Simultaneous Analysis of 53 Pesticides in Safflower (Carthamus tinctorius L.) by Using LC-MS/MS Coupled with a Modified QuEChERS Technique.

OBJECTIVE: An optimized quick, easy, cheap, effective, rugged, and safe (QuEChERS) technique was investigated and compared with the conventional QuEChERS technique for the simultaneous analysis of fifty-three pesticide residues in safflower using ultra-high performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS). METHOD: Graphitic carbon nitride (g-C3N4) consisting of a major amount of carbon and nitrogen with a large surface area was used as a QuEChERS adsorbent instead of graphitized carbon black (GCB) for safflower extraction purification. Validation experiments were performed using spiked pesticide samples, and real samples were analyzed. RESULTS: The linearity of the modified QuEChERS technique was evaluated with high coefficients of determination (R-2) being higher than 0.99. The limits of detection were <10 µg/kg. The spiked recoveries ranged from 70.4% to 97.6% with a relative standard deviation of less than 10.0%. The fifty-three pesticides exhibited negligible matrix effects (<20%). Thiamethoxam, acetamiprid, metolachlor, and difenoconazole were detected in real samples using an established method. CONCLUSION: This work provides a new g-C3N4-based modified QuEChERS technique for multi-pesticide residue analysis in complex food matrices.

Rapid determination of 134 pesticides in tea through multi-functional filter cleanup followed by UPLC-QTOF-MS.

Ensuring the safety of tea requires effective methods for the simultaneous analysis of pesticide residues in the product. A sensitive and reliable method to scan for 134 pesticide residues in tea was developed that employs a novel Multi-Functional Filter (MFF) based on d-SPE extraction and ultra-high-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry. The adsorption material was developed by porous polyvinylpolypyrrolidone (PVPP) for the removal of polyphenols. Acetonitrile extraction was passed through a syringe and then detected by UPLC-Q-TOF-MS. Method validation revealed satisfactory linearity with correlation coefficients higher than 0.985 for all pesticides. All limits of quantification were below 10 µg/kg. The matrix effects of 133 of the pesticides were nearly negligible (<20%), except for Sebutylazine (=22%). The recoveries at two spiked levels (50, 100 µg/kg) were 66.83-118.33%, and the Relative standard deviation (RSD) was lower than 20%, indicating accuracy and precision of the new method.

Using molecular functional networks to manifest connections between obesity and obesity-related diseases.

Obesity is a primary risk factor for many diseases such as certain cancers. In this study, we have developed three algorithms including a random-walk based method OBNet, a shortest-path based method OBsp and a direct-overlap method OBoverlap, to reveal obesity-disease connections at protein-interaction subnetworks corresponding to thousands of biological functions and pathways. Through literature mining, we also curated an obesity-associated disease list, by which we compared the methods. As a result, OBNet outperforms other two methods. OBNet can predict whether a disease is obesity-related based on its associated genes. Meanwhile, OBNet identifies extensive connections between obesity genes and genes associated with a few diseases at various functional modules and pathways. Using breast cancer and Type 2 diabetes as two examples, OBNet identifies meaningful genes that may play key roles in connecting obesity and the two diseases. For example, TGFB1 and VEGFA are inferred to be the top two key genes mediating obesity-breast cancer connection in modules associated with brain development. Finally, the top modules identified by OBNet in breast cancer significantly overlap with modules identified from TCGA breast cancer gene expression study, revealing the power of OBNet in identifying biological processes involved in the disease.