Notes

Speedy high-intensity light-curing of bulk-fill compounds: A quantitative analysis associated with minor integrity.
This research study highlights the catalytic usage of hetero atoms doped and undoped biogenic carbon nano dots (BCNDs) in the reduction of Alizarine yellow R (AYR) dye. selleck chemicals Hydrothermal route was followed to synthesize the eco-friendly and fluorescent undoped as well as, N, B & S doped BCNDs from Syzygium cumini (S. cumini) fruit extract. Synthesized BCNDs exhibited good fluorescent and optical properties. From the HR-TEM results, the sizes of the spherically shaped undoped, N, B & S doped BCNDs were found to be 4.75 nm, 4.31 nm, 4.07 nm & 3.96 nm respectively. XRD results highlighted their amorphous nature. Functional groups and elemental percentages were elucidated from the results of FT-IR, EDS and XPS. Graphitic texture of the BCNDs were explained from Raman spectroscopy results and SAED. Thermal stability of BCNDs was evident from the results of TGA analysis. Further, BCNDs were used as green catalyst in the reduction of Alizarine Yellow R (AYR) dye. Langmuir- Hinshelwood mechanism was applied to evaluate the catalytic influence of BCNDs on AYR dye.Cumin (Cuminum cyminum) and fennel (Foeniculum vulgare) are widely used seasonings and play a very important role in industries such as breeding, cosmetics, winemaking, drug discovery, and nano-synthetic materials. However, studies have shown that cumin and fennel from different regions not only differ greatly in the content of lipids, phenols and proteins but also the substances contained in their essential oils are also different. Therefore, realizing precise identification of cumin and fennel from different regions will greatly help in quality control, market fraud and production industrialization. In this experiment, cumin and fennel samples were collected from each region, a total of 480 NIR spectra were collected. We used deep learning and traditional machine learning algorithms combined with near infrared (NIR) spectroscopy to identify their origin. To obtain the model with the best generalization performance and classification accuracy, we used principal component analysis (PCA) to reduce spectral datrch and medical diagnosis in the future.GdNbTiO6 Sm3+ phosphors with various Sm3+ concentrations were prepared via a high temperature solid-state reaction method. The crystal structure of the samples was characterized by means of X-ray diffraction (XRD) and the as-prepared samples were confirmed to be orthorhombic phase GdNbTiO6. Photoluminescence properties were investigated by measuring the concentration- and temperature-dependent photoluminescence spectra. Concentration-dependent luminescence quenching and luminescent thermal quenching behaviors were observed and they were respectively ascribed to the electric dipole-dipole interaction between Sm3+ ions and the cooperation of energy transfer and crossover process. The chromatic characteristics were found to be dependent on the excitation wavelength and Sm3+ concentration. In addition, temperature-induced redshift of charge transfer band of GdNbTiO6 host was found in temperature-dependent excitation spectra and the opposite variations of different excitation peaks were utilized for optical thermometry. Finally, the optical transition property was studied on the basis of the diffuse reflectance spectra and Judd-Ofelt (J-O) theory, meanwhile, its accuracy was evaluated by the result of emission spectra.The World Health Organization (WHO) grade diagnosis of cancer is essential for surgical outcomes and patient treatment. Traditional pathological grading diagnosis depends on dyes or other histological approaches, and the result interpretation highly relies on the pathologists, making the process time-consuming (>60 min, including the steps of dewaxing to water and H&E staining), resource-wasting, and labor-intensive. In the present study, we report an alternative workflow that combines the Fourier transform infrared (FTIR) microscopy and artificial neural network (ANN) to diagnose the grade of human glioma in a way that is faster (~20 min, including the processes of sample dewaxing, spectra acquisition and analysis), accurate (the prediction accuracy, specificity and sensitivity can reach above 99%), and without reagent. Moreover, this method is much superior to the common classification method of principal component analysis-linear discriminate analysis (PCA-LDA) (the prediction accuracy, specificity and sensitivity are only 87%, 89% and 86%, respectively). The ANN mainly learned the characteristic region of 800-1800 cm-1 to classify the major histopathologic classes of human glioma. These results demonstrate that the grade diagnosis of human glioma by FTIR microscopy plus ANN can be streamlined, and could serve as a complementary pathway that is independent of the traditional pathology laboratory.Acute myeloid leukemia (AML) is a common acute leukemia in both adults and children, with poor early detection and diagnosis. Therefore, identifying new indicators for AML detection is significant for effective treatment. Here, we developed a supramolecular probe that exhibits high specificity and sensitivity to G-quadruplex structures in physiological buffer solution, chromosomes, and cells. Using this probe, we tested the DNA extracted from different types of cells and found that the DNA extracted from human acute myeloid leukemia cells HL-60 and KG-1 enhanced the probe fluorescence more significantly than the DNA extracted from other cells. This phenomenon may be related to a large number of G-quadruplexes in acute myeloid leukemia cells, implicating that G-quadruplex levels may be a potential indicator for the detection of acute myeloid leukemia.
One of the most important steps in combating breast cancer is early and accurate diagnosis. Unfortunately, breast cancer is asymptomatic at the early stage, although some symptoms are presented at a later time, but at symptomatic stage treatment could be complicated or even become impossible thereby leading to death. Proper risk assessment is hence very important in reducing mortality. Some computational techniques have been developed for breast cancer risk assessment in the developed world, but such techniques do not work well in Africa because of the difference in risk profiles of African women e.g. later menarche, low drug abuse and low smoking rate.

In this work, we propose a bespoke risk prediction model for African women using Random Forest Classifier (RFC) machine learning technique.

A total of 180 subjects were studied out of which 90 were confirmed cases of breast cancer and 90 were benign. Twenty-five risk factors were included, for example, smoking, alcohol intake, occupational hazards and age at menopause.
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