Artículos de revistas sobre el tema "Tuskegee Institute (Tuskegee, Ala.)"

In august, 1927, Zora Neale Hurston posed with Langston Hughes and Jessie Fauset at the foot of the statue of Booker T. Washington on the campus of Tuskegee Normal and Industrial Institute in Tuskegee, Alabama. Now, after six months of collecting African-American folklore – customs, games, jokes, lies, songs, superstitions, and tales – Hurston was ready to return to New York City and to finish her Bachelor of Arts in anthropology at Barnard. She had left New York City the previous February and had spent most of her time in and around her hometown of Eatonville and Tallahassee, Florida, before driving across the Florida panhandle to Mobile, Alabama. There she interviewed Cudjo Lewis, reputed to be the only living survivor of the last ship to bring slaves from Africa to America. By chance, Hurston also met Hughes, who had just arrived in Mobile by train from New Orleans. Soon after, she and Hughes drove up to Tuskegee, joined Fauset to lecture to summer students, then continued on their way to New York City.

Abstract By exploring agricultural education initiatives in South Africa in the period from the 1910s to the 1930s, this article seeks to expand the historiography of both Black internationalism and global progressivism to include Black agrarianism and questions of race from a transatlantic perspective. In their efforts to promote scientific farming, Black progressive farmers in the Eastern Cape reserves made strategic use of the famous educational philosophy of the Tuskegee Institute in Alabama as an enabling discourse that allowed them to gain an audience for their specific, localized claims to economic and political participation in a segregationist country. While the existing historiography has discussed Tuskegee’s impact in Africa as an example of oppressive labor education, this article argues that Black agrarian progressivism should be taken seriously as a form of antiracism and Pan-Africanism, albeit one that was classist and patriarchal.

AbstractWilliam Levi Dawson (1899–1990) is remembered chiefly for his masterful choral arrangements of Negro spirituals and for his multi-decade leadership of the Tuskegee Institute Choir. In 1934, however, his career seemed to be headed in a very different direction: Leopold Stokowski programmed Dawson's Negro Folk Symphony on four Philadelphia Orchestra concerts that met with acclaim from critics and audiences alike. The broadcast of one of these concerts on the Columbia Broadcasting System had a particularly powerful impact on the many African Americans in the radio audience. Materials in the William Levi Dawson Collection at Emory University illuminate both the momentousness of the symphony's debut and its provocatively minor impact on the trajectory of its composer's career. This article examines the premiere of the Negro Folk Symphony as a groundbreaking event both public and personal, offers an explanation for the symphony's startlingly rapid descent into obscurity, and argues that this effective and fascinating work merits renewed attention from conductors and scholars today.

In late 1902, exhaustion, financial distress, and the desire for a political appointment—combined with aspirations to serve as a broker for the export of African American labor abroad—led famed African American journalist T. Thomas Fortune to secure a temporary appointment with the Roosevelt administration to investigate trade and labor in Hawaii and the Philippines. In Hawaii, Fortune was fêted by the planter class, and allied himself publicly with the educational and political philosophies of Booker T. Washington. His hopes for black emigration and land ownership, however, were vigorously opposed by most newspapers connected to the oligarchy. Hawaii's robust in-language indigenous and ethnic newspapers, meanwhile, voiced their own position on black labor. In Manila, a fiercely entrepreneurial and militaristic American press attacked Fortune. Recent scholarship ties Washington's Tuskegee Institute to a kind of “Jim Crow colonialism” abroad. An in-depth look at Fortune's journey both supports and troubles such a view. Both men hoped U.S. “expansion,” and African American participation in it, might expose not only the power of race, but also its instability and vulnerability; Fortune, in particular, saw newspapers as vital to this task.

Abstract African Americans (AA) have higher incidence and mortality rates for several cancer types in comparison to their European American (EA) counterparts. Increasing participation in clinical research and patient registries, related to precision cancer medicine, will significantly improve cancer health equity. Many AA cancer patients are treated in community oncology clinics. Unfortunately, these health systems have limited access to Clinical Laboratory Improvement Amendments (CLIA) next generation sequence (NGS) germline and somatic DNA and RNA testing that are used to inform oncologists on the best treatment and/or clinical trial options for cancer patients. Indeed, AA CLIA NGS sample sets are poorly represented, which could presumably result in incomplete knowledge of genomic variants that could affect their treatment and overall outcomes. Hence, it is crucial to implement CLIA NGS efforts for all cancer patients. To address these disparities, Morehouse School of Medicine has formed the Comprehensive Approach to Reimagine health Equity Solutions (CARhES) consortium with Tuskegee University that has engaged community oncology practices in Alabama and Georgia - two of five Black Belt states. The CARhES consortium aims to implement precision cancer medicine to underserved and underrepresented communities that will improve the standard of cancer care by providing access to CLIA NGS testing, clinical trials, and personalized cancer care. Here we describe the first proof of concept of this approach with community oncology partners, i.e., Grady Health System, Wellstar Health System, Georgia Urology, Midtown Urology, and Maui Memorial Medical Center. At the time of consent, saliva, buccal, and tumor samples were collected from participants. Germline and somatic CLIA NGS was performed, and medical reports were returned to practitioners within 14 days. Prior to the COVID pandemic, the study enrolled over 880 patients with a 88% consent rate (n = 1000) in the first 11months of the program. At the start of the COVID pandemic, recruitment efforts were suspended for four months with a slow restart by June 2020. A decrease in the number of staff, office visits (67% reduction), and increase in COVID cases significantly limited recruitment efforts. During this slowdown, we established and improved eConsenting capabilities, which exist today. Community anxiety, due to the pandemic and SARS-CoV-19 vaccine efforts, resulted in a significant reduction in consent rates (88% to 60%). Nevertheless, this study began in April of 2019 and consented 1,750 participants in less than 2 years. Taken together, our study shows that a community-focused precision medicine approach requires meeting people where they are and providing them with access and understanding the benefit of clinical trial participation. The approximate 2,000 clinically annotated genomic AA datasets will greatly contribute to our understanding of cancer health disparities and among the first steps to democratize precision medicine. This study was funded by the American Association for Cancer Research, The Pelotonia Foundation, the M2Gen Foundation, and the National Cancer Institute (U54CA118638). Citation Format: Latrisha L. Horne, Gabriella M. Oprea-Ilies, Eddie R. Stanley, Carla M. Holloway, Margaret P. Hooker, Amina Isom, Derrick J. Beech, Ana Gaonkar, Shavette Harden, Jeffrey F. Hines, LaTonya Randolph, James K. Bennett, Daniel Canter, Darrell J. Carmen, Pooja Mishra, Giuseppe Del Priore, Roland Matthews, Brian M. Rivers, James W. Lillard. AACR President's initiative - 2020 by 2020: Democratizing precision cancer medicine and advancing health equity in the black belt [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr LB173.

Background: Across lymphoma subtypes, disparities have been uncovered in age of onset, stage, treatment, and outcomes by socioeconomic status, insurance status, and race. For several lymphoma subtypes, Black patients present at a mean age approximately 10 years younger than White patients and have worse survival. Racial disparities in access to care and clinical trial enrollment have been described across cancers and contribute to these differences in survival. We sought to clarify patients' perceptions of the needs and priorities for care during diagnosis, treatment, and survivorship. Objective: We conducted qualitative research with Black lymphoma patients in order to determine their perceptions and understanding regarding treatment and research, barriers to care, and future research priorities. Methods: Semi-structured phone interviews were conducted with 12 Black patients with lymphoma who had received treatment in Georgia. Participants were recruited from patients who were previously agreed to be contacted about research when identified through the state cancer registry and a current research participant pool at a university research hospital in Atlanta, Georgia. Interviews were recorded and transcribed verbatim. MAXQDA software (version 18.0.8) was used for coding and identifying themes and grounded theory processes were used to generate codes. Thematic analysis will be used to generate further themes and create a constant-comparative coding process. Results: Emerging themes from this ongoing data collection include endurance, lack of information surrounding diagnosis, trust in medical staff, and willingness to participate in research. Several participants described their cancer journey as simply another hurdle to overcome. They did not consider the emotional toll of their diagnosis and instead focused on positivity, refusing to feel sorry for themselves. A lack of information during the process of care was the most significant barrier for patients. The majority of patients felt that they were not given enough information regarding their diagnosis, with some stating that they had never heard of lymphoma before they were diagnosed with it. Others felt that this lack of information continued into their treatment with some patients being unaware of their cancer status or not being informed about procedures before they occurred. This gap in information left participants feeling disconnected from their own treatment process and uninvolved in their care. Despite this, patients expressed great trust in medical staff. Nearly all participants stated that they trusted their doctor most in their clinical care team and were therefore more willing to proceed with treatments and procedures at their doctor's recommendation. Those with a weaker connection to their doctor and care team reported feeling less satisfied with their care experience. Patients were also very willing to participate in research. Most expressed interest to participate in order to help others that may be diagnosed in the future. Some stated specific factors that would deter them from enrolling in a study, with one participant referencing the Tuskegee Syphilis Study as her reason for avoiding drug trials. Participants expressed interest in a wide range of topics for future research, with several focused on the origins of lymphoma. These participants assumed that they may have been exposed to hazardous materials while serving in the military or through an environmental contaminant and wanted more knowledge in this area. Conclusions: These preliminary findings suggest that the Black population in Georgia has specific needs that are unaddressed by current scientific and medical communities. A lack of information leaves patients feeling uninvolved with their care and potentially disconnected from their care team. This study presents the opportunity for clinicians and research personnel to collaborate with patients to develop communication tools and strategies that keep this population informed during their treatment. Clinicians and medical providers can utilize these data to improve support for this population. Disclosures Flowers: TG Therapeutics: Research Funding; Karyopharm: Consultancy; Gilead: Consultancy, Research Funding; National Cancer Institute: Research Funding; BeiGene: Consultancy, Research Funding; Celgene: Consultancy, Research Funding; Denovo Biopharma: Consultancy; Genentech, Inc./F. Hoffmann-La Roche Ltd: Consultancy, Research Funding; Spectrum: Consultancy; Pharmacyclics/Janssen: Consultancy, Research Funding; AbbVie: Consultancy, Research Funding; Acerta: Research Funding; Bayer: Consultancy; V Foundation: Research Funding; AstraZeneca: Consultancy; Millenium/Takeda: Research Funding; Optimum Rx: Consultancy; Eastern Cooperative Oncology Group: Research Funding; Burroughs Wellcome Fund: Research Funding.

AbstractThe AIFARMS Artificial Intelligence for Future Agricultural Resilience, Management, and Sustainability national AI institute brings together over 40 world‐class AI and agriculture researchers, with the common mission to develop foundational advances in AI and use them to ensure that future agriculture is environmentally friendly, sustainable, affordable, and accessible to diverse farming communities. Since its establishment in 2020, AIFARMS has advanced the state of the art in autonomous farming, cover crop planting, machine learning for improved outcomes from remote sensing, dynamic estimation of yield loss from weeds, and livestock management. The institute has prioritized the creation and utilization of high‐quality, openly available data sets for advancing foundational AI and tackling agricultural challenges. AIFARMS leverages a close partnership between UIUC and Tuskegee University to build programming for a skilled and diverse next‐generation workforce in digital agriculture. We are expanding the reach of AIFARMS outside of the current partners to collaborate with national AI institutions and international partners.

This essay examines the portrayal of modern Black motherhood in Nella Larsen’s Harlem Renaissance novel,Quicksand(1928). Writing in a cultural landscape dominated by discourses of racial uplift, scientific motherhood and eugenics, I argue that Larsen critiques and ultimately refuses the limited literary, medical and political terms available for representing Black motherhood in the early twentieth century. My readings centre Larsen’s understudied career as a nurse; prior to becoming a writer, Larsen worked as Head Nurse at the Tuskegee Institute in Alabama and as a public health nurse for the Department of Health in the Bronx. I consider how this professional experience informed her fictional depiction of modern Black motherhood, drawing on archival materials to demonstrate how her novel complicates contemporaneous medical and cultural attitudes towards Black motherhood and resists the eugenic demands delineating what constitutes ‘good’ and ‘bad’ motherhood. Engaging contemporary Black feminist theories of refusal and Black motherhood, I show howQuicksandis not only a critique of racist stigmatising discourses and practices but also of how racism limits the ways in which Black mothers’ complexity has historically been represented.

Abstract In the mid-1920s, Solomon Tshekisho Plaatje toured the South African countryside showing films he brought from the Tuskegee Institute in the United States. Plaatje’s cinema tours complemented his educational talks on the status of Africans in the Union of South Africa alongside the material he collected for books, speeches and political tours. Focusing on the itinerant cinema as an element of fieldwork, our article asks what can be learned from approaching Plaatje’s research practices. We consider Plaatje’s methods of research in relation to conventional notions of social scientific fieldwork, which also relied on modern media but were often entangled in colonial projects that projected an image of African rural life. Drawing on letters, novels, and accounts of his film screenings, our essay argues for an interdisciplinary engagement with cinema practices in African history that is attentive to the uses of mass media in research and the pedagogical valences of itinerant film screenings. Considering Plaatje’s cinema alongside the value he attached to travelling and mobility, we argue that his cinema puts the field to work and inspires new practices of research in African Studies.

Intrauterine and perinatal maternal stress increase the risk for cardiovascular disease in adult offspring. Upregulation of the renin‐angiotensin‐aldosterone system (RAAS) occurs early in the pathogenesis of hypertension and is associated with the initiation of a pro‐fibrotic myocardial environment. Our previous work with Spontaneously Hypertensive Rats (SHR) demonstrated that short‐term, prenatal and perinatal exposure of SHR dams to the angiotensin converting enzyme inhibitor (ACE1) captopril reduced hypertension and cardiac fibrosis in adult offspring. The goal of this study was to determine if these beneficial are associated with altered maternal imprinting of cardiac RAAS and its pro‐fibrotic targets. Cardiac lysates were prepared from normotensive WKY or SHR male offspring (14 day old) for analysis of RNA (qRT‐PCR) and protein (western blots, enzyme assays) content. AT1R receptor density was assessed by 125I‐Angiotesin II binding to isolated cardiac fibroblasts. Compared to WKY (n=5, p<0.05), ACE1 mRNA expression, tissue ACE1 activity and AT1R receptor density were significantly increased in SHR vs. WKY and were attenuated by maternal captopril treatment. The mRNA expression of anti‐fibrotic ACE2 and bradykinin type 2 receptor were also increased (P< 0.006) in SHR vs. WKY. Interestingly, there were no changes in the expression of other known RAAS targets, including transforming growth factor‐β, (TGF‐β), Col1a, Col3a, or connective tissue growth factor. Compared to WKY, both plasminogen activator inhibitor‐ 1, TGF‐β receptor II protein levels were decreased by ~80% (p<0.01), although mRNA expression was either unchanged or increased (1.32±0.08 fold, P<0.02), respectively. Taken together, these data suggest that in early stages of postnatal development, SHR cardiac RAAS up‐regulation is compensated by down‐regulation of known pro‐fibrotic targets and up‐regulation of the anti‐fibrotic ACE2 and BK2R. In conclusion, maternal captopril treatment may alter the epigenetic regulation of genes involved in the development and progression of hypertensive heart disease.Support or Funding InformationNIH 2RO1‐HL5604‐12, Research Institute at Nationwide Children's Hospital (PAL) and start‐up funds Tuskegee College (CB)

Photo by Clément Hélardot on Unsplash INTRODUCTION The history of biomedical research in the United States is both inspiring and haunting. From the first public demonstration of anesthesia in surgery at Massachusetts General Hospital to the infamous Tuskegee Experiment, we see the significant advances made for the medical field and the now exposed power dynamics that contribute to injustices when they are left unmonitored.[1] Over the past century, biomedical research led to positive change but also reinforced structural racism. Henrietta Lacks, whose tissue was used without her consent to generate HeLa cells, and the Tuskegee study research subjects, who were denied an existing treatment for syphilis, exemplify how biomedical research in the US has been a vector for exploiting minority groups in exchange for knowledge creation. As we usher in the age of computational medicine, leaders of the field must listen to calls from communities around the country and world to decrease the prevalence of structural racism in the next wave of medical advances.[2] We are vulnerable to perpetuating structural racism through algorithms and databases that will drive biomedical research and aid healthcare systems in developing new methods for diagnosing and treating illness. With guidelines from governmental funding agencies and inclusivity of racial and ethnic minorities in research and development communities, we can inch closer to a more just future for our nation's health. BACKGROUND Many health systems rely on commercial software to store and process their patient’s data. This software commonly comes with patented predictive algorithms that help providers assign a risk score to patients based on health needs. However, biases held by algorithm developers can reflect racial disparities and incorporate them in the algorithms if proper counterbalances are not in place to audit the work of algorithm designers.[3] Despite the recent digitization of healthcare data across the United States, racial bias has already found its way into healthcare algorithms that manage populations. ANALYSIS l. Use of Algorithms One landmark study that interrogated a widely used algorithm demonstrated that Black patients were considerably sicker than white patients at a given risk score, evidenced by signs of uncontrolled disease.[4] The algorithm predicted the need for additional help based on past expenditures, and we historically spend less on Black patients than white patients. Rectifying this bias would lead to three times as many Black patients receiving additional resources. The algorithm produces a treatment gap due to a history of unequal access to care and lower spending on people of color compared to white people in the healthcare system. The disconnect between the clinical situation and historical resource allocation exemplifies how certain predictors may produce an outcome that harms patients. The study shows that using a proxy measure for healthcare spending to quantify how sick a patient is instead of using physiologic data can amplify racial disparities. This example highlights the need for collaboration between clinicians, data scientists, ethicists, and epidemiologists of diverse backgrounds to ensure model parameters do not perpetuate racial biases. ll. Use of Big Data in Algorithm Creation In addition to eradicating algorithms that make decisions based on proxy measures encoding racial inequities, we must also be diligent about the content of databases employed in algorithm development. Racial disparities in a database may result from the intentional selection of a homogenous population or unintentional exclusion due to systemic issues such as unequal distribution of resources. For example, a genetic study conducted in a Scandinavian country is more likely to be racially homogenous and not generalizable to a broader population. Applying algorithms derived from homogenous populations to either diverse populations or to different homogenous populations would fail to account for biological differences and could result in a recess of care when used beyond the appropriate population. Additionally, companies like Apple or FitBit could de-identify consumer data collected using their wearable sensors and make it available in research. This is problematic because the demographic distribution of people who have access to their technology may not reflect the general population. To combat these potential disparities, we must construct freely accessible research databases containing patients with diverse demographic characteristics that better model the actual populations that a given model will serve. lll. Government-Based Safeguards Armed with an understanding of how systemic bias is integrated into algorithms and databases, we must strive to construct safeguards that minimize systemic racism in computational biomedical research. A potential way to step forward as a society would be aligning incentives to produce the desired results. Governmental agencies wield enormous power over the trajectory of publicly funded research. Therefore, it is crucial that computational biomedical research funding is regulated by procedures that encourage diverse researchers to investigate and develop healthcare algorithms and databases that promote our nation's health. For example, the National Institutes of Health (NIH) has set forth two large initiatives to catalyze equitable growth of knowledge and research in healthcare artificial intelligence (AI). The first initiative is the Artificial Intelligence/Machine Learning Consortium to Advance Health Equity and Researcher Diversity (AIM-AHEAD), which focuses on increasing diversity in researchers and data within AI/machine learning (ML). The program states that “these gaps pose a risk of creating and continuing harmful biases in how AI/ML is used, how algorithms are developed and trained, and how findings are interpreted.” Increased participation of researchers and communities currently underrepresented in AI/ML modeling can prevent continued health disparities and inequities. Programs like AIM-AHEAD are crucial to reducing the risk of creating and continuing harmful biases in biomedical research. With the four key focus areas of partnerships, research, infrastructure, and data science training, AIM-AHEAD and its future incarnations can promote health equity for the next era of medicine and biomedical research. The second initiative announced by the NIH is known as the Bridge to Artificial Intelligence (Bridge2AI) program.[5] This program has a different approach to tackling systemic racism and bias by focusing on the content and process of AI/ML research. Two key components of AI/ML research are rich databases and algorithm development protocols. To develop reproducible and actionable algorithms, researchers must have access to large, well-labeled databases and follow best practices in their development of algorithms. However, large databases are not readily available across the healthcare research ecosystem. As a result, many investigators struggle to gain access to databases that would enable them to carry out AI/ML research at their home institution. A movement toward more freely available databases like the Medical Information Mart for Intensive Care (MIMIC) and electronic Intensive Care Unit (eICU) through the PhysioNet platform created at the Massachusetts Institute of Technology Laboratory for Computational Physiology can improve access to data for research.[6] By adopting the practice of freely available databases commonly used in the AI/ML research communities outside of medicine, MIMIC and eICU lowered the barrier to entry for data scientists interested in health care. The improved access from MIMIC and eICU has led to over 2,000 publications to date. While this is a solid foundational step for the healthcare AI/ML research community, it is essential to reflect on progress and ensure that freely accessible databases are racially and geographically diverse. In this manner, Bridge2AI will facilitate the expansion of healthcare databases that are ethically sourced, trustworthy, and accessible. Without government programs such as AIM-AHEAD and Bridge2AI, the US biomedical research community is at higher risk of perpetuating systemic racism and biases in how AI/ML is used, how algorithms are developed, and how clinical decision support results are interpreted when delivering patient care. lV. Private Sector Standards Even with the proper incentives delivered from governmental agencies, there can be a disconnect between the public and private sectors, leading to racial bias in algorithms used in patient care. Privately funded AI/ML algorithms used in care decision-making should be held to the same ethical standards as those developed by publicly funded research at academic institutions. Publicly funded research is usually peer-reviewed before publication, giving reviewers a chance to evaluate algorithmic bias or deficiencies. Algorithms used in care may have avoided similar scrutiny. Corporations have an inherent conflict between protecting intellectual property and providing transparency of algorithmic design and inputs. The Food and Drug Administration (FDA) is responsible for regulating AI/ML algorithms. It has classified them as Software as a Medical Device (SaMD), focusing on the development process and benchmarking.[7] The importance of holding privately funded algorithm development to the same standards as publicly funded research is highlighted in a September 2020 review of FDA-approved AI/ML algorithms. All SaMD approved by the FDA are registered by private companies.[8] Regulators must be well-versed in structural racism and equipped to evaluate proprietary algorithms for racial bias and maintain oversight as population data drifts occur and the algorithms continue to optimize themselves. The FDA role is crucial to clinical use of SaMD. CONCLUSION Computational decision support using algorithms and large databases has the potential to transform the way we deliver care. However, governments should plan how they will prevent structural racism and associated inequities from running rampant in the new systems. Racial bias and mistreatment are engrained in the history of medical research. In the newly formulated digital world, these biases are potentially even more dangerous. They now can propagate quietly in the background, masked under layers of computer code that very few people understand how to write and interpret. It will be possible for a doctor to unconsciously propagate bias because an algorithm is nudging the doctor’s behavior to make the best treatment decision for their patient. Healthcare leaders must be vigilant in guiding the development of algorithms and databases to minimize systemic racism. Guidance from funding agencies to uphold minimum quality standards, a transparent vetting process for algorithms by governing bodies like the FDA, and a diverse community of researchers and developers will allow us to curb the spread of structural racism in research and build equitable tools for diagnosing and treating illness. The real question is: will the age of digital medicine also lead to a more equitable healthcare system? - [1] About the USPHS Syphilis Study. Accessed August 18 2021. https://www.tuskegee.edu/about-us/centers-of-excellence/bioethics-center/about-the-usphs-syphilis-study; MGH. MGH Firsts. Accessed August 18 2021. https://libguides.massgeneral.org/mghhistory/firsts. [2] Geneviève LD, Martani A, Shaw D, Elger BS, Wangmo T. Structural racism in precision medicine: leaving no one behind. BMC medical ethics. 2020;21(1):1-13. [3] Barocas S, Selbst AD. Big data's disparate impact. Calif L Rev. 2016;104:671. [4] Obermeyer Z, Powers B, Vogeli C, Mullainathan S. Dissecting racial bias in an algorithm used to manage the health of populations. Science. 2019;366(6464):447-453. [5] NIH. Bridge to Artificial Intelligence. Accessed August 17 2021. https://commonfund.nih.gov/bridge2ai. [6] PhysioNet. PhysioNet: The Research Resource for Complex Physiologic Signals. Accessed August 17 2021. https://physionet.org. [7] FDA. Software as a Medical Device (SaMD). Accessed August 16 2021. https://www.fda.gov/medical-devices/digital-health-center-excellence/software-medical-device-samd. [8] Benjamens S, Dhunnoo P, Meskó B. The state of artificial intelligence-based FDA-approved medical devices and algorithms: an online database. npj Digital Medicine. 2020;3(1):1-8.