This is my daily update for COVID-19 trends for July 2, 2020.
Prior analyses and the R program code can be found at https://github.com/StevenLShafer/COVID19
This is my daily update for COVID-19 trends for July 3, 2020.
Prior analyses and the R program code can be found at https://github.com/StevenLShafer/COVID19
This is my daily update for COVID-19 trends for July 4, 2020.
Prior analyses and the R program code can be found at https://github.com/StevenLShafer/COVID19
This is my daily update for COVID-19 trends for July 4, 2020.
Prior analyses and the R program code can be found at https://github.com/StevenLShafer/COVID19
This is my daily update for COVID-19 trends for July 2, 2020.
Prior analyses and the R program code can be found at https://github.com/StevenLShafer/COVID19
This is my daily update for COVID-19 trends for July 3, 2020.
Prior analyses and the R program code can be found at https://github.com/StevenLShafer/COVID19
This is my daily update for COVID-19 trends for July 4, 2020.
Prior analyses and the R program code can be found at https://github.com/StevenLShafer/COVID19
This is my daily update for COVID-19 trends for July 4, 2020.
Prior analyses and the R program code can be found at https://github.com/StevenLShafer/COVID19
This is my daily update for COVID-19 trends for July 8, 2020.
Prior analyses and the R program code can be found at https://github.com/StevenLShafer/COVID19
This is my daily update for COVID-19 trends for July 6, 2020.
Prior analyses and the R program code can be found at https://github.com/StevenLShafer/COVID19
This is my daily update for COVID-19 trends for July 10, 2020.
Prior analyses and the R program code can be found at https://github.com/StevenLShafer/COVID19
This is my daily update for COVID-19 trends for July 9, 2020.
Prior analyses and the R program code can be found at https://github.com/StevenLShafer/COVID19
This is my daily update for COVID-19 trends for July 8, 2020.
Prior analyses and the R program code can be found at https://github.com/StevenLShafer/COVID19
This is my daily update for COVID-19 trends for July 6, 2020.
Prior analyses and the R program code can be found at https://github.com/StevenLShafer/COVID19
This is my daily update for COVID-19 trends for July 10, 2020.
Prior analyses and the R program code can be found at https://github.com/StevenLShafer/COVID19
This is my daily update for COVID-19 trends for July 9, 2020.
Prior analyses and the R program code can be found at https://github.com/StevenLShafer/COVID19
Explore natural remedies for syphilis treatment in Singapore. Discover alternative therapies, herbal remedies, and lifestyle changes that may complement conventional treatments. Learn about holistic approaches to managing syphilis symptoms and supporting overall health.
Report Back from SGO 2024: What’s the Latest in Cervical Cancer?bkling
Are you curious about what’s new in cervical cancer research or unsure what the findings mean? Join Dr. Emily Ko, a gynecologic oncologist at Penn Medicine, to learn about the latest updates from the Society of Gynecologic Oncology (SGO) 2024 Annual Meeting on Women’s Cancer. Dr. Ko will discuss what the research presented at the conference means for you and answer your questions about the new developments.
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
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
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.
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.
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Evaluation of antidepressant activity of clitoris ternatea in animals
COVID-19 Analysis: June 19, 2020
1. Caveats and Comments
1
Overview:
This is my analysis, not Stanford’s. It is simply a set of regressions in R to understand the trajectory of COVID. It is not confidential and can be freely shared. The R
program code and all previous PowerPoint files are available at https://github.com/StevenLShafer/COVID19/. Please contact me at steven.shafer@Stanford.edu if
you would like to be added or removed from the recipient list. Suggestions are most welcome! You are welcome to use the R code on GitHub for any purpose. If
you create new graphs, please let me know, as I may want to add them to my analyses.
I am attempting to keep the analysis and commentary as apolitical as possible. I am now including partisan lean as a metric. This is just more data to understand
the COVID epidemic.
I try to provide a daily update in the morning. However, as an anesthesiologist at Stanford, when I have clinical duties and USAFacts has not updated the US data
by the time I leave for Stanford the analysis will be delayed.
There is a lot of information on the figures. If something isn’t clear, please see the explanation on slide 2.
Data sources:
• USA Case Data: https://usafactsstatic.blob.core.windows.net/public/data/covid-19/covid_confirmed_usafacts.csv
• USA Death Data: https://usafactsstatic.blob.core.windows.net/public/data/covid-19/covid_deaths_usafacts.csv
• USA Testing Data: https://raw.githubusercontent.com/COVID19Tracking/covid-tracking-data/master/data/states_daily_4pm_et.csv
• Global Case Data: https://github.com/CSSEGISandData/COVID-19/raw/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv
• Global Death Data: https://github.com/CSSEGISandData/COVID-19/raw/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv
• Global Testing Data: https://raw.githubusercontent.com/owid/covid-19-data/master/public/data/owid-covid-data.csv
• Mobility Data: https://www.gstatic.com/covid19/mobility/Global_Mobility_Report.csv
• Partisan Lean: MIT Election Data and Science Lab: https://doi.org/10.7910/DVN/VOQCHQ/HEIJCQ
Models:
1. Future projections are based on the Gompertz function: log 𝑐𝑢𝑚𝑢𝑙𝑎𝑡𝑖𝑣𝑒 𝑐𝑎𝑠𝑒𝑠 = 𝑐𝑢𝑟𝑟𝑒𝑛𝑡 𝑐𝑎𝑠𝑒𝑠 + 𝑚𝑎𝑥𝑖𝑚𝑢𝑚 𝑐𝑎𝑠𝑒𝑠 − 𝑐𝑢𝑟𝑟𝑒𝑛𝑡 𝑐𝑎𝑠𝑒𝑠 1 − 𝑒−𝑘 𝑡 . This is a
naïve asymptotic model. k is the rate constant, such that log(2) / k = time to 50% rise. t is the number of days. Wikipedia has a good description:
https://en.wikipedia.org/wiki/Gompertz_function. The Gompertz function is estimated from the last 3 weeks of data for cumulative cases. These points
appear as red dots in the figures.
2. The rate of changed is based on a simple linear regression of new cases over the last 10 days, divided by the number of new cases yesterday.
Locations
The locations for the modeling are where Pamela and I have family and friends, or locations of interest to friends and colleagues. Additionally, some locations
figure prominently in news reports (e.g., China, South Korea, Sweden, Brazil) or have significant economic impact on the United States (e.g., Japan, Canada,
Mexico). I am happy to add locations, just let me know.
Stay safe, well, resilient, and kind.
Steve Shafer
steven.shafer@Stanford.edu
2. 2,586,092
152,804
1
10
100
1,000
10,000
100,000
1,000,000
10,000,000
100,000,000
Actual(points)/Predicted(line)
Phase
Pre-Model
Modeled
Deaths
Tests
USA projection as of 2020-05-27
0
10,000
20,000
30,000
0
2,000
4,000
6,000
Cases/Day
Deaths/Day
Cases: 1,662,302 -- Deaths: 98,220 -- Deaths per 10,000: 3.1 -- Case Mortality: 5.9% -- Daily Change in Cases: -0.5%
2
Brown dots:
cumulative tests
Red dots: cumulative cases
used to estimate Gompertz
function, presently set to last
3 weeks
Red line: predicted cumulative
cases based on the Gompertz
function estimated from the red
dots
Red number: total cases
on June 30th, based on
the Gompertz function
estimated from the red
dots
Black number: total
cases on June 30th,
based on the total
cases (see above) *
case mortality (see
below)
Black line: predicted
cumulative deaths, calculated
as red line (above) * mortality
(see below)
Red line: predicted new cases
based on the Gompertz
function estimated from the
red dots
Axis for deaths / day, usually
1/5th of the axis for cases /
day on the left side of the
figure.
Funny bump: an artifact of the
Gompertz function, which
mathematically requires an
inflection in total cases.
Green line: linear regression
over 10 days, used to
calculate percent increase /
decrease (see below)
Daily change in cases,
calculated as the slope of the
green line (above left) /
number of new cases
yesterday.
Case mortality:
cumulative deaths
/ cumulative cases.
Cases / day calculated
from cumulative cases
used to estimate the
Gompertz function
Cases / day calculated
from cumulative cases
not used to estimate the
Gompertz function
Deaths / day,
axis is on the left
Cumulative deaths
/ population *
10,000
Blue line: today
Blue dots: cumulative cases not
used to estimate Gompertz
function
Yesterday’s
total cases
and deaths
Axis for cases / day.
Axis for deaths / day
appears to the right.
Geographic
location
Date of analysis,
also shown as
blue vertical line
below
Explanation of the figures
6. Change in New Cases per Day
New cases are:
Increasing > +3%
Increasing between +1% and +3%
No Change (-1% to +1%)
Decreasing between -1% and -3%
Decreasing > -3%
New cases by state as of 2020-06-19
6
7. Change in New Deaths per Day
New deaths are:
Increasing > +0.5%
Increasing between +0.1% and +0.5%
No Change (-0.1% to +0.1%)
Decreasing between -0.1% and -0.5%
Decreasing > -0.5%
New deaths by state as of 2020-06-19
7
9. Cases as a Percent of Peak Cases
HI TX FL
OK LA MS AL GA
AZ NM KS AR TN NC SC DC
CA UT CO NE MO KY WV VA MD DE
OR NV WY SD IA IN OH PA NJ CT RI
WA ID MT ND MN IL MI NY MA
WI VT NH
AK ME
0
25
50
75
100
0
25
50
75
100
0
25
50
75
100
0
25
50
75
100
0
25
50
75
100
0
25
50
75
100
0
25
50
75
100
0
25
50
75
100
PercentofPeak
Daily Cases as a Percent of Peak Cases
9
10. Deaths as a Percent of Peak Deaths
HI TX FL
OK LA MS AL GA
AZ NM KS AR TN NC SC DC
CA UT CO NE MO KY WV VA MD DE
OR NV WY SD IA IN OH PA NJ CT RI
WA ID MT ND MN IL MI NY MA
WI VT NH
AK ME
0
25
50
75
100
0
25
50
75
100
0
25
50
75
100
0
25
50
75
100
0
25
50
75
100
0
25
50
75
100
0
25
50
75
100
0
25
50
75
100
PercentofPeak
Daily Deaths as a Percent of Peak Deaths
10
11. Percent Population Tested
HI TX FL
OK LA MS AL GA
AZ NM KS AR TN NC SC DC
CA UT CO NE MO KY WV VA MD DE
OR NV WY SD IA IN OH PA NJ CT RI
WA ID MT ND MN IL MI NY MA
WI VT NH
AK ME
0
5
10
15
20
0
5
10
15
20
0
5
10
15
20
0
5
10
15
20
0
5
10
15
20
0
5
10
15
20
0
5
10
15
20
0
5
10
15
20
PercentPopulationTested
Percent Population
11
12. Percent Population Tested
HI TX FL
OK LA MS AL GA
AZ NM KS AR TN NC SC DC
CA UT CO NE MO KY WV VA MD DE
OR NV WY SD IA IN OH PA NJ CT RI
WA ID MT ND MN IL MI NY MA
WI VT NH
AK ME
0
5
10
15
20
0
5
10
15
20
0
5
10
15
20
0
5
10
15
20
0
5
10
15
20
0
5
10
15
20
0
5
10
15
20
0
5
10
15
20
PercentPopulationTested
Percent Population
12
13. Percent Positive Tests
HI TX FL
OK LA MS AL GA
AZ NM KS AR TN NC SC DC
CA UT CO NE MO KY WV VA MD DE
OR NV WY SD IA IN OH PA NJ CT RI
WA ID MT ND MN IL MI NY MA
WI VT NH
AK ME
0
5
10
15
0
5
10
15
0
5
10
15
0
5
10
15
0
5
10
15
0
5
10
15
0
5
10
15
0
5
10
15
PercentPositive(%)
Percent Positive Tests Over Last 28 Days
13
15. Change in New Cases per Day
Direction
Increasing > +2%
Increasing between +0.5% and +2%
No Change (-0.5% to +0.5%)
Decreasing between -0.5% and -2%
Decreasing > -2%
NA
Trends by county as of 2020-06-19
NA = Inadequate data
15
16. Percent Change by Partisan Lean
-10
-5
0
5
10
0 25 50 75 100
Percent Republican
Percentchangeinnewcasesperday
25
50
75
Republican
Counties by 2016 presidential election results
Dark green line is a Friedman's supersmoother
16
17. Percent Change by Population
-10
-5
0
5
10
1,000 10,000 100,000 1,000,000 10,000,000
Population
Percentchangeinnewcasesperday
25
50
75
Republican
Counties by Population
Dark green line is a Friedman's 'super smoother'
17
18. Partisan Lean vs Population and Direction
1,000
10,000
100,000
1,000,000
10,000,000
0 25 50 75 100
Percent Republican
Population
Direction
Increasing > +2%
Increasing between +0.5% and +2%
No Change (-0.5% to +0.5%)
Decreasing between -0.5% and -2%
Decreasing > -2%
Partisan Lean vs Population and Direction
Dark green line is a Friedman's 'super smoother'
18
19. Cases as a Percent of Population
0.001%
0.01%
0.1%
1%
10%
20%
1,000 10,000 100,000 1,000,000 10,000,000
County Population
Totalcases
Total Cases as a Percent of County Population
Slanted lines are counties with small integer numbers of cases, green line: Friedman's 'super smoother'
19
20. Deaths as a Percent of Population
0.0001%
0.001%
0.01%
0.1%
1%
1,000 10,000 100,000 1,000,000 10,000,000
County Population
Totaldeaths
Total Deaths as a Percent of County Population
Slanted lines are counties with small integer numbers of cases, green line: Friedman's 'super smoother'
20
21. Case Mortality vs. Population
0.1%
1%
10%
100%
1,000 10,000 100,000 1,000,000 10,000,000
County Population
Casemortality
Case Mortality vs. County Population
21
116. Case Mortality vs. Testing
AL
AK
AZ
AR
CA
CO
CT
DE
DC
FL
GA
HIID
IL
IN
IA
KS
KY
LA
ME
MD
MA
MI
MN
MS
MO
MT
NE
NV
NH
NJ
NM
NY
NC ND
OH
OK
OR
PA
RI
SC
SD
TN
TX
UT
VT
VA
WA
WV
WI
WY
0.0
2.5
5.0
7.5
10.0
0 5 10 15 20
% Tested
%Mortality
Mortality vs. Testing as of 2020-06-19
116
143. Case Mortality vs. Testing
BHR
LUX
ISL
DNK
LTU
RUS
QAT
PRT
ISR
BLRKWT
ITA
IRL
USA
EST
AUS
BEL
LVA
MDV
NZL
KAZ
ESP
AUT
CAN
GBR
DEU
SGP
CHE
NOR
CZE
SRB
CHL
SVN
FIN
SWE
SVK
SAU
TUR
ROU
NLD
GRC
POL
HUN
KORPANZAF
MYS
SLV
HRV
BGR
IRN
URY
CUB
UKR
COL
RWA
PRY
THA
GHA
PER
TUN
ARG
ECU
CRI
IND
NPL
PAK
PHL
BOL
SENBGDTWN
JPN
MEX
UGAVNM
MAR
BRA
KEN
ETH
ZWE
IDN
MMR
NGA
0
5
10
15
0 10 20
% Tested
%CaseMortality
Case Mortality vs. Testing as of 2020-06-19
143