Publications by authors named "D G Tiezzi"
Article Synopsis
- - The study aimed to find specific microRNAs (miRNAs) and their mRNA targets that influence how well patients with advanced invasive ductal carcinoma respond to chemotherapy.
- - Researchers compared miRNA expression in patients who responded positively to treatment versus those who did not, utilizing data from The Cancer Genome Atlas (TCGA) for deeper analysis.
- - A predictive model based on 24 differentially expressed miRNAs was developed, revealing key miRNAs linked to chemotherapy success, and proposed further exploration to validate these findings.
Although autologous bone grafting is the most widely used treatment for bone defects, the most effective preparation remains unclear. This animal study aimed to compare different autologous bone grafting preparation for the treatment of rat́s calvaria critical bone defect. 122 rats were randomly allocated into three groups: Simulado, Macerated and Chopped.
View Article and Find Full Text PDFReal-world data on adjuvant capecitabine after standard neoadjuvant chemotherapy for triple negative breast cancer.
Rev Bras Ginecol Obstet
August 2024
Objective: Neoadjuvant chemotherapy (NACT) has become the standard of care for patients with triple-negative breast cancer (TNBC) with tumors > 1 cm or positive axillary nodes. Pathologic complete response (pCR) has been used as an endpoint to select patients for treatment scaling. This study aimed to examine the benefit of adding adjuvant capecitabine for TNBC patients who did not achieve pCR after standard NACT in a real-world scenario.
View Article and Find Full Text PDFPurpose: To establish a reliable machine learning model to predict malignancy in breast lesions identified by ultrasound (US) and optimize the negative predictive value to minimize unnecessary biopsies.
Methods: We included clinical and ultrasonographic attributes from 1526 breast lesions classified as BI-RADS 3, 4a, 4b, 4c, 5, and 6 that underwent US-guided breast biopsy in four institutions. We selected the most informative attributes to train nine machine learning models, ensemble models and models with tuned threshold to make inferences about the diagnosis of BI-RADS 4a and 4b lesions (validation dataset).