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  • Paratesticular Fibrous Pseudotumor in a Pediatric Patient: A Case Report.
    Publication . Coelho Mogárrio, Inês; Jalles, Filipa; Knoblich, Maria; Alves, Rui
    Paratesticular fibrous pseudotumors are rare benign tumors. This case reports paratesticular fibrous pseudotumors in a very young patient. A previously healthy 16-month-old boy was seen due to a growing scrotal mass. On clinical examination, there was a painless, multinodular scrotal mass. Tumor markers were normal, and a testicular ultrasound with Doppler revealed a solid, avascular, and hypoechoic mass (50x20 mm). The patient underwent excision of the scrotal mass and adjacent skin. The histological analysis revealed a paratesticular fibrous pseudotumor. Definitive treatment is surgical excision, and if there is any concern for malignancy, an extemporaneous examination should be done to confirm the diagnosis. The prognosis with fibrous pseudotumors is excellent.
    2024-10Periodical Open access Show more
  • Acoustic and Clinical Data Analysis of Vocal Recordings: Pandemic Insights and Lessons.
    Publication . Carreiro-Martins, Pedro; Paixão, Paulo; Caires, Iolanda; Matias, Pedro; Gamboa, Hugo; Soares, Filipe; Gomez, Pedro; Sousa, Joana; Neuparth, Nuno
    Background/Objectives: The interest in processing human speech and other human-generated audio signals as a diagnostic tool has increased due to the COVID-19 pandemic. The project OSCAR (vOice Screening of CoronA viRus) aimed to develop an algorithm to screen for COVID-19 using a dataset of Portuguese participants with voice recordings and clinical data. Methods: This cross-sectional study aimed to characterise the pattern of sounds produced by the vocal apparatus in patients with SARS-CoV-2 infection documented by a positive RT-PCR test, and to develop and validate a screening algorithm. In Phase II, the algorithm developed in Phase I was tested in a real-world setting. Results: In Phase I, after filtering, the training group consisted of 166 subjects who were effectively available to train the classification model (34.3% SARS-CoV-2 positive/65.7% SARS-CoV-2 negative). Phase II enrolled 58 participants (69.0% SARS-CoV-2 positive/31.0% SARS-CoV-2 negative). The final model achieved a sensitivity of 85%, a specificity of 88.9%, and an F1-score of 84.7%, suggesting voice screening algorithms as an attractive strategy for COVID-19 diagnosis. Conclusions: Our findings highlight the potential of a voice-based detection strategy as an alternative method for respiratory tract screening.
    2024-10-12Text Open access Show more