How high frequency ultrasound imaging is supporting preclinical research appl...Scintica Instrumentation
This free webinar hosted by Scintica Instrumentation introduced participants to some of the basics of high frequency ultrasound imaging and reviewed the most common preclinical research applications for this exciting technology. Our presenter, Ms. Tonya Coulthard, discussed the basic principles of ultrasound imaging, presented sample images from various organs and tissues as a way of surveying common research applications, and introduced a few novel techniques that will be of interest to many researchers. Throughout the webinar, technical information, product specifications and sample images where shown from the Prospect T1 compact, high-frequency, preclinical ultrasound imaging system.
How high frequency ultrasound imaging is supporting preclinical research appl...Scintica Instrumentation
This free webinar hosted by Scintica Instrumentation introduced participants to some of the basics of high frequency ultrasound imaging and reviewed the most common preclinical research applications for this exciting technology. Our presenter, Ms. Tonya Coulthard, discussed the basic principles of ultrasound imaging, presented sample images from various organs and tissues as a way of surveying common research applications, and introduced a few novel techniques that will be of interest to many researchers. Throughout the webinar, technical information, product specifications and sample images where shown from the Prospect T1 compact, high-frequency, preclinical ultrasound imaging system.
Nursing Care of Clients with Peripheral Vascular Disorders Part 2 of 3 Carmela Domocmat
Nursing Care of Clients with Peripheral Vascular Disorders Part 2 of 3: Arterial disorders such as Arterial occlusive disease, Arterial embolism, Arterial thrombosis, Thromboangiitis obliterans (Buerger’s disease), Aortitis, Aortoiliac disease, Aneurysms, Raynaud’s disease, and Thoracic outlet syndrome
Nursing Care of Clients with Peripheral Vascular Disorders Part 2 of 3 Carmela Domocmat
Nursing Care of Clients with Peripheral Vascular Disorders Part 2 of 3: Arterial disorders such as Arterial occlusive disease, Arterial embolism, Arterial thrombosis, Thromboangiitis obliterans (Buerger’s disease), Aortitis, Aortoiliac disease, Aneurysms, Raynaud’s disease, and Thoracic outlet syndrome
http://www.theheart.org/web_slides/1254947.do
A prospective multicenter study to determine the diagnostic performance of noninvasive fractional flow reserve (FFRCT) ws invasively measured fractional flow reserve (FFR)
The Life-Changing Impact of AI in HealthcareKalin Hitrov
For IT Leaders in the healthcare and pharmaceutical industries looking to understand the impact of AI on their industries and how to overcome the ethical and efficiency challenges that come with its use.
The Gram stain is a fundamental technique in microbiology used to classify bacteria based on their cell wall structure. It provides a quick and simple method to distinguish between Gram-positive and Gram-negative bacteria, which have different susceptibilities to antibiotics
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- 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
New Drug Discovery and Development .....NEHA GUPTA
The "New Drug Discovery and Development" process involves the identification, design, testing, and manufacturing of novel pharmaceutical compounds with the aim of introducing new and improved treatments for various medical conditions. This comprehensive endeavor encompasses various stages, including target identification, preclinical studies, clinical trials, regulatory approval, and post-market surveillance. It involves multidisciplinary collaboration among scientists, researchers, clinicians, regulatory experts, and pharmaceutical companies to bring innovative therapies to market and address unmet medical needs.
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
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
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/kqbnxVAZs-0
- Video recording of this lecture in Arabic language: https://youtu.be/SINlygW1Mpc
- 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
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.
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
Local Advanced Lung Cancer: Artificial Intelligence, Synergetics, Complex Sys...Oleg Kshivets
Overall life span (LS) was 1671.7±1721.6 days and cumulative 5YS reached 62.4%, 10 years – 50.4%, 20 years – 44.6%. 94 LCP lived more than 5 years without cancer (LS=2958.6±1723.6 days), 22 – more than 10 years (LS=5571±1841.8 days). 67 LCP died because of LC (LS=471.9±344 days). AT significantly improved 5YS (68% vs. 53.7%) (P=0.028 by log-rank test). Cox modeling displayed that 5YS of LCP significantly depended on: N0-N12, T3-4, blood cell circuit, cell ratio factors (ratio between cancer cells-CC and blood cells subpopulations), LC cell dynamics, recalcification time, heparin tolerance, prothrombin index, protein, AT, procedure type (P=0.000-0.031). Neural networks, genetic algorithm selection and bootstrap simulation revealed relationships between 5YS and N0-12 (rank=1), thrombocytes/CC (rank=2), segmented neutrophils/CC (3), eosinophils/CC (4), erythrocytes/CC (5), healthy cells/CC (6), lymphocytes/CC (7), stick neutrophils/CC (8), leucocytes/CC (9), monocytes/CC (10). Correct prediction of 5YS was 100% by neural networks computing (error=0.000; area under ROC curve=1.0).
2. Neural Networks In Medical
Diagnosis
A neural network system:
• does not suffer from fatigue or psychological factors
that can affect the reliability of the diagnosis
procedure.
• once trained, can offer the expertise of an expert
radiologist in interpreting the scans when an expert
radiologist is not accessible.
• has the promise for a more accurate diagnosis than
is possible with human interpretation.
3. Pulmonary Embolism (PE)
• Blood clots break off from their
source and become emboli.
• Emboli travel through the heart
into the pulmonary arteries.
• They occlude the arteries to
various anatomic regions of the
lung.
300,000 to 600,000 hospitalizations and 50,000 People die each
year from PE [NIH Consensus Statement cited August 1999]
4. Various Diagnostic Criterias
• Modified PIOPED - Prospective Investigation
of Pulmonary Embolism Diagnosis [1995].
• Biello’s Criteria [1979].
• Inputs from Expert Radiologists.
The modified PIOPED criteria was followed in this project
5. Modified PIOPED Criteria
High Probability
• > = 2 Large segmental perfusion
defects (SPD).
• 1 Large SPD and >= 2 Moderate
SPD.
• > = 4 Moderate SPD.
Intermediate Probability
• 1 Moderate to < 2 Large SPD.
• Corresponding V/Q defect and
CXR opacity in lower lung.
• Single moderately matched V/Q
defect.
• Corresponding V/Q defect and
small Pleural Effusion.
Low Probability
• Multiple Matching V/Q defects.
• Corresponding V/Q defects and
CXR parenchymal opacity in upper
or middle lung zone.
• Corresponding V/Q defects and
large Pleural Effusion.
• > 3 Small SPD.
Very Low Probability
• < = 3 Small SPD.
Normal
• No perfusion defects and perfusion
outlines the shape of the lung seen
on CXR
*CXR = Chest Radiograph
**V/Q = Ventilation-Perfusion
6. Architecture of the Neural
Diagnosis System
Architecture of the Neural Diagnosis System
Output
Inputs to ANN
Image
Processing
System
Artificial
Neural
Network
Committee
Machine
V/Q
Scans
and
Chest
X-Ray
Graphical
User
Interface
(GUI)
7. The ANN Committee Machine
• Dynamic committee
machine
– 13 MLPs to classify
(divided into 5 groups
for various
probabilitites)
– 14 RBFNNs as Gating
Networks (Part of
Integrator)
Confidence
Integrator
(14 RBFNNs)
Output
Inputs
1 perceptron
1 Perceptron
High Probability
Intermediate Probability
2 perceptrons
MLP-1 2 hidden nodes
MLP-2 3 hidden nodes
Low Probability
7 perceptrons
MLP
2 hidden node
Very Low Probability
Normal
8. Inputs to the ANN Committee
Machine
1) Size of the largest perfusion defect with respect to the size of the lung.
2) Number of small (< 25% of a segment) segmental perfusion defects with a normal CXR.
3) Number of matched V/Q defects with normal CXR
4) Number of non-segmental perfusion defects
5) Number of perfusion defects surrounded by normally perfused lung
6) Number of corresponding V/Q defects with CXR parenchymal opacity in upper or middle
lung zone.
7) Number of corresponding V/Q defects with large pleural effusion.
8) Number of perfusion defects with substantially larger CXR abnormality.
9) Number of moderate matched V/Q defects with normal CXR.
10) Number of corresponding V/Q defects with CXR parenchymal opacity in lower lung zone.
11) Number of corresponding V/Q defects with small pleural effusion.
12) Number of large (>75% of a segment) perfusion defect with normal CXR.
13) Number of moderate (25% - 75% of a segment) perfusion defects without CXR
abnormality.
9. Outputs
• Classification -
• Normal
• Very Low Probability
• Low Probability
• Intermediate Probability
• High Probability
• Confidence
• Range 0 to 1
10. The Integrator
• Produces confidences in the MLP outputs
• Confidences depends on distance of input point from decision boundary of
the particular MLP (Gaussian Function used)
Confidence = |r -1| where, r= RBFNN output
Distance from Decision Boundary (x)
RBFNN Output (y)
1
0
RBFNN Output v/s Distance from Decision Boundaries
11. Image Enhancement
• Intensity adjustment done to raise the average pixel intensity in the image
to a value between 65% and 70%
• Nonlinear mapping using an ‘S’ curve used to improve the contrast of the
image
Mapped Intensity = I(x,y) * a * m
0
255 * a
127 * a
0
1
0
255 * m
200 * a
Mapping function (m)Image intensity
range (a <= 1)
Resulting Intensity
Intensity mapping done during enhancement
12. Architecture of the GUI
Opening Screen
Identify Defective Views
Case of Normal Ventilation scan and Chest Xray
Number of segmental defects in each view
Number of non-segmental defects in each view
Number of perfusion defects surrounded by normally perfused lung
Case of abnormal Ventilation scan and/or Chest XRay
Number of segmental defects in each view
Number of non-segmental defects in each view
Number of perfusion defects surrounded by normally perfused lung
Defects with parenchymal Opacity in Lower, Upper or Middle Lung
Pleural Effusion
Perfusion Defects with substantially larger CXR abnormality
Case of Segmental Perfusion Defects
Identify Lung
Identify defect
Identify defective Segment
Result
13. The Opening Screen
Opening screen of the user interface
•Go through the set of
images
•Identify images that show
a defect
•Select “Bigger View”
button for a better view
14. Case of Normal Ventilation
Scans and Chest XRay
Screen shown in case of Perfusion defects only
For each defective view
•Number of Segmental Perfusion
Defects in the view
•Screen for identifying area of
defect and segment with defect
•Number of Non-segmental defects
•Number of Perfusion defects
surrounded by normally perfused lung
15. Case of segmental perfusion
defects
Screen for marking the segmental perfusion defects
•Identify Lung(s)
•Identify defect(s)
•Identify Segment(s)
16. Case of abnormal Ventilation
scan and/or Chest XRay
Screen shown in cases where Ventilation and/or Perfusion
defects are present.
• Number of segmental defects in each
view
• Number of non-segmental defects in
each view
• Number of perfusion defects
surrounded by normally perfused lung
• Defects with parenchymal Opacity in
Lower, Upper or Middle Lung
• Pleural Effusion
• Perfusion defects with substantially
larger CXR abnormality
18. Stage 1
• The MLPs in the committee
machine were trained and tested
individually.
• Testing was done to identify and
confirm the positions of the
decision boundaries in Input space.
• RBFNNs were trained to find
cluster centers at the decision
bondaries created by the MLPs (A
distance function was used for
this)
Training/Testing/Simulation
Stage 2
• The committee machine was
integrated (the MLP system and
the Integrator were connected) and
testing was done using a different
set of data.
• The Committee machine was
integrated with the User Interface.
Alpha Phase
19. • Currently being implemented. In this phase the radiologist will have
a hands on experience. This will ensure that the software has a high
degree of usability and physicians will not be intimidated by it.
Training/Testing/Simulation
Beta Phase
20. Conclusions
• Implementation of Artificial Neural Network Systems in
the diagnosis of medical diseases is feasible and can be
very easily extended to cover different diseases.
• The methods utilized to diagnose Pulmonary Embolism
effectively capture the spirit of the modified PIOPED
criteria.
• This system has the ability to make accurate and quick
diagnosis.
21. The Future...
• Total Automation (Radiologists not required to identify
defects)
• Improved Diagnostic capabilities beyond the modified
PIOPED criteria by training using angiography results.
• Network output in terms of presence or absence of PE (not
probabilities).
• Use of other Artificial Intelligence paradigms such as
Fuzzy Logic Systems and Expert Systems in combination
with Artificial Neural Network System.