Everything below concerns research chemical. We keep the language plain, cite what the science says, and separate well-supported claims from open questions.
Updated 2026-07-01. Numbers and descriptions here follow the published literature rather than marketing material.
Because REV-ERB receptors are core clock components, SR9009 has been examined for effects on daily rhythms as well as metabolism. Research has explored whether the compound can shift or reinforce circadian gene expression in tissues such as liver and muscle. Some studies report improved metabolic markers in obese or diabetic mice, while others show context-dependent responses. Questions remain about which effects are direct, which are secondary to timing, and how they might differ across species.
SR9009 is a synthetic small molecule studied as an agonist of REV-ERB nuclear receptors. REV-ERB alpha and REV-ERB beta help regulate circadian rhythms and metabolic gene expression. In laboratory research, SR9009 has been used to probe how these receptors affect skeletal muscle, liver, and adipose tissue. The compound was identified in academic drug-discovery work and is often described in scientific literature by its chemical name and research code. It is not an approved medicine, and human clinical data remain limited or absent.
SR9009 binds REV-ERB receptors and alters their repressive activity on target genes. This action can change transcription of genes involved in lipid handling, glucose metabolism, and mitochondrial function. In rodent studies, treated animals have shown changes in muscle oxidative capacity and exercise performance, though effects vary by dose, duration, and model. The precise molecular steps connecting receptor binding to whole-body outcomes are still an active area of investigation. Findings in animals do not automatically translate to humans.
Research interest in SR9009 grew from studies showing improved running endurance in mice after short treatment periods. Those experiments linked the compound to increased mitochondrial content and fatty acid oxidation in muscle, but the findings come from animal models and specific dosing schedules. Independent replication has been limited, and the pathways connecting REV-ERB activation to exercise performance are still being mapped. Whether similar responses occur in humans is an open question.
SR9009 is often grouped with compounds studied for circadian and metabolic regulation rather than with classical anabolic steroids. Its interactions with nuclear receptors differ from those of androgen receptor ligands, and its proposed mechanisms involve transcriptional control rather than direct hormone signaling. Some sources classify it as a metabolic modulator because of observed effects on energy utilization. The distinction matters for regulation and for interpreting research results across different compound classes.
| Property | Value | Notes |
|---|---|---|
| Chemical class | Synthetic REV-ERB agonist | Small-molecule nuclear receptor ligand |
| Appearance | Off-white to pale yellow solid | Typical for research-grade powder |
| Solubility class | Soluble in DMSO; low water solubility | Common stock solutions use organic solvent |
| Typical stock solvent | Dimethyl sulfoxide | Used for laboratory assays |
| Common analytical method | HPLC with UV or mass detection | Used for identity and purity checks |
SR9009 is not approved as a medicine by major regulatory agencies. It is commonly sold as a research chemical, a category that may fall outside customary drug approval and quality rules. In sports, the World Anti-Doping Agency lists SR9009 as a prohibited substance. Athletes who use it can face sanctions if it is detected in a sample. Legal status varies by country, and importation may be restricted. Enforcement practices differ across borders.
Detection of SR9009 in biological samples usually employs liquid chromatography coupled with tandem mass spectrometry. This method can identify the parent compound and sometimes metabolites in urine or blood. Because exposure can be low and clearance may be rapid, sample timing and limits of detection matter. Laboratories validate assays for sensitivity and specificity. Results are interpreted alongside chain-of-custody and quality-control records. Urine is the common matrix for anti-doping analysis, while blood may be used in research settings.
=== Host immune response === Fibroblasts from different anatomical sites in the body express many genes that code for immune mediators and proteins. These mediators of immune response enable the cellular communication with hematopoietic immune cells. The immune activity of non-hematopoietic cells, such as fibroblasts, is referred to as "structural immunity". In order to facilitate a fast response to immunological challenges, fibroblasts encode crucial aspects of the structural cell immune response in the epigenome.
=== Screenwriter === Culp wrote scripts for seven I Spy episodes, one of which he also directed. He later wrote and directed two episodes of The Greatest American Hero, including the series finale. Culp also wrote scripts for other television series, including Trackdown, a two-part episode from The Rifleman, and Cain's Hundred.
An artificial neural network is based on a collection of nodes also known as artificial neurons, which loosely model the neurons in a biological brain. It is trained to recognise patterns; once trained, it can recognise those patterns in fresh data. There is an input, at least one hidden layer of nodes and an output. Each node applies a function and once the weight crosses its specified threshold, the data is transmitted to the next layer. A network is typically called a deep neural network if it has at least 2 hidden layers. Learning algorithms for neural networks use local search to choose the weights that will get the right output for each input during training. The most common training technique is the backpropagation algorithm. Neural networks learn to model complex relationships between inputs and outputs and find patterns in data. In theory, a neural network can learn any function. In feedforward neural networks the signal passes in only one direction. The term perceptron typically refers to a single-layer neural network. In contrast, deep learning uses many layers. Recurrent neural networks (RNNs) feed the output signal back into the input, which allows short-term memories of previous input events. Long short-term memory networks (LSTMs) are recurrent neural networks that better preserve longterm dependencies and are less sensitive to the vanishing gradient problem. Convolutional neural networks (CNNs) use layers of kernels to more efficiently process local patterns.
Platt, Harris & Tishkoff (2026) reconstruct likely patterns of interbreeding between Neanderthals and anatomically modern humans on the basis of the study of their X chromosomes, interpreted as indicating that their interbreeding predominantly involved Neanderthal men mating with anatomically modern women. Evidence from the study of Middle and Upper Paleolithic assemblages, indicating that overall anatomically modern human occupations can be distinguished from Neanderthal ones on the basis of tighter and more cohesive clusters of archaeological remains, is presented by Merino-Pelaz & Cobo-Sánchez (2026). Evidence of utility of the study of nonmetric traits at the enamel-dentine junction for distinguishing teeth of Neanderthals and modern humans is presented by Becam, Chevalier & Colard (2026). Kanis et al. (2026) identify amino acid changes in the growth hormone receptor of Neanderthals, including a change driving faster cell growth, and report evidence of more muscle mass in modern humans who inherited the gene encoding the Neanderthal growth hormone receptor through admixture. Zhang et al. (2026) present a new method for identification of evidence of archaic ancestry in modern human genomes, and report evidence of an introgression from an unknown archaic lineage into the ancestors of modern humans before their migration out of Africa. Evidence of effectiveness of the imputation in detection of Neanderthal and Denisovan ancestry in low-coverage ancient genomes is presented by Capodiferro et al. (2026) . Rao et al.
Sources: en.wikipedia.org
is the molecular mass. In general, however, the viscosity of a system depends in detail on how the molecules constituting the system interact, and there are no simple but correct formulas for it. The simplest exact expressions are the Green–Kubo relations for the linear shear viscosity or the transient time correlation function expressions derived by Evans and Morriss in 1988. Although these expressions are each exact, calculating the viscosity of a dense fluid using these relations currently requires the use of molecular dynamics computer simulations. Somewhat more progress can be made for a dilute gas, as elementary assumptions about how gas molecules move and interact lead to a basic understanding of the molecular origins of viscosity. More sophisticated treatments can be constructed by systematically coarse-graining the equations of motion of the gas molecules. An example of such a treatment is Chapman–Enskog theory, which derives expressions for the viscosity of a dilute gas from the Boltzmann equation.
=== D-amino acids === Some amino acids contain the opposite absolute chirality, chemicals that are not available from normal ribosomal translation and transcription machinery. Most bacterial cells walls are formed by peptidoglycan, a polymer composed of amino sugars crosslinked with short oligopeptides bridged between each other. The oligopeptide is non-ribosomally synthesised and contains several peculiarities including D-amino acids, generally D-alanine and D-glutamate. A further peculiarity is that the former is racemised by a PLP-binding enzymes (encoded by alr or the homologue dadX), whereas the latter is racemised by a cofactor independent enzyme (murI). Some variants are present, in Thermotoga spp. D-Lysine is present and in certain vancomycin-resistant bacteria D-serine is present (vanT gene).
== History == Historically this equation arose as a variant on the Prony equation; this variant was developed by Henry Darcy of France, and further refined into the form used today by Julius Weisbach of Saxony in 1845. Initially, data on the variation of fD with velocity was lacking, so the Darcy–Weisbach equation was outperformed at first by the empirical Prony equation in many cases. In later years it was eschewed in many special-case situations in favor of a variety of empirical equations valid only for certain flow regimes, notably the Hazen–Williams equation or the Manning equation, most of which were significantly easier to use in calculations. However, since the advent of the calculator, ease of calculation is no longer a major issue, and so the Darcy–Weisbach equation's generality has made it the preferred one.
Sources: en.wikipedia.org
SR9009 is a synthetic research compound that acts on REV-ERB nuclear receptors. It is not approved for human use and is sold only as a research chemical. Its effects have been studied mainly in cells and rodents.
It binds REV-ERB alpha and REV-ERB beta and changes the expression of genes tied to metabolism and circadian rhythm. These changes can affect mitochondria and energy use in animal models. The exact chain from receptor binding to physiological outcome is still being mapped.
No reliable human trials show that SR9009 improves athletic performance. Some rodent studies report endurance changes, but these results are not proof of human benefit. Its use in sport is prohibited, and quality and safety data are lacking.
It is a synthetic REV-ERB agonist used mainly in preclinical research. It is not an approved medicine for human use.