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Article: Higher ETV5 Expression Associates With Poor 5-Florouracil-Based Adjuvant Therapy Response in Colon Cancer.

Giri, Anil K

2021 Volume 11, Page(s) 620811

Abstract: Discovery of markers predictive for 5-Fluorouracil (5-FU)-based adjuvant chemotherapy (adjCTX) response in patients with locally advanced stage II and III colon cancer (CC) is necessary for precise identification of potential therapy responders. PEA3 ... ...

Abstract	Discovery of markers predictive for 5-Fluorouracil (5-FU)-based adjuvant chemotherapy (adjCTX) response in patients with locally advanced stage II and III colon cancer (CC) is necessary for precise identification of potential therapy responders. PEA3 subfamily of ETS transcription factors (ETV1, ETV4, and ETV5) are upregulated in multiple cancers including colon cancers. However, the underlying epigenetic mechanism regulating their overexpression as well as their role in predicting therapy response in colon cancer are largely unexplored. In this study, using gene expression and methylation data from The Cancer Genome Atlas (TCGA) project, we showed that promoter DNA methylation negatively correlates with ETV4 expression (
Language	English
Publishing date	2021-02-09
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2587355-6
ISSN	1663-9812
ISSN	1663-9812
DOI	10.3389/fphar.2020.620811
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Book ; Online: COVID-19 working paper

Giri, Anil K. / Subedi, Deepak / Kassel, Kathleen

distribution and examination of Coronavirus Food Assistance Program payments and forgivable Paycheck Protection Program loans at the state level in 2020

(COVID-19 working paper ; XXXX ; AP ; 116)

2023

Abstract: Government payments to the farm sector were a record-high $45.7 billion in calendar year 2020. COVID-related payments from two programs--USDA's Coronavirus Food Assistance Program (CFAP) at $23.5 billion and Small Business Administration's (SBA) Paycheck ...

Title variant	Distribution and examination of Coronavirus Food Assistance Program payments and forgivable Paycheck Protection Program loans at the state level in 2020
Institution	Coronavirus Food Assistance Program (U.S.) Paycheck Protection Program (U.S.) United States. / Department of Agriculture
Author's details	Anil K. Giri, Dipak Subedi, and Kathleen Kassel
Series title	COVID-19 working paper XXXX AP ; 116
Abstract	Government payments to the farm sector were a record-high $45.7 billion in calendar year 2020. COVID-related payments from two programs--USDA's Coronavirus Food Assistance Program (CFAP) at $23.5 billion and Small Business Administration's (SBA) Paycheck Protection Program (PPP) at $6.0 billion--accounted for nearly two-thirds of those 2020 payments. This report analyzes the distribution of direct Government payments relative to cash receipts in calendar year 2020. We find that USDA COVID-related payments from CFAP relative to cash receipts at the State level were closely aligned with distribution of cash receipts.
Keywords	Food relief/States. ; Government aid to small business/States. ; COVID-19 Pandemic, 2020-/Influence. ; United States.
Language	English
Dates of publication	2023-08
Size	1 online resource (ii, 17 pages) ;, color illustrations
Publisher	Economic Research Service, U.S. Department of Agriculture
Publishing place	Washington, DC
Document type	Book ; Online
DOI	10.327472023.8134138.ers
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: High-throughput screening for drug discovery targeting the cancer cell-microenvironment interactions in hematological cancers.

Giri, Anil K / Ianevski, Aleksander

Expert opinion on drug discovery

2021 Volume 17, Issue 2, Page(s) 181–190

Abstract: Introduction: The interactions between leukemic blasts and cells within the bone marrow environment affect oncogenesis, cancer stem cell survival, as well as drug resistance in hematological cancers. The importance of this interaction is increasingly ... ...

Abstract	Introduction: The interactions between leukemic blasts and cells within the bone marrow environment affect oncogenesis, cancer stem cell survival, as well as drug resistance in hematological cancers. The importance of this interaction is increasingly being recognized as a potentially important target for future drug discoveries and developments. Recent innovations in the high throughput drug screening-related technologies, novel ex-vivo disease-models, and freely available machine-learning algorithms are advancing the drug discovery process by targeting earlier undruggable proteins, complex pathways, as well as physical interactions (e.g. leukemic cell-bone microenvironment interaction). Area covered: In this review, the authors discuss the recent methodological advancements and existing challenges to target specialized hematopoietic niches within the bone marrow during leukemia and suggest how such methods can be used to identify drugs targeting leukemic cell-bone microenvironment interactions. Expert opinion: The recent development in cell-cell communication scoring technology and culture conditions can speed up the drug discovery by targeting the cell-microenvironment interaction. However, to accelerate this process, collecting clinical-relevant patient tissues, developing culture model systems, and implementing computational algorithms, especially trained to predict drugs and their combination targeting the cancer cell-bone microenvironment interaction are needed.
MeSH term(s)	Drug Discovery ; Early Detection of Cancer ; Hematologic Neoplasms/drug therapy ; High-Throughput Screening Assays ; Humans ; Tumor Microenvironment
Language	English
Publishing date	2021-11-08
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't ; Review
ZDB-ID	2259618-5
ISSN	1746-045X ; 1746-0441
ISSN (online)	1746-045X
ISSN	1746-0441
DOI	10.1080/17460441.2022.1991306
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Zs.A 6388: Show issues

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Article: Analysis of the payments from the coronavirus food assistance program and the market facilitation program to minority producers.

Giri, Anil K / Subedi, Dipak / Kassel, Kathleen

Applied economic perspectives and policy

2022

Abstract: This paper examines the payments made to minority producers, focused on African American producers, from the COVID-19 program, Coronavirus Food Assistance Program (CFAP), of the United States Department of Agriculture (USDA) and compares it with one of ... ...

Abstract	This paper examines the payments made to minority producers, focused on African American producers, from the COVID-19 program, Coronavirus Food Assistance Program (CFAP), of the United States Department of Agriculture (USDA) and compares it with one of the other more recent ad hoc program payments, the Market Facilitation Program (MFP). There were two rounds of the CFAP, and combinedly (as of March 2022), the program made direct payments of $31.0 billion ($11.8 billion from CFAP 1 and $19.2 billion from CFAP 2) starting in 2020. The MFP made a total payment of $23.5 billion (in two rounds, MFP 2018 and MFP 2019) to producers affected by the retaliatory tariffs placed on US producers by trade partners across multiple years. CFAP made almost $600 million in direct payments to minority producers, including Black or African American producers. Black or African American only producers received more than $52 million in CFAP payments. CFAP payments were proportional to the value of agricultural commodity sold for most minority producers. The 2017 Census of Agriculture showed that the majority of minority producers, including African American producers but excluding Asian producers, raised livestock. CFAP made the highest payments to livestock minority producers. The CFAP payment distribution pattern shows that payments reached minority producers who often did not receive Government payments. CFAP made more payments and as a share of total program outlays to minority producers compared to MFP. However, for Black or African American only producers, even though the magnitude increased (because CFAP disbursed more funds compared to MFP), the share of payment received did not increase.
Language	English
Publishing date	2022-10-05
Publishing country	England
Document type	Journal Article
ZDB-ID	2518384-9
ISSN	2040-5790
ISSN	2040-5790
DOI	10.1002/aepp.13325
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Article ; Online: Charting the challenges behind the testing of COVID-19 in developing countries: Nepal as a case study.

Giri, Anil K / Rana, Divya Rsjb

Biosafety and health

2020 Volume 2, Issue 2, Page(s) 53–56

Abstract: The infrastructure needed to detect Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection (COVID-19) that complies completely with WHO guidelines is lacking across many parts of the globe, especially in developing countries, including ... ...

Abstract	The infrastructure needed to detect Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection (COVID-19) that complies completely with WHO guidelines is lacking across many parts of the globe, especially in developing countries, including Nepal. We outline the problems faced by such countries and suggest that the national and international community should collaborate in the development and adoption of novel protocols for the rapid detection of COVID-19 according to locally available infrastructure, in order to fight against the outbreak.
Language	English
Publishing date	2020-05-13
Publishing country	Netherlands
Document type	Editorial
ISSN	2590-0536
ISSN (online)	2590-0536
DOI	10.1016/j.bsheal.2020.05.002
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Article ; Online: Fully-automated and ultra-fast cell-type identification using specific marker combinations from single-cell transcriptomic data

Aleksandr Ianevski / Anil K. Giri / Tero Aittokallio

Nature Communications, Vol 13, Iss 1, Pp 1-

2022 Volume 10

Abstract: Cell types are typically identified in single cell transcriptomic data by manual annotation of cell clusters using established marker genes. Here the authors present a fully-automated computational platform that can quickly and accurately distinguish ... ...

Abstract	Cell types are typically identified in single cell transcriptomic data by manual annotation of cell clusters using established marker genes. Here the authors present a fully-automated computational platform that can quickly and accurately distinguish between cell types.
Keywords	Science ; Q
Language	English
Publishing date	2022-03-01T00:00:00Z
Publisher	Nature Portfolio
Document type	Article ; Online
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Article ; Online: Fully-automated and ultra-fast cell-type identification using specific marker combinations from single-cell transcriptomic data.

Ianevski, Aleksandr / Giri, Anil K / Aittokallio, Tero

Nature communications

2022 Volume 13, Issue 1, Page(s) 1246

Abstract: Identification of cell populations often relies on manual annotation of cell clusters using established marker genes. However, the selection of marker genes is a time-consuming process that may lead to sub-optimal annotations as the markers must be ... ...

Abstract	Identification of cell populations often relies on manual annotation of cell clusters using established marker genes. However, the selection of marker genes is a time-consuming process that may lead to sub-optimal annotations as the markers must be informative of both the individual cell clusters and various cell types present in the sample. Here, we developed a computational platform, ScType, which enables a fully-automated and ultra-fast cell-type identification based solely on a given scRNA-seq data, along with a comprehensive cell marker database as background information. Using six scRNA-seq datasets from various human and mouse tissues, we show how ScType provides unbiased and accurate cell type annotations by guaranteeing the specificity of positive and negative marker genes across cell clusters and cell types. We also demonstrate how ScType distinguishes between healthy and malignant cell populations, based on single-cell calling of single-nucleotide variants, making it a versatile tool for anticancer applications. The widely applicable method is deployed both as an interactive web-tool ( https://sctype.app ), and as an open-source R-package.
MeSH term(s)	Animals ; Mice ; Sequence Analysis, RNA ; Single-Cell Analysis ; Software ; Transcriptome/genetics ; Whole Exome Sequencing
Language	English
Publishing date	2022-03-10
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2553671-0
ISSN	2041-1723 ; 2041-1723
ISSN (online)	2041-1723
ISSN	2041-1723
DOI	10.1038/s41467-022-28803-w
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Article ; Online: SynergyFinder 3.0: an interactive analysis and consensus interpretation of multi-drug synergies across multiple samples.

Ianevski, Aleksandr / Giri, Anil K / Aittokallio, Tero

Nucleic acids research

2022 Volume 50, Issue W1, Page(s) W739–W743

Abstract: SynergyFinder (https://synergyfinder.fimm.fi) is a free web-application for interactive analysis and visualization of multi-drug combination response data. Since its first release in 2017, SynergyFinder has become a popular tool for multi-dose ... ...

Abstract	SynergyFinder (https://synergyfinder.fimm.fi) is a free web-application for interactive analysis and visualization of multi-drug combination response data. Since its first release in 2017, SynergyFinder has become a popular tool for multi-dose combination data analytics, partly because the development of its functionality and graphical interface has been driven by a diverse user community, including both chemical biologists and computational scientists. Here, we describe the latest upgrade of this community-effort, SynergyFinder release 3.0, introducing a number of novel features that support interactive multi-sample analysis of combination synergy, a novel consensus synergy score that combines multiple synergy scoring models, and an improved outlier detection functionality that eliminates false positive results, along with many other post-analysis options such as weighting of synergy by drug concentrations and distinguishing between different modes of synergy (potency and efficacy). Based on user requests, several additional improvements were also implemented, including new data visualizations and export options for multi-drug combinations. With these improvements, SynergyFinder 3.0 supports robust identification of consistent combinatorial synergies for multi-drug combinatorial discovery and clinical translation.
MeSH term(s)	Consensus ; Drug Combinations ; Software ; Drug Discovery
Chemical Substances	Drug Combinations
Language	English
Publishing date	2022-05-17
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	186809-3
ISSN	1362-4962 ; 1362-4954 ; 0301-5610 ; 0305-1048
ISSN (online)	1362-4962 ; 1362-4954
ISSN	0301-5610 ; 0305-1048
DOI	10.1093/nar/gkac382
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Article ; Online: Epigenome-wide methylation study identified two novel CpGs associated with T2DM risk and a network of co-methylated CpGs capable of patient's classifications.

Giri, Anil K / Prasad, Gauri / Parekatt, Vaisak / Rajashekar, Donaka / Tandon, Nikhil / Bharadwaj, Dwaipayan

Human molecular genetics

2023 Volume 32, Issue 16, Page(s) 2576–2586

Abstract: Prevention of Type 2 diabetes mellitus (T2DM) pandemic needs markers that can precisely predict the disease risk in an individual. Alterations in DNA methylations due to exposure towards environmental risk factors are widely sought markers for T2DM risk ... ...

Abstract	Prevention of Type 2 diabetes mellitus (T2DM) pandemic needs markers that can precisely predict the disease risk in an individual. Alterations in DNA methylations due to exposure towards environmental risk factors are widely sought markers for T2DM risk prediction. To identify such individual DNA methylation signatures and their effect on disease risk, we performed an epigenome-wide association study (EWAS) in 844 Indian individuals of Indo-European origin. We identified and validated methylation alterations at two novel CpG sites in MIR1287 (cg01178710) and EDN2-SCMH1 (cg04673737) genes associated with T2DM risk at the epigenome-wide-significance-level (P < 1.2 × 10-7). Further, we also replicated the association of two known CpG sites in TXNIP, and CPT1A in the Indian population. With 535 EWAS significant CpGs (P < 1.2 × 10-7) identified in the discovery phase samples, we created a co-methylation network using weighted correlation network analysis and identified four modules among the CpGs. We observed that methylation of one of the module associates with T2DM risk factors (e.g. BMI, insulin and C-peptide) and can be used as markers to segregate T2DM patients with good glycemic control (e.g. low HbA1c) and dyslipidemia (low HDL and high TG) from the other patients. Additionally, an intronic SNP (rs6503650) in the JUP gene, a member of the same module, associated with methylation at all the 14 hub CpG sites of that module as methQTL. Our network-assisted EWAS is the first to systematically explore DNA methylation variations conferring risks to T2DM in Indians and use the identified risk CpG sites for patient segregation with different clinical outcomes. These findings can be useful for better stratification of patients to improve the clinical management and treatment effects.
MeSH term(s)	Humans ; Epigenome/genetics ; Epigenesis, Genetic/genetics ; Diabetes Mellitus, Type 2/genetics ; Genome-Wide Association Study ; CpG Islands/genetics ; DNA Methylation/genetics ; MicroRNAs
Chemical Substances	MIRN1287 microRNA, human ; MicroRNAs
Language	English
Publishing date	2023-05-12
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	1108742-0
ISSN	1460-2083 ; 0964-6906
ISSN (online)	1460-2083
ISSN	0964-6906
DOI	10.1093/hmg/ddad084
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Article ; Online: Charting the challenges behind the testing of COVID-19 in developing countries

Giri, Anil K. / Rana, Divya RSJB

Biosafety and Health

Nepal as a case study

2020 Volume 2, Issue 2, Page(s) 53–56

Keywords	covid19
Language	English
Publisher	Elsevier BV
Publishing country	us
Document type	Article ; Online
ISSN	2590-0536
DOI	10.1016/j.bsheal.2020.05.002
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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