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Early audiologic screening is needed to ascertain type of HL and to efficiently direct patient care in this population.

Children and young adults with Marfan syndrome have a high likelihood of hearing loss, with high rates of CHL, chronic otitis media, and Eustachian tube dysfunction. SNHL is also prevalent in this syndrome; hypertension increased the likelihood of SNHL. Early audiologic screening is needed to ascertain type of HL and to efficiently direct patient care in this population.The cricoid is a circular "ring" of cartilage in the airway. When the lateral walls of the cricoid approximate, it takes the shape of an ellipse. In severe cases, this also reduces the glottic aperture and causes respiratory distress, stridor, and failure to thrive. The elliptical cricoid has limited surgical options outside of open laryngotracheal procedures and tracheostomy. Recently, alternatives to autologous grafts have been utilized in airway reconstruction to reduce harvest site morbidity and increase operating room efficiency. Herein a case is presented that demonstrates the successful use of a resorbable plate in augmenting the posterior larynx in an infant with a severely elliptical cricoid to avoid a tracheostomy.Ultrasonic-assisted glass molding (UGM) has recently gained a promising start in fast replication of tailored functional structures onto glasses; however, the underlying mechanisms of the unique thermomechanical and micro-filling behaviors of glasses in UGM remain largely unrevealed. This study presents a full demonstration and elucidation of the ultrasonic-induced thermal/tribological effects on viscoelastic responses and filling capacity of the typical optical glass L-BAL42. First, conventional precision glass molding (PGM) and UGM experiments with partial-filling settings are implemented, whereby glass arrays with surface protrusions of varied depths (460-780 μm) are directly formed. Subsequently, the molding force, forming time and filling depth of the glass under varying pressing speeds/loads are comparatively evaluated. Furthermore, experimental quantifications of ultrasonic-induced heat increment and friction reduction are performed to account for the differentiated molding effects in UGM and PGM. The results indicate that compared with PGM, the molding force and forming time in UGM are greatly reduced, while the average filling depth of the UGM-formed glass array is effectively improved. This overall enhancement can be attributed to the ultrasonic-induced thermal softening, friction reduction and stress superposition effects, among which the thermal contribution is dominant. The findings in this study will provide new references for ultrasonic-assisted precision molding of glass-based micro/meso components.This work aims at studying the effect of porosity in particulate reinforced metal-matrix composites on the statistical amplitude distribution of backscattered laser-induced ultrasonic pulses in these composites. PT2399 A special laser-ultrasonic transducer used in experiments combines laser excitation and piezoelectric detection of broadband ultrasonic pulses in composite specimens with only one plane surface available for laser irradiation. We studied stir cast hypereutectic aluminium-silicon alloy A336 matrix composites reinforced with the SiC micro particles (volume fractions of 0.033-0.135) and in-situ reactive cast aluminum matrix composites reinforced with the Al3Ti intermetallic particles (volume fractions of 0.04-0.115). The amplitude distribution width of the backscattered ultrasonic pulse was determined by approximating the experimental data by the Gaussian probability distribution applicable for statistics of large number of independent random variables. The results show that the amplitude distribution width increases with the growth in the specimen porosity independent of sizes and fractions of the reinforcing particles. The empirical relationship between the local porosity and distribution width of the backscattered ultrasonic signal amplitudes was obtained for porosities up to 4.5%. This relationship can be used for nondestructive testing of the local porosity in engineering products fabricated from the studied composite materials. The proposed laser-ultrasonic technique is especially promising for structural health monitoring of particulate reinforced metal-matrix composites during their service.

The study aims to assess the impact of radiomics in the clinical practice of breast ultrasound, to determine which lesions are undetermined by the software, and to discuss the future of the radiologist's role.

Consecutive analyses of 207 ultrasound masses from January 2018 to April 2019 referred for percutaneous breast biopsy. Breast masses were classified using dedicated ultrasound software (AI). The AI software automatically classified the masses on a scale of 0-100, where 100 is the most suspicious. We adopt the histology results as the gold standard. The cut-off point of malignancy by radiomics was determined, with ±10 % of margin error according to the Youden's index. We considered these lesions as undetermined masses. The performance of the AI software and the radiologist classification was compared using the area under roc curves (AUROC). We also discuss the impact of radiologist validation of AI results, especially in undetermined lesions.

Of the 207 evaluated masses, 143 were benign, and 64 were malignant. The Youden's index was 0.516, including undetermined masses with a varied range of 10 % (0.464-0.567). Twenty-one (14.58 %) benign and twelve (19.05 %) malignant masses were in this range. The best accuracy performance to classify masses was the combination of the reader and AI (0.829). The most common undetermined masses in AI were fibroadenoma, followed by phyllodes tumor, steatonecrosis as benign. Whereas, low-grade, and high-grade invasive ductal carcinoma represents the malignant lesions.

Artificial Intelligence has a reliable performance in ultrasound breast masses classification. Radiologist validation is critical to determine the final BI-RADS assessment, especially in undetermined masses to obtain the best classification performance.

Artificial Intelligence has a reliable performance in ultrasound breast masses classification. Radiologist validation is critical to determine the final BI-RADS assessment, especially in undetermined masses to obtain the best classification performance.