Clinical application of high-resolution spiral CT scanning in the diagnosis of auriculotemporal and ossicle.

Precision and intelligence in evaluating the complexities of middle ear structures are required to diagnose auriculotemporal and ossicle-related diseases within otolaryngology. Due to the complexity of the anatomical details and the varied etiologies of illnesses such as trauma, chronic otitis media, and congenital anomalies, traditional diagnostic procedures may not yield accurate diagnoses. This research intends to enhance the diagnosis of diseases of the auriculotemporal region and ossicles by combining High-Resolution Spiral Computed Tomography (HRSCT) scanning with Deep Learning Techniques (DLT). This study employs a deep learning method, Convolutional Neural Network-UNet (CNN-UNet), to extract sub-pixel information from medical photos. This method equips doctors and researchers with cutting-edge resources, leading to groundbreaking discoveries and better patient healthcare. The research effort is the interaction between the CNN-UNet model and high-resolution Computed Tomography (CT) scans, automating activities including ossicle segmentation, fracture detection, and disruption cause classification, accelerating the diagnostic process and increasing clinical decision-making. The suggested HRSCT-DLT model represents the integration of high-resolution spiral CT scans with the CNN-UNet model, which has been fine-tuned to address the nuances of auriculotemporal and ossicular diseases. This novel combination improves diagnostic efficiency and our overall understanding of these intricate diseases. The results of this study highlight the promise of combining high-resolution CT scanning with the CNN-UNet model in otolaryngology, paving the way for more accurate diagnosis and more individualized treatment plans for patients experiencing auriculotemporal and ossicle-related disruptions.

IL-35 Inhibited Th17 Response in Children with Allergic Rhinitis.

OBJECTIVE: Previous studies have proved that Th17 (T helper 17) cell subsets, a unique proinflammatory CD4+ T cell lineage, are deeply involved in the pathophysiology of allergic rhinitis (AR). IL-35, secreted mainly by natural Treg (nTreg) and depending on the expression of Foxp3, can effectively alleviate allergen-induced specific airway inflammation. However, the regulation of IL-35 in AR is not clear. METHODS: Twenty AR children and 20 healthy controls were enrolled. The expression of serum IL-35 protein was detected and the correlation with Th17 cytokines (IL-17, IL-23, IL-27) expression was analyzed by enzyme-linked immunosorbent assay (ELISA). Peripheral blood mononuclear cells were prepared and stimulated by IL-35 to explore its effect on Th17 inflammation. RESULTS: Serum IL-35 levels in AR were negatively correlated with serum IL-17 and IL-23 levels in AR. Recombinant IL-35 inhibits the Th17 response of PBMCs, which were mediated by the mitogen-activated protein kinase (MEK) and c-Jun N-terminal kinase (JNK) pathways. CONCLUSIONS: Our data demonstrate that IL-35 can inhibit Th17 response in AR through MEK and JNK pathways.

[The ability of pleth variability index to predict fluid responsiveness in mechanically ventilated patients under general anaesthesia].

OBJECTIVE: To evaluate the ability of pleth variability index (PVI) in predicting fluid responsiveness in mechanically ventilated patients under general anesthesia. METHODS: From August to November 2009, 25 patients were enclosed in this study following anesthesia induction. PVI was continuously displayed by the Masimo Radical 7. All patients were also monitored with Vigileo/FloTrac system. Haemodynamic data such as cardiac index (CI), stroke volume variability (SVV), mean arterial pressure, heart rate, central venous pressure, PVI, perfusion index were recorded before and after volume expansion (hetastar 6%, 7 ml/kg). Fluid responsiveness was defined as an increase in CI ≥ 15% (ΔCI ≥ 15). RESULTS: SVV and PVI were significantly higher in the responders (16.0% ± 2.6% and 20.5% ± 3.7%) than those in non-responders (11.6% ± 1.4% and 13.8% ± 2.6%) respectively (P < 0.05). The SVV threshold of 13.5% before volume expansion was able to discriminate the responders from the non-responders with a sensitivity of 88.2% and a specificity of 87.5%. The threshold for PVI was 15.5%, the same sensitivity of 88.2% and specificity of 87.5% were obtained. There was a significant relationship between PVI before volume expansion and change in CI after volume expansion (r = 0.683, P < 0.01), the same as the changes of SVV (r = 0.600, P < 0.01). CONCLUSION: PVI as a new dynamic indices can predict fluid responsiveness non-invasively in mechanically ventilated patients during general anesthesia.