Automated sample preparation for electrospray ionization mass spectrometry based on CLOCK-controlled autonomous centrifugal microfluidics.

We report a centrifugal microfluidic device that automatically performs sample preparation under steady-state rotation for clinical applications using mass spectrometry. The autonomous microfluidic device was designed for the control of liquid operation on centrifugal hydrokinetics (CLOCK) paradigm. The reported device was highly stable, with less than 7% variation with respect to the time of each unit operation (sample extraction, mixing, and supernatant extraction) in the preparation process. An agitation mechanism with bubbling was used to mix the sample and organic solvent in this device. We confirmed that the device effectively removed the protein aggregates from the sample, and the performance was comparable to those of conventional manual sample preparation procedures that use high-speed centrifugation. In addition, probe electrospray ionization mass spectrometry (PESI-MS) was performed to compare the device-treated and manually treated samples. The obtained PESI-MS spectra were analyzed by partial least squares discriminant analysis, and the preparation capability of the device was found to be equivalent to that of the conventional method.

ADAMTS4 is involved in the production of the Alzheimer disease amyloid biomarker APP669-711.

Amyloid-ß (Aß) deposition in the brain parenchyma is one of the pathological hallmarks of Alzheimer disease (AD). We have previously identified amyloid precursor protein (APP)669-711 (a.k.a. Aß(-3)-40) in human plasma using immunoprecipitation combined with matrix-assisted laser desorption ionization time-of-flight mass spectrometry (IP-MALDI-MS). Furthermore, we found that the level of a composite biomarker, i.e., a combination of APP669-711/Aß1-42 ratio and Aß1-40/Aß1-42 ratio in human plasma, correlates with the amyloid PET status of AD patients. However, the production mechanism of APP669-711 has remained unclear. Using in vitro and in vivo assays, we identified A Disintegrin and Metalloproteinase with a Thrombospondin type 1 motif, type 4 (ADAMTS4) as a responsible enzyme for APP669-711 production. ADAMTS4 cleaves APP directly to generate the C-terminal stub c102, which is subsequently proteolyzed by γ-secretase to release APP669-711. Genetic knockout of ADAMTS4 reduced the production of endogenous APP669-711 by 30% to 40% in cultured cells as well as mouse plasma, irrespectively of Aß levels. Finally, we found that the endogenous murine APP669-711/Aß1-42 ratio was increased in aged AD model mice, which shows Aß deposition as observed in human patients. These data suggest that ADAMTS4 is involved in the production of APP669-711, and a plasma biomarker determined by IP-MALDI-MS can be used to estimate the level of Aß deposition in the brain of mouse models.

An Automated, Clip-Type, Small Internet of Things Camera-Based Tomato Flower and Fruit Monitoring and Harvest Prediction System.

Automated crop monitoring using image analysis is commonly used in horticulture. Image-processing technologies have been used in several studies to monitor growth, determine harvest time, and estimate yield. However, accurate monitoring of flowers and fruits in addition to tracking their movements is difficult because of their location on an individual plant among a cluster of plants. In this study, an automated clip-type Internet of Things (IoT) camera-based growth monitoring and harvest date prediction system was proposed and designed for tomato cultivation. Multiple clip-type IoT cameras were installed on trusses inside a greenhouse, and the growth of tomato flowers and fruits was monitored using deep learning-based blooming flower and immature fruit detection. In addition, the harvest date was calculated using these data and temperatures inside the greenhouse. Our system was tested over three months. Harvest dates measured using our system were comparable with the data manually recorded. These results suggest that the system could accurately detect anthesis, number of immature fruits, and predict the harvest date within an error range of ±2.03 days in tomato plants. This system can be used to support crop growth management in greenhouses.

Aqueous counter collision using paired water jets as a novel means of preparing bio-nanofibers.

This study involved a detailed investigation of a novel approach to reducing naturally occurring cellulose fibers into nanofibers solely by the use of aqueous counter collision (ACC) without any chemical modification. In this process, equivalent aqueous suspensions of cellulose are ejected from dual nozzles and collide at high speed and pressure. Even a few repetitions of the collision process are sufficient to produce nano-sized fibers dispersed in water. This work compared the ACC nano-pulverization of stable Iß-rich and meta-stable Iα-rich cellulose samples. The ACC method is applicable to various kinds of polymeric materials with hierarchical structures, either natural or synthetic, as a means of preparing aqueous dispersions of nano-sized structures.