Fourier-transform infrared spectroscopy: a universal optical sensing technique with auspicious application prospects in the diagnosis and management of autoimmune diseases

Rapid differentiation of patients with lung cancers from benign lung nodule based on dried serum Fourier-transform infrared spectroscopy combined with machine learning algorithms

Lung cancer (LC) is associated with poor 5-year survival rates when diagnosed at advanced stages. While low-dose computed tomography (LDCT) screening enables earlier detection, its high false-positive rate, primarily due to benign lung nodules (BLN), necessitates more accurate diagnostic tools. This study developed a rapid and precise LC discrimination method by integrating Fourier transform infrared (FTIR) spectroscopy of dried serum samples with machine learning algorithms. We analyzed dried serum from 58 LC patients, 37 BLN patients, and 36 healthy controls. Five machine learning models, linear discriminant analysis (LDA), support vector machine (SVM), random forest, multilayer perceptron (MLP), and LightGBM, were optimized using FTIR spectral data (1800-900 cm cm-1 band). All algorithms successfully differentiated the three groups, with LDA achieving the highest accuracy (93.9%). These results demonstrate that dried serum FTIR spectroscopy coupled with machine learning, particularly LDA, offers a promising approach for distinguishing LC from BLN, potentially augmenting LDCT screening to reduce unnecessary interventions.

Development of disease diagnosis technology based on coattention cross-fusion of multiomics data

BACKGROUND

Early diagnosis is vital for increasing the rates of curing diseases and patient survival in medicine. With the advancement of biotechnology, the types of bioomics data are increasing. The integration of multiomics data can provide more comprehensive biological information, thereby achieving more accurate diagnoses than single-omics data can. Nevertheless, current multiomics research is often limited to the intelligent diagnosis of a single disease or a few types of omics data and lacks a multiomics disease diagnosis model that can be widely applied to different diseases. Therefore, developing a model that can effectively utilize multiomics data and accurately diagnose diseases has become an important challenge in medical research.

RESULTS

On the basis of vibrational spectroscopy and metabolomics data, this study proposes an innovative coattention cross-fusion model for disease diagnosis on the basis of interactions of multiomics data. The model not only integrates the information of different omics data but also simulates the interactions between these data to achieve accurate diagnosis of diseases. Through comprehensive experiments, our method achieved accuracies of 95.00 %, 94.95 %, and 97.22 % and area under the curve (AUC) values of 95.00 %, 96.77 %, and 99.31 % on the cervical lymph node metastasis of the thyroid, systemic lupus erythematosus, and cancer datasets, respectively, indicating excellent performance in the diagnosis of multiple diseases.

SIGNIFICANCE

The proposed model outperforms existing multiomics models, enhancing medical diagnostic accuracy and offering new approaches for multiomics data use in disease diagnosis. The innovative coattention cross-fusion module enables more effective multiomics data processing and analysis, serving as a potent tool for early and precise disease diagnosis with substantial clinical and research implications.

Integrative computational and experimental study of propolis, polyvinyl alcohol, and alhagi maurorum complex as anticancer and antibacterial agents

The study examined the potential applications of propolis, polyvinyl alcohol (PVA), and Alhagi maurorum extracts in drug delivery systems, utilizing both computer and lab methods. The study uses molecular docking probes along with DFT (density functional theory) to investigate molecular interactions and examine the binding of drugs to carrier materials. The HOMO (Highest Occupied Molecular Orbital)-LUMO (Lowest Occupied Molecular Orbital) gap for the mix of PVA, galangin, and triterpene glycoside is -0.07621 eV, which matches the experiment results. This small gap enhances responsiveness in drug delivery applications, which is crucial for successful interactions with biological targets. It's possible that a delivery system that combines galangin and triterpene glycosides would work better and be more compatible with living things.The experimental results of the Methyl Thiazole Tetrazolium (MTT) show consistent findings: The viability of MCF7, a human breast cancer cell line, significantly decreased at all concentrations of propolis and polyvinyl alcohol compared to WRL68, a fetal liver cell line. Within-group comparisons showed less viability in both groups at 400 µg/ml. Mean ± SD: 42.05267 ± 1.951655; 67.12533 ± 7.401263.In the positive control group, the average number of malignant cells was 47.06, but the average number of cells in the fourth treatment (Propolis + PVA) and the third combination (Propolis + Alhagi maurorum + PVA) were 42.05267 and 42.97800, respectively. The Sustainable Development Goals in Industry and Innovation are focusing on developing a new combination of alhagi and propolis using PVA as a polymer carrier.

Application of Fourier transform infrared spectroscopy (FTIR) for protozoan analysis: A systematic review

Protozoa present in water for human consumption represent a significant public health risk to a greater extent in the most vulnerable populations. Identifying protozoa in a traditional way through microscopy or with more advanced technologies such as molecular biology may present limitations in sensitivity, specificity, time, and costs. Fourier Transform Infrared (FTIR) spectroscopy have potential as an alternative for the detection of protozoa in water used for human consumption. An exhaustive search was carried out in the databases, SCIELO, PubMed, SCOPUS and Google Scholar, with the search terms "protozoa," "protozoan," "parasite," "FTIR," "infrared spectroscopy." Only six articles met the inclusion criteria. FTIR spectroscopy can detect changes in biochemical composition but has not been used for the identification of parasites in human clinical or environmental samples. The present systematic review identified a lack of studies in this area and the need to conduct research aimed at developing standardized methods and creating spectral database banks of protozoan species that will allow for the precise identification of protozoa such as Cryptosporidium spp. and Giardia spp. in water for human consumption.