Kappa is a graphic emoticon commonly used by trolls as a postscript to a sentence to convey sarcasm on the live streaming video platform Twitch; it's popularity has also lead to the emoticon being used as a form of spam.
The Kappa emote is based on a grey-scale photograph of Josh DeSeno, then an employee of Justin.TV working on the chat client, which was uploaded during the early days of Justin.TV, along with others emotes based on JTV employees.
As of February 2014, the emote is used 900,000 times on average per day; by June 2015 this had already increased to around 1 million times on average per day. Twitch has added a total of four emotes featuring Kappa: Kappa (), Keepo (), MiniK () and KappaHD (); another unique Kappa titled KappaRoss ( ), featuring the original with Bob Ross' hair, was added in October 2015 in celebration of Twitch Creative. On February 18th, 2012, r/kappa/subreddit was established. It has got over 13,900 readers. On July 13th, 2014, first submission for Kappa on Urban Dictionary was made, and on October 13th, the top definition of the "Kappa" emote was submitted by R4D1AT10N.
In December of 2015, Twitch introduced the KappaClaus emote, a version of the Kappa emote with a Santa Claus hat on his head (shown below). The emote has typically seen an increase in use around December.
KappaPride is a Twitch emote featuring Kappa with a rainbow background used to affirm or inquire about one's homosexuality. A thread about the emote was posted to Bungie forums in October of 2015. In October of 2015, a study by FiveThirtyEight found that the emote was the second most-used emote on Twitch behind regular Kappa. On November 15th, 2016, Urban Dictionary user defined it as "Rainbow version of twitch emote Kappa. Can be used to affirm or ask about someone's homosexuality." There are several variations on the image available on Twitch.
There's also a popular ASCII used as a replacement for emoticon variantion:
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The latest from KYM
About a week ago, this pop-punk adjacent jingle got stuck into the internet's head started from an October 12th TikTok. TikTokers started singing the 'Burger Queen' anthem in their own videos, spreading the lyrics as a catchphrase to other platforms.
Oct 21st, 2021 11:27 AM
The annual trend of putting increasingly absurd things inside Halloween candy and making PSAs about it started in 2011 with Jacksfilms and is still very alive to this day.
Oct 21st, 2021 11:29 AM
"Only '90s Kids" is a series of memes in which the punchline can be genuine nostalgia-bait or an ironic take on the thinking that only '90s kids are able to have certain experiences or understanding. Existing in multiple forms for over 15 years, this catchphrase has gone through many meme phases multiple times.
Oct 21st, 2021 08:35 AM
-They hate SF5.
-They hate Capcom.
-Often have been claimed as not having actually played the fighting games they maliciously joke about and hate.
-Openly mock and belittle the FGC and its practices.
-Sometimes claim to be the people who wish to shape and "save" the FGC.
Opinions? Are they the "true" FGC? Or are they just a bunch of s***posters who only wish to sow chaos and disorder among the community?
SF5: M. Bison, Urien, Seth, Akira | GGST: Leo
Mankind knew that they cannot change society, so instead of reflecting on themselves, they blamed the developers.
"Equivalent exchange, you syrup demon."
Oh, and it's reddit and kappa, so probably full of idiots.
GGS - Faust | Nick Brawl - April O'Neil | KoF02UM - Mai/Kasumi/Vice | GGXXAC+R - Testament
SF5: M. Bison, Urien, Seth, Akira | GGST: Leo
Mankind knew that they cannot change society, so instead of reflecting on themselves, they blamed the developers.
Openly mock and belittle the FGC and its practices.
Well they can't be all that bad then.
Super Mario Maker 2 ID: V50-YX1-NKG
8/8/18 - K. Rool in Smash; 6/11/19 - Banjo & Kazooie in Smash; 3/24/21 - RIP KOF's storyline integrity
Lurked in the place a few times. They seem more interested in porn than in FGs. That seems to be the one notable difference between this place and r/Kappa.this board isn't filled with porn because gamefaqs is smart enough to stop sin and the only one here who wouldn't post wack s*** is prettytonytiger
"Equivalent exchange, you syrup demon."
nowadays, the subreddit is a porn dump and flame war
Inter-Rater Reliability Measures in R
Cohen’s Kappa in R: For Two Categorical Variables
Cohen’s kappa(Jacob Cohen 1960, J Cohen (1968)) is used to measure the agreement of two raters (i.e., “judges”, “observers”) or methods rating on categorical scales. This process of measuring the extent to which two raters assign the same categories or score to the same subject is called inter-rater reliability.
Traditionally, the inter-rater reliability was measured as simple overall percent agreement, calculated as the number of cases where both raters agree divided by the total number of cases considered.
This percent agreement is criticized due to its inability to take into account random or expected agreement by chance, which is the proportion of agreement that you would expect two raters to have based simply on chance.
The Cohen’s kappa is a commonly used measure of agreement that removes this chance agreement. In other words, it accounts for the possibility that raters actually guess on at least some variables due to uncertainty.
There are many situation where you can calculate the Cohen’s Kappa. For example, you might use the Cohen’s kappa to determine the agreement between two doctors in diagnosing patients into “good”, “intermediate” and “bad” prognostic cases.
The Cohen’s kappa can be used for two categorical variables, which can be either two nominal or two ordinal variables. Other variants exists, including:
- Weighted kappa to be used only for ordinal variables.
- Light’s Kappa, which is just the average of all possible two-raters Cohen’s Kappa when having more than two categorical variables (Conger 1980).
- Fleiss kappa, which is an adaptation of Cohen’s kappa for n raters, where n can be 2 or more.
This chapter describes how to measure the inter-rater agreement using the Cohen’s kappa and Light’s Kappa.
You will learn:
- The basics, formula and step-by-step explanation for manual calculation
- Examples of R code to compute Cohen’s kappa for two raters
- How to calculate Light’s kappa for more than two raters
- Interpretation of the kappa coefficient
Related BookInter-Rater Reliability Essentials: Practical Guide in R
Basics and manual calculations
The formula of Cohen’s Kappa is defined as follow:
- Po: proportion of observed agreement
- Pe: proportion of chance agreement
kappa can range form -1 (no agreement) to +1 (perfect agreement).
- when k = 0, the agreement is no better than what would be obtained by chance.
- when k is negative, the agreement is less than the agreement expected by chance.
- when k is positive, the rater agreement exceeds chance agreement.
Kappa for 2x2 tables
For explaining how to calculate the observed and expected agreement, let’s consider the following contingency table. Two clinical psychologists were asked to diagnose whether 70 individuals are in depression or not.
- a, b, c and d are the observed (O) counts of individuals;
- N = a + b + c + d, that is the total table counts;
- R1 and R2 are the total of row 1 and 2, respectively. These represent row margins in the statistics jargon.
- C1 and C2 are the total of column 1 and 2, respectively. These are column margins.
Example of data:
Proportion of observed agreement. The total observed agreement counts is the sum of the diagonal entries. The proportion of observed agreement is: , where N is the total table counts.
- 25 participants were diagnosed yes by the two doctors
- 20 participants were diagnosed no by both
Proportion of chance agreement. The expected proportion of agreement is calculated as follow.
Step 1. Determine the probability that both doctors would randomly say Yes:
- Doctor 1 says yes to 35/70 (0.5) participants. This represents the row 1 marginal proportion, which is .
- Doctor 2 says yes to 40/70 (0.57) participants. This represents the column 1 marginal proportion, which is .
- Total probability of both doctors saying yes randomly is . This is the product of row 1 and column 1 marginal proportions.
Step 2. Determine the probability that both doctors would randomly say No:
- Doctor 1 says no to 35/70 (0.5) participants. This is the row 2 marginal proportion: .
- Doctor 2 says no to 30/70 (0.428) participants. This is the column 2 marginal proportion: .
- Total probability of both doctors saying no randomly is . This is the product of row 2 and column 2 marginal proportions.
so, the total expected probability by chance is . Technically, this can be seen as the sum of the product of rows and columns marginal proportions: .
Cohen’s kappa. Finally, the Cohen’s kappa is .
Kappa for two categorical variables with multiple levels
In the previous section, we demonstrated how to manually compute the kappa value for 2x2 table (binomial variables: yes vs no). This can be generalized to categorical variables with multiple levels as follow.
The ratings scores from the two raters can be summarized in a k×k contingency table, where k is the number of categories.
Example of kxk contingency table to assess agreement about k categories by two different raters:
- The column “Total” () indicates the sum of each row, known as row margins or marginal counts. Here, the total sum of a given row is named .
- The row “Total” () indicates the sum of each column, known as column margins. Here, the total sum of a given column is named
- N is the total sum of all table cells
- For a give row/column, the marginal proportion is the row/column margin divide by N. This is also known as the marginal frequencies or probabilities. For a row , the marginal proportion is . Similarly, for a given column , the marginal proportion is .
- For each table cell, the proportion can be calculated as the cell count divided by N.
The proportion of observed agreement (Po) is the sum of diagonal proportions, which corresponds to the proportion of cases in each category for which the two raters agreed on the assignment.
The proportion of chance agreement (Pe) is the sum of the products of the rows and columns marginal proportions:
So, the Cohen’s kappa can be calculated by plugging Po and Pe in the formula: .
Kappa confidence intervals. For large sample size, the standard error (SE) of kappa can be computed as follow (J. L. Fleiss and Cohen 1973, J. L. Fleiss, Cohen, and Everitt (1969), Friendly, Meyer, and Zeileis (2015)):
Once SE(k) is calculated, a confidence interval for kappa may be computed using the standard normal distribution as follows:
For example, the formula of the 95% confidence interval is: .
R code to compute step by step the Cohen’s kappa:
In the following sections, you will learn a single line R function to compute Kappa.
Interpretation: Magnitude of the agreement
In most applications, there is usually more interest in the magnitude of kappa than in the statistical significance of kappa. The following classifications has been suggested to interpret the strength of the agreement based on the Cohen’s Kappa value (Altman 1999, Landis JR (1977)).
|0.01 - 0.20||Slight|
|0.81 - 1.00||Almost perfect|
However, this interpretation allows for very little agreement among raters to be described as “substantial”. According to the table 61% agreement is considered as good, but this can immediately be seen as problematic depending on the field. Almost 40% of the data in the dataset represent faulty data. In healthcare research, this could lead to recommendations for changing practice based on faulty evidence. For a clinical laboratory, having 40% of the sample evaluations being wrong would be an extremely serious quality problem (McHugh 2012).
This is the reason that many texts recommend 80% agreement as the minimum acceptable inter-rater agreement. Any kappa below 0.60 indicates inadequate agreement among the raters and little confidence should be placed in the study results.
Fleiss et al. (2003) stated that for most purposes,
- values greater than 0.75 or so may be taken to represent excellent agreement beyond chance,
- values below 0.40 or so may be taken to represent poor agreement beyond chance, and
- values between 0.40 and 0.75 may be taken to represent fair to good agreement beyond chance.
Another logical interpretation of kappa from (McHugh 2012) is suggested in the table below:
|0 - 0.20||None||0 - 4‰|
|0.21 - 0.39||Minimal||4 - 15%|
|0.40 - 0.59||Weak||15 - 35%|
|0.60 - 0.79||Moderate||35 - 63%|
|0.80 - 0.90||Strong||64 - 81%|
|Above 0.90||Almost Perfect||82 - 100%|
In the table above, the column “% of data that are reliable” corresponds to the squared kappa, an equivalent of the squared correlation coefficient, which is directly interpretable.
Assumptions and requirements
Your data should met the following assumptions for computing Cohen’s Kappa.
- You have two outcome categorical variables, which can be ordinal or nominal variables.
- The two outcome variables should have exactly the same categories
- You have paired observations; each subject is categorized twice by two independent raters or methods.
- The same two raters are used for all participants.
- Null hypothesis (H0): . The agreement is the same as chance agreement.
- Alternative hypothesis (Ha): . The agreement is different from chance agreement.
Example of data
We’ll use the psychiatric diagnoses data provided by two clinical doctors. 30 patients were enrolled and classified by each of the two doctors into 5 categories (J. Fleiss and others 1971): 1. Depression, 2. Personality Disorder, 3. Schizophrenia, 4. Neurosis, 5. Other.
The data is organized into the following 5x5 contingency table:
Kappa for two raters
The R function [vcd package] can be used to compute unweighted and weighted Kappa. The unweighted version corresponds to the Cohen’s Kappa, which are our concern in this chapter. The weighted Kappa should be considered only for ordinal variables and are largely described in Chapter @ref(weighted-kappa).
Note that, in the above results is the asymptotic standard error of the kappa value.
In our example, the Cohen’s kappa (k) = 0.65, which represents a fair to good strength of agreement according to Fleiss e al. (2003) classification. This is confirmed by the obtained p-value (p < 0.05), indicating that our calculated kappa is significantly different from zero.
Kappa for more than two raters
If there are more than 2 raters, then the average of all possible two-raters kappa is known as Light’s kappa(Conger 1980). You can compute it using the function [irr package], which takes a matrix as input. The matrix columns are raters and rows are individuals.
Cohen’s kappa was computed to assess the agreement between two doctors in diagnosing the psychiatric disorders in 30 patients. There was a good agreement between the two doctors, kappa = 0.65 (95% CI, 0.46 to 0.84), p < 0.0001.
This chapter describes the basics and the formula of the Cohen’s kappa. Additionally, we show how to compute and interpret the kappa coefficient in R. We also provide examples of R code for computing the Light’s Kappa, which is the average of all possible two-raters kappa when you have more than two raters.
Other variants of Cohen’s kappa include: the weighted kappa (for two ordinal variables, Chapter @ref(weighted-kappa)) and Fleiss kappa (for two or more variables, Chapter @ref(weighted-kappa)).
Altman, Douglas G. 1999. Practical Statistics for Medical Research. Chapman; Hall/CRC Press.
Cohen, J. 1968. “Weighted Kappa: Nominal Scale Agreement with Provision for Scaled Disagreement or Partial Credit.” Psychological Bulletin 70 (4): 213—220. doi:10.1037/h0026256.
Cohen, Jacob. 1960. “A Coefficient of Agreement for Nominal Scales.” Educational and Psychological Measurement 20 (1): 37–46. doi:10.1177/001316446002000104.
Conger, A. J. 1980. “Integration and Generalization of Kappas for Multiple Raters.” Psychological Bulletin 88 (2): 322–28.
Fleiss, J.L., and others. 1971. “Measuring Nominal Scale Agreement Among Many Raters.” Psychological Bulletin 76 (5): 378–82.
Fleiss, Joseph L., and Jacob Cohen. 1973. “The Equivalence of Weighted Kappa and the Intraclass Correlation Coefficient as Measures of Reliability.” Educational and Psychological Measurement 33 (3): 613–19. doi:10.1177/001316447303300309.
Fleiss, Joseph L., Jacob Willem Cohen, and Brian Everitt. 1969. “Large Sample Standard Errors of Kappa and Weighted Kappa.” Psychological Bulletin 72: 332–27.
Friendly, Michael, D. Meyer, and A. Zeileis. 2015. Discrete Data Analysis with R: Visualization and Modeling Techniques for Categorical and Count Data. 1st ed. Chapman; Hall/CRC.
Landis JR, Koch GG. 1977. “The Measurement of Observer Agreement for Categorical Data” 1 (33). Biometrics: 159–74.
McHugh, Mary. 2012. “Interrater Reliability: The Kappa Statistic.” Biochemia Medica : Časopis Hrvatskoga Društva Medicinskih Biokemičara / HDMB 22 (October): 276–82. doi:10.11613/BM.2012.031.
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Kappa Alpha Theta
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Beta Pi/Michigan State
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|Aliases||OPRK1, K-OR-1, KOR, KOR-1, OPRK, opioid receptor kappa 1, KOR1, KOP|
|External IDs||OMIM: 165196MGI: 97439HomoloGene: 20253GeneCards: OPRK1|
|Location (UCSC)||Chr 8: 53.23 – 53.25 Mb||Chr 1: 5.59 – 5.61 Mb|
The κ-opioid receptor or kappa opioid receptor, abbreviated KOR or KOP, is a G protein-coupled receptor that in humans is encoded by the OPRK1gene. The KOR is coupled to the G proteinGi/G0 and is one of four related receptors that bind opioid-like compounds in the brain and are responsible for mediating the effects of these compounds. These effects include altering nociception, consciousness, motor control, and mood. Dysregulation of this receptor system has been implicated in alcohol and drug addiction.
The KOR is a type of opioid receptor that binds the opioid peptidedynorphin as the primary endogenous ligand (substrate naturally occurring in the body). In addition to dynorphin, a variety of natural alkaloids, terpenes and synthetic ligands bind to the receptor. The KOR may provide a natural addiction control mechanism, and therefore, drugs that target this receptor may have therapeutic potential in the treatment of addiction.
There is evidence that distribution and/or function of this receptor may differ between sexes.
KORs are widely distributed in the brain, spinal cord (substantia gelatinosa), and in peripheral tissues. High levels of the receptor have been detected in the prefrontal cortex, periaqueductal gray, raphe nuclei (dorsal), ventral tegmental area, substantia nigra, dorsal striatum (putamen, caudate), ventral striatum (nucleus accumbens, olfactory tubercle), amygdala, bed nucleus stria terminalis, claustrum, hippocampus, hypothalamus, midline thalamic nuclei, locus coeruleus, spinal trigeminal nucleus, parabrachial nucleus, and solitary nucleus.
Based on receptor binding studies, three variants of the KOR designated κ1, κ2, and κ3 have been characterized. However, only one cDNA clone has been identified, hence these receptor subtypes likely arise from interaction of one KOR protein with other membrane associated proteins.
All opioid receptors exist as obligate dimers. The implications this may have are not totally known.
Similarly to μ-opioid receptor (MOR) agonists, KOR agonists are potently analgesic, and have been employed clinically in the treatment of pain. However, KOR agonists also produce side effects such as dysphoria, hallucinations, and dissociation, which has limited their clinical usefulness. Examples of KOR agonists that have been used medically as analgesics include butorphanol, nalbuphine, levorphanol, levallorphan, pentazocine, phenazocine, and eptazocine. Difelikefalin (CR845, FE-202845) and CR665 (FE-200665, JNJ-38488502) are peripherally restricted KOR agonists lacking the CNS side effects of centrally active KOR agonists and are currently under clinical investigation as analgesics.
Centrally active KOR agonists have hallucinogenic or dissociative effects, as exemplified by salvinorin A (the active constituent in Salvia divinorum). These effects are generally undesirable in medicinal drugs. It is thought that the hallucinogenic and dysphoric effects of opioids such as butorphanol, nalbuphine, and pentazocine serve to limit their abuse potential. In the case of salvinorin A, a structurally novel neoclerodanediterpene KOR agonist, these hallucinogenic effects are sought by recreational users, despite the dysphoria experienced by some users. Another KOR agonist with comparable effects is ibogaine, which has possible medical application in addiction treatment. While these KOR agonists possess hallucinogenic and dissociative effects, they are mechanistically and qualitatively different from those of the 5HT2AR agonist psychedelic hallucinogens such as lysergic acid diethylamide (LSD) or psilocybin and those of NMDAR antagonist dissociatives/anesthetics ketamine and phencycldine.
The claustrum is the region of the brain in which the KOR is most densely expressed. It has been proposed that this area, based on its structure and connectivity, has "a role in coordinating a set of diverse brain functions", and the claustrum has been elucidated as playing a crucial role in consciousness. As examples, lesions of the claustrum in humans are associated with disruption of consciousness and cognition, and electrical stimulation of the area between the insula and the claustrum has been found to produce an immediate loss of consciousness in humans along with recovery of consciousness upon cessation of the stimulation. On the basis of the preceding knowledge, it has been proposed that inhibition of the claustrum (as well as, "additionally, the deep layers of the cortex, mainly in prefrontal areas") by activation of KORs in these areas is primarily responsible for the profound consciousness-altering/dissociative hallucinogen effects of salvinorin A and other KOR agonists. In addition, it has been stated that "the subjective effects of S. divinorum indicate that salvia disrupts certain facets of consciousness much more than the largely serotonergic hallucinogen [LSD]", and it has been postulated that inhibition of a brain area that is apparently as fundamentally involved in consciousness and higher cognitive function as the claustrum may explain this. However, these conclusions are merely tentative, as "[KORs] are not exclusive to the claustrum; there is also a fairly high density of receptors located in the prefrontal cortex, hippocampus, nucleus accumbens and putamen", and "disruptions to other brain regions could also explain the consciousness-altering effects [of salvinorin A]".
In supplementation of the above, according to Addy et al.:
Theories suggest the claustrum may act to bind and integrate multisensory information, or else to encode sensory stimuli as salient or nonsalient (Mathur, 2014). One theory suggests the claustrum harmonizes and coordinates activity in various parts of the cortex, leading to the seamless integrated nature of subjective conscious experience (Crick and Koch, 2005; Stiefel et al., 2014). Disrupting claustral activity may lead to conscious experiences of disintegrated or unusually bound sensory information, perhaps including synesthesia. Such theories are in part corroborated by the fact that [salvia divinorum], which functions almost exclusively on the KOR system, can cause consciousness to be decoupled from external sensory input, leading to experiencing other environments and locations, perceiving other "beings" besides those actually in the room, and forgetting oneself and one's body in the experience.
Mood, stress, and addiction
See also: κ-opioid receptor § Role in treatment of drug addiction
The involvement of the KOR in stress, as well as in consequences of chronic stress such as depression, anxiety, anhedonia, and increased drug-seeking behavior, has been made clear. KOR agonists are notably dysphoric and aversive at sufficient doses. The KOR antagonists buprenorphine, as ALKS-5461 (a combination formulation with samidorphan), and CERC-501 (LY-2456302) are currently in clinical development for the treatment of major depressive disorder and substance use disorders.JDTic and PF-4455242 were also under investigation but development was halted in both cases due to toxicity concerns.
The depressive-like behaviors following prolonged morphine abstinence appear to be mediated by upregulation of the KOR/dynorphin system in the nucleus accumbens, as the local application of a KOR antagonist prevented the behaviors. As such, KOR antagonists might be useful for the treatment of depressive symptoms associated with opioid withdrawal.
In a small clinical study, pentazocine, a KOR agonist, was found to rapidly and substantially reduce symptoms of mania in patients with bipolar disorder. It was postulated that the efficacy observed was due to KOR activation-mediated amelioration of excessive dopaminergic signaling in the reward pathways.[failed verification]
A variety of other effects of KOR activation are known:
- Activation of the KOR appears to antagonize many of the effects of the MOR, including analgesia, tolerance, euphoria, and memory regulation.Nalorphine and nalmefene are dual MOR antagonists and KOR agonists that have been used clinically as antidotes for opioid overdose, although the specific role and significance of KOR activation in this indication, if any, is uncertain. In any case however, KOR agonists notably do not affect respiratory drive, and hence do not reverse MOR activation-induced respiratory depression.
- KOR agonists suppress itching, and the selective KOR agonist nalfurafine is used clinically as an antipruritic (anti-itch drug).
- Eluxadoline is a peripherally restricted KOR agonist as well as MOR agonist and DOR antagonist that has been approved for the treatment of diarrhea-predominant irritable bowel syndrome. Asimadoline and fedotozine are selective and similarly peripherally restricted KOR agonists that were also investigated for the treatment of irritable bowel syndrome and reportedly demonstrated at least some efficacy for this indication but were ultimately never marketed.
- KOR agonists are known for their characteristic diuretic effects, due to their negative regulation of vasopressin, also known as antidiuretic hormone (ADH).
- KOR agonism is neuroprotective against hypoxia/ischemia.
- The selective KOR agonist U-50488 protected rats against supramaximal electroshockseizures, indicating that KOR agonism may have anticonvulsant effects.
KOR activation by agonists is coupled to the G proteinGi/G0, which subsequently increases phosphodiesterase activity. Phosphodiesterases break down cAMP, producing an inhibitory effect in neurons. KORs also couple to inward-rectifier potassium and to N-type calcium ion channels. Recent studies have also demonstrated that agonist-induced stimulation of the KOR, like other G-protein coupled receptors, can result in the activation of mitogen-activated protein kinases (MAPK). These include extracellular signal-regulated kinase, p38 mitogen-activated protein kinases, and c-Jun N-terminal kinases.
The synthetic alkaloid ketazocine and terpenoid natural product salvinorin A are potent and selective KOR agonists. The KOR also mediates the dysphoria and hallucinations seen with opioids such as pentazocine.
Nalfurafine (Remitch), which was introduced in 2009, is the first selective KOR agonist to enter clinical use.
- 5'-Acetamidinoethylnaltrindole (ANTI) – selective 
- 5'-Guanidinonaltrindole (5'-GNTI) – selective, long-acting
- 6'-Guanidinonaltrindole (6'-GNTI) – biased ligand: G protein agonist, β-arrestin antagonist
- Amentoflavone – non-selective; naturally-occurring
- AT-076 – non-selective, likely long acting; JDTic analogue
- Binaltorphimine – selective, long-acting
- BU09059 – selective, short-acting; JDTic analogue
- Buprenorphine – non-selective; silent antagonist or weak partial agonist, depending on source
- CERC-501 – selective, short-acting
- Dezocine – non-selective; silent antagonist
- DIPPA – selective, long-acting 
- JDTic – selective, long-acting
- LY-255582 - non-selective
- LY-2459989 – selective, short-acting
- LY-2795050 – selective, short-acting
- Methylnaltrexone – non-selective
- ML190 – selective 
- ML350 – selective, short-acting
- MR-2266 – non-selective
- Naloxone – non-selective
- Naltrexone – non-selective
- Noribogaine – non-selective; naturally-occurring; biased ligand: G protein agonist, β-arrestin antagonist
- Norbinaltorphimine – selective, long-acting
- Pawhuskin A – selective; naturally-occurring
- PF-4455242 – selective, short-acting
- Quadazocine – non-selective; silent antagonist; preference for κ2
- RB-64 (22-thiocyanatosalvinorin A) – G protein biased agonist with a bias factor of 96; β-arrestin antagonist
- Zyklophin – selective peptide antagonist; dynorphin A analogue
Main article: menthol
Found in numerous species of mint, (including peppermint, spearmint, and watermint), the naturally-occurring compound menthol is a weak KOR agonist owing to its antinociceptive, or pain blocking, effects in rats. In addition, mints can desensitize a region through the activation of TRPM8 receptors (the 'cold'/menthol receptor).
Main article: Salvia divinorum
The key compound in Salvia divinorum, salvinorin A, is known as a powerful, short-acting KOR agonist.
Main article: ibogaine
Used for the treatment of addiction in limited countries, ibogaine has become an icon of addiction management among certain underground circles. Despite its lack of addictive properties, ibogaine is listed as a Schedule I compound in the US because it is a psychoactive substance, hence it is considered illegal to possess under any circumstances. Ibogaine is also a KOR agonist and this property may contribute to the drug's anti-addictive efficacy.
Role in treatment of drug addiction
KOR agonists had been investigated for their therapeutic potential in the treatment of addiction and evidence points towards dynorphin, the endogenous KOR agonist, to be the body's natural addiction control mechanism. Childhood stress/abuse is a well known predictor of drug abuse and is reflected in alterations of the MOR and KOR systems. In experimental "addiction" models the KOR has also been shown to influence stress-induced relapse to drug seeking behavior. For the drug-dependent individual, risk of relapse is a major obstacle to becoming drug-free. Recent reports demonstrated that KORs are required for stress-induced reinstatement of cocaine seeking.
One area of the brain most strongly associated with addiction is the nucleus accumbens (NAcc) and striatum while other structures that project to and from the NAcc also play a critical role. Though many other changes occur, addiction is often characterized by the reduction of dopamine D2 receptors in the NAcc. In addition to low NAcc D2 binding, cocaine is also known to produce a variety of changes to the primate brain such as increases prodynorphin mRNA in caudate putamen (striatum) and decreases of the same in the hypothalamus while the administration of a KOR agonist produced an opposite effect causing an increase in D2 receptors in the NAcc.
Additionally, while cocaine overdose victims showed a large increase in KORs (doubled) in the NAcc, KOR agonist administration is shown to be effective in decreasing cocaine seeking and self-administration. Furthermore, while cocaine abuse is associated with lowered prolactin response, KOR activation causes a release in prolactin, a hormone known for its important role in learning, neuronal plasticity and myelination.
It has also been reported that the KOR system is critical for stress-induced drug-seeking. In animal models, stress has been demonstrated to potentiate cocaine reward behavior in a kappa opioid-dependent manner. These effects are likely caused by stress-induced drug craving that requires activation of the KOR system. Although seemingly paradoxical, it is well known that drug taking results in a change from homeostasis to allostasis. It has been suggested that withdrawal-induced dysphoria or stress-induced dysphoria may act as a driving force by which the individual seeks alleviation via drug taking. The rewarding properties of drug are altered, and it is clear KOR activation following stress modulates the valence of drug to increase its rewarding properties and cause potentiation of reward behavior, or reinstatement to drug seeking. The stress-induced activation of KORs is likely due to multiple signaling mechanisms. The effects of KOR agonism on dopamine systems are well documented, and recent work also implicates the mitogen-activated protein kinase cascade and pCREB in KOR-dependent behaviors.
While the predominant drugs of abuse examined have been cocaine (44%), ethanol (35%), and opioids (24%). As these are different classes of drugs of abuse working through different receptors (increasing dopamine directly and indirectly, respectively) albeit in the same systems produce functionally different responses. Conceptually then pharmacological activation of KOR can have marked effects in any of the psychiatric disorders (depression, bipolar disorder, anxiety, etc.) as well as various neurological disorders (i.e. Parkinson's disease and Huntington's disease). Not only are genetic differences in dynorphin receptor expression a marker for alcohol dependence but a single dose of a KOR antagonist markedly increased alcohol consumption in lab animals. There are numerous studies that reflect a reduction in self-administration of alcohol, and heroin dependence has also been shown to be effectively treated with KOR agonism by reducing the immediate rewarding effects and by causing the curative effect of up-regulation (increased production) of MORs that have been down-regulated during opioid abuse.
The anti-rewarding properties of KOR agonists are mediated through both long-term and short-term effects. The immediate effect of KOR agonism leads to reduction of dopamine release in the NAcc during self-administration of cocaine and over the long term up-regulates receptors that have been down-regulated during substance abuse such as the MOR and the D2 receptor. These receptors modulate the release of other neurochemicals such as serotonin in the case of MOR agonists and acetylcholine in the case of D2. These changes can account for the physical and psychological remission of the pathology of addiction. The longer effects of KOR agonism (30 minutes or greater) have been linked to KOR-dependent stress-induced potentiation and reinstatement of drug seeking. It is hypothesized that these behaviors are mediated by KOR-dependent modulation of dopamine, serotonin, or norepinephrine and/or via activation of downstream signal transduction pathways.
Of significant note, while KOR activation blocks many of the behavioral and neurochemical responses elicited by drugs of abuse as stated above. These results are indicative of the KOR induced negative affective states counteracting the rewarding effects of drugs of abuse. Implicating the KOR/dynorphin system as an anti-reward system, supported by the role of KOR signaling and stress, mediating both stress-induced potentiation of drug reward and stress-induced reinstatement of seeking behavior.  This in turn addresses what was thought to be paradoxical above. That is, rather, KOR signaling is activated/upregulated by stress, drugs of abuse and agonist administration - resulting in negative affective state. As such drug addiction is maintained by avoidance of negative affective states manifest in stress, craving, and drug withdrawal. Consistent with KOR induced negative affective states and role in drug addiction, KOR antagonists are efficacious at blocking negative affect induced by drug withdrawal and at decreasing escalated drug intake in pre-clinical trial involving extended drug access. Clinically there has been little advancement to evaluate the effects of KOR antagonists due to adverse effects and undesirable pharmacological profiles for clinical testing (i.e. long half-life, poor bioavailability). More recently, a selective, high-affinity KOR antagonist LY2456302 was well-tolerated in CUD patients.  Showing feasibility a subsequent proof-of-mechanism trial evaluated JNJ-67953964 (previously LY2456302) potential for treating anhedonia in a double-blind, placebo-controlled, randomized trial in patients with anhedonia and a mood or anxiety disorder.  The KOR antagonist significantly increased fMRI ventral striatum activation during reward anticipation while accompanied by therapeutic effects on clinical measures of anhedonia, further reinforces the promise of KOR antagonism and proceeding assessment of clinical impact.  Additionally a positron emission tomography (PET) study in cocaine use disorder (CUD) patients utilizing a KOR selective agonist [11C]GR103545 radioligand showed CUD individuals with higher KOR availability were more prone to stress-induced relapse.  A subsequent PET scan following a three day cocaine binge showed a decrease in KOR availability, interpreted as increased endogenous dynorphin competing with the radioligand at the KOR binding sites.  Taken together these findings are in support of the negative affect state and further implicate the KOR/dynorphin system clinically and therapeutically relevant in humans with CUD. Taken together, in drug addiction the KOR/dynorphin system is implicated as a homeostatic mechanism to counteract the acute effects of drugs of abuse. Chronic drug use and stress up-regulate the system in turn leading to a dysregulated state which induces negative affective states and stress reactivity. 
KOR has been shown to interact with sodium-hydrogen antiporter 3 regulator 1,ubiquitin C,5-HT1A receptor, and RGS12.
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