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Although it is well known dietary factors are closely correlated with bone health, the association between macronutrients intake distribution and bone mineral density (BMD) is still unclear. The aims of this study were to investigate how macronutrients distribution was correlated with BMD, and to evaluate how the substitution between macronutrients could be associated with BMD.We conducted a cross-sectional study based on data from National Health and Nutrition Examination Survey. Dietary recall method was used to assessed the intake of macronutrients. Macronutrient intake distribution including carbohydrate, protein and fat was calculated as percentages of energy intake from total energy. BMD was converted to T-score and low BMD was defined as T-score less than -1.0. The association between the percentages of energy intake from carbohydrate, protein and fat with T-score and risk of low BMD was evaluated using multivariate regression models. Isocaloric substitution analysis was conducted using the multivariate nutrient density method.Data form 4447 adults aged 20 years and older who underwent BMD examination were included in this study. Higher percentage of energy intake from carbohydrate was associated with lower T-score (-0.03 [95%CI, -0.05 to -0.01]; P = 0.001) and higher risk of low BMD (1.05 [95%CI, 1.02-1.08]; P = 0.003), while higher percentage of energy intake from protein was associated with higher T-score (0.05 [95%CI, 0.01-0.08]; P = 0.009) and lower odds of low BMD (0.92 [95%CI, 0.87-0.98]; P = 0.007). The percentage of energy intake from fat seemed to be positively correlated with T-score, but the correlation became insignificant after adjusting for metabolism related confounders. Isocaloric substitution analysis showed that only the substitution between carbohydrate and protein was significantly and independently associated with T-score (-0.05 [95%CI, -0.08 to -0.01]; P = 0.01) and the risk of low BMD (1.08 [95%CI, 1.02-1.15]; P = 0.008).Based on the results from this study, we hypothesized that a high-protein diet coupled with low carbohydrate intake would be beneficiary for prevention of bone loss in adults. However, randomized clinical trials or longitudinal studies are needed to further assessed our findings.

Objective We aimed to determine the real-world prevalence and investigate risk factors for rheumatoid arthritis (RA)-related lung disease on chest computed tomography (CT) imaging. We also investigated the impact of RA-related lung disease on mortality. Methods We studied chest CT imaging abnormalities among RA patients. We determined the presence and type of abnormalities using the chest CT imaging radiologic report. RA-related lung disease was defined as interstitial lung disease (ILD), bronchiectasis, or pleural disease. We examined whether demographics and RA characteristics were associated with RA-related lung disease using logistic regression. RA-related lung disease and mortality was described using survival curves and Cox regression. Results We analyzed 190 patients who had chest CT imaging performed for clinical indications. Mean age was 64.2 years (SD 11.8), 80.0% were female, and 75.3% were seropositive. RA-related lung disease was detected in 54 patients (28.4%); 30 (15.8%) had ILD, 27 (14.2%) had bronchiectasis, and 18 (9.5%) had pleural disease. RA-related lung disease was reported in both seropositive and seronegative RA (28.7% vs. 27.7%, p = 1.00). Male sex (OR 2.62, 95%CI 1.17–5.88) and current methotrexate use (OR 2.73, 95%CI 1.27–5.61 vs. not current) were associated with RA-related lung disease. Twenty-four (44.4%) patients with RA-related lung disease died during mean 7.0 years of follow-up. RA-related lung disease had HR of 5.35 (95%CI 0.72–39.9) for mortality compared to normal chest CT. Conclusions In this real-world study, RA-related lung disease was commonly detected on chest CT imaging regardless of RA serostatus. RA-related lung disease had high mortality, emphasizing the importance in close monitoring of these patients.

The use of electronic health records (EHR) systems has grown over the past decade, and with it, the need to extract information from unstructured clinical narratives. Clinical notes, however, frequently contain acronyms with several potential senses (meanings) and traditional natural language processing (NLP) techniques cannot differentiate between these senses. In this study we introduce a semi-supervised method for binary acronym disambiguation, the task of classifying a target sense for acronyms in the clinical EHR notes.We developed a semi-supervised ensemble machine learning (CASEml) algorithm to automatically identify when an acronym means a target sense by leveraging semantic embeddings, visit-level text and billing information. The algorithm was validated using note data from the Veterans Affairs hospital system to classify the meaning of three acronyms: RA, MS, and MI. We compared the performance of CASEml against another standard semi-supervised method and a baseline metric selecting the most frequent acronym sense. Along with evaluating the performance of these methods for specific instances of acronyms, we evaluated the impact of acronym disambiguation on NLP-driven phenotyping of rheumatoid arthritis.CASEml achieved accuracies of 0.947, 0.911, and 0.706 for RA, MS, and MI, respectively, higher than a standard baseline metric and (on average) higher than a state-of-the-art semi-supervised method. As well, we demonstrated that applying CASEml to medical notes improves the AUC of a phenotype algorithm for rheumatoid arthritis.CASEml is a novel method that accurately disambiguates acronyms in clinical notes and has advantages over commonly used supervised and semi-supervised machine learning approaches. In addition, CASEml improves the performance of NLP tasks that rely on ambiguous acronyms, such as phenotyping.

Giant Cell Arteritis (GCA) is a systemic large and medium-size vessel vasculitis. A 59-year-old female presented with right lower extremity (RLE) claudication and was diagnosed with peripheral artery disease (PAD) and referred to outpatient vascular surgery. CT-angiography (CTA) demonstrated

Cardiac involvement is a major cause of morbidity and mortality in hypereosinophilic syndromes. A 35 year-old man presented with 4 weeks of fevers and malaise and was found to have an eosinophil count of 2,800 per microliter. Electrocardiogram (ECG) demonstrated diffuse T wave inversions and