Advancing necrotizing enterocolitis prediction through iterative monitoring.

Background: Necrotizing enterocolitis (NEC) is a severe inflammatory intestinal disease in preterm infants, marked by heightened morbidity and mortality. Timely prediction of NEC is significant in the management of critical neonates. However, it is difficult to predict NEC accurately because of the multi-factorial pathogenesis. This study aimed to develop a prediction model through repeated measurement data to further improve the accuracy of prediction in NEC. Methods: We retrospectively collected clinical data of premature infants admitted to the Neonatology Department of the First Affiliated Hospital of Anhui Medical University from January 2016 to December 2023. The infants were categorized into the NEC group (Bell's stage ≥ II) (n=150) and the non-NEC group (n=150). The clinical baseline data of the NEC and non-NEC groups were matched. Laboratory examination indicators were collected on the 1st day, the 7th day after birth, and the day of NEC onset. Univariate and multivariate logistic regression analyses were conducted to identify independent factors influencing NEC. A nomogram was constructed based on these factors to predict NEC. The concordance index and calibration plot were used to assess the efficiency of the nomogram in the training and validation cohorts. Results: This study demonstrated that antenatal steroids, antenatal antibiotics, probiotics treatment before NEC, anion gap (AG, day 7), and mean corpuscular volume (MCV, day 7) were independent risk factors which combined to accurately predict NEC. A nomogram of NEC was created utilizing these five predictors. With an area under the receiver operator characteristic (ROC) curve of 0.835 [95% confidence interval (CI): 0.785-0.884]. Concordance index for the training and validation groups were 0.835 and 0.848, respectively. As the calibration plots indicate, the predicted probability of NEC is highly consistent with the actual observation. Conclusions: The risk estimation nomogram for NEC offers clinical value by guiding early prediction, targeted prevention, and early intervention strategies for NEC.

A rapid near-infrared turn on fluorescence probe for the detection of sulfite in food and its application in biological imaging.

A near-infrared fluorescent "turn on" probe DTMI featuring simple skeleton was constructed easily. It undergoes a structure transformation from an A-π-A to a D-π-A framework towards SO32-. Besides, DTMI is capable of distinctive sensing sulfite with a fast response and a significant Stokes shift as well as with high sensitivity, excellent selectivity, long-term stability of fluorescence signals, and good anti-interference ability. The detection limit (LOD) of DTMI for sulfite within the linear concentration range of 0.5-10 µM is 27.39 nM. More importantly, DTMI has been favorably utilized for detecting sulfite in food samples such as red wine and vermicelli. Based on its low biotoxicity, DTMI has been successfully applied in imaging experiments involving HeLa cells, onion inner epidermal cells, and zebrafish. Therefore, the results show that the presented probe possesses potential sensing activity towards sulfite in complex biological system and food samples.

Silicon dioxide nanoparticles induce anxiety-like behavior in a size-specific manner via the microbiota-gut-brain axis.

The impacts of silicon dioxide nanoparticles (SiO2-NPs) on human health have attracted increasing interest due to their widespread utilization in medicine and food additives. However, the size-dependent effects of SiO2-NPs on brain health remain sparse. Herein we investigated alterations in behavioral patterns, the gut microbiota, inflammation and oxidative stress of mice after a 12-week exposure to SiO2-NPs with either small size (NP-S) or large size (NP-L). A more pronounced deleterious effect of NP-S was found on anxiety-like behavior in mice relative to NP-L. We also found that SiO2-NPs exposure induced inflammation and oxidative stress in the colon, hippocampus and cortex of mice in a size-specific manner. Correlation network analysis revealed potential links between anxiety-like behavior and SiO2-NPs-induced shifts in the gut microbiota including Parvibacter, Faecalibaculum, Gordonibacter and Ileibacterium. Furthermore, anxiety-like behavior caused by SiO2-NPs exposure exhibited correlations with decreased levels of hippocampal IL-10 and cortex Nqo1 as well as increased levels of intestinal Acox1 and hippocampal TNF-α. Therefore, our findings suggest that exposure to SiO2-NPs promoted anxiety-like behavior through the mediation of interplay between the gut and the brain, and SiO2-NPs of smaller size may generate a more adverse effect on brain health.

Application of intensive management of risk awareness combined with cardiac rehabilitation nursing in elderly patients with acute myocardial infarction and heart failure.

The aim of this study is to assess the impact of intensive risk awareness management along with cardiac rehabilitation nursing in elderly patients with acute myocardial infarction and heart failure. We selected 101 elderly patients with acute myocardial infarction and heart aging treated from January 2022 to March 2023. They were divided into control and observation groups based on hospitalization numbers. The control group (n = 50) received routine nursing, while the observation group (n = 51) received intensive risk awareness management and cardiac rehabilitation nursing. We compared medication possession ratio (MPR), cardiac function, self-care ability scale scores, quality-of-life, incidents, and satisfaction between the 2 groups. Before intervention, there was no significant difference in MPR values between the 2 groups (P > .05). After intervention, MPR values increased in both groups, with a greater increase in the observation group (P < .05). Cardiac function showed no significant difference before intervention (P > .05), but after intervention, the observation group had lower left ventricular end-systolic and diastolic diameters and higher left ventricular ejection fraction compared to the control group (P < .05). Self-care skills, health knowledge, self-responsibility, and self-concept scores improved in both groups after intervention, with higher scores in the observation group (P < .05). The observation group had higher scores in various quality-of-life domains (P < .05). The total incidence of adverse events was lower in the observation group (5.88%) compared to the control group (20.00%) (P < .05). Patient satisfaction was significantly higher in the observation group (96.08%) than in the control group (84.00%) (P < .05). Intensive risk awareness management combined with cardiac rehabilitation nursing in elderly patients with acute myocardial infarction and heart aging can enhance medication compliance, improve quality-of-life, enhance self-care abilities, boost cardiac function, reduce incidents, and increase patient satisfaction.

A colorimetric sensor based on 4-MPBA Au@AgNPs for accurately identification of EnshiYulu tea grade.

Enshi Yulu green tea (ESYL) is the most representative traditional steamed green tea in Enshi, Hubei. Different ESYL grades exhibit distinct flavors, tastes, and prices. In this study, a visual sensor based on 4-MPBA Au@AgNPs was developed for the rapid and accurate identification of ESYL grades. The recognition mechanism involved the binding of 4-MPBA Au@AgNPs with polyphenolic compounds in ESYL to form borate esters and the conversion of Ag+ to Ag0, with the generated Ag0 depositing on the surface of 4-MPBA Au@AgNPs. The results showed that the sensor can amplify the color differences of different grades of ESYL. The visual results were also validated by the partial least squares discriminant analysis model, demonstrating an enhancement in recognition accuracy from 68.2 % to 95.5 % compared to the original extraction solution. The colorimetric sensor developed in this study is expected to provide a new approach for traceability research of other foods.

LKB1 regulates autophagy through AMPK/TOR signaling pathway to alleviate the damage caused by Vibrio alginolyticus infection.

LKB1 (liver kinase B1) is a key upstream kinase of AMPK and plays an important role in various cellular activities. While the function and mechanism of LKB1 have been widely reported in the study of tumor, there are few reports on its role in bacterial infectious diseases, especially in shrimp. In the present study, molecular characterization revealed that LvLKB1 has an open reading frame (ORF) of 1266 bp encoding 421 amino acids with a molecular weight of about 48 KDa, including the kinase region, N-terminal regulatory domain and C-terminal regulatory domain. LvLKB1 in hepatopancreas and hemocytes was significantly upregulated after infection with Vibrio alginolyticus (V. alginolyticus). After silencing LvLKB1 gene in Litopenaeus vannamei (L. vannamei) and artificially infecting V. alginolyticus, the survival rate of L. vannamei was significantly decreased. Subsequently, it was found that the expression of inflammatory factors in hepatopancreas and hemocytes of shrimp was up-regulated, and the expression of lipid oxidation factors was decreased after silencing LKB1, leading to the phenomenon of lipid accumulation in hepatopancreas. In order to explore the mechanism, autophagy levels of shrimp were detected after silencing LKB1, which showed that autophagy levels in hepatopancreas and hemocytes were significantly reduced. Further studies conclusively showed that silencing LvLKB1 inhibited AMPK phosphorylation induced by V. alginolyticus infection, thereby activating TOR pathway and inhibiting autophagy in shrimp. These results indicate that LvLKB1 regulates autophagy through AMPK/TOR signaling pathway to alleviate the damage caused by V. alginolyticus infection.

The Vicious Cycle between Loneliness and Problematic Smartphone Use among Adolescents: A Random Intercept Cross-Lagged Panel Model.

Despite extensive research on the psychological impacts of digital technology, the nuanced dynamics between adolescent loneliness and problematic smartphone use, particularly across different educational levels and genders, remain underexplored. This study aims to fill this gap by employing a Random Intercept Cross-Lagged Panel Model to dissect the bidirectional relationship between loneliness and problematic smartphone use among adolescents, with a focus on the moderating roles of educational levels and gender. Engaging 3132 students from various educational institutions in China, the research conducted a three-wave longitudinal analysis across 2022-2023. The final number of participants included 1120 adolescents (53.5% female; age in 2022: M = 14.57 years, SD = 1.57). Results reveal that loneliness significantly predicts problematic smartphone use, but not vice versa, highlighting a unidirectional influence. The study uncovers crucial differences across educational levels and gender, emphasizing the stronger effect of loneliness on problematic smartphone use among junior high students and female adolescents. These findings underscore the complexity of adolescent loneliness and its relationship with digital behavior, suggesting a need for tailored interventions considering both gender and developmental stages.

Discrimination and prediction of Qingzhuan tea storage year using quantitative chemical profile combined with multivariate analysis: Advantages of MRMHR based targeted quantification metabolomics.

The duration of storage significantly influences the quality and market value of Qingzhuan tea (QZT). Herein, a high-resolution multiple reaction monitoring (MRMHR) quantitative method for markers of QZT storage year was developed. Quantitative data alongside multivariate analysis were employed to discriminate and predict the storage year of QZT. Furthermore, the content of the main biochemical ingredients, catechins and alkaloids, and free amino acids (FAA) were assessed for this purpose. The results show that targeted marker-based models exhibited superior discrimination and prediction performance among four datasets. The R2Xcum, R2Ycum and Q2cum of orthogonal projection to latent structure-discriminant analysis discrimination model were close to 1. The correlation coefficient (R2) and the root mean square error of prediction of the QZT storage year prediction model were 0.9906 and 0.63, respectively. This study provides valuable insights into tea storage quality and highlights the potential application of targeted markers in food quality evaluation.

UHPLC-QTOF-MS-based untargeted metabolomic authentication of Chinese red wines according to their grape varieties.

Wine is a very popular alcoholic drink owing to its health benefits of antioxidant effects. However, profits-driven frauds of wine especially false declarations of variety frequently occurred in markets. In this work, an UHPLC-QTOF-MS-based untargeted metabolomics method was developed for metabolite profiling of 119 bottles of Chinese red wines from four varieties (Cabernet Sauvignon, Merlot, Cabernet Gernischt, and Pinot Noir). The metabolites of red wines from different varieties were assessed using orthogonal partial least-squares discriminant analysis (OPLS-DA) and analyzed using KEGG metabolic pathway analysis. Results showed that the differential compounds among different varieties of red wines are mainly flavonoids, phenols, indoles and amino acids. The KEGG metabolic pathway analysis showed that indoles metabolism and flavonoids metabolism are closely related to wine varieties. Based on the differential compounds, OPLS-DA models could identify external validation wine samples with a total correct rate of 90.9 % in positive ionization mode and 100 % in negative ionization mode. This study indicated that the developed untargeted metabolomics method based on UHPLC-QTOF-MS is a potential tool to identify the varieties of Chinese red wines.

Raspberry polysaccharides attenuate hepatic inflammation and oxidative stress in diet-induced obese mice by enhancing butyrate-mediated intestinal barrier function.

Obesity and associated liver diseases are becoming global public health challenges. Raspberry (Rubus chingii Hu.), as a medicine food homology plant, possesses a series of health-promoting properties, but its protective effect on obesity-related liver injury and the potential mechanisms remain obscure. Herein high-fat diet (HFD)-fed mice were orally treated with raspberry polysaccharides (RCP) for 14 weeks. Treatment with RCP alleviated obesity and associated symptoms including hyperglycemia, hyperlipemia, endotoxemia, as well as hepatic inflammation and oxidant stress in HFD-induced obese mice. RCP restructured the gut microbiota and host metabolism especially by increasing the levels of Dubosiella and its metabolite butyrate. Besides, exogenous butyrate supplementation protected against intestinal barrier disruption, and thereby reduced inflow of lipopolysaccharide and mitigated inflammation and oxidative injury in the liver of obese mice. Therefore, we suggest that RCP can be utilized as a novel prebiotics to improve obesity-induced hepatic oxidative injury by enhancing butyrate-mediated intestinal barrier function.

Third-order calibration applied to process surfactant-modulated excitation-emission matrix four-way fluorescence data for the direct determination of four polycyclic aromatic hydrocarbons in oilfield produced water.

Fluorescence spectroscopy is a powerful tool to determine polycyclic aromatic hydrocarbons (PAHs) owing to the strong endogenous fluorescence of these compounds. However, the presence of unknown interferences and overlapped spectra hinders the accurate determination of PAHs in oilfield produced water. Moreover, surfactants frequently coexist in oilfield produced water and will seriously affect the fluorescence signals of PAHs. Herein, a new methodology applying third-order calibration to process four-way (4D) fluorescence data was proposed to solve these problems and achieve accurate determination of pyrene, fluorene, phenanthrene, and fluoranthene as an example in oilfield produced water. The methodology is based on excitation-emission matrix fluorescence modulated by different concentrations of sodium dodecyl benzene sulfonate (SDBS) in the analyzed samples. The 4D fluorescence data were processed by third-order calibration methods including four-way parallel factor analysis (4-PARAFAC) and alternating weighted residue constraint quadrilinear decomposition (AWRCQLD), and the results were compared with those of second-order calibration methods. It was proved that third-order calibration was capable of accurately identifying and quantifying PAHs together with SDBS in oilfield produced water, which has better quantitative results and figures of merit compared to second-order calibration. This study provided a new approach to generating 4D fluorescence data and opened up an avenue for the accurate determination of PAHs in complex oilfield produced water with surfactants.

Untargeted metabolomics analysis reveals spatial metabolic heterogeneity in different intestinal segments of type 1 diabetic mice.

Type 1 diabetes (T1D) has been reported to cause systematic metabolic disorders, but metabolic changes in different intestinal segments of T1D remain unclear. In this study, we analyzed metabolic profiles in the jejunum, ileum, cecum and colon of streptozocin-induced T1D and age-matched control (CON) mice by an LC-MS-based metabolomics method. The results show that segment-specific metabolic disorders occurred in the gut of T1D mice. In the jejunum, we found that T1D mainly led to disordered amino acid metabolism and most amino acids were significantly lower relative to CON mice. Moreover, fatty acid metabolism was disrupted mainly in the ileum, cecum and colon of T1D mice, such as arachidonic acid, alpha-linolenic acid and linoleic acid metabolism. Thus, our study reveals spatial metabolic heterogeneity in the gut of T1D mice and provides a metabolic view on diabetes-associated intestinal diseases.

Revealing the chemical differences and their application in the storage year prediction of Qingzhuan tea by SWATH-MS based metabolomics analysis.

It's generally believed that the longer the storage, the better the quality of dark tea, but the chemical differences of Qingzhuan tea (QZT) with different storage years is still unclear. Herein, in this work, an untargeted metabolomic approach based on SWATH-MS was established to investigate the differential compounds of QZT with 0-9 years' storage time. These QZT samples were roughly divided into two categories by principal component analysis (PCA). After orthogonal projections to latent structures discriminant analysis (OPLS-DA), 18 differential compounds were putatively identified as chemical markers for the storage year variation of QZT. Heatmap visualization showed that the contents of catechins, fatty acids, and some phenolic acids significantly reduced, flavonoid glycosides, triterpenoids, and 8-C N-ethyl-2-pyrrolidinone-substituted flavan-3-ols (EPSFs) increased with the increase of storage time. Furthermore, these chemical markers were verified by the peak areas corresponding to MS2 ions from SWATH-MS. Based on the extraction chromatographic peak areas of MS and MS2 ions, a duration time prediction model was built for QZT with correlation coefficient R2 of 0.9080 and 0.9701, and RMSEP value of 0.85 and 1.24, respectively. This study reveals the chemical differences of QZT with different storage years and provides a theoretical basis for the quality evaluation of stored dark tea.

Neonatal sequential organ failure assessment score within 72†h after delivery reliably predicts bronchopulmonary dysplasia in very preterm infants.

Background: The neonatal sequential organ failure assessment (nSOFA) score is an operational definition of organ dysfunction employed to predict sepsis-associated mortality. However, the relationship between the nSOFA score and bronchopulmonary dysplasia (BPD) has not been investigated clearly. This study evaluates whether the nSOFA score within 72 h after delivery could be used to predict the occurrence of BPD in very preterm infants. Methods: In this retrospective, single-center cohort study, preterm infants born between 2019 and 2021 were investigated, the nSOFA score was calculated from medical records after admission to the neonatal intensive care unit (NICU) within 72 h after delivery, and the peak value was used for calculation. A logistic regression model was used to evaluate the relationship between the nSOFA score and BPD. Propensity score matching and subgroup analysis were performed to verify the reliability of the results. Results: Of 238 infants meeting the inclusion criteria, 93 infants (39.1%) were diagnosed with BPD. The receiver operating characteristic curve of the nSOFA score in predicting BPD was 0.790 [95% confidence interval (CI): 0.731-0.849]. The logistic regression model showed that an increment of one in the nSOFA score was related to a 2.09-fold increase in the odds of BPD (95% CI: 1.57-2.76) and 6.36-fold increase when the nSOFA score was higher than 1.5 (95% CI: 2.73-14.79). Conclusions: The nSOFA score within 72 h after delivery is independently related to BPD and can be used to identify high-risk infants and implement early interventions.

[Progress in the application of novel nano-materials to the safety analysis of agricultural products].

The quality and safety of agricultural products are strongly related to human livelihood. Thus, the government and consumers have recently paid increased attention to the quality and safety of agricultural products. The development of efficient, rapid, and sensitive analytical methods for detecting pesticides, veterinary drugs, heavy metals, mycotoxins, and environmental pollutants in agricultural products is of great significance. Owing to the complexity of many sample matrices and the low concentration of pollutants in a typical sample, appropriate sample pretreatment steps are necessary to enrich pollutants in agricultural products. Solid-phase extraction (SPE) is the most widely used sample pretreatment technology; in this technique, the adsorbent generally determines the selectivity and efficiency of the extraction process. An increasing number of novel materials have been used as SPE adsorbents. The extraction efficiency, extraction selectivity, and analytical throughput of SPE could be greatly improved by combining these novel materials with various extraction modes (e. g., solid-phase microextraction, dispersed SPE, and magnetic SPE (MSPE)) during sample preparation. Because of their large specific surface area and high affinity toward target analytes, nanomaterials are often used as SPE adsorbents, thereby greatly improving the selectivity and sensitivity of the analytical technology. More importantly, these materials have become a priority area of research on preconcentration technologies for trace compounds in agricultural products. This paper summarizes the adsorption characteristics of several new nanomaterials, including magnetic materials, carbon-based materials, metal nanomaterials (MNs), metal oxide nanomaterials (MONs), metal organic frameworks (MOFs), and covalent organic frameworks (COFs). These nanomaterials present numerous advantages, such as large specific surface areas, high adsorption capacities, and tailorable structural designs. MSPE employs magnetic materials as sorbents to afford fast dispersion and efficient recycling when applied to complex sample matrices under an external magnetic field. The use of MSPE can avoid several typical problems associated with SPE such as poor adsorbent packing and high pressure, thereby greatly simplifying the pretreatment process and providing a high flux for sample analysis. Carbon-based materials are powdered or bulk nonmetallic solid materials with carbon as the main component; carbon and nitrogen materials, mesoporous carbon, carbon nanotubes, and graphene are some examples of these materials. These materials provide large specific surface areas, abundant pore structures, good thermal stability, high mechanical strength and adsorption capacity, and controllable morphology. Pure and modified carbon nanomaterials have been successfully used to purify target analytes from agricultural products. Given their unique physical and chemical properties, MNs and MONs have attracted significant interest for use in sample preparation. MNs and MONs with excellent thermal and mechanical stabilities show good resistance to a wide pH range and diverse organic solvents, which is crucial in adsorbent-based extraction methods. The surface of these materials can be easily modified with various ligands to improve their selectivity. MOFs and COFs present many advantages such as large specific surface areas, high porosity, adjustable pore performance, and good thermal stability. Several methods that employ novel adsorbent materials to analyze pollutants in a variety of agricultural products, such as chromatography, spectroscopy, mass spectrometry, and other detection technologies, have been established. This paper also reviews the application of adsorbent materials in the analysis of agricultural product quality and safety, and discusses the future development trends of these sorbents in sample preparation for the safety analysis of agricultural products.

Microbiota from healthy mice alleviates cognitive decline via reshaping the gut-brain metabolic axis in diabetic mice.

Diabetic cognitive decline has been associated with the gut microbial disorders, but its potential gut-brain axis mechanisms remain unclear. Herein we transplanted the gut microbiota from healthy mice into type 1 diabetic (T1D) mice and then investigated the effect of fecal microbiota transplantation (FMT) on cognitive function and the gut-brain metabolic axis. The results demonstrate that FMT from healthy mice effectively improved the learning and memory abilities in T1D mice, and significantly reduced neuroinflammation and neuron injury in the cortex and hippocampus. Moreover, FMT partly reversed the gut microbiota and gut-brain metabolic disorders, particularly glutamate metabolism. In vitro study, we found that glutamate notably decreased microglia activation and the expression levels of proinflammatory factor. Hence, our study suggests that glutamate serves as a key signal metabolite connecting the gut to brain and affects cognitive functions.

Association of obesity with high retears and complication rates, and low functional scores after rotator cuff repair: a systematic review and meta-analysis.

BACKGROUND: Obesity influences the outcomes of orthopedic surgeries such as total knee arthroplasty and spinal surgery. However, the effect of obesity on the outcomes of rotator cuff repair is unknown. This systematic review and meta-analysis aimed to examine the effect of obesity on rotator cuff repair outcomes. METHODS: PubMed, EMBASE, Web of Science, and Cochrane Library databases were searched to identify relevant studies published from their inception till July 2022. Two reviewers independently screened titles and abstracts using the specified criteria. Articles were included if they indicated the effect of obesity on rotator cuff repair and the related outcomes after surgery. Review Manager 5.4.1 software was used to perform statistical analysis. RESULTS: Thirteen articles involving 85,497 patients were included. Obese patients had higher retear rates than nonobese patients (odds ratio [OR] 2.58, 95% confidence interval [CI] 1.23-5.41, P = .01), lower American Shoulder and Elbow Surgeons scores (mean difference [MD]: -3.59, 95% CI: -5.45 to [-1.74]; P = .0001), higher visual analog scale for pain (mean difference: 0.73, 95% CI: 0.29-1.17; P = .001), higher reoperation rates (OR 1.31, 95% CI 1.21-1.42, P < .00001), and higher rates of complications (OR 1.57, 95% CI 1.31-1.87, P = .000). Obesity did not affect the duration of surgery (MD: 6.03, 95% CI: -7.63 to 19.69; P = .39) or external rotation of the shoulder (MD: -1.79, 95% CI: -5.30 to 1.72; P = .32). CONCLUSION: Obesity is a significant risk factor for retear and reoperation after rotator cuff repair. Furthermore, obesity increases the risk of postoperative complications and leads to lower postoperative American Shoulder and Elbow Surgeons scores and higher shoulder visual analog scale for pain.

Insights from ß-Conglycinin and Glycinin into the Mechanism of Nanoliposome-Soybean Protein Isolate Interactions.

The interaction mechanism between nanoliposomes (NL) and a soybean protein isolate (SPI) was investigated via the complexation between NL and two major components of SPI, i.e., ß-conglycinin (7S) and glycinin (11S). The endogenous fluorescence emissions of 7S and 11S were statically quenched after complexation with NL, and the polarity of the SPI fluorophore increased. The interaction between NL and SPI was exothermic and spontaneous, 7S/11S secondary structures were altered, and more hydrophobic groups were exposed on protein surfaces. Moreover, the NL-SPI complex had a large zeta potential to attain system stability. Hydrophobic forces and hydrogen bonds played vital roles in the interaction between NL and 7S/11S, and a salt bridge was also involved in the NL-11S interaction. The binding characteristics between NL and 7S/11S were chiefly governed by the protein characteristics, such as amino acid composition, surface hydrophobicity, and advanced structure. These findings could deepen the understanding of the interaction mechanism between NL and SPI.

Target-switched artificial biochemical circuit for a versatile and sensitive colorimetric detection platform.

The presence of the target causes the structural switch of a metastable hairpin template probe (MH) for triggering a self-primed artificial biochemical circuit to produce numerous DNAzyme replicates, which in turn results in substantial signal amplification for highly sensitive colorimetric determination of nucleic acids.

A Comprehensive Review on the Excitation-Emission Matrix Fluorescence Spectroscopic Characterization of Petroleum-Containing Substances: Principles, Methods, and Applications.

Petroleum-containing substance (PCS) is a general term used for petroleum and its derivatives. A comprehensive characterization of PCSs is crucial for resource exploitation, economic development and environmental protection. Fluorescence spectroscopy, especially excitation-emission matrix fluorescence (EEMF) spectroscopy, has been proved to be a powerful tool to characterize PCSs since its remarkable sensitivity, selectivity, simplicity and high efficiency. However, there is a lack of systematic review focusing on this field in the literature. This paper reviews the fundamental principles and measurements of EEMF for characterizing PCSs, and makes a systematic introduction to various information mining methods including basic peak information extraction, spectral parameterization and some commonly used chemometric methods. In addition, recent advances in applying EEMF to characterize PCSs during the whole life-cycle process of petroleum are also revisited. Furthermore, the current limitations of EEMF in the measurement and characterization of PCSs are discussed and corresponding solutions are provided. For promoting the future development of this field, the urgent need to build a relatively complete EEMF fingerprint library to trace PCSs, not only pollutants but also crude oil and petroleum products, is proposed. Finally, the extensions of EEMF to high-dimensional chemometrics and deep learning are prospected, with the expectation of solving more complex systems and problems.