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BackgroundTeduglutide, the first glucagon-like peptide 2 analogue, has been demonstrated to facilitate the absorption of gut nutrient and lessen the need for parenteral assistance in patients with Short Bowel Syndrome (SBS). However, its adverse drug events (AEs) are primarily documented in clinical trials, with a deficit in real-world data. This study evaluates the AEs profile of teduglutide based on Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) data.MethodA disproportionality analysis of FAERS data from Quarter 1 (Q1) 2013 to Quarter 3 (Q3) 2023 was conducted to examine the association between teduglutide and adverse events, employing Reporting Odds Ratio (ROR), Proportional Reporting Ratio (PRR), Bayesian Confidence Propagation Neural Network (BCPNN), and Empirical Bayesian Geometric Mean (EBGM) methods.ResultsOut of 13,809,302 reports in the FAERS database, 10,114 reports identified teduglutide as the “primary suspect” in AEs identification. During the dosing observation period, the median occurrence of adverse events was 393 days (interquartile range [IQR] 97–996 days). Teduglutide-associated AEs occurred in 27 System Organ Classes (SOC), of which renal and urinary disorders is not mentioned in the specification. Based on the four algorithms, a total of 260 major disproportionality preferred terms (PTs) were filtered out, including previously unreported AEs including weight decreased (n = 805), vascular device infection (n = 683), dehydration (n = 596) and nephrolithiasis (n = 146).ConclusionOur findings corroborate the AEs listed in the teduglutide prescribing information and additionally unveil new adverse reaction signals such as nephrolithiasis. These discoveries could aid in clinical monitoring and risk identification for teduglutide.

Abstract Roundabout guidance receptor 2 (Robo2) is closely related to malignant tumors such as pancreatic cancer and liver fibrosis, but there is no relevant research on the role of Robo2 in HCC. The study will further explore the function and mechanism of Robo2 and its downstream target genes in HCC. Firstly, Robo2 protein levels in human HCC tissues and paired adjacent normal liver tissues were detected. Then we established HepG2 and Huh7 hepatoma cell lines with knock-down Robo2 by transfection with lentiviral vectors, and examined the occurrence of EMT, proliferation and apoptosis abilities in HCC cells by western blot, flow cytometry, wound healing assay and TUNEL staining. Then we verified the interaction between Robo2 and its target gene by Co-IP and immunofluorescence co-staining, and further explored the mechanism of Robo2 and YB-1 by rescue study. The protein expression level of Robo2 in HCC was considerably higher than that in the normal liver tissues. After successfully constructing hepatoma cells with knock-down Robo2, it was confirmed that down-regulated Robo2 suppressed EMT and proliferation of hepatoma cells, and accelerated the cell apoptosis. High-throughput sequencing and validation experiments verified that YB-1 was the downstream target gene of Robo2, and over-expression of YB-1 could reverse the apoptosis induced by Robo2 down-regulation and its inhibitory effect on EMT and proliferation. Robo2 deficiency inhibits EMT and proliferation of hepatoma cells and augments the cell apoptosis by regulating YB-1, thus inhibits the occurrence of HCC and provides a new strategy for the treatment of HCC.

Effective resource allocation can exploit the advantage of intelligent reflective surface (IRS) assisted mobile edge computing (MEC) fully. However, it is challenging to balance the limited energy of MTs and the strict delay requirement of their tasks. In this paper, in order to tackle the challenge, we jointly optimize the offloading delay and energy consumption of mobile terminals (MTs) to realize the delay-energy tradeoff in an IRS-assisted MEC network, in which non-orthogonal multiple access (NOMA) and multiantenna are applied to improve spectral efficiency. To achieve the optimal delay-energy tradeoff, an offloading cost minimization model is proposed, in which the edge computing resource allocation, signal detecting vector, uplink transmission power, and IRS phase shift coefficient are needed to be jointly optimized. The optimization of the model is a multi-level fractional problem in complex fields with some coupled high dimension variables. To solve the intractable problem, we decouple the original problem into a computing subproblem and a wireless transmission subproblem based on the uncoupled relationship between different variable types. The computing subproblem is proved convex and the closed-form solution is obtained for the edge computing resource allocation. Further, the wireless transmission subproblem is solved iteratively through decoupling the residual variables. In each iteration, the closed-form solution of residual variables is obtained through different successive convex approximation (SCA) methods. We verify the proposed algorithm can converge to an optimum with polynomial complexity. Simulation results indicate the proposed method achieves average saved costs of 65.64%, 11.24%, and 9.49% over three benchmark methods respectively.

ABSTRACTAs the boom of data storage and processing, brain-inspired computing provides an effective approach to solve the current problem. Various emerging materials and devices have been reported to promote the development of neuromorphic computing. Thereinto, the neuromorphic device represented by memristor has attracted extensive research due to its outstanding property to emulate the brain’s functions from synaptic plasticity, sensory-memory neurons to some intelligent behaviors of living creatures. Herein, we mainly review the progress of these brain functions mimicked by neuromorphic devices, concentrating on synapse (i.e. various synaptic plasticity trigger by electricity and/or light), neurons (including the various sensory nervous system) and intelligent behaviors (such as conditioned reflex represented by Pavlov’s dog experiment). Finally, some challenges and prospects related to neuromorphic devices are presented.

Abstract Purpose To develop new and effective biomarkers for the diagnosis of colorectal cancer (CRC). Experimental design The serum expression of ITGB4 (49 CRC and 367 HC) was detected by enzyme‐linked immunosorbent assay (ELISA), and its diagnostic value was analyzed using the receiver operating characteristic (ROC) curve. The sensitivity and specificity of ITGB4 in CRC diagnosis were calculated through statistical analysis. The optimal clinical cutoff value was calculated using the Youden index, and diagnostic efficacy was analyzed in a larger serum sample (98 CRC and 1631 non‐CRC). The expression of ITGB4 was measured by CyTOF (cell experimental technology) at the single‐cell level, and characteristics were analyzed using viSNE and SPADE TREE. Results Serum ITGB4 and CEA levels were significantly higher in CRC patients than in HC and non‐CRC patients. The use of serum ITGB4 levels for the diagnosis of CRC has a high sensitivity (79%) but not high specificity when the clinical cutoff value was 0.70 ng/mL. However, the optimal cutoff value was 1.6 ng/mL with 86.2% specificity and 52.0% sensitivity, and the diagnostic efficacy was greatly improved with high specificity (82.0%) and sensitivity (71.4%) when combined with CEA. ITGB4 expression characteristics were measured and related to the expression of EpCAM, Ck8/18, and perforin at the single‐cell level. Single‐cell analysis showed that cell clusters with low expression of CK8/18 and ITGB4 were more sensitive to 5FU and radiotherapy (RT). Conclusions ITGB4 is an effective diagnostic serum biomarker and a potential therapeutic target for CRC.