Thermal annealing behaviour and gel to crystal transition of a low molecular weight hydrogelator

The thermal annealing behaviour of an electrolyte-triggered calixarene hydrogelator is found to depend strongly on the specific metal chloride used. While the lithium chloride gel showed typical gel-sol transitions as a function of temperature, the magnesium chloride gel was found to repeatedly strengthen with heat-cool cycles. Structural investigations using small-angle neutron scattering, and scanning probe microscopy, suggest that the annealing behaviour is associated with a change in morphology of the fibrous structures supporting the gel. On prolonged standing at room temperature, the magnesium chloride gel underwent a gel-crystal transition, with the collapsing gel accompanied by the deposition of crystals of a magnesium complex of the proline-functionalised calix[4]arene gelator

Identification of the Chromophores in Prussian blue

Prussian blue was the world's first synthetic dye. Its structural, optical and magnetic properties have led to many applications in technology and medicine, and provide paradigms for understanding coordination polymers, framework materials and mixed-valence compounds. The intense red absorption of Prussian blue that characterises chemical and physical properties critical to many of these applications is now shown to arise from localised intervalence charge transfer transitions within two chromophoric variants (ligand isomers) of an idealised "dimer" fragment {(NC)5FeII}(mu-CN){FeIII(NC)3(H2O)2}. This fragment is only available in modern interpretations of the material's crystal structure, with the traditional motif {(NC)5FeII}(mu-CN){FeIII(NC)5} shown not to facilitate visible absorption. Essential to the analysis is the demonstration, obtained independently using absorption and magnetic circular dichroism spectroscopies, that spectra of Prussian blues are strongly influenced by particle size and (subsequent) light scattering. These interpretations are guided and supported by density functional theory calculations (CAM-B3LYP), supplemented by coupled cluster and Bethe-Salpeter spectral simulations, as well as electron paramagnetic resonance spectroscopy of Prussian blue and a model molecular dimeric ion [Fe2(CN)11]6-

Sea-ice-related halogen enrichment at Law Dome, coastal East Antarctica

The Law Dome site is ideal for the evaluation of sea ice proxies due to its location near to the Antarctic coast, regular and high accumulation throughout the year, an absence of surface melting or remobilization, and minimal multiyear sea ice. We present records of bromine and iodine concentrations and their enrichment beyond seawater compositions and compare these to satellite observations of first-year sea ice area in the 90–130° E sector of the Wilkes coast. Our findings support the results of previous studies of sea ice variability from Law Dome, indicating that Wilkes coast sea ice area is currently at its lowest level since the start of the 20th century. From the Law Dome DSS1213 firn core, 26 years of monthly deposition data indicate that the period of peak bromine enrichment is during austral spring–summer, from November to February. Results from a traverse along the lee (western) side of Law Dome show low levels of sodium and bromine deposition, with the greatest fluxes in the vicinity of the Law Dome summit. Finally, multidecadal variability in iodine enrichment appears well correlated to bromine enrichment, suggesting a common source of variability that may be related to the Interdecadal Pacific Oscillation (IPO)

Molecular characterization of the uncultivatable hemotropic bacterium Mycoplasma haemofelis

Mycoplasma haemofelis is a pathogenic feline hemoplasma. Despite its importance, little is known about its metabolic pathways or mechanism of pathogenicity due to it being uncultivatable. The recently sequenced M. haemofelis str. Langford 1 genome was analysed and compared to those of other available hemoplasma genomes

Degenerative encephalopathy in Nova Scotia Duck Tolling Retrievers presenting with a rapid eye movement sleep behavior disorder

BACKGROUND: Neurodegenerative diseases are a heterogeneous group of disorders characterized by loss of neurons and are commonly associated with a genetic mutation. HYPOTHESIS/OBJECTIVES: To characterize the clinical and histopathological features of a novel degenerative neurological disease affecting the brain of young adult Nova Scotia Duck Tolling Retrievers (NSDTRs). ANIMALS: Nine, young adult, related NSDTRs were evaluated for neurological dysfunction and rapid eye movement sleep behavior disorder. METHODS: Case series review. RESULTS: Clinical signs of neurological dysfunction began between 2 months and 5 years of age and were progressive in nature. They were characterized by episodes of marked movements during sleep, increased anxiety, noise phobia, and gait abnormalities. Magnetic resonance imaging documented symmetrical, progressively increasing, T2‐weighted image intensity, predominantly within the caudate nuclei, consistent with necrosis secondary to gray matter degeneration. Abnormalities were not detected on clinicopathological analysis of blood and cerebrospinal fluid, infectious disease screening or urine metabolite screening in most cases. Postmortem examination of brain tissue identified symmetrical malacia of the caudate nuclei and axonal dystrophy within the brainstem and spinal cord. Genealogical analysis supports an autosomal recessive mode of inheritance. CONCLUSIONS AND CLINICAL IMPORTANCE: A degenerative encephalopathy was identified in young adult NSDTRs consistent with a hereditary disease. The prognosis is guarded due to the progressive nature of the disease, which is minimally responsive to empirical treatment

Accurate brain-age models for routine clinical MRI examinations

Convolutional neural networks (CNN) can accurately predict chronological age in healthy individuals from structural MRI brain scans. Potentially, these models could be applied during routine clinical examinations to detect deviations from healthy ageing, including early-stage neurodegeneration. This could have important implications for patient care, drug development, and optimising MRI data collection. However, existing brain-age models are typically optimised for scans which are not part of routine examinations (e.g., volumetric T1-weighted scans), generalise poorly (e.g., to data from different scanner vendors and hospitals etc.), or rely on computationally expensive pre-processing steps which limit real-time clinical utility. Here, we sought to develop a brain-age framework suitable for use during routine clinical head MRI examinations. Using a deep learning-based neuroradiology report classifier, we generated a dataset of 23,302 'radiologically normal for age' head MRI examinations from two large UK hospitals for model training and testing (age range = 18-95 years), and demonstrate fast (&lt; 5 seconds), accurate (mean absolute error [MAE] &lt; 4 years) age prediction from clinical-grade, minimally processed axial T2-weighted and axial diffusion-weighted scans, with generalisability between hospitals and scanner vendors (Δ MAE &lt; 1 year). The clinical relevance of these brain-age predictions was tested using 228 patients whose MRIs were reported independently by neuroradiologists as showing atrophy 'excessive for age'. These patients had systematically higher brain-predicted age than chronological age (mean predicted age difference = +5.89 years, 'radiologically normal for age' mean predicted age difference = +0.05 years, p &lt; 0.0001). Our brain-age framework demonstrates feasibility for use as a screening tool during routine hospital examinations to automatically detect older-appearing brains in real-time, with relevance for clinical decision-making and optimising patient pathways.</p

Automated triaging of head MRI examinations using convolutional neural networks

The growing demand for head magnetic resonance imaging (MRI) examinations, along with a global shortage of radiologists, has led to an increase in the time taken to report head MRI scans around the world. For many neurological conditions, this delay can result in increased morbidity and mortality. An automated triaging tool could reduce reporting times for abnormal examinations by identifying abnormalities at the time of imaging and prioritizing the reporting of these scans. In this work, we present a convolutional neural network for detecting clinically-relevant abnormalities in $\text{T}_2$-weighted head MRI scans. Using a validated neuroradiology report classifier, we generated a labelled dataset of 43,754 scans from two large UK hospitals for model training, and demonstrate accurate classification (area under the receiver operating curve (AUC) = 0.943) on a test set of 800 scans labelled by a team of neuroradiologists. Importantly, when trained on scans from only a single hospital the model generalized to scans from the other hospital ($\Delta$AUC $\leq$ 0.02). A simulation study demonstrated that our model would reduce the mean reporting time for abnormal examinations from 28 days to 14 days and from 9 days to 5 days at the two hospitals, demonstrating feasibility for use in a clinical triage environment.Comment: Accepted as an oral presentation at Medical Imaging with Deep Learning (MIDL) 202

Optimising brain age estimation through transfer learning:A suite of pre-trained foundation models for improved performance and generalisability in a clinical setting

Estimated age from brain MRI data has emerged as a promising biomarker of neurological health. However, the absence of large, diverse, and clinically representative training datasets, along with the complexity of managing heterogeneous MRI data, presents significant barriers to the development of accurate and generalisable models appropriate for clinical use. Here, we present a deep learning framework trained on routine clinical data (N up to 18,890, age range 18–96 years). We trained five separate models for accurate brain age prediction (all with mean absolute error ≤4.0 years, R2 ≥.86) across five different MRI sequences (T2-weighted, T2-FLAIR, T1-weighted, diffusion-weighted, and gradient-recalled echo T2*-weighted). Our trained models offer dual functionality. First, they have the potential to be directly employed on clinical data. Second, they can be used as foundation models for further refinement to accommodate a range of other MRI sequences (and therefore a range of clinical scenarios which employ such sequences). This adaptation process, enabled by transfer learning, proved effective in our study across a range of MRI sequences and scan orientations, including those which differed considerably from the original training datasets. Crucially, our findings suggest that this approach remains viable even with limited data availability (as low as N = 25 for fine-tuning), thus broadening the application of brain age estimation to more diverse clinical contexts and patient populations. By making these models publicly available, we aim to provide the scientific community with a versatile toolkit, promoting further research in brain age prediction and related areas.</p

Labelling imaging datasets on the basis of neuroradiology reports: a validation study

Natural language processing (NLP) shows promise as a means to automate the labelling of hospital-scale neuroradiology magnetic resonance imaging (MRI) datasets for computer vision applications. To date, however, there has been no thorough investigation into the validity of this approach, including determining the accuracy of report labels compared to image labels as well as examining the performance of non-specialist labellers. In this work, we draw on the experience of a team of neuroradiologists who labelled over 5000 MRI neuroradiology reports as part of a project to build a dedicated deep learning-based neuroradiology report classifier. We show that, in our experience, assigning binary labels (i.e. normal vs abnormal) to images from reports alone is highly accurate. In contrast to the binary labels, however, the accuracy of more granular labelling is dependent on the category, and we highlight reasons for this discrepancy. We also show that downstream model performance is reduced when labelling of training reports is performed by a non-specialist. To allow other researchers to accelerate their research, we make our refined abnormality definitions and labelling rules available, as well as our easy-to-use radiology report labelling app which helps streamline this process