Reliable Event Generation With Invertible Conditional Normalizing Flow.

Event streams provide a novel paradigm to describe visual scenes by capturing intensity variations above specific thresholds along with various types of noise. Existing event generation methods usually rely on one-way mappings using hand-crafted parameters and noise rates, which may not adequately suit diverse scenarios and event cameras. To address this limitation, we propose a novel approach to learn a bidirectional mapping between the feature space of event streams and their inherent parameters, enabling the generation of reliable event streams with enhanced generalization capabilities. We first randomly generate a vast number of parameters and synthesize massive event streams using an event simulator. Subsequently, an event-based normalizing flow network is proposed to learn the invertible mapping between the representation of a synthetic event stream and its parameters. The invertible mapping is implemented by incorporating an intensity-guided conditional affine simulation mechanism, facilitating better alignment between event features and parameter spaces. Additionally, we impose constraints on event sparsity, edge distribution, and noise distribution through novel event losses, further emphasizing event priors in the bidirectional mapping. Our framework surpasses state-of-the-art methods in video reconstruction, optical flow estimation, and parameter estimation tasks on synthetic and real-world datasets, exhibiting excellent generalization across diverse scenes and cameras.

Phase II, Open-Label Study of Ciltacabtagene Autoleucel, an Anti-B-Cell Maturation Antigen Chimeric Antigen Receptor-T-Cell Therapy, in Chinese Patients With Relapsed/Refractory Multiple Myeloma (CARTIFAN-1).

PURPOSE: CARTIFAN-1 aimed to evaluate the efficacy and safety of ciltacabtagene autoleucel (cilta-cel), a B-cell maturation antigen-targeting chimeric antigen receptor T-cell therapy, in Chinese patients with relapsed/refractory multiple myeloma (RRMM). METHODS: This pivotal phase II, open-label study (ClinicalTrials.gov identifier: NCT03758417), conducted across eight sites in China, enrolled adult patients with RRMM who had received ≥ 3 lines of prior therapy, including a proteasome inhibitor and immunomodulatory drug. Patients received a single infusion of cilta-cel (target dose 0.75 × 106 chimeric antigen receptor-positive viable T cells/kg). The primary end point was overall response rate. Secondary end points included progression-free survival (PFS), overall survival (OS), and incidence and severity of adverse events (AEs). RESULTS: As of the clinical cutoff of July 19, 2021, 48 patients received a cilta-cel infusion. At an 18-month median follow-up, the overall response rate was 89.6% (95% CI, 77.3 to 96.5), with a median time to first response of approximately 1 month; 77.1% of patients (95% CI, 62.7 to 88.0) achieved complete response or better. Medians for duration of response, PFS, and OS were not reached. The 18-month PFS and OS rates were 66.8% (95% CI, 49.4 to 79.4) and 78.7% (95% CI, 64.0 to 88.0), respectively. Hematologic AEs were common, including anemia (100%), neutropenia (97.9%), lymphopenia (95.8%), and thrombocytopenia (87.5%). Cytokine release syndrome occurred in 97.9% of patients (35.4% grade 3/4); the median time to onset was 7 days, and the median duration was 5 days. Infections occurred in 85.4% of patients (37.5% grade 3/4). Ten deaths occurred after cilta-cel infusion, eight of which were due to treatment-related AEs. CONCLUSION: These data demonstrate a favorable risk-benefit profile for a single infusion of cilta-cel, resulting in early, deep, and durable responses in heavily pretreated patients with RRMM in China.

Deblurring Dynamic Scenes via Spatially Varying Recurrent Neural Networks.

Deblurring images captured in dynamic scenes is challenging as the motion blurs are spatially varying caused by camera shakes and object movements. In this paper, we propose a spatially varying neural network to deblur dynamic scenes. The proposed model is composed of three deep convolutional neural networks (CNNs) and a recurrent neural network (RNN). The RNN is used as a deconvolution operator on feature maps extracted from the input image by one of the CNNs. Another CNN is used to learn the spatially varying weights for the RNN. As a result, the RNN is spatial-aware and can implicitly model the deblurring process with spatially varying kernels. To better exploit properties of the spatially varying RNN, we develop both one-dimensional and two-dimensional RNNs for deblurring. The third component, based on a CNN, reconstructs the final deblurred feature maps into a restored image. In addition, the whole network is end-to-end trainable. Quantitative and qualitative evaluations on benchmark datasets demonstrate that the proposed method performs favorably against the state-of-the-art deblurring algorithms.

Learning Spatially Variant Linear Representation Models for Joint Filtering.

Joint filtering mainly uses an additional guidance image as a prior and transfers its structures to the target image in the filtering process. Different from existing approaches that rely on local linear models or hand-designed objective functions to extract the structural information from the guidance image, we propose a new joint filtering method based on a spatially variant linear representation model (SVLRM), where the target image is linearly represented by the guidance image. However, learning SVLRMs for vision tasks is a highly ill-posed problem. To estimate the spatially variant linear representation coefficients, we develop an effective approach based on a deep convolutional neural network (CNN). As such, the proposed deep CNN (constrained by the SVLRM) is able to model the structural information of both the guidance and input images. We show that the proposed approach can be effectively applied to a variety of applications, including depth/RGB image upsampling and restoration, flash deblurring, natural image denoising, and scale-aware filtering. In addition, we show that the linear representation model can be extended to high-order representation models (e.g., quadratic and cubic polynomial representations). Extensive experimental results demonstrate that the proposed method performs favorably against the state-of-the-art methods that have been specifically designed for each task.

Physics-Based Generative Adversarial Models for Image Restoration and Beyond.

We present an algorithm to directly solve numerous image restoration problems (e.g., image deblurring, image dehazing, and image deraining). These problems are ill-posed, and the common assumptions for existing methods are usually based on heuristic image priors. In this paper, we show that these problems can be solved by generative models with adversarial learning. However, a straightforward formulation based on a straightforward generative adversarial network (GAN) does not perform well in these tasks, and some structures of the estimated images are usually not preserved well. Motivated by an interesting observation that the estimated results should be consistent with the observed inputs under the physics models, we propose an algorithm that guides the estimation process of a specific task within the GAN framework. The proposed model is trained in an end-to-end fashion and can be applied to a variety of image restoration and low-level vision problems. Extensive experiments demonstrate that the proposed method performs favorably against state-of-the-art algorithms.

Effects of canagliflozin versus glimepiride on adipokines and inflammatory biomarkers in type 2 diabetes.

OBJECTIVE: Type 2 diabetes and obesity are pro-inflammatory states associated with increased risk of cardiovascular disease. Canagliflozin, an SGLT2 inhibitor, demonstrated superiority in lowering HbA1c versus glimepiride with less hypoglycemia and greater weight reduction via loss of fat mass in a 52-week trial of type 2 diabetes patients. This post hoc, exploratory analysis assessed the effects of canagliflozin versus glimepiride on select adipokines, inflammatory biomarkers, and chemokines. METHODS: Changes from baseline to Week 52 in serum leptin, adiponectin, IL-6, TNFα, CRP, PAI-1, VCAM-1, and MCP-1 were measured in a randomly selected subset of type 2 diabetes patients on metformin receiving canagliflozin 300 mg (n = 100) or glimepiride (n = 100) in the overall study. Correlations between change in biomarkers and change in select metabolic and anthropometric variables were assessed. RESULTS: At Week 52, canagliflozin decreased median serum leptin by 25% (95% CI: -34%, -15%) and increased median serum adiponectin by 17% (95% CI: 11%, 23%) compared with glimepiride. There was a 22% reduction in median serum IL-6 (95% CI: -34%, -10%) and a 7% increase in median serum TNFα (95% CI: 1%, 12%) with canagliflozin versus glimepiride. No between-group differences were observed with the other biomarkers. The decrease in serum leptin with canagliflozin was correlated with change in weight (r ≥ 0.3) only; the increase in adiponectin and decrease in IL-6 with canagliflozin occurred independently of changes in HbA1c, weight, or lipids. CONCLUSIONS: These results indicate that canagliflozin may improve adipose tissue function and induce changes in serum leptin, adiponectin, and IL-6 that favorably impact insulin sensitivity and cardiovascular disease risk.

Sodium-glucose co-transporter (SGLT) and glucose transporter (GLUT) expression in the kidney of type 2 diabetic subjects.

The sodium-glucose co-transporters (SGLTs) are responsible for the tubular reabsorption of filtered glucose from the kidney into the bloodstream. The inhibition of SGLT2-mediated glucose reabsorption is a novel and highly effective strategy to alleviate hyperglycaemia in patients with type 2 diabetes mellitus (T2DM). However, the effectiveness of SGLT2 inhibitor therapy is diminished due, in part, to a compensatory increase in the maximum reabsorptive capacity (Tm) for glucose in patients with T2DM. We hypothesized that this increase in Tm could be explained by an increase in the tubular expression of SGLT and glucose transporters (GLUT) in these patients. To examine this, we obtained human kidney biopsy specimens from patients with or without T2DM and examined the mRNA expression of SGLTs and GLUTs. The expression of SGLT1 is markedly increased in the kidney of patients with T2DM, and SGLT1 mRNA is highly and significantly correlated with fasting and postprandial plasma glucose and HbA1c. In contrast, our data demonstrate that the levels of SGLT2 and GLUT2 mRNA are downregulated in diabetic patients, but not to a statistically significant level. These important findings are clinically significant and may have implications for the treatment of T2DM using strategies that target SGLT transporters in the kidney.

Effects of canagliflozin, a sodium glucose co-transporter 2 inhibitor, on blood pressure and markers of arterial stiffness in patients with type 2 diabetes mellitus: a post hoc analysis.

BACKGROUND: Physiologic determinants, such as pulse pressure [difference between systolic blood pressure (SBP) and diastolic BP (DBP)], mean arterial pressure (2/3 DBP + 1/3 SBP), and double product [beats per minute (bpm) × SBP], are linked to cardiovascular outcomes. The effects of canagliflozin, a sodium glucose co-transporter 2 (SGLT2) inhibitor, on pulse pressure, mean arterial pressure, and double product were assessed in patients with type 2 diabetes mellitus (T2DM). METHODS: This post hoc analysis was based on pooled data from four 26-week, randomized, double-blind, placebo-controlled studies evaluating canagliflozin in patients with T2DM (N = 2313) and a 6-week, randomized, double-blind, placebo-controlled, ambulatory BP monitoring (ABPM) study evaluating canagliflozin in patients with T2DM and hypertension (N = 169). Changes from baseline in SBP, DBP, pulse pressure, mean arterial pressure, and double product were assessed using seated BP measurements (pooled studies) or averaged 24-h BP assessments (ABPM study). Safety was assessed based on adverse event reports. RESULTS: In the pooled studies, canagliflozin 100 and 300 mg reduced SBP (-4.3 and -5.0 vs -0.3 mmHg) and DBP (-2.5 and -2.4 vs -0.6 mmHg) versus placebo at week 26. Reductions in pulse pressure (-1.8 and -2.6 vs 0.2 mmHg), mean arterial pressure (-3.1 and -3.3 vs -0.5 mmHg), and double product (-381 and -416 vs -30 bpm × mmHg) were also seen with canagliflozin 100 and 300 mg versus placebo. In the ABPM study, canagliflozin 100 and 300 mg reduced mean 24-h SBP (-4.5 and -6.2 vs -1.2 mmHg) and DBP (-2.2 and -3.2 vs -0.3 mmHg) versus placebo at week 6. Canagliflozin 300 mg provided reductions in pulse pressure (-3.3 vs -0.8 mmHg) and mean arterial pressure (-4.2 vs -0.6 mmHg) compared with placebo, while canagliflozin 100 mg had more modest effects on these parameters. Canagliflozin was generally well tolerated in both study populations. CONCLUSIONS: Canagliflozin improved all three cardiovascular physiologic markers, consistent with the hypothesis that canagliflozin may have beneficial effects on some cardiovascular outcomes in patients with T2DM. Trial registration ClinicalTrials.gov Identifier: NCT01081834 (registered March 2010); NCT01106677 (registered April 2010); NCT01106625 (registered April 2010); NCT01106690 (registered April 2010); NCT01939496 (registered September 2013).

Reductions in Mean 24-Hour Ambulatory Blood Pressure After 6-Week Treatment With Canagliflozin in Patients With Type 2 Diabetes Mellitus and Hypertension.

This randomized, double-blind, placebo-controlled study evaluated the early effects of canagliflozin on blood pressure (BP) in patients with type 2 diabetes mellitus (T2DM) and hypertension. Patients were randomized to canagliflozin 300 mg, canagliflozin 100 mg, or placebo for 6 weeks and underwent 24-hour ambulatory BP monitoring before randomization, on day 1 of treatment, and after 6 weeks. The primary endpoint was change in mean 24-hour systolic BP (SBP) from baseline to week 6. Overall, 169 patients were included (mean age, 58.6 years; glycated hemoglobin, 8.1%; seated BP 138.5/82.7 mm Hg). At week 6, canagliflozin 300 mg provided greater reductions in mean 24-hour SBP than placebo (least squares mean -6.2 vs -1.2 mm Hg, respectively; P=.006). Numerical reductions in SBP were observed with canagliflozin 100 mg. Canagliflozin was generally well tolerated, with side effects similar to those reported in previous studies. These results suggest that canagliflozin rapidly reduces BP in patients with T2DM and hypertension.

Muraglitazar, a novel dual (alpha/gamma) peroxisome proliferator-activated receptor activator, improves diabetes and other metabolic abnormalities and preserves beta-cell function in db/db mice.

Muraglitazar, a novel dual (alpha/gamma) peroxisome proliferator-activated receptor (PPAR) activator, was investigated for its antidiabetic properties and its effects on metabolic abnormalities in genetically obese diabetic db/db mice. In db/db mice and normal mice, muraglitazar treatment modulates the expression of PPAR target genes in white adipose tissue and liver. In young hyperglycemic db/db mice, muraglitazar treatment (0.03-50 mg . kg(-1) . day(-1) for 2 weeks) results in dose-dependent reductions of glucose, insulin, triglycerides, free fatty acids, and cholesterol. In older hyperglycemic db/db mice, longer-term muraglitazar treatment (30 mg . kg(-1) . day(-1) for 4 weeks) prevents time-dependent deterioration of glycemic control and development of insulin deficiency. In severely hyperglycemic db/db mice, muraglitazar treatment (10 mg . kg(-1) . day(-1) for 2 weeks) improves oral glucose tolerance and reduces plasma glucose and insulin levels. In addition, treatment increases insulin content in the pancreas. Finally, muraglitazar treatment increases abnormally low plasma adiponectin levels, increases high-molecular weight adiponectin complex levels, reduces elevated plasma corticosterone levels, and lowers elevated liver lipid content in db/db mice. The overall conclusions are that in db/db mice, the novel dual (alpha/gamma) PPAR activator muraglitazar 1) exerts potent and efficacious antidiabetic effects, 2) preserves pancreatic insulin content, and 3) improves metabolic abnormalities such as hyperlipidemia, fatty liver, low adiponectin levels, and elevated corticosterone levels.

Design and synthesis of N-[(4-methoxyphenoxy)carbonyl]-N-[[4-[2-(5- methyl-2-phenyl-4-oxazolyl)ethoxy]phenyl]methyl]glycine [Muraglitazar/BMS-298585], a novel peroxisome proliferator-activated receptor alpha/gamma dual agonist with efficacious glucose and lipid-lowering activities.

Muraglitazar/BMS-298585 (2) has been identified as a non-thiazolidinedione PPAR alpha/gamma dual agonist that shows potent activity in vitro at human PPARalpha (EC(50) = 320 nM) and PPARgamma(EC(50) = 110 nM). Compound 2 shows excellent efficacy for lowering glucose, insulin, triglycerides, and free fatty acids in genetically obese, severely diabetic db/db mice and has a favorable ADME profile. Compound 2 is currently in clinical development for the treatment of type 2 diabetes and dyslipidemia.