Association between dynapenic obesity phenotypes and physical performance in middle-age and older women living in community.

Background: Dynapenic obesity (DO) is the coexistence of excess adipose tissue/body weight and low muscle strength. This condition is associated with an increased risk of suffering from various chronic diseases and physical deterioration in older people. Aim: To analyze the association between DO phenotypes and physical performance in middle-aged women living in the community. Methods: This cross-sectional study was conducted on middle-aged and older women (≥50 years) residing in Guayaquil, Ecuador. Dynapenia was diagnosticated by a handgrip strength (HGS) < 16 kg; obesity was determined based on body mass index (BMI) ≥ 30 kg/m2. Participants were categorized into four groups based on their dynapenia and obesity status: non-dynapenic/non-obesity (ND/NO), obesity/non-dynapenic (O/ND), dynapenic/non-obesity (D/NO) and dynapenic/obesity (D/O). Physical performance was assessed by the Short Physical Performance Battery (SPPB). Results: A total of 171 women were assessed. The median (IQR) age of the sample was 72.0 (17.0) years. Obesity and dynapenia were 35% (n = 60) and 57.8% (n = 99) of the participants, respectively. The prevalence of ND/NO was 25.1% (n = 43), O/ND 17% (n = 29), D/NO 39.8% (n = 68) and DO 18.1% (n = 31). The mean SPPB total score was 6.5 ± 3.2. Participants of D/NO and DO groups presented significantly lower mean SPPB scores (p < 0.001) compared to those of NO/ND and O/ND groups. Conclusion: Women with DO and D/NO exhibited significantly lower SPPB scores, indicating poorer physical performance. These findings emphasize the importance of incorporating a comprehensive assessment of muscle strength and obesity in middle-aged and older women.

Echocardiogram Vector Embeddings Via R3D Transformer for the Advancement of Automated Echocardiography.

Background: Ejection fraction (EF) estimation informs patient plans in the ICU, and low EF can indicate ventricular systolic dysfunction, which increases the risk of adverse events including heart failure. Automated echocardiography models are an attractive solution for high-variance human EF estimation, and key to this goal are echocardiogram vector embeddings, which are a critical resource for computational researchers. Objectives: The authors aimed to extract the vector embeddings from each echocardiogram in the EchoNet dataset using a classifier trained to classify EF as healthy (>50%) or unhealthy (<= 50%) to create an embeddings dataset for computational researchers. Methods: We repurposed an R3D transformer to classify whether patient EF is below or above 50%. Training, validation, and testing were done on the EchoNet dataset of 10,030 echocardiograms, and the resulting model generated embeddings for each of these videos. Results: We extracted 400-dimensional vector embeddings for each of the 10,030 EchoNet echocardiograms using the trained R3D model, which achieved a test AUC of 0.916 and 87.5% accuracy, approaching the performance of comparable studies. Conclusions: We present 10,030 vector embeddings learned by this model as a resource to the cardiology research community, as well as the trained model itself. These vectors enable algorithmic improvements and multimodal applications within automated echocardiography, benefitting the research community and those with ventricular systolic dysfunction (https://github.com/Team-Echo-MIT/r3d-v0-embeddings).

Understanding and training for the impact of large language models and artificial intelligence in healthcare practice: a narrative review.

Reports of Large Language Models (LLMs) passing board examinations have spurred medical enthusiasm for their clinical integration. Through a narrative review, we reflect upon the skill shifts necessary for clinicians to succeed in an LLM-enabled world, achieving benefits while minimizing risks. We suggest how medical education must evolve to prepare clinicians capable of navigating human-AI systems.

Unmasking biases and navigating pitfalls in the ophthalmic artificial intelligence lifecycle: A narrative review.

Over the past 2 decades, exponential growth in data availability, computational power, and newly available modeling techniques has led to an expansion in interest, investment, and research in Artificial Intelligence (AI) applications. Ophthalmology is one of many fields that seek to benefit from AI given the advent of telemedicine screening programs and the use of ancillary imaging. However, before AI can be widely deployed, further work must be done to avoid the pitfalls within the AI lifecycle. This review article breaks down the AI lifecycle into seven steps-data collection; defining the model task; data preprocessing and labeling; model development; model evaluation and validation; deployment; and finally, post-deployment evaluation, monitoring, and system recalibration-and delves into the risks for harm at each step and strategies for mitigating them.

Simultaneous Measurement of the Half-Life and Spectral Shape of

Current bounds on the neutrino Majorana mass are affected by significant uncertainties in the nuclear calculations for neutrinoless double-beta decay. A key issue for a data-driven improvement of the nuclear theory is the actual value of the axial coupling constant g_{A}, which can be investigated through forbidden ß decays. We present the first measurement of the 4th-forbidden ß decay of ^{115}In with a cryogenic calorimeter based on indium iodide. Exploiting the enhanced spectrum-shape method for the first time to this isotope, our study accurately determines simultaneously spectral shape, g_{A}, and half-life. The interacting shell model, which best fits our data, indicates a half-life for this decay at T_{1/2}=(5.26±0.06)×10^{14} yr.

A toolbox for surfacing health equity harms and biases in large language models.

Large language models (LLMs) hold promise to serve complex health information needs but also have the potential to introduce harm and exacerbate health disparities. Reliably evaluating equity-related model failures is a critical step toward developing systems that promote health equity. We present resources and methodologies for surfacing biases with potential to precipitate equity-related harms in long-form, LLM-generated answers to medical questions and conduct a large-scale empirical case study with the Med-PaLM 2 LLM. Our contributions include a multifactorial framework for human assessment of LLM-generated answers for biases and EquityMedQA, a collection of seven datasets enriched for adversarial queries. Both our human assessment framework and our dataset design process are grounded in an iterative participatory approach and review of Med-PaLM 2 answers. Through our empirical study, we find that our approach surfaces biases that may be missed by narrower evaluation approaches. Our experience underscores the importance of using diverse assessment methodologies and involving raters of varying backgrounds and expertise. While our approach is not sufficient to holistically assess whether the deployment of an artificial intelligence (AI) system promotes equitable health outcomes, we hope that it can be leveraged and built upon toward a shared goal of LLMs that promote accessible and equitable healthcare.

Technical principles, benefits, challenges, and applications of photon counting computed tomography in coronary imaging: a narrative review.

Background and Objective: The introduction of photon-counting computed tomography (PCCT) represents the most recent groundbreaking advancement in clinical computed tomography (CT). PCCT has the potential to overcome the limitations of traditional CT and to provide new quantitative imaging information. This narrative review aims to summarize the technical principles, benefits, and challenges of PCCT and to provide a concise yet comprehensive summary of the applications of PCCT in the domain of coronary imaging. Methods: A review of PubMed, Scopus, and Google Scholar was performed until October 2023 by using relevant keywords. Articles in English were considered. Key Content and Findings: The main advantages of PCCT over traditional CT are enhanced spatial resolution, improved signal and contrast characteristics, diminished electronic noise and image artifacts, lower radiation exposure, and multi-energy capability with enhanced material discrimination. These key characteristics have made room for improved assessment of plaque volume and severity of stenosis, more precise assessment of coronary artery calcifications, also preserved in the case of a reduced radiation dose, improved assessment of plaque composition, possibility to provide details regarding the biological processes occurring within the plaque, enhanced quality and accuracy of coronary stent imaging, and improved radiomic analyses. Conclusions: PCCT can significantly impact diagnostic and clinical pathways and improve the management of patients with coronary artery diseases (CADs).

Analysis of the Volatile and Enantiomeric Compounds Emitted by Plumeria rubra L. Flowers Using HS-SPME-GC.

The volatile components emitted by fresh aromatic flowers of Plumeria rubra L., harvested in southern Ecuador during three different months were determined to evaluate the fluctuation of secondary metabolites. The volatile compounds were analyzed using headspace solid-phase microextraction (HS-SPME) followed by gas chromatography coupled to mass spectrometry (GC-MS) and a flame ionization detector (GC-FID) using two types of columns: a non-polar (DB-5ms) and polar column (HP-INNOWax). The principal chemical groups were hydrocarbon sesquiterpenes (43.5%; 40.0%), oxygenated sesquiterpenes (23.4%; 26.4%), oxygenated monoterpenes (14.0%; 11.2%), and hydrocarbon monoterpenes (12.7%; 9.3%). The most representative constituents were (E,E)-α-Farnesene (40.9-41.2%; 38.5-50.6%), (E)-nerolidol (21.4-32.6%; 23.2-33.0%), (E)-ß-ocimene (4.2-12.5%; 4.5-9.1%), (Z)-dihydro-apofarnesol (6.5-9.9%; 7.6-8.6%), linalool (5.6-8.3%; 3.3-7.8%), and perillene (3.1-5.9%; 3.0-3.2%) in DB-5ms and HP-INNOWax, respectively. Finally, we reported for the first time the enantiomeric distribution of P. rubra flowers, where the enantiomers (1R,5R)-(+)-α-pinene, (S)-(-)-limonene, (S)-(+)-Linalool, and (1S,2R,6R,7R,8R)-(+)-α-copaene were present as enantiomerically pure substances, whereas (S)-(+)-(E)-Nerolidol and (R)-(+)-(E)-Nerolidol were observed as scalemic mixtures. This study provides the first comprehensive and comparative aroma profile of Plumeria rubra cultivated in southern Ecuador and gave us a clue to the variability of P. rubra chemotypes depending on the harvesting time, which could be used for future quality control or applications in phytopharmaceutical and food industries.

Low-cost and convenient screening of disease using analysis of physical measurements and recordings.

In recent years, there has been substantial work in low-cost medical diagnostics based on the physical manifestations of disease. This is due to advancements in data analysis techniques and classification algorithms and the increased availability of computing power through smart devices. Smartphones and their ability to interface with simple sensors such as inertial measurement units (IMUs), microphones, piezoelectric sensors, etc., or with convenient attachments such as lenses have revolutionized the ability collect medically relevant data easily. Even if the data has relatively low resolution or signal to noise ratio, newer algorithms have made it possible to identify disease with this data. Many low-cost diagnostic tools have been created in medical fields spanning from neurology to dermatology to obstetrics. These tools are particularly useful in low-resource areas where access to expensive diagnostic equipment may not be possible. The ultimate goal would be the creation of a "diagnostic toolkit" consisting of a smartphone and a set of sensors and attachments that can be used to screen for a wide set of diseases in a community healthcare setting. However, there are a few concerns that still need to be overcome in low-cost diagnostics: lack of incentives to bring these devices to market, algorithmic bias, "black box" nature of the algorithms, and data storage/transfer concerns.

Photon-Counting Computed Tomography Angiography of Carotid Arteries: A Topical Narrative Review with Case Examples.

Photon counting computed tomography (PCCT) represents a paradigm shift from conventional CT imaging, propelled by a new generation of X-ray detectors capable of counting individual photons and measuring their energy. The first part of this narrative review is focused on the technical aspects of PCCT and describes its key advancements and benefits compared to conventional CT but also its limitations. By synthesizing the existing literature, the second part of the review seeks to elucidate the potential of PCCT as a valuable tool for assessing carotid artery disease. Thanks to the enhanced spatial resolution and image quality, PCCT allows for an accurate evaluation of carotid luminal stenosis. With its ability to finely discriminate between different tissue types, PCCT allows for detailed characterization of plaque morphology and composition, which is crucial for assessing plaque vulnerability and the risk of cerebrovascular events.

Encapsulation of Phenolic Compounds Extracted from Beet By-Products: Analysis of Physical and Chemical Properties.

Beet is a nutritious and health-promoting food with important bioactive compounds in its industrial by-products. The encapsulation of antioxidants from beet by-products has been proposed for valorization. For this, an ethanol-water extract was mixed with polyvinylpyrrolidone (PVP) (used as a carrier agent) and then encapsulated. The encapsulation was performed by spray drying, where the effects of temperature (140-160 °C), extract input flow rate (10-30%), and extraction solvent (ethanol-water 50/50 v/v and ethanol) were evaluated for the total phenol content and the spray-drying yield. The yields obtained were between 60 and 89%, and total phenols were between 136 and 1026 mg gallic acid equivalents/g of encapsulated product. Both responses were affected (p < 0.05) by the extraction solvent. The optimal spray-drying conditions were determined by response surface methodology (RSM). The encapsulated product obtained at optimal conditions was characterized by infrared spectrometry, X-ray fluorescence, Ultra-High Performance Liquid Chromatography, and scanning electron microscopy analysis. The results show that the encapsulated product has a high content of total phenols and compounds such as betanin, isobetanin, and neobetanin. Considering the results of physicochemical properties and the bioactive compounds, the optimized encapsulated product could be applied in the food industry as a bioactive ingredient or natural colorant. However, the further investigation of alternative carrier agents needs to be performed to reduce caking.

Assessment of Clinical Metadata on the Accuracy of Retinal Fundus Image Labels in Diabetic Retinopathy in Uganda: Case-Crossover Study Using the Multimodal Database of Retinal Images in Africa.

BACKGROUND: Labeling color fundus photos (CFP) is an important step in the development of artificial intelligence screening algorithms for the detection of diabetic retinopathy (DR). Most studies use the International Classification of Diabetic Retinopathy (ICDR) to assign labels to CFP, plus the presence or absence of macular edema (ME). Images can be grouped as referrable or nonreferrable according to these classifications. There is little guidance in the literature about how to collect and use metadata as a part of the CFP labeling process. OBJECTIVE: This study aimed to improve the quality of the Multimodal Database of Retinal Images in Africa (MoDRIA) by determining whether the availability of metadata during the image labeling process influences the accuracy, sensitivity, and specificity of image labels. MoDRIA was developed as one of the inaugural research projects of the Mbarara University Data Science Research Hub, part of the Data Science for Health Discovery and Innovation in Africa (DS-I Africa) initiative. METHODS: This is a crossover assessment with 2 groups and 2 phases. Each group had 10 randomly assigned labelers who provided an ICDR score and the presence or absence of ME for each of the 50 CFP in a test image with and without metadata including blood pressure, visual acuity, glucose, and medical history. Specificity and sensitivity of referable retinopathy were based on ICDR scores, and ME was calculated using a 2-sided t test. Comparison of sensitivity and specificity for ICDR scores and ME with and without metadata for each participant was calculated using the Wilcoxon signed rank test. Statistical significance was set at P<.05. RESULTS: The sensitivity for identifying referrable DR with metadata was 92.8% (95% CI 87.6-98.0) compared with 93.3% (95% CI 87.6-98.9) without metadata, and the specificity was 84.9% (95% CI 75.1-94.6) with metadata compared with 88.2% (95% CI 79.5-96.8) without metadata. The sensitivity for identifying the presence of ME was 64.3% (95% CI 57.6-71.0) with metadata, compared with 63.1% (95% CI 53.4-73.0) without metadata, and the specificity was 86.5% (95% CI 81.4-91.5) with metadata compared with 87.7% (95% CI 83.9-91.5) without metadata. The sensitivity and specificity of the ICDR score and the presence or absence of ME were calculated for each labeler with and without metadata. No findings were statistically significant. CONCLUSIONS: The sensitivity and specificity scores for the detection of referrable DR were slightly better without metadata, but the difference was not statistically significant. We cannot make definitive conclusions about the impact of metadata on the sensitivity and specificity of image labels in our study. Given the importance of metadata in clinical situations, we believe that metadata may benefit labeling quality. A more rigorous study to determine the sensitivity and specificity of CFP labels with and without metadata is recommended.

Global inequities in adverse pregnancy outcomes: what can we do?

The Health Equity Leadership & Exchange Network states that "health equity exists when all people, regardless of race, sex, sexual orientation, disability, socioeconomic status, geographic location, or other societal constructs, have fair and just access, opportunity, and resources to achieve their highest potential for health." It is clear from the wide discrepancies in maternal and infant mortalities, by race, ethnicity, location, and social and economic status, that health equity has not been achieved in pregnancy care. Although the most obvious evidence of inequities is in low-resource settings, inequities also exist in high-resource settings. In this presentation, based on the Global Pregnancy Collaboration Workshop, which addressed this issue, the bases for the differences in outcomes were explored. Several different settings in which inequities exist in high- and low-resource settings were reviewed. Apparent causes include social drivers of health, such as low income, inadequate housing, suboptimal access to clean water, structural racism, and growing maternal healthcare deserts globally. In addition, a question is asked whether maternal health inequities will extend to and be partially due to current research practices. Our overview of inequities provides approaches to resolve these inequities, which are relevant to low- and high-resource settings. Based on the evidence, recommendations have been provided to increase health equity in pregnancy care. Unfortunately, some of these inequities are more amenable to resolution than others. Therefore, continued attention to these inequities and innovative thinking and research to seek solutions to these inequities are encouraged.

Quantifying benefits of renewable investments for German residential Prosumers in times of volatile energy markets.

The COVID-19 pandemic and the Russian invasion of Ukraine have led to unseen disruptions in the global energy markets since the end of 2021. Residential renewable investments like photovoltaic systems, battery home storage systems, and heat pumps are therefore gaining traction. However, the benefits of those technologies during the energy crisis and beyond have not been fully quantified yet. Therefore, in this study, we benchmark renewable investments for a broad variety of single-family homes by evaluating potential cost savings and emission reductions. In addition, the study considers the influence of recent political incentives and subsidies. The results show that photovoltaic systems are a no-regret investment decision, both economically and environmentally. At the climax of the energy crisis, a typical German household with a heat pump could save 1850 € and reduce equivalent CO2 emissions by 250 g/kWh annually. Politically introduced price breaks on electricity and natural gas do not reverse this advantage. Furthermore, when owning an electric vehicle renewable investments are often more beneficial.

A Clinician's Guide to Understanding Bias in Critical Clinical Prediction Models.

This narrative review focuses on the role of clinical prediction models in supporting informed decision-making in critical care, emphasizing their 2 forms: traditional scores and artificial intelligence (AI)-based models. Acknowledging the potential for both types to embed biases, the authors underscore the importance of critical appraisal to increase our trust in models. The authors outline recommendations and critical care examples to manage risk of bias in AI models. The authors advocate for enhanced interdisciplinary training for clinicians, who are encouraged to explore various resources (books, journals, news Web sites, and social media) and events (Datathons) to deepen their understanding of risk of bias.

The Coexistence of Microscopic Polyangiitis and Rheumatoid Arthritis: A Case Report.

Microscopic polyangiitis (MPA) is a rare autoimmune disease characterized by the inflammation and necrosis of small vessels, primarily affecting kidneys and lungs. It is classified as an antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV) due to the presence of ANCA. MPA can manifest as diffuse alveolar hemorrhage (DAH) and rapidly progressive glomerulonephritis. In contrast, rheumatoid arthritis (RA) is an inflammatory disease that mainly targets the synovial joints. The coexistence of these two conditions presents significant diagnostic challenges, highlighting the need for further research and understanding. We report a case of a 58-year-old male with a past medical history of RA, chronic bronchitis, tobacco use, and recent Legionella pneumonia who presented with acute dyspnea. The patient was intubated for acute hypoxemic respiratory failure. Laboratory workup revealed anemia, hyponatremia, and acute kidney injury. Urinalysis showed hematuria and proteinuria. A CT scan of the chest exhibited bilateral extensive patchy infiltrates. He was transfused with one packed red blood cell (PRBC) unit. Hemoglobin decreased below 6 g/dL after transfusion. A bronchoscopy revealed erythema throughout the tracheobronchial tree, and blood on bronchial alveolar lavage suggested DAH. High-dose steroids were started. Subsequent laboratory results were positive for rheumatoid factor (RF), perinuclear ANCA (p-ANCA), anti-myeloperoxidase (anti-MPO), and antinuclear antibody (ANA). The kidney biopsy demonstrated focal crescentic necrotizing glomerulonephritis pauci-immune type, confirming MPA. RA pathogenesis involves immune dysregulation and activation of various cells, leading to the release of cytokines. Antibodies such as RF and anti-cyclic citrullinated peptide (anti-CCP) can be detected up to 10 years before the clinical manifestation of RA. Recent studies have revealed a predominance of MPA in AAV while coexisting with RA. The underlying mechanism of its occurrence remains unclear. Our patient had recurrent respiratory symptoms and renal dysfunction before hospitalization. MPA-RA overlap syndrome is potentially treatable and clinicians should maintain a high index of suspicion when encountering patients with preexisting RA. Timely initiation of immunosuppressive therapy at early stages is essential to prevent renal and pulmonary complications. ANCA serology should be assessed in these cases.