A. N. DIBA

BIOMEDICINE -INFO

Alzheimer

Disease

And

APOE

Gene

19 Nov 2020

The 2019 issue (Vol. 17 No. 64) of BMC Medicine journal( by BioMed Central) published an article entitled "ApoE4: an emerging therapeutic target for Alzheimer’s disease" authored by Mirna Safieh, Amos D. Korczyn & Daniel M. Michaelson.

In an abstract the authors write about the bacground and address, "The growing body of evidence indicating the heterogeneity of Alzheimer’s disease (AD), coupled with disappointing clinical studies directed at a fit-for-all therapy, suggest that the development of a single magic cure suitable for all cases may not be possible."

Source:

ApoE4:
an
emerging
therapeutic
target
for
Alzheimer’s
disease

The writers write, "This calls for a shift in paradigm where targeted treatment is developed for specific AD subpopulations that share distinct genetic or pathological properties."

"Apolipoprotein E4 (apoE4), the most prevalent genetic risk factor of AD, is expressed in more than half of AD patients and is thus an important possible AD therapeutic target."

Source:

ApoE4:
an
emerging
therapeutic
target
for
Alzheimer’s
disease

The authors in the review explain, "This review focuses initially on the pathological effects of apoE4 in AD, as well as on the corresponding cellular and animal models and the suggested cellular and molecular mechanisms which mediate them."

They note, "The second part of the review focuses on recent apoE4-targeted (from the APO gene to the apoE protein and its interactors) therapeutic approaches that have been developed in animal models and are ready to be translated to human."

Source:

ApoE4:
an
emerging
therapeutic
target
for
Alzheimer’s
disease

Mirna Safieh and her colleagues in their article add, "Further, the issue of whether the pathological effects of apoE4 are due to loss of protective function or due to gain of toxic function is discussed herein."

The authors explain, "It is possible that both mechanisms coexist, with certain constituents of the apoE4 molecule and/or its downstream signaling mediating a toxic effect, while others are associated with a loss of protective function."

Mirna Safieh and her colleagues in the conclusion address, "ApoE4 is a promising AD therapeutic target that remains understudied."

"Recent studies are now paving the way for effective apoE4-directed AD treatment approaches."

Source:

ApoE4:
an
emerging
therapeutic
target
for
Alzheimer’s
disease

The

Bad

Version

of

APOE

Gene

Is

E4

19 Nov 2020

Matt Ridley in chapter 19 of his 1999 book (Genome: The Autobiogeraphy of A Species in 23 Chapters; New York, Harper Colins) writes the following:

"There is a family of genes called the apolipoprotein genes, or APO genes."(p.259)

"They come in four basic varieties, called A, B, C and -strangely- E, though there are various different version of each on different chromosomes." (p.259)

The writer explains, "The one that interests us most is APO E, which happens to lie here on chromosome 19."(p.259)

Matt Ridley in his book writes, "What marks APO E out as special is that it is so 'polymorphic'.(p.260)

Instead of us all having one version of the gene, with rare exceptions, APO E is like eye colour: it comes in three common kinds, known as E2, E3 and E4."(p.260)

The author explains, "In Europe, E3 is both the 'best' and the commonest kind: more than eighty per cent of people have at least one copy of E3 and thirty-nine per cent have two copies."(p.260)

"But the seven per cent of people who have two copies of E4," the author refers to work of Eto M. et al (1989), "are at markedly high risk of early heart disease, and so, in a slightly different way, are the four per cent of people who have two copies of E2."(p.260)

*Eto, M., Watanabe, K. and Makino, I. (1989). Increased frequencies of apolipoprotein E2 and E4 alleles in patients with ischemic heart disease. Clinical Genetics 36:183-8.

Matt Ridley in his book refers to the works of Kamboh, M. I. (1995) about Apoliporotein E and Alzheimer disease and Corder E. H. et al. (1994) about protective effect of apolipprotein E type 2 allele for late onset Alzheimer disease and writes, "After all, "It had been noticed for some time that Alzheimer's victims quite often had high cholestrol."(p.262)

The author with a reference to the works of Komboh M. I. (1995) and Corder E. H. (1994) notes, "Once again, the 'bad' version of the gene is E4."(p.262)

"In families that are specually prone to Alzheimer's disease, the chances of getting Alzheimer's are twenty percent for those with no E4 gene and the mean age is eighty-four."(p.262)

* Kamboh, M.I.(1995) Apolipoprotein E polymorphism and suseptibility to Alzheimer's disease. Human Biology 67:195-215.

**

Corder, E. H. et al. (1994). Protective effect of apolipoprotein E type 2 alleke for late onset Alzheimer disease. Nature Genetics 7:180-84.

The aurhor addresses, "None the less, the scale of the effect came as a schock."(p.269)

"Once again," the writer of the book emphasizes, "the 'bad' version of the gene is E4."(p.262)

He adds, "In families that are espcially prone to Alzheimer's dusease, the chances of getting Alzheimer's are twenty per cent for those with no E4 gene and the mean age of obset is eighty-four."(p.262)

"For those with one E4 gene, the probability rises to forty-seven per cent and the mean age of onset drpos to seventy-five."(p. 262)

Matt Ridley with a reference to the work of Kamboh, M. I. (1995) in Human Biology 67 about Apolipoprotein E polymorphism and syseptibilty to Azheimer disease and also the work of Corder, E. H. et al. (1994) in Nature Genetics 7 about protective effect of apoliporotein E type 2 allele for the late onset Alzheimer's disease, writes the following (p.262):

"In many people who show no symptoms of memory loss, the classic plaques of Alzheimer's are none the less present, and are usually worse in E4 carriers than E3."(p.262)

The author with a reference to above mentioned sources explains, "Those with at least one E2 version of the gene are even less likely to get Alzheimer's than those with E3 genes, though the difference is small."(p.262)

Matt Ridley explains, "For those with two E4 genes, the probablity is ninety-one per cent and the mean age of onset sixty-eight years."(p.262)

The author with a reference to the work of Wilkie, I. (1996) entitle "The people who want to look inside your genes" (Guardian, 3 October 1996) addresses:

"Discriminating on the basis of APOE genes is like discriminating on the basis of skin colour or gender."(p.269)

Matt Ridley referes to the paper of Wilkie I. and addresses the following:

"A non-smoker might justifiably object to subsiding the premium of a smoker by being lumped with him in the same risk category, but if an E3/E3 objected to subsiding the premium of an E4/E4, he would be expressing bigotry and prejudice against somebody who was guilty of nothing but bad luck.(p.269)

Age-Specific

Epigenetic Drift

In Late-Onset

Alzheimer's

Disease

19 Nov 2020

A July 16, 2008 article entitled "Age-Specific Epigenetic Drift in Late-Onset Alzheimer's Disease" authored by Sun-Chong Wang, Beatrice Oelze, Axel Schumacher, published in PLOS "plos.org"

In an abstract the authors write, "Despite an enormous research effort, most cases of late-onset Alzheimer's disease (LOAD) still remain unexplained and the current biomedical science is still a long way from the ultimate goal of revealing clear risk factors that can help in the diagnosis, prevention and treatment of the disease."

"Current theories about the development of LOAD hinge on the premise that Alzheimer's arises mainly from heritable causes."

Source:

Age-
Specific
Epigenetic
Drift
in
Late-
Onset
Alzheimer's
Disease

Sun-Chong Wang and the colleagues point out, "Yet, the complex, non-Mendelian disease etiology suggests that an epigenetic component could be involved."

The authors address, "Using MALDI-TOF mass spectrometry in post-mortem brain samples and lymphocytes, we have performed an analysis of DNA methylation across 12 potential Alzheimer's susceptibility loci."

According to their explanation, "In the LOAD brain samples we identified a notably age-specific epigenetic drift, supporting a potential role of epigenetic effects in the development of the disease."

They note, "Additionally, we found that some genes that participate in amyloid-β processing (PSEN1, APOE) ad methylation homeostasis (MTHFR, DNMT1) show a significant interindividual epigenetic variability, which may contribute to LOAD predisposition."

Sun-Chong Wang and the colleagues address, "The APOE gene was found to be of bimodal structure, with a hypomethylated CpG-poor promoter and a fully methylated 3′-CpG-island, that contains the sequences for the ε4-haplotype, which is the only undisputed genetic risk factor for LOAD."

"Aberrant epigenetic control in this CpG-island may contribute to LOAD pathology."

The authors write, "We propose that epigenetic drift is likely to be a substantial mechanism predisposing individuals to LOAD and contributing to the course of disease."

Citation: Wang S-C, Oelze B, Schumacher A (2008) Age-Specific Epigenetic Drift in Late-Onset Alzheimer's Disease. PLoS ONE 3(7): e2698. doi:10.1371/journal.pone.0002698

Link:


31 October
2018
Clinical
implications
of
APOE
genotyping
for
late-
onset
Alzheimer’s
disease
(LOAD)
risk
estimation:
a
review
of
the
literature

James

Watson's

Wishes

For

APO E

Risk Status

Anonymity

20 Nov 2020

The 23 October 2008 issue of Wired magazine published an article entitled "PGP volunteers note: it's hard to hide your APOE status" written by DANIEL MACARTHUR.

The author writes, "When James Watson published his genome sequence online, he kept just one region a secret - the APOE gene, which is linked with late-onset Alzheimer's disease."

The writer notes, "Now an Australian team has shown that Watson needs to delete far more DNA to remove this risk information from public scrutiny."

Source:


PGP
volunteers
note:
it's
hard
to
hide
your
APOE
status

DANIEL MACARTHUR in his article writes:

"When James Watson's genome sequence was publicly released earlier this year, Watson famously kept only one region of his DNA a secret - the region encoding the APOE gene, which contains common variants that contribute substantially to the risk of late-onset Alzheimer's, and also affect predisposition to other diseases."

The author of this article (Wired magazine) addresses, "A recent article in the European Journal of Human Genetics shows something that shouldn't have come as a surprise to anyone familiar with human genetics:
simply removing the APOE gene was not enough to prevent someone from inferring whether or not Watson carries the riskier versions of this gene, because other markers around the gene can also indirectly convey this information through the magic oflinkage disequilibrium."

DANIEL MACARTHUR writes in the Wired magazine the following:

"The authors kindly don't reveal Watson's APOE status, and in fact note that they warned Watson prior to publishing their paper so that he had time to take appropriate actions."

"He has since responded by removing an additional 2 million bases around the APOE gene from his public sequence."

The wtiter of the Wired magazine explains, "That action largely removes the possibility of inferring his risk genotype using linkage - in fact, the authors note with dry Australian understatement that the removal of 2 million bases is "likely excessive"."

DANIEL MACARTHUR writes:

"Watson could have used linkage information from the HapMap project to delineate the smallest required region, but apparently decided that overkill was the best policy."

The author of the article in the Wired magazine notes, "It's worth noting that once we have complete genome sequences from sufficient individuals it will be straightforward to determine which DNA positions provide linkage-based information about a particular risk polymorphism (in a specific population, at least)."

"That would allow the clean excision of only those bases that are absolutely required, thus having a smaller impact on research into the rest of the genome."

(Of course, that relies on at least some people releasing their APOE sequence into the public domain, even if it turns out to carry the riskier version - I guess it's lucky for us we have anonymous genome sequencing projects like1000 Genomes.)

DANIEL MACARTHUR writes:

"The whole episode must be raising questions in the mind of some of the Personal Genome Project volunteers as they consider the prospect of releasing their own genome sequences to the world (participant number 8 has already raised the prospect of redacting his APOE sequence, while Misha Angrist is reserving the right to hold back, well, anything)."

The writer of the Wired magazine asks:

"Are there genes they should be hiding? If so, how much sequence do they need to delete?

DANIEL MACARTHUR writes:

"Ultimatelow do projects like the PGP reconcile the desire for partial genome privacy with the need to get sequences out there in the public domain to further genomic research?"

The author in his article writes, "Mind you, giventhe quality of the sequence data released so far, they probably don't need to worry too much for the moment..."

Dale R Nyholt, Chang-En Yu, Peter M Visscher (2008). On Jim Watson's APOE status: genetic information is hard to hide European Journal of Human Genetics DOI: 10.1038/ejhg.2008.198

European Journal of Human Genetics published an article entitled "On Jim Watson's APOE status: genetic information is hard to hide" authored by Dale R Nyholt, Chang-En Yu, and Peter M Visscher.

Source:

On
Jim
Watson's
APOE
status:
genetic
information
is
hard
to
hide

The authors write, "The recent publication and release to public databases of Dr James Watson's sequenced genome,1a with the exception of all gene information about apolipoprotein E (ApoE), provides a pertinent example of the challenges concerning privacy and the complexities of informed consent in the era of personalized genomics.2

The writers address, "Dr Watson requested that his ApoE gene (APOE) information be redacted, citing concerns about the association that has been shown with late onset Alzheimer's disease (LOAD), which is currently incurable and claimed one of his grandmothers."3

Dale R Nyholt and the colleagues note, "In this letter, without any ‘analysis' of Dr Watson's genome, and thus respecting Dr Watson's wishes for APOE risk status anonymity, we highlight the challenges concerning the privacy and the complexities of informed consent by pointing out that the deletion of the APOE gene information only may not prevent accurate prediction of Dr Watson's risk for LOAD conveyed by APOErisk alleles."

The writers point out, "Specifically, linkage disequilibrium (LD) between one or multiple polymorphisms and APOEcan be used to predict APOEstatus using advanced computational tools."

"Therefore, simply blanking out genotypes at known risk factors is generally not sufficient if the aim is to hide genetic information at these loci."

The authors write, "We note that Dr Watson received genetic counseling and after being made aware of the privacy risks associated with public data broadcast, Dr Watson decided to share his personal genome by releasing it into a publicly accessible scientific database (for full details concerning Dr Watson and Protection of human subjects, Returning research results to research participants, and Data release and data flow, see Box 1 of Wheeler et al1)."

The writers note, "Nevertheless, during the preparation of this Letter, we contacted Dr Watson and colleagues in December 2007 and February 2008 informing them of the possibility of inferring his risk for LOAD conveyed by APOE risk alleles using surrounding SNP data."

"As a consequence, the online James Watson Genome Browser (JWGB) has nominally removed all data from the 2-Mb region surround in APOE."

The authors explain, "To demonstrate our point that genetic information is hard to hide, without contravening Dr Watson's wishes for APOE risk status anonymity (see Box 1 of Wheeler et al1), we utilized SNP genotypes identified in Dr J Craig Venter's genome sequence."11

"Furthermore, Dr Venter's sequence data reports that he is heterozygote for both the LOAD high-risk APOE SNP rs429358 (T/C) and APOC1 SNP rs4420638 (A/G)."

Links:

David A
Wheeler
et al.
Nature.
2008.:
"The
complete
genome
of
an
individual
by
massively
parallel
DNA
sequencing


Blaine
Bettinger
(27 October
2008)
"Another
Consider
ation
For
Genetic
Sequencing
and
Privacy"


By
(May 16,
2017):
in Dr.
Niman's
Corner
"Personal
DNA
Testing
For
Late
Onset
Alzheimer's
Disease-
APO E4

BREAST

CANCER

DETECTION

AND

A.I.

TOOL

On

MAMMO-


GRAPHY

19 Nov 2020

On 4 Nov 2020, the website of EurekAlert "eurekalert.org" reported about a news with a title "AI tool improves breast cancer detection on mammography".

The EUREKALERT's report says:

/> "OAK BROOK, Ill. - Artificial intelligence (AI) can enhance the performance of radiologists in reading breast cancer screening mammograms, according to a study published in Radiology: Artificial Intelligence."

Source:

AI
tool
improves
breast
cancer
detection
on
mammography
****

RSNA is an association of radiologists, radiation oncologists, medical physicists and related scientists promoting excellence in patient care and health care delivery through education, research and technologic innovation.

The Society is based in Oak Brook, Illinois. (RSNA.org)

****

The EurekaAlert's website continues to address about the study published in Radiology: Artificial Intelligence. Serena Pacilè, Ph.D. was the clinical research manager at Therapixel where the software was developed. The news says:

"Breast cancer screening with mammography has been shown to improve prognosis and reduce mortality by detecting disease at an earlier, more treatable stage."

"However," the research report from EurekAlert adds, "many cancers are missed on screening mammography, and suspicious findings often turn out to be benign."

Source:

AI
tool
improves
breast
cancer
detection
on
mammography

The EurekAlert's report cites Dr. Serena Pacilè, the clinical research manager at Therapixel where the software was developed as saying:

"In March, the U.S. Food and Drug Administration cleared MammoScreen for use in the clinic, where it could help reduce the workload of radiologists."

The EurekAlert's news addresses, "The researchers plan to explore the behavior of the AI tool on a large screening-based population and its ability to detect breast cancer earlier."

Source:

AI
tool
improves
breast
cancer
detection
on
mammography

The report published in the EurekAlert's research news about this tool in Radiology: Artificial Intelligence" also says:

"An earlier study from Radiology found that, on average, only 10% of women recalled from screening for additional diagnostic workup based on suspicious findings are ultimately found to have cancer."

The EurekAlert's resrarch news (about the study published in Radiology: Artificial Intelligence) notes:

"AI-based algorithms represent a promising avenue for improving the accuracy of digital mammography."

"Developers "train" the AI on existing images, teaching it to recognize abnormalities associated with cancer and distinguish them from benign findings."

Source:

AI
tool
improves
breast
cancer
detection
on
mammography

The EurekAlert's report also explains, "The programs can then be tested on different sets of images."

The report in EurekAlert's website says, "AI offers not only the possibility of better cancer detection but also improved efficiency for radiologists."

The EurejAlert's report details:

"For the study, researchers used MammoScreen, an AI tool that can be applied with mammography to aid in cancer detection."

"The AI system," the EurekAlert's report addressws, "is designed to identify regions suspicious for breast cancer on 2D digital mammograms and assess their likelihood of malignancy."

Source:

AI
tool
improves
breast
cancer
detection
on
mammography

The same research report (EurekAlert's website) explains, "The system takes as input the complete set of four views composing a mammogram and outputs a set of image positions with a related suspicion score."

The EurekAlert's research report about this study writes:

"Fourteen radiologists assessed a dataset of 240 2D digital mammography images acquired between 2013 and 2016 that included different types of abnormalities."

"Half of the dataset was read without AI and the other half with the help of AI during a first session and without during a second session."

The EurekAlert's research news adds, "Average sensitivity for cancer increased slightly when using AI support."

Source:

AI
tool
improves
breast
cancer
detection
on
mammography

The EurekAlert's news provides the following information:

"AI also helped reduce the rate of false negatives, or findings that look normal even though cancer is present."

The news about talks about the results:

"The results show that MammoScreen may help to improve radiologists' performance in breast cancer detection," said Serena Pacilè, Ph.D., clinical research manager at Therapixel, where the software was developed.

Source:

AI
tool
improves
breast
cancer
detection
on
mammography

The same research report writes, "The improved diagnostic performance of radiologists in the detection of breast cancer was achieved without prolonging their workflow."

"In cases with a low likelihood of malignancy," the repot says, "reading time decreased in the second reading session."

The research news citesb the researchers who explain, "This reduced reading time could increase overall radiologists' efficiency, allowing them to focus their attention on the more suspicious examinations."

Source:

AI
tool
improves
breast
cancer
detection
on
mammography
*****

"Improving Breast Cancer Detection Accuracy of Mammography with the Concurrent Use of an Artificial Intelligence Tool."

Collaborating with Dr. Pacilè were January Lopez, M.D., Pauline Chone, M.Phil., Thomas Bertinotti, M.Sc., Jean Marie Grouin, Ph.D., and Pierre Fillard, Ph.D.

Radiology: Artificial Intelligence is edited by Charles E. Kahn Jr., M.D., M.S., Perelman School of Medicine at the University of Pennsylvania, and owned and published by the Radiological Society of North America, Inc.

RSNA

RSNA is an association of radiologists, radiation oncologists, medical physicists and related scientists promoting excellence in patient care and health care delivery through education, research and technologic innovation.

The Society is based in Oak Brook, Illinois. (RSNA.org)

A. I.

MAY

HELP

IMPROVE

DETECTION

OF

BREAST

CANCER

ON

MAMMO-


GRAPHY

19 Nov 2020

On Nov 4, 2020 the DOCWIRENEWS "docwirenews.com" published a report entitled "Artificial Intelligence May Help Improve Detection of Breast Cancer on Mammography" written by Kaitlyn. D’Onofrio

Source:

Artificial Intelligence May Help Improve Detection of Breast Cancer on Mammography

Kaitlyn. D’Onofrio in her research report writes the folllowing:

"The use of Artificial intelligence (AI) is becoming more widespread in medicine, including in the oncology field."

"Among its potental uses may be to aid in breast cancer detection on mammography, according to the findings of a study."

The author of this research news addresses:

"Despite its widespread use for breast cancer detection, mammography comes with several limitations."

Source:

Artificial Intelligence May Help Improve Detection of Breast Cancer on Mammography

D'Onofrio cites the researchers as saying, "Up to 30% to 40% of breast cancers can be missed during screening and on average, only 10% of women recalled from screening for diagnostic workup are ultimately found to have cancer."

"The current literature is conflicting on the use of traditional computer-aided detection systems in digital mammography examinations, however."

Source:

Artificial Intelligence May Help Improve Detection of Breast Cancer on Mammography

The writer of this research news informs about the evaluation by radiologists and reports, "Fourteen radiologists evaluated a dataset that encompassed 240 digital mammography images during two sessions."

"In the fist session, half of the images were viewed with AI and half without, and in the second session, the opposite was done."

It says, "The main outcomes were the area under the receiver operaticharacteristic curve (AUC), sensitivity, specificity, and reading time."

Source:

Artificial Intelligence May Help Improve Detection of Breast Cancer on Mammography

Kaitlyn. D’Onofrio continues to report, "When AI was not used, the average AUC of the readers was 0.769 (95% confidence interval [CI], 0.724–0.814), compared to 0.797 (95% CI, 0.754–0.840) with AI, for an average difference in AUC of 0.028 (95% CI, 0.002–0.055; P=0.035)."

According to this research report, "The use of AI increased the average sensitivity by 0.033 (P=0.021)."

"Changes in reading time were dependent on the AI-tool score."

The report abou the reading time says, "The reading time in cases with low odds of malignancy (smaller than 2.5%) was similar in the first session and slightly decreased in the second session."

"In cases with higher odds of malignancy, AI use was generally associated with an increased reading time."

The study was published in Radiology: Artificial Intelligence.

Kaitlyn. D’Onofrio in her news report writes the researcher concluded:

“The concurrent use of this AI tool improved the diagnostic performance of radiologists in the mammographic detection of breast cancer."

"In addition, the use of AI was shown to reduce false negatives without affecting the specificity.”

Source:

Artificial Intelligence May Help Improve Detection of Breast Cancer on Mammography

LINKS:

Explorathon -
iCAIRD:
Artificial
Intelligence
in
Breast
Cancer
Screening


Artificial
Intelligence
in
Cancer
Treatment





Effect
of
artificial
intelligence
-based
triaging
of
breast
cancer
screening
mammograms
on
cancer
detection
and
radiologist
workload:
a
retrospective
simulation
study


New
genetic
mutations
discovered
in
people
with
Schizophrenia