Fluorescence spectral detection of acute lymphoblastic leukemia (ALL) and acute myeloid leukemia (AML): A novel photodiagnosis strategy

Laser induced fluorescence spectroscopy analysis of kidney tissues: A pilot study for the identification of renal cell carcinoma

The 325 nm-excited autofluorescence spectra from cancerous and normal renal tissues were collected ex vivo biopsy tissue samples, through an optical fiber probe-based system. Noticeable changes in intensity/wavelength were observed in the fluorescence emissions from endogenous fluorophores such as collagen, Nicotinamide adenine dinucleotide (NADH), Vitamin A (retinol), and flavin adenine dinucleotide, in pathological conditions with respect to the normal state. The energy metabolism involved in clear cell renal cell carcinoma (ccRCC) and chromophobe renal cell carcinoma (chRCC) are reflected in the fluorescence emission band at 445 nm due to bound NADH attributed to enhanced oxidative phosphorylation in chRCC and emission at 465 nm contributed by free NADH showing higher glycolytic action in ccRCC. The principal component analysis and one-way ANOVA effectively discriminate ccRCC from chRCC. It is shown that laser induced fluorescence technique with 325 nm excitation can be a suitable technique for optical pathology and in vivo surgical boundary demarcation in renal cell carcinoma.

Circulating Biomarkers Associated with the Diagnosis and Prognosis of B-Cell Progenitor Acute Lymphoblastic Leukemia

Acute lymphoblastic leukemia (ALL) is a hematological disease characterized by the dysfunction of the hematopoietic system that leads to arrest at a specific stage of stem cells development, suppressing the average production of cellular hematologic components. BCP-ALL is a neoplasm of the B-cell lineage progenitor. BCP-ALL is caused and perpetuated by several mechanisms that provide the disease with its tumor potential and genetic and cytological characteristics. These pathological features are used for diagnosis and the prognostication of BCP-ALL. However, most of these paraclinical tools can only be obtained by bone marrow aspiration, which, as it is an invasive study, can delay the diagnosis and follow-up of the disease, in addition to the anesthetic risk it entails for pediatric patients. For this reason, it is crucial to find noninvasive and accessible ways to supply information concerning diagnosis, prognosis, and the monitoring of the disease, such as circulating biomarkers. In oncology, a biomarker is any measurable indicator that demonstrates the presence of malignancy, tumoral behavior, prognosis, or responses to treatments. This review summarizes circulating molecules associated with BCP-ALL with potential diagnostic value, classificatory capacity during monitoring specific clinic features of the disease, and/or capacity to identify each BCP-ALL stage regarding its evolution and outcome of the patients with BCP-ALL. In the same way, we provide and classify biomarkers that may be used in further studies focused on clinical approaches or therapeutic target identification for BCP-ALL.

Rapid detection of cholecystitis by serum fluorescence spectroscopy combined with machine learning

While cholecystitis is a critical public health problem, the conventional diagnostic methods for its detection are time consuming, expensive and insufficiently sensitive. This study examined the possibility of using serum fluorescence spectroscopy and machine learning for the rapid and accurate identification of patients with cholecystitis. Significant differences were observed between the fluorescence spectral intensities of the serum of cholecystitis patients (n = 74) serum and those of healthy subjects (n = 71) at 455, 480, 485, 515, 625 and 690 nm. The ratios of characteristic fluorescence spectral peak intensities were first calculated, and principal component analysis (PCA)-linear discriminant analysis (LDA) and PCA-support vector machine (SVM) classification models were then constructed using the ratios as variables. Compared with the PCA-LDA model, the PCA-SVM model displayed better diagnostic performance in differentiating cholecystitis patients from healthy subjects, with an overall accuracy of 96.55%. This exploratory study showed that serum fluorescence spectroscopy combined with the PCA-SVM algorithm has significant potential for the development of a rapid cholecystitis screening method.

Optical Detection of Cancer Cells Using Lab-on-a-Chip

The global need for accurate and efficient cancer cell detection in biomedicine and clinical diagnosis has driven extensive research and technological development in the field. Precision, high-throughput, non-invasive separation, detection, and classification of individual cells are critical requirements for successful technology. Lab-on-a-chip devices offer enormous potential for solving biological and medical problems and have become a priority research area for microanalysis and manipulating cells. This paper reviews recent developments in the detection of cancer cells using the microfluidics-based lab-on-a-chip method, focusing on describing and explaining techniques that use optical phenomena and a plethora of probes for sensing, amplification, and immobilization. The paper describes how optics are applied in each experimental method, highlighting their advantages and disadvantages. The discussion includes a summary of current challenges and prospects for cancer diagnosis.

A deep learning method and device for bone marrow imaging cell detection

BACKGROUND

Morphological analysis of bone marrow cells is considered as the gold standard for the diagnosis of leukemia. However, due to the diverse morphology of bone marrow cells, extensive experience and patience are needed for morphological examination. automatic diagnosis system through the comprehensive application of image analysis and pattern recognition technology is urgently needed to reduce work intensity, error probability and improves work efficiency.

METHODS

In this article, we establish a new morphological diagnosis system for bone marrow cell detection based on the deep learning object detection framework. The model is based on the Faster Region-Convolutional Neural Network (R-CNN), a classical object detection model. The system automatically detects bone marrow cells and determines their types. As specimens have severe long-tail distribution, i.e., the frequency of different types of cells varies dramatically, we proposed a general score ranking loss to solve such a problem. The general score ranking loss considers the ranking relationship between positive and negative samples and optimizes the positive sample with a higher classification probability value.

RESULTS

We verified this system with 70 bone marrow specimens of leukemia patients, which proved that it can realize intelligent recognition with high efficiency. The software is finally integrated into the microscope system to build an augmented reality system.

CONCLUSIONS

Clinical tests show that the response speed of the newly developed diagnostic system is faster than that of trained diagnostic experts.

Fluorescence spectroscopy as a novel technique for premarital screening of sickle cell disorders

Spectral diagnostic screening for sickle cell disease was carried out on volunteer blood samples (N = 100). The samples were subjected to different diagnostic methods including conventional complete blood count (CBC), hemoglobin electrophoresis (HBE) and spectral diagnosis. For the spectral diagnostic method, we discriminated three different characteristic spectral features. In total, 15 samples were sickle cell trait (SCT), 34 samples were sickle cell disease (SCD), and the rest of the samples (N = 51) were normal controls. The spectral discrimination of the three different sets of samples was distinguished on the quantification of fluorescent biomolecules such as tyrosine, tryptophan, NADH, FAD, and porphyrins. The results were compared with the conventional standard CBC and capillary electrophoresis findings. The spectral diagnosis method exhibited a sensitivity and specificity greater than 90 % for the tested samples. This technique requires only 5 mL blood samples, has an analysis time of 20 min, exhibits high accuracy and may be used in small clinics in remote villages.

An experimental and algorithm-based study of the spectral features of breast cancer patients by a photodiagnosis approach

In the present study, the spectral diagnosis of blood plasma samples of breast cancer patients and an equal number of normal controls was investigated. A set of ratio parameters was acquired by employing SXS and FES. The samples were also analyzed statistically by employing Welch two-sample t-tests, and the effects of three ratio parameters, R₁, R₂, and R₃, were also studied by plotting them against the subject numbers. A linear discriminant was also applied to verify the exact classification of normal control and breast cancer patients. It was observed that the levels of biofluorophores such as porphyrin, NADH, tryptophan and flavins were elevated 2- to 3-fold for breast cancer patients compared to normal controls, with an accuracy of approximately 100 %. We have also confirmed the validity of the obtained experimental results by using an advanced robust diagnostic algorithm. The experimental results of the current study may have a vital and substantial impact on the detection and screening protocols used for future breast cancer patients. The spectral analysis of body fluid could be of great value to add to and enhance the current procedures with an accuracy of approximately 100 % with limited number of samples. The results and objectives of this preliminary study were encouraging and useful for the discrimination of the features of breast cancer patients compared to those of normal controls.

NOVEL USE OF FLUORESCEIN DYE IN DETECTION OF ORAL DYSPLASIA AND ORAL CANCER

BACKGROUND

India is now regarded as the country with one of the highest incidence of oral cancer in the world. Considering poor survival in cases with late diagnosis, early detection can reduce morbidity and mortality of cancer patients and may impede malignant transformation in cases of oral potentially malignant disorders (OPMD). Most of the diagnostic aids are expensive and not available for mass screenings in developing countries. There is a need to develop a sensitive and affordable technique for screening of oral cancer, which can be accurate even in hands of health care workers with limited experience. Fluorescein dye has been used for tumour detection in colon, stomach, breast and brain. However, its utility in the diagnosis of oral cancer and OPMD has not yet been explored.

METHODS

This is the first study to report the role of fluorescein in the detection of oral cancer and OPMD. The present cross sectional study was conducted at a tertiary care dental centre. It included 100 individuals presenting with 42 OPMDs, 40 oral squamous cell carcinoma (OSCC) and 18 controls.

RESULTS

The sensitivity and specificity for the fluorescein detection method for OPMDs and OSCC was found to be 96.6% and 52.4% respectively. The positive predictive value was 73.7% and the negative predictive value was 91.7% for the fluorescein method. The likelihood ratios stood at 2.03 for a positive test and 0.066 for a negative test.

CONCLUSION

We conclude that fluorescein staining along with blue light is likely to improve detection of early oral cancers and dysplasia and can play a vital role in mass screening programmes of oral cancer.