27740 research outputs found
Sort by
Systematic Evaluation of Batch Effects in The Cancer Genome Atlas Program Underscores the Need for Batch-Controlled Analysis
No full textPurpose: To systematically assess the impact of batch effects on differential expression of isomiR, mRNA, and non-coding RNA (sncRNA) across 13 The Cancer Genome Atlas (TCGA) cancer types and develop a standardized, batch-controlled analysis protocol for TCGA datasets.
Methods: Data analysis and visualization were performed using Python v3.12. Plots were generated using matplotlib and seaborn. isomiR, mRNA, and sncRNA sequences across 13 TCGA cancer types were used. Principal Component Analysis (PCA) was performed using scikit-learn for dimensionality reduction. Differential expression was conducted using DESeq2 under the following protocols: 1) primary tumor samples vs matched normal samples processed in the same batch as the control; 2) all primary tumor vs all normal samples with batch correction; 3) randomly down-sampled primary tumor to the same sample size as normal vs all normal samples without batch correction; and 4) randomly down-sampled primary tumor to the same sample size as normal vs all normal samples with batch correction.
Results: PCA revealed distinct clustering of normal and primary tumor samples across 13 cancer types and all RNA molecules, indicating the presence of batch effects. Moreover, using differential expression outcomes from normal and primary tumor samples processed within the same batch as control, we found that existing batch effects introduce false-positive differentially expressed molecules and obscure molecules that are potentially truly differentially expressed. After correcting batch effects using DESeq2, we observed a greater overlap in differentially expressed molecules between the control and the batch-corrected analyses across 13 cancer types. Additionally, we identified a set of non-overlapping differentially expressed molecules that appeared exclusively in the control analysis.
Conclusions: TCGA plays a critical role in guiding the development of molecular markers used in clinical research and translational oncology. However, because batch effects in TCGA data sets are not comprehensively evaluated in the literature, researchers may be misled by batch-driven artifacts. We recommend implementing a batch-controlled protocol when analyzing TCGA datasets to minimize false positives and identify true differentially expressed molecules that may be masked by batch effects.https://jdc.jefferson.edu/aoa_research_symposium_posters/1015/thumbnail.jp
Domains that Influence Underrepresented Community Wellbeing and Engagement: A Literature Review
No full textIntroduction
Inner-city neighborhoods, such as those in Philadelphia, vary in demographics, resources, access, safety, and overall health. Local non-profits that serve these communities require more understanding of the areas they serve to efficiently align resources and meet the local community needs. Community partnerships, including youth in the community, are key informants as they share lived experiences, navigate neighborhood conditions, and identify barriers and priorities
Assessing Familiarity and Comfortability of Phase 2 SKMC Students in Working with the Intellectually Disabled Population
No full textPurpose: People living with an intellectual or developmental disability (IDD) are at increased risk of poor health outcomes due to inadequate access to quality healthcare. Literature suggests that trainees are underprepared to adjust their clinical practice to treat these patients, which may implicate undergraduate medical education curriculum. This study assessed the comfortability of SKMC students who have completed Phase 2 of the JeffMD curriculum in performing clinical tasks while caring for patients with IDD, and if that comfort is affected by prior experience. We predict that students will report low levels of comfort and be engaged in an educational intervention.
Methods: Curricular assessment was performed via quantitative survey, administered to SKMC students who completed Phase 2 of the JeffMD curriculum. The survey was designed and delivered via REDCap. Survey assessment of comfortability employed matrices and other rating scales. Demographics and prior experience data were collected. Simple descriptive and non-parametric statistical testing were completed to analyze data.
Results: Students indicated the least comfort with reviewing care plans with IDD patients, adapting exam skills, and identifying comorbidities. Students with prior experience working with the IDD population were more comfortable (p=0.012). The majority of students, regardless of planned specialty, identified need and interest in curricular intervention.
Conclusions: Results indicate a lack of comfort among students in caring for patients with IDD. Prior experience reduces this deficit. This study identifies an opportunity to fill an educational gap in caring for individuals with IDD, which can lessen the burden of disparities and improve healthcare outcomes for this vulnerable population.https://jdc.jefferson.edu/aoa_research_symposium_posters/1017/thumbnail.jp
Designing a Wellness Initiative Residents Actually Use: Data from Four Years of Opt-Out Check-Ins
No full textBackground Resident well-being remains a critical concern in GME, with increasing rates of burnout, mental health challenges, and persistent barriers to accessing care.2,4 Burnout among residents is linked to higher rates of medical errors and lower patient satisfaction.4,5 ACGME emphasizes the importance of fostering a culture of wellness including the opportunity for residents to attend healthcare appointments during working hours; however, programs often struggle to translate these goals into sustainable, practical interventions.1 Opt-out programs have been used to target residents and medical students to increase help-seeking and provide access to mental health care with limited barriers. Stigma, time constraints, financial barriers, and long waitlists can further deter residents from seeking support.3,4,5 Opt-in programs require residents to self-schedule and often have low engagement; opt-out programs automatically schedule sessions, increasing participation and normalizing mental health care.4,5https://jdc.jefferson.edu/phbposters/1008/thumbnail.jp
MicroRNAs in Prostate Cancer Liquid Biopsies: Early Detection, Prognosis, and Treatment Monitoring
No full textProstate cancer (PCa) is a common malignancy in men worldwide, with incidence projected to rise in the coming years. Traditional screening and diagnostic methods, such as prostate-specific antigen (PSA) testing and biopsy, face limitations in specificity and invasiveness. Circulating microRNAs (miRNAs) have emerged as stable, non-invasive biomarkers obtainable via liquid biopsies (blood, urine, semen) that could transform PCa management. These small regulatory RNAs reflect underlying tumor biology and are detectable at early disease stages, enabling improved early detection when used alongside or in place of PSA. Distinct miRNA expression patterns correlate with tumor aggressiveness. For example, miR-141 and miR-375 are elevated in metastatic cases, whereas let-7 family members and miR-326 are upregulated in aggressive disease, highlighting their prognostic value. Moreover, dynamic changes in reported miRNAs during therapy provide real-time insights into treatment response. In androgen-deprivation therapy (ADT), oncogenic miRNAs, such as miR-21 and miR-125b, increase upon resistance, whereas a decline in tumor-suppressive miRNAs, such as miR-23b/-27b, flags the transition to castration-resistant PCa (CRPC). Similarly, baseline levels of miRNAs (e.g., miR-200b/c, miR-20a) can predict chemotherapy outcomes. Integrating multi-miRNA panels has demonstrated superior accuracy for risk stratification and monitoring, paving the way for personalized treatment. Although promising, clinical implementation of miRNA-based assays requires further validation, standardization of protocols, and large-scale prospective studies. Harnessing circulating miRNAs could usher in a new era of precision oncology for PCa, improving early diagnosis, prognostication, and real-time therapeutic guidance
Large Language Models Improve Readability of Patient Education Materials on Vascular Conditions
No full textObjective: Patient education materials frequently exceed the recommended sixth-grade reading level. Although large language models (LLMs) have shown inconsistent accuracy in medical query responses, they have demonstrated promise in simplifying complex text. This capability has not yet been studied in vascular patient education materials. This study evaluates whether ChatGPT-4o and Gemini 1.5 Pro can improve the readability of Society for Vascular Surgery (SVS) patient education flyers. Methods: SVS health flyers were selected based on five common vascular conditions: abdominal aortic aneurysm, carotid artery disease, deep vein thrombosis, peripheral artery disease, and varicose veins. Each flyer was submitted to ChatGPT-4o and Gemini 1.5 Pro, which generated simplified versions using structured Extensible Markup Language prompts to guide consistent editing. Vascular surgeons, who were blinded to the source of each flyer, independently scored the original and LLM-modified flyers on accuracy, comprehensiveness, and understandability using a 0 to 10 Likert scale. Readability was assessed using the Average Reading Level Consensus tool, and textual features—including word count, sentence count, syllables per word, and percentage of complex words—were quantified. Paired t-tests were used to analyze differences in readability scores. Analysis of variance with Tukey honestly significant difference post hoc testing was used to assess textual characteristics. Results: The original SVS flyers had an average reading grade level of 10.61 (standard deviation [SD], 0.88). Gemini and ChatGPT-4o significantly reduced the reading level to 8.18 (SD, 1.24; P = .012) and 8.37 (SD, 0.88; P = .00013), respectively. SVS flyers averaged 605 words, 29.8 sentences, 1.7 syllables per word, and 20.4% complex words. Both LLMs significantly reduced syllables per word (Gemini: 1.52; P \u3c .0001; ChatGPT: 1.53; P \u3c .0001) and the proportion of complex words (Gemini: 12.7%; P \u3c .0001; ChatGPT: 13.6%; P \u3c .0001). There were no significant differences between the Gemini and ChatGPT outputs in readability or textual metrics. Physician scores for accuracy, comprehensiveness, and understandability showed no significant differences between SVS and either LLM model, nor between the two LLMs. Conclusions: LLMs significantly improved the readability of SVS patient education materials by approximately two grade levels without compromising content accuracy. These findings support the use of LLMs to enhance the accessibility of medical information when grounded in trusted source material, rather than relying on unprompted content generation
Harm Reduction and Public Health
No full textLecture Objectives Describe 3 adverse outcomes related to substance use List 2 principles of harm reduction Identify 2 evidence-based harm reduction program
Plans4Care, a Web Application Providing Caregivers Personalized Solutions to Manage Dementia-Related Care Challenges: Proof of Concept and Efficacy Trial
No full textOBJECTIVE: Few dementia caregivers have access to evidence-based support programs. Web applications (app) may address this gap. Plans4Care, a web app, provides caregivers with on-demand personalized solutions to address care challenges. We present results of a proof-of-concept study and describe a trial protocol to test efficacy.
METHODS: To use Plans4Care, caregivers respond to brief onboarding questions, assess dementia patients\u27 cognitive function, identify care challenges, and generate action plans (personalized strategies). Telehealth sessions with dementia-trained care advisors are available. A proof-of-concept study evaluated a clickable prototype using standardized technology scales to determine if \u3e75 % scored positively on four criteria (acceptability, feasibility, appropriateness, ease-of-use). The fully developed app will be tested in a prospective randomized trial (n = 160 caregivers). Caregivers will be assigned to an immediate treatment or 6-month delayed control group to evaluate short (3, 6-months) and long-term (12-month) outcomes on caregiver wellbeing and healthcare utilization of caregivers and people with dementia. App use patterns and care advisor interactions will be evaluated.
FINDINGS/RESULTS: Proof-of-concept testing (N = 25 caregivers) resulted in high ratings (100 % achieved for acceptability and feasibility; 80 % for appropriateness; 96 % for usability), supporting full app development. The app contains \u3e100 care challenges, \u3e2700 nonpharmacological strategies, 60+ education-oriented guidance documents, brief how-to videos, novel assessment of cognitive function, an algorithm personalizing strategies to cognitive function and care context, and a care advisor portal. The trial will yield outcome data and utilization patterns to inform commercialization and scaling.
CONCLUSIONS: Plans4Care addresses a critical gap in dementia care with potential for commercialization and scalability
Disease Burden of Asthma Patients Utilizing Short-Acting Beta-2 Agonist-Only Inhalers as Rescue in the United States
No full textPurpose: We aimed to describe the disease burden of asthma and examine relationships between short-acting beta-2 agonist (SABA) use and asthma exacerbations. Patients and Methods: This retrospective cohort study used geographically diverse Premier Healthcare Database (PHD) and linked insurance claims (10/01/2021-09/30/2022) for US patients aged ≥18 years with a SABA-only prescription and a history of asthma exacerbation within 12 months before index date (the earliest SABA prescription date). Three asthma control groups were defined based on SABA usage: well controlled-low (0–1 prescriptions), not well controlled-medium (2–3 prescriptions), very poorly controlled-high (≥4 prescriptions). Four asthma severity groups were defined using asthma medication usage: intermittent, mild persistent, moderate persistent, and severe persistent. Results: A total of 12,692 patients were included: mean age 38.7 years, 73% female, 54% white, 31% Black, and 70% Medicaid patients. During the 12-month post-index period, 31% (n = 3,889) experienced an exacerbation at a mean rate 0.51 (SD 1.05) per patient. The percentage of patients with low, medium, and high SABA-only prescriptions were 42%, 21%, and 37%, respectively. A greater proportion of high-SABA users had an asthma exacerbation (41%) versus medium- (32%) or low- (20%) SABA users (both p \u3c 0.0001). The proportion of patients with asthma maintenance controller use was the highest (71%) among high-SABA users, followed by medium- (56%), and low- (35%) SABA users (all p \u3c 0.0001). Mean rates of asthma exacerbation during 12-month post-index period were 0.34 (SD 0.75) in the intermittent, 0.43 (SD 0.87) in the mild persistent, 0.43 (SD 0.82) in the moderate persistent, and 0.73 (SD 1.37) in the severe persistent groups (p \u3c 0.0001). Almost a quarter (24%) of patients with intermittent asthma experienced an exacerbation during this period. Conclusion: Patients with greater use of SABA-only rescue inhalers experienced higher rates of exacerbations, despite having greater asthma controller use. New rescue therapy approaches are needed to decrease the burden of illness in asthma patients