Lung Ultrasound Efficacy in Monitoring Post-SARS-CoV-2 Pneumonia and Inflammatory Biomarkers in Pediatric Patients.

: Recognizing the crucial gaps in our understanding of pediatric pneumonia post-SARS-CoV-2 infection, this study aimed to assess the relationship between Pediatric Pneumonia Ultrasound Scores (PedPne) and inflammatory biomarkers. The primary objective of this study is to evaluate the predictive value of PedPne in comparison with inflammatory biomarkers (IL-6 and dNLR) for the development of pneumonia in pediatric patients following SARS-CoV-2 infection. : This longitudinal observational study collected data from pediatric patients diagnosed with pneumonia after an acute SARS-CoV2 infection.

Unmet needs in EGFR exon 20 insertion mutations in Central and Eastern Europe: reimbursement, diagnostic procedures, and treatment availability.

Lung cancer remains the leading cause of cancer-related deaths in Europe, with non-small cell lung cancer (NSCLC) accounting for approximately 85% of cases. NSCLC is a heterogeneous disease encompassing various oncogenic alterations. Among them, EGFR exon 20 insertion mutations, constituting 0.

The Unmet Diagnostic and Treatment Needs in Large Cell Neuroendocrine Carcinoma of the Lung.

Large cell neuroendocrine carcinoma of the lung (LCNEC) is currently classified as a rare lung cancer subtype, but given the high incidence of lung cancer, the overall number of cases is considerable. The pathologic diagnosis of LCNEC is mainly based on the microscopic appearance of the tumor cells, the mitotic rate, the amount of intra-tumoral necrosis, and the presence of positive neuroendocrine markers identified by immunohistochemistry. Recently, a subdivision into two main categories was proposed based on mutation signatures involving the RB1, TP53, KRAS, and STK11/LKB1 genes, into SCLC-like (small cell lung cancer-like) and NSCLC-like (non-small cell lung cancer-like) LCNEC.

Deep Layer Aggregation Architectures for Photorealistic Universal Style Transfer.

This paper introduces a deep learning approach to photorealistic universal style transfer that extends the PhotoNet network architecture by adding extra feature-aggregation modules. Given a pair of images representing the content and the reference of style, we augment the state-of-the-art solution mentioned above with deeper aggregation, to better fuse content and style information across the decoding layers. As opposed to the more flexible implementation of PhotoNet (i.