Generating Text Sequence Images for Recognition

Recently, methods based on deep learning have dominated the field of text recognition. With a large number of training data, most of them can achieve the state-of-the-art performances. However, it is hard to harvest and label sufficient text sequence images from the real scenes. To mitigate this issue, several methods to synthesize text sequence images were proposed, yet they usually need complicated preceding or follow-up steps. In this work, we present a method which is able to generate infinite training data without any auxiliary pre/post-process. We tackle the generation task as an image-to-image translation one and utilize conditional adversarial networks to produce realistic text sequence images in the light of the semantic ones. Some evaluation metrics are involved to assess our method and the results demonstrate that the caliber of the data is satisfactory. The code and dataset will be publicly available soon

Computational profiling and prognostic modeling based on lysosome-related genes in colorectal cancer

Background: Despite significant advances over the past decade, patients diagnosed with advanced colorectal cancer (CRC) continue to face unfavorable prognoses. Recent studies have underscored the pivotal role of lysosomes in tumor development and progression. This led us to postulate and develop a novel lysosomal-centric model for predicting CRC risk and therapeutic response.Methods: CRC tissue samples were sourced from the TCGA database, while lysosome-associated genes were collated from the GSEA database. Differentially expressed lysosome-related genes (DE-LRGs) were discerned by contrasting tumor samples with normal tissue. Based on the expression profile of DE-LRGs, patients were stratified into two distinct clusters. Survival disparities between the clusters were delineated using Kaplan-Meier estimators. For tumor microenvironment assessment, we employed ESTIMATE and ssGSEA. Functional pathway enrichment was ascertained using both GSVA and GSEA. Subsequent uni- and multi-variate Cox regression analyses pinpointed risk-associated DE-LRGs. Leveraging these genes, we constructed a novel risk prediction model and derived risk scores. The model’s prognostic capability was externally validated using dataset GSE39084. The mutational landscape across risk categories was evaluated using the Maftools algorithm. The potential efficacy of targeted and immunotherapeutic interventions for each patient cohort was gauged using pRRophetic, CYT, and IMvigor210.Results: We identified 46 DE-LRGs. Tumor Immune MicroEnvironment (TIME) assessment revealed that cluster 2 patients exhibited elevated ESTIMATE, Immunocore, and stromal scores, yet diminished tumor purity relative to cluster 1. Notable differences in immune cell infiltration patterns were observed between clusters, and distinct pathway enrichments were evident. Cluster 2 manifested a pronounced expression of immune checkpoint-related genes. Four DE-LRGs (ATP6V0A4, GLA, IDUA, and SLC11A1) were deemed critical for risk association, leading to the formulation of our novel risk model. The model exhibited commendable predictive accuracy, which was corroborated in an external validation cohort. A palpable survival advantage was observed in high-TMB, low-risk subgroups. Moreover, the low-risk cohort displayed heightened sensitivity to both targeted and immunotherapeutic agents.Conclusion: Our findings underscore the potential of lysosome-associated genes as robust prognostic and therapeutic response markers in CRC patients

Focus-Enhanced Scene Text Recognition with Deformable Convolutions

Recently, scene text recognition methods based on deep learning have sprung up in computer vision area. The existing methods achieved great performances, but the recognition of irregular text is still challenging due to the various shapes and distorted patterns. Consider that at the time of reading words in the real world, normally we will not rectify it in our mind but adjust our focus and visual fields. Similarly, through utilizing deformable convolutional layers whose geometric structures are adjustable, we present an enhanced recognition network without the steps of rectification to deal with irregular text in this work. A number of experiments have been applied, where the results on public benchmarks demonstrate the effectiveness of our proposed components and shows that our method has reached satisfactory performances. The code will be publicly available at https://github.com/Alpaca07/dtr soon

Unified Chinese License Plate Detection and Recognition with High Efficiency

Recently, deep learning-based methods have reached an excellent performance on License Plate (LP) detection and recognition tasks. However, it is still challenging to build a robust model for Chinese LPs since there are not enough large and representative datasets. In this work, we propose a new dataset named Chinese Road Plate Dataset (CRPD) that contains multi-objective Chinese LP images as a supplement to the existing public benchmarks. The images are mainly captured with electronic monitoring systems with detailed annotations. To our knowledge, CRPD is the largest public multi-objective Chinese LP dataset with annotations of vertices. With CRPD, a unified detection and recognition network with high efficiency is presented as the baseline. The network is end-to-end trainable with totally real-time inference efficiency (30 fps with 640p). The experiments on several public benchmarks demonstrate that our method has reached competitive performance. The code and dataset will be publicly available at https://github.com/yxgong0/CRPD

LncRNAs: the bridge linking RNA and colorectal cancer.

Long noncoding RNAs (lncRNAs) are transcribed by genomic regions (exceeding 200 nucleotides in length) that do not encode proteins. While the exquisite regulation of lncRNA transcription can provide signals of malignant transformation, lncRNAs control pleiotropic cancer phenotypes through interactions with other cellular molecules including DNA, protein, and RNA. Recent studies have demonstrated that dysregulation of lncRNAs is influential in proliferation, angiogenesis, metastasis, invasion, apoptosis, stemness, and genome instability in colorectal cancer (CRC), with consequent clinical implications. In this review, we explicate the roles of different lncRNAs in CRC, and the potential implications for their clinical application

Hypophyseal Involvement in Immunoglobulin G4-Related Disease: A Retrospective Study from a Single Tertiary Center

This study aims to outline the clinical features and outcomes of IgG4-related hypophysitis (IgG4-RH) patients in a tertiary medical center. We reviewed clinical manifestations and imaging and pituitary function tests at baseline, as well as during follow-up. Ten patients were included. The mean age at diagnosis of IgG4-RH was 48.4 (16.0–64.0) years. An average of 3 (0–9) extrapituitary organs were involved. Five patients had panhypopituitarism, three had only posterior hypopituitarism, one had only anterior hypopituitarism, and one had a normal pituitary function. One patient in our study had pituitary mass biopsy, lacking IgG4-positive cells despite lymphocyte infiltration forming an inflammatory pseudotumor. Five patients with a clinical course of IgG4-RH less than nine months and a whole course of IgG4-RD less than two years were managed with glucocorticoids, while three patients with a longer history were administered glucocorticoids plus immunosuppressive agents. One patient went through surgical excision, and one patient was lost to follow-up. All patients showed a prompt response clinically, but only three patients had normalized serum IgG4 levels. Two patients who took medications for less than six months relapsed. Conclusions. IgG4-RD is a broad disease, and all physicians involved have to be aware of the possibility of pituitary dysfunction. Younger patients should be expected. The histopathological feature of pituitary gland biopsy could be atypical. For patients with a longer history, the combination of GC and immunosuppressive agents is favorable. Early and adequate courses of treatment are crucial for the management of IgG4-RH. With GC and/or immunosuppressant treatment, however, pituitary function or diabetes insipidus did not improve considerably

Association between Vitamin D supplementation and mortality in critically ill patients: A systematic review and meta-analysis of randomized clinical trials.

BACKGROUND: Observational studies suggest that low 25-hydroxyvitamin D status is common and has been associated with higher mortality in critically ill patients. This study aim to investigate whether vitamin D supplementation is associated with lower mortality in critically ill patients. METHOD: We searched Medline, Embase, and Cochrane databases from inception to January 12, 2020, without language restrictions, for randomized controlled trials comparing the effect of vitamin D supplementation with placebo in critically ill patients. Two authors independently performed data extraction and assessed study quality. The primary outcome was all-cause mortality at the longest follow-up. RESULT: We identified nine trials with a total of 2066 patients. Vitamin D supplementation was not associated with reduced all-cause mortality at the longest follow-up (RR 0.90, 95% CI 0.74 to 1.09, I2 = 20%), at 30 days (RR 0.81, 95% CI 0.56 to 1.15), at 90 days (RR 1.15, 95% CI 0.92 to 1.44), and at 180 days (RR 0.82, 95% CI 0.65 to 1.03). Results were similar in the sensitivity analysis. The sample size met the optimum size in trial sequential analysis. Similarly, supplemental vitamin D was not associated with length of ICU stay, hospital stay, or mechanical ventilation. CONCLUSION: Vitamin D supplement was not associated with reduced all-cause mortality in critically ill patients. SYSTEMATIC REVIEW REGISTRATION: Open Science Framework https://osf.io/bgsjq