The inflammatory process is a conserved and adaptive biological response to infection or tissue damage. Despite its substantial energy demands, inflammation triggers centrally regulated changes in behavior, commonly referred to as sickness behavior, which includes anorexia and consequent negative energy balance. Although these responses have been extensively modeled through infection or cytokine administration, they remain less explored in a more dynamic spectrum of clinical conditions, such as inflammatory bowel disease (IBD). In this study, we used the dextran sodium sulfate (DSS) model of colitis, which mimics key features of human IBD. We assessed food and water intake, locomotor activity, and body composition over the disease progression. We further assessed neuronal activation and transcriptional changes in metabolic-sensing brain regions at key disease stages. Acute DSS-induced disease progression was associated with metabolic alterations, including anorexia, energy conservation, reduced physical activity, and changes in body mass composition. A positive correlation between disease severity and neuronal activation in the hypothalamus and the caudal brainstem was also found. Transcriptomic analysis revealed changes in hypothalamic gene expression associated with the immune response. Furthermore, targeted colocalization studies identified the activation of hypothalamic hunger-promoting AgRP/NPY-expressing neurons as a neuronal population recruited during colitis, suggesting a role for these neurons in coordinating allostatic metabolic adaptations to intestinal inflammation. This study provides evidence that the DSS model is a clinically relevant, dynamic, and tractable tool for studying the progression of sickness-like behavior in IBD, as well as the underlying neurometabolic adaptations that extend beyond the gut.NEW & NOTEWORTHY By showing that experimental colitis induced by DSS in mice triggers metabolic adaptations and activation of brain regions regulating energy balance, this study expands the model's relevance beyond intestinal inflammation. These findings provide a framework to investigate gut-brain interactions and the neurometabolic components of sickness-like behavior in inflammatory bowel disease.

The function of the neocortex is fundamentally determined by its repeating microcircuit motif, but also by its rich, interregional connectivity. We present a data-driven computational model of the anatomy of non-barrel primary somatosensory cortex of juvenile rat, integrating whole-brain scale data while providing cellular and subcellular specificity. The model consists of 4.2 million morphologically detailed neurons, placed in a digital brain atlas. They are connected by 14.2 billion synapses, comprising local, mid-range and extrinsic connectivity. We delineated the limits of determining connectivity from neuron morphology and placement, finding that it reproduces targeting by Sst+ neurons, but requires additional specificity to reproduce targeting by PV+ and VIP+ interneurons. Globally, connectivity was characterized by local clusters tied together through hub neurons in layer 5, demonstrating how local and interegional connectivity are complicit, inseparable networks. The model is suitable for simulation-based studies, and the model is made openly available to the community.

Animals respond to environmental cues to time phenological events, but the intrinsic mechanism of circannual timing remains elusive. We used transcriptomic sequencing and frequent sampling of multiple hypothalamic nuclei in Djungarian hamsters to examine the neural and molecular architecture of circannual interval timing. Our study identified three distinct phases of transcript changes, with deiodinase type-3 (Dio3) expression activated during the early induction phase. Subsequent work demonstrated that targeted mutation of Dio3 using CRISPR-Cas resulted in a shorter period for circannual interval timing. Hamsters that are non-responsive to short photoperiods and fail to show any winter adaptations do not display changes in Dio3 expression and do not show any change in body mass or pelage. Our work demonstrates that changes in Dio3 induction are essential for setting the period of circannual interval timing.

We aimed to estimate how rheumatology outpatient hospital attendances have changed since the COVID-19 pandemic and determine demographic characteristics associated with observed changes. Using three primary and secondary care electronic health record datasets in England (with the approval of NHS England), Scotland and Wales, we identified people with a diagnosis of RA before 1 April 2019. We determined the proportion of people with rheumatology hospital outpatient appointments each month [April 2019 to December 2022 (Wales and Scotland), November 2023 (England)] and quantified changes using interrupted time-series analysis. We used logistic regression to determine characteristics associated with having fewer appointments compared with 2019. We identified 145 065, 3813 and 13 637 people coded with RA in England, Scotland and Wales, respectively. At the start of the COVID-19 pandemic the number of rheumatology outpatient appointments dropped sharply across all nations. In England and Scotland, the percentage of monthly appointments has continued to decline. In Wales, while there was a gradual recovery, rheumatology services have not returned to pre-pandemic levels. In contrast, the number of appointments for other specialties has recovered in all nations. People with no rheumatology outpatient appointments were more often aged over 80, male and living in rural areas. Ethnic minorities, those living in more deprived and urban areas had fewer appointments after the start of the pandemic compared with 2019. For the first time, we compared healthcare use across three UK nations and found rheumatology outpatient appointments had not recovered to pre-COVID-19 pandemic levels, particularly in Scotland and England.

Tau undergoes fibrillogenesis in a group of neurodegenerative diseases, termed tauopathies. Each tauopathy is characterized by tau fibrils with disease-specific conformations, highlighting the complexity of tau self-assembly. This has led to debate surrounding the precise mechanisms that govern the self-assembly of tau in disease, especially the involvement of disulfide bonding (DSB) between cysteine residues. In this study, we use a truncated form of tau, dGAE, capable of forming filaments identical to those in disease. We reveal the impact of DSB on dGAE assembly and propagation by resolving the global mechanisms that dominate its assembly. We found evidence of surface-mediated secondary nucleation and fragmentation being active in dGAE assembly. The inhibition of DSB during dGAE assembly leads to an enhanced aggregation rate through a reduced lag phase but with no effect on the global assembly mechanisms. We suggest this is due to the formation of a dominant, seed-competent species in the absence of DSB that facilitates elongation and secondary nucleation, resulting in enhanced assembly. In vitro seeding assays reveal the recruitment of endogenous tau in a cell model only when using dGAE species formed under conditions that inhibit DSB. Our results further support the use of the in vitro dGAE tau aggregation model for investigating the mechanism of tau assembly, show the effect of varying conditions on tau assembly, and how these conditions affect the resultant species. Further studies may utilize dGAE and its aggregates to investigate tau seeding, propagation, and to highlight or test potential targets for therapies that reduce the spread of pathological tau throughout the brain.

Two different machine learning architectures - sequential convolutional neural networks (CNN) and parallel inception models were evaluated with respect to their ability to identify nematic liquid crystal variants, including the ferroelectric and the twist-bend nematic phases. Varying levels of model complexity were employed from 1- to 5-layer CNNs, to 1- to 3-block inception models. Various types of augmentations like flip, contrast and brightness were used, together with dropout-layer regularisation. Flip was the only augmentation trialled to yield positive results with an acceptable level of accuracy and error, while the inclusion of dropout regularisation almost exclusively led to lower accuracies. From the systematic investigation it is advised that different variants of the nematic phase can be distinguished to an accuracy better than 0.96-0.98 ± 0.01 by the use of 3-layer CNNs or a model with a single inception block, if flip augmentation is applied. Computational restraints therefore suggest that a sequential CNN is sufficient to characterise phase sequences with four or fewer different phases. Higher accuracies, closer to 100%, can be achieved for extended and class-balanced datasets. In the latter case an inception approach would possibly be beneficial, depending on the size of the dataset, but overfitting needs to be avoided.