Lifestyle parameters of Japanese agricultural and non-agricultural workers aged 60 years or older and less than 60 years: A cross-sectional observational study.

OBJECTIVES: Improving the lifestyle of occupational workers is essential for extending healthy life expectancy. We investigated various lifestyle-related items in a rural Japanese population and compared them between agricultural and non-agricultural workers. METHODS: This cross-sectional study was conducted as a part of the "Iwaki Health Promotion Project." Lifestyle-related items such as sleep, work hours, nutrition, health-related quality of life, and proportion of time spent performing each daily activity were compared between agricultural and non-agricultural workers in the ≥60 years (n = 251) and <60 years (n = 560) age groups. RESULTS: Agricultural workers had significantly lower Pittsburgh Sleep Quality Index total scores than non-agricultural workers in the <60 years group. The proportion of participants with more than 5 weekly working days was high among agricultural workers in both groups. Additionally, the proportion of people who worked more than 8 h per day was high among agricultural workers in both age groups. Energy intake per day was high among agricultural workers in the <60 years group. In both age groups, agricultural workers slept and woke up approximately 40 min earlier than did non-agricultural workers. CONCLUSIONS: Agricultural workers have better sleep habits but work longer than non-agricultural workers, with some differences in energy intake and proportion of time spent on each daily activity. These differences should be considered when planning lifestyle intervention programs for agricultural workers.

Enhancement of protein thermostability by three consecutive mutations using loop-walking method and machine learning.

We developed a method to improve protein thermostability, "loop-walking method". Three consecutive positions in 12 loops of Burkholderia cepacia lipase were subjected to random mutagenesis to make 12 libraries. Screening allowed us to identify L7 as a hot-spot loop having an impact on thermostability, and the P233G/L234E/V235M mutant was found from 214 variants in the L7 library. Although a more excellent mutant might be discovered by screening all the 8000 P233X/L234X/V235X mutants, it was difficult to assay all of them. We therefore employed machine learning. Using thermostability data of the 214 mutants, a computational discrimination model was constructed to predict thermostability potentials. Among 7786 combinations ranked in silico, 20 promising candidates were selected and assayed. The P233D/L234P/V235S mutant retained 66% activity after heat treatment at 60 °C for 30 min, which was higher than those of the wild-type enzyme (5%) and the P233G/L234E/V235M mutant (35%).

Machine Learning-Based Amino Acid Substitution of Short Peptides: Acquisition of Peptides with Enhanced Inhibitory Activities against α-Amylase and α-Glucosidase.

We developed a method for efficiently activating functional peptides with a large structural contribution using the peptide-searching method with machine learning. The physicochemical properties of the amino acids were employed as variables. As a model peptide, we used GHWYYRCW, which is a functional peptide that inhibits α-amylase derived from human pancreatic juice. First, training data were acquired. A total of 153 peptides were prepared in which 1 amino acid in GHWYYRCW was replaced to construct a 1-amino acid substitution coverage peptide library. The inhibitory activity of each peptide against α-amylase and α-glucosidase was evaluated. Second, random forest (RF) regression analysis was performed using 120 variables, and the enzyme inhibitory activity of the peptide was related to the physicochemical properties. The constructed model had many features describing the charge of the amino acid (isoelectric point and pK2). Then, high inhibitory (HI) peptides were predicted using a library of peptides with 2- or 3-amino acid substitution as test data, which were called HI2 and HI3 peptides. As results, the first or seventh amino acid of the HI2 peptide was replaced with Arg, Trp, or Tyr. We found that all 30 HI2 peptides had significantly higher activity than the original sequence (100%) and 26 of the 30 HI3 peptides were significantly active (86.7%). However, the actual inhibitory activity of the HI3 peptides was improved to a lesser extent. The docking simulation clarified that the CDOCKER energy decrease was roughly correlated with the inhibitory activity. The machine learning-based predictive model was a promising tool for design of substituted peptides with high activity values, and it was assumed that the advanced model that forecasts the interaction index such as the CDOCKER energy substituting for the inhibitory activity would be used to design HI peptides, even in the case of the HI3 peptides.

Novel cannabis flavonoid, cannflavin A displays both a hormetic and neuroprotective profile against amyloid ß-mediated neurotoxicity in PC12 cells: Comparison with geranylated flavonoids, mimulone and diplacone.

BACKGROUND: Flavonoids form a diverse class of naturally occurring polyphenols ascribed various biological activities, including inhibition of amyloid ß (Aß) fibrillisation and neurotoxicity of relevance to Alzheimer's disease. Cannabis contains a unique subset of prenylated flavonoids, the cannflavins. While selected conventional flavonoids have demonstrated anti-amyloid and neuroprotective potential, any neuroprotective bioactivity of prenylated flavonoids has not been determined. We evaluated the in vitro neuroprotective and anti-aggregative properties of the novel geranylated cannabis-derived flavonoid, cannflavin A against Aß1-42 and compared it to two similarly geranylated flavonoids, mimulone and diplacone, to compare the bioactive properties of these unique flavonoids more broadly. METHODS: Neuronal viability were assessed in PC12 cells biochemically using the MTT assay in the presence of each flavonoid (1-200 µM) for 48 h. Sub-toxic threshold test concentrations of each flavonoid were then applied to cells, alone or with concomitant incubation with the lipid peroxidant tert-butyl hyrdroperoxide (t-bhp) or amyloid ß (Aß1-42; 0-2 µM). Fluorescent staining was used to indicate effects of Aß1-42 on PC12 cellular morphology, while direct effects of each flavonoid on Aß fibril formation and aggregation were assessed using the Thioflavin T (ThT) fluorometric kinetic assay and transmission electron microscopy (TEM) to visualise fibril and aggregate morphology. RESULTS: Cannflavin A demonstrated intrinsic hormetic effects on cell viability, increasing viability by 40% from 1 to 10 µM but displaying neurotoxicity at higher (>10-100 µM) concentrations. Neither mimulone nor diplacone exhibited such a biphasic effect, instead showing only concentration-dependent neurotoxicity, with diplacone the more potent (from >1 µM). However at the lower concentrations (<10 µM), cannflavin A increased cell viability by up to 40%, while 10 µM cannflavin A inhibited the neurotoxicity elicited by Aß1-42 (0-2 µM), reducing Aß aggregate adherence to PC-12 cells and associated neurite loss. The neuroprotective effects of cannflavin A were associated with a direct inhibition of Aß1-42 fibril and aggregate density, evidenced by attenuated ThT fluorescence kinetics and microscopic evidence of both altered and diminished density of Aß aggregate and fibril morphology via electron microscopy. CONCLUSIONS: These findings highlight a concentration-dependent hormetic and neuroprotective role of cannflavin A against Aß-mediated neurotoxicity, associated with an inhibition of Aß fibrillisation. The efficacy of the cannabis flavone may itself direct further lead development targeting neurodegeneration in Alzheimer's disease. However, the geranylated flavonoids generally displayed a comparatively potent neurotoxicity not observed with many conventional flavonoids in vitro.

Morphology-based non-invasive quantitative prediction of the differentiation status of neural stem cells.

Neural stem cells (NSCs) are multipotent and are considered ideal source for regenerating damaged neural cells for neurological disorders. During culture of NSCs, both the measurement and the evaluation of their differentiation potential are important to maintain stable quality-assured NSCs for regenerative treatments since the rate of differentiation into certain lineages from NSCs is still not fully controllable. However, conventional cell evaluation techniques using biological molecular are still invasive, costly, and time-consuming. Therefore, a non-invasive, low-cost, and rapid cell evaluation method is required to expand the possibilities of regenerative therapy, especially in the facilities that produce cells for therapy. To address these such technological limitations in non-invasive cell evaluation, we propose the efficacy of computer-aided morphology-based prediction of potentials of stem cells by using multiple and time-course morphological parameters from phase-contrast microscopic images combined with experimentally determined differentiation potentials. In this work, we quantified the morphological parameters of NSCs during three types of differentiation culture and investigated two applications with NSCs: (i) evaluation of their differentiation type and (ii) early prediction of neural differentiation rate. Our data demonstrate that it is possible to non-invasively evaluate neural differentiation types and quantitatively predict future differentiation rates by using morphological information from the first 4 days. Our findings indicate the potential application of morphology-based non-invasive evaluation for optimizing effective differentiation protocols, screening of compounds to mediate NSC differentiation, and quality maintenance of regenerative medicine products.