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Sporadic Creutzfeldt–Jakob disease (sCJD) is a transmissible brain proteinopathy. Five main clinicopathological subtypes (sCJD-MM(V)1, -MM(V)2C, -MV2K, -VV1, and -VV2) are currently distinguished. Histopathological evidence suggests that the localisation of prion aggregates and spongiform lesions varies among subtypes. Establishing whether there is an initial site with detectable imaging abnormalities (epicentre) and an order of lesion propagation would be informative for disease early diagnosis, patient staging, management and recruitment in clinical trials. Diffusion magnetic resonance imaging (MRI) is the most-used and most-sensitive test to detect spongiform degeneration. This study was designed to identify, in vivo and for the first time, subtype-dependent epicentre and lesion propagation in the brain using diffusion-weighted images (DWI), in the largest known cross-sectional dataset of autopsy-proven subjects with sCJD. We estimate lesion propagation by cross-sectional DWI using event-based modelling, a well-established data-driven technique. DWI abnormalities of 594 autopsy-diagnosed subjects (448 patients with sCJD) were scored in 12 brain regions by 1 neuroradiologist blind to the diagnosis. We used the event-based model to reconstruct sequential orderings of lesion propagation in each of five pure subtypes. Follow-up data from 151 patients validated the estimated sequences. Results showed that epicentre and ordering of lesion propagation are subtype specific. The two most common subtypes (-MM1 and -VV2) showed opposite ordering of DWI abnormality appearance: from the neocortex to subcortical regions, and vice versa, respectively. The precuneus was the most likely epicentre also in -MM2 and -VV1 although at variance with -MM1, abnormal signal was also detected early in cingulate and insular cortices. The caudal-rostral sequence of lesion propagation that characterises -VV2 was replicated in -MV2K. Combined, these data-driven models provide unprecedented dynamic insights into subtype-specific epicentre at onset and propagation of the pathologic process, which may also enhance early diagnosis and enable disease staging in sCJD.

Pain recognition is fundamental for safeguarding animal welfare. Facial expressions have been investigated in several species and grimace scales have been developed as pain assessment tool in many species including horses (HGS) and mice (MGS). This study is intended to progress the validation of grimace scales, by proposing a statistical approach to identify a classifier that can estimate the pain status of the animal based on Facial Action Units (FAUs) included in HGS and MGS. To achieve this aim, through a validity study, the relation between FAUs included in HGS and MGS and the real pain condition was investigated. A specific statistical approach (Cumulative Link Mixed Model, Inter-rater reliability, Multiple Correspondence Analysis, Linear Discriminant Analysis and Support Vector Machines) was applied to two datasets. Our results confirm the reliability of both scales and show that individual FAU scores of HGS and MGS are related to the pain state of the animal. Finally, we identified the optimal weights of the FAU scores that can be used to best classify animals in pain with an accuracy greater than 70%. For the first time, this study describes a statistical approach to develop a classifier, based on HGS and MGS, for estimating the pain status of animals. The classifier proposed is the starting point to develop a computer-based image analysis for the automatic recognition of pain in horses and mice.

•The literature lacks consensus on specific white matter tract alterations in Tourette Syndrome.•This study aims to investigate the microstructural integrity of the Frontal Aslant Tract in patients with Tourette Syndrome using Neurite Orientation Dispersion and Density Imaging (NODDI).•Our results show reduced Fractional Anisotropy and Neurite Density, as well as increased Mean Diffusivity, in the bilateral Frontal Aslant Tract of patients compared to healthy controls.•Moreover, this study suggests that diffusion metrics are mutually correlated: FA and ND are directly proportional, and both are inversely proportional to MD and ODI. Tourette syndrome (TS) is a neurodevelopmental disorder characterized by motor and phonic tics, affecting up to 1% of the adult population. While its etiology is unknown, increasing evidence highlights the key role of brain connectivity changes. Given its involvement in motor and speech inhibition, the Frontal Aslant Tract (FAT), a white matter tract connecting the posterior Superior and Inferior Frontal Gyri, may present structural alterations in individuals with TS. To investigate this hypothesis, a prospective case-control study was conducted from 2020 to 2023, enrolling 11 consecutive right-handed adults diagnosed with TS at a single referral center, alongside 12 age- and sex-matched healthy controls. We examined microstructural alterations of the FAT using metrics derived from two diffusion MRI techniques: Neurite Orientation Dispersion and Density Imaging (NODDI) and Constrained Spherical Deconvolution (CSD). We also examined correlations between diffusion metrics and clinical scores, as well as relationships among the diffusion metrics within both the TS and control groups. After False Discovery Rate corrections, patients’ bilateral FAT showed reduced Fractional Anisotropy (FA) and Neurite Density (ND), and higher Mean Diffusivity (MD), indicating a more isotropic water diffusion and a decreased density in axons and dendrites. No significant correlations between diffusion metrics and clinical measures were found. FA and ND were positively correlated, and they also showed negative correlations with MD and Orientation Dispersion Index (ODI). These results strongly indicate that patients with TS may have reduced integrity of the bilateral FAT, which could be considered a potential target of stimulation techniques in TS treatment. Data and Code Availability Statement: Raw Data will be made available upon a reasonable request to the corresponding author. [Display omitted]

The methylation of the O6-methylguanine-DNA methyltransferase (MGMT) promoter is a molecular marker associated with a better response to chemotherapy in patients with glioblastoma (GB). Standard pre-operative magnetic resonance imaging (MRI) analysis is not adequate to detect MGMT promoter methylation. This study aims to evaluate whether the radiomic features extracted from multiple tumor subregions using multiparametric MRI can predict MGMT promoter methylation status in GB patients. This retrospective single-institution study included a cohort of 277 GB patients whose 3D post-contrast T1-weighted images and 3D fluid-attenuated inversion recovery (FLAIR) images were acquired using two MRI scanners. Three separate regions of interest (ROIs) showing tumor enhancement, necrosis, and FLAIR hyperintensities were manually segmented for each patient. Two machine learning algorithms (support vector machine (SVM) and random forest) were built for MGMT promoter methylation prediction from a training cohort (196 patients) and tested on a separate validation cohort (81 patients), based on a set of automatically selected radiomic features, with and without demographic variables (i.e., patients’ age and sex). In the training set, SVM based on the selected radiomic features of the three separate ROIs achieved the best performances, with an average of 83.0% (standard deviation: 5.7%) for accuracy and 0.894 (0.056) for the area under the curve (AUC) computed through cross-validation. In the test set, all classification performances dropped: the best was obtained by SVM based on the selected features extracted from the whole tumor lesion constructed by merging the three ROIs, with 64.2% (95% confidence interval: 52.8–74.6%) accuracy and 0.572 (0.439–0.705) for AUC. The performances did not change when the patients’ age and sex were included with the radiomic features into the models. Our study confirms the presence of a subtle association between imaging characteristics and MGMT promoter methylation status. However, further verification of the strength of this association is needed, as the low diagnostic performance obtained in this validation cohort is not sufficiently robust to allow clinically meaningful predictions.

Reliable recognition of pain is difficult in ferrets as many currently available parameters are non-specific, inconsistent and/or impractical. Grimace scales have successfully been applied to assess pain in different animal species and might also be applicable to ferrets. To compose a Ferret Grimace Scale (FGS), we studied the facial musculature of ferrets and compared lateral photographs of 19 ferret faces at six time points before and after intraperitoneal telemetry probe implantation. We identified the Action Units (AUs) orbital tightening, nose bulging, cheek bulging, ear changes and whisker retraction as potential indicators of pain in ferrets. To evaluate whether these AUs could reliably be used to identify photographs taken before and after surgery, the photographs were scored 0, 1 or 2 (not, moderately or obviously present) by 11 observers that were blinded to the treatment and timing of the photographs. All AU-scores assigned to the photographs taken five hours after surgery were significantly higher compared to their time-matched baseline scores. Further analysis using the weights that were obtained using a Linear Discriminant Analysis revealed that scoring orbital tightening alone was sufficient to make this distinction with high sensitivity, specificity and accuracy. Including weighted scores for nose bulging, cheek bulging and ear change did not change this. As these AUs had more missing values than orbital tightening, their descriptions should be re-evaluated. Including whisker retraction, which had a negative weight, resulted in lower accuracy and should therefore in its current form be left out of the FGS. Overall, the results of this study suggest that the FGS and the AU orbital tightening in particular could be useful in a multifactorial pain assessment protocol for ferrets. However, before applying the FGS in practice, it should be further validated by incorporating more time points before and after applying (different) painful stimuli, and different levels of analgesia.

Coccidiosis is still one of the major parasitic infections in poultry. It is caused by protozoa of the genus Eimeria, which cause concrete economic losses due to malabsorption, bad feed conversion rate, reduced weight gain, and increased mortality. The greatest damage is registered in commercial poultry farms because birds are reared together in large numbers and high densities. Unfortunately, these enteric pathologies are not preventable, and their diagnosis is only available when the disease is full-blown. For these reasons, the preventive use of anticoccidials—some of these with antimicrobial action—is a common practice in intensive farming, and this type of management leads to the release of drugs in the environment which contributes to the phenomenon of antibiotic resistance. Due to the high relevance of this issue, the early detection of any health problem is of great importance to improve animal welfare in intensive farming. Three prototypes, previously calibrated and adjusted, were developed and tested in three different experimental poultry farms in order to evaluate whether the system was able to identify the coccidia infection in intensive poultry farms early. For this purpose, a data-driven machine learning algorithm was built, and specific critical values of volatile organic compounds (VOCs) were found to be associated with abnormal levels of oocystis count at an early stage of the disease. This result supports the feasibility of building an automatic data-driven machine learning algorithm for an early warning of coccidiosis.

Background In recent years, the seed amplification assay (SAA) has enabled the identification of pathological TDP-43 in the cerebrospinal fluid (CSF) and olfactory mucosa (OM) of patients with genetic forms of frontotemporal dementia (FTD) and amyotrophic lateral sclerosis (ALS). Here, we investigated the seeding activity of TDP-43 in OM samples collected from patients with sporadic ALS. Methods OM samples were collected from patients with (a) sporadic motor neuron diseases (MND), including spinal ALS ( n  = 35), bulbar ALS ( n  = 18), primary lateral sclerosis ( n  = 10), and facial onset sensory and motor neuronopathy ( n  = 2); (b) genetic MND, including carriers of C9orf72 exp ( n  = 6), TARDBP ( n  = 4), SQSTM1 ( n  = 3), C9orf72 exp  + SQSTM1 ( n  = 1), OPTN ( n  = 1), GLE1 ( n  = 1), FUS ( n  = 1) and SOD1 ( n  = 4) mutations; (c) other neurodegenerative disorders (OND), including Alzheimer’s disease ( n  = 3), dementia with Lewy bodies ( n  = 8) and multiple system atrophy ( n  = 6); and (d) control subjects ( n  = 22). All samples were subjected to SAA analysis for TDP-43 (TDP-43_SAA). Plasmatic levels of TDP-43 and neurofilament-light chain (NfL) were also assessed in a selected number of patients. Results TDP-43_SAA was positive in 29/65 patients with sporadic MND, 9/21 patients with genetic MND, 6/17 OND patients and 3/22 controls. Surprisingly, one presymptomatic individual also tested positive. As expected, OM of genetic non-TDP-43-related MND tested negative. Interestingly, fluorescence values from non-MND samples that tested positive were consistently and significantly lower than those obtained with sporadic and genetic MND. Furthermore, among TDP-43-positive samples, the lag phase observed in MND patients was significantly longer than that in non-MND patients. Plasma TDP-43 levels were significantly higher in sporadic MND patients compared to controls and decreased as the disease progressed. Similarly, plasma NfL levels were higher in both sporadic and genetic MND patients and positively correlated with disease progression rate (ΔFS). No significant correlations were detected between TDP-43_SAA findings and the biological, clinical, or neuropsychological parameters considered. Conclusions The OM of a subset of patients with sporadic MND can trigger seeding activity for TDP-43, as previously observed in genetic MND. Thus, TDP-43_SAA analysis of OM can improve the clinical characterization of ALS across different phenotypes and enhance our understanding of these diseases. Finally, plasma TDP-43 could serve as a potential biomarker for monitoring disease progression. However, further research is needed to confirm and expand these findings.

To identify feasible indicators to evaluate animals’ emotional states as a parameter to assess animal welfare, the present study aimed at investigating the accuracy of free choice profiling (FCP) and fixed list (FL) approach of Qualitative Behaviour Assessment (QBA) in horses during the loading phase of transport. A total of 13 stakeholders were trained to score 2 different sets of videos of mixed breed horses loaded for road transport, using both FCP and FL, in 2 sessions. Generalized Procustes Analysis (GPA) consensus profile explained a higher percentage of variation (80.8%) than the mean of 1000 randomized profiles (41.2 ± 1.6%; p = 0.001) for the FCP method, showing an excellent inter-observer agreement. GPA identified two main factors, explaining 65.1% and 3.7% of the total variation. Factor 1 ranging from ‘anxious/ to ‘calm/relaxed’, described the valence of the horses’ emotional states. Factor 2, ranging from ‘bright’ to ‘assessing/withdrawn’, described the arousal. As for FL, Principal Component Analysis (PCA) first and second components (PC1 and PC2, respectively), explaining on average 59.8% and 12.6% of the data variability, had significant agreement between observers. PC1 ranges from relaxed/confident to anxious/frightened, while PC2 from alert/inquisitive to calm. Our study highlighted the need for the use of descriptors specifically selected, throughout a prior FCP process for the situation we want to evaluate to get a good QBA accuracy level.

To quantitatively evaluate the possible advantages of quantifying and differentiating various soft tissues using virtual monochromatic images (VMI) derived from different dual-energy computed tomography (DECT) technologies. This study involved four DECT scanners with different technologies. CIRS phantom images were acquired in single-energy (SECT) and DECT modes with each scanner. The analysis focused on five equivalent soft-tissue inserts: adipose, breast, liver, muscle, and bone (200 mg). The signal-to-noise ratio (SNR) was calculated for each equivalent soft-tissue insert. Finally, the contrasts of tissue pairs between DECT and SECT images were compared using Wilcoxon signed-rank tests adjusted for multiple comparisons. Average CT numbers and noise showed a significant difference pattern between DECT with respect to SECT for each CT scanner. Generally, energy levels of 70 keV or higher led to improved SNR in VMI for most of the equivalent soft-tissue inserts. However, energy levels of 40–50 keV showed significantly higher contrasts in most of the equivalent soft-tissue insert pairs. DECT images at low energies, especially at 40–50 keV, outperform SECT images in discriminating soft tissues across all four DECT technologies. The combined use of DECT images reconstructed at different energy levels provides a more comprehensive set of information for diagnostic and/or radiotherapy evaluation compared to SECT. Some differences between scanners are evident, depending on the DECT acquisition technique and reconstruction method.