Rapid and sensitive detection of esophageal cancer by FTIR spectroscopy of serum and plasma

Development of a multi-spectroscopy method coupling μ-FTIR and μ-Raman analysis for one-stop detection of microplastics in environmental and biological samples

Current techniques for microplastics (MPs) analysis are diverse. However, most techniques have individual limitations like the detection limit of spatial resolution, susceptibility, high cost, and time-consuming detection. In this study, we proposed a multi-spectroscopy method coupling μ-FTIR and μ-Raman analysis for one-stop MPs detection, in which barium fluoride was used as the substrate alternative to the filter membrane. Compared with commonly used filter membranes (alumina, silver, PTFE and nylon membranes), the barium fluoride substrate showed better spectroscopic detection performance on microscopic observation, broader transmittable wavenumber range for μ-FTIR (750-4000 cm-1) and μ-Raman (250-4000 cm-1) detection, thus suitable for the multi-spectroscopy analysis of spiked samples. Further, the real environmental and biological samples (indoor air, bottled water and human exhaled breath) were collected and detected to verify the applicability of the developed multi-spectroscopy method. The results indicated that the average content of detected MPs could be increased by 30.4 ± 29.9 % for indoor air, 17.1 ± 13.2 % for bottled water and 38.4 ± 16.0 % for human exhaled breath, respectively in comparison with widely used μ-Raman detection, which suggested that MPs exposure might be underestimated using single spectroscopy detection. Moreover, the majority of underestimated MPs were colored and smaller sized (<50 μm) MPs, which could pose higher risks to human body. In addition, the proposed method consumed lower sample pre-treatment costs and was environmental-friendly since the barium fluoride substrate could be used repeatedly after being cleaned by organic solvent with reliable results (n = 10, CV = 10 %, ICC = 0.961), which reduced the cost of MPs detection by at least 2.49 times compared with traditional methods using silver membrane.

Cervical Squamous Cell Carcinoma Diagnosis by FTIR Microspectroscopy

Cervical cancer was considered the fourth most common cancer worldwide in 2020. In order to reduce mortality, an early diagnosis of the tumor is required. Currently, this type of cancer occurs mostly in developing countries due to the lack of vaccination and screening against the Human Papillomavirus. Thus, there is an urgent clinical need for new methods aiming at a reliable screening and an early diagnosis of precancerous and cancerous cervical lesions. Vibrational spectroscopy has provided very good results regarding the diagnosis of various tumors, particularly using Fourier transform infrared microspectroscopy, which has proved to be a promising complement to the currently used histopathological methods of cancer diagnosis. This spectroscopic technique was applied to the analysis of cryopreserved human cervical tissue samples, both squamous cell carcinoma (SCC) and non-cancer samples. A dedicated Support Vector Machine classification model was constructed in order to categorize the samples into either normal or malignant and was subsequently validated by cross-validation, with an accuracy higher than 90%.

Comparison of serum from lung cancer patients and from patients with benign lung nodule using FTIR spectroscopy

Lungcancer remains the leading cause of cancer related deaths in worldwide. Earlydiagnosis oflungcancer can significantly improve survival rate. However, due to its close resemblance to the malignant nodules, the possible existence of benign nodules often leads to erroneous decisions. The aim of this study was to explore whether fourier transform infrared (FTIR) spectroscopy could improve the accuracy of early diagnosis of lung cancer by distinguishing lung cancer patients' (LCP') serum from patients with benign lung nodules' (PBLN') serum. In this study, A1243+1081/A1652+1539 ratio in LCP group was increased significantly compared with that in PBLN group, indicating that the ratio could be used to distinguish the serum of LCP from that of PBLN. In addition, the ratios of A2926/A2969, A1744/A2926+2859, A2926+2859/A1652+1539 were also increased significantly in LCP group compared with that in PBLN group. These findings suggest that FTIR spectroscopy might be a potentially effective method for the early diagnosis of lung cancer.

Infrared Spectroscopy for Rapid Triage of Cancer Using Blood Derivatives: A Reality Check

Infrared (IR) spectroscopy of serum/plasma represents an alluring molecular diagnostic tool, especially for cancer, as it can provide a molecular fingerprint of clinical samples based on vibrational modes of chemical bonds. However, despite the superior performance, the routine adoption of this technique for clinical settings has remained elusive. This is due to the potential confounding factors that are often overlooked and pose a significant barrier to clinical translation. In this Perspective, we summarize the concerns associated with various confounding factors, such as fluid sampling, optical effects, hemolysis, abnormal cardiovascular and/or hepatic functions, infections, alcoholism, diet style, age, and gender of a patient or normal control cohort, and improper selection of numerical methods that ultimately would lead to improper spectral diagnosis. We also propose some precautionary measures to overcome the challenges associated with these confounding factors.

Providing potential solutions by using FT-IR spectroscopy for biofluid analysis: Clinical impact of optical screening and diagnostic tests

BACKGROUND

Currently, the potential of FT-IR spectroscopy for rapid diagnosis of many pathologies has been demonstrated by numerous research studies including those targeting COVID-19 detection. However, the number of clinicians aware of this potential and who are willing to use spectroscopy in their clinics and hospitals is still negligible. In addition, lack of awareness creates a huge gap between clinicians and researchers involved in clinical translation of current FT-IR technology hence hindering initiatives to bring basic and applied research together for the direct benefit of patients.

METHODS

Knowledge and medical training on FT-IR on the side of clinicians should be one of the first steps to be able to integrate it into the list of complementary exams which may be requested by health professionals. Countless FT-IR applications could have a life-changing impact on patients' lives, especially screening and diagnostic tests involving biofluids such as blood, saliva and urine which are routinely non-invasively or minimally-invasively.

RESULTS

Blood may be the most difficult to obtain by the invasive method of collection, but much can be evaluated in its components, and areas such as hematology, infectiology, oncology and endocrinology can be directly benefited. Urine with a relatively simple collection method can provide pertinent information from the entire urinary system, including the actual condition of the kidneys. Saliva collection can be simpler for the patient and can provide information on diseases affecting the mouth and digestive system and can be used to diagnose diseases such as oral cancer in its early-stages. An unavoidable second step is the active involvement of industries to design robust and portable instruments for specific purposes, as the medical community requires user-friendly instruments of advanced computational algorithms. A third step resides in the legal situation involving the global use of the technique as a new diagnostic modality.

CONCLUSIONS

It is important to note that decentralized funds for variety of technologies hinders the training of clinical and medical professionals for the use of newly arising technologies and affect the engagement of these professionals with technology developers. As a result of decentralized funding, research efforts are spread out over a range of technologies which take a long time to get validated and translated to the clinic. Partnership over similar groups of technologies and efforts to test the same technologies while overcoming barriers posed to technology validation in different areas around the globe may benefit the clinical/medical, research and industry community globally.

A Review of Machine Learning Methods Recently Applied to FTIR Spectroscopy Data for the Analysis of Human Blood Cells

Machine learning (ML) is a broad term encompassing several methods that allow us to learn from data. These methods may permit large real-world databases to be more rapidly translated to applications to inform patient-provider decision-making. This paper presents a review of articles that discuss the use of Fourier transform infrared (FTIR) spectroscopy and ML for human blood analysis between the years 2019-2023. The literature review was conducted to identify published research of employed ML linked with FTIR for distinction between pathological and healthy human blood cells. The articles' search strategy was implemented and studies meeting the eligibility criteria were evaluated. Relevant data related to the study design, statistical methods, and strengths and limitations were identified. A total of 39 publications in the last 5 years (2019-2023) were identified and evaluated for this review. Diverse methods, statistical packages, and approaches were used across the identified studies. The most common methods included support vector machine (SVM) and principal component analysis (PCA) approaches. Most studies applied internal validation and employed more than one algorithm, while only four studies applied one ML algorithm to the data. A wide variety of approaches, algorithms, statistical software, and validation strategies were employed in the application of ML methods. There is a need to ensure that multiple ML approaches are used, the model selection strategy is clearly defined, and both internal and external validation are necessary to be sure that the discrimination of human blood cells is being made with the highest efficient evidence.

The current state of artificial intelligence in endoscopic diagnosis of early esophageal squamous cell carcinoma

Esophageal squamous cell carcinoma (ESCC) is a common malignant tumor of the digestive tract. The most effective method of reducing the disease burden in areas with a high incidence of esophageal cancer is to prevent the disease from developing into invasive cancer through screening. Endoscopic screening is key for the early diagnosis and treatment of ESCC. However, due to the uneven professional level of endoscopists, there are still many missed cases because of failure to recognize lesions. In recent years, along with remarkable progress in medical imaging and video evaluation technology based on deep machine learning, the development of artificial intelligence (AI) is expected to provide new auxiliary methods of endoscopic diagnosis and the treatment of early ESCC. The convolution neural network (CNN) in the deep learning model extracts the key features of the input image data using continuous convolution layers and then classifies images through full-layer connections. The CNN is widely used in medical image classification, and greatly improves the accuracy of endoscopic image classification. This review focuses on the AI-assisted diagnosis of early ESCC and prediction of early ESCC invasion depth under multiple imaging modalities. The excellent image recognition ability of AI is suitable for the detection and diagnosis of ESCC and can reduce missed diagnoses and help endoscopists better complete endoscopic examinations. However, the selective bias used in the training dataset of the AI system affects its general utility.