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The reproducibility crisis (or replication crisis) in biomedical research is a particularly existential and under-addressed issue in the field of behavioral neuroscience, where, in spite of efforts to standardize testing and assay protocols, several known and unknown sources of confounding environmental factors add to variance. Human interference is a major contributor to variability both within and across laboratories, as well as novelty-induced anxiety. Attempts to reduce human interference and to measure more \"natural\" behaviors in subjects has led to the development of automated home-cage monitoring systems. These systems enable prolonged and longitudinal recordings, and provide large continuous measures of spontaneous behavior that can be analyzed across multiple time scales. In this review, a diverse team of neuroscientists and product developers share their experiences using such an automated monitoring system that combines Noldus PhenoTyper ® home-cages and the video-based tracking software, EthoVision ® XT, to extract digital biomarkers of motor, emotional, social and cognitive behavior. After presenting our working definition of a “home-cage”, we compare home-cage testing with more conventional out-of-cage tests (e.g., the open field) and outline the various advantages of the former, including opportunities for within-subject analyses and assessments of circadian and ultradian activity. Next, we address technical issues pertaining to the acquisition of behavioral data, such as the fine-tuning of the tracking software and the potential for integration with biotelemetry and optogenetics. Finally, we provide guidance on which behavioral measures to emphasize, how to filter, segment, and analyze behavior, and how to use analysis scripts. We summarize how the PhenoTyper has applications to study neuropharmacology as well as animal models of neurodegenerative and neuropsychiatric illness. Looking forward, we examine current challenges and the impact of new developments. Examples include the automated recognition of specific behaviors, unambiguous tracking of individuals in a social context, the development of more animal-centered measures of behavior and ways of dealing with large datasets. Together, we advocate that by embracing standardized home-cage monitoring platforms like the PhenoTyper, we are poised to directly assess issues pertaining to reproducibility, and more importantly, measure features of rodent behavior under more ethologically relevant scenarios.

Electrophysiological and behavioral alterations, including sleep and cognitive impairments, are critical components of age-related decline and neurodegenerative diseases. In preclinical investigation, many refined techniques are employed to probe these phenotypes, but they are often conducted separately. Herein, we provide a protocol for one-time surgical implantation of EMG wires in the nuchal muscle and a skull-surface EEG headcap in mice, capable of 9-to-12-month recording longevity. All data acquisitions are wireless, making them compatible with simultaneous EEG recording coupled to multiple behavioral tasks, as we demonstrate with locomotion/sleep staging during home-cage video assessments, cognitive testing in the Barnes maze, and sleep disruption. Time-course EEG and EMG data can be accurately mapped to the behavioral phenotype and synchronized with neuronal frequencies for movement and the location to target in the Barnes maze. We discuss critical steps for optimizing headcap surgery and alternative approaches, including increasing the number of EEG channels or utilizing depth electrodes with the system. Combining electrophysiological and behavioral measurements in preclinical models of aging and neurodegeneration has great potential for improving mechanistic and therapeutic assessments and determining early markers of brain disorders.

Large number of promising preclinical psychiatric studies in rodents later fail in clinical trials, raising concerns about the efficacy of this approach to generate novel pharmacological interventions. In this mini-review we argue that over-reliance on behavioral tests that are brief and highly sensitive to external factors play a critical role in this failure and propose that automated home-cage monitoring offers several advantages that will increase the translational utility of preclinical psychiatric research in rodents. We describe three of the most commonly used approaches for automated home cage monitoring in rodents [e.g., operant wall systems (OWS), computerized visual systems (CVS), and automatic motion sensors (AMS)] and review several commercially available systems that integrate the different approaches. Specific examples that demonstrate the advantages of automated home-cage monitoring over traditional tests of anxiety, depression, cognition, and addiction-like behaviors are highlighted. We conclude with recommendations on how to further expand this promising line of preclinical research.

Traumatic brain injury (TBI) is a primary global health concern and one of the most common causes of neurological impairments in people under 50. Mild TBI (mTBI) accounts for the majority of TBI cases. Anxiety is the most common complaint after mTBI in humans. This study aims to evaluate behavioral tests designed to assess anxiety-like phenotypes in a mice model of mTBI. ICR mice underwent mTBI using the weight-drop model. Seven days post-injury, mice were subjected to one of five different behavioral tests: Elevated Plus Maze (EPM), Open Field apparatus (OF), Marble Burying test (MBT), Light Dark Box (LDB), and the Light Spot test within the PhenoTyper home cage (LS). In the EPM and OF tests, there were no significant differences between the groups. During the 30-min test period of the MBT, mTBI mice buried significantly more marbles than control mice. In the LDB, mTBI mice spent significantly less time on the far side of the arena than control mice. In addition, the time it took for mTBI mice to get to the far side of the arena was significantly longer compared to controls. Results of LS show significant within-group mean differences for total distance traveled for mTBI mice but not for the control. Furthermore, injured mice moved significantly more than control mice. According to the results, the anxiety traits exhibited by mTBI mice depend upon the time of exposure to the aversive stimulus, the apparatus, and the properties of the stressors used. Therefore, the characterization of anxiety-like behavior in mTBI mice is more complicated than was initially suggested. Based on our findings, we recommend incorporating a variety of stressors and test session lengths when assessing anxiety-like behavior in experimental models of mTBI.

Cognitive function declines substantially with age in both humans and animal models. In humans, this decline is associated with decreases in independence and quality of life. Although the methodology for analysis of cognitive function in human models is relatively well established, similar analyses in animal models have many technical issues (e.g., unintended experimenter bias, motivational issues, stress, and testing during the light phase of the light dark cycle) that limit interpretation of the results. These caveats, and others, potentially bias the interpretation of studies in rodents and prevent the application of current tests of learning and memory as part of an overall healthspan assessment in rodent models of aging. The goal of this study was to establish the methodology to assess cognitive function in aging animals that addresses many of these concerns. Here, we use a food reward-based discrimination procedure with minimal stress in C57Bl/6J male mice at 6, 21, and 27 months of age, followed by a reversal task to assess behavioral flexibility. Importantly, the procedures minimize issues related to between-experimenter confounds and are conducted during both the dark and light phases of the light dark cycle in a home-cage setting. During cognitive testing, we were able to assess multiple measures of spontaneous movement and diurnal activity in young and aged mice including, distance moved, velocity, and acceleration over a 90-h period. Both initial discrimination and reversal learning significantly decreased with age and, similar to rats and humans, not all old mice demonstrated impairments in learning with age. These results permitted classification of animals based on their cognitive status. Analysis of movement parameters indicated decreases in distance moved as well as velocity and acceleration with increasing age. Based on these data, we developed preliminary models indicating, as in humans, a close relationship exists between age-related movement parameters and cognitive ability. Our results provide a reliable method for assessing cognitive performance with minimal stress and simultaneously provide key information on movement and diurnal activity. These methods represent a novel approach to developing non-invasive healthspan measures in rodent models that allow standardization across laboratories.

Die Gruppe der Kardiomyopathien hat in den letzten Jahren verstärkt Aufmerksamkeit erhalten, nachdem einige ihrer Ursachen identifiziert und sie mithilfe moderner Bildgebungsmethoden genauer charakterisiert werden konnten. Regelmäßig wurden von nationalen und internationalen Fachgesellschaften neue Definitionen und Klassifikationsschemata bereitgestellt. Die neue Leitlinie der European Society of Cardiology (ESC) von 2023 zum Management der Kardiomyopathien ist nun international die erste Guideline, die umfassend alle Kardiomyopathien in einem Dokument behandelt. Es handelt sich um eine neue Leitlinie, sodass die meisten Empfehlungen ebenso neu sind. Eine Ausnahme bildet der Abschnitt zur hypertrophen Kardiomyopathie (HCM), bei dem es sich um eine Aktualisierung der ESC-Leitlinie von 2014 zur Diagnose und Behandlung der HCM handelt. Das Hauptziel dieser Leitlinie besteht darin, einen klaren Leitfaden für die Diagnose von Kardiomyopathien bereitzustellen, allgemeine Bewertungs- und Managementprobleme zu betonen und den Leser auf die relevante wissenschaftliche Evidenzbasis für die Empfehlungen hinzuweisen. Aufgrund des Umfangs können keine detaillierten Beschreibungen und Empfehlungen für jede spezifische Kardiomyopathie bereitgestellt werden, jedoch wird auf die entsprechende Literatur verwiesen.

Malignant melanoma is notorious for its inter- and intratumour heterogeneity, based on transcriptionally distinct melanoma cell phenotypes. It is thought that these distinct phenotypes are plastic in nature and that their transcriptional reprogramming enables heterogeneous tumours both to undergo different stages of melanoma progression and to adjust to drug exposure during treatment. Recent advances in genomic technologies and the rapidly expanding availability of large gene expression datasets have allowed for a refined definition of the gene signatures that characterize these phenotypes and have revealed that phenotype plasticity plays a major role in the resistance to both targeted therapy and immunotherapy. In this Review we discuss the definition of melanoma phenotypes through particular transcriptional states and reveal the prognostic relevance of the related gene expression signatures. We review how the establishment of phenotypes is controlled and which roles phenotype plasticity plays in melanoma development and therapy. Because phenotype plasticity in melanoma bears a great resemblance to epithelial–mesenchymal transition, the lessons learned from melanoma will also benefit our understanding of other cancer types.

ObjectivesMyofibroblasts are key effector cells in the extracellular matrix remodelling of systemic sclerosis-associated interstitial lung disease (SSc-ILD); however, the diversity of fibroblast populations present in the healthy and SSc-ILD lung is unknown and has prevented the specific study of the myofibroblast transcriptome. We sought to identify and define the transcriptomes of myofibroblasts and other mesenchymal cell populations in human healthy and SSc-ILD lungs to understand how alterations in fibroblast phenotypes lead to SSc-ILD fibrosis.MethodsWe performed droplet-based, single-cell RNA-sequencing with integrated canonical correlation analysis of 13 explanted lung tissue specimens (56 196 cells) from four healthy control and four patients with SSc-ILD, with findings confirmed by cellular indexing of transcriptomes and epitopes by sequencing in additional samples.ResultsExamination of gene expression in mesenchymal cells identified two major, SPINT2hi and MFAP5hi, and one minor, WIF1hi, fibroblast populations in the healthy control lung. Combined analysis of control and SSc-ILD mesenchymal cells identified SPINT2hi, MFAP5hi, few WIF1hi fibroblasts and a new large myofibroblast population with evidence of actively proliferating myofibroblasts. We compared differential gene expression between all SSc-ILD and control mesenchymal cell populations, as well as among the fibroblast subpopulations, showing that myofibroblasts undergo the greatest phenotypic changes in SSc-ILD and strongly upregulate expression of collagens and other profibrotic genes.ConclusionsOur results demonstrate previously unrecognised fibroblast heterogeneity in SSc-ILD and healthy lungs, and define multimodal transcriptome-phenotypes associated with these populations. Our data indicate that myofibroblast differentiation and proliferation are key pathological mechanisms driving fibrosis in SSc-ILD.

We investigated replicated ecological speciation in the livebearing fish Poecilia mexicana and P. sulphuraria (Poeciliidae), which inhabit freshwater habitats and have also colonized multiple sulfidic springs in southern Mexico. These springs exhibit extreme hypoxia and high concentrations of hydrogen sulfide, which is lethal to most metazoans. We used phylogenetic analyses to test whether springs were independently colonized, performed phenotypic assessments of body and gill morphology variation to identify convergent patterns of trait differentiation, and conducted an eco-toxicological experiment to detect differences in sulfide tolerances among ecotypes. Our results indicate that sulfidic springs were colonized by three different lineages, two within P. mexicana and one representing P. sulphuraria. Colonization occurred earlier in P. sulphuraria, whereas invasion of sulfidic springs in P. mexicana was more recent, such that each population is more closely related to neighboring populations from adjacent nonsulfidic habitats. Sulfide spring fish also show divergence from nonsulfidic phenotypes and a phenotypic convergence toward larger heads, larger gills, and increased tolerance to H₂S. Together with previous studies that indicated significant reproductive isolation between fish from sulfidic and nonsulfidic habitats, this study provides evidence for repeated ecological speciation in the independent sulfide spring populations of P. mexicana and P. sulphuraria.

BackgroundLung involvement is a prevalent extraarticular manifestation of rheumatoid arthritis (RA) that remains a significant clinical challenge. Few studies have comprehensively quantified lung abnormalities of RA patients using artificial intelligence-based (AI) technology.ObjectivesThe aim of this study was to quantify lung lesions in RA patients and classify them based on their lung parameters.MethodsAn AI-based quantitative computed tomography (CT) image analysis software (AIQCT) was applied to high-resolution CT scans of RA patients in a cross-sectional manner. AIQCT automatically classified and quantified 10 types of parenchymal image patterns, expressing the volumes as percentages of total lung volume [1]. Hierarchical Ward’s linkage clustering based on these patterns identified five clusters. Visual assessments (ILD, and airway lesions) of HRCT and clinical phenotypes were assessed.ResultsA total of 408 RA patients were included in the study. The lung profiles of the five clusters were as follows: Cluster I (68.6%), characterized by nearly normal lungs; Cluster II (23.5%), characterized by slight lung lesions with honeycombs or ground-glass opacities (GGOs); Cluster III (5.6%), characterized by the predominance of GGOs; Cluster IV (1.0%), characterized by a predominant hyperlucent area; and Cluster V (1.2%), characterized by extensive lung abnormalities (Figure 1, Table 1). The number of patients in each cluster with ILD and airway lesions, based on visual assessments, were as follows: [ILD] Cluster I, 11/280 (3.9%), Cluster II, 19/96 (19.8%), Cluster III, 5/23 (21.7%), Cluster IV, 0/4 (0%), and Cluster V, 5/5 (100%) (p < 0.001): [airway lesions] Cluster I, 31/280 (11.1%), Cluster II, 29/96 (30.2%), Cluster III, 6/23 (26.1%), Cluster IV, 0/4 (0%), and Cluster V, 1/5 (20%) (p < 0.001). Clinical characteristics of each cluster were described in Table 1. The disease activity of RA in each cluster were as follows: [DAS28ESR, mean (SD)] Cluster I, 2.4 (1.0), Cluster II, 3.0 (1.2), Cluster III, 2.8 (1.0), Cluster IV, 2.2 (0.7), and Cluster V, 3.0 (0.8) (p < 0.001); [HAQ, mean (SD)] Cluster I, 0.4 (0.6), Cluster II, 0.9 (0.9), Cluster III, 1.1 (0.9), Cluster IV, 0.3 (0.3), and Cluster V, 0.8 (0.9) (p < 0.001).ConclusionThis study is the first to classify lung lesions in comprehensive RA patients using quantitative data derived from novel AI technology. The AIQCT-derived clustering of RA patients appears to be associated with their clinical backgrounds and characteristics.Reference[1] Ann Am Thorac Soc 2022;19:399-406Table 1.Scores of ten lung parenchymal image patterns and clinical characteristics in each cluster.ClusterI (n=280)II (n=96)III (n=23)IV (n=4)V (n=5)p valueAIQCT lung score (%), mean (SD)Normal92.6 (1.5)90.7 (2.5)87.4 (1.8)76.3 (5.1)80.2 (1.6)<0.001GGOs0.7 (0.3)1.0 (0.4)2.9 (1.4)0.6 (0.3)2.4 (0.5)<0.001Reticulation0.1 (0.1)0.4 (0.4)0.5 (0.5)0.2 (0.2)3.8 (3.3)<0.001Consolidation0.1 (0.05)0.3 (0.3)0.3 (0.2)0.4 (0.6)1.6 (1.1)<0.001Honeycombs0 (0.02)0.08 (0.2)0.01 (0.02)0.07 (0.05)1.7 (1.8)<0.001Small nodules0.1 (0.06)0.3 (0.2)0.5 (0.6)0.1 (0.04)0.5 (0.4)<0.001Interlobular septum0.1 (0.06)0.3 (0.2)0.4 (0.3)0.1 (0.07)0.5 (0.3)<0.001Hyperluency0.1 (0.3)0.7 (1.6)0.1 (0.1)17.1 (3.9)0.4 (0.4)<0.001Bronchi1.8 (0.4)2.3 (0.6)2.5 (0.7)1.9 (0.3)4.2 (1.4)<0.001Blood vessels4.4 (1.0)4 (1.1)5.5 (1.1)3.3 (0.6)4.7 (0.5)<0.001Age, mean (SD)62.9 (12.0)72.2 (8.0)70.4 (9.9)68.5 (4.8)60.0 (8.0)<0.001Sex; female, n (%)240 (86)81 (84)19 (83)1 (25)5 (100)0.015Smoking history, n (%)94 (34)25 (26)8 (35)4 (100)0 (0)0.019RF titer, mean (SD)94 (165)151 (216)184 (328)120 (130)288 (425)0.012ACPA titer, mean (SD)132 (189)181 (306)132 (161)92 (41)677 (1018)<0.001MTX, n (%)201 (72)64 (67)13 (57)3 (75)1 (20)0.065PSL, n (%)45 (16)32 (33)8 (35)1 (25)4 (80)<0.001bDMARDs, n (%)136 (49)45 (47)9 (39)2 (50)5 (100)0.183Figure 1.Clustering constellation tree diagram for ten lung parenchymal HRCT image patterns of rheumatoid arthritis.Acknowledgements:NIL.Disclosure of InterestsYoichi Nakayama: None declared, Ran Nakashima: None declared, Tomohiro Handa: None declared, Kiminobu Tanizawa: None declared, Hideo Onizawa: None declared, Takayuki Fujii: None declared, Koichi Murata Speakers bureau: AbbVie GK, Eisai Co., Ltd., Pfizer Inc., Chugai Pharmaceutical Co., Ltd., Mitsubishi Tanabe Pharma Corporation, Pfizer Inc., Bristol-Myers Squibb, Daiichi Sankyo Co. Ltd., and Asahi Kasei Pharma Corp., Grant/research support from: Daiichi Sankyo Co. Ltd, Kosaku Murakami: None declared, Akira Onishi: None declared, Masao Tanaka: None declared, Mirei Shirakashi: None declared, Ryosuke Hiwa: None declared, Hideaki Tsuji: None declared, Koji Kitagori: None declared, Syuji Akizuki: None declared, Hajime Yoshifuji: None declared, Akio Morinobu: None declared.