Self-directed learning of basic musculoskeletal ultrasound among rheumatologists in the United States

Objective Because musculoskeletal ultrasound (MSUS) is highly user dependent, we aimed to establish whether non-mentored learning of MSUS is sufficient to achieve the same level of diagnostic accuracy and scanning reliability as has been achieved by rheumatologists recognized as international experts in MSUS. Methods A group of 8 rheumatologists with more experience in MSUS and 8 rheumatologists with less experience in MSUS participated in an MSUS exercise to assess patients with musculoskeletal abnormalities commonly seen in a rheumatology practice. Patients' established diagnoses were obtained from chart review (gout, osteoarthritis, rotator cuff syndrome, rheumatoid arthritis, and seronegative arthritis). Two examining groups were formed, each composed of 4 less experienced and 4 more experienced examiners. Each group scanned 1 predefined body region (hand, wrist, elbow, shoulder, knee, or ankle) in each of 8 patients, blinded to medical history and physical examination. Structural abnormalities were noted with dichotomous answers, and an open-ended answer was used for the final diagnosis. Results Less experienced and more experienced examiners achieved the same diagnostic accuracy (US-established diagnosis versus chart review diagnosis). The interrater reliability for tissue pathology was slightly higher for more experienced versus less experienced examiners (Κ = 0.43 versus Κ = 0.34; P = 0.001). Conclusion Non-mentored training in MSUS can lead to the achievement of diagnostic accuracy in MSUS comparable to that achieved by highly experienced international experts. Reliability may increase slightly with additional experience. Further study is needed to determine the minimal training requirement to achieve proficiency in MSUS.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/65036/1/20063_ftp.pd

Factors affecting response to medical management in patients of filarial chyluria: A prospective study

Introduction: Filarial chyluria is a common problem in filarial endemic countries. Its management begins with medical therapy but some patients progress to require surgery. The present study aimed to determine factors affecting response to medical management in patients of filarial chyluria. Materials and Methods: This prospective study conducted between August 2008 and November 2012, included conservatively managed patients of chyluria. Demographic profile, clinical presentation, treatment history and urinary triglycerides (TGs) and cholesterol levels at baseline were compared between the responders and non-responders. Apart from the clinical grade of chyluria, hematuria was evaluated as an independent risk factor. Results: Out of the 222 patients (mean age, 37.99 ± 13.29 years, 129 males), 31 patients failed to respond while 35 had a recurrence after initial response; the overall success rate being 70.3% at a mean follow-up of 25 months. No difference was observed in demographics, clinical presentation, presence of hematuria, disease duration and mean urinary TGs loss between responders and non-responders. On multivariate analysis, patients with treatment failure were found to have a higher-grade disease (14.3% Grade-I, 36.6% Grades-II and 60% Grade-III), higher number of pretreatment courses (1.59 ± 1.08 vs. 1.02 ± 0.79) and heavier cholesterol (26.54 ± 23.46 vs. 8.81 ± 8.55 mg/dl) loss at baseline compared with responders (P < 0.05). Conclusion: Conservative management has a success rate in excess of 70%, not affected by the disease chronicity, previous episodes and recurrent nature. However, higher-grade disease, extensive pre-treatment with drugs and higher urinary cholesterol loss at baseline are the predictors of poor response. Hematuria is not an independent poor risk factor for conservative management

Similar Improvements in Patient-reported Outcomes among Rheumatoid Arthritis Patients Treated with two Different Doses of Methotrexate in Combination with Adalimumab: Results from the MUSICA Trial

<p><b>Article full text</b></p><p><br></p><p>The full text of this article can be found here<b>.</b> <a href="https://link.springer.com/article/10.1007/s40744-018-0105-7">https://link.springer.com/article/10.1007/s40744-018-0105-7</a></p><p></p><p><br></p><p><b>Provide enhanced content for this article</b></p><p><br></p><p>If you are an author of this publication and would like to provide additional enhanced content for your article then please contact <a href="http://www.medengine.com/Redeem/”mailto:[email protected]”"><b>[email protected]</b></a>.</p><p><br></p><p>The journal offers a range of additional features designed to increase visibility and readership. All features will be thoroughly peer reviewed to ensure the content is of the highest scientific standard and all features are marked as ‘peer reviewed’ to ensure readers are aware that the content has been reviewed to the same level as the articles they are being presented alongside. Moreover, all sponsorship and disclosure information is included to provide complete transparency and adherence to good publication practices. This ensures that however the content is reached the reader has a full understanding of its origin. No fees are charged for hosting additional open access content.</p><p><br></p><p>Other enhanced features include, but are not limited to:</p><p><br></p><p>• Slide decks</p><p>• Videos and animations</p><p>• Audio abstracts</p><p> </p><p>• Audio slides</p><p> </p><p> </p> <p> </p

A Physical Data Fusion Approach to Optimize Compositional Stability of Halide Perovskites

Compositional search within multinary perovskites employing brute force synthesis are prohibitively expensive in large chemical spaces. To identify the most stable multi-cation lead iodide perovskites containing Cs, formamidinium (FA) and methylammonium (MA), we fuse results from density functional theory (DFT) calculations and in situ thin-film degradation test within an end-to-end machine learning (ML) algorithm to inform the compositional optimization of CsxMAyFA1-x-yPbI3. We integrate phase thermodynamics modelling as a probabilistic constraint in a Bayesian optimization (BO) loop, which effectively guides the experimental search while considering both structural and environmental stability. After three optimization rounds and only sampling 1.8% of the compositional space, we identify thin-film compositions centred at Cs0.17MA0.03FA0.80PbI3 that achieve a 3x delay in macroscopic degradation onset under elevated temperature, humidity, and light compared with the more complex state-of-the-art Cs0.05(MA0.17FA0.83)0.95Pb(I0.83Br0.17)3. We find up to 8% of MA can be incorporated into the perovskite structure before stability is significantly compromised. Cs is beneficial at low concentrations, however, beyond 17% is found to contribute to reduced stability. Synchrotron-based grazing-incidence wide-angle X-ray scattering (GIWAXS) further validates that the interplay of chemical decomposition and phase separation governs the non-linear instability landscape of this compositional space. We reveal the detrimental role of the ẟ-CsPbI3 minority phase in accelerating degradation and it can be kinetically suppressed by co-optimising Cs and MA content, providing insights into simplifying perovskite compositions for further environmental stability enhancement. Our approach realizes the effectiveness of ML-enabled data fusion in achieving a holistic, efficient, and physics-informed experimentation for multinary systems, potentially generalisable to materials search in the vast structural and alloyed spaces beyond halide perovskites.<br /

A data fusion approach to optimize compositional stability of halide perovskites

Despite recent intensive efforts to improve the environmental stability of halide perovskite materials for energy harvesting and conversion, traditional trial-and-error explorations face bottlenecks in the navigation of vast chemical and compositional spaces. We develop a closed-loop optimization framework that seamlessly marries data from first-principle calculations and high-throughput experimentation into a single machine learning algorithm. This framework enables us to achieve rapid optimization of compositional stability for CsxMAyFA1−x−yPbI3 perovskites while taking the human out of the decision-making loop. We envision that this data fusion approach is generalizable to directly tackle challenges in designing multinary materials, and we hope that our successful showcase on perovskites will encourage researchers in other fields to incorporate knowledge of physics into the search algorithms, applying hybrid machine learning models to guide discovery of materials in high-dimensional spaces