AIDS since 1984: no evidence for a new, viral epidemic--not even in Africa.

Since the discoveries of a putative AIDS virus in 1984 and of millions of asymptomatic carriers in subsequent years, no general AIDS epidemic has occurred by 2011. In 2008, however, it has been proposed that between 2000 and 2005 the new AIDS virus, now called HIV, had killed 1.8 million South Africans at a steady rate of 300,000 per year and that anti-HIV drugs could have saved 330,000 of those. Here we investigate these claims in view of the paradoxes that HIV would cause a general epidemic in Africa but not in other continents, and a steady rather than a classical bell-shaped epidemic like all other new pathogenic viruses. Surprisingly, we found that South Africa attributed only about 10,000 deaths per year to HIV between 2000 and 2005 and that the South African population had increased by 3 million between 2000 and 2005 at a steady rate of 500,000 per year. This gain was part of a monotonic growth trajectory spanning from 29 million in 1980 to 49 million in 2008. During the same time Uganda increased from 12 to 31 million, and Sub-Saharan Africa as a whole doubled from 400 to 800 million, despite high prevalence HIV. We deduce from this demographic evidence that HIV is not a new killer virus. Based on a review of the known toxicities of antiretroviral drugs we like to draw the attention of scientists, who work in basic and clinical medical fields, including embryologists, to the need of rethinking the risk-and-benefit balance of antiretroviral drugs for pregnant women, newborn babies and all others who carry antibodies against HIV.

DATE analysis: A general theory of biological change applied to microarray data.

In contrast to conventional data mining, which searches for specific subsets of genes (extensive variables) to correlate with specific phenotypes, DATE analysis correlates intensive state variables calculated from the same datasets. At the heart of DATE analysis are two biological equations of state not dependent on genetic pathways. This result distinguishes DATE analysis from other bioinformatics approaches. The dimensionless state variable F quantifies the relative overall cellular activity of test cells compared to well-chosen reference cells. The variable pi(i) is the fold-change in the expression of the ith gene of test cells relative to reference. It is the fraction phi of the genome undergoing differential expression-not the magnitude pi-that controls biological change. The state variable phi is equivalent to the control strength of metabolic control analysis. For tractability, DATE analysis assumes a linear system of enzyme-connected networks and exploits the small average contribution of each cellular component. This approach was validated by reproducible values of the state variables F, RNA index, and phi calculated from random subsets of transcript microarray data. Using published microarray data, F, RNA index, and phi were correlated with: (1) the blood-feeding cycle of the malaria parasite, (2) embryonic development of the fruit fly, (3) temperature adaptation of Killifish, (4) exponential growth of cultured S. pneumoniae, and (5) human cancers. DATE analysis was applied to aCGH data from the great apes. A good example of the power of DATE analysis is its application to genomically unstable cancers, which have been refractory to data mining strategies.

WITHDRAWN: HIV-AIDS hypothesis out of touch with South African AIDS - A new perspective.

This Article-in-Press has been permanently withdrawn. The editorial policy of Medical Hypotheses makes it clear that the journal considers "radical, speculative, and non-mainstream scientific ideas", and articles will only be acceptable if they are "coherent and clearly expressed." However, we received serious expressions of concern about the quality of this article, which contains highly controversial opinions about the causes of AIDS, opinions that could potentially be damaging to global public health. Given these important signals of concern, we commissioned an external expert panel to investigate the circumstances in which this article came to be published online. The panel recommended that the article should be externally peer-reviewed. Following a peer-review process managed by The Lancet editorial team, all five external reviewers recommended rejection, as a result of which the expert panel recommended permanent withdrawal. The Publisher apologizes for any inconvenience this may cause. The full Elsevier Policy on Article Withdrawal can be found at http://www.elsevier.com/locate/withdrawalpolicy.

The chemical bases of the various AIDS epidemics: recreational drugs, anti-viral chemotherapy and malnutrition.

In 1981 a new epidemic of about two-dozen heterogeneous diseases began to strike non-randomly growing numbers of male homosexuals and mostly male intravenous drug users in the US and Europe. Assuming immunodeficiency as the common denominator the US Centers for Disease Control (CDC) termed the epidemic, AIDS, for acquired immunodeficiency syndrome. From 1981-1984 leading researchers including those from the CDC proposed that recreational drug use was the cause of AIDS, because of exact correlations and of drug-specific diseases. However, in 1984 US government researchers proposed that a virus, now termed human immunodeficiency virus (HIV), is the cause of the non-random epidemics of the US and Europe but also of a new, sexually random epidemic in Africa. The virus-AIDS hypothesis was instantly accepted, but it is burdened with numerous paradoxes, none of which could be resolved by 2003: Why is there no HIV in most AIDS patients, only antibodies against it? Why would HIV take 10 years from infection to AIDS? Why is AIDS not self-limiting via antiviral immunity? Why is there no vaccine against AIDS? Why is AIDS in the US and Europe not random like other viral epidemics? Why did AIDS not rise and then decline exponentially owing to antiviral immunity like all other viral epidemics? Why is AIDS not contagious? Why would only HIV carriers get AIDS who use either recreational or anti-HIV drugs or are subject to malnutrition? Why is the mortality of HIV-antibody-positives treated with anti-HIV drugs 7-9%, but that of all (mostly untreated) HIV-positives globally is only 1.4%? Here we propose that AIDS is a collection of chemical epidemics, caused by recreational drugs, anti-HIV drugs, and malnutrition. According to this hypothesis AIDS is not contagious, not immunogenic, not treatable by vaccines or antiviral drugs, and HIV is just a passenger virus. The hypothesis explains why AIDS epidemics strike non-randomly if caused by drugs and randomly if caused by malnutrition, why they manifest in drug- and malnutrition-specific diseases, and why they are not self-limiting via anti-viral immunity. The hypothesis predicts AIDS prevention by adequate nutrition and abstaining from drugs, and even cures by treating AIDS diseases with proven medications.

Aneuploidy theory explains tumor formation, the absence of immune surveillance, and the failure of chemotherapy.

The autocatalyzed progression of aneuploidy accounts for all cancer-specific phenotypes, the Hayflick limit of cultured cells, carcinogen-induced tumors in mice, the age distribution of human cancer, and multidrug-resistance. Here aneuploidy theory addresses tumor formation. The logistic equation, phi(n)(+1) = rphi(n) (1 - phi(n)), models the autocatalyzed progression of aneuploidy in vivo and in vitro. The variable phi(n)(+1) is the average aneuploid fraction of a population of cells at the n+1 cell division and is determined by the value at the nth cell division. The value r is the growth control parameter. The logistic equation was used to compute the probability distribution for values of phi after numerous divisions of aneuploid cells. The autocatalyzed progression of aneuploidy follows the laws of deterministic chaos, which means that certain values of phi are more probable than others. The probability map of the logistic equation shows that: 1) an aneuploid fraction of at least 0.30 is necessary to sustain a population of cancer cells; and 2) the most likely aneuploid fraction after many population doublings is 0.70, which is equivalent to a DNA(index)=1.7, the point of maximum disorder of the genome that still sustains life. Aneuploidy theory also explains the lack of immune surveillance and the failure of chemotherapy.