Sensitivity Analysis and Uncertainty Quantification of Nanoparticle Deposition from Tongue Morphological Variations.

The human tongue has highly variable morphology. Its role in regulating respiratory flows and deposition of inhaled aerosols remains unclear. The objective of this study was to quantify the uncertainty of nanoparticle deposition from the variability in tongue shapes and positions and to rank the importance of these morphological factors. Oropharyngeal models with different tongue postures were reconstructed by modifying an existent anatomically accurate upper airway geometry. An LRN k-ω model was applied to solve the multiregime flows, and the Lagrangian tracking approach with near-wall treatment was used to simulate the behavior and fate of inhaled aerosols. Once the database of deposition rates was completed, a surrogate model was trained using Gaussian process regression with polynomial kernels and was validated by comparing its predictions to new CFD simulations. Input sensitivity analysis and output updateability quantification were then performed using the surrogate model. Results show that particle size is the most significant parameter in determining nanoparticle deposition in the upper airway. Among the morphological factors, the shape variations in the central tongue had a higher impact on the total deposition than those in the back tongue and glottal aperture. When considering subregional deposition, mixed sensitivity levels were observed among morphological factors, with the back tongue being the major factor for throat deposition and the central tongue for oral deposition. Interaction effects between flow rate and morphological factors were much higher than the effects from individual parameters and were most significant in the throat (pharyngolaryngeal region). Given input normal variances, the nanoparticle deposition exhibits logarithmical normal distributions, with much lower uncertainty in 100-nm than 2-nm aerosols.

exKidneyBERT: a language model for kidney transplant pathology reports and the crucial role of extended vocabularies.

Background: Pathology reports contain key information about the patient's diagnosis as well as important gross and microscopic findings. These information-rich clinical reports offer an invaluable resource for clinical studies, but data extraction and analysis from such unstructured texts is often manual and tedious. While neural information retrieval systems (typically implemented as deep learning methods for natural language processing) are automatic and flexible, they typically require a large domain-specific text corpus for training, making them infeasible for many medical subdomains. Thus, an automated data extraction method for pathology reports that does not require a large training corpus would be of significant value and utility. Objective: To develop a language model-based neural information retrieval system that can be trained on small datasets and validate it by training it on renal transplant-pathology reports to extract relevant information for two predefined questions: (1) "What kind of rejection does the patient show?"; (2) "What is the grade of interstitial fibrosis and tubular atrophy (IFTA)?" Methods: Kidney BERT was developed by pre-training Clinical BERT on 3.4K renal transplant pathology reports and 1.5M words. Then, exKidneyBERT was developed by extending Clinical BERT's tokenizer with six technical keywords and repeating the pre-training procedure. This extended the model's vocabulary. All three models were fine-tuned with information retrieval heads. Results: The model with extended vocabulary, exKidneyBERT, outperformed Clinical BERT and Kidney BERT in both questions. For rejection, exKidneyBERT achieved an 83.3% overlap ratio for antibody-mediated rejection (ABMR) and 79.2% for T-cell mediated rejection (TCMR). For IFTA, exKidneyBERT had a 95.8% exact match rate. Conclusion: ExKidneyBERT is a high-performing model for extracting information from renal pathology reports. Additional pre-training of BERT language models on specialized small domains does not necessarily improve performance. Extending the BERT tokenizer's vocabulary library is essential for specialized domains to improve performance, especially when pre-training on small corpora.

Store operated calcium channels are associated with diabetic cystopathy in streptozotocin­induced diabetic rats.

Store operated calcium channels (SOCCs) have been suggested to play a critical role in many diabetic complications. Diabetic cystopathy (DCP) is common in patients with diabetes, but the role of SOCCs in DCP is still unclear. The aim of the present study was to investigate the role of SOCCs in DCP with streptozocin (STZ)­induced diabetic rats. Specifically, the authors investigated whether SOCCs were altered in streptozocin (STZ)­induced diabetic rats and, if so, how this may contribute to the contraction of bladder detrusor strips and the intracellular Ca2+ concentration of bladder smooth muscle cells in diabetic rats. Cyclopiazonic acid (CPA, 10 µM) and SKF­96365 (10 µM) were used to activate and inhibit SOCCs respectively, to research the effects of SOCCs on the contraction of the bladder detrusor strips in normal and STZ­induced diabetic rats at the 4th, 8th and 12th week after the diabetic rat model was established. The changes of intracellular Ca2+ were also evaluated under confocal microscopy with pretreated Fluo­4AM. In addition, the expressions of Orai1 and STIM1 were detected by reverse transcription­quantitative polymerase chain reaction and western blotting at different time points. According to the results, the contractive frequency of diabetic bladder muscle strips was higher than that of controls in the 4th and 8th week. The increased fluorescence intensity was detected after using CPA and SKF­96365 in diabetic groups. The expressions of Orai1 and STIM1 changed in a time­dependent manner.