Abstract :
Bio photonics is a branch of science dealing with the interaction of light in biological substances such as tissues and cells
at scales ranging from microns to the nano-level. This quality of biophotonics leads to understand hidden knowledge of
cell-cell interaction, cell-tissue interaction and so-on. It needs to be more explore in HIV/AIDS for treatment and
diagnosis. Artificial Intelligence based predictive models help to develop such chips which are cost effective, ready to use
and require less time for diagnosis. These photonics based methods also need to develop therapeutics for HIV.
Keywords: Biophotonics; Artificial intelligence; Therapeutics; HIV
Introduction
Introduction
Advancement in technology is of need today. With the
growing cutting edge technology, diagnosis and treatment
time is reduced. Bio photonic technology is one of them.
The discipline of bio photonics deals with the interaction
of light, or electromagnetic radiations with living
organisms and biologically active macromolecules such as
proteins(hemoglobin),nucleic acids (DNA and RNA), and
metabolites (glucose and lactose). In both high (x ray) and
low (radio frequency (RF) energies the body is almost
transparent, this allows the non-invasive imaging of the
internal structure of organs and bones. This focused light
of lasers with colors can be used for a wide variety of
unique therapeutic interventions of specific regions of
organs and tissues.
HIV (human immunodeficiency virus) detection in
biological samples is critical. Recently an optical
biosensor is patented that is able to detect the virus a
week after being infected, with a total test time of 4 hours
and 45 minutes thereby allowing clinical results to be
obtained on the same day [1]. The biosensor combines
micromechanical silicon structures with gold
nanoparticles, both functionalize with p24-specific
antibodies. The gold nanoparticles have optical
resonances known as Plasmon’s, which are capable of
scattering light very efficiently. Micromechanical
structures are excellent mechanical sensors capable of
detecting interactions as small as intermolecular forces.
The combination of these two structures produces both
mechanical and optical signals that amplify one another,
producing remarkable sensitivity to detect the p24
(protein marker for HIV) [2].
Detecting and quantifying biomarkers and viruses in
biological samples have broad applications in early
disease diagnosis and treatment monitoring. It has been
demonstrated that a label-free optical sensing mechanism
using nanostructured photonic crystals (PC) can capture
and quantify intact viruses (HIV-1) from biologically
relevant samples [3,4
growing cutting edge technology, diagnosis and treatment
time is reduced. Bio photonic technology is one of them.
The discipline of bio photonics deals with the interaction
of light, or electromagnetic radiations with living
organisms and biologically active macromolecules such as
proteins(hemoglobin),nucleic acids (DNA and RNA), and
metabolites (glucose and lactose). In both high (x ray) and
low (radio frequency (RF) energies the body is almost
transparent, this allows the non-invasive imaging of the
internal structure of organs and bones. This focused light
of lasers with colors can be used for a wide variety of
unique therapeutic interventions of specific regions of
organs and tissues.
HIV (human immunodeficiency virus) detection in
biological samples is critical. Recently an optical
biosensor is patented that is able to detect the virus a
week after being infected, with a total test time of 4 hours
and 45 minutes thereby allowing clinical results to be
obtained on the same day [1]. The biosensor combines
micromechanical silicon structures with gold
nanoparticles, both functionalize with p24-specific
antibodies. The gold nanoparticles have optical
resonances known as Plasmon’s, which are capable of
scattering light very efficiently. Micromechanical
structures are excellent mechanical sensors capable of
detecting interactions as small as intermolecular forces.
The combination of these two structures produces both
mechanical and optical signals that amplify one another,
producing remarkable sensitivity to detect the p24
(protein marker for HIV) [2].
Detecting and quantifying biomarkers and viruses in
biological samples have broad applications in early
disease diagnosis and treatment monitoring. It has been
demonstrated that a label-free optical sensing mechanism
using nanostructured photonic crystals (PC) can capture
and quantify intact viruses (HIV-1) from biologically
relevant samples [3,4
Bio photonics and Artificial Intelligence
Techniques
With the applications of optics and photonics, various
methods are developed by spectrophotometer,
microscopy, and lasers etc. for disease diagnosis in not
only molecular level but also in tissue level. Some of them
are described below:
Hyper spectral Imaging (HSI): It is also called imaging
spectrometer which involves various medical applications
specifically in image guided surgery and diagnosis of
diseases. It has been assumed that the scattering,
fluorescence and absorption properties of tissues vary as
the disease progresses. Therefore, the transmitted,
reflected, and fluorescent light from tissue has been
captured by HIS having quantitative diagnostic data.
Which can be used by artificial intelligence based machine
learning techniques to interpret HIV disease and use of
ART in particular time [5].
Diffuse optical Imaging: This technique is classified in
two groups:
a) Diffuse optical topography---If the tissue optical
properties get modified after a period of time, there are
possibilities for the photon to reach same detector by
altering the measurable intensity. A 2D topographical
data set can be constructed by measuring the changes
between every set of source and detector. This helps to
study HIV at a tissue level [6].
b) Reconstructed topography—the absorption changes
determined by high resolution images leads to
production of 3D reconstruction of the image. It is
usually done via the detection of the absorption
distribution, where the data measured is matched with
simulation results of numerous absorption
distributions inside the 3D images. Hence, the
recording of the signal at various distances between
source and detector is essential and measured [7].
These results would be used input for Artificial
Intelligence based machine learning techniques for
disease diagnosis in particular stage as well as
molecular level of HIV infection is well understood.
Diffuse optical tomography: This techniques needs
computed tomography in reconstructing the 3D images
where recording of sequence of tissue measurements is
performed. The 3D image has to be acquired from various
angles which will give complete information of HIV
affected cells/tissues [8].
Lasers: Its better if Lasers system is used with sensor
control. Because sensor controlled laser systems are of
focus in the field of therapy. This was previously used in
treating cancer. But it can be used for monitoring of HIV
infection spread in body.
Flow Cytometer (FC): A flow cytometer is a machine
driven instrument that can be used to examine single cell
properties i.e., allow only one cell to be analyzed at a time.
It can be used to measure the cell granularity, cell size,
and to quantify various cell components that include
newly synthesized DNA and the total DNA, the number of
specific cell surface receptors, gene expression as the
amount of messenger RNA for a particular gene, and
amounts of transient signaling events and intracellular
protein in living cells. Hence FC is most adaptable to study
HIV features. It is used in diagnosis of HIV disease as well
as its progression [6].
Techniques
With the applications of optics and photonics, various
methods are developed by spectrophotometer,
microscopy, and lasers etc. for disease diagnosis in not
only molecular level but also in tissue level. Some of them
are described below:
Hyper spectral Imaging (HSI): It is also called imaging
spectrometer which involves various medical applications
specifically in image guided surgery and diagnosis of
diseases. It has been assumed that the scattering,
fluorescence and absorption properties of tissues vary as
the disease progresses. Therefore, the transmitted,
reflected, and fluorescent light from tissue has been
captured by HIS having quantitative diagnostic data.
Which can be used by artificial intelligence based machine
learning techniques to interpret HIV disease and use of
ART in particular time [5].
Diffuse optical Imaging: This technique is classified in
two groups:
a) Diffuse optical topography---If the tissue optical
properties get modified after a period of time, there are
possibilities for the photon to reach same detector by
altering the measurable intensity. A 2D topographical
data set can be constructed by measuring the changes
between every set of source and detector. This helps to
study HIV at a tissue level [6].
b) Reconstructed topography—the absorption changes
determined by high resolution images leads to
production of 3D reconstruction of the image. It is
usually done via the detection of the absorption
distribution, where the data measured is matched with
simulation results of numerous absorption
distributions inside the 3D images. Hence, the
recording of the signal at various distances between
source and detector is essential and measured [7].
These results would be used input for Artificial
Intelligence based machine learning techniques for
disease diagnosis in particular stage as well as
molecular level of HIV infection is well understood.
Diffuse optical tomography: This techniques needs
computed tomography in reconstructing the 3D images
where recording of sequence of tissue measurements is
performed. The 3D image has to be acquired from various
angles which will give complete information of HIV
affected cells/tissues [8].
Lasers: Its better if Lasers system is used with sensor
control. Because sensor controlled laser systems are of
focus in the field of therapy. This was previously used in
treating cancer. But it can be used for monitoring of HIV
infection spread in body.
Flow Cytometer (FC): A flow cytometer is a machine
driven instrument that can be used to examine single cell
properties i.e., allow only one cell to be analyzed at a time.
It can be used to measure the cell granularity, cell size,
and to quantify various cell components that include
newly synthesized DNA and the total DNA, the number of
specific cell surface receptors, gene expression as the
amount of messenger RNA for a particular gene, and
amounts of transient signaling events and intracellular
protein in living cells. Hence FC is most adaptable to study
HIV features. It is used in diagnosis of HIV disease as well
as its progression [6].
Fluorescent Markers: A significant feature of Bio
photonics involves the visualization and detection of cells
and tissues. Which includes injection of fluorescent
markers, into a living system, to follow dynamics of a cell
and drug delivery? This visualization and detection of
fluorescent markers can be counted and effect on living
system is shared by AI based ML techniques. By this
method cell reaction and drug delivery to tissues are
monitored in specific time. This can be used in
Fluorescence lifetime imaging microscopy (FLIM). The
information obtained by FILM is used in local
environment sensing, detection of molecular interactions,
detection of conformational changes, discrimination of
multiple labels or background removal, tissue
characterization by auto fluorescence and
characterization and quality control of new materials. All
these parameters based study are required for input data
of machine learning techniques. And our expected
outcome would able to predict which parameter is best
for study cell based interactions of HIV. Confocal
microscopy and multiphoton microscopy are also found
to be suitable for HIV cells. Because in multiphoton
microscopy, instantaneous absorption of two incident
photons from a pulsed infrared laser source is observed.
So this will helpful in how a particular HIV infected cell
response [7].
and tissues. Which includes injection of fluorescent
markers, into a living system, to follow dynamics of a cell
and drug delivery? This visualization and detection of
fluorescent markers can be counted and effect on living
system is shared by AI based ML techniques. By this
method cell reaction and drug delivery to tissues are
monitored in specific time. This can be used in
Fluorescence lifetime imaging microscopy (FLIM). The
information obtained by FILM is used in local
environment sensing, detection of molecular interactions,
detection of conformational changes, discrimination of
multiple labels or background removal, tissue
characterization by auto fluorescence and
characterization and quality control of new materials. All
these parameters based study are required for input data
of machine learning techniques. And our expected
outcome would able to predict which parameter is best
for study cell based interactions of HIV. Confocal
microscopy and multiphoton microscopy are also found
to be suitable for HIV cells. Because in multiphoton
microscopy, instantaneous absorption of two incident
photons from a pulsed infrared laser source is observed.
So this will helpful in how a particular HIV infected cell
response [7].
Targeted molecular imaging: It involves analyzingmicron-level biological processes. It is used to analyze theshape and role of the molecular system by generating the
signals incident from the molecules. Therefore the
produced image describes the 3-D spatial distribution of
the targeted molecules in the tissue, specifies the
diagnostic data at the molecular level, and shows the
functional cell properties. These properties are essential
to study HIV infected cells at molecular level with its
functionality opens new avenues of HIV [8
Other methods like optical trapping, second harmonic
trapping, and cell transfection are used to explore in case
of HIV.
Artificial intelligence makes our computer to solve the
complicated problem by training and testing the data
given by us. Hence our intelligence and computer learning
intelligence works together to develop the model or our
knowledge for better use.
Machine learning which evolved from pattern
recognition and computational learning theory, is able to
construct algorithms that can learn and make predictions
with data. There are many machines learning software
tools. We use decision tree induction algorithms and
Naïve bayes algorithm of WEKA software package [6] to
classify and compare a given HIV data set. CD4+count and
IL-10,p24, IFN- biomarkers were used to determine
diagnosis and screening of HIV/AIDS [9,10]. There
interaction and progression during HIV course was
detected by above discussed bio photonics methods:
produced image describes the 3-D spatial distribution of
the targeted molecules in the tissue, specifies the
diagnostic data at the molecular level, and shows the
functional cell properties. These properties are essential
to study HIV infected cells at molecular level with its
functionality opens new avenues of HIV [8
Other methods like optical trapping, second harmonic
trapping, and cell transfection are used to explore in case
of HIV.
Artificial intelligence makes our computer to solve the
complicated problem by training and testing the data
given by us. Hence our intelligence and computer learning
intelligence works together to develop the model or our
knowledge for better use.
Machine learning which evolved from pattern
recognition and computational learning theory, is able to
construct algorithms that can learn and make predictions
with data. There are many machines learning software
tools. We use decision tree induction algorithms and
Naïve bayes algorithm of WEKA software package [6] to
classify and compare a given HIV data set. CD4+count and
IL-10,p24, IFN- biomarkers were used to determine
diagnosis and screening of HIV/AIDS [9,10]. There
interaction and progression during HIV course was
detected by above discussed bio photonics methods:
Decision tree: A decision tree (or tree diagram) is a
decision support tool that uses a tree-like graph or model
of decisions and their possible consequences, including
chance event outcomes, resource costs, and utility.
Another use of decision trees is as a descriptive means for
calculating conditional probabilities. In data mining and
machine learning, a decision tree is a predictive model;
that is, a mapping from observations about an item to
conclusions about its target value. More descriptive
names for such tree models are classification tree
(discrete outcome) or regression tree (continuous
outcome). In these tree structures, leaves represent
classifications and branches represent conjunctions of
features that lead to those classifications. The machine
learning technique for inducing a decision tree from data
is called decision tree learning, or (colloquially) decision
trees [11,12]. With the help of symptoms of HIV infection,
and above mentioned biomarkers, a decision tree model
is developed which would tell whether the person is
infected with HIV and is in which stage. Accordingly anti-
retroviral therapy will started. As this is very fast and
accurate it can be used in POC diagnostics for HIV/AIDS.
Naïve-bayes: This classifier is a simple probabilistic
classifier based on applying Bayes' theorem with strong
(naive) independence assumptions. A more descriptive
term for the underlying probability model would be
"independent feature model". In simple terms, a naive
Bayes classifier assumes that the presence (or lack of
presence) of a particular feature of a class is unrelated to
the presence (or lack of presence) of any other feature
Depending on the precise nature of the probability model,
naive Bayes classifiers can be trained very efficiently in a
supervised learning setting. In many practical
applications, parameter estimation for naive Bayes
models uses the method of maximum likelihood [13,14].
Other machine learning algorithms are also used based
on the data provided by bio photonics techniques and
accordingly model development and validation can be
done for further investigation.
Targeted molecular imaging: It involves analyzing
micron-level biological processes. It is used to analyze the
shape and role of the molecular system by generating the
signals incident from the molecules. Therefore the
produced image describes the 3-D spatial distribution of
the targeted molecules in the tissue, specifies the
diagnostic data at the molecular level, and shows the
functional cell properties. These properties are essential
to study HIV infected cells at molecular level with its
functionality opens new avenues of HIV [8
Other methods like optical trapping, second harmonic
trapping, and cell transfection are used to explore in case
of HIV.
Artificial intelligence makes our computer to solve the
complicated problem by training and testing the data
given by us. Hence our intelligence and computer learning
intelligence works together to develop the model or our
knowledge for better use.
Machine learning which evolved from pattern
recognition and computational learning theory, is able to
construct algorithms that can learn and make predictions
with data. There are many machines learning software
tools. We use decision tree induction algorithms and
Naïve bayes algorithm of WEKA software package [6] to
classify and compare a given HIV data set. CD4+count and
IL-10,p24, IFN- biomarkers were used to determine
diagnosis and screening of HIV/AIDS [9,10]. There
interaction and progression during HIV course was
detected by above discussed bio photonics methods:
Decision tree: A decision tree (or tree diagram) is a
decision support tool that uses a tree-like graph or model
of decisions and their possible consequences, including
chance event outcomes, resource costs, and utility.
Another use of decision trees is as a descriptive means for
calculating conditional probabilities. In data mining and
machine learning, a decision tree is a predictive model;
that is, a mapping from observations about an item to
conclusions about its target value. More descriptive
names for such tree models are classification tree
(discrete outcome) or regression tree (continuous
outcome). In these tree structures, leaves represent
classifications and branches represent conjunctions of
features that lead to those classifications. The machine
learning technique for inducing a decision tree from data
is called decision tree learning, or (colloquially) decision
trees [11,12]. With the help of symptoms of HIV infection,
and above mentioned biomarkers, a decision tree model
is developed which would tell whether the person is
infected with HIV and is in which stage. Accordingly anti-
retroviral therapy will started. As this is very fast and
accurate it can be used in POC diagnostics for HIV/AIDS.
Naïve-bayes: This classifier is a simple probabilistic
classifier based on applying Bayes' theorem with strong
(naive) independence assumptions. A more descriptive
term for the underlying probability model would be
"independent feature model". In simple terms, a naive
Bayes classifier assumes that the presence (or lack of
presence) of a particular feature of a class is unrelated to
the presence (or lack of presence) of any other feature
Depending on the precise nature of the probability model,
naive Bayes classifiers can be trained very efficiently in a
supervised learning setting. In many practical
applications, parameter estimation for naive Bayes
models uses the method of maximum likelihood [13,14].
Other machine learning algorithms are also used based
on the data provided by bio photonics techniques and
accordingly model development and validation can be
done for further investigation.
specific HIV-1 diagnostic tools [7,17,18]. If the kit is
developed which not only shows viral load also gives
genotype information then it would be breakthrough and
innovative research in the field of HIV/AIDS therapeutics.
Here the issues of accuracy cost and time were important
for the purpose of the research based on POC of HIV/AIDS
being a serious disease. To resolve the issues Artificial
intelligence based machine learning techniques are
required to overcome the problem.
"Concluding Remark and Future Prospects
Bio photonics techniques increase the chances for
understanding the HIV disease better at molecular level.
With the handshake of AI techniques, biomedical
scientists will be able to provide environment friendly
techniques as they are fast and economical, accurate and
reliable to use. The data of molecular analysis of Bio
photonics techniques are very diversified which need to
be handled properly for further investigation, here AI
based methods are very helpful for understanding the HIV
mechanism far better. Diagnostic and therapeutic
techniques available for HIV/AIDS are need to be
improved. This will bring major insights for beneficial of
mankind. Most importantly it would be very cost effective
and reachable to all the medical centers so nobody would
die in absence of treatment !
of decisions and their possible consequences, including
chance event outcomes, resource costs, and utility.
Another use of decision trees is as a descriptive means for
calculating conditional probabilities. In data mining and
machine learning, a decision tree is a predictive model;
that is, a mapping from observations about an item to
conclusions about its target value. More descriptive
names for such tree models are classification tree
(discrete outcome) or regression tree (continuous
outcome). In these tree structures, leaves represent
classifications and branches represent conjunctions of
features that lead to those classifications. The machine
learning technique for inducing a decision tree from data
is called decision tree learning, or (colloquially) decision
trees [11,12]. With the help of symptoms of HIV infection,
and above mentioned biomarkers, a decision tree model
is developed which would tell whether the person is
infected with HIV and is in which stage. Accordingly anti-
retroviral therapy will started. As this is very fast and
accurate it can be used in POC diagnostics for HIV/AIDS.
Naïve-bayes: This classifier is a simple probabilistic
classifier based on applying Bayes' theorem with strong
(naive) independence assumptions. A more descriptive
term for the underlying probability model would be
"independent feature model". In simple terms, a naive
Bayes classifier assumes that the presence (or lack of
presence) of a particular feature of a class is unrelated to
the presence (or lack of presence) of any other feature
Depending on the precise nature of the probability model,
naive Bayes classifiers can be trained very efficiently in a
supervised learning setting. In many practical
applications, parameter estimation for naive Bayes
models uses the method of maximum likelihood [13,14].
Other machine learning algorithms are also used based
on the data provided by bio photonics techniques and
accordingly model development and validation can be
done for further investigation.
Targeted molecular imaging: It involves analyzing
micron-level biological processes. It is used to analyze the
shape and role of the molecular system by generating the
signals incident from the molecules. Therefore the
produced image describes the 3-D spatial distribution of
the targeted molecules in the tissue, specifies the
diagnostic data at the molecular level, and shows the
functional cell properties. These properties are essential
to study HIV infected cells at molecular level with its
functionality opens new avenues of HIV [8
Other methods like optical trapping, second harmonic
trapping, and cell transfection are used to explore in case
of HIV.
Artificial intelligence makes our computer to solve the
complicated problem by training and testing the data
given by us. Hence our intelligence and computer learning
intelligence works together to develop the model or our
knowledge for better use.
Machine learning which evolved from pattern
recognition and computational learning theory, is able to
construct algorithms that can learn and make predictions
with data. There are many machines learning software
tools. We use decision tree induction algorithms and
Naïve bayes algorithm of WEKA software package [6] to
classify and compare a given HIV data set. CD4+count and
IL-10,p24, IFN- biomarkers were used to determine
diagnosis and screening of HIV/AIDS [9,10]. There
interaction and progression during HIV course was
detected by above discussed bio photonics methods:
Decision tree: A decision tree (or tree diagram) is a
decision support tool that uses a tree-like graph or model
of decisions and their possible consequences, including
chance event outcomes, resource costs, and utility.
Another use of decision trees is as a descriptive means for
calculating conditional probabilities. In data mining and
machine learning, a decision tree is a predictive model;
that is, a mapping from observations about an item to
conclusions about its target value. More descriptive
names for such tree models are classification tree
(discrete outcome) or regression tree (continuous
outcome). In these tree structures, leaves represent
classifications and branches represent conjunctions of
features that lead to those classifications. The machine
learning technique for inducing a decision tree from data
is called decision tree learning, or (colloquially) decision
trees [11,12]. With the help of symptoms of HIV infection,
and above mentioned biomarkers, a decision tree model
is developed which would tell whether the person is
infected with HIV and is in which stage. Accordingly anti-
retroviral therapy will started. As this is very fast and
accurate it can be used in POC diagnostics for HIV/AIDS.
Naïve-bayes: This classifier is a simple probabilistic
classifier based on applying Bayes' theorem with strong
(naive) independence assumptions. A more descriptive
term for the underlying probability model would be
"independent feature model". In simple terms, a naive
Bayes classifier assumes that the presence (or lack of
presence) of a particular feature of a class is unrelated to
the presence (or lack of presence) of any other feature
Depending on the precise nature of the probability model,
naive Bayes classifiers can be trained very efficiently in a
supervised learning setting. In many practical
applications, parameter estimation for naive Bayes
models uses the method of maximum likelihood [13,14].
Other machine learning algorithms are also used based
on the data provided by bio photonics techniques and
accordingly model development and validation can be
done for further investigation.
specific HIV-1 diagnostic tools [7,17,18]. If the kit is
developed which not only shows viral load also gives
genotype information then it would be breakthrough and
innovative research in the field of HIV/AIDS therapeutics.
Here the issues of accuracy cost and time were important
for the purpose of the research based on POC of HIV/AIDS
being a serious disease. To resolve the issues Artificial
intelligence based machine learning techniques are
required to overcome the problem.
"Concluding Remark and Future Prospects
Bio photonics techniques increase the chances for
understanding the HIV disease better at molecular level.
With the handshake of AI techniques, biomedical
scientists will be able to provide environment friendly
techniques as they are fast and economical, accurate and
reliable to use. The data of molecular analysis of Bio
photonics techniques are very diversified which need to
be handled properly for further investigation, here AI
based methods are very helpful for understanding the HIV
mechanism far better. Diagnostic and therapeutic
techniques available for HIV/AIDS are need to be
improved. This will bring major insights for beneficial of
mankind. Most importantly it would be very cost effective
and reachable to all the medical centers so nobody would
die in absence of treatment !
Corresponding author:
Dr.ANUBHA DUBEY EDUCATION DIRECTOR & TRAINER - KANISHKSOCIALMEDIA
(PHD,BIOINFORMATICS & MBA HR,)
PG,RESEARCH CONSALTANT IN CAREER CONSULING NATIONAL & INTERNATIONAL,GUIDANCE,UG,PG ETC.
OUR MISSION: EDUCATION REVOLUTION,We provide Ph.D. advisory 1. Paper writing like a review, survey, communication, proposed method, etc. 2.Journal referencing, editing, publication. 3.Project proposal and writing. 4.Thesis writing and publication. As per the above service charges will apply.
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