Intravenous meloxicam for the treatment of moderate to severe acute pain: a pooled analysis of safety and opioid-reducing effects.

BACKGROUND AND OBJECTIVES: To describe the safety and tolerability of intravenous meloxicam compared with placebo across all phase II/III clinical trials. METHODS: Safety data and opioid use from subjects with moderate to severe postoperative pain who received ≥1 dose of intravenous meloxicam (5-60 mg) or placebo in 1 of 7 studies (4 phase II; 3 phase III) were pooled. Data from intravenous meloxicam 5 mg, 7.5 mg and 15 mg groups were combined (low-dose subset). RESULTS: A total of 1426 adults (86.6% white; mean age: 45.8 years) received ≥1 dose of meloxicam IV; 517 (77.6% white; mean age: 46.7 years) received placebo. The incidence of treatment-emergent adverse events (TEAEs) in intravenous meloxicam and placebo-treated subjects was 47% and 57%, respectively. The most commonly reported TEAEs across treatment groups (intravenous meloxicam 5-15 mg, 30 mg, 60 mg and placebo, respectively) were nausea (4.3%, 20.8%, 5.8% and 25.3%), headache (1.5%, 5.6%, 1.6% and 10.4%), vomiting (2.8%, 4.6%, 1.6% and 7.4%) and dizziness (0%, 3.5%, 1.1% and 4.8%). TEAE incidence was generally similar in subjects aged \u3e65 years with impaired renal function and the general population. Similar rates of cardiovascular events were reported between treatment groups. One death was reported (placebo group; unrelated to study drug). There were 35 serious adverse events (SAEs); intravenous meloxicam 15 mg (n=5), intravenous meloxicam 30 mg (n=15) and placebo (n=15). The SAEs in meloxicam-treated subjects were determined to be unrelated to study medication. Six subjects withdrew due to TEAEs, including three treated with intravenous meloxicam (rash, localized edema and postprocedural pulmonary embolism). In trials where opioid use was monitored, meloxicam reduced postoperative rescue opioid use. CONCLUSIONS: Intravenous meloxicam was generally well tolerated in subjects with moderate to severe postoperative pain. TRIAL REGISTRATION NUMBERS: NCT01436032, NCT00945763, NCT01084161, NCT02540265, NCT02678286, NCT02675907 and NCT02720692

Nonlinear dynamics of the interface of dielectric liquids in a strong electric field: Reduced equations of motion

The evolution of the interface between two ideal dielectric liquids in a strong vertical electric field is studied. It is found that a particular flow regime, for which the velocity potential and the electric field potential are linearly dependent functions, is possible if the ratio of the permittivities of liquids is inversely proportional to the ratio of their densities. The corresponding reduced equations for interface motion are derived. In the limit of small density ratio, these equations coincide with the well-known equations describing the Laplacian growth.Comment: 10 page

Admissible Policy Teaching through Reward Design

We study reward design strategies for incentivizing a reinforcement learning agent to adopt a policy from a set of admissible policies. The goal of the reward designer is to modify the underlying reward function cost-efficiently while ensuring that any approximately optimal deterministic policy under the new reward function is admissible and performs well under the original reward function. This problem can be viewed as a dual to the problem of optimal reward poisoning attacks: instead of forcing an agent to adopt a specific policy, the reward designer incentivizes an agent to avoid taking actions that are inadmissible in certain states. Perhaps surprisingly, and in contrast to the problem of optimal reward poisoning attacks, we first show that the reward design problem for admissible policy teaching is computationally challenging, and it is NP-hard to find an approximately optimal reward modification. We then proceed by formulating a surrogate problem whose optimal solution approximates the optimal solution to the reward design problem in our setting, but is more amenable to optimization techniques and analysis. For this surrogate problem, we present characterization results that provide bounds on the value of the optimal solution. Finally, we design a local search algorithm to solve the surrogate problem and showcase its utility using simulation-based experiments

Radiation tolerance studies of silicon microstrip sensors for the CBM Silicon Tracking System

Double-sided silicon microstrip sensors will be used in the Silicon Tracking System of the CBM experiment. During experimental run they will be exposed to a radiation field of up to 1x1014 1 MeV neq cm-2. Radiation tolerance studies were made on prototypes from two different vendors. Results from these prototype detectors before and after irradiation to twice that neutron fluence are discussed

Specifying and Testing kk-Safety Properties for Machine-Learning Models

Machine-learning models are becoming increasingly prevalent in our lives, for instance assisting in image-classification or decision-making tasks. Consequently, the reliability of these models is of critical importance and has resulted in the development of numerous approaches for validating and verifying their robustness and fairness. However, beyond such specific properties, it is challenging to specify, let alone check, general functional-correctness expectations from models. In this paper, we take inspiration from specifications used in formal methods, expressing functional-correctness properties by reasoning about $k$ different executions, so-called $k$-safety properties. Considering a credit-screening model of a bank, the expected property that "if a person is denied a loan and their income decreases, they should still be denied the loan" is a 2-safety property. Here, we show the wide applicability of $k$-safety properties for machine-learning models and present the first specification language for expressing them. We also operationalize the language in a framework for automatically validating such properties using metamorphic testing. Our experiments show that our framework is effective in identifying property violations, and that detected bugs could be used to train better models