added

.Rhistory

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 setwd("C:/Users/BhosaleS/Documents/GitHub/santoshdbhosale.github.io")
 utils::packageVersion("rmarkdown")
 utils::packageVersion("rmarkdown")
+esquisser()
+esquisser()
+esquisse::
+esquisse::
+install.packages('esquisse')
+install.packages("esquisse")
+esquisser()
+library(esquisse)
+esquisser()
+esquisser()
+esquisser()
+esquisser()
+esquisser()
+esquisser()

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 [1,"\r[10/11] cv\\index.qmd\n"]
-[1,"failed to create process.\r\nWARNING: Unable to perform code-link (code-link requires R packages rmarkdown, downlit, and xml2)\n\r[11/11] index.qmd\n"]
+[1,"failed to create process.\r\nWARNING: Unable to perform code-link (code-link requires R packages rmarkdown, downlit, and xml2)\n"]
+[1,"\r[11/11] index.qmd\n"]
 [1,"\n"]
 [1,"Watching files for changes\nBrowse at http://localhost:22222/\n"]
 [1,"GET: /\n"]
 [1,"  /img/logo.png (404: Not Found)\n"]
+[1,"GET: /about/\n"]
+[1,"  /img/logo.png (404: Not Found)\n"]

.Rproj.user/224B9E6C/pcs/files-pane.pper

@@ -5,5 +5,5 @@
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+    "path": "~/GitHub/santoshdbhosale.github.io/about"
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.Rproj.user/224B9E6C/persistent-state

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.Rproj.user/224B9E6C/sources/session-b6384cda/12378BAC-contents

@@ -0,0 +1,27 @@
+---
+author: ""
+title-block-banner: false
+page-layout: full
+description-meta: "Welcome to my blog, Here, you will find a collection of posts for some of the previously published articles"
+listing:
+    contents: 
+      - "Serum-Proteomics-Pre-diabetic/index.qmd"
+      - "Serum-Proteomics-Atherosclerosis/index.qmd"
+      - "Mass-Spectrometry-Based-Serum-Proteomics/index.qmd"
+      - "AP-MS-to-Study-FOSL-Related-Proteins-Interactome/index.qmd"
+      - "Serum-Proteomics-of-mother-infant-dyads-T1D-risk/index.qmd"
+    sort: "date desc"
+    categories: true
+toc-title: Year
+toc-location: right
+date-format: "MMMM D, YYYY"
+image: ""
+code-tools: false
+comments: false
+---
+
+Welcome to my blog, here, you will find a collection of posts for some of the previously published articles!
+
+The snapshot of my research work is visualized in the following WordCloud using [Scholar Googler](https://shiny.rcg.sfu.ca/u/rdmorin/scholar_googler3/) shiny app. This app extracts information from research publications by using individual's Google Scholar ID.
+
+![](/img/wordcloud.png){width="500"}

.Rproj.user/224B9E6C/sources/session-b8d86596/27DEA4F6 → .Rproj.user/224B9E6C/sources/session-b6384cda/27DEA4F6

@@ -3,7 +3,7 @@
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.Rproj.user/224B9E6C/sources/session-b6384cda/27DEA4F6-contents

@@ -0,0 +1,15 @@
+---
+title: "Serum Proteomics of mother infant dyads: T1D risk"
+date: 2024-04-28
+categories:
+  - publications
+  - serum
+image: fig/Study_design.png
+---
+
+**Background:**
+The physiology of the mother during pregnancy and in the infant during early stages of life may be affected by exogenous factors with later implications on the development of the immune system. The Early Dietary Intervention and Later Signs of Beta-Cell Autoimmunity study  was conducted to examine that how usage of two different research infant influenced the development of beta-cell autoimmunity. The samples collected in the study included serum form the mother during pregnancy, at and after delivery, and from the children at different time intervals in the first year of life. Using a proteomics approach we have determine protein expression profiles for the mothers and children samples and evaluated the links between the mother and child, dietary intervention and other metadata.
+
+**Methods:**
+Undepleted serum samples (Mother-child pairs) were denatured, reduced and alkylated, and digested with trypsin. The desalted peptides were analysed in randomized batches in triplicate using tandem mass spectrometry (LC-MS/MS) with a label free quantification (LFQ) approach. A Q Exactive Orbitrap mass spectrometer was used in data dependent acquisition mode. The mass spectrometry raw files were searched using MaxQuant to enable the protein identification and quantification. The LFQ data was then analysed using Perseus and R.
+

.Rproj.user/224B9E6C/sources/session-b8d86596/8B9F7118 → .Rproj.user/224B9E6C/sources/session-b6384cda/583AEBDB

@@ -1,18 +1,18 @@
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@@ -22,11 +22,11 @@
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@@ -9,6 +9,7 @@ listing:
       - "Serum-Proteomics-Atherosclerosis/index.qmd"
       - "Mass-Spectrometry-Based-Serum-Proteomics/index.qmd"
       - "AP-MS-to-Study-FOSL-Related-Proteins-Interactome/index.qmd"
+      - "Serum-Proteomics-of-mother-infant-dyads-T1D-risk/index"
     sort: "date desc"
     categories: true
 toc-title: Year

.Rproj.user/224B9E6C/sources/session-b6384cda/B103A6A6-contents

@@ -0,0 +1,21 @@
+---
+title: "Serum Proteomics Atherosclerosis"
+date: 2020-11-11
+categories:
+  - publications
+  - serum
+image: fig/roc-plot.png
+---
+Atherosclerotic cardiovascular diseases are the major causes of mortality and morbidity in developed world. Thickening of the carotid arterial wall (plaque formation) are indicative of active atherosclerotic process. 
+Myocardial infarction and stroke are the clinical events associated with an acute rupture of a critically located atherosclerotic plaque. Currently, ultrasonic assessment of carotid artery intima media thickness is used as a pre-clinical marker of the disease process. 
+Nevertheless, atherosclerotic process may remain asymptomatic for several decades and whether thickening of intima-media layer of carotid artery and carotid plaque represents two different phenotypes or single traits of disease process is still unclear. 
+
+To gain insights into the pathophysiology of pre-clinical atherosclerosis and identify novel biomarkers, we conducted the **[serum proteomics measurements](https://www.nature.com/articles/s41598-018-27265-9#Sec1) on the unique sample set of participants recruited in [The Cardiovascular Risk in Young Finns Study](https://youngfinnsstudy.utu.fi/studydesign.html).** 
+We performed label free quantitative MS analysis of serum samples obtained from the subjects in whom early signs of plaques were discerned together with matched controls. The serum samples were immunodepleted to remove high abundant proteins prior to LC-MS/MS analysis.
+
+The profiling results indicated differential abundances in a set of proteins. 
+Furthermore, selected reaction monitoring mass spectrometry analysis were performed on undepleted serum samples to verify the observed differences. 
+Finally, machine learning analysis identified a panel of three proteins **P23142-4 (Fibulin 1 proteoform C), P02649 (Apolipoprotein E) and P55290 (Cadherin-13)** which segregated the cases from controls with best discrimination (an area under receiver-operating characteristic curve (AUROC) value of 0.79.). 
+
+![](fig/roc-plot.png){}
+More details about the data analysis can be found **[here](https://github.com/santoshdbhosale/Carotid_Atherosclerosis_LFQ).**

.Rproj.user/224B9E6C/sources/session-b8d86596/A21CD174 → .Rproj.user/224B9E6C/sources/session-b6384cda/C0CC5753

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+---
+about:
+  template: jolla
+  id: about-block
+---
+
+::: {#about-block}
+:::
+
+I am currently employed as an associate biomedical scientist at Precision biomarker laboratories, a commercial proteomics contract research organization owned by Cedars-Sinai Medical Center, Los Angeles, CA, USA.
+
+During my postdoctoral fellowship, I worked in Prof. Martin R. Larsen's lab at the University of Southern Denmark on developing a pipeline for identifying post-translationally modified biomarkers in clinical samples.
+
+I pursued my PhD from University of Turku under the joint advisorship of Prof. Riitta Lahesmaa and Dr. Robert Moulder. During the course of studies, I worked on identifying and validating the serum protein biomarkers for type 1 diabetes and carotid atherosclerosis.
+
+Before enrollment into the PhD study, I gained my first level of research experience with my master thesis in the area of proteomics and mass spectrometry at National Chemical Laboratory (NCL) under the joint supervision of Drs. Mahesh J. Kulkarni and B.Santhakumari, after which I continued as a teacher in college of pharmacy and then as a research assistant again at NCL.
+

.Rproj.user/224B9E6C/sources/session-b6384cda/EC8B4BB9-contents

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+data = read.delim("mg_kegg.tsv")
+S1<- ggplot(data, aes(x= reorder (Pathways, +FDR), y= FDR, size=DEGs, color=FDR, group=Group)) + geom_point(alpha = 0.8) + 
+  theme_classic() + coord_flip() 
+S1 = S1+scale_color_gradient(low = "red2",  high = "mediumblue", space = "Lab")
+S1 = S1 + labs(x = "Group", y = "KEGG pathways", title = "KEGG Pathways Enriched for Protein DE in ENRC and ENRI Cells")
+S1 + facet_wrap(~Group, scales = "free")

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