ARTICLES
Original Article
Turkish Title : Analgesic Effect of Cannabidiol and Tetrahydrocannabinol on Cold Hypersensitivity in Reserpine Model of Parkinson’s Disease
Muhammad Tahir,Isa Ahmed-Sherif,Paul Philemon,Tekanyi Amat Abdoulie
JNBS, 2026, 13(2), p:0-0
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.
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.
Original Article
Turkish Title : Compact Deep Learning for Major Depressive Disorder Classification from Resting-State EEG: Independent Cohort Validation and Channel-Level Explainability
Caglar Uyulan
JNBS, 2026, 13(2), p:0-0
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: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.
Original Article
Neurophysiological Correlates of Consumer Responses: A PRISMA-Based Meta-Analysis
Turkish Title : Neurophysiological Correlates of Consumer Responses: A PRISMA-Based Meta-Analysis
Pamfili Candan,Ülker Selami Varol
JNBS, 2026, 13(2), p:0-0
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.
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.
Review Article
Disinhibition: A Conceptual Hypothesis to Explain Near-Death Experiences And Out-of-Body Experiences
Turkish Title : Disinhibition: A Conceptual Hypothesis to Explain Near-Death Experiences And Out-of-Body Experiences
Lugten Peter
JNBS, 2026, 13(2), p:0-0
Living beings bring more order to the world, and are required by the second law of thermodynamics, to simultaneously produce more disorder through metabolic heat dissipation. Consciousness in living beings produces even more order, enhancing survival and reproduction, and incurs an entropy debt that must be paid separately, and non-metabolically, through heat dissipation produced by Landauer’s principle: the erasure of information within the "explanatory gap" of the hard problem. Consciousness is assumed to depend on the properties of tryptophan in microtubules and their ability to dissipate heat through superradiation as required by Landauer. After the post-mortem decomposition of microtubules, consciousness will have no mechanism for the necessary heat discharge, which would violate Landauer’s principle and thus the second law. Any actual such survival would imply life-before-death to be solipsism. The aim is to distinguish near-death experiences and out-of-body experiences from life-after-death in biophysical terms, and to explain how they might arise as a result of natural forces. It is hypothesized that near-death and out-of-body experiences, and their vivid nature, arise from the disinhibition of microtubular consciousness after electroencephalographic brain activity has stopped, but while microtubular Tryptophan continues to be activated by ultraviolet photons. These are hypothesized to be transmitted to the brain primarily via light falling on the retina or skin, carried to the brain through mitochondria and microtubules acting as optical waveguides. Additionally, bioluminescent ultra-weak photon emissions accompany membrane depolarization at the moment of death. It is hypothesized that the relativistic transactional interpretation of quantum mechanics could explain the entanglement of this photonic microtubular input, as potentialities, with environmental photons in order to account for rare, veridical near-death extraocular perceptions. Veridical Auditory near-death and out-of-body experiences require a coordinated stimulation of Heschl’s gyrus by ambient sound, which might be accomplished by adiabatic mechanical propulsion of axonal solitons along the Auditory nerve, or possibly transcranial perception of low frequency sound waves.Ultimately, the approach of death can be accompanied by a pseudo-solipsism of "added time" that could seem like an eternity for the deceased person even as microtubular stimulation decreases.
Living beings bring more order to the world, and are required by the second law of thermodynamics, to simultaneously produce more disorder through metabolic heat dissipation. Consciousness in living beings produces even more order, enhancing survival and reproduction, and incurs an entropy debt that must be paid separately, and non-metabolically, through heat dissipation produced by Landauer’s principle: the erasure of information within the "explanatory gap" of the hard problem. Consciousness is assumed to depend on the properties of tryptophan in microtubules and their ability to dissipate heat through superradiation as required by Landauer. After the post-mortem decomposition of microtubules, consciousness will have no mechanism for the necessary heat discharge, which would violate Landauer’s principle and thus the second law. Any actual such survival would imply life-before-death to be solipsism. The aim is to distinguish near-death experiences and out-of-body experiences from life-after-death in biophysical terms, and to explain how they might arise as a result of natural forces. It is hypothesized that near-death and out-of-body experiences, and their vivid nature, arise from the disinhibition of microtubular consciousness after electroencephalographic brain activity has stopped, but while microtubular Tryptophan continues to be activated by ultraviolet photons. These are hypothesized to be transmitted to the brain primarily via light falling on the retina or skin, carried to the brain through mitochondria and microtubules acting as optical waveguides. Additionally, bioluminescent ultra-weak photon emissions accompany membrane depolarization at the moment of death. It is hypothesized that the relativistic transactional interpretation of quantum mechanics could explain the entanglement of this photonic microtubular input, as potentialities, with environmental photons in order to account for rare, veridical near-death extraocular perceptions. Veridical Auditory near-death and out-of-body experiences require a coordinated stimulation of Heschl’s gyrus by ambient sound, which might be accomplished by adiabatic mechanical propulsion of axonal solitons along the Auditory nerve, or possibly transcranial perception of low frequency sound waves.Ultimately, the approach of death can be accompanied by a pseudo-solipsism of "added time" that could seem like an eternity for the deceased person even as microtubular stimulation decreases.
Review Article
Neurotoxic Effects of Electronic Cigarette Exposure on the Central Nervous System: A Scoping Review
Turkish Title : Neurotoxic Effects of Electronic Cigarette Exposure on the Central Nervous System: A Scoping Review
Emre Taner Özcan
JNBS, 2026, 13(2), p:0-0
Electronic cigarettes (e-cigarettes) have achieved global adoption, with an estimated 82 million users worldwide and disproportionate uptake among adolescents. Despite marketing as a safer tobacco alternative, e-cigarette aerosols contain neurotoxic constituents including nicotine, flavoring agents, carbonyl compounds, and heavy metals. This review maps empirical evidence on CNS effects of e-cigarette exposure across biological model types. A systematic scoping review was conducted per the Arksey and O'Malley framework and reported per PRISMA-ScR guidelines. PubMed and Scopus were searched in January 2026 for studies from 2015 to 2026 examining quantifiable CNS outcomes following e-cigarette exposure in human, animal, or in vitro models. Data were extracted across eight domains and synthesized narratively. Seventy-four studies met inclusion criteria: 49 animal in vivo (66.2%), 17 human (23.0%), and 8 in vitro (10.8%), spanning 14 countries. Seven CNS outcome domains were identified: neuroinflammation and oxidative stress, blood-brain barrier (BBB) integrity, cognitive and behavioral outcomes, neuroimaging and pharmacokinetics, neurotransmitter systems and electrophysiology, developmental neurotoxicology, and other outcomes. Neuroinflammation and BBB disruption showed high directional consistency. Cognitive impairment, hippocampal volume reductions, and reward circuit reorganization were consistently observed. Developmental studies identified epigenomic reprogramming and disrupted GABAergic interneuron migration. Non-nicotine constituents were independently implicated in CNS toxicity. E-cigarette exposure is associated with a consistent and biologically plausible pattern of CNS harm. The developing brain represents a priority risk population. Current evidence does not support the neurological safety of e-cigarettes; regulatory frameworks should mandate CNS-specific toxicity assessment for electronic nicotine delivery systems.
Electronic cigarettes (e-cigarettes) have achieved global adoption, with an estimated 82 million users worldwide and disproportionate uptake among adolescents. Despite marketing as a safer tobacco alternative, e-cigarette aerosols contain neurotoxic constituents including nicotine, flavoring agents, carbonyl compounds, and heavy metals. This review maps empirical evidence on CNS effects of e-cigarette exposure across biological model types. A systematic scoping review was conducted per the Arksey and O'Malley framework and reported per PRISMA-ScR guidelines. PubMed and Scopus were searched in January 2026 for studies from 2015 to 2026 examining quantifiable CNS outcomes following e-cigarette exposure in human, animal, or in vitro models. Data were extracted across eight domains and synthesized narratively. Seventy-four studies met inclusion criteria: 49 animal in vivo (66.2%), 17 human (23.0%), and 8 in vitro (10.8%), spanning 14 countries. Seven CNS outcome domains were identified: neuroinflammation and oxidative stress, blood-brain barrier (BBB) integrity, cognitive and behavioral outcomes, neuroimaging and pharmacokinetics, neurotransmitter systems and electrophysiology, developmental neurotoxicology, and other outcomes. Neuroinflammation and BBB disruption showed high directional consistency. Cognitive impairment, hippocampal volume reductions, and reward circuit reorganization were consistently observed. Developmental studies identified epigenomic reprogramming and disrupted GABAergic interneuron migration. Non-nicotine constituents were independently implicated in CNS toxicity. E-cigarette exposure is associated with a consistent and biologically plausible pattern of CNS harm. The developing brain represents a priority risk population. Current evidence does not support the neurological safety of e-cigarettes; regulatory frameworks should mandate CNS-specific toxicity assessment for electronic nicotine delivery systems.
| ISSN (Print) | 2149-1909 |
| ISSN (Online) | 2148-4325 |
2020 Ağustos ayından itibaren yalnızca İngilizce yayın kabul edilmektedir.

