N1,N12-diacetylspermine (DiAcSpm), a promising biomarker for cancer diagnosis, presents significant quantification challenges due to the structural homology within the polyamine family. To address this issue, we engineered a molecularly imprinted monolithic (MIM) column functionalized with biomimetic phosphorylcholine (PC) as functional monomer for the selective recognition of DiAcSpm in human urine. The zwitterionic polymer was synthesized via thermally initiated polymerization, with its morphology and pore architecture characterized through scanning electron microscopy (SEM) and brunauer-emmett-teller (BET) analysis. After optimizing capillary microextraction (CME) parameters, the MIM demonstrated a broad linear response (10-500 μM), a low detection limit (3.3 μM, S/N = 3), and high recoveries (76.8-91.2%) when coupled with HPLC-UV analysis. The biomimetic PC-based recognition significantly improved selectivity against key structural analogs, such as spermine, in complex biological matrices. This study underscores the potential of zwitterionic-based MIMs as a robust and efficient platform for the sensitive and selective monitoring of acetylated polyamines in clinical settings.
Alicia Manzanares-Pedrosa, Joanna Szumilas, Teresa Ayats
et al.
Thermophilic Campylobacter spp. are the main cause of gastrointestinal illness in humans through contaminated food. Poultry and poultry products are the main sources of Campylobacter infection. Epidemiological data on Campylobacter prevalence and load in broiler livers remain limited and its presence in this offal may be associated with the caecal load. Hence, this study aimed to determine the prevalence and levels of Campylobacter in chicken livers, both from the surface and inner tissue, compared with that of caeca, by sampling 56 flocks from two slaughterhouses in Spain. Three carcasses per flock were randomly collected during evisceration (n = 168 livers and caecal contents). Overall Campylobacter prevalence was 57.1 % in caecal samples, 77.9 % in surface liver samples and 35.7 % in the inner tissue liver. C. jejuni was the most common species in all sample types and coinfections with C. coli were more prevalent in livers than in the caeca samples. However, there was no relationship between Campylobacter species (C. jejuni, C. coli) and sample type (P > 0.05). The data highlights the role of chicken offal as a potential source of human campylobacteriosis, particularly because of the high Campylobacter load (>103 CFU/liver) in a high proportion of the surface liver samples (40.1 %). However, this high load was only detected in 6.6 % of the inner tissue livers. Restriction fragment length polymorphism (RFLP) analysis revealed a high genetic diversity with 107 different profiles among 473 genotyped Campylobacter isolates. Translocation of Campylobacter strains was demonstrated, with the same RFLP profile identified in isolates from the caeca and the inner liver tissue of the same carcass (14.9 %). Cross-contamination was also revealed, since the same RFLP profile was identified in isolates from the caeca and the surface of the liver from the same carcass (11.9 %). Targeted measures on broiler farms and slaughterhouses to reduce Campylobacter prevalence and cross-contamination in chicken offal will help to reduce the risk of campylobacteriosis for consumers.
Aleksanteri Hamalainen, Aku Karhinen, Jesse Miettinen
et al.
Rotating machines are extremely common in many industries, and their maintenance involves substantial costs and labor. Most recent studies aiming to automate fault diagnosis have focused on deep learning, but industry adoption has been slow owing to the lack of well-curated datasets and the complexity of the methods. We propose a new method called Rapid Few-shot Condition Monitoring (Rapid-FSCM), which enables the rapid deployment of deep learning-based condition monitoring models and is readily extensible to future advancements in the field. This will make it simpler for the industry to conduct machine condition monitoring without the cost of an expert. Rapid-FSCM utilizes few-shot learning and the InceptionTime convolutional neural network to enable training on data from a related base domain more readily available than data from the target domain. In addition, the prototypical networks method for few-shot learning is modified to enable the deployment of the model as an anomaly detector, even before any fault samples have been recorded. After faults have occurred and been recorded, the model demonstrates the ability to initiate fault diagnosis without further retraining. Validated with three datasets, two gear datasets from a test bench with complex features, and the CWRU bearing dataset, the model was shown to have high accuracy in target domains containing unseen faults, sensors, operating conditions, and even entirely new components. The developed method can be used to rapidly deploy a condition monitoring model for any rotating machine without the need to first conduct a large data acquisition process.