Construction and Validation of a Novel Ferroptosis-Related Prognostic Model for Acute Myeloid Leukemia

Acute myeloid leukemia (AML) is a clonal malignant proliferative blood disorder with a poor prognosis. Ferroptosis, a novel form of programmed cell death, holds great promise for oncology treatment, and has been demonstrated to interfere with the development of various diseases. A range of genes are involved in regulating ferroptosis and can serve as markers of it. Nevertheless, the prognostic significance of these genes in AML remains poorly understood. Transcriptomic and clinical data for AML patients were acquired from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO). Univariate Cox analysis was performed to identify ferroptosis-related genes with prognostic value, and the least absolute shrinkage and selection operator (LASSO) algorithm and stepwise multivariate Cox regression analysis were utilized to optimize gene selection from the TCGA cohort (132 samples) for model construction. Tumor samples from the GEO database (136 samples and 104 samples) were used as validation groups to estimate the predictive performance of the risk model. Finally, an eight-gene prognostic signature (including CHAC1, CISD1, DPP4, GPX4, AIFM2, SQLE, PGD, and ACSF2) was identified for the prediction of survival probability and was used to stratify AML patients into high- and low-risk groups. Survival analysis illustrated significantly prolonged overall survival and lower mortality in the low-risk group. The area under the receiver operating characteristic curve demonstrated good results for the training set (1-year: 0.846, 2-years: 0.826, and 3-years: 0.837), which verified the accuracy of the model for predicting patient survival. Independent prognostic analysis indicated that the model could be used as a prognostic factor (p ≤ 0.001). Functional enrichment analyses revealed underlying mechanisms and notable differences in the immune status of the two risk groups. In brief, we conducted and validated a novel ferroptosis-related prognostic model for outcome prediction and risk stratification in AML, with great potential to guide individualized treatment strategies in the future.

Adaptations and plastic phenotypic responses of marine animals to the environmental challenges of the high intertidal zone

The high intertidal zone is home to an incredible variety of marine animals, as it offers an escape from low intertidal/subtidal predation and competition, among other advantages. However, this area of the shore also comes with many tide-driven and emersion-associated environmental stressors, such as desiccation, high temperatures and freezing stress, hypoxia, salinity fluctuations, nitrogenous waste accumulation, ultraviolet (UV) radiation, wave and ice disturbance, and hydrogen sulphide (H2S) toxicity. This review explores the diversity of evolutionary adaptations and plastic phenotypic responses that high intertidal animals use to cope with these challenges. Examples are provided of behavioural, morphological, physiological and biochemical adaptations/responses, along with some of the underlying molecular mechanisms that have been elucidated to date. Adaptations of many different worms, anemones, molluscs, crustaceans and fishes are highlighted. Many adaptations and mechanisms of plasticity are universal among animal phyla, and some are multifunctional (serve more than one function) or provide tolerance to multiple stressors (i.e., ‘cross-tolerance’). High intertidal animals have received considerable attention by scientists, given their accessibility and that they can provide valuable insights in the transition from a marine to a terrestrial lifestyle. Nevertheless, further research is needed to understand the adaptations/responses of these animals more thoroughly, and the future holds great promise for accomplishing this with recent advances in epigenetics, transcriptomics, protein biochemistry and other molecular tools.

COVID-19 vaccine hesitancy in the UK: a longitudinal household cross-sectional study

Background The approved COVID-19 vaccines have shown great promise in reducing disease transmission and severity of outcomes. However, the success of the COVID-19 vaccine rollout is dependent on public acceptance and willingness to be vaccinated. In this study, we aim to examine how the attitude towards public sector officials and the government impact vaccine willingness. The secondary aim is to understand the impact of ethnicity on vaccine-willingness after we explicitly account for trust in public institutions. Methods This cross-sectional study used data from a UK population based longitudinal household survey (Understanding Society COVID-19 study, Understanding Society: the UK Household Longitudinal Study) between April 2020-January 2021. Data from 22,421 participants in Waves 6 and 7 of the study were included after excluding missing data. Demographic details in addition to previous survey responses relating to public sector/governmental trust were included as covariates in the main analysis. A logit model was produced to describe the association between public sector/governmental mistrust and the willingness for vaccination with interaction terms included to account for ethnicity/socio-economic status. Results In support of existing literature, we identified those from BAME groups were more likely to be unwilling to take the COVID-19 vaccine. We found that positive opinions towards public sector officials (OR 2.680: 95% CI 1.888 – 3.805) and the UK government (OR 3.400; 95% CI 2.454—4.712) led to substantive increase in vaccine willingness. Most notably we identified this effect to vary across ethnicity and socio-economic status with those from South Asian background (OR 4.513; 95% CI 1.012—20.123) and possessing a negative attitude towards public officials and the government being the most unwilling to be vaccinated. Conclusions These findings suggests that trust in public sector officials play a key factor in the low vaccination rates particularly seen in at-risk groups. Given the additional morbidity/mortality risk posed by COVID-19 to those from lower socio-economic or ethnic minority backgrounds, there needs to be urgent public health action to review how to tailor health promotion advice given to these groups and examine methods to improve trust in public sector officials and the government.

Fully distributed rainfall-runoff modeling using spatial-temporal graph neural network

Recent studies using latest deep learning algorithms such as LSTM (Long Short-Term Memory) have shown great promise in time-series modeling. There are many studies focusing on the watershed-scale rainfall-runoff modeling or streamflow forecasting, often considering a single watershed with limited generalization capabilities. To improve the model performance, several studies explored an integrated approach by decomposing a large watershed into multiple sub-watersheds with semi-distributed structure. In this study, we propose an innovative physics-informed fully-distributed rainfall-runoff model, NRM-Graph (Neural Runoff Model-Graph), using Graph Neural Networks (GNN) to make full use of spatial information including the flow direction and geographic data. Specifically, we applied a time-series model on each grid cell for its runoff production. The output of each grid cell is then aggregated by a GNN as the final runoff at the watershed outlet. The case study shows that our GNN based model successfully represents the spatial information in predictions. NRM-Graph network has shown less over-fitting and a significant improvement on the model performance compared to the baselines with spatial information. Our research further confirms the importance of spatially distributed hydrological information in rainfall-runoff modeling using deep learning, and we encourage researchers to incorporate more domain knowledge in modeling.

Probabilistic modeling of ambient noise in single-cell omics data

Droplet-based single-cell omics, including single-cell RNA sequencing (scRNAseq), single cell CRISPR perturbations (e.g., CROP-seq) and single-cell protein and transcriptomic profiling (e.g., CITE-seq) hold great promise for comprehensive cell profiling and genetic screening at the single cell resolution, yet these technologies suffer from substantial noise, among which ambient signals present in the cell suspension may be the predominant source. Current efforts to address this issue are highly specific to a certain technology, while a universal model to describe the noise across these technologies may reveal this common source thereby improving the denoising accuracy. To this end, we explicitly examined these unexpected signals and observed a predictable pattern in multiple datasets across different technologies. Based on the finding, we developed single cell Ambient Remover (scAR) which uses probabilistic deep learning to deconvolute the observed signals into native and ambient composition. scAR provides an efficient and universal solution to count denoising for multiple types of single-cell omics data, including single cell CRISPR screens, CITE-seq and scRNAseq. It will facilitate the application of single-cell omics technologies.

All-fiber source and sorter for multimode correlated photons

Photons occupying multiple spatial modes hold a great promise for implementing high-dimensional quantum communication. We use spontaneous four-wave mixing to generate multimode photon pairs in a few-mode fiber. We show the photons are correlated in the fiber mode basis using an all-fiber mode sorter. Our demonstration offers an essential building block for realizing high-dimensional quantum protocols based on standard, commercially available fibers, in an all-fiber configuration.

Online Pilot Grocery Intervention among Rural and Urban Residents Aimed to Improve Purchasing Habits

Online grocery shopping has the potential to improve access to food, particularly among low-income households located in urban food deserts and rural communities. The primary aim of this pilot intervention was to test whether a three-armed online grocery trial improved fruit and vegetable (F&V) purchases. Rural and urban adults across seven counties in Kentucky, Maryland, and North Carolina were recruited to participate in an 8-week intervention in fall 2021. A total of 184 adults were enrolled into the following groups: (1) brick-and-mortar “BM” (control participants only received reminders to submit weekly grocery shopping receipts); (2) online-only with no support “O” (participants received weekly reminders to grocery shop online and to submit itemized receipts); and (3) online shopping with intervention nudges “O+I” (participants received nudges three times per week to grocery shop online, meal ideas, recipes, Facebook group support, and weekly reminders to shop online and to submit itemized receipts). On average, reported food spending on F/V by the O+I participants was USD 6.84 more compared to the BM arm. Online shopping with behavioral nudges and nutrition information shows great promise for helping customers in diverse locations to navigate the increasing presence of online grocery shopping platforms and to improve F&V purchases.

Affordances, Architecture and the Action Possibilities of Learning Environments: A Critical Review of the Literature and Future Directions

This paper critically reviews the body of literature on affordances relating to the design and inhabitation of school buildings. Focusing on the influence of learning spaces on pedagogical practices, we argue that links between affordances, architecture and the action possibilities of school-based environments have largely been overlooked and that such links hold great promise for better aligning space and pedagogy—especially amidst changing expectations of what effective teaching and learning ‘looks like’. Emerging innovative learning environments (ILEs) are designed to enable a wider pedagogical repertoire than traditional classrooms. In order to transcend stereotypical understandings about how the physical environment in schools may afford teaching and learning activities, it is becoming increasingly recognised that both design and practice reconceptualisation is required for affordances of new learning environments to be effectively actualised in support of contemporary education. With a focus on the environmental perceptions of architects, educators and learners, we believe affordance theory offers a useful framework for thinking about the design and use of learning spaces. We argue that Gibson’s affordance theory should be more commonly applied to help situate conversations between designers and users about how physical learning environments are conceived, perceived and actioned for effective teaching and learning.

Titanium alloy composited with dual-cytokine releasing polysaccharide hydrogel to enhance osseointegration via osteogenic and macrophage polarization signaling pathways

Titanium alloy has been widely used in orthopedic surgeries as bone defect filling. However, the regeneration of high-quality new bones is limited due to the pro-inflammatory microenvironment around implants, resulting in a high occurrence rate of implant loosening or failure in osteological therapy. In this study, extracellular matrix (ECM)-mimetic polysaccharide hydrogel co-delivering BMP-2 and IL-4 was composited with 3D printed titanium alloy to promote the osseointegration and regulate macrophage response to create a pro-healing microenvironment in bone defect. Notably, it is discovered from the bioinformatics data that IL-4 and BMP-2 could affect each other through multiple signal pathways to achieve a synergistic effect towards osteogenesis. The composite scaffold significantly promoted the osteoblast differentiation and proliferation of human bone marrow mesenchyme stem cells (hBMSCs). The repair of large-scale femur defect in rat indicated that the dual-cytokine-delivered composite scaffold could manipulate a lower inflammatory level in situ by polarizing macrophages to M2 phenotype, resulting in superior efficacy of mature new bone regeneration over the treatment of native titanium alloy or that with an individual cytokine. Collectively, this work highlights the importance of M2-type macrophages-enriched immune-environment in bone healing. The biomimetic hydrogel-metal implant composite is a versatile and advanced scaffold for accelerating in vivo bone regeneration, holding great promise in treating orthopedic diseases.

Immune infiltration and a ferroptosis-related gene signature for predicting the prognosis of patients with cholangiocarcinoma

Background Cholangiocarcinoma (CHOL) is a digestive tract tumor with high malignancy and poor prognosis and is extremely challenging to treat. At present, induced cell death holds great promise in tumor therapy. Ferroptosis is a recently proposed pattern of programmed cell death, and numerous studies have shown that it is intimately involved in tumors. However, the roles of differentially expressed ferroptosis-related genes (DEFRGs) in CHOL have not been investigated. Methods Our study was based on the The Cancer Genome Atlas (TCGA) database, DEFRGs were obtained to construct a prognostic riskScore model of CHOL by univariate and multivariate Cox regression analyses. Subsequently, the model was evaluated by nomogram construction, survival analysis, receiver operating characteristic (ROC) analysis and exploration of the immune microenvironment, and the mRNA and protein expression levels of each gene in the model were validated by Gene Expression Omnibus (GEO) database and quantitative real-time PCR (qRT-PCR). Results We screened four DEFRGs from the TCGA database to construct a prognostic model. The construction of a nomogram confirmed the predictive value of the model for overall survival (OS), and it was confirmed to have high diagnostic value by ROC analysis. The GSEA results suggested that these genes were mainly enriched in ferroptosis- and metabolism-related pathways. Finally, our experimental results validated the expression levels of the four DEFRGs, which were almost consistent with our bioinformatics results. Conclusion Our study found that the prognostic model showed extremely high diagnostic and prognostic value and could predict the possibility of immunotherapy, thus providing a new direction for individualized treatment of patients with CHOL.