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The retinal ganglion cells (RGCs) serve as the critical pathway for transmitting visual information from the retina to the brain, yet they can be dramatically impacted by diseases such as glaucoma. When investigating disease processes affecting RGCs in mouse models, accurately quantifying affected cells becomes essential. However, the use of pan RGC markers like RBPMS or THY1 presents challenges in accurate total cell counting. While Brn3a serves as a reliable RGC nuclear marker for automated counting, it fails to encompass all RGC subtypes in mice. To address this limitation and enable precise automated counting, our research endeavors to develop a method for labeling nuclei in all RGC subtypes. Investigating RGC subtypes labeled with the nuclear marker POU6F2 revealed that numerous RGCs unlabeled by Brn3a were, in fact, labeled with POU6F2. We hypothesize that using antibodies against both Brn3a and POU6F2 would label virtually all RGC nuclei in the mouse retina. Our experiments confirmed that staining retinas with both markers resulted in the labeling of all RGCs. Additionally, when using the cell body marker RBPMS known to label all mouse RGCs, all RBPMS-labeled cells also exhibited Brn3a or POU6F2 labeling. This combination of Brn3a and POU6F2 antibodies provides a pan-RGC nuclear stain, facilitating accurate automated counting by labeling cell nuclei in the retina.
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http://dx.doi.org/10.1016/j.exer.2024.109881 | DOI Listing |
J Clin Lab Anal
September 2025
Department of Nursing, National Tainan Junior College of Nursing, Tainan, Taiwan.
Background: Improving efficiency and reducing turnaround time are crucial in clinical laboratories. While automated analyzers such as the Beckman Coulter DxH 900 streamline workflow, subtle abnormalities like blasts and immature granulocytes (IGs) may be missed, especially in the absence of WBC-related suspect messages. This study evaluated whether integrating cell population data (CPD) with instrument messages could enhance detection accuracy.
View Article and Find Full Text PDFFront Plant Sci
August 2025
College of Mathematics and Computer Science, Yan'an University, Yan'an, Shaanxi, China.
To address the challenge of real-time kiwifruit detection in trellised orchards, this paper proposes YOLOv10-Kiwi, a lightweight detection model optimized for resource-constrained devices. First, a more compact network is developed by adjusting the scaling factors of the YOLOv10n architecture. Second, to further reduce model complexity, a novel C2fDualHet module is proposed by integrating two consecutive Heterogeneous Kernel Convolution (HetConv) layers as a replacement for the traditional Bottleneck structure.
View Article and Find Full Text PDFComput Methods Programs Biomed
August 2025
Zhengzhou University, School of Computer and Artificial Intelligence, Zhengzhou, 450001, China. Electronic address:
Background And Objective: The early detection of breast cancer plays a critical role in improving survival rates and facilitating precise medical interventions. Therefore, the automated identification of breast abnormalities becomes paramount, significantly enhancing the prospects of successful treatment outcomes. To address this imperative, our research leverages multiple modalities such as MRI, CT, and mammography to detect and screen for breast cancer.
View Article and Find Full Text PDFJ Anim Sci
September 2025
Department of Animal Science, South Dakota State University, Brookings, SD 57007, USA.
Flaxseed oil contains elevated levels of omega-3 fatty acids (n-3 FA), which have been shown to impact reproductive performance. This study aimed to determine the effects of a flaxseed oil-based supplement on reproductive parameters, feeding behavior, and lipid profile in beef heifers. Sixty Angus and Simmental × Angus heifers (14 months old ± 2 months), blocked by full body weight (BW; 396.
View Article and Find Full Text PDFAppl Microbiol Biotechnol
September 2025
School of Plant Sciences, The University of Arizona, 1140 E South Campus Drive, Forbes 303, Tucson, AZ, 85721, USA.
Fungal endophytes and epiphytes associated with plant leaves can play important ecological roles through the production of specialized metabolites encoded by biosynthetic gene clusters (BGCs). However, their functional capacity, especially in crops like lettuce (Lactuca sativa L.), remains poorly understood.
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