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The authors describe a novel assay for the detection of methylated DNA site. Rolling circle amplification and CdSe/ZnS quantum dots with high fluorescence efficiency are applied in this method. The CdSe/ZnS quantum dots act as electron donors, and hemin and oxygen (derived from hydrogen peroxide act as acceptors in photoinduced electron transfer. The assay, best performed at excitation/emission peaks of 450/620 nm, is sensitive and specific. Fluorometric response is linear in the 1 pM to 100 nM DNA concentration range, and the lowest detectable concentration of methylated DNA is 142 fM (S/N = 3). The method is capable of recognizing 0.01% methylated DNA in a mixture of methylated/unmethylated DNA. Graphical abstract A novel method for methylated sites detection in DNA is established. Rolling circle amplification and photoinduced electron transfer. CdSe/ZnS quantum dots with high fluorescence efficiency act as the electron donor, while G-quadruplex/hemin and hydrogen peroxide derived oxygen act as electron acceptor. It presents a linear response towards 1 pM to 100 nM methylated DNA with a correlation coefficient of 0.9968, and the lowest detectable concentration of methylated DNA was 142 fM, with selectivity significantly superior to other methods.

A colorimetric assay is described for the detection of BCR/ABL fusion genes. Polyamidoamine (PAMAM) dendrimers were placed on peroxidase (POx) mimicking Au@Pt nanoparticles to form a nanocomposite of type Au@Pt-PAMAM. Capture DNA probe is a designed nucleic acid strand that specifically binds target DNA to the surface of the electrode. The capture probe was attached to magnetic beads via biotin and avidin interaction. The hairpin structure of the capture probe can only be opened by the complementary BCR/ABL DNA. This results in a highly specific assay. The POx-mimicking property of the Au@Pt-PAMAM causes the formation of a blue dye by reaction of HO and 3,3,3',3'-tetramethylbenzidine (TMB) which is measured by a microplate reader. Under optimum conditions, the absorbance increases linearly the 1 pM to 100 nM BCR/ABL concentration range, and the detection limit is as low as 190 fM. The method is highly selective and was successfully applied to the determination of fusion genes in spiked real samples. Conceivably, it possesses a large potential in clinical testing of patients suffering from chronic myeloid leukemia. Graphical abstract Au@PtNP, an efficient catalyst, is bound with polyamidoamine (PAMAM) dendrimer to amplify the colorimetric signal. With the introduction of streptavidin-magnetic beads to remove non-specific signals, a novel colorimetric sensor is constructed to detect BCR/ABL fusion genes.

The emerging technologies from the field of artificial intelligence (AI) have been deployed in many industries such as various online services, construction, car, and cybersecurity. Such technologies are advantageous because they can process and react to their environment or inputs in a way similar to human intelligence, giving them the capability to complete tasks that otherwise only humans can complete. Most traditionally defined sports including golf have incorporated the use of computer technologies over the past decades. However, the applications of AI technologies in golf and sports, in general, have been very limited. This paper explores the current applications, research, future research directions, and possibilities of AI specifically in golf by examining and summarizing recent studies on this topic. It was found that existing applications of AI technologies are extremely limited, are neither widely used nor endorsed and offer no definite advantage. Currently, research into applying AI technologies in golf is in its early stages, are largely independent of each other, and only focuses on a small task or a very specific aspect, for instance, swing discrepancy detection. The resulting models from these studies often only produced numerical results which demonstrate their sufficiency in their specific task, however, cannot be interpreted in useful ways. Their proficiencies in completing their designated tasks, however, demonstrate potential for a strong AI specialized in golf that essentially plays the role of or even surpasses coaches. Nevertheless, extensive research and the development of large sets of labelled.