Endoscopic Retrograde Cholangiopancreatography in Elderly Patients

SUMMARYEndoscopic retrograde cholangiopancreatography (ERCP) is effective in the investigation and treatment of pan-creatic and biliary disease. As the prevalence of bile duct stones and malignant disease and the risk of surgery rise with age, studies on the therapeutic success of ERCP in the elderly become more popular. There have been publications documenting the safety of ERCP in elderly patients from the age of 65 to 85 years. Recent studies have also shown that ERCP is safe and effective in those aged 90 years and older. Outcomes of ERCP in terms of success and complication rates are similar to those in younger patients. Therefore, the decision to undergo ERCP should be determined by clinical need, and age alone should not be a contraindication to endoscopic intervention. Here, we review the indications, pre-procedure preparation, sedation and analgesia, monitoring/procedural care, complications, and outcomes of diagnostic and therapeutic ERCP in the elderly

CARBEN: Composite Adversarial Robustness Benchmark

Prior literature on adversarial attack methods has mainly focused on attacking with and defending against a single threat model, e.g., perturbations bounded in Lp ball. However, multiple threat models can be combined into composite perturbations. One such approach, composite adversarial attack (CAA), not only expands the perturbable space of the image, but also may be overlooked by current modes of robustness evaluation. This paper demonstrates how CAA's attack order affects the resulting image, and provides real-time inferences of different models, which will facilitate users' configuration of the parameters of the attack level and their rapid evaluation of model prediction. A leaderboard to benchmark adversarial robustness against CAA is also introduced.Comment: IJCAI 2022 Demo Track; The demonstration is at https://hsiung.cc/CARBEN

Towards Compositional Adversarial Robustness: Generalizing Adversarial Training to Composite Semantic Perturbations

Model robustness against adversarial examples of single perturbation type such as the $\ell_{p}$-norm has been widely studied, yet its generalization to more realistic scenarios involving multiple semantic perturbations and their composition remains largely unexplored. In this paper, we first propose a novel method for generating composite adversarial examples. Our method can find the optimal attack composition by utilizing component-wise projected gradient descent and automatic attack-order scheduling. We then propose generalized adversarial training (GAT) to extend model robustness from $\ell_{p}$-ball to composite semantic perturbations, such as the combination of Hue, Saturation, Brightness, Contrast, and Rotation. Results obtained using ImageNet and CIFAR-10 datasets indicate that GAT can be robust not only to all the tested types of a single attack, but also to any combination of such attacks. GAT also outperforms baseline $\ell_{\infty}$-norm bounded adversarial training approaches by a significant margin

NeuralFuse: Learning to Improve the Accuracy of Access-Limited Neural Network Inference in Low-Voltage Regimes

Deep neural networks (DNNs) have become ubiquitous in machine learning, but their energy consumption remains a notable issue. Lowering the supply voltage is an effective strategy for reducing energy consumption. However, aggressively scaling down the supply voltage can lead to accuracy degradation due to random bit flips in static random access memory (SRAM) where model parameters are stored. To address this challenge, we introduce NeuralFuse, a novel add-on module that addresses the accuracy-energy tradeoff in low-voltage regimes by learning input transformations to generate error-resistant data representations. NeuralFuse protects DNN accuracy in both nominal and low-voltage scenarios. Moreover, NeuralFuse is easy to implement and can be readily applied to DNNs with limited access, such as non-configurable hardware or remote access to cloud-based APIs. Experimental results demonstrate that, at a 1% bit error rate, NeuralFuse can reduce SRAM memory access energy by up to 24% while improving accuracy by up to 57%. To the best of our knowledge, this is the first model-agnostic approach (i.e., no model retraining) to address low-voltage-induced bit errors. The source code is available at https://github.com/IBM/NeuralFuse

AutoVP: An Automated Visual Prompting Framework and Benchmark

Visual prompting (VP) is an emerging parameter-efficient fine-tuning approach to adapting pre-trained vision models to solve various downstream image-classification tasks. However, there has hitherto been little systematic study of the design space of VP and no clear benchmark for evaluating its performance. To bridge this gap, we propose AutoVP, an end-to-end expandable framework for automating VP design choices, along with 12 downstream image-classification tasks that can serve as a holistic VP-performance benchmark. Our design space covers 1) the joint optimization of the prompts; 2) the selection of pre-trained models, including image classifiers and text-image encoders; and 3) model output mapping strategies, including nonparametric and trainable label mapping. Our extensive experimental results show that AutoVP outperforms the best-known current VP methods by a substantial margin, having up to 6.7% improvement in accuracy; and attains a maximum performance increase of 27.5% compared to linear-probing (LP) baseline. AutoVP thus makes a two-fold contribution: serving both as an efficient tool for hyperparameter tuning on VP design choices, and as a comprehensive benchmark that can reasonably be expected to accelerate VP's development. The source code is available at https://github.com/IBM/AutoVP.Comment: ICLR 202

Picomolar Dichotomous Activity of Gnidimacrin Against HIV-1

Highly active antiretroviral therapy (HAART) has offered a promising approach for controlling HIV-1 replication in infected individuals. However, with HARRT, HIV-1 is suppressed rather than eradicated due to persistence of HIV-1 in latent viral reservoirs. Thus, purging the virus from latent reservoirs is an important strategy toward eradicating HIV-1 infection. In this study, we discovered that the daphnane diterpene gnidimacrin, which was previously reported to have potent anti-cancer cell activity, activated HIV-1 replication and killed persistently-infected cells at picomolar concentrations. In addition to its potential to purge HIV-1 from latently infected cells, gnidimacrin potently inhibited a panel of HIV-1 R5 virus infection of peripheral blood mononuclear cells (PBMCs) at an average concentration lower than 10 pM. In contrast, gnidimacrin only partially inhibited HIV-1 ×4 virus infection of PBMCs. The strong anti-HIV-1 R5 virus activity of gnidimacrin was correlated with its effect on down-regulation of the HIV-1 coreceptor CCR5. The anti-R5 virus activity of gnidimacrin was completely abrogated by a selective protein kinase C beta inhibitor enzastaurin, which suggests that protein kinase C beta plays a key role in the potent anti-HIV-1 activity of gnidimacrin in PBMCs. In summary, these results suggest that gnidimacrin could activate latent HIV-1, specifically kill HIV-1 persistently infected cells, and inhibit R5 viruses at picomolar concentrations

New Betulinic Acid Derivatives for Bevirimat-Resistant Human Immunodeficiency Virus Type-1

Bevirimat (1, BVM) is an anti-HIV agent that blocks HIV-1 replication by interfering with HIV-1 Gag-SP1 processing at a late stage of viral maturation. However, clinical trials of 1 have revealed a high baseline drug resistance that is attributed to naturally-occurring polymorphisms in HIV-1 Gag. To overcome the drug resistance, 28 new derivatives of 1 were synthesized and tested against compound 1-resistant (BVM-R) HIV-1 variants. Among them, compound 6 exhibited much improved activity against several HIV-1 strains carrying BVM-R polymorphisms. Compound 6 was at least 20-fold more potent than 1 against the replication of NL4-3/V370A, which carries the most prevalent clinical BVM-R polymorphism in HIV-1 Gag-SP1. Thus, compound 6 merits further development as a potential anti-AIDS clinical trial candidate

Synthesis of betulinic acid derivatives as entry inhibitors against HIV-1 and bevirimat-resistant HIV-1 variants

Betulinic acid derivatives modified at the C28 position are HIV-1entry inhibitors such as compound A43D; however, modified at the C3 position instead of C28 give HIV-1 maturation inhibitor such as bevirimat. Bevirimat exhibited promising pharmacokinetic profiles in clinical trials, but its effectiveness was compromised by the high baseline drug resistance of HIV-1 variants with polymorphism in the putative drug binding site. In an effort to determine whether the viruses with bevirimat resistant polymorphism also altered their sensitivities to the betulinic acid derivatives that inhibit HIV-1 entry, a series of new betulinic acid entry inhibitors were synthesized and tested for their activities against HIV-1 NL4-3 and NL4-3 variants resistant to bevirimat. The results show that the bevirimat resistant viruses were approximately 5- to10-fold more sensitive to three new glutamine ester derivatives (13, 15 and 38) and A43D in an HIV-1 multi-cycle replication assay. In contrast, the wild type NL4-3 and the bevirimat resistant variants were equally sensitive to the HIV-1 RT inhibitor AZT. In addition, these three new compounds markedly improved microsomal stability compared to A43D

Design, Synthesis, and Preclinical Evaluations of Novel 4-Substituted 1,5-Diarylanilines as Potent HIV-1 Non-Nucleoside Reverse Transcriptase Inhibitor (NNRTI) Drug Candidates

Twenty-one new 4-substituted diarylaniline compounds (DAANs) (Scheme 2, series 13, 14, and 15) were designed, synthesized, and evaluated against wild-type and drug resistant HIV-1 viral strains. As a result, approximately a dozen new DAANs showed high potency with low nano- to sub-nanomolar EC50 values ranging from 0.2 to 10 nM. The three most promising compounds 14e, 14h, and 15h exhibited high potency against wild-type and drug-resistant viral strains with EC50 values at the sub-nanomolar level (0.29–0.87 nM), and were comparable to or more potent than the new NNRTI drug riplivirine (2) in the same assays. Drug-like physicochemical property assessments revealed that the most active DAANs (EC50 1–90 μg/mL at pH 7.4 and pH 2) and metabolic stability in vitro than 2, as well as desirable log P values (2). These promising results warrant further development of this novel compound class as potential potent anti-AIDS clinical trial candidates