Retrospective Evaluation of Clinical Experience With Intravenous Ascorbic Acid in Patients With Cancer.

BACKGROUND: Intravenous ascorbic acid (IV AA) has been used extensively in cancer patients throughout the United States. Currently, there are limited data on the safety and clinical effects of IV AA. The purpose of this study was to expand the current literature using a retrospective analysis of adverse events and symptomatic changes of IV AA in a large sample of cancer patients. METHODS: We conducted a retrospective chart review of all patients receiving IV AA for cancer at the Thomas Jefferson University Hospital over a 7-year period. We assessed all reports of adverse events, laboratory findings, and hospital or emergency department admissions. We also reviewed quality-of-life data, including fatigue, nausea, pain, appetite, and mood. RESULTS: There were 86 patients who received a total of 3034 doses of IV AA ranging from 50 to 150g. In all, 32 patients received only ascorbic acid as part of their cancer management (1197 doses), whereas 54 patients received ascorbic acid in conjunction with chemotherapy (1837 doses). The most common adverse events related to ascorbic acid were temporary nausea and discomfort at the injection site. All events reported in the ascorbic acid alone group were associated with less than 3% of the total number of infusions. Patients, overall, reported improvements in fatigue, pain, and mood while receiving ascorbic acid. CONCLUSIONS: The results of this retrospective analysis support the growing evidence that IV AA is generally safe and well tolerated in patients with cancer, and may be useful in symptom management and improving quality of life

Multi-nutrient supplement improves hormone ratio associated with cancer risk.

BACKGROUND: Gynecological cancers are among the most common in women and are directly related to a variety of hormonal factors. One potential risk factor associated with developing a gynecological malignancy is the ratio of two hormone metabolites, 2-Hydroxyestrone (2-HE) and 16alpha-Hydroxyestrone (16alpha-HE). A number of botanical constituents such as indoles, flavonoids, and resveratrol have been shown to have a favorable effect on the metabolic pathways that affect this ratio. The present study was designed to evaluate if a multi-nutrient supplement containing targeted botanical constituents would affect the 2-HE/16 alpha-HE ratio in middle-aged women. METHODS: A retrospective analysis was performed on 76 female patients (mean age 54 years) who received 2-HE/16 alpha-HE ratio assessments at two separate time points. The ratio assessment was part of standard care for women who presented with risk indicators associated with a high proliferative state. All patients who completed pre and post assessments were included. Sixty-five of the patients received a multi-nutrient supplement, Lucentia Peak, during the study period. Eleven patients chose not to take the supplement, but did receive ratio assessments at similar time points as the treatment group, allowing for between group comparisons. Paired t-tests were used to compare the changes in the 2-HE and 16alpha-HE measures as well as their ratio, both within groups and between groups. RESULTS: The results demonstrated a significant increase in the 2-HE/16alpha-HE ratio in the treated group (pre 0.38 to post 0.57, p CONCLUSIONS: The results demonstrated that women receiving the Lucentia Peak multi-nutrient supplement had significant increases in their 2-HE:16alpha-HE ratio, which appears to be mediated primarily by increasing the 2-HE levels. These results suggest further research on phytonutrients that might positively affect estrogen metabolism is warranted

Stem Cell Therapy for Autism

Autism spectrum disorders (ASD) are a group of neurodevelopmental conditions whose incidence is reaching epidemic proportions, afflicting approximately 1 in 166 children. Autistic disorder, or autism is the most common form of ASD. Although several neurophysiological alterations have been associated with autism, immune abnormalities and neural hypoperfusion appear to be broadly consistent. These appear to be causative since correlation of altered inflammatory responses, and hypoperfusion with symptology is reported. Mesenchymal stem cells (MSC) are in late phases of clinical development for treatment of graft versus host disease and Crohn's Disease, two conditions of immune dysregulation. Cord blood CD34+ cells are known to be potent angiogenic stimulators, having demonstrated positive effects in not only peripheral ischemia, but also in models of cerebral ischemia. Additionally, anecdotal clinical cases have reported responses in autistic children receiving cord blood CD34+ cells. We propose the combined use of MSC and cord blood CD34+cells may be useful in the treatment of autism

Identification of Chronic Mild Traumatic Brain Injury Using Resting State Functional MRI and Machine Learning Techniques

Mild traumatic brain injury (mTBI) is a major public health concern that can result in a broad spectrum of short-term and long-term symptoms. Recently, machine learning (ML) algorithms have been used in neuroscience research for diagnostics and prognostic assessment of brain disorders. The present study aimed to develop an automatic classifier to distinguish patients suffering from chronic mTBI from healthy controls (HCs) utilizing multilevel metrics of resting-state functional magnetic resonance imaging (rs-fMRI). Sixty mTBI patients and forty HCs were enrolled and allocated to training and testing datasets with a ratio of 80:20. Several rs-fMRI metrics including fractional amplitude of low-frequency fluctuation (fALFF), regional homogeneity (ReHo), degree centrality (DC), voxel-mirrored homotopic connectivity (VMHC), functional connectivity strength (FCS), and seed-based FC were generated from two main analytical categories: local measures and network measures. Statistical two-sample t-test was employed comparing between mTBI and HCs groups. Then, for each rs-fMRI metric the features were selected extracting the mean values from the clusters showing significant differences. Finally, the support vector machine (SVM) models based on separate and multilevel metrics were built and the performance of the classifiers were assessed using five-fold cross-validation and via the area under the receiver operating characteristic curve (AUC). Feature importance was estimated using Shapley additive explanation (SHAP) values. Among local measures, the range of AUC was 86.67-100% and the optimal SVM model was obtained based on combined multilevel rs-fMRI metrics and DC as a separate model with AUC of 100%. Among network measures, the range of AUC was 80.42-93.33% and the optimal SVM model was obtained based on the combined multilevel seed-based FC metrics. The SHAP analysis revealed the DC value in the left postcentral and seed-based FC value between the motor ventral network and right superior temporal as the most important local and network features with the greatest contribution to the classification models. Our findings demonstrated that different rs-fMRI metrics can provide complementary information for classifying patients suffering from chronic mTBI. Moreover, we showed that ML approach is a promising tool for detecting patients with mTBI and might serve as potential imaging biomarker to identify patients at individual level. Clinical trial registration: [clinicaltrials.gov], identifier [NCT03241732]

Treatment effects of N-acetyl cysteine on resting-state functional MRI and cognitive performance in patients with chronic mild traumatic brain injury: a longitudinal study

Mild traumatic brain injury (mTBI) is a significant public health concern, specially characterized by a complex pattern of abnormal neural activity and functional connectivity. It is often associated with a broad spectrum of short-term and long-term cognitive and behavioral symptoms including memory dysfunction, headache, and balance difficulties. Furthermore, there is evidence that oxidative stress significantly contributes to these symptoms and neurophysiological changes. The purpose of this study was to assess the effect of N-acetylcysteine (NAC) on brain function and chronic symptoms in mTBI patients. Fifty patients diagnosed with chronic mTBI participated in this study. They were categorized into two groups including controls (CN, n = 25), and patients receiving treatment with N-acetyl cysteine (NAC, n = 25). NAC group received 50 mg/kg intravenous (IV) medication once a day per week. In the rest of the week, they took one 500 mg NAC tablet twice per day. Each patient underwent rs-fMRI scanning at two timepoints including the baseline and 3 months later at follow-up, while the NAC group received a combination of oral and IV NAC over that time. Three rs-fMRI metrics were measured including fractional amplitude of low frequency fluctuations (fALFF), degree centrality (DC), and functional connectivity strength (FCS). Neuropsychological tests were also assessed at the same day of scanning for each patient. The alteration of rs-fMRI metrics and cognitive scores were measured over 3 months treatment with NAC. Then, the correlation analysis was executed to estimate the association of rs-fMRI measurements and cognitive performance over 3 months (p < 0.05). Two significant group-by-time effects demonstrated the changes of rs-fMRI metrics particularly in the regions located in the default mode network (DMN), sensorimotor network, and emotional circuits that were significantly correlated with cognitive function recovery over 3 months treatment with NAC (p < 0.05). NAC appears to modulate neural activity and functional connectivity in specific brain networks, and these changes could account for clinical improvement. This study confirmed the short-term therapeutic efficacy of NAC in chronic mTBI patients that may contribute to understanding of neurophysiological effects of NAC in mTBI. These findings encourage further research on long-term neurobehavioral assessment of NAC assisting development of therapeutic plans in mTBI

Deep learning-based multimodality classification of chronic mild traumatic brain injury using resting-state functional MRI and PET imaging

Mild traumatic brain injury (mTBI) is a public health concern. The present study aimed to develop an automatic classifier to distinguish between patients with chronic mTBI (n = 83) and healthy controls (HCs) (n = 40). Resting-state functional MRI (rs-fMRI) and positron emission tomography (PET) imaging were acquired from the subjects. We proposed a novel deep-learning-based framework, including an autoencoder (AE), to extract high-level latent and rectified linear unit (ReLU) and sigmoid activation functions. Single and multimodality algorithms integrating multiple rs-fMRI metrics and PET data were developed. We hypothesized that combining different imaging modalities provides complementary information and improves classification performance. Additionally, a novel data interpretation approach was utilized to identify top-performing features learned by the AEs. Our method delivered a classification accuracy within the range of 79–91.67% for single neuroimaging modalities. However, the performance of classification improved to 95.83%, thereby employing the multimodality model. The models have identified several brain regions located in the default mode network, sensorimotor network, visual cortex, cerebellum, and limbic system as the most discriminative features. We suggest that this approach could be extended to the objective biomarkers predicting mTBI in clinical settings

Resting-State Functional MRI Metrics in Patients With Chronic Mild Traumatic Brain Injury and Their Association With Clinical Cognitive Performance.

Mild traumatic brain injury (mTBI) accounts for more than 80% of people experiencing brain injuries. Symptoms of mTBI include short-term and long-term adverse clinical outcomes. In this study, resting-state functional magnetic resonance imaging (rs-fMRI) was conducted to measure voxel-based indices including fractional amplitude of low-frequency fluctuation (fALFF), regional homogeneity (ReHo), and functional connectivity (FC) in patients suffering from chronic mTBI; 64 patients with chronic mTBI at least 3 months post injury and 40 healthy controls underwent rs-fMRI scanning. Partial correlation analysis controlling for age and gender was performed within mTBI cohort to explore the association between rs-fMRI metrics and neuropsychological scores. Compared with controls, chronic mTBI patients showed increased fALFF in the left middle occipital cortex (MOC), right middle temporal cortex (MTC), and right angular gyrus (AG), and increased ReHo in the left MOC and left posterior cingulate cortex (PCC). Enhanced FC was observed from left MOC to right precuneus; from right MTC to right superior temporal cortex (STC), right supramarginal, and left inferior parietal cortex (IPC); and from the seed located at right AG to left precuneus, left superior medial frontal cortex (SMFC), left MTC, left superior temporal cortex (STC), and left MOC. Furthermore, the correlation analysis revealed a significant correlation between neuropsychological scores and fALFF, ReHo, and seed-based FC measured from the regions with significant group differences. Our results demonstrated that alterations of low-frequency oscillations in chronic mTBI could be representative of disruption in emotional circuits, cognitive performance, and recovery in this cohort

Machine Learning-Based Classification of Chronic Traumatic Brain Injury Using Hybrid Diffusion Imaging

BACKGROUND AND PURPOSE: Traumatic brain injury (TBI) can cause progressive neuropathology that leads to chronic impairments, creating a need for biomarkers to detect and monitor this condition to improve outcomes. This study aimed to analyze the ability of data-driven analysis of diffusion tensor imaging (DTI) and neurite orientation dispersion imaging (NODDI) to develop biomarkers to infer symptom severity and determine whether they outperform conventional T1-weighted imaging. MATERIALS AND METHODS: A machine learning-based model was developed using a dataset of hybrid diffusion imaging of patients with chronic traumatic brain injury. We first extracted the useful features from the hybrid diffusion imaging (HYDI) data and then used supervised learning algorithms to classify the outcome of TBI. We developed three models based on DTI, NODDI, and T1-weighted imaging, and we compared the accuracy results across different models. RESULTS: Compared with the conventional T1-weighted imaging-based classification with an accuracy of 51.7-56.8%, our machine learning-based models achieved significantly better results with DTI-based models at 58.7-73.0% accuracy and NODDI with an accuracy of 64.0-72.3%. CONCLUSION: The machine learning-based feature selection and classification algorithm based on hybrid diffusion features significantly outperform conventional T1-weighted imaging. The results suggest that advanced algorithms can be developed for inferring symptoms of chronic brain injury using feature selection and diffusion-weighted imaging

Phase I Evaluation of Intravenous Ascorbic Acid in Combination with Gemcitabine and Erlotinib in Patients with Metastatic Pancreatic Cancer

Preclinical data support further investigation of ascorbic acid in pancreatic cancer. There are currently insufficient safety data in human subjects, particularly when ascorbic acid is combined with chemotherapy.14 subjects with metastatic stage IV pancreatic cancer were recruited to receive an eight week cycle of intravenous ascorbic acid (three infusions per week), using a dose escalation design, along with standard treatment of gemcitabine and erlotinib. Of 14 recruited subjects enrolled, nine completed the study (three in each dosage tier). There were fifteen non-serious adverse events and eight serious adverse events, all likely related to progression of disease or treatment with gemcitabine or erlotinib. Applying RECIST 1.0 criteria, seven of the nine subjects had stable disease while the other two had progressive disease.These initial safety data do not reveal increased toxicity with the addition of ascorbic acid to gemcitabine and erlotinib in pancreatic cancer patients. This, combined with the observed response to treatment, suggests the need for a phase II study of longer duration.Clinicaltrials.gov NCT00954525

N-Acetyl Cysteine May Support Dopamine Neurons in Parkinson\u27s Disease: Preliminary Clinical and Cell Line Data.

BACKGOUND: The purpose of this study was to assess the biological and clinical effects of n-acetyl-cysteine (NAC) in Parkinson\u27s disease (PD). METHODS: The overarching goal of this pilot study was to generate additional data about potentially protective properties of NAC in PD, using an in vitro and in vivo approach. In preparation for the clinical study we performed a cell tissue culture study with human embryonic stem cell (hESC)-derived midbrain dopamine (mDA) neurons that were treated with rotenone as a model for PD. The primary outcome in the cell tissue cultures was the number of cells that survived the insult with the neurotoxin rotenone. In the clinical study, patients continued their standard of care and were randomized to receive either daily NAC or were a waitlist control. Patients were evaluated before and after 3 months of receiving the NAC with DaTscan to measure dopamine transporter (DAT) binding and the Unified Parkinson\u27s Disease Rating Scale (UPDRS) to measure clinical symptoms. RESULTS: The cell line study showed that NAC exposure resulted in significantly more mDA neurons surviving after exposure to rotenone compared to no NAC, consistent with the protective effects of NAC previously observed. The clinical study showed significantly increased DAT binding in the caudate and putamen (mean increase ranging from 4.4% to 7.8%; p CONCLUSIONS: The results of this preliminary study demonstrate for the first time a potential direct effect of NAC on the dopamine system in PD patients, and this observation may be associated with positive clinical effects. A large-scale clinical trial to test the therapeutic efficacy of NAC in this population and to better elucidate the mechanism of action is warranted. TRIAL REGISTRATION: ClinicalTrials.gov NCT02445651