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The influence of physical activity in the progression of experimental lung cancer in mice

Renato Batista Paceli 1Rodrigo Nunes CalCarlos Henrique Ferreira dos SantosJosé Antonio CordeiroCassiano Merussi NeivaKazuo Kawano NagaminePatrícia Maluf Cury


GRUPO_AF1GROUP AFA1 – Aerobic Physical Activity – Atividade Física Aeróbia – ´´My´´ Dissertation – Faculty of Medicine of Sao Jose do Rio Preto

GRUPO AFAN 1GROUP AFAN1 – Anaerobic Physical ActivityAtividade Física Anaeróbia – ´´My´´ Dissertation – Faculty of Medicine of Sao Jose do Rio Preto

GRUPO_AF2GROUP AFA2 – Aerobic Physical ActivityAtividade Física Aeróbia – ´´My´´ Dissertation – Faculty of Medicine of Sao Jose do Rio Preto

GRUPO AFAN 2GROUP AFAN 2 – Anaerobic Physical ActivityAtividade Física Anaeróbia´´My´´ Dissertation – Faculty of Medicine of Sao Jose do Rio Preto

Slides – mestrado´´My´´ Dissertation – Faculty of Medicine of Sao Jose do Rio Preto



Avaliação da influência da atividade física aeróbia e anaeróbia na progressão do câncer de pulmão experimental – Summary – Resumo´´My´´ Dissertation Faculty of Medicine of Sao Jose do Rio Preto



Lung cancer is one of the most incident neoplasms in the world, representing the main cause of mortality for cancer. Many epidemiologic studies have suggested that physical activity may reduce the risk of lung cancer, other works evaluate the effectiveness of the use of the physical activity in the suppression, remission and reduction of the recurrence of tumors. The aim of this study was to evaluate the effects of aerobic and anaerobic physical activity in the development and the progression of lung cancer. Lung tumors were induced with a dose of 3mg of urethane/kg, in 67 male Balb – C type mice, divided in three groups: group 1_24 mice treated with urethane and without physical activity; group 2_25 mice with urethane and subjected to aerobic swimming free exercise; group 3_18 mice with urethane, subjected to anaerobic swimming exercise with gradual loading 5-20% of body weight. All the animals were sacrificed after 20 weeks, and lung lesions were analyzed. The median number of lesions (nodules and hyperplasia) was 3.0 for group 1, 2.0 for group 2 and 1.5-3 (p=0.052). When comparing only the presence or absence of lesion, there was a decrease in the number of lesions in group 3 as compared with group 1 (p=0.03) but not in relation to group 2. There were no metastases or other changes in other organs. The anaerobic physical activity, but not aerobic, diminishes the incidence of experimental lung tumors.

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The most innovative cancer drugs take longest to reach patients, study suggests

The Pharmaceutical Journal29 JAN 2020By Emma Wilkinson

Examples of innovative cancer drugs that have been delayed in the authorisation process include mifamurtide (Mepact; Takeda UK) and trabectedin (Yondelis; Immedica).

The most innovative new cancer drugs are taking longer to pass through clinical trials, licensing and eventual appraisal by the National Institute for Health and Care Excellence (NICE), an analysis carried out by the Institute of Cancer Research (ICR) has suggested.

European Medicines Agency (EMA) authorisation data from 2000 to 2016 showed that the numbers of cancer drugs being authorised has increased in the past two decades; however, the most innovative treatments — including those that have a novel target or mechanism of action — took 3.2 years longer to move from patent filing to NHS patients.

Further scrutiny by researchers at the ICR, published on 29 January 2019 in Drug Discovery Today, revealed that much of this delay seemed to happen in the period between starting phase I trials and authorisation by the EMA which lasted an average of 8.9 years for the most innovative drugs.

The ‘most innovative drugs’ were categorised as those that acted against a new molecular target or via a novel mechanism; represented a novel class of compound in an area of high unmet need; was novel in its application; or offered improved targeting through use of a biomarker. The equivalent delay for the least innovative drugs was 6.8 years.

Examples ofdelayed cancer drugsinclude mifamurtide (Mepact; Takeda UK), which took 20 years to go from patent to NICE approval for osteosarcoma, and trabectedin (Yondelis; Immedica), which took 22 years to navigate clinical trials and approval processes for advanced soft tissue carcinoma.

For some cancer types, including brain and oesophageal cancer, there were no new treatments licensed from 2000–2016. Only 8% of the drugs licensed in the same time period were for use in children, the study found.

Study leader Paul Workman, chief executive of the Institute of Cancer Research

Source: Courtesy of Paul Workman

Paul Workman, chief executive of the Institute of Cancer Research, and colleagues revealed that the delay in authorising innovative cancer drugs seemed to happen between starting phase I trials and authorisation by the European Medicines Agency

The research team — led by Paul Workman, chief executive of the Institute of Cancer Research, has called for streamlined clinical trial regulations and licensing processes, as well as stronger incentives for innovation in drug discovery and development.

The team pointed out that NICE had reduced the lag time between EMA authorisation and starting the technology appraisal process from 21.0 months to 6.5 months over the 16-year period, although the appraisal process was taking the same amount of time.

Workman said that while the study highlights major progress in the number of new drugs coming through the process, it does highlight shortcomings in the regulatory process. 

“[Our study] makes clear that our regulatory systems are not keeping pace with advances in the science. It is taking longer for new drugs to reach patients and, alarmingly, the delays are longest for the most exciting, innovative treatments, with the greatest potential to transform the lives of patients.”

Commenting on the research, Simon Cheesman, lead pharmacist for cancer at University College London Hospitals NHS Foundation Trust, said the results were not completely surprising, but that ensuring a treatment really did confer benefits to patients was tricky to balance against speed of access.

“It can be hard to assess the true benefit, safety and cost-effectiveness of a new medicine following initial clinical trials with surrogate endpoints and short-term follow up, and to balance this with rapid access to get the drug to those patients who might benefit as soon as possible,” he said.

Citation: The Pharmaceutical Journal, PJ January 2020 online, online | DOI: 10.1211/PJ.2020.20207628

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From Wikipedia, the free encyclopediaJump to navigationJump to searchFor other uses, see Imagination (disambiguation).Olin Levi WarnerImagination (1896). Library of Congress Thomas Jefferson Building, Washington, D.C.

Imagination is the ability to produce and simulate novel objects, peoples and ideas in the mind without any immediate input of the senses. It is also described as the forming of experiences in the mind, which can be re-creations of past experiences such as vivid memories with imagined changes, or they can be completely invented and possibly fantastic scenes.[1] Imagination helps make knowledge applicable in solving problems and is fundamental to integrating experience and the learning process.[2][3][4][5] A basic training for imagination is listening to storytelling (narrative),[2][6] in which the exactness of the chosen words is the fundamental factor to “evoke worlds”.[7]

Imagination is a cognitive process used in mental functioning and sometimes used in conjunction with psychological imagery. It is considered as such because it involves thinking about possibilities.[8] The cognate term of mental imagery may be used in psychology for denoting the process of reviving in the mind recollections of objects formerly given in sense perception. Since this use of the term conflicts with that of ordinary language, some psychologists have preferred to describe this process as “imaging” or “imagery” or to speak of it as “reproductive” as opposed to “productive” or “constructive” imagination. Constructive imagination is further divided into active imagination driven by the prefrontal cortex (PFC) and spontaneous PFC-independent imagination such as REM-sleep dreamingdaydreaminghallucinations, and spontaneous insight. The active types of imagination include integration of modifiers, and mental rotation. Imagined images, both novel and recalled, are seen with the “mind’s eye“.

Imagination, however, is not considered to be exclusively a cognitive activity because it is also linked to the body and place, particularly that it also involves setting up relationships with materials and people, precluding the sense that imagination is locked away in the head.[9]

Imagination can also be expressed through stories such as fairy tales or fantasies. Children often use such narratives and pretend play in order to exercise their imaginations. When children develop fantasy they play at two levels: first, they use role playing to act out what they have developed with their imagination, and at the second level they play again with their make-believe situation by acting as if what they have developed is an actual reality.[10]


The mind’s eye[edit]

The notion of a “mind’s eye” goes back at least to Cicero‘s reference to mentis oculi during his discussion of the orator’s appropriate use of simile.[11]

In this discussion, Cicero observed that allusions to “the Syrtis of his patrimony” and “the Charybdis of his possessions” involved similes that were “too far-fetched”; and he advised the orator to, instead, just speak of “the rock” and “the gulf” (respectively) — on the grounds that “the eyes of the mind are more easily directed to those objects which we have seen, than to those which we have only heard”.[12]

The concept of “the mind’s eye” first appeared in English in Chaucer’s (c.1387) Man of Law’s Tale in his Canterbury Tales, where he tells us that one of the three men dwelling in a castle was blind, and could only see with “the eyes of his mind”; namely, those eyes “with which all men see after they have become blind”.[13]

The condition of not being able to internally visualize (the lack of a ”mind’s eye”) is called Aphantasia.


The common use of the term is for the process of forming new images in the mind that have not been previously experienced with the help of what has been seen, heard, or felt before, or at least only partially or in different combinations. This could also be involved with thinking out possible or impossible outcomes of something or someone in life’s abundant situations and experiences. Some typical examples follow:

Imagination, not being limited to the acquisition of exact knowledge by the requirements of practical necessity is largely free from objective restraints. The ability to imagine one’s self in another person’s place is very important to social relations and understanding. Albert Einstein said, “Imagination … is more important than knowledge. Knowledge is limited. Imagination encircles the world.”[14]

The same limitations beset imagination in the field of scientific hypothesis. Progress in scientific research is due largely to provisional explanations which are developed by imagination, but such hypotheses must be framed in relation to previously ascertained facts and in accordance with the principles of the particular science.

Imagination is an experimental partition of the mind used to develop theories and ideas based on functions. Taking objects from real perceptions, the imagination uses complex IF-functions[citation needed] to develop new or revised ideas. This part of the mind is vital to developing better and easier ways to accomplish old and new tasks. In sociology, Imagination is used to part ways with reality and have an understanding of social interactions derived from a perspective outside of society itself. This leads to the development of theories through questions that wouldn’t usually be asked. These experimental ideas can be safely conducted inside a virtual world and then, if the idea is probable and the function is true, the idea can be actualized in reality. Imagination is the key to new development of the mind and can be shared with others, progressing collectively.

Regarding the volunteer effort, imagination can be classified as:

  • involuntary (the dream from the sleep, the daydream)
  • voluntary (the reproductive imagination, the creative imagination, the dream of perspective)


Psychologists have studied imaginative thought, not only in its exotic form of creativity and artistic expression but also in its mundane form of everyday imagination.[15] Ruth M.J. Byrne has proposed that everyday imaginative thoughts about counterfactual alternatives to reality may be based on the same cognitive processes on which rational thoughts are also based.[16] Children can engage in the creation of imaginative alternatives to reality from their very early years.[17] Cultural psychology is currently elaborating a view of imagination as a higher mental function involved in a number of everyday activities both at the individual and collective level[18] that enables people to manipulate complex meanings of both linguistic and iconic forms in the process of experiencing.

The phenomenology of imagination is discussed In The Imaginary: A Phenomenological Psychology of the Imagination (FrenchL’Imaginaire: Psychologie phénoménologique de l’imagination), also published under the title The Psychology of the Imagination, is a 1940 book by Jean-Paul Sartre, in which he propounds his concept of the imagination and discusses what the existence of imagination shows about the nature of human consciousness.[19]

The imagination is also active in our perception of photographic images in order to make them appear real.[20]


See also: Mental image and Imagery

Memory and mental imagery, often seen as a part of the process of imagination, have been shown to be affected by one another.[21] “Images made by functional magnetic resonance imaging technology show that remembering and imagining sends blood to identify parts of the brain.”[21] Various psychological factors can influence the mental processing of and can to heighten the chance of the brain to retain information as either long-term memories or short-term memories. John Sweller indicated that experiences stored as long-term memories are easier to recall, as they are ingrained deeper in the mind. Each of these forms require information to be taught in a specific manner so as to use various regions of the brain when being processed.[22] This information can potentially help develop programs for young students to cultivate or further enhance their creative abilities from a young age. The neocortex and thalamus are responsible for controlling the brain’s imagination, along with many of the brain’s other functions such as consciousness and abstract thought.[23] Since imagination involves many different brain functions, such as emotions, memory, thoughts, etc., portions of the brain where multiple functions occur—such as the thalamus and neocortex—are the main regions where imaginative processing has been documented.[24] The understanding of how memory and imagination are linked in the brain, paves the way to better understand one’s ability to link significant past experiences with their imagination. Aphantasia is a disease that makes you not imagine.


Piaget posited that perceptions depend on the world view of a person. The world view is the result of arranging perceptions into existing imagery by imagination. Piaget cites the example of a child saying that the moon is following her when she walks around the village at night. Like this, perceptions are integrated into the world view to make sense. Imagination is needed to make sense of perceptions.[25]

Versus belief[edit]

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Imagination is different from belief because the subject understands that what is personally invented by the mind does not necessarily affect the course of action taken in the apparently shared world, while beliefs are part of what one holds as truths about both the shared and personal worlds. The play of imagination, apart from the obvious limitations (e.g. of avoiding explicit self-contradiction), is conditioned only by the general trend of the mind at a given moment. Belief, on the other hand, is immediately related to practical activity: it is perfectly possible to imagine oneself a millionaire, but unless one believes it one does not, therefore, act as such. Belief endeavors to conform to the subject’s experienced conditions or faith in the possibility of those conditions; whereas imagination as such is specifically free.

The dividing line between imagination and belief varies widely in different stages of technological development. Thus in more extreme cases, someone from a primitive culture who ill frames an ideal reconstruction of the causes of his illness, and attributes it to the hostile magic of an enemy based on faith and tradition rather than science. In ignorance of the science of pathology the subject is satisfied with this explanation, and actually believes in it, sometimes to the point of death, due to what is known as the nocebo effect. It follows that the learned distinction between imagination and belief depends in practice on religion, tradition, and culture.[according to whom?]

Brain activation[edit]

A study using fMRI while subjects were asked to imagine precise visual figures, to mentally disassemble them, or mentally blend them, showed activity in the occipital, frontoparietal, posterior parietalprecuneus, and dorsolateral prefrontal regions of the subject’s brains.[26]

Evolution of imagination[edit]

Phylogenesis and ontogenesis of various components of imagination

Phylogenetic acquisition of imagination was a gradual process. The simplest form of imagination, REM-sleep dreaming, evolved in mammals with acquisition of REM sleep 140 million years ago.[27] Spontaneous insight improved in primates with acquisition of the lateral prefrontal cortex 70 million years ago. After hominins split from the chimpanzee line 6 million years ago they further improved their imagination. Prefrontal analysis was acquired 3.3 million years ago when hominins started to manufacture Mode One stone tools.[28] Progress in stone tools culture to Mode Two stone tools by 2 million years ago signify remarkable improvement of prefrontal analysis. The most advanced mechanism of imagination, prefrontal synthesis, was likely acquired by humans around 70,000 years ago and resulted in behavioral modernity.[29] This leap toward modern imagination has been characterized by paleoanthropologists as the “Cognitive revolution”,[30] “Upper Paleolithic Revolution”,[31] and the “Great Leap Forward”.[32]

As a reality[edit]

The world as experienced is an interpretation of data arriving from the senses; as such, it is perceived as real by contrast to most thoughts and imaginings. Users of hallucinogenic drugs are said to have a heightened imagination, or perhaps, a kind of heightened imaginative output. This difference is only one of degree and can be altered by several historic causes, namely changes to brain chemistry, hypnosis or other altered states of consciousnessmeditation, many hallucinogenic drugs, and electricity applied directly to specific parts of the brain. The difference between imagined and perceived reality can be proven by psychosis. Many mental illnesses can be attributed to this inability to distinguish between the sensed and the internally created worlds. Some cultures and traditions even view the apparently shared world as an illusion of the mind as with the Buddhist Maya, or go to the opposite extreme and accept the imagined and dreamed realms as of equal validity to the apparently shared world as the Australian Aborigines do with their concept of dreamtime.

Imagination, because of having freedom from external limitations, can often become a source of real pleasure and unnecessary suffering. Consistent with this idea, imagining pleasurable and fearful events is found to engage emotional circuits involved in emotional perception and experience.[33] A person of vivid imagination often suffers acutely from the imagined perils besetting friends, relatives, or even strangers such as celebrities. Also crippling fear can result from taking an imagined painful future too seriously.

See also[edit]


  1. ^ Szczelkun, Stefan (2018-03-03). SENSE THINK ACT: a collection of exercises to experience total human ability. Stefan Szczelkun. ISBN 9781870736107.
  2. Jump up to:a b Norman 2000 pp. 1-2
  3. ^ Brian Sutton-Smith 1988, p. 22
  4. ^ Archibald MacLeish 1970, p. 887
  5. ^ Kieran Egan 1992, pp. 50
  6. ^ Northrop Frye 1963, p. 49
  7. ^ As noted by Giovanni Pascoli
  8. ^ Byrne, Ruth (2007). The Rational Imagination: How People Create Alternatives to Reality. Cambridge, MA: MIT Press. p. 38. ISBN 978-0262025843.
  9. ^ Janowski, Dr Monica; Ingold, Professor Tim (2012-09-01). Imagining Landscapes: Past, Present and Future. Ashgate Publishing, Ltd. ISBN 9781409461449.
  10. ^ Laurence Goldman (1998). Child’s play: myth, mimesis and make-believe. Oxford New York: Berg Publishers. ISBN 978-1-85973-918-1Basically what this means is that the children use their make-believe situation and act as if what they are acting out is from a reality that already exists even though they have made it up.imagination comes after story created.
  11. ^ Cicero, De Oratore, Liber III: XLI: 163.
  12. ^ J.S. (trans. and ed.), Cicero on Oratory and Orators, Harper & Brothers, (New York), 1875: Book III, C.XLI, p.239.
  13. ^ The Man of Laws Tale, lines 550-553.
  14. ^ Viereck, George Sylvester (October 26, 1929). “What life means to Einstein: an interview”. The Saturday Evening Post.
  15. ^ Ward, T.B., Smith, S.M, & Vaid, J. (1997). Creative thought. Washington DC: APA
  16. ^ Byrne, R.M.J. (2005). The Rational Imagination: How People Create Alternatives to Reality. Cambridge, MA: MIT Press.
  17. ^ Harris, P. (2000). The work of the imagination. London: Blackwell.
  18. ^ Tateo, L. (2015). Giambattista Vico and the psychological imagination. Culture and Psychology, vol. 21(2):145-161.
  19. ^ Sartre, Jean-Paul (1995). The psychology of imagination. London: Routledge. ISBN 9780415119542OCLC 34102867.
  20. ^ Wilson, John G. (2016-12-01). “Sartre and the Imagination: Top Shelf Magazines”. Sexuality & Culture20 (4): 775–784. doi:10.1007/s12119-016-9358-xISSN 1095-5143.
  21. Jump up to:a b Long, Priscilla (2011). My Brain On My Mind. p. 27. ISBN 978-1612301365.
  22. ^ Leahy, Wayne; John Sweller (5 June 2007). “The Imagination Effect Increases with an Increased Intrinsic Cognitive Load”. Applied Cognitive Psychology22 (2): 273–283. doi:10.1002/acp.1373.
  23. ^ “Welcome to Brain Health and Puzzles!”. Retrieved 2011-03-05.
  24. ^ “Welcome to ScienceForums.Net!”.
  25. ^ Piaget, J. (1967). The child’s conception of the world. (J. & A. Tomlinson, Trans.). London: Routledge & Kegan Paul. BF721 .P5 1967X
  26. ^ Alexander Schlegel, Peter J. Kohler, Sergey V. Fogelson, Prescott Alexander, Dedeepya Konuthula, and Peter Ulric Tse (Sep 16, 2013) Network structure and dynamics of the mental workspace PNAS early edition
  27. ^ Hobson, J. Allan (1 October 2009). “REM sleep and dreaming: towards a theory of protoconsciousness”. Nature Reviews Neuroscience10 (11): 803–813. doi:10.1038/nrn2716PMID 19794431.
  28. ^ Harmand, Sonia; Lewis, Jason E.; Feibel, Craig S.; Lepre, Christopher J.; Prat, Sandrine; Lenoble, Arnaud; Boës, Xavier; Quinn, Rhonda L.; Brenet, Michel; Arroyo, Adrian; Taylor, Nicholas; Clément, Sophie; Daver, Guillaume; Brugal, Jean-Philip; Leakey, Louise; Mortlock, Richard A.; Wright, James D.; Lokorodi, Sammy; Kirwa, Christopher; Kent, Dennis V.; Roche, Hélène (20 May 2015). “3.3-million-year-old stone tools from Lomekwi 3, West Turkana, Kenya”. Nature521 (7552): 310–315. Bibcode:2015Natur.521..310Hdoi:10.1038/nature14464PMID 25993961.
  29. ^ Vyshedsky, Andrey (2019). “Neuroscience of Imagination and Implications for Human Evolution” (PDF). Curr Neurobiol10 (2): 89–109.
  30. ^ Harari, Yuval N. (2014). Sapiens : a brief history of humankind. London. ISBN 9781846558245OCLC 890244744.
  31. ^ Bar-Yosef, Ofer (October 2002). “The Upper Paleolithic Revolution”. Annual Review of Anthropology31 (1): 363–393. doi:10.1146/annurev.anthro.31.040402.085416ISSN 0084-6570.
  32. ^ Diamond, Jared M. (2006). The third chimpanzee : the evolution and future of the human animal. New York: HarperPerennial. ISBN 0060845503OCLC 63839931.
  33. ^ Costa, VD, Lang, PJ, Sabatinelli, D, Bradley MM, and Versace, F (2010). “Emotional imagery: Assessing pleasure and arousal in the brain’s reward circuitry”Human Brain Mapping31 (9): 1446–1457. doi:10.1002/hbm.20948PMC 3620013PMID 20127869.
Wikiquote has quotations related to: imagination
Wikisource has the text of the 1905 New International Encyclopedia article Imagination.

Further reading[edit]



  • Watkins, Mary: “Waking Dreams” [Harper Colophon Books, 1976] and “Invisible Guests – The Development of Imaginal Dialogues” [The Analytic Press, 1986]
  • Moss, Robert: “The Three “Only” Things: Tapping the Power of Dreams, Coincidence, and Imagination” [New World Library, September 10, 2007]
  •  This article incorporates text from a publication now in the public domain: Chisholm, Hugh, ed. (1911). “Imagination“. Encyclopædia Britannica14 (11th ed.). Cambridge University Press. pp. 304–305.

Three philosophers for whom imagination is a central concept are Kendall WaltonJohn Sallis and Richard Kearney. See in particular:

  • Kendall WaltonMimesis as Make-Believe: On the Foundations of the Representational Arts. Harvard University Press, 1990. ISBN 0-674-57603-9 (pbk.).
  • John SallisForce of Imagination: The Sense of the Elemental (2000)
  • John SallisSpacings-Of Reason and Imagination. In Texts of Kant, Fichte, Hegel (1987)
  • Richard KearneyThe Wake of Imagination. Minneapolis: University of Minnesota Press (1988); 1st Paperback Edition- (ISBN 0-8166-1714-7)
  • Richard Kearney, “Poetics of Imagining: Modern to Post-modern.” Fordham University Press (1998)

External links[edit]

 The dictionary definition of imagination at Wiktionary

hidevteMental processes
CognitionAwarenessCognitive dissonanceDecision-makingComprehensionConsciousnessImaginationIntuitionProblem solving
PerceptionAmodalHaptic (touch)Sound pitchharmonicsspeechSocialPerception as interpretationVisualColor RGB modelPeripheralDepthForm
OtherAttentionHigher nervous activityIntentionLearningMental fatigueMental setThinkingVolition
Authority control GND4064032-2


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Speaking of Research

Improving understanding about Animal Research / Animal Testing

Computer models are not replacing animal research, and probably never will

January 7th 2020
Juan Carlos Marvizon, Ph.D.
David Geffen School of Medicine at UCLA, VA Greater Los Angeles

The buzz is everywhere when animal research is mentioned: experiments in animals are outdated because computer models and other modern techniques are replacing them. For example, you may have heard statements like these:

“Researchers have developed a wide range of sophisticated computer models that simulate human biology and the progression of developing diseases. Studies show that these models can accurately predict the ways that new drugs will react in the human body and replace the use of animals in exploratory research and many standard drug tests.” Says PETA.

“Scientists at private companies, universities and government agencies are developing new cell and tissue tests, computer models and other sophisticated methods to replace existing animal tests. These alternatives are not only humane; they also tend to be more cost-effective, rapid and reliable than traditional animal tests.” Says the Humane Society of the United States.

“But research shows computer simulations of the heart have the potential to improve drug development for patients and reduce the need for animal testing.” Says Scientific American.

“Computer models could replace animal testing.” Reads the headline of Global Biotech Insights.

“During the past several years, OCSPP and ORD have worked together to make significant progress to reduce, replace and refine animal testing requirements. Beginning in 2012, the Endocrine Disruptor Screening Program began a multi-year transition to validate and more efficiently use computational toxicology methods and high-throughput approaches that allow the EPA to more quickly and cost-effectively screen for potential endocrine effects. In 2017 and 2018, ORD and OCSPP worked with other federal partners to compile a large body of legacy toxicity studies that was used to develop computer-based models to predict acute toxicity without the use of animals.” Reads the memo by Andrew R. Wheeler, the Administrator of the Environmental Protection Agency, in which he announced a reduction in animal testing of potentially toxic chemicals.

Data mining in PubMed

There is an easy way to check if these claims about computer models replacing animal research are true. Since the final product of scientific research are scientific articles, or “papers”, we can compare the number of papers generated with computer models and with animals to evaluate the actual productivity of the two approaches. There is a freely-accessible repository of all the papers published anywhere in the world: PubMed. It is run by the United States government, specifically by the US National Library of Medicine, part of the National Institutes of Health (NIH). In PubMed you can do keyword searches to find articles on any topic, so I used it for data-mining to compare the number of papers using animal research and computer models. In the “Search results” page there is a nifty graphic on the top left, with bars representing the number of papers per year containing the keyword. Below is a “Download CSV” link that allows you to get those numbers in a spreadsheet. I imported the numbers into a graphics program (Prism 8, by GraphPad) to create the graphics that I am going to show you.

There are several ways to enter a keyword in a search. You can search for the keyword anywhere in the article (“All Fields”). However, this is not useful for my goal because if an article mentions “computer model”, this does not mean that this was the main method used in the paper. My favorite method to restrict a search is to look for the keyword only in the title or the abstract of the paper (“Title/Abstract”). Still, this is not optimal because different authors may use different words for the same concept. For example, the terms “computer model” and “computer simulation” are synonyms. To deal with the problem of synonyms PubMed uses Medical Subject Headings (MeSH, homepageWikipedia), a sort of a thesaurus to facilitate searching by linking synonymous terms, so if you enter one of them it retrieves all the terms that are related. This is called doing an “extended search”. PubMed can perform MeSH searches by MeSH Major TopicMeSH Subheading or MeSH Terms. These different types of MeSH record types are explained here. A descriptor, Main Heading or Major Topic are terms used to describe the subject of each article. Qualifiers or Subheadings are used together with descriptors to provide more specificity. Entry Terms are “synonyms or closely related terms that are cross-referenced to descriptors”. Therefore, I performed my searches using MeSH Terms to avoid having to find the exact wording of a MeSH Major Topic. When you introduce a keyword as MeSH Term, for example ‘mice’, PubMed searches that word and all its synonyms, in this case ‘mouse’, ‘Mus’ and ‘Mus Musculus’.

Figures show the number of papers in the period 1975-2017, because 1975 seems to be the year when PubMed starts gathering most of the papers written in the world. Records appear incomplete before that date. It also seems that it takes up to two years for PubMed to complete its collection of citations, since the number of papers in every search drops substantially during the last two years. Hence, I excluded data from 2018 or 2019.

Papers using animals, mice, rats and non-mammals

A good place to start is to look at the number of papers using all animals, mice, rats, mice or rats, and non-mammals. Figure 1 shows that the number of papers using any kind of animals has been increasing linearly since 1975 and presently amounts to more than 100,000 papers per year. A large fraction of these papers uses rats or mice, and their number increase linearly in parallel with the number of papers using all animals. However, studies using rats have remained constant since 1990, while the number of papers using mice has been increasing exponentially. The blue line in the graph is an exponential curve, which provides an excellent fit for the mouse data. Therefore, scientists have been dropping rats in favor of mice, likely because of the increasing availability of transgenic mice, which allow performing sophisticated experiments. The number of papers using non-mammals (mostly birds, fish, insects and worms) has also been increasing exponentially, and recently surpassed the number of studies using rats.

Papers on humans and clinical trials

A search with the MeSH Term ‘animal’ without excluding humans yields a very high number of papers. This is because there a large number of papers on humans, which are shown in Figure 2 together with the results for non-human animals and mice or rats. Clearly, there many more papers on humans than on animals. They increase exponentially while the studies on animals increase linearly, so that the difference becomes greater with time. While in 1975 the number of papers on humans was roughly double of the papers on animals, today there are six studies on humans for every study on animals.

However, this does not mean that animal research is being replaced with research on humans. Strictly speaking, research on humans is conducted in clinical trials, so let us see what happens when we do a PubMed search on clinical trials. Looking at the Y-axis scale of Figure 3 we can see that there are many fewer papers reporting clinical trials than papers on humans. Today there is one clinical for every 50 papers on humans. This is because papers on humans are medical case reports, epidemiological studies and other medical observations. These could be considered research, but certainly not the kind of research on physiological and biochemical mechanisms that can replace animal research.

The number of clinical trials has increased over time but does not follow a clear trend, either linear or exponential. There was a steep drop around 1990 followed by a rapid increase until 2003. Since then, the number of clinical trials has remained constant. The result of this search is consistent with the reports in ClinicalTrials.gov, which lists 34,128 studies. Since a clinical trial run for several years, this could produce the number of annual papers shown in Figure 3.

Papers using computer models

Now we have enough background information to compare the number of papers on computer models with different types of animal and human research. Figure 4 shows the evolution in the number of papers using computer models over time. Barely any papers were published before 1985. After that, the number of studies increased slowly until 2001 and rapidly from 2001 to 2008, when it stopped growing.

At that point, the number of studies using computer models was similar to the number of clinical trials and 40 times less than the number of animal studies. Overall, their number fits reasonably well an exponential curve, but this is largely due to their initial growth. However, many of these studies use computer models in combination with animal studies. As shown in Figure 4, excluding the papers that used animals reduced the number of papers by almost two thirds. Moreover, the stagnation in the number of computer model studies after 2008 becomes more apparent when we exclude the studies that also use animals. If computer models were replacing animal studies what we would see is an increase in the papers exclusively using computer models. Instead, what we see is that a large number of papers use both computer models and animals, either because the models are used to analyze results obtained with animals or because animal experiments are used to validate the model.

The MeSH Term ‘computer simulation’ has five different subcategories: Augmented Reality, Molecular Docking Simulation, Molecular Dynamics Simulation, Patient-Specific Modeling and Virtual Reality. Searches with ‘augmented reality’ and ‘virtual reality’ as MeSH Terms yielded just a few hits. According to Wikipedia, molecular dynamics “is a computer simulation method for analyzing the physical movements of atoms and molecules” and is used in biomedical research to study the 3-dimensional structures of proteins and other biomolecules. Molecular docking is used to study the interaction of small molecules with their ‘docking pockets’ or ‘binding sites’ in proteins like enzymes or neurotransmitter receptors. This is a great tool to design new drugs that interact with these proteins. Patient-specific modeling is used to plan surgeries and to model organ function. Clearly, none of these techniques can be used to replace animal research; rather, they complement it. As we can see in Figure 4, molecular dynamics and molecular docking comprise a good fraction of the recent papers using computer models. Patient-specific modeling generates a very small number of papers.

Figure 5 shows a comparison between the number of papers generated in 2015 with computer models, with or without animals, and the papers derived from clinical trials or the use of different animal species. Most of the papers that year used mice or rats. Computer models produced many fewer papers, but this number was similar to the number of papers on clinical trials. When we consider papers using exclusively computer models, their number was much smaller and comparable with those using single animal species like dogs, cats and primates. Interestingly, papers using non-human primates are similar in number to those using zebrafish, the fruitfly Drosophila or the worm C. elegans, showing the relative importance of studies in non-mammals and invertebrates. If we add the number of papers using these species, they vastly outnumber the papers using computer models exclusively. Figure 1 shows that the number of papers using any kind of animal in 2015 was 120,000.

Implications for the 3Rs: reduce, replace and refine

Figure 1 shows that, overall, the use of animals in research has been increasing since 1975 and will likely continue to grow in the future. A major part of this increase is due to the exponential growth in the use of mice and non-mammal species. I will examine the question of whether mammals are being replaced by non-mammals or by in vitro methods in a future article. However, it is clear is that computer models are not replacing animals in research. The number of studies using computer models is relatively small and has not increased much in the last 10 years. When we count only studies that use computer models without animals, their number is much smaller and has not increased at all since 2008. Moreover, at present many of the papers using computer models deal with molecular dynamics and molecular docking, methods that complement but do not replace animal experiments. These types of papers have been increasing and some of them may include the use of animals.

Of course, the number of papers using animals does not reflect the actual number of animals used in research. Papers using monkeys use just a few of them; papers on mice and rats typically use hundreds of animals, while papers using fruit flies use tens of thousands of them. However, the number of papers does tell us the relative contribution of each species to the scientific endeavor. Also, given that the number of animals per paper for a given species is not likely to change much over time, an increase in the number of papers for that species is likely to reflect an increase in the number of animals used.

Therefore, the use of animals in research is not being reduced overall but continues to increase linearly. Regarding replacement, it is likely that charismatic species like dogs, cats and monkeys are being replaced by mice and non-mammals (an issue that I will examine in a future article). However, animal research is clearly not being replaced by computer models.

Why computer models will not replace animal research

But, how about the future? Surely, the enormous growth of computer power and artificial intelligence will determine that sooner or later biomedical research will move from animals to computer models, right?

Well, no. There are some fundamental issues that determine that, for the foreseeable future, we will need the actual bodies of animals or humans to extract information from them. Even though future computers will help enormously to accelerate biomedical research, they will not be able to tell us what happens inside our bodies or the bodies of animals. We will have to tell them.

The reason for this lies in the nature of life itself. Living beings have been created by evolution, which is a contingent process. The word ‘contingent’ means that there is an element of randomness in a process that makes it impossible to predict its outcome. In the words of evolutionary biologist Stephen Jay Gould, if we went back in time and run evolution again, we would end up with a completely different set of living beings. All the enzymes, intracellular signaling pathways, ion channels, neurotransmitter receptors, hormone receptors, membrane transporters, etc., responsible for the functioning of our bodies were created by contingent processes. Not entirely random, but still impossible to predict. For example, imagine that you were to design a new car. You will be constrained by physics if you wanted the car to work, but the car could still have infinite different looks. It may have four wheels, or three, or six. It could ride high as an SV or low as a sport car. An external observer could not predict how it would look and how it would work. Likewise, if you told a computer ‘find out how neurons in the spinal cord process pain’, the computer would not be able to tell you. Somebody would have to look at those neurons and find out. You have to feed that information to a computer before it can do anything with it.

The amount of information in our bodies, in each of our cells, is staggering. We have barely started to scratch it. The human genome contains 20,000 to 25,000 genes, and we still don’t know what most of them do. A computer, no matter how powerful, is not going to tell us. And knowing what each of those genes does is only a small part of the story. We need to know how the proteins encoded by those genes interact with each other to generate metabolism. The only way to do that is to take the body of an animal and look inside. A computer cannot guess what goes on inside the body, just like it cannot guess the content of a book that it has not read.

The advancement of computer technology in the information revolution has been so amazing that we have become convinced that there is nothing an advanced computer can’t do. That is why it is so easy for animal rights organizations to convince the public that we can eliminate animal research and replace it with computer models. Even organizations that supposedly defend animal research have helped this misconception by promoting the idea that eventually it will be Replaced (one of the three Rs) by computer models, in vitro research or clinical trials. That is simply not true. As I have shown here, as scientific productivity increases, so does the use of animals. It has not decreased, we are just using fewer animals of some species (dogs, cats, rabbits, primates) by using more animals of other species, like mice and zebrafish. And, as Figure 4 shows, research using computer models is relatively small and is not growing fast enough to ever catch up with animal research.

In conclusion, computer models are not replacing and likely will never replace animal research. Computers can do amazing things, but they cannot guess information that they do not have. There are limits to what is possible, and this is one of them.

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  1. RSMACLEODExcellent summary and use of publication data. I develop computer simulations as a tool that I hope might one day assist in the diagnosis and management of heart disease but I could not even begin the process without the results from large-mammal experiments that I also carry out. Each day, it gets easier to add complexity and sophistication to my models but, sadly, it also gets harder and more costly to conduct the essential animal experiments. Your article shows that many others are also successfully overcoming the resistance and pursuing the best science possible in a hostile environment. Nice to know that we are persevering. Keep up the great and challenging task of representing animal research accurately and fairly!Reply


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New nanosensor can detect cancer from a SINGLE DROP of blood

13 Feb, 2020 15:56 / Updated 2 days agoGet short URL

New nanosensor can detect cancer from a SINGLE DROP of blood

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Follow RT onResearchers in the Netherlands have developed an incredibly accurate nanosensor which can detect metastatic cancer cells from just a single drop of blood in a major breakthrough for early detection and treatment of the disease.

PhD students Dilu Mathew from University of Twente and Pepijn Beekman from Wageningen University pooled their resources and developed a tiny system to detect tumor-derived extracellular vesicles (tdEVs), a particular type of cancer biomarker. 

Their nanosensor is so sensitive it can detect cancer biomarkers on a broad spectrum of concentrations from 10 particles per microliter to 1 million particles per microliter, thanks to its incredibly small and delicate electrodes, shaped like two combs facing each other, with a gap of just 120 nanometers between them.ALSO ON RT.COMChemicals in tap water are causing thousands of cancer deaths across Europe – but the EU probably won’t do anything about it

“The most unique feature of this sensor is that its sensitivity spans over six orders of magnitude. In contrast to other sensors, it covers most of the clinically relevant range for tdEV detection in blood,” Mathew said. 

The pair’s method is far less time- and resource-intensive than Magnetic Resonance Imaging (MRI) and other traditional methods of detecting metastatic cancers.

Their ‘lab on a chip’ can detect individual nanoparticles – and could soon provide a quick and convenient method for healthcare professionals the world over to test for certain diseases in an incredibly non-invasive way.

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Biological Clock Key to Reducing Heart Damage From Radiation

Oncology Times: February 5, 2020 – Volume 42 – Issue 3 – p 24doi: 10.1097/01.COT.0000654744.98288.d9

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Shobhan Gaddameedhi, PhD, and Panshak Dakup, PhD
Shobhan Gaddameedhi, PhD, and Panshak Dakup, PhD: Study authors Shobhan Gaddameedhi, PhD, and Panshak Dakup, PhD, look at cell cultures used in experiments that examine the biological clock’s role in protecting from cell damage caused by radiation treatment.

Treatment for breast cancer commonly includes radiation therapy, which offers good chances of success but comes with a serious long-term side effect: toxicity due to radiation that reaches the heart, causing DNA damage in healthy heart cells. Over time, this can lead to heart disease and eventually heart failure.

A new study conducted by researchers in the Washington State University College of Pharmacy and Pharmaceutical Sciences suggests that a preventive solution may lie in the biological clock, the built-in time-keeping mechanism that keeps us on a 24-hour cycle of rest and activity and regulates a wide variety of processes in our bodies.

Published in the FASEB Journal, their study used a rodent model to determine whether the biological clock is involved in heart toxicity from radiation therapy and could be used as part of a strategy to reduce this toxicity (2020; https://doi.org/10.1096/fj.201901850RR). Their findings showed that, after receiving a dose of radiation to the heart, mice with disrupted biological clocks had significantly worse heart function than control mice.

In addition, the researchers demonstrated that a protein known as Bmal1—which drives 24-hour rhythms in the expression of many genes—plays an important role in protecting the heart from radiation-related damage.

“Our findings suggest that Bmal1 serves as a biomarker for the susceptibility to radiation-induced DNA damage to the heart,” said Shobhan Gaddameedhi, PhD, Assistant Professor in the Department of Pharmaceutical Sciences and the study’s senior author.

Though more research is needed, the researchers are hopeful that their discovery could someday be used to improve treatment outcomes for breast cancer patients. Panshak Dakup—the study’s first author and a PhD in pharmaceutical sciences student—said their finding holds promise for personalized medicine. “For example, in breast cancer patients who have a long history of working night shifts, the expression of biological clock proteins such as Bmal1 may be compromised, and it could be that radiation therapy is not the best option for them.”

Gaddameedhi added that it could also be used to optimize the timing of radiation therapy so it is provided when a patient’s Bmal1 level offers the greatest level of protection from heart damage. That timing may vary depending on a person’s chronotype—whether they are early birds or night owls—as well as on other factors that influence the status of the master biological clock, such as shiftwork or frequent travel across time zones.

Dakup conducted the experiments for the study as part of a predoctoral fellowship supported by the American Heart Association. Additional major support for the study came from the National Institutes of Health.

Study Details

In the study’s main experiment, Dakup looked at the heart function of two groups of mice with disrupted clocks, as compared to that of control mice. One group had a genetic mutation that eliminated Per1 and Per2—two genes that control the body’s master biological clock. The second group included wild-type mice that were put on a simulated rotating shift schedule in which light-dark cycles were reversed weekly, throwing off their clocks. The control group consisted of wild-type mice with healthy biological clocks that were on a simulated day shift schedule. All mice received radiation treatment to the chest that included all of the heart.

Collaborating with Zhaokang Cheng, PhD, Assistant Professor of Pharmaceutical Sciences and Cardiovascular Biology, Dakup used ultrasound echocardiography technology to compare heart function among the three groups, both prior to and up to 6 weeks after radiation treatment. In clock-disrupted mice, the heart’s ability to pump blood out and into circulation was compromised due to a loss of elasticity in the heart ventricle. Those mice also had more heart scar tissue than control mice.

Additional analyses focused on determining a potential relationship with the biological clock protein Bmal1. The researchers showed that Bmal1 levels across 24 hours were significantly lower in clock-disrupted mice versus control mice and peaked at a later time. They also found that higher levels of Bmal1 were associated with lower DNA damage levels, and vice versa.

Finally, the researchers found that Bmal1 interacts with the BRCA1, BRCA2, and ATM genes, three DNA damage response genes they said are important in fighting against radiation-induced DNA damage and cell death.

“When Bmal1 binds to these genes, it is potentially trying to elevate or activate their function against the collateral damage caused by radiation therapy,” Gaddameedhi said.

The researchers’ next step is to test their hypothesis in a cancer model. This will help them pin down the exact mechanism by which the biological clock protects the heart from radiation damage. They could then use this knowledge to develop new treatment strategies to minimize heart damage while maximizing the ability to kill tumor cells. Any such strategies would first need to be tested in clinical trials before they could be adopted.Copyright © 2020 Wolters Kluwer Health, Inc. All rights reserved.


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February 5, 2020, Vol. 42, Issue 3

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Gene Editing, AI Can Build New Generation of Low-Cost Health Solutions, Gates Says


14 February 2020by: Becky Ham

Bill Gates

Bill Gates calls for reducing health care inequities in a AAAS Annual Meeting plenary address. | Robb Cohen Photography & VideoBill Gates calls for reducing health care inequities in a AAAS Annual Meeting plenary address. | Robb Cohen Photography & Video

The number of child deaths around the world has declined by half in the past two decades, but that number could be cut even further with the help of artificial intelligence and gene editing tools, said Bill Gates at the American Association for the Advancement of Science’s Annual Meeting in Seattle.

One of the aims of The Bill & Melinda Gates Foundation, co-chaired by Bill Gates, is to reduce inequities in global health by reducing infectious disease and childhood mortality in developing countries. In his plenary address, Gates said the new technologies will help with “things we’re just on the verge of being able to do, that will advance that mission very dramatically.”

“We have an opportunity with the advancement in tools like artificial intelligence and gene-based editing technologies to build this new generation of health solutions so that they are available to everyone on the planet,” Gates said.

These solutions also could benefit wealthy countries seeking to bring down their own health care costs, he said.

“Today, most investments in health R&D are really focused almost entirely on the rich countries and in some cases because of pricing policies, only on the United States,” said Gates. “And this is causing the priorities of what gets done and the requirement that it be simple and low-cost to be missed, and therefore we’re not taking the best of science in treating something that can have a potential impact.”

In 1990, more than 12 million children under the age of 5 died worldwide. By 2015, the number was 5.9 million, according to the World Economic Forum. Children in the developing world, particularly sub-Saharan Africa, remain threatened by malnutrition and infectious diseases including HIV, tuberculosis and malaria.

Artificial intelligence, including machine learning, are already being used to model disease spread, find new drugs and understand complex biological systems. Machine learning may be especially useful for examining the microbiome, the full set of genetic materials from bacteria, fungi and viruses that live in and on the body, Gates said.

The contents — and sometimes the disruption — of the microbiome have been linked to malnutrition in the developing world, as well as diseases like obesity and diabetes in the developed world.

“This is an area that needed these [gene] sequencing tools and the high-scale data processing, including AI, to be able to find a path,” said Gates. “There’s just too much going on there if you had to do it, say, with paper and pencil, to understand the hundred trillion organisms and the large amount of genetic material.”

Gene editing tools like CRISPR technology, which allow scientists to alter DNA sequences and change gene function, are already being tested against diseases like malaria, where it is used to change the gut of mosquitos that carry the malaria parasite. Researchers also are using gene editing to modify the HIV virus and sickle cell anemia mutations.

For the moment, many gene editing treatments require an expensive and complicated procedure that alters cells outside of the body, replacing them only after old cells have been wiped out with chemotherapy. These procedures “definitely keep them out of broad, low-income country usage,” said Gates, who added that the goal is to find ways to alter genes inside the body in simpler outpatient procedures.

As with many global challenges, climate change looms large against the backdrop of global health, Gates said. Scientists and policymakers have paid less attention to developing adaptations to climate change, in favor of looking for ways to mitigate greenhouse gas emissions.

“And that’s partly because the vast proportion of the suffering that will be caused is for subsistence farmers near equatorial regions,” said Gates. “And so you have this mismatch between the place where the money and science are and the place where the historical emissions are, which is almost entirely separate from where the suffering will be.”

[Associated image: Robb Cohen Photography & Video]

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