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Introduction to Atherosclerosis and PCSK9 Inhibitor Leqvio

Atherosclerosis is a leading cause of cardiovascular diseases worldwide. Understanding its pathophysiology and exploring innovative treatment options like PCSK9 inhibitors are crucial for advancing cardiovascular health.

Pathophysiology of Atherosclerosis

Atherosclerosis is a chronic condition characterized by the accumulation of fatty and fibrous materials within the innermost layer of the arteries, known as the intima. This process begins with the buildup of low-density lipoprotein cholesterol (LDL-C), which leads to the formation of atherosclerotic plaques. These plaques can become calcified and fibrous over time, narrowing the arterial lumen and restricting blood flow, potentially causing tissue ischemia.

Atherosclerosis is a major contributor to cardiovascular diseases (CVD), which include conditions such as myocardial infarction (heart attack), ischemic stroke, and peripheral artery disease. The pathogenesis of atherosclerosis involves

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Highlights from the American College of Rheumatology Conference

Introduction:
The American College of Rheumatology's annual meeting, held from November 10th to 15th in San Diego, showcased groundbreaking research in rheumatology. Although I was not present, I am proud that my contributions at Novartis played a key role in some of the significant research presented.

Presented Work:

• Systemic Lupus Erythematosus Treatment:
  Ianalumab has shown promise in a Phase 2 trial, significantly reducing B cell counts and autoantibody levels in Systemic Lupus Erythematosus (SLE) patients. The clinical responses were effective irrespective of the initial interferon gene signature status. Read abstract here

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IgA Nephropathy (IgAN) and the Promise of Iptacopan: Insights from a Phase 2 Clinical Trial

Glossary

• Proteinuria: The presence of excess proteins in the urine. It is often a sign of kidney damage.
• eGFR (estimated Glomerular Filtration Rate): A test that measures how well the kidneys are filtering blood. It's an estimate of the filtering capacity of the kidneys.
• Glomerulonephritis: A type of kidney disease where the glomeruli, tiny filters in the kidneys, become inflamed, affecting the kidney's ability to filter waste and fluids.

Introduction

IgA Nephropathy (IgAN), the most common form of glomerulonephritis, is characterized by the deposition of galactose-deficient IgA in the glomerular mesangium. With a global incidence of approximately 25 per million per year, it predominantly affects young adults. Alarmingly, around 15–40% of IgAN patients may progress to kidney failure within a decade or two post-diagnosis, emphasizing the urgent need for effective treatments.

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Nephrology: The Study of Kidneys and Kidney Week 2023

Introduction to Nephrology

Originating from the ancient Greek word 'nephros' meaning kidney, nephrology is a specialized medical discipline dedicated to the diagnosis, treatment, and management of kidney diseases. This field covers a spectrum of conditions, from acute kidney injuries to chronic kidney diseases, delving into the kidney's anatomy, physiology, and related disorders.

Kidney Diseases: An Integrated Overview

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clean `soluble_responder_tbl` dataframe from cols with suffix.

```{r}
my_table %>%
  dplyr::rename_with( ~ stringr::str_remove(., '.x')) %>%
  dplyr::select(
    !dplyr::ends_with(".y")
  )
```

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#### BiomaRt Biotype Analysis

See BioMartR documentation [here](https://cran.r-project.org/web/packages/biomartr/vignettes/Functional_Annotation.html)

Load ensembl db annotation.

```{r}
# load ensembl 
ensembl <- useEnsembl(
  biomart = "genes", 

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## Limma::Camera pipeline

This block of code will run camera: Competitive Gene Set Test Accounting for Inter-gene Correlation.

See reference here:

Wu, D, and Smyth, GK (2012). Camera: a competitive gene set test accounting for inter-gene correlation. Nucleic Acids Research 40, e133. 
http://nar.oxfordjournals.org/content/40/17/e133

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library(DT)

#' render_dt
#' render a DT table within Rmarkdown.
#' @param table a data frame
render_dt <- function(table) {
  table %>% 
    DT::datatable(
      extensions = 'Buttons',
      options = list(dom = 'Blfrtip', scrollX = TRUE, scrollY=TRUE,

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plot_df <-df %>% group_by(feature) %>%
  do(
    plots = ggplot(data = .) + aes(x = xcol, y = ycol) +
      geom_point() + ggtitle(.$feature)
  )

# show plots
plot_df$plots

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# from http://www.sthda.com/english/wiki/ggplot2-line-plot-quick-start-guide-r-software-and-data-visualization
#+++++++++++++++++++++++++
# Function to calculate the mean and the standard deviation
  # for each group
#+++++++++++++++++++++++++
# data : a data frame
# varname : the name of a column containing the variable
  #to be summariezed
# groupnames : vector of column names to be used as
  # grouping variables