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Research suggests affective symptoms are associated with reduced habitual use of reappraisal as an emotion regulation strategy in individuals with mental health problems. Less is known, however, about whether mental health problems are related to reduced reappraisal capacity per se. The current study investigates this question using a film-based emotion regulation task that required participants to use reappraisal to downregulate their emotional response to highly evocative real-life film footage. We pooled data (N = 512, age: 18-89 years, 54% female) from 6 independent studies using this task. In contrast to our predictions, symptoms of depression and anxiety were unrelated to self-reported negative affect after reappraisal or to emotional reactivity to negative films. Implications for the measurement of reappraisal as well as future directions for research in the field of emotion regulation are discussed., (© 2023. The Author(s).)

Tumour cells exhibit altered metabolic pathways that lead to radiation resistance and disease progression. Raman spectroscopy (RS) is a label-free optical modality that can monitor post-irradiation biomolecular signatures in tumour cells and tissues. Convolutional Neural Networks (CNN) perform automated feature extraction directly from data, with classification accuracy exceeding that of traditional machine learning, in cases where data is abundant and feature extraction is challenging. We are interested in developing a CNN-based predictive model to characterize clinical tumour response to radiation therapy based on their degree of radiosensitivity or radioresistance. In this work, a CNN architecture is built for identifying post-irradiation spectral changes in Raman spectra of tumour tissue. The model was trained to classify irradiated versus non-irradiated tissue using Raman spectra of breast tumour xenografts. The CNN effectively classified the tissue spectra, with accuracies exceeding 92.1% for data collected 3 days post-irradiation, and 85.0% at day 1 post-irradiation. Furthermore, the CNN was evaluated using a leave-one-out- (mouse, section or Raman map) validation approach to investigate its generalization to new test subjects. The CNN retained good predictive accuracy (average accuracies 83.7%, 91.4%, and 92.7%, respectively) when little to no information for a specific subject was given during training. Finally, the classification performance of the CNN was compared to that of a previously developed model based on group and basis restricted non-negative matrix factorization and random forest (GBR-NMF-RF) classification. We found that CNN yielded higher classification accuracy, sensitivity, and specificity in mice assessed 3 days post-irradiation, as compared with the GBR-NMF-RF approach. Overall, the CNN can detect biochemical spectral changes in tumour tissue at an early time point following irradiation, without the need for previous manual feature extraction. This study lays the foundation for developing a predictive framework for patient radiation response monitoring., (© 2023. The Author(s).)

This work combines Raman spectroscopy (RS) with supervised learning methods-group and basis restricted non-negative matrix factorisation (GBR-NMF) and linear discriminant analysis (LDA)-to aid in the prediction of clinical indicators of disease progression in a cohort of 9 patients receiving high dose rate brachytherapy (HDR-BT) as the primary treatment for intermediate risk (D'Amico) prostate adenocarcinoma. The combination of Raman spectroscopy and GBR-NMF-sparseLDA modelling allowed for the prediction of the following clinical information; Gleason score, cancer of the prostate risk assessment (CAPRA) score of pre-treatment biopsies and a Ki67 score of < 3.5% or > 3.5% in post treatment biopsies. The three clinical indicators of disease progression investigated in this study were predicted using a single set of Raman spectral data acquired from each individual biopsy, obtained pre HDR-BT treatment. This work highlights the potential of RS, combined with supervised learning, as a tool for the prediction of multiple types of clinically relevant information to be acquired simultaneously using pre-treatment biopsies, therefore opening up the potential for avoiding the need for multiple immunohistochemistry (IHC) staining procedures (H&E, Ki67) and blood sample analysis (PSA) to aid in CAPRA scoring., (© 2022. The Author(s).)

This work combines single cell Raman spectroscopy (RS) with group and basis restricted non-negative matrix factorisation (GBR-NMF) to identify individual biochemical changes associated with radiation exposure in three human cancer cell lines. The cell lines analysed were derived from lung (H460), breast (MCF7) and prostate (LNCaP) tissue and are known to display varying degrees of radio sensitivity due to the inherent properties of each cell type. The GBR-NMF approach involves the deconstruction of Raman spectra into component biochemical bases using a library of Raman spectra of known biochemicals present in the cells. Subsequently, scores are obtained on each of these bases which can be directly correlated with the contribution of each chemical to the overall Raman spectrum. We validated GBR-NMF through the correlation of GBR-NMF-derived glycogen scores with scores that were previously observed using principal component analysis (PCA). Phosphatidylcholine, glucose, arginine and asparagine showed a distinct differential score pattern between radio-resistant and radio-sensitive cell types. In summary, the GBR-NMF approach allows for the monitoring of individual biochemical radiation-response dynamics previously unattainable with more traditional PCA-based approaches.

We have prepared and studied a family of cyanobiphenyl dimers with varying linking groups with a view to exploring how molecular structure dictates the stability of the nematic and twist-bend nematic mesophases. Using molecular modelling and 1D 1 H NOESY NMR spectroscopy, we determine the angle between the two aromatic core units for each dimer and find a strong dependency of the stability of both the nematic and twist-bend mesophases upon this angle, thereby satisfying earlier theoretical models.

Group 1 metabotropic glutamate receptors (mGluR1/mGluR5) play an integral role in neurodevelopment and are implicated in psychiatric disorders, such as schizophrenia. mGluR1 and mGluR5 are expressed as homodimers, which is important for their functionality and pharmacology. We examined the protein expression of dimeric and monomeric mGluR1α and mGluR5 in the prefrontal cortex (PFC) and hippocampus throughout development (juvenile/adolescence/adulthood) and in the perinatal phencyclidine (PCP) model of schizophrenia. Under control conditions, mGluR1α dimer expression increased between juvenile and adolescence (209-328%), while monomeric levels remained consistent. Dimeric mGluR5 was steadily expressed across all time points; monomeric mGluR5 was present in juveniles, dramatically declining at adolescence and adulthood (-97-99%). The mGluR regulators, Homer 1b/c and Norbin, significantly increased with age in the PFC and hippocampus. Perinatal PCP treatment significantly increased juvenile dimeric mGluR5 levels in the PFC and hippocampus (37-50%) but decreased hippocampal mGluR1α (-50-56%). Perinatal PCP treatment also reduced mGluR1α dimer levels in the PFC at adulthood (-31%). These results suggest that Group 1 mGluRs have distinct dimeric and monomeric neurodevelopmental patterns, which may impact their pharmacological profiles at specific ages. Perinatal PCP treatment disrupted the early expression of Group 1 mGluRs which may underlie neurodevelopmental alterations observed in this model.