Parkinson’s disease is the second most common neurodegenerative disease after Alzheimer’s disease characterized by early degeneration of dopaminergic neurons in the substantia nigra pars compacta and accumulation of Lewy bodies. Parkinson’s disease is traditionally known to be associated with motor symptoms; however, it is also associated with non-motor symptoms like pain which may precede motor symptoms by more than a decade. Therapy for Parkinson’s disease primarily involves the use of levodopa which is linked to polyneuropathy and predominantly targets motor symptoms neglecting the non-motor symptoms cold hypersensitivity. The aim of the study was to investigate the effect of Cannabidiol (CBD) and Tetrahydrocannabinol (THC) on Cold Hypersensitivity in reserpine induced Parkinson’s disease in mice. Forty-two (42) mice were randomly divided in to seven groups of six mice each. Group I was the control group that were administered distilled water (10 ml/kg). Group II received reserpine (0.5 mg/kg), Group III received reserpine (0.5 mg/kg) and Cannabidiol (CBD) (30 mg/kg), Group IV received reserpine (0.5 mg/kg) and CBD (60 mg/kg), Group V received reserpine (0.5 mg/kg) and; Tetrahydrocannabinol (THC) (4 mg/kg), Group VI received reserpine (0.5 mg/kg) and; Tetrahydrocannabinol(THC) (6 mg/kg) and Group VII received reserpine (0.5 mg/kg) and; CBD (60 mg/kg) +THC (6 mg/kg). All administration were carried out intraperitoneally for three weeks. Cold pain threshold increased significantly (P < 0.05) in reserpine (0.5 mg/kg) + CBD (60 mg/kg) and reserpine (0.5 mg/kg) + THC (6 mg/kg) groups. Malonaldehyde (MDA) concentration decreased significantly in the reserpine (0.5 mg/kg) + CBD (30 mg/kg), reserpine (0.5mg/kg) + THC (4mg/kg) and reserpine (0.5 mg/kg) + CBD (60 mg/kg) +THC (6 mg/kg) treatment groups respectively. Superoxide dismutase (SOD) concentration increased significantly (P < 0.05) in reserpine (0.5 mg/kg) + CBD (60 mg/kg), reserpine (0.5 mg/kg) + THC (6 mg/kg) and reserpine (0.5 mg/kg) + CBD (60 mg/kg) +THC (6 mg/kg). Reduced glutathione level (GSH) increased significantly (p < 0.05) in reserpine (0.5 mg/kg) + THC (6 mg/kg) and reserpine (0.5 mg/kg) + CBD (60 mg/kg) + THC (6 mg/kg) groups. In conclusion, CBD and THC were able to improve cold pain threshold and at the same time decreasing MDA concentration and increasing SOD activity and GSH concentration.

Aims:Major depressive disorder (MDD) is a common psychiatric disorder, and objective measures that can support clinical assessment are increasingly being investigated. Electroencephalography (EEG) provides a non-invasive and relatively low-cost approach for examining brain activity and has shown potential for EEG-based MDD classification. This study investigated whether short resting-state EEG segments could distinguish individuals with MDD from healthy controls (HC) using two compact deep learning architectures: a Lightweight 1D Convolutional Neural Network (Light 1D-CNN) and a CNN-MiniTransformer. Materials and Methods: The study included 112 resting-state EEG recordings from 56 individuals with MDD and 56 HC. Ninety-two recordings were used for model development and internal evaluation, while 20 independent recordings were reserved for external validation. Nineteen-channel eyes-closed EEG recordings were standardized to 125 Hz and divided into non-overlapping 1-s segments. Channel-ablation analysis was performed to examine spatial EEG importance. Results: On the internal test set, the Light 1D-CNN achieved 96.43% accuracy, a 96.39% F1-score, and an AUROC of 0.9941, while the CNN-MiniTransformer achieved 95.26%, 95.31%, and 0.9906, respectively. On external validation, the Light 1D-CNN achieved 93.72% accuracy and an AUROC of 0.9785, whereas the CNN-MiniTransformer achieved 94.65% accuracy and an AUROC of 0.9872. P4 was the most influential channel in both global and MDD-specific analyses, with Fz and F4 also showing strong contributions. The parietal region showed the highest importance, followed by the frontal region. Conclusion: Both compact deep learning models showed strong performance for MDD–HC classification using short resting-state EEG segments and maintained high performance on independently held-out recordings. Similar channel-importance patterns across the two architectures also provide an interpretable basis for further investigation of spatial EEG characteristics associated with MDD.

Aims: Consumer neuroscience increasingly integrates neurophysiological measurement with self-report methods, yet a comprehensive quantitative synthesis across different neurophysiological modalities within a common meta-analytic framework has remained limited. This meta-analysis systematically evaluates the association between EEG, eye-tracking, galvanic skin response (GSR), and multimodal measures and consumer outcomes (purchase intention, brand attitude, advertisement evaluation, and preference) reported in empirical studies published between 2010 and 2024. Materials and Methods: Following PRISMA 2020 guidelines, 3,109 records were screened, resulting in 22 primary studies included in the final analysis. Effect sizes were synthesized in two independent pools (standardized mean differences and Fisher z-transformed correlations) using random-effects models (REML with Knapp-Hartung adjustment). Results: The pooled effect sizes were moderate and statistically significant (SMD: d = 0.47, 95% CI [0.18, 0.75]; correlation: r ≈ 0.33, 95% CI [0.07, 0.61]), with high heterogeneity observed across studies (I² = 77.3%–84.1%). None of the tested moderators reached statistical significance after FDR correction. Publication bias was detected in the SMD pool, but corrected estimates remained significant. Conclusion: Neurophysiological measures provide meaningful but context-dependent insights into consumer behavior, functioning as complementary rather than universal predictors whose explanatory value varies depending on stimulus characteristics, measurement modality, and experimental design.