Workflow of CDM

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Clinical Data Management (CDM) is a critical component of clinical research that involves the collection, cleaning, validation, and management of clinical trial data to ensure its accuracy, integrity, and compliance with regulatory requirements. The workflow of CDM typically consists of several key stages, each with specific activities and processes. Here is an overview of the typical workflow of CDM: Study Startup: Protocol Review: CDM teams begin by reviewing the clinical trial protocol to understand the study's objectives, endpoints, data collection requirements, and timelines. Database Design: Based on the protocol, the team designs a data capture system or electronic data capture (EDC) system. This includes creating data entry forms, defining data validation checks, and setting up data dictionaries. Data Collection: Case Report Form (CRF) Design: CDM professionals design electronic or paper CRFs to collect data during the trial. CRFs capture specific data points required by the protocol. Data Entry: Data is entered into the CRFs, either electronically by site personnel or through paper CRFs. Data Validation: CDM teams implement validation checks to ensure data quality and consistency. Data validation checks may include range checks, consistency checks, and logic checks. Query Management: Queries are generated when data discrepancies or inconsistencies are identified. CDM teams send queries to investigational sites for resolution. Data Cleaning and Quality Control: Data Cleaning: Data are cleaned to resolve discrepancies, discrepancies, and inconsistencies. This involves querying data discrepancies with clinical trial sites. Data Review: CDM teams review data to ensure completeness and accuracy, and any outstanding queries are resolved. Quality Control: Quality control processes are applied to verify the integrity and accuracy of data. Database Lock: Once the data are cleaned, reviewed, and validated, the database is locked, indicating that no further changes can be made to the data. Database lock is a critical step before data analysis begins. Data Export and Analysis: Data is exported from the database and provided to biostatisticians and researchers for statistical analysis. This analysis is conducted to determine the study's outcomes, efficacy, and safety profile. Data listings, summaries, and tables are generated for regulatory submissions, reports, and publications. Final Study Reporting: After data analysis, CDM teams contribute to the preparation of final study reports, which provide a comprehensive overview of the trial's results, data quality, and regulatory compliance. Archiving and Documentation: Clinical trial data, documentation, and databases are archived to ensure their long-term availability for regulatory audits and future reference. Regulatory Submission: CDM teams provide support for regulatory submissions.

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WORKFLOW OF CLINICAL DATA MANAGMENT
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Student’s Name: Pawar Manisha
Student’s Qualification :M. pharmacy
Student ID :200/092023

INDEX
What is clinical Data Management
Workflow Clinical Data Management
Objectives of CDM
References
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What is clinical Data Management
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Clinical data management (CDM) is a critical process in clinical
research, which leads to generation of high-quality, reliable, and
statistically sound data from clinical trials.
Clinical data management ensures collection, integration and
availability of data at appropriate quality and cost.
CDM is the process collecting and managing research data accordance
with regulatory standards to obtained quality information that is complete
and error-free.
.

Objectives of CDM:
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To Ensure:
That collected data is complete & accurate so that results are correct.
That trial database is complete, accurate & a true representation of what took
place in trial.
That trial database is sufficiently clean to support statistical analysis & its
subsequent presentation & interpretation.

Workflow Clinical Data Management
 The workflow starts when the CDM team generates a case report from
(CRF) and ends when the database lock.
Clinical data management consists of five stages, which span data
collection, archiving, and presentation. ‘
 The data manager executes quality checks and data cleaning throughout
the workflow.
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CDM Workflow
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 Patient Recorded Data:
• Where the subject is required to record data (e.g. daily symptoms) then a
diary is provided for completion.
• Data management of this data requires a different approach to CRF data as,
for example, it is generally not practical to raise data queries.
• Patient diaries may be developed in either paper or electronic (e Diary)
formats.
Investigator:
Investigator are responsible for supervising the proper handling,
administration ,storage , and destruction of investigational agent .
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 laboratory data
• Samples collected during a clinical trial may be sent to a single central
laboratory for analysis.
• The clinical data manager liaises with the central laboratory and agrees
data formats and transfer schedules in Data Transfer Agreement.
• The sample collection date and time may be reconciled against the CRF
to ensure that all samples collected have been analyzed.
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 Data manager:
• Data managers are primarily responsible for all data collection and
distribution, making them an integral part of successful clinical data
management.
• They are responsible for ensuring life sciences organizations get
maximum utility out of trial data, from assuring its overall accuracy
and integrity to facilitating easy access and analysis by data analysts.
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 Statistician:
• A statistician can contribute to the accuracy and validity of a database by
providing input into the design of both the database structure and data
plausibility checks, and the quality control procedures performed to evaluate
the integrity of a database.
Regulatory Authorities:
• National Designated Authorities (NDAs) are government institutions that serve
as the interface between each country and the Fund.
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References
 https://en.wikipedia.org/wiki/Clinical_data_management.
 https://xcelcareer.com/Clinical_data_management-course/
 https://www.scilife.io/golssary/Clinical_data_management
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Visit:www.acriindia.com ACRI is a leading Clinical data management training Institute in Bangalore India. ACRI creates a value add for every degree. Our PGDCRCDM course is approved by the Mysore University. Graduates and Post Graduates and even PhDs have trained with us and got enviable positions in the Clinical Research Industry. ACRI supplements University training with Industry based training, coupled with hands-on internships and projects based on real case studies. The ACRI brand gives the individual the confidence and expertise to join the ever-growing workforce both in the country and abroad.

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A Trial Master File (TMF) is set up at the beginning of the trial. It is a collection of all essential documents pertaining to the trial, which in turn will allow for effective monitoring and supervision (audit). In order to demonstrate compliance with the applicable regulations, Good Clinical Practice (GCP) guidelines and the protocol – a well organised TMF is essential. According to the GCP guidelines, it is the responsibility of the sponsor to ensure that the TMF includes all relevant essential documents, and is stored in a secure location, with restricted access. Generally, the TMF is maintained at the sponsor’s office, co-ordinating site or by the Contract Research Organisation (CRO), if contracted. In addition to the TMF, copies of all relevant documents must be kept at each participating site, in an Investigator Site File (ISF). The ISF will also include all site-specific essential documents. For example, site preparedness log or site visit logs, etc. A member of the research or trial team should be delegated with the task of updating, maintaining and reviewing the TMF and ISF, periodically throughout the course of the clinical trial as per the defined SOPs. Ideally, the documents included in the TMF are: Trial documents (protocol, investigator’s brochure, participant information documents, SOPs, instructions, manuals, guidelines, etc.) Documents related to the Investigational Product (certificates of analysis, shipment records, storage records, etc.) Training documentation for the trial team Details of the laboratories, if applicable. Contracts, agreements, budgets, etc. Monitoring visit reports (for each site visit onsite or central) Documents related to the safety reporting Ethics Committees documents (composition of the EC, approvals, notifications, reports, etc.) Site-specific documents (list of site staff and their curriculum vitae, investigator’s undertaking, site preparedness documents, training of site staff, etc.) Audit related documents, if available (if an audit was conducted). Significant communications Others The GCP guidelines provide comprehensive guidance regarding the documents to be included in a Trial Master File categorised according to the lifecycle of the trial. This information can also be accessed here. It shall be the responsibility of the sponsor to make arrangements for the safe and secure custody of all study-related documents and material for a period of three years after the completion of the study or submission of the data to the regulatory authority(ies) whichever is later.

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Intentional re-challenge, a practice within clinical data management, is a deliberate and controlled re-administration of a drug to a patient who has previously experienced an adverse drug reaction or drug-related problem during the course of a clinical trial. This process is undertaken to confirm the causality between the drug and the observed adverse event, gain a deeper understanding of the event's mechanisms, and assess the potential for its recurrence. Clinical data management plays a pivotal role in documenting and tracking these intentional re-challenges, meticulously recording patient information, medical history, dosage regimens, and the nature and severity of the adverse event. The data management process ensures that the re-challenge is conducted in a controlled and ethical manner, with a keen focus on patient safety. This comprehensive data management approach aids in evaluating the drug's safety profile, informing regulatory decisions, and ultimately contributing to the overall pharmacovigilance and risk assessment of the drug under investigation.

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Patient-Centric Clinical Trials: Empowering Participants and Enhancing Outcomes'

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Drug regulatory agencies play a pivotal role in overseeing and regulating clinical research to ensure the safety, efficacy, and quality of pharmaceutical products. Their primary responsibility is to protect public health by evaluating the data generated from clinical trials and determining whether a drug can be approved for marketing and use in patients. Here are some key roles of drug regulatory agencies in clinical research: Approval and Oversight of Clinical Trials: Regulatory agencies review and approve clinical trial protocols, ensuring that they adhere to ethical and scientific standards. They assess the design, methodology, and objectives of trials to ensure patient safety and the reliability of data generated. Regulatory Guidance and Standards: These agencies provide guidance and establish regulations governing the conduct of clinical trials, including Good Clinical Practice (GCP) guidelines, which outline standards for trial design, conduct, monitoring, and reporting. Review and Evaluation of Data: Regulatory agencies review the data collected from clinical trials to assess the safety and efficacy of investigational drugs. They evaluate study results, adverse events, and other relevant information to make informed decisions about drug approval. Drug Approval and Labeling: Based on the evaluation of clinical trial data, regulatory agencies decide whether to approve a drug for marketing and use. They also determine the appropriate labeling, including indications, dosages, contraindications, and warnings, to ensure safe and effective use by healthcare professionals and patients. Post-Marketing Surveillance: Regulatory agencies continue to monitor the safety and effectiveness of approved drugs through post-marketing surveillance programs. They collect and analyze real-world data on adverse events and drug utilization to identify potential risks and take appropriate regulatory actions if safety concerns arise. Enforcement of Regulations: Regulatory agencies enforce compliance with regulatory requirements and take enforcement actions against sponsors, investigators, or manufacturers who fail to adhere to ethical or regulatory standards in clinical research. International Collaboration: Many regulatory agencies collaborate with counterparts in other countries to harmonize regulatory standards, exchange information, and streamline the drug approval process, facilitating global drug development and access to new therapies.

Role of Drug Regulatory agencies in Clinical Research.

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Data privacy and consent management are critical aspects of ensuring that individuals' personal information is handled responsibly and ethically, particularly in healthcare settings where sensitive medical data is involved. Data privacy refers to the protection of personal information from unauthorized access, use, or disclosure, while consent management involves obtaining and managing individuals' permissions for the collection, storage, and processing of their data. In healthcare, patients entrust providers with their sensitive medical information, expecting that it will be kept confidential and used only for legitimate purposes related to their care. Robust data privacy measures include encryption, access controls, and anonymization techniques to safeguard patient data from unauthorized access or breaches. Additionally, healthcare organizations must adhere to regulatory standards such as HIPAA in the United States or GDPR in the European Union, which outline specific requirements for the protection of patient information and impose penalties for non-compliance. Consent management plays a crucial role in ensuring that individuals have control over how their data is used. Patients should be informed about the purposes for which their data will be collected and processed, as well as any potential risks or benefits associated with its use. Obtaining informed consent involves providing individuals with clear and transparent information about their privacy rights and giving them the opportunity to consent to or decline the use of their data for specific purposes. Consent management systems help healthcare organizations track and manage patients' consent preferences, ensuring that data is used in accordance with their wishes and legal requirements. Effective data privacy and consent management practices not only protect individuals' privacy rights but also foster trust and transparency in healthcare relationships. By implementing robust security measures, respecting patients' autonomy, and promoting informed decision-making, healthcare organizations can uphold the principles of data privacy and consent while leveraging data responsibly to improve patient care and outcomes.

Data Privacy and consent management .. .

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Clinical data security and integrity are paramount in maintaining patient confidentiality, protecting sensitive medical information, and ensuring the reliability of healthcare data. Security measures encompass various strategies and technologies aimed at safeguarding clinical data from unauthorized access, breaches, or manipulation. Encryption techniques, access controls, and secure authentication mechanisms are commonly employed to restrict access to patient records and prevent unauthorized individuals from viewing or altering sensitive data. Additionally, robust network security protocols, such as firewalls and intrusion detection systems, help safeguard against external threats and cyberattacks targeting healthcare systems.

Clinical data security and Integrity in Clinical Research

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Pharmacovigilance in pediatrics and geriatrics is of paramount importance due to the unique physiological and pharmacokinetic characteristics of these populations. In pediatrics, factors such as rapid growth, organ maturation, and differences in drug metabolism can influence the safety and efficacy of medications. Therefore, monitoring for adverse drug reactions (ADRs), including both known and unexpected effects, is crucial in ensuring the safe use of drugs in children. Additionally, since pediatric patients often receive off-label or unlicensed medications due to limited clinical trials in this population, pharmacovigilance efforts play a critical role in gathering real-world data to inform pediatric drug use.

Pharmacovigilance in Patriatics and Geriatrics

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Signal mining in pharmacovigilance involves the systematic analysis of large datasets to identify potential safety signals associated with medications. It encompasses a variety of computational and statistical methods aimed at detecting patterns or trends that may indicate previously unrecognized adverse drug reactions (ADRs). Signal mining relies on data from sources such as spontaneous reporting systems, electronic health records, clinical trials, and medical literature. Advanced algorithms and data mining techniques, including disproportionality analysis, Bayesian data mining, and machine learning, are employed to sift through vast amounts of data to uncover potential signals of concern. These signals are then subjected to further evaluation to determine their clinical relevance and potential impact on patient safety. By leveraging the power of big data and analytics, signal mining plays a crucial role in enhancing pharmacovigilance efforts, enabling proactive identification and mitigation of medication-related risks, and ultimately contributing to improved patient care and drug safety.

Presenting- Signal mining in Pharmacovigilance

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Patient-centered pharmacovigilance represents a pivotal shift in the landscape of healthcare, emphasizing the active involvement of patients in the monitoring and reporting of adverse drug reactions. Unlike traditional pharmacovigilance, which primarily relies on healthcare professionals to identify and document adverse events, this approach recognizes patients as critical stakeholders in ensuring medication safety. By empowering patients to share their experiences, concerns, and observations regarding medication effects, whether positive or negative, healthcare systems can gain a comprehensive understanding of drug safety and efficacy in real-world settings. Patient-centered pharmacovigilance fosters a collaborative partnership between patients, healthcare providers, and regulatory agencies, promoting transparency, accountability, and ultimately, better patient outcomes. Through increased patient engagement and the utilization of patient-reported data, this approach enables healthcare systems to identify potential safety issues earlier, tailor treatment strategies to individual needs, and enhance overall drug safety surveillance efforts.

Patient centered Pharmacovigilance .. .

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Blogs, presentations, and review articles stand as dynamic mediums, each with its unique power to engage, inform, and influence. Blogs, the conversational storytellers; presentations, the visual orchestrators; and review articles, the comprehensive knowledge curators, collectively play pivotal roles in shaping how we share, learn, and explore diverse subjects. This exploration delves into the distinct characteristics and purposes of these forms of communication, unveiling the art of effective expression and knowledge sharing in the digital age.

Introduction to Blogs, Presentations and Review Articles- Noorush Shifa Nizami

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Precision medicine, with its focus on tailoring medical treatment to the individual characteristics of each patient, has ushered in a new era in healthcare. Within this paradigm, clinical trials play a pivotal role in testing and validating targeted therapies. This article explores the importance of adopting patient-centric approaches in precision medicine trials and outlines strategies to enhance their success. By prioritizing patient engagement, leveraging digital technologies, and fostering collaborative partnerships, precision medicine trials can not only advance scientific understanding but also ensure that patient perspectives and experiences are integral to the research process.

Patient-Centric Approaches in Precision Medicine Trials: Strategies for Success

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Collaborative initiatives involving patient advocacy groups play a crucial role in advancing the success of precision medicine. Precision medicine, also known as personalized medicine, tailors medical treatment and interventions to the characteristics of each patient, considering factors such as genetic makeup, lifestyle, and environment. Patient advocacy groups contribute to the success of precision medicine in several ways: Raising Awareness and Education: Patient advocacy groups are instrumental in raising awareness about precision medicine among their communities. They provide education and resources to patients, caregivers, and the general public, promoting a better understanding of the benefits and implications of precision medicine. Patient Empowerment: Advocacy groups empower patients by providing them with information about their conditions and treatment options. They help patients understand the importance of participating in precision medicine initiatives, including clinical trials and genetic testing. Supporting Research and Development: Patient advocacy groups often collaborate with researchers and industry stakeholders to support the development of targeted therapies and diagnostics. By actively participating in research initiatives, advocacy groups contribute to the identification of genetic markers, biomarkers, and other factors that influence treatment response. Influencing Policy and Regulation: Advocacy groups advocate for policies that support the advancement of precision medicine. They work to ensure that regulations promote patient access to personalized treatments and protect patient rights, privacy, and data security. Fostering Collaboration: Patient advocacy groups facilitate collaboration among patients, researchers, healthcare providers, and industry partners. They create platforms for sharing information, experiences, and best practices, fostering a collaborative environment that accelerates progress in precision medicine. Clinical Trial Recruitment: Advocacy groups play a crucial role in recruiting patients for clinical trials related to precision medicine. Their engagement helps ensure diverse and representative participant populations, leading to more robust and applicable research outcomes. Feedback and Patient-Centric Approaches: Advocacy groups provide valuable feedback on the patient experience, preferences, and priorities. This patient-centric approach helps researchers and healthcare professionals tailor precision medicine strategies to better meet the needs and expectations of the individuals they serve. Championing Access to Treatments: Patient advocacy groups work to improve patient access to precision medicine treatments. They advocate for affordable and equitable access to innovative therapies, ensuring that the benefits of precision medicine are accessible to a broader population. In summary, patient advocacy groups play a multifaceted role in advancing precision medicine by fostering awareness,

Collaborative Initiatives: How Patient Advocacy Groups Contribute to the Succ...

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The Ethical dilemmas in precision medicine arise from balancing the potential benefits of tailored treatments with concerns about privacy, consent, equity, and the implications of genetic information. These Dilemmas include issues around informed consent for data sharing, ensuring equitable access to precision medicine technologies, and safeguarding against discrimination based on Information. The potential for misuse or unintended consequences further complicate the ethical landscape of precision medicine. The Precision medicine widely used in Data Integration, Biomarkers, Targeted Therapies, predictive modeling, Clinical Decision Support. Precision Medicine improves the Patient outcomes, enhancing treatment efficacy, and reducing healthcare costs by minimizing trial and error in treatment selection. The precision medicine also raises Ethical legal and social complications related to data privacy, consent and equitable access and the potential for Discrimination based on information.

Ethical Dilemmas in Precision Medicine: Navigating the Intersection of Scienc...

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Regulatory Considerations in Precision Medicine: Navigating the Approval Process

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Emerging Technologies and Tools in Precision Medicine Research

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Precision medicine, an innovative approach tailoring medical treatment to individual characteristics, holds great promise for improved patient outcomes. In this paradigm, pharmacovigilance plays a crucial role in monitoring and ensuring the safety of personalized treatments. The integration of big data and artificial intelligence (AI) into pharmacovigilance practices becomes paramount for handling the complexities of individualized therapies and identifying potential safety concerns. This article explores the synergies between big data, AI, and pharmacovigilance in the context of precision medicine.

Precision Medicine in Action: Case Studies and Success Stories

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Harnessing Big Data and Artificial Intelligence for Pharmacovigilance in Prec...

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Pharmacovigilance, a critical component of the healthcare and pharmaceutical industry, plays a pivotal role in monitoring and ensuring the safety of drugs. As personalized treatment approaches gain prominence, the responsibilities of PV companies become even more significant. This article explores the key functions of pharmacovigilance companies in the context of personalized medicine, focusing on their role in ensuring drug safety and mitigating potential risks associated with tailored therapeutic interventions.

Pharmacovigilance (PV) Companies: Ensuring Drug Safety in Personalized Treatm...

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Clinical trials play a pivotal role in advancing medical research and ensuring the efficacy of new treatments. In the context of precision medicine, where therapies are tailored to individual characteristics, adaptive design emerges as a powerful strategy to optimize clinical trial outcomes. This approach deviates from traditional fixed trial designs, allowing for modifications based on accumulating data, thus enhancing efficiency, flexibility, and the likelihood of success in precision medicine endeavors.

Adoptive Design: Optimizing Clinical Trial Strategies for Precision Medicine

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Artificial Intelligence In Clinical Trial

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Precision medicine, with its focus on tailoring healthcare interventions to individual characteristics, is undergoing a transformative evolution propelled by emerging technologies. From advanced genomic tools to artificial intelligence, these technologies are revolutionizing our ability to understand, diagnose, and treat diseases at an unprecedented level of specificity. This article explores the key emerging technologies shaping the future of precision medicine.

Emerging Technologies Shaping the Future of Precision Medicine

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Precision medicine, with its focus on tailoring medical interventions to individual characteristics, benefits significantly from the integration of machine learning (ML) algorithms for predictive analytics. These algorithms analyze complex datasets, identify patterns, and generate predictions that inform personalized treatment strategies. This article explores the diverse applications of ML in precision medicine, highlighting the key algorithms driving predictive analytics in this transformative field.

Innovations in Liquid Biopsies for Precision Cancer Diagnosis

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The integration of machine learning algorithms for predictive analytics in precision medicine represents a powerful tool for extracting meaningful insights from diverse and complex datasets. As the field continues to evolve, these algorithms play a crucial role in advancing our understanding of individualized treatment strategies, improving patient outcomes, and ultimately shaping the future of personalized healthcare.

Machine Learning Algorithms for Predictive Analytics in Precision Medicine

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