This document discusses survival analysis and Cox regression for cancer clinical trials. It begins with an introduction to Cox regression analysis and how it can be used to analyze the effects of covariates on survival rates in cancer trials. The document then provides examples of Cox regression outputs and how to interpret the results, including checking the proportional hazards assumption. It cautions against some invalid methods of survival analysis that do not properly account for censored or time-dependent data.
Introduction to survival analysis Providing intuition of hazard function, survival function, cumulative failure function. Life table, KM and log-rank test
These annotated slides will help you interpret an OR or RR in clinical terms. Please download these slides and view them in PowerPoint so you can view the annotations describing each slide.
Use Proportional Hazards Regression Method To Analyze The Survival of Patient...Waqas Tariq
The Kaplan Meier method is used to analyze data based on the survival time. In this paper used Kaplan Meier procedure and Cox regression with these objectives. The objectives are finding the percentage of survival at any time of interest, comparing the survival time of two studied groups and examining the effect of continuous covariates with the relationship between an event and possible explanatory variables. The variables (Age, Gender, Weight, Drinking, Smoking, District, Employer, Blood Group) are used to study the survival patients with cancer stomach. The data in this study taken from Hiwa/Hospital in Sualamaniyah governorate during the period of (48) months starting from (1/1/2010) to (31/12/2013) .After Appling the Cox model and achieve the hypothesis we estimated the parameters of the model by using (Partial Likelihood) method and then test the variables by using (Wald test) the result show that the variables age and weight are influential at the survival of time.
Introduction to survival analysis Providing intuition of hazard function, survival function, cumulative failure function. Life table, KM and log-rank test
These annotated slides will help you interpret an OR or RR in clinical terms. Please download these slides and view them in PowerPoint so you can view the annotations describing each slide.
Use Proportional Hazards Regression Method To Analyze The Survival of Patient...Waqas Tariq
The Kaplan Meier method is used to analyze data based on the survival time. In this paper used Kaplan Meier procedure and Cox regression with these objectives. The objectives are finding the percentage of survival at any time of interest, comparing the survival time of two studied groups and examining the effect of continuous covariates with the relationship between an event and possible explanatory variables. The variables (Age, Gender, Weight, Drinking, Smoking, District, Employer, Blood Group) are used to study the survival patients with cancer stomach. The data in this study taken from Hiwa/Hospital in Sualamaniyah governorate during the period of (48) months starting from (1/1/2010) to (31/12/2013) .After Appling the Cox model and achieve the hypothesis we estimated the parameters of the model by using (Partial Likelihood) method and then test the variables by using (Wald test) the result show that the variables age and weight are influential at the survival of time.
Evaluating Racial Disparities in Survival after AIDS Diagnosis using Standard...fhardnett
The presentation was given at the Joint Statistical Meetings 2005 in Minneapolis, MN. The presentation describes the use of standardized Kaplan-Meier estimation to compare survival across population subgroups when covariate adjustment is necessary and the proportional hazards assumption does not hold.
Objectives
To provide an introduction to the statistical analysis of
failure time data
To discuss the impact of data censoring on data analysis
To demonstrate software tools for reliability data analysis
Organization
Reliability definition
Characteristics of reliability data
Statistical analysis of censored reliability data
ARTFICIAL INTELLIGENCE, SYSTEM ANALYSIS AND SIMULATION MODELING IN OPTIMIZATION OF TREATMENT FOR ESOPHAGEAL CANCER PATIENTS AFTER COMPLETE ESOPHAGECTOMIES
El paquete TestSurvRec implementa las pruebas estadíıticas para comparar dos curvas de supervivencia con eventos recurrentes. Este software ofrece herramientas ´utiles para el an´alisis de la supervivencia en el campo de la biomedicina, epidemiolog´ıa, farmac´eutica y otras áreas. El paquete TestSurvRec contiene dos conjuntos de datos con eventos recurrentes, un conjunto de datos referido al experimento de Byar que contiene los tiempos de recurrencia de tumores de c´ancer de vejiga en los pacientes tratados con piridoxina, tiotepa o considerado como un placebo. Y otro conjunto de datos que contiene los tiempos de rehospitalizaci´on despu´es de la cirug´ıa en pacientes con cáncer colorrectal. Estos datos provienen de un estudio que se llev´o a cabo en el Hospital de Bellvitge, un hospital universitario p´ublico en Barcelona (España).
Probability Models for Estimating Haplotype Frequencies and Bayesian Survival...Université de Dschang
M. Kum Cletus Kwa a soutenu une thèse de Doctorat/Phd en mathématiques ce 14 juin 2016 à l'Université de Dschang. Le jury lui a décerné à l'issue des échanges la mention très honorable.
Este manual es útil e indispensable para el uso del "Package TesSurvRec_1.2.1" de CRAN. Importante para estadístico, médicos, farmacéuticos, seguros, bancos, ingenieros, psicólogos, astrónomos, entre otras profesiones. Son pruebas estadísticas que se utilizan para medir diferencias entre funciones del análisis de supervivencias de grupos de poblaciones que manifiestan eventos recurrentes.
Frequency Measures Used in EpidemiologyIntroductionIn e.docxMARRY7
Frequency Measures Used in Epidemiology
Introduction
In epidemiological studies, many qualitative variables have only two possible categories, such as
Alive or dead
Case or control
Exposed and unexposed
The frequency measures for dichotomous variable include:
Ratio
Proportion
Rate
( All the above 3 measure are based on the same formula: )
Ratios, Proportion, and Rates Compared
In a ratio, the values of x and y may be completely independent from each other or x is a part of y
For example , the gender of the children attending a specific program could be compared in one of the following ways:
Proportion is a ratio in which X is included in Y
For example , the gender of the children attending a specific program
Rate is a proportion that measures the occurrence of an event in a population over time
Rate = X
Ratios, Proportion, and Rates Compared
Example 1: The following table was part of an article published by Dr. Mshana and his colleagues. The title of this study is “Outbreak of a novel Enterobacter sp. carrying blaCTX-M-15 in a neonatal unit of a tertiary care hospital in Tanzania. ". Please use this table to answer the following questions.
Source: Mshana SE, Gerwing L, Minde M, Hain T, Domann E, Lyamuya E, et al. Outbreak of a novel Enterobacter sp. carrying blaCTX-M-15 in a neonatal unit of a tertiary care hospital in Tanzania. International journal of antimicrobial agents. 2011;38(3):265-9.
4
Example 1
What is the ratio of males to females? 7 : 10
What proportion of premature babies? 12/17=0.706
What proportion of patients were discharged? 11/17=0.647
What is the ratio of prematurity to birth asphyxia ? 12 : 5
Source: Mshana SE, Gerwing L, Minde M, Hain T, Domann E, Lyamuya E, et al. Outbreak of a novel Enterobacter sp. carrying blaCTX-M-15 in a neonatal unit of a tertiary care hospital in Tanzania. International journal of antimicrobial agents. 2011;38(3):265-9.
5
Example 2:
In 1989, 733,151 new cases of gonorrhea were reported among the United States civilian population. The 1989 mid-year U.S. civilian population was estimated to be 246,552,000. What is the 1989 gonorrhea incidence rate for the U.S. civilian population? (For these data we will use a value of 105 for 10n ).
Answer:
Incidence rate = X
Incidence rate = X = 297.4 per 100,000
6
Measures of association:
They are used to quantify the relationship between exposure and disease among two groups
They are used to compare the disease occurrence among one group with the disease occurrence in the another group
They include the following measures based on the study design:
Risk Ratio (RR):
It also called relative risk
It is used to compare the risk of health related events in two groups
The following formula cis used to calculate the RR:
A risk ratio of 1.0 indicates identical risk in the two groups
A risk ratio greater than 1.0 indicates an increased risk for the numerator group
A risk ratio greater than 1.0 ...
Logistic Loglogistic With Long Term Survivors For Split Population ModelWaqas Tariq
Split population models are also known as mixture model . The data used in this paper is Stanford Heart Transplant data. Survival times of potential heart transplant recipients from their date of acceptance into the Stanford Heart Transplant program [3]. This set consists of the survival times, in days, uncensored and censored for the 103 patients and with 3 covariates are considered Ages of patients in years, Surgery and Transplant, failure for these individuals is death. Covariate methods have been examined quite extensively in the context of parametric survival models for which the distribution of the survival times depends on the vector of covariates associated with each individual. See [6] for approaches which accommodate censoring and covariates in the ordinary exponential model for survival. Currently, such mixture models with immunes and covariates are in use in many areas such as medicine and criminology. See for examples [4][5][7]. In our formulation, the covariates are incorporated into a split loglogistic model by allowing the proportion of ultimate failures and the rate of failure to depend on the covariates and the unknown parameter vectors via logistic model. Within this setup, we provide simple sufficient conditions for the existence, consistency, and asymptotic normality of a maximum likelihood estimator for the parameters involved. As an application of this theory, the likelihood ratio test for a difference in immune proportions is shown to have an asymptotic chi-square distribution. These results allow immediate practical applications on the covariates and also provide some insight into the assumptions on the covariates and the censoring mechanism that are likely to be needed in practice. Our models and analysis are described in section 5.
2019/ 10/ 11 Originality Report
iginalityReport/ultra?attemptId=929ba456-7401-4cb2-8818-df87061d712f&course_id=_65931_1&includeDeleted=true&print=true&download=true… 1/3
%33
SafeAssign Originality Report
(Current Semester - الفصل الحالي)HCM-506: Appli… • Turnitin Plagiarism Checker
%33Total Score: Medium risk
MOHAMMED ALAHMARI
Submission UUID: b770c976-29f1-45b0-6be2-a23c9f149d8d
Total Number of Repo…
1
Highest Match
33 %
133333.docx
Average Match
33 %
Submitted on
10/11/19
10:36 AM GMT+3
Average Word Count
362
Highest: 133333.docx
%33Attachment 1
Global database (3)
Student paper Student paper Student paper
Top sources (3)
Excluded sources (0)
View Originality Report - Old Design
Word Count: 362
133333.docx
3 2 1
3 Student paper 2 Student paper 1 Student paper
https://lms.seu.edu.sa/webapps/mdb-sa-BBLEARN/originalityReport?attemptId=929ba456-7401-4cb2-8818-df87061d712f&course_id=_65931_1&download=true&includeDeleted=true&print=true&force=true
2019/ 10/ 11 Originality Report
iginalityReport/ultra?attemptId=929ba456-7401-4cb2-8818-df87061d712f&course_id=_65931_1&includeDeleted=true&print=true&download=true… 2/3
Source Matches (6)
Student paper
86%
Student paper
98%
Student paper
96%
Student paper
92%
Module 13
Name: Date:
Kaplan-Meier plots: The goal is to estimate a population survival curve from a sample. i) If every patient is followed until
death, the curve may be estimated simply by computing the fraction surviving at each time. ii) However, in most studies
patients tend to drop out, become lost to follow-up, move away, etc. iii) A Kaplan-Meier analysis allows estimation of
survival over time, even when points drop out or are studied for different lengths of time.
H0 The survival time is not related to the patient treatment group. (Null Hypothesis) H1 The survival time is related to the
patient treatment group. (Alternative Hypothesis)
Baseline Survivor Function (at predictor means)... 0.0000 0.8465 1.0000 0.3114
Interpretation of the curve: Vertical axis represents estimated probability of survival for a hypothetical cohort, not actual %
surviving. • Precision of estimates depends on # observations; therefore, estimates at left-hand side are more precise than at
right-hand side (because of small #’s due to deaths and dropouts). • Curves may give the impression that a given event
occurs more frequently early than late, because of high survival rate and large # people at beginning
Yes, Kaplan-Meier survival curves prognostic indicator of UCEC because it can be generated by selecting the cancer type,
survival type and protein(s) of interest. This technique may also be used for breast, colon and other cancers because its a
statistical technique which gives proper result of survival data of the patients undergone cancer or other treatment.
References Hazra, A., & Gogtay, N. (2016). Biostatistics series module 6: Correlation and linear regression. Indian Journal of
Dermatology, 61(6), 593-601. Retr.
Surveillance of healthcare-associated infections: understanding and utilizing...Evangelos Kritsotakis
Presented at the EUCIC Basic Module for Infection Prevention and Control, Groningen, May 2022.
This module is organised by the European Committee on Infection Control (EUCIC) is taught face-to-face by top experts from different academic centres in Europe, who cover all major aspects of Infection Prevention and Control in the hospital.
Abstract- Statistical models include issues such as statistical characterization of numerical data, estimating the probabilistic future behaviour of a system based on past behaviour, extrapolation or interpolation of data based on some best-fit, error estimates of observations or model generated output. If the statistical model is used to analyse the survival data it is known as statistical model in survival analysis. There are different statistical data. Censored data is one of its kinds. Censoring means the actual survival time is unknown. Censoring may occur when a person does not experience the event before the study ends or lost to follow-up during the study period or withdraws from the study. For this type of censored data the suitable model is survival models. Survival models are classified as non-parametric, semi-parametric and parametric models. The survival probability can be obtained using these models. Using the health data of cancer registry in Tiruchirappalli, Tamil Nadu , a study on survival pattern of cancer patients was explored, the non-parametric modelling that is Kaplan-Meier method was used to estimate the survival probability and the comparison of survival probability of obtained by life table and Kaplan Meier methods for each stage of the disease were made. Log rank test has been used for the comparison between the estimates obtained at the different stages of the disease.
Root cause Analysis (RCA) & Corrective and Preventive action (CAPA) in MRCT d...Bhaswat Chakraborty
This presentation describes Identification & differentiation of Protocol deviation & violation; Different methods of RCA & best suitable method for Multiregional Clinical Trial; CAPA management and CAPA application to other trial sites/CRO/SMO/ Country that is involved in same trial (Strategic Management and application of CAPA in MRCT)
This presentation gives effective solutions to outliers issue in bioequivalence trials. It described what would be acceptable to Regulatory agencies as well as some new approaches.
Equivalence approches for complex generics DIA 11 april 2019 Bhaswat Chakraborty
This is a workshop that i gave a few days ago on bioequivalence of complex generics like peptides, polymers, liposomes, colloids, ophthamic and topical produtcts.
Clinical trials that are needed for efficacy & safety evidence of Medical devices include feasibility (pilot) and Pivotal trials. An extended battery of preclinical trials are also needed for high risk devices.
Writing Science papers for for publication requires something more thatn creativity. Target journals, content organization, wrting style, elegance and referencing are equally important.
Multidisc review of NDAs and BLAs nipicon 2018 Dr. ChakrabortyBhaswat Chakraborty
NDAS and BLAs cannot be authoritatively reviewed these days until experts from different disciplines act together like a team. This presentation give some foundational points and an illustrative example in that regard.
Teaching by stories, anecdotes and historical facts sept 25 2018Bhaswat Chakraborty
Many difficult principles in science and humanities can be taught best by a story (of its discovery), by an anecdote or some historical facts about them.
Orientation and Adaptation for Post-Graduate Pharmacy ProgramsBhaswat Chakraborty
PG Pharmacy programs are more focused and professionally oriented than the undergraduate counterpart. Many soft skills are required along with the curricular competence for excellence at the PG level.
Scientific integrity calls for some basic originality. Plagiarism can destroy this original creativity and ideation. This presentation defines plagiarism (stealing from others' works) and some of the creative and systematic remedies.
Best Practices to Risk Based Data Integrity at Data Integrity Conference, Lon...Bhaswat Chakraborty
Data integrity can be implemented using several approaches. One of the most effective ways to implement DI is a risk based approach. The speaker elaborates this.
There are several dimensions in Pharmaceutical ethics -- Practice-, research- and community oriented. This presentation mainly deals with Clinical research oriented Ethics.
Young pharmaceutical scientists are and can get involved in all aspects of new drug discovery and development. They have to be appropriately qualified, trained and experienced though,
This presentation mainly deals with clinical development of biosimilar products. It also gives enough on non-clinical development so that the audience is well oriented.
High variability in PK can be a characteristic of certain drug products which require different from ordinary strategies and study designs for establishing bioequivalence.
High variability in PK can be a characteristic of certain drug products which require different from ordinary strategies and study designs for establishing bioequivalence.
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.
Follow us on: Pinterest
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
New Directions in Targeted Therapeutic Approaches for Older Adults With Mantl...i3 Health
i3 Health is pleased to make the speaker slides from this activity available for use as a non-accredited self-study or teaching resource.
This slide deck presented by Dr. Kami Maddocks, Professor-Clinical in the Division of Hematology and
Associate Division Director for Ambulatory Operations
The Ohio State University Comprehensive Cancer Center, will provide insight into new directions in targeted therapeutic approaches for older adults with mantle cell lymphoma.
STATEMENT OF NEED
Mantle cell lymphoma (MCL) is a rare, aggressive B-cell non-Hodgkin lymphoma (NHL) accounting for 5% to 7% of all lymphomas. Its prognosis ranges from indolent disease that does not require treatment for years to very aggressive disease, which is associated with poor survival (Silkenstedt et al, 2021). Typically, MCL is diagnosed at advanced stage and in older patients who cannot tolerate intensive therapy (NCCN, 2022). Although recent advances have slightly increased remission rates, recurrence and relapse remain very common, leading to a median overall survival between 3 and 6 years (LLS, 2021). Though there are several effective options, progress is still needed towards establishing an accepted frontline approach for MCL (Castellino et al, 2022). Treatment selection and management of MCL are complicated by the heterogeneity of prognosis, advanced age and comorbidities of patients, and lack of an established standard approach for treatment, making it vital that clinicians be familiar with the latest research and advances in this area. In this activity chaired by Michael Wang, MD, Professor in the Department of Lymphoma & Myeloma at MD Anderson Cancer Center, expert faculty will discuss prognostic factors informing treatment, the promising results of recent trials in new therapeutic approaches, and the implications of treatment resistance in therapeutic selection for MCL.
Target Audience
Hematology/oncology fellows, attending faculty, and other health care professionals involved in the treatment of patients with mantle cell lymphoma (MCL).
Learning Objectives
1.) Identify clinical and biological prognostic factors that can guide treatment decision making for older adults with MCL
2.) Evaluate emerging data on targeted therapeutic approaches for treatment-naive and relapsed/refractory MCL and their applicability to older adults
3.) Assess mechanisms of resistance to targeted therapies for MCL and their implications for treatment selection
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
These simplified slides by Dr. Sidra Arshad present an overview of the non-respiratory functions of the respiratory tract.
Learning objectives:
1. Enlist the non-respiratory functions of the respiratory tract
2. Briefly explain how these functions are carried out
3. Discuss the significance of dead space
4. Differentiate between minute ventilation and alveolar ventilation
5. Describe the cough and sneeze reflexes
Study Resources:
1. Chapter 39, Guyton and Hall Textbook of Medical Physiology, 14th edition
2. Chapter 34, Ganong’s Review of Medical Physiology, 26th edition
3. Chapter 17, Human Physiology by Lauralee Sherwood, 9th edition
4. Non-respiratory functions of the lungs https://academic.oup.com/bjaed/article/13/3/98/278874
Couples presenting to the infertility clinic- Do they really have infertility...Sujoy Dasgupta
Dr Sujoy Dasgupta presented the study on "Couples presenting to the infertility clinic- Do they really have infertility? – The unexplored stories of non-consummation" in the 13th Congress of the Asia Pacific Initiative on Reproduction (ASPIRE 2024) at Manila on 24 May, 2024.
Title: Sense of Taste
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 structure and function of taste buds.
Describe the relationship between the taste threshold and taste index of common substances.
Explain the chemical basis and signal transduction of taste perception for each type of primary taste sensation.
Recognize different abnormalities of taste perception and their causes.
Key Topics:
Significance of Taste Sensation:
Differentiation between pleasant and harmful food
Influence on behavior
Selection of food based on metabolic needs
Receptors of Taste:
Taste buds on the tongue
Influence of sense of smell, texture of food, and pain stimulation (e.g., by pepper)
Primary and Secondary Taste Sensations:
Primary taste sensations: Sweet, Sour, Salty, Bitter, Umami
Chemical basis and signal transduction mechanisms for each taste
Taste Threshold and Index:
Taste threshold values for Sweet (sucrose), Salty (NaCl), Sour (HCl), and Bitter (Quinine)
Taste index relationship: Inversely proportional to taste threshold
Taste Blindness:
Inability to taste certain substances, particularly thiourea compounds
Example: Phenylthiocarbamide
Structure and Function of Taste Buds:
Composition: Epithelial cells, Sustentacular/Supporting cells, Taste cells, Basal cells
Features: Taste pores, Taste hairs/microvilli, and Taste nerve fibers
Location of Taste Buds:
Found in papillae of the tongue (Fungiform, Circumvallate, Foliate)
Also present on the palate, tonsillar pillars, epiglottis, and proximal esophagus
Mechanism of Taste Stimulation:
Interaction of taste substances with receptors on microvilli
Signal transduction pathways for Umami, Sweet, Bitter, Sour, and Salty tastes
Taste Sensitivity and Adaptation:
Decrease in sensitivity with age
Rapid adaptation of taste sensation
Role of Saliva in Taste:
Dissolution of tastants to reach receptors
Washing away the stimulus
Taste Preferences and Aversions:
Mechanisms behind taste preference and aversion
Influence of receptors and neural pathways
Impact of Sensory Nerve Damage:
Degeneration of taste buds if the sensory nerve fiber is cut
Abnormalities of Taste Detection:
Conditions: Ageusia, Hypogeusia, Dysgeusia (parageusia)
Causes: Nerve damage, neurological disorders, infections, poor oral hygiene, adverse drug effects, deficiencies, aging, tobacco use, altered neurotransmitter levels
Neurotransmitters and Taste Threshold:
Effects of serotonin (5-HT) and norepinephrine (NE) on taste sensitivity
Supertasters:
25% of the population with heightened sensitivity to taste, especially bitterness
Increased number of fungiform papillae
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
- Video recording of this lecture in English language: https://youtu.be/lK81BzxMqdo
- Video recording of this lecture in Arabic language: https://youtu.be/Ve4P0COk9OI
- Link to download the book free: https://nephrotube.blogspot.com/p/nephrotube-nephrology-books.html
- Link to NephroTube website: www.NephroTube.com
- Link to NephroTube social media accounts: https://nephrotube.blogspot.com/p/join-nephrotube-on-social-media.html
Factory Supply Best Quality Pmk Oil CAS 28578–16–7 PMK Powder in Stockrebeccabio
Factory Supply Best Quality Pmk Oil CAS 28578–16–7 PMK Powder in Stock
Telegram: bmksupplier
signal: +85264872720
threema: TUD4A6YC
You can contact me on Telegram or Threema
Communicate promptly and reply
Free of customs clearance, Double Clearance 100% pass delivery to USA, Canada, Spain, Germany, Netherland, Poland, Italy, Sweden, UK, Czech Republic, Australia, Mexico, Russia, Ukraine, Kazakhstan.Door to door service
Hot Selling Organic intermediates
Pulmonary Thromboembolism - etilogy, types, medical- Surgical and nursing man...VarunMahajani
Disruption of blood supply to lung alveoli due to blockage of one or more pulmonary blood vessels is called as Pulmonary thromboembolism. In this presentation we will discuss its causes, types and its management in depth.
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.
Ozempic: Preoperative Management of Patients on GLP-1 Receptor Agonists Saeid Safari
Preoperative Management of Patients on GLP-1 Receptor Agonists like Ozempic and Semiglutide
ASA GUIDELINE
NYSORA Guideline
2 Case Reports of Gastric Ultrasound
Ozempic: Preoperative Management of Patients on GLP-1 Receptor Agonists
Part 2 Cox Regression
1. Survival Analysis and Cox
Regression for Cancer Trials
Presented at PG Department of Statistics,
Sardar Patel University January 29, 2013
Dr. Bhaswat S. Chakraborty
Sr. VP & Chair, R&D Core Committee
Cadila Pharmaceuticals Ltd., Ahmedabad
1
2. Part 2: Cox Regression
Analysis of Cancer CTs
2
3. Clinical Trials
Organized scientific efforts to get direct answers from
relevant patients on important scientific questions on
(doses and regimens of) actions of drugs (or devices or
other interventions).
Questions are mainly about differences or null
Modern trials (last 40 years or so) are large, multicentre,
often international and co-operative endeavors
Ideally, primary objectives are consistent with mechanism
of action
Results can be translated to practice
Would stand the regulatory and scientific scrutiny
3
4. Cancer Trials (Phases I–IV)
Highly complex trials involving cytotoxic drugs, moribund patients,
time dependent and censored variables
Require prolonged observation of each patient
Expensive, long term and resource intensive trials
Heterogeneous patients at various stages of the disease
Prognostic factors of non-metastasized and metastasized diseases are
different
Adverse reactions are usually serious and frequently include death
Ethical concerns are numerous and very serious
Trial management is difficult and patient recruitment extremely
challenging
Number of stopped trials (by DSMB or FDA) is very high
Data analysis and interpretation are very difficult by any standard
5
7. India: 2010
7137 of 122 429 study deaths were due to cancer, corresponding to 556 400 national
cancer deaths in India in 2010.
395 400 (71%) cancer deaths occurred in people aged 30—69 years (200 100 men
and 195 300 women).
At 30—69 years, the three most common fatal cancers were oral (including lip and
pharynx, 45 800 [22·9%]), stomach (25 200 [12·6%]), and lung (including trachea
and larynx, 22 900 [11·4%]) in men, and cervical (33 400 [17·1%]), stomach
(27 500 [14·1%]), and breast (19 900 [10·2%]) in women.
Tobacco-related cancers represented 42·0% (84 000) of male and 18·3% (35 700) of
female cancer deaths and there were twice as many deaths from oral cancers as lung
cancers.
Age-standardized cancer mortality rates per 100 000 were similar in rural (men 95·6
[99% CI 89·6—101·7] and women 96·6 [90·7—102·6]) and urban areas (men 102·4
[92·7—112·1] and women 91·2 [81·9—100·5]), but varied greatly between the
states.
Cervical cancer was far less common in Muslim than in Hindu women (study deaths
24, age-standardized mortality ratio 0·68 [0·64—0·71] vs 340, 1·06 [1·05—1·08]).
8
9. Survival Analysis
Survival analysis is studying the time between entry
to a study and a subsequent event (such as death).
Also called “time to event analysis”
Survival analysis attempts to answer questions such
as:
which fraction of a population will survive past a certain
time ?
at what rate will they fail ?
at what rate will they present the event ?
How do particular factors benefit or affect the probability of
survival ?
11
10. What kind of time to event data?
Survival Analysis typically focuses on time to event data.
In the most general sense, it consists of techniques for
positive-valued random variables, such as
time to death
time to onset (or relapse) of a disease
length of stay in a hospital
money paid by health insurance
viral load measurements
Kinds of survival studies include:
clinical trials
prospective cohort studies
retrospective cohort studies
retrospective correlative studies
12
11. Definition and Characteristics of Variables
Survival time (t) random variables (RVs) are always non-
negative, i.e., t ≥ 0.
T can either be discrete (taking a finite set of values, e.g.
a1, a2, …, an) or continuous [defined on (0,∞)].
A random variable t is called a censored survival time RV
if x = min(t, u), where u is a non-negative censoring
variable.
For a survival time RV, we need:
(1) an unambiguous time origin (e.g. randomization to clinical
trial)
(2) a time scale (e.g. real time (days, months, years)
(3) defnition of the event (e.g. death, relapse)
13
12. Event
Test
Non-Event
Sample of Target Randomize
Population
Event
Control
Non-Event
Time to Event
14
14. Why Regression for Survival Data?
Survival, in the form of hazard function, and one or more
explanatory co-variables can be very interesting research
investigation
The relation with risk factors can be studied using group-
specific Kaplan-Meier estimates, together with Logrank and/or
Wilcoxon tests
Investigating the relation with covariates, requires a
regression-type model
Relating the outcome to several factors and/or covariates
simultaneously requires multiple regression, ANOVA, or
ANCOVA models
The most frequently used model is the Cox (proportional
hazards) model
16
16. Cox Proportional Hazards Regression
Most common Cox are linear-like models for the log
hazard
For example, a parametric regression model based on the
exponential distribution:
loge hi(t) = α + β1xi1 + β2xi2 + … + βkxik
or, equivalently,
hi(t) = exp (α + β1xi1 + β2xi2 + … + βkxik)
= eα x eβ1xi1 x eβ2xi2 x … x eβkxik
Where
i indexes subjects and
xi1, xi2, …, xik are the values of the co-variates for the ith
18
subject
17. Cox Model contd..
This is therefore a linear model for the log-hazard or a multiplicative
model for the hazard itself
The model is parametric because, once the regression parameters α,
β1, … βk are specified, the hazard function hi(t) is fully characterized by
the model
The regression constant α represents a kind of baseline hazard, since
loge hi(t) = α, or equivalently, hi(t) = eα, when all of the x’s are 0
Other parametric hazard regression models are based on other
distributions commonly used in modeling survival data, such as the
Gompertz and Weibull distributions.
Parametric hazard models can be estimated with standards softwares
19
Source: John Fox
18. Cox Regression is a Proportional Hazards
Model
Consider two observations, h1(t): hazard for the experimental group and
h0(t): hazard for the control group
h1(t)/h0(t) = exp(β)
exp (β) indicates how large (small) is the hazard in experimental group with
the respect to the hazard in the reference group
and it is constant, does not depend on time. Hence, it is called “proportional
hazards” over time
Other qualities:
Usually provides better estimates of survival probabilities and
cumulative hazard than those provided by the Kaplan-Meier function
when assumptions are met
The coefficients in a Cox regression relate to hazard
a positive coefficient indicates a worse prognosis
a negative coefficient indicates a protective effect of the variable with which it is
associated
20
20. Interpretation of Results
h1 (t,X) = h0(t) exp (β1 gender + β2 treatment)
Gender: 1 = male, 0 = female; treament: 1 = experimental,
0 = control
h1 (t,X) = h0(t) exp (−0.51 gender + 0.69 treatment) and
exp(β1 ) = exp(−0.51 ) = 0.6 and exp(β2 ) = exp(0.69 ) = 2.0
This means a reduction of hazards for males, i.e., males have
larger probabilities of survival than females
The experimental treatment increases hazard, i.e., patients
receiving the new experimental treatment have lower survival
probabilities than patients on the control (standard) treatment
22
21. Checking Proportionality of Hazards
Check to see if the estimated survival curves cross
If they do, then this is evidence that the hazards are not
proportional
More formal test: e.g., scaled Schoenfeld Residuals show
interactions between covariates and time
Testing the time dependent covariates is equivalent to testing
for a non-zero slope in a generalized linear regression of the
scaled Schoenfeld residuals on functions of time
A non-zero slope is an indication of a violation of the
proportional hazard assumption.
23
23. Cox Regression is a Proportional Hazards
Model
Cox regression (or proportional hazards regression) is method
for investigating the effect of several variables upon the time a
specified event takes to happen
When an outcome is death this is known as Cox regression for
survival analysis
Assumptions:
the effects of the predictor variables upon survival are constant over time
are additive in one scale
Usually provides better estimates of survival probabilities and
cumulative hazard than those provided by the Kaplan-Meier
function when assumptions are met
The coefficients in a Cox regression relate to hazard
a positive coefficient indicates a worse prognosis
a negative coefficient indicates a protective effect of the variable with
which it is associated
25
28. Cox Hazard Analysis
95% Conf. Hazard =
Coefficient (±) Std.Error P Exp(Coef.)
Group
Surv -0.5861172 0.6726008 0.343165 0.0876 0.55648
The significance test for the coefficient b1 tests the null hypothesis that it
equals zero and thus that its exponent equals one
The confidence interval for b1 is therefore the confidence interval for the
relative death rate or hazard ratio
What is your conclusion of this analysis?
30
30. Case Study: Results
Cox proportional hazards:
Factors associated with increased mortality risk were male
sex, poor KPS (< 80), presence of liver metastases, high
serum lactate dehydrogenase, and low serum albumin.
Adjusted for these variables, there was no statistically
significant difference in survival rates between patients
treated with gemcitabine and marimastat 25 mg, but patients
receiving either marimastat 10 or 5 mg were found to have a
significantly worse survival rate than those receiving
gemcitabine
32
32. Bad or Wrong Methods of Analysis
Comparison of life tables at one point in time ignoring their structure
elsewhere (except very rapid processes)
If a few patients are at risk for more than a certain time but do not die, this
should not be taken as evidence of cure. Look at all the data of all the
patients
Median survival times are not very reliable unless the death rate around that
median is very high
A simple count of number of death in each group is inefficient as it ignores
the rate of death
The best estimate of the probability of survival for a certain time (say 5
years), is given by the life table value at that time. Other simplistic
calculations may be misleading
Randomized controls are always better than historical controls
34
33. Bad or Wrong Methods of Analysis contd.
Estimation of survival is best done from randomization time. If it is done
from the time of 1st treatment it can be misleading (as initiating time for two
treatments can be different)
Superficial comparison of the slopes of survival graphs as it biases the
proportion surviving at each given time
Declaring ITT is better than per protocol analysis or the reverse
Check all the data carefully especially the P values associated with either
type of analysis
When you get an overall non-significant treatment effect, do not insist that
a sub-stratum can still benefit from the treatment even if that stratum
analysis is significant
Realistically not checking the actual number of survivors on the last day of
the study (follow up)
Be sure of your reason to use and report one-sided vs. two-sided t-tests
35
34. Overall Conclusions
Survival time is measured for each patient from his/her date of
randomization
The life table is a table or graph estimating the proportion of
surviving patients at different times after randomization
The Log Rank test is a comparison of observed and expected
death in each experimental group
P value of Log Rank can be estimated by a chi square (χ2 ) test.
A patients are divided into strata (prospectively or
retrospectively), K-M life tables or Log Rank can be used to
compare prognosis in each stratum, for testing heterogeneity,
etc.
Usually Cox regression yields slightly better analysis of cancer
trial data provided assumptions are met
36