Metabolic profile, bioavailability and toxicokinetics of zearalenone-14-glucoside in rats after oral and intravenous administration by liquid chromatography high-resolution mass spectrometry and tandem mass spectrometry

Zearalenone-14-glucoside (ZEN-14G), a key modified mycotoxin, has attracted a great deal of attention due to the possible conversion to its free form of zearalenone (ZEN) exerting toxicity. In this study, the toxicokinetics of ZEN-14G were investigated in rats after oral and intravenous administration. The plasma concentrations of ZEN-14G and its major five metabolites were quantified using a validated liquid chromatography tandem mass spectrometry (LC-MS/MS) method. The data were analyzed via non-compartmental analysis using software WinNonlin 6.3. The results indicated that ZEN-14G was rapidly hydrolyzed into ZEN in vivo. In addition, the major parameters of ZEN-14G following intravenous administration were: area under the plasma concentration-time curve (AUC), 1.80 h.ng/mL; the apparent volume of distribution (V-Z), 7.25 L/kg; and total body clearance (CL), 5.02 mL/h/kg, respectively. After oral administration, the typical parameters were: AUC, 0.16 h.ng/mL; V-Z, 6.24 mL/kg; and CL, 4.50 mL/h/kg, respectively. The absolute oral bioavailability of ZEN-14G in rats was about 9%, since low levels of ZEN-14G were detected in plasma, which might be attributed to its extensive metabolism. Therefore, liquid chromatography high-resolution mass spectrometry (LC-HRMS) was adopted to clarify the metabolic profile of ZEN-14G in rats' plasma. As a result, eight metabolites were identified in which ZEN-14-glucuronic acid (ZEN-14GlcA) had a large yield from the first time-point and continued accumulating after oral administration, indicating that ZEN-14-glucuronic acid could serve a potential biomarker of ZEN-14G. The obtained outcomes would prompt the accurate safety evaluation of ZEN-14G

Mycotoxin exposure assessments in a multi-center European validation study by 24-hour dietary recall and biological fluid sampling

The European Food Consumption Validation (EFCOVAL) project includes 600 men and women from Belgium, the Czech Republic, France, the Netherlands, and Norway, who had given serum and 24-hour urine samples, and completed 24-hour dietary recall (24-HDR) interviews. Consumption, according to 24-HDR, was matched against the European Food Safety Authority (EFSA) databases of mycotoxin contaminations, via the FoodEx1 standard classifications, producing an indirect external estimate of dietary mycotoxin exposure. Direct, internal measurements of dietary mycotoxin exposure were made in serum and urine by ultra-performance liquid chromatography coupled to tandem mass spectrometry. For the first time, mycotoxin exposures were thoroughly compared between two 24-HDRs, and two 24-hour urine samples collected during the same days covered by the 24-HDRs. These measurements were compared to a single-time point serum measurement to investigate evidence of chronic mycotoxin exposure. According to 24-HDR data, all 600 individuals were exposed to between 4 and 34 mycotoxins, whereof 10 found to exceed the tolerable daily intake. Correlations were observed between two time points, and significant correlations were observed between concentrations in serum and urine. However, only acetyldeoxynivalenol, ochratoxin A, and sterigmatocystin were found to have significant positive correlations between 24-HDR exposures and serum, while aflatoxin G1 and G2, HT-2 toxin, and deoxynivalenol were associated between concurrent 24-HDR and 24-hour urine. Substantial agreements on quantitative levels between serum and urine were observed for the groups Type B Trichothecenes and Zearalenone. Further research is required to bridge the interpretation of external and internal exposure estimates of the individual on a time scale of hours. Additionally, metabolomic profiling of dietary mycotoxin exposures could help with a comprehensive assessment of single time-point exposures, but also with the identification of chronic exposure biomarkers. Such detailed characterization informs population exposure assessments, and aids in the interpretation of epidemiological health outcomes related to multi-mycotoxin exposure

EUS assisted transmural cholecystogastrostomy fistula creation as a bridge for endoscopic internal gallbladder therapy using a novel fully covered metal stent

BACKGROUND: Laparoscopic cholecystectomy (LC) has become the “gold standard” for treating symptomatic gallstones. Innovative methods, such as a scarless therapeutic procedure through a natural orifice are being introduced, and include transgastric or transcolonic endoscopic cholecystectomy. However, before clinical implementation, instruments still need modification, and a more convenient treatment is still needed. The aim of this study was to evaluate the feasibility of endoscopic internal gallbladder therapy such as cholecystolithotomy in an animal survival model. METHODS: Four pigs underwent endoscopic-ultrasound (EUS)-guided cholecystogastrostomy and the placement of a novel covered mental stent. Four weeks later the stents were removed and an endoscope was advanced into the gallbladder via the fistula, and cholecystolithotomy was performed. Two weeks later the pigs were sacrificed, and the healing of the fistulas was assessed. RESULTS: EUS-guided cholecystogastrostomy with mental stent deployment was successfully performed in all the animals. Four weeks after the procedure, the fistulas had formed and all the stents were removed. Endoscopic cholecystolithotomy was performed through each fistula. All the animals survived until they were sacrificed 2 weeks later. The fistulas were found to be completely healed. CONCLUSIONS: This study reports the first endoscopic transmural cholecystolithotomy after placement of a novel mental stent in an animal survival model

The Solution for The PST-KDD-2024 OAG-Challenge

In this paper, we introduce the second-place solution in the KDD-2024 OAG-Challenge paper source tracing track. Our solution is mainly based on two methods, BERT and GCN, and combines the reasoning results of BERT and GCN in the final submission to achieve complementary performance. In the BERT solution, we focus on processing the fragments that appear in the references of the paper, and use a variety of operations to reduce the redundant interference in the fragments, so that the information received by BERT is more refined. In the GCN solution, we map information such as paper fragments, abstracts, and titles to a high-dimensional semantic space through an embedding model, and try to build edges between titles, abstracts, and fragments to integrate contextual relationships for judgment. In the end, our solution achieved a remarkable score of 0.47691 in the competition

Vector spectrometer with Hertz-level resolution and super-recognition capability

High-resolution optical spectrometers are crucial in revealing intricate characteristics of signals, determining laser frequencies, measuring physical constants, identifying substances, and advancing biosensing applications. Conventional spectrometers, however, often grapple with inherent trade-offs among spectral resolution, wavelength range, and accuracy. Furthermore, even at high resolution, resolving overlapping spectral lines during spectroscopic analyses remains a huge challenge. Here, we propose a vector spectrometer with ultrahigh resolution, combining broadband optical frequency hopping, ultrafine microwave-photonic scanning, and vector detection. A programmable frequency-hopping laser was developed, facilitating a sub-Hz linewidth and Hz-level frequency stability, an improvement of four and six orders of magnitude, respectively, compared to those of state-of-the-art tunable lasers. We also designed an asymmetric optical transmitter and receiver to eliminate measurement errors arising from modulation nonlinearity and multi-channel crosstalk. The resultant vector spectrometer exhibits an unprecedented frequency resolution of 2 Hz, surpassing the state-of-the-art by four orders of magnitude, over a 33-nm range. Through high-resolution vector analysis, we observed that group delay information enhances the separation capability of overlapping spectral lines by over 47%, significantly streamlining the real-time identification of diverse substances. Our technique fills the gap in optical spectrometers with resolutions below 10 kHz and enables vector measurement to embrace revolution in functionality.Comment: 21 pages, 6 figure

Advancing Transformer Architecture in Long-Context Large Language Models: A Comprehensive Survey

Transformer-based Large Language Models (LLMs) have been applied in diverse areas such as knowledge bases, human interfaces, and dynamic agents, and marking a stride towards achieving Artificial General Intelligence (AGI). However, current LLMs are predominantly pretrained on short text snippets, which compromises their effectiveness in processing the long-context prompts that are frequently encountered in practical scenarios. This article offers a comprehensive survey of the recent advancement in Transformer-based LLM architectures aimed at enhancing the long-context capabilities of LLMs throughout the entire model lifecycle, from pre-training through to inference. We first delineate and analyze the problems of handling long-context input and output with the current Transformer-based models. We then provide a taxonomy and the landscape of upgrades on Transformer architecture to solve these problems. Afterwards, we provide an investigation on wildly used evaluation necessities tailored for long-context LLMs, including datasets, metrics, and baseline models, as well as optimization toolkits such as libraries, frameworks, and compilers to boost the efficacy of LLMs across different stages in runtime. Finally, we discuss the challenges and potential avenues for future research. A curated repository of relevant literature, continuously updated, is available at https://github.com/Strivin0311/long-llms-learning.Comment: 40 pages, 3 figures, 4 table