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- Mishra, Abhay, et al.
(författare)
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Spectroscopic insight into breast cancer: profiling small extracellular vesicles lipids via infrared spectroscopy for diagnostic precision
- 2024
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Ingår i: Scientific Reports. - : Springer Nature. - 2045-2322. ; 14
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Tidskriftsartikel (refereegranskat)abstract
- Breast cancer, a leading cause of female mortality due to delayed detection owing to asymptomatic nature and limited early diagnostic tools, was investigated using a multi-modal approach. Plasma-derived small EVs from breast cancer patients (BrCa, n = 74) and healthy controls (HC, n = 30) were analyzed. Small EVs (n = 104), isolated through chemical precipitation, underwent characterization via transmission electron microscopy (TEM) and nanoparticle tracking analysis (NTA). Validation involved antibody-based tests (TSG101, CD9, CD81, CD63). Infrared spectra of small EVs were obtained, revealing significant differences in lipid acyl chains, particularly in the C–H stretching of CH3. The study focused on the lipid region (3050–2900 cm−1), identifying peaks (3015 cm−1, 2960 cm−1, 2929 cm−1) as distinctive lipid characteristics. Spectroscopic lipid-to-lipid ratios [(I3015/I2929), (I2960/I2929)] emerged as prominent breast cancer markers. Exploration of protein, nucleic acid, and carbohydrate ratios indicated variations in alpha helices, asymmetric C–H stretching vibrations, and C–O stretching at 1033 cm−1. Principal component analysis (PCA) successfully differentiated BrCa and HC small EVs, and heatmap analysis and receiver operating characteristic (ROC) curve evaluations underscored the discriminatory power of lipid ratios. Notably, (I2960/I2929) exhibited 100% sensitivity and specificity, highlighting its potential as a robust BrCa sEV marker for breast cancer detection.
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| 2. |
- Ranjan, Piyush, et al.
(författare)
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Japanese encephalitis symptom prediction using machine learning algorithm
- 2024
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Ingår i: Intelligent systems. - Singapore : Springer. - 9789819939312 - 9789819939329 ; , s. 99-112
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Konferensbidrag (refereegranskat)abstract
- In India Japanese Encephalitis (JEV) has been a major public health problem. In endemic districts of country each year there is a large-scale outbreak occurring of JEV. Research says that Japanese Encephalitis is a flavivirus related to West Nile Virus, Yellow Fever and Dengue and it is escalated by mosquitoes. Japanese Encephalitis is although rare, but the fatality rate is around 30%. Till now there is no cure for JEV, the entire treatment is focused for supporting the patient to overcome disease and relieving severe clinical sign. Maximum number of JEV cases in India are of infants and the fatality rate is around 30% which is a great matter of concern. Here Force of Infection denotes the rate at which sensitive individuals acquire an infectious disease. In India, states which report major outbreak of Japanese Encephalitis are Uttar pradesh, Andhra Pradesh, West Bengal, Karnataka, Assam, Tamil Nadu, Bihar, Goa and Manipur. The impacting factors include Climate, Rice Distribution, Livestock Distribution, Population Density, Specific Age Group Density, Urban/Rural Category and Elevation. Impacting Factors may change with the location. Here we have used Machine learning algorithms like Ridge Regression, Lasso Regression, ElasticNet Regression and Multi-layer Perceptron for the prediction of Force of Infection of Japanese Encephalitis Virus. ElasticNet Regression Algorithm is also used for extracting the significant attribute from the JEV Dataset. The proposed model generated an optimum performance in context to the error rate and accuracy of prediction.
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