The current evaluation of functional foods primarily focuses on active constituents, often neglecting a holistic approach to quality assessment. To address this gap, a comprehensive “composition-activity-sense” method was developed, integrating a portable near-infrared spectrometer with the analytic hierarchy process (AHP) and the entropy weight method (EWM). Tangerine peel was chosen as a case study to illustrate this approach. Near-infrared spectroscopy was employed to predict various attributes, including origin, aging years, hesperidin, nobiletin, tangeretin, anti-inflammatory activity, umami, sweetness, bitterness, and overall liking. The comprehensive index (CI) of tangerine peel was calculated using AHP-EWM, and a quantitative CI model was constructed to evaluate and predict its quality. The optimal CI model demonstrated strong predictive performance, with an Rp value of 0.92 and an RPD value of 2.0, underscoring its potential for rapid and comprehensive functional food quality assessment.

ETHNOPHARMACOLOGICAL RELEVANCE:Rubus suavissimus S.Lee (RS), a traditional ethnomedicine Guangxi, China, has long been used to manage diabetes and its complications. Existing studies have demonstrated the antidiabetic activity of RS and its complications, but the pharmacological material basis and molecular mechanism of its efficacy have not been clarified. AIM OF THE STUDY:This study aimed to elucidate the active constituents and specific molecular mechanisms underlying the therapeutic effects of RS in diabetic kidney disease (DKD). MATERIALS AND METHODS:Ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) was employed to identify major active components within RS polyphenol extracts. Core signaling pathways and key active components were screened using network pharmacology analysis and enrichment methods. Subsequently, an in vitro MPC-5 podocyte cell model was established under high-glucose and high-lipid conditions. Data-independent acquisition (DIA) mass spectrometry was used to analyze differentially expressed proteins in the cellular secretome. Mitochondrial ultrastructure was assessed using transmission electron microscopy (TEM). Key protein expression changes were validated by Western blotting. RESULTS:Network pharmacology screening identified myricetin (Myr) as the compound exhibiting the highest binding affinity to PIK3R1, suggesting its role as a key active component in the anti-DKD effects of RS polyphenols. In vitro, high-glucose and high-lipid exposed MPC-5 cells exhibited pronounced mitochondrial swelling and cristae disruption. Myr treatment significantly preserved mitochondrial morphology and induced the formation of double-membrane autophagic vesicles encapsulating damaged mitochondria, indicative of activated mitophagy. Proteomic analysis corroborated these findings. This study demonstrates for the first time that Myr, a principal active component of RS polyphenols, exerts its therapeutic potential in DKD by inhibiting PIK3R1. This inhibition promotes XBP1 expression, indirectly activating both the PI3K/Akt and PINK1/Parkin pathways, ultimately enhancing autophagic flux. CONCLUSIONS:Myr effectively activated autophagy and mitophagy by targeting the PI3K/Akt and PINK1/Parkin signaling pathways, facilitating the removal of dysfunctional mitochondria and mitigating cellular damage in DKD models. These findings provide a mechanistic foundation for the use of RS-derived polyphenols in chronic kidney disease management and highlight Myr's potential as a natural therapeutic agent for DKD.

Bile acids and gut microbiota participate in the pathogenesis of liver fibrosis (LF). The total alkaloids of Corydalis saxicola Bunting (TACS) is a traditional Chinese medicine extract that has been used to treat LF, but the underlying mechanisms are not clear. This study performed integrated metabolomics and gut microbiome analysis to study the anti-LF mechanism of TACS using a rat model. Ultra-performance liquid chromatography quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF/MS) was used to identify the chemical compounds in TACS. Biochemical and histopathological analysis were performed to determine the efficacy of TACS. Bile acid-targeted metabolomics was used to assess changes in the bile acid (BA) profiles in TACS-treated LF rats. 16S rRNA gene sequencing and metagenomics were used to assess changes in the gut microbiota of the TACS-treated LF rats. Antibiotic cocktail treatment and fecal microbiota transplantation (FMT) were used to determine the relationship between the gut microbiota and the anti-LF effects of TACS. Metagenomics was used to identify significantly enriched gut microbiota after TACS treatment and its correlation with the anti-LF effects was verified by in vivo experiments. TACS treatment significantly reduced the levels of serum liver enzymes, fibrosis and pro-inflammatory cytokines in the liver. TACS significantly increased the levels of chenodeoxycholic acid (CDCA) and taurochenodeoxycholic acid (TCDCA) in the cecum and decreased the levels of cholic acid (CA) and deoxycholic acid (DCA) in the liver of the LF rats. TACS significantly increased the abundances of Lactobacillus and Akkermansia in the LF rats. Antibiotic cocktail treatment and FMT have shown that the effect of TACS cure liver fibrosis depends on the gut microbiota. The abundance of Lactobacillus reuteri was significantly increased by TACS. Administration of Lactobacillus reuteri via gavage ameliorated LF. TACS exerted anti-LF effects in rats by modulating bile acid metabolism and gut microbiome.

BACKGROUND:Oyster polypeptide (OP) is a mixture of oligopeptides extracted from oysters through enzyme lysis, separation, and purification. It is associated with immunomodulatory effects, but the underlying mechanisms are not known. This study therefore combined proton nuclear magnetic resonance (1H-NMR) urinary metabolomics and 16S rRNA gene sequencing of the gut microbiome to determine the immunoprotective mechanisms of OP in rats subjected to cyclophosphamide-induced immunosuppression. RESULTS:Oyster polypeptide restored the body weight and the structure of spleen and thymus in rats with cyclophosphamide-induced immunosuppression. It upregulated the levels of white blood cells (WBCs), hemoglobin (HGB), platelets (PLT), red blood cells (RBCs), immunoglobulin G (IgG), immunoglobulin M (IgM), cytokines such as interleukin‑6 (IL-6) and tumor necrosis factor-α (TNF-α), and increased the numbers of CD3+ and CD4+ T cells in the immunosuppressed rats. The 1H-NMR metabolomics results showed that OP significantly reversed the levels of ten metabolites in urine, including 2-oxoglutarate, citrate, dimethylamine, taurine, N-phenylacetylglycine, alanine, betaine, creatinine, uracil, and benzoate. The 16S rRNA gene sequencing results showed that OP restored the gut microbiome homeostasis by increasing the abundance of beneficial bacteria and reducing the abundance of pathogenic bacteria. Finally, a combination of metabolomics and microbiomics found that the metabolism of taurine and hypotaurine, and the metabolism of alanine, aspartate, and glutamate were disturbed, but these metabolic pathways were restored by OP. CONCLUSION:This study demonstrated that OP had immunoprotective effects in rats with cyclophosphamide-induced immunosuppression by restoring key metabolic pathways and the gut microbiome homeostasis. Our findings provide a framework for further research into the immunoregulatory mechanisms of OP and its potential use in drugs and nutritional supplements. © 2024 Society of Chemical Industry.

Oyster polypeptide (OP) is a mixture of oligopeptides extracted from oysters through enzyme lysis, separation, and purification. It is associated with immunomodulatory effects, but the underlying mechanisms are not known. This study therefore combined proton nuclear magnetic resonance (

Conventional methods for detecting unsaturated fatty acids (UFAs) pose challenges for rapid analyses due to the need for complex pretreatment and expensive instruments. Here, we developed an intelligent platform for facile and low-cost analysis of UFAs by combining a smartphone-assisted colorimetric sensor array (CSA) based on MnO2 nanozymes with "image segmentation-feature extraction" deep learning (ISFE-DL). Density functional theory predictions were validated by doping experiments using Ag, Pd, and Pt, which enhanced the catalytic activity of the MnO2 nanozymes. A CSA mimicking mammalian olfactory system was constructed with the principle that UFAs competitively inhibit the oxidization of the enzyme substrate, resulting in color changes in the nanozyme-ABTS substrate system. Through linear discriminant analysis coupled with the smartphone App "Quick Viewer" that utilizes multihole parallel acquisition technology, oleic acid (OA), linoleic acid (LA), alpha-linolenic acid (ALA), and their mixtures were clearly discriminated; various edible vegetable oils, different camellia oils (CAO), and adulterated CAOs were also successfully distinguished. Furthermore, the ISFE-DL method was combined in multicomponent quantitative analysis. The sensing elements of the CSA (3 x 4) were individually segmented for single-hole feature extraction containing information from 38,868 images of three UFAs, thereby allowing for the extraction of more features and augmenting sample size. After training with the MobileNetV3 small model, the determination coefficients of OA, LA, and ALA were 0.9969, 0.9668, and 0.7393, respectively. The model was embedded in the smartphone App "Intelligent Analysis Master" for one-click quantification. We provide an innovative approach for intelligent and efficient qualitative and quantitative analysis of UFAs and other compounds with similar characteristics.

Achieving rapid, cost effective, and intelligent identification and quantification of flavonoids is challenging. For fast and uncomplicated flavonoid determination, a sensing platform of smartphone-coupled colorimetric sensor arrays (electronic noses) was developed, relying on the differential competitive inhibition of hesperidin, nobiletin, and tangeretin on the oxidation reactions of nanozymes with a 3,3 ',5,5 '-tetramethylbenzidine substrate. First, density functional theory calculations predicted the enhanced peroxidase-like activities of CeO2 nanozymes after doping with Mn, Co, and Fe, which was then confirmed by experiments. The self-designed mobile application, Quick Viewer, enabled a rapid evaluation of the red, green, and blue values of colorimetric images using a multi-hole parallel acquisition strategy. The sensor array based on three channels of CeMn, CeFe, and CeCo was able to discriminate between different flavonoids from various categories, concentrations, mixtures, and the various storage durations of flavonoid-rich Citri Reticulatae Pericarpium through a linear discriminant analysis. Furthermore, the integration of a "segmentation-extraction-regression" deep learning algorithm enabled single-hole images to be obtained by segmenting from a 3 x 4 sensing array to augment the featured information of array images. The MobileNetV3-small neural network was trained on 37,488 single-well images and achieved an excellent predictive capability for flavonoid concentrations (R2 = 0.97). Finally, MobileNetV3-small was integrated into a smartphone as an application (Intelligent Analysis Master), to achieve the one-click output of three concentrations. This study developed an innovative approach for the qualitative and simultaneous multiingredient quantitative analysis of flavonoids.

基于深度学习在侧信道攻击中的应用,在Chipwhisperer平台中实现AES算法,在其加密过程中测量相应能量迹,再利用CPA技术分析得出兴趣点位置,并针对兴趣点做出模型训练.在卷积神经网络(CNN),长短时记忆网络(LSTM)和CNN_LSTM混合模型三种网络模型上,结合数据预处理技术训练同步和异步能量迹.实验结果表明三种模型同步状态下的准确率相当,另外在保证模型训练参数不变的情况下逐渐增大异步数据时,三个模型训练集和测试集的准确率都在减少,但新提出的混合模型下降速度变化是最慢的,在实验异步数加大到50时,仍可以保证准确率在90％之上,即几乎一条能量迹就可恢复出正确密钥.所以,CNN_LSTM模型可以更好地适应能量迹发生异步的情况.

In the research field of side-channel attacks, label-based modeling attacks based on machine learning have been successfully applied to block cipher AES. Compared with non-modeling attacks, modeling attacks have a large workload of data collection and processing in the early stage, but their advantage lies in the high reusability of the model. Once the model is established successfully, the key can be recovered with less traces. First, implement the AES algorithm on the Chipwhisperer platform and collect the energy traces of the process, borrow the CPA technology to extract the points of interest, and the obtained points of interest are the data set for model training. This paper applies deep reinforcement learning to the research of symmetric cipher AES, and realizes a label-free training model. Experiments show that deep reinforcement learning technology can be successfully applied to side-channel attacks. The model can predict that the correct rate of both synchronous and asynchronous energy trajectories is as high as 91% or more. This data shows that the model can attack the correct key with an energy trace.