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This multicenter retrospective study evaluated the diagnostic performance of a deep learning (DL)-based application for detecting, classifying, and highlighting suspected aortic dissections (ADs) on chest and thoraco-abdominal CT angiography (CTA) scans. CTA scans from over 200 U.S. and European cities acquired on 52 scanner models from six manufacturers were retrospectively collected and processed by CINA-CHEST (AD) (Avicenna.AI, La Ciotat, France) device. The diagnostic performance of the device was compared with the ground truth established by the majority agreement of three U.S. board-certified radiologists. Furthermore, the DL algorithms time to notification was evaluated to demonstrate clinical effectiveness. The study included 1303 CTAs (mean age 58.8 ± 16.4 years old, 46.7% male, 10.5% positive). The device demonstrated a sensitivity of 94.2% [95% CI: 88.8-97.5%] and a specificity of 97.3% [95% CI: 96.2-98.1%]. The application classified positive cases by the AD type with an accuracy of 99.5% [95% CI: 98.9-99.8%] for type A and 97.5 [95% CI: 96.4-98.3%] for type B. The application did not miss any type A cases. The device flagged 32 cases incorrectly, primarily due to acquisition artefacts and aortic pathologies mimicking AD. The mean time to process and notify of potential AD cases was 27.9 ± 8.7 s. This deep learning-based application demonstrated a strong performance in detecting and classifying aortic dissection cases, potentially enabling faster triage of these urgent cases in clinical settings.

Micronutrient deficiencies, undernutrition, and overweight/obesity are prevalent in low- and middle-income countries (LMICs). Nutrient profiling models (NPMs), initially developed to help reduce the prevalence of diet-related chronic diseases in Western countries, could be one solution to promote nutrient-dense foods in LMICs. This study reviewed government-endorsed NPMs implemented in LMICs and assessed their key components in relation to country-specific nutritional challenges. The peer-reviewed and grey literature were systematically reviewed to identify government-endorsed NPMs implemented in LMICs to promote healthier choices among adults. Their key metrics, including scope, components, units, and validation method, were extracted. The prevalence of undernutrition; overweight/obesity; and iron, vitamin A, and iodine deficiencies were extracted from the Global Health Observatory and the Global Burden of Disease study. NPMs have been implemented in 16 LMICs to encourage healthier choices, mostly through front-of-pack labeling schemes. Warning Label schemes are used to strongly discourage the consumption of energy-dense products in countries where overnutrition affects most of the population, such as Latin American LMICs. A “Keyhole” front-of-pack labeling scheme was implemented only in North Macedonia. It limits sugar, fat, and salt while promoting fibers, fruits, vegetables, nuts, and legumes to prevent overnutrition and diet-related chronic diseases. “Choices” schemes that focus on positive messages have been implemented in Southeast Asia and Zambia where over- and undernutrition coexist. “Choices” criteria encourage the consumption of category-specific vitamins and minerals, in addition to advocating limiting certain nutrients. In LMICs, NPMs focus on discouraging the consumption of sugar, fat, and salt. Additionally, NPMs promote category-specific micronutrients in countries where undernutrition remains prevalent or food components associated with a reduced risk of diet-related chronic diseases, including whole grains and fibers, in countries where overnutrition is the main nutrition-related public health issue.This study was registered at PROSPERO as CRD42023468807.

Summary: There are 574 federally recognized Tribes in the United States. Tribes have experienced increased rates of domestic violence (DV) due to structural determinants like gender violence, loss of control, discrimination, marginalization, oppression, and political violence. American Indian and Alaska Native girls and women experience the highest rates of DV and abuse in the US, yet policy change, funding, and advocacy has been slow to address high DV rates. In this commentary, we comprehensively review the structural determinants of DV in American Indian and Alaska Native populations. We review policies and the complexities of criminal jurisdiction in Tribal nations and provide key recommendations to build a new framework that addresses DV in American Indian Alaska Native women in the future. Less than half of federally recognized Tribes receive funding through the Family Violence Prevention and Services Act. Structural determinants explain why housing and funding are advocacy issues, where unequal access and differential effects lead to increased risk for DV. We advocate for future work that addresses the interplay of multiple structural determinants while advocating for funding and culturally safe research and support to address DV in American Indian Alaska Native populations.

PURPOSE: Since the prompt recognition of acute pulmonary embolism (PE) and the immediate initiation of treatment can significantly reduce the risk of death, we developed a deep learning (DL)-based application aimed to automatically detect PEs on chest computed tomography angiograms (CTAs) and alert radiologists for an urgent interpretation. Convolutional neural networks (CNNs) were used to design the application. The associated algorithm used a hybrid 3D/2D UNet topology. The training phase was performed on datasets adequately distributed in terms of vendors, patient age, slice thickness, and kVp. The objective of this study was to validate the performance of the algorithm in detecting suspected PEs on CTAs. METHODS: The validation dataset included 387 anonymized real-world chest CTAs from multiple clinical sites (228 U.S. cities). The data were acquired on 41 different scanner models from five different scanner makers. The ground truth (presence or absence of PE on CTA images) was established by three independent U.S. board-certified radiologists. RESULTS: The algorithm correctly identified 170 of 186 exams positive for PE (sensitivity 91.4% [95% CI: 86.4-95.0%]) and 184 of 201 exams negative for PE (specificity 91.5% [95% CI: 86.8-95.0%]), leading to an accuracy of 91.5%. False negative cases were either chronic PEs or PEs at the limit of subsegmental arteries and close to partial volume effect artifacts. Most of the false positive findings were due to contrast agent-related fluid artifacts, pulmonary veins, and lymph nodes. CONCLUSIONS: The DL-based algorithm has a high degree of diagnostic accuracy with balanced sensitivity and specificity for the detection of PE on CTAs.

Background: Over the last 3 decades, hematopoietic stem cell transplantation (HSCT) has been successfully used to treat severe and refractory autoimmune diseases (AIDs). A multidisciplinary appraisal of potential benefits and risks by disease and transplant specialists is essential to determine individual suitability for HSCT. Objective: Our aim was to observe that patient-reported outcomes (PROs) and health-related quality of life instruments can capture the unique patient perspective on disease burden and impact of treatment. Methods: Herein, we describe the basis and complexity of end points measuring patient-reported perceptions of efficacy and tolerability used in clinical practice and trials for patients with AIDs undergoing autologous HSCT. Results: PRO measures and patient-reported experience measures are key tools to evaluate the impact and extent of disease burden for patients affected by AIDs. For formal scientific assessment, it is essential that validated general instruments are used, whereas adaptations have resulted in disease-specific instruments that may help guide tailored interventions. An additional approach relates to qualitative evaluations, from carefully structured qualitative research to informal narratives, as patient stories. The patients’ subjectively reported responses to HSCT may be influenced by their preprocedure expectations and investment in the HSCT journey. Conclusions: The complexity of AIDs advocates for individualized and multidisciplinary approach to positively affect the patient journey. PROs and health-related quality of life need to be collected using validated instruments in clinical practice and trials to enable robustness of data and to ensure the impact of the intervention is comprehensively assessed, addressing the main questions and needs of the involved stakeholders.

This multicenter retrospective study evaluated the diagnostic performance of a deep learning (DL)-based application for detecting, classifying, and highlighting suspected aortic dissections (ADs) on chest and thoraco-abdominal CT angiography (CTA) scans. CTA scans from over 200 U.S. and European cities acquired on 52 scanner models from six manufacturers were retrospectively collected and processed by CINA-CHEST (AD) (Avicenna.AI, La Ciotat, France) device. The diagnostic performance of the device was compared with the ground truth established by the majority agreement of three U.S. board-certified radiologists. Furthermore, the DL algorithm’s time to notification was evaluated to demonstrate clinical effectiveness. The study included 1303 CTAs (mean age 58.8 ± 16.4 years old, 46.7% male, 10.5% positive). The device demonstrated a sensitivity of 94.2% [95% CI: 88.8–97.5%] and a specificity of 97.3% [95% CI: 96.2–98.1%]. The application classified positive cases by the AD type with an accuracy of 99.5% [95% CI: 98.9–99.8%] for type A and 97.5 [95% CI: 96.4–98.3%] for type B. The application did not miss any type A cases. The device flagged 32 cases incorrectly, primarily due to acquisition artefacts and aortic pathologies mimicking AD. The mean time to process and notify of potential AD cases was 27.9 ± 8.7 s. This deep learning-based application demonstrated a strong performance in detecting and classifying aortic dissection cases, potentially enabling faster triage of these urgent cases in clinical settings.

Translational research needs to show value through impact on measures that matter to the public, including health and societal benefits. To this end, the Translational Science Benefits Model (TSBM) identified four categories of impact: Clinical, Community, Economic, and Policy. However, TSBM offers limited guidance on how these areas of impact relate to equity. Central to the structure of our Center for American Indian and Alaska Native Diabetes Translation Research are seven regional, independent Satellite Centers dedicated to community-engaged research. Drawing on our collective experience, we provide empirical evidence about how TSBM applies to equity-focused research that centers community partnerships and recognizes Indigenous knowledge. For this special issue – “Advancing Understanding and Use of Impact Measures in Implementation Science” – our objective is to describe and critically evaluate gaps in the fit of TSBM as an evaluation approach with sensitivity to health equity issues. Accordingly, we suggest refinements to the original TSBM Logic model to add: 1) community representation as an indicator of providing community partners “a seat at the table” across the research life cycle to generate solutions (innovations) that influence equity and to prioritize what to evaluate, and 2) assessments of the representativeness of the measured outcomes and benefits.

BackgroundElderly breast cancer (BC) patients have been underrepresented in clinical trials whereas ~60% of deaths from BC occur in women aged 70 years and older. Only limited data are available on the prognostic impact of age according to treatment, especially in the triple-negative (TN) and Her2-positive because of the lower frequency of these subtypes in elderly patients. We report herein the results of a multicenter retrospective study analyzing the prognostic impact of age according to treatment delivered in TN and Her2-positive BC patients of 70 years or older, including comparison by age groups.MethodsThe medical records of 31,473 patients treated from January 1991 to December 2018 were retrieved from 13 French cancer centers for retrospective analysis. Our study population included all ≥70 patients with TN or Her2-positive BC treated by upfront surgery. Three age categories were determined: 70-74, 75-80, and > 80 years.ResultsOf 528 patients included, 243 patients were 70-74 years old (46%), 172 were 75-80 years (32.6%) and 113 were >80 years (21.4%). Half the population (51.9%, 274 patients) were TN, 30.1% (159) Her2-positive/hormone receptors (HR)-positive, and, 18% (95) Her2-positive/endocrine receptors (ER)-negative BC. Advanced tumor stage was associated with older age but no other prognostic factors (tumor subtype, tumor grade, LVI). Adjuvant chemotherapy delivery was inversely proportional to age. With 49 months median follow-up, all patient outcomes (overall survival (OS), disease-free survival (DFS), breast cancer-specific survival (BCSS), and recurrence-free survival (RFS)) significantly decreased as age increased. In multivariate analysis, age >80, pT2-3 sizes, axillary macrometastases, lymphovascular involvement, and HR-negativity tumor negatively affected DFS and OS. Comparison between age >80 and 80 (HR=1.771, p=0.031).ConclusionTN and Her2-positive subtypes occur at similar frequency in elderly patients. Older age is associated with more advanced tumor stage presentation. Chemotherapy use decreases with older age without worse other pejorative prognostic factors. Age >80, but not ≤80, independently affected DFS and OS.

Abstract Background Previous studies found high prevalence of inadequate intakes of vitamins E, D and K, calcium and potassium among Brazilian pre-school children, with suboptimal consumption of dairy products. Dietary modelling was applied to determine the theoretical impact of improving dairy products consumption on nutrient adequacy in 4–5-year-old Brazilian children. Methods Adherence to the dairy recommendation of two servings/day was calculated using data from the Brazil Kids Nutrition and Health Study (KNHS) (n = 228). Two modelling scenarios were applied to test the impact on nutrient intakes of (1) adding one or two servings of a frequently consumed cow’s milk or a widely available fortified alternative: pre-school children milk (PCM), and of (2) substituting the current milk consumed by PCM. Mean nutrient intakes and percentage of children adhering to the nutrient recommendations were determined at baseline and after applying modelling scenarios. Results Seventy-six percent (n = 174) of children did not meet the recommended daily two servings of dairy products, 56% had less than one serving of dairy products on the day of recall. The mean consumption of whole milk (fortified and unfortified) was 147 g/d, yoghurt 114 g/d and cheese 34 g/d. The addition of one serving of cow’s milk demonstrated a 17% reduction in calcium inadequacy, 18% reduction in vitamin A and 3% reduction in zinc inadequacy. Adding one serving of PCM further reduced calcium inadequacy from 87 to 41%, vitamin E from 81 to 37%, and zinc inadequacy by 10%. Replacing the child’s current milk with a PCM resulted in further reduction of micronutrient inadequacies, including calcium, vitamin D and vitamin E. Conclusions Dairy products consumption in pre-school children should be encouraged to reduce nutrient inadequacies. In particular, consumption of PCM would help to reduce calcium, vitamin D and vitamin E inadequacy, nutrients of concern in this population.

Background Dysthyroidism (DT) is a common toxicity of immune checkpoint inhibitors (ICIs) and prior work suggests that dysthyroidism (DT) might be associated with ICI efficacy.Patients and methods ConSoRe, a new generation data mining solution, was used in this retrospective study, to extract data from electronic patient records of adult cancer patients treated with ICI at Institut Paoli-Calmettes (Marseille, France). Every DT was verified and only ICI-induced DT was retained. Survival analyses were performed by Kaplan-Meier method (log-rank test) and Cox model. To account for immortal time bias, a conditional landmark analysis was performed (2 months and 6 months), together with a time-varying Cox model.Results Data extraction identified 1385 patients treated with ICI between 2011 and 2021. DT was associated with improved overall survival (OS) (HR 0.46, (95% CI 0.33 to 0.65), p

This pilot study is founded on an exhaustive investigation of representative and well-documented cases (120) of carbonate systems during the Phanerozoic. We investigated the coupling between geodynamic settings and geometry, physiography, and stratigraphic architecture of carbonate platforms. The objective is to identify and quantify the impact of geodynamic controls on carbonate platforms and possible global trends during the Phanerozoic. Carbonate platforms are characterized in terms of geometry, stratigraphic architecture, time duration, preservation rate, progradation and rates of progradation, platform type, and thickness. Geodynamic parameters are characterized in terms of basin physiography, basement depth and type, tectonic setting, and subsidence origin. Relationships between carbonate parameters and geodynamic characteristics are investigated, leading to nine models of Geodynamic Carbonate Platforms. Passive margin is the most favorable geodynamic setting for the development of carbonate platforms in three dimensions. The continental crust hosts most of the carbonate platforms independently of time and geodynamic settings. Carbonate platforms developed above the exhumed mantle or oceanic crust (volcano) are the exception or very small and isolated. Global trends during the Phanerozoic of carbonate platform lateral extend, progradation, or thickness are tentatively interpreted in relation to geodynamic and eustatic parameters. The concept of spatial intersection between geodynamic and climatic windows favorable to carbonate platforms is introduced with its possible retroaction to the global carbon cycle.

Purpose and Objectives: American Indian/Alaska Native (AI/AN) hypertension contributes to cardiovascular disease, the leading cause of premature death in this population. The purpose of this article is to document strategies, concerns, and barriers related to hypertension and cardiovascular disease from Native-Controlling Hypertension and Risks through Technology (Native-CHART) symposiums facilitated by the Center for Native American Health (CNAH). The objectives of this evaluation were to combine Health Needs Assessment (HNA) data and explore barriers and strategies related to hypertension while assessing changes in participants’ perspectives over time (2017-2021). Approach: CNAH followed an iterative process each year for planning the HNA, facilitating the HNA, and refining and reflecting on HNA findings over time. This involved 3 interconnected steps: (1) developing a shared understanding for the HNA, “Why are we here?,” (2) facilitating the HNA during annual symposiums “What do we do?,” and (3) reflecting on “What did we learn?”. Evaluation Methods: Data were collected using a culturally centered HNA co-created by the CNAH team and tribal partners. Qualitative data analysis utilized a culturally centered thematic approach and NVivo software version 12.0. Quantitative data analysis included summarizing frequency counts and descriptive statistics using Microsoft Excel. Results: Over the 5-year period, 212 Native-CHART symposium participants completed HNAs. Data collected from HNAs show persistent barriers and concerns and illuminate potential strategies to address AI/AN hypertension. Future efforts must explore effective strategies that build on community strengths, culture and traditions, and existing resources. This is the path forward. Implications for Public Health: CNAH’s culturally centered and unique HNA approach helped assess participant perspectives over time. CNAH facilitated symposiums over multiple years, even amid a global pandemic. This demonstrates resilience and continuity of community outreach when it is needed the most. Other universities and tribal partners could benefit from this iterative approach as they work to design HNAs with tribal populations.

Food and beverage companies reformulate packaged foods and to better align their products with public health policies and evolving consumer needs. The nutritional quality of packaged foodscan be tracked using nutrient profiling methods. The present study compared the nutritional quality of packaged foods launched globally between 2016 and 2018 and those launched in 2018–2020, as reported in the Mintel Global New Products Database. Nutrient profiling analyses showed that the nutrient composition of product categories shifted for almost 40% of newly launched products between 2016–2018 and 2018–2020. For example, pizzas that had been assigned to one nutritionally homogenous category in 2016–2018 separated in two nutritionally distinct subgroups in 2018–2020. The new products that were reduced in protein, saturated fat, and sodium were now nutritionally distinct from the traditional pizza offer. By 2018–2020 “best of category” products were significantly lower in sugar and sodium than before; however, no clear trend was observed for saturated fats, proteins, or fibers. The relative speed of product reformulation was category specific. This analysis of the Mintel Global New Products Database suggests that the WHO nutritional recommendations focusing on sugar and sodium reduction did have a positive impact on the composition of new packaged foods, whether through reformulation of existing products, launches of line extensions or new product development.

Post-traumatic stress disorder (PTSD), a common condition with potentially devastating individual, family, and societal consequences, is highly associated with substance use disorders (SUDs). The association between PTSD and SUD is complex and may involve adverse childhood experiences (ACEs), historical and multi-generational traumas, and social determinants of health as well as cultural and spiritual contexts. Current psychosocial and pharmacological treatments for PTSD are only modestly effective, and there is a need for more research on therapeutic interventions for co-occurring PTSD and SUD, including whether to provide integrated or sequential treatments. There is a current resurgence of interest in psychedelics as potential treatment augmentation for PTSD and SUDs with an appreciation of the risks in this target population. This paper reviews the historical perspective of psychedelic research and practices, as well as the intersection of historical trauma, ACEs, PTSD, and SUDs through the lens of New Mexico. New Mexico is a state with high populations of Indigenous and Hispanic peoples as well as high rates of trauma, PTSD, and SUDs. Researchers in New Mexico have been leaders in psychedelic research. Future directions for psychedelic researchers to consider are discussed, including the importance of community-based participatory approaches that are more inclusive and respectful of Indigenous and other minority communities.

Purpose: Since the prompt recognition of acute pulmonary embolism (PE) and the immediate initiation of treatment can significantly reduce the risk of death, we developed a deep learning (DL)-based application aimed to automatically detect PEs on chest computed tomography angiograms (CTAs) and alert radiologists for an urgent interpretation. Convolutional neural networks (CNNs) were used to design the application. The associated algorithm used a hybrid 3D/2D UNet topology. The training phase was performed on datasets adequately distributed in terms of vendors, patient age, slice thickness, and kVp. The objective of this study was to validate the performance of the algorithm in detecting suspected PEs on CTAs. Methods: The validation dataset included 387 anonymized real-world chest CTAs from multiple clinical sites (228 U.S. cities). The data were acquired on 41 different scanner models from five different scanner makers. The ground truth (presence or absence of PE on CTA images) was established by three independent U.S. board-certified radiologists. Results: The algorithm correctly identified 170 of 186 exams positive for PE (sensitivity 91.4% [95% CI: 86.4–95.0%]) and 184 of 201 exams negative for PE (specificity 91.5% [95% CI: 86.8–95.0%]), leading to an accuracy of 91.5%. False negative cases were either chronic PEs or PEs at the limit of subsegmental arteries and close to partial volume effect artifacts. Most of the false positive findings were due to contrast agent-related fluid artifacts, pulmonary veins, and lymph nodes. Conclusions: The DL-based algorithm has a high degree of diagnostic accuracy with balanced sensitivity and specificity for the detection of PE on CTAs.