We present the first known case of a patient with BRD2::NUTM1-driven NUT carcinoma. A 59-year-old woman presented with poorly differentiated squamous cell lung cancer metastatic to the pleura. ...Eventually, a positive NUT immunohistochemistry, NUT fluorescence in situ hybridization, and RNA next-generation sequencing with a BRD2::NUTM1 fusion led to the diagnosis of NUT carcinoma. She received multiple lines of chemotherapy with response and is still alive at 2 years postdiagnosis. This report expands on the known fusions in NUT carcinoma and highlights potential differences in patient prognosis on the basis of gene fusion partners.
We present the first known case of a patient with BRD2::NUTM1-driven NUT carcinoma. A 59-year-old woman presented with poorly differentiated squamous cell lung cancer metastatic to the pleura. ...Eventually, a positive NUT immunohistochemistry, NUT fluorescence in situ hybridization, and RNA next-generation sequencing with a BRD2::NUTM1 fusion led to the diagnosis of NUT carcinoma. She received multiple lines of chemotherapy with response and is still alive at 2 years postdiagnosis. This report expands on the known fusions in NUT carcinoma and highlights potential differences in patient prognosis on the basis of gene fusion partners.
Physical activity plays an important role in children's cardiovascular health, musculoskeletal health, mental and behavioral health, and physical, social, and cognitive development. Despite the ...importance in children's lives, pediatricians are unfamiliar with assessment and guidance regarding physical activity in children. With the release of the 2018 Physical Activity Guidelines by the US Department of Health and Human Services, pediatricians play a critical role in encouraging physical activity in children through assessing physical activity and physical literacy; providing guidance toward meeting recommendations by children and their families; advocating for opportunities for physical activity for all children in schools, communities, and hospitals; setting an example and remaining physically active personally; advocating for the use of assessment tools and insurance coverage of physical activity and physical literacy screening; and incorporating physical activity assessment and prescription in medical school curricula.
BackgroundThe commercial application Openfit allows for automatic identification and quantification of food intake through short video capture without a physical reference marker. There are no known ...peer-reviewed publications on the validity of this Nutrition Artificial Intelligence (AI).ObjectivesTo test the validity of Openfit to identify food automatically and semiautomatically (with user correction), test the validity of Openfit at quantifying energy intake (kcal) automatically and semiautomatically, and assess satisfaction and usability of Openfit.MethodsDuring a laboratory-based visit, adults (7 male and 17 female), used Openfit to automatically and semiautomatically record provided meals, which were covertly weighed. Foods logged were identified as an "exact match," "far match," or an "intrusion" using Food and Nutrient Database for Dietary Studies (FNDDS) codes. Descriptive data were stratified by meal, food item, and FNDDS group, and presented with or without beverages. Bland-Altman analyses assessed errors over levels of energy intake. Participants completed a User Satisfaction Survey (USS) and the Computer Systems Usability Questionnaire (CSUQ). Open-ended questions were assessed with qualitative methods.ResultsExact matches, far matches, and intrusions were 46%, 41%, and 13% for automated identification, and 87%, 23%, and 0% for semiautomated identification, respectively. Error for automated and semiautomated energy estimates were 43% and 33% with beverages, and 16% and 42% without beverages. Bland-Altman analyses indicated larger error for higher energy meals. Overall mean scores were 2.4 for the CSUQ and subscale means scores ranged from 4.1 to 5.5. for the USS. Participants recommended improvements to Openfit's Nutrition AI, manual estimation, and overall app.ConclusionOpenfit worked relatively well for automatically and semiautomatically identifying foods. Error in automated energy estimates was relatively high; however, after excluding beverages, error was relatively low (16%). For semiautomated energy estimates, error was comparable to previous studies. Improvements to the Nutrition AI, manual estimation and overall application may increase Openfit's usability and validity.This trial was registered at clinicaltrials.gov as NCT05343585.
Type 2 diabetes mellitus (T2DM) and obstructive sleep apnea (OSA) are common, increasingly recognized as comorbid conditions, and individually implicated in the development of cardiovascular disease ...(CVD). We sought to determine the association between OSA and CVD in an overweight and obese population with T2DM.
Cross-sectional.
Ancillary study to the Look AHEAD trial.
Three hundred five participants of the Sleep AHEAD study who underwent unattended full polysomnography at home with measurement of the apnea-hypopnea index (AHI).
Self-reported prevalent CVD was obtained at the initial assessment of the parent study and included a history of the following conditions: stroke, carotid endarterectomy, myocardial infarction, coronary artery bypass grafting, and percutaneous coronary intervention. Logistic regression was used to assess the association of OSA, measured continuously and categorically, with prevalent CVD. OSA was present (AHI ≥ 5) in 86% of the population, whereas the prevalence of all forms of CVD was just 14%. The AHI was associated with stroke with an adjusted odds ratio (95% confidence interval) of 2.57 (1.03, 6.42). Neither the continuously measured AHI nor the categories of OSA severity were significantly associated with the other forms of CVD assessed.
We found suggestive evidence of a greater prevalence of stroke at greater values of the AHI. OSA was not associated with prevalent coronary heart disease in the Sleep AHEAD trial. Future studies should confirm the link between OSA and stroke and examine mechanisms that link OSA to stroke in adults with T2DM.
The commercial app Openfit allows for automatic identification and quantification of food intake through short video capture without a physical reference marker. There are no known peer reviewed ...publications on the validity of this Nutrition Artificial Intelligence (AI).
Test the validity of Openfit to identify food automatically and semi-automatically (with user correction), test the validity of Openfit at quantifying energy intake (kcal) automatically and semi-automatically, and assess satisfaction and usability of Openfit.
During a lab-based visit, adults (7 male, 17 female), used Openfit to automatically and semi-automatically record provided meals, which were covertly weighed. Foods logged were identified as an “exact match”, “far match”, or an “intrusion” using Food and Nutrient Database for Dietary Studies (FNDDS) codes. Descriptive data were stratified by meal, food item, FNDDS group, and presented with or without beverages. Bland-Altman analyses assessed error over levels of energy intake. Participants completed a User Satisfaction Survey (USS) and the Computer Systems Usability Questionnaire (CSUQ). Open-ended questions were assessed with qualitative methods.
Exact matches, far matches and intrusions were 46%, 41%, and 13% for automated identification, and 87%, 23%, and 0% for semi-automated identification, respectively. Error for automated and semi-automated energy estimates were 43% and 33% with beverages, and 16% and 42% without beverages. Bland-Altman analyses indicated larger error for higher energy meals. Overall mean scores were 2.4 for the CSUQ and subscale means scores ranged from 4.1 to 5.5. for the USS. Participants recommended improvements to Openfit’s Nutrition AI, manual estimation, and overall app.
Openfit worked relatively well for automatically and semi-automatically identifying foods. Error in automated energy estimates was relatively high; however, after excluding beverages, error was relatively low (16%). For semi-automated energy estimates, error was comparable to previous studies. Improvements to the Nutrition AI, manual estimation and overall app may increase Openfit’s usability and validity.
Clinical Trials Registry number and website: NCT05343585, https://www.clinicaltrials.gov/study/NCT05343585
Nutrition Artificial Intelligence (AI) available in commercial apps, such as Openfit, is used to automatically identify foods and quantify food intake. This pilot study tested the validity of this AI.
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