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Literature review on Serum auto fluorescence technique for deduction of lung cancer by Li et al., Serum was taken as a sample detect target. The serum samples of both lung cancer patients and the healthy patients i.e., the control groups were taken for detecting lung cancer by using a technique called Laser Induced Fluorescence spectroscopy. To analyze the data of fluorescence spectra they have used Principle Component Analysis (PCA) and Linear Discriminant Analysis (LDA) and achieved an accuracy rate of 84%.
According to the estimation of the 21st World Anti-cancer Conference hold in Shenzhen China, there were about 2.6 million incidences of cancer, and 1.8 million mortality each year.
The mortality rate of cancer has increased 80% in the past 30 years. Cancer has become the first cause of death in urban and rural China, and lung cancer has been the first killer in China [1]. Lung cancer is the world's leading cause of death from disease, and existing treatments are misleading [2].
Most lung cancer originate from the epithelium of bronchial mucosa.
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So, the lung cancer is otherwise called as “Bronchogenic Carcinoma” [3]. In this paper, the fluorescence spectroscopy of serum from both lung cancer patients as well as healthy groups were measured using the Principal Component Analysis and Linear Discriminant Analysis and these methods are more recommendable as they achieved an accuracy rate of about 84%. The results showed that auto-fluorescence of serum can provide early diagnosis and treatment of lung cancer [3].
Lung cancer is the most common cause of death due to cancer in both men and women throughout the world.
Statistics from the American Cancer Society estimated that in 2019 there will be about 228,000 new cases of lung cancer in the U.S. occurred and over 142,000 deaths were due to the disease.
According to the U.S. National Cancer Institute, approximately 6.5% of men and women in the U.S. will be diagnosed with cancer of the lung at some point in their lifetime based on data from 2011- 2013. Lung cancer is predominantly a disease of the elderly; almost 70% of people diagnosed with lung cancer are over 65 years of age, while less than 3% of lung cancers occur in people under 45 years of age [4].
Lymph nodes are small, bean-shaped structures that are part of the lymph system throughout your body. The lymphatic system is a network of organs, tubes, and lymph nodes which help distribute body fluids and defend the body against irregular microbes and cells. When an infection, trauma, or cancer occurs in a part of the body, the area's lymph nodes grow larger. For example, in figure 1, the lymph nodes in your neck get swollen when you have a cold or a sore throat.
Occasionally cancer cells from a malignant tumor break away and pass through the channels of blood or lymph. The cancer cells will lodge in neighboring lymph nodes which usually flush out harmful microbes and cells. They will expand and divide there, forming a new tumor that can shed more cancer cells, which can then spread further in the body. An essential factor in determining the degree or phase of that cancer is the spread of cancer to the lymph nodes.
It is the natural emission of light by biological structures such as mitochondria and lysosomes when they have absorbed light and is used to distinguish the light originating from artificially added fluorescent markers (fluorophores) [5]. In fluorescence microscopy, autofluorescence may be troublesome.
Light-emitting stains are added to samples to allow different structures to be visualized. Auto-fluorescence interferes with the detection of specific fluorescent signals, particularly when the signals of interest are very dim, causing structures to become visible other than those of interest. In a few cases, autofluorescence may illuminate the structures of interest, or serve as a useful diagnostic indicator.[5]
Fluorescence is a material that has absorbed light or other electromagnetic radiation from the absorption of light. It's a luminescence form. In most cases, the light released has a longer wavelength and thus less energy than the radiation absorbed. The most striking example of fluorescence happens when the absorbed radiation is in the ultraviolet region of the spectrum and thus invisible to the human eye, while the released light is in the visible region, which gives a distinct color to the fluorescent material that can only be seen when exposed to UV light.
The fluorescence lifetime refers to the average time the molecule stays in its excited state before emitting a photon. Fluorescence typically follows first-order kinetics: [S1] is the concentration of excited state molecules at time ‘t’. [S1]0 is the initial concentration. This is an instance of exponential decay.
Various radiative and non-radiative processes can de-populate the excited state. In such case the total decay rate is the sum over all rates: If the rate of spontaneous emission, or any of the other rates are fast, the lifetime is short. For commonly used fluorescent compounds, typical excited state decay times for photon emissions with energies from the UV to near infrared are within the range of 0.5 to 20 nanoseconds. The fluorescence lifetime is an important parameter for practical applications of fluorescence such as fluorescence resonance energy transfer and fluorescence-lifetime imaging microscopy [6].
A Jablonski diagram is typically used to illustrate the physics of fluorescence. In figure 2 electronic (energy) states are indicated by bold horizontal lines. The thin horizontal lines above them represent vibrational/rotational sublevels. Electrons are normally at the lowest energy state, indicated by S0. When a photon with appropriate energy interacts with a molecule the photon may be absorbed, causing an electron to jump to one of the levels of an excited state.
By ‘appropriate energy’ we mean an amount corresponding to the energy difference between the ground and excited states. Thus, not all incident photons are equally likely to be absorbed. This transition process is very fast, on the order of 10-15 seconds. An excited-state electron rapidly (on the order of 10-12 seconds) loses its energy to vibration a process called internal conversion and falls to the lowest level of the first (S1) excited state.
From there the electron may fall to one of the sub-levels of the ground (S0) state, emitting a photon with energy equivalent to the energy difference of the transition. This happens on a time scale of nanoseconds (10-9 – 10-8 seconds) after the initial photon was absorbed. Since the emitted photon has less energy than the absorbed photon it is at a longer wavelength. The probability that a photon will be absorbed varies with wavelength. Even for those photons that are absorbed there are other processes that compete with fluorescence for de-excitation of the excited-state electrons. The number of photons fluoresced relative to the number absorbed is the quantum efficiency.
The higher the absorption and quantum efficiency, the brighter the fluorescence [7]. Fluorescence spectroscopy is a type of electromagnetic spectroscopy that analyses a sample fluorescence. This includes using a light beam, usually ultraviolet light, which excites the electrons in certain compounds ' molecules and causes them to emit light; typically, but not always, visible light. Spectroscopy of absorption is a complementary technique. In the case of single molecule fluorescence spectroscopy, frequency differences from the emitted light are determined either from individual fluorophores, or from pairs of fluorophores.
Three methods are used for the detection of lung cancer using auto-fluorescence of serum by fluorescence spectroscopy. It includes:
a) Sample Preparation
b) Spectroscopy Collection
c) Statistical Analysis
Sample Preparation
Spectroscopy Collection
The block diagram of the system is shown in the figure 3. Laser beam produced by Ar-ion laser irradiate in the sample cell after the modulation of a chopper. Fluorescence were collected in a double-slit monochromator through a focusing lens, and then detected by PMT. The fluorescence signals were amplified by a lock-in amplifier and were input into computer by an A/D card. Wavelength of 488.0 nm were used to excite the serum sample, scanning range were 520 - 620 nm, collection interval was 1 Å, time delay was 0 ms [3].
Argon ion laser
Chopper
Double-Slit Monochromator
Photo Multiplier tube ( PMT )
Lock in Amplifier
Auto Fluorescence spectrum of serum sample
Auto fluorescence spectrum were obtained from normal healthy subjects and lung cancer patients as shown in the figure 4. As we can see the two spectrums it is evident that auto fluorescence obtained from healthy subjects had lot of variations at different wavelength when compared to lung cancer patient spectrum. a. Normal serum sample b. Lung cancer serum sample Fig 4 Auto fluorescence spectroscopy [3],[8],[9].
Finding the key features from the spectrum is the challenging part which determine the accuracy of their system. They have chosen 5 different features at different wavelength point from both the spectrums for each subject. First is the starting point of fluorescence intensity 520nm, main peak intensity (555nm – normal; 538 - lung cancer), Main trough wavelength point, main trough plus 10nm, and the end point 620nm were chosen for both healthy subjects and lung cancer [3],[8]. Table 1 shows the obtained data from 50 serum samples by Li et al., Plotting all these data would be difficult and complicated for analysis part so a standard technique called Principle component analysis and Linear discriminant analysis were used to analyze the data.
Principle Component Analysis
The basic idea behind PCA is to reduce the no. of dimensions to smaller value like 2D 0r 3D. In general, PCA finds the variations between the same class rather than different categories. PCA is the standard dimension deduction method generally used on Raman spectroscopy by extraction orthogonal basic vectors [8]. It converts set of values which are possibly correlated variables into set of values of linearly uncorrelated variables called principle components. Using very less PCs large amount of Raman spectrum can be represented.
O(λ)=PC1*P1(λ)+ PC2*P2(λ)+…. PCn*Pn(λ)
O(λ) is the original spectroscopy, Pn(λ) are called principal component spectroscopy, and PCn are the principal components [8]. New principle space is created, and every spectroscopy point has a spot in that new space. The principle component contains key feature information about lung cancer disease in auto fluorescence spectrum [8] which means that each point in the new space represent single serum sample.
The five features were converted as a principle component which are represented as PCA1, PCA2, PC3, PCA4, and PCA5. PCA analysis finds eigen value and eigen vectors, these are the important terms in PCA. Eigen vectors are principle component which determine the direction of new space and eigen values represent the magnitude of each principle component. These eigen values were plotted as shown in the figure 5.
This plot gives us the information about which two principle component has more variations. It can be seen in the figure that PCA 1 and PCA2 has more variations when compared to PCA2 and PCA3 and so on. This means that PCA 1 and PCA 2 has more information and thus these two components were chosen for further analysis. In figure 6 scattered plot was plotted between PCA1 and PCA2 in which empty circles represent lung cancer and filled triangle represent healthy subject. This scree plot is the reduced dimension plot i.e. 5 features of all the subjects were reduced to two key features based on PCA analysis.
Linear discriminant analysis
Linear discriminant analysis is a method that characterizes or separates two or more class of objects or events. LDA is like ANOVA and regression analysis but LDA uses continuous independent variable and categorical dependent variable. LDA tries to sketch the difference between classes while PCA in contrast does not consider difference in classes. PCA-LDA are widely used in spectroscopy analysis purpose. In figure 6 the line in between the graph is called discriminant line which divides the two groups healthy and cancer subject [8].
Auto fluorescence can be affected by various biomolecules present in the serum thus finding the key feature that differentiate healthy sample and lung cancer sample was very important in this study. PCA and LDA were effectively used to reduce the dimensions of the data obtained and differentiate both the samples. Researches on low concentration of blood, serum and body fluids are very less but when we compare with blood plasma between the patient groups and the controls, analysis of blood has been used for diagnosis of oral malignancy, gastric cancer, and breast cancer [9],[10].
All these diagnoses gave high accuracy than this system. Standard methods like mass spectrometry, ELISA, HPLC are more efficient and accurate than this auto fluorescence serum system but they are all more complicated system, time consuming, expensive when compared to this serum auto fluorescence system. Main drawback of this system is that using low concertation of body fluid is very complicated for spectrum analysis. Accuracy of this system is 84% and they were able to predict 25 cancer sample out of 30 and 17 healthy sample out of 20. Table 2 shows the results of discriminant analysis.
Analyzing Lung Cancer with Serum Auto-Fluorescence: A Spectroscopic Approach. (2024, Feb 21). Retrieved from https://studymoose.com/document/analyzing-lung-cancer-with-serum-auto-fluorescence-a-spectroscopic-approach
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