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BackgroundPredicting neurological recovery after spinal cord injury (SCI) is challenging. Using topological data analysis, we have previously shown that mean arterial pressure (MAP) during SCI surgery predicts long-term functional recovery in rodent models, motivating the present multicenter study in patients.MethodsIntra-operative monitoring records and neurological outcome data were extracted (n = 118 patients). We built a similarity network of patients from a low-dimensional space embedded using a non-linear algorithm, Isomap, and ensured topological extraction using persistent homology metrics. Confirmatory analysis was conducted through regression methods.ResultsNetwork analysis suggested that time outside of an optimum MAP range (hypotension or hypertension) during surgery was associated with lower likelihood of neurological recovery at hospital discharge. Logistic and LASSO (least absolute shrinkage and selection operator) regression confirmed these findings, revealing an optimal MAP range of 76-[104-117] mmHg associated with neurological recovery.ConclusionsWe show that deviation from this optimal MAP range during SCI surgery predicts lower probability of neurological recovery and suggest new targets for therapeutic intervention.FundingNIH/NINDS: R01NS088475 (ARF); R01NS122888 (ARF); UH3NS106899 (ARF); Department of Veterans Affairs: 1I01RX002245 (ARF), I01RX002787 (ARF); Wings for Life Foundation (ATE, ARF); Craig H. Neilsen Foundation (ARF); and DOD: SC150198 (MSB); SC190233 (MSB).

Primary closure of suborbital skin defects can cause tension along the closure resulting in ectropion. The bicycle wheel analogy is a simple yet effective guide to aid in reducing tension vectors resulting in ectropion.

BACKGROUND AND PURPOSE:Our aim was to use 2D convolutional neural networks for automatic segmentation of the spinal cord and traumatic contusion injury from axial T2-weighted MR imaging in a cohort of patients with acute spinal cord injury. MATERIALS AND METHODS:Forty-seven patients who underwent 3T MR imaging within 24 hours of spinal cord injury were included. We developed an image-analysis pipeline integrating 2D convolutional neural networks for whole spinal cord and intramedullary spinal cord lesion segmentation. Linear mixed modeling was used to compare test segmentation results between our spinal cord injury convolutional neural network (Brain and Spinal Cord Injury Center segmentation) and current state-of-the-art methods. Volumes of segmented lesions were then used in a linear regression analysis to determine associations with motor scores. RESULTS:Compared with manual labeling, the average test set Dice coefficient for the Brain and Spinal Cord Injury Center segmentation model was 0.93 for spinal cord segmentation versus 0.80 for PropSeg and 0.90 for DeepSeg (both components of the Spinal Cord Toolbox). Linear mixed modeling showed a significant difference between Brain and Spinal Cord Injury Center segmentation compared with PropSeg (P < .001) and DeepSeg (P < .05). Brain and Spinal Cord Injury Center segmentation showed significantly better adaptability to damaged areas compared with PropSeg (P < .001) and DeepSeg (P < .02). The contusion injury volumes based on automated segmentation were significantly associated with motor scores at admission (P = .002) and discharge (P = .009). CONCLUSIONS:Brain and Spinal Cord Injury Center segmentation of the spinal cord compares favorably with available segmentation tools in a population with acute spinal cord injury. Volumes of injury derived from automated lesion segmentation with Brain and Spinal Cord Injury Center segmentation correlate with measures of motor impairment in the acute phase. Targeted convolutional neural network training in acute spinal cord injury enhances algorithm performance for this patient population and provides clinically relevant metrics of cord injury.

An itch during surgery can be distracting. Sterile cotton tipped applicators are inexpensive and multipurpose tools that may be used for a variety of simple surgical tasks such as scratching an itch on the surgeon or patient’s face. They may also be used to adjust glasses and turn on surgical lights or the electrocautery machine.

Nine new molecular complexes of the proton sponge 1,8-bis(dimethylamino)naphthalene (DMAN) with substituted benzoic acid co-formers have been engineered with varying component stoichiometries (1:1, 1:2 or 1:3). These complexes are all ionic in nature, following proton transfer between the acid co-former and DMAN; the extracted proton is held by DMAN in all instances in an intramolecular [N-H⋯N]+ hydrogen bond. A number of structural features are common to all complexes and are found to be tunable in a predictable way using systematic acid co-former substitution. These features include charge-assisted hydrogen bonds formed between acid co-formers in hydrogen bonding motifs consistent with complex stoichiometry, and weak hydrogen bonds which facilitate the crystal packing of DMAN and acid co-former components into a regular motif. Possible crystal structure tuning by co-former substitution can aid the rational design of such materials, offering the potential to target solid-state properties that may be influenced by these interactions.

Selective, robust and cost-effective chemical sensors for detecting small volatile-organic compounds (VOCs) have widespread applications in industry, healthcare and environmental monitoring. Here we design a Pt(II) pincer-type material with selective absorptive and emissive responses to methanol and water. The yellow anhydrous form converts reversibly on a subsecond timescale to a red hydrate in the presence of parts-per-thousand levels of atmospheric water vapour. Exposure to methanol induces a similarly-rapid and reversible colour change to a blue methanol solvate. Stable smart coatings on glass demonstrate robust switching over 104 cycles, and flexible microporous polymer membranes incorporating microcrystals of the complex show identical vapochromic behaviour. The rapid vapochromic response can be rationalised from the crystal structure, and in combination with quantum-chemical modelling, we provide a complete microscopic picture of the switching mechanism. We discuss how this multiscale design approach can be used to obtain new compounds with tailored VOC selectivity and spectral responses.

Selective, robust and cost-effective chemical sensors for detecting small volatile-organic compounds (VOCs) have widespread applications in industry, healthcare and environmental monitoring. Here we design a Pt(II) pincer-type material with selective absorptive and emissive responses to methanol and water. The yellow anhydrous form converts reversibly on a subsecond timescale to a red hydrate in the presence of parts-per-thousand levels of atmospheric water vapour. Exposure to methanol induces a similarly-rapid and reversible colour change to a blue methanol solvate. Stable smart coatings on glass demonstrate robust switching over 104 cycles, and flexible microporous polymer membranes incorporating microcrystals of the complex show identical vapochromic behaviour. The rapid vapochromic response can be rationalised from the crystal structure, and in combination with quantum-chemical modelling, we provide a complete microscopic picture of the switching mechanism. We discuss how this multiscale design approach can be used to obtain new compounds with tailored VOC selectivity and spectral responses.