Emergence and impact of theranostic‐nanoformulation of triple therapeutics for combination cancer therapy

Smart Medicine, Journal Year: 2024, Volume and Issue: 3(1)

Published: Jan. 30, 2024

Cancer remains a major global health threat necessitating the multipronged approaches for its prevention and management. Traditional in form of chemotherapy, surgery, radiotherapy are often encountered with poor patient outcomes evidenced by high mortality morbidity, compelling need precision medicine cancer patients to enable personalized targeted treatment. There has been an emergence smart multimodal theranostic nanoformulation triple combination therapy last few years, which dramatically enhances overall safety vivo potential clinical applications minimal toxicity. However, it is imperative gain insight into limitations this system terms translation, cost-effectiveness, accessibility, multidisciplinary collaboration. This review paper aims highlight compare impact recent nanoformulations therapeutics single nanocarrier effective management provide new dimension diagnostic treatment simultaneously.

Language: Английский

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Human blood plasma SERS analysis using silver nanoparticles for cardiovascular diseases detection

Journal of Biomedical Photonics & Engineering, Journal Year: 2024, Volume and Issue: 10(1), P. 010301 - 010301

Published: Jan. 16, 2024

In recent years, the use of Raman and surface enhanced spectroscopy for disease detection has grown. The motives their increased have commonly been attributed to well-known benefits, such as creation narrow spectral bands that are characteristic molecular components present, high sensitivity specificity they can provide. aim this work is analysis features plasma in patients with cardiovascular diseases utilizing determine presence or absence disease. investigation revealed spectrum difference between patient healthy volunteers’ groups at observed bands. 146 67 subjects were analyzed. Classification group was made based on projection latent structures 99% accuracy. Stability classifier checked implementation cross-validation separation analyzed data into training test sets. obtained results demonstrate proposed SERS technique stable significant potential clinical diagnostic applications.

Language: Английский

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Deep learning of surface-enhanced Raman spectroscopy data for multiple sclerosis diagnostics

The European Physical Journal Special Topics, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 7, 2025

Language: Английский

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Comparative Study Into the Effect of Detector Noises and Sensitivity on the Serum SERS Analysis: Example of Non‐Communicable Diseases Discrimination

Journal of Biophotonics, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 6, 2025

ABSTRACT The aim of the study is to compare performance surface‐enhanced Raman spectroscopy (SERS) analysis serum using a non‐cooled detector ( EnSpectr R785 ) and high spectral resolution Renishaw in task discrimination between patients with chronic heart failure obstructive pulmonary disease. SERS‐based solution classification problem demonstrates an insignificant relationship disease accuracy quality (classification for high‐resolution setup 0.84 low‐cost 0.81). In data recorded on setup, most significant bands are 611, 675, 720, 804, 1187, 1495, 1847 cm −1 ; setup—721, 1051, 1665 . results have revealed equal capabilities setups; however, has more prospects identifying contribution pathologically associated analytes.

Language: Английский

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Avoiding Overestimation and the ‘Black Box’ Problem in Biofluids Multivariate Analysis by Raman Spectroscopy: Interpretation and Transparency With the SP‐LIME Algorithm

Journal of Raman Spectroscopy, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 29, 2024

ABSTRACT Raman spectroscopy, in combination with multivariate analysis, is a powerful analytical tool for solving regression and classification problems various fields—from materials science to clinical practice. However, practical applications, experimental studies the implementation of spectroscopy present numerous challenges, including multicollinearity spectral data ‘black box’ problem complex models. To avoid these problems, proposed models require proper interpretation. This study makes use comparative analysis explanation methods based on SP‐LIME (local interpretable model‐agnostic explanations submodular pick) algorithm bilinear model (projection onto latent structures [PLS]) nonlinear (one‐dimensional convolutional neural network [CNN]). The be interpreted are trained solve task blood serum characteristics urea levels. Effective evaluation spectra revealed that both PLS CNN models, important band at 1003 cm −1 . approach value root mean square error estimation only when single analyzed. aim this paper develop an explain operation provides way reveal exact bands biggest impact performance.

Language: Английский

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Comment on “Early cancer detection by serum biomolecular fingerprinting spectroscopy with machine learning”

Light Science & Applications, Journal Year: 2025, Volume and Issue: 14(1)

Published: Jan. 20, 2025

In their recent publication, Dong et al. 1 demonstrated high performance of SERS-based liquid biopsy human serum analysis for earlier cancer detection.Moreover, this study was highlighted in News & Vies section Light Science and Applications journal by Shi 2 .No doubt, different optical techniques nowadays are widely used the biofluids 3-10 demonstrate accuracy possible clinical applications.At same time, paper may be treated incorrectly due to drawbacks spectral data analysis.First all, proposed evaluate provided classification models based on separation acquired into training test sets.This is an adequate approach machine learning prove stability 11 .In it required model both set (comparability proves stability).Dong divided 4:1 ratio (training:test), but surprisingly only one value characterize models.Actually mixed from groups such value.It clearly seen data, presented figures text, that demonstrates whole dataset instead two values test.As example collected spectra 244 lung (LC) patients 324 healthy controls (HC).

Language: Английский

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Surface-enhanced Raman spectroscopy for the characterization of filtrate portions of hepatitis B blood serum samples using 100 kDa ultra filtration devices

Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy, Journal Year: 2025, Volume and Issue: 333, P. 125883 - 125883

Published: Feb. 9, 2025

Language: Английский

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Multi-cancer early detection based on serum surface-enhanced Raman spectroscopy with deep learning: a large-scale case–control study

BMC Medicine, Journal Year: 2025, Volume and Issue: 23(1)

Published: Feb. 21, 2025

Early detection of cancer can help patients with more effective treatments and result in better prognosis. Unfortunately, established screening technologies are limited for use, especially multi-cancer early detection. In this study, we described a serum-based platform integrating surface-enhanced Raman spectroscopy (SERS) technology resampling strategy, feature dimensionality enhancement, deep learning interpretability analysis methods sensitive accurate pan-cancer screening. Totally, 1655 early-stage breast (BC, n = 569), lung (LC, 513), thyroid (TC, 220), colorectal (CC, 215), gastric (GC, 100), esophageal (EC, 38), 1896 healthy controls (HC) were enrolled. The serum SERS spectra obtained from each participant. Data dimension enhancement was conducted by heatmap transformation continuous wavelet transform (CWT). dimensionalization spectral data subsequently analyzed residual neural network (ResNet) as convolutional (CNN) algorithm. Class activation mapping (CAM) method performed to elucidate the potential biological significance classification. All participants divided into training set test ratio 7:3. BorderlineSMOTE selected most appropriate strategy (DNN) model achieved desirable performance among all groups (accuracy rate: 93.15%, precision 88:46%, recall 85.68%, F1-score: 86.98%), generated AUC values 0.991 HC, 0.995 BC, 0.979 LC, 0.996 TC, 0.994 CC, 0.982 GC, 0.941 EC, respectively. Furthermore, combination use ResNet (form heatmap) also capable effectively distinguishing different categories making predictions 94.75%, 89.02, 86.97, 87.88), 0.988 0.999 0.993 0.985 0.940 Additionally, strong wave number range observed CAM analysis. Our study has offered highly SERS-based approach detection, which might shed new light on clinical practice.

Language: Английский

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Overestimation of the classification model for Raman spectroscopy data of biological samples

mSystems, Journal Year: 2025, Volume and Issue: unknown

Published: March 10, 2025

Language: Английский

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Detection of Respiratory Disease Based on Surface-Enhanced Raman Scattering and Multivariate Analysis of Human Serum

Diagnostics, Journal Year: 2025, Volume and Issue: 15(6), P. 660 - 660

Published: March 8, 2025

Background/Objectives: Chronic obstructive pulmonary disease (COPD) is a significant public health concern, affecting millions of people worldwide. This study aims to use Surface-Enhanced Raman Scattering (SERS) technology detect the presence respiratory conditions, with focus on COPD. Methods: The samples human serum from 41 patients diseases (11 COPD, 20 bronchial asthma (BA), and 10 asthma–COPD overlap syndrome) 103 ischemic heart disease, complicated by chronic failure (CHF), were analyzed using SERS. A multivariate analysis SERS characteristics was performed Partial Least Squares Discriminant Analysis (PLS-DA) classify following groups: (1) all versus pathological referent group, which included CHF patients, (2) COPD those BA. Results: We found that combination at 638 1051 cm−1 could help identify diseases. PLS-DA model achieved mean predictive accuracy 0.92 for classifying group (0.85 sensitivity, 0.97 specificity). However, in case differentiating between BA, only 0.61. Conclusions: Therefore, metabolic proteomic composition shows differences compared but BA are less significant, suggesting similarity general pathogenetic mechanisms these two conditions.

Language: Английский

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