Smartphone-Assisted Nanozyme Colorimetric Sensor Array Combined "Image Segmentation-Feature Extraction" Deep Learning for Detecting Unsaturated Fatty Acids.

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), α-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 × 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.

Nanozyme-based colorimetric sensor arrays coupling with smartphone for discrimination and "segmentation-extraction-regression" deep learning assisted quantification of flavonoids.

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 × 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 multi-ingredient quantitative analysis of flavonoids.

Quality control for a traditional Chinese medicine, Millettia speciosa Champ, using ultra-high-performance liquid chromatography fingerprint, serum pharmacochemistry and network pharmacology.

Millettia speciosa (M. speciosa) Champ (MSC) is a healthy food type with medicinal and edible homology, which is now considered a clinically significant anti-rheumatoid arthritis medicine. However, there is currently no standardized or generally accepted research strategy by which we can assess M. speciosa. Thus, it is essential to develop novel theories, strategies and evaluation methods for the scientific quality control of M. speciosa. Herein, our use ultra-high-performance liquid chromatography (UPLC)-MS/MS analysis identified 12 common bioactive components absorbed into MSC serum. Next, network pharmacology analysis exhibited that 5 MSC components may be those active components in treating rheumatoid arthritis and may be considered potential quality markers. These 5 components were then quantified using a fast UPLC approach, based on the quality marker of measurability, showing that lenticin can be regarded as the MSC quality marker. The cumulative study findings, based on systematic assessment of chemical composition both in vivo and in vitro, and the potential efficacy of M. speciosa, provide a novel approach for M. speciosa quality control.

Evaluation of aristolochic acid Ι nephrotoxicity in mice via 1H NMR quantitative metabolomics and network pharmacology approaches.

Background: Although many studies have shown that herbs containing aristolochic acids can treat various human diseases, AAΙ in particular has been implicated as a nephrotoxic agent. Methods and results: Here, we detail the nephrotoxic effect of AAΙ via an approach that integrated 1H NMR-based metabonomics and network pharmacology. Our findings revealed renal injury in mice after the administration of AAΙ. Metabolomic data confirmed significant differences among the renal metabolic profiles of control and model groups, with significant reductions in 12 differential metabolites relevant to 23 metabolic pathways. Among them, there were seven important metabolic pathways: arginine and proline metabolism; glycine, serine, and threonine metabolism; taurine and hypotaurine metabolism; ascorbate and aldehyde glycolate metabolism; pentose and glucosinolate interconversion; alanine, aspartate, and glutamate metabolism; and glyoxylate and dicarboxylic acid metabolism. Relevant genes, namely, nitric oxide synthase 1 (NOS1), pyrroline-5-carboxylate reductase 1 (PYCR1), nitric oxide synthase 3 (NOS3) and glutamic oxaloacetic transaminase 2 (GOT2), were highlighted via network pharmacology and molecular docking techniques. Quantitative real-time PCR findings revealed that AAI administration significantly downregulated GOT2 and NOS3 and significantly upregulated NOS1 and PYCR1 expression and thus influenced the metabolism of arginine and proline. Conclusion: This work provides a meaningful insight for the mechanism of AAΙ renal injury.

Preventive effect of tilapia skin collagen hydrolysates on ulcerative colitis mice based on metabonomic and 16 S rRNA gene sequencing.

BACKGROUND: Tilapia skin collagen hydrolysates (TSCHs) are the product of enzymatic hydrolysis of collagen, which is mainly extracted from tilapia skin. The components of TSCHs have recently been reported to play a preventive role in dextran sulfate sodium (DSS)-induced ulcerative colitis (UC). However, it has not been illustrated whether TSCHs can prevent against DSS-induced UC via the gut microbiota and its derived metabolites. RESULTS: TSCHs are mainly composed of amino acids, which have similar characteristics to collagen, with most having a molecular weight below 5 kDa. In a mouse model of UC, TSCHs had no toxic effect at a dose of 60 g kg-1 and could reduce body weight changes, colon length, histopathological changes and score, and the level of the serum inflammatory cytokine interleukin (IL)-6. Concurrently, 16 S rRNA sequencing showed that TSCHs significantly reduced the abundance of Bacteroidetes and Proteobacteria at the phylum level and norank_f__Muribaculaceae and Escherichia-Shigella at the genus level, while they increased the abundance of Firmicutes at the phylum level and Lachnoclostridium, Allobaculum, Enterorhabdus, and unclassified__f__Ruminococcaceae at the genus level. Target metabolomic analysis showed that TSCHs elevated the concentration of total acid, acetic acid, propanoic acid, and butanoic acid, but reduced isovaleric acid concentrations. Moreover, Pearson correlation analysis revealed that Allobaculum, unclassified_Ruminococcaceae, and Enterorhabdus were positively correlated with acetic acid and butyric acid, but not Escherichia-Shigella. CONCLUSION: These findings suggest that TSCHs can prevent UC by modulating gut microbial and microbiota-derived metabolites. © 2023 Society of Chemical Industry.

Design, synthesis and biological evaluation of rasagiline-clorgyline hybrids as novel dual inhibitors of monoamine oxidase-B and amyloid-ß aggregation against Alzheimer's disease.

A series of rasagiline-clorgyline hybrids was designed, synthesized and investigated in vitro for their inhibition of monoamine oxidase and amyloid-ß aggregation. Most of compounds were found to be selective and highly potent hMAO-B inhibitors showing IC50 values in the nanomolar, and exhibited a moderate inhibition of amyloid-ß aggregation. 7-((5-(methyl(prop-2-yn-1-yl)amino) pentyl)oxy)chroman-4-one (6j) was the most interesting compound identified in this research, endowed with higher hMAO-B potency (IC50 = 4 nM) and selectivity (SI > 25000) compared to the reference selective inhibitor rasagiline (IC50 = 141 nM, SI > 355), and exhibited good inhibitory activity against Aß1-42 aggregation (40.78%, 25 µM). Kinetic and molecular modeling studies revealed that 6j was a competitive reversible inhibitor for hMAO-B. Moreover, compound 6j displayed low toxicity and good neuroprotective effects in SH-SY5Y cell assay, and could penetrate the blood-brain barrier according to the parallel artificial membrane permeability assay. Pharmacokinetics assay revealed that compound 6j possessed good pharmacokinetic profiles after intravenous and oral administrations. Overall, these results highlighted that compound 6j was an effective and promising multitarget agent against Alzheimer's disease.