The impact of extensive intraductal component (EIC) on the genomic risk of recurrence in early hormone receptor positive breast cancer.

Background: Early invasive ductal carcinoma (IDC) breast cancer often presents with a coexisting ductal carcinoma in situ (DCIS) component, while about 5 % of cases present with an extensive (>25 %) intraductal component (EIC). The impact of EIC on the genomic risk of recurrence is unclear.

Methods: Patients with early hormone receptor-positive HER2neu-negative (HR + HER2-) IDC breast cancer and a known OncotypeDX Breast Recurrence Score® (RS) who underwent breast surgery at our institute were included.

[NOVEL TECHNOLOGIES AND FUTURE TRENDS IN TRANSLATIONAL RESEARCH IN ONCOLOGY].

Translational research in medicine has undergone significant changes in the last decade, primarily due to the remarkable technological advancements made during this period. Oncology research is at the forefront of translational research in medicine and is heavily influenced by these changes. In this article, we briefly review the technologies that form the basis for the "next generation of translational research" in oncology in the coming decades, as well as the emerging trends in translational research in oncology through the implementation of these technologies.

Masking important information to assess the robustness of a multimodal classifier for emotion recognition.

Deep neural networks have been proven effective in classifying human interactions into emotions, especially by encoding multiple input modalities. In this work, we assess the robustness of a transformer-based multimodal audio-text classifier for emotion recognition, by perturbing the input at inference time using attacks which we design specifically to corrupt information deemed important for emotion recognition. To measure the impact of the attacks on the classifier, we compare between the accuracy of the classifier on the perturbed input and on the original, unperturbed input.

HybridMouse: A Hybrid Convolutional-Recurrent Neural Network-Based Model for Identification of Mouse Ultrasonic Vocalizations.

Mice use ultrasonic vocalizations (USVs) to convey a variety of socially relevant information. These vocalizations are affected by the sex, age, strain, and emotional state of the emitter and can thus be used to characterize it. Current tools used to detect and analyze murine USVs rely on user input and image processing algorithms to identify USVs, therefore requiring ideal recording environments.

Morphological characteristics of spoken language in schizophrenia patients - an exploratory study.

Psychosis is diagnosed based on disruptions in the structure and use of language, including reduced syntactic complexity, derailment, and tangentiality. With the development of computational analysis, natural language processing (NLP) techniques are used in many areas of life to make evaluations and inferences regarding people's thoughts, feelings and behavior. The present study explores morphological characteristic of schizophrenia inpatients using NLP.

The impact of tumor detection method on genomic and clinical risk and chemotherapy recommendation in early hormone receptor positive breast cancer.

Background: Symptomatic breast cancers share aggressive clinico-pathological characteristics compared to screen-detected breast cancers. We assessed the association between the method of cancer detection and genomic and clinical risk, and its effect on adjuvant chemotherapy recommendations.

Patients And Methods: Patients with early hormone receptor positive (HR+) HER2neu-negative (HER2-) breast cancer, and known OncotypeDX Breast Recurrence Score test were included.