Effect of nanobubbles on powder morphology in the spray drying process.

This study investigated the influence of gas-injected nanobubbles on the morphology of particles during spray drying under various experimental conditions. The nanoparticle tracking system was used to measure the generation, size, and concentration of nanobubbles. Experiments were conducted at different temperatures (160°C-260°C) and feed rates (0.2-0.26 g/s) to examine the effect of nanobubbles on spray drying and present diverse results. The deionized (DI) water with generated nanobubbles had a particle concentration of 1.8 × 108 particles/mL and a mean particle size of 242.6 nm, which was ∼3.31 × 107 particles/mL higher untreated DI water. The maltodextrin solution containing nanobubbles also showed a significant increase in particle generation, with a concentration of 1.62 × 109 particles/mL. The viscosity of the maltodextrin solution containing nanobubbles decreased by ∼18%, from 9.3 mPa·s to 7.5 mPa·s. Overall, the size of the generated particles was similar regardless of nanobubble treatment, but there was a tendency for particle size to increase under specific temperature (260°C) and feed flow rate (0.32 g/s) conditions. Furthermore, it was observed that the Hausner ratio significantly varied with increasing temperature and feed flow rate, and these results were explained through scanning electron microscopy images. These findings confirm that the gas nanobubbles mixed in the feed can exert diverse effects on the spray drying system and powder characteristics depending on the operating conditions. This study suggests that nanobubbles can contribute to a more efficient process in spray drying and can influence the morphological characteristics of particles depending on the spray drying conditions.

Transforming Thyroid Cancer Diagnosis and Staging Information from Unstructured Reports to the Observational Medical Outcome Partnership Common Data Model.

BACKGROUND: Cancer staging information is an essential component of cancer research. However, the information is primarily stored as either a full or semistructured free-text clinical document which is limiting the data use. By transforming the cancer-specific data to the Observational Medical Outcome Partnership Common Data Model (OMOP CDM), the information can contribute to establish multicenter observational cancer studies. To the best of our knowledge, there have been no studies on OMOP CDM transformation and natural language processing (NLP) for thyroid cancer to date. OBJECTIVE: We aimed to demonstrate the applicability of the OMOP CDM oncology extension module for thyroid cancer diagnosis and cancer stage information by processing free-text medical reports. METHODS: Thyroid cancer diagnosis and stage-related modifiers were extracted with rule-based NLP from 63,795 thyroid cancer pathology reports and 56,239 Iodine whole-body scan reports from three medical institutions in the Observational Health Data Sciences and Informatics data network. The data were converted into the OMOP CDM v6.0 according to the OMOP CDM oncology extension module. The cancer staging group was derived and populated using the transformed CDM data. RESULTS: The extracted thyroid cancer data were completely converted into the OMOP CDM. The distributions of histopathological types of thyroid cancer were approximately 95.3 to 98.8% of papillary carcinoma, 0.9 to 3.7% of follicular carcinoma, 0.04 to 0.54% of adenocarcinoma, 0.17 to 0.81% of medullary carcinoma, and 0 to 0.3% of anaplastic carcinoma. Regarding cancer staging, stage-I thyroid cancer accounted for 55 to 64% of the cases, while stage III accounted for 24 to 26% of the cases. Stage-II and -IV thyroid cancers were detected at a low rate of 2 to 6%. CONCLUSION: As a first study on OMOP CDM transformation and NLP for thyroid cancer, this study will help other institutions to standardize thyroid cancer-specific data for retrospective observational research and participate in multicenter studies.

Second primary malignancy risk in thyroid cancer and matched patients with and without radioiodine therapy analysis from the observational health data sciences and informatics.

PURPOSE: Risk of second primary malignancy (SPM) after radioiodine (RAI) therapy has been continuously debated. The aim of this study is to identify the risk of SPM in thyroid cancer (TC) patients with RAI compared with TC patients without RAI from matched cohort. METHODS: Retrospective propensity-matched cohorts were constructed across 4 hospitals in South Korea via the Observational Health Data Science and Informatics (OHDSI), and electrical health records were converted to data of common data model. TC patients who received RAI therapy constituted the target group, whereas TC patients without RAI therapy constituted the comparative group with 1:1 propensity score matching. Hazard ratio (HR) by Cox proportional hazard model was used to estimate the risk of SPM, and meta-analysis was performed to pool the HRs. RESULTS: Among a total of 24,318 patients, 5,374 patients from each group were analyzed (mean age 48.9 and 49.2, women 79.4% and 79.5% for target and comparative group, respectively). All hazard ratios of SPM in TC patients with RAI therapy were ≤ 1 based on 95% confidence interval(CI) from full or subgroup analyses according to thyroid cancer stage, time-at-risk period, SPM subtype (hematologic or non-hematologic), and initial age (< 30 years or ≥ 30 years). The HR within the target group was not significantly higher (< 1) in patients who received over 3.7 GBq of I-131 compared with patients who received less than 3.7 GBq of I-131 based on 95% CI. CONCLUSION: There was no significant difference of the SPM risk between TC patients treated with I-131 and propensity-matched TC patients without I-131 therapy.