University Of Pennsylvania Research Articles

Learning on a Limb: An outreach module to engage high school students in orthopaedics.

Orthopaedic researchers need new strategies for engaging diverse students. Our field has demonstrated noticeable gaps in racial, ethnic, and gender diversity, which inhibit our ability to innovate and combat the severe socioeconomic burden of musculoskeletal disorders. Towards this goal, we designed, implemented, and evaluated Learning on a Limb, an orthopaedic research outreach module to teach diverse high school students about orthopaedic research. During the 4-hr module, students completed hands-on activities to learn how biomechanical testing, microcomputed tomography, cell culture, and histology are used in orthopaedic research. Over three years, we recruited 32 high school students from the Greater Philadelphia Area to participate in Learning on a Limb. Most participants identified as racial/ethnic or gender minorities in orthopaedic research. Using pre/post-tests, we found that students experienced significant learning gains of 51 percentage points from completing Learning on a Limb. In addition to teaching students about orthopaedic research, post-survey data demonstrated that participating in Learning on a Limb strongly influenced students' interest in orthopaedic research. Several students acted on this interest by completing summer research experiences in the McKay Orthopaedic Research Laboratory at the University of Pennsylvania. Learning on a Limb instructors also benefited by having the opportunity to "pay it forward" to the next generation of students and build community within their department. Empowering institutions to host modules like Learning on a Limb would synergistically inspire diverse high school students and strengthen community within orthopaedic departments to ultimately enhance orthopaedic research innovations.

Proceedings of the University of Pennsylvania 15th annual conference on statistical issues in clinical trials: Advances in time-to-event analyses in clinical trials-challenges and opportunities.

Proceedings of the University of Pennsylvania 15th annual conference on statistical issues in clinical trials: Advances in time-to-event analyses in clinical trials-challenges and opportunities.

Bascom Headen Palmer, Jr, MD (1889-1954)

PurposeMost ophthalmologists in the US recognize the name “Bascom Palmer” but few are familiar with the individual for whom the Eye Institute is named. This article reviews the biography of Bascom Headen Palmer, Jr., MD (1889-1954). DesignHistorical review. MethodsHistorical documents were reviewed, including the archives of the University of Miami, the Miami Lighthouse for the Blind and Visually Impaired, and other sources. ResultsPalmer, born in Lake City, FL in 1889, was the younger son of a Florida politician. He graduated from what was then called the College of Medicine of the Tulane University of Louisiana (now the Tulane University School of Medicine) in 1914, followed by a two-year internship at Touro Infirmary, service in the army during World War I, and finally a post-graduate course in ophthalmology at the University of Pennsylvania in 1923. He then moved to Miami, where he became influential in two new organizations: the University of Miami, founded in 1925, and the Florida Association of Workers for the Blind (now the Miami Lighthouse for the Blind and Visually Impaired), founded in 1930. He forged collaborations between these two organizations which eventually led to the foundation of the eponymous Eye Institute. ConclusionsThe decades-long collaboration between the Miami Lighthouse and the University of Miami, facilitated by Palmer, ultimately led to the founding of the Eye Institute.

B - 57 Examining Symptom Patterns in Anomalous Health Incidents: a Spectral Co-Clustering Analysis Using the Post-Concussion Symptom Scale

Abstract Objective The ImPACT Post-Concussion Symptom Scale (PCSS) is widely used to document post-concussion symptoms. This study investigated the structural patterns of PCSS using spectral co-clustering on data from U.S. diplomats and their families who served in Cuba and China, who reported experiencing Anomalous Health Incidents (AHIs). Method Clinicians at the University of Pennsylvania evaluated 65 individuals with potential AHI exposure (Median age-group = 35–45 years; 73.84% in Cuba; 49.23% females). PCSS feature scores were discretized into 4-level symptom severity expressions: none, mild, moderate, and severe. The preprocessed data were fit with spectral co-clustering model to extract optimal number of biclusters, by measuring scree plot with principal component analysis and Calinski-Harabasz (CH) score. Final model was also evaluated by CH score and a random label t-test to compare the significance of the clustering results. Results Overall, spectral co-clustering yielded two biclusters with CH score of 20.29 and random label t-test of p < 0.001, suggesting reasonable clustering quality. Biclustering identified two sets of interpretable PCSS symptom characteristics. Cluster centroids X and Y (IDs retracted) were estimated using Manhattan distance, summarizing symptom characteristics of each cluster. More precisely, centroid X represented mild to moderate symptom profiles (e.g., headache, dizziness, drowsiness, and nervousness), while centroidY represented a relative absence of symptoms, except for mild nervousness and emotionality. Conclusion The spectral co-clustering of PCSS reveals patterns of elevated symptoms in one cluster and minimal symptom burden in the other, similar to outcomes observed after mild TBI. These patterns can guide future research directions and ultimately improve patient outcomes in various medical contexts.

Long-Term Dementia Risk in Parkinson Disease.

It is widely cited that dementia occurs in up to 80% of patients with Parkinson disease (PD), but studies reporting such high rates were published over two decades ago, had relatively small samples, and had other limitations. We aimed to determine long-term dementia risk in PD using data from two large, ongoing, prospective, observational studies. Participants from the Parkinson's Progression Markers Initiative (PPMI), a multisite international study, and a long-standing PD research cohort at the University of Pennsylvania (Penn), a single site study at a tertiary movement disorders center, were recruited. PPMI enrolled de novo, untreated PD participants and Penn a convenience cohort from a large clinical center. For PPMI, a cognitive battery is administered annually, and a site investigator makes a cognitive diagnosis. At Penn, a comprehensive cognitive battery is administered either annually or biennially, and a cognitive diagnosis is made by expert consensus. Interval-censored survival curves were fit for time from PD diagnosis to stable dementia diagnosis for each cohort, using cognitive diagnosis of dementia as the primary end point and Montreal Cognitive Assessment (MoCA) score <21 and Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS) Part I cognition score ≥3 as secondary end points for PPMI. In addition, estimated dementia probability by PD disease duration was tabulated for each study and end point. For the PPMI cohort, 417 participants with PD (mean age 61.6 years, 65% male) were followed, with an estimated probability of dementia at year 10 disease duration of 9% (site investigator diagnosis), 15% (MoCA), or 12% (MDS-UPDRS Part I cognition). For the Penn cohort, 389 participants with PD (mean age 69.3 years, 67% male) were followed, with 184 participants (47% of cohort) eventually diagnosed with dementia. The interval-censored curve for the Penn cohort had a median time to dementia of 15 years (95% CI 13-15); the estimated probability of dementia was 27% at 10 years of disease duration, 50% at 15 years, and 74% at 20 years. Results from two large, prospective studies suggest that dementia in PD occurs less frequently, or later in the disease course, than previous research studies have reported.

The AISP Network of cross-sector data linkage efforts: Results &amp; lessons from a national survey of 40 state and local systems in the US

Objectives and ApproachThis session will share findings from a national survey of 40 state and local cross-sector data linkage efforts using data to impact policy and outcomes across the United States (US). The survey, conducted in February 2023 by Actionable Intelligence for Social Policy (AISP) at the University of Pennsylvania, captured each effort’s purpose(s) for sharing data (the “why”), approach to linkage (the “how”), and the data sources being integrated (the “what”). Respondents are all current or prospective members of the AISP network, representing a cross-section of systems at different stages of development and engaged in peer exchange. ResultsThe presentation will share results that describe the core purposes of system development and how systems operate; this includes governance structures to institutionalize sharing relationships; staffing, funding sources, and operating budgets; legal frameworks; and technical infrastructure. The presentation will also explore what data systems share, and the role a peer-led community of practice plays in developing and honing approaches to public sector data use. ConclusionsNetwork members have demonstrated widespread impact in their communities. In the last two years, respondents collectively reported that their integrated data enabled 365 reports and briefings for policymakers to affect policy change and 160 research articles to advance knowledge for impact. They also widely reported improved relationships and trust across agencies and improved ability to evaluate what works. ImplicationsThis presentation showcases the impact of the US community of practice in supporting diverse models for impact-oriented data systems.

Dr. Anandibai Joshi: An Indian Medical Pioneer.

Dr. Anandibai Joshi was the first Indian female physician to complete her studies in Western medicine at the Women's Medical College of Pennsylvania. A pioneer in medicine, she was a trailblazing Indian woman, who had faced many hurdles during her quest for education. In her short span of life, she faced a lot of emotional, mental, physical, and social turbulence and had to prove herself at every step to the orthodox patriarchal Indian society the need to study medicine. Studying medicine in India was impossible for females during the 19th century. During those days, the females in India were uneducated due to gender differences prevalent in society, so the only option was to pursue medicine in a foreign land. Her life was full of hardships and challenges; she got married at the age of nine and, at the young age of 14, delivered a baby boy who could not survive beyond 10 days due to lack of medical care. This was the turning point in her life, and she was determined to become a physician so that the other women would not suffer the way she had. Thus began her educational journey, to pursue a career in Western medicine. Before Anandibai went to the United States, she gave a motivating speech in a public gathering, expressing the need for female doctors in India and her keenness to pursue medicine in the United States. She challenged all the social norms in those days, whether it was fighting to break gender stereotypes or convincing the male-dominated society to study medicine in the United States. This article is a reflection of Anandibai's accomplishments in her life and medical career and her dedication, determination, and strong commitment to improving the lives of Indian women. Her short life is a reflection of hope, perseverance, determination, and success and an inspiration to many Indian women to pursue education and medicine.

Reviewer Acknowledgements for Journal of Education and Training, Vol. 11, No. 2

Journal of Education and Training (JET) would like to acknowledge the following reviewers for their assistance with peer review of manuscripts for this issue. Many authors, regardless of whether JET publishes their work, appreciate the helpful feedback provided by the reviewers. Their comments and suggestions were of great help to the authors in improving the quality of their papers. Each of the reviewers listed below returned at least one review for this issue.Reviewers for Volume 11, Number 2 A. F. Elgamal, Faculty of Specific Education- Computer Science Dep.Mansoura University, EgyptBryan Spiegelberg, Rider University, USAJohn Cowan, Edinburgh Napier University, ScotlandJukdao Potisaen, Rajabhat Mahasarakham University, ThailandLazarus Ndiku Makewa, University of Eastern AfricaBaraton, KenyaMontasser Mohamed AbdelWahab Mahmoud, Imam Abdulrahman bin Faisal University, Saudi ArabiaPaola Molteni, Università Cattolica del Sacro Cuore di Milano, ItalyShijuan Liu, Indiana University of Pennsylvania, USA Dora WangEditorial AssistantJournal of Education and TrainingMacrothink Institute-----------------------------------------5348 Vegas Dr.#825Las VegasNevada 89108United StatesPhone: 1-702-953-1852 ext.534E-mail 1: jet@macrothink.orgE-mail 2: jet@macrothink.comWebsite: http://jet.macrothink.org

Predicting peritumoral glioblastoma infiltration and subsequent recurrence using deep-learning-based analysis of multi-parametric magnetic resonance imaging.

Glioblastoma (GBM) is the most common and aggressive primary adult brain tumor. The standard treatment approach is surgical resection to target the enhancing tumor mass, followed by adjuvant chemoradiotherapy. However, malignant cells often extend beyond the enhancing tumor boundaries and infiltrate the peritumoral edema. Traditional supervised machine learning techniques hold potential in predicting tumor infiltration extent but are hindered by the extensive resources needed to generate expertly delineated regions of interest (ROIs) for training models on tissue most and least likely to be infiltrated. We developed a method combining expert knowledge and training-based data augmentation to automatically generate numerous training examples, enhancing the accuracy of our model for predicting tumor infiltration through predictive maps. Such maps can be used for targeted supra-total surgical resection and other therapies that might benefit from intensive yet well-targeted treatment of infiltrated tissue. We apply our method to preoperative multi-parametric magnetic resonance imaging (mpMRI) scans from a subset of 229 patients of a multi-institutional consortium (Radiomics Signatures for Precision Diagnostics) and test the model on subsequent scans with pathology-proven recurrence. Leave-one-site-out cross-validation was used to train and evaluate the tumor infiltration prediction model using initial pre-surgical scans, comparing the generated prediction maps with follow-up mpMRI scans confirming recurrence through post-resection tissue analysis. Performance was measured by voxel-wised odds ratios (ORs) across six institutions: University of Pennsylvania (OR: 9.97), Ohio State University (OR: 14.03), Case Western Reserve University (OR: 8.13), New York University (OR: 16.43), Thomas Jefferson University (OR: 8.22), and Rio Hortega (OR: 19.48). The proposed model demonstrates that mpMRI analysis using deep learning can predict infiltration in the peri-tumoral brain region for GBM patients without needing to train a model using expert ROI drawings. Results for each institution demonstrate the model's generalizability and reproducibility.

Citation Analysis of Marketing Research: Key Contributions and Future Directions

Citation analysis measures the relative importance or impact of an author, article, or publication by counting the number of times other works have cited it. This research conducted a thorough examination within the marketing academic field, exploring the most cited articles and authors, the most published authors, publishing countries, universities, and the years with maximum contributions. The methodology involved selecting the top ten marketing journals based on the Scimago Journal and Country Rank (as of April 29, 2024). Data were collected from the Scopus database, encompassing 12,445 journal articles and 782,442 cited references. The results revealed Barry J. Babin as the most prolific author with 447 articles. The most cited article was "Uniting the Tribes: Using Text for Marketing Insight" with 772 citations. The United States led with 67,302 publications and 2,500,668 citations, while the University of Pennsylvania, Wharton School, had the highest number of publications at 235 articles. As of December 2019, Philip Kotler was identified as the most cited author in marketing, with 75,355 citations for his paper "Marketing Management: A South Asian Perspective" since 2009. This analysis provides valuable insights into the recent trends and future directions in the marketing field, highlighting significant contributions and influential works. However, the study's scope was limited to the top ten marketing journals from the SciMago list. Future research should consider a broader range of journals to gain a more comprehensive understanding of the marketing discipline.

An African American Burial Ground in Philadelphia: “Discovered,” Protected, Eradicated

ABSTRACT Construction in 2018 brought to light a previously unknown Black graveyard that operated from 1826 to 1882 on the outskirts of densely populated Philadelphia. To assure dignified burials, founders of the African Friends to Harmony Burial Ground created a “beneficial,” a form widely used by contemporary African Americans. Burials ended in 1882 as urban development encroached on the graveyard. Within a few decades, it had been erased from property deeds and public memory. In 2019–20, the two churches deemed descendant communities by the property owner, the University of Pennsylvania, chose to relocate the remains. This article points to instances where Black cemeteries have been retained in place or emerged as crucial instances of public engagement. Exploring the aspirations and struggles of those who established and tended African Friends to Harmony ground, the article reveals the richness of its history but the missed opportunity to commemorate the region’s complex past.

Optimizing Regulatory Reviews for Clinical Protocols that Use Radiopharmaceuticals: Findings of the University of Pennsylvania Radiation Research Safety Committee.

Institutional radiation safety committees review research studies with radiation exposure. However, ensuring that the potential patient benefit and knowledge gained merit the radiation risks involved often necessitates revisions that inadvertently delay protocol activations. This quality-improvement study analyzed protocols, identified factors associated with approval time by a radiation safety committee, and developed guidelines to expedite reviews without compromising quality. Clinical protocols submitted to the University of Pennsylvania's Radiation Research Safety Committee (RRSC) for review between 2017 and 2021 were studied. Protocol characteristics, review outcome, stipulations, and approval times were summarized. Statistical analysis (Spearman's rho) was used to investigate stipulations and approval time; rank-sum analysis (Kruskal-Wallis or Wilcoxon) was used to determine whether approval time differed by protocol characteristics. One hundred ten (110) protocols were analyzed. Approximately two-thirds of protocols used approved radiopharmaceuticals to aid investigational therapy trials. Twenty-three percent (23%) of protocols received RRSC approval, and 73% had approval withheld with stipulations, which included requests for edits or additional information. Submissions had a median of three stipulations. Median and mean RRSC approval times were 62 and 80.1 d, and 41% of protocols received RRSC approval after IRB approval. RRSC approval time was positively correlated with stipulations (Spearman's rho = 0. 632, p < 0.001). RRSC approval time was longer for studies using investigational new drugs (median 80 d) than approved radiopharmaceuticals (median 57 d, p = 0.05). The review process is lengthy and may benefit from changes, including publishing standardized radiation safety language and commonly required documents and encouraging timely response to stipulations.

Clinical outcomes and risk stratification in unresectable biliary tract cancers undergoing radiation therapy

BackgroundBiliary tract cancers (BTC) are rare and aggressive malignancies originating from intrahepatic and extrahepatic bile ducts and the gallbladder. Surgery is the only curative option, but due to late-stage diagnosis, is frequently not feasible, leaving chemotherapy as the primary treatment. Radiotherapy (RT) can be an effective alternative for patients with unresectable, non-metastatic BTC despite the generally poor prognosis and significant variability. To help manage patients with unresectable BTC who receive RT, we aimed to identify prognostic markers that could aid in predicting overall survival (OS).MethodsA retrospective cohort study was conducted at the University of Pennsylvania, involving seventy-eight patients with unresectable BTC treated with definitive intent RT. Comprehensive demographic, clinical, and treatment-related data were extracted from the electronic medical records. Univariate and multivariate Cox regressions were employed to identify predictors of OS after RT. A biomarker model was developed for refined survival prediction.ResultsThe cohort primarily comprised patients with good performance status without significant hepatic dysfunction at presentation. The predominant treatment approach involved hypofractionated RT or concurrent 5FU-based chemoRT. Median OS after RT was 12.3 months, and 20 patients (15.6%) experienced local progression with a median time of 30.1 months. Univariate and multivariate analyses identified CA19-9 (above median) and higher albumin-bilirubin (ALBI) grades at presentation as significant predictors of poor OS. Median OS after RT was 24 months for patients with no risk factors and 6.3 months for those with both.ConclusionsOur study demonstrates generally poor but significantly heterogeneous OS in patients with unresectable BTC treated with RT. We have developed a biomarker model based on CA19-9 and ALBI grade at presentation that can distinguish sub-populations with markedly diverse prognoses. This model can aid the clinical management of this challenging disease.

Predicting a successful match among applicants to reproductive endocrinology and infertility fellowship

To identify independent predictors of a successful match to reproductive endocrinology and infertility (REI) fellowships, and to develop and internally validate a prediction model for REI match results. Retrospective cohort study. REI fellowship applications sent to the University of Pennsylvania from 2019 to 2023 (excluding 2020), which represented nearly all REI applicants nationally according to National Resident Matching Program (NRMP) data. Demographics, education, training, and academic achievements. Match result, confirmed through online search and communication with program administrators. Univariate analyses identified variables associated with match, which were then included in multivariable models to identify independent predictors. Bootstrapping was used to assess model discrimination and calibration. The final model was integrated into a web-based tool. Of 286 applications (99.0% of REI applications to the NRMP), 199 (69.6%) resulted in a successful match. In univariate analyses, variables associated with match were younger age, attendance at an allopathic U.S. medical school, USMLE and CREOG scores, residency rank, residency affiliation with a fellowship, research experiences, first-author publications, abstracts/articles in progress, and poster presentations. In the adjusted model, independent predictors of match included residency affiliation with an REI fellowship (aOR 5.43, 2.02-14.64), residency rank (aOR 1.77, 1.25-2.50), USMLE score (aOR 1.05, 1.02-1.08), at least one first-author publication (aOR 2.32, 1.08-4.96), projects in progress (aOR 1.26 (1.02-1.55), and poster presentations (aOR 1.07, 1.00-1.15). Attendance at an international medical school was a negative predictor (aOR 0.32, 0.11-0.88). The model achieved an area under the curve (AUC) of 0.883, with 88.5% sensitivity and 65.8% specificity. A refined model without USMLE scores maintained strong performance (C-statistic 0.85, 0.81-0.91; calibration slope 0.91, 0.72-1.24). Affiliation with an REI fellowship, residency reputation, and research output strongly predicted match success. Gender, race, and ethnicity were not major predictors, yet underrepresentation of certain racial and ethnic groups limited the power to detect potential differences. Our prediction model correctly classified >75% of candidates' match results. These findings may help candidates optimize applications and estimate chances of a successful match into REI fellowship, as well as assist programs in critically reviewing their selection criteria for fellowship match.

Pathway to Independence - an interview with Louis Prahl.

Louis Prahl is a postdoctoral fellow in Alex Hughes' lab in the Department of Bioengineering and the Center for Soft and Living Matter at the University of Pennsylvania, Philadelphia, USA. He is interested in how epithelial cells organise into complex structures and in developing engineering strategies to control tissue organisation. Louis is one of the 2024 fellows of the Pathway to Independence Programme. We caught up with him to talk about his physics and bioengineering background, his interdisciplinary research interests, and his plans as an independent researcher.

Adaptive fine-tuning based transfer learning for the identification of MGMT promoter methylation status.

Background.Glioblastoma Multiforme (GBM) is an aggressive form of malignant brain tumor with a generally poor prognosis.O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation has been shown to be a predictive bio-marker for resistance to treatment of GBM, but it is invasive and time-consuming to determine methylation status. There has been effort to predict the MGMT methylation status through analyzing MRI scans using machine learning, which only requires pre-operative scans that are already part of standard-of-care for GBM patients.Purpose.To improve the performance of conventional transfer learning in the identification of MGMT promoter methylation status, we developed a 3D SpotTune network with adaptive fine-tuning capability. Using the pretrained weights of MedicalNet with the SpotTune network, we compared its performance with a randomly initialized network for different combinations of MR modalities.Methods.Using a ResNet50 as the base network, three categories of networks are created: (1) A 3D SpotTune network to process volumetric MR images, (2) a network with randomly initialized weights, and (3) a network pre-trained on MedicalNet. These three networks are trained and evaluated using a public GBM dataset provided by the University of Pennsylvania. The MRI scans from 240 patients are used, with 11 different modalities corresponding to a set of perfusion, diffusion, and structural scans. The performance is evaluated using 5-fold cross validation with a hold-out testing dataset.Results.The SpotTune network showed better performance than the randomly initialized network. The best performing SpotTune model achieved an area under the Receiver Operating Characteristic curve (AUC), average precision of the precision-recall curve (AP), sensitivity, and specificity values of 0.6604, 0.6179, 0.6667, and 0.6061 respectively.Conclusions.SpotTune enables transfer learning to be adaptive to individual patients, resulting in improved performance in predicting MGMT promoter methylation status in GBM using equivalent MRI modalities as compared to a randomly initialized network.

Evolving Dynamics of Higher Education Institutions and Their Cities (1980-2020)

This research examines the significance of higher education institutions (HEIs) as anchors in their host cities and their community engagement strategies from 1980 to 2020. By examining four case studies of urban-based universities that are members of the Coalition of Urban and Metropolitan Universities (CUMU) across diverse geographical and socio-economic contexts, this study analyzes the impact of anchor strategies on local communities, economic growth, and urban revitalization. Two public institutions—Rutgers University-Newark (RU-N) and the University of Pretoria (Mamelodi Campus, South Africa)—and two private institutions—Syracuse University (S.U.) and the University of Pennsylvania (UPenn)—comprise the four selected institutions. Comparative analysis of the case studies reveals that effective anchor strategies frequently involve multidimensional approaches that address their respective cities' social, economic, and public safety challenges. In addition, I identify the key factors contributing to the efficacy of anchor strategies, such as strong leadership, stakeholder collaboration, and maximizing the role of foundations and long-term vision. The findings of my study highlight the significance of universities as significant agents of urban change in guiding policymakers, urban planners, and higher education administrators who seek to harness the potential of universities in shaping sustainable and inclusive community engagement. My findings highlight the interplay between universities and their environments rooted in their host cities.

XCP-D: A Robust Pipeline for the Post-Processing of fMRI data

Abstract Functional neuroimaging is an essential tool for neuroscience research. Pre-processing pipelines produce standardized, minimally pre-processed data to support a range of potential analyses. However, post-processing is not similarly standardized. While several options for post-processing exist, they may not support output from different pre-processing pipelines, may have limited documentation, and may not follow generally accepted data organization standards (e.g. BIDS). In response, we present XCP-D: a collaborative effort between PennLINC at the University of Pennsylvania and the DCAN lab at the University of Minnesota. XCP-D uses an open development model on GitHub and incorporates continuous integration testing; it is distributed as a Docker container or Apptainer image. XCP-D generates denoised BOLD images and functional derivatives from resting-state data in either NIfTI or CIFTI files following pre-processing with fMRIPrep, HCP, or ABCD-BIDS pipelines. Even prior to its official release, XCP-D has been downloaded &amp;gt;5,000 times from DockerHub. Together, XCP-D facilitates robust, scalable, and reproducible post-processing of fMRI data.

Progression of Customer Relationship Management 2010–2023: A Bibliometric Analysis

The Customer Relationship Management (CRM) has evolved into a key business application, by gradually creating its impact over the years, where the organization made it one of the imperative requirements of effective Business operation. CRM in recent years emerged as, a more Business and customer-oriented application. This study aims to analyze the relevance of Customer relationships concerning progress from Customer Relationship Management (CRM) in fields of Subject area, Countries that contributed to the field of research, Sources, affiliations, Authors, and funding sponsors. Science mapping techniques and performance analysis were applied in this process by Vos-Viewer Bibliometric software by extracting 1,940 Publications between 2010 and 2023 that are indexed in the SCOPUS database. The Bibliometric analysis denotes that 2010 was the year with a maximum of 144 publications indexed in the Scopus Database and 2023 had a dip at only 105 indexing. Author Smith, A.D. with 23 articles publications. The countries that participated the most are the United States of America followed by India and United kingdom. Commendably China was the highest funding sponsor to be specific it’s from the National Natural Science Foundation of China. The research papers had major Affiliations from the Hong Kong Polytechnic University followed by Robert Morris University in Pennsylvania and Georgia State University. The research significantly shows the progress and impact all over the world and is not restricted to any specific industry.

Bibliometric analysis of chimeric antigen receptor T-cell therapy

Background: CAR-T therapy refers to the genetic material with a specific antigen recognition domain and T cell activation signal is transferred into T cells through gene modification technology so that T cells are activated directly by combining with specific antigens on the surface of tumor cells, and directly kill tumor cells by releasing perforin, granzymin B, etc. This review aims to point out the emerging trends in different classes of immune cell therapies and provide insights for future research. Methods: All relevant literature on CAR-T therapy has been screened from the Web of Science and analyzed for its research trends with the help of VOS viewer, Pajek, Microsoft Excel and Endnote software, Results: According to the findings, the United States is the most productive country in this field. The institution with the highest number of publications is the University of Pennsylvania. Frontiers in Immunology is one of the most productive journals. Gottschalk Stephen publishes the largest number of articles. Keyword co-occurrence analysis shows that the current research trend is more focused on immune cell therapy as well as CAR-T, and the mechanisms and modalities were explored in CAR-T therapy. Conclusion: This study provides a comprehensive summary and analysis of global research trends in CAR-T therapy. In detail, CAR-T therapy has provided hope for tumor treatment, truly bringing new hope to the majority of tumor patients, patients with major diseases, and sub-healthy people.