Clinical practice guidelines for the foot and ankle in rheumatoid arthritis: a critical appraisal

Background: Clinical practice guidelines are recommendations systematically developed to assist clinical decision-making and inform healthcare. In current rheumatoid arthritis (RA) guidelines, management of the foot and ankle is under-represented and the quality of recommendation is uncertain. This study aimed to identify and critically appraise clinical practice guidelines for foot and ankle management in RA. Methods: Guidelines were identified electronically and through hand searching. Search terms 'rheumatoid arthritis', 'clinical practice guidelines' and related synonyms were used. Critical appraisal and quality rating were conducted using the Appraisal of Guidelines for Research and Evaluation (AGREE) II instrument. Results: Twenty-four guidelines were included. Five guidelines were high quality and recommended for use. Five high quality and seven low quality guidelines were recommended for use with modifications. Seven guidelines were low quality and not recommended for use. Five early and twelve established RA guidelines were recommended for use. Only two guidelines were foot and ankle specific. Five recommendation domains were identified in both early and established RA guidelines. These were multidisciplinary team care, foot healthcare access, foot health assessment/review, orthoses/insoles/splints, and therapeutic footwear. Established RA guidelines also had an 'other foot care treatments' domain. Conclusions: Foot and ankle management for RA features in many clinical practice guidelines recommended for use. Unfortunately, supporting evidence in the guidelines is low quality. Agreement levels are predominantly 'expert opinion' or 'good clinical practice'. More research investigating foot and ankle management for RA is needed prior to inclusion in clinical practice guidelines

An essential function for the ATR-Activation-Domain (AAD) of TopBP1 in mouse development and cellular senescence

ATR activation is dependent on temporal and spatial interactions with partner proteins. In the budding yeast model, three proteins – Dpb11TopBP1, Ddc1Rad9 and Dna2 - all interact with and activate Mec1ATR. Each contains an ATR activation domain (ADD) that interacts directly with the Mec1ATR:Ddc2ATRIP complex. Any of the Dpb11TopBP1, Ddc1Rad9 or Dna2 ADDs is sufficient to activate Mec1ATR in vitro. All three can also independently activate Mec1ATR in vivo: the checkpoint is lost only when all three AADs are absent. In metazoans, only TopBP1 has been identified as a direct ATR activator. Depletion-replacement approaches suggest the TopBP1-AAD is both sufficient and necessary for ATR activation. The physiological function of the TopBP1 AAD is, however, unknown. We created a knock-in point mutation (W1147R) that ablates mouse TopBP1-AAD function. TopBP1-W1147R is early embryonic lethal. To analyse TopBP1-W1147R cellular function in vivo, we silenced the wild type TopBP1 allele in heterozygous MEFs. AAD inactivation impaired cell proliferation, promoted premature senescence and compromised Chk1 signalling following UV irradiation. We also show enforced TopBP1 dimerization promotes ATR-dependent Chk1 phosphorylation. Our data suggest that, unlike the yeast models, the TopBP1-AAD is the major activator of ATR, sustaining cell proliferation and embryonic development

Pre-registration of CT pulmonary volumetric image data

Bakalárska práca sa zaoberá predregistráciou pľúcnych objemových CT obrazových dát. Predregistrácia je riešená metódou fázovej korelácie pri rozklade 3D obrazu na 2D rezy usporiadané za sebou. Práca ďalej popisuje geometrické transformácie, interpolácie, výpočet podobnostných kritérií, optimalizáciu registrácie obrazu a proces samotnej registrácie obrazu. Predregistračný softvér je navrhnutý v programovom prostredí MATLAB^®, kde prebieha predregistrácia 3D reálnych CT obrazových dát s dôrazom na rýchlosť procesu.This bachelor thesis is dealing with pre-registration of CT pulmonary volumetric image data. Pre-registration is solved by phase correlation method, which decomposes 3D images into 2D slices arranged in a row. It further describes the geometric transformations, interpolation, calculations of similarity criteria, optimization of registration of images and the image registration process itself. The pre-registration software runs in MATLAB^®, which works with 3D images of real CT image data with an emphasis on process speed.