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We present here a passive and label-free droplet microfluidic platform to sort cells stepwise by lactate and proton secretion from glycolysis. A technology developed in our lab, Sorting by Interfacial Tension (SIFT), sorts droplets containing single cells into two populations based on pH by using interfacial tension. Cellular glycolysis lowers the pH of droplets through proton secretion, enabling passive selection based on interfacial tension and hence single-cell glycolysis. The SIFT technique is expanded here by exploiting the dynamic droplet acidification from surfactant adsorption that leads to a concurrent increase in interfacial tension. This allows multiple microfabricated rails at different downstream positions to isolate cells with distinct glycolytic levels. The device is used to correlate sorted cells with three levels of glycolysis with a conventional surface marker for T-cell activation. As glycolysis is associated with both disease and cell state, this technology facilitates the sorting and analysis of crucial cell subpopulations for applications in oncology, immunology and immunotherapy.

Background: Hypertension is the most prevalent risk factor for mortality globally. Uncontrolled hypertension is associated with excess morbidity and mortality, and nearly one-half of individuals with hypertension do not have the condition under control. Data from electronic health record (EHR) systems may be useful for community hypertension surveillance, filling a gap in local public health departments' community health assessments and supporting the public health data modernization initiatives currently underway. To identify patients with hypertension, computable phenotypes are required. These phenotypes leverage available data elements-such as vitals measurements and medications-to identify patients diagnosed with hypertension. However, there are multiple methodologies for creating a phenotype, and the identification of which method most accurately reflects real-world prevalence rates is needed to support data modernization initiatives., Objective: This study sought to assess the comparability of 6 different EHR-based hypertension prevalence estimates with estimates from a national survey. Each of the prevalence estimates was created using a different computable phenotype. The overarching goal is to identify which phenotypes most closely align with nationally accepted estimations., Methods: Using the 6 different EHR-based computable phenotypes, we calculated hypertension prevalence estimates for Marion County, Indiana, for the period from 2014 to 2015. We extracted hypertension rates from the Behavioral Risk Factor Surveillance System (BRFSS) for the same period. We used the two 1-sided t test (TOST) to test equivalence between BRFSS- and EHR-based prevalence estimates. The TOST was performed at the overall level as well as stratified by age, gender, and race., Results: Using both 80% and 90% CIs, the TOST analysis resulted in 2 computable phenotypes demonstrating rough equivalence to BRFSS estimates. Variation in performance was noted across phenotypes as well as demographics. TOST with 80% CIs demonstrated that the phenotypes had less variance compared to BRFSS estimates within subpopulations, particularly those related to racial categories. Overall, less variance occurred on phenotypes that included vitals measurements., Conclusions: This study demonstrates that certain EHR-derived prevalence estimates may serve as rough substitutes for population-based survey estimates. These outcomes demonstrate the importance of critically assessing which data elements to include in EHR-based computer phenotypes. Using comprehensive data sources, containing complete clinical data as well as data representative of the population, are crucial to producing robust estimates of chronic disease. As public health departments look toward data modernization activities, the EHR may serve to assist in more timely, locally representative estimates for chronic disease prevalence., (©Katie S Allen, Nimish Valvi, P Joseph Gibson, Timothy McFarlane, Brian E Dixon. Originally published in the Online Journal of Public Health Informatics (https://ojphi.jmir.org/), 13.03.2024.)

Background: Electronic health record (EHR) systems are widely used in the United States to document care delivery and outcomes. Health information exchange (HIE) networks, which integrate EHR data from the various health care providers treating patients, are increasingly used to analyze population-level data. Existing methods for population health surveillance of essential hypertension by public health authorities may be complemented using EHR data from HIE networks to characterize disease burden at the community level., Objective: We aimed to derive and validate computable phenotypes (CPs) to estimate hypertension prevalence for population-based surveillance using an HIE network., Methods: Using existing data available from an HIE network, we developed 6 candidate CPs for essential (primary) hypertension in an adult population from a medium-sized Midwestern metropolitan area in the United States. A total of 2 independent clinician reviewers validated the phenotypes through a manual chart review of 150 randomly selected patient records. We assessed the precision of CPs by calculating sensitivity, specificity, positive predictive value (PPV), F 1 -score, and validity of chart reviews using prevalence-adjusted bias-adjusted κ. We further used the most balanced CP to estimate the prevalence of hypertension in the population., Results: Among a cohort of 548,232 adults, 6 CPs produced PPVs ranging from 71% (95% CI 64.3%-76.9%) to 95.7% (95% CI 84.9%-98.9%). The F 1 -score ranged from 0.40 to 0.91. The prevalence-adjusted bias-adjusted κ revealed a high percentage agreement of 0.88 for hypertension. Similarly, interrater agreement for individual phenotype determination demonstrated substantial agreement (range 0.70-0.88) for all 6 phenotypes examined. A phenotype based solely on diagnostic codes possessed reasonable performance (F 1 -score=0.63; PPV=95.1%) but was imbalanced with low sensitivity (47.6%). The most balanced phenotype (F 1 -score=0.91; PPV=83.5%) included diagnosis, blood pressure measurements, and medications and identified 210,764 (38.4%) individuals with hypertension during the study period (2014-2015)., Conclusions: We identified several high-performing phenotypes to identify essential hypertension prevalence for local public health surveillance using EHR data. Given the increasing availability of EHR systems in the United States and other nations, leveraging EHR data has the potential to enhance surveillance of chronic disease in health systems and communities. Yet given variability in performance, public health authorities will need to decide whether to seek optimal balance or declare a preference for algorithms that lean toward sensitivity or specificity to estimate population prevalence of disease., (©Nimish Valvi, Timothy McFarlane, Katie S Allen, P Joseph Gibson, Brian Edward Dixon. Originally published in JMIR Formative Research (https://formative.jmir.org), 27.12.2023.)

Objective: The objective of this study is to validate and report on portability and generalizability of a Natural Language Processing (NLP) method to extract individual social factors from clinical notes, which was originally developed at a different institution., Materials and Methods: A rule-based deterministic state machine NLP model was developed to extract financial insecurity and housing instability using notes from one institution and was applied on all notes written during 6 months at another institution. 10% of positively-classified notes by NLP and the same number of negatively-classified notes were manually annotated. The NLP model was adjusted to accommodate notes at the new site. Accuracy, positive predictive value, sensitivity, and specificity were calculated., Results: More than 6 million notes were processed at the receiving site by the NLP model, which resulted in about 13,000 and 19,000 classified as positive for financial insecurity and housing instability, respectively. The NLP model showed excellent performance on the validation dataset with all measures over 0.87 for both social factors., Discussion: Our study illustrated the need to accommodate institution-specific note-writing templates as well as clinical terminology of emergent diseases when applying NLP model for social factors. A state machine is relatively simple to port effectively across institutions. Our study. showed superior performance to similar generalizability studies for extracting social factors., Conclusion: Rule-based NLP model to extract social factors from clinical notes showed strong portability and generalizability across organizationally and geographically distinct institutions. With only relatively simple modifications, we obtained promising performance from an NLP-based model., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Objective: The annual American College of Medical Informatics (ACMI) symposium focused discussion on the national public health information systems (PHIS) infrastructure to support public health goals. The objective of this article is to present the strengths, weaknesses, threats, and opportunities (SWOT) identified by public health and informatics leaders in attendance., Materials and Methods: The Symposium provided a venue for experts in biomedical informatics and public health to brainstorm, identify, and discuss top PHIS challenges. Two conceptual frameworks, SWOT and the Informatics Stack, guided discussion and were used to organize factors and themes identified through a qualitative approach., Results: A total of 57 unique factors related to the current PHIS were identified, including 9 strengths, 22 weaknesses, 14 opportunities, and 14 threats, which were consolidated into 22 themes according to the Stack. Most themes (68%) clustered at the top of the Stack. Three overarching opportunities were especially prominent: (1) addressing the needs for sustainable funding, (2) leveraging existing infrastructure and processes for information exchange and system development that meets public health goals, and (3) preparing the public health workforce to benefit from available resources., Discussion: The PHIS is unarguably overdue for a strategically designed, technology-enabled, information infrastructure for delivering day-to-day essential public health services and to respond effectively to public health emergencies., Conclusion: Most of the themes identified concerned context, people, and processes rather than technical elements. We recommend that public health leadership consider the possible actions and leverage informatics expertise as we collectively prepare for the future., (© The Author(s) 2023. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

The COVID-19 pandemic exposed multiple weaknesses in the nation's public health system. Therefore, the American College of Medical Informatics selected "Rebuilding the Nation's Public Health Informatics Infrastructure" as the theme for its annual symposium. Experts in biomedical informatics and public health discussed strategies to strengthen the US public health information infrastructure through policy, education, research, and development. This article summarizes policy recommendations for the biomedical informatics community postpandemic. First, the nation must perceive the health data infrastructure to be a matter of national security. The nation must further invest significantly more in its health data infrastructure. Investments should include the education and training of the public health workforce as informaticians in this domain are currently limited. Finally, investments should strengthen and expand health data utilities that increasingly play a critical role in exchanging information across public health and healthcare organizations., (© The Author(s) 2023. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Objective: This study sought to create natural language processing algorithms to extract the presence of social factors from clinical text in 3 areas: (1) housing, (2) financial, and (3) unemployment. For generalizability, finalized models were validated on data from a separate health system for generalizability., Materials and Methods: Notes from 2 healthcare systems, representing a variety of note types, were utilized. To train models, the study utilized n-grams to identify keywords and implemented natural language processing (NLP) state machines across all note types. Manual review was conducted to determine performance. Sampling was based on a set percentage of notes, based on the prevalence of social need. Models were optimized over multiple training and evaluation cycles. Performance metrics were calculated using positive predictive value (PPV), negative predictive value, sensitivity, and specificity., Results: PPV for housing rose from 0.71 to 0.95 over 3 training runs. PPV for financial rose from 0.83 to 0.89 over 2 training iterations, while PPV for unemployment rose from 0.78 to 0.88 over 3 iterations. The test data resulted in PPVs of 0.94, 0.97, and 0.95 for housing, financial, and unemployment, respectively. Final specificity scores were 0.95, 0.97, and 0.95 for housing, financial, and unemployment, respectively., Discussion: We developed 3 rule-based NLP algorithms, trained across health systems. While this is a less sophisticated approach, the algorithms demonstrated a high degree of generalizability, maintaining >0.85 across all predictive performance metrics., Conclusion: The rule-based NLP algorithms demonstrated consistent performance in identifying 3 social factors within clinical text. These methods may be a part of a strategy to measure social factors within an institution., Competing Interests: None declared., (© The Author(s) 2023. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

Abstract: Chlamydia trachomatis (CT) and Neisseria gonorrhoeae (GC) are the 2 most common reported sexually transmitted infections in the United States. Current recommendations are to presumptively treat CT and/or GC in persons with symptoms or known contact. This review characterizes the literature around studies with presumptive treatment, including identifying rates of presumptive treatment and overtreatment and undertreatment rates. Of the 18 articles that met our inclusion criteria, 6 pertained to outpatient settings. In the outpatient setting, presumptive treatment rates, for both asymptomatic and symptomic patients, varied from 12% to 100%, and the percent positive of those presumptively treated ranged from 25% to 46%. Three studies also reported data on positive results in patients not presumptively treated, which ranged from 2% to 9%. Two studies reported median follow-up time for untreated, which was roughly 9 days. The remaining 12 articles pertained to the emergency setting where presumptive treatment rates, for both asymptomatic and symptomic patients, varied from 16% to 91%, the percent positive following presumptive treatment ranged from 14% to 59%. Positive results without presumptive treatment ranged from 4% to 52%. Two studies reported the percent positive without any treatment (6% and 32%, respectively) and one reported follow-up time for untreated infections (median, 4.8 days). Rates of presumptive treatment, as well as rates of overtreatment or undertreatment vary widely across studies and within care settings. Given the large variability in presumptive treatment, the focus on urban settings, and minimal focus on social determinants of health, additional studies are needed to guide treatment practices for CT and GC in outpatient and emergency settings., Competing Interests: Conflict of Interest: None declared., (Copyright © 2022 American Sexually Transmitted Diseases Association. All rights reserved.)

Plant xylem colonization is the hallmark of vascular wilt diseases caused by phytopathogens within the Fusarium oxysporum species complex. Recently, xylem colonization has also been reported among endophytic F. oxysporum strains, resulting in some uncertainty. This study compares xylem colonization processes by pathogenic versus endophytic strains in Arabidopsis thaliana and Solanum lycopersicum , using Arabidopsis pathogen Fo5176, tomato pathogen Fol4287, and the endophyte Fo47, which can colonize both plant hosts. We observed that all strains were able to advance from epidermis to endodermis within 3 days postinoculation (dpi) and reached the root xylem at 4 dpi. However, this shared progression was restricted to lateral roots and the elongation zone of the primary root. Only pathogens reached the xylem above the primary-root maturation zone (PMZ). Related to the distinct colonization patterns, we also observed stronger induction of callose at the PMZ and lignin deposition at primary-lateral root junctions by the endophyte in both plants. This observation was further supported by stronger induction of Arabidopsis genes involved in callose and lignin biosynthesis during the endophytic colonization (Fo47) compared with the pathogenic interaction (Fo5176). Moreover, both pathogens encode more plant cell wall-degrading enzymes than the endophyte Fo47. Therefore, observed differences in callose and lignin deposition could be the combination of host production and the subsequent fungal degradation. In summary, this study demonstrates spatial differences between endophytic and pathogenic colonization, strongly suggesting that further investigations of molecular arm-races are needed to understand how plants differentiate friend from foe. [Formula: see text] Copyright © 2023 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

Objectives: The Chronic Pain Treatment Tracker (Tx Tracker) is a prototype decision support tool to aid primary care clinicians when caring for patients with chronic noncancer pain. This study evaluated clinicians' perceived utility of Tx Tracker in meeting information needs and identifying treatment options, and preferences for visual design., Methods: We conducted 12 semi-structured interviews with primary care clinicians from four health systems in Indiana. The interviews were conducted in two waves, with prototype and interview guide revisions after the first six interviews. The interviews included exploration of Tx Tracker using a think-aloud approach and a clinical scenario. Clinicians were presented with a patient scenario and asked to use Tx Tracker to make a treatment recommendation. Last, participants answered several evaluation questions. Detailed field notes were collected, coded, and thematically analyzed by four analysts., Results: We identified several themes: the need for clinicians to be presented with a comprehensive patient history, the usefulness of Tx Tracker in patient discussions about treatment planning, potential usefulness of Tx Tracker for patients with high uncertainty or risk, potential usefulness of Tx Tracker in aggregating scattered information, variability in expectations about workflows, skepticism about underlying electronic health record data quality, interest in using Tx Tracker to annotate or update information, interest in using Tx Tracker to translate information to clinical action, desire for interface with visual cues for risks, warnings, or treatment options, and desire for interactive functionality., Conclusion: Tools like Tx Tracker, by aggregating key information about past, current, and potential future treatments, may help clinicians collaborate with their patients in choosing the best pain treatments. Still, the use and usefulness of Tx Tracker likely relies on continued improvement of its functionality, accurate and complete underlying data, and tailored integration with varying workflows, care team roles, and user preferences., Competing Interests: None declared., (Thieme. All rights reserved.)

Background: Nonclinical determinants of health are of increasing importance to health care delivery and health policy. Concurrent with growing interest in better addressing patients' nonmedical issues is the exponential growth in availability of data sources that provide insight into these nonclinical determinants of health., Objective: This review aimed to characterize the state of the existing literature on the use of nonclinical health indicators in conjunction with clinical data sources., Methods: We conducted a rapid review of articles and relevant agency publications published in English. Eligible studies described the effect of, the methods for, or the need for combining nonclinical data with clinical data and were published in the United States between January 2010 and April 2018. Additional reports were obtained by manual searching. Records were screened for inclusion in 2 rounds by 4 trained reviewers with interrater reliability checks. From each article, we abstracted the measures, data sources, and level of measurement (individual or aggregate) for each nonclinical determinant of health reported., Results: A total of 178 articles were included in the review. The articles collectively reported on 744 different nonclinical determinants of health measures. Measures related to socioeconomic status and material conditions were most prevalent (included in 90% of articles), followed by the closely related domain of social circumstances (included in 25% of articles), reflecting the widespread availability and use of standard demographic measures such as household income, marital status, education, race, and ethnicity in public health surveillance. Measures related to health-related behaviors (eg, smoking, diet, tobacco, and substance abuse), the built environment (eg, transportation, sidewalks, and buildings), natural environment (eg, air quality and pollution), and health services and conditions (eg, provider of care supply, utilization, and disease prevalence) were less common, whereas measures related to public policies were rare. When combining nonclinical and clinical data, a majority of studies associated aggregate, area-level nonclinical measures with individual-level clinical data by matching geographical location., Conclusions: A variety of nonclinical determinants of health measures have been widely but unevenly used in conjunction with clinical data to support population health research., (©Elizabeth Golembiewski, Katie S Allen, Amber M Blackmon, Rachel J Hinrichs, Joshua R Vest. Originally published in JMIR Public Health and Surveillance (http://publichealth.jmir.org), 07.10.2019.)

Current and emerging plant diseases caused by obligate parasitic microbes such as rusts, downy mildews, and powdery mildews threaten worldwide crop production and food safety. These obligate parasites are typically unculturable in the laboratory, posing technical challenges to characterize them at the genetic and genomic level. Here we have developed a data analysis pipeline integrating several bioinformatic software programs. This pipeline facilitates rapid gene discovery and expression analysis of a plant host and its obligate parasite simultaneously by next generation sequencing of mixed host and pathogen RNA (i.e., metatranscriptomics). We applied this pipeline to metatranscriptomic sequencing data of sweet basil (Ocimum basilicum) and its obligate downy mildew parasite Peronospora belbahrii, both lacking a sequenced genome. Even with a single data point, we were able to identify both candidate host defense genes and pathogen virulence genes that are highly expressed during infection. This demonstrates the power of this pipeline for identifying genes important in host-pathogen interactions without prior genomic information for either the plant host or the obligate biotrophic pathogen. The simplicity of this pipeline makes it accessible to researchers with limited computational skills and applicable to metatranscriptomic data analysis in a wide range of plant-obligate-parasite systems.

Melanoma patients exhibit changes in immune responsiveness in the local tumor environment, draining lymph nodes, and peripheral blood. Immune-targeting therapies are revolutionizing melanoma patient care increasingly, and studies show that patients derive clinical benefit from these newer agents. Nonetheless, predicting which patients will benefit from these costly therapies remains a challenge. In an effort to capture individual differences in immune responsiveness, we are analyzing patterns of gene expression in human peripheral blood cells using RNAseq. Focusing on CD4+ peripheral blood cells, we describe multiple categories of immune regulating genes, which are expressed in highly ordered patterns shared by cohorts of healthy subjects and stage IV melanoma patients. Despite displaying conservation in overall transcriptome structure, CD4+ peripheral blood cells from melanoma patients differ quantitatively from healthy subjects in the expression of more than 2000 genes. Moreover, 1300 differentially expressed genes are found in transcript response patterns following activation of CD4+ cells ex vivo, suggesting that widespread functional discrepancies differentiate the immune systems of healthy subjects and melanoma patients. While our analysis reveals that the transcriptome architecture characteristic of healthy subjects is maintained in cancer patients, the genes expressed differentially among individuals and across cohorts provide opportunities for understanding variable immune states as well as response potentials, thus establishing a foundation for predicting individual responses to stimuli such as immunotherapeutic agents., Competing Interests: No author has a financial and/or commercial conflict of interest.

Inflammation and coagulation are so tightly linked that the cytokine storm which accompanies the development of sepsis initiates thrombin activation and the development of an intravascular coagulopathy. This review examines the interaction between the inflammatory and coagulation cascades, as well as the role of endogenous anticoagulants in regulating this interaction and dampening the activity of both pathways. Clinical trials attempting to improve outcomes in patients with severe sepsis by inhibiting thrombin generation with heparin and or endogenous anticoagulants are reviewed. In general, these trials have failed to demonstrate that anticoagulant therapy is associated with improvement in mortality or morbidity. While it is possible that selective patients who are severely ill with a high expected mortality may be shown to benefit from such therapy, at the present time none of these anticoagulants are neither approved nor can they be recommended for the treatment of sepsis.

Between January and April 2012, the city of Long Beach Department of Health and Human Services investigated an outbreak involving 19 case patients who had tested positive for Salmonella enterica serotype Typhimurium with indistinguishable pulsed-field gel electrophoresis patterns. All cases were residents of or traveled to the city of Long Beach, California, during their incubation period, and the majority of patients reported eating at one of two restaurants in Long Beach. This article describes the outbreak investigation that traced the source to an asymptomatic food handler working at both restaurants and highlights the importance of maintaining a high index of suspicion for food handlers when faced with local outbreaks of diarrheal illness.

Picornaviruses have been developed as potential therapies for gene delivery and vaccination. One drawback to their use is the potential for recombination and viral persistence. Therefore, the engineering strategies used must take into account the possibility for virus escape. We have developed Theiler's murine encephalomyelitis virus (TMEV) as a potential vaccine vector for use in immunotherapy. This study shows that insertion of a vaccine epitope at a unique site within the TMEV leader protein can dramatically increase the type I interferon (IFN) response to infection and promote rapid viral clearance. This live virus vaccine maintains its ability to drive antigen-specific CD8(+) T-cell responses to a model antigen as well as to the weakly immunogenic tumor antigen Her2/neu. Furthermore, the epitope integration site does not affect the efficacy of this vaccine as cancer immunotherapy for treating models of melanoma and breast cancer as demonstrated by delayed tumor outgrowth and increased survival in animals implanted with these tumors. These findings show that an attenuated virus retaining limited ability to replicate nonetheless can effectively mobilize CD8(+) cellular immunity and will be important for the design of picornavirus vectors used as immunotherapy in clinical settings.

MRI is sensitive to tissue pathology in multiple sclerosis (MS); however, most lesional MRI findings have limited correlation with disability. Chronic T1 hypointense lesions or "T1 black holes" (T1BH), observed in a subset of MS patients and thought to represent axonal damage, show moderate to strong correlation with disability. The pathogenesis of T1BH remains unclear. We previously reported the first and as of yet only model of T1BH formation in the Theiler's murine encephalitis virus induced model of acute CNS neuroinflammation induced injury, where CD8 T-cells are critical mediators of axonal damage and related T1BH formation. The purpose of this study was to further analyze the role of CD8 and CD4 T-cells through adoptive transfer experiments and to determine if the relevant CD8 T-cells are classic epitope specific lymphocytes or different subsets. C57BL/6 mice were used as donors and RAG-1 deficient mice as hosts in our adoptive transfer experiments. In vivo 3-dimensional MRI images were acquired using a 7 Tesla small animal MRI system. For image analysis, we used semi-automated methods in Analyze 9.1; transfer efficiency was monitored using FACS of brain infiltrating lymphocytes. Using a peptide depletion method, we demonstrated that the majority of CD8 T-cells are classic epitope specific cytotoxic cells. CD8 T-cell transfer successfully restored the immune system's capability to mediate T1BH formation in animals that lack adaptive immune system, whereas CD4 T-cell transfer results in an attenuated phenotype with significantly less T1BH formation. These findings demonstrate contrasting roles for these cell types, with additional evidence for a direct pathogenic role of CD8 T-cells in our model of T1 black hole formation.

The induction of sterilizing T-cell responses to tumors is a major goal in the development of T-cell vaccines for treating cancer. Although specific components of anti-viral CD8+ immunity are well characterized, we still lack the ability to mimic viral CD8+ T-cell responses in therapeutic settings for treating cancers. Infection with the picornavirus Theiler's murine encephalomyelitis virus (TMEV) induces a strong sterilizing CD8+ T-cell response. In the absence of sterilizing immunity, the virus causes a persistent infection. We capitalized on the ability of TMEV to induce strong cellular immunity even under conditions of immune deficiency by modifying the virus to evaluate its potential as a T-cell vaccine. The introduction of defined CD8+ T-cell epitopes into the leader sequence of the TMEV genome generates an attenuated vaccine strain that can efficiently drive CD8+ T-cell responses to the targeted antigen. This virus activates T-cells in a manner that is capable of inducing targeted tissue damage and glucose dysregulation in an adoptive T-cell transfer model of diabetes mellitus. As a therapeutic vaccine for the treatment of established melanoma, epitope-modified TMEV can induce strong cytotoxic T-cell responses and promote infiltration of the T-cells into established tumors, ultimately leading to a delay in tumor growth and improved survival of vaccinated animals. We propose that epitope-modified TMEV is an excellent candidate for further development as a human T-cell vaccine for use in immunotherapy.

Individual Practice Associations (IPAs) must be able to recruit physicians from the community to compete in the future. This article reports the results of a study to assess the factors that influenced physicians in the Research Triangle area of North Carolina to join a primary care network type of IPA. Results indicate that physicians with lower incomes and fewer physician visits and those who were newly established in the community were more likely to join. Peers had a strong influence on their decisions, while Blue Cross/Blue Shield marketing representatives did not.