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Optogenetic therapy is a promising vision restoration method where light-sensitive opsins are introduced to the surviving inner retina following photoreceptor degeneration. The cell type targeted for opsin expression will likely influence the quality of restored vision. However, a like-for-like preclinical comparison of visual responses evoked following equivalent opsin expression in the two major targets, ON bipolar (ON BCs) or retinal ganglion cells (RGCs), is absent. We address this deficit by comparing stimulus-response characteristics at single-unit resolution in the retina and dorsal lateral geniculate nucleus of retinally degenerate mice genetically engineered to express the opsin ReaChR in Grm6- or Brn3c-expressing cells (ON BC vs. RGCs, respectively). For both targeting strategies, we find ReaChR-evoked responses have equivalent sensitivity and can encode contrast across different background irradiances. Compared with ON BCs, targeting RGCs decreased response reproducibility and resulted in more stereotyped responses with reduced diversity in response polarity, contrast sensitivity, and temporal frequency tuning. Recording ReaChR-driven responses in visually intact retinas confirmed that RGC-targeted ReaChR expression disrupts visual feature selectivity of individual RGCs. Our data show that, while both approaches restore visual responses with impressive fidelity, ON BC targeting produces a richer visual code closer to that of wild-type mice.
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http://dx.doi.org/10.1016/j.ymthe.2025.01.030 | DOI Listing |
Neural Netw
September 2025
School of Cyberspace Security (School of Cryptology), Hainan University, No. 58, Renmin Avenue, Haikou, 570228, Hainan, China. Electronic address:
The primary challenge of large-margin learning lies in designing classifiers with strong discriminative power. Although existing large margin methods have achieved success in various classification tasks, they often suffer from weak task generalization and imbalanced handling of easy and hard samples. In this paper, we propose a margin adaptive synthetic virtual Softmax loss (SV-Softmax), which dynamically generates virtual prototypes by synthesizing embedded features and their corresponding prototypes.
View Article and Find Full Text PDFNeural Netw
September 2025
School of Automation, Southeast University, Nanjing, 210096, China; Advanced Ocean Institute of Southeast University Nantong, Nantong, 226010, China. Electronic address:
Unmanned Aerial Vehicle (UAV) tracking requires accurate target localization from aerial top-down perspectives while operating under the computational constraints of aerial platforms. Current mainstream UAV trackers, constrained by the limited resources, predominantly employ lightweight Convolutional Neural Network (CNN) extractor, coupled with an appearance-based fusion mechanism. The absence of comprehensive target perception significantly constrains the balance between tracking accuracy and computational efficiency.
View Article and Find Full Text PDFIEEE J Biomed Health Inform
September 2025
Thyroid eye disease (TED) is a prevalent autoimmune orbital disorder that can severely impair visual function and significantly diminish patients' quality of life. In recent years, several studies have attempted to automate TED diagnosis using optical coherence tomography (OCT) images. However, existing approaches primarily rely on convolutional neural networks (CNNs) combined with attention mechanisms and are mostly trained using traditional cross-entropy loss.
View Article and Find Full Text PDFNat Aging
September 2025
Aging Biomarker Consortium (ABC), Beijing, China.
The global surge in the population of people 60 years and older, including that in China, challenges healthcare systems with rising age-related diseases. To address this demographic change, the Aging Biomarker Consortium (ABC) has launched the X-Age Project to develop a comprehensive aging evaluation system tailored to the Chinese population. Our goal is to identify robust biomarkers and construct composite aging clocks that capture biological age, defined as an individual's physiological and molecular state, across diverse Chinese cohorts.
View Article and Find Full Text PDFGeroscience
September 2025
Department of Neurology, Albert Einstein College of Medicine, Bronx, NY, USA.
Cognitive decline is common in multiple sclerosis (MS), although neural mechanisms are not fully understood. The objective was to investigate the impact of mild cognitive impairment (MCI) on the relationship between resting state functional connectivity (RSFC) and cognitive function in older adults with multiple sclerosis (OAMS) and age matched healthy controls. Participants underwent magnetic resonance imaging (MRI) scans and cognitive assessments.
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