Information-Theoretic GAN Compression with Variational Energy-based Model

We propose an information-theoretic knowledge distillation approach for the compression of generative adversarial networks, which aims to maximize the mutual information between teacher and student networks via a variational optimization based on an energy-based model. Because the direct computation of the mutual information in continuous domains is intractable, our approach alternatively optimizes the student network by maximizing the variational lower bound of the mutual information. To achieve a tight lower bound, we introduce an energy-based model relying on a deep neural network to represent a flexible variational distribution that deals with high-dimensional images and consider spatial dependencies between pixels, effectively. Since the proposed method is a generic optimization algorithm, it can be conveniently incorporated into arbitrary generative adversarial networks and even dense prediction networks, e.g., image enhancement models. We demonstrate that the proposed algorithm achieves outstanding performance in model compression of generative adversarial networks consistently when combined with several existing models.Comment: Accepted at Neurips202

Directing ricin-based immunotoxins with targeting affibodies and KDEL signal peptide to cancer cells effectively induces apoptosis and tumor suppression

<jats:title>Abstract</jats:title><jats:p>The plant toxin ricin, especially its cytotoxic A chain (RTA), can be genetically engineered with targeting ligands to develop specific anti-cancer recombinant immunotoxins (RITs). Here, we used affibody molecules targeting two cancer biomarkers, the receptors HER2 and EGFR, along with the KDEL signal peptide to construct two cancer-specific ricin-based RITs, HER2Afb-RTA-KDEL and EGFRAfb-RTA-KDEL. The affibodies successfully provided target-specificity and subsequent receptor-mediated endocytosis and the KDEL signal peptide routed the RITs through the retrograde transport pathway, effectively delivering RTA to the cytosol as well as avoiding the alternate recycling pathway that typical cancer cells frequently have. The in vivo efficacy of RITs was enhanced by introducing the albumin binding domain (AlBD) to construct AlBD/HER2Afb/RTA-KDEL. Systemic administration of AlBD-containing RITs to tumor-bearing mice significantly suppressed tumor growth without any noticeable side-effects. Collectively, combining target-selective affibody molecules, a cytotoxic RTA, and an intracellularly designating peptide, we successfully developed cancer-specific and efficacious ricin-based RITs. This approach can be applied to develop novel protein-based ???magic bullets??? to effectively suppress tumors that are resistant to conventional anti-cancer drugs.</jats:p> <jats:p><jats:bold>Graphical Abstract</jats:bold></jats:p&gt

Association of type 2 diabetes mellitus with lung cancer in patients with chronic obstructive pulmonary disease

BackgroundPatients with chronic obstructive pulmonary disease (COPD) have an increased risk of developing lung cancer. Some studies have also suggested that diabetes mellitus (DM) may increase the risk of developing lung cancer. This study aimed to investigate whether type 2 DM (T2DM) is associated with an increased risk of lung cancer in patients with COPD.Materials and methodsWe conducted a retrospective analysis on two cohorts: the National Health Insurance Service-National Sample Cohort (NHIS-NSC) of Korea and the Common Data Model (CDM) database of a university hospital. Among patients newly diagnosed with COPD in each cohort, those with a lung cancer diagnosis were included, and a control group was selected through propensity score matching. We used the Kaplan–Meier analysis and Cox proportional hazard models to compare lung cancer incidence between patients with COPD and T2DM and those without T2DM.ResultsIn the NHIS-NSC and CDM cohorts, we enrolled 3,474 and 858 patients with COPD, respectively. In both cohorts, T2DM was associated with an increased risk of lung cancer [NHIS-NSC: adjusted hazard ratio (aHR), 1.20; 95% confidence interval (CI), 1.02–1.41; and CDM: aHR, 1.45; 95% CI, 1.02–2.07). Furthermore, in the NHIS-NSC, among patients with COPD and T2DM, the risk of lung cancer was higher in current smokers than in never-smokers (aHR, 1.45; 95% CI, 1.09–1.91); in smokers with ≥30 pack-years than in never-smokers (aHR, 1.82; 95% CI, 1.49–2.25); and in rural residents than in metropolitan residents (aHR, 1.33; 95% CI, 1.06–1.68).ConclusionOur findings suggest that patients with COPD and T2DM may have an increased risk of developing lung cancer compared to those without T2DM

Lactate oxidase/catalase-displaying nanoparticles efficiently consume lactate in the tumor microenvironment to effectively suppress tumor growth

<jats:title>Abstract</jats:title><jats:p>The aggressive proliferation of tumor cells often requires increased glucose uptake and excessive anaerobic glycolysis, leading to the massive production and secretion of lactate to form a unique tumor microenvironment (TME). Therefore, regulating appropriate lactate levels in the TME would be a promising approach to control tumor cell proliferation and immune suppression. To effectively consume lactate in the TME, lactate oxidase (LOX) and catalase (CAT) were displayed onto <jats:italic>Aquifex aeolicus</jats:italic> lumazine synthase protein nanoparticles (AaLS) to form either AaLS/LOX or AaLS/LOX/CAT. These complexes successfully consumed lactate produced by CT26 murine colon carcinoma cells under both normoxic and hypoxic conditions. Specifically, AaLS/LOX generated a large amount of H<jats:sub>2</jats:sub>O<jats:sub>2</jats:sub> with complete lactate consumption to induce drastic necrotic cell death regardless of culture condition. However, AaLS/LOX/CAT generated residual H<jats:sub>2</jats:sub>O<jats:sub>2</jats:sub>, leading to necrotic cell death only under hypoxic condition similar to the TME. While the local administration of AaLS/LOX to the tumor site resulted in mice death, that of AaLS/LOX/CAT significantly suppressed tumor growth without any severe side effects. AaLS/LOX/CAT effectively consumed lactate to produce adequate amounts of H<jats:sub>2</jats:sub>O<jats:sub>2</jats:sub> which sufficiently suppress tumor growth and adequately modulate the TME, transforming environments that are favorable to tumor suppressive neutrophils but adverse to tumor-supportive tumor-associated macrophages. Collectively, these findings showed that the modular functionalization of protein nanoparticles with multiple metabolic enzymes may offer the opportunity to develop new enzyme complex-based therapeutic tools that can modulate the TME by controlling cancer metabolism.</jats:p> <jats:p><jats:bold>Graphical Abstract</jats:bold></jats:p&gt

Effect of Family Caregiving on Depression in the First 3 Months After Spinal Cord Injury

ObjectiveTo investigate the effect of family caregiving on depression in the first 3 months after spinal cord injury (SCI).MethodsA retrospective study was carried out on 76 patients diagnosed with an SCI from January 2013 to December 2016 at the Department of Physical Medicine and Rehabilitation of Kyungpook National University Hospital, Korea. Clinical characteristics including age, gender, level of injury, completeness of the injury, time since injury, caregiver information, etiology, and functional data were collected through a retrospective review of medical records. Depression was assessed using the Beck Depression Inventory (BDI). Patients with 14 or more points were classified as depressed and those with scores of 13 or less as non-depressed group.ResultsOf the 76 patients, 33 were in the depressed group with an average BDI of 21.27±6.17 and 43 patients included in the non-depressed group with an average BDI of 4.56±4.20. The BDI score of patients cared by unlicensed assistive personnel (UAP) was significantly higher than that of patients cared by their families (p=0.020). Univariate regression analysis showed that motor complete injury (p=0.027), UAP caregiving (p=0.022), and Ambulatory Motor Index (p=0.019) were associated with depression after SCI. Multivariate binary logistic regression analysis showed that motor completeness (p=0.002) and UAP caregiving (p=0.002) were independent risk factors.ConclusionCompared with UAP, family caregivers lowered the prevalence of depression in the first 3 months after SCI

Fluctuating risk of acute kidney injury-related mortality for four weeks after exposure to air pollution: A multi-country time-series study in 6 countries.

BACKGROUND: Recent studies have reported that air pollution is related to kidney diseases. However, the global evidence on the risk of death from acute kidney injury (AKI) owing to air pollution is limited. Therefore, we investigated the association between short-term exposure to air pollution-particulate matter ≤ 2.5 μm (PM2.5), ozone (O3), and nitrogen dioxide (NO2)-and AKI-related mortality using a multi-country dataset. METHODS: This study included 41,379 AKI-related deaths in 136 locations in six countries during 1987-2018. A novel case time-series design was applied to each air pollutant during 0-28 lag days to estimate the association between air pollution and AKI-related deaths. Moreover, we calculated AKI deaths attributable to non-compliance with the World Health Organization (WHO) air quality guidelines. RESULTS: The relative risks (95% confidence interval) of AKI-related deaths are 1.052 (1.003, 1.103), 1.022 (0.994, 1.050), and 1.022 (0.982, 1.063) for 5, 10, and 10 µg/m3 increase in lag 0-28 days of PM2.5, warm-season O3, and NO2, respectively. The lag-distributed association showed that the risk appeared immediately on the day of exposure to air pollution, gradually decreased, and then increased again reaching the peak approximately 20 days after exposure to PM2.5 and O3. We also found that 1.9%, 6.3%, and 5.2% of AKI deaths were attributed to PM2.5, warm-season O3, and NO2 concentrations above the WHO guidelines. CONCLUSIONS: This study provides evidence that public health policies to reduce air pollution may alleviate the burden of death from AKI and suggests the need to investigate the several pathways between air pollution and AKI death

Deep Convolutional Framelet Denosing for Low-Dose CT via Wavelet Residual Network

Model-based iterative reconstruction algorithms for low-dose X-ray computed tomography (CT) are computationally expensive. To address this problem, we recently proposed a deep convolutional neural network (CNN) for low-dose X-ray CT and won the second place in 2016 AAPM Low-Dose CT Grand Challenge. However, some of the textures were not fully recovered. To address this problem, here we propose a novel framelet-based denoising algorithm using wavelet residual network which synergistically combines the expressive power of deep learning and the performance guarantee from the framelet-based denoising algorithms. The new algorithms were inspired by the recent interpretation of the deep CNN as a cascaded convolution framelet signal representation. Extensive experimental results confirm that the proposed networks have significantly improved performance and preserve the detail texture of the original images