LISTSERV mailing list manager LISTSERV 16.5

Help for SCIENCE-FOR-THE-PEOPLE Archives


SCIENCE-FOR-THE-PEOPLE Archives

SCIENCE-FOR-THE-PEOPLE Archives


SCIENCE-FOR-THE-PEOPLE@LIST.UVM.EDU


View:

Message:

[

First

|

Previous

|

Next

|

Last

]

By Topic:

[

First

|

Previous

|

Next

|

Last

]

By Author:

[

First

|

Previous

|

Next

|

Last

]

Font:

Proportional Font

LISTSERV Archives

LISTSERV Archives

SCIENCE-FOR-THE-PEOPLE Home

SCIENCE-FOR-THE-PEOPLE Home

SCIENCE-FOR-THE-PEOPLE  April 2007

SCIENCE-FOR-THE-PEOPLE April 2007

Subject:

Re: Beyond the HIV-Causes-AIDS Model

From:

Alex Dajkovic <[log in to unmask]>

Reply-To:

Science for the People Discussion List <[log in to unmask]>

Date:

Tue, 3 Apr 2007 19:01:05 +0200

Content-Type:

text/plain

Parts/Attachments:

Parts/Attachments

text/plain (604 lines)

An alternative (and simpler) explanation is that the models are wrong because
ordinary differential equations do not capture the nature of HIV infection.
This could be due to a number of factors, not the least of which is the
'continuous' nature of the models, in contrast to the stochastic nature of
biological events.  There could be missing (or wrong) parameters, missing (or
wrong) equations, etc. For example, the fact that HIV interacts with many
different cells in many different tissues is not accounted for by these
models.   Many such objections could (and should) be raised against any
positive interpretation of the predictions of the models.

Alex



On Tue, 3 Apr 2007 12:10:21 -0400, Mitchel Cohen wrote
> This article can be found on the I-SIS website at
> http://www.i-sis.org.uk/beyondHIV-CausesAIDS.php
> 
> ========================================================
> ISIS Press Release 03/04/07
> 
> Beyond the HIV-Causes-AIDS Model
> ##############################
> 
> Dr. Mae-Wan Ho follows the trail of how a bad mathematical
> model has misled AIDS policies with disastrous consequences,
> and recent attempts to find a better model
> 
> An electronic version of this report, or any other ISIS
> report, with full references, can be sent to you via e-mail
> for a donation of £3.50. Please e-mail the title of the
> report to: [log in to unmask]
> 
> Alternatively, order you copy of ISIS Report Unraveling AIDS
> and get a free CD of all new articles and others on AIDS
> from SiS archives to bring you right up to date
> http://www.i-sis.org.uk/onlinestore/books.php#236
> 
> “A model lacking in predictive and explanatory power” “More
> than 20 years into the AIDS era, it has become increasingly
> clear that the current single-virus causation model is
> lacking in predictive and explanatory power.” This is how
> Rebecca Culshaw, assistant professor of mathematics at the
> University of Texas, Tyler, USA, begins her most recent
> paper published in the winter 2006 issue of the Journal of
> American Physicians and Surgeons [1].
> 
> Culshaw has announced why she “quit HIV” in March 2006 [2]
> (On Quitting HIV, this series), and this had lured me onto
> the fascinating trail of how a bad mathematical model has
> misguided AIDS policies for so many years worldwide, and
> more importantly, alerted me to recent attempts to find
> alternative, more realistic models.
> 
> Perhaps quitting HIV is not the same as quitting AIDS, as
> the world is desperately in need of a good model, to save
> lives and end human suffering on a gigantic scale.
> 
> AIDS disease is generally characterised by a decline in CD4+
> T lymphocytes circulating in the blood, which are
> responsible for cell-mediated immunity. As a result, the
> patient becomes susceptible to opportunistic infections
> (those affecting weakened immune systems) such as
> tuberculosis, pneumonia, meningitis, and other diseases
> caused by parasites, bacteria and viruses that can enter and
> multiply in the cells of the body.
> 
> But models that assume the human immunodeficiency 
> virus (HIV) plays a central role in disease 
> progression run into considerable difficulties. 
> If the decline in CD4+ cells is due to HIV 
> killing the cells, then there should be a 
> correlation between the ‘viral load’, which 
> estimates the amount of virus in the body, and 
> the CD4+ cell count. But that is not the case. 
> CD4+ cell count is not a reliable indicator of 
> disease progression at all, nor for that matter 
> is viral load [3] (Chapter 2, 
> <http://www.i-sis.org.uk/unravelingAIDS.php>Unraveling 
> AIDS, ISIS Report), and they bear little 
> relationship to each another. This has been 
> confirmed in a recent study on untreated HIV+ individuals [4].
> 
> Although higher viral loads are associated with 
> greater CD4+ cell decline, only a very small 
> proportion of CD4+ cell loss, about 4 – 6 
> percent, is influenced by viral load. The authors 
> reporting the new study called for future efforts 
> [4] “to delineate the relative contribution of 
> other mechanisms.” In short, as Culshaw states 
> [1]: “It has been extremely difficult to 
> construct a realistic theoretical model of immune 
> suppression that is entirely mediated by HIV.”
> 
> Why is it important to have a realistic model of 
> the disease? A realistic model not only can 
> predict how the disease will progress, it can 
> also help in developing effective treatment and 
> prevention. Since the discovery of HIV, 
> mathematical models have been constructed 
> precisely for those purposes: to determine the 
> rates of progression to AIDS, to define optimal 
> drug regimens for therapy, to develop vaccines, 
> and as a desperate last resort, microbicide 
> vaginal gels [5] 
> (<http://www.i-sis.org.uk/HIVsexualTransmission.php>Concentrating 
> Exclusively on Sexual Transmission of HIV is 
> Misplaced, this series). However, the vast 
> majority of the models lack predictive power 
> because the mechanisms of disease and the 
> fundamental nature of the immune system are both 
> poorly understood. Meanwhile, the consequences of 
> models based on a wrong hypothesis are all too 
> clear, as Culshaw has starkly stated [2].
> 
> The toxicity of HAART treatment is now widely 
> accepted [3] 
> (<http://www.i-sis.org.uk/unravelingAIDS.php>Unraveling 
> AIDS, Chapter 7). The scandal of toxic drugs 
> being tested on defenceless foster children in 
> New York City and mothers and babies in Uganda 
> [6-8] (<http://www.i-sis.org.uk/USFCUADT.php>US 
> Foster Children Used in AIDS Drugs Tests; 
> <http://www.i-sis.org.uk/GPKADT.php>Guinea Pig 
> Kids in AIDS Drugs Trials ; 
> <http://www.i-sis.org.uk/NSADTMB.php>NIH-Sponsored 
> AIDS Drugs Tests on Mothers and Babies ; SiS 27) 
> was widely publicised more recently in Harper’s 
> Magazine [9]. This has reopened the acrimonious 
> debate [10] between AIDS ‘dissidents’ and the 
> orthodox community of researchers and activists 
> led by Robert Gallo, the controversial 
> co-discoverer of HIV. The litany of vaccine 
> failures has reached epic, controversial 
> proportions [3] 
> (<http://www.i-sis.org.uk/unravelingAIDS.php>Unraveling 
> AIDS, Chapters 9-13); and the third large-scale 
> clinical trials of anti-HIV gels has just been 
> terminated because it was not only ineffective, 
> but actually increased the risk of HIV infection 
> [5]. There are many compelling reasons to confront the bad model itself.
> 
> The Ho/Shaw model on why “hit hard hit early”
> 
> The model of HIV causes AIDS disease that has 
> come to dominate global policies on AIDS, from 
> diagnosis to therapy and prevention, is barely 12 
> years old. It was created in two high profile 
> papers published in the 12 January 1995 issue of 
> the journal Nature [11, 12]. Two research teams, 
> led respectively by David Ho of the Aaron Diamond 
> AIDS Research Centre NYU School of Medicine, New 
> York, and George Shaw of the University of 
> Alabama at Birmingham, used experimental 
> antiretroviral drugs to follow how HIV viral load 
> and CD4+ cell counts change after drug 
> administration. From the changes, they estimated 
> the rates of viral replication and elimination 
> from the body as well as the rates at which CD4+ 
> cells are killed and replaced by cell proliferation.
> 
> The results were astonishing; they were touted as 
> giving a radically new understanding of HIV 
> infection, one in which the immune system is in a 
> constant battle with HIV from the moment of 
> initial infection. As the distinguished late 
> mathematician Serge Lang, a prominent AIDS 
> dissident wrote [13]: “These papers largely 
> provided the justification for the new phase of 
> protease inhibitor and cocktail treatments, as 
> well as for the expanded use of surrogate markers 
> such as “viral load” and CD4 counts for AIDS 
> disease. Each of these represented a significant 
> departure in terms of HIV/AIDS diagnosis, 
> maintenance, treatment, and epidemiological reporting.”
> 
> In the Ho study [11], a protease inhibitor code 
> named ABT-538 was given at 600 to 1 200 mg per 
> day to 20 HIV+ individuals whose pre-treatment 
> CD4+ lymphocyte counts ranged from 36 to 490 per 
> mm3 and viral load, measured by a new 
> quantitative branch polymerase chain reaction 
> from 15 – 554 x 103 virus particles per ml.
> 
> Following treatment, every patient had a rapid 
> and dramatic decline in plasma viral load over 
> the first two weeks, between 11 and 275-fold 
> reduction, with a mean of 66-fold, i.e., a 98.5 
> percent drop. The initial decline was assumed to 
> be exponential, allowing the half time of viral 
> decay (time it takes for half of the virus 
> particles to disappear) to be estimated as 2.1 + 
> 0.4 days. That showed HIV-replication must be 
> “highly productive”, the authors claimed; and the 
> virus particles were cleared as fast as they were 
> produced. In other words, a steady state standoff 
> was established in the body, so that the viral 
> load measured at any time remained roughly the 
> same. The estimated minimum production rate – the 
> same as the minimum clearance rate - averaged 
> 0.68 + 0.13 x 109 virus particles per day, which 
> is really quite modest, considering that each 
> infected cell can produce a hundred virus particles.
> 
> The paper was heavily criticised. The estimates 
> depended on the assumption that drug treatment 
> does not affect viral clearance, and that there 
> was a pre-existing steady state between viral 
> production and viral clearance, regardless of the 
> amount of virus in circulation. Curiously, the 
> estimated viral clearance/production rate bore no 
> relationship to the initial viral load or to the 
> CD4+ lymphocyte count, which was difficult to 
> reconcile with the idea that the virus was 
> killing the CD4+ cells by invading the cells to 
> replicate and burst the cells. In that case, the 
> more virus particles and the more cells, the 
> higher should be the production/clearance rate.
> 
> After ABT-538 treatment, CD4+ lymphocyte counts 
> rose in each of 18 patients that could be 
> evaluated. Some increases were dramatic and 
> others quite modest. From the slope of the line 
> depicting the rise in CD4+ lymphocyte counts 
> assuming an exponential increase, a doubling time 
> of about 15 days was estimated during the 
> (assumed) pre-treatment steady state. The slopes 
> were inversely correlated with baseline CD4+ cell 
> counts, however, which too was difficult to 
> explain. In patients with lower initial CD4 cell 
> counts, more prominent rises were obtained. 
> Nevertheless, the authors claimed: “This 
> demonstrates convincingly that the CD4+ 
> lymphocyte depletion seen in AIDS is primarily a 
> consequence of the destruction of these cells 
> induced by HIV-1, not a lack of their 
> production.”  They explained that such an inverse 
> correlation would be expected if T-cell 
> proliferation were governed by some kind of 
> homeostatic mechanism. From the inverse 
> correlation, it was estimated that the minimum 
> number of CD4+ cells in blood produced or 
> destroyed each day ranged from 4.3 x 106 to 109 x 
> 106, with a mean of 35.1 x 106. As the blood 
> lymphocyte pool is about 2 percent of the total 
> population, the overall CD4+ lymphocytes turnover 
> in the patients was calculated to vary from 0.2 x 
> 109 to 5.4 x 109 cells per day, with a mean of 
> 1.8 x109 cells per day. This number of cells was 
> about the same as the number of putative viruses 
> produced (and cleared) each day, far too many 
> cells killed for the number of viruses produced. Things didn’t add up.
> 
> The increase in CD4+ lymphocyte counts following 
> ABT-538 administration was also modelled 
> linearly, and using the same arguments as for the 
> decline in viral load, the minimum estimates of 
> total CD4+ lymphocytes production or destruction 
> rates at baseline were determined to vary between 
> 0.1 x 109 to 7.8 x 109 cells per day with a mean 
> of 2.6 x 109 cells per day, sufficiently close to the estimate above.
> 
> The authors commented that the CD4+ lymphocyte 
> depletion seen in advanced HIV-1 infection “may 
> be likened to a sink containing a low water 
> level, with the tap and drain both equally wide 
> open.” As the regenerative capacity of the immune 
> system is not infinite, it is not difficult to 
> see why the sink eventually empties (when CD4+ cells are all 
> depleted).
> 
> Now comes the crucial conclusion that has 
> justified the “hit hard, hit early” [14] strategy 
> of HAART that has gone so disastrously wrong for 
> otherwise healthy HIV+ individuals: “It is also 
> evident from this analogy that our primary 
> strategy to reverse the immunodeficiency ought to 
> be to target virally mediated destruction (plug 
> the drain) rather than to emphasize lymphocyte 
> reconstitution (put in a second tap).”
> 
> And: “We believe our new kinetic data have 
> important implications for HIV-1 therapy and 
> pathogenesis. It is self evident that, with rapid 
> turnover of HIV-1, generation of viral diversity 
> and the attendant increased opportunities for 
> viral escape from therapeutic agents are 
> unavoidable sequelae. Treatment strategies, if 
> they are to have a dramatic clinical impact, must 
> therefore be initiated as early in the infection 
> course as possible, perhaps seen during seroconversion…”
> 
> The Shaw study [12] used the protease inhibitors 
> ABT-538, L-735.524, or the non-nucleoside reverse 
> transcriptase inhibitor Nevirepine on a total of 
> 22 patients, as part of a phase I/IIA clinical 
> trial, and came to the same conclusions. In 
> addition, it found drug resistant mutant viruses 
> in all subjects soon after treatment started. The 
> lowest point of viral load was at two weeks in 
> all subjects after treatment started, when the 
> CD4+ cell count rose to a peak. Thereafter, viral 
> load increased rapidly, despite increased drug 
> dosage, and by week four, 100 percent of the 
> virus in blood was drug resistant. The CD4+ cell 
> counts dropped more slowly, and were back to baseline within 6-20 weeks.
> 
> Critics faulted the Shaw study for the same 
> unwarranted assumptions that underlie the Ho 
> study. Neither study included a control group. 
> The clinical outcomes of the drugs on the 
> patients were not reported, so it was impossible 
> to tell whether the patients benefited from the 
> transient reduction in viral load or the 
> transient increase in CD4+ cells. The 
> mathematical model had no contact with the 
> observations other than dubious fitting of a 
> straight line through two or three data points [15].
> 
> The Ho/Shaw model began to unravel almost as soon 
> as it was proposed, but the “hit hard hit early” 
> HAART approach continued at least until 2001 when 
> the US government’s expert panel on anti-HIV 
> therapy finally recommended restricting the 
> prescription of anti-HIV drugs for as long as 
> possible for people without symptoms, on account 
> of the serious side effects [3]
> 
> (<http://www.i-sis.org.uk/unravelingAIDS.php>Unraveling AIDS,
>  Chapter 7).
> 
> “The final nails in the coffin” of Ho/Shaw models
> 
> Mario Rodoerer at Stanford University Beckman 
> Center, writing in News and Views of the February 
> 1998 issue of Nature Medicine commented [15] that 
> two papers published in the same issue [16, 
> 17]  “provide the final nails in the coffin for 
> models of T cell dynamics in which a major reason 
> for changes in T cell numbers is the death of 
> HIV-infected cells [i.e., the Ho/Shaw models].”
> 
> The papers presented extensive data on the 
> remodelling of the T cell compartment in 
> HIV-infected individuals after treatment with 
> HAART. Throughout the early stages of HIV 
> infection, CD4+ cells decline, whereas the total 
> CD8+ cells expand. However, the application of 
> flow cytometry techniques that accurately 
> identified subsets of T cells showed that this 
> increase in CD8+ cells is made up entirely of 
> memory and activated T cells, while naïve T cells 
> (precursor of memory and activated T cells) 
> declined at the same rate as naïve CD4+ cells 
> (precursor of memory and activated CD4+ cells). 
> Activated T cells are found only in peripheral 
> tissues - the spleen and lymph nodes – and their 
> expansion in the blood in HIV-infected 
> individuals indicated an active immune response 
> even during the later stages of disease.
> 
> Within weeks after starting HAART, there were 
> significant increases in the number of B cells, 
> and of CD4+ and CD8+ cell in the blood, but these 
> were only memory cells that can maintain 
> long-term residence in lymph nodes, and not naïve 
> T cells, which do not dwell in lymph nodes and do 
> not immediately respond to HAART.
> 
> Essentially, the studies provided evidence for 
> the ‘redistribution hypothesis’: the increase in 
> CD4+ cell counts observed shortly after the start 
> of HAART are T lymphocytes redistributed from the 
> lymph nodes, and not produced by cell 
> proliferation. During active viral replication 
> and the concomitant cellular immune response, a 
> large number of B and T cells may be trapped in 
> peripheral sites (for example, by antigen, 
> cytokine or chemokine signals). After initiation 
> of HAART, when HIV is effectively removed from 
> the system, the immune response begins to resolve 
> and cells pour out of the inflamed lymph nodes back into the blood.
> 
> The first study [17] suggested that the degree of 
> T lymphocyte trapping increases as disease 
> progresses. That would explain why the response 
> to HAART tends to be greater in individuals with lower CD4+ cell counts.
> 
> Functional recovery of the T cell compartment is 
> only complete when the repertoire of T cell 
> receptors is restored, so that potentially all 
> antigens can be recognized. The decrease in naïve 
> and memory T cell populations during disease 
> progression means that the repertoire becomes 
> increasingly restricted, finally resulting in immunodeficiency.
> 
> The second study [18] confirmed earlier findings 
> that the T cell receptor repertoire in 
> HIV-infected individuals is significantly 
> different from the normal distribution found in 
> healthy adults. This is due to a loss of unique T 
> cell clones and an expansion of antigen-specific 
> clones caused by an over-representation of certain receptor types.
> 
> In individuals responding to HAART, the number of 
> naïve T cells slowly increases over a six-month 
> period after initiation of HAART and a 
> reconstitution of the T cell repertoire can take 
> place (but see later). Notably, this 
> reconstitution occurs only in individuals who 
> show reductions in viral load in response to 
> HAART. It is also likely that failure of HAART, 
> which occurs in many patients over time, will 
> also be accompanied by a re-initiation of cell 
> losses and repertoire restriction.
> 
> AIDS and an over-stimulated and unbalanced immune system
> 
> The use of radioisotope labelling has enabled 
> researchers to identify different populations of 
> T lymphocytes in the human body [19]. There are 
> long-lived and short-lived cells, and the size of 
> the total T lymphocyte pool appears to be 
> regulated mainly at the level of the long-lived 
> cells. During the course of an antigen-driven 
> cell proliferation response, some T cells 
> differentiate into effector cells that clear the 
> antigens from the body, and typically have a 
> short life span. Others become memory T cells, 
> which, by contrast, are long-lived and serve as 
> reservoirs for subsequent activation by antigen 
> to proliferate and produce effector cells. Naïve 
> T cells also have a long life span. In advanced 
> HIV-1 infection, a much higher proportion of T 
> cells are short-lived, compared to healthy 
> controls, and effective HAART tends to restore 
> the values towards the normal. Advanced HIV-1 
> infection greatly reduces the percentage and 
> total number of CD4+ cells that are long-lived. 
> Because these cells represent the regenerative 
> source of newly formed CD4+ effector T cells, 
> their loss may underlie the immunodeficiency of 
> HIV-1 disease. These abnormalities may not be 
> present in early HIV-1 infection and may represent a marker of 
> disease stage.
> 
> However, many questions remain unanswered 
> [20].  Why is HIV so uniquely powerful, among 
> chronic viruses, in inducing a chronic state of 
> immune activation?  And why is the HIV- induced 
> immune activation is so disruptive of the proper 
> overall functioning of the immune system?
> 
> Of course, there remains the lingering doubt that 
> HIV is not actually causing the disease
> 
> A radical new model is needed
> 
> None of the models so far has taken into account 
> the role of nutrition in AIDS or AIDS-like 
> diseases, and the ability of good nutrition to 
> reverse or delay disease progression [3] 
> (<http://www.i-sis.org.uk/unravelingAIDS.php>Unraveling 
> AIDS, Chapters 15-17). In particular, AIDS is a 
> disease in which the immune system is out of 
> balance, not only in being chronically activated, 
> but also in the predominance of the humoural 
> (type 2) at the expense of cellular (type 1) 
> immunity [3]
> 
> (<http://www.i-sis.org.uk/unravelingAIDS.php>Unraveling AIDS,
>  Chapter 12).
> 
> All HIV models so far have considered the CD4+ 
> cells as a single entity. But it has been known 
> for some that the pool of CD4+ cells (commonly 
> known as T-helper or Th cells) contained two 
> different subsets: Th 1, responsible for 
> cell-mediated immunity and Th 2, responsible for 
> extracellular or humoural immunity. The majority 
> of the CD4+ Th 1 cells reside in the peripheral 
> blood and it is their depletion that occurs in 
> the progression to AIDS [1 and references 
> therein]. Th2 cells reside mainly in the bone 
> marrow and to a lesser extent in the lymph nodes, 
> and do not appear to become depleted in the 
> progression to AIDS. If anything they have been observed to increase 
> [21].
> 
> As AIDS progresses there appears to be a gradual 
> shift from Th1- to Th2-dominance, which is why 
> patients experience mainly fungal and 
> mycobacterial infections, but very few 
> “classical” bacterial diseases. Furthermore, 
> elevated levels of antibodies, including 
> autoantibodies, are characteristic of all AIDS 
> patients, as consistent with an increase in Th2 
> subset. Contrary to what one might expect, HIV is 
> expressed primarily in Th0 (precursor of Th1 and 
> Th2) and Th2 cells and is scarcely
> 
> Expressed in the Th1 subset [22]. Yet it is the 
> Th1 cells that are depleted, whereas the cells in 
> which HIV prefers to reside do not decrease. So 
> what mediates the Th1 to Th2 shift, and how can 
> it be prevented or reversed so as to restore balance to the immune system?
> 
> Culshaw [1] suggests using bifurcation theory, a 
> branch of mathematics that deals with changes in 
> critical parameters that determines major or 
> abrupt changes, such as the commitment of Th0 to become either Th1,
>  or Th2.
> 
> One crucial component in the Th1 to Th2 shift is 
> the release of nitrous oxide (NO) from the 
> cell-mediated arm of the immune system [23]. NO 
> can diffuse though cell membranes without the 
> help of receptors in cell-cell communication, and 
> nitrogen oxides are regulated by the oxidative 
> state of the immune cells. Excessive oxidation 
> negatively affects immune function through the 
> production of cytokines from the immune cells. 
> Oxidative processes are counterbalanced by 
> reduction, which is accomplished by 
> sulphur-containing molecules that serve as 
> electron donors, the main one is glutathione, a 
> tripeptide consisting of cysteine, glutamine and 
> glycine. Glutathione is found in both the reduced 
> (GSH) and the oxidized (GSSG) form. The ratio of 
> GSH:GSSG has been shown to be important in 
> regulating Th1/Th2 balance [24, 25]. If the 
> GSH:GSSH ratio declines, Th2 cells are 
> preferentially made from Th0, thus resulting in 
> Th2 dominating at the expense of Th1 cells.
> 
> HAART causes a transient increase in T-cell 
> counts in the peripheral blood because it damages 
> B-cells as they mature and disrupts antibody 
> production. Unable to make contact with 
> antibody-producing B-cells in the bone marrow, 
> the CD4+ Th2 cells return to the peripheral 
> blood. So, although CD4+ T cell count increases, 
> the cells are ineffective against opportunistic 
> infections. This gives rise to the phenomenon of 
> ‘immune reconstitution syndrome’, in which 
> patients experience the “irony” of an increase in 
> opportunistic infections after initiating HAART therapy [26].
> 
> Culshaw sketches out an alternative mathematical 
> model based on the GSH:GSSG ratio and Th1/Th2 
> balance, which are crucial in the development of 
> AIDS, and proposes that HIV itself need not even 
> be included as a variable. The proposed model 
> tracks the Th0, Th1 and Th2 subsets of the T-cell 
> pool over time, with the ratio of GSH:GSSG as a 
> possible bifurcation parameter. As GSSG 
> increases, and the ratio declines, a greater 
> proportion of the Th0 cells mature into Th2 cells 
> and are diverted from the Th1 pool. Such a model 
> could enable researchers to determine the 
> critical ratio of GSH:GSSG below which a shift to Th2-dominance occurs.
> 
> There are several advantages to such a model. 
> First, it replaces viral load measurements, which 
> have not been shown to have good clinical 
> predictive value as a therapeutic endpoint. 
> Second, determining a critical ratio of GSH:GSSH 
> rather than a critical value gets around the 
> problem of variability among individual patients. 
> Finally, the model explicitly considers the 
> Th1/Th2 ratio, an important measure in the 
> progression to AIDS that has been largely neglected in theoretical modelling.
> 
> Circumstance evidence in favour of such a model 
> is that selenium and other antioxidants appear to 
> be effective in preventing and treating AIDS [3] 
> (<http://www.i-sis.org.uk/unravelingAIDS.php>Unraveling 
> AIDS, Chapter 17). Another advantage of Culshaw’s 
> new model is that it can make direct contact with 
> nutritional status, an important determinant in disease progression.
> 
> ---------------------------------------------------------------------
> http://www.i-sis.org.uk/beyondHIV-CausesAIDS.php
> 
> This is the second article in the current AIDS series
> Previous articles in the series:
> 28/3/07 On Quitting HIV
> http://www.i-sis.org.uk/OnQuittingHIV.php
> 
> The rest will be released over the next two weeks
> 
> Or read other articles in the Health & Disease
> section of the Institute of Science in Society
> Website
> http://www.i-sis.org.uk/scihealth.php
> 
> ========================================================
> This article can be found on the I-SIS website at
> http://www.i-sis.org.uk/beyondHIV-CausesAIDS.php


Alex Dajkovic
Institut Curie

Top of Message | Previous Page | Permalink

Advanced Options


Options

Log In

Log In

Get Password

Get Password


Search Archives

Search Archives


Subscribe or Unsubscribe

Subscribe or Unsubscribe


Archives

August 2019
July 2019
June 2019
May 2019
April 2019
March 2019
February 2019
January 2019
December 2018
November 2018
October 2018
September 2018
August 2018
July 2018
June 2018
May 2018
April 2018
March 2018
February 2018
January 2018
December 2017
November 2017
October 2017
September 2017
August 2017
July 2017
June 2017
May 2017
April 2017
March 2017
February 2017
January 2017
December 2016
November 2016
October 2016
September 2016
August 2016
July 2016
June 2016
May 2016
April 2016
March 2016
February 2016
January 2016
December 2015
November 2015
October 2015
September 2015
August 2015
July 2015
June 2015
May 2015
April 2015
March 2015
February 2015
January 2015
December 2014
November 2014
October 2014
September 2014
August 2014
July 2014
June 2014
May 2014
April 2014
March 2014
February 2014
January 2014
December 2013
November 2013
October 2013
September 2013
August 2013
July 2013
June 2013
May 2013
April 2013
March 2013
February 2013
January 2013
December 2012
November 2012
October 2012
September 2012
August 2012
July 2012
June 2012
May 2012
April 2012
March 2012
February 2012
January 2012
December 2011
November 2011
October 2011
September 2011
August 2011
July 2011
June 2011
May 2011
April 2011
March 2011
February 2011
January 2011
December 2010
November 2010
October 2010
September 2010
August 2010
July 2010
June 2010
May 2010
April 2010
March 2010
February 2010
January 2010
December 2009
November 2009
October 2009
September 2009
August 2009
July 2009
June 2009
May 2009
April 2009
March 2009
February 2009
January 2009
December 2008
November 2008
October 2008
September 2008
August 2008
July 2008
June 2008
May 2008
April 2008
March 2008
February 2008
January 2008
December 2007
November 2007
October 2007
September 2007
August 2007
July 2007
June 2007
May 2007
April 2007
March 2007
February 2007
January 2007
December 2006
November 2006
October 2006
September 2006
August 2006
July 2006
June 2006
May 2006
April 2006
March 2006
February 2006
January 2006
December 2005
November 2005
October 2005
September 2005
August 2005
July 2005
June 2005
May 2005
April 2005
March 2005
February 2005
January 2005
December 2004
November 2004
October 2004
September 2004
August 2004
July 2004
June 2004
May 2004
April 2004
March 2004
February 2004
January 2004
December 2003
November 2003
October 2003
September 2003
August 2003
July 2003
June 2003
May 2003
April 2003
March 2003
February 2003
January 2003
December 2002
November 2002
October 2002
September 2002
August 2002
July 2002
June 2002
May 2002
April 2002
March 2002
February 2002
January 2002
December 2001
November 2001
October 2001
September 2001
August 2001
May 2001
March 2001
February 2001
January 2001
December 2000
November 2000
October 2000
September 2000
August 2000
July 2000
May 2000
April 2000
March 2000
February 2000
January 2000
December 1999
November 1999
October 1999
September 1999
August 1999
July 1999
June 1999
May 1999
April 1999
March 1999
February 1999
January 1999
December 1998
November 1998
September 1998
August 1998
July 1998
June 1998
May 1998

ATOM RSS1 RSS2



LIST.UVM.EDU

CataList Email List Search Powered by the LISTSERV Email List Manager