Search

Nuclear medicine treatment requires patient-specific dose assessment as treatment plan reference. Voxelized dose results can give three-dimensional dose distribution. The conventional MIRD method assumes that the source is uniformly distributed within the organ, and the calculated dose results in an organ-averaged dose. In this study, based on molecular imaging data, the non-uniform distribution of the source term within the organ was obtained, and the voxel-level dose distribution was calculated for nuclear medicine patients using the following method. With accurate quantitative analyze of 131I SPECT images and CT images of patients, voxel S-value method and direct Monte Carlo (MC) method were used to calculate voxel-level dose distribution in nuclear medicine patients. The voxel S-factor is defined as the average absorbed dose of each radioactive decay in the source voxel to the target voxel, both of which are contained in an infinite homogeneous tissue. The voxel S-value is calculated to evaluate the dose of multiple surrounding source voxels to the target voxel. The three-dimensional dose distribution is calculated based on the source distribution obtained from the SPECT images. The MC simulation was processed based on the source distribution obtained from SPECT images and CT images. The HU value of each voxel in the patient’s body was obtained from the CT images, and the density value of each voxel was further obtained. The material of each voxel was divided according to the density to construct the voxel phantom. The voxel-level activity distribution was obtained based on the SPECT images to construct the source term distribution model. The MC simulation based on CT images was used to count the energy deposition of each voxel to obtain the accurate voxel-level dose distribution in the patient’s body. A 400x speedup was achieved using GPU MC simulation. The results of the S-value method and the MC method show a maximum difference of over 40% in the lung area. Voxel-level dose simulation can obtain the dose distribution in sub-organ and tumor, which provides a valuable reference for precision treatment of clinical nuclear medicine. The S-factor method can quickly give the dose distribution of patients, but this method ignores the tissue inhomogeneity, and the dose calculation results are not accurate in tissues and organs with large density differences, such as lung and bone, etc. The MC voxelized simulation can accurately give the dose distribution in patients, which is important for the accurate assessment of clinical dose in nuclear medicine.

Idiopathic short stature (ISS) is a common childhood condition with largely unknown underlying causes. Recent research highlights the role of circulating exosomes in the pathogenesis of various disorders, but their connection to ISS remains unexplored. In the experiments, human chondrocytes are cocultured with plasma exosomes from ISS patients, leading to impaired chondrocyte growth and bone formation. Elevated levels of a specific long non‐coding RNA (lncRNA), ISSRL, are identified as a distinguishing factor in ISS, boasting high specificity and sensitivity. Silencing ISSRL in ISS plasma exosomes reverses the inhibition of chondrocyte proliferation and bone formation. Conversely, overexpression of ISSRL in chondrocytes impedes their growth and bone formation, revealing its mechanism of action through the miR‐877‐3p/GZMB axis. Subsequently, exosomes (CT‐Exo‐siISSRL‐oeGH) with precise cartilage‐targeting abilities are engineered, loaded with customized siRNA for ISSRL and growth hormone. This innovative approach offers a therapeutic strategy to address ISS by rectifying abnormal non‐coding RNA expression in growth plate cartilage and delivering growth hormone with precision to promote bone growth. This research provides valuable insights into ISS diagnosis and treatment, highlighting the potential of engineered exosomes. [ABSTRACT FROM AUTHOR]

Objective To explore the application value of common indicators in the risk assessment model for diabetic foot (DF) complicated with necrotizing fasciitis (NF). Methods This was a cross-sectional study. A retrospective analysis was performed on 815 patients with DF, who were admitted to the First Affiliated Hospital of Chongqing Medical University from October 2018 to April 2022. Patients were divided into DF group (703 cases) and DF with NF group (DNF, 112 cases) according to whether NF was combined. Clinical data (age, body mass index, course of disease, diabetic complications, necrosis of the foot skin, body temperature, blood routine, creatinine, fibrinogen, albumin, glycated hemoglobin A1c, hypersensitive C-reactive protein, etc.) of the two groups were analyzed, and indicators of statistical and clinical significance were converted into categorical variables. Multivariate logistic regression was used to develop a new risk assessment model for DNF (NRADNF) and to construct a nomogram. Model discriminability was assessed by area under receiver operating characteristic (ROC) curve (AUC), accuracy was evaluated by Hosmer-Lemeshow goodness-fit test, effectiveness was evaluated by decision curve analysis, and stability was evaluated by bootstrap repeated sampling. Results The constructed NRANDF model includes 6 indicators: age ≥60 years old (OR=0.378, P<0.001), body temperature ≥38 °C (OR=3.119, P=0.001), foot skin necrosis (OR=1.974, P=0.012), neutrophil-lymphocyte ratio ≥8.5 (OR=2.500, P=0.002), hs-CRP>20 mg/L (OR=2.455, P=0.006), hemoglobin ≤100 g/L (OR=1.833, P=0.026). The AUC (95%CI) was 0.815 (0.773, 0.857), and Hosmer-Lemeshow goodness-fit test showed that the model performed well overall (χ² =8.736, P=0.272). The result of decision curve analysis suggested that the model had a net clinical benefit, and internal verification using bootstrap showed that ROC curves were essentially identical before and after repeated sampling. Conclusion The NRADNF model has good predictive efficiency, clinical utility and the included indicators are easy to obtain, it can be used in the clinic for preliminary screening of DNF. [ABSTRACT FROM AUTHOR]

We proposed a passenger control strategy and train operation plan collaborative optimization (PCS&TOP) model to schedule the train operation that improves the efficiency of passengers' travel and reduces the cost of train operation for a rail transit network. The model is an integer non linear programming model that aims to minimize the entrance and platform waiting time of passengers and the operation cost of trains. The timetable and variable train formation are integrated optimized, and the turnaround of rolling stock is also considered by constructing the train operation constraints for the model. The coordination of timetables on different lines and the coordination of passenger control strategies at different stations are mentioned by considering the transfer passengers when constructing the passenger control constraints. To solve the model, a multi-operator simulated annealing (MOSA) algorithm that includes three types of execution operators corresponding to three main decision variables is proposed. A numerical case that includes 2 bidirectional crossed lines and a real-world case from China are introduced to test the efficiency of the proposed method, which demonstrates better performance than the single and respective optimization solutions., Author(s): Yonghao Yin [1]; Jun Chen (corresponding author) [1]; Wenrui Zhao [2] 1. Introduction The rail transit system is developing rapidly in recent years, the passenger demand is increasing greatly, [...]