The interaction of molecular oxygen on LaO terminated surfaces of La2NiO4

Rare-earth metal oxides with perovskite-type crystal structures are under consideration for use as air electrode materials for intermediate to high temperature electrochemical device applications. The surface chemistry of these materials plays a critical role in determining the kinetics of oxygen reduction and exchange reactions. Among various perovskite-structured oxides, certain members of the Ruddlesden–Popper series, e.g. La2NiO4, have been identified as significantly active for surface oxygen interactions. However, the challenge remains to be the identification of the structure and composition of active surfaces, as well as the influence of these factors on the mechanisms of surface exchange reactions. In this contribution, the changes in the electronic structure and the energetics of oxygen interactions on the surfaces of La2NiO4 are analysed using first principles calculations in the Density Functional Theory (DFT) formalism. As for the surface chemistry, LaO termination rather than NiO2 termination is presumed due to recent experimental evidence of the surfaces of various perovskite structured oxides after heat treatment in oxidizing environments being transition metal free. Our findings substantiate the fact that the LaO-terminated surface can indeed participate in the formation of surface superoxo species. Detailed charge transfer analyses revealed that it is possible for such a surface to be catalytically active owing to the enhanced electronic configurations on the neighbouring La sites to surface species. In addition, positively charged oxygen vacancies, relative to the crystal lattice, can act as active sites and catalyse the O–O bond cleavage

Translation of tissue-based artificial intelligence into clinical practice: from discovery to adoption.

Digital pathology (DP), or the digitization of pathology images, has transformed oncology research and cancer diagnostics. The application of artificial intelligence (AI) and other forms of machine learning (ML) to these images allows for better interpretation of morphology, improved quantitation of biomarkers, introduction of novel concepts to discovery and diagnostics (such as spatial distribution of cellular elements), and the promise of a new paradigm of cancer biomarkers. The application of AI to tissue analysis can take several conceptual approaches, within the domains of language modelling and image analysis, such as Deep Learning Convolutional Neural Networks, Multiple Instance Learning approaches, or the modelling of risk scores and their application to ML. The use of different approaches solves different problems within pathology workflows, including assistive applications for the detection and grading of tumours, quantification of biomarkers, and the delivery of established and new image-based biomarkers for treatment prediction and prognostic purposes. All these AI formats, applied to digital tissue images, are also beginning to transform our approach to clinical trials. In parallel, the novelty of DP/AI devices and the related computational science pipeline introduces new requirements for manufacturers to build into their design, development, regulatory and post-market processes, which may need to be taken into account when using AI applied to tissues in cancer discovery. Finally, DP/AI represents challenge to the way we accredit new diagnostic tools with clinical applicability, the understanding of which will allow cancer patients to have access to a new generation of complex biomarkers

Exploring the immune microenvironment in small bowel adenocarcinoma using digital image analysis.

BACKGROUND: Small bowel adenocarcinoma (SBA) is a rare malignancy of the small intestine associated with late stage diagnosis and poor survival outcome. High expression of immune cells and immune checkpoint biomarkers especially programmed cell death ligand-1 (PD-L1) have been shown to significantly impact disease progression. We have analysed the expression of a subset of immune cell and immune checkpoint biomarkers in a cohort of SBA patients and assessed their impact on progression-free survival (PFS) and overall survival (OS). METHODS: 25 patient samples in the form of formalin fixed, paraffin embedded (FFPE) tissue were obtained in tissue microarray (TMAs) format. Automated immunohistochemistry (IHC) staining was performed using validated antibodies for CD3, CD4, CD8, CD68, PD-L1, ICOS, IDO1 and LAG3. Slides were scanned digitally and assessed in QuPath, an open source image analysis software, for biomarker density and percentage positivity. Survival analyses were carried out using the Kaplan Meier method. RESULTS: Varying expressions of biomarkers were recorded. High expressions of CD3, CD4 and IDO1 were significant for PFS (p = 0.043, 0.020 and 0.018 respectively). High expression of ICOS was significant for both PFS (p = 0.040) and OS (p = 0.041), while high PD-L1 expression in tumour cells was significant for OS (p = 0.033). High correlation was observed between PD-L1 and IDO1 expressions (Pearson correlation co-efficient = 1) and subsequently high IDO1 expression in tumour cells was found to be significant for PFS (p = 0.006) and OS (p = 0.034). CONCLUSIONS: High levels of immune cells and immune checkpoint proteins have a significant impact on patient survival in SBA. These data could provide an insight into the immunotherapeutic management of patients with SBA

Subcellular Epithelial HMGB1 Expression Is Associated with Colorectal Neoplastic Progression, Male Sex, Mismatch Repair Protein Expression, Lymph Node Positivity, and an 'Immune Cold' Phenotype Associated with Poor Survival.

New treatment targets are needed for colorectal cancer (CRC). We define expression of High Mobility Group Box 1 (HMGB1) protein throughout colorectal neoplastic progression and examine the biological consequences of aberrant expression. HMGB1 is a ubiquitously expressed nuclear protein that shuttles to the cytoplasm under cellular stress. HMGB1 impacts cellular responses, acting as a cytokine when secreted. A total of 846 human tissue samples were retrieved; 6242 immunohistochemically stained sections were reviewed. Subcellular epithelial HMGB1 expression was assessed in a CRC Tissue Microarray (n = 650), normal colonic epithelium (n = 75), adenomatous polyps (n = 52), and CRC polyps (CaP, n = 69). Stromal lymphocyte phenotype was assessed in the CRC microarray and a subgroup of CaP. Normal colonic epithelium has strong nuclear and absent cytoplasmic HMGB1. With progression to CRC, there is an emergence of strong cytoplasmic HMGB1 (p < 0.001), pronounced at the leading cancer edge within CaP (p < 0.001), and reduction in nuclear HMGB1 (p < 0.001). In CRC, absent nuclear HMGB1 is associated with mismatch repair proteins (p = 0.001). Stronger cytoplasmic HMGB1 is associated with lymph node positivity (p < 0.001) and male sex (p = 0.009). Stronger nuclear (p = 0.011) and cytoplasmic (p = 0.002) HMGB1 is associated with greater CD4+ T-cell density, stronger nuclear HMGB1 is associated with greater FOXP3+ (p < 0.001) and ICOS+ (p = 0.018) lymphocyte density, and stronger nuclear HMGB1 is associated with reduced CD8+ T-cell density (p = 0.022). HMGB1 does not directly impact survival but is associated with an 'immune cold' tumour microenvironment which is associated with poor survival (p < 0.001). HMGB1 may represent a new treatment target for CRC

International Veterinary Epilepsy Task Force Consensus Proposal: Outcome of therapeutic interventions in canine and feline epilepsy

Common criteria for the diagnosis of drug resistance and the assessment of outcome are needed urgently as a prerequisite for standardized evaluation and reporting of individual therapeutic responses in canine epilepsy. Thus, we provide a proposal for the definition of drug resistance and partial therapeutic success in canine patients with epilepsy. This consensus statement also suggests a list of factors and aspects of outcome, which should be considered in addition to the impact on seizures. Moreover, these expert recommendations discuss criteria which determine the validity and informative value of a therapeutic trial in an individual patient and also suggest the application of individual outcome criteria. Agreement on common guidelines does not only render a basis for future optimization of individual patient management, but is also a presupposition for the design and implementation of clinical studies with highly standardized inclusion and exclusion criteria. Respective standardization will improve the comparability of findings from different studies and renders an improved basis for multicenter studies. Therefore, this proposal provides an in-depth discussion of the implications of outcome criteria for clinical studies. In particular ethical aspects and the different options for study design and application of individual patient-centered outcome criteria are considered

Cognitive dysfunction in naturally occurring canine idiopathic epilepsy

Globally, epilepsy is a common serious brain disorder. In addition to seizure activity, epilepsy is associated with cognitive impairments including static cognitive impairments present at onset, progressive seizure-induced impairments and co-morbid dementia. Epilepsy occurs naturally in domestic dogs but its impact on canine cognition has yet to be studied, despite canine cognitive dysfunction (CCD) recognised as a spontaneous model of dementia. Here we use data from a psychometrically validated tool, the canine cognitive dysfunction rating (CCDR) scale, to compare cognitive dysfunction in dogs diagnosed with idiopathic epilepsy (IE) with controls while accounting for age. An online cross-sectional study resulted in a sample of 4051 dogs, of which n = 286 had been diagnosed with IE. Four factors were significantly associated with a diagnosis of CCD (above the diagnostic cut-off of CCDR ≥50): (i) epilepsy diagnosis: dogs with epilepsy were at higher risk; (ii) age: older dogs were at higher risk; (iii) weight: lighter dogs (kg) were at higher risk; (iv) training history: dogs with more exposure to training activities were at lower risk. Impairments in memory were most common in dogs with IE, but progression of impairments was not observed compared to controls. A significant interaction between epilepsy and age was identified, with IE dogs exhibiting a higher risk of CCD at a young age, while control dogs followed the expected pattern of low-risk throughout middle age, with risk increasing exponentially in geriatric years. Within the IE sub-population, dogs with a history of cluster seizures and high seizure frequency had higher CCDR scores. The age of onset, nature and progression of cognitive impairment in the current IE dogs appear divergent from those classically seen in CCD. Longitudinal monitoring of cognitive function from seizure onset is required to further characterise these impairments

Encrypted federated learning for secure decentralized collaboration in cancer image analysis.

Artificial intelligence (AI) has a multitude of applications in cancer research and oncology. However, the training of AI systems is impeded by the limited availability of large datasets due to data protection requirements and other regulatory obstacles. Federated and swarm learning represent possible solutions to this problem by collaboratively training AI models while avoiding data transfer. However, in these decentralized methods, weight updates are still transferred to the aggregation server for merging the models. This leaves the possibility for a breach of data privacy, for example by model inversion or membership inference attacks by untrusted servers. Somewhat-homomorphically-encrypted federated learning (SHEFL) is a solution to this problem because only encrypted weights are transferred, and model updates are performed in the encrypted space. Here, we demonstrate the first successful implementation of SHEFL in a range of clinically relevant tasks in cancer image analysis on multicentric datasets in radiology and histopathology. We show that SHEFL enables the training of AI models which outperform locally trained models and perform on par with models which are centrally trained. In the future, SHEFL can enable multiple institutions to co-train AI models without forsaking data governance and without ever transmitting any decryptable data to untrusted servers

Chagas disease: an impediment in achieving the Millennium Development Goals in Latin America

<p>Abstract</p> <p>Background</p> <p>Achieving sustainable economic and social growth through advances in health is crucial in Latin America within the framework of the United Nations Millennium Development Goals.</p> <p>Discussion</p> <p>Health-related Millennium Development Goals need to incorporate a multidimensional approach addressing the specific epidemiologic profile for each region of the globe. In this regard, addressing the cycle of destitution and suffering associated with infection with <it>Trypanosoma cruzi</it>, the causal agent of Chagas disease of American trypanosomiasis, will play a key role to enable the most impoverished populations in Latin America the opportunity to achieve their full potential. Most cases of Chagas disease occur among forgotten populations because these diseases persist exclusively in the poorest and the most marginalized communities in Latin America.</p> <p>Summary</p> <p>Addressing the cycle of destitution and suffering associated with <it>T. cruzi </it>infection will contribute to improve the health of the most impoverished populations in Latin America and will ultimately grant them with the opportunity to achieve their full economic potential.</p

A novel AKT3 mutation in melanoma tumours and cell lines

Recently, a rare activating mutation of AKT1 (E17K) has been reported in breast, ovarian, and colorectal cancers. However, analogous activating mutations in AKT2 or AKT3 have not been identified in any cancer lineage. To determine the prevalence of AKT E17K mutations in melanoma, the most aggressive form of skin cancer, we analysed 137 human melanoma specimens and 65 human melanoma cell lines for the previously described activating mutation of AKT1, and for analogous mutations in AKT2 and AKT3. We identified a single AKT1 E17K mutation. Remarkably, a previously unidentified AKT3 E17K mutation was detected in two melanomas (from one patient) as well as two cell lines. The AKT3 E17K mutation results in activation of AKT when expressed in human melanoma cells. This represents the first report of AKT mutations in melanoma, and the initial identification of an AKT3 mutation in any human cancer lineage. We have also identified the first known human cell lines with naturally occurring AKT E17K mutations