Axillary nodes The lymph nodes under the arm.

Benign Not cancer.

Bilateral Affecting or about both the right and left sides of body. For example, a bilateral mastectomy is removal of both breasts.

Biobank (tissue repository) A large collection of tissue samples and medical data that is used for research studies.

Bioinformatics The field of endeavor that relates to the collection, organization and analysis of large amounts of biological data using networks of computers and databases.

Biopsy Removal of tissue to be looked at under a microscope.

BRCA1/BRCA2 Genes (breast cancer genes) Genes that help limit cell growth. A mutation in one of these genes increases a persons risk of breast, ovarian and certain other cancers.

Breast cancer An uncontrolled growth of abnormal breast cells.

Breast density A measure used to describe the relative amounts of fat and tissue in the breasts as seen on a mammogram.

Calcifications Deposits of calcium in the breast that appear as bright, white spots on a mammogram.

Cell The basic unit of any living organism.

Chemotherapy A drug or combination of drugs that kills cancer cells in various ways.

Clinical breast examination A physical exam done by a health care provider to check the look and feel of the breasts and underarm for any changes or abnormalities, such as lumps.

Clinical trials Research studies that test the benefits of possible new ways to detect, diagnose, treat or prevent disease. People volunteer to take part in these studies.

Core needle biopsy A needle biopsy that uses a hollow needle to remove samples of tissue from an abnormal area in the breast.

Ct scan (computerized tomography scan) A series of pictures created by a computer linked to an X-ray machine.

The scan gives detailed internal images of the body.

Cyst A fluid-filled sac.

Data mining The ability to query very large databases in order to satisfy a hypothesis (top-down data mining); or to interrogate a database in order to generate new hypotheses based on rigorous statistical correlations (bottom-up data mining).

DNA (deoxyribonucleic acid) The information contained in a gene.

DNA sequencing The technique in which the specific sequence of bases forming a particular DNA region is deciphered.

Expression (gene or protein) A measure of the presence, amount, and time-course of one or more gene products in a particular cell or tissue. Expression studies are typically performed at the RNA (mRNA) or protein level in order to determine the number, type and level of genes that may be up-regulated or down-regulated during a cellular process, in response to an external stimulus, or in sickness or disease.

Family history A record of the current and past health conditions of a persons blood-related family members that may help show a pattern of certain diseases within a family.

Genes The part of a cell that contains DNA. The DNA information in a persons genes is inherited from both sides of a persons family.

Gene expression Process in which a gene gets turned on in a cell to make RNA and proteins.

Genetic testing Analyzing DNA to look for a gene mutation that may show an increased risk for developing a specific disease.

Genome The total genetic information of an organism.

Genomic testing Analyzing DNA to check for gene mutations of a cancer tumor.

Genomics The study of genes and their functions.

Immunotherapy Therapies that use the immune system to fight cancer. These therapies target something specific to the biology of the cancer cell, as opposed to chemotherapy, which attacks all rapidly dividing cells.

Implant An envelope containing silicone, saline or both, that is used to restore the breast form after a mastectomy.

Informatics The science of information; the collection, classification, storage, retrieval, and dissemination of recorded knowledge treated both as a pure and as an applied science.

Invasive breast cancer Cancer that has spread from the original location into the surrounding breast tissue and possibly into the lymph nodes and other parts of the body.

Lesion Area of abnormal tissue.

Linear accelerator The device used during radiation therapy to direct X-rays into the body.

Lumpectomy (breast conserving surgery) Surgery that removes part of the breast the area containing and closely surrounding the tumor.

Lymph nodes Small groups of immune cells that act as filters for the lymphatic system. Clusters of lymph nodes are found in the underarms, groin, neck, chest and abdomen.

Lymphedema Swelling due to poor draining of lymph fluid that can occur after surgery to remove lymph nodes or after radiation therapy to the area.

Malignant Cancerous.

Mammogram An X-ray image of the breast.

Mastectomy Surgical removal of the breast. The exact procedure depends on the diagnosis.

Medical oncologist A physician specializing in the treatment of cancer using chemotherapy, hormone therapy and targeted therapy.

Metastasize When cancer cells spread to other organs through the lymphatic and/or circulatory system.

MRI (magnetic resonance imaging) An imaging technique that uses a magnet linked to a computer to make detailed pictures of organs or soft tissues in the body.

Mutation Any change in the DNA of a cell. Gene mutations can be harmful, beneficial or have no effect.

Nipple-sparing mastectomy A breast reconstruction procedure that removes the tumor and margins as well as the fat and other tissue in the breast, but leaves the nipple and areola intact.

PET (positron emission tomography) A procedure where a short-term radioactive sugar is given through an IV so that a scanner can show which parts of the body are consuming more sugar. Cancer cells tend to consume more sugar than normal cells do. PET is sometimes used as part of breast cancer diagnosis or treatment, but is not used for breast cancer screening.

Protein Any of various naturally occurring extremely complex substances that consist of amino-acid residues joined by peptide bonds, contain the elements carbon, hydrogen, nitrogen, oxygen, usually sulfur and occasionally other elements.

Proteomics The cataloging of all the expressed proteins in a particular cell or tissue type, obtained by identifying the proteins from cell extracts.

Prophylactic mastectomy Preventive surgery where one or both breasts are removed in order to prevent breast cancer.

Radiation oncologist A physician specializing in the treatment of cancer using targeted, high energy X-rays.

Radiation Therapy Treatment given by a radiation oncologist that uses targeted, high energy X-rays to kill cancer cells.

Radiologist A physician who reads and interprets X-rays, mammograms and other scans related to diagnosis or follow-up.

Radiologists also perform needle biopsies and wire localization procedures.

RNA (ribonucleic acid) A molecule made by cells containing genetic information that has been copied from DNA. RNA performs functions related to making proteins.

Sentinel node biopsy The surgical removal and testing of the sentinel nodes the first axillary nodes in the underarm area filtering lymph fluid from the tumor site to see if the node contains cancer cells.

Stage of cancer A way to indicate the extent of the cancer within the body. The most widely used staging method for breast cancer is the TNM system, which uses Tumor size, lymph Node status and the absence or presence of Metastases to classify breast cancers.

Targeted therapy Drug therapies designed to attack specific molecular agents or pathways involved in the development of cancer. Herceptin is an example of a targeted therapy used to treat breast cancer Tomosynthesis (3D Mammography, Digital Tomosynthesis) A tool that uses a digital mammography machine to take multiple two dimensional X-ray images of the breast. Computer software combines the multiple 2D images into a three dimensional image.

Tumor An abnormal growth or mass of tissue that may be benign (not cancerous) or malignant (cancerous).

Ultrasound Diagnostic test that uses sound waves to make images of tissues and organs. Tissues of different densities reflect sound waves differently.

Sources: Susan G. Komen; Federal University of Rio Grande do Sul, Brazil.

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A glossary of commonly used breast cancer terminology - Meadville Tribune

Agility is critical for South African manufacturers looking to modernise their environments. They need to embrace the concept of Industry 4.0 to leverage cloud-based technology and cyber-physical systems to operate the smart factory required in todays digital world.

By Morn de Villiers: integration architect and project manager at TechSoft International

An intelligent manufacturing solution must be considered if a business is to bridge the gap between existing legacy solutions and the connected ones needed for the cloud. This will help local operators gain supply chain efficiencies and compete in the global marketplace.

When a business can make intelligent decisions, it can optimally allocate its people and resources to minimise machine and equipment downtime. Process automation becomes essential in this regard.

When a manufacturer uses the data generated through this intervention and power intelligent automation, it can reduce manual processes, increase productivity, refocus its workers on more value-added tasks, and enable better decision-making across the organisational footprint.

Data integration

Manufacturers collect, store, and use data across various machines, systems, and data repositories. This data needs to be unified to optimise efficiency to provide business and technology leaders with complete access. Protocols are numerous, which presents a challenge in connecting the data across operations. Connecting the various data stores takes cloud-based intelligence, data mining, and analytics.

A connected intelligence platform that integrates data across processes, equipment, and Industrial Internet of Things devices becomes the cornerstone of the shift toward Industry 4.0. Furthermore, this platform can intelligently unify data for greater access and control and confidently predict the future to help reduce costs, improve operational efficiencies, and increase profitability.

South African manufacturers understand all too well the need for consistent production processes. Intelligent automation does provide for this. Quality control (QC) is used to identify defects or flawed products, but this takes up valuable time and resources. It is, therefore, better to have consistent production that ensures each product is flawless.

Reducing or eliminating inconsistencies saves time and resources in QC. It also lowers defect rates on products delivered to buyers. Artificial intelligence (AI) models empower manufacturers to combine historical and real-time supply chain data to find quality issues early. The AI learns from automated root-cause analysis and other processes to dynamically improve quality consistency.

Injecting resilience

Supply chain optimisation is vital if a manufacturer is to be competitive in modern distribution channels. Buyers throughout the distribution channel rely on on-time delivery of products. Any delays or bottlenecks in production and distribution provide competitors with a significant advantage in filling orders.

Industry 4.0 and automated intelligence enable proactive responses to supply chain changes. Instead of manually recognising demand changes or problems, the data reveals inventory insights and allows the business to optimise transportation and logistics as required.

Locally, manufacturers must contend with significant industry regulations impacting production processes and products. And while South Africa is not unique in this regard, it does introduce a new layer of complexity into the process. Fortunately, Industry 4.0 automation enables greater efficiency and accuracy in meeting compliance by reducing opportunities for human error, improving audit and tracking information, and providing analytics that helps identify potential compliance issues before they arise.

Driving revenue and profit

Companies will see optimised revenue and profit by adopting automated processes and benefitting from greater accuracy resulting from manufacturing intelligence. They can maximise resource utilisation to deliver products on time while keeping costs minimum. Automation also enables agility as manufacturers scale to accommodate new customers or products being brought to market.

As a result of the events over the past two years, Industry 4.0 is foundational to the manufacturing ecosystems success. Automation brings benefits such as integrated data insight, process optimisation, supply chain resilience, and compliance with industry regulations that cannot be ignored.

Getting a connected intelligence platform in place to provide manufacturing intelligence for the modern smart factory is no longer a luxury but a business necessity for local companies.

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Connected intelligence helps SA manufacturers transition to Industry 4.0 - IT-Online

The U.S. Centers for Disease Control and Prevention (CDC) will not release its review of post-COVID-19-vaccination heart inflammation.

The CDC has been performing abstractions on reports of post-vaccination myocarditis, a form of heart inflammation, submitted to theVaccine Adverse Event Reporting System.

But the agency is saying that federal law prevents it from releasing the results.

The abstractions are considered medical records which are withheld in full from disclosure, the CDC told The Epoch Times in a recent letter, responding to a Freedom of Information Act request.

One of the exemptions in the act says that agencies can withhold materials that are specifically exempted from disclosure by statute, if that statute(i) requires that the matters be withheld from the public in such a manner as to leave no discretion on the issue; or (ii) establishes particular criteria for withholding or refers to particular types of matters to be withheld; and (B) if enacted after the date of enactment of the OPEN FOIA Act of 2009, specifically cites to this paragraph.

The CDC pointed to the Public Health Service Act, which was enacted in 1944, and says that vaccine injury reports and other information that may identify a person shall not be made available to any person except the person who received the vaccine or a legal representative for that person.

The information sought is available through the CDC websitewithout details that would identify patients, the agency also said.

The CDC said that it does not have a formal definition of abstraction but that it means the process of reviewing medical records, including autopsy reports and death certificates, and recording data in a database. Please note that this definition means that any abstracted data, because they originate from medical records, is also considered medical records, a CDC records officer told The Epoch Times in an email.

Refusing to release the data raises concerns about transparency, according to Barbara Loe Fisher, co-founder and president of the National Vaccine Information Center.

The stubborn refusal of officials heading up federal health agencies responsible for protecting the public health to come clean with Americans about what they know about COVID vaccine risks is stunning, Fisher told The Epoch Times in an email.

Fisher noted that the CDC has funded electronic medical record systems that collect personal health information and that the agency shares the data with a number of third parties, such as contractors and researchers.

Yet, CDC officials are claiming they cannot release de-identified abstraction information curated from the medical records of individuals, who have suffered myocarditis or died after COVID shots? This looks and feels like a coverup of the true risks of COVID vaccines, Fisher said.

Fisher called for a congressional probe into what she described as the disturbing lack of transparency on the part of federal agency officials, who granted COVID vaccine manufacturers an Emergency Use Authorization (EUA) to widely distribute the vaccines in December 2020 and have recommended and aggressively promoted the vaccines for mandated use ever since.

In response to a separate Freedom of Information Act request, the CDC initially said that it did not perform any abstractions or produce any reports on post-vaccination myocarditis. That request was for reports betweenApril 2, 2021, and Oct. 2, 2021.

The agency alsofalsely said that a link between myocarditis and the messenger RNA COVID-19 vaccines was not known during that time.

A possible link between those vaccines, made by Pfizer and Moderna, became known in early 2021. Many experts now acknowledge the link is likely or definitely causal.

The CDC later issued a correction on the false claim, as well as the claim that the agency started performing a type of data mining on VAERS data as early as February 2021.

The CDC said in its correction that myocarditis abstractions began being performed in May 2021.

Notified that its response was false and asked to do a fresh search, the records office did not respond.

Appeals have been lodged in that case and after the more recent response withholding the records.

Dr. Rochelle Walensky, the CDCs director, said in a press conference in April 2021 that the agency had not detected a link between the vaccines and myocarditis. The basis for that statement remains unclear.

The refusal to provide the myocarditis abstractions is part of a pattern with the CDCand its partner, the Food and Drug Administration (FDA).

The CDC still hasnt released the results of the data mining, to The Epoch Times, Sen. Ron Johnson (R-Wis.), or a nonprofit called Childrens Health Defense. The agency also declined to provide results from a different monitoring system, V-safe, to a nonprofit called Informed Consent Action Network, which then sued the agency and just recently received the first tranche of data.

The FDA, meanwhile, has refused to release the results of a different type of analysis on the VAERS data, claiming it cannot separate the results from protected internal communications. The agency is also withholding autopsies conducted on people who died after getting COVID-19 vaccines, pointing to exceptions laid out in the Freedom of Information Act.

Along with Johnson, several other lawmakers are pressing at least one of the agencies to release the data, asserting that not doing so is illegal.

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Zachary Stieber covers U.S. and world news for The Epoch Times. He is based in Maryland.

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EXCLUSIVE: CDC Won't Release Review of Post-Vaccination Heart Inflammation - The Epoch Times

A snapshot of key trends and developments in data handling according to selected panellists from the chromatography sector.

Q. What is currently the biggest problem in data management for chromatographers?

Christoph Nickel: Currently one of the major challenges is the increasing number of samples to run, analyses to conduct, and data to review while keeping data quality high and detecting any potential error. A major driver for this is increasingly complex separation and detection techniques that are required to analyze biotherapeutics. The result being that the chromatographer increasingly needs to use mass spectrometry (MS). Furthermore, the consolidation of all this information into an easily viewable and sharable format at a central location is a massive challenge. This is particularly important for information that is required to take an informed final review and approval. A typical example is the weighing results for calibration standards generated from a balance that should be connected to the calibration data in the chromatography data system (CDS) for confirmation of proper calibration and eventual accurate quantitation of the unknown compounds.

Ofrit Pinco: One of the biggest challenges for chromatographers is that data from different vendors cannot be incorporated together and analyzed collectively due to a lack of a unified data format. Chromatographers can only review data from one system at a time and answer specific questions. This makes it harder to access and conduct secondary analysis across multiple data systems. To address this challenge, several pharmaceutical companies have sponsored the Allotrope Foundation (1) whose initiative is to unify data formats. In addition, some startups are building tools to translate data into a common format. However, both initiatives will take some time and collaboration to overcome this challenge.

Anne Marie Smith: Chromatographers use a variety of different instruments from various vendors, each with their own proprietary data formats. One big problem area is bringing together and managing the data from the different electronic systems. The ability to normalize all that disparate data while retaining the ability to interrogate it, as in native data processing software, is very beneficial to chromatographers. Since chromatography data are so ubiquitous, effective management in a central, accessible place is essential.

Bjrn-Thoralf Erxleben: Handling large quantities of data requires a lot of time for data processing and interpretation. Additionally, depending on the local situation, secure data storage and archiving can be time consuming, and administration of these processes gets more and more complex.

Although there are network-based multiinstrument-capable CDSs, all vendors support and maintain their proprietary data format firstdata file formats for photodiode array detectors (PDA) and for MS instruments are closed. Even when providing drivers to other CDS systems, still several requests/wishes are not satisfied. Hardware-wise, hybrid configurations may contain different operation workflows, and parameters cannot easily be transferred between vendors. Direct comparison of data between different instruments can be difficult.

Q. What is the future of data handling solutions for chromatographers?

Christoph Nickel: I see three main trends: first, radically streamlined and simplified user experience with more fit-for-purpose applications; second, an agglomeration of data from different sources in a single central repository in a consolidated formatoften referred to as a Data Lake. This will reduce the time for data review/analysis because it eliminates any manual data transfers or manual consolidation of spreadsheets or PDF files. Third, more and more automation of routine tasks using machine learning (ML) (for routine reviews) and algorithmassisted data mining to identify patterns, trends, outliers, or deviations.

In addition, data will continue to become available anywhere, anytime, so there will be no further need to be in the laboratory or at the instrument to analyze your data, and no need for installation and maintaining software applications on your device. Everything will be available online.

Ofrit Pinco: The future of data handling goes well beyond acquiring and analyzing data generated by a single chromatography system. As new tools and solutions are being developed, and as researchers are being expected to extract more information from their samples, chromatographers will need to access and analyze data from multiple instruments and data systems simultaneously. Right now, chromatographers have multiple tools to help them focus on multiple areas, but future tools will allow them to review information from the whole workflow in one space. This has potential to enable researchers to answer more questions. This will also be valuable as requirements and regulations for compliance become stricter. New tools will also give research teams insight into historical instrument performance data, leading to increased operational efficiency and even predictive maintenance. Data handling will only continue to become more streamlined and more advanced through the utilization of these types of tools combined with artificial intelligence (AI) and ML. These are the next steps needed to reach the lab of the future and Industry 4.0.

Anne Marie Smith: The cloud is the future of data handling. All systems will connect to the cloud. Its secure, simplifies the infrastructure thereby reducing costs, provides better performance, and is scalable. Depending on the system you choose, it can also be future proof. It is important, however, that systems architects take into account the scientists data access requirements. Whether the data needs to be accessed immediately upon generation, or at a later date, should inform how data management solutions are architected to ensure a seamless transition to cloud-based data access.

Bjrn-Thoralf Erxleben: We already see cloud-based data storage options at several CDS installations, and this trend will continue because it renders data access and sharing far easier. At the same time, this will require a new level of data security and data protection. A positive aspect is that data storage and archiving is outsourced and will not bind IT resources on-site.

AI software will be implemented in standard software, for peak picking, processing, and identification using database packages. Selflearning algorithms will support method optimization and provide an estimation of retention time, based on structural information of the analyte.

Developing and maintaining special programs and databases for research use is a time- and resource-intensive task. If such a standard is being accepted and used in the industry, instrument vendors have to provide data compatible with these programs. There might be also agreements about new standard data formats, which will be used or supported via conversion.

Last, but not leastit would be nice to see that workflows and parameter definition is adjusted between the vendors and that data processing, at least for two-dimensional (2D) data, becomes a common piece of software accessible via the web, to be used by all chromatographers, after logging on to the dedicated cloud.

Q. What one recent development in Big Data is most important for chromatographers from a practical perspective?

Christoph Nickel: While it might sound boring, the biggest impact on the analytical laboratory is the ability to bring data together from all instruments and all devices working on the same function, the same research, or the same discipline. The availability of data in one location is a mandatory prerequisite for every analysis, insight, or application of algorithms. So, any effort that chromatographers can make to bring their data together brings them a major step closer to fast, efficient, and error-free analysis, moving from reactive review or error handling to proactive problem prevention. This can be realized from the availability of unlimited computing power in the cloud, which is becoming more mainstream for deployment of globally connected systems.

Ofrit Pinco: AI and ML have been growing rapidly in the last few years and people are realizing more of their advantages on a daily basis. Take search engines for example, Google has drastically changed the way we search for answers, plan our travels, and consume news. As AI and ML technologies mature, more scientists with this skill set will enter the chromatography field and apply these technologies to the laboratory.

In the current state, chromatographers analyze data based on specific questions, with the aim of confirming predefined hypotheses. Through AI and ML, chromatographers may be able to uncover new trends and patterns from a large set of unstructured data, giving chromatographers insights they didnt know existed. This will greatly facilitate the progress of scientific research in the long run.

Anne Marie Smith: AI and ML can help find relationships and insights in an otherwise overwhelming amount of data, providing potential predicted outcomes. While AI and ML can drastically improve processes, it is only as good as the data that is input. For instance, for chromatographers where there are a multitude of possible instrument combinations, if data collection is of poor quality or incomplete, the results may be biased.

Bjrn-Thoralf Erxleben: Analytical intelligence features such as autorecovery, start-up, self-check, and feedback. Apart from additional automation, this enables quick and easy hardware diagnostics and helps to decrease downtime of the systems. By applying more process analytical techniques (PAT) features and more feedback from the system to the central server, chromatographers can focus on their work and need to care less for the hardware.

Q. What obstacles do you think stand in the way of chromatographers adopting new data solutions?

Christoph Nickel: One of the greatest challenges is the need to comply with good manufacturing practice (GMP) and data integrity guidelines. The validation guidelines were drafted for on-premise deployment of software, and laboratories now need to transform their validation principles into a more decentralized, globally connected world with often abstracted storage. In simple termsthe demands to prove that your data integrity is maintained now require you to include at least one additional playerthe host of your data. This increases complexity of your validation tasks and requires a change in thinking and conducting validation.

Another significant obstacle is the potential delay of data access driven from the need to transfer the data from the laboratory to the central location/entity and access it there. While the internet and cloud performance are fast enough to provide a positive user experience, the in-house infrastructure is often the rate-limiting step. For example, a single low performance element in your local area network such as an old 10 MB switch can slow down your entire data traffic by a factor of 10. Suitability of the infrastructure is a critical prerequisite for transferring the data into the central repositories and increases dependency on your IT infrastructure.

Ofrit Pinco: A few factors contribute to this slow adoption. First is the complex laboratory ecosystem. Due to the interconnectedness of systems and solutions, any change must be evaluated for its impact on all components within the ecosystem. Also, downtime needs to be minimized, as many laboratories are in production and operate on a 24/7 schedule. After implementation, regulated labs require validation for the change. Additional training is also required for technicians to adopt new standard operating procedures (SOPs) and avoid errors. As a result, adopting new solutions is difficult and time-consuming.

Anne Marie Smith: Adopting new data solutions is a daunting task. It involves time to set up the system in a useful way, time for validation and implementationensuring the system meets the data integrity requirements and ensuring the data are secureand time to learn the new system. These factors often lead to reluctance to change, which can stand in the way of adoption of useful solutions.

Bjrn-Thoralf Erxleben: Changing an established workflow is a critical matter for analytical laboratories and operators do not always come with a strong analytical background and experience. New user interfaces, new operation workflows, and, in the worst cases, new definitions for known parameters in the software present a lot of training for users until a new solution is finally adapted. Risk of longer downtime is high. Right now, we are confronted with objection to installation of necessary service packs or patches to be compatible with modern operating systems and virus protection.

New features and functionality need to prove their advantage first before new software is rolled out and established. Another aspect is the data comparison and transfer, what happens with the old data? Legislations require that old data and results have to be kept and provided for inspection if neededis maintaining a piece of the old software a good solution? Especially when it means some knowledge of how to operate it needs to be available.

Q. What was the biggest accomplishment or news in 2021/2022 for data handling?

Christoph Nickel: The adoption of the cloud with unlimited storage, computing power that enables data agglomeration, and new levels of advanced and super-fast analysis of data.

Ofrit Pinco: In the past two years, more data scientists have entered and brought changes to the analytical industry. Data scientists are skilled at analyzing and extracting insights from structured and unstructured data by using scientific methods, algorithms, and systems. They can be good complementary partners to application scientists, who have backgrounds in chemistry and understand the cases and workflows in the laboratory. Together with application scientists, data scientists can utilize models and algorithms to analyze and visualize historical data and let application scientists relate new findings to workflows and experiments.

In addition to scientific findings, data scientists may also improve laboratory operation efficiency by evaluating instrument performance and data management metrics. Data scientists may provide new perspectives on how laboratories can better store, organize, and manage data.

Anne Marie Smith: Streaming live data as its acquired locally and storing it in a cloud instance of a CDS has improved IT systems. With the recent development in Big Data, this simplifies data movement for downstream data analytics.

Reference

Christoph Nickel is the Director of Software Marketing at ThermoFisher Scientific.

Ofrit Pinco is Senior Product Manager at Agilent Technologies.

Anne Marie Smith is Product Manager, Mass Spectrometry & Chromatography at ACD/Labs.

Bjrn-Thoralf Erxleben is Senior Manager at Shimadzu Europa in charge of the pharmaceutical and biopharmaceutical market.

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Trends and Developments in Data Handling 2022 - LCGC Chromatography Online