The Encino Hospital Medical Center is a hospital in Encino, California.The hospital's ownership changed in June 2008 when Tenet Healthcare sold it to the current owner, Prime Healthcare Services. Previously, the hospital was one of the campuses of the Encino-Tarzana Regional Medical Center..
BACKGROUND:Nasolabial folds (NLFs) are common age-related facial lines, often treated with dermal fillers. Princess FILLER Lidocaine (PFL; now saypha filler Lidocaine) and Juvéderm Ultra XC (JUXC) are both hyaluronic acid-based fillers used for this purpose. OBJECTIVES:The aim of the authors of this study is to evaluate the effectiveness and safety of PFL in reducing NLF severity compared with JUXC using a split-face study design. METHODS:In this randomized, subject- and investigator-blinded multicenter study, patients with moderate-to-severe NLFs received PFL on one side of the face and JUXC on the other. Baseline NLF severity was assessed using the 5-point NLF-Severity Rating Scale (NLF-SRS). Follow-up assessments occurred at Weeks 12, 24, 36, and/or 48. The primary endpoint was the proportion of NLF-SRS responders at Week 24. Secondary endpoints included assessments by photographic reviewers and treating investigators, along with Global Aesthetic Improvement Scale (GAIS) ratings. Safety was monitored by adverse event reporting and patient diaries. FACE-Q questionnaires evaluated patient satisfaction. Repeat treatment was permitted at Week 36 or 48 if needed. RESULTS:At Week 24, PFL demonstrated noninferiority to JUXC (82.2% vs 81.9% responders; difference 0.37%, P < .0001). Secondary assessments confirmed this finding. Adverse events occurred in 24.4% of patients post PFL, with most being mild to moderate. Serious treatment-emergent adverse events were rare (1.1%). CONCLUSIONS:PFL is a noninferior alternative to JUXC for treating moderate-to-severe NLFs, with comparable efficacy, safety, and patient satisfaction. LEVEL OF EVIDENCE: 1 (THERAPEUTIC):For image description, please refer to the figure legend and surrounding text.
In this chapter, we will demonstrate how to query and clone data in the Snowflake Data Cloud. We will provide examples of SQL queries to show you how to get required information out of the data in tables and views within Snowflake. You will also learn how to clone data within Snowflake, which is one of its key differentiated features.
In this chapter, we will cover how the Snowflake Data Cloud handles semi-structured data such as JSON and XML. As data capture and sources have grown significantly in the last few years especially, semi-structured data has also grown. JSON semi-structured data especially grew significantly with the popularity of NoSQL databases. It has become essential for organizations to be able to process semi-structured data and combine it with structured data for analysis. However, analyzing semi-structured data using traditional methods has been more difficult due to added levels of complexity. Many businesses have struggled to combine structured with semi-structured data. Most analytical cloud databases also treated semi-structured data as a second-class data, making it hard to easily use both semi-structured data and structured data.
This chapter will cover the essentials of the Snowflake Data Cloud architecture that has made Snowflake widely popular. This hybrid architecture provides Snowflake with ease of use as well as fast and scalable performance. When the founders decided to build a new relational database completely based on the cloud, they were able to create architectural advantages beyond existing database architectures. One of the key architectural beliefs they were founded on was that tying storage to compute created challenges with scaling on the cloud.
