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Background: As the common antibacterial drugs in folic acid oral liquid, parabens are listed as mandatory substances in the quality standard. Both the Chinese Pharmacopoeia and the United States Pharmacopoeia use high performance liquid chromatography for the determination of folic acid, but the quantitative methods of parabens are different. Pharmacopoeias use different instruments to quantify folic acid and parabens, resulting in cumbersome and cumbersome detection methods. Objective: Without changing the type of instrument and mobile phase, two methods were established for the determination of folic acid and parabens (methyl paraben; ethyl paraben; propyl paraben) using respective wavelengths and flow comparisons Propyl benzoate) high performance liquid chromatography method. Method: Chromatographic separation was achieved on an Agilent 5 TC-C18 HPLC column (5 μm; 250 μm × 4.6 mm) maintained at 25 °C (column temperature). The mobile phase consisted of phosphate buffer (pH 4.0)-methanol. When the ratio is 99:1, it is used to determine the content of folic acid, and when the ratio is 79:21, it is used to determine the content of antimicrobial agents. The flow rate used was 1.2 mL/min, the injection volume of folic acid was 20 µL, and the injection volume of bacteriostatic agent was 50 µL. In addition, the blue applicability grade index (BAGI) and analytical greenness (AGREE) metric tools were used to evaluate the greenness and environmental friendliness of the developed methods. Results: The method has a good linear relationship with R2 ≥ 0.9995, the average recovery rate of the two methods is ≥ 95%, and the relative standard deviation (RSD%) accuracy is less than 0.21%. The BAGI tool characterizes the developed method as green. The AGREE score is around 0.5, and the method is also largely consistent with the principles of green analytical chemistry. Conclusions: The HPLC method was established for the rapid determination of folic acid and antibacterial agent of parabens in folic acid. The method has high accuracy, strong specificity, high recovery rate, good stability and environmental friendliness. Compared with the method in the pharmacopoeia, it has strong resistance to complex matrix interference, greatly shortens the detection time, and has little damage to the instrument and chromatographic column. It can be used for the quality standard of folic acid oral liquid. [ABSTRACT FROM AUTHOR]

Background: The etiopathogenesis of hepatic stellate cells (HSC) activation has yet to be completely comprehended, and there has been broad concern about the interplay between amino acid transporter and cell proliferation. This study proposed exploring the molecular mechanism from amino acid transport-related genes in HSC activation by bioinformatic methods, seeking to identify the potentially crucial biomarkers. Methods: GSE68000, the mRNA expression profile dataset of activated HSC, was applied as the training dataset, and GSE67664 as the validation dataset. Differently expressed amino acid transport-related genes (DEAATGs), GO, DO, and KEGG analyses were utilized. We applied the protein-protein interaction analysis and machine learning of LASSO and random forests to identify the target genes. Moreover, single-gene GESA was executed to investigate the potential functions of target genes via the KEGG pathway terms. Then, a ceRNA network and a drug-gene interaction network were constructed. Ultimately, correlation analysis was explored between target genes and collagen alpha I (COL1A), alpha-smooth muscle actin (α-SMA), and immune checkpoints. Results: We identified 15 DEAATGs, whose enrichment analyses indicated that they were primarily enriched in the transport and metabolic process of amino acids. Moreover, two target genes (SLC7A5 and SLC1A5) were recognized from the PPI network and machine learning, confirmed through the validation dataset. Then single-gene GESA analysis revealed that SLC7A5 and SLC1A5 had a significant positive correlation to ECM−receptor interaction, cell cycle, and TGF−β signaling pathway and negative association with retinol metabolism conversely. Furthermore, the mRNA expression of target genes was closely correlated with the COL1A and α-SMA, as well as immune checkpoints. Additionally, 12 potential therapeutic drugs were in the drug-gene interaction network, and the ceRNA network was constructed and visualized. Conclusion: SLC7A5 and SLC1A5, with their relevant molecules, could be potentially vital biomarkers for the activation of HSC. [ABSTRACT FROM AUTHOR]

The objective of this study was to compare the effects of biochar extract (BE) and mineral potassium fulvic acid (MPFA) on the salt tolerance and growth of Bok Choy (Shanghai Bok Choy) under saline conditions, aiming to utilize saline groundwater resources in the Yellow River Delta region. Based on the mineralization level of saline groundwater in the Yellow River Delta region, all the treatments were cultured in a nutrient solution containing 6 g·L−1 NaCl. Nine treatments were set up: BE (C1: 1 g, C2: 5 g, C3: 10 g, C4: 15 g·L−1), MPFA (H1: 0.2 g, H2: 0.4 g, H3: 0.6 g, H4: 0.8 g·L−1), and a control (CK). The results showed that both the BE and MPFA reduced MDA accumulation, increased SOD and CAT enzyme activities, thereby increasing the number of effective leaves, the maximum leaf length, and the maximum leaf width of Shanghai Bok Choy. However, MPFA was better than the BE at increasing the antioxidant enzyme activities and total chlorophyll content. Correlation analysis revealed that the hydrogen peroxide content and CAT enzyme activity were the most important factors affecting Shanghai Bok Choy yield. Compared to the CK, the BE treatments reduced the hydrogen peroxide content by 1.1–46.4% and increased the CAT enzyme activity by 4.1–68.0%, while the MPFA treatments increased the hydrogen peroxide content and decreased the CAT enzyme activity. Consequently, the fresh weight of Shanghai Bok Choy treated with the BE was 11.01–112.21% higher than the MPFA treatments and 5.17–49.70% higher than the CK. Overall, C3 had significantly lower hydrogen peroxide content than the CK and the highest CAT enzyme activity, which was markedly better than the MPFA treatments. This suggests that C3 may be an effective method for improving the salt tolerance and growth of Shanghai Bok Choy under salt stress. This study provides favorable data to support the solution to the problem of agricultural water use and the realization of sustainable development in the Yellow River Delta. [ABSTRACT FROM AUTHOR]

Assessing leakage risks in water distribution networks (WDNs) and implementing preventive monitoring for high-risk pipelines has become a widely accepted approach for leakage control. However, existing methods face significant data barriers between Geographic Information System (GIS) and leakage prediction systems. These barriers hinder traditional pipeline risk assessment methods, particularly when addressing challenges such as data imbalance, poor model interpretability, and lack of intuitive prediction results. To overcome these limitations, this study proposes a leakage assessment framework for water distribution networks based on multiple attention mechanisms and a generative model-based data balancing method. Extensive comparative experiments were conducted using water distribution network data from B2 and B3 District Metered Areas in Zhengzhou. The results show that the proposed model, optimized with a balanced data method, achieved a 40.76% improvement in the recall rate for leakage segment assessments, outperforming the second-best model using the same strategy by 1.7%. Furthermore, the strategy effectively enhanced the performance of all models, further proving that incorporating more valid data contributes to improved assessment results. This study comprehensively demonstrates the application of data-driven models in the field of "smart water management", providing practical guidance and reference cases for advancing the development of intelligent water infrastructure. [ABSTRACT FROM AUTHOR]

As water distribution networks expand, evaluating pipeline network leakage risk has become increasingly crucial. Contrary to traditional evaluation methods, which are often hampered by subjective weight assignment, data scarcity, and high expenses, data-driven models provide advantages like autonomous weight learning, comprehensive coverage, and cost-efficiency. This study introduces a data-driven framework leveraging graph neural networks to assess leakage risk in water distribution networks. Employing geographic information system (GIS) data from a central Chinese city, encompassing pipeline network details and historical repair records, the model achieved superior performance compared to other data-driven approaches, evidenced by metrics such as precision, accuracy, recall, and the Matthews correlation coefficient. Further analysis of risk factors underscores the importance of factors like pipe age, material, prior failures, and length. This approach demonstrates robust predictive accuracy and offers significant reference value for leakage risk evaluation. [ABSTRACT FROM AUTHOR]

Accurate water demand forecasting is crucial for optimizing the strategies across multiple water sources. This paper proposes the Ensemble Empirical Mode Decomposition Granger causality test Dynamic Graph Attention Transformer Network (EG-DGATN) for multi-sensor cross-temporal granularity water demand forecasting, which combines the Transformer and Graph Neural Networks. It employs the EEMD–Granger test to delineate the interconnections among sensors and extracts the spatiotemporal features within the causal domain by stacking dynamical graph spatiotemporal attention layers. The experimental results demonstrate that compared to baseline models, the EG-DGATN improves the MAPE metrics by 2.12%, 4.33%, and 6.32% in forecasting intervals of 15 min, 45 min, and 90 min, respectively. The model achieves an R2 score of 0.97, indicating outstanding predictive accuracy and exceptional explanatory power for the target variable. This research highlights significant potential applications in predictive tasks within smart water management systems. [ABSTRACT FROM AUTHOR]

The work function can serve as a characteristic quantity to evaluate the catalytic activity due to its relationship with the surface structure of a material. However, what factors determine the influence of the work function on the electrochemical performance are still unclear. Herein, we elucidate the effect of the work function of Ag on the electrochemical reduction of CO2 to CO by controlling the ratio of exposed crystalline planes. To this end, the exposed surface of Ag powder was regulated by high-energy ball milling and its influence on CO2 reduction was investigated. The surface structure with more Ag(110) surface achieves higher activity and selectivity for CO production, resulting from the lower work function of Ag(110), which dramatically enhances the electron tunnelling probability during CO2 electroreduction. We found that a higher ratio of Ag(110) to Ag(100) leads to a lower work function and thus better electrochemical activity and selectivity. This study demonstrates a promising strategy to enhance the electrochemical performance of metal catalysts through tuning their work functions via regulating exposed crystalline planes. [ABSTRACT FROM AUTHOR]

Objectives: To develop and validate radiomics models utilizing endoscopic ultrasonography (EUS) images to distinguish insulinomas from non-functional pancreatic neuroendocrine tumors (NF-PNETs)., Methods: A total of 106 patients, comprising 61 with insulinomas and 45 with NF-PNETs, were included in this study. The patients were randomly assigned to either the training or test cohort. Radiomics features were extracted from both the intratumoral and peritumoral regions, respectively. Six machine learning algorithms were utilized to train intratumoral prediction models, using only the nonzero coefficient features. The researchers identified the most effective intratumoral radiomics model and subsequently employed it to develop peritumoral and combined radiomics models. Finally, a predictive nomogram for insulinomas was constructed and assessed., Results: A total of 107 radiomics features were extracted based on EUS, and only features with nonzero coefficients were retained. Among the six intratumoral radiomics models, the light gradient boosting machine (LightGBM) model demonstrated superior performance. Furthermore, a peritumoral radiomics model was established and evaluated. The combined model, integrating both the intratumoral and peritumoral radiomics features, exhibited a comparable performance in the training cohort (AUC=0.876) and achieved the highest accuracy in predicting outcomes in the test cohorts (AUC=0.835). The Delong test, calibration curves, and decision curve analysis (DCA) were employed to validate these findings. Insulinomas exhibited a significantly smaller diameter compared to NF-PNETs. Finally, the nomogram, incorporating diameter and radiomics signature, was constructed and assessed, which owned superior performance in both the training (AUC=0.929) and test (AUC=0.913) cohorts., Conclusion: A novel and impactful radiomics model and nomogram were developed and validated for the accurate differentiation of NF-PNETs and insulinomas utilizing EUS images., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as potential conflicts of interest., (Copyright © 2024 Mo, Huang, Wang, Zhao, Wu, Jiang and Qin.)

The work function can serve as a characteristic quantity to evaluate the catalytic activity due to its relationship with the surface structure of a material. However, what factors determine the influence of the work function on the electrochemical performance are still unclear. Herein, we elucidate the effect of the work function of Ag on the electrochemical reduction of CO 2 to CO by controlling the ratio of exposed crystalline planes. To this end, the exposed surface of Ag powder was regulated by high-energy ball milling and its influence on CO 2 reduction was investigated. The surface structure with more Ag(110) surface achieves higher activity and selectivity for CO production, resulting from the lower work function of Ag(110), which dramatically enhances the electron tunnelling probability during CO 2 electroreduction. We found that a higher ratio of Ag(110) to Ag(100) leads to a lower work function and thus better electrochemical activity and selectivity. This study demonstrates a promising strategy to enhance the electrochemical performance of metal catalysts through tuning their work functions via regulating exposed crystalline planes.

Objective To investigate the effect of sphingosine kinase 1 (SphK1) gene silence on the sensitivity to cisplatin (DDP) in RKO colon cancer cell line and the potential mechanism. Methods Targeted SphK1 gene lentivirus virus was constructed to infect RKO cells. The relative mRNA expression of SphK1 was detected by quantitative real-time PCR (qRT-PCR) and the protein level of SphK1 was determined by Western blotting. Then RKO cells were divided into three groups: down-regulated SphK1 group (shSphK1 group), negative control group (shControl group) and blank control group (control group). Cells of these groups were incubated for 0, 24, 48 and 72 hours with 0, 2, 4, 8, 16, 32 μg/mL DDP. After treatment, cell viability was evaluated by MTT assay. Cell apoptosis index was determined by TUNEL. The expressions of ki67, Bcl-2, caspase-9 and caspase-3 were tested by Western blotting. Results Down-regulation of SphK1 inhibited cell proliferation and enhanced apoptosis of RKO cells, expecially after exposed to DDP. Silence of SphK1 sensitized RKO cells to DDP in a concentration- and time-dependent manner. Cell proliferation of shSphK1 group was obviously reduced compared with control group or shControl group, and cell apoptosis rate of shSphK1 group significantly increased compared with control group or shControl group. Moreover, with the down-regulation of SphK1, the expressions of ki67 and Bcl-2 were depressed; the expressions of caspase-9 and caspase-3 were raised, especially after treated with DDP. Conclusion Down-regulation of SphK1 may decrease the expression of Bcl-2, increase the expressions of caspase-9 and caspase-3, inhibit cell proliferation, and promote cell apoptosis, thus improving chemosensitivity of colon cancer RKO cells to DDP.

Background: MicroRNA (miRNA) expressive alterations are associated with cancer and have potential diagnostic and prognostic values in various malignancies. Here, we summarize the global predictive role of miR-210 expression for survival in patients with a variety of carcinomas., Methods: Eligible studies were identified through multiple search strategies. Data were assembled from studies investigating the relationship between miR-210 expression and survival in cancer patients. Hazard ratio (HR) was used as the common measure of association across studies: relative risk (RR) was considered equivalent to HR. Combined hazard ratios (HRs) of miR-210 for outcome were analyzed., Results: A total of 10 studies dealing with various carcinomas were included for this global meta-analysis. For overall survival (OS), the pooled hazard ratio (HR) of higher miR-210 expression in cancerous tissue was 2.41 (95% CI: 1.31-4.44), which could significantly predict poorer survival in general carcinomas. For distant-free, relapse-free or progressive-free survival, elevated miR-210 was also a significant predictor, with a pooled HR of 2.84 (95% CI: 2.10-3.83). Importantly, subgroup analysis suggested that higher expression of miR-210 correlated with worse OS in breast cancer: HR 4.34, 95% CI: 1.63-11.55., Conclusions: Our findings reveal that miR-210 detection has a prognostic value in patients with cancer, especially in breast cancer.

Objective: To explore the methods to realize the growth curve fitting of coefficients of skewness-median-coefficient of variation (LMS) using different software, and to optimize growth curve statistical method for grass-root child and adolescent staffs., Methods: Regular physical examination data of head circumference for normal infants aging 3, 6, 9 and 12 months in Baotou City were analyzed. Statistical software such as SAS, R, STATA and SPSS were used to fit the LMS growth curve and the results were evaluated upon the user 's convenience, study circle, user interface, results display forms, software update and maintenance and so on., Results: Growth curve fitting results showed the same calculation outcome and each of statistical software had its own advantages and disadvantages. With all the evaluation aspects in consideration, R software excelled others in LMS growth curve fitting., Conclusion: R software have the advantage over other software in grass roots child and adolescent staff.