Microcystin detection is of great significance and an urgent need because of its damage to water environments and human health. In this paper, an electrochemical aptasensor was developed by combining a 3D cobalt-based oxide modified boron and nitrogen co-doped graphene hydrogel (3D BNG/Co) with a DNA aptamer for sensitive detection of microcystin (MC-LR) through differential pulse voltammetry (DPV) technology. By using 3D BNG/Co as a catalyst and [Fe(CN)6]3-/4- as a redox probe, the catalytic current signal was 3.8 times higher than that of the bare glassy carbon electrode, which can better monitor the electron conduction on the electrode surface and then improve the sensitivity. The as-fabricated electrochemical aptasensor displayed a wide detection range (0.1-1000 pmol L-1), low detection limit (0.03 pmol L-1), good sensitivity, and repeatability, which has potential applications for the protection of the ecological environment and human health.Portable smartphone-based fluorescent microscopes are becoming popular owing to their ability to provide major functionalities offered by regular benchtop microscopes at a fraction of the cost. However, smartphone-based microscopes are still limited to a single fluorophore, fixed magnification, the inability to work with a different smartphones, and limited usability to either glass slides or cover slips. To overcome these challenges, here we present a modular smartphone-based microscopic attachment. The modular design allows the user to easily swap between different sets of filters and lenses, thereby enabling utility of multiple fluorophores and magnification levels. Our microscopic smartphone attachment can also be used with different smartphones and was tested with Nokia Lumia 1020, Samsung Galaxy S9+, and an iPhone XS. Further, we showed imaging results of samples on glass slides, cover slips, and microfluidic devices. A 1951 USAF resolution test target was used to quantify the maximum resolution of the microscope which was found to be 3.9 μm. The performance of the smartphone-based microscope was compared with a benchtop microscope and we found an R2 value of 0.99 using polystyrene beads and blood cells isolated from human blood samples collected from Robert Wood Johnson Medical Hospital. Additionally, to count the particles (cells and beads) imaged from the smartphone-based fluorescent microscope, we developed artificial neural networks (ANNs) using multiple training algorithms, and evaluated their performances compared to the control (ImageJ). Finally, we did ANOVA and Tukey's post-hoc analysis and found a p-value of 0.97 which shows that no statistical significant difference exists between the performance of the trained ANN and control (ImageJ).For highlighting the predominance of phenolic acid nutraceutical ferulic acid (FR) in regulating the in vivo/vitro performances of anticancer drug 5-fluorouracil (Flu) and strengthening their cooperativity in antitumor effect, thus achieving a major breakthrough in the development of drug-nutraceutical cocrystal with synergistic antitumor action, a cocrystallization strategy of dual optimization is created, in which both the in vivo and vitro natures of Flu are improved by exploiting the FR's excellent physicochemical property. Moreover, Flu's anticancer effects were promoted by exerting the assistant antitumor peculiarity of FR. Such dual optimization of FR for Flu in physicochemical properties and anticancer activities is beneficial for realizing synergistic augmentation effect by taking the benefit of the cooperativeness of Flu and FR in the anticancer ability. Based on this idea, a novel cocrystal of Flu and FR, namely, Flu-FR-H2O, is successfully assembled as the first 5-fluorouracil-nutraceutical cocrys strategy for Flu to optimize its physicochemical properties and antitumor activities simultaneously but also offers some opinions for the development of synergistic antitumor pharmaceutical cocrystals.An aqueous solution containing unsaturated fatty acids (100 μM) or lipids (50 μg mL-1) and chloroauric acid (HAuCl4, 10 μM) is electrosprayed (-4.5 kV for unsaturated fatty acids and +4.0 kV for lipids) from a 50 μm diameter capillary with N2 nebulizing gas (60 psi), and the resulting microdroplets enter a mass spectrometer with a flight distance of 10 mm for chemical analysis. The HAuCl4 oxidizes the C[double bond, length as m-dash]C double bond to cause the formation of an aldehyde group or a hydroxyl group on one side and a carboxyl group on the other (i.e., CHO-R-COOH or HO-R-COOH), allowing the location of the double bond to be identified. This approach was successfully applied to four unsaturated fatty acids [linoleic acid (LA), ricinoleic acid (RA), isooleic acid (IA), and nervonic acid (NA)] and two phospholipids [1,2-dioleoyl-sn-glycero-3-phosphocholine (DOPC) and L-α-lysophosphatidylcholine (lysoPC)]. A mechanism for this transformation is proposed, which involves epoxidation of the double bond, followed by the formation of the final products. check details This method has the advantages of being simple and rapid, and requiring a small amount of analyte.As with other proteins, the conformation of the silk protein is critical for determining the mechanical, optical and biological performance of materials. However, an efficient, accurate and time-efficient method for evaluating the protein conformation from Fourier transform infrared (FTIR) spectra is still desired. A set of convolutional neural network (CNN)-based deep learning models was developed in this study to identify the silk proteins and evaluate their relative content of each conformation from FTIR spectra. Compared with the conventional deconvolution algorithm, our CNN models are highly accurate and time-efficient, showing promise in processing massive FTIR data sets, such as data from FTIR imaging, and in quick analysis feedback, such as on-line and time-resolved FTIR measurements. We compiled an open-source and user-friendly graphical Python program that allows users to analyze their own FTIR data set, which can be from the silk protein or other proteins, for the encouragement and convenience of interested researchers to use the CNN models.