Diaphragmatic morphological post-mortem findings in critically ill COVID-19 patients: an observational study.

Our study investigates the post-mortem findings of the diaphragm's muscular structural changes in mechanically ventilated COVID-19 patients. Diaphragm samples of the right side from 42 COVID-19 critically ill patients were analyzed and correlated with the type and length of mechanical ventilation (MV), ventilatory parameters, prone positioning, and use of sedative drugs. The mean number of fibers was 550±626. The cross-sectional area was 4120±3280 µm2, while the muscular fraction was 0.607±0.126. The overall population was clustered into two distinct populations (clusters 1 and 2). Cluster 1 showed a lower percentage of slow myosin fiber and higher fast fiber content than cluster 2, 68% versus 82%, p<0.00001, and 29.8% versus 18.8%, p=0.00045 respectively. The median duration of MV was 180 (41-346) hours. In cluster 1, a relationship between assisted ventilation and fast myosin fiber percentage (R2=-0.355, p=0.014) was found. In cluster 2, fast fiber content increased with increasing the length of the controlled MV (R2=0.446, p=0.006). A high grade of fibrosis was reported. Cluster 1 was characterized by fibers' atrophy and cluster 2 by hypertrophy, supposing different effects of ventilation on the diaphragm but without excluding a possible direct viral effect on diaphragmatic fibers.

Secretagogue effect of PDE4 inhibitor apremilast on human salivary gland organoids obtained from primary Sjögren's syndrome patients.

OBJECTIVES: The aim of the study was to culture vital salivary gland organoids obtained through labial or parotid biopsy of primary Sjögren's syndrome (pSS) patients in order to evaluate their morphological and functional features in basal condition and after stimulation with Cystic Fibrosis Transmembrane Conductance Regulator (CFTR) activator forskolin and phosphodiesterase 4 (PDE4) inhibitor apremilast, their in vitro regenerative capacity and the immune-histological resemblance with original tissue. METHODS: Salivary gland tissues from five pSS patients were processed to obtain vital organoids; swelling assay and cell proliferation tests were performed after forskolin and apremilast application. Immunochemistry evaluation on original salivary gland tissue and corresponding organoids was performed, and secretomics analysis was conducted to assess their functional status. REULTS: After application of forskolin and apremilast, we observed organoid swelling after 30 minutes, compatible with a positive functional status and enhancement of saliva production. In 3 cases, apremilast induced organoid proliferation. All cases were positive for cytokeratin 14 (CK14) and most for cytokeratin 5 (CK5). All the cases were positive for amylase; its secretion, and thus functional status of organoids, was confirmed by its high concentration in the culture medium. A focal ductal differentiation was found in some cases, highlighted by epithelial membrane antigen (EMA) positivity. The more differentiated EMA positive areas were negative for the staminal marker CK14, showing a sort of "complementary staining". CONCLUSIONS: Our data highlighted that differentiated cells and vital functional organoids that recapitulate the development of original salivary glands can be obtained from pSS epithelium. For the first time, the direct stimulating effect of PDE4 inhibitor apremilast on pSS human salivary gland organoids is reported, opening new perspectives on targeting oral dryness with drugs that combine secretagogue and immunomodulatory effects.

Optimizing Vision Transformers for Histopathology: Pretraining and Normalization in Breast Cancer Classification.

This paper introduces a self-attention Vision Transformer model specifically developed for classifying breast cancer in histology images. We examine various training strategies and configurations, including pretraining, dimension resizing, data augmentation and color normalization strategies, patch overlap, and patch size configurations, in order to evaluate their impact on the effectiveness of the histology image classification. Additionally, we provide evidence for the increase in effectiveness gathered through geometric and color data augmentation techniques. We primarily utilize the BACH dataset to train and validate our methods and models, but we also test them on two additional datasets, BRACS and AIDPATH, to verify their generalization capabilities. Our model, developed from a transformer pretrained on ImageNet, achieves an accuracy rate of 0.91 on the BACH dataset, 0.74 on the BRACS dataset, and 0.92 on the AIDPATH dataset. Using a model based on the prostate small and prostate medium HistoEncoder models, we achieve accuracy rates of 0.89 and 0.86, respectively. Our results suggest that pretraining on large-scale general datasets like ImageNet is advantageous. We also show the potential benefits of using domain-specific pretraining datasets, such as extensive histopathological image collections as in HistoEncoder, though not yet with clear advantages.

Predictive and prognostic biomarkers in urological tumours.

Advancements in cutting-edge molecular profiling techniques, such as next-generation sequencing and bioinformatic analytic tools, have allowed researchers to examine tumour biology in detail and stratify patients based on factors linked with clinical outcome and response to therapy. This manuscript highlights the most relevant prognostic and predictive biomarkers in kidney, bladder, prostate and testicular cancers with recognised impact in clinical practice. In bladder and prostate cancer, new genetic acquisitions concerning the biology of tumours have modified the therapeutic scenario and led to the approval of target directed therapies, increasing the quality of patient care. Thus, it has become of paramount importance to choose adequate molecular tests, i.e., FGFR screening for urothelial cancer and BRCA1-2 alterations for prostate cancer, to guide the treatment plan for patients. While no tissue or blood-based biomarkers are currently used in routine clinical practice for renal cell carcinoma and testicular cancers, the field is quickly expanding. In kidney tumours, gene expression signatures might be the key to identify patients who will respond better to immunotherapy or anti-angiogenic drugs. In testicular germ cell tumours, the use of microRNA has outperformed conventional serum biomarkers in the diagnosis of primary tumours, prediction of chemoresistance, follow-up monitoring, and relapse prediction.