The giant reed, Arundo donax (A. donax) is a fast and naturally growing species in the Mediterranean Area indicated as one of the 15 invasive species with greatest impact by the European Commission within the Ecosystem Vulnerability Key Actrion.It is a greatly available but non-fully exploited material regarded as a problem both in agriculture and in watercourse management.This study explores the potential use of A. donax as an alternative material in the production of particleboard panels.The research, conducted in collaboration with the industrial sector, evaluates the mech. and phys. properties of sandwich particleboards in which part of the recycled wood chips are replaced with varying percentages of A. donax chips only in the core of the board.The work demonstrates the feasibility of such a board using industrial procedures and the capability of A. donax to improve the phys. and mech. performance of the recycled wood particleboard without altering the production process or adding resin.The particleboards were manufactured in three densities (550, 680 and 750 kg/m³) and tested for thickness swelling, surface soundness, internal bond and bending strength.The results reveal that particleboards containing 20-35 % of A. donax by mass, particularly for high densities, improved mech. properties and reduced the thickness swelling, meeting the requirements for class P4 particleboards resulting in an upgrade of the wood recycled panel′s classification.This investigation highlights the viability of integrating A. donax into particleboard production, potentially reducing reliance on imported wood, improving the mech. properties of recycled wood particleboards and promoting sustainable and locally sourced materials.

01 Jan 2025·Sensors and Actuators B: Chemical

Microscale cephalosporin ion-selective amperometric/voltammetric sensors based on a micropipette-supported liquid/liquid interface: Multiple purposes of transmembrane studies and real-time determinations

Author: Bai, Silan ; Xu, Zhidan ; Wang, Lishi ; Liu, Cheng ; You, Yongtao

The permeability of cephalosporins through cell membranes is crucial for their efficacy against gram-neg. bacteria.To address this, a microsoft biphasic sensor utilizing the interface between two immiscible electrolyte solutions (ITIES) has been introduced for studying the ion transfer of cefotiam (CTM), ceftazidime (CAZ) and cefepime (CPM).The typical voltammograms of cephalosporin ion transfer can be further used to determine the formal ion transfer potentials (Δwoφ'A), ion transfer Gibbs free energies (ΔwoG'A), effective hydrophilicity (logPi), geometry of micro-ITIES, diffusion coefficient (Dw) and ion transfer rate constant (k0).In addition, the partitioning of CTM+, CAZ+, and CPM+ in two phases at different pH values was revealed by the corresponding ion partition diagrams (IPDs).The IPDs can further elucidate the mechanism of cephalosporin ion transfer across the ITIES at different pH values.Finally, the determination of CTM+, CAZ+ and CPM+ was achieved via real-time chronoamperometry.An excellent linear range for cephalosporin ions with a satisfactory limit of detection was obtained.The findings show the superb selectivity and anti-interferent ability of micro-ITIES sensors toward cephalosporins.The constructed micro-ITIES sensors for cephalosporin could contribute to medicinal design, single microorganism metabolism anal., clin. diagnosis, and food safety inspection.

01 Dec 2024·Computational and Structural Biotechnology Journal

Predicting antimicrobial resistance in E. coli with discriminative position fused deep learning classifier

Article

Author: Jin, Canghong ; Yue, Min ; Shen, Yanyi ; Jia, Chenghao ; Hu, Wenkang ; Xu, Haidong

Escherichia coli (E. coli) has become a particular concern due to the increasing incidence of antimicrobial resistance (AMR) observed worldwide. Using machine learning (ML) to predict E. coli AMR is a more efficient method than traditional laboratory testing. However, further improvement in the predictive performance of existing models remains challenging. In this study, we collected 1937 high-quality whole genome sequencing (WGS) data from public databases with an antimicrobial resistance phenotype and modified the existing workflow by adding an attention mechanism to enable the modified workflow to focus more on core single nucleotide polymorphisms (SNPs) that may significantly lead to the development of AMR in E. coli. While comparing the model performance before and after adding the attention mechanism, we also performed a cross-comparison among the published models using random forest (RF), support vector machine (SVM), logistic regression (LR), and convolutional neural network (CNN). Our study demonstrates that the discriminative positional colors of Chaos Game Representation (CGR) images can selectively influence and highlight genome regions without prior knowledge, enhancing prediction accuracy. Furthermore, we developed an online tool (https://github.com/tjiaa/E.coli-ML/tree/main) for assisting clinicians in the rapid prediction of the AMR phenotype of E. coli and accelerating clinical decision-making.

100 Deals associated with Ceftezole Sodium

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Indication	Country/Location	Organization	Date
Bacterial Infections	-	-	-
Peritonitis	China	Shinpoong Pharmaceutical Co., Ltd.	-
Respiratory Tract Infections	China	Shinpoong Pharmaceutical Co., Ltd.	-
Sepsis	China	Shinpoong Pharmaceutical Co., Ltd.	-
Urinary Tract Infections	China	Shinpoong Pharmaceutical Co., Ltd.	-

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