Estimating systemic fibrosis by combining galectin-3 and ST2 provides powerful risk stratification value for patients after acute decompensated heart failure

Background: Two fibrosis biomarkers, galectin-3 (Gal-3) and suppression of tumorigenicity 2 (ST2), provide prognostic value additive to natriuretic peptides and traditional risk factors in patients with heart failure (HF). However, it is to be investigated whether their combined measurement before discharge provides incremental risk stratification for patients after acute HF. Methods: A total of 344 patients with acute HF were analyzed with Gal-3, and ST2 measured. Patients were prospectively followed for 3.7 ± 1.3 years for deaths, and composite events (death/HF-related re-hospitalizations). Results: The levels of Gal-3 and ST2 were only slightly related (r = 0.20, p < 0.001). The medians of Gal-3 and ST2 were 18 ng/mL and 32.4 ng/mL, respectively. These biomarkers compensated each other and characterized patients with different risk factors. According to the cutoff at median values, patients were separated into four subgroups based on high and low Gal-3 (HG and LG, respectively) and ST2 levels (HS and LS, respectively). Kaplan-Meier survival curves showed that HGHS powerfully identified patients at risk of mortality (Log rank = 21.27, p < 0.001). In multivariable analysis, combined log(Gal-3) and log(ST2) was an in­dependent predictor. For composite events, Kaplan-Meier survival curves showed a lower event- -free survival rate in the HGHS subgroup compared to others (Log rank = 34.62, p < 0.001; HGHS vs. HGLS, Log rank = 4.00, p = 0.045). In multivariable analysis, combined log(Gal-3) and log(ST2) was also an independent predictor. Conclusions: Combination of biomarkers involving heterogeneous fibrosis pathways may identify patients with high systemic fibrosis, providing powerful risk stratification value

Critical Acceptance Factors of Cloud-Based Public Health Records

Personal health records (PHR) is a tool that can be used to assist patients in health management, and cloud-based PHR is expected to effectively integrate medical resources and information, elevate overall healthcare quality, and reduce unnecessary medical costs. This study tends to explore the factors that affect users’ intention to use with regard to the Microsoft HealthVault hybrid cloud health system in Taiwan. A research model combined with Unified Theory of Acceptance and Use of Technology (UTAUT) and Task-Technology Fit (TTF) models as well as perceived risks and trust is proposed including 10 hypotheses. After conducting a series survey, in total, 254 valid questionnaires in Taiwan were received. Some preliminary findings are discussed, and it is hoped that this model can be used to explore the key factors influencing usage intent toward the HealthVault

Treatment Learning Causal Transformer for Noisy Image Classification

Current top-notch deep learning (DL) based vision models are primarily based on exploring and exploiting the inherent correlations between training data samples and their associated labels. However, a known practical challenge is their degraded performance against "noisy" data, induced by different circumstances such as spurious correlations, irrelevant contexts, domain shift, and adversarial attacks. In this work, we incorporate this binary information of "existence of noise" as treatment into image classification tasks to improve prediction accuracy by jointly estimating their treatment effects. Motivated from causal variational inference, we propose a transformer-based architecture, Treatment Learning Causal Transformer (TLT), that uses a latent generative model to estimate robust feature representations from current observational input for noise image classification. Depending on the estimated noise level (modeled as a binary treatment factor), TLT assigns the corresponding inference network trained by the designed causal loss for prediction. We also create new noisy image datasets incorporating a wide range of noise factors (e.g., object masking, style transfer, and adversarial perturbation) for performance benchmarking. The superior performance of TLT in noisy image classification is further validated by several refutation evaluation metrics. As a by-product, TLT also improves visual salience methods for perceiving noisy images.Comment: Accepted to IEEE WACV 2023. The first version was finished in May 201

Korean Red Ginseng Improves Blood Pressure Stability in Patients with Intradialytic Hypotension

Introduction. Intradialytic hypotension (IDH) is a common complication during hemodialysis which may increase mortality risks. Low dose of Korean red ginseng (KRG) has been reported to increase blood pressure. Whether KRG can improve hemodynamic stability during hemodialysis has not been examined. Methods. The 8-week study consisted of two phases: observation phase and active treatment phase. According to prehemodialysis blood pressure (BP), 38 patients with IDH were divided into group A (BP ≥ 140/90 mmHg, n = 18) and group B (BP < 140/90 mmHg, n = 20). Patients were instructed to chew 3.5 gm KRG slices at each hemodialysis session during the 4-week treatment phase. Blood pressure changes, number of sessions disturbed by symptomatic IDH, plasma levels of vasoconstrictors, blood biochemistry, and adverse effects were recorded. Results. KRG significantly reduced the degree of blood pressure drop during hemodialysis (P < 0.05) and the frequency of symptomatic IDH (P < 0.05). More activation of vasoconstrictors (endothelin-1 and angiotensin II) during hemodialysis was found. The postdialytic levels of endothelin-1 and angiotensin II increased significantly (P < 0.01). Conclusion. Chewing KRG renders IDH patients better resistance to acute BP reduction during hemodialysis via activation of vasoconstrictors. Our results suggest that KRG could be an adjuvant treatment for IDH