Clinical development of liposome-based drugs: formulation, characterization, and therapeutic efficacy

Research on liposome formulations has progressed from that on conventional vesicles to new generation liposomes, such as cationic liposomes, temperature sensitive liposomes, and virosomes, by modulating the formulation techniques and lipid composition. Many research papers focus on the correlation of blood circulation time and drug accumulation in target tissues with physicochemical properties of liposomal formulations, including particle size, membrane lamellarity, surface charge, permeability, encapsulation volume, shelf time, and release rate. This review is mainly to compare the therapeutic effect of current clinically approved liposome-based drugs with free drugs, and to also determine the clinical effect via liposomal variations in lipid composition. Furthermore, the major preclinical and clinical data related to the principal liposomal formulations are also summarized

Detach and Adapt: Learning Cross-Domain Disentangled Deep Representation

While representation learning aims to derive interpretable features for describing visual data, representation disentanglement further results in such features so that particular image attributes can be identified and manipulated. However, one cannot easily address this task without observing ground truth annotation for the training data. To address this problem, we propose a novel deep learning model of Cross-Domain Representation Disentangler (CDRD). By observing fully annotated source-domain data and unlabeled target-domain data of interest, our model bridges the information across data domains and transfers the attribute information accordingly. Thus, cross-domain joint feature disentanglement and adaptation can be jointly performed. In the experiments, we provide qualitative results to verify our disentanglement capability. Moreover, we further confirm that our model can be applied for solving classification tasks of unsupervised domain adaptation, and performs favorably against state-of-the-art image disentanglement and translation methods.Comment: CVPR 2018 Spotligh

Comparison of the Prevalence of Metabolic Syndrome Between the Criteria for Taiwanese and Japanese and the Projected Probability of Stroke in Elderly Hypertensive Taiwanese

SummaryBackgroundThe cutoff of abdominal circumference for metabolic syndrome (MS) defined by the Bureau of Health Promotion (BHP) of Taiwan for Taiwanese (men, 90cm; women, 80cm) and by the International Diabetes Federation (IDF) for Japanese (men, 85cm; women, 90cm) differs. This study aimed to examine the impact of this difference on the prevalence of MS and the impact of an MS diagnosis on the projected risk of stroke in hypertensive Taiwanese.MethodsMS was examined in a sample of 3,472 hypertensive patients (aged 55–80 years; 1,709 women) across Taiwan. The 10-year probability of stroke estimated from the Framingham equation was compared between MS and non-MS patients.ResultsThe prevalence of MS using the BHP criteria was 59.2% using the BHP criteria (95% confidence interval, CI, 57.6–60.8%; men, 52.5%; women, 66.1%) and 48.9% by the IDF criteria (95% CI, 47.2–50.5%; men, 61.3%; women, 36.1%). Both criteria showed that, compared with non-MS, MS has higher predicted 10-year probability of stroke (BHP, 0.153 ± 0.115 vs. 0.133 ± 0.105; IDF, 0.159 ± 0.109 vs. 0.132 ± 0.112; both p < 0.001) because of the difference in women (BHP, 0.143 ± 0.124 vs. 0.102 ± 0.091; IDF, 0.147 ± 0.121 vs. 0.118 ± 0.110; both p < 0.001) rather than men (BHP, p = 0.21; IDF, p = 0.29).ConclusionBoth criteria demonstrate that MS is highly prevalent in elderly hypertensive patients in Taiwan. Additionally in women, but not men, the predicted probability of stroke is higher in MS than in non-MS patients. The diagnosis of MS is potentially useful for identifying elderly hypertensive females with an elevated risk of stroke in Taiwan

Thrombolysis in Myocardial Infarction Frame Count in Single-Vessel Disease After Angioplasty

SUMMARYBackgroundWe compared the thrombolysis in myocardial infarction (TIMI) frame count and examined the impact of angioplasty on the count between patients with normal coronary angiograms and those with single-vessel disease (SVD).MethodsIn 780 consecutive patients referred for coronary angiography, TIMI frame count was measured for 149 patients who had SVD and 32 patients with normal angiograms who underwent the procedure for electro-physiologic study or valvular heart disease survey.ResultsComparison of each of the three vessels in the normal vessel group with the corresponding non-stenotic vessels in the SVD group showed similar counts in each of the left anterior descending artery (LAD), left circumflex artery (LCX), and right coronary artery (RCA). For the stenotic vessels, after successful angioplasty, the counts were all reduced (LAD, 54.5 ±28.8 vs. 34.0 ±19.3; LCX, 67.3 ±31.1 vs. 34.1 ±19.0; RCA, 33.2 ±28.1 vs. 19.3 ±7.9; all p <0.05). In addition, the count in the RCA after angioplasty was lower, compared with the RCA of the normal group (19.3 ±7.9 vs. 29.1 ±14.6, p = 0.001). Multivariate analysis showed that the use of oral calcium channel blockers was the only independent predictor for the reduction in RCA after angioplasty.ConclusionIn patients with SVD, the data of TIMI frame count in the nonstenotic vessels were similar to those without the disease, suggesting that the count in the normal artery is not affected by the adjacent stenotic artery. For the stenotic vessels, angioplasty had differential effects on each of the three arteries, indicating the existence of distinct properties, which is affected by calcium channel blockers, for individual coronary arteries in response to atherosclerosis and/or angioplasty

Decoupled Contrastive Learning

Contrastive learning (CL) is one of the most successful paradigms for self-supervised learning (SSL). In a principled way, it considers two augmented "views" of the same image as positive to be pulled closer, and all other images as negative to be pushed further apart. However, behind the impressive success of CL-based techniques, their formulation often relies on heavy-computation settings, including large sample batches, extensive training epochs, etc. We are thus motivated to tackle these issues and establish a simple, efficient, yet competitive baseline of contrastive learning. Specifically, we identify, from theoretical and empirical studies, a noticeable negative-positive-coupling (NPC) effect in the widely used InfoNCE loss, leading to unsuitable learning efficiency concerning the batch size. By removing the NPC effect, we propose decoupled contrastive learning (DCL) loss, which removes the positive term from the denominator and significantly improves the learning efficiency. DCL achieves competitive performance with less sensitivity to sub-optimal hyperparameters, requiring neither large batches in SimCLR, momentum encoding in MoCo, or large epochs. We demonstrate with various benchmarks while manifesting robustness as much less sensitive to suboptimal hyperparameters. Notably, SimCLR with DCL achieves 68.2% ImageNet-1K top-1 accuracy using batch size 256 within 200 epochs pre-training, outperforming its SimCLR baseline by 6.4%. Further, DCL can be combined with the SOTA contrastive learning method, NNCLR, to achieve 72.3% ImageNet-1K top-1 accuracy with 512 batch size in 400 epochs, which represents a new SOTA in contrastive learning. We believe DCL provides a valuable baseline for future contrastive SSL studies.Comment: Accepted by ECCV202