ABSTRACT
We present a mechanically sheared image acquisition format for upright and open-top light-sheet microscopes that automatically places data in its proper spatial context. This approach, which reduces computational post-processing and eliminates unnecessary interpolation or duplication of the data, is demonstrated on an upright variant of axially swept light-sheet microscopy (ASLM) that achieves a field of view, measuring 774 × 435 microns, that is 3.2-fold larger than previous models and a raw and isotropic resolution of â¼460â nm. Combined, we demonstrate the power of this approach by imaging sub-diffraction beads, cleared biological tissues, and expanded specimens.
ABSTRACT
We present a mechanically sheared image acquisition format for upright and open-top light-sheet microscopes that automatically places data in its proper spatial context. This approach, which reduces computational post-processing and eliminates unnecessary interpolation or duplication of the data, is demonstrated on an upright variant of Axially Swept Light-Sheet Microscopy (ASLM) that achieves a field of view, measuring 774 x 435 microns, that is 3.2-fold larger than previous models and a raw and isotropic resolution of â¼420 nm. Combined, we demonstrate the power of this approach by imaging sub-diffraction beads, cleared biological tissues, and expanded specimens.
ABSTRACT
navigate is a turnkey, open-source software solution designed to enhance light-sheet fluorescence microscopy (LSFM) by integrating smart microscopy techniques into a user-friendly framework. It enables automated, intelligent imaging with a Python-based control system that supports GUI-reconfigurable acquisition routines and the integration of diverse hardware sets. As a comprehensive package, navigate democratizes access to advanced LSFM capabilities, facilitating the development and implementation of smart microscopy workflows without requiring deep programming knowledge or specialized expertise in light-sheet microscopy.
ABSTRACT
Osteopontin (OPN)-CD44 signaling plays an important role in promoting tumor progression and metastasis. In cancer, OPN and CD44 overexpression is a marker of aggressive disease and poor prognosis, and correlates with therapy resistance. In this study, we aimed to evaluate the association of single nucleotide polymorphisms (SNPs) in the OPN and CD44 genes with clinical outcomes in 307 non-small cell lung cancer (NSCLC) patients treated with radiotherapy or chemoradiotherapy. The potential impact of the variants on plasma OPN levels was also investigated. Multivariate analysis showed that OPN rs11730582 CC carriers had a significantly increased risk of death (p = 0.029), while the CD44 rs187116 A allele correlated with a reduced risk of locoregional recurrence (p = 0.016) in the curative treatment subset. The rs11730582/rs187116 combination was associated with an elevated risk of metastasis in these patients (p = 0.016). Furthermore, the OPN rs1126772 G variant alone (p = 0.018) and in combination with rs11730582 CC (p = 7 × 10-5) was associated with poor overall survival (OS) in the squamous cell carcinoma subgroup. The rs11730582 CC, rs187116 GG, and rs1126772 G, as well as their respective combinations, were independent risk factors for unfavorable treatment outcomes. The impact of rs11730582-rs1126772 haplotypes on OS was also observed. These data suggest that OPN and CD44 germline variants may predict treatment effects in NSCLC.