Obesity Management: Clinical Review and Update of the Pharmacologic Treatment Options

The toolbox of medications available for medical weight management is more robust than ever and includes a wide variety of mechanisms of actions and options for patients

Ground-Based HPA Pre-Distorter Using Machine Learning and Artificial Intelligent for Satellite Communication Applications

This chapter describes an innovative design and implementation approach of a ground-based pre-distorter framework using machine learning and artificial intelligence (ML-AI) technology for high power amplifier (HPA) pre-distortion. The ML-AI technology enabler proposed is a combined multi-objective reinforce learning-and-adaptive neural network (MORL-ANN) and an operating environment predictor (OEP). The proposed framework addresses the signal distortions caused by a nonlinear HPA on the ground transmitter and a nonlinear HPA located at a satellite communication (SATCOM) transponder (TXDER). The TXDER’s HPA is assumed to operate under unknown conditions. The objective is twofold, namely, to demonstrate (i) an advanced decision science technique using ML-AI for future SATCOM applications and (ii) the feasibility of the proposed ground-based ML-AI framework using an end-to-end SATCOM emulator. A new OEP concept using a deterministic and Bayesian approach to improve the MORL-ANN pre-distorter (PD) performance will also be presented

Chemosensitivity Predicted by BluePrint 80-Gene Functional Subtype and MammaPrint in the Prospective Neoadjuvant Breast Registry Symphony Trial (NBRST).

PURPOSE: The purpose of the NBRST study is to compare a multigene classifier to conventional immunohistochemistry (IHC)/fluorescence in situ hybridization (FISH) subtyping to predict chemosensitivity as defined by pathological complete response (pCR) or endocrine sensitivity as defined by partial response. METHODS: The study includes women with histologically proven breast cancer, who will receive neoadjuvant chemotherapy (NCT) or neoadjuvant endocrine therapy. BluePrint in combination with MammaPrint classifies patients into four molecular subgroups: Luminal A, Luminal B, HER2, and Basal. RESULTS: A total of 426 patients had definitive surgery. Thirty-seven of 211 (18 %) IHC/FISH hormone receptor (HR)+/HER2- patients were reclassified by Blueprint as Basal (n = 35) or HER2 (n = 2). Fifty-three of 123 (43 %) IHC/FISH HER2+ patients were reclassified as Luminal (n = 36) or Basal (n = 17). Four of 92 (4 %) IHC/FISH triple-negative (TN) patients were reclassified as Luminal (n = 2) or HER2 (n = 2). NCT pCR rates were 2 % in Luminal A and 7 % Luminal B patients versus 10 % pCR in IHC/FISH HR+/HER2- patients. The NCT pCR rate was 53 % in BluePrint HER2 patients. This is significantly superior (p = 0.047) to the pCR rate in IHC/FISH HER2+ patients (38 %). The pCR rate of 36 of 75 IHC/FISH HER2+/HR+ patients reclassified as BPLuminal is 3 %. NCT pCR for BluePrint Basal patients was 49 of 140 (35 %), comparable to the 34 of 92 pCR rate (37 %) in IHC/FISH TN patients. CONCLUSIONS: BluePrint molecular subtyping reclassifies 22 % (94/426) of tumors, reassigning more responsive patients to the HER2 and Basal categories while reassigning less responsive patients to the Luminal category. These findings suggest that compared with IHC/FISH, BluePrint more accurately identifies patients likely to respond (or not respond) to NCT

Opportunities for organoids as new models of aging.

The biology of aging is challenging to study, particularly in humans. As a result, model organisms are used to approximate the physiological context of aging in humans. However, the best model organisms remain expensive and time-consuming to use. More importantly, they may not reflect directly on the process of aging in people. Human cell culture provides an alternative, but many functional signs of aging occur at the level of tissues rather than cells and are therefore not readily apparent in traditional cell culture models. Organoids have the potential to effectively balance between the strengths and weaknesses of traditional models of aging. They have sufficient complexity to capture relevant signs of aging at the molecular, cellular, and tissue levels, while presenting an experimentally tractable alternative to animal studies. Organoid systems have been developed to model many human tissues and diseases. Here we provide a perspective on the potential for organoids to serve as models for aging and describe how current organoid techniques could be applied to aging research

GN and C Subsystem Concept for Safe Precision Landing of the Proposed Lunar MARE Robotic Science Mission

The Lunar MARE (Moon Age and Regolith Explorer) Discovery Mission concept targets delivery of a science payload to the lunar surface for sample collection and dating. The mission science is within a 100-meter radius region of smooth lunar maria terrain near Aristarchus crater. The location has several small, sharp craters and rocks that present landing hazards to the spacecraft. For successful delivery of the science payload to the surface, the vehicle Guidance, Navigation and Control (GN&C) subsystem requires safe and precise landing capability, so design infuses the NASA Autonomous precision Landing and Hazard Avoidance Technology (ALHAT) and a gimbaled, throttleable LOX/LCH4 main engine. The ALHAT system implemented for Lunar MARE is a specialization of prototype technologies in work within NASA for the past two decades, including a passive optical Terrain Relative Navigation (TRN) sensor, a Navigation Doppler Lidar (NDL) velocity and range sensor, and a Lidar-based Hazard Detection (HD) sensor. The landing descent profile is from a retrograde orbit over lighted terrain with landing near lunar dawn. The GN&C subsystem with ALHAT capabilities will deliver the science payload to the lunar surface within a 20-meter landing ellipse of the target location and at a site having greater than 99% safety probability, which minimizes risk to safe landing and delivery of the MARE science payload to the intended terrain region

Measurement challenge : protocol for international case–control comparison of mammographic measures that predict breast cancer risk

Introduction: For women of the same age and body mass index, increased mammographic density is one of the strongest predictors of breast cancer risk. There are multiple methods of measuring mammographic density and other features in a mammogram that could potentially be used in a screening setting to identify and target women at high risk of developing breast cancer. However, it is unclear which measurement method provides the strongest predictor of breast cancer risk. Methods and analysis: The measurement challenge has been established as an international resource to offer a common set of anonymised mammogram images for measurement and analysis. To date, full field digital mammogram images and core data from 1650 cases and 1929 controls from five countries have been collated. The measurement challenge is an ongoing collaboration and we are continuing to expand the resource to include additional image sets across different populations (from contributors) and to compare additional measurement methods (by challengers). The intended use of the measurement challenge resource is for refinement and validation of new and existing mammographic measurement methods. The measurement challenge resource provides a standardised dataset of mammographic images and core data that enables investigators to directly compare methods of measuring mammographic density or other mammographic features in case/control sets of both raw and processed images, for the purposes of the comparing their predictions of breast cancer risk. Ethics and dissemination: Challengers and contributors are required to enter a Research Collaboration Agreement with the University of Melbourne prior to participation in the measurement challenge. The Challenge database of collated data and images are stored in a secure data repository at the University of Melbourne. Ethics approval for the measurement challenge is held at University of Melbourne (HREC ID 0931343.3)