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The ginsenoside isomers were analyzed and distinguished by using inlet ionization-high resolution mass spectrometry technology under positive and negative ion modes with optimum inlet temperatures. The results showed that under the full scanning mode of positive and negative ion modes with the optimum experimental temperature, different types of ions were formed according to the characteristics of different numbers of polymers formed by isomeric monomers of ginsenoside, and the identification of ginsenoside isomers was realized. For the identification of ginsenoside CK and Rh2, there was no obvious difference in their MS spectra under positive ion mode. However, under negative ion mode with the inlet temperature of 200 ℃, ginsenoside CK tended to form polymers of [M+Cl]－, Rh2 tended to form polymers of [M+COOH]－, and the number of polymers of CK was significantly higher than that of Rh2. For the identification of ginsenoside Rb2 and Rc, under positive ion mode with the inlet temperature of 200 ℃, ginsenoside Rb2 and Rc could produce polymers of [M+Na]+, but the polymers peak intensity of Rb2 was significantly higher than that of Rc. Under negative ion mode with the inlet temperature of 200 ℃, Rb2 tended to form polymers of [M+Cl]－, and Rc tended to form polymers of [M+COOH]－. For the identification of ginsenoside Rg1 and Rf, there was no obvious difference in their MS spectra under the positive ion mode. Under negative ion mode with the inlet temperature of 200 ℃, Rg1 and Rf tended to form polymers of [M+Cl]－ and [M−H]−, respectively. In this study, the three pairs of isomers in ginsenosides were basically identified by using the full MS scanning method and the control of the mass spectrometry inlet temperature. The experimental method is simple, fast, and has the characteristics of low consumption. This study confirms that the entrance ionization mass spectrometry technique can provide a theoretical reference for the rapid discrimination and identification of ginsenoside isomers.

Photonic reservoir computing (RC) is a simple and efficient neuromorphic computing framework for human cortical circuits, which is featured with fast training speed and low training cost. Photonic time delay RC, as a simple hardware implementation method of RC, has attracted widespread attention. In this paper, we present and experimentally demonstrate a time delay RC system based on a Fabry Perot (FP) laser for multiple tasks processing. Here, the various tasks are attempted to perform in parallel in the multiple longitudinal modes of the FP laser. It is found that the time delay RC system based on the FP laser can successfully handle different tasks across multiple longitudinal modes simultaneously. The experimental results demonstrate the potential of the time delay RC system based on the FP laser to achieve multiple tasks processing, providing various possibilities for improving the information processing ability of neural morphology RC systems, and promoting the development of RC systems.

Abstract Wu-tou decoction (WTD), a traditional Chinese medicine prescription, is used to treat rheumatoid arthritis (RA). It works by controlling intestinal flora and its metabolites, which in turn modulates the inflammatory response and intestinal barrier function. Small molecular compounds (SM) and polysaccharides (PS) were the primary constituents of WTD extract. In this work, a model of adjuvant-induced arthritis (AIA) in rats was established and treated with WTD, SM, and PS, respectively. 16S rRNA gene sequencing was used to examine the regulatory impact of the various groups on the disturbance of the gut flora induced by RA. Further, since PS cannot be absorbed into the blood, the influence of PS on the absorption and metabolism of SM was studied by examining their pharmacokinetic (PK) parameters of 23 active components in SM by UPLC-MS/MS. WTD was found to be more effective than PS and SM in alleviating arthritis in AIA rats, which may be related to changes in gut flora. The PK properties of 13 active compounds were altered after PS intervene. Based on the findings, PS may be able to manage the disruption of intestinal microbiota, enhance the intestinal environment of model animals, and hence influence SM absorption and metabolism.

Tenghuang Jiangu Wan (THJGW) is a commonly utilized treatment for osteoarthritis (OA), yet its pharmacodynamic material basis and molecular mechanism remain inadequately understood. This study systematically characterized the in vitro and in vivo chemical components of THJGW using ultra-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS). The pharmacodynamic material basis of THJGW in the treatment of OA was explored through a target network pharmacology approach and molecular docking technology. Cellular experiments were subsequently employed to validate the molecular mechanisms. Consequently, a total of 134 components from THJGW were identified in vitro, comprising 45 flavonoids, 10 iridoid glycosides, 39 phenylethanoid glycosides, 9 phenylpropanoids, 11 organic acids, 4 phenolics, and 16 other compounds. Additionally, 38 prototype absorbed components in serum were characterized for the purpose of network construction. 11 key components and 10 core targets (VEGFA, STAT3, RELA/NF-κB p65, PIK3R1, PIK3CA, MMP9, MMP1, IL-6, HDAC1, and FGF2) were determined by the target network pharmacology. Through Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, it was found that these intersection targets are associated with various signaling pathways such as pathways in cancer, lipid and atherosclerosis, and prostate cancer. Notably, the lipid and atherosclerosis pathway was closely linked to OA. The molecular docking results demonstrated strong binding affinities of the three core targets: IL-6, STAT3, and RELA/NF-κB p65. These results suggest that these targets may play a significant role for THJGW in treating OA. In vitro experiments showed that THJGW had notable protective effects on LPS-induced RAW264.7 macrophages by reducing levels of TNF-α and IL-6, and inhibiting phosphorylation expression of STAT3 and NF-κB p65 proteins related to the lipid and atherosclerosis pathway. This study provides insight into the pharmacodynamic material basis and mechanism of action for THJGW in the treatment of OA, which provide scientific evidence for the scientific application and improvement of quality standards for THJGW.

BackgroundMalnutrition is the most common nutritional issue in Alzheimer’s disease (AD) patients, but there is still a lack of a comprehensive evaluation of the nutritional status in AD patients. This study aimed to determine the potential association of various nutritional indices with AD at different stages.MethodsSubjects, including individuals with normal cognition (NC) and patients diagnosed with AD, were consecutively enrolled in this cross-sectional study. Demographics, body composition, dietary patterns, nutritional assessment scales and nutrition-related laboratory variables were collected. Binary logistics regression analyses and receiver operating characteristic (ROC) curves were used to indicate the association between nutrition-related variables and AD at different stages.ResultsTotals of 266 subjects, including 73 subjects with NC, 72 subjects with mild cognitive impairment due to AD (AD-MCI) and 121 subjects with dementia due to AD (AD-D) were included. There was no significant difference in dietary patterns, including Mediterranean diet and Mediterranean-DASH diet intervention for neurodegenerative delay (MIND) diet between the three groups. Lower BMI value, smaller hip and calf circumferences, lower Mini Nutritional Assessment (MNA) and Geriatric Nutritional Risk Index (GNRI) scores, and lower levels of total protein, albumin, globulin, and apolipoprotein A1 were associated with AD (all p

The chemical complexity and toxicity of volatile organic compounds (VOCs) are primarily encountered through intensive anthropogenic emissions in suburban areas. Here, pollution characteristics, impacts on secondary pollution formation, and health risks were investigated through continuous in-field measurements from 1–30 June 2020 in suburban Nanjing, adjacent to national petrochemical industrial parks in China. On average, the total VOCs concentration was 34.47 ± 16.08 ppb, which was comprised mostly by alkanes (41.8%) and halogenated hydrocarbons (29.4%). In contrast, aromatics (17.4%) dominated the ozone formation potential (OFP) and secondary organic aerosol formation potential (SOAFP) with 59.6% and 58.3%, respectively. Approximately 63.5% of VOCs were emitted from the petrochemical industry and from solvent usage based on source apportionment results, followed by biogenic emissions of 22.3% and vehicle emissions of 14.2%. Of the observed 46 VOC species, hexachlorobutadiene, dibromoethane, butadiene, tetrachloroethane, and vinyl chloride contributed as high as 98.8% of total carcinogenic risk, a large fraction of which was ascribed to the high-level emissions during ozone pollution episodes and nighttime. Therefore, the mitigation of VOC emissions from petrochemical industries would be an effective way to reduce secondary pollution and potential health risks in conurbation areas.

Background and Context: Computational thinking (CT) has been increasingly added to K-12 curricula, prompting teachers to grade more and more CT artifacts. This has led to a rise in automated CT assessment tools. Objective: This study examines the scope and characteristics of publications that use machine learning (ML) approaches to assess students' CT competencies from four perspectives: the educational context in which the assessments were implemented, the data used to train and validate ML algorithms, the specific ML algorithms used, and the aspects of CT assessed. Method: The PRISMA approach and Arksey and O'Malley's methodological framework for scoping reviews were adopted to search and screen studies. Findings: ML algorithms have been increasingly used to assess CT competencies. However, this study identified several research gaps in the literature: existing studies were mostly conducted in the context of programming or other learning activities related to computing science; datasets used by the ML algorithms were generally small; the most frequently used algorithms were regression techniques, naive Bayes, neural networks, clustering, and natural language processing, whereas no studies used reinforcement learning; and CT competencies were not comprehensively assessed. Implications: The applications of ML in CT assessments have the potential to enable personalized learning, improve assessment validity, reduce the workload of graders, and gain insights from large datasets by uncovering complex and subtle patterns.