While the association between sperm selection with Hyaluronic Acid (HA) and DNA quality is well-accepted, the underlying mechanism remains unclear. In order to shed light on this issue, micro-Raman spectroscopy was utilized to analyze the differences in the Raman spectral response of sperm cells bound with or without HA. Our results demonstrate that the Raman spectra of HA-bound sperm and HA-unbound sperm display distinct differences in Raman spectral response. Furthermore, we conducted nucleoprotein and nuclear DNA fluorescent staining to validate our findings, demonstrating that HAbound sperm exhibit enhanced maturity in nucleoproteins and heightened DNA duplex quality. These findings substantiate the effectiveness of HA in selectively identifying highquality sperm cells. In summary, our research suggests that HA can be a valuable component for efficiently selecting high-quality sperm cells, and the integration of HA screening with micro-Raman spectroscopy presents a promising, non-invasive approach for objectively evaluating and screening strictly high-quality such cells.
Quantitative methods for noninvasive diagnosis of scars are a challenging issue in medicine. This work aims to implement a texture analysis method for quantitatively discriminating abnormal scars from normal scars based on second-harmonic generation (SHG) images. A local difference local binary pattern (LD-LBP) operator combined with a wavelet transform was explored to extract diagnosis features from scar SHG images that were related to the alteration in collagen morphology. Based on the quantitative parameters including the homogeneity, directional and coarse features in SHG images, the scar collagen SHG images were classified into normal or abnormal scars by a support vector machine classifier in a leave-one-out cross-validation procedure. Our experiments and data analyses demonstrated apparent differences between normal and abnormal scars in terms of their morphological structure of collagen. By comparing with gray level co-occurrence matrix, wavelet transform, and combined basic local binary pattern and wavelet transform with respect to the accuracy and receiver operating characteristic analysis, the method proposed herein was demonstrated to achieve higher accuracy and more reliable classification of SHG images. This result indicated that the extracted texture features with the proposed method were effective in the classification of scars. It could provide assistance for physicians in the diagnostic process.
Secreted proteins, the promising source of biomarkers for early detection and diagnosis of cancer, have received considerable attention. Raman spectroscopy and principal component analysis (PCA) were used to characterize the secreted proteins collected from the cell cultures of human hepatoma cell line HepG2 and normal human liver cell line LO2 in this paper. We found the major difference of secreted proteins Raman spectra between HepG2 and LO2 cells were in the range of 1200cm-1-1800cm-1. Compared with LO2 cells, some significant changes based on secondary structure of secreted proteins in HepG2 cells were observed, including the increase in the relative intensity of the band at 1004cm-1, 1445cm-1, 1674cm-1 and the decrease at 1074cm-1. These variations of Raman bands indicated that the species and conformation of secreted proteins in HepG2 cells changed. The measured Raman spectra of the two groups were separated into two distinct clusters with no overlap and high specificity and sensitivity by PCA. These results show that the combination of Raman spectroscopy and PCA analysis may be a powerful tool for distinguishing the secreted proteins between human hepatoma cells and normal human liver cells, provide a new thought to analyze the secreted proteins from cancer cells and find a novel cancer biomarker.
Early detection of hepatocellular carcinoma is difficult due to the absence of recognizable physical symptoms. In this study, Raman spectra of liver normal tissues and hepatocellular carcinoma tissues were measured by using silver nanoparticles based surface enhanced Raman spectroscopy (SERS), respectively. The mean Raman spectra of two groups are roughly similar. But the peaks intensity of hepatocellular carcinoma tissues at 722 cm-1 and 1049 cm-1 are obviously higher than those of normal tissues. Some peaks of hepatocellular carcinoma tissues have shifted by different degree. Besides, Raman peaks at 1004cm-1 had disappeared in normal tissue. The result suggested that SERS spectra can feature liver normal tissue and hepatocellular carcinoma tissue. Principal component analysis (PCA) coupled with linear discriminant analysis (LDA) was performed on the measured spectra. There were three most diagnostically significant PCs (PC3, PC9, and PC15, p<0.05) for discriminating these two groups. The diagnostic sensitivity and specificity both were 84.6%. The whole analysis of each sample needs less time-consumed and cost than other traditional methods in detecting and diagnosing HCC. The preliminary result suggests that SERS spectra can be a potential medical technology to detect and diagnose HCC.
Gold nanoparticles are delivered into living cells by transient electroporation method to obtain intracellular surface-enhanced Raman spectroscopy (SERS). The subcellular localization of gold nanoparticles is characterized by transmission electron microscopy, and the forming large gold nanoaggregates are mostly found in the cytoplasm. The SERS detection of cells indicates that this kind of gold nanostructures induces a high signal enhancement of cellular chemical compositions, in addition to less cellular toxicity than that of silver nanoparticles. These results demonstrate that rapid incorporation of gold nanoparticles by electroporation into cells has great potential applications in the studies of cell biology and biomedicine.
Despite the introduction of high-technology methods of detection and diagnosis, screening of primary liver cancer (PLC) remains imperfect. To diagnosis PLC earlier, Surface-enhanced Raman spectroscopy (SERS) coupled with cellulose-acetate membrane electrophoresis were introduced to separate human serum albumin and SERS spectra. Three groups (15 normal persons’ samples, 17 hepatitis/cirrhosis samples, 15 cases of PLC) of serum albumin were tested. Silver colloid was used to obtain SERS spectra of human serum albumin. Principal component analysis (PCA) and linear discriminant analysis (LDA) were also employed for statistical analysis. The mean Raman spectra of three groups and the difference spectra of any two suggested that the albumin has changed in liver patients. Compared to normal groups, some Raman peaks have shifted or even disappeared in hepatitis/cirrhosis and PLCs groups. The sensitivity and specificity between PLCs and normal groups is 80% and 93.3%. Among hepatitis/cirrhosis and normal groups, the sensitivity is 88.2% and specificity is also 93.3%. Besides, the sensitivity and specificity between PLCs and hepatitis/cirrhosis groups is 86.7% and 76.5%. All the above data and results indicated that early screening of PLC is potential by SERS in different stages of liver disease before cancer occurs.
Texture analysis plays a crucial role in identifying objects or regions of interest in an image. It has been applied to a variety of medical image processing, ranging from the detection of disease and the segmentation of specific anatomical structures, to differentiation between healthy and pathological tissues. Second harmonic generation (SHG) microscopy as a potential noninvasive tool for imaging biological tissues has been widely used in medicine, with reduced phototoxicity and photobleaching. In this paper, we clarified the principles of texture analysis including statistical, transform, structural and model-based methods and gave examples of its applications, reviewing studies of the technique. Moreover, we tried to apply texture analysis to the SHG images for the differentiation of human skin scar tissues. Texture analysis method based on local binary pattern (LBP) and wavelet transform was used to extract texture features of SHG images from collagen in normal and abnormal scars, and then the scar SHG images were classified into normal or abnormal ones. Compared with other texture analysis methods with respect to the receiver operating characteristic analysis, LBP combined with wavelet transform was demonstrated to achieve higher accuracy. It can provide a new way for clinical diagnosis of scar types. At last, future development of texture analysis in SHG images were discussed.
The use of normal Raman (NR) spectroscopy and surface enhanced Raman scattering (SERS) spectroscopy to analyze the biochemical information of human serum proteins and hence distinguish between normal and primary hepatic carcinoma (PHC) serum samples was investigated. The serum samples were obtained from patients who were clinically diagnosed with PHC (n=20) and healthy volunteers (n=20). All spectra were collected in the spectral range of 400-1800 cm-1 and analyzed through the multivariate statistical methods of principal component analysis (PCA). The results showed that both NR and SERS combined with PCA had good performance in distinguishing the human serum proteins between PHC patients and healthy volunteers with high sensitivity and specificity of 100%. And we can get more detail information of component and conformation of human serum proteins by considering NR and SERS spectrum. Our results support the concept again that serum protein Raman and SERS spectroscopy combined with PCA analysis both can become noninvasive and rapid diagnostic tools to detect the primary hepatic carcinoma.
Surface-enhanced Raman scattering (SERS) spectroscopy combined with membrane electrophoresis (ME) was firstly employed to detect albumin variation in type II diabetic development. Albumin was first purified from human serum by ME and then mixed with silver nanoparticles to perform SERS spectral analysis. SERS spectra were obtained from blood albumin samples of 20 diabetic patients and 19 healthy volunteers. Subtle but discernible changes in the acquired mean spectra of the two groups were observed. Tentative assignment of albumin SERS bands indicated specific structural changes of albumin molecule with diabetic development. Meanwhile, PCA-LDA diagnostic algorithms were employed to classify the two kinds of albumin SERS spectra, yielding the diagnostic sensitivity of 90% and specificity of 94.7%. The results from this exploratory study demonstrated that the EM-SERS method in combination with multivariate statistical analysis has great potential for the label-free detection of albumin variation for improving type II diabetes screening.
Molecular characterization of semen that can be used to provide an objective diagnosis of semen quality is still lacking. Raman spectroscopy measures vibrational modes of molecules, thus can be utilized to characterize biological fluids. Here, we employed Raman spectroscopy to characterize and compare normal and abnormal semen samples in the fingerprint region (400-1800cm-1). Multivariate analysis methods including principal component analysis (PCA) and partial least square-discriminant analysis (PLS-DA) were used for spectral analysis to differentiate between normal and abnormal semen samples. Compared with PCA-LDA analysis, PLS-DA improved the diagnostic results, showing a sensitivity of 77% and specificity of 73%. Furthermore, our preliminary quantitative analysis based on PLS algorithm demonstrated that spermatozoa concentration were relatively well predicted (R2=0.825). In conclusion, this study demonstrated that micro-Raman spectroscopy combined with multivariate methods can provide as a new diagnostic technique for semen analysis and differentiation between normal and abnormal semen samples.
Surface-enhanced Raman scattering (SERS) spectra of serum proteins purified from human serum samples were employed to detect colorectal cancer. Acetic acid as a new aggregating agent was introduced to increase the magnitude of the SERS enhancement. High-quality SERS spectra of serum proteins were acquired from 103 cancer patients and 103 healthy volunteers. Tentative assignments of SERS bands reflect that some specific biomolecular contents and protein secondary structures change with colorectal cancer progression. Principal component analysis combined with linear discriminant analysis was used to assess the capability of this approach for identifying colorectal cancer, yielding diagnostic accuracies of 100% (sensitivity: 100%; specificity: 100%) based on albumin SERS spectroscopy and 99.5% (sensitivity: 100%; specificity: 99%) based on globulin SERS spectroscopy, respectively. A partial least squares (PLS) approach was introduced to develop diagnostic models. An albumin PLS model successfully predicted the unidentified subjects with a diagnostic accuracy of 93.5% (sensitivity: 95.6%; specificity: 91.3%) and the globulin PLS model gave a diagnostic accuracy of 93.5% (sensitivity: 91.3%; specificity: 95.6%). These results suggest that serum protein SERS spectroscopy can be a sensitive and clinically powerful means for colorectal cancer detection.
In this study, a rapid and simple method which combines drop coating deposition and Raman spectroscopy (DCDR) was developed to characterize the dry embryo culture media (ECM) droplet. We demonstrated that Raman spectra obtained from the droplet edge presented useful and characteristic signatures for protein and amino acids assessment. Using a different analytical method, scanning electron microscopy coupled with energy dispersive X-ray analysis, we further confirmed that Na, K, and Cl were mainly detected in the central area of the dry ECM droplet while sulphur, an indicative of the presence of macromolecules such as proteins, was mainly found at the periphery of the droplet. In addition, to reduce sample preparation time, different temperatures for drying the droplets were tested. The results showed that drying temperature at 50°C can effectively reduce the sample preparation time to 6 min (as compared to 50 min for drying at room temperature, ∼25°C ) without inducing thermal damage to the proteins. This work demonstrated that DCDR has potential for rapid and reliable metabolomic profiling of ECM in clinical applications.
The capabilities of micro-Raman spectroscopy for differentiating normal and malignant nasopharyngeal tissues were evaluated. Raman scattering signals were acquired from 22 normal and 52 malignant nasopharyngeal tissue samples. Distinctive spectral differences in Raman spectra between normal and malignant nasopharyngeal tissues were found, particularly in the spectral ranges of 853, 937, 1094, 1209, 1268, 1290 to 1340, 1579, and 1660 cm −1 , which primarily contain signals related to proteins, DNA, and lipids. Compared to normal tissues, the band intensity located at 853, and 937 cm −1 were significantly lower for cancerous tissues (p<0.05 ), while the band intensity located at 1094, 1209, 1268, and 1579 cm −1 were significantly higher (p<0.05 ). The band intensity located at 1290 to 1340, and 1660 cm −1 were also higher for cancerous tissues; but the differences were not statistically significant (p>0.05 ). Principal component analysis (PCA) and linear discriminate analysis (LDA) were employed to generate diagnostic algorithms for classification of Raman spectra of the two nasopharyngeal tissue types. The PCA-LDA algorithms together with leave-one-out, cross-validation technique yielded diagnostic sensitivity of 92% and specificity of 82%. This work demonstrated that the Raman spectroscopy technique associated with PCA-LDA diagnostic algorithms has potential for improving the diagnosis of nasopharyngeal cancers.
The molecular characterization of ABO blood types, which is clinically significant in blood transfusion, has clinical and
anthropological importance. Polymerase chain reaction sequence-based typing (PCR-SBT) is one of the most commonly
used methods for the analysis of genetic bases of ABO blood types. However, such methods as PCR-SBT are
time-consuming and are high in demand of equipments and manipulative skill. Here we showed that membrane
electrophoresis based SERS method employed for studying the molecular bases of ABO blood types can provide rapidand
easy-operation with high sensitivity and specificity. The plasma proteins were firstly purified by membrane
electrophoresis and then mixed with silver nanoparticles to perform SERS detection. We use this method to classify
different blood types, including blood type A (n=13), blood type B (n=9) and blood type O (n=10). Combination of
principal component analysis (PCA) and liner discriminant analysis (LDA) was then performed on the SERS spectra of
purified albumin, showing good classification results among different blood types. Our experimental outcomes represent
a critical step towards the rapid, convenient and accurate identification of ABO blood types.
Growth in the percentage of male infertility has caused extensive concerns. The fast and reliable method is urgently
required for diagnosis of semen samples. In our study, micro-Raman spectroscopy was employed to characterize and
differentiate the normal and abnormal semen samples based on the differences of their specific Raman spectra which
originated from biochemical components. Our preliminary results demonstrate that micro-Raman spectroscopy combined
with multivariate analysis methods has the potential of being used to detect and differentiate semen samples.
Studies with circulating ribonucleic acid (RNA) not only provide new targets for cancer detection, but also open up the possibility of noninvasive gene expression profiling for cancer. In this paper, we developed a surface-enhanced Raman scattering (SERS), platform for detection and differentiation of serum RNAs of colorectal cancer. A novel three-dimensional (3-D), Ag nanofilm formed by dry MgSO4 aggregated silver nanoparticles, Ag NP, as the SERS-active substrate was presented to effectively enhance the RNA Raman signals. SERS measurements were performed on two groups of serum RNA samples. One group from patients, n = 55 with pathologically diagnosed colorectal cancer and the other group from healthy controls, n = 45. Tentative assignments of the Raman bands in the normalized SERS spectra demonstrated that there are differential expressions of cancer-related RNAs between the two groups. Linear discriminate analysis, based on principal component analysis, generated features can differentiate the colorectal cancer SERS spectra from normal SERS spectra with sensitivity of 89.1 percent and specificity of 95.6 percent. This exploratory study demonstrated great potential for developing serum RNA SERS analysis into a useful clinical tool for label-free, noninvasive screening and detection of colorectal cancers.
Raman spectroscopy (RS) was applied for the analysis of seminal plasma in order to detect spectral parameters, which might be used for differentiating the normal and abnormal semen samples. Raman spectra of seminal plasma separated from normal and abnormal semen samples, showed a distinct difference in peak ratios between 1449 and 1418 cm−1 (P < 0.05). More efficient alternative method of using principal component analysis-linear discriminate analysis based on Raman spectroscopic data yielded a diagnostic sensitivity of 73% and specificity of 82%. The results suggest that RS combined with the multivariate analysis method has the potential for differentiating semen samples by examination of the corresponding seminal plasma.
Singlet oxygen (1O2) can be generated in a living cell upon focused laser irradiation of an intracellular photosensitizer. In
this study, 1O2 generation from the plasma membrane-targeted protoporphyrin IX (PpIX) in human nasopharyngeal
carcinoma CNE2 cells was monitored indirectly by using the fluorescence probe Singlet Oxygen Sensor Green agent
(SOSG). The confocal images indicate that the fluorescence of SOSG in the vicinity of the cells that incubated with PpIX
was dramatically enhanced with the increased irradiation time, while there is no significant enhancement for the control
cells. Moreover, the fluorescence of SOSG is dramatically enhanced with the increase of the intracellular PpIX in CNE2
cells for the same photoirradiation time. These observations imply that the 1O2 generated from the plasma
membrane-targeted PpIX in the CNE2 cells can be escaped into the extracellular medium and to react with the SOSG to
produce SOSG-EP, and the fluorescence enhancement of SOSG around the cells mainly depends on the intracellular
PpIX. Our findings may be useful for further monitored the 1O2 that can be escaping from the living cells.
Nasopharyngeal carcinoma (NPC) is one of the most common malignancies in china, with a deep and hidden
localization. Recently, methods for early diagnosis of NPC has become one of the most important research topics in
medical field. Early monitoring of morphological change of NPC cells during the carcinogenesis is of great importance,
and early information extracted from the NPC cells during the initial stage of NPC is critical for diagnosis and treatment.
In this paper, image processing methods for two-photon microscopic image of NPC cells was investigated with the
purpose of providing useful information for early diagnosis and treatment of NPC.
There is abundant information in a two-photon microscopic image of NPC cells, which can be analyzed and processed
by means of computer and image pattern processing algorithm. In this paper, firstly, a mathematical method of transform
of Bottom-hat based on Matlab platform was employed to enhance the image of NPC cells, making the image easier to
distinguish; Then, several classical edge detection algorithms were compared and discussed, for example, Roberts
operator, Prewitt operator, and Canny operator etc. According to the inherent characteristics of two-photon microscopic
image of NPC cells, corrosion algorithm was used to define the edge of NPC cells. Furthermore, the article gets the
iterative threshold segmentation after noise denoising, on the other hand, improved discriminant analysis was adopted for
threshold segmentation of NPC cells, better results were obtained.
Biomedical images denosing based on Partial Differential Equation are well-known for their good
processing results. General denosing methods based on PDE can remove the noises of images with gentle
changes and preserve more structure details of edges, but have a poor effectiveness on the denosing of
biomedical images with many texture details. This paper attempts to make an overview of biomedical images
texture detail denosing based on PDE. Subsequently, Three kinds of important image denosing schemes are
introduced in this paper: one is image denosing based on the adaptive parameter estimation total variation
model, which denosing the images based on region energy distribution; the second is using G norm on the
perception scale, which provides a more intuitive understanding of this norm; the final is multi-scale denosing
decomposition. The above methods involved can preserve more structures of biomedical images texture detail.
Furthermore, this paper demonstrates the applications of those three methods. In the end, the future trend of
biomedical images texture detail denosing Based on PDE is pointed out.
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