Saturday, April 21, 2018

A quick description of New Cancer detection, tumor fighting, and metastatic fighting cancer cures.

There are several techniques recently developed that allow for the detection, selective cell-destroying, and eventually, cure for Metastasis in early trials in the last 5 years.  These are breakthrough technologies, if released to the public, could greatly lengthen human lifespans. 
The first technology that will be discussed is the ability to screen for cancer.  Imaging techniques of in-vivo tumors have several techniques.  This fluophore in the first paper discussed is a targetable “activatable” fluophore that can be controlled to only fluoresce when a current target molecule: “fluorophores could be controlled and predicted precisely by using the concept of intramolecular photoinduced electron transfer”[1].  Several fluophores were created DPAX and DMAX for singlet oxygen, DAF, DAMBO and DACals for nitric oxide. HPF, APF and APC for highly reactive oxygen, NiSPYs for peroxynitrite, DNAT-Me for glutathione S-transferase, TG-BetalGal for Beta-galactosidase and some more.

These fluophores are specifically targeted for specific ions and compounds known in cancer cells.  The reactions of each to determine which fluophore for which molecule is beyond my skill in metallo-organic chemistry.  Here is the table.



In-vivo cancer visualization is created by making targeted activatable fluorescent imagine probes.  Novel acidic pH-activatable probes based on the BODIPY fluophore were developed using the PeT – based rational design strategies, and conjugated them to a cancer-targeting monoclonal antibody.  This agent is activated after endocytotic internalization by sensing the pH change in the lysosome.  Here is a chart of the functioning of the fluorescence dye.



This reaction occurs within 1 minute after being applied.  Tiny tumors less than 1 mm size were successfully detected based on the concept of signal activation by using cancer-specific antibodies labeled with acidic-pH activatable fluorescence probes.

After the cancer is detected there is a lot of research into which drugs to use to kill the cancer.  A programmed drug-delivery system that can transport different anticancer therapeutics to their distinct targets holds vast promise for cancer treatment.  Herein, a core–shell-based “nanodepot” consisting of a liposomal core and a crosslinked-gel shell (designated Gelipo) is developed for the sequential and site-specific delivery (SSSD) of tumor necrosis factor-related apoptosis inducing ligand (TRAIL) and doxorubicin (Dox). As a small-molecule drug intercalating the nuclear DNA, Dox is loaded in the aqueous core of the liposome, while TRAIL, acting on the death receptor (DR) on the plasma membrane, is encapsulated in the outer shell made of crosslinked hyaluronic acid (HA). The degradation of the HA shell by HAase that is concentrated in the tumor environment results in the rapid extracellular release of TRAIL and subsequent internalization of the liposomes. The parallel activity of TRAIL and Dox show synergistic anticancer efficacy. The half-maximal inhibitory concentration (IC 50 ) of TRAIL and Dox co-loaded Gelipo (TRAIL/Dox-Gelipo) toward human breast cancer (MDA-MB-231) cells is 83 ng mL –1 (Dox concentration), which presents a 5.9-fold increase in the cytotoxicity compared to 569 ng mL –1 of Dox-loaded Gelipo (Dox-Gelipo). Moreover, with the programmed choreography, Gelipo significantly improves the inhibition of the tumor growth in the MDA-MB-231 xenograft tumor animal model.[2]



The conventional chemotherapeutic drugs attack the tumors by interrupting processes or inhibiting substances essential for the replication and proliferation of the tumor cells. For example, co-delivery of doxorubicin (Dox) and paclitaxel (Ptx) by a polymeric nanoparticle could release both drugs simultaneously and efficiently within the cells. The released Ptx inhibits the intracytoplasmic microtubules disassembly that is required for cell proliferation, while Dox intercalates into the nuclear DNA and induced cell apoptosis. For the cancer gene therapy, siRNA for silencing the target genes in cancer cells and pDNA for implanting corrective genetic material into the cells, have been applied to coordinate with small-molecule drugs. A typical example involves a micellar nanocarrier for co-delivery of MDR-1 siRNA and Dox, the released siRNA in the cells downregulates the P-glycoprotein expression to improve the efficacy of Dox in the multi-drug resistant
cancer cells. [2]


Here is the results of the drugs administered to the tumor and the testing of various drugs. [C]  In vitro cytotoxicity of TRAIL-Gelipo, Dox-Gelipo and TRAIL/Dox-Gelipo after 30 min
of HAase pre-treatment toward MDA-MB-231 cells for 24 h.


As you can see, the chemical cocktail has a dramatic effect on the tumor after 14 days.  A further problem with tumors is the metastatic ability to grow in regions far away from the original tumor.  A recent technology was developed at Duke University to combat this.  Metastatic spread is the mechanism in more than 90% of cancer deaths, and current chemical options, such as systemic chemotherapy, are often ineffective.  This paper is about Synergistic Immuno Photothermal Therapy (SYMPHONY).    Immune checkpoint inhibition is a promising immunotherapy that aims to reverse signals from immunosuppressive tumor microenvironment. Programmed death-ligand 1 (PD-L1), a
protein overexpressed by many cancers, contributes to the suppression of the immune system and cancer immune evasion. PD-L1 binds to its receptor, PD-1 found on activated T cells, and inhibits
cytotoxic T-cell function, thus escaping the immune response. To reverse tumor-mediated immunosuppression, therapeutic anti-PD-1/PD-L1 antibodies have been designed to block the PD-L1/PD-1 interaction.  nanoparticle (NP)-mediated thermal therapy has recently demonstrated the potential to combine the advantages of precise cancer cell ablation with benefits of mild HT in tumor microenvironments. NPs have a natural propensity to extravasate from the tumor vascular network and accumulate in and around cancer cells due to the enhanced permeability and retention (EPR) effect. Among various types of nanoparticles, gold nanostars (GNS), whose sharp branches create a “lightning rod” effect that dramatically enhances the local electromagnetic (EM) field, are the most effective in converting light into heat for photothermal therapy (PTT).  The unique tip-enhanced plasmonics property of GNS can be optimally tuned in the near infrared (NIR) tissue optical window, where photons can travel further in healthy tissue to be ‘captured’ and converted into heat by GNS taken up preferentially in cancer cells. We have investigated the PEGylated GNS bio-distribution in mice as well as GNS uptake at both macroscopic and microscopic scales by using radiolabeling, CT and optical imaging methods. In addition, a recent toxicity study of aptamer-loaded GNS found no signs of acute toxicity.



In this experiment, only 1 tumor was treated, and the other tumor was not.  It was found that using the GNS+Laser+Anti-PD-L1 had the only group of mice that survived the cancer at all.  It is shown that this cocktail has the only chance of survival in bladder cancer tumors for any mice at all.

Hopefully, these cancer treatments will assist in the future for detecting, treating, and then treating metastatic cancer in the future and can be applied to human use.  Unfortunately, even in experiments, the survival rate is not very high at this point for cancer cures, but some chance of survival is better than no chance of survival.  It appears that Phototherapy, in addition with chemical cocktails gives the best chance of survival.

[1] Urano, Yasuteru. “Novel Live Imaging Techniques of Cellular Functions and in Vivo Tumors Based on Precise Design of Small Molecule-Based ‘Activatable’ Fluorescence Probes.” Current Opinion in Chemical Biology, vol. 16, no. 5-6, 2012, pp. 602–608., doi:10.1016/j.cbpa.2012.10.023.
[2] Jiang, Tianyue, et al. “Gel-Liposome-Mediated Co-Delivery of Anticancer Membrane-Associated Proteins and Small-Molecule Drugs for Enhanced Therapeutic Efficacy.” Advanced Functional Materials, vol. 24, no. 16, 2014, pp. 2295–2304., doi:10.1002/adfm.201303222.

Liu, Yang, et al. “Synergistic Immuno Photothermal Nanotherapy (SYMPHONY) for the Treatment of Unresectable and Metastatic Cancers.” Scientific Reports, vol. 7, no. 1, 2017, doi:10.1038/s41598-017-09116-1.

Sunday, November 16, 2014

Econometrics paper (Difficult Subject)

.


 

4.1 Trends in Entrepreneurship


 


 

"How has entrepreneurship fared over the same period of time in which unemployment

rates have increased rapidly and the housing market has dropped significantly? Figure 3

displays annual estimates of the monthly entrepreneurship rate from 1996 to 2009. As

noted above the entrepreneurship rate measures the percentage of the adult, nonbusiness

owner population that starts a business each month. It captures all new business owners,

including those who own incorporated or unincorporated business, and those who are

employers or non employers. In 2009, an average of 0.34% of the adult population, or

340 out of 100,000 adults created a new business each month. The business formation

rate increased from 2008 when it was 0.32%. It was the third straight year that the

index increased, resulting in an increase from 0.29% in 2006 to 0.34% in 2009.


 

The recent increase is the largest over the 14-year sample period. In fact, over the period from 1996
to 2009, the business creation rate fluctuated within the range of 0.27–0.31%. It was not
until 2008 and 2009 that it rose above the high end of this range, which coincides with the
recent recession. In the late 1990s, the entrepreneurship rate decreased slightly, then rose
from 2001 to 2003. It remained relative constant over the next 3 years before increasing
in the recent recession."[7]


 



 


 

"Another interesting finding is that home owners are more likely to start businesses.

The coefficient is positive and statistically significant, although relatively small. Home

owners have a 0.012 percentage point higher rate of entrepreneurship than non home

owners, which is roughly a 4% higher rate relative to the mean. In the presence of

liquidity constraints, the ability of owners to borrow against the value of their homes,

such as home equity loans, may make it easier to finance new business ventures."[7]


 

"Figure 4 displays the entrepreneurship rate against the national median home price

in $2009. The negative relationship between the two trends in the recent recession is very

clear. Home prices have dropped sharply over the past few years as entrepreneurship

rates have increased. These patterns run counter to the decline in home equity decreasing

entrepreneurship and are likely due to stronger positive effects of rising unemployment

rates. Entrepreneurship rates also dropped in the late 1990s when home prices were

rising. Interestingly, however, both entrepreneurship and home prices rose steadily in

the early 2000s. In this period, rising home equity may have provided capital for would be-

entrepreneurs to start businesses."[7]


 

"At the national level, trends in entrepreneurship appear to be primarily counter

cyclical—rising in economic downturns and declining in strong economic growth periods.

The national patterns for entrepreneurship, however, are weaker than unemployment

patterns over the business cycle. Trends in home prices and their effects on access

to capital may have offset some of the business cycle effects. But, these are only broad

strokes based on national trends. Instead, it is important to focus on variation in local

labor market and housing conditions. Unemployment rates and housing prices differ

substantially across metropolitan areas, and these differences can be used to more carefully

examine the relationship between entrepreneurship, and unemployment and home

prices."[7]


 



 

"Summary measures of goodness of fit are useful for comparing the performance of different models fit to the same data. Measures include information criteria or R2-type measures. Calculation of these summary measures usually depends on the sample size (or range of data used) and model structure used (such as linear or logistic regression), and so comparisons should only be made between models of the same structure built using the same data. It should be noted that for non-linear models there are multiple different ways to calculate R2-type measures, with no consensus as to which should be used. For non-linear models, goodness-of-fit measures based on the residuals (such as the deviance) or information criteria are preferred. There are sometimes also measures specific to the model type, such as the Hosmer– Lemeshow test for logistic regression; for further details see Campbell. If Markov Chain Monte Carlo methods have been used, additional properties such as the convergence of the chains, autocorrelation plots, and deviance information criteria should be reported [16]."


 


 


 

"The central puzzle in economic development is to explain what accounts for differences in output per capita (inequality) across nations. This is what Lucas (1988) posited as the problem of economic development. Based on the neo-classical production function, differences in output per capita can be attrib-uted to differences in physical capital, human capital and total factor productivity (TFP). Chari et al. (1997) argue that observed differences in output per capita can be explained by differences in factors of production (e.g., physical and human capital). However, Hall and Jones (hereinafter HJ 1999), and Parente and Prescott (1999, 2000) show that the difference in TFP is the key determinant of differences in international incomes. Thus, to be able to answer Lucas' question, we must first answer the question: What explains international differences in TFP?[17]


 

"Recently, considerable attention has been given to the role of institutions in explaining not only differences in productivity across countries, but also why some countries invest more in physical and human capital (North 1990, Knack and Keefer 1995; Nugent and Robinson 1998; HJ 1999; Parente and Prescott 2000; Acemoglu et al. 2001, Easterly and Levine 2002, among others). By institutions, North (1990) means the formal (laws, constitutions) and informal (customs, traditions) constraints, and government policies (enforcement, punishment) that shape the interactions of economic actors1. For instance, countries with more secure property rights have, in general, higher productivity and therefore higher levels of income per capita. According to North (1990, p.107), institutions ''are the underlying determinant of the long-run performance of economies.''[17]


 


 


 



 


 


 

The output per worker gap among nations, would tend to widen. Quantile regression provides the appropriate tools to determine whether there are different marginal responses of output per worker to changes in institutions. In Fig. 2, we depict the IV estimates (horizontal dashed lines) along with the corresponding quantile regression estimates.


 

The shaded areas represent 90% confidence intervals; at all estimated quantiles, institutions are statistically and foremost economically significant. As expected, at higher quantiles ðsÞ the return for each additional ''unit'' of institutions decreases relative to lower conditional quantiles of output per worker. Returns vary from approximately 6.2 to 3.8 as s increases. This first difference relative to HJ, as a result of applying the more robust and descriptive methodology of quantile regression, reinforces the importance attributed to institutions in promoting not only development, but also in closing differences in output per worker across nations."




 


 

"The state housing collateral ratio is computed using the Lustig and van Nieuwerburgh (2005) method. The unemployment rates are from the BLS. The relative unemployment rate is the ratio of the current unemployment rate to the moving average of the unemployment rates from the previous 16 quarters. Labor income is from the BEA. U.S. cay and U.S. They are downloaded from Sydney Ludvigson's and Stijn van Nieuwerburgh's web sites, respectively.


 

The three spreads use quarterly data obtained from the Board of Governors of the Federal Reserve System web site. We use 13(f) institutional holdings data from Thomson Reuters to compute the institutional trading variables, while the retail trading variables are computed using retail holdings data from a large U.S. discount brokerage house. The retail data are available only for the 1991 to 1996 period.


 

The change in local institutional or retail holdings is the percentage change in the value of local holdings (i.e., the total value of shares of firms headquartered in a state held by investors in the same state). The change in nonlocal institutional or retail holdings is the percentage change in the value of nonlocal holdings (i.e., the total value of shares of firms headquartered in a state but held by out-of-state investors). To compute the state economic activity index, we add the standardized values of state income growth and state, subtract the standardized value of relative unemployment, and divide this sum by three.


 



 


 

"Low-paid, repetitive positions are most likely to go, with people earning less than £30,000 a year five times more likely to see their jobs taken over by machines than those paid £100,000, new research has warned. Huge advances in technology risks creating an under-class of low-skilled people whose jobs have been automated, according to a joint report from Deloitte, the Big Four accountancy firm, and the University of Oxford.[18]"


 


 


 


 

Year

Entrepeneurship Rate

Unemployment Rate

Median home price

Entrepeneurship

1996

0.33

0.28

160000

210000

1997

0.27

0.26

165000

185000

1998

0.28

0.24

167000

180000

1999

0.26

0.23

180000

182000

2000

0.27

0.24

182000

183000

2001

0.26

0.23

185000

185000

2002

0.28

0.25

187000

190000

2003

0.29

0.27

205000

210000

2004

0.29

0.26

215000

215000

2005

0.28

0.27

220000

225000

2006

0.27

0.24

240000

185000

2007

0.31

0.24

230000

192000

2008

0.33

0.27

210000

210000

2009

0.34

0.38

185000

225000

2010

0.35

0.4

175000

235000


 


 


 


 

SUMMARY OUTPUT ENTREPENEURSHIP AND UNEMPLOYMENT

       
         

Regression Statistics

       

Multiple R

0.804484

       

R Square

0.647195

       

Adjusted R Square

0.620056

       

Standard Error

0.018609

       

Observations

15

       
         

ANOVA

        

  

df

SS

MS

F

Significance F

   

Regression

1

0.008258

0.008258

23.84756

0.000299

   

Residual

13

0.004502

0.000346

     

Total

14

0.01276

  

  

  

   
         

  

Coefficients

Standard Error

t Stat

P-value

Lower 95%

Upper 95%

Lower 95.0%

Upper 95.0%

Intercept

0.165243

0.026801

6.165666

3.4E-05

0.107344

0.223142

0.107344

0.223142

X Variable 1

0.475703

0.097412

4.883397

0.000299

0.265257

0.68615

0.265257

0.68615

         
       


 

 
         


 

SUMMARY OUTPUT MEDIUM HOME PRICE AND ENTRPENEURSHIP

       
         

Regression Statistics

       

Multiple R

0.086584

       

R Square

0.007497

       

Adjusted R Square

-0.06885

       

Standard Error

0.031212

       

Observations

15

       
         

ANOVA

        

  

df

SS

MS

F

Significance F

   

Regression

1

9.57E-05

9.57E-05

0.098195

0.758977

   

Residual

13

0.012664

0.000974

     

Total

14

0.01276

  

  

  

   
         

  

Coefficients

Standard Error

t Stat

P-value

Lower 95%

Upper 95%

Lower 95.0%

Upper 95.0%

Intercept

0.314501

0.065916

4.771234

0.000365

0.172098

0.456903

0.172098

0.456903

X Variable 1

-1.1E-07

3.38E-07

-0.31336

0.758977

-8.4E-07

6.24E-07

-8.4E-07

6.24E-07

         
         
         


 

CONCLUSION:


 

Through many tests of Data analysis, regression, studies of quotations, that has shown through multiple T-Tests, that

Entrepreneurship and unemployment and home prices are correlated through regression, I have not been able to prove that Institutions are increasing Entrepeneurship rates because of lack of Data available, except through graphs available but not data.


 

ENTREPENEURSHIP_i = .16*UNEMPLOYMENT_i + .32*HOMEPRICE_i +Log(C*INSTITUTIONS(0))+ e_i

(6.16)     (4.77)


 

N=15 R^2=.64


 

Bibliography:


 

References

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  2. Chien-Chung Nieh, Russel, P. S., Hung, K., & Ya-Kai Chang. The asymmetric impact of financial intermediaries development on economic growth. International Journal of Finance, 21(2), 6035-6069.
  3. Chris Forman, Avi Goldfarb, & Shane Greenstein. The internet and local wages: A puzzle. The American Economic Review, 102(1), 556-575.
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18. Ten Million Jobs at Risk from Advancing Technology." The Telegraph. Telegraph Media Group, 28 Apr. 0010. Web. 10 Nov. 2014.