Occult Basal Cell Carcinoma Arising in Seborrheic Keratosis

Both seborrheic keratosis and basal cell carcinoma are common skin tumors in daily clinical practice. However, the coexistence of seborrheic keratosis and basal cell carcinoma is rare. In this report, we present a case of occult microscopic basal cell carcinoma arising in a lesion of seborrheic keratosis. This case indicates that the basal cell carcinoma could arise from seborrheic keratosis and might help to clarify the origin of basal cell carcinoma

Bilateral Pneumothorax Associated With Lung and Pleural Metastases of Breast Cancer

A rare case of bilateral pneumothorax in a 54-year-old woman with advanced breast cancer associated with lung and pleural metastases is presented. The patient was admitted to our hospital complaining of unexpected severe dyspnea. A chest X-ray showed bilateral pneumothorax associated with multiple lung metastases and pleural effusions, followed by immediate pleural drainage. Although air leak and effusions of the right lung were well controlled by the conservative management, massive air leaks of the left lung had continued for 40 days. Because of patient's poor general status a surgical closure of the leaking site was selected using video-assisted thoracoscopic surgery techniques. Thoracoscopy revealed a ruptured bulla in the lower lobe (S6), thus, followed by a successful bullectomy with a stapling device. We speculate that multiple pleural metastasis may disturb the normal repair mechanism of the lung tissue and cause prolonged persistent air leaks

Pituitary adenylate cyclase-activating polypeptide type 1 receptor signaling evokes long-lasting nociceptive behaviors through the activation of spinal astrocytes in mice

AbstractIntrathecal (i.t.) administration of pituitary adenylate cyclase-activating polypeptide (PACAP) induces long-lasting nociceptive behaviors for more than 60 min in mice, while the involvement of PACAP type1 receptor (PAC1-R) has not been clarified yet. The present study investigated signaling mechanisms of the PACAP-induced prolonged nociceptive behaviors. Single i.t. injection of a selective PAC1-R agonist, maxadilan (Max), mimicked nociceptive behaviors in a dose-dependent manner similar to PACAP. Pre- or post-treatment of a selective PAC1-R antagonist, max.d.4, significantly inhibited the nociceptive behaviors by PACAP or Max. Coadministration of a protein kinase A inhibitor, Rp-8-Br-cAMPS, a mitogen-activated protein kinase/extracellular signal-regulated kinase (ERK) kinase inhibitor, PD98059 or a c-Jun N-terminal kinase (JNK) inhibitor, SP600125, significantly inhibited the nociceptive behaviors by Max. Immunohistochemistry and immunoblotting analysis revealed that spinal administration of Max-induced ERK phosphorylation and JNK phosphorylation, and also augmented an astrocyte marker, glial fibrillary acidic protein in mouse spinal cord. Furthermore, an astroglial toxin, l-α-aminoadipate, significantly attenuated the development of the nociceptive behaviors and ERK phosphorylation by Max. These results suggest that the activation of spinal PAC1-R induces long-lasting nociception through the interaction of neurons and astrocytes

RipsNet: a general architecture for fast and robust estimation of the persistent homology of point clouds

International audienceThe use of topological descriptors in modern machine learning applications, such as persistence diagrams (PDs) arising from Topological Data Analysis (TDA), has shown great potential in various domains. However, their practical use in applications is often hindered by two major limitations: the computational complexity required to compute such descriptors exactly, and their sensitivity to even low-level proportions of outliers. In this work, we propose to bypass these two burdens in a data-driven setting by entrusting the estimation of (vectorization of) PDs built on top of point clouds to a neural network architecture that we call RipsNet. Once trained on a given data set, RipsNet can estimate topological descriptors on test data very efficiently with generalization capacity. Furthermore, we prove that RipsNet is robust to input perturbations in terms of the 1-Wasserstein distance, a major improvement over the standard computation of PDs that only enjoys Hausdorff stability, yielding RipsNet to substantially outperform exactly-computed PDs in noisy settings. We showcase the use of RipsNet on both synthetic and real-world data. Our implementation will be made freely and publicly available as part of the open-source library Gudhi