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Intradural spinal cord compression impairs perfusion pressure and is putatively rate-limiting for recovery after traumatic spinal cord injury (tSCI). After cervical tSCI, even minimally improved tissue preservation may help promote neurological recovery. To assess the nature and extent of spinal cord swelling and compression post-acute cervical tSCI, we evaluated several baseline MRI parameters including BASIC score, intramedullary lesion (IML) length, maximal canal compromise (MCC), maximal spinal cord compression (MSCC), extent of cord compression (ECC), maximal swollen anteroposterior diameter adjacent to injury site (Dmax), and maximal cord swelling (MCS) in 169 consecutive patients across 2 centers. In patients with either primarily intradural or combined (MSCC ≤5% or >5%, respectively) cord compression, we examined the predictive value of clinical and imaging admission parameters on American Spinal Injury Association Impairment Scale (AIS) severity and conversion up to 1-year follow-up. 37 (21.9%) patients presented with primarily intradural while 132 (78.1%) had combined cord compression. MSCC, MCS, and Dmax values differed significantly between the two groups (p < 0.0001, < 0.01 and < 0.001, respectively). MSCC was associated with age, MCC and MCS at baseline, while MCS was associated with age, MSCC and Dmax, on multivariable analysis. Logistic regression analysis of areas under receiver operating characteristic curve (AUROC) confirmed ECC (AUC 0.678) and MCS (AUC 0.922) as good and excellent predictors, respectively of AIS-conversion at 1-year for intradural compression participants. Additionally, MCS was significantly more accurate in predicting AIS-conversion in intradural group and the probability of AIS-conversion significantly decreased with each 1% increase in MCS (p = 0.003; OR 0.949), for both compression subtypes. In conclusion, baseline measures of cord swelling predict AIS-conversion likelihood up to 1-year. The deleterious effects of intradural cord compression, either isolated or presenting with extradural compression, may benefit from supplemental decompression strategies in addition to current standard-of-care.

\"Hurry home, honey. I'm in labor.\" This was the message one U.S. serviceman stationed in the Saudi Arabian desert heard from his wife in a personal greeting card. Yes, heard -- not read. The card was one of thousands sent to American military personnel participating in...

The open sharing of genomic data provides an incredibly rich resource for the study of bacterial evolution and function and even anthropogenic activities such as the widespread use of antimicrobials. However, these data consist of genomes assembled with different tools and levels of quality checking, and of large volumes of completely unprocessed raw sequence data. In both cases, considerable computational effort is required before biological questions can be addressed. Here, we assembled and characterised 661,405 bacterial genomes retrieved from the European Nucleotide Archive (ENA) in November of 2018 using a uniform standardised approach. Of these, 311,006 did not previously have an assembly. We produced a searchable COmpact Bit-sliced Signature (COBS) index, facilitating the easy interrogation of the entire dataset for a specific sequence (e.g., gene, mutation, or plasmid). Additional MinHash and pp-sketch indices support genome-wide comparisons and estimations of genomic distance. Combined, this resource will allow data to be easily subset and searched, phylogenetic relationships between genomes to be quickly elucidated, and hypotheses rapidly generated and tested. We believe that this combination of uniform processing and variety of search/filter functionalities will make this a resource of very wide utility. In terms of diversity within the data, a breakdown of the 639,981 high-quality genomes emphasised the uneven species composition of the ENA/public databases, with just 20 of the total 2,336 species making up 90% of the genomes. The overrepresented species tend to be acute/common human pathogens, aligning with research priorities at different levels from individual interests to funding bodies and national and global public health agencies.

The response rate to immune checkpoint inhibitor therapy for non-small-cell lung cancer (NSCLC) is just 20%. To improve this figure, several early phase clinical trials combining novel immunotherapeutics with immune checkpoint blockade have been initiated. Unfortunately, these trials have been designed without a strong foundational knowledge of the immune landscape present in NSCLC. Here, we use a flow cytometry panel capable of measuring 51 immune cell populations to comprehensively identify the immune cell composition and function in NSCLC. The results show that the immune cell composition is fundamentally different in lung adenocarcinoma as compared with lung squamous cell carcinoma, and that neutrophils are the most prevalent immune cell type. Using T-cell receptor-β sequencing and tumour reactivity assays, we predict that tumour reactive T cells are frequently present in NSCLC. These results should help to guide the design of clinical trials and the direction of future research in this area. Tumour immune evasion can involve multiple strategies. Here, the authors characterize the immune populations from clinical specimens of lung cancer in conjunction with TCR-β sequencing and show abundant neutrophils affecting cytotoxic T-cell content and the frequent presence of tumour-specific T-cell clones.

The 8-point Penetration-Aspiration Scale (PAS) was introduced to the field of dysphagia in 1996 and has become the standard method used by both clinicians and researchers to describe and measure the severity of airway invasion during swallowing. In this article, we review the properties of the scale and explore what has been learned over 20 years of use regarding the construct validity, ordinality, intervality, score distribution, and sensitivity of the PAS to change. We propose that a categorical revision of the PAS into four levels of increasing physiological severity would be appropriate. The article concludes with a discussion of common errors made in the statistical analysis of the PAS, proposing that frequency distributions and ordinal logistic regression approaches are most appropriate given the properties of the scale. A hypothetical dataset is included to illustrate both the problems and strengths of different statistical approaches.

Most current evidence on risk factors for hospitalization because of coronavirus disease 2019 (COVID-19) comes from studies using data abstracted primarily from electronic health records, limited to specific populations, or that fail to capture over-the-counter medications and adjust for potential confounding factors. Properly understanding risk factors for hospitalization will help improve clinical management and facilitate targeted prevention messaging and forecasting and prioritization of clinical and public health resource needs. To identify risk factors for hospitalization using patient questionnaires and chart abstraction. We randomly selected 600 of 1,738 laboratory-confirmed Colorado COVID-19 cases with known hospitalization status and illness onset during March 9-31, 2020. In April 2020, we collected demographics, social history, and medications taken in the 30 days before illness onset via telephone questionnaire and collected underlying medical conditions in patient questionnaires and medical record abstraction. Overall, 364 patients participated; 128 were hospitalized and 236 were non-hospitalized. In multivariable analysis, chronic hypoxemic respiratory failure with oxygen requirement (adjusted odds ratio [aOR] 14.64; 95% confidence interval [CI] 1.45-147.93), taking opioids (aOR 8.05; CI 1.16-55.77), metabolic syndrome (aOR 5.71; CI 1.18-27.54), obesity (aOR 3.35; CI 1.58-7.09), age [greater than or equal to]65 years (aOR 3.22; CI 1.20-7.97), hypertension (aOR 3.14; CI 1.47-6.71), arrhythmia (aOR 2.95; CI 1.00-8.68), and male sex (aOR 2.65; CI 1.44-4.88), were significantly associated with hospitalization. We identified patient characteristics, medications, and medical conditions, including some novel ones, associated with hospitalization. These data can be used to inform clinical and public health resource needs.

Many microbial enzymes are metal-dependent, and the microbe must acquire scarce metals from the environment. Microbes that use methane as a carbon source have a copper-dependent enzyme that oxidizes the methane. Peptides known as methanobactins (Mbns) acquire copper by using a pair of ligands comprising a nitrogen-containing ring and an adjacent thioamide. Kenney et al. describe the biosynthetic machinery that adds the copper-binding groups to a precursor peptide. This involves a complex of two homologs: MbnB, a member of a functionally uncharacterized protein family that includes a diiron cluster, and MbnC, which is even less well characterized. The iron cofactor is required for ligand synthesis. MbnB and MbnC homologs are encoded in many genomes, suggesting that they may have roles beyond Mbn biosynthesis. Science , this issue p. 1411 An enzyme complex uses iron and dioxygen to generate copper-binding ligands in the methanobactin family of natural products. Metal homeostasis poses a major challenge to microbes, which must acquire scarce elements for core metabolic processes. Methanobactin, an extensively modified copper-chelating peptide, was one of the earliest natural products shown to enable microbial acquisition of a metal other than iron. We describe the core biosynthetic machinery responsible for the characteristic posttranslational modifications that grant methanobactin its specificity and affinity for copper. A heterodimer comprising MbnB, a DUF692 family iron enzyme, and MbnC, a protein from a previously unknown family, performs a dioxygen-dependent four-electron oxidation of the precursor peptide (MbnA) to install an oxazolone and an adjacent thioamide, the characteristic methanobactin bidentate copper ligands. MbnB and MbnC homologs are encoded together and separately in many bacterial genomes, suggesting functions beyond their roles in methanobactin biosynthesis.

Massively parallel reporter assays (MPRAs) can simultaneously measure the function of thousands of candidate regulatory sequences (CRSs) in a quantitative manner. In this method, CRSs are cloned upstream of a minimal promoter and reporter gene, alongside a unique barcode, and introduced into cells. If the CRS is a functional regulatory element, it will lead to the transcription of the barcode sequence, which is measured via RNA sequencing and normalized for cellular integration via DNA sequencing of the barcode. This technology has been used to test thousands of sequences and their variants for regulatory activity, to decipher the regulatory code and its evolution, and to develop genetic switches. Lentivirus-based MPRA (lentiMPRA) produces ‘in-genome’ readouts and enables the use of this technique in hard-to-transfect cells. Here, we provide a detailed protocol for lentiMPRA, along with a user-friendly Nextflow-based computational pipeline—MPRAflow—for quantifying CRS activity from different MPRA designs. The lentiMPRA protocol takes ~2 months, which includes sequencing turnaround time and data processing with MPRAflow. This protocol describes a lentivirus-based massively parallel reporter assay (lentiMPRA) for quantitative assessment of the activity of gene regulatory elements and an accompanying Nextflow-based computational pipeline for analysis.