Single molecule fluorescence measurements of complex systems

2017 Summer.Includes bibliographical references.Single molecule methods are powerful tools for investigating the properties of complex systems that are generally concealed by ensemble measurements. Here we use single molecule fluorescent measurements to study two different complex systems: 1/ƒ noise in quantum dots and diffusion of the membrane proteins in live cells. The power spectrum of quantum dot (QD) fluorescence exhibits 1/ƒ noise, related to the intermittency of these nanosystems. As in other systems exhibiting 1/ƒ noise, this power spectrum is not integrable at low frequencies, which appears to imply infinite total power. We report measurements of individual QDs that address this long-standing paradox. We find that the level of 1/ƒβ noise for QDs decays with the observation time. We show that the traditional description of the power spectrum with a single exponent is incomplete and three additional critical exponents characterize the dependence on experimental time. A broad range of membrane proteins display anomalous diffusion on the cell surface. Different methods provide evidence for obstructed subdiffusion and diffusion on a fractal space, but the underlying structure inducing anomalous diffusion has never been visualized due to experimental challenges. We addressed this problem by imaging the cortical actin at high resolution while simultaneously tracking individual membrane proteins in live mammalian cells. Our data show that actin introduces barriers leading to compartmentalization of the plasma membrane and that membrane proteins are transiently confined within actin fences. Furthermore, superresolution imaging shows that the cortical actin is organized into a self-similar fractal

1/f noise for intermittent quantum dots exhibits non-stationarity and critical exponents

The power spectrum of quantum dot fluorescence exhibits $1/f^\beta$ noise, related to the intermittency of these nanosystems. As in other systems exhibiting $1/f$ noise, this power spectrum is not integrable at low frequencies, which appears to imply infinite total power. We report measurements of individual quantum dots that address this long-standing paradox. We find that the level of $1/f^\beta$ noise decays with the observation time. The change of the spectrum with time places a bound on the total power. These observations are in stark contrast with most measurements of noise in macroscopic systems which do not exhibit any evidence for non-stationarity. We show that the traditional description of the power spectrum with a single exponent $\beta$ is incomplete and three additional critical exponents characterize the dependence on experimental time.Comment: 16 pages, 4 figure

Survey of Expression of Aflatoxin Production Regulator Gene (aflR) in Aspergillus Parasiticus by Alpinia Galanga L and Dorema Aucheri

Background: Aflatoxins are one of the highly toxic secondary metabolites, which are mainly produced by Aspergillus parasiticus. This species frequently cause of food and agricultural products contamination including cereals, peanuts, and crops in the field. During recent years, researchers have considered research on elimination of aflatoxin and antifungal effects of medicinal herbals, such as Alpinia galanga L and Dorema aucheri. In this study, the effect of A.galanga L and D.aucheri a natural compound was examined on Aspergillus parasiticus growth, aflatoxins production and the aflR gene expression.Materials and Methods: Antifungal susceptibility A.galanga L and D.aucheri was performed according to CLSI document M38-A2. Quantitative changes in aflR gene level of expression were analyzed by Real-time PCR method.Results: Our result obtained that the MIC of extracts on A. parasiticus growth 250 mg/mL for D.aucheri and 800 mg/mL for A.galanga L. D.aucheri has antitoxic properties as well as its effective ability to decrease aflatoxin production. The level of aflR gene expression was decreased significantly after the exposure of fungal cell to D.aucheri extract, but A.galanga L didn’t have significant effect.Conclusion: This research indicated that D.aucheri has antifungal effects more than A.galanga L. Due to our obtained result we can suggest that D.aucheri herbal extract may have antifungal potential in medicine or agriculture

Association of Anthropometric Indexes With Disease Severity in Male Patients With Chronic Obstructive Pulmonary Disease in Qazvin, Iran

Malnutrition is one of the most important factors that lead to lower quality of life in patients suffering from chronic obstructive pulmonary disease (COPD). There are several methods for assessing malnutrition including anthropometric indexes. The aim of this study was to determine the association of anthropometric indexes with disease severity in male patients with COPD in Qazvin, Iran. This cross-sectional study was conducted on 72 male patients with COPD in Qazvin, Iran, from May to December 2014. Spirometry was performed for all participants. Disease severity was determined using the Global Initiative for Chronic Obstructive Lung Disease (GOLD) guideline. Body mass index (BMI), mid-arm muscle circumference (MAMC), and triceps skinfold thickness (TSF) were measured. MAMC and TSF were categorized into three subgroups as 75th P (Where P is the abbreviation for percentile.). Data were analyzed using ANOVA and logistic regression analysis. Mean age was 60.23 ± 11.39 years. Mean BMI was 23.23 ± 4.42 Kg/m2 , mean MAMC was 28.34 ± 3.72 cm2 , and mean TSF was 10.15 ± 6.03 mm. Mean BMI and MAMC in the GOLD stage IV were significantly lower than other stages. Of 72, 18.1% were underweight while 6.9% were obese. The GOLD stage IV was associated with 16 times increased risk of underweight and nine times increased risk of MAMC < 25th P. Disease severity was associated with BMI and MAMC as indexes of malnutrition in patients with COPD in the present study. The GOLD stage IV was associated with increased risk of underweight and low MAMC. Keywords Chronic obstructive pulmonary disease, malnutrition, body mass index, thinness, obesit

Efficient non-degenerate two-photon excitation for fluorescence microscopy

Non-degenerate two-photon excitation (ND-TPE) has been explored in two-photon excitation microscopy. However, a systematic study of the efficiency of ND-TPE to guide the selection of fluorophore excitation wavelengths is missing. We measured the relative non-degenerate two-photon absorption cross-section (ND-TPACS) of several commonly used fluorophores (two fluorescent proteins and three small-molecule dyes) and generated 2-dimensional ND-TPACS spectra. We observed that the shape of a ND-TPACS spectrum follows that of the corresponding degenerate two-photon absorption cross-section (D-TPACS) spectrum, but is higher in magnitude. We found that the observed enhancements are higher than theoretical predictions.Published versio

Precision Diagnostics in Cardiac Tumours:Integrating Echocardiography and Pathology with Advanced Machine Learning on Limited Data

This study pioneers the integration of echocardiography and pathology data with advanced machine learning (ML) techniques to significantly enhance the diagnostic accuracy of cardiac tumours, a critical yet challenging aspect of cardiology. Despite advancements in diagnostic methods, cardiac tumours' nuanced complexity and rarity necessitate more precise, non-invasive, and efficient diagnostic solutions. Our research aims to bridge this gap by developing and validating ML models—Support Vector Machines (SVM), Random Forest (RF), and Gradient Boosting Machines (GBM)—optimized for limited datasets prevalent in specialized medical fields. Utilizing a dataset comprising clinical features from 399 patients at the Heart Hospital, our study meticulously evaluated the performance of these models against traditional diagnostic metrics. The RF model emerged superior, achieving a groundbreaking accuracy of 96.25% and a perfect ROC AUC score of 0.99, significantly outperforming existing diagnostic approaches. Key predictors identified include age, echo malignancy, and echo position, underscoring the value of integrating diverse data types. Clinical validation conducted at the Heart Hospital further confirmed the models' applicability and reliability, with the RF model demonstrating a diagnostic accuracy of 94% in a real-world setting. These findings advocate for the potential of ML in revolutionizing cardiac tumour diagnostics, offering pathways to more accurate, non-invasive, and patient-centric diagnostic processes. This research not only highlights the capabilities of ML to enhance diagnostic precision in the realm of cardiac tumours but also sets a foundation for future explorations into its broader applicability across various domains of medical diagnostics, emphasizing the need for expanded datasets and external validation

Precision Diagnostics in Cardiac Tumours:Integrating Echocardiography and Pathology with Advanced Machine Learning on Limited Data

This study pioneers the integration of echocardiography and pathology data with advanced machine learning (ML) techniques to significantly enhance the diagnostic accuracy of cardiac tumours, a critical yet challenging aspect of cardiology. Despite advancements in diagnostic methods, cardiac tumours' nuanced complexity and rarity necessitate more precise, non-invasive, and efficient diagnostic solutions. Our research aims to bridge this gap by developing and validating ML models—Support Vector Machines (SVM), Random Forest (RF), and Gradient Boosting Machines (GBM)—optimized for limited datasets prevalent in specialized medical fields. Utilizing a dataset comprising clinical features from 399 patients at the Heart Hospital, our study meticulously evaluated the performance of these models against traditional diagnostic metrics. The RF model emerged superior, achieving a groundbreaking accuracy of 96.25% and a perfect ROC AUC score of 0.99, significantly outperforming existing diagnostic approaches. Key predictors identified include age, echo malignancy, and echo position, underscoring the value of integrating diverse data types. Clinical validation conducted at the Heart Hospital further confirmed the models' applicability and reliability, with the RF model demonstrating a diagnostic accuracy of 94% in a real-world setting. These findings advocate for the potential of ML in revolutionizing cardiac tumour diagnostics, offering pathways to more accurate, non-invasive, and patient-centric diagnostic processes. This research not only highlights the capabilities of ML to enhance diagnostic precision in the realm of cardiac tumours but also sets a foundation for future explorations into its broader applicability across various domains of medical diagnostics, emphasizing the need for expanded datasets and external validation

Precision diagnostics in cardiac tumours: Integrating echocardiography and pathology with advanced machine learning on limited data

This study pioneers the integration of echocardiography and pathology data with advanced machine learning (ML) techniques to significantly enhance the diagnostic accuracy of cardiac tumours, a critical yet challenging aspect of cardiology. Despite advancements in diagnostic methods, cardiac tumours' nuanced complexity and rarity necessitate more precise, non-invasive, and efficient diagnostic solutions. Our research aims to bridge this gap by developing and validating ML models—Support Vector Machines (SVM), Random Forest (RF), and Gradient Boosting Machines (GBM)—optimized for limited datasets prevalent in specialized medical fields. Utilizing a dataset comprising clinical features from 399 patients at the Heart Hospital, our study meticulously evaluated the performance of these models against traditional diagnostic metrics. The RF model emerged superior, achieving a groundbreaking accuracy of 96.25 % and a perfect ROC AUC score of 0.99, significantly outperforming existing diagnostic approaches. Key predictors identified include age, echo malignancy, and echo position, underscoring the value of integrating diverse data types. Clinical validation conducted at the Heart Hospital further confirmed the models' applicability and reliability, with the RF model demonstrating a diagnostic accuracy of 94 % in a real-world setting. These findings advocate for the potential of ML in revolutionizing cardiac tumour diagnostics, offering pathways to more accurate, non-invasive, and patient-centric diagnostic processes. This research not only highlights the capabilities of ML to enhance diagnostic precision in the realm of cardiac tumours but also sets a foundation for future explorations into its broader applicability across various domains of medical diagnostics, emphasizing the need for expanded datasets and external validation