Modelling the effects of post-FESS middle turbinate synechiae on sinonasal physiology: A computational fluid dynamics study

Preliminary study on AI-assisted diagnosis of bone remodeling in chronic maxillary sinusitis

OBJECTIVE

To construct the deep learning convolution neural network (CNN) model and machine learning support vector machine (SVM) model of bone remodeling of chronic maxillary sinusitis (CMS) based on CT image data to improve the accuracy of image diagnosis.

METHODS

Maxillary sinus CT data of 1000 samples in 500 patients from January 2018 to December 2021 in our hospital was collected. The first part is the establishment and testing of chronic maxillary sinusitis detection model by 461 images. The second part is the establishment and testing of the detection model of chronic maxillary sinusitis with bone remodeling by 802 images. The sensitivity, specificity and accuracy and area under the curve (AUC) value of the test set were recorded, respectively.

RESULTS

Preliminary application results of CT based AI in the diagnosis of chronic maxillary sinusitis and bone remodeling. The sensitivity, specificity and accuracy of the test set of 93 samples of CMS, were 0.9796, 0.8636 and 0.9247, respectively. Simultaneously, the value of AUC was 0.94. And the sensitivity, specificity and accuracy of the test set of 161 samples of CMS with bone remodeling were 0.7353, 0.9685 and 0.9193, respectively. Simultaneously, the value of AUC was 0.89.

CONCLUSION

It is feasible to use artificial intelligence research methods such as deep learning and machine learning to automatically identify CMS and bone remodeling in MSCT images of paranasal sinuses, which is helpful to standardize imaging diagnosis and meet the needs of clinical application.

Temperature influences commensal-pathogen dynamics in a nasal epithelial cell co-culture model

Chronic rhinosinusitis (CRS) is an inflammatory disease of the paranasal sinuses, and microbial dysbiosis associated with CRS is thought to be a key driver of host inflammation that contributes to disease progression. Staphylococcus aureus is a common upper respiratory tract (URT) pathobiont associated with higher carriage rates in CRS populations, where S. aureus-secreted toxins can be identified in CRS tissues. Although many genera of bacteria colonize the URT, few account for the majority of sequencing reads. These include S. aureus and several species belonging to the genus Corynebacterium, including Corynebacterium propinquum and Corynebacterium pseudodiphtheriticum, which are observed at high relative abundance in the healthy URT. Studies have examined bacterial interactions between major microbionts of the URT and S. aureus, but few have done so in the context of a healthy versus diseased URT environment. Here, we examine the role of temperature in commensal, pathogen, and epithelial dynamics using an air-liquid interface cell culture model mimicking the nasal epithelial environment. Healthy URT temperatures change from the nares to the nasopharynx and are increased during disease. Temperatures representative of the healthy URT increase persistence and aggregate formation of commensal C. propinquum and C. pseudodiphtheriticum, reduce S. aureus growth, and lower epithelial cytotoxicity compared to higher temperatures correlating with the diseased CRS sinus. Dual-species colonization revealed species-specific interactions between Corynebacterium species and S. aureus dependent on temperature. Our findings suggest URT mucosal temperature plays a significant role in mediating polymicrobial and host-bacterial interactions that may exacerbate microbial dysbiosis in chronic URT diseases.IMPORTANCEChronic rhinosinusitis is a complex inflammatory disease with a significant healthcare burden. Although presence of S. aureus and microbial dysbiosis are considered mediators of inflammation in CRS, no studies have examined the influence of temperature on S. aureus interactions with the nasal epithelium and the dominant genus of the healthy URT, Corynebacterium. Interactions between Corynebacterium species and S. aureus have been documented in several studies, but none to date have examined how environmental changes in the URT may alter their interactions with the epithelium or each other. This study utilizes a polarized epithelial cell culture model at air-liquid interface to study the colonization and spatial dynamics of S. aureus and clinical isolates of Corynebacterium from people with CRS to characterize the role temperature has in single- and dual-species dynamics on the nasal epithelium.

Physical Conditions Prevailing in the Nasal and Maxillary Sinus Cavities Based on Numerical Simulation

Background and Objectives: This paper presents a unique study that links the physical conditions in the nasal passage with conditions that favour the development of bacterial strains and the colonization of the mucous membranes of the nose and paranasal sinuses. The physical parameters considered were air flow, pressure, humidity, and temperature. Materials and Methods: Numerical models of the human nose and maxillary sinus were retrospectively reconstructed from CT images of generally healthy young subjects. The state-of-the-art numerical methods and tools were then used to determine the temperature, humidity, airflow velocity, and pressure at specific anatomical locations. Results: The results were compared with optimal conditions for bacterial growth in the nose and sinuses. Conclusions: Temperature, humidity, air velocity, and pressure were shown to play critical roles in the selection and distribution of microorganisms. Furthermore, certain combinations of physical parameters can favour mucosal colonisation by various strains of bacteria.