All Publications

  • A Man With Leg Pain. Annals of emergency medicine Ghaith, S., Perez-Cruet, J., Moody, G., Hill, C., Lin, M., Batchelor, T. J. 2026; 88 (3): 417-418

    View details for DOI 10.1016/j.annemergmed.2026.03.005

    View details for PubMedID 42618177

  • Systematic review of implementing stewardship activities to promote appropriate antimicrobial use ANTIMICROBIAL STEWARDSHIP & HEALTHCARE EPIDEMIOLOGY McKay, V., Tetteh, E., Bono, K., McGovern, C., Sattler, M., Zabotka, L., Yaeger, L., Obeng, H., Perez-Cruet, J. M., Facer, E., Newland, J. G., Malone, S. 2026; 6 (1)

    View details for DOI 10.1017/ash.2026.10769

    View details for Web of Science ID 001837761000001

  • A Man With Neck Pain and Swelling. Journal of the American College of Emergency Physicians open Perez-Cruet, J. M., Ghaith, S., Cortez, B., Kendall, J. L., Batchelor, T. J. 2026; 7 (3): 100408

    View details for DOI 10.1016/j.acepjo.2026.100408

    View details for PubMedID 42094253

    View details for PubMedCentralID PMC13140020

  • An Integrable and Interactive Session for Developing Action-Oriented Foundational Climate Change and Health Competencies in Medical Students. MedEdPORTAL : the journal of teaching and learning resources Perez-Cruet, J. M., Scherer, N., Haines, E., Tan, W., Craig, H., Duncan, M., Fishman, S., Sanchez, J. S., Truel, J., Zhao, S., Troyer, S., Catley, C., Hobson, A., Hubert, A., Li, J., Mattar, C., Valko, P., Hanson, J. L. 2025; 21: 11560

    Abstract

    Introduction: Climate change is the greatest threat to global health, yet there are few foundational climate resources available for integration into medical school curricula. We describe an interactive session for equipping medical students with practical and empowering foundational climate-health competencies.Methods: We developed a 2-hour interactive lecture+ preceded by 30 minutes of required prep work. Knowledge was assessed using two-question quizzes. A postsession survey evaluated session effectiveness and self-assessed attitudes and preparedness.Results: A total of 375 students participated; 164 completed all assessment and evaluation measures. The average knowledge quiz score after required prep was 80%. Of all students, 82% reported that more than half of the session's climate change mitigative strategies were new to them. Ratings of preparedness for five tasks linked to learning objectives significantly improved in all classes (p < .001), with 8%-58% of students before the session and 89%-100% of students after the session reporting being fairly/completely prepared. Qualitative responses also supported achievement of learning objectives. Rates of satisfaction with the required prep and lecture+ were 79% and 89%, respectively. Cited strengths included overall quality and the use of cases to highlight health care environmental impacts and opportunities for mitigation.Discussion: This resource fills an urgent need for an integrable session for medical schools hoping to achieve action-oriented, foundational climate-health competencies. Key characteristics of this work include the diversity of the development team, ease and flexibility of session implementation, a focus on empowerment, and strong assessment and evaluation data supporting achievement of learning objectives.

    View details for DOI 10.15766/mep_2374-8265.11560

    View details for PubMedID 41306713

  • Build Deep Neural Network Models to Detect Common Edible Nuts from Photos and Estimate Nutrient Portfolio NUTRIENTS An, R., Perez-Cruet, J. M., Wang, X., Yang, Y. 2024; 16 (9)

    Abstract

    Nuts are nutrient-dense foods and can be incorporated into a healthy diet. Artificial intelligence-powered diet-tracking apps may promote nut consumption by providing real-time, accurate nutrition information but depend on data and model availability. Our team developed a dataset comprising 1380 photographs, each in RGB color format and with a resolution of 4032 × 3024 pixels. These images feature 11 types of nuts that are commonly consumed. Each photo includes three nut types; each type consists of 2-4 nuts, so 6-9 nuts are in each image. Rectangular bounding boxes were drawn using a visual geometry group (VGG) image annotator to facilitate the identification of each nut, delineating their locations within the images. This approach renders the dataset an excellent resource for training models capable of multi-label classification and object detection, as it was meticulously divided into training, validation, and test subsets. Utilizing transfer learning in Python with the IceVision framework, deep neural network models were adeptly trained to recognize and pinpoint the nuts depicted in the photographs. The ultimate model exhibited a mean average precision of 0.7596 in identifying various nut types within the validation subset and demonstrated a 97.9% accuracy rate in determining the number and kinds of nuts present in the test subset. By integrating specific nutritional data for each type of nut, the model can precisely (with error margins ranging from 0.8 to 2.6%) calculate the combined nutritional content-encompassing total energy, proteins, carbohydrates, fats (total and saturated), fiber, vitamin E, and essential minerals like magnesium, phosphorus, copper, manganese, and selenium-of the nuts shown in a photograph. Both the dataset and the model have been made publicly available to foster data exchange and the spread of knowledge. Our research underscores the potential of leveraging photographs for automated nut calorie and nutritional content estimation, paving the way for the creation of dietary tracking applications that offer real-time, precise nutritional insights to encourage nut consumption.

    View details for DOI 10.3390/nu16091294

    View details for Web of Science ID 001220025800001

    View details for PubMedID 38732541

    View details for PubMedCentralID PMC11085677

  • We got nuts! use deep neural networks to classify images of common edible nuts NUTRITION AND HEALTH An, R., Perez-Cruet, J., Wang, J. 2024; 30 (2): 301-307

    Abstract

    Nuts are nutrient-dense foods that contribute to healthier eating. Food image datasets enable artificial intelligence (AI) powered diet-tracking apps to help people monitor daily eating patterns.This study aimed to create an image dataset of commonly consumed nut types and use it to build an AI computer vision model to automate nut type classification tasks.iPhone 11 was used to take photos of 11 nut types-almond, brazil nut, cashew, chestnut, hazelnut, macadamia, peanut, pecan, pine nut, pistachio, and walnut. The dataset contains 2200 images, 200 per nut type. The dataset was randomly split into the training (60% or 1320 images), validation (20% or 440 images), and test sets (20% or 440 images). A neural network model was constructed and trained using transfer learning and other computer vision techniques-data augmentation, mixup, normalization, label smoothing, and learning rate optimization.The trained neural network model correctly predicted 338 out of 440 images (40 per nut type) in the validation set, achieving 99.55% accuracy. Moreover, the model classified the 440 images in the test set with 100% accuracy.This study built a nut image dataset and used it to train a neural network model to classify images by nut type. The model achieved near-perfect accuracy on the validation and test sets, demonstrating the feasibility of automating nut type classification using smartphone photos. Being made open-source, the dataset and model can assist the development of diet-tracking apps that facilitate users' adoption and adherence to a healthy diet.

    View details for DOI 10.1177/02601060221113928

    View details for Web of Science ID 000830274300001

    View details for PubMedID 35861193

  • Effects of Subterranean Limestone Sinks and Agricultural Development on Benthic Macroinvertebrate Communities of Canyon Creek (Wyoming) BULLETIN OF THE PEABODY MUSEUM OF NATURAL HISTORY Perez-Cruet, J. M. 2020; 61 (2): 103-122

    View details for DOI 10.3374/014.061.0203

    View details for Web of Science ID 000586801300003

  • Identification of a Na<SUP>+</SUP>-Binding Site near the Oxygen-Evolving Complex of Spinach Photosystem II BIOCHEMISTRY Wang, J., Perez-Cruet, J. M., Huang, H., Reiss, K., Gisriel, C. J., Banerjee, G., Kaur, D., Ghosh, I., Dziarski, A., Gunner, M. R., Batista, V. S., Brudvig, G. W. 2020; 59 (30): 2823-2831

    Abstract

    The oxygen-evolving complex (OEC) of photosystem II (PSII) is an oxomanganese cluster composed of four redox-active Mn ions and one redox-inactive Ca2+ ion, with two nearby bound Cl- ions. Sodium is a common counterion of both chloride and hydroxide anions, and a sodium-specific binding site has not been identified near the OEC. Here, we find that the oxygen-evolution activity of spinach PSII increases with Na+ concentration, particularly at high pH. A Na+-specific binding site next to the OEC, becomes available after deprotonation of the D1-H337 amino acid residue, is suggested by the analysis of two recently published PSII cryo-electron microscopy maps in combination with quantum mechanical calculations and multiconformation continuum electrostatics simulations.

    View details for DOI 10.1021/acs.biochem.0c00303

    View details for Web of Science ID 000558751200008

    View details for PubMedID 32650633