Neuroblastoma (NB) is the most common extracranial cancer diagnosed in children. NB represents 6% of pediatric cancer cases yet accounts for 15% of childhood cancer deaths. The disproportionate NB mortality rate is attributed to clinical heterogeneity, which ranges from spontaneous regression to aggressive metastasis in high risk (HR) patients. In recent years, an increasing number of bioinformatics studies suggest that long non-coding RNAs (lncRNAs) may play crucial roles in cancer dysregulation, including the modulation of an immune response. Thus, this study aimed to identify a candidate long non-coding RNA associated with greater survival (i.e., overall survival > 5.0 years) and increased natural killer cytotoxic tumor responses. A bulk RNA-seq dataset containing primary NB tumor samples (n = 152) was downloaded from the TCGABioLinks database and further separated into poor survival (n = 69) and good survival (n = 83) groups. A differential gene expression analysis demonstrated that lncRNA RP11-277P12.20 was upregulated in patients with good survival and a Kaplan-Meier survival analysis further demonstrated an association between a higher expression of RP11-277P12.20 with survival greater than 5 years (p < 0.05). CIBERSORT and functional annotation analyses revealed a potential role of RP11-277P12.20 in modulating NK cytotoxicity as a promising target for the first lncRNA-based therapy for NB. However further research should include analysis of MHC class I expression to exploit CD8+ T cells in therapy. The development of new and robust therapeutic options may enhance multimodal therapeutic strategies to increase HR NB patient survival.
The U.S Endangered Species Act (ESA) is legislation implemented to repopulate endangered species affected by the ongoing sixth mass extinction. In this study, the links between species charisma and population trends are investigated to assess the efficacy of the ESA from a non-economics perspective. 367 vertebrate species were ranked based on charisma (through a novel point value system) and their rankings were compared to respective qualitative population trends. Species with higher charisma levels had a greater chance of possessing a “beneficial trend” in their population levels. These findings suggest a detrimental bias, based on outward appearances, present in the ESA’s effort to repopulate at-risk species.
The EPA reports nearly half a million contaminated sites throughout the US. Current strategies to remedy this issue are ineffective. Rhizoremediation is a promising solution to this global public health concern. NCBI was first used to obtain raw sequences of 16s rRNA from 96 soil samples, which were then processed through Qiime2, a microbiome analysis package, to examine bacterial taxonomy in contaminated and uncontaminated soil rhizospheres. Rhizobium, Pseudomonas, and Burkholderia constituted over 43% of the microbial community in the non-contaminated rhizosphere. Three different soil microbes - Pseudomonas fluorescens, Rhizobium leguminosarum, and Burkholderia vietnamiensis were inoculated in the rhizosphere of Brassica juncea, Oryza sativa, and Pisum sativum in soil contaminated with 500 ppm of lead. Soil lead content was measured at various stages of plant growth. After four weeks, soil lead content decreased from 500 to 150 ppm, a 70% decline in pots with Pseudomonas-Brassica juncea, from 500 to 175 ppm, a 65% decline with Burkholderia-Oryza sativa, and from 500 to 200 ppm, a 60% decline with Rhizobium-Pisum sativum. Soil lead content decreased from 500 to 50 ppm in pots with Pseudomonas-Rhizobium-Burkholderia triple combination with Brassica juncea, a 90% decline in soil lead content. Statistical significance was proven using a two-way ANOVA. Spectrophotometric analysis of chlorophyll content of the dried leaves of plant groups showed similar optical density (OD) compared to control leaves, indicating that lead decontamination in the soil did not negatively affect plant health. Therefore, Rhizoremediation is an effective bioremediation strategy and significantly increases crop productivity.
Bacterial biofilms account for 80% of all chronic microbial infections. ESKAPE pathogens form dense, multispecies biofilms that are resistant to most antibiotics. In this project, a combination treatment of FDA approved concentrations of quorum quenching agents was devised to target three major interspecies biofilm-pathways: Chlorogenic Acid (CA) inhibiting Pseudomonas fluorescens-Staphylococcus epidermidis HONO production by targeting Pas Quorum Sensing (QS), carvacrol inhibiting P. fluorescens-Burkholderia pyrrocinia N-acyl-homoserine-lactone production by targeting Las QS, and 6-gingerol inhibiting P. fluorescens-Candida albicans phenazine production by targeting Rhl QS. Computational analysis of the cystic fibrosis lung microbiome using QIIME2 and PICRUSt2 confirmed the prevalence of these biofilm pathways. Docking analysis supported the binding affinities of each treatment with targeted enzymes (LasR/Lasl/PqsR). Dual combinations of microbes were treated with 0.75% carvacrol, 30g/ml CA, or 32g/ml 6-gingerol, and the multispecies group was treated with all agents. Of the dual-species groups had a 70% efficacy in inhibiting P. fluorescens-S. epidermidis biofilm, carvacrol a 60% efficacy on P. fluorescens-B. pyrrocinia biofilm, and 6-gingerol a 45% efficacy on P. fluorescens-C. albicans biofilm. The combination treatment demonstrated an 80% efficacy in inhibiting P. fluorescens-S. epidermidis-B. pyrrocinia-C. albicans biofilm. Statistical significance was confirmed using ANOVA and T-test. Treatment safety was supported by a cytotoxicity assay on A549 human alveolar epithelial cells. These findings support the delivery of the cocktail therapy as an adjuvant to antibiotics in-vivo, thus reducing morbidity and mortality from chronic biofilm-related infections.
Chemotherapy-Induced Peripheral Neuropathy (CIPN) is a side effect of chemotherapy that can lead to symptoms ranging from numbness and pain to organ failure or paralysis. It may necessitate a reduction or cessation of chemotherapy, negatively impacting treatment. By running differential gene expression analysis (DESeq2) using RStudio, it was possible to find which genes are highly over or under-regulated in patients that develop CIPN. After comparing the mechanisms of CIPN with the significant genes (p-value 0.05), 11 genes that had a direct connection to CIPN were identified. As this research is continued into other chemotherapy side effects, such as cardiomyopathy, the information will allow better-personalized care of cancer in deciding whether or not to use chemotherapy or which chemotherapy to use.
Drug discovery is incredibly time-consuming and expensive, and researchers have yet to uncover most of the biochemical properties that make drugs viable. In this study, Machine Learning (ML) was used to reveal which molecular structures were most responsible for viable drugs. An ML pipeline was used to group drugs into those that bind strongly and weakly to target proteins. Correlation analysis was performed to validate which drug fragments were most responsible for the grouping. It was found that the CCCH and CCCCCH molecular fragments are most responsible, revealing significant biochemical insight into the molecular binding properties of viable drugs.
Dense multi-species bacterial biofilms are resistant to most antibiotics. In this study, a combination treatment consisting of FDA-approved concentrations of quorum quenching agents was devised to target three major interspecies biofilm-pathways. Computational analysis (Qiime2/PICRUSt2) of the CF lung microbiome confirmed the necessity of QS in targeted pathways, and docking analysis supported the binding affinities of each treatment and targeted enzyme (LasR/LasI/PqsR). The combination treatment demonstrated a near 80% inhibition efficacy on P. aeruginosa-S. aureus-B. cepacia-C. albicans complex in-vitro. These findings can be translated into the development of novel adjuvants to deliver the cocktail treatment in-vivo, thus reducing mortality from chronic biofilm-related infections.
Despite the clinical similarity between systemic lupus erythematosus (SLE) types and inflammatory breast cancer (IBC), IBC is not considered classically “inflammatory” because its inflamed appearance is caused by lymphatic blockage, and genetic characteristics common to both diseases are underexplored. This study sought to reveal those characteristics and reassess IBC’s “inflammatory” status. Shared SLE and IBC genes were found using Python and tested for pathway and transcription factor (TF) enrichment using Enrichr and ChEA3. These analyses yielded inflammatory pathways and cell cycle-related TFs in the upregulated genes, and translational pathways and cell proliferation TFs in the downregulated genes. The similarity between the diseases, in inflammation and altered translational function, potentiates the need for new treatment development.
Gastrointestinal Ulcers are linked to diseases that result in a risk of Colorectal Cancer. As such, analyzing Capsule Endoscopies, which record the digestive tract, for GastroIntestinal Ulcers could assist in the diagnosis of Cancer. Therefore, I implemented a state-of-the-art Machine Learning Model to localize and detect ulcers with higher precision than existing models. The results show significant progress but seem to suggest issues that will be fixed through Data Augmentation. After these improvements, this model will be capable of accurately detecting Ulcers in endoscopy footage, allowing it to assist in diagnosing Crohn’s disease, Ulcerative Colitis, and Colorectal Cancer.
Nearly seven million individuals in the U.S. have Essential Tremor (ET). Differential Gene Expression (DGE) on ET RNA-seq data identified 86 differentially expressed gene transcripts. The gene transcripts were input into Gene Set Enrichment Analysis (GSEA) where five dysregulated gene pathways were identified. After filtering the genes to 32 with optimization, a Random Forest model predicted ET from control 85% of the time. Logistic regression was utilized to analyze the 32 genes individually, and 8 genes had a higher accuracy than 80%. Identifying these biomarkers shows that ET may be related to other neurological diseases more than once thought.
Dense multi-species bacterial biofilms are resistant to most antibiotics. In this study, a combination treatment consisting of FDA-approved concentrations of quorum quenching agents was devised to target three major interspecies biofilm-pathways. Computational analysis (Qiime2/PICRUSt2) of the CF lung microbiome confirmed the necessity of QS in targeted pathways, and docking analysis supported the binding affinities of each treatment and targeted enzyme (LasR/LasI/PqsR). The combination treatment demonstrated a near 80% inhibition efficacy on P. aeruginosa-S. aureus-B. cepacia-C. albicans complex in-vitro. These findings can be translated into the development of novel adjuvants to deliver the cocktail treatment in-vivo, thus reducing mortality from chronic biofilm-related infections.
Coral bleaching is a fatal process in which corals expel their symbiotic dinoflagellates. Restoration efforts have attempted to repair damaged reefs, however, there are over 360,000 square miles of coral reefs worldwide, making it challenging to target conservation efforts. Herein, a machine learning model was developed to predict global locations at risk for coral bleaching. Data was obtained from BCO-DMO and consisted of various coral bleaching events and the parameters under which the bleaching occurred. Various models and classifiers were tested and a 95% accuracy in predicting the likelihood of a bleaching event was obtained.
Shoulder pain is one of the most common physical injuries, as two out of three people experience this condition at some point in their lives. Typical causes of shoulder pain include biceps tendinitis, rotator cuff tear and tendinitis, and bursitis. This study aimed to use convolutional neural network (CNN) models for evaluating ultrasound images to help primary care physicians diagnose shoulder pain. The CNN models implemented in this study are DenseNet 201, Xception, and InceptionResNet. The three models had satisfactory performances that demonstrate the potential of assisting primary care physicians by increasing diagnostic accuracy and providing rapid preliminary results.
The U.S Endangered Species Act (ESA) is legislation implemented to repopulate endangered species affected by the ongoing sixth mass extinction. In this study, the links between species charisma and population trends are investigated to assess the efficacy of the ESA from a non-economics perspective. 367 vertebrate species were ranked based on charisma (through a novel point value system) and their rankings were compared to respective qualitative population trends. Species with higher charisma levels had a greater chance of possessing a “beneficial trend” in their population levels. These findings suggest a detrimental bias, based on outward appearances, present in the ESA’s effort to repopulate at-risk species.
