Association of Longitudinal Changes in Cerebral Microstructure with Cognitive Functioning in Breast Cancer Survivors after Adjuvant Chemotherapy.

Background: Adjuvant chemotherapy for breast cancer might impact cognitive function and brain structure. Methods: In this study, we investigated the cerebral microstructural changes in breast cancer survivors after adjuvant chemotherapy and the correlation with cognitive function with both cross-sectional and longitudinal study designs. All participants underwent structural MRI. In total, we recruited 67 prechemotherapy patients (BB), 67 postchemotherapy patients (BA), and 77 healthy controls (BH). For the follow-up study, 28 participants in the BH and 28 in the BB groups returned for imaging and assessment (BHF, BBF). Voxel-based morphometry analysis was performed to evaluate differences in brain volume; vertex-based shape analysis was used to assess the shape alterations of subcortical regions. Moreover, multiple regression was applied to assess the association between the changes in neuropsychological assessment and brain volume. Results: The results showed brain volume reduction in the temporal and parietal gyrus in BB and BA patients. Among each group, we also found significant shape alterations in the caudate and thalamus. Volume reductions in the temporal regions and shape changes in the caudate and hippocampus were also observed in patients from time point 1 to time point 2 (postchemotherapy). An association between brain volume and cognitive performance was also found in the limbic system. Conclusions: Based on our findings, we can provide a better understanding of the cerebral structural changes in breast cancer survivors, establish a subsequent prediction model, and serve as a reference for subsequent treatment.

Longitudinal assessment of chemotherapy-induced brain connectivity changes in cerebral white matter and its correlation with cognitive functioning using the GQI.

Objective: Breast cancer was the most prevalent type of cancer and had the highest incidence rate among women worldwide. The wide use of adjuvant chemotherapy might have a detrimental effect on the human brain and result in chemotherapy-related cognitive impairment (CICI) among breast cancer patients. Furthermore, prior to chemotherapy, patients reported cancer-related cognitive impairment (CRCI), which might be due to physiological factors or mood symptoms. The present longitudinal study aimed to investigate microstructural and macroscale white matter alterations by generalized q-sampling imaging (GQI). Methods: The participants were categorized into a pre-chemotherapy group (BB) if they were diagnosed with primary breast cancer and an age-matched noncancer control group (HC). Some participants returned for follow-up assessment. In the present follow up study, 28 matched pairs of BB/BBF (follow up after chemotherapy) individuals and 28 matched pairs of HC/HCF (follow up) individuals were included. We then used GQI and graph theoretical analysis (GTA) to detect microstructural alterations in the whole brain. In addition, we evaluated the relationship between longitudinal changes in GQI indices and neuropsychological tests as well as psychiatric comorbidity. Findings: The results showed that disruption of white matter integrity occurred in the default mode network (DMN) of patients after chemotherapy, such as in the corpus callosum (CC) and middle frontal gyrus (MFG). Furthermore, weaker connections between brain regions and lower segregation ability were observed in the post-chemotherapy group. Significant correlations were observed between neuropsychological tests and white matter tracts of the CC, MFG, posterior limb of the internal capsule (PLIC) and superior longitudinal fasciculus (SLF). Conclusion: The results provided evidence of white matter alterations in breast cancer patients, and they may serve as potential imaging markers of cognitive changes. In the future, the study may be beneficial to create and evaluate strategies designed to maintain or improve cognitive function in breast cancer patients undergoing chemotherapy.

Detecting microstructural alterations of cerebral white matter associated with breast cancer and chemotherapy revealed by generalized q-sampling MRI.

Objective: Previous studies have discussed the impact of chemotherapy on the brain microstructure. There is no evidence of the impact regarding cancer-related psychiatric comorbidity on cancer survivors. We aimed to evaluate the impact of both chemotherapy and mental health problem on brain microstructural alterations and consequent cognitive dysfunction in breast cancer survivors. Methods: In this cross-sectional study conducted in a tertiary center, data from 125 female breast cancer survivors who had not received chemotherapy (BB = 65; 49.86 ± 8.23 years) and had received chemotherapy (BA = 60; 49.82 ± 7.89 years) as well as from 71 age-matched healthy controls (47.18 ± 8.08 years) was collected. Chemotherapeutic agents used were docetaxel and epirubicin. We used neuropsychological testing and questionnaire to evaluate psychiatric comorbidity, cognitive dysfunction as well as generalized sampling imaging (GQI) and graph theoretical analysis (GTA) to detect microstructural alterations in the brain. Findings: Cross-comparison between groups revealed that neurotoxicity caused by chemotherapy and cancer-related psychiatric comorbidity may affect the corpus callosum and middle frontal gyrus. In addition, GQI indices were correlated with the testing scores of cognitive function, quality of life, anxiety, and depression. Furthermore, weaker connections between brain regions and lower segregated ability were found in the post-treatment group. Conclusion: This study suggests that chemotherapy and cancer-related mental health problem both play an important role in the development of white matter alterations and cognitive dysfunction.

Assessment of brain connectome alterations in male chronic smokers using structural and generalized q-sampling MRI.

An association has been shown between chronic cigarette smoking and structural abnormalities in the brain areas related to several functions relevant to addictive behavior. However, few studies have focused on the structural alternations of chronic smoking by using magnetic resonance imaging (MRI). Also, it remains unclear how structural alternations are associated with tobacco-dependence severity and the positive/negative outcome expectances. The q-sampling imaging (GQI) is an advanced diffusion MRI technique that can reconstruct more precise and consistent images of complex oriented fibers than other methods. We aimed to use GQI to evaluate the impact of the neurological structure caused by chronic smoking. Sixty-seven chronic smokers and 43 nonsmokers underwent a MRI scan. The tobacco dependence severity and the positive/negative outcome expectancies were assessed via self-report. We used GQI with voxel-based statistical analysis (VBA) to evaluate structural brain and connectivity abnormalities. Graph theoretical analysis (GTA) and network-based statistical (NBS) analysis were also performed to identify the structural network differences among groups. Chronic smokers had smaller GM and WM volumes in the bilateral frontal lobe and bilateral frontal region. The GM/WM volumes correlated with dependence severity and outcome expectancies in the brain areas involving high-level functions. Chronic smokers had shape changes in the left hippocampal head and tail and the inferior brain stem. Poorer WM integrity in chronic smokers was found in the left middle frontal region, the right superior fronto-occipital fasciculus, the right temporal region, the left parahippocampus, the left anterior internal capsule, and the right inferior parietal region. WM integrity correlated with dependence severity and outcome expectancies in brain areas involving high-level functions. Chronic smokers had decreased local segregation and global integration among the brain regions and networks. Our results provide further evidence indicating that chronic smoking may be associated with brain structure and connectivity changes.

Magnetic Resonance-Based Synthetic Computed Tomography Using Generative Adversarial Networks for Intracranial Tumor Radiotherapy Treatment Planning.

The purpose of this work is to develop a reliable deep-learning-based method that is capable of synthesizing needed CT from MRI for radiotherapy treatment planning. Simultaneously, we try to enhance the resolution of synthetic CT. We adopted pix2pix with a 3D framework, which is a conditional generative adversarial network, to map the MRI data domain into the CT data domain of our dataset. The original dataset contains paired MRI and CT images of 31 subjects; 26 pairs were used for model training and 5 were used for model validation. To identify the correctness of the synthetic CT of models, all of the synthetic CTs were calculated by the quantized image similarity formulas: cosine angle distance, Euclidean distance, mean square error, peak signal-to-noise ratio, and mean structural similarity. Two radiologists independently evaluated the satisfaction score, including spatial, detail, contrast, noise, and artifacts, for each imaging attribute. The mean (±standard deviation) of the structural similarity indices (CAD, L2 norm, MSE, PSNR, and MSSIM) between five real CT scans and the synthetic CT scans were 0.96 ± 0.015, 76.83 ± 12.06, 0.00118 ± 0.00037, 29.47 ± 1.35, and 0.84 ± 0.036, respectively. For synthetic CT, radiologists rated the results as evincing excellent satisfaction in spatial geometry and noise level, good satisfaction in contrast and artifacts, and fair imaging details. The similarity index and clinical evaluation results between synthetic CT and original CT guarantee the usability of the proposed method.

Assessment of disrupted brain functional connectome in tuberous sclerosis complex using resting-state fMRI.

ABSTRACT: Tuberous sclerosis complex (TSC) is a rare genetic disorder with multisystem involvement. TSC is characterized by benign hamartomas in multiple organs, including the brain, and its clinical phenotypes may be associated with abnormal functional connections. We aimed to use resting-state functional connectivity to provide findings of disrupted functional brain networks in TSC patients using graph theoretical analysis (GTA) and network-based statistic (NBS) analysis.Forty TSC patients (age = 24.11+/-11.44 years old) and 18 age-matched (25.13+/- 10.01 years old) healthy controls were recruited; they underwent resting-state functional magnetic resonance imaging using a 3T magnetic resonance imaging scanner. After image preprocessing and removing physiological noises, GTA was used to calculate the topological parameters of the brain network. NBS analysis was then used to determine the differences in cerebrum functional connectivity between the 2 groups.In GTA, several topological parameters, including the clustering coefficient, local efficiency, transitivity, and modularity, were better in controls than in TSC patients (P < .05). In NBS analysis, the edges of the brain networks between the groups were compared. One subnetwork showed more edges in controls than in TSC patients (P < .05), including the connections from the frontal lobe to the temporal and parietal lobe.The study results provide the findings on disrupted functional connectivity and organization in TSC patients compared with controls. The findings may help better understand the underlying physiological mechanisms of brain connection in TSC.

Classification and Visualization of Chemotherapy-Induced Cognitive Impairment in Volumetric Convolutional Neural Networks.

Breast cancer is the most common female cancer worldwide, and breast cancer accounts for 30% of female cancers. Of all the treatment modalities, breast cancer survivors who have undergone chemotherapy might complain about cognitive impairment during and after cancer treatment. This phenomenon, chemo-brain, is used to describe the alterations in cognitive functions after receiving systemic chemotherapy. Few reports detect the chemotherapy-induced cognitive impairment (CICI) by performing functional MRI (fMRI) and a deep learning analysis. In this study, we recruited 55 postchemotherapy breast cancer survivors (C+ group) and 65 healthy controls (HC group) and extracted mean fractional amplitudes of low-frequency fluctuations (mfALFF) from resting-state fMRI as our input feature. Two state-of-the-art deep learning architectures, ResNet-50 and DenseNet-121, were transformed to 3D, embedded with squeeze and excitation (SE) blocks and then trained to differentiate cerebral alterations based on the effect of chemotherapy. An integrated gradient was applied to visualize the pattern that was recognized by our model. The average performance of SE-ResNet-50 models was an accuracy of 80%, precision of 78% and recall of 70%; on the other hand, the SE-DenseNet-121 model reached identical results with an average of 80% accuracy, 86% precision and 80% recall. The regions with the greatest contributions highlighted by the integrated gradients algorithm for differentiating chemo-brain were the frontal, temporal, parietal and occipital lobe. These regions were consistent with other studies and strongly associated with the default mode and dorsal attention networks. We constructed two volumetric state-of-the-art models and visualized the patterns that are critical for identifying chemo-brains from normal brains. We hope that these results will be helpful in clinically tracking chemo-brain in the future.

Comparison of functional dorsal attention network alterations in breast cancer survivors before and after chemotherapy.

ABSTRACT: Breast cancer is the leading type of cancer among women worldwide, and a high number of breast cancer patients are suffering from psychological and cognitive disorders. This cross-sectional study used resting-state functional magnetic resonance imaging (rs-fMRI) and clinical neuropsychological tests to evaluate the possible underlying mechanisms.We enrolled 32 breast cancer patients without chemotherapy (BC), 32 breast cancer patients within 6 to 12 months after the completion of chemotherapy (BC_CTx) and 46 healthy controls. Participants underwent neuropsychological tests and rs-fMRI with mean fractional amplitude of low-frequency fluctuation and mean regional homogeneity analyses. Between groups whole-brain voxel-wise rs-fMRI comparisons were calculated using two-sample t test. rs-fMRI and neuropsychological tests correlation analyses were calculated using multiple regression. Age and years of education were used as covariates. A false discovery rate-corrected P-value of less than .05 was considered statistically significant.We found significantly alteration of mean fractional amplitude of low-frequency fluctuation and mean regional homogeneity in the frontoparietal lobe and occipital lobe in the BC group compared with the other 2 groups, indicating alteration of functional dorsal attention network (DAN). Furthermore, we found the DAN alteration was correlated with neuropsychological impairment.The majority of potential underlying mechanisms of DAN alteration in BC patients may due to insufficient frontoparietal lobe neural activity to drive DAN and may be related to the effects of neuropsychological distress. Further longitudinal studies with comprehensive images and neuropsychological tests correlations are recommended.

A CNN-Based Autoencoder and Machine Learning Model for Identifying Betel-Quid Chewers Using Functional MRI Features.

Betel quid (BQ) is one of the most commonly used psychoactive substances in some parts of Asia and the Pacific. Although some studies have shown brain function alterations in BQ chewers, it is virtually impossible for radiologists' to visually distinguish MRI maps of BQ chewers from others. In this study, we aimed to construct autoencoder and machine-learning models to discover brain alterations in BQ chewers based on the features of resting-state functional magnetic resonance imaging. Resting-state functional magnetic resonance imaging (rs-fMRI) was obtained from 16 BQ chewers, 15 tobacco- and alcohol-user controls (TA), and 17 healthy controls (HC). We used an autoencoder and machine learning model to identify BQ chewers among the three groups. A convolutional neural network (CNN)-based autoencoder model and supervised machine learning algorithm logistic regression (LR) were used to discriminate BQ chewers from TA and HC. Classifying the brain MRIs of HC, TA controls, and BQ chewers by conducting leave-one-out-cross-validation (LOOCV) resulted in the highest accuracy of 83%, which was attained by LR with two rs-fMRI feature sets. In our research, we constructed an autoencoder and machine-learning model that was able to identify BQ chewers from among TA controls and HC, which were based on data from rs-fMRI, and this might provide a helpful approach for tracking BQ chewers in the future.

Cedrus atlantica Extract Suppress Glioblastoma Growth through Promotion of Genotoxicity and Apoptosis: In Vitro and In Vivo Studies.

Glioblastoma (GBM) is the most common malignant primary brain tumor in humans, exhibiting highly infiltrative growth and drug resistance to conventional chemotherapy. Cedrus atlantica (CAt) extract has been shown to decrease postoperative pain and inhibit the growth of K562 leukemia cells. The aim of this study was to assess the anti-GBM activity and molecular mechanism of CAt extract in vitro and in vivo. The results showed that CAt extract greatly suppressed GBM cells both in vitro and in vivo and enhanced the survival rate in subcutaneous and orthotopic animal models. Moreover, CAt extract increased the level of ROS and induced DNA damage, resulting in cell cycle arrest at the G0/G1 phase and cell apoptosis. Western blotting results indicated that CAt extract regulates p53/p21 and CDK4/cyclin D1 protein expression and activates extrinsic and intrinsic apoptosis. Furthermore, CAt extract enhanced the cytotoxicity of Temozolomide and decreased AKT/mTOR signaling by combination treatment. In toxicity assays, CAt extract exhibited low cytotoxicity toward normal cells or organs in vitro and in vivo. CAt extract suppresses the growth of GBM by induction of genotoxicity and activation of apoptosis. The results of this study suggest that CAt extract can be developed as a therapeutic agent or adjuvant for GBM treatment in the future.

Usefulness of triggered non-contrast-enhanced magnetic resonance angiography in assessing lower extremity venous disease.

ABSTRACT: Although venous duplex ultrasonography (USG) is reliable for diagnosing lower extremity venous disease (LEVD), cross-sectional imaging studies were usually required before intervention or surgery. Patients of LEVD with renal insufficiency usually restrict the use of contrast-enhanced imaging modalities. In seeking an alternative imaging solution for these patients, we explore the clinical utility of triggered angiography non-contrast-enhanced magnetic resonance imaging (TRANCE-MRI) in the assessment of LEVD.We collected data from patients presenting to a tertiary wound-care center with symptoms of LEVD from April 2017-November 2019. Each participant underwent baseline USG followed by TRANCE-MRI on a 1.5T MR scanner (Philips Ingenia, Philips Healthcare, Best, The Netherlands). Inter-rater reliability was measured using Cohen's kappa (κ).All 80 participants (mean age, 61.9 ±â€Š14.8 years; 35 males, 45 females) were assessed and were classified into one of five disease groups, deep vein thrombosis (n = 38), venous static ulcer (n = 16), symptomatic varicose veins (n = 18), recurrent varicose veins (n = 3), and lymphoedema (n = 5). The inter-rater reliability between TRANCE-MRI and doppler USG showed substantial agreement (κ, 0.73). The sensitivity, specificity, and accuracy of TRANCE-MRI were 90.5%, 88.1%, and 88.8%, respectively. In 59 (73.8%) USG-negative patients, we were able to diagnose positive findings (deep venous thrombosis, n = 7; varicose veins, n = 15; lymphedema, n = 10; iliac vein compression with thrombosis, n = 6; external venous compression, n = 5; vena cava anomaly, n = 2; occult peripheral artery disease, n = 5; ccluded bypass graft, n = 1) by using TRANCE-MRI. Of these, 9 (15.3%) patients underwent additional vascular surgery based on positive TRANCE-MRI findings.TRANCE technique provides the limb's entire venous drainage in clear images without background contamination by associated arterial imaging. Additionally, simultaneous evaluation of bilateral lower extremities can help determine the lesion's exact site. Although TRANCE-MRI can provide MR arteriography and MR venography, we recommend performing only MR venography in symptomatic LEVD patients because the incidence of occult arterial disease is low.

Association between functional brain alterations and neuropsychological scales in male chronic smokers using resting-state fMRI.

RATIONALE: Recent studies have demonstrated that cigarette smoking is related to changes in brain structure and function. However, few studies focus on functional brain differences between male chronic smokers and nonsmokers in both local spontaneous activity and whole-brain functional networks. OBJECTIVES: Our study recruited 67 chronic smokers and 43 nonsmokers who underwent functional magnetic resonance imaging (fMRI) scans to investigate functional activity and connectivity alterations in chronic smokers. METHODS: We used the mean fractional amplitude of the low-frequency fluctuation (mfALFF) and mean regional homogeneity (mReHo) methods to investigate resting-state spontaneous activity in chronic smokers and nonsmokers. The graph theoretical analysis (GTA) and network-based statistical (NBS) analysis were also used to investigate functional connectivity alterations. RESULTS: Compared with nonsmokers, chronic smokers exhibited higher activation in the reward system and portions of the prefrontal cortex but lower activation in the default mode networks (DMN) and visual-related regions. In addition, correlation analysis was conducted to assess the associations between neuroimaging findings and the severity of nicotine dependence or expectations of smoking effects. Our results showed that certain brain regions correlated with the Fagerström Test for Nicotine Dependence (FTND), the positive aspect of the Drug Use Disorders Identification Test Extended (DUDIT-E), and the negative aspect of the DUDIT-E, especially in the attentional control networks and hippocampus. The graph theoretical analysis (GTA) results indicated chronic smokers exhibited a trend toward increased assortativity. Our network-based statistical (NBS) analysis revealed reduced functional connections between the subnetwork in the prefrontal cortex, olfactory cortex, angular gyrus, and cingulate gyrus of chronic smokers. CONCLUSIONS: We concluded that chronic smokers have neural adaptations in local spontaneous activity but remain healthy brain functional networks.

Circulating cytokines as predictors of depression in patients with breast cancer.

Depression is a common comorbid disorder associated with breast cancer, and it can have considerable physical and psychological impacts. Circulating cytokines have been proposed as a potential tool to predict depression in various diseases; however, limited studies have specifically examined it in breast cancer. In this study, we examined and compared the prediction ability of various circulating cytokines for depression in patients with breast cancer. Eighty-three patients with a new diagnosis of breast cancer not receiving chemotherapy were recruited; among them, 15 patients had depression and 68 did not have depression. Depression was evaluated using the Patient Health Questionnaire 9 (PHQ-9). Cytokine levels in the serum were measured using an immunology multiplex assay. Two types of cytokines were assayed: (1) proinflammatory cytokines (interleukin [IL]-1ß, IL-2, IL-6, IL-12, IL-17A, interferon [IFN]γ, and tumor necrosis factor [TNF]α) and (2) anti-inflammatory cytokines (IL-4, IL-5, IL-10, and IL-13). Receiver operating characteristic (ROC) analysis was performed to calculate the area under the curves (AUCs), sensitivities, and specificities of circulating cytokines for predicting depression. As a result, IL-2 (AUC = 0.78) and IL-5 (AUC = 0.76) demonstrated good predictability for depression, even after controlling for the covariates (i.e. age, education, stage of cancer, surgery, radiation therapy, and hormone therapy). The optimal cut-off value of IL-2 for predicting depression was 1.06 pg/mL with a sensitivity of 86.7% and a specificity of 52.9%; this cytokine also had the best prediction ability in this study. Owing to the prediction ability and practical feasibility of circulating cytokines, they may be used as a valid laboratory diagnostic tool for depression in breast cancer.

Disrupted white matter connectivity and organization of brain structural connectomes in tuberous sclerosis complex patients with neuropsychiatric disorders using diffusion tensor imaging.

OBJECTIVE: Tuberous sclerosis complex (TSC) is a genetic neurocutaneous syndrome with variable and unpredictable neurological comorbidity that includes epilepsy, intellectual disability (ID), autism spectrum disorder, and neurobehavioral abnormalities. The degree of white matter involvement is believed to be associated with the severity of neurological impairment. The goal of the present study was to evaluate diffusion characteristics of tubers, white matter lesions, and brain structural network alterations in TSC patients using diffusion tensor imaging (DTI), graph theoretical analysis (GTA), and network-based statistical (NBS) analysis. MATERIALS AND METHODS: Forty-two patients with a definitive diagnosis of TSC were recruited for this study. All patients underwent brain DTI examination using a 3 T magnetic resonance imaging system. Mean diffusivity (MD), axial diffusivity (AD), radial diffusivity (RD) values, and fractional anisotropy (FA) mapping in 52 tubers and white matter lesions were measured and compared with those of contralateral normal regions. GTA was performed on the inter-regional connectivity matrix, and NBS analysis was used to identify the significance of any connected subnetworks evident in the set of altered connections. For neurological severity subgrouping, a neurological severity score was assigned to TSC patients including those with ID, seizure, autism, and other neuropsychiatric disorders (NPDs). RESULTS: Significantly higher MD, AD, and RD, and lower FA values, were found in TSC lesions compared with those measured in contralateral normal regions for tubers (P < 0.05). GTA and NBS analysis provided better local segregation but worse global integration of the structural network (regular-like network) in TSC patients with ID, seizure, and higher Neurological Severity Score. Disrupted subnetworks in TSC patients with severe status included connections from the frontal lobe to the parietal lobe, temporal lobe to the caudate, and temporal lobe to the insula. DISCUSSION: DTI has the potential to provide valuable information about cytoarchitectural changes in TSC lesions beyond morphological MRI findings alone. Using GTA and NBS, current results provide the information of disrupted white matter connectivity and organization in TSC patients with different neuropsychological impairments.

Subjective and objective cognitive functioning among patients with breast cancer: effects of chemotherapy and mood symptoms.

BACKGROUND: Previous findings regarding declines in cognitive functioning among patients with breast cancer (BC) before and after chemotherapy have been inconsistent. The present study explored the effect of BC and cancer-related chemotherapies on cognitive functioning. METHODS: A cross-sectional design was adopted to compare BC patients before their chemotherapy treatment, BC patients 3 ~ 9 months after the completion of chemotherapy, and noncancer controls. Evaluations of cognitive functioning included subjective and objective dimensions, with focus on memory, executive functioning, attention, and language. ANCOVA and Pearson's correlation analysis were used to examine the relationship among cancer, chemotherapy, cognitive performance, and psychological distress. RESULTS: After adjustment for intelligence quotient, anxiety, and depression, we found significant differences in the Semantic Association of Verbal Fluency between post-chemotherapy (C/T) patients and noncancer controls. Specifically, post-C/T patients scored lower than controls (p = 0.03, η2 = 0.07). No significant differences were found in other objective cognitive measures. However, both subjective and objective cognitive scores were significantly associated with depression, anxiety, and fatigue. In BC patients, levels of anxiety were positively correlated with measures of executive function. Among pre-C/T patients, self-perceived interference by fatigue was positively associated with better performances in some of the objective cognitive measures. CONCLUSION: Our findings suggest cognitive impairments in the domain of executive functioning among patients with BC who received chemotherapy. Providing relevant suggestions or strategies of managements for these negative consequences may help increase the long-term quality of life of patients with BC.

Functional and Structural Connectome Features for Machine Learning Chemo-Brain Prediction in Women Treated for Breast Cancer with Chemotherapy.

Breast cancer is the leading cancer among women worldwide, and a high number of breast cancer patients are struggling with psychological and cognitive disorders. In this study, we aim to use machine learning models to discriminate between chemo-brain participants and healthy controls (HCs) using connectomes (connectivity matrices) and topological coefficients. Nineteen female post-chemotherapy breast cancer (BC) survivors and 20 female HCs were recruited for this study. Participants in both groups received resting-state functional magnetic resonance imaging (rs-fMRI) and generalized q-sampling imaging (GQI). Logistic regression (LR), decision tree classifier (CART), and xgboost (XGB) were the models we adopted for classification. In connectome analysis, LR achieved an accuracy of 79.49% with the functional connectomes and an accuracy of 71.05% with the structural connectomes. In the topological coefficient analysis, accuracies of 87.18%, 82.05%, and 83.78% were obtained by the functional global efficiency with CART, the functional global efficiency with XGB, and the structural transitivity with CART, respectively. The areas under the curves (AUCs) were 0.93, 0.94, 0.87, 0.88, and 0.84, respectively. Our study showed the discriminating ability of functional connectomes, structural connectomes, and global efficiency. We hope our findings can contribute to an understanding of the chemo brain and the establishment of a clinical system for tracking chemo brain.

Extract Derived from Cedrus atlantica Acts as an Antitumor Agent on Hepatocellular Carcinoma Growth In Vitro and In Vivo.

Cedrus atlantica is widely used in herbal medicine. However, the anti-cancer activity of C. atlantica extract (CAt extract) has not been clarified in hepatocellular carcinoma. In the study, we elucidated the anti-hepatoma capacity of CAt extract on HCC in vitro and in vivo. To explore the anti-hepatoma mechanisms of the CAt extract in vitro, HCC and normal cells were treated with the CAt extract, which showed marked inhibitory effects on HCC cells in a dose-dependent manner; in contrast, the CAt extract treatment was less cytotoxic to normal cells. In addition, our results indicate that the CAt extract induced apoptosis via caspase-dependent and independent apoptosis pathways. Furthermore, the CAt extract inhibited HCC tumor cell growth by restraining cell cycle progression, and it reduced the signaling of the AKT, ERK1/2, and p38 pathways. In the xenograft model, the CAt extract suppressed HCC tumor cell growth and prolonged lifespan by inhibiting PCNA protein expression, repressing part of the VEGF-induced autocrine pathway, and triggering strong expression of cleaved caspase-3, which contributed to cell apoptosis. Moreover, the CAt extract did not induce any obvious changes in pathological morphology or body weight, suggesting it had no toxicity. CAt extract exerted anti-tumor effects on HCC in vitro and in vivo. Thus, CAt extract could be used as a potential anti-cancer therapeutic agent against HCC.

Cedrol suppresses glioblastoma progression by triggering DNA damage and blocking nuclear translocation of the androgen receptor.

Glioblastoma (GBM) is the most common and aggressive primary brain tumor with great invasiveness and resistance to chemotherapy, which presents a treatment challenge. In this study, we investigated the antitumor effect of Cedrol, a sesquiterpene alcohol isolated from Cedrus atlantica, against GBM cells in vitro and in vivo. Cedrol was found to potently inhibit cell growth and induce intracellular ROS generation and DNA damage response. In addition, Cedrol induced significant G0/G1 cell cycle arrest and cell apoptosis via the extrinsic (Fas/FasL/Caspase-8) and intrinsic (Bax/Bcl-2/Caspase-9) pathways. In addition, Cedrol had a synergistic effect with temozolomide (TMZ) and reduced drug resistance by blockage of the AKT/mTOR pathway. Cedrol suppressed tumor growth in both orthotopic and xenograft GBM animal models with low or no short-term acute toxicity or long-term accumulative toxicity. In a molecular docking study, Cedrol targeted the androgen receptor (AR), and reduced DHT-mediated AR nuclear translocation, downstream gene KLK3/TMPRSS2 expression and cell proliferation. Our study demonstrates that Cedrol may be a potential candidate for drug development for single or combination treatment with TMZ in GBM therapy.

Differential associations of proinflammatory and anti-inflammatory cytokines with depression severity from noncancer status to breast cancer course and subsequent chemotherapy.

BACKGROUND: In this study, we examined the differential associations of various proinflammatory and anti-inflammatory cytokines with depression severity from the development of breast cancer to subsequent chemotherapy treatment. METHODS: A cross-sectional study was conducted on a sample of 116 women: 29 controls without cancer, 55 patients with breast cancer who were not receiving chemotherapy, and 32 patients with breast cancer who were receiving chemotherapy. Blood samples were assayed to evaluate serum levels of the following cytokines: interferon-γ, interleukin (IL)-12 (p70), IL-1ß, IL-2, tumor necrosis factor (TNF)-α, IL-4, IL-5, IL-10, IL-13, IL-6, and IL-17A. Depression severity was assessed using the Patient Health Questionnaire. RESULTS: After adjustment for sociodemographics, consistent patterns of the association between cytokine and depression were noted in the different groups. No significant associations were observed in the controls. Inverse associations were observed between cytokines levels and depression severity in patients with breast cancer who were not receiving chemotherapy, whereas positive associations were noted in patients with breast cancer who were receiving chemotherapy. Specific differential relationships between IL-5 levels and depression severity were found between patients with breast cancer who were receiving and not receiving chemotherapy. CONCLUSIONS: Our study revealed differential relationships between cytokine levels and depression severity with the development of cancer. Immunostimulation and immunosuppression in breast cancer and cancer treatment may account for the differential responses with the development of breast cancer.

New Use for Old Drugs: The Protective Effect of Risperidone on Colorectal Cancer.

Background: The potential of old drugs in novel indications is being greatly valued. We propose a triple-model study involving population-based, cell, and animal studies to investigate the effects of risperidone, a type of second-generation antipsychotic (SGA) drug, on colorectal cancer. Methods: We used data from Taiwan's National Health Insurance Research Database between 1997 and 2013 to compare 101,989 patients with colorectal cancer and 101,989 controls. Conditional logistic regression analyses were used to explore the association between SGA exposure and the risk of colorectal cancer. The following bench studies were performed to evaluate the findings of the population-based study. Results: We found that SGAs had been less commonly used in colorectal cancer patients than in controls. The colorectal cancer risk was reduced with an increase in the cumulative defined daily dose (cDDD) of SGAs. The adjusted odds ratio of antipsychotic use for cDDD days was 0.32 (95% CI: 0.25-0.42). Risperidone exhibited the most prominent tumor inhibition effect in a cell screen study. Bench data revealed that risperidone significantly induced apoptosis and elevated intracellular ROS in human SW480 cells and suppressed the proliferation of the xenografted SW480 tumor in nude mice. Conclusion: This triple-model study demonstrates the association between risperidone usage and a lower risk of colorectal cancer.