Whether a drug can effectively bind to its target protein, or how different parts of a virus or a cell interact, depends on processes taking place at the level of individual molecules. Their behaviour is determined not only by their shape, but also by the electrostatic forces acting between them. Understanding how these forces are distributed helps scientists explain why certain molecules attract each other, how they bind and how they interact.This is where the <a target="_blank" rel="noopener noreferrer" href="https://acc.biodata.ceitec.cz/">Atomic Charge Calculator (ACC)</a> comes in. Researchers at Masaryk University have been developing this computational tool for more than a decade. Its third and most advanced generation to date was developed at CEITEC Masaryk University and the Faculty of Science at Masaryk University, in collaboration with the Faculty of Informatics at Masaryk University and Palacký University Olomouc.ACC III calculates the electric charges of individual atoms and visualises their distribution directly within three-dimensional molecular structures. It brings together twenty different calculation methods in a single environment and can work with a wide range of molecules – from small organic compounds to proteins and other large biomolecular structures.A key advantage of Atomic Charge Calculator III is that it combines a broad range of computational methods with ease of access. Basic calculations can be performed directly in a web browser, with no need to install specialised software or register for an account. The resulting charges can be displayed directly on a 3D model of the molecule. For advanced users and large-scale analyses, the platform also supports automated data processing and allows its computational core to be run locally.<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/CEITEC_Co_pritahuje_molekuly_Nova_generace_nastroje_z_CEITEC_zviditelnuje_sily_mezi_atomy_d32d7bd2cd.jpg" alt="CEITEC/Biological Data Management and Analysis Core Facility: What Attracts Molecules? A New Generation of a CEITEC Tool Makes the Forces Between Atoms Visible: Oseltamivir bound to influenza virus neuraminidase (blue: positive partial charges, red: negative charges). A strong electrostatic interaction occurs between a negatively charged region of the drug and positively charged regions of the protein." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_CEITEC_Co_pritahuje_molekuly_Nova_generace_nastroje_z_CEITEC_zviditelnuje_sily_mezi_atomy_d32d7bd2cd.jpg 140w," sizes="100vw" width="1600" height="1786"><h3>Why are scientists interested in the electric charges of atoms?</h3>Electrons are not distributed evenly within molecules. Individual atoms can therefore carry different partial electric charges. These charges play an important role in determining how molecules behave towards their surroundings and towards one another.Positively and negatively charged regions of molecules can attract each other, while regions with the same charge repel each other. The distribution of these charges therefore helps determine which molecules approach each other, how they orient themselves and how strongly they may bind. In biology, this is important, for example, in molecular recognition or when a drug binds to its target protein. Understanding partial charges therefore helps scientists gain insight into the interactions underlying the behaviour of many molecular systems.There is, however, no single universal way to determine partial charges. Different computational methods can produce different values, and the choice of method also depends on how the results will be used. Accurate quantum-chemical calculations can also become very computationally demanding when large numbers of structures are involved. ACC III therefore brings together empirical methods that allow these calculations to be performed more quickly and on a large scale.“From the outset, we designed ACC III to make calculating partial atomic charges as straightforward as possible. Users do not need to install anything or deal with complicated settings – they simply upload a molecule and start the calculation with a single click. The application then suggests suitable methods from the twenty available and can handle both small molecules and large biomolecular structures,” says <a target="_blank" rel="noopener noreferrer" href="https://www.ceitec.eu/tomas-racek-ph-d/u94476?page=publication">Tomáš Raček</a>, who has been developing and maintaining ACC since 2018.<h3>How an antiviral drug binds to an influenza virus protein</h3>The authors demonstrate what charge distribution can reveal using the well-known antiviral drug oseltamivir, marketed as Tamiflu. The drug binds to neuraminidase, a protein of the influenza A virus. ACC III can show directly within the three-dimensional structure where positive and negative partial charges are located and how they contribute to the interaction between the drug and the protein.“The example of oseltamivir clearly shows why it is useful not only to calculate the charges, but also to visualise them directly within the structure. This allows us to see which parts of the two molecules interact electrostatically. The same approach can be used to study many other molecular systems,” explains Tomáš Raček.ACC III is not limited to small molecules. It can also work with proteins and other large biomolecular structures. The authors demonstrate this using a second example: the SARS-CoV-2 pore complex. Coronaviruses form these pores during replication in infected cells and use them to transport viral RNA across membranes. Visualising partial charges helps distinguish the parts of the complex that pass through the non-polar environment of the membrane from its more highly charged regions.<h3>From calculation straight to a 3D model</h3>The new generation of the Atomic Charge Calculator also offers more advanced visualisation. It uses Mol*, which allows calculated charges to be mapped directly onto a three-dimensional structure and explored interactively. A visualisation created using ACC III was also selected for the cover of the special Nucleic Acids Research Web Server Issue. Created by Tomáš Raček, the cover image shows the distribution of partial charges in a membrane protein system.<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/CEITEC_Co_pritahuje_molekuly_Nova_generace_nastroje_z_CEITEC_zviditelnuje_sily_mezi_atomy_3_85cc310925.jpeg" alt="CEITEC/Oxford University Press: What Attracts Molecules? A New Generation of a CEITEC Tool Makes the Forces Between Atoms Visible: Cover page in Nucleic Acids Research journal." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_CEITEC_Co_pritahuje_molekuly_Nova_generace_nastroje_z_CEITEC_zviditelnuje_sily_mezi_atomy_3_85cc310925.jpeg 124w," sizes="100vw" width="1600" height="2011">Results can be downloaded in several standard formats and, for the first time, stored directly in the mmCIF format commonly used for three-dimensional biomolecular structures. Information about the structure and its calculated charges can therefore remain together in a single file and be used by other tools.For researchers who need to process large numbers of structures, ACC III also provides an API and a computational core that can be run locally. The platform can therefore be used both for individual calculations through its web interface and as part of larger automated analyses.<h3>More than a decade of development</h3>Scientists introduced the first Atomic Charge Calculator in 2015, followed by its second generation in 2020. In the meantime, the team also developed specialised tools: PDBCharges for structures from the Protein Data Bank and AlphaCharges for structures from the AlphaFold Database.“The Atomic Charge Calculator was never a one-off project,” says <a target="_blank" rel="noopener noreferrer" href="https://www.ceitec.eu/prof-radka-svobodova-ph-d/u17518?page=publication">Radka Svobodová</a>, Head of the Biological Data Management and Analysis Core Facility at CEITEC Masaryk University. “For more than a decade, we have been continuously developing tools that respond both to new possibilities in structural bioinformatics and to the needs of their users. The third generation combines straightforward web-based use with capabilities that are important for the automated processing of large numbers of molecular structures.”<blockquote>Atomic Charge Calculator III is freely available through its web interface and does not require registration for basic use. Its development was supported by the ELIXIR CZ research infrastructure.</blockquote><h5>The authors presented the new generation of the tool in <a target="_blank" rel="noopener noreferrer" href="https://academic.oup.com/nar/article/54/W1/W211/8662890">Nucleic Acids Research</a>.</h5>

What Attracts Molecules? A New Generation of a CEITEC Tool Makes the Forces Between Atoms Visible 

Not every concentration job can tolerate heat — and not every concentrator that handles heat well can handle cold. <a target="_blank" rel="noopener noreferrer" href="https://labrulez.com/en-us/all/products/2441">Refrigerated centrifugal vacuum concentrators</a> exist for a specific reason: proteins, RNA, enzymes, and synthetic peptides degrade quickly under thermal stress, and standard concentrators that rely on heat alone can compromise sample integrity before evaporation finishes. We compared two of the leading refrigerated systems — the Labconco Refrigerated CentriVap and the <a target="_blank" rel="noopener noreferrer" href="https://labrulez.com/en-us/all/products/2611">Thermo Fisher Refrigerated SpeedVac SRF110</a> — to see how they stack up for labs where sample preservation is non-negotiable.<blockquote><a target="_blank" rel="noopener noreferrer" href="https://explore.organomation.com/labconco-refrigerated-centrivap-vs-thermo-fisher-refrigerated-speedvac">Download the full comparison with pricing here!</a></blockquote><h3>The Problem Refrigeration Actually Solves</h3>It's a detail that gets lost in most spec-sheet comparisons: running a<a target="_blank" rel="noopener noreferrer" href="https://labrulez.com/en-us/lcms/products?manufacturerItems=540&amp;search=centrivap"> vacuum concentrator </a>without temperature control can cause a sample to over-cool itself. As solvent evaporates under vacuum, the process pulls latent heat from the sample — the same evaporative cooling effect that makes sweat feel cold on skin. For a heat-stable solvent, that's irrelevant. For a temperature-sensitive biomolecule, it can drop the sample low enough to risk unwanted freezing or sublimation mid-run. Labconco's own engineering notes for the Refrigerated CentriVap describe this directly: the unit pairs a refrigeration system with a heater so operators can hold a precise set-point temperature and avoid sublimated samples caused by evaporative cooling. That's the whole reason this product category exists, and it's worth understanding before comparing specs line by line.<h3>Price: A Wider Gap at the High End</h3>The Labconco Refrigerated CentriVap is typically priced around $13,000 USD. The Thermo Fisher SRF110 runs higher, generally $15,000–$17,000 USD, and that's before factoring in the modular kit components — Thermo Fisher sells the SRF110 with a separate vacuum system, rotor, vapor trap, and heat transfer fluid bundled into the total kit price. The price gap between these two refrigerated units is proportionally larger than what we found comparing their non-refrigerated counterparts, which makes the value case for the CentriVap even more pronounced at this tier.<h3>Capacity: The Difference That Matters Most Right Now</h3>This is where the two systems really separate, and it lines up with where lab demand is heading. The Refrigerated CentriVap processes up to 312 samples per run; the SRF110 tops out at 132. Both handle volumes up to 50 mL, so this isn't a volume trade-off the way it was in our earlier comparison — it's a straight throughput advantage for the CentriVap.That gap matters because<a target="_blank" rel="noopener noreferrer" href="https://peptidestaff.com/industry-trends/peptide-therapy-market-forecast-2026/"> peptide-based therapeutics </a>have become one of the fastest-growing classes of drugs in the pharmaceutical industry, and manufacturing capacity — not demand — is widely cited as the primary bottleneck holding the sector back. <a target="_blank" rel="noopener noreferrer" href="https://labrulez.com/en-us/all/article/7541">GLP-1 programs</a> in particular are pushing <a target="_blank" rel="noopener noreferrer" href="https://www.bachem.com/knowledge-center/glp-1-demand-for-peptide-manufacturers/">peptide manufacturers</a> toward industrialized, large-scale production platforms rather than one-off batch work. <a target="_blank" rel="noopener noreferrer" href="https://www.organomation.com/">Sample prep equipment</a> sits squarely inside that bottleneck: every purification run, HPLC fraction, and concentration step has to scale alongside the chemistry. A concentrator that processes more than double the samples per run, like the Refrigerated CentriVap, is a meaningfully different proposition for a peptide synthesis or purification lab than it would have been five years ago.<h3>Temperature Range: How Cold You Can Go</h3>The CentriVap's refrigeration reaches as low as -10°C; the SRF110 bottoms out at -4°C. Both units heat up to +100°C. That six-degree gap on the cold end isn't dramatic on paper, but for the most heat-labile peptides and enzymes, the lower floor gives operators more margin before a sample is at risk — particularly during longer runs where evaporative cooling has more time to compound any thermal load on the sample.<h3>Vortex Speed and Mechanical Flexibility</h3>The CentriVap offers an adjustable vortex range from 500 to 1,800 rpm, while the SRF110 runs up to 1,725 rpm. The CentriVap's lower floor is the more useful distinction here — fragile, large, or shear-sensitive samples (think intact proteins or larger peptide constructs) sometimes need to run at reduced rotor speed to avoid mechanical stress, and the CentriVap gives operators that headroom on both ends rather than just a ceiling.<h3>Lid Material: Visibility vs. Durability</h3>The Refrigerated CentriVap uses a glass lid, letting operators see into the chamber during a run. The SRF110 uses an epoxy-coated aluminum lid, which trades visibility for added durability. For labs running visually distinct sample batches — colored buffers, multiple project codes, HPLC fractions pulled at different time points — the ability to glance at the chamber without interrupting a run has real workflow value, especially in a core facility setting where one instrument serves multiple research groups.<h3>What This Means for Core Facilities</h3>Shared instrumentation cores feel throughput and cold-temperature constraints differently than a single-PI lab does. A<a target="_blank" rel="noopener noreferrer" href="https://blog.organomation.com/blog/an-appreciation-for-academic-core-labs"> core running samples from multiple research groups </a>— some doing routine RNA cleanup, others running heat-labile peptide fractions from an HPLC purification step — needs an instrument that won't bottleneck on either axis. The CentriVap's combination of higher per-run capacity and a lower refrigeration floor gives a shared facility more room to serve mixed workloads without forcing every group onto the same conservative settings a less flexible unit would require.<h3>Built for the Same Footprint</h3>Both units are sized for fume hood placement, with the CentriVap measuring 15.0 x 27.2 x 13.9 inches against the SRF110's 16.62 x 25 x 13.75 inches — close enough that floor planning isn't a deciding factor either way. Both carry standard one-year warranties.<h3>The Bottom Line</h3>For labs concentrating proteins, RNA, enzymes, or peptide fractions at scale — particularly anyone supporting peptide therapeutic development, where production capacity is already the limiting factor industry-wide — the Labconco Refrigerated CentriVap's combination of higher throughput, lower cold-temperature floor, and lower price makes it the stronger fit. The SRF110 remains a capable option for labs with smaller batch sizes already standardized on Thermo Fisher's modular vacuum ecosystem.Organomation distributes the full Labconco CentriVap line, including the Refrigerated and Refrigerated Pro models. Contact our team at sales@organomation.com to talk through rotor configuration, cold trap pairing, and throughput planning for your lab.&nbsp;<blockquote><h5><a target="_blank" rel="noopener noreferrer" href="https://explore.organomation.com/labconco-refrigerated-centrivap-vs-thermo-fisher-refrigerated-speedvac">Download the full comparison with pricing here!</a></h5></blockquote>

Refrigerated SpeedVac SRF110 vs. CentriVap - Why Sample Capacity Matters More Than Ever for Peptide and Protein Labs

What if you could assess a tumor's metastatic potential — without extensive sample prep — directly from tissue? Rachel Wood is working on exactly that.Rachel Wood is a PhD candidate at Queen's University (Kingston, Ontario),&nbsp;working jointly in the labs of Dr. Richard Oleschuk (Chemistry) and Dr. Chris&nbsp;Nicoll (Pathology &amp; Molecular Medicine). Her research applies Ambient Ionization Mass Spectrometry — specifically the Liquid Microjunction Surface Sampling Probe &nbsp;(LMJ-SSP) and Desorption Electrospray Ionization (DESI) — to profile the metabolomics of non-metastatic and metastatic clear cell Renal Cell Carcinoma (ccRCC) cell lines. The long-term goal: a rapid, minimally invasive MS-based prognostic tool for kidney cancer.This is the first reported use of LMJ-SSP to assess metastatic status in ccRCC —&nbsp;and early PCA results are already showing distinct separation between cell line&nbsp; profiles.<h5>In this episode, David and Rachel cover:</h5><ul><li>Why ccRCC is such a resistant biomarker problem despite active genomic, proteomic, and metabolomic research</li><li>The clinical stakes of "diagnosed by accident" — and what a prognostic biomarker actually needs to do for a clinician</li><li>How AIMS complements (not replaces) LC-MS in a discovery-phase cancer workflow</li><li>LMJ-SSP vs. DESI: what each technique sees that the other misses, including spatial resolution differences (~1mm vs. ~50µm)</li><li>Why Rachel runs LMJ-SSP on both a triple quad and a Q-TOF — and what each instrument contributes to the same research question</li><li>How her lab repurposed a 3D printer as a custom automated XYZ sampling stage with 50µm movement precision</li><li>Managing high-dimensional metabolomics data across two polarities, two cell lines, and two mass analyzers — the pipeline explained</li><li>What unsupervised machine learning (PCA) showed in early results</li><li>The path from cell lines to excised tumor tissue to, ultimately, liquid biopsies</li><li>Advice for analytical chemists considering work at the chemistry–oncology interface</li></ul><h3>Video Transcription</h3>Kidney cancer remains a difficult biomarker problem, particularly when researchers try to distinguish tumors that are likely to behave aggressively from those that are not. In a recent episode of Concentrating on Chromatography, PhD researcher Rachel Wood discussed how she is combining rapid ambient ionization mass spectrometry, LMJ-SSP, DESI, multivariate data analysis, and conventional LC-MS workflows to investigate the molecular differences associated with clear cell renal cell carcinoma and its metastatic potential.<h4>Returning to the research question that started it all</h4>Wood first encountered kidney cancer research during her undergraduate studies. Although she later moved into a more instrumentation-focused master’s project involving breast cancer, the subject of kidney cancer remained unfinished business.When the opportunity arose to choose a PhD project, she returned to the field that had originally sparked her interest in research. She is now conducting her doctoral work at Queen’s University in Kingston, Ontario, in an interdisciplinary environment spanning chemistry, pathology, molecular medicine, computer science, and clinical research.<h4>Why clear cell renal cell carcinoma is such a difficult biomarker problem</h4>Wood’s research focuses on clear cell renal cell carcinoma (ccRCC), which represents the majority of kidney cancer cases discussed in the interview.The difficulty is not a lack of research effort. Genomics, proteomics, and metabolomics studies have all produced promising biomarker candidates. The challenge is that different studies often identify different sets of markers, with relatively little overlap between them.Patient-to-patient biological variability further complicates the picture. A candidate biomarker panel that performs well in one cohort may not reproduce equally well in another. Selecting which features deserve further investigation therefore becomes a major analytical and biological challenge.<h4>From incidental diagnosis to prognostic information</h4>Another difficulty is the way kidney cancer is often discovered.Rather than being detected through a dedicated screening program, tumors may be found incidentally during imaging performed for another reason. According to Wood, the ideal solution would be a diagnostic biomarker capable of detecting kidney cancer early. Such a marker is not currently available within the scope of the work she described.That places additional importance on prognostic biomarkers. Once a tumor has been detected, clinicians need as much information as possible about how aggressive it may be and how likely it is to progress.Wood’s current work is therefore not attempting to declare directly that a particular patient has metastatic disease. Instead, the goal is to understand what changes biologically as a tumor moves from a less aggressive to a more aggressive state.That means asking which molecular pathways change, which mechanisms become altered, and which analytes may eventually provide useful indicators of tumor behavior.<h4>AIMS and LC-MS answer different questions</h4>A central theme of the interview was the relationship between rapid ambient ionization mass spectrometry and conventional LC-MS.Wood is clear that AIMS is not intended to replace LC-MS. Instead, she sees the two approaches as complementary.AIMS provides a rapid way to obtain a broad molecular overview of a sample with minimal preparation. This makes it especially useful during the discovery phase, when the goal is to gather as much molecular information as possible and identify features that distinguish biological groups.LC-MS then becomes valuable when those features need to be separated, characterized more carefully, and quantified with greater confidence.In Wood’s workflow, rapid MS screening can therefore help identify which analytes deserve closer attention before more targeted LC-MS investigations are performed.<h4>What “minimal sample preparation” actually means</h4>The amount of sample preparation required depends strongly on the type of material being analyzed.Wood currently works mainly with cultured human cell lines, which means much of the preparation begins with growing the cells themselves. Depending on the experiment, the cells may then be chemically lysed or subjected to fixation, paraffin embedding, and sectioning before analysis by LMJ-SSP or DESI.She also notes that the possibilities would be different for freshly excised tumor material. In an appropriate perioperative or biosafety laboratory setting, LMJ-SSP could potentially be used to analyze tumor margins more directly.The practical meaning of “minimal preparation” is therefore highly dependent on the sample and the analytical question being asked.<h4>Why combine LMJ-SSP and DESI?</h4>Rather than choosing between LMJ-SSP and DESI, Wood uses the two approaches because they provide different types of information.LMJ-SSP offers rapid, localized analysis and can accommodate samples with uneven surface topography, which is particularly useful for excised tissue.DESI, by contrast, provides substantially higher spatial resolution. In the workflow described in the interview, DESI can achieve spatial resolution on the order of tens of micrometers, whereas LMJ-SSP operates at a much larger sampling scale depending on probe design.That higher spatial resolution is important when moving from simplified cell-line models toward actual tumor tissue, where the molecular organization of different regions may reveal biologically relevant differences.Combining both techniques therefore enables researchers to balance speed, surface compatibility, and spatial information.<h4>Two mass analyzers for two parts of the same problem</h4>Wood also uses two different mass spectrometers in her LMJ-SSP work.Most of the discovery work is carried out using a triple quadrupole mass spectrometer, particularly because her project focuses strongly on lower-mass analytes and small molecules.Once statistically important analytes have been identified, a Q-TOF instrument can be used for MS/MS experiments and tentative structural identification.The two instruments therefore address different stages of the workflow: one supports broad discovery and comparison, while the other provides additional structural information needed to connect selected features with possible metabolic pathways.<h4>Turning a 3D printer into an automated sampling platform</h4>One of the most unusual elements of the project is the automated sampling system used with LMJ-SSP.Historically, LMJ-SSP measurements could be performed manually or with custom-built XYZ positioning stages. Wood’s laboratory instead adapted a 3D printer into an automated positioning system.Custom software controls the printer and moves the sampling probe with precise control over its X, Y, and Z positions. According to Wood, the system enables movements down to approximately 50 µm and also controls parameters such as the contact time between the probe and sample and the delay between consecutive sampling events.This level of automation improves reproducibility because variables that would otherwise depend on manual operation can be controlled systematically.Even the Z-position can be programmed, allowing the probe to operate at constant or varying heights depending on the sample surface.<h4>Managing high-dimensional MS data</h4>The experimental design creates a large data-analysis challenge.Wood is working with multiple cell lines, both ionization polarities, and more than one mass analyzer. To maintain traceability, she analyzes the data in clearly defined subsets.A typical workflow might begin with one mass analyzer and one polarity. Within that condition, the lower-aggressiveness and higher-aggressiveness cell models can then be compared directly.The data processing pipeline includes steps such as:<ul><li>binning,</li><li>normalization,</li><li>transformation,</li><li>dimensionality reduction,</li><li>identification of discriminating molecular features.</li></ul>Once relevant features are identified, MS/MS analysis can be used to investigate them further. The same process is then repeated across other polarities and instruments.<h4>Unsupervised machine learning reveals distinct molecular profiles</h4>Machine learning is particularly valuable because the AIMS datasets are highly dimensional, containing very large numbers of molecular features across multiple samples.Wood’s initial analysis has included principal component analysis (PCA), an unsupervised dimensionality-reduction technique.The early results are encouraging: the non-metastatic and metastatic cell-line models separate clearly in PCA space.That does not itself identify a definitive biomarker panel, but it indicates that the two biological states have distinguishable molecular profiles even though both belong to the same kidney cancer subtype.This provides an important foundation for identifying which specific metabolites or molecular pathways may be responsible for the separation.<h4>Testing LMJ-SSP for metastatic status</h4>A particularly novel aspect of Wood’s project is the use of LMJ-SSP to investigate differences associated with metastatic status.She had previously worked on distinguishing healthy and cancerous tissues, where the biological differences are relatively large. Comparing two states within the same cancer type is more demanding because the molecular changes may be much subtler.The scientific motivation, however, is strong. Tumors discovered at later stages carry greater concern for metastatic progression, making it important to understand the molecular changes associated with increasingly aggressive behavior.For Wood, this made the methodological risk worthwhile: the analytical challenge directly addresses an area where better prognostic information could ultimately have clinical value.<h4>A long-term goal: rapid MS-based prognostic testing</h4>The long-term vision extends beyond cell-line research.Wood is interested in whether rapid, minimally invasive mass spectrometry could eventually contribute to clinical decision-making.One possibility would be identifying a diagnostic biomarker in a liquid biopsy, potentially using blood or urine. A urine-based marker capable of contributing to earlier detection would be particularly attractive for kidney cancer.For prognostic applications, the goal would be slightly different. Once a tumor is discovered, molecular information could help characterize its likely behavior and provide clinicians with additional evidence when deciding between immediate treatment, surgery, or other management strategies.Wood emphasizes that such a tool would not replace standard clinical workflows. Instead, it would provide additional molecular information to complement existing diagnostic and pathological approaches.<h4>Why interdisciplinary work matters</h4>Throughout the interview, Wood repeatedly returns to the importance of collaboration.Her research sits at the intersection of analytical chemistry, pathology, oncology, computation, and clinical medicine. No single researcher can remain equally current in all of these fields.Working closely with pathologists, computer scientists, surgeons, medical students, and other specialists therefore provides both scientific expertise and a way to stay connected with developments outside one’s immediate discipline.For Wood, the biological context has also changed how she thinks about analytical chemistry. Developing a new instrument or method is important, but understanding who may ultimately benefit from that analytical capability makes the research more meaningful.That patient-centered perspective is now an integral part of how she approaches the project.<h4>An intersection full of opportunities</h4>Wood describes the interface between analytical chemistry and oncology as an especially exciting area for young researchers.The work can range from troubleshooting instrumentation to interpreting pathology, developing statistical methods, and collaborating across disciplines. That diversity creates constant opportunities to learn and to apply analytical chemistry to clinically relevant questions.Her project illustrates that modern mass spectrometry research is increasingly about more than generating spectra. It involves connecting instrumentation, spatial sampling, multivariate statistics, machine learning, biological interpretation, and clinical needs into a single analytical strategy.And in kidney cancer, where reliable prognostic biomarkers remain difficult to establish, that integrated approach may be particularly valuable.<blockquote>This text has been automatically transcribed from a video presentation using AI technology. It may contain inaccuracies and is not guaranteed to be 100% correct.</blockquote><blockquote><h4>Concentrating on Chromatography Podcast</h4>Dive into the frontiers of chromatography, mass spectrometry, and sample preparation with host David Oliva. Each episode features candid conversations with leading researchers, industry innovators, and passionate scientists who are shaping the future of analytical chemistry. From decoding PFAS detection challenges to exploring the latest in AI-assisted liquid chromatography, this show uncovers practical workflows, sustainability breakthroughs, and the real-world impact of separation science. Whether you’re a chromatographer, lab professional, or researcher you'll discover inspiring content!You can find Concentrating on Chromatography Podcast in podcast apps:<ul><li><a target="_blank" rel="noopener noreferrer" href="https://open.spotify.com/show/1wZg67rCjoGju4yhqxI3tL">Spotify</a></li><li><a target="_blank" rel="noopener noreferrer" href="https://podcasts.apple.com/tz/podcast/concentrating-on-chromatography/id1788429185">Apple podcast</a></li><li><a target="_blank" rel="noopener noreferrer" href="https://podscan.fm/podcasts/concentrating-on-chromatography">podscan</a></li></ul>and on <a target="_blank" rel="noopener noreferrer" href="https://www.youtube.com/@ChromatographyTalk/videos">YouTube channel</a></blockquote>

AIMS vs. LC-MS for Cancer Diagnostics: ccRCC Metabolomics with Rachel Wood

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Since its establishment in 2011, CEITEC has quickly developed into a cutting-edge infrastructure for research which performs highly alongside the best institutes in Europe. Among the main priorities of CEITEC are the promotion of a motivating and dynamic international scientific environment, the provision of state-of-the-art research infrastructure, and the policy of open communication and equal opportunities.

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Organomation, founded in 1959, designs and manufactures high-quality nitrogen evaporators and extraction systems. Known for innovation and durability, their lab instruments are used globally for efficient sample preparation with strong customer support.

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Phenomenex Axia Preparative LC Columns

Available in over 40 unique achiral and chiral selectivities, Axia advanced preparative column packings and column hardware designs offer several advantages.

Guaranteed, Longest Lasting Preparative HPLC and SFC ColumnAvailable in over 40 unique achiral and chiral selectivities, Axia advanced preparative column packings and column hardware designs offer several advantages. Unlike traditional preparative HPLC column packing methods, the Axia packing method offers increased sorbent bed density for increased performance and eliminates media bed collapse as a source of premature column failure in preparative HPLC/SFC columns.<h3>Axia Packed Preparative HPLC Columns</h3><h4>Axia Single Axial Compression Step</h4>Axia packed preparative columns involve a single axial compression step, unlike conventional packed preparative columns. The ideal prep HPLC column bed density is custom calculated and automated for each specific media and column size. Computer control of the entire process ensures both proper bed density and column uniformity every time.<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Phenomenex_Axia_Packing_Technology_1785ff0442.jpg" alt="Phenomenex: Axia Packing Technology" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Phenomenex_Axia_Packing_Technology_1785ff0442.jpg 245w," sizes="100vw" width="917" height="560"><h4>Unmatched Column Reproducibility</h4>The automated Axia™ packing system provides feedback control and infinite tuning of packing density for specific media characteristics such as mechanical strength and porosity. An optimum higher bed density can be consistently reproduced column-to-column. This directly translates into consistent efficiency and peak asymmetry measurements and decreases the column variability seen in traditionally packed preparative columns.<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Phenomenex_Unmatched_Column_Reproducibility_a752be8dd5.jpg" alt="Phenomenex: Unmatched Column Reproducibility" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Phenomenex_Unmatched_Column_Reproducibility_a752be8dd5.jpg 245w," sizes="100vw" width="995" height="534"><h3>Seamless Scalability to Increase Sample Load</h3><h4>Increase Column Length</h4>Increase sample load without increasing your flow rate by using a longer column. With Axia technology, each preparative column is optimized for:<ul><li>Analytical-like efficiency</li><li>Long column lifetime</li><li>High sample load with high surface area media such as Kinetex, Aeris, Gemini, Luna, Luna Omega, or Synergi</li></ul><h4>Increase Column ID</h4>For maximizing load without increasing the run time, consider scaling up to a larger column ID. Axia packed columns provide the three important benefits you need.<ul><li>Reproducible performance across all column diameters</li><li>Increased throughput without sacrificing purity</li><li>High efficiency from analytical to preparative</li></ul><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Phenomenex_Increase_Column_ID_a45d7f8aa3.jpg" alt="Phenomenex: Increase Column ID" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Phenomenex_Increase_Column_ID_a45d7f8aa3.jpg 156w," sizes="100vw" width="908" height="910"><h3>Axia Technology Outperforms Traditional Packing Processes!</h3>Because of the constant pressure placed on the integrated packing piston, Axia packed columns possess the dynamic capability of maintaining a consistent, homogeneous media bed. This results in superior column performance no matter which media selectivity you choose.<h3>How to choose the right column</h3><h5>Acidic Compounds</h5><ul><li>Kinetex XB-C18</li><li>Gemini C18</li></ul><h5>Aromatic</h5><ul><li>Luna Phenyl-Hexyl</li><li>Kinetex Biphenyl</li></ul><h5>Closely Related Compounds</h5><ul><li>Kinetex F5</li><li>Luna PFP(2)</li></ul><h5>Extremely Hydrophobic</h5><ul><li>Kinetex C8</li><li>Luna C8(2)</li></ul><h5>Extremely Polar</h5><ul><li>Luna HILIC(2)</li><li>Kinetex HILIC</li></ul><h5>Hydrophobic Compounds</h5><ul><li>Luna C18(2)</li><li>Kinetex C18</li></ul><h5>Polar Acids</h5><ul><li>Luna Omega Polar C18</li><li>Kinetex Polar C18</li></ul><h5>Polar Bases</h5><ul><li>Luna Omega PS C18</li><li>Kinetex PS C18</li></ul><h5>Sugars</h5><ul><li>Luna NH(2)</li></ul><h5>Chiral</h5><ul><li>Lux i-Amylose-3</li><li>Lux Cellulose-1</li></ul>

Products focused on PrepLC from Phenomenex - page 1

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