Changes in the physicochemical properties and in vitro protein digestibility of peanut hulls treated via mechanical activation.

In this study, peanut hulls powder (PHP) was treated via mechanical activation (MA) and divided into three groups (control, PHP150 and PHP250). Physicochemical properties including mean particle size distribution (MPSD), powder properties, solubility and in vitro protein digestibility of PHP were then investigated. The results showed that MA could decrease the particle size of PHP by destroying its crystal structure, resulting in an increase of amorphization and a decrease of crystallinity and crystalline size. The results of in vitro protein digestibility and crude fiber contents showed that MA increased the protein digestibility of PHP by 43.32% and 74.70% (P < 0.05), while crude fiber content was decreased by 0.42% and 26.65% (P < 0.05). These findings indicated a large application potential of MA in PHP treatment. Supplementary Information: The online version contains supplementary material available at 10.1007/s10068-022-01084-1.

Lead toxicity induces autophagy to protect against cell death through mTORC1 pathway in cardiofibroblasts.

Heavy metals, such as lead (Pb(2+)), are usually accumulated in human bodies and impair human's health. Lead is a metal with many recognized adverse health side effects and yet the molecular processes underlying lead toxicity are still poorly understood. In the present study, we proposed to investigate the effects of lead toxicity in cultured cardiofibroblasts. After lead treatment, cultured cardiofibroblasts showed severe endoplasmic reticulum (ER) stress. However, the lead-treated cardiofibroblasts were not dramatically apoptotic. Further, we found that these cells determined to undergo autophagy through inhibiting mammalian target of rapamycin complex 1 (mTORC1) pathway. Moreover, inhibition of autophagy by 3-methyladenine (3-MA) may dramatically enhance lead toxicity in cardiofibroblasts and cause cell death. Our data establish that lead toxicity induces cell stress in cardiofibroblasts and protective autophagy is activated by inhibition of mTORC1 pathway. These findings describe a mechanism by which lead toxicity may promote the autophagy of cardiofibroblasts cells, which protects cells from cell stress. Our findings provide evidence that autophagy may help cells to survive under ER stress conditions in cardiofibroblasts and may set up an effective therapeutic strategy for heavy metal toxicity.

[Preparation and characteristic analysis of ractopamine molecularly imprinted polymers].

A molecularly imprinted polymers (MIPs) of Ractopamine (RCT) was prepared by thermal polymerization method, and the adsorptive characters of the MIPs was investigated with ultraviolet spectrophotometric method. The results showed that RCT had the maximum absorbance value at the wavelength of 272 nm, the regression equation of RCT was y = 7.354 1x + 0.001 0, R2 = 0.999 9, and the average adsorption rate of MIPs was 83.4%. According to the adsorption kinetics, the adsorption time should be controlled within 10 minutes. Infrared spectrum analysis indicated that the MIPs was formed by hydrogen bonds between RCT and functional monomer methacrylic acid, the MIPs of RCT recognized RCT and combined with it exclusively via hydrogen bonds. The investigation is very useful and important for establishing RCT detection methods based on molecularly imprinted technology.

Two new glycosides from the fruits of Morinda citrifolia L.

To study the chemical constituents of the fruits of noni (Morinda citrifolia L.), and find novel compounds, an n-butanol extract of the ethanol soluble fraction was subjected to repeated silica gel and ODS column chromatography and HPLC. Two new glycosides were isolated and their structures elucidated by NMR and HRFAB-MS spectrometry as (2E,4E,7Z)-deca-2,4,7-trienoate-2-O-ß-D-glucopyranosyl-ß-D-glucopyranoside and amyl-1-O-ß-D-apio-furanosyl-1,6-O-ß-D-glucopyranoside, respectively.

Classification of protein quaternary structure with support vector machine.

MOTIVATION: Since the gap between sharply increasing known sequences and slow accumulation of known structures is becoming large, an automatic classification process based on the primary sequences and known three-dimensional structure becomes indispensable. The classification of protein quaternary structure based on the primary sequences can provide some useful information for the biologists. So a fully automatic and reliable classification system is needed. This work tries to look for the effective methods of extracting attribute and the algorithm for classifying the quaternary structure from the primary sequences. RESULTS: Both of the support vector machine (SVM) and the covariant discriminant algorithms have been first introduced to predict quaternary structure properties from the protein primary sequences. The amino acid composition and the auto-correlation functions based on the amino acid index profile of the primary sequence have been taken into account in the algorithms. We have analyzed 472 amino acid indices and selected the four amino acid indices as the examples, which have the best performance. Thus the five attribute parameter data sets (COMP, FASG, NISK, WOLS and KYTJ) were established from the protein primary sequences. The COMP attribute data set is composed of amino acid composition, and the FASG, NISK, WOLS and KYTJ attribute data sets are composed of the amino acid composition and the auto-correlation functions of the corresponding amino acid residue index. The overall accuracies of SVM are 78.5, 87.5, 83.2, 81.7 and 81.9%, respectively, for COMP, FASG, NISK, WOLS and KYTJ data sets in jackknife test, which are 19.6, 7.8, 15.5, 13.1 and 15.8%, respectively, higher than that of the covariant discriminant algorithm in the same test. The results show that SVM may be applied to discriminate between the primary sequences of homodimers and non-homodimers and the two protein sequence descriptors can reflect the quaternary structure information. Compared with previous Robert Garian's investigation, the performance of SVM is almost equal to that of the Decision tree models, and the methods of extracting feature vector from the primary sequences are superior to Robert's binning function method. AVAILABILITY: Programs are available on request from the authors.