Reactive axillary lymph nodes after COVID-19 mRNA vaccination: comparison of mRNA vs. attenuated whole-virus vaccines.

Objective: To compare the incidence and natural course of reactive axillary lymph nodes (RAL) between mRNA and attenuated whole-virus vaccines using Deauville criteria.

Methods: In this multi-institutional PET-CT study comprising multiple vaccine types (Pfizer-BioNTech/Comirnaty, Moderna/Spikevax, Sinovac/CoronaVac and Janssen vaccines), we evaluated the incidence and natural course of RAL in a large cohort of oncological patients utilizing a standardized Deauville scaling system (n=522; 293 Female, Deauville 3-5 positive for RAL). Univariate and multivariate analyses were conducted to evaluate the predictive value of clinical parameters (absolute neutrophil count [ANC], platelets, age, sex, tumor type, and vaccine-to-PET interval) for PET positivity.

Morphology-based deep learning approach for predicting adipogenic and osteogenic differentiation of human mesenchymal stem cells (hMSCs).

Human mesenchymal stem cells (hMSCs) are multipotent progenitor cells with the potential to differentiate into various cell types, including osteoblasts, chondrocytes, and adipocytes. These cells have been extensively employed in the field of cell-based therapies and regenerative medicine due to their inherent attributes of self-renewal and multipotency. Traditional approaches for assessing hMSCs differentiation capacity have relied heavily on labor-intensive techniques, such as RT-PCR, immunostaining, and Western blot, to identify specific biomarkers.

A review of machine learning applications in life cycle assessment studies.

Life Cycle Assessment (LCA) is a foundational method for quantitative assessment of sustainability. Increasing data availability and rapid development of machine learning (ML) approaches offer new opportunities to advance LCA. Here, we review current progress and knowledge gaps in applying ML techniques to support LCA, and identify future research directions for LCAs to better harness the power of ML.

Inter-pass motion correction for whole-body dynamic PET and parametric imaging.

Whole-body dynamic FDG-PET imaging through continuous-bed-motion (CBM) mode multi-pass acquisition protocol is a promising metabolism measurement. However, inter-pass misalignment originating from body movement could degrade parametric quantification. We aim to apply a non-rigid registration method for inter-pass motion correction in whole-body dynamic PET.

CT-free attenuation correction for dedicated cardiac SPECT using a 3D dual squeeze-and-excitation residual dense network.

Background: Attenuation correction (AC) using CT transmission scanning enables the accurate quantitative analysis of dedicated cardiac SPECT. However, AC is challenging for SPECT-only scanners. We developed a deep learning-based approach to generate synthetic AC images from SPECT images without AC.