Altered Mental Status at the Extreme: Behavioral Evaluation of Disorders of Consciousness.

Disorders of consciousness represent altered mental status at its most severe, comprising a continuum between coma, the vegetative state/unresponsive wakefulness syndrome, the minimally conscious state, and emergence from the minimally conscious state. Patients often transition between these levels throughout their recovery, and determining a patient's current level can be challenging, particularly in the acute care setting. Although healthcare providers have classically relied on a bedside neurological exam or the Glasgow Coma Scale to aid with assessment of consciousness, studies have identified multiple limitations of doing so. Neurobehavioral assessment measures, such as the Coma Recovery Scale-Revised, have been developed to address these shortcomings. Each behavioral metric has strengths as well as weaknesses when applied in the acute care setting. In this review, we appraise common assessment approaches, outline alternative measures for fine-tuning these assessments in the acute care setting, and highlight strategies for implementing these practices in an interdisciplinary manner.

scTab: Scaling cross-tissue single-cell annotation models.

Identifying cellular identities is a key use case in single-cell transcriptomics. While machine learning has been leveraged to automate cell annotation predictions for some time, there has been little progress in scaling neural networks to large data sets and in constructing models that generalize well across diverse tissues. Here, we propose scTab, an automated cell type prediction model specific to tabular data, and train it using a novel data augmentation scheme across a large corpus of single-cell RNA-seq observations (22.2 million cells). In this context, we show that cross-tissue annotation requires nonlinear models and that the performance of scTab scales both in terms of training dataset size and model size. Additionally, we show that the proposed data augmentation schema improves model generalization. In summary, we introduce a de novo cell type prediction model for single-cell RNA-seq data that can be trained across a large-scale collection of curated datasets and demonstrate the benefits of using deep learning methods in this paradigm.

Coma Prognostication After Acute Brain Injury: A Review.

Importance: Among the most impactful neurologic assessments is that of neuroprognostication, defined here as the prediction of neurologic recovery from disorders of consciousness caused by severe, acute brain injury. Across a range of brain injury etiologies, these determinations often dictate whether life-sustaining treatment is continued or withdrawn; thus, they have major implications for morbidity, mortality, and health care costs. Neuroprognostication relies on a diverse array of tests, including behavioral, radiologic, physiological, and serologic markers, that evaluate the brain's functional and structural integrity. Observations: Prognostic markers, such as the neurologic examination, electroencephalography, and conventional computed tomography and magnetic resonance imaging (MRI), have been foundational in assessing a patient's current level of consciousness and capacity for recovery. Emerging techniques, such as functional MRI, diffusion MRI, and advanced forms of electroencephalography, provide new ways of evaluating the brain, leading to evolving schemes for characterizing neurologic function and novel methods for predicting recovery. Conclusions and Relevance: Neuroprognostic markers are rapidly evolving as new ways of assessing the brain's structural and functional integrity after brain injury are discovered. Many of these techniques remain in development, and further research is needed to optimize their prognostic utility. However, even as such efforts are underway, a series of promising findings coupled with the imperfect predictive value of conventional prognostic markers and the high stakes of these assessments have prompted clinical guidelines to endorse emerging techniques for neuroprognostication. Thus, clinicians have been thrust into an uncertain predicament in which emerging techniques are not yet perfected but too promising to ignore. This review illustrates the current, and likely future, landscapes of prognostic markers. No matter how much prognostic markers evolve and improve, these assessments must be approached with humility and individualized to reflect each patient's values.

Photo- and Cobalt-Catalyzed Synthesis of Heterocycles via Cycloisomerization of Unactivated Olefins.

We report a general, intramolecular cycloisomerization of unactivated olefins with pendant nucleophiles. The reaction proceeds under mild conditions and tolerates ethers, esters, protected amines, acetals, pyrazoles, carbamates, and arenes. It is amenable to N-, O-, as well as C-nucleophiles, yielding a number of different heterocycles including, but not limited to, pyrrolidines, piperidines, oxazolidinones, and lactones. Use of both a benzothiazinoquinoxaline as organophotocatalyst and a Co-salen catalyst obviates the need for stoichiometric oxidant or reductant. We showcase the utility of the protocol in late-stage drug diversification and synthesis of several small natural products.

Disclosing Results of Tests for Covert Consciousness: A Framework for Ethical Translation.

The advent of neurotechnologies including advanced functional magnetic resonance imaging and electroencephalography to detect states of awareness not detectable by traditional bedside neurobehavioral techniques (i.e., covert consciousness) promises to transform neuroscience research and clinical practice for patients with brain injury. As these interventions progress from research tools into actionable, guideline-endorsed clinical tests, ethical guidance for clinicians on how to responsibly communicate the sensitive results they yield is crucial yet remains underdeveloped. Drawing on insights from empirical and theoretical neuroethics research and our clinical experience with advanced neurotechnologies to detect consciousness in behaviorally unresponsive patients, we critically evaluate ethical promises and perils associated with disclosing the results of clinical covert consciousness assessments and describe a semistructured approach to responsible data sharing to mitigate potential risks.

Swallowing dysfunctions in patients with disorders of consciousness: Evidence from neuroimaging data, assessment, and management.

Following severe brain injuries, a subset of patients may remain in an altered state of consciousness; most of these patients require artificial feeding. Currently, a functional oral phase and the presence of exclusive oral feeding may constitute signs of consciousness. Additionally, the presence of pharyngo-laryngeal secretions, saliva aspiration, cough reflex and tracheostomy are related to the level of consciousness. However, the link between swallowing and consciousness is yet to be fully understood. The primary aim of this review is to establish a comprehensive overview of the relationship between an individual's conscious behaviour and swallowing (reflexive and voluntary). Previous studies of brain activation during volitional and non-volitional swallowing tasks in healthy subjects are also reviewed. We demonstrate that the areas activated by voluntary swallowing tasks (primary sensorimotor, cingulate, insula, premotor, supplementary motor, cerebellum, and operculum) are not specific to deglutitive function but are shared with other motor tasks and brain networks involved in consciousness. This review also outlines suitable assessment and treatment methods for dysphagic patients with disorders of consciousness. Finally, we propose that markers of swallowing could contribute to the development of novel diagnostic guidelines for patients with disorders of consciousness.

Modeling fragment counts improves single-cell ATAC-seq analysis.

Single-cell ATAC sequencing coverage in regulatory regions is typically binarized as an indicator of open chromatin. Here we show that binarization is an unnecessary step that neither improves goodness of fit, clustering, cell type identification nor batch integration. Fragment counts, but not read counts, should instead be modeled, which preserves quantitative regulatory information. These results have immediate implications for single-cell ATAC sequencing analysis.

Neuroimaging in Disorders of Consciousness and Recovery.

There is a clinical need for more accurate diagnosis and prognostication in patients with disorders of consciousness (DoC). There are several neuroimaging modalities that enable detailed, quantitative assessment of structural and functional brain injury, with demonstrated diagnostic and prognostic value. Additionally, longitudinal neuroimaging studies have hinted at quantifiable structural and functional neuroimaging biomarkers of recovery, with potential implications for the management of DoC.

Programs, Origins, and Niches of Immunomodulatory Myeloid Cells in Gliomas.

Gliomas are incurable malignancies notable for an immunosuppressive microenvironment with abundant myeloid cells whose immunomodulatory properties remain poorly defined. Here, utilizing scRNA-seq data for 183,062 myeloid cells from 85 human tumors, we discover that nearly all glioma-associated myeloid cells express at least one of four immunomodulatory activity programs: Scavenger Immunosuppressive, C1Q Immunosuppressive, CXCR4 Inflammatory, and IL1B Inflammatory. All four programs are present in IDH1 mutant and wild-type gliomas and are expressed in macrophages, monocytes, and microglia whether of blood or resident myeloid cell origins. Integrating our scRNA-seq data with mitochondrial DNA-based lineage tracing, spatial transcriptomics, and organoid explant systems that model peripheral monocyte infiltration, we show that these programs are driven by microenvironmental cues and therapies rather than myeloid cell type, origin, or mutation status. The C1Q Immunosuppressive program is driven by routinely administered dexamethasone. The Scavenger Immunosuppressive program includes ligands with established roles in T-cell suppression, is induced in hypoxic regions, and is associated with immunotherapy resistance. Both immunosuppressive programs are less prevalent in lower-grade gliomas, which are instead enriched for the CXCR4 Inflammatory program. Our study provides a framework to understand immunomodulatory myeloid cells in glioma, and a foundation to develop more effective immunotherapies.

Scaling cross-tissue single-cell annotation models.

Identifying cellular identities (both novel and well-studied) is one of the key use cases in single-cell transcriptomics. While supervised machine learning has been leveraged to automate cell annotation predictions for some time, there has been relatively little progress both in scaling neural networks to large data sets and in constructing models that generalize well across diverse tissues and biological contexts up to whole organisms. Here, we propose scTab, an automated, feature-attention-based cell type prediction model specific to tabular data, and train it using a novel data augmentation scheme across a large corpus of single-cell RNA-seq observations (22.2 million human cells in total). In addition, scTab leverages deep ensembles for uncertainty quantification. Moreover, we account for ontological relationships between labels in the model evaluation to accommodate for differences in annotation granularity across datasets. On this large-scale corpus, we show that cross-tissue annotation requires nonlinear models and that the performance of scTab scales in terms of training dataset size as well as model size - demonstrating the advantage of scTab over current state-of-the-art linear models in this context. Additionally, we show that the proposed data augmentation schema improves model generalization. In summary, we introduce a de novo cell type prediction model for single-cell RNA-seq data that can be trained across a large-scale collection of curated datasets from a diverse selection of human tissues and demonstrate the benefits of using deep learning methods in this paradigm. Our codebase, training data, and model checkpoints are publicly available at https://github.com/theislab/scTab to further enable rigorous benchmarks of foundation models for single-cell RNA-seq data.

Cobalt-Catalyzed Aerobic Aminocyclization of Unsaturated Amides for the Synthesis of Functionalized γ- and δ-Lactams.

We report the cobalt-catalyzed aminocyclization of unsaturated N-acyl sulfonamides in the presence of oxygen to provide γ- and δ-lactam aldehydes. Use of an optically active cobalt catalyst resulted in the formation of enantiomerically enriched γ-and δ-lactam alcohols. The γ-lactam aldehydes and alcohols obtained were elaborated into useful building blocks.

Organophotocatalytic carbo-heterofunctionalization of unactivated olefins with pendant nucleophiles.

We report the difunctionalization of unactivated, terminal olefins through intermolecular addition of α-bromoketones, -esters, and -nitriles followed by formation of 4- to 6-membered heterocycles with pendant nucleophiles. The reaction can be conducted with alcohols, acids, and sulfonamides as nucleophiles furnishing products bearing 1,4 functional group relationships that offer various handles for further manipulation. Salient features of the transformations are the use of 0.5 mol% of a benzothiazinoquinoxaline organophotoredox catalyst and their robustness with respect to air and moisture. Mechanistic investigations are carried out and a catalytic cycle for the reaction is proposed.

Intermolecular Organophotocatalytic Cyclopropanation of Unactivated Olefins.

Intermolecular cyclopropanation of mono-, di-, and trisubstituted olefins with α-bromo-ß-ketoesters and α-bromomalonates under organophotocatalysis is reported. The reaction displays broad functional group tolerance, including substrates bearing acids, alcohols, halides, ethers, ketones, nitriles, esters, amides, carbamates, silanes, stannanes, boronic esters, as well as arenes, and furnishes highly substituted cyclopropanes. The transformation may be performed in the presence of air and moisture with 0.5 mol % of a benzothiazinoquinoxaline as organophotocatalyst. Mechanistic investigations, involving Stern-Volmer quenching, quantum yield determination, and deuteration experiments, are carried out, and a catalytic cycle for the transformation is discussed.

Epinephrine autoinjectors: individualizing device and dosage to optimize anaphylaxis management in the community setting.

Background: Anaphylaxis is the most severe manifestation of a systemic allergic reaction, and, in the community setting, the immediate administration of an epinephrine autoinjector (EAI) can be life-saving. Physicians are tasked with selecting the most appropriate EAI for each individual and counseling patients and/or their caregivers to maximize the likelihood of successful deployment of the EAI. Objective: To offer an evidence-based expert clinical perspective on how physicians might best tailor EAI selection to their patients with anaphylaxis. Methods: A group of eight adult and pediatric allergists with expertise in anaphylaxis management reviewed and assessed the published data and guidelines on anaphylaxis management and EAI device selection. Results: Personalized EAI selection is influenced by intrinsic individual factors, extrinsic factors such as the properties of the individual EAI (e.g., dose, needle length, overall design) as well as cost and coverage. The number and the variety of EAIs available have expanded in most jurisdictions in recent years, which provide a greater diversity of options to meet the characteristics and needs of patients with anaphylaxis. Conclusion: There currently are no EAIs with customizable dose and needle length. Although precise personalization of each patient's EAI remains an optimistic future aspiration, careful consideration of all variables when prescribing EAIs can support optimal management of anaphylaxis.

RNA-based drug discovery for spinal muscular atrophy: a story of small molecules and antisense oligonucleotides.

INTRODUCTION: Spinal Muscular Atrophy (SMA), the second most prevalent autosomal genetic disease affecting infants, is caused by the lack of SMN1, which encodes a neuron functioning vital protein, SMN. Improving exon 7 splicing in the paralogous gene SMN2, also coding for SMN protein, increases protein production efficiency from SMN2 to overcome the genetic deficit in SMN1. Several molecular mechanisms have been investigated to improve SMN2 functional splicing. AREAS COVERED: This manuscript will cover two of the three mechanistically distinct available treatment options for SMA, both targeting the SMN2 splicing mechanism. The first therapeutic, nusinersen (Spinraza®, 2017), is an antisense oligonucleotide (ASO) targeting the splicing inhibitory sequence in the intron downstream of exon 7 from SMN2, thus increasing exon 7 inclusion. The second drug is a small molecule, risdiplam (Evrysdi®, 2021), that enhances the binding of splice factors and also promotes exon 7 inclusion. Both therapies, albeit through different mechanisms, increase full-length SMN protein expression. EXPERT OPINION: Nusinersen and risdiplam have directly helped SMA patients and families, but they also herald a sea change in drug development for genetic diseases. This piece aims to draw parallels between both development histories; this may help chart the course for future targeted agents.

Modeling intercellular communication in tissues using spatial graphs of cells.

Models of intercellular communication in tissues are based on molecular profiles of dissociated cells, are limited to receptor-ligand signaling and ignore spatial proximity in situ. We present node-centric expression modeling, a method based on graph neural networks that estimates the effects of niche composition on gene expression in an unbiased manner from spatial molecular profiling data. We recover signatures of molecular processes known to underlie cell communication.

The biomechanical signature of loss of consciousness: computational modelling of elite athlete head injuries.

Sports related head injuries can cause transient neurological events including loss of consciousness and dystonic posturing. However, it is unknown why head impacts that appear similar produce distinct neurological effects. The biomechanical effect of impacts can be estimated using computational models of strain within the brain. Here, we investigate the strain and strain rates produced by professional American football impacts that led to loss of consciousness, posturing or no neurological signs. We reviewed 1280 National Football League American football games and selected cases where the team's medical personnel made a diagnosis of concussion. Videos were then analysed for signs of neurological events. We identified 20 head impacts that showed clear video signs of loss of consciousness and 21 showing clear abnormal posturing. Forty-one control impacts were selected where there was no observable evidence of neurological signs, resulting in 82 videos of impacts for analysis. Video analysis was used to guide physical reconstructions of these impacts, allowing us to estimate the impact kinematics. These were then used as input to a detailed 3D high-fidelity finite element model of brain injury biomechanics to estimate strain and strain rate within the brain. We tested the hypotheses that impacts producing loss of consciousness would be associated with the highest biomechanical forces, that loss of consciousness would be associated with high forces in brainstem nuclei involved in arousal and that dystonic posturing would be associated with high forces in motor regions. Impacts leading to loss of consciousness compared to controls produced higher head acceleration (linear acceleration; 81.5 g ± 39.8 versus 47.9 ± 21.4; P = 0.004, rotational acceleration; 5.9 krad/s2 ± 2.4 versus 3.5 ± 1.6; P < 0.001) and in voxel-wise analysis produced larger brain deformation in many brain regions, including parts of the brainstem and cerebellum. Dystonic posturing was also associated with higher deformation compared to controls, with brain deformation observed in cortical regions that included the motor cortex. Loss of consciousness was specifically associated with higher strain rates in brainstem regions implicated in maintenance of consciousness, including following correction for the overall severity of impact. These included brainstem nuclei including the locus coeruleus, dorsal raphé and parabrachial complex. The results show that in head impacts producing loss of consciousness, brain deformation is disproportionately seen in brainstem regions containing nuclei involved in arousal, suggesting that head impacts produce loss of consciousness through a biomechanical effect on key brainstem nuclei involved in the maintenance of consciousness.

Protecting isolated reptile populations outside their main area of distribution: a predictive model of the Dice snake, Natrixtessellata, distribution in the Czech Republic.

Marginal populations of animals are highly susceptible to environmental pressures associated with climatic changes. Understanding their distribution and ecological requirements is, thus, essential for the development of efficient conservation strategies. The dice snake, Natrixtessellata, is listed as critically endangered in the Czech Republic. In certain regions (Bohemia and Silesia), its populations are located beyond the northern border of the continuous range of the species, while the south Moravian populations are connected to it. Based on the statewide database of the Czech Nature Conservation Agency, we created a predictive model and determined key factors influencing the species distribution. The most relevant factors were: watercourses and bodies, average annual temperatures, altitude, slope inclination and precipitation seasonality. The model fits the presence records well and is applicable in both theory and practice of the species conservation - for example, focusing faunistic research to certain areas, critical analysis of controversial presence reports and as an input for species management in the form of repatriation and introduction.