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Article ; Online: Unfolded Protein Response Signaling in Hepatic Stem Cell Activation in Liver Fibrosis.

Salimi, Zohreh / Rostami, Mehdi / Milasi, Yaser Eshaghi / Mafi, Alireza / Raoufinia, Ramin / Kiani, Amirhossein / Sakhaei, Fariba / Ghezelbash, Behrooz / Butler, Alexandra E / Mohammad-Sadeghipour, Maryam / Sahebkar, Amirhossein

Current protein & peptide science

2024 Volume 25, Issue 1, Page(s) 59–70

Abstract: Frequent exposure to various external and internal adverse forces (stresses) disrupts cell protein homeostasis through endoplasmic reticulum (ER) capacity saturation. This process leads to the unfolded protein response (UPR), which aims to re-establish/ ... ...

Abstract	Frequent exposure to various external and internal adverse forces (stresses) disrupts cell protein homeostasis through endoplasmic reticulum (ER) capacity saturation. This process leads to the unfolded protein response (UPR), which aims to re-establish/maintain optimal cellular equilibrium. This complex mechanism is involved in the pathogenesis of various disorders, such as metabolic syndrome, fibrotic diseases, neurodegeneration, and cancer, by altering cellular metabolic changes integral to activating the hepatic stellate cells (HSCs). The development of hepatic fibrosis is one of the consequences of UPR activation. Therefore, novel therapies that target the UPR pathway effectively and specifically are being studied. This article covers the involvement of the UPR signaling pathway in cellular damage in liver fibrosis. Investigating the pathogenic pathways related to the ER/UPR stress axis that contribute to liver fibrosis can help to guide future drug therapy approaches.
MeSH term(s)	Humans ; Unfolded Protein Response ; Liver Cirrhosis/pathology ; Endoplasmic Reticulum Stress/physiology ; Signal Transduction ; Stem Cells/metabolism
Language	English
Publishing date	2024-01-02
Publishing country	United Arab Emirates
Document type	Journal Article
ZDB-ID	2045662-1
ISSN	1875-5550 ; 1389-2037
ISSN (online)	1875-5550
ISSN	1389-2037
DOI	10.2174/1389203724666230822085951
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Zs.A 5884: Show issues

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Article ; Online: Sperm DNA fragmentation and apoptosis in the sperm of men with oligozoospermia are closely related to anti-ODF2 autoantibodies.

Kiani, Amirhossein / Döğüş, Yusuf / Saadatnia, Sahar / Yazdani, Yalda / Asadi, Fatemeh / Al-Naqeeb, Bashar Zuhair Talib / Masouleh, Sahand Saeidpour / Merza, Muna S / Daemi, Amin / Rahimi, Asiye

Pathology, research and practice

2023 Volume 245, Page(s) 154434

Abstract: Background: Around 15% of couples of childbearing age suffer from infertility; in 50% of these cases, the male factor is present. In this study, we investigated the association between anti-ODF2 autoantibody existence and the DNA fragmentation and ... ...

Abstract	Background: Around 15% of couples of childbearing age suffer from infertility; in 50% of these cases, the male factor is present. In this study, we investigated the association between anti-ODF2 autoantibody existence and the DNA fragmentation and apoptosis of sperm in oligozoospermia men. Material and methods: 35 fertile men and 57 oligozoospermia men are enrolled in this study as control and case groups, respectively. After the identification of ODF2 as a possible target of anti-sperm antibodies in sera of oligozoospermia men using two-dimensional gel electrophoresis followed by western blotting and mass spectrometry, the case group serums were screened for anti-ODF2 autoantibodies and divided into anti-ODF2 negative (N = 24) and positive (N = 33) subgroups to follow assays. The mRNA expression levels of ODF2, Caspases 3, 8, 9, BAX, and BCL-2 were evaluated via qRT-PCR in spermatozoa samples of mentioned groups. DNA fragmentation and apoptosis rate of spermatozoa in studied groups were assessed using an SDF kit and flow cytometry, respectively. Results: Mass spectrometry showed that ODF2 is one of the anti-sperm antibodies targeted in oligozoospermia patients. 33 of 57 oligozoospermia men had anti-ODF2 autoantibody in their sera. An elevated expression of ODF2 mRNA was observed in spermatozoa of anti-ODF2 Conclusion: Our results revealed that ODF2 is one of the main spermatozoa structural proteins, which is one of the anti-sperm antibodies targets, and its dysregulated expression may result in an increased rate of sperm DNA fragmentation and apoptosis.
MeSH term(s)	Humans ; Male ; Apoptosis/genetics ; Autoantibodies ; bcl-2-Associated X Protein ; DNA Fragmentation ; Oligospermia/genetics ; RNA, Messenger ; Spermatozoa
Chemical Substances	Autoantibodies ; bcl-2-Associated X Protein ; RNA, Messenger ; ODF2 protein, human
Language	English
Publishing date	2023-04-01
Publishing country	Germany
Document type	Journal Article
ZDB-ID	391889-0
ISSN	1618-0631 ; 0344-0338
ISSN (online)	1618-0631
ISSN	0344-0338
DOI	10.1016/j.prp.2023.154434
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Book ; Online: Sepsis World Model

Kiani, Amirhossein / Wang, Chris / Xu, Angela

A MIMIC-based OpenAI Gym "World Model" Simulator for Sepsis Treatment

2019

Abstract: Sepsis is a life-threatening condition caused by the body's response to an infection. In order to treat patients with sepsis, physicians must control varying dosages of various antibiotics, fluids, and vasopressors based on a large number of variables in ...

Abstract	Sepsis is a life-threatening condition caused by the body's response to an infection. In order to treat patients with sepsis, physicians must control varying dosages of various antibiotics, fluids, and vasopressors based on a large number of variables in an emergency setting. In this project we employ a "world model" methodology to create a simulator that aims to predict the next state of a patient given a current state and treatment action. In doing so, we hope our simulator learns from a latent and less noisy representation of the EHR data. Using historical sepsis patient records from the MIMIC dataset, our method creates an OpenAI Gym simulator that leverages a Variational Auto-Encoder and a Mixture Density Network combined with a RNN (MDN-RNN) to model the trajectory of any sepsis patient in the hospital. To reduce the effects of noise, we sample from a generated distribution of next steps during simulation and have the option of introducing uncertainty into our simulator by controlling the "temperature" variable. It is worth noting that we do not have access to the ground truth for the best policy because we can only evaluate learned policies by real-world experimentation or expert feedback. Instead, we aim to study our simulator model's performance by evaluating the similarity between our environment's rollouts with the real EHR data and assessing its viability for learning a realistic policy for sepsis treatment using Deep Q-Learning. Comment: This project was done as a class project for CS221 at Stanford University
Keywords	Computer Science - Machine Learning ; Statistics - Machine Learning
Subject code	006
Publishing date	2019-12-15
Publishing country	us
Document type	Book ; Online
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Article ; Online: Targeting AMPK by Statins: A Potential Therapeutic Approach.

Dehnavi, Sajad / Kiani, Amirhossein / Sadeghi, Mahvash / Biregani, Ali Farhadi / Banach, Maciej / Atkin, Stephen L / Jamialahmadi, Tannaz / Sahebkar, Amirhossein

Drugs

2021 Volume 81, Issue 8, Page(s) 923–933

Abstract: Statins are a group of lipid-lowering drugs that inhibit cholesterol biosynthesis and have anti-inflammatory, anti-tumor, and immunomodulatory properties. Several lines of evidence indicate that statins regulate multiple proteins associated with the ... ...

Abstract	Statins are a group of lipid-lowering drugs that inhibit cholesterol biosynthesis and have anti-inflammatory, anti-tumor, and immunomodulatory properties. Several lines of evidence indicate that statins regulate multiple proteins associated with the regulation of differing cellular pathways. The 5'-adenosine monophosphate-activated protein kinase (AMPK) pathway plays an important role in metabolism homeostasis with effects on cellular processes including apoptosis and the inflammatory responses through several pathways. Recently, it has been shown that statins can affect the AMPK pathway in differing physiological and pathological ways, resulting in anti-cancer, cardio-protective, neuro-protective, and anti-tubercular effects; additionally, they have therapeutic effects on non-alcoholic fatty liver disease and diabetes mellitus-associated complications. Statins activate AMPK as an energy sensor that inhibits cell proliferation and induces apoptosis in cancer cells, whilst exerting its cardio-protective effects through inhibition of inflammation and fibrosis, and promotion of angiogenesis. Furthermore, statin-associated AMPK activation leads to decreased lipid accumulation and decreased amyloid beta deposition in the liver and brain, respectively, and may have therapeutic effects on the liver and neurons. In this review, we summarize the results of studies of AMPK-associated therapeutic effects of statins in different pathological conditions.
MeSH term(s)	AMP-Activated Protein Kinases/drug effects ; AMP-Activated Protein Kinases/metabolism ; Angiogenesis Inducing Agents/pharmacology ; Animals ; Apoptosis/drug effects ; Cardiovascular Diseases/drug therapy ; Cell Line, Tumor ; Cell Proliferation ; Fibrosis/drug therapy ; Humans ; Hydroxymethylglutaryl-CoA Reductase Inhibitors/pharmacology ; Hypolipidemic Agents/pharmacology ; Inflammation/drug therapy ; Neoplasms/drug therapy ; Nervous System Diseases/drug therapy
Chemical Substances	Angiogenesis Inducing Agents ; Hydroxymethylglutaryl-CoA Reductase Inhibitors ; Hypolipidemic Agents ; AMP-Activated Protein Kinases (EC 2.7.11.31)
Language	English
Publishing date	2021-05-03
Publishing country	New Zealand
Document type	Journal Article ; Review
ZDB-ID	120316-2
ISSN	1179-1950 ; 0012-6667
ISSN (online)	1179-1950
ISSN	0012-6667
DOI	10.1007/s40265-021-01510-4
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Zs.A 762: Show issues

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Article ; Online: The evaluation of CD39, CD73, and HIF-1 α expression besides their related miRNAs in PBMCs of women with recurrent pregnancy loss.

Parhizkar, Forough / Kiani, Amirhossein / Darzi, Satinik / Motavalli, Roza / Noori Dolama, Fatemeh / Yousefzadeh, Yousef / Aghebati-Maleki, Leili / Pia, Helen / Abdollahi-Fard, Sedigheh / Mardi, Amirhossein / Danaii, Shahla / Ahmadian Heris, Javad / Yousefi, Mehdi / Soltani-Zangbar, Mohammad Sadegh

Journal of reproductive immunology

2023 Volume 156, Page(s) 103820

Abstract: The molecular mechanisms involved in the pathogenesis of recurrent pregnancy loss (RPL) are not completely recognized. The present study aimed to assess the molecules associated with ATP catabolism and hypoxia besides their related miRNAs in patients ... ...

Abstract	The molecular mechanisms involved in the pathogenesis of recurrent pregnancy loss (RPL) are not completely recognized. The present study aimed to assess the molecules associated with ATP catabolism and hypoxia besides their related miRNAs in patients with RPL. The frequency of Th17 and Treg cells in PBMCs of RPL women and healthy pregnant women were evaluated with Flow cytometry. The expression levels of CD39, CD73, and Hypoxia-inducible factor-alpha (HIF-1α), miR-18a, miR-30a, and miR-206 in PBMCs of two groups were measured with real-time PCR and western blotting. Then, serum levels of IGF-1, TGF-β, and HIF-1α were measured by ELISA. Our results indicated a higher (p = 0.0002) and lower (p < 0.0001) frequency of Th17 and Treg lymphocytes in RPL women, respectively. The expression level of CD39 decreased in PBMCs of RPL women whereas the level of CD73 and HIF-α increased (p = 0.0010, 0.0023, 0.0006 respectively). The results of CD39 and CD37 were also confirmed by protein analysis (p = 0.0047, 0.0364 respectively). Almost, the same results for CD39 and CD73 expression at mRNA and protein levels were observed in isolated Treg cells. Moreover, we found the higher expression of miR-206 and miRNA-30a (p = 0.0038, 0.0123), but the lower expression of miRNA-18a (p = 0.0101) in RPL. The concentration level of IGF-1, and TGF-β reduced (p = 0.0017, 0.0065 respectively) while the level of HIF-α elevated (p = 0.0235) in serum samples of RPL. In conclusion, we observed the dysregulation of molecules that are involved in ATP catabolism and hypoxia, including CD39, CD73, and HIF-1a which is related to miR-18a, miR-30a, and miR-206 change in RPL women. It may be potentially used for RPL prognosis by more comprehensive future studies.
MeSH term(s)	Female ; Humans ; Pregnancy ; Abortion, Habitual ; Adenosine Triphosphate ; Hypoxia ; Insulin-Like Growth Factor I ; MicroRNAs/genetics ; Transforming Growth Factor beta
Chemical Substances	Adenosine Triphosphate (8L70Q75FXE) ; Insulin-Like Growth Factor I (67763-96-6) ; MicroRNAs ; MIRN206 microRNA, human ; Transforming Growth Factor beta ; ENTPD1 protein, human (EC 3.6.1.5) ; NT5E protein, human (EC 3.1.3.5) ; HIF1A protein, human
Language	English
Publishing date	2023-01-31
Publishing country	Ireland
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	424421-7
ISSN	1872-7603 ; 0165-0378
ISSN (online)	1872-7603
ISSN	0165-0378
DOI	10.1016/j.jri.2023.103820
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Zs.A 1483: Show issues

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Book ; Online: CheXpedition

Rajpurkar, Pranav / Joshi, Anirudh / Pareek, Anuj / Chen, Phil / Kiani, Amirhossein / Irvin, Jeremy / Ng, Andrew Y. / Lungren, Matthew P.

Investigating Generalization Challenges for Translation of Chest X-Ray Algorithms to the Clinical Setting

2020

Abstract: Although there have been several recent advances in the application of deep learning algorithms to chest x-ray interpretation, we identify three major challenges for the translation of chest x-ray algorithms to the clinical setting. We examine the ... ...

Abstract	Although there have been several recent advances in the application of deep learning algorithms to chest x-ray interpretation, we identify three major challenges for the translation of chest x-ray algorithms to the clinical setting. We examine the performance of the top 10 performing models on the CheXpert challenge leaderboard on three tasks: (1) TB detection, (2) pathology detection on photos of chest x-rays, and (3) pathology detection on data from an external institution. First, we find that the top 10 chest x-ray models on the CheXpert competition achieve an average AUC of 0.851 on the task of detecting TB on two public TB datasets without fine-tuning or including the TB labels in training data. Second, we find that the average performance of the models on photos of x-rays (AUC = 0.916) is similar to their performance on the original chest x-ray images (AUC = 0.924). Third, we find that the models tested on an external dataset either perform comparably to or exceed the average performance of radiologists. We believe that our investigation will inform rapid translation of deep learning algorithms to safe and effective clinical decision support tools that can be validated prospectively with large impact studies and clinical trials. Comment: Accepted as workshop paper at ACM Conference on Health, Inference, and Learning (CHIL) 2020
Keywords	Electrical Engineering and Systems Science - Image and Video Processing ; Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Machine Learning
Subject code	006
Publishing date	2020-02-26
Publishing country	us
Document type	Book ; Online
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Article ; Online: CheXaid: deep learning assistance for physician diagnosis of tuberculosis using chest x-rays in patients with HIV.

Rajpurkar, Pranav / O'Connell, Chloe / Schechter, Amit / Asnani, Nishit / Li, Jason / Kiani, Amirhossein / Ball, Robyn L / Mendelson, Marc / Maartens, Gary / van Hoving, Daniël J / Griesel, Rulan / Ng, Andrew Y / Boyles, Tom H / Lungren, Matthew P

NPJ digital medicine

2020 Volume 3, Page(s) 115

Abstract: Tuberculosis (TB) is the leading cause of preventable death in HIV-positive patients, and yet often remains undiagnosed and untreated. Chest x-ray is often used to assist in diagnosis, yet this presents additional challenges due to atypical radiographic ... ...

Abstract	Tuberculosis (TB) is the leading cause of preventable death in HIV-positive patients, and yet often remains undiagnosed and untreated. Chest x-ray is often used to assist in diagnosis, yet this presents additional challenges due to atypical radiographic presentation and radiologist shortages in regions where co-infection is most common. We developed a deep learning algorithm to diagnose TB using clinical information and chest x-ray images from 677 HIV-positive patients with suspected TB from two hospitals in South Africa. We then sought to determine whether the algorithm could assist clinicians in the diagnosis of TB in HIV-positive patients as a web-based diagnostic assistant. Use of the algorithm resulted in a modest but statistically significant improvement in clinician accuracy (
Language	English
Publishing date	2020-09-09
Publishing country	England
Document type	Journal Article
ISSN	2398-6352
ISSN (online)	2398-6352
DOI	10.1038/s41746-020-00322-2
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Article ; Online: Fast and accurate read alignment for resequencing.

Mu, John C / Jiang, Hui / Kiani, Amirhossein / Mohiyuddin, Marghoob / Bani Asadi, Narges / Wong, Wing H

Bioinformatics (Oxford, England)

2012 Volume 28, Issue 18, Page(s) 2366–2373

Abstract: Motivation: Next-generation sequence analysis has become an important task both in laboratory and clinical settings. A key stage in the majority sequence analysis workflows, such as resequencing, is the alignment of genomic reads to a reference genome. ... ...

Abstract	Motivation: Next-generation sequence analysis has become an important task both in laboratory and clinical settings. A key stage in the majority sequence analysis workflows, such as resequencing, is the alignment of genomic reads to a reference genome. The accurate alignment of reads with large indels is a computationally challenging task for researchers. Results: We introduce SeqAlto as a new algorithm for read alignment. For reads longer than or equal to 100 bp, SeqAlto is up to 10 × faster than existing algorithms, while retaining high accuracy and the ability to align reads with large (up to 50 bp) indels. This improvement in efficiency is particularly important in the analysis of future sequencing data where the number of reads approaches many billions. Furthermore, SeqAlto uses less than 8 GB of memory to align against the human genome. SeqAlto is benchmarked against several existing tools with both real and simulated data. Availability: Linux and Mac OS X binaries free for academic use are available at http://www.stanford.edu/group/wonglab/seqalto Contact: whwong@stanford.edu.
MeSH term(s)	Algorithms ; Genome, Human ; Genomics ; High-Throughput Nucleotide Sequencing ; Humans ; INDEL Mutation ; Sequence Alignment/methods ; Sequence Analysis, DNA ; Software
Language	English
Publishing date	2012-07-18
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	1422668-6
ISSN	1367-4811 ; 1367-4803
ISSN (online)	1367-4811
ISSN	1367-4803
DOI	10.1093/bioinformatics/bts450
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Article ; Online: Impact of a deep learning assistant on the histopathologic classification of liver cancer.

NPJ digital medicine

2020 Volume 3, Page(s) 23

Abstract: Artificial intelligence (AI) algorithms continue to rival human performance on a variety of clinical tasks, while their actual impact on human diagnosticians, when incorporated into clinical workflows, remains relatively unexplored. In this study, we ... ...

Abstract	Artificial intelligence (AI) algorithms continue to rival human performance on a variety of clinical tasks, while their actual impact on human diagnosticians, when incorporated into clinical workflows, remains relatively unexplored. In this study, we developed a deep learning-based assistant to help pathologists differentiate between two subtypes of primary liver cancer, hepatocellular carcinoma and cholangiocarcinoma, on hematoxylin and eosin-stained whole-slide images (WSI), and evaluated its effect on the diagnostic performance of 11 pathologists with varying levels of expertise. Our model achieved accuracies of 0.885 on a validation set of 26 WSI, and 0.842 on an independent test set of 80 WSI. Although use of the assistant did not change the mean accuracy of the 11 pathologists (
Language	English
Publishing date	2020-02-26
Publishing country	England
Document type	Journal Article
ISSN	2398-6352
ISSN (online)	2398-6352
DOI	10.1038/s41746-020-0232-8
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