Approaches to the targeting of treatment for osteoporosis

Fractures are a clinical consequence of osteoporosis, and represent a major cause of morbidity and mortality worldwide. Several treatments have been shown to decrease the risk of fracture, but problems arise in identifying individuals at high fracture risk so that treatments can be effectively targeted. The case for widespread population screening using bone mineral density testing is weak, as these tests lack sensitivity. Case-finding algorithms are available in many countries, but differ markedly in their approaches. Recent developments in fracture risk assessment include the availability of the FRAX (WHO Collaborating Center for Bone Metabolic Disease, Sheffield, UK) tool, which integrates the weight of clinical risk factors for fracture risk with or without information on bone mineral density, and computes the 10-year probability of fracture. The tool increases sensitivity without trading specificity, and is now being used in the reappraisal of clinical guidelines

Подготовка, оформление реферата и магистерской диссертации

Содержит основные требования и описание этапов выполняемых работ над рефератом по общеобразовательной дисциплине «Основы информационных технологий» и магистерской диссертацией. Для студентов учреждений высшего образования II ступени обучения специальности 1-40 80 04 «Математическое моделирование, численные методы и комплексы программ» дневной и заочной форм обучения

Wastewater sequencing reveals early cryptic SARS-CoV-2 variant transmission.

As SARS-CoV-2 continues to spread and evolve, detecting emerging variants early is critical for public health interventions. Inferring lineage prevalence by clinical testing is infeasible at scale, especially in areas with limited resources, participation, or testing and/or sequencing capacity, which can also introduce biases1-3. SARS-CoV-2 RNA concentration in wastewater successfully tracks regional infection dynamics and provides less biased abundance estimates than clinical testing4,5. Tracking virus genomic sequences in wastewater would improve community prevalence estimates and detect emerging variants. However, two factors limit wastewater-based genomic surveillance: low-quality sequence data and inability to estimate relative lineage abundance in mixed samples. Here we resolve these critical issues to perform a high-resolution, 295-day wastewater and clinical sequencing effort, in the controlled environment of a large university campus and the broader context of the surrounding county. We developed and deployed improved virus concentration protocols and deconvolution software that fully resolve multiple virus strains from wastewater. We detected emerging variants of concern up to 14 days earlier in wastewater samples, and identified multiple instances of virus spread not captured by clinical genomic surveillance. Our study provides a scalable solution for wastewater genomic surveillance that allows early detection of SARS-CoV-2 variants and identification of cryptic transmission

Wastewater sequencing uncovers early, cryptic SARS-CoV-2 variant transmission

Summary As SARS-CoV-2 continues to spread and evolve, detecting emerging variants early is critical for public health interventions. Inferring lineage prevalence by clinical testing is infeasible at scale, especially in areas with limited resources, participation, or testing/sequencing capacity, which can also introduce biases. SARS-CoV-2 RNA concentration in wastewater successfully tracks regional infection dynamics and provides less biased abundance estimates than clinical testing. Tracking virus genomic sequences in wastewater would improve community prevalence estimates and detect emerging variants. However, two factors limit wastewater-based genomic surveillance: low-quality sequence data and inability to estimate relative lineage abundance in mixed samples. Here, we resolve these critical issues to perform a high-resolution, 295-day wastewater and clinical sequencing effort, in the controlled environment of a large university campus and the broader context of the surrounding county. We develop and deploy improved virus concentration protocols and deconvolution software that fully resolve multiple virus strains from wastewater. We detect emerging variants of concern up to 14 days earlier in wastewater samples, and identify multiple instances of virus spread not captured by clinical genomic surveillance. Our study provides a scalable solution for wastewater genomic surveillance that allows early detection of SARS-CoV-2 variants and identification of cryptic transmission