High coercivity cobalt carbide nanoparticles processed via polyol reaction: A new permanent magnet material

Cobalt carbide nanoparticles were processed using polyol reduction chemistry that offers high product yields in a cost effective single-step process. Particles are shown to be acicular in morphology and typically assembled as clusters with room temperature coercivities greater than 4 kOe and maximum energy products greater than 20 KJ/m3. Consisting of Co3C and Co2C phases, the ratio of phase volume, particle size, and particle morphology all play important roles in determining permanent magnet properties. Further, the acicular particle shape provides an enhancement to the coercivity via dipolar anisotropy energy as well as offering potential for particle alignment in nanocomposite cores. While Curie temperatures are near 510K at temperatures approaching 700 K the carbide powders experience an irreversible dissociation to metallic cobalt and carbon thus limiting operational temperatures to near room temperature.Comment: Total 28 pages, 10 figures, and 1 tabl

Changes in daily mental health service use and mortality at the commencement and lifting of COVID-19 'lockdown' policy in 10 UK sites: a regression discontinuity in time design.

OBJECTIVES: To investigate changes in daily mental health (MH) service use and mortality in response to the introduction and the lifting of the COVID-19 'lockdown' policy in Spring 2020. DESIGN: A regression discontinuity in time (RDiT) analysis of daily service-level activity. SETTING AND PARTICIPANTS: Mental healthcare data were extracted from 10 UK providers. OUTCOME MEASURES: Daily (weekly for one site) deaths from all causes, referrals and discharges, inpatient care (admissions, discharges, caseloads) and community services (face-to-face (f2f)/non-f2f contacts, caseloads): Adult, older adult and child/adolescent mental health; early intervention in psychosis; home treatment teams and liaison/Accident and Emergency (A&E). Data were extracted from 1 Jan 2019 to 31 May 2020 for all sites, supplemented to 31 July 2020 for four sites. Changes around the commencement and lifting of COVID-19 'lockdown' policy (23 March and 10 May, respectively) were estimated using a RDiT design with a difference-in-difference approach generating incidence rate ratios (IRRs), meta-analysed across sites. RESULTS: Pooled estimates for the lockdown transition showed increased daily deaths (IRR 2.31, 95% CI 1.86 to 2.87), reduced referrals (IRR 0.62, 95% CI 0.55 to 0.70) and reduced inpatient admissions (IRR 0.75, 95% CI 0.67 to 0.83) and caseloads (IRR 0.85, 95% CI 0.79 to 0.91) compared with the pre lockdown period. All community services saw shifts from f2f to non-f2f contacts, but varied in caseload changes. Lift of lockdown was associated with reduced deaths (IRR 0.42, 95% CI 0.27 to 0.66), increased referrals (IRR 1.36, 95% CI 1.15 to 1.60) and increased inpatient admissions (IRR 1.21, 95% CI 1.04 to 1.42) and caseloads (IRR 1.06, 95% CI 1.00 to 1.12) compared with the lockdown period. Site-wide activity, inpatient care and community services did not return to pre lockdown levels after lift of lockdown, while number of deaths did. Between-site heterogeneity most often indicated variation in size rather than direction of effect. CONCLUSIONS: MH service delivery underwent sizeable changes during the first national lockdown, with as-yet unknown and unevaluated consequences

On Evaluating MHC-II Binding Peptide Prediction Methods

Choice of one method over another for MHC-II binding peptide prediction is typically based on published reports of their estimated performance on standard benchmark datasets. We show that several standard benchmark datasets of unique peptides used in such studies contain a substantial number of peptides that share a high degree of sequence identity with one or more other peptide sequences in the same dataset. Thus, in a standard cross-validation setup, the test set and the training set are likely to contain sequences that share a high degree of sequence identity with each other, leading to overly optimistic estimates of performance. Hence, to more rigorously assess the relative performance of different prediction methods, we explore the use of similarity-reduced datasets. We introduce three similarity-reduced MHC-II benchmark datasets derived from MHCPEP, MHCBN, and IEDB databases. The results of our comparison of the performance of three MHC-II binding peptide prediction methods estimated using datasets of unique peptides with that obtained using their similarity-reduced counterparts shows that the former can be rather optimistic relative to the performance of the same methods on similarity-reduced counterparts of the same datasets. Furthermore, our results demonstrate that conclusions regarding the superiority of one method over another drawn on the basis of performance estimates obtained using commonly used datasets of unique peptides are often contradicted by the observed performance of the methods on the similarity-reduced versions of the same datasets. These results underscore the importance of using similarity-reduced datasets in rigorously comparing the performance of alternative MHC-II peptide prediction methods

Engineering T cells for cancer therapy

It is generally accepted that the immune system plays an important role in controlling tumour development. However, the interplay between tumour and immune system is complex, as demonstrated by the fact that tumours can successfully establish and develop despite the presence of T cells in tumour. An improved understanding of how tumours evade T-cell surveillance, coupled with technical developments allowing the culture and manipulation of T cells, has driven the exploration of therapeutic strategies based on the adoptive transfer of tumour-specific T cells. The isolation, expansion and re-infusion of large numbers of tumour-specific T cells generated from tumour biopsies has been shown to be feasible. Indeed, impressive clinical responses have been documented in melanoma patients treated with these T cells. These studies and others demonstrate the potential of T cells for the adoptive therapy of cancer. However, the significant technical issues relating to the production of natural tumour-specific T cells suggest that the application of this approach is likely to be limited at the moment. With the advent of retroviral gene transfer technology, it has become possible to efficiently endow T cells with antigen-specific receptors. Using this strategy, it is potentially possible to generate large numbers of tumour reactive T cells rapidly. This review summarises the current gene therapy approaches in relation to the development of adoptive T-cell-based cancer treatments, as these methods now head towards testing in the clinical trial setting

A stochastic individual-based model to explore the role of spatial interactions and antigen recognition in the immune response against solid tumours

FRM is funded by the Engineering and Physical Sciences Research Council (EPSRC).Spatial interactions between cancer and immune cells, as well as the recognition of tumour antigens by cells of the immune system, play a key role in the immune response against solid tumours. The existing mathematical models generally focus only on one of these key aspects. We present here a spatial stochastic individual-based model that explicitly captures antigen expression and recognition. In our model, each cancer cell is characterised by an antigen profile which can change over time due to either epimutations or mutations. The immune response against the cancer cells is initiated by the dendritic cells that recognise the tumour antigens and present them to the cytotoxic T cells. Consequently, T cells become activated against the tumour cells expressing such antigens. Moreover, the differences in movement between inactive and active immune cells are explicitly taken into account by the model. Computational simulations of our model clarify the conditions for the emergence of tumour clearance, dormancy or escape, and allow us to assess the impact of antigenic heterogeneity of cancer cells on the efficacy of immune action. Ultimately, our results highlight the complex interplay between spatial interactions and adaptive mechanisms that underpins the immune response against solid tumours, and suggest how this may be exploited to further develop cancer immunotherapies.PostprintPeer reviewe

The abilities of improved schizophrenia patients to work and live independently in the community: a 10-year long-term outcome study from Mumbai, India

Background: The outcome of schizophrenia has several determinants. Socioecological factors, particularly living conditions, migration, community and culture, not only affect the level of risk but also the outcome. Mega cities around the world show a unique socioecological condition that has several challenges for mental health. The present study reports on the long-term status of patients with schizophrenia in such a mega city: Mumbai, India. Aim This study aims to reveal the long-term outcome of patients suffering from schizophrenia with special reference to clinical symptoms and social functioning. Methods: The cohort for this study was drawn from a 10-year follow-up of first episode schizophrenia. Patients having completed 10 years of consistent treatment after first hospitalisation were assessed on psychopathological and recovery criteria. Clinical as well as social parameters of recovery were evaluated. Descriptive statistics with 95% confidence intervals are provided. Results: Of 200 patients recruited at the beginning of this study, 122 patients (61%) were present in the city of Mumbai at the end of 10-year follow-up study period. Among 122 available patients, 101 patients (50.5%) were included in the assessment at the end of 10-year follow-up study period, 6 patients (3.0%) were excluded from the study due to changed diagnosis, and 15 patients (7.5%) were excluded due to admission into long-term care facilities. This indicates that 107 out of 122 available patients (87.7%) were living in the community with their families. Out of 101 (50.5%) patients assessed at the end of 10 years, 61 patients (30.5%) showed improved recovery on the Clinical Global Impression Scale, 40 patients (20%) revealed no improvement in the recovery, 43 patients (72.9%) were able to live independently, and 24 patients (40%) were able to find employment. Conclusions: With 10 years of treatment, the recovery rate among schizophrenia patients in Mumbai was 30.5%. Among the patients, 87.7% of patients lived in the community, 72.9% of patients lived independently, and 40% of patients obtained employment. However, 60% of patients were unable to return to work, which highlights the need for continued monitoring and support to prevent the deterioration of health in these patients. It is likely that socioecological factors have played a role in this outcome

Triangle network motifs predict complexes by complementing high-error interactomes with structural information

BackgroundA lot of high-throughput studies produce protein-protein interaction networks (PPINs) with many errors and missing information. Even for genome-wide approaches, there is often a low overlap between PPINs produced by different studies. Second-level neighbors separated by two protein-protein interactions (PPIs) were previously used for predicting protein function and finding complexes in high-error PPINs. We retrieve second level neighbors in PPINs, and complement these with structural domain-domain interactions (SDDIs) representing binding evidence on proteins, forming PPI-SDDI-PPI triangles.ResultsWe find low overlap between PPINs, SDDIs and known complexes, all well below 10%. We evaluate the overlap of PPI-SDDI-PPI triangles with known complexes from Munich Information center for Protein Sequences (MIPS). PPI-SDDI-PPI triangles have ~20 times higher overlap with MIPS complexes than using second-level neighbors in PPINs without SDDIs. The biological interpretation for triangles is that a SDDI causes two proteins to be observed with common interaction partners in high-throughput experiments. The relatively few SDDIs overlapping with PPINs are part of highly connected SDDI components, and are more likely to be detected in experimental studies. We demonstrate the utility of PPI-SDDI-PPI triangles by reconstructing myosin-actin processes in the nucleus, cytoplasm, and cytoskeleton, which were not obvious in the original PPIN. Using other complementary datatypes in place of SDDIs to form triangles, such as PubMed co-occurrences or threading information, results in a similar ability to find protein complexes.ConclusionGiven high-error PPINs with missing information, triangles of mixed datatypes are a promising direction for finding protein complexes. Integrating PPINs with SDDIs improves finding complexes. Structural SDDIs partially explain the high functional similarity of second-level neighbors in PPINs. We estimate that relatively little structural information would be sufficient for finding complexes involving most of the proteins and interactions in a typical PPIN