Understanding the effects of ethanol on the liposome bilayer structure using microfluidic-based time-resolved small-angle X-ray scattering and molecular dynamics simulations.

Lipid nanoparticles (LNPs) are essential carrier particles in drug delivery systems, particularly in ribonucleic acid delivery. In preparing lipid-based nanoparticles, microfluidic-based ethanol injection may produce precisely size-controlled nanoparticles. Ethanol is critical in LNP formation and post-treatment processes and affects liposome size, structure, lamellarity, and drug-loading efficiency. However, the effects of time-dependent changes in the ethanol concentration on the structural dynamics of liposomes are not clearly understood. Herein, we investigated ethanol-induced lipid bilayer changes in liposomes on a time scale from microseconds to tens of seconds using a microfluidic-based small-angle X-ray scattering (SAXS) measurement system coupled with molecular dynamics (MD) simulations. The time-resolved SAXS measurement system revealed that single unilamellar liposomes were converted to multilamellar liposomes within 0.8 s of contact with ethanol, and the d-spacing was decreased from 6.1 (w/o ethanol) to 4.4 nm (80% ethanol) with increasing ethanol concentration. We conducted 1 µs MD simulations to understand the molecular-level structural changes in the liposomes. The MD simulations revealed that the changes in the lamellar structure caused by ethanol at the molecular level could explain the structural changes in the liposomes observed via time-resolved SAXS. Therefore, the post-treatment process to remove residual ethanol is critical in liposome formation.

Total Synthesis of Lobatamides A and C.

The total synthesis of lobatamides A (1 a) and C (1 c) via a common bislactone intermediate is reported. The allylic aryl moiety including a trisubstituted Z-olefin was constructed by hydroboration of a 1,1-disubstituted allene and subsequent Migita-Kosugi-Stille coupling. Although the seco acid proved to be highly unstable even in the presence of weak bases, Zhao macrolactonization under acidic conditions via the α-acyloxyenamide successfully provided the common bislactone intermediate. Hydrozirconation-iodination of the terminal alkyne and subsequent copper-mediated coupling with primary amides proceeded successfully in the presence of the sensitive bislactone framework. The developed synthetic route enables the late-stage installation of enamide side chains, which are crucial structures for V-ATPase inhibition.

Production of siRNA-Loaded Lipid Nanoparticles using a Microfluidic Device.

The development of functional lipid nanoparticles (LNPs) is one of the major challenges in the field of drug delivery systems (DDS). Recently, LNP-based RNA delivery systems, namely, RNA-loaded LNPs have attracted attention for RNA therapy. In particular, mRNA-loaded LNP vaccines were approved to prevent COVID-19, thereby leading to the paradigm shift toward the development of next-generation nanomedicines. For the LNP-based nanomedicines, the LNP size is a significant factor in controlling the LNP biodistribution and LNP performance. Therefore, a precise LNP size control technique is indispensable for the LNP production process. Here, we report a protocol for size controlled LNP production using a microfluidic device, named iLiNP. siRNA loaded LNPs are also produced using the iLiNP device and evaluated by in vitro experiment. Representative results are shown for the LNP size, including siRNA-loaded LNPs, Z-potential, siRNA encapsulation efficiency, cytotoxicity, and target gene silencing activity.

Microfluidic technologies and devices for lipid nanoparticle-based RNA delivery.

In 2021, mRNA vaccines against COVID-19 were approved by the Food and Drug Administration. mRNA vaccines are important for preventing severe COVID-19 and returning to normal life. The development of RNA-delivery technology, including mRNA vaccines, has been investigated worldwide for ~30 years. Lipid nanoparticles (LNPs) are a breakthrough technology that stably delivers RNA to target organs, and RNA-loaded LNP-based nanomedicines have been studied for the development of vaccines and nanomedicines for RNA-, gene-, and cell-based therapies. Recently, microfluidic devices and technologies have attracted attention for the production of LNPs, particularly RNA-loaded LNPs. Microfluidics provides many advantages for RNA-loaded LNP production, including precise LNP size controllability, high reproducibility, high-throughput optimization of LNP formulation, and continuous LNP-production processes. In this review, we summarize microfluidic-based RNA-loaded LNP production and its applications in RNA-based therapy and genome editing.

A new chemosensor for cyanide in blood based on the Pd complex of 2-(5-bromo-2-pyridylazo)-5-[N-n-propyl-N-(3-sulfopropyl)amino]phenol.

A new indirect chemosensor for the detection of cyanide in blood is developed. 2-(5-Bromo-2-pyridylazo)-5-[N-n-propyl-N-(3-sulfopropyl)amino]phenol, a yellow dye, forms a blue-coloured complex with palladium ions. The yellow colour of this complex is regained upon reaction with cyanide ions. The complex shows high selectivity for the detection of cyanide over 16 other anions. The system was applied to two different methods for the detection of cyanide in human whole blood. As a quantitative absorbance method, blood samples were mixed with acid, and the resulting vaporised hydrogen cyanide was absorbed in an alkaline solution containing the complex in a Conway cell. The resulting absorbance response of the solution at 450 nm is linear over the range 4-40 µM (R2 = 1.000), and the limit of detection is 0.6 µM. Furthermore, the complex-soaked paper is applicable as a test strip for cyanide detection. When a test strip is used with 0.5 mL of blood, the limit of detection is 15 µM. The detection limits of these two methods are below the toxic blood cyanide concentration (19 µM). Therefore, both methods allow the quantification and screening of cyanide in blood samples. Furthermore, the test strip is low cost and enables on-site analysis.

Resting-State Functional Connectivity Estimated With Hierarchical Bayesian Diffuse Optical Tomography.

Resting-state functional connectivity (RSFC) has been generally assessed with functional magnetic resonance imaging (fMRI) thanks to its high spatial resolution. However, fMRI has several disadvantages such as high cost and low portability. In addition, fMRI may not be appropriate for people with metal or electronic implants in their bodies, with claustrophobia and who are pregnant. Diffuse optical tomography (DOT), a method of neuroimaging using functional near-infrared spectroscopy (fNIRS) to reconstruct three-dimensional brain activity images, offers a non-invasive alternative, because fNIRS as well as fMRI measures changes in deoxygenated hemoglobin concentrations and, in addition, fNIRS is free of above disadvantages. We recently proposed a hierarchical Bayesian (HB) DOT algorithm and verified its performance in terms of task-related brain responses. In this study, we attempted to evaluate the HB DOT in terms of estimating RSFC. In 20 healthy males (21-38 years old), 10 min of resting-state data was acquired with 3T MRI scanner or high-density NIRS on different days. The NIRS channels consisted of 96 long (29-mm) source-detector (SD) channels and 56 short (13-mm) SD channels, which covered bilateral frontal and parietal areas. There were one and two resting-state runs in the fMRI and fNIRS experiments, respectively. The reconstruction performances of our algorithm and the two currently prevailing algorithms for DOT were evaluated using fMRI signals as a reference. Compared with the currently prevailing algorithms, our HB algorithm showed better performances in both the similarity to fMRI data and inter-run reproducibility, in terms of estimating the RSFC.

Potential of automatic diagnosis system with linked color imaging for diagnosis of Helicobacter pylori infection.

BACKGROUND AND AIM: It is necessary to establish universal methods for endoscopic diagnosis of Helicobacter pylori (HP) infection, such as computer-aided diagnosis. In the present study, we propose a multistage diagnosis algorithm for HP infection. METHODS: The aims of this study are to: (i) to construct an interpretable automatic diagnostic system using a support vector machine for HP infection; and (ii) to compare the diagnosis capability of our artificial intelligence (AI) system with that of endoscopists. Presence of an HP infection determined through linked color imaging (LCI) was learned through machine learning. Trained classifiers automatically diagnosed HP-positive and -negative patients examined using LCI. We retrospectively analyzed the new images from 105 consecutive patients; 42 were HP positive, 46 were post-eradication, and 17 were uninfected. Five endoscopic images per case taken from different areas were read into the AI system, and used in the HP diagnosis. RESULTS: Accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the diagnosis of HP infection using the AI system were 87.6%, 90.4%, 85.7%, 80.9%, and 93.1%, respectively. Accuracy of the AI system was higher than that of an inexperienced doctor, but there was no significant difference between the diagnosis of experienced physicians and the AI system. CONCLUSIONS: The AI system can diagnose an HP infection with significant accuracy. There remains room for improvement, particularly for the diagnosis of post-eradication patients. By learning more images and considering a diagnosis algorithm for post-eradication patients, our new AI system will provide diagnostic support, particularly to inexperienced physicians.