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Objective: Hypertension caused by vascular Behcet's disease (BD) is an important prognostic factor of paediatric BD. However, much less is known about its clinical features. The objective of this study was to investigate the clinical characteristics of paediatric vascular BD complicated by hypertension. Methods: A retrospective study was carried out in paediatric BD patients complicated by hypertension treated in the Children's Hospital Capital Institute of Paediatrics from Jan 2009 to Dec 2022. Results: Of 65 BD patients, 6 (9.2%) were complicated by hypertension, 5 patients were female, and the median ages of onset and diagnosis were 9.8 years and 11.3 years, respectively. Three patients were found to have cardiac involvement and hypertensive retinopathy secondary to hypertension. Five of the 6 patients with hypertension had right renal artery involvement, and all of them were treated with glucocorticoids and immunosuppressants. Four patients were treated with biological agents. One patient with severe renal artery stenosis underwent unsuccessful vascular interventional therapy. After 3–6 years of follow-up, five patients were found to have renal atrophy, and one patient was at stable condition. Conclusion: Hypertension in paediatric BD is mainly caused by renal artery involvement. Early recognition and treatment of vascular involvement in BD is important to prevent poor prognosis. [ABSTRACT FROM AUTHOR]

Clear delineation of tumor margins is essential for accurate resection and decreased recurrence rate in the clinic. Fluorescence imaging is emerging as a promising alternative to traditional visual inspection by surgeons for intraoperative imaging. However, traditional probes lack accuracy in tumor diagnosis, making it difficult to depict tumor boundaries accurately. Herein, we proposed an offensive and defensive integration (ODI) strategy based on the “attack systems (invasive peptidase) and defense systems (reductive microenvironment)” of multi‐dimensional tumor characteristics to design activatable fluorescent probes for imaging tumor boundaries precisely. Screened out from a series of ODI strategy‐based probes, ANQ performed better than traditional probes based on tumor unilateral correlation by distinguishing between tumor cells and normal cells and minimizing false‐positive signals from living metabolic organs. To further improve the signal‐to‐background ratio in vivo, derivatized FANQ, was prepared and successfully applied to distinguish orthotopic hepatocellular carcinoma tissues from adjacent tissues in mice models and clinical samples. This work highlights an innovative strategy to develop activatable probes for rapid diagnosis of tumors and high‐precision imaging of tumor boundaries, providing more efficient tools for future clinical applications in intraoperative assisted resection. [ABSTRACT FROM AUTHOR]

Simple Summary: A pig inventory is an important part of realizing accurate and large-scale farming. Distinguishing from the current, mostly overhead scene or static map counting, this study proposes a video-based dynamic counting method through which pigs can be counted automatically and continuously in complex scenes. The improved model effectively reduces the counting error and improves the operation efficiency, which provides data and experimental references for the study of automated pig counting, and it also provides technical support to improve the management efficiency of pig farms, reducing the cost and promoting the intelligent transformation of the animal husbandry industry. A pig inventory is a crucial component of achieving precise and large-scale farming. In complex pigsty environments, due to pigs' stress reactions and frequent obstructions, it is challenging to count them accurately and automatically. This difficulty contrasts with most current deep learning studies, which rely on overhead views or static images for counting. This research proposes a video-based dynamic counting method, combining YOLOv7 with DeepSORT. By utilizing the YOLOv7 network structure and optimizing the second and third 3 × 3 convolution operations in the head network ELAN-W with PConv, the model reduces the computational demand and improves the inference speed without sacrificing accuracy. To ensure that the network acquires accurate position perception information at oblique angles and extracts rich semantic information, we introduce the coordinate attention (CA) mechanism before the three re-referentialization paths (REPConv) in the head network, enhancing robustness in complex scenarios. Experimental results show that, compared to the original model, the improved model increases the mAP by 3.24, 0.05, and 1.00 percentage points for oblique, overhead, and all pig counting datasets, respectively, while reducing the computational cost by 3.6 GFLOPS. The enhanced YOLOv7 outperforms YOLOv5, YOLOv4, YOLOv3, Faster RCNN, and SSD in target detection with mAP improvements of 2.07, 5.20, 2.16, 7.05, and 19.73 percentage points, respectively. In dynamic counting experiments, the improved YOLOv7 combined with DeepSORT was tested on videos with total pig counts of 144, 201, 285, and 295, yielding errors of -3, -3, -4, and -26, respectively, with an average accuracy of 96.58% and an FPS of 22. This demonstrates the model's capability of performing the real-time counting of pigs in various scenes, providing valuable data and references for automated pig counting research. [ABSTRACT FROM AUTHOR]

Abnormal physiological processes and diseases can lead to content or activity fluctuations of biocomponents in organelles and whole blood. However, precise monitoring of these abnormalities remains extremely challenging due to the insufficient sensitivity and accuracy of available fluorescence probes, which can be attributed to the background fluorescence arising from two sources, 1) biocomponent autofluorescence (BCAF) and 2) probe intrinsic fluorescence (PIF). To overcome these obstacles, we have re‐engineered far‐red to NIR II rhodol derivatives that possess weak BCAF interference. And a series of "zero" PIF sensing‐platforms were created by systematically regulating the open‐loop/spirocyclic forms. Leveraging these advancements, we devised various ultra‐sensitive NIR indicators, achieving substantial fluorescence boosts (190 to 1300‐fold). Among these indicators, 8‐LAP demonstrated accurate tracking and quantifying of leucine aminopeptidase (LAP) in whole blood at various stages of tumor metastasis. Furthermore, coupling 8‐LAP with an endoplasmic reticulum‐targeting element enabled the detection of ERAP1 activity in HCT116 cells with p53 abnormalities. This delicate design of eliminating PIF provides insights into enhancing the sensitivity and accuracy of existing fluorescence probes toward the detection and imaging of biocomponents in abnormal physiological processes and diseases. [ABSTRACT FROM AUTHOR]

Abnormal physiological processes and diseases can lead to content or activity fluctuations of biocomponents in organelles and whole blood. However, precise monitoring of these abnormalities remains extremely challenging due to the insufficient sensitivity and accuracy of available fluorescence probes, which can be attributed to the background fluorescence arising from two sources, 1) biocomponent autofluorescence (BCAF) and 2) probe intrinsic fluorescence (PIF). To overcome these obstacles, we have re‐engineered far‐red to NIR II rhodol derivatives that possess weak BCAF interference. And a series of "zero" PIF sensing‐platforms were created by systematically regulating the open‐loop/spirocyclic forms. Leveraging these advancements, we devised various ultra‐sensitive NIR indicators, achieving substantial fluorescence boosts (190 to 1300‐fold). Among these indicators, 8‐LAP demonstrated accurate tracking and quantifying of leucine aminopeptidase (LAP) in whole blood at various stages of tumor metastasis. Furthermore, coupling 8‐LAP with an endoplasmic reticulum‐targeting element enabled the detection of ERAP1 activity in HCT116 cells with p53 abnormalities. This delicate design of eliminating PIF provides insights into enhancing the sensitivity and accuracy of existing fluorescence probes toward the detection and imaging of biocomponents in abnormal physiological processes and diseases. [ABSTRACT FROM AUTHOR]