To explore the initial stages of amyloid β peptide (Aβ42) deposition on membranes, we have studied the interaction of Aβ42 in the monomeric form with lipid monolayers and with bilayers in either the liquid-disordered or the liquid-ordered (L(o)) state, containing negatively charged phospholipids. Molecular dynamics (MD) simulations of the system have been performed, as well as experimental measurements. For bilayers in the L(o) state, in the absence of the negatively charged lipids, interaction is weak and it cannot be detected by isothermal calorimetry. However, in the presence of phosphatidic acid, or of cardiolipin, interaction is detected by different methods and in all cases interaction is strongest with lower (2.5-5 mol%) than higher (10-20 mol%) proportions of negatively charged phospholipids. Liquid-disordered bilayers consistently allowed a higher Aβ42 binding than L(o) ones. Thioflavin T assays and infrared spectroscopy confirmed a higher proportion of β-sheet formation under conditions when higher peptide binding was measured. The experimental results were supported by MD simulations. We used 100 ns MD to examine interactions between Aβ42 and three different 512 lipid bilayers consisting of palmitoylsphingomyelin, dimyristoyl phosphatidic acid, and cholesterol in three different proportions. MD pictures are different for the low- and high-charge bilayers, in the former case the peptide is bound through many contact points to the bilayer, whereas for the bilayer containing 20 mol% anionic phospholipid only a small fragment of the peptide appears to be bound. The MD results indicate that the binding and fibril formation on the membrane surface depends on the composition of the bilayer, and is the result of a subtle balance of many inter- and intramolecular interactions between the Aβ42 and membrane.

Colorectal cancer is one of the world leading death causes. Fortunately, an early diagnosis allows for effective treatment, increasing the survival rate. Deep learning techniques have shown their utility for increasing the adenoma detection rate at colonoscopy, but a dataset is usually required so the model can automatically learn features that characterize the polyps. In this work, we present the PICCOLO dataset, that comprises 3433 manually annotated images (2131 white-light images 1302 narrow-band images), originated from 76 lesions from 40 patients, which are distributed into training (2203), validation (897) and test (333) sets assuring patient independence between sets. Furthermore, clinical metadata are also provided for each lesion. Four different models, obtained by combining two backbones and two encoder–decoder architectures, are trained with the PICCOLO dataset and other two publicly available datasets for comparison. Results are provided for the test set of each dataset. Models trained with the PICCOLO dataset have a better generalization capacity, as they perform more uniformly along test sets of all datasets, rather than obtaining the best results for its own test set. This dataset is available at the website of the Basque Biobank, so it is expected that it will contribute to the further development of deep learning methods for polyp detection, localisation and classification, which would eventually result in a better and earlier diagnosis of colorectal cancer, hence improving patient outcomes.

Type II diabetes mellitus is associated with the deposition of fibrillar aggregates in pancreatic islets. The major protein component of islet amyloids is the glucomodulatory hormone islet amyloid polypeptide (IAPP). Islet amyloid fibrils are virtually always associated with several biomolecules, including apolipoprotein E, metals, glycosaminoglycans, and various lipids. IAPP amyloidogenesis has been originally perceived as a self-assembly homogeneous process in which the inherent aggregation propensity of the peptide and its local concentration constitute the major driving forces to fibrillization. However, over the last two decades, numerous studies have shown a prominent role of amyloid cofactors in IAPP fibrillogenesis associated with the etiology of type II diabetes. It is increasingly evident that the biochemical microenvironment in which IAPP amyloid formation occurs and the interactions of the polypeptide with various biomolecules not only modulate the rate and extent of aggregation, but could also remodel the amyloidogenesis process as well as the structure, toxicity, and stability of the resulting fibrils.