This document provides an overview of oncology and cancer clinical trials from a data standards and programming perspective. It begins with basic cancer definitions and epidemiology. Key aspects of clinical trials in oncology are then discussed, including complex efficacy endpoints, safety evaluations, and exposure assessments. Standardization efforts through CDISC are summarized, including SDTM and ADaM domains for oncology. Regulatory guidelines from the FDA and EMA are also covered. Throughout, challenges specific to oncology trials from a data and programming standpoint are highlighted. The aim of the PhUSE oncology wiki is also introduced as a resource for further information.
Presented at PhUSE 2013
The evaluation of efficacy in oncology studies, in particular for solid tumors, is pretty standard and well defined by several regulatory guidance (e.g. EMA and FDA), including some specific cancer type guidance (e.g. NSCLC from FDA).
Although some references will be also given for non-solid tumors, the paper will mainly focus on solid tumors efficacy
endpoints.
Overall Survival, Best Overall Response as per RECIST criteria, Progression Free Survival (PFS), Time to Progression (TTP), Best Overall Response Rate are some of the key efficacy indicators that will be discussed.
The presentation is intended for Clinical Trial programmers or statisticians who are working on the solid tumor studies in oncology. There are three types of studies in oncology: Solid Tumor, Lymphoma and Leukemia. The solid tumor study usually follow RECIST (Response Evaluation Criteria in Solid Tumor) while Lymphoma follows Cheson and Leukemia follows study-specific criteria. The presentation will provide the brief introduction of RECIST 1.1 such as lesions (target, non target and new) and their selection criteria (size, number and etc). It will also discuss how the changes in tumor measurements will lead to responses (Complete Response, Partial Response, Stable Disease, Progression Disease and Not Evaluable).
Then, the presentation will introduce how RECIST 1.1 data are streamlined in CDISC – mainly in SDTM and ADaM. The presentation will introduce the new oncology SDTM domains - TU (Tumor Identification), TR (Tumor Results) and RS (Response) according to RECIST 1.1. The presentation will also show how ADaM datasets can be created for the tumor response evaluation and analysis in ORR (Objective Response Rate), PFS (Progression Free Survival) and TTP (Time to Progression).
Presented at PhUSE 2013
The evaluation of efficacy in oncology studies, in particular for solid tumors, is pretty standard and well defined by several regulatory guidance (e.g. EMA and FDA), including some specific cancer type guidance (e.g. NSCLC from FDA).
Although some references will be also given for non-solid tumors, the paper will mainly focus on solid tumors efficacy
endpoints.
Overall Survival, Best Overall Response as per RECIST criteria, Progression Free Survival (PFS), Time to Progression (TTP), Best Overall Response Rate are some of the key efficacy indicators that will be discussed.
The presentation is intended for Clinical Trial programmers or statisticians who are working on the solid tumor studies in oncology. There are three types of studies in oncology: Solid Tumor, Lymphoma and Leukemia. The solid tumor study usually follow RECIST (Response Evaluation Criteria in Solid Tumor) while Lymphoma follows Cheson and Leukemia follows study-specific criteria. The presentation will provide the brief introduction of RECIST 1.1 such as lesions (target, non target and new) and their selection criteria (size, number and etc). It will also discuss how the changes in tumor measurements will lead to responses (Complete Response, Partial Response, Stable Disease, Progression Disease and Not Evaluable).
Then, the presentation will introduce how RECIST 1.1 data are streamlined in CDISC – mainly in SDTM and ADaM. The presentation will introduce the new oncology SDTM domains - TU (Tumor Identification), TR (Tumor Results) and RS (Response) according to RECIST 1.1. The presentation will also show how ADaM datasets can be created for the tumor response evaluation and analysis in ORR (Objective Response Rate), PFS (Progression Free Survival) and TTP (Time to Progression).
SDTM (Study Data Tabulation Model) defines a standard structure for human clinical trial (study) data tabulations and for nonclinical study data tabulations that are to be submitted as part of a product application to a regulatory authority such as the United States Food and Drug Administration (FDA).
In this presentation, Principal Statistical Scientist Ben Vaughn explains how clinical trial data moves from collection in the case report form to its presentation to FDA.
According to FDA Draft Guidance for Industry in Electronic Submission and Study Data Technical Conformance Guide, the pharmaceutical companies will need to provide CDISC Electronic submission to FDA. The paper will explain Data Standard Catalog which will dictate FDA Standards. The paper will discuss how to prepare CDISC electronic submission and what to prepare in CDISC electronic submission.
Two different use cases to obtain best response using recist 11 sdtm and a ...Kevin Lee
Each therapeutic area has its own unique data collection and analysis. Especially, Oncology has a unique way to collect and analyze the data and one of unique data points in oncology study is best response. The paper will be based on Solid Tumor and RECIST 1.1, and it will show use cases on how best response will be collected in SDTM domains and derived in ADaM datasets using RECIST 1.1 in solid tumor oncology study.
The paper will provide the brief introduction of RECIST 1.1 such as legions type (i.e., target, non-target and new) and their selection criteria(e.g., size and number). The paper will provide the practical application on how tumor measurements for target and non-target lesions are collected in TR domain, how those measurement are assessed according to RECIST 1.1, and eventually how responses are represented in RS domain based on the assessment from tumor measurements.
We will also put in prospective a pictorial road map on which way we choose to derive responses to give a prospective to the user and the process to get from beginning to end objective. This paper will also discuss a use case where the visit level response are been derived programmatically in ADaM and perform a sensitive analysis in comparison to investigator provides visit level response to SDTM RS domain. This case study will help user identify the differences between both the methodologies and help answer any anomalies from investigator inference prospective vs analytical calculations by the programmer.
SDTM (Study Data Tabulation Model) defines a standard for organizing and formatting data to streamline processes in collection, management, analysis and reporting of human clinical trial data tabulations and for non-clinical study data tabulations which are to be submitted as part of a product application(IND and NDA) to a regulatory authority such as the United States Food and Drug Administration (FDA) and PMDA (Japan)
Comparison of RECIST 1.0 and 1.1 - Impact on Data ManagementKevin Shea
A review of the two RECIST versions, noting similarities and differences, highlighting the improvements in v.1.1. This information is used to discuss how some of the challenges RECIST presents to data management can be addressed.
The presentation is intended for Clinical Trial programmers or statisticians who are working on the oncology lymphoma clinical trial studies. There are three types of studies in oncology: Solid Tumor, Lymphoma and Leukemia. The lymphoma studies usually follow Cheson while solid tumor follow RECIST (Response Evaluation Criteria in Solid Tumor) and Leukemia studies follow IWCLL(Internal Working Group on Chronic Lymphocytic Leukemia). There are two version of Cheson – 1999 and 2007. The presentation will be based on Cheson 2007.
The presentation will provide the brief introduction of Cheson 2007 such as legions (enlarged lymph node, nodal masses and extra nodal masses) and their types (target, non target and new) . The lymphoma studies need to collect the measurements of lesions (the longest diameter, its greatest transverse diameter and the sum of diameters), PET scan on those lesions, Bone Marrow assessment, Spleen and Liver assessment. Cheson 2007 explains how each assessment is made to determine responses (Complete Response, Partial Response, Stable Disease and Progression Disease).
Then, the paper will show how tumor data are streamlined in CDISC – mainly in SDTM and ADaM. The paper will introduce the new oncology SDTM domains - TU (Tumor Identification), TR (Tumor Results) and RS (Response) and oncology ADaM dataset – Time to Event (--TTE). The paper will show how Cheson 2007 data points are collected in SDTM domain - tumor measurements in TR and TU, PET scan in TR and TU, Bone Marrow in LB and FA, Spleen and Liver assessments in PE and response in RS. The paper will also show how ADaM time to event datasets can be used for oncology analysis such as OR(Overall Survival) and PFS (Progression Free Survival).
CDISC journey in Leukemia studies using IWCLL 2008Kevin Lee
The presentation is intended for those who are working on the oncology leukemia clinical trial studies. There are four types of leukemia studies : Acute Lymphoblastic Leukemia(ALL), Acute Myeloid Leukemia (AML), Chronic Lymphocytic Leukemia (CLL),Chronic Myeloid Leukemia (CML). The presentation is based on CLL.
The presentation will provide the brief introduction of International Workshop on Chronic Lymphocytic Leukemia (IWCLL) 2008 such as tumors measurement (enlarged lymph node), bone marrow assessment, liver and spleen enlargement assessment and blood counts (B-Lymphocytes, Neutrophils, Platelets, Hemoglobin) . The presentation will explains how each assessment based on IWCLL 2008 is made to determine responses (Complete Response, Partial Response, Stable Disease and Progression Disease).
Then, the presentation will show how tumor data are streamlined in CDISC – mainly in SDTM and ADaM. The paper will introduce the new oncology SDTM domains - TU (Tumor Identification), TR (Tumor Results) and RS (Response) and oncology ADaM dataset – Time to Event (--TTE). The presentation will show how IWCLL 2008 data points are collected in SDTM domain - tumor measurements in TR and TU, bone marrow in LB and FA, spleen and liver assessments in PE and FA, blood counts in LB and responses in RS. The presentation will also show how overall response rate will be derived in ADaM data set.
SDTM (Study Data Tabulation Model) defines a standard structure for human clinical trial (study) data tabulations and for nonclinical study data tabulations that are to be submitted as part of a product application to a regulatory authority such as the United States Food and Drug Administration (FDA).
In this presentation, Principal Statistical Scientist Ben Vaughn explains how clinical trial data moves from collection in the case report form to its presentation to FDA.
According to FDA Draft Guidance for Industry in Electronic Submission and Study Data Technical Conformance Guide, the pharmaceutical companies will need to provide CDISC Electronic submission to FDA. The paper will explain Data Standard Catalog which will dictate FDA Standards. The paper will discuss how to prepare CDISC electronic submission and what to prepare in CDISC electronic submission.
Two different use cases to obtain best response using recist 11 sdtm and a ...Kevin Lee
Each therapeutic area has its own unique data collection and analysis. Especially, Oncology has a unique way to collect and analyze the data and one of unique data points in oncology study is best response. The paper will be based on Solid Tumor and RECIST 1.1, and it will show use cases on how best response will be collected in SDTM domains and derived in ADaM datasets using RECIST 1.1 in solid tumor oncology study.
The paper will provide the brief introduction of RECIST 1.1 such as legions type (i.e., target, non-target and new) and their selection criteria(e.g., size and number). The paper will provide the practical application on how tumor measurements for target and non-target lesions are collected in TR domain, how those measurement are assessed according to RECIST 1.1, and eventually how responses are represented in RS domain based on the assessment from tumor measurements.
We will also put in prospective a pictorial road map on which way we choose to derive responses to give a prospective to the user and the process to get from beginning to end objective. This paper will also discuss a use case where the visit level response are been derived programmatically in ADaM and perform a sensitive analysis in comparison to investigator provides visit level response to SDTM RS domain. This case study will help user identify the differences between both the methodologies and help answer any anomalies from investigator inference prospective vs analytical calculations by the programmer.
SDTM (Study Data Tabulation Model) defines a standard for organizing and formatting data to streamline processes in collection, management, analysis and reporting of human clinical trial data tabulations and for non-clinical study data tabulations which are to be submitted as part of a product application(IND and NDA) to a regulatory authority such as the United States Food and Drug Administration (FDA) and PMDA (Japan)
Comparison of RECIST 1.0 and 1.1 - Impact on Data ManagementKevin Shea
A review of the two RECIST versions, noting similarities and differences, highlighting the improvements in v.1.1. This information is used to discuss how some of the challenges RECIST presents to data management can be addressed.
The presentation is intended for Clinical Trial programmers or statisticians who are working on the oncology lymphoma clinical trial studies. There are three types of studies in oncology: Solid Tumor, Lymphoma and Leukemia. The lymphoma studies usually follow Cheson while solid tumor follow RECIST (Response Evaluation Criteria in Solid Tumor) and Leukemia studies follow IWCLL(Internal Working Group on Chronic Lymphocytic Leukemia). There are two version of Cheson – 1999 and 2007. The presentation will be based on Cheson 2007.
The presentation will provide the brief introduction of Cheson 2007 such as legions (enlarged lymph node, nodal masses and extra nodal masses) and their types (target, non target and new) . The lymphoma studies need to collect the measurements of lesions (the longest diameter, its greatest transverse diameter and the sum of diameters), PET scan on those lesions, Bone Marrow assessment, Spleen and Liver assessment. Cheson 2007 explains how each assessment is made to determine responses (Complete Response, Partial Response, Stable Disease and Progression Disease).
Then, the paper will show how tumor data are streamlined in CDISC – mainly in SDTM and ADaM. The paper will introduce the new oncology SDTM domains - TU (Tumor Identification), TR (Tumor Results) and RS (Response) and oncology ADaM dataset – Time to Event (--TTE). The paper will show how Cheson 2007 data points are collected in SDTM domain - tumor measurements in TR and TU, PET scan in TR and TU, Bone Marrow in LB and FA, Spleen and Liver assessments in PE and response in RS. The paper will also show how ADaM time to event datasets can be used for oncology analysis such as OR(Overall Survival) and PFS (Progression Free Survival).
CDISC journey in Leukemia studies using IWCLL 2008Kevin Lee
The presentation is intended for those who are working on the oncology leukemia clinical trial studies. There are four types of leukemia studies : Acute Lymphoblastic Leukemia(ALL), Acute Myeloid Leukemia (AML), Chronic Lymphocytic Leukemia (CLL),Chronic Myeloid Leukemia (CML). The presentation is based on CLL.
The presentation will provide the brief introduction of International Workshop on Chronic Lymphocytic Leukemia (IWCLL) 2008 such as tumors measurement (enlarged lymph node), bone marrow assessment, liver and spleen enlargement assessment and blood counts (B-Lymphocytes, Neutrophils, Platelets, Hemoglobin) . The presentation will explains how each assessment based on IWCLL 2008 is made to determine responses (Complete Response, Partial Response, Stable Disease and Progression Disease).
Then, the presentation will show how tumor data are streamlined in CDISC – mainly in SDTM and ADaM. The paper will introduce the new oncology SDTM domains - TU (Tumor Identification), TR (Tumor Results) and RS (Response) and oncology ADaM dataset – Time to Event (--TTE). The presentation will show how IWCLL 2008 data points are collected in SDTM domain - tumor measurements in TR and TU, bone marrow in LB and FA, spleen and liver assessments in PE and FA, blood counts in LB and responses in RS. The presentation will also show how overall response rate will be derived in ADaM data set.
The Center for Medicare and Medicaid Innovation (CMS Innovation Center) hosted an introduction webinar about the Oncology Care Model (OCM) on Thursday, February 19, 2015 from 12:00pm – 1:00pm EST. The webinar focused on introducing core concepts of OCM and application instructions. Advance registration was not required.
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Gastro Intestinal Tract – By Prof.Dr.R.R.Deshpande
Uploaded on 10 July 16
This PPT is a part of First BAMS .Syllabus of Sharir Kriya .Paper 1 & Part B. Point 5 .Functional Anatomy of Gastro Intestinal Tract . Points in the syllabus are Mechanism of Secretion & composition of different digestive Juices ,Functions of salivary glands ,Stomach,Liver ,Pancreas ,small intestine & large intestine .The process of Digestion & Absorption .Movements of the gut ,Deglutition,Peristalsis ,Defecation & the control.Enteric Nervous System .
Mobile – 922 68 10 630
Web site – www.ayurvedicfriend.com
Health complications of various forms of tobacco such as Chewing tobacco, Snuff, Creamy snuff, Dipping tobacco, Gutka, Snus, Cigarette, Cigar, Bidi, Kretek and Hookah are discussed in this presentation.
Bioavailability and Bioequivalence Studies (BABE) & Concept of BiowaiversJaspreet Guraya
The presentation gives an insight on BABE studies, mathematical and statistical procedures involved in designing these studies, the official guidelines regarding study design. In the later part it also discusses about biowaivers and their role.
Beyond Cigarettes: The Risks of Non-Cigarette Nicotine Products and Implicati...Center on Addiction
Whereas much is known about the effects of tobacco use, the current state of knowledge regarding non-cigarette nicotine products, such as electronic nicotine delivery systems (e-cigarettes and other vaping devices), water pipe/hookah, smokeless tobacco, pipes, cigars, little cigars, and cigarillos, that do not contain tobacco is not robust enough to yield a definitive consensus regarding their relative risks and benefits.
Lung cancer is the most common cancer in males and second most common in females after breast cancer.
it is the third most commonly diagnosed and leading cause of cancer death in Pakistan, with an estimated 6,800 (4.6%) new cases and 6,013 (5.9%) deaths occurring in 2012
We have compared our data with the international statistics to see where do we stand.
In Pakistan, we do not have a valid central cancer registry at present which can provide a true picture of lung cancer. This calls for an urgent need to formulate a valid central cancer registry in the country in association with the local bodies.
Oncology is that department of medicine that deals with tumors. A medical professional who practices oncology is an oncologist.This includes the detection of any cancer in person , therapies, post care of cancer patients after treatments in the superspeciality hospitals
Rare cancers are those affecting very small numbers and up to now have been poorly diagnosed, researched, funded, and treated, resulting in very high death rates even as morbidity for common cancers decline.
Diagnostic breakthroughs like genome sequencing make earlier stage diagnosis possible and breakthroughs in personalized treatment, including cell and gene therapies, provide new hope, including potential cures.
CCSN welcomed our host panelist Durhane Wong-Rieger, President & CEO of Canadian Organization for Rare Disorders and Chair of Rare Disease International. Durhane was joined by Lisa Machado, Founder and Chair of the CML Network for this engaging and educational webinar on the unique issues presented in rare cancers.
The webinar was followed by a question & answer session.
The use of Adaptive designs is becoming quite popular and well-perceived by the regulatory agencies such as the FDA in the US. “Adaptation” can occur in different fashion and potentially make studies more efficient (e.g. shorter duration, fewer patients) more likely to demonstrate an effect of the drug if one exists, or more informative (see “Adaptive Design Clinical Trials for Drugs and Biologics” FDA guidance).
The aim of this presentation is to illustrate a case where an adaptive design was used in a Phase III oncology pivotal study having Overall Survival as a primary end-point. The particular adaptation implemented was an un-blinded SSR that applied a promising zone approach.
The main focus will be how the adaptive design impacted the SDTM modelling, the design of some ADaM datasets (e.g. those containing the time-to-event endpoints and therefore using ADTTE ADaM model) and later on how some mapping and analysis decisions were described in both the study and analysis reviewer guide.
The use of Adaptive designs is becoming quite popular and well-perceived by the regulatory agencies such as the FDA in the US. “Adaptation” can occur in different fashion and potentially make studies more efficient (e.g. shorter duration, fewer patients) more likely to demonstrate an effect of the drug if one exists, or more informative (see “Adaptive Design Clinical Trials for Drugs and Biologics” FDA guidance).
The aim of this presentation is to illustrate a case where an adaptive design was used in a Phase III oncology pivotal study having Overall Survival as a primary end-point. The particular adaptation implemented was an un-blinded SSR that applied a promising zone approach.
The main focus will be how the adaptive design impacted the SDTM modelling, the design of some ADaM datasets (e.g. those containing the time-to-event endpoints and therefore using ADTTE ADaM model) and later on how some mapping and analysis decisions were described in both the study and analysis reviewer guide.
While the evolution of information technology is bringing the data closer to customers for their own exploration, the need of a comprehensive understanding of the therapeutic area knowledge for programmers in clinical development is increasing. Starting with a basic understanding on the medical background, special assessment methods, ways of statistically analyzing and displaying the data, to name a few essential ones enables programmers to interact with partners (e.g. scientist, statisticians etc.) on equal par.
In this intent, activities to collect and provide comprehensive information around the Oncology and Rheumatoid Arthritis Therapeutic Areas (TA) via the PhUSE Wiki had started in February 2013 and continued throughout the year. Various PhUSE members have spent time and energy to provide and expand their knowledge and make it available to the entire community.
Today, although there is still much to do to complete and maintain the collected material, the two TA Wikis are a useful tool for Statistical Programmers approaching these TA for the first time or who want to improve their knowledge. Moreover the PhUSE Wiki can be seen as a basic tool for future developments to improve the way professionals in the different TA work. An established working relationship across organizations, pharmaceutical companies or external service providers, will help to support implementation of TA-specific standards from mapping raw data in SDTM, data analysis using ADaM and finally data presentation in standardized outputs. The PhUSE Wiki can be the central place to share important updates such as new CDISC TA standards or the availability of new TA regulatory guidance. On the other hand we see the Wiki as a place to discuss, to stimulate and inspire new initiatives among the “SAS-Programming Community”, be it Statisticians, Programmers, Data Managers or everyone else involved; this may include specific TA working related white papers and/or scripts being part of the FDA Working Groups WG5 “Development of Standard Scripts for Analysis and Programming” Project 08 “Create white papers providing recommended display and analysis including Table, List and Figure shells”.
Presented at PhUSE/FDA CSS 2014 in Silver Spring (US)
These lecture slides, by Dr Sidra Arshad, offer a quick overview of physiological basis of a normal electrocardiogram.
Learning objectives:
1. Define an electrocardiogram (ECG) and electrocardiography
2. Describe how dipoles generated by the heart produce the waveforms of the ECG
3. Describe the components of a normal electrocardiogram of a typical bipolar leads (limb II)
4. Differentiate between intervals and segments
5. Enlist some common indications for obtaining an ECG
Study Resources:
1. Chapter 11, Guyton and Hall Textbook of Medical Physiology, 14th edition
2. Chapter 9, Human Physiology - From Cells to Systems, Lauralee Sherwood, 9th edition
3. Chapter 29, Ganong’s Review of Medical Physiology, 26th edition
4. Electrocardiogram, StatPearls - https://www.ncbi.nlm.nih.gov/books/NBK549803/
5. ECG in Medical Practice by ABM Abdullah, 4th edition
6. ECG Basics, http://www.nataliescasebook.com/tag/e-c-g-basics
Anti ulcer drugs and their Advance pharmacology ||
Anti-ulcer drugs are medications used to prevent and treat ulcers in the stomach and upper part of the small intestine (duodenal ulcers). These ulcers are often caused by an imbalance between stomach acid and the mucosal lining, which protects the stomach lining.
||Scope: Overview of various classes of anti-ulcer drugs, their mechanisms of action, indications, side effects, and clinical considerations.
Flu Vaccine Alert in Bangalore Karnatakaaddon Scans
As flu season approaches, health officials in Bangalore, Karnataka, are urging residents to get their flu vaccinations. The seasonal flu, while common, can lead to severe health complications, particularly for vulnerable populations such as young children, the elderly, and those with underlying health conditions.
Dr. Vidisha Kumari, a leading epidemiologist in Bangalore, emphasizes the importance of getting vaccinated. "The flu vaccine is our best defense against the influenza virus. It not only protects individuals but also helps prevent the spread of the virus in our communities," he says.
This year, the flu season is expected to coincide with a potential increase in other respiratory illnesses. The Karnataka Health Department has launched an awareness campaign highlighting the significance of flu vaccinations. They have set up multiple vaccination centers across Bangalore, making it convenient for residents to receive their shots.
To encourage widespread vaccination, the government is also collaborating with local schools, workplaces, and community centers to facilitate vaccination drives. Special attention is being given to ensuring that the vaccine is accessible to all, including marginalized communities who may have limited access to healthcare.
Residents are reminded that the flu vaccine is safe and effective. Common side effects are mild and may include soreness at the injection site, mild fever, or muscle aches. These side effects are generally short-lived and far less severe than the flu itself.
Healthcare providers are also stressing the importance of continuing COVID-19 precautions. Wearing masks, practicing good hand hygiene, and maintaining social distancing are still crucial, especially in crowded places.
Protect yourself and your loved ones by getting vaccinated. Together, we can help keep Bangalore healthy and safe this flu season. For more information on vaccination centers and schedules, residents can visit the Karnataka Health Department’s official website or follow their social media pages.
Stay informed, stay safe, and get your flu shot today!
The prostate is an exocrine gland of the male mammalian reproductive system
It is a walnut-sized gland that forms part of the male reproductive system and is located in front of the rectum and just below the urinary bladder
Function is to store and secrete a clear, slightly alkaline fluid that constitutes 10-30% of the volume of the seminal fluid that along with the spermatozoa, constitutes semen
A healthy human prostate measures (4cm-vertical, by 3cm-horizontal, 2cm ant-post ).
It surrounds the urethra just below the urinary bladder. It has anterior, median, posterior and two lateral lobes
It’s work is regulated by androgens which are responsible for male sex characteristics
Generalised disease of the prostate due to hormonal derangement which leads to non malignant enlargement of the gland (increase in the number of epithelial cells and stromal tissue)to cause compression of the urethra leading to symptoms (LUTS
Title: Sense of Smell
Presenter: Dr. Faiza, Assistant Professor of Physiology
Qualifications:
MBBS (Best Graduate, AIMC Lahore)
FCPS Physiology
ICMT, CHPE, DHPE (STMU)
MPH (GC University, Faisalabad)
MBA (Virtual University of Pakistan)
Learning Objectives:
Describe the primary categories of smells and the concept of odor blindness.
Explain the structure and location of the olfactory membrane and mucosa, including the types and roles of cells involved in olfaction.
Describe the pathway and mechanisms of olfactory signal transmission from the olfactory receptors to the brain.
Illustrate the biochemical cascade triggered by odorant binding to olfactory receptors, including the role of G-proteins and second messengers in generating an action potential.
Identify different types of olfactory disorders such as anosmia, hyposmia, hyperosmia, and dysosmia, including their potential causes.
Key Topics:
Olfactory Genes:
3% of the human genome accounts for olfactory genes.
400 genes for odorant receptors.
Olfactory Membrane:
Located in the superior part of the nasal cavity.
Medially: Folds downward along the superior septum.
Laterally: Folds over the superior turbinate and upper surface of the middle turbinate.
Total surface area: 5-10 square centimeters.
Olfactory Mucosa:
Olfactory Cells: Bipolar nerve cells derived from the CNS (100 million), with 4-25 olfactory cilia per cell.
Sustentacular Cells: Produce mucus and maintain ionic and molecular environment.
Basal Cells: Replace worn-out olfactory cells with an average lifespan of 1-2 months.
Bowman’s Gland: Secretes mucus.
Stimulation of Olfactory Cells:
Odorant dissolves in mucus and attaches to receptors on olfactory cilia.
Involves a cascade effect through G-proteins and second messengers, leading to depolarization and action potential generation in the olfactory nerve.
Quality of a Good Odorant:
Small (3-20 Carbon atoms), volatile, water-soluble, and lipid-soluble.
Facilitated by odorant-binding proteins in mucus.
Membrane Potential and Action Potential:
Resting membrane potential: -55mV.
Action potential frequency in the olfactory nerve increases with odorant strength.
Adaptation Towards the Sense of Smell:
Rapid adaptation within the first second, with further slow adaptation.
Psychological adaptation greater than receptor adaptation, involving feedback inhibition from the central nervous system.
Primary Sensations of Smell:
Camphoraceous, Musky, Floral, Pepperminty, Ethereal, Pungent, Putrid.
Odor Detection Threshold:
Examples: Hydrogen sulfide (0.0005 ppm), Methyl-mercaptan (0.002 ppm).
Some toxic substances are odorless at lethal concentrations.
Characteristics of Smell:
Odor blindness for single substances due to lack of appropriate receptor protein.
Behavioral and emotional influences of smell.
Transmission of Olfactory Signals:
From olfactory cells to glomeruli in the olfactory bulb, involving lateral inhibition.
Primitive, less old, and new olfactory systems with different path
Tom Selleck Health: A Comprehensive Look at the Iconic Actor’s Wellness Journeygreendigital
Tom Selleck, an enduring figure in Hollywood. has captivated audiences for decades with his rugged charm, iconic moustache. and memorable roles in television and film. From his breakout role as Thomas Magnum in Magnum P.I. to his current portrayal of Frank Reagan in Blue Bloods. Selleck's career has spanned over 50 years. But beyond his professional achievements. fans have often been curious about Tom Selleck Health. especially as he has aged in the public eye.
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Introduction
Many have been interested in Tom Selleck health. not only because of his enduring presence on screen but also because of the challenges. and lifestyle choices he has faced and made over the years. This article delves into the various aspects of Tom Selleck health. exploring his fitness regimen, diet, mental health. and the challenges he has encountered as he ages. We'll look at how he maintains his well-being. the health issues he has faced, and his approach to ageing .
Early Life and Career
Childhood and Athletic Beginnings
Tom Selleck was born on January 29, 1945, in Detroit, Michigan, and grew up in Sherman Oaks, California. From an early age, he was involved in sports, particularly basketball. which played a significant role in his physical development. His athletic pursuits continued into college. where he attended the University of Southern California (USC) on a basketball scholarship. This early involvement in sports laid a strong foundation for his physical health and disciplined lifestyle.
Transition to Acting
Selleck's transition from an athlete to an actor came with its physical demands. His first significant role in "Magnum P.I." required him to perform various stunts and maintain a fit appearance. This role, which he played from 1980 to 1988. necessitated a rigorous fitness routine to meet the show's demands. setting the stage for his long-term commitment to health and wellness.
Fitness Regimen
Workout Routine
Tom Selleck health and fitness regimen has evolved. adapting to his changing roles and age. During his "Magnum, P.I." days. Selleck's workouts were intense and focused on building and maintaining muscle mass. His routine included weightlifting, cardiovascular exercises. and specific training for the stunts he performed on the show.
Selleck adjusted his fitness routine as he aged to suit his body's needs. Today, his workouts focus on maintaining flexibility, strength, and cardiovascular health. He incorporates low-impact exercises such as swimming, walking, and light weightlifting. This balanced approach helps him stay fit without putting undue strain on his joints and muscles.
Importance of Flexibility and Mobility
In recent years, Selleck has emphasized the importance of flexibility and mobility in his fitness regimen. Understanding the natural decline in muscle mass and joint flexibility with age. he includes stretching and yoga in his routine. These practices help prevent injuries, improve posture, and maintain mobilit
Lung Cancer: Artificial Intelligence, Synergetics, Complex System Analysis, S...Oleg Kshivets
RESULTS: Overall life span (LS) was 2252.1±1742.5 days and cumulative 5-year survival (5YS) reached 73.2%, 10 years – 64.8%, 20 years – 42.5%. 513 LCP lived more than 5 years (LS=3124.6±1525.6 days), 148 LCP – more than 10 years (LS=5054.4±1504.1 days).199 LCP died because of LC (LS=562.7±374.5 days). 5YS of LCP after bi/lobectomies was significantly superior in comparison with LCP after pneumonectomies (78.1% vs.63.7%, P=0.00001 by log-rank test). AT significantly improved 5YS (66.3% vs. 34.8%) (P=0.00000 by log-rank test) only for LCP with N1-2. Cox modeling displayed that 5YS of LCP significantly depended on: phase transition (PT) early-invasive LC in terms of synergetics, PT N0—N12, cell ratio factors (ratio between cancer cells- CC and blood cells subpopulations), G1-3, histology, glucose, AT, blood cell circuit, prothrombin index, heparin tolerance, recalcification time (P=0.000-0.038). Neural networks, genetic algorithm selection and bootstrap simulation revealed relationships between 5YS and PT early-invasive LC (rank=1), PT N0—N12 (rank=2), thrombocytes/CC (3), erythrocytes/CC (4), eosinophils/CC (5), healthy cells/CC (6), lymphocytes/CC (7), segmented neutrophils/CC (8), stick neutrophils/CC (9), monocytes/CC (10); leucocytes/CC (11). Correct prediction of 5YS was 100% by neural networks computing (area under ROC curve=1.0; error=0.0).
CONCLUSIONS: 5YS of LCP after radical procedures significantly depended on: 1) PT early-invasive cancer; 2) PT N0--N12; 3) cell ratio factors; 4) blood cell circuit; 5) biochemical factors; 6) hemostasis system; 7) AT; 8) LC characteristics; 9) LC cell dynamics; 10) surgery type: lobectomy/pneumonectomy; 11) anthropometric data. Optimal diagnosis and treatment strategies for LC are: 1) screening and early detection of LC; 2) availability of experienced thoracic surgeons because of complexity of radical procedures; 3) aggressive en block surgery and adequate lymph node dissection for completeness; 4) precise prediction; 5) adjuvant chemoimmunoradiotherapy for LCP with unfavorable prognosis.
Prix Galien International 2024 Forum ProgramLevi Shapiro
June 20, 2024, Prix Galien International and Jerusalem Ethics Forum in ROME. Detailed agenda including panels:
- ADVANCES IN CARDIOLOGY: A NEW PARADIGM IS COMING
- WOMEN’S HEALTH: FERTILITY PRESERVATION
- WHAT’S NEW IN THE TREATMENT OF INFECTIOUS,
ONCOLOGICAL AND INFLAMMATORY SKIN DISEASES?
- ARTIFICIAL INTELLIGENCE AND ETHICS
- GENE THERAPY
- BEYOND BORDERS: GLOBAL INITIATIVES FOR DEMOCRATIZING LIFE SCIENCE TECHNOLOGIES AND PROMOTING ACCESS TO HEALTHCARE
- ETHICAL CHALLENGES IN LIFE SCIENCES
- Prix Galien International Awards Ceremony
micro teaching on communication m.sc nursing.pdfAnurag Sharma
Microteaching is a unique model of practice teaching. It is a viable instrument for the. desired change in the teaching behavior or the behavior potential which, in specified types of real. classroom situations, tends to facilitate the achievement of specified types of objectives.
3. Disclaimer
The information contained in this presentation is based on research of
personal PhUSE Wiki authors. PhUSE Wiki authors may or may not be
experts in the field of the specific disease.
Neither PhUSEwiki.org nor the PhUSE guarantee for the correctness of the
displayed information.
Text
Text
5. Overview
Oncology (cont.)
• Endpoints – Tools used
• Data Challenges
• SDTM and ADaM
• Regulatory Setting
• FDA Guideline
• EMA Guideline
• References
6. Standards help to collaborate,
the PhUSE Wiki helps to understand
CDASH/annotated CRF
SDTM
ADaM
PhUSE Wiki
Protocol,
CRF,
SAP
Text
Text
SAS
Programming
Standardized
Analysis
7. Aim of PhUSE (TA-)Wiki
THE central place to share knowledge and information
Basics
• Background
Information
• Etiology
• Pathophysiology
• Statistics
• Symptoms
• Treatment
options
Specific
Standardisation
• TA-specific
• Regulatory
Setting
• Endpoints
• Assessment
tools
• (Data)
Challenges
Text
Text
• SDTM mapping
• Tumore
Response
• Anti-Cancer
Medications
• Survival Fup
• ADaM concepts
• TTE Endpoints
• References
11. Oncology - Basics
Definition (short)
• Cancer is a term used for diseases in which
abnormal cells divide without control and are able to
invade other tissues. Cancer cells can spread to
other parts of the body through the blood and lymph
systems.
• Oncology is a branch of medicine that specializes
in the diagnosis and treatment of cancer. It includes
medical oncology (the use of chemotherapy,
hormone therapy, and other drugs to treat cancer),
radiation oncology (the use of radiation therapy to
treat cancer), and surgical oncology (the use of
surgery and other procedures to treat cancer).
12. Oncology - Basics
Text
Text
Invasive colorectal cancer
Apoptosis is the process of
programmed cell death
Cancer are caused by a
series of mutations
Source: http://en.wikipedia.org
Chest x-ray showing lung
cancer in the left lung
13. Oncology - Basics
Epidemiology
• Cancer is a leading cause of disease
worldwide with about 13 million new cancer
cases occurred worldwide
• Just five cancer sites –lung, female breast,
colon-rectum, stomach and prostate –
accounted for half (48%) of the world’s
total cancer diagnoses in 2008
• Men are more often affected than women.
Source: Cancer Research UK http://www.cancerresearchuk.org
14. Oncology - Basics
Risk Factors
•The most common risk factors for cancer:
Tobacco
Sunlight
Ionizing radiation
Certain chemicals and other substances
Some viruses and bacteria
Certain hormones
Family history of cancer
Alcohol
Poor diet, lack of physical activity, or being overweight
15. Oncology - Basics
Type of Cancer
•Solid Tumors Cancer involving solid tumor,
typically originates in a specific body organ, such
a lung, breast, ovarian, etc. Types of solid tumors
includes sarcomas, carcinomas, adenocarcinomas,
blastomas, carcinoid tumors
•Hematologic malignancies Arrise in the bloodforming cells; typically present as systemic disease,
as blood and lymphatic organs located throughout the
body are affected. Types of Hematologic malignancies
includes leukemias, acute lymphoblastic leukemia
(ALL), acute myeloid leukemia (AML), multiple
myeloma (MM)
17. Oncology - Basics
Diagnosis
•Not easy to diagnose
•Symptoms only appears as the mass grow or ulceration
E.g. mass effects from lung cancer can cause blockage of the
bronchus resulting in cough or pneumonia
•Metastasis when cancer spread to other locations
•Screening (Periodic Assessment)
•Mammography for Breast Cancer
•PSA for Prostate Cancer
•Sigmoidoscopy or Colonscopy for Colorectal Cancer
•Pap test for Cervix
18. Oncology - Basics
Diagnosis
•Primary vs Metastatic vs Recurrent Cancer
•Resistant/Refractory Cancer
•Location of the Cancer
•Stage (TNM): extent of the disease and
whether or not the cancer has spread in the
body (metastasis)
•Histology: type of normal tissue the tumor
cells most closely resemble
•Grading: cells differentiation/proliferation
19. Oncology - Basics
Treatment
•The treatment plan depends mainly on the type of
cancer, the stage of the disease, age and general
health
•Treatment to Cure or Control or Reduce Symptoms
•The treatment plan may change over time
•The treatment plan includes
•Surgery (local therapy removes or destroys
cancer)
•Radiation (to shrink or destroy a tumor)
•Systemic Therapies
•Vaccines to prevent and ‘cure’
20. Oncology - Basics
Treatment
Systemic Therapies
Drugs or substances are used (through the bloodstream)
to destroy cancer cells all over the body. The therapies
kill or slow the growth of cancer cells that may have
spread beyond the original tumor:
•Chemotherapy
•Biological therapy
•Monotherapy vs Combination therapies
•Adjuvant vs Neo-Adjuvant Treatment
21. Oncology - Basics
Treatment
Therapies with molecular/biological target
•Use of Biomarkers to target the population
E.g. drugs used in the therapy target specific
markers
- Herceptin in breast cancer with HER2++
- Gefitinib in lung cancer with mutant EGFR
23. Oncology - Basics
Treatment
•Because cancer treatments often damage
healthy cells and tissues, side effects are
common:
Type and extent of the treatment.
Side effects may not be the same for each
person, and they may change from one
treatment session to the next.
24. Oncology - Basics
Summary
•
•
•
•
One Disease/Several Diseases
Not easy to diagnose
Complex pattern of therapies
Challenging disease therefore challenging in
programming
25. Oncology – Specific
Clinical Trials in Oncology
• Placebos are never used in place of
treatment when an existing standard
therapy exists.
• Patient recruitment is more complicated.”
• Longer follow-up
26. Oncology – Specific
Clinical Trials in Oncology
• Phase I
• Toxicity
• Optimal Dose Determination
• Initial Drug Activity
• PK
• Phase II
• Activity Signals / Tumor Response
• Confirmation about tollerability
27. Oncology – Specific
Clinical Trials in Oncology
• Phase III
• To show better clinical risk/benefit profile
based on the efficacy and safety data
analysis.
• Efficacy through survival endpoints
28. Oncology – Specific
Phase I in Oncology
In alternative
• Accelerated Titration
• Intra-Patient Titration
• Continuation
Reassessment Method
(CRM)
29. Oncology – Specific
Phase I in Oncology
• A peculiarity of Oncoloy
• The concept of DLT (Dose Limiting Toxicity)
and DLT period
• Extended lab (hematology/chemistry)
assessments
• Nadir / Time to Nadir
• Recovery / Time to Recovery
• „Screening“ for future indication to develop
(Phase II)
30. Oncology - Specific
Primary Efficacy Outcome Measures
• Overall Survival (OS) is the gold standard
• Several surrogate endpoints can be used in
place of OS
• Best Overall Response (BOR)
• Objective Response Rate
• Duration of Response
• Time to Progression (TTP) / Disease Free
Survival (DFS)
• Progression Free Survival (PFS)
31. Oncology - Specific
Primary Efficacy Outcome Measures
Quality of Life (EORTC QLQ-C30) and indication specific
questionnaires
http://groups.eortc.be/qol/eortc-modules
32. Oncology - Specific
Primary Efficacy Outcome Measures
•Standardised Tumor response evaluation:
•RECIST for solid tumors
•CHESON for Acute Myeloid Leukemia
•Modified version
•Modified PFS for Prostate Cancer
(PCWG2)
•mRECIST for Hepatocellular Carcinoma
33. Oncology - Specific
Efficacy Analysis
• Primarly survival analysis (Cox Model)
• Graphical Representation
• Kaplan Meier Plot
• Forest Plot
• Waterfall Plot
• Sensitivity Analysis
E.g. for incorrect periodicity of tumor
assessment
35. Oncology - Specific
The concept of cycle
• Commonly defined as a Number of days (or
weeks), e.g. 21 / 28 days (3/4 weeks), where
treatment is repeted
• Different type of schedule
• With combinations studies drugs might have a
different schedule
• The sequence of treatment is repeated (recycled) under certain condition usually safety
and/or efficacy related.
36. Oncology - Specific
Exposure assessment
• Usually described by means of DoseIntensity and Relative Dose-Intensity
• Cumulative dose (mg/sqm) / Treatment
duration (weeks)
• Dose Modifications e.g.:
• Delays
• Reductions
• Overdoses
• Omissions
37. Oncology - Specific
Laboratory data and the CTCAE Grade
Very often Laboratory results comes from local labs
For some of the hematology, chemistry and coagulation
parameters, a categorisation of the value is
possibleNCI-CTCAE criteria
• Each lab value is assigned a grade between 1 and 4
• The grade depends on the actual value and the
normal ranges defined by the labs where the sample
was analyzed
• The classification can be mono or bi-directional
• Hypo
• Hyper
• Hypo and Hyper
•
•
39. Oncology - Specific
Tumor response evaluation with RECIST
• Tumor response measures the changes in tumor
mass, growth (progression) or shrinkage
(response)
• Lesion classified as target (measurable) or nontarget (non-measurable)
• Periodically assessed with CT-SCAN (every 6/8
weeks)
• Progression evaluated vs Nadir (best ‘response’ prior
to current assessment)
• Response evaluated vs Baseline
• Best Overall Response as the best response
assessed since the subject is on-study (on-treatment)
Applicable to Solid Tumors
41. Oncology - Specific
Data Challenges
• Use of Local Labs
• Advers Events and Treatment Emergent
Definition
• Periodic Tumor Assessments
• Tumor Assesment and Treatment having
different schedule
• Blinded Tumor Assessments (Independent
Review)
• Follow-up when OS is primary endpoint
• Use of Biomarkers in the analysis
42. Oncology - Specific
Summary
•
•
•
•
Choice of endpoints depends on
several factors
Efficacy evaluation of response is
standardised and validated for solid
tumors and for certain non-solid
tumors (e.g. AML)
Revised standard for specific
cancer-type
Peculiarity in handling Safety and
Exposure
43. Oncology - Standardisation
CDISC
•SDTM version 3.1.3 contains oncology
specific data domains for tumor response
evaluation
•TU: Tumor Identification
•TR: Tumor Results
•RS: Tumor Response
•Upcoming version of SDTM
•PR: Procedures
•SS: Subject Status (Follow-up)
•TS: Trial Design Assessment
RG02: “CDISC Journey on Solid Tumor Studies using RECIST” Kevin Lee, ; PhUSE 2013
44. Oncology - Standardisation
CDISC ADaM
• No specific oncology-standard have
been developped
• ADTTE for most of efficacy
endpoints (time-to-event) including
composite endpoints
45. Oncology - Standardisation
CRF SDTM ADaM TLF
PFS as a composite endpoints
EVENT
CENSOR
Progression
From Tumor Assessment /
Response
Death
From Survival Follo-up
Last Tumor
Assessment
From Tumor Assessment /
Response
48. Oncology - Standardisation
CDISC – Oncology Open Questions
• [SDTM] Where to store prior anti-cancer
therapiesCommon approach is to store them
in CM with appropriate CMCAT and CMSCAT
• [SDTM] Prior Cancer history stored in several
different domain e.g. MH, CM, SUPPQUAL of
MH, sponsor domains
• [SDTM] Follow-up in DSLack of details
• [SDTM] The use of Oncology Domains to store
non-efficacy information
• [ADaM] Cycles date as TRxxSDT/TRxxEDT?
49. Oncology - Standardisation
CDISC – Coming Version (SDTM 3.1.4)
• PR Procedures
For Prior prior/post anti-cancer treatments
• SS Subject Status
For survival follow-up
• TD Trial Disease Assessments
For efficacy schedule of assessments
53. Oncology – Regulatory
Regulatory Setting (FDA)
FDA Clinical Trial Endpoints for the Approval of
Cancer Drugs and Biologics (2007)
General regulatory requirements for efficacy
Detailed description of endpoints and how
they can be used in various clinical settings
• Pros and Cons
• Protocol and SAP design requirements
• Data Collection for Tumor Measurement
54. Oncology – Regulatory
Regulatory Setting (FDA)
• Issues to consider in PFS analysis
•
•
•
•
Progression and Censoring Date
How to handle Missing Data
Lesions evaluation
Sensitivity Analysis
55. Oncology – Regulatory
Regulatory Setting (EMA)
• ………
Guideline on the evaluation of anticancer medical
products in man
All stages of clinical drug development
Appendices covering methodologial aspects related to:
• Use of Progression Free Survival (PFS) and
Disease Free Survival (DFS) in confirmatory trials
• Confirmatory Studies in Haematological Malignancies
• Condition specific Guidance such as NSCLC, Prostate
The EMA is also planning to provide an additional
appendix for Quality of Life/Patient Reported Outcome.
61. Oncology
Overall Summary
•Cancer one diseases, several diseases
•Complex study endpoints derivation in
efficacy but also in safety
•Unsual concepts e.g. a „cycle“ is not a
„visit“
•If you get involved in a Oncology-study
you may take a look at the PhUSE Wiki
before you start
62. PhUSE (TA-Onco)Wiki
What next
•Seeking for feedback
•Structure, Sections, Topics covered
•Enough or more details
•Link to source or source
•Seeking for contributions
•Complete sections, provide missing details
•Review
•Maintenance
63. PhUSE (TA-Onco)Wiki
What next to develop
•Identify tumor type specific characteristics
from the data and analysis point of view
•E.g. What make different colorectal cancer
from lung cancer?
•Key requirements for submission
•E.g. Differences between indications, type of
cancer and / or line of therapy
•Complete the following area:
•Phase II and Phase III design
•Statistical Analysis
•Quality of Life
•Any missing important item?
64. Oncology
Everyone is invited to contribute!
http://www.phusewiki.org
For further information:
wikiadmin@phusewiki.org
angelo.tinazzi@cytel.com