety of cancers including gliomas.

Increasing insight also is emerging into the significant DNA methylation that occurs in cancer, which alters gene expression, resulting in changes like silencing tumor suppressor genes and producing additional cancer-causing genetic mutations.

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Read the full study.

Source: https://bioengineer.org/new-approach-emerges-to-better-classify-treat-brain-tumors/

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Lasers & molecular tethers create perfectly patterned platforms for tissue engineering

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Imagine going to a surgeon to have a diseased or injured organ switched out for a fully functional, laboratory-grown replacement. This remains science fiction and not reality because researchers today struggle to organize cells into the complex 3D arrangements that our bodies can master on their own.

There are two major hurdles to overcome on the road to laboratory-grown organs and tissues. The first is to use a biologically compatible 3D scaffold in which cells can grow. The second is to decorate that scaffold with biochemical messages in the correct configuration to trigger the formation of the desired organ or tissue.

In a major step toward transforming this hope into reality, researchers at the University of Washington have developed a technique to modify naturally occurring biological polymers with protein-based biochemical messages that affect cell behavior. Their approach, published the week of Jan. 18 in the Proceedings of the National Academy of Sciences, uses a near-infrared laser to trigger chemical adhesion of protein messages to a scaffold made from biological polymers such as collagen, a connective tissue found throughout our bodies.

Mammalian cells responded as expected to the adhered protein signals within the 3D scaffold, according to senior author Cole DeForest, a UW associate professor of chemical engineering and of bioengineering. The proteins on these biological scaffolds triggered changes to messaging pathways within the cells that affect cell growth, signaling and other behaviors.

These methods could form the basis of biologically based scaffolds that might one day make functional laboratory-grown tissues a reality, said DeForest, who is also a faculty member with the UW Molecular Engineering and Sciences Institute and the UW Institute for Stem Cell and Regenerative Medicine.

“This approach provides us with the opportunities we’ve been waiting for to exert greater control over cell function and fate in naturally derived biomaterials — not just in three-dimensional space but also over time,” said DeForest. “Moreover, it makes use of exceptionally precise photochemistries that can be controlled in 4D while uniquely preserving protein function and bioactivity.”

DeForest’s colleagues on this project are lead author Ivan Batalov, a former UW postdoctoral researcher in chemical engineering and bioengineering, and co-author Kelly Stevens, a UW assistant professor of bioengineering and of laboratory medicine and pathology.

Their method is a first for the field, spatially controlling cell function inside naturally occurring biological materials as opposed to those that are synthetically derived. Several research groups, including DeForest’s, have developed light-based methods to modify synthetic scaffolds with protein signals. But natural biological polymers can be a more attractive scaffold for tissue engineering because they innately possess biochemical characteristics that cells rely on for structure, communication and other purposes.

“A natural biomaterial like collagen inherently includes many of the same signaling cues as those found in native tissue,” said DeForest. “In many cases, these types of materials keep cells ‘happier’ by providing them with similar signals to those they would encounter in the body.”

They worked with two types of biological polymers: collagen and fibrin, a protein involved in blood clotting. They assembled each into fluid-filled scaffolds known as hydrogels.

The signals that the team added to the hydrogels are proteins, one of the main messengers for cells. Proteins come in many forms, all with their own unique chemical properties. As a result, the researchers designed their system to employ a universal mechanism to attach proteins to a hydrogel — the binding between two chemical groups, an alkoxyamine and an aldehyde. Prior to hydrogel assembly, they decorated the collagen or fibrin precursors with alkoxyamine groups, all physically blocked with a “cage” to prevent the alkoxyamines from reacting prematurely. The cage can be removed with ultraviolet light or a near-infrared laser.

Using methods previously developed in DeForest’s laboratory, the researchers also installed aldehyde groups to one end of the proteins they wanted to attach to the hydrogels. They then combined the aldehyde-bearing proteins with the alkoxyamine-coated hydrogels, and used a brief pulse of light to remove the cage covering the alkoxyamine. The exposed alkoxyamine readily reacted with the aldehyde group on the proteins, tethering them within the hydrogel. The team used masks with patterns cut into them, as well as changes to the laser scan geometries, to create intricate patterns of protein arrangements in the hydrogel — including an old UW logo, Seattle’s Space Needle, a monster and the 3D layout of the human heart.

The tethered proteins were fully functional, delivering desired signals to cells. Rat liver cells — when loaded onto collagen hydrogels bearing a protein called EGF, which promotes cell growth — showed signs of DNA replication and cell division. In a separate experiment, the researchers decorated a fibrin hydrogel with patterns of a protein called Delta-1, which activates a specific pathway in cells called Notch signaling. When they introduced human bone cancer cells into the hydrogel, cells in the Delta-1-patterned regions activated Notch signaling, while cells in areas without Delta-1 did not.

These experiments with multiple biological scaffolds and protein signals indicate that their approach could work for almost any type of protein signal and biomaterial system, DeForest said.

“Now we can start to create hydrogel scaffolds with many different signals, utilizing our understanding of cell signaling in response to specific protein combinations to modulate critical biological function in time and space,” he added.

With more-complex signals loaded on to hydrogels, scientists could then try to control stem cell differentiation, a key step in turning science fiction into science fact.

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The research was funded by the National Science Foundation, the National Institutes of Health and Gree Real Estate.

For more information, contact DeForest at [email protected]

Source: https://bioengineer.org/lasers-molecular-tethers-create-perfectly-patterned-platforms-for-tissue-engineering/

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