Treatment of HER2+ metastatic salivary ductal carcinoma in a pregnant woman: a case report

Salivary duct carcinoma (SDC) is a rare and aggressive malignancy with a high mortality and poor response to treatment in the advanced setting. Human epidermal growth factor 2 (HER2) can be amplified in a fraction of SDC. We describe the case of HER2+ metastatic SDC of the submandibular gland in a young pregnant woman treated by multimodal treatment (chemotherapy, radiotherapy and targeted therapy). During pregnancy, a 27-year-old woman developed SDC of the left submandibular gland with lung and bone metastases. Given the HER2 overexpression, she was treated with trastuzumab, paclitaxel and cisplatin. Since the tumor had arisen during pregnancy, triptorelin was administered after delivery. A complete remission was observed, and after eight cycles of chemotherapy, radiotherapy was started in association with trastuzumab and triptorelin. A prolonged disease control and complete visceral remission were observed. Multimodal therapy based on patient's tumor characteristics showed good clinical efficacy in the treatment of metastatic SDC

The role of patient-and treatment-related factors and early functional imaging in late radiation-induced xerostomia in oropharyngeal cancer patients

The advent of quantitative imaging in personalized radiotherapy (RT) has offered the opportunity for a better understanding of individual variations in intrinsic radiosensitivity. We aimed to assess the role of magnetic resonance imaging (MRI) biomarkers, patient-related factors, and treatment-related factors in predicting xerostomia 12 months after RT (XER12 ) in patients affected by oropharyngeal squamous cell carcinoma (OSCC). Patients with locally advanced OSCC underwent diffusion-weighted imaging (DWI) and dynamic-contrast enhanced MRI at baseline; DWI was repeated at the 10th fraction of RT. The Radiation Therapy Oncology Group (RTOG) toxicity scale was used to evaluate salivary gland toxicity. Xerostomia-related questionnaires (XQs) were administered weekly during and after RT. RTOG toxicity ≥ grade 2 at XER12 was considered as endpoint to build prediction models. A Decision Tree classification learner was applied to build the prediction models following a five-fold cross-validation. Of the 89 patients enrolled, 63 were eligible for analysis. Thirty-six (57.1%) and 21 (33.3%) patients developed grade 1 and grade 2 XER12, respectively. Including only baseline variables, the model based on DCE-MRI and V65 (%) (volume of both glands receiving doses ≥ 65 Gy) had a fair accuracy (77%, 95% CI: 66.5–85.4%). The model based on V65 (%) and XQ-Intmid (integral of acute XQ scores from the start to the middle of RT) reached the best accuracy (81%, 95% CI: 71–88.7%). In conclusion, non-invasive biomarkers from DCE-MRI, in combination with dosimetric variables and self-assessed acute XQ scores during treatment may help predict grade 2 XER12 with a fair to good accuracy

Connecting Neurons to a Mobile Robot: An In Vitro Bidirectional Neural Interface

One of the key properties of intelligent behaviors is the capability to learn and adapt to changing environmental conditions. These features are the result of the continuous and intense interaction of the brain with the external world, mediated by the body. For this reason “embodiment” represents an innovative and very suitable experimental paradigm when studying the neural processes underlying learning new behaviors and adapting to unpredicted situations. To this purpose, we developed a novel bidirectional neural interface. We interconnected in vitro neurons, extracted from rat embryos and plated on a microelectrode array (MEA), to external devices, thus allowing real-time closed-loop interaction. The novelty of this experimental approach entails the necessity to explore different computational schemes and experimental hypotheses. In this paper, we present an open, scalable architecture, which allows fast prototyping of different modules and where coding and decoding schemes and different experimental configurations can be tested. This hybrid system can be used for studying the computational properties and information coding in biological neuronal networks with far-reaching implications for the future development of advanced neuroprostheses