A Prospective Controlled Study of Magnetic Resonance Imaging of the Brain in Gay Men and Parenteral Drug Users with Human Immunodeficiency Virus Infection

To detect the earliest structural changes in the brain in human immunodeficiency virus (HIV) infection, 118 gay men and 115 parenteral drug users enrolled in a study of the natural history of HIV infection underwent magnetic resonance imaging evaluations. Routine T2-weighted and heavily T2-weighted scans for quantification of brain water were obtained, blinded to HIV serostatus. Atrophy and foci of increased signal did not correlate with any medical, immunologic, neurologic, or neuropsychologic parameters in the group as a whole, or in the gay men or parenteral drug user subgroups. Three subjects had progressive multifocal leukoencephalopathy and one had central nervous system lymphoma. In a subgroup in whom intracranial water percent was calculated, correlations were found with CD4 counts and CD4/CD8 ratios. We conclude that standard magnetic resonance imaging of the brain does not differentiate asymptomatic and mildly symptomatic HIV-positive individuals from HIV-negative individuals, regardless of risk group. However, intracranial water percent may distinguish HIV-positive from HIV-negative individuals because it correlates with raw CD4 counts and CD4/CD8 ratios

Prostate Clinical Outlook Visualization System for Patients and Clinicians Considering Cyberknife Treatment—A Personalized Approach

Background: When a patient presents with localized prostate cancer, referral for radiation oncology consultation includes a discussion of likely outcomes of therapy. Among current radiation treatments for prostate cancers, hypo-fractionated stereotactic body radiation therapy (SBRT) has gained clinical acceptance based on efficacy, short duration of treatment, and the potential radiobiological advantages. The Prostate Clinical Outlook Visualization System (PCOVS) was developed to provide the patient and the clinician with a tool to visualize probable treatment outcomes using institutional, patient specific data for comparing results of treatment. Methods: We calculated the prostate cancer outcomes—for each prospective patient using the EPIC-26 quality of life parameters based on clinical outcomes data of 580 prostate cancer patients who were treated with SBRT. We applied Kaplan-Meier analysis using the ASTRO definition for biochemical recurrence (BCR) free survival and likely outcome and the PCOVS nomogram to calculate parameters for quality of life. Open-source R, RShiny, and MySQL were used to develop a modularized architecture system. Results: The PCOVS presents patient specific risk scores in a gauge chart style and risk free probability bar plots to compare the treatment data of patients treated with SBRT. The PCOVS generates reports, in PDF, which consists of a comparison charts of risk free probabilities late effects and gauge charts of risk scores. This system is now being expanded as a web-based service to patients. Conclusions: The PCOVS visualized patient specific likely outcomes were compared to treatment data from a single department, helping the patient and the clinician to visualize likely outcomes. The PCOVS approach can be expanded to other specialties of oncology with the flexible, modularized architecture, which can be customized by changing independent modules

G&I Genomics & Informatics Perspectives on Clinical Informatics: Integrating Large-Scale Clinical, Genomic, and Health Information for Clinical Care

The advances in electronic medical records (EMRs) and bioinformatics (BI) represent two significant trends in healthcare. The widespread adoption of EMR systems and the completion of the Human Genome Project developed the technologies for data acquisition, analysis, and visualization in two different domains. The massive amount of data from both clinical and biology domains is expected to provide personalized, preventive, and predictive healthcare services in the near future. The integrated use of EMR and BI data needs to consider four key informatics areas: data modeling, analytics, standardization, and privacy. Bioclinical data warehouses integrating heterogeneous patient-related clinical or omics data should be considered. The representative standardization effort by the Clinical Bioinformatics Ontology (CBO) aims to provide uniquely identified concepts to include molecular pathology terminologies. Since individual genome data are easily used to predict current and future health status, different safeguards to ensure confidentiality should be considered. In this paper, we focused on the informatics aspects of integrating the EMR community and BI community by identifying opportunities, challenges, and approaches to provide the best possible care service for our patients and the population

Perspectives on Clinical Informatics: Integrating Large-Scale Clinical, Genomic, and Health Information for Clinical Care

The advances in electronic medical records (EMRs) and bioinformatics (BI) represent two significant trends in healthcare. The widespread adoption of EMR systems and the completion of the Human Genome Project developed the technologies for data acquisition, analysis, and visualization in two different domains. The massive amount of data from both clinical and biology domains is expected to provide personalized, preventive, and predictive healthcare services in the near future. The integrated use of EMR and BI data needs to consider four key informatics areas: data modeling, analytics, standardization, and privacy. Bioclinical data warehouses integrating heterogeneous patient-related clinical or omics data should be considered. The representative standardization effort by the Clinical Bioinformatics Ontology (CBO) aims to provide uniquely identified concepts to include molecular pathology terminologies. Since individual genome data are easily used to predict current and future health status, different safeguards to ensure confidentiality should be considered. In this paper, we focused on the informatics aspects of integrating the EMR community and BI community by identifying opportunities, challenges, and approaches to provide the best possible care service for our patients and the population

The Prostate Clinical Outlook (PCO) Classifier Application for Predicting Biochemical Recurrences in Patients Treated by Stereotactic Body Radiation Therapy (SBRT)

(1) Background: Prostate cancer risk classifiers have been used for predicting surgical and radiation therapy outcomes; however, a classifier for predicting biochemical recurrence (BCR) in patients undergoing stereotactic body radiation therapy (SBRT) is not available. We attempted to develop a model that creates a risk classifier to predict BCR in patients considering SBRT. (2) Methods: We studied the outcomes of 809 patients treated with SBRT between August 2007 and November 2016. We used Cox regression analysis with time to BCR as the outcome to develop a model that calculates a prostate clinical outlook (PCO) score based on age at diagnosis, clinical-radiological staging, and a modified risk level. We then created the PCO classifier application, which uses the model we created to categorize patients into risk groups based on multiple factors. We assessed the concordance index (c-index) to determine the accuracy of the PCO classifier application and compared the results to the D’Amico and Kattan nomogram classifications. (3) Results: The calculated PCO scores ranged from 0 to 156 points. The PCO classifier application categorized patients into three risk-groups, with 5-year BCR-free survival rates of 98.3% for low risk (n = 137), 95.4% for intermediate risk (n = 570), and 86.4% for high risk (n = 102). We demonstrated the improved prognostic power of the PCO classifier application, with a c-index of 0.75 (training set) and 0.67 (validation set); the c-index of the Kattan nomogram was 0.62 and 0.63, respectively, and that of the D’Amico classifier was 0.64 and 0.64, respectively. (4) Conclusions: The PCO classifier application is a predictive tool for employing readily available clinical parameters to stratify prostate cancer patients and to predict the probability of BCR after SBRT

A Prospective Controlled Study of Magnetic Resonance Imaging of the Brain in Gay Men and Parenteral Drug Users with Human Immunodeficiency Virus Infection

To detect the earliest structural changes in the brain in human immunodeficiency virus (HIV) infection, 118 gay men and 115 parenteral drug users enrolled in a study of the natural history of HIV infection underwent magnetic resonance imaging evaluations. Routine T2-weighted and heavily T2-weighted scans for quantification of brain water were obtained, blinded to HIV serostatus. Atrophy and foci of increased signal did not correlate with any medical, immunologic, neurologic, or neuropsychologic parameters in the group as a whole, or in the gay men or parenteral drug user subgroups. Three subjects had progressive multifocal leukoencephalopathy and one had central nervous system lymphoma. In a subgroup in whom intracranial water percent was calculated, correlations were found with CD4 counts and CD4/CD8 ratios. We conclude that standard magnetic resonance imaging of the brain does not differentiate asymptomatic and mildly symptomatic HIV-positive individuals from HIV-negative individuals, regardless of risk group. However, intracranial water percent may distinguish HIV-positive from HIV-negative individuals because it correlates with raw CD4 counts and CD4/CD8 ratios

A Soybean Transcript Map: Gene Distribution, Haplotype and Single-Nucleotide Polymorphism Analysis

The first genetic transcript map of the soybean genome was created by mapping one SNP in each of 1141 genes in one or more of three recombinant inbred line mapping populations, thus providing a picture of the distribution of genic sequences across the mapped portion of the genome. Single-nucleotide polymorphisms (SNPs) were discovered via the resequencing of sequence-tagged sites (STSs) developed from expressed sequence tag (EST) sequence. From an initial set of 9459 polymerase chain reaction primer sets designed to a diverse set of genes, 4240 STSs were amplified and sequenced in each of six diverse soybean genotypes. In the resulting 2.44 Mbp of aligned sequence, a total of 5551 SNPs were discovered, including 4712 single-base changes and 839 indels for an average nucleotide diversity of θ = 0.000997. The analysis of the observed genetic distances between adjacent genes vs. the theoretical distribution based upon the assumption of a random distribution of genes across the 20 soybean linkage groups clearly indicated that genes were clustered. Of the 1141 genes, 291 mapped to 72 of the 112 gaps of 5–10 cM in the preexisting simple sequence repeat (SSR)-based map, while 111 genes mapped in 19 of the 26 gaps >10 cM. The addition of 1141 sequence-based genic markers to the soybean genome map will provide an important resource to soybean geneticists for quantitative trait locus discovery and map-based cloning, as well as to soybean breeders who increasingly depend upon marker-assisted selection in cultivar improvement