Pharmacogenetics in Type 2 Diabetes: Potential Implications for Clinical Practice

Pharmacogenetic research aims to study how genetic variation may influence drug efficacy and/or toxicity; pharmacogenomics expands this quest to the entire genome. Pharmacogenetic findings may help to uncover new drug targets, illuminate pathophysiology, clarify disease heterogeneity, aid in the fine-mapping of genetic associations, and contribute to personalized treatment. In diabetes, there is precedent for the successful application of pharmacogenetic concepts to monogenic forms of the disease, such as maturity onset diabetes of the young or neonatal diabetes. Whether similar insights will be produced for the common form of type 2 diabetes remains to be seen. With recent advances in genetic approaches, the successive application of candidate gene studies, large-scale genotyping studies and genome-wide association studies has begun to generate suggestive results that may lead to changes in clinical practice. However, many potential barriers to the translation of pharmacogenetic discoveries to the clinical management of diabetes still remain. Here, we offer a contemporary overview of the field in its current state, identify potential obstacles, and highlight future directions

Estimation of Shade Losses in Unlabeled PV Data

We provide a methodology for estimating the losses due to shade in power generation data sets produced by real-world photovoltaic (PV) systems. We focus this work on estimating shade loss from data that are unlabeled, i.e. power measurements with time stamps but no other information such as site configuration or meteorological data. This approach enables, for the first time, the analysis of data generated by small scale, distributed PV systems, which do not have the data quality or richness of large, utility-scale PV systems or research-grade installations. This work is an application of the newly published signal decomposition (SD) framework, which provides an extensible approach for estimating hidden components in time-series data.Comment: 8 pages in double-column format, 12 figures, and 2 table

A roadmap to achieve pharmacological precision medicine in diabetes

Current pharmacological treatment of diabetes is largely algorithmic. Other than for cardiovascular disease or renal disease, where sodium–glucose cotransporter 2 inhibitors and/or glucagon-like peptide-1 receptor agonists are indicated, the choice of treatment is based upon overall risks of harm or side effect and cost, and not on probable benefit. Here we argue that a more precise approach to treatment choice is necessary to maximise benefit and minimise harm from existing diabetes therapies. We propose a roadmap to achieve precision medicine as standard of care, to discuss current progress in relation to monogenic diabetes and type 2 diabetes, and to determine what additional work is required. The first step is to identify robust and reliable genetic predictors of response, recognising that genotype is static over time and provides the skeleton upon which modifiers such as clinical phenotype and metabolic biomarkers can be overlaid. The second step is to identify these metabolic biomarkers (e.g. beta cell function, insulin sensitivity, BMI, liver fat, metabolite profile), which capture the metabolic state at the point of prescribing and may have a large impact on drug response. Third, we need to show that predictions that utilise these genetic and metabolic biomarkers improve therapeutic outcomes for patients, and fourth, that this is cost-effective. Finally, these biomarkers and prediction models need to be embedded in clinical care systems to enable effective and equitable clinical implementation. Whilst this roadmap is largely complete for monogenic diabetes, we still have considerable work to do to implement this for type 2 diabetes. Increasing collaborations, including with industry, and access to clinical trial data should enable progress to implementation of precision treatment in type 2 diabetes in the near future. GRAPHICAL ABSTRACT: [Image: see text] SUPPLEMENTARY INFORMATION: The online version contains a slideset of the figures for download, which is available at 10.1007/s00125-022-05732-3

The GuideView System for Interactive, Structured, Multi-modal Delivery of Clinical Guidelines

GuideView is a computerized clinical guideline system which delivers clinical guidelines in an easy-to-understand and easy-to-use package. It may potentially enhance the quality of medical care or allow non-medical personnel to provide acceptable levels of care in situations where physicians or nurses may not be available. Such a system can be very valuable during space flight missions when a physician is not readily available, or perhaps the designated medical personnel is unable to provide care. Complex clinical guidelines are broken into simple steps. At each step clinical information is presented in multiple modes, including voice,audio, text, pictures, and video. Users can respond via mouse clicks or via voice navigation. GuideView can also interact with medical sensors using wireless or wired connections. The system's interface is illustrated and the results of a usability study are presented

Nuevas Técnicas de Transmisión de Muy Alta Velocidad para Redes de Computadores: Una aproximación mediante ondículas.

Indoor multpropagation channel is modeled by the Kaiser electromagnetic wavelet. A method for channel characterization is proposed by modeling all the reflections of indoor propagation in a kernel function instead of its impulse response. This led us to consider a fractal modulation scheme in which Kaiser wavelets substitute the traditional sinusoidal carrier

Genome-Wide Association with Diabetes-Related Traits in the Framingham Heart Study

BACKGROUND: Susceptibility to type 2 diabetes may be conferred by genetic variants having modest effects on risk. Genome-wide fixed marker arrays offer a novel approach to detect these variants. METHODS: We used the Affymetrix 100K SNP array in 1,087 Framingham Offspring Study family members to examine genetic associations with three diabetes-related quantitative glucose traits (fasting plasma glucose (FPG), hemoglobin A1c, 28-yr time-averaged FPG (tFPG)), three insulin traits (fasting insulin, HOMA-insulin resistance, and 0–120 min insulin sensitivity index); and with risk for diabetes. We used additive generalized estimating equations (GEE) and family-based association test (FBAT) models to test associations of SNP genotypes with sex-age-age2-adjusted residual trait values, and Cox survival models to test incident diabetes. RESULTS: We found 415 SNPs associated (at p 1%) 100K SNPs in LD (r2 > 0.05) with ABCC8 A1369S (rs757110), KCNJ11 E23K (rs5219), or SNPs in CAPN10 or HNFa. PPARG P12A (rs1801282) was not significantly associated with diabetes or related traits. CONCLUSION: Framingham 100K SNP data is a resource for association tests of known and novel genes with diabetes and related traits posted at. Framingham 100K data replicate the TCF7L2 association with diabetes.National Heart, Lung, and Blood Institute's Framingham Heart Study (N01-HC-25195); National Institutes of Health National Center for Research Resources Shared Instrumentation grant (1S10RR163736-01A1); National Center for Research Resources General Clinical Research Center (M01-RR-01066); American Diabetes Association Career Developement Award; GlaxoSmithKline; Merck; Lilly; National Institutes of Health Research Career Award (K23 DK659678-03

Diplomado de profundización – CISCO.

Este documento corresponde a la actividad final del Diplomado de Profundización CCNA, y por intermedio de estas actividades, divididas en dos (2) escenarios nos permitirán identificar el grado de desarrollo de competencias y habilidades que adquirimos durante el diplomado. Lo principal de esta prueba de habilidades es poner a prueba los niveles de comprensión y solución que aprendimos en los diferentes ejercicios y problemas relacionados con el campo emergente de las Redes y Telecomunicaciones. De igual manera se pretende profundizar y demostrar las capacidades de responder aún mundo real con escenarios físicos que tienen pocas diferencias con las simulaciones hechas de topologías de red, configuraciones de dispositivos, insertar paquetes y simulaciones hechas con las herramientas: Packet Tracer o GNS3. En esta oportunidad se configuraran e interconectaran entre sí cada uno de los dispositivos que forman parte de los escenarios a resolver, acorde con los lineamientos establecidos para el direccionamiento IP, protocolos de enrutamiento y demás aspectos que forman parte de la topología de red. Palabras Claves: Cisco, Ping, Traceroute, Packet Tracer, Show ip route, Enable, EIGRP, Router, DHCP, VLAN.This document corresponds to the final activity of the CCNA Deepening Diploma, and through these activities, divided into two (2) scenarios will allow us to identify the degree of development of skills and abilities that we acquired during the diploma. The main thing about this skills test is to test the levels of understanding and solution we learned in the different exercises and problems related to the emerging field of Networks and Telecommunications. Similarly, it is intended to deepen and demonstrate the capabilities of responding even in real world with physical scenarios that have few differences with simulations made of network topologies, device configurations, insert packages and simulations made with the tools: Packet Tracer or GNS3. In this occasion, each of the devices that are part of the scenarios to be resolved will be configured and interconnected, in accordance with the guidelines established for IP addressing, routing protocols and other aspects that are part of the network topology. Keywords: Cisco, ping, traceroute, Packet Tracer, show ip route, enable, EIGRP, Router, DHCP, VLAN

Automatic parameter tuning for functional regionalization methods

The methods used to define functional regions for public statistics and policy purposes need to establish several parameter values. This is typically achieved using expert knowledge based on qualitative judgements and lengthy consultations with local stakeholders. We propose to support this process by using an optimization algorithm to calibrate any regionalization method by identifying the parameter values that produce the best regionalization for a given quantitative indicator. The approach is exemplified by using a grid search and a genetic algorithm to configure the official methods employed in the UK and Sweden for the definition of their respective official concepts of local labour markets.Los métodos utilizados para definir las regiones funcionales con fines de estadística y políticas públicas deben establecer una serie de valores de ciertos parámetros. Esto se logra generalmente utilizando conocimiento experto basado en juicios cualitativos y largas consultas con las partes interesadas locales. Se propone apoyar este proceso utilizando un algoritmo de optimización para calibrar los métodos de regionalización mediante la identificación de los valores de los parámetros que producen la mejor regionalización para un determinado indicador cuantitativo. El enfoque se ejemplifica mediante el uso de una búsqueda por cuadrículas y un algoritmo genético para configurar los métodos oficiales empleados en el Reino Unido y en Suecia para la definición de sus respectivos conceptos oficiales de los mercados laborales locales.This work was supported by the Spanish Ministry of Economy and Competitiveness (grant numbers CSO2011-29943-C03-02 and CSO2014-55780-C3-2-P, National R&D&i Plan)