Quantitative patterns of visual impairment and recovery in children with brain injury.

Brain injury can cause many distinct types of visual impairment in children, but these deficits are difficult to quantify due to co-morbid deficits in communication and cognition. Clinicians must instead rely on low-resolution, subjective judgements of simple reactions to handheld stimuli, which limits treatment potential. We have developed an interactive assessment program called the Visual Ladder, which uses gaze-based responses to intuitive, game-like tasks to address the lack of broad-spectrum quantified data on the visual abilities of children with brain injury. Here, we present detailed metrics on eye movements, field asymmetries, contrast sensitivity, and other critical visual abilities measured longitudinally using the Ladder in hospitalized children with varying types and degrees of brain injury, many of whom were previously considered untestable. Our findings show which abilities are most likely to exhibit recovery and reveal how distinct patterns of task outcomes defined unique diagnostic clusters of visual impairment.

Single-cell decoding of drug induced transcriptomic reprogramming in triple negative breast cancers.

BACKGROUND: The encoding of cell intrinsic drug resistance states in breast cancer reflects the contributions of genomic and non-genomic variations and requires accurate estimation of clonal fitness from co-measurement of transcriptomic and genomic data. Somatic copy number (CN) variation is the dominant mutational mechanism leading to transcriptional variation and notably contributes to platinum chemotherapy resistance cell states. Here, we deploy time series measurements of triple negative breast cancer (TNBC) single-cell transcriptomes, along with co-measured single-cell CN fitness, identifying genomic and transcriptomic mechanisms in drug-associated transcriptional cell states. RESULTS: We present scRNA-seq data (53,641 filtered cells) from serial passaging TNBC patient-derived xenograft (PDX) experiments spanning 2.5 years, matched with genomic single-cell CN data from the same samples. Our findings reveal distinct clonal responses within TNBC tumors exposed to platinum. Clones with high drug fitness undergo clonal sweeps and show subtle transcriptional reversion, while those with weak fitness exhibit dynamic transcription upon drug withdrawal. Pathway analysis highlights convergence on epithelial-mesenchymal transition and cytokine signaling, associated with resistance. Furthermore, pseudotime analysis demonstrates hysteresis in transcriptional reversion, indicating generation of new intermediate transcriptional states upon platinum exposure. CONCLUSIONS: Within a polyclonal tumor, clones with strong genotype-associated fitness under platinum remained fixed, minimizing transcriptional reversion upon drug withdrawal. Conversely, clones with weaker fitness display non-genomic transcriptional plasticity. This suggests CN-associated and CN-independent transcriptional states could both contribute to platinum resistance. The dominance of genomic or non-genomic mechanisms within polyclonal tumors has implications for drug sensitivity, restoration, and re-treatment strategies.

Beyond benchmarking and towards predictive models of dataset-specific single-cell RNA-seq pipeline performance.

BACKGROUND: The advent of single-cell RNA-sequencing (scRNA-seq) has driven significant computational methods development for all steps in the scRNA-seq data analysis pipeline, including filtering, normalization, and clustering. The large number of methods and their resulting parameter combinations has created a combinatorial set of possible pipelines to analyze scRNA-seq data, which leads to the obvious question: which is best? Several benchmarking studies compare methods but frequently find variable performance depending on dataset and pipeline characteristics. Alternatively, the large number of scRNA-seq datasets along with advances in supervised machine learning raise a tantalizing possibility: could the optimal pipeline be predicted for a given dataset? RESULTS: Here, we begin to answer this question by applying 288 scRNA-seq analysis pipelines to 86 datasets and quantifying pipeline success via a range of measures evaluating cluster purity and biological plausibility. We build supervised machine learning models to predict pipeline success given a range of dataset and pipeline characteristics. We find that prediction performance is significantly better than random and that in many cases pipelines predicted to perform well provide clustering outputs similar to expert-annotated cell type labels. We identify characteristics of datasets that correlate with strong prediction performance that could guide when such prediction models may be useful. CONCLUSIONS: Supervised machine learning models have utility for recommending analysis pipelines and therefore the potential to alleviate the burden of choosing from the near-infinite number of possibilities. Different aspects of datasets influence the predictive performance of such models which will further guide users.

Characterizing the impacts of dataset imbalance on single-cell data integration.

Computational methods for integrating single-cell transcriptomic data from multiple samples and conditions do not generally account for imbalances in the cell types measured in different datasets. In this study, we examined how differences in the cell types present, the number of cells per cell type and the cell type proportions across samples affect downstream analyses after integration. The Iniquitate pipeline assesses the robustness of integration results after perturbing the degree of imbalance between datasets. Benchmarking of five state-of-the-art single-cell RNA sequencing integration techniques in 2,600 integration experiments indicates that sample imbalance has substantial impacts on downstream analyses and the biological interpretation of integration results. Imbalance perturbation led to statistically significant variation in unsupervised clustering, cell type classification, differential expression and marker gene annotation, query-to-reference mapping and trajectory inference. We quantified the impacts of imbalance through newly introduced properties-aggregate cell type support and minimum cell type center distance. To better characterize and mitigate impacts of imbalance, we introduce balanced clustering metrics and imbalanced integration guidelines for integration method users.

A quantitative study examining infographic assessment guidelines for undergraduate nursing students with specific learning difficulties (SpLDs).

This research investigates the perceived clarity and usefulness of infographic versus traditional text-based assessment guidelines among undergraduate nursing students with and without specific learning difficulties (SpLDs). Through quantitative analysis, the study reveals that undergraduate nursing students with SpLDs significantly prefer infographics over text-based guidelines, both in terms of clarity and usefulness (p < .001). Interestingly, there were no statistically significant differences in the perceptions of students without SpLDs. These findings suggest that the use of infographics as a tool for presenting assessment guidelines could contribute to more inclusive educational practices. The research further highlights the potential of infographics to not only make complex information more accessible but also to cater to diverse learning needs. As higher education institutions strive to be more inclusive, adapting assessment guidelines to suit the varied learning styles and cognitive needs of all students, particularly those with SpLDs, becomes increasingly important. This paper provides initial evidence to support the adoption of infographic-based assessment guidelines as a step towards achieving this goal.

The impacts of active and self-supervised learning on efficient annotation of single-cell expression data.

A crucial step in the analysis of single-cell data is annotating cells to cell types and states. While a myriad of approaches has been proposed, manual labeling of cells to create training datasets remains tedious and time-consuming. In the field of machine learning, active and self-supervised learning methods have been proposed to improve the performance of a classifier while reducing both annotation time and label budget. However, the benefits of such strategies for single-cell annotation have yet to be evaluated in realistic settings. Here, we perform a comprehensive benchmarking of active and self-supervised labeling strategies across a range of single-cell technologies and cell type annotation algorithms. We quantify the benefits of active learning and self-supervised strategies in the presence of cell type imbalance and variable similarity. We introduce adaptive reweighting, a heuristic procedure tailored to single-cell data-including a marker-aware version-that shows competitive performance with existing approaches. In addition, we demonstrate that having prior knowledge of cell type markers improves annotation accuracy. Finally, we summarize our findings into a set of recommendations for those implementing cell type annotation procedures or platforms. An R package implementing the heuristic approaches introduced in this work may be found at https://github.com/camlab-bioml/leader .

Vibrational spectroscopic profiling of biomolecular interactions between oak powdery mildew and oak leaves.

Oak powdery mildew, caused by the biotrophic fungus Erysiphe alphitoides, is a prevalent disease affecting oak trees, such as English oak (Quercus robur). While mature oak populations are generally less susceptible to this disease, it can endanger young oak seedlings and new leaves on mature trees. Although disruptions of photosynthate and carbohydrate translocation have been observed, accurately detecting and understanding the specific biomolecular interactions between the fungus and the leaves of oak trees is currently lacking. Herein, via hybrid Raman spectroscopy combined with an advanced artificial neural network algorithm, the underpinning biomolecular interactions between biological soft matter, i.e., Quercus robur leaves and Erysiphe alphitoides, are investigated and profiled, generating a spectral library and shedding light on the changes induced by fungal infection and the tree's defence response. The adaxial surfaces of oak leaves are categorised based on either the presence or absence of Erysiphe alphitoides mildew and further distinguishing between covered or not covered infected leaf tissues, yielding three disease classes including healthy controls, non-mildew covered and mildew-covered. By analysing spectral changes between each disease category per tissue type, we identified important biomolecular interactions including disruption of chlorophyll in the non-vein and venule tissues, pathogen-induced degradation of cellulose and pectin and tree-initiated lignification of cell walls in response, amongst others, in lateral vein and mid-vein tissues. Via our developed computational algorithm, the underlying biomolecular differences between classes were identified and allowed accurate and rapid classification of disease with high accuracy of 69.6% for non-vein, 73.5% for venule, 82.1% for lateral vein and 85.6% for mid-vein tissues. Interfacial wetting differences between non-mildew covered and mildew-covered tissue were further analysed on the surfaces of non-vein and venule tissue. The overall results demonstrated the ability of Raman spectroscopy, combined with advanced AI, to act as a powerful and specific tool to probe foliar interactions between forest pathogens and host trees with the simultaneous potential to probe and catalogue molecular interactions between biological soft matter, paving the way for exploring similar relations in broader forest tree-pathogen systems.

Radiomics analysis to predict pulmonary nodule malignancy using machine learning approaches.

BACKGROUND: Low-dose CT screening can reduce lung cancer-related mortality. However, most screen-detected pulmonary abnormalities do not develop into cancer and it often remains challenging to identify malignant nodules, particularly among indeterminate nodules. We aimed to develop and assess prediction models based on radiological features to discriminate between benign and malignant pulmonary lesions detected on a baseline screen. METHODS: Using four international lung cancer screening studies, we extracted 2060 radiomic features for each of 16 797 nodules (513 malignant) among 6865 participants. After filtering out low-quality radiomic features, 642 radiomic and 9 epidemiological features remained for model development. We used cross-validation and grid search to assess three machine learning (ML) models (eXtreme Gradient Boosted Trees, random forest, least absolute shrinkage and selection operator (LASSO)) for their ability to accurately predict risk of malignancy for pulmonary nodules. We report model performance based on the area under the curve (AUC) and calibration metrics in the held-out test set. RESULTS: The LASSO model yielded the best predictive performance in cross-validation and was fit in the full training set based on optimised hyperparameters. Our radiomics model had a test-set AUC of 0.93 (95% CI 0.90 to 0.96) and outperformed the established Pan-Canadian Early Detection of Lung Cancer model (AUC 0.87, 95% CI 0.85 to 0.89) for nodule assessment. Our model performed well among both solid (AUC 0.93, 95% CI 0.89 to 0.97) and subsolid nodules (AUC 0.91, 95% CI 0.85 to 0.95). CONCLUSIONS: We developed highly accurate ML models based on radiomic and epidemiological features from four international lung cancer screening studies that may be suitable for assessing indeterminate screen-detected pulmonary nodules for risk of malignancy.

Designer Benzodiazepines Gidazepam and Desalkygidazepam (Bromonordiazepam): What Do We Know?

Designer benzodiazepines are one of the primary new psychoactive substance (NPS) threats around the world, being found in large numbers in postmortem, driving under the influence of drugs and drug-facilitated sexual assault cases. Even though when compared to many other NPS types, such as opioids and cathinones, there are relatively few designer benzodiazepines being monitored. Recently, a new NPS benzodiazepine has been reported in Europe, the USA and Canada, desalkygidazepam, also known as bromonordiazepam. This substance is a metabolite of the prodrug gidazepam, a drug licensed for use in Ukraine and Russia under the name Gidazepam IC®. In the paper, we review what is currently known about the use, pharmacology and analytical detection of gidazepam, its metabolite desalkygidazepam and their other possible metabolites.

The blood-to-plasma ratio and predicted GABAA-binding affinity of designer benzodiazepines.

PURPOSE: The number of benzodiazepines appearing as new psychoactive substances (NPS) is continually increasing. Information about the pharmacological parameters of these compounds is required to fully understand their potential effects and harms. One parameter that has yet to be described is the blood-to-plasma ratio. Knowledge of the pharmacodynamics of designer benzodiazepines is also important, and the use of quantitative structure-activity relationship (QSAR) modelling provides a fast and inexpensive method of predicting binding affinity to the GABAA receptor. METHODS: In this work, the blood-to-plasma ratios for six designer benzodiazepines (deschloroetizolam, diclazepam, etizolam, meclonazepam, phenazepam, and pyrazolam) were determined. A previously developed QSAR model was used to predict the binding affinity of nine designer benzodiazepines that have recently appeared. RESULTS: Blood-to-plasma values ranged from 0.57 for phenazepam to 1.18 to pyrazolam. Four designer benzodiazepines appearing since 2017 (fluclotizolam, difludiazepam, flualprazolam, and clobromazolam) had predicted binding affinities to the GABAA receptor that were greater than previously predicted binding affinities for other designer benzodiazepines. CONCLUSIONS: This work highlights the diverse nature of the designer benzodiazepines and adds to our understanding of their pharmacology. The greater predicted binding affinities are a potential indication of the increasing potency of designer benzodiazepines appearing on the illicit drugs market.

Loss of apelin blocks the emergence of sprouting angiogenesis in experimental tumors.

Angiogenesis inhibitor drugs targeting vascular endothelial growth factor (VEGF) signaling to the endothelial cell (EC) are used to treat various cancer types. However, primary or secondary resistance to therapy is common. Clinical and pre-clinical studies suggest that alternative pro-angiogenic factors are upregulated after VEGF pathway inhibition. Therefore, identification of alternative pro-angiogenic pathway(s) is critical for the development of more effective anti-angiogenic therapy. Here we study the role of apelin as a pro-angiogenic G-protein-coupled receptor ligand in tumor growth and angiogenesis. We found that loss of apelin in mice delayed the primary tumor growth of Lewis lung carcinoma 1 and B16F10 melanoma when combined with the VEGF receptor tyrosine kinase inhibitor, sunitinib. Targeting apelin in combination with sunitinib markedly reduced the tumor vessel density, and decreased microvessel remodeling. Apelin loss reduced angiogenic sprouting and tip cell marker gene expression in comparison to the sunitinib-alone-treated mice. Single-cell RNA sequencing of tumor EC demonstrated that the loss of apelin prevented EC tip cell differentiation. Thus, apelin is a potent pro-angiogenic cue that supports initiation of tumor neovascularization. Together, our data suggest that targeting apelin may be useful as adjuvant therapy in combination with VEGF signaling inhibition to inhibit the growth of advanced tumors.

Sero-Epidemiology of Pneumocystis Infection among Infants, Children, and Adults in Chile.

Previous serologic surveys show >80% of infants in Chile have anti-Pneumocystis antibodies by 2 years of age, but the seroepidemiology of Pneumocystis infection beyond infancy is unknown. We describe the sero-epidemiology in infants, children, and adults at different locations in Chile. Serum samples were prospectively obtained from 681 healthy adults (age ≥ 17 years) and 690 non-immunocompromised infants/children attending eight blood banks or outpatient clinics (2 in Santiago) in Chile. ELISA was used to measure serum IgM and IgG antibodies to Pneumocystis jirovecii major surface antigen (Msg) constructs MsgA and MsgC1. Serologic responses to Pneumocystis Msg showed a high frequency of reactivity, inferring infection. Among infants/children increasing age and the proportion with detectable IgM responses to MsgA, and IgG responses to MsgA, and MsgC1 were positively associated. Among adults there was almost universal seropositivity to one or more Pneumocystis Msg constructs. In infants and children rates of detectable IgM responses to MsgC1 and MsgA were greater than IgG responses. In Santiago, rates of seropositivity among infants/children were greater in clinics located in a more socio-economically deprived part of the city. In Chile, a serological response to Pneumocystis Msg constructs was common across ages regardless of geographical location and climatic conditions. Observed higher rates of IgM responses than IgG responses is consistent with concept of recent/ongoing exposure to Pneumocystis in children and adults. Higher rates of seropositivity in infants/children residing in more densely populated areas of Santiago infers crowding poses an increased risk of transmission.

The Landscape of COVID-19 Research in the United States: a Cross-sectional Study of Randomized Trials Registered on ClinicalTrials.Gov.

IMPORTANCE: SARS-CoV-2 has infected over 200 million people worldwide, resulting in more than 4 million deaths. Randomized controlled trials are the single best tool to identify effective treatments against this novel pathogen. OBJECTIVE: To describe the characteristics of randomized controlled trials of treatments for COVID-19 in the United States launched in the first 9 months of the pandemic. Design, Setting, and Participants We conducted a cross-sectional study of all completed or actively enrolling randomized, interventional, clinical trials for the treatment of COVID-19 in the United States registered on www.clinicaltrials.gov as of August 10, 2020. We excluded trials of vaccines and other interventions intended to prevent COVID-19. Main Outcomes and Measures We used descriptive statistics to characterize the clinical trials and the statistical power for the available studies. For the late-phase trials (i.e., phase 3 and 2/3 studies), we compared the geographic distribution of the clinical trials with the geographic distribution of people diagnosed with COVID-19. RESULTS: We identified 200 randomized controlled trials of treatments for people with COVID-19. Across all trials, 87 (43.5%) were single-center, 64 (32.0%) were unblinded, and 80 (40.0%) were sponsored by industry. The most common treatments included monoclonal antibodies (N=46 trials), small molecule immunomodulators (N=28), antiviral medications (N=24 trials), and hydroxychloroquine (N=20 trials). Of the 9 trials completed by August 2020, the median sample size was 450 (IQR 67-1113); of the 191 ongoing trials, the median planned sample size was 150 (IQR 60-400). Of the late-phase trials (N=54), the most common primary outcome was a severity scale (N=23, 42.6%), followed by a composite of mortality and ventilation (N=10, 18.5%), and mortality alone (N=6, 11.1%). Among these late-phase trials, all trials of antivirals, monoclonal antibodies, or chloroquine/hydroxychloroquine had a power of less than 25% to detect a 20% relative risk reduction in mortality. Had the individual trials for a given class of treatments instead formed a single trial, the power to detect that same reduction in mortality would have been greater than 98%. There was large variability in access to trials with the highest number of trials per capita in the Northeast and the lowest in the Midwest. CONCLUSIONS AND RELEVANCE: A large number of randomized trials were launched early in the pandemic to evaluate treatments for COVID-19. However, many trials were underpowered for important clinical endpoints and substantial geographic disparities were observed, highlighting the importance of improving national clinical trial infrastructure.

The Basics of Machine Learning.

The Basics of Machine LearningWhen a person is pregnant, a key question is how to establish the "date" of the pregnancy. Classically, the date was based on the last menstrual period (LMP). For the past 3 decades or more, in high-resource countries, this has been done using "hospital-grade" ultrasound machines, with testing performed by trained sonographers. In many parts of the world, neither the machines nor the trained sonographers are accessible. In an article published in NEJM Evidence, Pokaprakarn et al.1 asked whether a low-cost handheld ultrasound device combined with artificial intelligence (AI) could substitute for the expensive machines and trained sonographers.

Automated assignment of cell identity from single-cell multiplexed imaging and proteomic data.

A major challenge in the analysis of highly multiplexed imaging data is the assignment of cells to a priori known cell types. Existing approaches typically solve this by clustering cells followed by manual annotation. However, these often require several subjective choices and cannot explicitly assign cells to an uncharacterized type. To help address these issues we present Astir, a probabilistic model to assign cells to cell types by integrating prior knowledge of marker proteins. Astir uses deep recognition neural networks for fast inference, allowing for annotations at the million-cell scale in the absence of a previously annotated reference. We apply Astir to over 2.4 million cells from suspension and imaging datasets and demonstrate its scalability, robustness to sample composition, and interpretable uncertainty estimates. We envision deployment of Astir either for a first broad cell type assignment or to accurately annotate cells that may serve as biomarkers in multiple disease contexts. A record of this paper's transparent peer review process is included in the supplemental information.

Clonal fitness inferred from time-series modelling of single-cell cancer genomes.

Progress in defining genomic fitness landscapes in cancer, especially those defined by copy number alterations (CNAs), has been impeded by lack of time-series single-cell sampling of polyclonal populations and temporal statistical models1-7. Here we generated 42,000 genomes from multi-year time-series single-cell whole-genome sequencing of breast epithelium and primary triple-negative breast cancer (TNBC) patient-derived xenografts (PDXs), revealing the nature of CNA-defined clonal fitness dynamics induced by TP53 mutation and cisplatin chemotherapy. Using a new Wright-Fisher population genetics model8,9 to infer clonal fitness, we found that TP53 mutation alters the fitness landscape, reproducibly distributing fitness over a larger number of clones associated with distinct CNAs. Furthermore, in TNBC PDX models with mutated TP53, inferred fitness coefficients from CNA-based genotypes accurately forecast experimentally enforced clonal competition dynamics. Drug treatment in three long-term serially passaged TNBC PDXs resulted in cisplatin-resistant clones emerging from low-fitness phylogenetic lineages in the untreated setting. Conversely, high-fitness clones from treatment-naive controls were eradicated, signalling an inversion of the fitness landscape. Finally, upon release of drug, selection pressure dynamics were reversed, indicating a fitness cost of treatment resistance. Together, our findings define clonal fitness linked to both CNA and therapeutic resistance in polyclonal tumours.

Modelling hereditary diffuse gastric cancer initiation using transgenic mouse-derived gastric organoids and single-cell sequencing.

Hereditary diffuse gastric cancer (HDGC) is a cancer syndrome caused by germline variants in CDH1, the gene encoding the cell-cell adhesion molecule E-cadherin. Loss of E-cadherin in cancer is associated with cellular dedifferentiation and poor prognosis, but the mechanisms through which CDH1 loss initiates HDGC are not known. Using single-cell RNA sequencing, we explored the transcriptional landscape of a murine organoid model of HDGC to characterize the impact of CDH1 loss in early tumourigenesis. Progenitor populations of stratified squamous and simple columnar epithelium, characteristic of the mouse stomach, showed lineage-specific transcriptional programs. Cdh1 inactivation resulted in shifts along the squamous differentiation trajectory associated with aberrant expression of genes central to gastrointestinal epithelial differentiation. Cytokeratin 7 (CK7), encoded by the differentiation-dependent gene Krt7, was a specific marker for early neoplastic lesions in CDH1 carriers. Our findings suggest that deregulation of developmental transcriptional programs may precede malignancy in HDGC. © 2021 The Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Quantitative phosphoproteomic analysis reveals involvement of PD-1 in multiple T cell functions.

Programmed cell death protein 1 (PD-1) is a critical inhibitory receptor that limits excessive T cell responses. Cancer cells have evolved to evade these immunoregulatory mechanisms by upregulating PD-1 ligands and preventing T cell-mediated anti-tumor responses. Consequently, therapeutic blockade of PD-1 enhances T cell-mediated anti-tumor immunity, but many patients do not respond and a significant proportion develop inflammatory toxicities. To improve anti-cancer therapy, it is critical to reveal the mechanisms by which PD-1 regulates T cell responses. We performed global quantitative phosphoproteomic interrogation of PD-1 signaling in T cells. By complementing our analysis with functional validation assays, we show that PD-1 targets tyrosine phosphosites that mediate proximal T cell receptor signaling, cytoskeletal organization, and immune synapse formation. PD-1 ligation also led to differential phosphorylation of serine and threonine sites within proteins regulating T cell activation, gene expression, and protein translation. In silico predictions revealed that kinase/substrate relationships engaged downstream of PD-1 ligation. These insights uncover the phosphoproteomic landscape of PD-1-triggered pathways and reveal novel PD-1 substrates that modulate diverse T cell functions and may serve as future therapeutic targets. These data are a useful resource in the design of future PD-1-targeting therapeutic approaches.