Emerging innovations in portable chemical sensing devices: Advancements from microneedles to hydrogel, microfluidic, and paper-based platforms.

With the public heightened emphasis on mitigating the occurrence risks of health-related ailment and optimizing personal physical performance, portable chemical sensing devices emerged as an indispensable component of pervasive health monitoring. Chemical sensing enabled the immediate and on-site identification of biomarkers in biological fluids by integrating colorimetry, fluorescence, electrochemical, and other methods into portable sensor devices. These sensor devices incorporated microneedles, hydrogels, microfluidic modules, and papers, facilitating conformal human-device contact and providing several visual sensing options for disease prevention and healthcare management. This review systematically overviewed recent advancements in chemical sensors for marker detection, categorizing them based on monitoring device types. Furthermore, we also offered recommendations and opportunities for developing portable chemical sensing devices by summarizing sensor integration methods and tracking sites on the human body.

A prediction model of high-risk population for cardiovascular diseases / 预防医学

Objective@#To investigate the proportion of high-risk population for cardiovascular diseases (CVD) among residents at ages of 35 to 79 in Nanjing City, and establish a prediction model of high-risk population for CVD.@*Methods@#Residents at ages of 35 to 79 years were selected from Nanjing City using a multi-stage stratified cluster random sampling method from 2020 to 2021. Participants' demographic information, characteristics of lifestyle and blood biochemical index were collected using questionnaire surveys, physical examination and laboratory testing. The high-risk population for CVD were determined according to the Chinese Guidelines for Cardiovascular Disease Risk Assessment and Management, and the Chinese Guidelines for the Prevention and Treatment of Adult Dyslipidemia (2016 Revision). Predictive factors for high-risk population for CVD were screened using a multivariable logistic regression model. A nomogram was established and verified with receiver operation characteristic (ROC) curve. Hosmer-Lemeshow goodness of fit test was used to evaluate the fitting effect and Bootstrap method was used for internal verification.@*Results@#A total of 38 428 individuals were surveyed, including 17 970 males (46.76%) and 20 458 females (53.24%), and 25 714 individuals aged 35 to 59 years. There were 8 905 high-risk population for CVD, with a detection rate of 23.17%. Multivariable logistic regression analysis identified 9 factors affecting high-risk population for CVD. A prediction model was established for ln[P/(1-P)]=-7.305+2.107×age-0.366×gender+0.299×marital status-0.297×educational level+0.631×body mass index+0.013×sleep duration+0.096×edible salt intake+0.444×smoke-0.069×alcohol consumption. The area under ROC curve was 0.799 (95%CI: 0.794-0.805), the sensitivity and specificity were 0.731 and 0.753, indicating good differentiation. The nomogram based on the above factors indicated good calibration and stability.@*Conclusion@#The nomogram constructed by age, gender, marital status, educational level, body mass index, sleep duration, edible salt intake, smoking and alcohol consumption can be used to predict high-risk population for CVD.

Discovery of quality markers in Rubus Chingii Hu using UPLC-ESI-QTOF-MS.

Raspberry, the fruit of Rubus Chingii Hu, has been used as a traditional Chinese medicine (TCM) to nourish kidney and strengthen Yang-qi. In order to determine the quality of raspberry, the quality markers (Q-markers) of raspberry that can improve renal function were investigated using UPLC-ESI-QTOF-MS in this study. The results of serum pharmacochemistry indicated that six components rutin, ellagic acid, kaempferol-3-rutinoside, astragalin, tiliroside, and goshonoside F5 in raspberry were absorbed into rat blood. The HEK293 cells treated with cisplatin were used to evaluate the kidney-protecting activity of these absorbed components. All these components could markedly inhibit cell damage induced by cisplatin and restore the levels of malondialdehyde (MDA) and catalase (CAT) in the cells, suggesting that these components may be the Q-markers of raspberry. More importantly, except for ellagic acid, other five Q-markers in raspberries from Dexing of Jiangxi province were higher than those from most of other areas. It is well known that Dexing raspberry is the Dao-di herbs raspberry used in the clinic of Chinese Medicine, demonstrating that these components could be used as Q-markers of raspberry. This study provides a reliable and valuable method for quality evaluation of raspberry.

A strategy combining solid-phase extraction, multiple mass defect filtering and molecular networking for rapid structural classification and annotation of natural products: characterization of chemical diversity in Citrus aurantium as a case study.

Medicinal plants are complex chemical systems containing thousands of secondary metabolites. The rapid classification and characterization of the components in medicinal plants using mass spectrometry (MS) remains an immense challenge. Herein, a novel strategy is presented for MS through the combination of solid-phase extraction (SPE), multiple mass defect filtering (MMDF) and molecular networking (MN). This strategy enables efficient classification and annotation of natural products. When combined with SPE and MMDF, the improved analytical method of MN can perform the rapid annotation of diverse natural products in Citrus aurantium according to the tandem mass spectrometry (MS/MS) fragments. In MN, MS2LDA can be initially applied to recognize substructures of natural products, according to the common fragmentation patterns and neutral losses in multiple MS/MS spectra. MolNetEnhancer was adopted here to obtain chemical classifications provided by ClassyFire. The results suggest that the integrated SPE-MMDF-MN method was capable of rapidly annotating a greater number of natural products from Citrus aurantium than the classical MN strategy alone. Moreover, SPE and MMDF enhanced the effectiveness of MN for annotating, classifying and distinguishing different types of natural products. Our workflow provides the foundation for the automated, high-throughput structural classification and annotation of secondary metabolites with various chemical structures. The developed approach can be widely applied in the analysis of constituents in natural products.

Discovery and validation of quality markers of Fructus Aurantii against acetylcholinesterase using metabolomics and bioactivity assays.

Fructus Aurantii is a traditional medicated diet in East Asia. To determine the underlying chemical markers responsible for the quality and efficacy of Fructus Aurantii, a sensitive metabolomic method was applied to distinguish Fructus Aurantii in Jiangxi Province from other two geographical locations (Hunan Province and Chongqing City) in China. In the present study, multivariate analyses were adopted to compare chemical compositions in 21 batches of Fructus Aurantii samples. Among three geographical origins, 23 differential compounds were structurally identified. Serum pharmacochemistry exhibited that 22 components could be detected in rat serum. Six differential and absorbed components were selected as six potential markers. Statistical analysis revealed that the content of six markers varied widely in three origins of Fructus Aurantii. Six differential and absorbed components were evaluated further by biological activity. Neohesperidin, naringin, and meranzin showed inhibitory effect on acetylcholinesterase that regulates gastrointestinal motility in vitro and in silico, suggesting that these three components may be determined as the active biomarkers of Fructus Aurantii. These findings demonstrate the potential of biomarkers for identification and quality control of Fructus Aurantii.

Metabolic profiling of tyrosine kinase inhibitor nintedanib using metabolomics.

Nintedanib is a promising tyrosine kinase inhibitor for clinically treating idiopathic pulmonary fibrosis (IPF). Some clinical cases reported that nintedanib treatment can cause hepatotoxicity and myocardial toxicity. U. S. FDA warns the potential drug-drug interaction when it is co-administrated with other drugs. In order to understand the potential toxicity of nintedanib and avoid drug-drug interaction, the metabolism of nintedanib was systematically investigated in human liver microsomes and mice using metabolomics approach, and the toxicity of metabolites was predicted by ADMET lab. Nineteen metabolites were detected in vivo and in vitro metabolism, and 8 of them were undescribed. Calculated partition coefficients (Clog P) were used to distinguish the isomers of nintedanib metabolites in this study. The major metabolic pathways of nintedanib majorly included hydroxylation, demethylation, glucuronidation, and acetylation reactions. The ADMET prediction indicated that nintedanib was a substrate of the cytochrome P450 3A4 (CYP3A4) and P-glycoprotein (P-gp). And nintedanib and most of its metabolites might possess potential hepatotoxicity and cardiotoxicity. This study provided a global view of nintedanib metabolism, which could be used to understand the mechanism of adverse effects related to nintedanib and its potential drug-drug interaction.