Prognostic value of growth differentiation factor-15 in Chinese patients with heart failure: A prospective observational study

  Background: Growth differentiation factor-15 (GDF-15), a biomarker associated with remodeling, oxidative stress and inflammation, has been used to stratify heart failure (HF) patients. However, its prognostic value in Chinese HF patients is still unknown. Methods: GDF-15 levels were examined on admission in 272 consecutive HF patients in Beijing Hospital (a Chinese tertiary medical center) by a commercial enzyme-linked immunosorbent assay. We recorded the incidence of all-cause mortality and/or readmission for HF during a median follow-up period of 558 days. Patients were stratified according to the tertiles of GDF-15. Results: Fifty-three (19.5%) patients died and 103 (37.9%) patients had major adverse cardiac events (MACE) which included the composite outcome of all-cause mortality or readmission for HF at the end of follow-up. Kaplan-Meier survival curves showed that the third tertile of GDF-15 was associated with increased rate of all-cause mortality (compared with the first and second tertiles, log rank p = 0.001 and 0.001, respectively) or MACE (compared with the first and second tertiles, log rank p = 0.002 and p < 0.001, respectively). In addition, multivariate Cox regression model showed that the highest tertile of GDF-15 was independently associated with increased risk of all-cause death (hazard ratio = 5.95, 95% confidence interval 1.88–18.78, p = 0.002) compared with the lowest tertile after adjustment for related clinical variables such as age, renal function or N-terminal pro-B-type natriuretic peptide.  Conclusions: Plasma GDF-15 is an independent predictor of all-cause mortality in Chinese patients with HF. It may potentially be used to stratify and prognosticate HF patients

PVP: Pre-trained Visual Parameter-Efficient Tuning

Large-scale pre-trained transformers have demonstrated remarkable success in various computer vision tasks. However, it is still highly challenging to fully fine-tune these models for downstream tasks due to their high computational and storage costs. Recently, Parameter-Efficient Tuning (PETuning) techniques, e.g., Visual Prompt Tuning (VPT) and Low-Rank Adaptation (LoRA), have significantly reduced the computation and storage cost by inserting lightweight prompt modules into the pre-trained models and tuning these prompt modules with a small number of trainable parameters, while keeping the transformer backbone frozen. Although only a few parameters need to be adjusted, most PETuning methods still require a significant amount of downstream task training data to achieve good results. The performance is inadequate on low-data regimes, especially when there are only one or two examples per class. To this end, we first empirically identify the poor performance is mainly due to the inappropriate way of initializing prompt modules, which has also been verified in the pre-trained language models. Next, we propose a Pre-trained Visual Parameter-efficient (PVP) Tuning framework, which pre-trains the parameter-efficient tuning modules first and then leverages the pre-trained modules along with the pre-trained transformer backbone to perform parameter-efficient tuning on downstream tasks. Experiment results on five Fine-Grained Visual Classification (FGVC) and VTAB-1k datasets demonstrate that our proposed method significantly outperforms state-of-the-art PETuning methods

Towards Semantic Segmentation of Urban-Scale 3D Point Clouds: A Dataset, Benchmarks and Challenges

An essential prerequisite for unleashing the potential of supervised deep learning algorithms in the area of 3D scene understanding is the availability of large-scale and richly annotated datasets. However, publicly available datasets are either in relative small spatial scales or have limited semantic annotations due to the expensive cost of data acquisition and data annotation, which severely limits the development of fine-grained semantic understanding in the context of 3D point clouds. In this paper, we present an urban-scale photogrammetric point cloud dataset with nearly three billion richly annotated points, which is three times the number of labeled points than the existing largest photogrammetric point cloud dataset. Our dataset consists of large areas from three UK cities, covering about 7.6 km^2 of the city landscape. In the dataset, each 3D point is labeled as one of 13 semantic classes. We extensively evaluate the performance of state-of-the-art algorithms on our dataset and provide a comprehensive analysis of the results. In particular, we identify several key challenges towards urban-scale point cloud understanding. The dataset is available at https://github.com/QingyongHu/SensatUrban.Comment: CVPR 2021, Code: https://github.com/QingyongHu/SensatUrba

Effect of the combination of cognitive behavioral therapy and oral paroxetine hydrochloride in patients with post-stroke depression

Purpose: To determine the effects of combined use of cognitive behavioral therapy (CBT) and paroxetine hydrochloride tablets in patients with post-stroke depression (PSD), and its effect on scores on Hamilton Rating Scale for Depression (HAMD) and Stroke Specific Quality of Life Scale (SS-QOL). Methods: Clinical data for 96 patients with PSD who were treated in Dongying Traditional Chinese Hospital, Dongying City, China from June 2018 to June 2019 were retrospectively analyzed. Patients who met the inclusion criteria were divided into treatment group (TG, n = 48) and reference group (RG, n = 48) based on odd and even hospitalization numbers. Both groups received conventional treatment, but RG patients were in addition given clopidogrel, while TG received CBT in combination with paroxetine hydrochloride tablets. Clinical indices were evaluated in both groups before and after treatment. Moreover, therapeutic effects in the two different treatment methods on PSD, as well as on Hamilton Rating Scale for Depression (HAMD) and Stroke Specific Quality of Life Scale (SS-QOL) scores were analyzed. Results: After treatment, TG had lower HAMD score (p < 0.001), lower scores on modified Rankin scale, and few incidences of adverse reactions at 3, 7, 15 and 30 days of treatment (p < 0.05), but higher total clinical effectiveness and mean SS-QOL score (p < 0.05), when compared with RG. Conclusion: Combined use of CBT and oral paroxetine hydrochloride tablets may be a promising strategy for treating depression and enhancing the quality of life of PSD patients, as it greatly improves neurological deficit and prognosis. However, further clinical trials should be carried out prior to introducing it in clinical practice

Accelerating Magnetic Resonance Parametric Mapping Using Simultaneously Spatial Patch-based and Parametric Group-based Low-rank Tensors (SMART)

Quantitative magnetic resonance (MR) parametric mapping is a promising approach for characterizing intrinsic tissue-dependent information. However, long scan time significantly hinders its widespread applications. Recently, low-rank tensor has been employed and demonstrated good performance in accelerating MR parametricmapping. In this study, we propose a novel method that uses spatial patch-based and parametric group-based low rank tensors simultaneously (SMART) to reconstruct images from highly undersampled k-space data. The spatial patch-based low-rank tensor exploits the high local and nonlocal redundancies and similarities between the contrast images in parametric mapping. The parametric group based low-rank tensor, which integrates similar exponential behavior of the image signals, is jointly used to enforce the multidimensional low-rankness in the reconstruction process. In vivo brain datasets were used to demonstrate the validity of the proposed method. Experimental results have demonstrated that the proposed method achieves 11.7-fold and 13.21-fold accelerations in two-dimensional and three-dimensional acquisitions, respectively, with more accurate reconstructed images and maps than several state-of-the-art methods. Prospective reconstruction results further demonstrate the capability of the SMART method in accelerating MR quantitative imaging.Comment: 15 pages, 12 figure