Bibliometric analysis of the uveitis literature and research trends over the past two decades.

OBJECTIVE: This study aimed to examine the publication patterns and present a current view of the field of uveitis using a bibliometric analysis. DESIGN: Bibliometric analysis. METHODS AND ANALYSIS: A comprehensive search of three databases including MEDLINE, EMBASE and Cochrane was conducted from 1 January 2000 to 31 December 2022. Search results from all three databases were subjected to analysis by Bibliometrix, an R programme that analyses large literature dataset with statistical and mathematical models. Visualisation of collaboration networks and relevance between countries was presented with VOSviewer. RESULTS: A total of 26 296 articles were included in the analysis. The field of uveitis has undergone a significant exponential growth since 2000, with an average growth rate of 4.14%. The most substantial annual growth was between the years 2021 and 2022 (36%). According to the corresponding author's countries, the three most productive countries were Turkey (3288, 12.6%), the USA (3136, 12%) and Japan (1981, 7.6%). The USA (243, 31.4%), England (117, 15%) and Germany (62, 8%) are the top three countries that contributed to clinical trials. The average international collaboration of all countries was 2.5%. CONCLUSIONS: Uveitis literature has undergone significant growth in the past two decades. The demographic factors of publishing countries lead to their various productivity and types of these uveitis studies, which is closely associated with the countries' scientific research resources and patient populations.

A Lightweight Framework For Chromatin Loop Detection at the Single-Cell Level.

Single-cell Hi-C (scHi-C) has made it possible to analyze chromatin organization at the single-cell level. However, scHi-C experiments generate inherently sparse data, which poses a challenge for loop calling methods. The existing approach performs significance tests across the imputed dense contact maps, leading to substantial computational overhead and loss of information at the single-cell level. To overcome this limitation, a lightweight framework called scGSLoop is proposed, which sets a new paradigm for scHi-C loop calling by adapting the training and inferencing strategies of graph-based deep learning to leverage the sequence features and 1D positional information of genomic loci. With this framework, sparsity is no longer a challenge, but rather an advantage that the model leverages to achieve unprecedented computational efficiency. Compared to existing methods, scGSLoop makes more accurate predictions and is able to identify more loops that have the potential to play regulatory roles in genome functioning. Moreover, scGSLoop preserves single-cell information by identifying a distinct group of loops for each individual cell, which not only enables an understanding of the variability of chromatin looping states between cells, but also allows scGSLoop to be extended for the investigation of multi-connected hubs and their underlying mechanisms.

GILoop: Robust chromatin loop calling across multiple sequencing depths on Hi-C data.

Graph and image are two common representations of Hi-C cis-contact maps. Existing computational tools have only adopted Hi-C data modeled as unitary data structures but neglected the potential advantages of synergizing the information of different views. Here we propose GILoop, a dual-branch neural network that learns from both representations to identify genome-wide CTCF-mediated loops. With GILoop, we explore the combined strength of integrating the two view representations of Hi-C data and corroborate the complementary relationship between the views. In particular, the model outperforms the state-of-the-art loop calling framework and is also more robust against low-quality Hi-C libraries. We also uncover distinct preferences for matrix density by graph-based and image-based models, revealing interesting insights into Hi-C data elucidation. Finally, along with multiple transfer-learning case studies, we demonstrate that GILoop can accurately model the organizational and functional patterns of CTCF-mediated looping across different cell lines.