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Abstract
Detecting cancer at an early stage is crucial for timely treatment and better chances of survival. This research focuses on a scanning method for detecting cancer by examining the nonlinear optical characteristics of blood plasma samples. The study used both cancerous and noncancerous plasma samples and presented the results statistically by utilizing an incident laser power-dependent nonlinear optical phase shift variable called ζ in the Z-scan technique. The results showed a clear difference between the cancerous and non-cancerous samples with an accuracy of 92%. Furthermore, the study suggests the potential for measuring the cancer staging from the cancerous plasma. The study also confirmed a significant difference in ζ for plasma samples undergoing chemotherapy. A red laser with high power (above 18mW) was used to avoid the involvement of fluorophores or other chemical reagents in the plasma samples during the measurement.
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1. Introduction
The use of laser light is a very standard noninvasive method for detecting diseases and offers much promise for doing in-vitro and in-vivo diagnosis and treatments. The nonlinear optical phase shift occurs in a material due to the change in the nonlinear refractive index of the material under an intense laser beam. With a laser having a focused beam, Bahae et. al. described a way of measuring this quantity for a material showing a nonlinear optical response to the incident laser beam [1–3]. Based on this method, the nonlinear refractive index of materials in solid and liquid phases, including proteins and freely circulating DNA in plasma, are measured [4–6]. Such studies on the bio-molecules offer an opportunity to participate in the most critical clinical diagnosis methods for cancer detection.
A literature survey reveals various laser-based studies for the detection of cancer. Starting from the study of the nonlinear optical investigation of normal ovarian cells of animals and cancerous ovarian cells of humans [7] to the use of Z-Scan for the determination of free circulating DNA in the blood of bladder cancer patients, the nonlinear optical method has been gaining importance for obtaining standardized tests resulting in better detection and treatment [6]. Further, the measurement of the nonlinear refractive index of blood porphyrin by the Z-scan has been proposed as a technique to diagnose prostate cancer [8]. The measurement of protein concentration in human blood is another attractive use of the Z-scan method [9].
The uncontrolled growth of living cells in the human body produces neoplasia that impacts every cancer patient differently [10]. Due to such variation in symptoms, the diagnosis processes start with the patients’ physical examination and complete medical history. In most cases, cancer detection becomes late due to the direct estimation and corresponding diagnosis for neoplasia.
Since cancer can originate in any organ, the immediate detection and diagnosis methods vary, including CT-scan, MRI, nuclear-scan, Bone-scan, pet-scan, ultrasound, and X-ray. However, the confirmation method for detecting cancer remains the performing of biopsies involving complicated and painful procedures [11]. A new approach for cancer diagnosis has been proposed using the Z-scan technique for identifying cancer in liquid biopsy [12].
Despite the most delicate medical service facilities available in some specific countries, most patients worldwide suffer from a lack of early cancer detection and appropriate diagnosis methods. This intricately defined scenario demands a simplified method of a cancer diagnosis. Researchers all over the world are trying to develop a method for analyzing and implementing the optical and chemical characteristics, including artificial intelligence (AI) and neural network technologies [13,14].
In recent decades, the nonlinear optical imaging techniques, including multi-photon excitation and multi-photon fluorescence, show encouraging outcomes for neoplasia diagnosis along with the conventional methods [15–18]. Since the nonlinear optical phenomena directly depend on the highly intense incident laser beam on a material, the damaging of biological samples can restrict the feasibility of its use in vivo application. Researchers are also becoming interested in the nonlinear refractive index of proteins like bio-molecules to quantify their concentration. Blood glucose concentration levels can also be measured using the laser-biomolecule interactions [19].
The common cancer factor is the uncontrolled growth of living cells. Researchers are finding the signature of such cells by identifying specific types of proteins known as biomarkers. In this work, we have looked into this phenomenon so that the proportion of the plasma constituent will vary if there exist uncontrolled cells. Due to the carrier protein or malignant tumor, we have assumed that there will be a proportional variation in the constituent of the plasma [20–22]. Since the nonlinear optical properties of materials are susceptible to the energy of the incident laser along with the material’s density variation [4,5,23], we hypothesized that the nonlinear optical phase shift, third-order nonlinear optical phenomena, can detect the variation (Fig. 1). It will be enough to find a distinct quantity through the Z-scan technique for the cancerous and control plasma.
Fig. 1. Variation (hypothesised) in nonlinear optical phase shift for plasmas extracted from different cell conditions.
The simplest possible way of measuring a material’s nonlinear refractive index is the Z-scan technique that allows us to obtain the nonlinear optical phase shift. In this article, we have extended the quantitative analyses depending on the nonlinear optical phase shift of the blood plasma from the control and cancerous patient to show the prospective implementation in Neoplasia diagnosis.
A detailed outcome of the work is presented in the results and analyses with a possible diagnosis procedure. Arguments on achieving this diagnosis strategy, including the experimental apparatus and possible future, are provided in the discussion. The conclusion contains the summary, including the path of the intended research.
2. Methodology
Due to the simplicity in the experimental setup, coupled with the efficiency in the output, the Z-scan technique has gained popularity in the scientific community after the proposal of Sheik-Bahae et. al. [1,2]. In this technique, the sample material of choice is placed on a translation stage experiencing a focused gradient incident laser beam. The transmitted power T, through the material, is measured to obtain the nonlinear refractive index associated with the nonlinear optical phase shift, $\Delta \phi$ as,
The brief of the experiment is provided in Supplement 1 section 1. Using this $\Delta \phi$, the nonlinear refractive index is measured by [4],
where $n_2$ is the nonlinear refractive index of the material, $\lambda$ is the wavelength of the incident laser beam, $\omega _0$ is the spot size of the laser beam at the waist, $L_{eff}$ is the effective sample length, $\Delta \phi$ is the nonlinear optical phase shift (a fitting parameter) and $P$ is the incident laser power. From Eq. (2), the constant $n_2$ of a material will be provided by our definition of a constant ratio $\frac {\Delta \phi }{P} = \zeta$. Since the $L_{eff}$ depends on other scientific parameters, we are choosing only the constant ratio $\zeta$ (unit: $W^{-1}$) for describing and analyzing the results.The standard Z-scan technique requires a Gaussian-shaped incident laser beam to observe any optical nonlinearity occurring in the material. We have used a high-power red diode laser for the nonlinear optical characterization of the plasmas. Usually, laser power fluctuates and decreases when the diode lasers get heated. To avoid such laser power output issues for the nonlinear optical measurement, we have developed the necessary circuitry for the provided driver circuit that helps stabilize the laser output power. Also, we corrected the shape of the laser beam by introducing a pinhole aperture along the beam’s path. Details are in Supplement 1 section 2.
To avoid contamination, we restrict ourselves to using the cuvettes only once. In order to cut the expenses, we developed and used a cuvette with research-grade quartz cells glued with wax that have a high research potential with commercial value. Details are in Supplement 1 section 3.
In the experiment, we have used the human blood plasma as a nonlinear optical material and observed the optical nonlinearity in terms of the nonlinear optical phase shift for the incident laser power ($\zeta = \Delta \phi /P$). The spot size at beam waist $\omega _0$, was $3.3 \times 10^{-3}$cm giving rise to a Rayleigh length $z_0 \approx 0.3$cm. We displaced the sample from $-2$cm to $+2$cm with $z=0$ at the beam waist. We have used a set of "non-Neoplasia" plasmas as the "control", along with the various types of Neoplasia plasmas namely lungs, colon, breast, prostate, cervical etc.
In the preparation of plasma for the Z-scan experiment, we have collected the blood in an EDTA vacutainer tube and allowed the sample to settle down for an hour. With the help of a centrifuge (Thermo Scientific, Sorvall ST 8R), the plasma was separated from blood at 4000 rpm for 10 minutes. The plasma is then diluted to 50% (v/v) with the 5% Normal IV saline and transferred to a cuvette of 2mm optical path.
Recent research articles explain how methylation of DNA molecules increases hydrophobicity in aqueous solution leading to large, micron-sized aggregates [20,22]. In the biological system, such as plasma from blood containing different types of biomolecules, can be aggregated to form a system that can interact with the incident laser beam to produce the Z-scan responses. The formation of the thermal lens like media, due to the laser beam with the same wavelength on different biological systems, will yield different responses. The change in nonlinear optical phase will be different for samples having methylated and unmethylated biomolecules in aqueous solution. Thorough z-scan studies for any pure methylated and unmethylated systems, in aqueous solution, are yet to be done to arrive at any precise conclusion.
After the preparation of the cancerous and the control samples, we have measured the nonlinear optical phase shift from the transmittance. Since optical nonlinearity depends on the incident laser power, we have provided a range in the incident powers from 40 to $90$mW for the prepared bio-samples. The defined $\zeta$ parameter can then be considered to be the signature value of a bio-sample. After comparing such trend lines among the control, cancerous and treated cancerous plasmas, we have concluded that such trend lines have a potential application in the field of medical diagnosis of the Neoplasia.
3. Results and analyses
The use of nonlinear optics in biological sensing is a promising field of research, which has seen significant advances in recent years. In particular, we demonstrated that the Z-scan technique is a powerful tool for detecting cancerous samples with the help of $\zeta$.
During our experiments, we tested different laser wavelengths to obtain a noteworthy response from the plasma. We discovered that the 660 nm (red laser) provided significantly measurable responses in the Z-scan experiment. Consequently, there was no need to use fluorophores with plasma for the quantitative nonlinear optical response. By doing so, we eliminated the additional effort and precautions required for preparing samples with fluorophores (such as Bradford or other chemicals) and the associated noise from these chemicals in the experimental results.
To identify the responses from the protein in blood plasma, a color reagent like Bradford provides the necessary responses in various optical methods of detection [4]. To avoid the color reagent in measuring the nonlinear phase shift, we have tried various incident wavelengths of continuous lasers at different incident powers (Fig. 2). Consequently, the nonlinear optical responses, the "Z-shape" in the outcome of the experiments, were found to develop with the red laser after 18mW of incident laser power. This nonlinear phase shift was detected to have a linear relationship with the incident laser power ranging from 40mW to 90mW, indicating the absence of nonlinear absorption.
Fig. 2. The nonlinear phase shift $\Delta \phi$, obtained from a plasma of the cancer patient, shows a linear relationship with the incident laser power $P$. In case of the lower incident laser powers, the Z-profile is not prominent (red inset). When the power is 20mW or higher, the signature Z-curve becomes prominent (green inset).
Since there is no direct measurement for the confirmation (without Biopsy) of the presence of cancer in humans, we chose to use 23 cancerous and 15 non-cancerous types of plasma for the nonlinear optical studies (Fig. 3). The closed-aperture Z-scan technique, the most straightforward technique for determining a material’s third-order nonlinear optical property, was used to determine those samples’ nonlinear optical phase shift. Since the nonlinear optical phase shift depends on the incident laser power, we measured the nonlinear phase shift over a range of incident laser powers to obtain the $\zeta$ parameter. Finally, the $\zeta$’s are defined to be a comparable quantity for each of the plasmas in identifying whether they are cancerous or not. The Kernel Density Estimation (KDE) study utilized the $\zeta$, providing the non-parametric quantity’s probability distribution in a finite dataset. Table 1 shows the measured $\zeta$ for the samples.
Fig. 3. The defined NLO parameter $\zeta = \Delta \phi /P$, obtained from the plasmas of cancer (Red) and controlled samples (Green). (a) The Z-profile for a normalized transmittance at the incident laser power of $\sim 75$mW. (b) The slope of the lines, for a cancerous and noncancerous sample ($\zeta$). (c) Cancer or noncancer sample’s magnitude in $\zeta$. The horizontal arrows indicate the difference between the clusters’ edges. (d) The probability density distribution for the measured $\zeta$, approximated with the Kernel Density estimation. In this figure, the responses of two separate groups of cancer samples are denoted by $C_1$ and $C_2$, while the non-cancerous samples are represented by $N$.
Table 1. The $\zeta$ (in $W^{-1}$) of the blood plasma for all the samples.
The $\zeta$ parameter shows that the non-cancerous and cancerous plasmas have a significant difference of $2.0W^{-1}$ as the plasmas produce a magnitude lower than $5W^{-1}$ and higher than $6.8W^{-1}$ respectively (Fig. 3(c)). Our $\zeta$ parameter indicates that we can detect the cancerous and non-cancerous plasmas with an accuracy of about 92%. This high accuracy in characterizing the plasmas holds a promising future for doing these types of studies in cancer diagnosis research. The data indicates that there are a couple of clusters in $\zeta$ values in the cancerous region, as shown in Fig. 3(c). These clusters are represented as $C_1$ and $C_2$ in Fig. 3(d), with a difference of $2.5 W^{-1}$ between them. This robust set may be potentially significant in further analysis regarding the different stages of cancer.
The non-parametric probability distribution analysis of the experimental data for the samples shows that a difference exists in the mean values for non-cancerous compared to cancerous plasma, including two bands in the cancerous region (Fig. 3(d)). Despite the overlap at the lower probability values, the difference in peaks becomes approximately $5W^{-1}$ for the non-cancer $(N)$ to cancer $C_1$. A more considerable difference of about $6W^{-1}$ is found in the peak for cancer $C_1$ to cancer $C_2$. In this study, for the samples, we obtained a p-value for the t-test to be $2.93\times 10^{-7}$ indicating the difference in the distribution is significant.
In addition to comparing the cancerous and non-cancerous plasmas, we studied five sets of plasmas from cancer patients treated with chemotherapy (Fig. 4). We collected the blood from each patient one day before and one day after chemotherapy. A significant difference in those samples’ $\zeta$ indicates a valuable means to monitor the status of cancer patients (Table 2).
Fig. 4. The nonlinear optical phase shift shows a significant difference, after the application of chemotherapy drugs to the patients. (a) The $\zeta$ becomes lower for the same patient after chemotherapy. (b) The comparison in the $\zeta$ for the plasmas of the treated cancer patients.
Table 2. The $\zeta$ values (in $W^{-1}$) of the blood plasma samples administered with chemotherapy.
The third-order optical nonlinearity provides a modern physics technique for identifying molecular interaction in liquid samples. Studies have presently been conducted in our laboratory using Z-scan, targeting the identification of tumor circulating cell-free DNA in the plasmas of cancer patients [12]. The $\zeta$ of the plasmas could open up new possibilities for cancer diagnosis due to its simplicity, relatively non-invasive nature, and rapid assessment. Countless lives can be saved if early screening can prevent delays in the diagnosis and treatment.
4. Discussion
We have chosen the incident laser power range in this work to get the linear dependence of $\Delta \phi$ on $P$. In this region of the Z-scan measurements, the multiphoton absorption phenomena are absent [3]. For the lower powers, the signature Z-profile becomes obscure, and for the higher powers, the dependence of $\Delta \phi$ on $P$ becomes nonlinear [2]. The cause of the responses observed for the higher laser powers requires further thorough investigation.
Since the Z-scan experimental setup contains an essential moving part, we took three readings for each of the samples, at a single incident laser power, to cancel any discrepancy. To obtain the necessary results from the experimental data, we have developed a python code according to the formalism provided by Bahae et. al. [1,2]. Details are in Supplement 1 section 4.
Blood is a rich source of biomarkers. The presence of cells from tumors and increased levels of DNA fragments in the blood of cancer patients offer the opportunity of developing a new diagnostic method for the detection of cancer. Our results indicate that the controlled and cancerous plasmas show very distinctive characteristics of third-order nonlinear optical properties. However, we believe that such a simple nonlinear optical measurement technique to detect the presence of cancer in a human body may lead to the possibility of non-invasive early cancer diagnosis and identification of the various stages of cancer in humans in the future. In addition, this process will help monitor and track the condition of patients undergoing chemotherapy and other types of cancer treatment outcomes.
5. Conclusion
The cancer diagnosis depends on the medical history and sophisticated and expensive diagnosis tools like MRI, CT-scan, Mammograms, Bone-scan. In most cases, the confirmation of diagnosis depends on the biopsy alone, and a significant delay occurs for effective treatment. In this research article, we focused on detecting various types of neoplasia by a comparatively simple means using the nonlinear optical properties of plasmas. We have used the third-order nonlinear optical property as an indicator to distinguish between the cancerous and the non-cancerous plasmas. As we are working with biomolecules, our focus was on thermal responses. Due to the CW incident laser, the responses from the samples in the Z-scan technique was thermal in origin [4,5]. Our promising findings hold vast potential for further advancement in the research on cancer diagnosis.
The nonlinear optical properties depend entirely on the incident laser power/energy and the beam’s frequency. We have tried various continuous wave laser beams, ranging from blue to the near-infrared region, to initiate a measurable nonlinear optical response from the plasma. Only the red laser responds to the plasma at sufficiently high power. Two of the most critical concluding remarks to be noted from these experimental results are (1) The requirement of chemical reagent is not necessary for our measurements, and (2) Only laser powers above 18mW produce the significant nonlinear optical responses with the plasmas (Fig. 2).
Despite the difference in the signature Z-curve obtained in the closed aperture Z-scan technique (Fig. 3(a)), we have used the $\zeta$’s for all the plasmas. A distinguishable difference is observed between the cancerous and the non-cancerous plasmas, with an accuracy above 90% for diagnosis purposes of neoplasia (Fig. 3(c)). Further analysis of the data with the help of the Kernel Density Distribution function gives the peak-to-peak difference between the cancerous and the non-cancerous plasmas as $4.99W^{-1}$ - this is large enough for diagnosis purposes (Fig. 3(d)). We have also found two distinguishable peaks in the distribution of the cancerous plasmas, which requires further investigation.
The $\zeta$’s for the blood samples of cancer patients subjected to chemotherapy were also measured using the same protocol. The analysis of these samples was done before and after applying the chemotherapy drugs. A notable drop in the $\zeta$ provides an opportunity for cancer monitoring and tracking (Fig. 4). We hope this may lead to monitoring the progression of cancer in the patients.
We conducted this research exclusively for only cancerous and non-cancerous plasmas. Similar additional work, investigating the plasma of various diseases that can contribute to either proteins or modified DNA, is required before applying our findings. We also expect to use this method to identify the different stages of cancer by the presence of distinct peaks in the probability distribution of the cancerous plasmas.
Based on the third-order nonlinear optical characterization, our research findings suggest that there is a comparable contribution to the optical response of cancerous and noncancerous blood plasma. The identity of the contributing biomolecules for such responses is an area of active research interest. These results could potentially be leveraged to develop a rapid screening tool for cancer. By providing a noninvasive and quick assessment of the optical response of blood plasma, such a tool could potentially offer significant benefits for early detection and diagnosis of cancer, enabling patients to receive timely treatment and improve their chances of survival.
Funding
Ministry of Education, Government of the People's Republic of Bangladesh (CP-4044, CP-557, HEQEP, UGC, Bangladesh, HEQEP, UGC, Bangladesh).
Acknowledgment
We acknowledge the contribution of National Institute of Cancer Research and Hospital, Dhaka, Sylhet Women’s Medical College and Hospital and Sylhet Osmani Medical College for providing the anonymous blood samples from cancer patients. Invent Technologies Ltd. also helped in acquiring and transportation of the blood samples. Thanks to Prof. Md. Zafar Iqbal for cricital academic and experimental discussions and Prof. Md. Syed Badiuzzaman Faruque for useful feedback.
Disclosures
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
MB, EH, SS, SD and YH made substantial contributions to the conception and design of the work. EH, MB, MH and SB completed the relevant studies and created plots related to the study. MH and KH contributed to the circuit development for the diode laser operation. The blood samples from cancer patients were acquired from NICRH and each result was cross-validated by at least three authors. EH prepared the final draft. YH, MB, SD critically revised the manuscript for important intellectual content. All the authors met regularly to discuss the outcome of each experiment. All authors are responsible for acquiring, analysing, and interpreting the data for this article. All the authors approved the version to be published.
Data availability
Data underlying the results presented in this paper are not publicly available at this time but may be obtained from the authors upon reasonable request
Supplemental document
See Supplement 1 for supporting content.
References
1. M. Sheik-Bahae, A. A. Said, and E. W. Van Stryland, “High-sensitivity, single-beam n 2 measurements,” Opt. Lett. 14(17), 955–957 (1989). [CrossRef]
2. M. Sheik-Bahae, A. A. Said, T.-H. Wei, D. J. Hagan, and E. W. Van Stryland, “Sensitive measurement of optical nonlinearities using a single beam,” IEEE J. Quantum Electron 26(4), 760–769 (1990). [CrossRef]
3. M. Sheik-Bahae, A. A. Said, T.-H. Wei, Y.-Y. Wu, D. J. Hagan, M. Soileau, and E. W. Van Stryland, “Z-scan: a simple and sensitive technique for nonlinear refraction measurements,” in Nonlinear Optical Properties Of Materials, vol. 1148 (International Society for Optics and Photonics, 1990), pp. 41–51.
4. E. Hoque, M. Biswas, H. Syfuddin, S. Sharafuddin, S. Das, and Y. Haque, “Nonlinear optical characteristic curve of protein (bsa),” J. Opt. 49(3), 392–396 (2020). [CrossRef]
5. M. Biswas, M. Amin, P. Das, E. Hoque, S. Sharafuddin, M. Younus, S. Das, and Y. Haque, “Single beam z-scan for the calculation of nonlinear refractive index (n2) for poly (2, 5-dimethylaniline),” J. Nonlinear Opt. Phys. Mater. 24(03), 1550039 (2015). [CrossRef]
6. S. I. P. M. do Nascimento Alves, M. L. Hallack, M. M. Perez, B. da Costa Aguiar Alves, L. H. da Silva, and F. L. A. Fonseca, “Application of the z-scan technique for the detection of cfcdna (cell-free circulating dna) and urine dna (udna) in patients with bladder cancer,” Photodiagn. Photodyn. Ther. 26, 131–133 (2019). [CrossRef]
7. M. Salman, M. A. M. Hosein, and N. Mohammad, “Nonlinear optical investigation of normal ovarian cells of animal and cancerous ovarian cells of human in-vitro,” Optik 127(8), 3867–3870 (2016). [CrossRef]
8. C. T. Nabeshima, S. I. P. Alves, A. M. F. Neto, F. R. Silva, R. E. Samad, and L. C. Courrol, “Z-scan technique: A new concept for diagnosis of prostate cancer in blood,” in Laser Science, (Optica Publishing Group, 2016), pp. JTh2A–132.
9. E. Ule, Measurement of the nonlinear refractive index by z-scan technique, University of Ljubljana, Slovenia pp. 4–5 (2015).
10. M. M. Koo, R. Swann, S. McPhail, G. A. Abel, L. Elliss-Brookes, G. P. Rubin, and G. Lyratzopoulos, “Presenting symptoms of cancer and stage at diagnosis: evidence from a cross-sectional, population-based study,” Lancet Oncol. 21(1), 73–79 (2020). [CrossRef]
11. D. M. Mintzer and B. A. Mason, “On the need for biopsy confirmation at suspected first recurrence of cancer,” Am. J. Clin. Oncol. 26(2), 192–196 (2003). [CrossRef]
12. F. L. A. Fonseca, G. L. da Veiga, B. da Costa Aguiar Alves, and S. I. P. M. do Nascimento Alves, “Liquid biopsy in cancer using the z-scan technique: a new approach to discover biomarkers in cancer,” Future Sci. OA p. FSO638 (2020).
13. S. Huang, J. Yang, S. Fong, and Q. Zhao, “Artificial intelligence in cancer diagnosis and prognosis: Opportunities and challenges,” Cancer Lett. 471, 61–71 (2020). [CrossRef]
14. L. Á. Menéndez, F. J. de Cos Juez, F. S. Lasheras, and J. Á. Riesgo, “Artificial neural networks applied to cancer detection in a breast screening programme,” Math. Comput. Model. 52(7-8), 983–991 (2010). [CrossRef]
15. R. Pal, J. Yang, D. Ortiz, S. Qiu, V. Resto, S. McCammon, and G. Vargas, “In-vivo nonlinear optical microscopy (nlom) of epithelial-connective tissue interface (ecti) reveals quantitative measures of neoplasia in hamster oral mucosa,” PLoS One 10(1), e0116754 (2015). [CrossRef]
16. D. A. Sipkins, X. Wei, J. W. Wu, J. M. Runnels, D. Côté, T. K. Means, A. D. Luster, D. T. Scadden, and C. P. Lin, “In vivo imaging of specialized bone marrow endothelial microdomains for tumour engraftment,” Nature 435(7044), 969–973 (2005). [CrossRef]
17. S.-J. Lin, S.-H. Jee, C.-J. Kuo, R.-J. Wu, W.-C. Lin, J.-S. Chen, Y.-H. Liao, C.-J. Hsu, T.-F. Tsai, Y.-F. Chen, and C.-Y. Dong, “Discrimination of basal cell carcinoma from normal dermal stroma by quantitative multiphoton imaging,” Opt. Lett. 31(18), 2756–2758 (2006). [CrossRef]
18. N. D. Kirkpatrick, M. A. Brewer, and U. Utzinger, “Endogenous optical biomarkers of ovarian cancer evaluated with multiphoton microscopy,” Cancer Epidemiol., Biomarkers Prev. 16(10), 2048–2057 (2007). [CrossRef]
19. T. Kitamori, K. Yasuda, Y. Nomura, K. Suzuki, and H. Fujimori, “Method and apparatus for detecting particular particulate substance,” (1990). US Patent 4,890,925.
20. A. A. I. Sina, L. G. Carrascosa, Z. Liang, Y. S. Grewal, A. Wardiana, M. J. A. Shiddiky, R. A. Gardiner, H. Samaratunga, M. K. Gandhi, R. J. Scott, D. Korbie, and M. Trau, “Epigenetically reprogrammed methylation landscape drives the dna self-assembly and serves as a universal cancer biomarker,” Nat. Commun. 9(1), 4915 (2018). [CrossRef]
21. A. A. I. Sina, S. Howell, L. G. Carrascosa, S. Rauf, M. J. Shiddiky, and M. Trau, “emethylsorb: electrochemical quantification of dna methylation at cpg resolution using dna–gold affinity interactions,” Chem. Commun. 50(86), 13153–13156 (2014). [CrossRef]
22. A. A. I. Sina, T.-Y. Lin, R. Vaidyanathan, Z. Wang, S. Dey, J. Wang, A. Behren, A. Wuethrich, L. G. Carrascosa, and M. Trau, “Methylation dependent gold adsorption behaviour identifies cancer derived extracellular vesicular dna,” Nanoscale Horiz. 5(9), 1317–1323 (2020). [CrossRef]
23. M. Biswas, P. Das, E. Hoque, S. Sharafuddin, S. Das, and Y. Haque, “Optical properties of benzene and derivatives by the single beam thermal lens technique,” J. Nonlinear Opt. Phys. Mater. 26(02), 1750025 (2017). [CrossRef]
