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Companion diagnostics using biomarkers have gained prominence in guiding radiotherapy. However, biopsy-based techniques fail to account for real-time variations in target response and tumor heterogeneity. Herein, we design an activated afterglow/MRI probe as a companion diagnostics tool for dynamically assessing biomarker apurinic/apyrimidinic endonuclease 1(APE1) during radiotherapy in vivo. We employ ultrabright afterglow nanoparticles and ultrasmall FeMnOx nanoparticles as dual contrast agents, significantly broadening signal change range and enhancing the sensitivity of APE1 imaging (limit of detection: 0.0092 U/mL in afterglow imaging and 0.16 U/mL in MRI). We devise longitudinally and transversely subtraction-enhanced imaging (L&T-SEI) strategy to markedly enhance MRI contrast and signal-to-noise ratio between tumor and normal tissue of living female mice. The combined afterglow and MRI facilitate both anatomical and functional imaging of APE1 activity. This probe enables correlation of afterglow and MRI signals with APE1 expression, radiation dosage, intratumor ROS, and DNA damage, enabling early prediction of radiotherapy outcomes (as early as 3 h), significantly preceding tumor size reduction (6 days). By monitoring APE1 levels, this probe allows for early and sensitive detection of liver organ injury, outperforming histopathological analysis. Furthermore, MRI evaluates APE1 expression in radiation-induced abscopal effects provides insights into underlying mechanisms, and supports the development of treatment protocols. Companion diagnostics using biomarkers are promising for guiding radiotherapy, but biopsy-based techniques normally fail to detect real-time variations in target response and tumor heterogeneity. Herein, the authors report an activated afterglow/MRI probe as a companion diagnostics tool for dynamically assessing biomarker apurinic/apyrimidinic endonuclease 1(APE1) during radiotherapy in vivo. [ABSTRACT FROM AUTHOR]

Sulfonate esters, one of the genetic impurities, have gained significant attention in recent years due to their potential to cause genetic mutations and cancer. In this study, we reported a novel on‐line column‐switching liquid chromatography method for the quantitative analysis of three p‐toluene sulfonate alkyl esters in active pharmaceutical ingredients (APIs). The sensitivity was improved by large‐volume injection. A diluting modulation‐based sampling procedure was specifically devised to mitigate the impact of solvent effects and resolve the poor stability of isopropyl p‐toluene sulfonate alkyl ester. The established method was validated in terms of linearity, sensitivity, accuracy, stability, and robustness. The limit of quantitation level was determined to be 1 ng/mL (equivalent to 1 ppm relative to 1 mg/mL API samples). Besides, the method was applied for four APIs Tosuloxacin toluene sulfonate, Lapatinib toluene sulfonate, Nirapalib toluene sulfonate, and Capecitabine. The recoveries of all target analytes in the four‐drug substances were within 89%–110%. The expanded uncertainties of the measurements ranged from 6.8% to 8.4% at a coverage level of k = 2 and a confidence level of approximately 95%. The method described in this paper was compared with other published literature, demonstrating its simplicity and ease of standardization which could be further applied in the quality control and safety assessment of drug substances. [ABSTRACT FROM AUTHOR]

Modern convolutional neural networks (CNNs) play a crucial role in computer vision applications. The intricacy of the application scenarios and the growing dataset both significantly raise the complexity of CNNs. As a result, they are often overparameterized and have significant computational costs. One potential solution for optimizing and compressing the CNNs is to replace convolutional layers with low-rank tensor decomposition. The most suitable technique for this is Canonical Polyadic (CP) decomposition. However, there are two primary issues with CP decomposition that lead to a significant loss in accuracy. Firstly, the selection of tensor ranks for CP decomposition is an unsolved issue. Secondly, degeneracy and instability are common problems in the CP decomposition of contractional tensors, which makes fine-tuning the compressed model difficult. In this study, a novel approach was proposed for compressing CNNs by using CP decomposition. The first step involves using the sensitivity of convolutional layers to determine the tensor ranks for CP decomposition effectively. Subsequently, to address the degeneracy issue and enhance the stability of the CP decomposition, two novel techniques were incorporated: optimization with sensitivity constraints and iterative fine-tuning based on sensitivity order. Finally, the proposed method was examined on common CNN structures for image classification tasks and demonstrated that it provides stable performance and significantly fewer reductions in classification accuracy. [ABSTRACT FROM AUTHOR]

Aims Pulsed field ablation (PFA) is emerging as a non-thermal, tissue-specific technique for pulmonary vein isolation (PVI) in atrial fibrillation therapy. This pre-clinical study aims to investigate the feasibility and safety of PVI using a novel PFA system including a nanosecond-scale PFA generator, a novel lotos PFA catheter, and a customized 12 Fr steerable sheath. Methods and results A total of 11 Yorkshire swine were included in this study, with 4 in the acute cohort and 7 in the chronic cohort. Under general anaesthesia, transseptal puncture and pulmonary vein (PV) angiography was initially performed. The PFA catheter was navigated to position at the right and left PV antrum after the electroanatomic reconstruction of the left atrium. Biphasic PFA applications were performed on PVs in both the spindle-shaped and the lotos-shaped poses. Pulmonary vein isolation and PFA-associated safety were assessed 30 min after ablation in both cohorts and 30 days later in the chronic cohort. Detailed necropsy and histopathology were performed. Additional intracardiac echocardiography and coronary angiogram were evaluated for safety. All target PVs (n = 20) were successfully isolated on the first attempt. No spasm of coronary artery or microbubble was seen during the procedure. Eleven of 12 PVs (91.6%) remained in isolation at the 30-day invasive study. No evidence of PV stenosis was observed in any targets. However, transient diaphragm capture occurred in 17.6%. Histopathological examinations showed no evidence of collateral injury. Conclusion This study provides scientific evidence demonstrating the safety and efficacy of the novel PFA catheter and system for single-shot PVI, which shows great potential. [ABSTRACT FROM AUTHOR]

Magnetic resonance imaging (MRI) is a non‐invasive, radiation‐free imaging technique widely used for disease detection and therapeutic evaluation due to its infinite penetration depth. Magnetic nanoparticles (MNPs) have unique magnetic and physicochemical properties, making them ideal as contrast agents for MRI. However, the in vivo toxicity of MNPs, resulting from metal ion leakage and long‐term accumulation in the reticuloendothelial system (RES), limits their clinical application. To overcome these challenges, there is considerable interest in the development of renal‐clearable MNPs that can be completely cleared through the kidney, reducing retention time and potential toxic risks. In this review, we provide an overview of recent advancements in the development of renal‐clearable MNPs for disease imaging and treatment. We discuss the factors influencing renal clearance, summarize the types of renal‐clearable MNPs, their synthesis methods, and biomedical applications. This review aims to offer comprehensive information for the design and clinical translation of renal‐clearable MNPs. This article is categorized under:Nanotechnology Approaches to Biology > Nanoscale Systems in BiologyDiagnostic Tools > Biosensing [ABSTRACT FROM AUTHOR]

In order to improve the efficiency of agricultural product logistics transportation, based on big data technology, this paper starts from the factors that affect the efficiency of agricultural product logistics and combines big data technology to conduct the research on the mechanism and efficiency of agricultural products logistics transportation. Moreover, this paper analyzes how to improve the efficiency of agricultural product logistics by studying the dynamic relationship between influencing factors and the efficiency of agricultural product logistics. At the same time, this paper takes the efficiency evaluation of agricultural product logistics transportation network as the research object to improve the agricultural product data analysis algorithm, and constructs the system model from the perspective of the supply chain. In addition, this paper constructs system function modules according to the actual needs of agricultural product logistics transportation, and analyzes agricultural product transportation logistics by factor analysis. Finally, this paper verifies the effect of the model proposed in this paper through experimental research. From the test results, it can be seen that the model proposed in this paper basically meets the actual needs. Core Ideas: Agricultural product logistics are efficient and combine big data technology.The mechanism of agricultural products logistics transportation is efficient.Improve the efficiency of agricultural product logistics by studying the dynamic relationship between influencing. [ABSTRACT FROM AUTHOR]