Determination of the time of refractive stability after uneventful phacoemulsification in Indian eyes.

BACKGROUND: Knowledge about refractive stabilization and the accuracy of postoperative refractive error measurements are crucial for improved patient outcomes after phacoemulsification. Existing guidelines typically recommend waiting 4-6 wk before prescribing corrective lenses. Our research focused on identifying factors that influence refractive errors in the early stages of post-cataract surgery, thus contributing to the existing literature on this topic. AIM: To investigate the time required for refraction stability after uneventful phacoemulsification surgery. METHODS: We compared the variation and statistical significance of the difference in spherical, cylindrical components, and the spherical equivalent between the 1- and 6-wk follow-up period in a group of 257 eyes that underwent uneventful phacoemulsification with foldable intraocular lens implantation, all performed by a single experienced surgeon. The Wilcoxon-Signed Rank Test was utilized to assess the magnitude of the change and determine its statistical significance. The refractive stability was defined as the point at which the change in spherical equivalent was within ± 0.5 dioptres for two consecutive visits. RESULTS: The average age of the patients was 64.9 ± 8.9 yr. The differences observed in both the visits in spherical power (0.1 ± 0.2), cylinder power (0.3 ± 0.4), and spherical equivalent (0.2 ± 0.2) were minimal and not statistically significant. The majority of eyes (93.4%) achieved refractive stability within 6 wk after the surgery. The cylindrical power differed between age groups at the 6th wk post-operative and the difference was statistically significant (P value 0.013). There were no significant differences in refractive stability when considering sex and axial length. CONCLUSION: Phacoemulsification with foldable intraocular lens implantation results in no significant changes in refraction for the majority of cases during the 6-wk follow-up period. Therefore, a spectacle prescription can be given at the completion of 1 wk.

SpatialPrompt: spatially aware scalable and accurate tool for spot deconvolution and domain identification in spatial transcriptomics.

Efficiently mapping of cell types in situ remains a major challenge in spatial transcriptomics. Most spot deconvolution tools ignore spatial coordinate information and perform extremely slow on large datasets. Here, we introduce SpatialPrompt, a spatially aware and scalable tool for spot deconvolution and domain identification. SpatialPrompt integrates gene expression, spatial location, and single-cell RNA sequencing (scRNA-seq) dataset as reference to accurately infer cell-type proportions of spatial spots. SpatialPrompt uses non-negative ridge regression and graph neural network to efficiently capture local microenvironment information. Our extensive benchmarking analysis on Visium, Slide-seq, and MERFISH datasets demonstrated superior performance of SpatialPrompt over 15 existing tools. On mouse hippocampus dataset, SpatialPrompt achieves spot deconvolution and domain identification within 2 minutes for 50,000 spots. Overall, domain identification using SpatialPrompt was 44 to 150 times faster than existing methods. We build a database housing 40 plus curated scRNA-seq datasets for seamless integration with SpatialPrompt for spot deconvolution.

Single-cell transcriptome analysis identifies novel biomarkers involved in major liver cancer subtypes.

Hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC) are the two aggressive subtypes of liver cancer (LC). Immense cellular heterogeneity and cross-talk between cancer and healthy cells make it challenging to treat these cancer subtypes. To address these challenges, the study aims to systematically characterize the tumor heterogeneity of LC subtypes using single-cell RNA sequencing (scRNA-seq) datasets. The study combined 51,927 single cells from HCC, ICC, and healthy scRNA-seq datasets. After integrating the datasets, cell groups with similar gene expression patterns are clustered and cluster annotation has been performed based on gene markers. Cell-cell communication analysis (CCA) was implemented to understand the cross-talk between various cell types. Further, differential gene expression analysis and enrichment analysis were carried out to identify unique molecular drivers associated with HCC and ICC. Our analysis identified T cells, hepatocytes, epithelial cells, and monocyte as the major cell types present in the tumor microenvironment. Among them, abundance of natural killer (NK) cells in HCC, epithelial cells, and hepatocytes in ICC was detected. CCA revealed key interaction between T cells to NK cells in HCC and smooth muscle cells to epithelial cells in the ICC. Additionally, SOX4 and DTHD1 are the top differentially expressed genes (DEGs) in HCC, while keratin and CCL4 are in ICC. Enrichment analysis of DEGs reveals major upregulated genes in HCC affect protein folding mechanism and in ICC alter pathways involved in cell adhesion. The findings suggest potential targets for the development of novel therapeutic strategies for the treatment of these two aggressive subtypes of LC.