Gender-affirming hormone therapy

From Wikipedia, the free encyclopedia

https://en.wikipedia.org/wiki/Gender-affirming_hormone_therapy

Gender-affirming hormone therapy (GAHT), also called hormone replacement therapy (HRT) or transgender hormone therapy, is a form of hormone therapy in which sex hormones and other hormonal medications are administered to transgender or gender nonconforming individuals for the purpose of more closely aligning their secondary sexual characteristics with their gender identity. This form of hormone therapy is given as one of two types, based on whether the goal of treatment is masculinization or feminization:

• Masculinizing hormone therapy – for transgender men or transmasculine people; consists of androgens and occasionally antiestrogens.
• Feminizing hormone therapy – for transgender women or transfeminine people; consists of estrogens with or without antiandrogens.

Eligibility for GAHT may require an assessment for gender dysphoria or persistent gender incongruence; or many medical institutions now use an informed consent model, which ensures patients are informed of the procedure process, including possible benefits and risks, while removing many of the historical barriers needed to start hormone therapy. Treatment guidelines for therapy have been developed by several medical associations.

Non-binary people may also engage in hormone therapy in order to achieve a desired balance of sex hormones or to help align their bodies with their gender identities. Many transgender people obtain hormone therapy from a licensed health care provider and others obtain and self-administer hormones.

History

Further information: Feminizing hormone therapy § History, and Masculinizing hormone therapy § History

Requirements

The formal requirements to begin gender-affirming hormone therapy vary widely depending on geographic location and specific institution. Gender-affirming hormones can be prescribed by a wide range of medical providers including, but not limited to, primary care physicians, endocrinologists, and gynecologists. Requirements generally include a minimum age; according to the Endocrine Society, there has been little research on taking cross-sex hormones before the age of about 14.

Historically, many health centers required a psychiatric evaluation and/or a letter from a therapist before beginning therapy. Many centers now use an informed consent model that does not require any routine formal psychiatric evaluation but instead focuses on reducing barriers to care by ensuring a person can understand the risks, benefits, alternatives, unknowns, limitations, and risks of no treatment. Some LGBT health organizations (notably Chicago's Howard Brown Health Center and Planned Parenthood) advocate for this type of informed consent model.

The World Professional Association for Transgender Health (WPATH) Standards of Care, 7th edition, note that both of these approaches to care are appropriate.

Gender dysphoria

Many international guidelines and institutions require persistent, well-documented gender dysphoria as a pre-requisite to starting gender-affirmation therapy. Gender dysphoria refers to the psychological discomfort or distress that an individual can experience if their sex assigned at birth is incongruent with that person's gender identity. Signs of gender dysphoria can include comorbid mental health stressors such as depression, anxiety, low self-esteem, and social isolation. Not all gender nonconforming individuals experience gender dysphoria, and measuring a person's gender dysphoria is critical when considering medical intervention for gender nonconformity.

Treatment options

Guidelines

For transgender youth, the Dutch protocol existed as among the earlier guidelines for hormone therapy by delaying puberty until age 16. The World Professional Association for Transgender Health (WPATH) and the Endocrine Society later formulated guidelines that created a foundation for health care providers to care for transgender patients. UCSF guidelines are also sometimes used. There is no generally agreed-upon set of guidelines, however.

Delaying puberty in adolescents

Main article: Puberty blocker

Tanner Stages for Female Sexual Characteristics

Tanner Stages for Male Sexual Characteristics

Adolescents experiencing gender dysphoria may opt to undergo puberty-suppressing hormone therapy at the onset of puberty. The Standards of Care set forth by WPATH recommend individuals pursuing puberty-suppressing hormone therapy wait until at least experiencing Tanner Stage 2 pubertal development. Tanner Stage 2 is defined by the appearance of scant pubic hair, breast bud development, and/or slight testicular growth. WPATH classifies puberty-suppressing hormone therapy as a "fully reversible" intervention. Delaying puberty allows individuals more time to explore their gender identity before deciding on more permanent interventions and prevents the physical changes associated with puberty.

The preferred puberty-suppressing agent for both individuals assigned male at birth and individuals assigned female at birth is a GnRH Analogue. This approach temporarily shuts down the Hypothalamic-Pituitary-Gonadal (HPG) Axis, which is responsible for the production of hormones (estrogen, testosterone) that cause the development of secondary sexual characteristics in puberty.

Feminizing hormone therapy

Main article: Feminizing hormone therapy

Feminizing hormone therapy is typically used by transgender women, who desire the development of feminine secondary sex characteristics. Individuals who identify as non-binary may also opt-in for feminizing hormone treatment to better align their body with their desired gender expression. Feminizing hormone therapy usually includes medication to suppress testosterone production and induce feminization. Types of medications include estrogens, antiandrogens (testosterone blockers), and progestogens. Most commonly, an estrogen is combined with an antiandrogen to suppress and block testosterone. This allows for demasculinization and promotion of feminization and breast development. Estrogens are administered in various modalities including injection, transdermal patch, and oral tablets.

The desired effects of feminizing hormone therapy focus on the development of feminine secondary sex characteristics. These desired effects include: breast tissue development, redistribution of body fat, decreased body hair, reduction of muscle mass, and more. The table below summarizes some of the effects of feminizing hormone therapy in transgender women:

Effects of feminizing hormone therapy

Effect	Time to expected onset of effect	Time to expected maximum effect	Permanency if hormone therapy is stopped
Breast development and nipple/areolar enlargement	2–6 months	1–5 years	Permanent
Thinning/slowed growth of facial/body hair	4–12 months	>3 years	Reversible
Cessation/reversal of male-pattern scalp hair loss	1–3 months	1–2 years	Reversible
Softening of skin/decreased oiliness and acne	3–6 months	Unknown	Reversible
Redistribution of body fat in a feminine pattern	3–6 months	2–5 years	Reversible
Decreased muscle mass/strength	3–6 months	1–2 years	Reversible
Widening and rounding of the pelvis	Unspecified	Unspecified	Permanent
Changes in mood, emotionality, and behavior	Unspecified	Unspecified	Reversible
Decreased sex drive	1–3 months	Temporary	Reversible
Decreased spontaneous/morning erections	1–3 months	3–6 months	Reversible
Erectile dysfunction and decreased ejaculate volume	1–3 months	Variable	Reversible
Decreased sperm production/fertility	Unknown	>3 years	Reversible or permanent
Decreased testicle size	3–6 months	2–3 years	Unknown
Decreased penis size	None	Not applicable	Not applicable
Decreased prostate gland size	Unspecified	Unspecified	Unspecified
Voice changes	None	Not applicable	Not applicable

Footnotes:

1. 
   

Estimates represent published and unpublished clinical observations.

Time at which further changes are unlikely at maximum maintained dose. Maximum effects vary widely depending on genetics, body habitus, age, and status of gonad removal. Generally, older individuals with intact gonads may have less feminization overall.

Complete removal of male facial and body hair requires electrolysis, laser hair removal, or both. Temporary hair removal can be achieved with shaving, epilating, waxing, and other methods.

Familial scalp hair loss may occur if estrogens are stopped.

Varies significantly depending on the amount of physical exercise.

Occurs only in individuals of pubertal age who have not yet completed epiphyseal closure.

Additional research is needed to determine permanency, but a permanent impact of estrogen therapy on sperm quality is likely and sperm preservation options should be counseled on and considered before initiation of therapy.

Conflicting reports, with none reported observed in transgender women but significant albeit minor reduction of penis size reported in men with prostate cancer on androgen deprivation therapy.

Treatment by speech pathologists for voice training is effective. Sources: Guidelines: Reviews/book chapters: Studies:

Masculinizing hormone therapy

Main article: Masculinizing hormone therapy

Masculinizing hormone therapy is typically used by transgender men, who desire the development of masculine secondary sex characteristics. Masculinizing hormone therapy usually includes testosterone to produce masculinization and suppress the production of estrogen. Treatment options include oral, subcutaneous injections or implant, and transdermal (patches, gels). Dosing is patient-specific, depending on the patient's rate of metabolism, and is discussed with the physician. The most commonly prescribed methods are intramuscular and subcutaneous injections. This dosing can be daily, weekly or biweekly depending on the route of administration and the individual patient.

Unlike feminizing hormone therapy, individuals undergoing masculinizing hormone therapy do not usually require additional hormone suppression such as estrogen suppression. Therapeutic doses of testosterone are usually sufficient to inhibit the production of estrogen to desired physiologic levels.

The desired effects of masculinizing hormone therapy focus on the development of masculine secondary sex characteristics. These desired effects include: increased muscle mass, development of facial hair, voice deepening, increase and thickening of body hair, and more.

Effects of masculinizing hormone therapy

Reversible Changes	Irreversible Changes
Increased libido	Deepening of voice
Redistribution of body fat	Growth of facial/body hair
Cessation of ovulation/menstruation	Male-pattern baldness
Increased muscle mass	Enlargement of clitoris
Increased perspiration	Growth spurt/closure of growth plates
Acne	Breast atrophy
Increased red blood cell count

Safety

Hormone therapy for transgender individuals has been shown in medical literature to be generally safe, when supervised by a qualified medical professional. There are potential risks with hormone treatment that will be monitored through screenings and lab tests such as blood count (hemoglobin), kidney and liver function, blood sugar, potassium, and cholesterol. Taking more medication than directed may lead to health problems such as increased risk of cancer, heart attack from thickening of the blood, blood clots, and elevated cholesterol.

Feminizing hormone therapy

The Standards of Care published by the World Professional Association for Transgender Health (WPATH) summarize many of the risks associated with feminizing hormone therapy (outlined below). For more in-depth information on the safety profile of estrogen-based feminizing hormone therapy visit the feminizing hormone therapy page.

Summary of Risks of Estrogen Therapy

Likely Increased Risk	Possible Increased Risk	Inconclusive/No Increased Risk
Venous thromboembolic disease	Type 2 diabetes	Breast cancer
Cardiovascular disease	Hypertension	Prostate cancer
Hypertriglyceridemia	Hyperprolactinaemia
Gallstones	Osteoporosis
Hyperkalemia
Cerebrovascular disease
Polyuria (or dehydration)
Meningioma

Only present in individuals taking spironolactone

Only present in individuals taking cyproterone

Masculinizing hormone therapy

The Standards of Care published by the World Professional Association for Transgender Health (WPATH) summarize many of the risks associated with masculinizing hormone therapy (outlined below). For more in-depth information on the safety profile of testosterone-based masculinizing hormone therapy visit the masculinizing hormone therapy page.

Summary of Risks of Testosterone Therapy

Likely Increased Risk	Possible Increased Risk	Inconclusive/No Increased Risk
Polycythemia	Type 2 diabetes	Osteoporosis
Weight gain		Breast cancer
Acne		Ovarian cancer
Pattern hair loss		Uterine cancer
Hypertension		Cervical cancer
Sleep apnea
Decreased HDL cholesterol
Decreased LDL cholesterol
Cardiovascular disease
Hypertriglyceridemia

Fertility consideration

GAHT may limit fertility potential. Should a transgender individual choose to undergo gender-affirming surgery, their fertility potential is lost completely. Before starting any treatment, individuals may consider fertility issues and fertility preservation. Options include semen cryopreservation, oocyte cryopreservation, and ovarian tissue cryopreservation.

A study presented at ENDO 2019 (the Endocrine Society's conference) shows that even after one year of treatment with testosterone, a transgender man can preserve his fertility potential.

Fake products

Some online scammers have been targeting trans consumers with products that do not contain any hormones or contain ones that are opposite of what is advertised. This can happen when legislations outlaw or restrict access to treatments by legitimate medical professionals.

Treatment eligibility

Many providers use informed consent, whereby someone seeking hormone therapy can sign a statement of informed consent and begin treatment without much gatekeeping. For other providers, eligibility is determined using major diagnostic tools such as ICD-11 or the Diagnostic and Statistical Manual of Mental Disorders (DSM) to classify a patient with gender dysphoria. The Endocrine Society requires physicians that diagnose gender dysphoria and gender incongruence to be trained in psychiatric disorders with competency in ICD-11 and DSM-5. The healthcare provider should also obtain a thorough assessment of the patient's mental health and identify potential psychosocial factors that can affect therapy.

WPATH Standards of Care

The WPATH Standards of Care, most recently published in 2022, outlines a series of guidelines which should be met before a patient should be allowed gender-affirming hormone therapy:

• Gender incongruence is marked and sustained
• Patient meets diagnostic criteria for gender incongruence prior to gender-affirming hormone treatment in regions where a diagnosis is necessary to access health care
• Patient has capacity to consent to hormone therapy treatment
• Other possible causes of apparent gender incongruence have been identified and excluded
• Mental health and physical conditions that could negatively impact the outcome of treatment have been assessed
• Understands the effect of gender-affirming hormone treatment on reproduction and they have explored reproductive options

The WPATH standards of care distinguish between gender-affirming hormone therapy, and hormone replacement therapy, with the latter referring to the replacement of endogenous hormones after a gonadectomy to prevent cardiovascular and musculoskeletal issues.

Readiness

See also: Real-life experience (transgender)

Some organizations – but fewer than in the past – require that patients spend a certain period of time living in their desired gender role before starting hormone therapy. This period is sometimes called real-life experience (RLE).

In Sweden, for instance, patients seeking to access gender affirming healthcare must first undergo extended evaluations with psychiatric professionals, during which they must - without any form of medical transition - successfully live for one full year as their desired gender in all professional, social, and personal matters. Gender clinics are recommended to provide patients with wigs and breast prostheses for the endeavor. The evaluation additionally involves, if possible, meetings with family members and/or other individuals close to the patient. Patients may be denied care for any number of "psychosocial dimensions", including their choice of job or their marital status.

Transgender and gender non-conforming activists, such as Kate Bornstein, have asserted that RLE is psychologically harmful and is a form of "gatekeeping", effectively barring individuals from transitioning for as long as possible, if not permanently.

In September 2022, the World Professional Association for Transgender Health (WPATH) Standards of Care for the Health of Transgender and Gender Diverse People (SOC) Version 8 were released and removed the requirement of RLE for all gender-affirming treatments, including gender-affirming surgery.

Accessibility

Some transgender people choose to self-administer hormone replacement medications, often because doctors have too little experience in this area, or because no doctor is available. Others self-administer because their doctor will not prescribe hormones without an approval letter from a psychotherapist. Many therapists require extended periods of continuous psychotherapy and/or real-life experience before they will write such a letter. Because many individuals must pay for evaluation and care out-of-pocket, costs can be prohibitive.

Access to medication can be poor even where health care is provided free. In a patient survey conducted by the United Kingdom's National Health Service in 2008, 5% of respondents acknowledged resorting to self-medication, and 46% were dissatisfied with the amount of time it took to receive hormone therapy. The report concluded in part: "The NHS must provide a service that is easy to access so that vulnerable patients do not feel forced to turn to DIY remedies such as buying drugs online with all the risks that entails. Patients must be able to access professional help and advice so that they can make informed decisions about their care, whether they wish to take the NHS or private route without putting their health and indeed their lives in danger." Self-administration of cross-gender hormones without medical supervision may have untoward health effects and risks.

A number of private companies have attempted to increase accessibility for hormone replacement medications and help transgender people navigate the complexities of access to treatment.

Sentiment analysis

From Wikipedia, the free encyclopedia

https://en.wikipedia.org/wiki/Sentiment_analysis

Sentiment analysis (also known as opinion mining or emotion AI) is the use of natural language processing, text analysis, computational linguistics, and biometrics to systematically identify, extract, quantify, and study affective states and subjective information. Sentiment analysis is widely applied to voice of the customer materials such as reviews and survey responses, online and social media, and healthcare materials for applications that range from marketing to customer service to clinical medicine. With the rise of deep language models, such as RoBERTa, also more difficult data domains can be analyzed, e.g., news texts where authors typically express their opinion/sentiment less explicitly.

Simple cases

• Coronet has the best lines of all day cruisers.
• Bertram has a deep V hull and runs easily through seas.
• Pastel-colored 1980s day cruisers from Florida are ugly.
• I dislike old cabin cruisers.

More challenging examples

• I do not dislike cabin cruisers. (Negation handling)
• Disliking watercraft is not really my thing. (Negation, inverted word order)
• Sometimes I really hate RIBs. (Adverbial modifies the sentiment)
• I'd really truly love going out in this weather! (Possibly sarcastic)
• Chris Craft is better looking than Limestone. (Two brand names, identifying the target of attitude is difficult)
• Chris Craft is better looking than Limestone, but Limestone projects seaworthiness and reliability. (Two attitudes, two brand names)
• The movie is surprising, with plenty of unsettling plot twists. (Negative term used in a positive sense in certain domains)
• You should see their decadent dessert menu. (Attitudinal term has shifted polarity recently in certain domains)
• I love my mobile but would not recommend it to any of my colleagues. (Qualified positive sentiment, difficult to categorise)
• Next week's gig will be right koide9! ("Quoi de neuf?", French for "what's new?". Newly minted terms can be highly attitudinal but volatile in polarity and often out of known vocabulary.)

Types

A basic task in sentiment analysis is classifying the polarity of a given text at the document, sentence, or feature/aspect level—whether the expressed opinion in a document, a sentence or an entity feature/aspect is positive, negative, or neutral. Advanced, "beyond polarity" sentiment classification looks, for instance, at emotional states such as enjoyment, anger, disgust, sadness, fear, and surprise.

Precursors to sentimental analysis include the General Inquirer, which provided hints toward quantifying patterns in text and, separately, psychological research that examined a person's psychological state based on analysis of their verbal behavior.

Subsequently, the method described in a patent by Volcani and Fogel, looked specifically at sentiment and identified individual words and phrases in text with respect to different emotional scales. A current system based on their work, called EffectCheck, presents synonyms that can be used to increase or decrease the level of evoked emotion in each scale.

Many other subsequent efforts were less sophisticated, using a mere polar view of sentiment, from positive to negative, such as work by Turney, and Pang who applied different methods for detecting the polarity of product reviews and movie reviews respectively. This work is at the document level. One can also classify a document's polarity on a multi-way scale, which was attempted by Pang and Snyder among others: Pang and Lee expanded the basic task of classifying a movie review as either positive or negative to predict star ratings on either a 3- or a 4-star scale, while Snyder performed an in-depth analysis of restaurant reviews, predicting ratings for various aspects of the given restaurant, such as the food and atmosphere (on a five-star scale).

First steps to bringing together various approaches—learning, lexical, knowledge-based, etc.—were taken in the 2004 AAAI Spring Symposium where linguists, computer scientists, and other interested researchers first aligned interests and proposed shared tasks and benchmark data sets for the systematic computational research on affect, appeal, subjectivity, and sentiment in text.

Even though in most statistical classification methods, the neutral class is ignored under the assumption that neutral texts lie near the boundary of the binary classifier, several researchers suggest that, as in every polarity problem, three categories must be identified. Moreover, it can be proven that specific classifiers such as the Max Entropy and SVMs can benefit from the introduction of a neutral class and improve the overall accuracy of the classification. There are in principle two ways for operating with a neutral class. Either, the algorithm proceeds by first identifying the neutral language, filtering it out and then assessing the rest in terms of positive and negative sentiments, or it builds a three-way classification in one step. This second approach often involves estimating a probability distribution over all categories (e.g. naive Bayes classifiers as implemented by the NLTK). Whether and how to use a neutral class depends on the nature of the data: if the data is clearly clustered into neutral, negative and positive language, it makes sense to filter the neutral language out and focus on the polarity between positive and negative sentiments. If, in contrast, the data are mostly neutral with small deviations towards positive and negative affect, this strategy would make it harder to clearly distinguish between the two poles.

A different method for determining sentiment is the use of a scaling system whereby words commonly associated with having a negative, neutral, or positive sentiment are given an associated number on a −10 to +10 scale (most negative up to most positive) or simply from 0 to a positive upper limit such as +4. This makes it possible to adjust the sentiment of a given term relative to its environment (usually on the level of the sentence). When a piece of unstructured text is analyzed using natural language processing, each concept in the specified environment is given a score based on the way sentiment words relate to the concept and its associated score. This allows movement to a more sophisticated understanding of sentiment, because it is now possible to adjust the sentiment value of a concept relative to modifications that may surround it. Words, for example, that intensify, relax or negate the sentiment expressed by the concept can affect its score. Alternatively, texts can be given a positive and negative sentiment strength score if the goal is to determine the sentiment in a text rather than the overall polarity and strength of the text.

There are various other types of sentiment analysis, such as aspect-based sentiment analysis, grading sentiment analysis (positive, negative, neutral), multilingual sentiment analysis and detection of emotions.

Subjectivity/objectivity identification

This task is commonly defined as classifying a given text (usually a sentence) into one of two classes: objective or subjective. This problem can sometimes be more difficult than polarity classification. The subjectivity of words and phrases may depend on their context and an objective document may contain subjective sentences (e.g., a news article quoting people's opinions). Moreover, as mentioned by Su, results are largely dependent on the definition of subjectivity used when annotating texts. However, Pang showed that removing objective sentences from a document before classifying its polarity helped improve performance.

Subjective and objective identification, emerging subtasks of sentiment analysis to use syntactic, semantic features, and machine learning knowledge to identify if a sentence or document contains facts or opinions. Awareness of recognizing factual and opinions is not recent, having possibly first presented by Carbonell at Yale University in 1979.

The term objective refers to the incident carrying factual information.

• Example of an objective sentence: 'To be elected president of the United States, a candidate must be at least thirty-five years of age.'

The term subjective describes the incident contains non-factual information in various forms, such as personal opinions, judgment, and predictions, also known as 'private states'. In the example down below, it reflects a private states 'We Americans'. Moreover, the target entity commented by the opinions can take several forms from tangible product to intangible topic matters stated in Liu (2010). Furthermore, three types of attitudes were observed by Liu (2010), 1) positive opinions, 2) neutral opinions, and 3) negative opinions.

• Example of a subjective sentence: 'We Americans need to elect a president who is mature and who is able to make wise decisions.'

This analysis is a classification problem.

Each class's collections of words or phrase indicators are defined for to locate desirable patterns on unannotated text. For subjective expression, a different word list has been created. Lists of subjective indicators in words or phrases have been developed by multiple researchers in the linguist and natural language processing field states in Riloff et al. (2003). A dictionary of extraction rules has to be created for measuring given expressions. Over the years, in subjective detection, the features extraction progression from curating features by hand to automated features learning. At the moment, automated learning methods can further separate into supervised and unsupervised machine learning. Patterns extraction with machine learning process annotated and unannotated text have been explored extensively by academic researchers.

However, researchers recognized several challenges in developing fixed sets of rules for expressions respectably. Much of the challenges in rule development stems from the nature of textual information. Six challenges have been recognized by several researchers: 1) metaphorical expressions, 2) discrepancies in writings, 3) context-sensitive, 4) represented words with fewer usages, 5) time-sensitive, and 6) ever-growing volume.

1. Metaphorical expressions. The text contains metaphoric expression may impact on the performance on the extraction. Besides, metaphors take in different forms, which may have been contributed to the increase in detection.
2. Discrepancies in writings. For the text obtained from the Internet, the discrepancies in the writing style of targeted text data involve distinct writing genres and styles.
3. Context-sensitive. Classification may vary based on the subjectiveness or objectiveness of previous and following sentences.
4. Time-sensitive attribute. The task is challenged by some textual data's time-sensitive attribute. If a group of researchers wants to confirm a piece of fact in the news, they need a longer time for cross-validation, than the news becomes outdated.
5. Cue words with fewer usages.
6. Ever-growing volume. The task is also challenged by the sheer volume of textual data. The textual data's ever-growing nature makes the task overwhelmingly difficult for the researchers to complete the task on time.

Previously, the research mainly focused on document level classification. However, classifying a document level suffers less accuracy, as an article may have diverse types of expressions involved. Researching evidence suggests a set of news articles that are expected to dominate by the objective expression, whereas the results show that it consisted of over 40% of subjective expression.

To overcome those challenges, researchers conclude that classifier efficacy depends on the precisions of patterns learner. And the learner feeds with large volumes of annotated training data outperformed those trained on less comprehensive subjective features. However, one of the main obstacles to executing this type of work is to generate a big dataset of annotated sentences manually. The manual annotation method has been less favored than automatic learning for three reasons:

1. Variations in comprehensions. In the manual annotation task, disagreement of whether one instance is subjective or objective may occur among annotators because of languages' ambiguity.
2. Human errors. Manual annotation task is a meticulous assignment, it require intense concentration to finish.
3. Time-consuming. Manual annotation task is an assiduous work. Riloff (1996) show that a 160 texts cost 8 hours for one annotator to finish.

All these mentioned reasons can impact on the efficiency and effectiveness of subjective and objective classification. Accordingly, two bootstrapping methods were designed to learning linguistic patterns from unannotated text data. Both methods are starting with a handful of seed words and unannotated textual data.

1. Meta-Bootstrapping by Riloff and Jones in 1999. Level One: Generate extraction patterns based on the pre-defined rules and the extracted patterns by the number of seed words each pattern holds. Level Two: Top 5 words will be marked and add to the dictionary. Repeat.
2. Basilisk (Bootstrapping Approach to Semantic Lexicon Induction using Semantic Knowledge) by Thelen and Riloff. Step One: Generate extraction patterns. Step Two: Move best patterns from Pattern Pool to Candidate Word Pool. Step Three: Top 10 words will be marked and add to the dictionary. Repeat.

Overall, these algorithms highlight the need for automatic pattern recognition and extraction in subjective and objective task.

Subjective and object classifier can enhance the several applications of natural language processing. One of the classifier's primary benefits is that it popularized the practice of data-driven decision-making processes in various industries. According to Liu, the applications of subjective and objective identification have been implemented in business, advertising, sports, and social science.

• Online review classification: In the business industry, the classifier helps the company better understand the feedbacks on product and reasonings behind the reviews.
• Stock price prediction: In the finance industry, the classifier aids the prediction model by process auxiliary information from social media and other textual information from the Internet. Previous studies on Japanese stock price conducted by Dong et al. indicates that model with subjective and objective module may perform better than those without this part.
• Social media analysis.
• Students' feedback classification.
• Document summarising: The classifier can extract target-specified comments and gathering opinions made by one particular entity.
• Complex question answering. The classifier can dissect the complex questions by classing the language subject or objective and focused target. In the research Yu et al.(2003), the researcher developed a sentence and document level clustered that identity opinion pieces.
• Domain-specific applications.
• Email analysis: The subjective and objective classifier detects spam by tracing language patterns with target words.

Feature/aspect-based

It refers to determining the opinions or sentiments expressed on different features or aspects of entities, e.g., of a cell phone, a digital camera, or a bank. A feature or aspect is an attribute or component of an entity, e.g., the screen of a cell phone, the service for a restaurant, or the picture quality of a camera. The advantage of feature-based sentiment analysis is the possibility to capture nuances about objects of interest. Different features can generate different sentiment responses, for example a hotel can have a convenient location, but mediocre food. This problem involves several sub-problems, e.g., identifying relevant entities, extracting their features/aspects, and determining whether an opinion expressed on each feature/aspect is positive, negative or neutral. The automatic identification of features can be performed with syntactic methods, with topic modeling, or with deep learning. More detailed discussions about this level of sentiment analysis can be found in Liu's work.

Intensity ranking

Emotions and sentiments are subjective in nature. The degree of emotions/sentiments expressed in a given text at the document, sentence, or feature/aspect level—to what degree of intensity is expressed in the opinion of a document, a sentence or an entity differs on a case-to-case basis. However, predicting only the emotion and sentiment does not always convey complete information. The degree or level of emotions and sentiments often plays a crucial role in understanding the exact feeling within a single class (e.g., 'good' versus 'awesome'). Some methods leverage a stacked ensemble method for predicting intensity for emotion and sentiment by combining the outputs obtained and using deep learning models based on convolutional neural networks, long short-term memory networks and gated recurrent units.

Methods and features

Existing approaches to sentiment analysis can be grouped into three main categories: knowledge-based techniques, statistical methods, and hybrid approaches. Knowledge-based techniques classify text by affect categories based on the presence of unambiguous affect words such as happy, sad, afraid, and bored. Some knowledge bases not only list obvious affect words, but also assign arbitrary words a probable "affinity" to particular emotions. Statistical methods leverage elements from machine learning such as latent semantic analysis, support vector machines, "bag of words", "Pointwise Mutual Information" for Semantic Orientation, semantic space models or word embedding models, and deep learning. More sophisticated methods try to detect the holder of a sentiment (i.e., the person who maintains that affective state) and the target (i.e., the entity about which the affect is felt). To mine the opinion in context and get the feature about which the speaker has opined, the grammatical relationships of words are used. Grammatical dependency relations are obtained by deep parsing of the text. Hybrid approaches leverage both machine learning and elements from knowledge representation such as ontologies and semantic networks in order to detect semantics that are expressed in a subtle manner, e.g., through the analysis of concepts that do not explicitly convey relevant information, but which are implicitly linked to other concepts that do so.

Open source software tools as well as range of free and paid sentiment analysis tools deploy machine learning, statistics, and natural language processing techniques to automate sentiment analysis on large collections of texts, including web pages, online news, internet discussion groups, online reviews, web blogs, and social media. Knowledge-based systems, on the other hand, make use of publicly available resources, to extract the semantic and affective information associated with natural language concepts. The system can help perform affective commonsense reasoning. Sentiment analysis can also be performed on visual content, i.e., images and videos (see Multimodal sentiment analysis). One of the first approaches in this direction is SentiBank utilizing an adjective noun pair representation of visual content. In addition, the vast majority of sentiment classification approaches rely on the bag-of-words model, which disregards context, grammar and even word order. Approaches that analyses the sentiment based on how words compose the meaning of longer phrases have shown better result, but they incur an additional annotation overhead.

A human analysis component is required in sentiment analysis, as automated systems are not able to analyze historical tendencies of the individual commenter, or the platform and are often classified incorrectly in their expressed sentiment. Automation impacts approximately 23% of comments that are correctly classified by humans. However, humans often disagree, and it is argued that the inter-human agreement provides an upper bound that automated sentiment classifiers can eventually reach.

Evaluation

The accuracy of a sentiment analysis system is, in principle, how well it agrees with human judgments. This is usually measured by variant measures based on precision and recall over the two target categories of negative and positive texts. However, according to research human raters typically only agree about 80% of the time (see Inter-rater reliability). Thus, a program that achieves 70% accuracy in classifying sentiment is doing nearly as well as humans, even though such accuracy may not sound impressive. If a program were "right" 100% of the time, humans would still disagree with it about 20% of the time, since they disagree that much about any answer.

On the other hand, computer systems will make very different errors than human assessors, and thus the figures are not entirely comparable. For instance, a computer system will have trouble with negations, exaggerations, jokes, or sarcasm, which typically are easy to handle for a human reader: some errors a computer system makes will seem overly naive to a human. In general, the utility for practical commercial tasks of sentiment analysis as it is defined in academic research has been called into question, mostly since the simple one-dimensional model of sentiment from negative to positive yields rather little actionable information for a client worrying about the effect of public discourse on e.g. brand or corporate reputation.

To better fit market needs, evaluation of sentiment analysis has moved to more task-based measures, formulated together with representatives from PR agencies and market research professionals. The focus in e.g. the RepLab evaluation data set is less on the content of the text under consideration and more on the effect of the text in question on brand reputation.

Because evaluation of sentiment analysis is becoming more and more task based, each implementation needs a separate training model to get a more accurate representation of sentiment for a given data set.

Web 2.0

See also: Reputation management, web 2.0, and web mining

The rise of social media such as blogs and social networks has fueled interest in sentiment analysis. With the proliferation of reviews, ratings, recommendations and other forms of online expression, online opinion has turned into a kind of virtual currency for businesses looking to market their products, identify new opportunities and manage their reputations. As businesses look to automate the process of filtering out the noise, understanding the conversations, identifying the relevant content and actioning it appropriately, many are now looking to the field of sentiment analysis. Further complicating the matter, is the rise of anonymous social media platforms such as 4chan and Reddit. If web 2.0 was all about democratizing publishing, then the next stage of the web may well be based on democratizing data mining of all the content that is getting published.

One step towards this aim is accomplished in research. Several research teams in universities around the world currently focus on understanding the dynamics of sentiment in e-communities through sentiment analysis.

The problem is that most sentiment analysis algorithms use simple terms to express sentiment about a product or service. However, cultural factors, linguistic nuances, and differing contexts make it extremely difficult to turn a string of written text into a simple pro or con sentiment. The fact that humans often disagree on the sentiment of text illustrates how big a task it is for computers to get this right. The shorter the string of text, the harder it becomes.

Even though short text strings might be a problem, sentiment analysis within microblogging has shown that Twitter can be seen as a valid online indicator of political sentiment. Tweets' political sentiment demonstrates close correspondence to parties' and politicians' political positions, indicating that the content of Twitter messages plausibly reflects the offline political landscape. Furthermore, sentiment analysis on Twitter has also been shown to capture the public mood behind human reproduction cycles globally, as well as other problems of public-health relevance such as adverse drug reactions.

While sentiment analysis has been popular for domains where authors express their opinion rather explicitly ("the movie is awesome"), such as social media and product reviews, only recently robust methods were devised for other domains where sentiment is strongly implicit or indirect. For example, in news articles - mostly due to the expected journalistic objectivity - journalists often describe actions or events rather than directly stating the polarity of a piece of information. Earlier approaches using dictionaries or shallow machine learning features were unable to catch the "meaning between the lines", but recently researchers have proposed a deep learning based approach and dataset that is able to analyze sentiment in news articles.

Scholars have utilized sentiment analysis to analyse the construction health and safety Tweets (which is called X now). The research revealed that there is a positive correlation between favorites and retweets in terms of sentiment valence. Others have examined the impact of YouTube on the dissemination of construction health and safety knowledge. They investigated how emotions influence users' behaviors in terms of viewing and commenting through semantic analysis. In another study, positive sentiment accounted for an overwhelming figure of 85% in knowledge sharing of construction safety and health via Instagram.

Application in recommender systems

See also: Recommender system

For a recommender system, sentiment analysis has been proven to be a valuable technique. A recommender system aims to predict the preference for an item of a target user. Mainstream recommender systems work on explicit data set. For example, collaborative filtering works on the rating matrix, and content-based filtering works on the meta-data of the items.

In many social networking services or e-commerce websites, users can provide text review, comment or feedback to the items. These user-generated text provide a rich source of user's sentiment opinions about numerous products and items. Potentially, for an item, such text can reveal both the related feature/aspects of the item and the users' sentiments on each feature. The item's feature/aspects described in the text play the same role with the meta-data in content-based filtering, but the former are more valuable for the recommender system. Since these features are broadly mentioned by users in their reviews, they can be seen as the most crucial features that can significantly influence the user's experience on the item, while the meta-data of the item (usually provided by the producers instead of consumers) may ignore features that are concerned by the users. For different items with common features, a user may give different sentiments. Also, a feature of the same item may receive different sentiments from different users. Users' sentiments on the features can be regarded as a multi-dimensional rating score, reflecting their preference on the items.

Based on the feature/aspects and the sentiments extracted from the user-generated text, a hybrid recommender system can be constructed. There are two types of motivation to recommend a candidate item to a user. The first motivation is the candidate item have numerous common features with the user's preferred items, while the second motivation is that the candidate item receives a high sentiment on its features. For a preferred item, it is reasonable to believe that items with the same features will have a similar function or utility. So, these items will also likely to be preferred by the user. On the other hand, for a shared feature of two candidate items, other users may give positive sentiment to one of them while giving negative sentiment to another. Clearly, the high evaluated item should be recommended to the user. Based on these two motivations, a combination ranking score of similarity and sentiment rating can be constructed for each candidate item.

Except for the difficulty of the sentiment analysis itself, applying sentiment analysis on reviews or feedback also faces the challenge of spam and biased reviews. One direction of work is focused on evaluating the helpfulness of each review. Review or feedback poorly written is hardly helpful for recommender system. Besides, a review can be designed to hinder sales of a target product, thus be harmful to the recommender system even it is well written.

Researchers also found that long and short forms of user-generated text should be treated differently. An interesting result shows that short-form reviews are sometimes more helpful than long-form, because it is easier to filter out the noise in a short-form text. For the long-form text, the growing length of the text does not always bring a proportionate increase in the number of features or sentiments in the text.

Lamba & Madhusudhan introduce a nascent way to cater the information needs of today's library users by repackaging the results from sentiment analysis of social media platforms like Twitter and provide it as a consolidated time-based service in different formats. Further, they propose a new way of conducting marketing in libraries using social media mining and sentiment analysis.

Transmissible spongiform encephalopathy

From Wikipedia, the free encyclopedia

https://en.wikipedia.org/wiki/Transmissible_spongiform_encephalopathy 

 

Transmissible spongiform encephalopathy (TSE)
Other names	Prion disease
Micrograph showing spongiform degeneration (vacuoles that appear as holes in tissue sections) in the cerebral cortex of a patient who had died of Creutzfeldt–Jakob disease. H&E stain, scale bar = 30 microns (0.03 mm).
Specialty	Infectious diseases
Symptoms	Dementia, seizures, tremors, insomnia, psychosis, delirium, confusion
Usual onset	Months to decades
Types	Bovine spongiform encephalopathy, Fatal familial insomnia, Creutzfeldt-Jakob disease, kuru, Huntington's disease-like 1, scrapie, variably protease-sensitive prionopathy, chronic wasting disease, Gerstmann-Sträussler-Scheinker syndrome, feline spongiform encephalopathy, transmissible mink encephalopathy, exotic ungulate encephalopathy, camel spongiform encephalopathy
Causes	Prion
Risk factors	Contact with infected fluids, ingestion of infected flesh, having one or two parents that have the disease (in case of fatal familial insomnia)
Diagnostic method	Currently there is no way to reliably detect prions except at post-mortem
Prevention	Varies
Treatment	Palliative care
Prognosis	Invariably fatal
Frequency	Rare

Transmissible spongiform encephalopathies (TSEs), also known as prion diseases, are a group of progressive, incurable, and fatal conditions that are associated with prions and affect the brain and nervous system of many animals, including humans, cattle, and sheep. According to the most widespread hypothesis, they are transmitted by prions, though some other data suggest an involvement of a Spiroplasma infection. Mental and physical abilities deteriorate and many tiny holes appear in the cortex causing it to appear like a sponge when brain tissue obtained at autopsy is examined under a microscope. The disorders cause impairment of brain function which may result in memory loss, personality changes, and abnormal or impaired movement which worsen over time.

TSEs of humans include Creutzfeldt–Jakob disease, Gerstmann–Sträussler–Scheinker syndrome, fatal familial insomnia, and kuru, as well as the recently discovered variably protease-sensitive prionopathy and familial spongiform encephalopathy. Creutzfeldt-Jakob disease itself has four main forms, the sporadic (sCJD), the hereditary/familial (fCJD), the iatrogenic (iCJD) and the variant form (vCJD). These conditions form a spectrum of diseases with overlapping signs and symptoms.

TSEs in non-human mammals include scrapie in sheep, bovine spongiform encephalopathy (BSE) in cattle – popularly known as "mad cow disease" – and chronic wasting disease (CWD) in deer and elk. The variant form of Creutzfeldt–Jakob disease in humans is caused by exposure to bovine spongiform encephalopathy prions.

Unlike other kinds of infectious disease, which are spread by agents with a DNA or RNA genome (such as virus or bacteria), the infectious agent in TSEs is believed to be a prion, thus being composed solely of protein material. Misfolded prion proteins carry the disease between individuals and cause deterioration of the brain. TSEs are unique diseases in that their aetiology may be genetic, sporadic, or infectious via ingestion of infected foodstuffs and via iatrogenic means (e.g., blood transfusion). Most TSEs are sporadic and occur in an animal with no prion protein mutation. Inherited TSE occurs in animals carrying a rare mutant prion allele, which expresses prion proteins that contort by themselves into the disease-causing conformation. Transmission occurs when healthy animals consume tainted tissues from others with the disease. In the 1980s and 1990s, bovine spongiform encephalopathy spread in cattle in an epidemic fashion. This occurred because cattle were fed the processed remains of other cattle, a practice now banned in many countries. In turn, consumption (by humans) of bovine-derived foodstuff which contained prion-contaminated tissues resulted in an outbreak of the variant form of Creutzfeldt–Jakob disease in the 1990s and 2000s.

Prions cannot be transmitted through the air, through touching, or most other forms of casual contact. However, they may be transmitted through contact with infected tissue, body fluids, or contaminated medical instruments. Normal sterilization procedures such as boiling or irradiating materials fail to render prions non-infective. However, treatment with strong, almost undiluted bleach and/or sodium hydroxide, or heating to a minimum of 134 °C, does destroy prions.

Classification

Differences in shape between the different prion protein forms are poorly understood.

Known spongiform encephalopathies

ICTVdb Code	Disease name	Natural host	Prion name	PrP isoform	Ruminant
Non-human mammals
90.001.0.01.001.	Scrapie	Sheep and goats	Scrapie prion	PrPSc	Yes
90.001.0.01.002.	Transmissible mink encephalopathy (TME)	Mink	TME prion	PrPTME	No
90.001.0.01.003.	Chronic wasting disease (CWD)	Elk, white-tailed deer, mule deer and red deer	CWD prion	PrPCWD	Yes
90.001.0.01.004.	Bovine spongiform encephalopathy (BSE) commonly known as "mad cow disease"	Cattle	BSE prion	PrPBSE	Yes
90.001.0.01.005.	Feline spongiform encephalopathy (FSE)	Cats	FSE prion	PrPFSE	No
90.001.0.01.006.	Exotic ungulate encephalopathy (EUE)	Nyala and greater kudu	EUE prion	PrPEUE	Yes
	Camel spongiform encephalopathy (CSE)	Camel	PrPCSE		Yes
Human diseases
90.001.0.01.007.	Kuru	Humans	Kuru prion	PrPKuru	No
90.001.0.01.008.	Creutzfeldt–Jakob disease (CJD)		CJD prion	PrPsCJD	No
	Variant Creutzfeldt–Jakob disease (vCJD, nvCJD)		vCJD prion	PrPvCJD
90.001.0.01.009.	Gerstmann-Sträussler-Scheinker syndrome (GSS)		GSS prion	PrPGSS	No
90.001.0.01.010.	Fatal familial insomnia (FFI)		FFI prion	PrPFFI	No
	Familial spongiform encephalopathy

Signs and symptoms

The degenerative tissue damage caused by human prion diseases (CJD, GSS, and kuru) is characterised by four features: spongiform change (the presence of many small holes), the death of neurons, astrocytosis (abnormal increase in the number of astrocytes due to the destruction of nearby neurons), and amyloid plaque formation. These features are shared with prion diseases in animals, and the recognition of these similarities prompted the first attempts to transmit a human prion disease (kuru) to a primate in 1966, followed by CJD in 1968 and GSS in 1981. These neuropathological features have formed the basis of the histological diagnosis of human prion diseases for many years, although it was recognized that these changes are enormously variable both from case to case and within the central nervous system in individual cases.

The clinical signs in humans vary, but commonly include personality changes, psychiatric problems such as depression, lack of coordination, and/or an unsteady gait (ataxia). Patients also may experience involuntary jerking movements called myoclonus, unusual sensations, insomnia, confusion, or memory problems. In the later stages of the disease, patients have severe mental impairment (dementia) and lose the ability to move or speak.

Early neuropathological reports on human prion diseases suffered from a confusion of nomenclature, in which the significance of the diagnostic feature of spongiform change was occasionally overlooked. The subsequent demonstration that human prion diseases were transmissible reinforced the importance of spongiform change as a diagnostic feature, reflected in the use of the term "spongiform encephalopathy" for this group of disorders.

Prions appear to be most infectious when in direct contact with affected tissues. For example, Creutzfeldt–Jakob disease has been transmitted to patients taking injections of growth hormone harvested from human pituitary glands, from cadaver dura allografts and from instruments used for brain surgery (Brown, 2000) (prions can survive the "autoclave" sterilization process used for most surgical instruments). It is also believed that dietary consumption of affected animals can cause prions to accumulate slowly, especially when cannibalism or similar practices allow the proteins to accumulate over more than one generation. An example is kuru, which reached epidemic proportions in the mid-20th century in the Fore people of Papua New Guinea, who used to consume their dead as a funerary ritual. Laws in developed countries now ban the use of rendered ruminant proteins in ruminant feed as a precaution against the spread of prion infection in cattle and other ruminants.

There exists evidence that prion diseases may be transmissible by the airborne route.

Note that not all encephalopathies are caused by prions, as in the cases of PML (caused by the JC virus), CADASIL (caused by abnormal NOTCH3 protein activity), and Krabbe disease (caused by a deficiency of the enzyme galactosylceramidase). Progressive Spongiform Leukoencephalopathy (PSL)—which is a spongiform encephalopathy—is also probably not caused by a prion, although the adulterant that causes it among heroin smokers has not yet been identified. This, combined with the highly variable nature of prion disease pathology, is why a prion disease cannot be diagnosed based solely on a patient's symptoms.

Cause

Genetics

Mutations in the PRNP gene cause prion disease. Familial forms of prion disease are caused by inherited mutations in the PRNP gene. Only a small percentage of all cases of prion disease run in families, however. Most cases of prion disease are sporadic, which means they occur in people without any known risk factors or gene mutations. In rare circumstances, prion diseases also can be transmitted by exposure to prion-contaminated tissues or other biological materials obtained from individuals with prion disease.

The PRNP gene provides the instructions to make a protein called the prion protein (PrP). Under normal circumstances, this protein may be involved in transporting copper into cells. The protein may also be involved in protecting brain cells and helping them communicate. Point mutations in this gene cause cells to produce an abnormal form of the prion protein, known as PrP^Sc. This abnormal protein builds up in the brain and destroys nerve cells, resulting in the signs and symptoms of prion disease.

Familial forms of prion disease are inherited in an autosomal dominant pattern, which means one copy of the altered gene in each cell is sufficient to cause the disorder. In most cases, an affected person inherits the altered gene from one affected parent.

In some people, familial forms of prion disease are caused by a new mutation in the PRNP gene. Although such people most likely do not have an affected parent, they can pass the genetic change to their children.

Protein-only hypothesis

Protein could be the infectious agent, inducing its own replication by causing conformational change of normal cellular PrP^C into PrP^Sc. Evidence for this hypothesis:

• Infectivity titre correlates with PrP^Sc levels. However, this is disputed.
• PrP^Sc is an isomer of PrP^C
• Denaturing PrP removes infectivity
• PrP-null mice cannot be infected
• PrP^C depletion in the neural system of mice with established neuroinvasive prion infection reverses early spongeosis and behavioural deficits, halts further disease progression and increases life-span

Multi-component hypothesis

See also: Protein misfolding cyclic amplification

While not containing a nucleic acid genome, prions may be composed of more than just a protein. Purified PrP^C appears unable to convert to the infectious PrP^Sc form, unless other components are added, such as RNA and lipids. These other components, termed cofactors, may form part of the infectious prion, or they may serve as catalysts for the replication of a protein-only prion.

Viral hypothesis

This hypothesis postulates that a yet undiscovered infectious viral agent is the cause of the disease. Although this was once the leading hypothesis, it is now a minority view. Evidence for this hypothesis is as follows:

• Incubation time is comparable to a lentivirus.
• Strain variation of different isolates of PrP^sc.

Diagnosis

There continues to be a very practical problem with diagnosis of prion diseases, including BSE and CJD. They have an incubation period of months to decades during which there are no symptoms, even though the pathway of converting the normal brain PrP protein into the toxic, disease-related PrP^Sc form has started. At present, there is virtually no way to detect PrP^Sc reliably except by examining the brain using neuropathological and immunohistochemical methods after death. Accumulation of the abnormally folded PrP^Sc form of the PrP protein is a characteristic of the disease, but it is present at very low levels in easily accessible body fluids like blood or urine. Researchers have tried to develop methods to measure PrP^Sc, but there are still no fully accepted methods for use in materials such as blood.

In 2010, a team from New York described detection of PrP^Sc even when initially present at only one part in a hundred billion (10⁻¹¹) in brain tissue. The method combines amplification with a novel technology called Surround Optical Fiber Immunoassay (SOFIA) and some specific antibodies against PrP^Sc. After amplifying and then concentrating any PrP^Sc, the samples are labelled with a fluorescent dye using an antibody for specificity and then finally loaded into a micro-capillary tube. This tube is placed in a specially constructed apparatus so that it is totally surrounded by optical fibres to capture all light emitted once the dye is excited using a laser. The technique allowed detection of PrP^Sc after many fewer cycles of conversion than others have achieved, substantially reducing the possibility of artefacts, as well as speeding up the assay. The researchers also tested their method on blood samples from apparently healthy sheep that went on to develop scrapie. The animals' brains were analysed once any symptoms became apparent. The researchers could therefore compare results from brain tissue and blood taken once the animals exhibited symptoms of the diseases, with blood obtained earlier in the animals' lives, and from uninfected animals. The results showed very clearly that PrP^Sc could be detected in the blood of animals long before the symptoms appeared.

Treatment

There are currently no known ways to cure or prevent prion disease. Certain medications can slow down the progression of the disease. But ultimately, supportive care is the only option for humans right now.

Epidemiology

Transmissible spongiform encephalopathies (TSE) are very rare but can reach epidemic proportions.^{[clarification needed]} It is very hard to map the spread of the disease due to the difficulty of identifying individual strains of the prions. This means that, if animals at one farm begin to show the disease after an outbreak on a nearby farm, it is very difficult to determine whether it is the same strain affecting both herds—suggesting transmission—or if the second outbreak came from a completely different source.

Classic Creutzfeldt-Jakob disease (CJD) was discovered in 1920. It occurs sporadically over the world but is very rare. It affects about one person per million each year. Typically, the cause is unknown for these cases. It has been found to be passed on genetically in some cases. 250 patients contracted the disease through iatrogenic transmission (from use of contaminated surgical equipment). This was before equipment sterilization was required in 1976, and there have been no other iatrogenic cases since then. In order to prevent the spread of infection, the World Health Organization created a guide to tell health care workers what to do when CJD appears and how to dispose of contaminated equipment. The Centers for Disease Control and Prevention (CDC) have been keeping surveillance on CJD cases, particularly by looking at death certificate information.

Chronic wasting disease (CWD) is a prion disease found in North America in deer and elk. The first case was identified as a fatal wasting syndrome in the 1960s. It was then recognized as a transmissible spongiform encephalopathy in 1978. Surveillance studies showed the endemic of CWD in free-ranging deer and elk spread in northeastern Colorado, southeastern Wyoming and western Nebraska. It was also discovered that CWD may have been present in a proportion of free-ranging animals decades before the initial recognition. In the United States, the discovery of CWD raised concerns about the transmission of this prion disease to humans. Many apparent cases of CJD were suspected transmission of CWD, however the evidence was lacking and not convincing.

In the 1980s and 1990s, bovine spongiform encephalopathy (BSE or "mad cow disease") spread in cattle at an epidemic rate. The total estimated number of cattle infected was approximately 750,000 between 1980 and 1996. This occurred because the cattle were fed processed remains of other cattle. Then human consumption of these infected cattle caused an outbreak of the human form CJD. There was a dramatic decline in BSE when feeding bans were put in place. On May 20, 2003, the first case of BSE was confirmed in North America. The source could not be clearly identified, but researchers suspect it came from imported BSE-infected cow meat. In the United States, the USDA created safeguards to minimize the risk of BSE exposure to humans.

Variant Creutzfeldt-Jakob disease (vCJD) was discovered in 1996 in England. There is strong evidence to suggest that vCJD was caused by the same prion as bovine spongiform encephalopathy. A total of 231 cases of vCJD have been reported since it was first discovered. These cases have been found in a total of 12 countries with 178 in the United Kingdom, 27 in France, five in Spain, four in Ireland, four in the United States, three in the Netherlands, three in Italy, two in Portugal, two in Canada, and one each in Japan, Saudi Arabia, and Taiwan.

History

In the 5th century BCE, Hippocrates described a disease like TSE in cattle and sheep, which he believed also occurred in humans. Publius Flavius Vegetius Renatus records cases of a disease with similar characteristics in the 4th and 5th centuries AD. In 1755, an outbreak of scrapie was discussed in the British House of Commons and may have been present in Britain for some time before that. Although there were unsupported claims in 1759 that the disease was contagious, in general it was thought to be due to inbreeding and countermeasures appeared to be successful. Early-20th-century experiments failed to show transmission of scrapie between animals, until extraordinary measures were taken such as the intra-ocular injection of infected nervous tissue. No direct link between scrapie and human disease was suspected then or has been found since. TSE was first described in humans by Alfons Maria Jakob in 1921. Daniel Carleton Gajdusek's discovery that Kuru was transmitted by cannibalism accompanied by the finding of scrapie-like lesions in the brains of Kuru victims strongly suggested an infectious basis to TSE. A paradigm shift to a non-nucleic infectious entity was required when the results were validated with an explanation of how a prion protein might transmit spongiform encephalopathy. Not until 1988 was the neuropathology of spongiform encephalopathy properly described in cows. The alarming amplification of BSE in the British cattle herd heightened fear of transmission to humans and reinforced the belief in the infectious nature of TSE. This was confirmed with the identification of a Kuru-like disease, called new variant Creutzfeldt–Jakob disease, in humans exposed to BSE. Although the infectious disease model of TSE has been questioned in favour of a prion transplantation model that explains why cannibalism favours transmission, the search for a viral agent was, as of 2007, being continued in some laboratories.