Everything below concerns quality control. We keep the language plain, cite what the science says, and separate well-supported claims from open questions.
Last reviewed on 2026-01-16. Where a claim depends on a specific study, the study is described rather than over-claimed.
Quality control samples are inserted at intervals to monitor accuracy and precision throughout a batch. Blank samples detect contamination, while spiked samples assess recovery from the sample matrix. Calibration standards establish the relationship between detector response and concentration, and control samples are prepared independently from them whenever possible. Laboratories also participate in proficiency testing and maintain audit trails, instrument logs, and reagent records. Ongoing review of control charts can reveal trends before they cause out-of-specification results.
Method validation demonstrates that an HPLC procedure is suitable for its intended purpose. Common validation parameters include accuracy, precision, specificity, linearity, range, limit of detection, limit of quantification, and robustness. Accuracy reflects agreement with a reference value, while precision describes repeatability under defined conditions. Specificity shows whether the method can measure the analyte in the presence of impurities or matrix components. Validation documents are reviewed before a method is used for routine testing or regulatory submissions.
System suitability testing is performed before and during analytical runs to confirm that the instrument and method are working as expected. Typical checks include retention time, peak area precision, resolution between critical pairs, tailing factor, and theoretical plate count. Acceptance criteria are set in the method or pharmacopeial monograph. If a suitability check fails, the run may be rejected and the instrument or sample preparation may need investigation. This practice helps prevent release of data from a system that has drifted out of control.
Data handling and documentation are central to HPLC quality control. Electronic systems should have audit trails that record changes to methods, sequences, and results. Integration parameters, such as peak baseline and threshold, can affect reported areas and must be defined in advance. Out-of-specification results trigger a structured investigation that may include reanalysis, instrument checks, and review of sample preparation. Regulatory inspections often examine raw data, audit trails, and training records to verify that reported results are traceable and reliable.
Method validation establishes that an HPLC procedure is suitable for its intended use. Key parameters include accuracy, precision, specificity, linearity, range, limit of detection, limit of quantitation, and robustness. Accuracy measures agreement with a true or accepted value, while precision describes repeatability and intermediate precision. Specificity confirms that the method measures the analyte without interference from impurities, degradants, or excipients. Validation is documented in a protocol and report, and acceptance criteria are set before experiments begin. Regulatory guidance varies by region, but the general principles are widely harmonized.
System suitability testing is performed before and during analytical runs to confirm that the instrument and method are working as expected. Common checks include retention time, peak area, resolution between critical pairs, tailing factor, and theoretical plate count. Results are compared with predefined limits, and a failed check requires investigation before sample results are reported. Quality control samples at low, middle, and high concentrations are injected at intervals to monitor accuracy and precision. Blank injections detect carryover and contamination, while control charts track performance over time.
| Property | Value | Notes |
|---|---|---|
| Validation parameter | Accuracy | Closeness to a reference value. |
| Validation parameter | Precision | Repeatability or intermediate precision. |
| Validation parameter | Linearity | Proportional response across a range. |
| System suitability check | Resolution | Separation between adjacent peaks. |
| Quality control tool | Control chart | Tracks results over time for trends. |
Instrumentation includes a solvent delivery system, an autosampler, a column oven, and one or more detectors. Reversed-phase columns with chemically modified silica are widely used, but normal-phase, ion-exchange, size-exclusion, and affinity modes exist for specific separations. Detectors may rely on ultraviolet absorbance, fluorescence, refractive index, or mass spectrometry. Column temperature, mobile phase composition, and flow rate are adjusted to improve resolution. System pressure is monitored because rising pressure can indicate column blockage or deteriorating packing.
Separation performance depends on particle size, pore size, column length, and the chemistry of the stationary phase. Smaller particles generally improve efficiency but require higher pressure and suitable instrumentation. The mobile phase often contains buffers and organic solvents that influence retention and selectivity. Testing labs select conditions based on the analytes, sample matrix, and required sensitivity. Method development frequently involves screening several columns and solvent mixtures before a final set of conditions is chosen.
High-performance liquid chromatography is an analytical technique that separates components in a liquid sample by passing them through a packed column under pressure. A pump delivers a mobile phase at a controlled flow rate, and an injector introduces the sample into the stream. Differences in how analytes partition between the mobile phase and the stationary phase cause them to exit the column at different times. Detection then records a signal proportional to the amount of each separated substance. The resulting chromatogram provides retention times and peak areas for identification and quantification.
Detection in HPLC testing commonly relies on ultraviolet-visible absorbance, fluorescence, refractive index, or mass spectrometry. A diode array detector records full spectra across a wavelength range, which helps identify co-eluting peaks. Mass spectrometry provides mass-to-charge ratios and can confirm molecular identity at low concentrations. The choice of detector depends on analyte structure, required sensitivity, and whether quantitation or identification is the goal. No single detector works for every compound, and method development often compares responses before selecting one.
High-performance liquid chromatography is an analytical technique that separates components of a liquid sample by passing it through a packed column under pressure. A pump delivers a mobile phase at a controlled flow rate, and an injector introduces the sample into the stream. Differences in interaction with the stationary phase cause analytes to migrate at different rates. Detectors record elution as peaks, and a data system converts signals into a chromatogram. The method is suited to compounds that dissolve in a liquid and are not volatile enough for gas chromatography.
Routine quality control includes blanks, duplicates, spiked samples, and certified reference materials. Calibration curves are prepared with standards at several concentrations, and the detector response is checked for linearity. Carryover, column aging, mobile phase evaporation, and temperature drift can shift retention times or peak areas. Maintenance such as replacing seals, filters, and columns helps prevent failures. Records of injections, integration, and deviations support traceability. Audits may request raw data and instrument logs for each batch.
Developing an HPLC test begins with defining the analytes, matrix, and required reporting limits. Chemists select a separation mode, column chemistry, mobile phase composition, flow rate, and detection wavelength or mass transition. Experiments then adjust these variables to achieve adequate retention, resolution, and peak shape. System suitability tests confirm that the instrument and method perform consistently before sample analysis. Without suitable resolution, quantitative results may be unreliable. Preliminary runs often use scouting gradients to locate retention windows.
Method validation demonstrates that an analytical procedure is suitable for its intended purpose. Typical validation characteristics include accuracy, precision, specificity, linearity, range, limit of detection, limit of quantitation, and robustness. Regulatory guidance from bodies such as the International Council for Harmonisation and the United States Pharmacopeia outlines expectations, though specific criteria depend on the product and method. System suitability tests are run before sample analysis to confirm resolution, peak symmetry, column efficiency, and injection repeatability. Failure of these checks can invalidate a batch of measurements.
Practical HPLC testing depends on careful sample preparation and instrument maintenance. Samples may require filtration, dilution, pH adjustment, or extraction to avoid column damage and matrix interference. Mobile phases are degassed and filtered, and columns are equilibrated before injection. Common problems include peak tailing, baseline drift, ghost peaks, carryover, and co-elution of analytes. Documentation of instrument logs, calibration records, and electronic audit trails supports data integrity and traceability. Ongoing training and routine maintenance help reduce variability between analysts and laboratories.
Quality control laboratories use HPLC to check identity, purity, concentration, and stability of raw materials and finished products. A validated method specifies the column, mobile phase, flow rate, detection wavelength, injection volume, and run time. Samples are prepared and compared against reference standards of known concentration. The resulting chromatogram provides quantitative data, such as assay values and impurity levels. This approach is common in pharmaceutical, food, environmental, and industrial testing where consistent measurements are required.
In Ethiopia, the Civil Aviation Accident Prevention and Investigation Bureau of the Ethiopian Civil Aviation Authority (ECAA), which is an agency of the Ministry of Transport and Communications, conducts aircraft accident investigations in Ethiopia or involving Ethiopian aircraft. In France, the agency responsible for investigation of civilian air crashes is the Bureau d'Enquêtes et d'Analyses pour la Sécurité de l'Aviation Civile (BEA). Its purpose is to establish the circumstances and causes of the accident and to make recommendations for their future avoidance. In Germany, the agency for investigating air crashes is the Federal Bureau of Aircraft Accidents Investigation (BFU). It is an agency of the Federal Ministry of Transport and Digital Infrastructure. The focus of the BFU is to improve safety by determining the causes of accidents and serious incidents and making safety recommendations to prevent recurrence.
Smith was hired to become the defensive quality control coach for the Tennessee Titans in 2011 under new head coach Mike Munchak. Smith then became the offensive quality coach the following season. In 2013, Smith was promoted to the assistant offensive line and assistant tight ends coach. Munchak was fired after the 2013 season and new head coach Ken Whisenhunt retained Smith as the assistant tight ends coach. Midway through the 2015 season, Whisenhunt was fired and replaced by tight ends coach Mike Mularkey. Mularkey was kept as head coach for the 2016 season and Smith was promoted to the new tight ends coach. When Mularkey was fired after the 2017 season, new head coach Mike Vrabel kept Smith as the tight ends coach for 2018. On January 21, 2019, Smith was promoted to offensive coordinator, replacing Matt LaFleur, who departed to become head coach of the Green Bay Packers two weeks prior. In his first year as offensive coordinator, Smith oversaw the highest-scoring Titans team in 16 years, with Derrick Henry, Ryan Tannehill, and Jonnu Smith having career years. Smith was praised for his play-calling in the Titans' 28–12 road victory over the top-seeded Baltimore Ravens in the AFC Divisional Round. In 2020, the Titans ranked fourth in scoring and second in total yards.
The medical equipment approach involves combining a continuous glucose monitor and an implanted insulin pump that can function together with a computer-controlled algorithm to replace the normal function of the pancreas. The development of continuous glucose monitors has led to the progress in artificial pancreas technology using this integrated system. Unlike the continuous sensor alone, the closed-loop system requires no user input in response to reading from the monitor; the monitor and insulin pump system automatically delivers the correct amount of hormone calculated from the readings transmitted. The system is what makes up the artificial pancreas device.
DeepMind is known to have trained the program on over 170,000 protein structures from the Protein Data Bank, a public repository of protein sequences and structures. The program uses a form of attention network, a deep learning technique that focuses on having the AI identify parts of a larger problem, then piece it together to obtain the overall solution. The overall training was conducted on processing power between 100 and 200 GPUs.
Altria, like other tobacco companies, has invested in science and medical companies which develop and produce medical products for conditions caused or aggravated by smoking. As of 2024, Altria's three medical subsidiaries included the following: Cronos group (maker of recreational cannabis), Lexaria Bioscience (Developer of proprietary drug delivery technology, DehydraTECH, to improve active pharmaceutical ingredients entrance into the bloodstream) and Micreos (biotech company focused on discovering and developing recombinant proteins for chronic dermatology and oncology conditions). It has also designed and marketed electronic cigarettes. Altria launched the MarkTen and Green Smoke brands. Both of these were discontinued in December 2018. The company returned to this market through the acquisition of NJOY.
Sources: en.wikipedia.org
The use of high-resolution ion-mobility mass spectrometry (IMS-MS) on HPLC-purified alpha-synuclein in vitro has shown alpha-synuclein to be autoproteolytic (self-proteolytic), generating a variety of small molecular weight fragments upon incubation. The 14.46 kDa protein was found to generate numerous smaller fragments, including 12.16 kDa (amino acids 14–133) and 10.44 kDa (40–140) fragments formed through C- and N-terminal truncation and a 7.27 kDa C-terminal fragment (72–140). The 7.27 kDa fragment, which contains the majority of the NAC region, aggregated considerably faster than full-length alpha-synuclein. It is possible that these autoproteolytic products play a role as intermediates or cofactors in the aggregation of alpha-synuclein in vivo.
RGD and other bioactive ligands can be presented on the surface of a biomaterial in a number of different spatial arrangements, and it has been demonstrated that these arrangements have a significant impact on cell behavior. In self-assembled monolayers, it was found that adhesion and proliferation of both human umbilical vein endothelial cells (HUVECs) and human mesenchymal stem cells (MSCs) increased as a function of RGD peptide density. These studies also showed that RGD density could change integrin expression, which has been postulated to enable control of biochemical signaling pathways. Further investigation of MSCs on self-assembled monolayers showed that modulating RGD density and the affinity of RGD for αvβ3 (through use of linear and cyclized RGD) could be used to control the differentiation of MSCs. The effect of RGD presentation on cells in 3D biomaterials, which more accurately replicate the in vivo environment, has also been evaluated. In degradable polyethylene glycol hydrogels, the length of capillary-like structures formed by HUVECs was directly proportional to the density of RGD in the hydrogel. Additionally, studies in nano-patterning have shown that, whereas an increase in global RGD density increases cell adhesion strength until saturation, an increase in local (mico/nano-scale) RGD density does not follow this trend.
Molecular biomarkers have been defined as biomarkers that can be discovered using basic and acceptable platforms such as genomics and proteomics. Many genomic and proteomics techniques are available for biomarker discovery and a few techniques that are recently being used can be found on that page. Apart from genomics and proteomics platforms biomarker assay techniques, metabolomics, lipidomics, glycomics, and secretomics are the most commonly used as techniques in identification of biomarkers. Biomarkers can be classified on their clinical applications as molecular biomarkers, cellular biomarkers or imaging biomarkers. Four of the main types of molecular biomarkers are genomic biomarkers, transcriptomic biomarkers, proteomic biomarkers and metabolic biomarkers.
The topology of a β-sheet describes the order of hydrogen-bonded β-strands along the backbone. For example, the flavodoxin fold has a five-stranded, parallel β-sheet with topology 21345; thus, the edge strands are β-strand 2 and β-strand 5 along the backbone. Spelled out explicitly, β-strand 2 is H-bonded to β-strand 1, which is H-bonded to β-strand 3, which is H-bonded to β-strand 4, which is H-bonded to β-strand 5, the other edge strand. In the same system, the Greek key motif described above has a 4123 topology. The secondary structure of a β-sheet can be described roughly by giving the number of strands, their topology, and whether their hydrogen bonds are parallel or antiparallel. β-sheets can be open, meaning that they have two edge strands (as in the flavodoxin fold or the immunoglobulin fold) or they can be closed β-barrels (such as the TIM barrel). β-Barrels are often described by their stagger or shear. Some open β-sheets are very curved and fold over on themselves (as in the SH3 domain) or form horseshoe shapes (as in the ribonuclease inhibitor). Open β-sheets can assemble face-to-face (such as the β-propeller domain or immunoglobulin fold) or edge-to-edge, forming one big β-sheet.
Sources: en.wikipedia.org
CHO cells are the most common mammalian cell line used for mass production of therapeutic proteins such as monoclonal antibodies, used in 70% of therapeutic mAbs. They can produce recombinant protein on the scale of 3–10 grams per liter of culture. Products of CHO cells are suitable for human applications, as these mammalian cells perform human-like post-translational modifications to recombinant proteins, which is key to the functioning of several proteins. It is believed that CHO-K1 transgenic cells are characterized by higher monoclonal antibody expression at the expense of biomass accumulation, for example, based on CHO-K1, monoclonal antibody‑producing cell lines with productivity up to 6.5 grams per liter have been selected. Cell culture Drug development Preclinical development
The core of comparative genome analysis is the establishment of the correspondence between genes (orthology analysis) or other genomic features in different organisms. Intergenomic maps are made to trace the evolutionary processes responsible for the divergence of two genomes. A multitude of evolutionary events acting at various organizational levels shape genome evolution. At the lowest level, point mutations affect individual nucleotides. At a higher level, large chromosomal segments undergo duplication, lateral transfer, inversion, transposition, deletion and insertion. Entire genomes are involved in processes of hybridization, polyploidization and endosymbiosis that lead to rapid speciation. The complexity of genome evolution poses many exciting challenges to developers of mathematical models and algorithms, who have recourse to a spectrum of algorithmic, statistical and mathematical techniques, ranging from exact, heuristics, fixed parameter and approximation algorithms for problems based on parsimony models to Markov chain Monte Carlo algorithms for Bayesian analysis of problems based on probabilistic models. Many of these studies are based on the detection of sequence homology to assign sequences to protein families.
AST is similar to alanine transaminase (ALT) in that both enzymes are associated with liver parenchymal cells. The difference is that ALT is found predominantly in the liver, with clinically negligible quantities found in the kidneys, heart, and skeletal muscle, while AST is found in the liver, heart (cardiac muscle), skeletal muscle, kidneys, brain, and red blood cells. As a result, ALT is a more specific indicator of liver inflammation than AST, as AST may be elevated also in diseases affecting other organs, such as myocardial infarction, acute pancreatitis, acute hemolytic anemia, severe burns, acute renal disease, musculoskeletal diseases, and trauma. The elevated AST level in hemorrhagic fever caused by crimean-congo hemorrhagic fever virus is associated with high mortality rate. AST was defined as a biochemical marker for the diagnosis of acute myocardial infarction in 1954. However, the use of AST for such a diagnosis is now redundant and has been superseded by the cardiac troponins. Laboratory tests should always be interpreted using the reference range from the laboratory that performed the test. Example reference ranges are shown below:
Adenylyl cyclase is regulated by G proteins, which can be found in the monomeric form or the heterotrimeric form, consisting of three subunits. Adenylyl cyclase activity is controlled by heterotrimeric G proteins. The inactive or inhibitory form exists when the complex consists of alpha, beta, and gamma subunits, with GDP bound to the alpha subunit. In order to become active, a ligand must bind to the receptor and cause a conformational change. This conformational change causes the alpha subunit to dissociate from the complex and become bound to GTP. This G-alpha-GTP complex then binds to adenylyl cyclase and causes activation and the release of cAMP. Since a good signal requires the help of enzymes, which turn on and off signals quickly, there must also be a mechanism in which adenylyl cyclase deactivates and inhibits cAMP. The deactivation of the active G-alpha-GTP complex is accomplished rapidly by GTP hydrolysis due to the reaction being catalyzed by the intrinsic enzymatic activity of GTPase located in the alpha subunit. It is also regulated by forskolin, as well as other isoform-specific effectors:
Angiogenesis, the formation of new blood vessels, is often critical for tumour cells to survive and grow in nutrient-depleted conditions. Akt is activated downstream of vascular endothelial growth factor (VEGF) in endothelial cells in the lining of blood vessels, promoting survival and growth. Akt also contributes to angiogenesis by activating endothelial nitric oxide synthase (eNOS), which increases production of nitric oxide (NO). This stimulates vasodilation and vascular remodelling. Signaling through the PI3K-Akt pathway increases translation of hypoxia-inducible factor α (HIF1α and HIF2α) transcription factors via mTOR. HIF promotes gene expression of VEGF and glycolytic enzymes, allowing metabolism in oxygen-depleted environments.
Sources: en.wikipedia.org
Method validation is the documented process of showing that an HPLC procedure produces reliable results for a defined purpose. It examines parameters such as accuracy, precision, specificity, linearity, and robustness. Regulators and quality systems often require validation before routine use.
System suitability is a set of checks run on the chromatographic system before sample analysis. It confirms that resolution, peak shape, retention time, and response meet predefined limits. Failure can invalidate the run and trigger corrective action.
Blank injections reveal peaks or baseline disturbances that come from solvents, reagents, or the instrument rather than the sample. They help distinguish contamination from actual analyte signals. Comparing blanks with sample runs supports accurate interpretation.
System suitability is a set of checks that confirm the instrument and method perform within limits before sample analysis. It typically includes resolution, tailing factor, retention time, and peak area reproducibility. If a check fails, the run is invalidated until the cause is resolved.