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Circular dichroism (CD) is a useful technique for an assessment of DNA-binding mode, being a more accessible, low-resolution complement to NMR and X-ray diffraction methods. Ligand–DNA interactions can be studied by virtue of the interpretation of induced ligand CD signals resulting from the coupling of electric transition moments of the ligand and DNA bases within the asymmetric DNA environment. This protocol outlines methods to determine the binding mode and affinity of ligand–DNA interactions and takes approximately 7.5 h.

DSC is used to determine thermally-induced conformational changes of biomolecules within a blood plasma sample. Recent research has indicated that DSC curves (or thermograms) may have different characteristics based on disease status and, thus, may be useful as a monitoring and diagnostic tool for some diseases. Since thermograms are curves measured over a range of temperature values, they are considered functional data. In this paper we apply functional data analysis techniques to analyze differential scanning calorimetry (DSC) data from individuals from the Lupus Family Registry and Repository (LFRR). The aim was to assess the effect of lupus disease status as well as additional covariates on the thermogram profiles, and use FD analysis methods to create models for classifying lupus vs. control patients on the basis of the thermogram curves. Thermograms were collected for 300 lupus patients and 300 controls without lupus who were matched with diseased individuals based on sex, race, and age. First, functional regression with a functional response (DSC) and categorical predictor (disease status) was used to determine how thermogram curve structure varied according to disease status and other covariates including sex, race, and year of birth. Next, functional logistic regression with disease status as the response and functional principal component analysis (FPCA) scores as the predictors was used to model the effect of thermogram structure on disease status prediction. The prediction accuracy for patients with Osteoarthritis and Rheumatoid Arthritis but without Lupus was also calculated to determine the ability of the classifier to differentiate between Lupus and other diseases. Data were divided 1000 times into separate 2/3 training and 1/3 test data for evaluation of predictions. Finally, derivatives of thermogram curves were included in the models to determine whether they aided in prediction of disease status. Functional regression with thermogram as a functional response and disease status as predictor showed a clear separation in thermogram curve structure between cases and controls. The logistic regression model with FPCA scores as the predictors gave the most accurate results with a mean 79.22% correct classification rate with a mean sensitivity = 79.70%, and specificity = 81.48%. The model correctly classified OA and RA patients without Lupus as controls at a rate of 75.92% on average with a mean sensitivity = 79.70% and specificity = 77.6%. Regression models including FPCA scores for derivative curves did not perform as well, nor did regression models including covariates. Changes in thermograms observed in the disease state likely reflect covalent modifications of plasma proteins or changes in large protein-protein interacting networks resulting in the stabilization of plasma proteins towards thermal denaturation. By relating functional principal components from thermograms to disease status, our Functional Principal Component Analysis model provides results that are more easily interpretable compared to prior studies. Further, the model could also potentially be coupled with other biomarkers to improve diagnostic classification for lupus.

Improved methods for the accurate identification of both the presence and severity of cervical intraepithelial neoplasia (CIN) and extent of spread of invasive carcinomas of the cervix (IC) are needed. Differential scanning calorimetry (DSC) has recently been shown to detect specific changes in the thermal behavior of blood plasma proteins in several diseases. This methodology is being explored to provide a complementary approach for screening of cervical disease. The present study evaluated the utility of DSC in differentiating between healthy controls, increasing severity of CIN and early and advanced IC. Significant discrimination was apparent relative to the extent of disease with no clear effect of demographic factors such as age, ethnicity, smoking status and parity. Of most clinical relevance, there was strong differentiation of CIN from healthy controls and IC, and amongst patients with IC between FIGO Stage I and advanced cancer. The observed disease-specific changes in DSC profiles (thermograms) were hypothesized to reflect differential expression of disease biomarkers that subsequently bound to and affected the thermal behavior of the most abundant plasma proteins. The effect of interacting biomarkers can be inferred from the modulation of thermograms but cannot be directly identified by DSC. To investigate the nature of the proposed interactions, mass spectrometry (MS) analyses were employed. Quantitative assessment of the low molecular weight protein fragments of plasma and urine samples revealed a small list of peptides whose abundance was correlated with the extent of cervical disease, with the most striking plasma peptidome data supporting the interactome theory of peptide portioning to abundant plasma proteins. The combined DSC and MS approach in this study was successful in identifying unique biomarker signatures for cervical cancer and demonstrated the utility of DSC plasma profiles as a complementary diagnostic tool to evaluate cervical cancer health.

The thermoanalytical technique differential scanning calorimetry (DSC) has been applied to characterize protein denaturation patterns (thermograms) in blood plasma samples and relate these to a subject's health status. The analysis and classification of thermograms is challenging because of the high-dimensionality of the dataset. There are various methods for group classification using high-dimensional data sets; however, the impact of using high-dimensional data sets for cancer classification has been poorly understood. In the present article, we proposed a statistical approach for data reduction and a parametric method (PM) for modeling of high-dimensional data sets for two- and three- group classification using DSC and demographic data. We compared the PM to the non-parametric classification method K-nearest neighbors (KNN) and the semi-parametric classification method KNN with dynamic time warping (DTW). We evaluated the performance of these methods for multiple two-group classifications: (i) normal versus cervical cancer, (ii) normal versus lung cancer, (iii) normal versus cancer (cervical + lung), (iv) lung cancer versus cervical cancer as well as for three-group classification: normal versus cervical cancer versus lung cancer. In general, performance for two-group classification was high whereas three-group classification was more challenging, with all three methods predicting normal samples more accurately than cancer samples. Moreover, specificity of the PM method was mostly higher or the same as KNN and DTW-KNN with lower sensitivity. The performance of KNN and DTW-KNN decreased with the inclusion of demographic data, whereas similar performance was observed for the PM which could be explained by the fact that the PM uses fewer parameters as compared to KNN and DTW-KNN methods and is thus less susceptible to the risk of overfitting. More importantly the accuracy of the PM can be increased by using a greater number of quantile data points and by the inclusion of additional demographic and clinical data, providing a substantial advantage over KNN and DTW-KNN methods.

Plasma thermograms (thermal stability profiles of blood plasma) are being utilized as a new diagnostic approach for clinical assessment. In this study, we investigated the ability of plasma thermograms to classify systemic lupus erythematosus (SLE) patients versus non SLE controls using a sample of 300 SLE and 300 control subjects from the Lupus Family Registry and Repository. Additionally, we evaluated the heterogeneity of thermograms along age, sex, ethnicity, concurrent health conditions and SLE diagnostic criteria. Thermograms were visualized graphically for important differences between covariates and summarized using various measures. A modified linear discriminant analysis was used to segregate SLE versus control subjects on the basis of the thermograms. Classification accuracy was measured based on multiple training/test splits of the data and compared to classification based on SLE serological markers. Median sensitivity, specificity, and overall accuracy based on classification using plasma thermograms was 86%, 83%, and 84% compared to 78%, 95%, and 86% based on a combination of five antibody tests. Combining thermogram and serology information together improved sensitivity from 78% to 86% and overall accuracy from 86% to 89% relative to serology alone. Predictive accuracy of thermograms for distinguishing SLE and osteoarthritis / rheumatoid arthritis patients was comparable. Both gender and anemia significantly interacted with disease status for plasma thermograms (p<0.001), with greater separation between SLE and control thermograms for females relative to males and for patients with anemia relative to patients without anemia. Plasma thermograms constitute an additional biomarker which may help improve diagnosis of SLE patients, particularly when coupled with standard diagnostic testing. Differences in thermograms according to patient sex, ethnicity, clinical and environmental factors are important considerations for application of thermograms in a clinical setting.

Multispectral optoacoustic tomography （MSOT） is an emerging imaging technology that offers several advantages over traditional modalities, particularly in its ability to resolve optical contrast at depth on the microscopic scale. While potential applications include the early detection of tumors below clinical thresholds set by current technology, the lack of tumor-specific contrast agents limits the use of MSOT imaging. Therefore, we constructed highly stable nano-contrast agents by coating gold nanorods （GNRs） with either polyacrylic acid （PAA） or amine- functionalized mesoporous silica （MS）. Syndecan-1, which has been shown to target insulin-like growth factor 1 receptor （IGF1-R） （upregulated in pancreatic tumors）, was conjugated on the surface of PAA-coated GNRs （PAA-GNRs） or MS-coated GNRs （MS-GNRs） to create tumor-targeted nanoparticles. In vitro, tumor targeting of nanoparticles was assessed with flow cytometry. In S2VP10L cells （positive for IGF1-R）, the syndecan-1 MS-GNRs （Syndecan-MS-GNRs） demonstrated an increase in OA signal, 10x, compared to syndecan-1 PAA- GNRs （Syndecan-PAA-GNRs）. Minimal binding was observed in MiaPaca-2 cells （negative for IGF1-R）. In vivo, tumor specific targeting of Syndecan-MS-GNRs was evaluated using a murine orthotopic pancreatic cancer model. The Syndecan- MS-GNRs demonstrated significantly greater accumulation within pancreatic tumors than in off-target organs such as the liver. Mice implanted with the IGF1-R negative MiaPaca-2 cells did not demonstrate specific tumor targeting. In summary, we report that targeted nano-contrast agents （Syndecan-MS-GNRs） can successfully detect orthotopic pancreatic tumors with minimum off-target binding in vivo using MSOT.

Background/Objectives: Differential scanning calorimetry (DSC) analysis of blood plasma, also known as thermal liquid biopsy (TLB), is a promising approach for disease detection and monitoring; however, its wider adoption in clinical settings has been hindered by labor-intensive data processing workflows, particularly baseline correction. Methods: We developed and tested two automated algorithms to address critical bottlenecks in TLB analysis: (1) a baseline-correction algorithm utilizing rolling-variance analysis for endpoint detection, and (2) a signal-detection algorithm that applies auto-regressive integrated moving average (ARIMA)-based stationarity testing to determine whether a profile contains interpretable thermal features. Both algorithms are implemented in ThermogramForge, an open-source R Shiny web application providing an end-to-end workflow for data upload, processing, and report generation. Results: The baseline-correction algorithm demonstrated excellent performance on plasma TLB data (characterized by high heat capacity), matching the quality of rigorous manual processing. However, its performance was less robust for low signal biofluids, such as urine, where weak thermal transitions reduce the reliability of baseline estimation. To address this, a complementary signal-detection algorithm was developed to screen for TLB profiles with discernable thermal transitions prior to baseline correction, enabling users to exclude non-informative data. The signal-detection algorithm achieved near-perfect classification accuracy for TLB profiles with well-defined thermal transitions and maintained a low false-positive rate of 3.1% for true noise profiles, with expected lower performance for borderline cases. The interactive review interface in ThermogramForge further supports quality control and expert refinement. Conclusions: The automated baseline-correction and signal-detection algorithms, together with their web-based implementation, substantially reduce analysis time while maintaining quality, supporting more efficient and reproducible TLB research.

Background: Over the past decade, saliva-based liquid biopsies have emerged as promising tools for the early diagnosis, prognosis, and monitoring of cancer, particularly in high-risk populations. However, challenges persist because of low concentrations and variable modifications of biomarkers linked to tumor development when compared to normal salivary components. Methods: This study explores the application of differential scanning calorimetry (DSC)-based thermal liquid biopsy (TLB) for analyzing saliva and blood plasma samples from head and neck cancer (HNC) patients. Results: Our research identified an effective saliva processing method via high-speed centrifugation and ultrafiltration, resulting in reliable TLB data. Notably, we recorded unique TLB profiles for saliva from 48 HNC patients and 21 controls, revealing distinct differences in thermal transition features that corresponded to salivary protein denaturation. These results indicated the potential of saliva TLB profiles in differentiating healthy individuals from HNC patients and identifying tumor characteristics. In contrast, TLB profiles for blood plasma samples exhibited smaller differences between HNC patients and had less utility for differentiation within HNC. Conclusions: Our findings support the feasibility of saliva-based TLB for HNC diagnostics, with further refinement in sample collection and the incorporation of additional patient variables anticipated to enhance accuracy, ultimately advancing non-invasive diagnostic strategies for HNC detection and monitoring.

Differential scanning calorimetry provides a new window into the plasma proteome. Plasma from normal individuals yields a characteristic, reproducible thermogram that appears to represent the weighted sum of denaturation profiles of the most abundant constituent plasma proteins. Plasma from diseased individuals yields dramatically different signature thermograms. Thermograms from individuals suffering from rheumatoid arthritis, systemic lupus, and Lyme disease were measured. Each disease appears to have a distinctive and characteristic thermogram. The difference in thermograms between normal and diseased individuals is not caused by radical changes in the concentrations of the most abundant plasma proteins but rather appears to result from interaction of as yet unknown biomarkers with the major plasma proteins. These results signal a novel use for calorimetry as a diagnostic tool.

Early detection of lung cancer (LC) significantly increases the likelihood of successful treatment and improves LC survival rates. Currently, screening (mainly low-dose CT scans) is recommended for individuals at high risk. However, the recent increase in the number of LC cases unrelated to the well-known risk factors, and the high false-positive rate of low-dose CT, indicate a need to develop new, non-invasive methods for LC detection. Therefore, we evaluated the use of differential scanning calorimetry (DSC) for LC patients’ diagnosis and predicted survival. Additionally, by applying mass spectrometry, we investigated whether changes in O- and N-glycosylation of plasma proteins could be an underlying mechanism responsible for observed differences in DSC curves of LC and control subjects. Our results indicate selected DSC curve features could be useful for differentiation of LC patients from controls with some capable of distinction between subtypes and stages of LC. DSC curve features also correlate with LC patients’ overall/progression free survival. Moreover, the development of classification models combining patients’ DSC curves with selected plasma protein glycosylation levels that changed in the presence of LC could improve the sensitivity and specificity of the detection of LC. With further optimization and development of the classification method, DSC could provide an accurate, non-invasive, radiation-free strategy for LC screening and diagnosis.