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Article: The relationship between single nucleotide polymorphisms and dental implant loss: a scoping review.

Zhang, Frederick / Finkelstein, Joseph

Clinical, cosmetic and investigational dentistry

2019 Volume 11, Page(s) 131–141

Abstract: Objectives: The purpose of this review was to evaluate the relationship between genetic polymorphisms and dental implant loss.: Materials and methods: All case-control studies examining single nucleotide polymorphisms (SNPs) and dental implant ... ...

Abstract	Objectives: The purpose of this review was to evaluate the relationship between genetic polymorphisms and dental implant loss. Materials and methods: All case-control studies examining single nucleotide polymorphisms (SNPs) and dental implant failure were considered. A Boolean search was conducted on PubMed and Scopus to find eligible studies. Results: The initial search produced 78 results. Twenty-one studies were considered for inclusion after review and 16 were included in the final review. Twenty-two different polymorphisms were analyzed and statistically significant correlation was found for IL-4, IL-1A, IL-1B, MMP-8, and MMP-1 polymorphisms for dental implant failure. Discussion: A limited number of comprehensive studies have been done in this field. Additional studies with larger sample sizes and different ethnic backgrounds need to be done to see if the results can be reproduced. Of the polymorphisms studied, the IL-4 (+33), MMP-8 (-799), MMP-1 (-519), and MMP-1 (-1607) polymorphisms show the greatest association with dental implant loss.
Language	English
Publishing date	2019-05-31
Publishing country	New Zealand
Document type	Journal Article ; Review
ZDB-ID	2508154-8
ISSN	1179-1357
ISSN	1179-1357
DOI	10.2147/CCIDE.S207445
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Article: Inconsistency in race and ethnic classification in pharmacogenetics studies and its potential clinical implications.

Zhang, Frederick / Finkelstein, Joseph

Pharmacogenomics and personalized medicine

2019 Volume 12, Page(s) 107–123

Abstract: Introduction: Racial and ethnic categories are frequently used in pharmacogenetics literature to stratify patients; however, these categories can be inconsistent across different studies. To address the ongoing debate on the applicability of traditional ...

Abstract	Introduction: Racial and ethnic categories are frequently used in pharmacogenetics literature to stratify patients; however, these categories can be inconsistent across different studies. To address the ongoing debate on the applicability of traditional concepts of race and ethnicity in the context of precision medicine, we aimed to review the application of current racial and ethnic categories in pharmacogenetics and its potential impact on clinical care. Methods: One hundred and three total pharmacogenetics papers involving the Results: The racial and ethnic categories used in the papers we analyzed were highly heterogeneous. In total, we found 66 different racial and ethnic categories used which fall under the NIH race category "White", 47 different racial and ethnic categories for "Asian", and 62 different categories for "Black". The number of categories used varied widely based on country of origin: Japan used the highest number of different categories for "White" with 17, Malaysia used the highest number for "Asian" with 24, and the US used the highest number for "Black" with 28. Significant variation in allele frequency between different ethnic subgroups was identified within 3 major continental racial categories. Conclusion: Our analysis showed that racial and ethnic classification is highly inconsistent across different papers as well as between different countries. Evidence-based consensus is necessary for optimal use of self-identified race as well as geographical ancestry in pharmacogenetics. Common taxonomy of geographical ancestry which reflects specifics of particular countries and is accepted by the entire scientific community can facilitate reproducible pharmacogenetic research and clinical implementation of its results.
Language	English
Publishing date	2019-07-02
Publishing country	New Zealand
Document type	Journal Article
ZDB-ID	2508173-1
ISSN	1178-7066
ISSN	1178-7066
DOI	10.2147/PGPM.S207449
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Article ; Online: Association Between Number of Actionable Pharmacogenetic Variants and Length of Hospital Stay.

Finkelstein, Joseph / Zhang, Frederick / Cabrera, Manuel

Studies in health technology and informatics

2020 Volume 272, Page(s) 195–198

Abstract: The goal of this study was to evaluate association between number of pharmacogenetic variants and length of hospital stay. Electronic medical records were combined with exome sequencing results in 450 hospitalized patients. De-identified data set was ... ...

Abstract	The goal of this study was to evaluate association between number of pharmacogenetic variants and length of hospital stay. Electronic medical records were combined with exome sequencing results in 450 hospitalized patients. De-identified data set was used to characterize urgent care utilization and to identify presence of 44 actionable pharmacogenetic variants according to the guidelines of the Clinical Pharmacogenetics Implementation Consortium. The average age was 58.03 ± 16.47 ranging from 20 to 91 years old, average number of pharmacogenetic variants was 61.22 ± 26.52 ranging from 20 to 169, and mean length of hospital stay was 6.50 ± 4.29 ranging between 1 and 42 days. After adjusting for patient socio-demographics and overall disease severity reflected by the Charlson comorbidity index, a significant association between mean length of stay and number of pharmacogenetic variants was found using generalized linear regression (p-value < 2.2e-16).
MeSH term(s)	Adult ; Aged ; Aged, 80 and over ; Electronic Health Records ; Humans ; Length of Stay ; Linear Models ; Middle Aged ; Pharmacogenetics ; Pharmacogenomic Variants ; Young Adult
Language	English
Publishing date	2020-06-30
Publishing country	Netherlands
Document type	Journal Article
ISSN	1879-8365
ISSN (online)	1879-8365
DOI	10.3233/SHTI200527
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Article: Informatics Approaches for Harmonized Intelligent Integration of Stem Cell Research.

Finkelstein, Joseph / Parvanova, Irena / Zhang, Frederick

Stem cells and cloning : advances and applications

2020 Volume 13, Page(s) 1–20

Abstract: As biomedical data integration and analytics play an increasing role in the field of stem cell research, it becomes important to develop ways to standardize, aggregate, and share data among researchers. For this reason, many databases have been developed ...

Abstract	As biomedical data integration and analytics play an increasing role in the field of stem cell research, it becomes important to develop ways to standardize, aggregate, and share data among researchers. For this reason, many databases have been developed in recent years in an attempt to systematically warehouse data from different stem cell projects and experiments at the same time. However, these databases vary widely in their implementation and structure. The aim of this scoping review is to characterize the main features of available stem cell databases in order to identify specifications useful for implementation in future stem cell databases. We conducted a scoping review of peer-reviewed literature and online resources to identify and review available stem cell databases. To identify the relevant databases, we performed a PubMed search using relevant MeSH terms followed by a web search for databases which may not have an associated journal article. In total, we identified 16 databases to include in this review. The data elements reported in these databases represented a broad spectrum of parameters from basic socio-demographic variables to various cells characteristics, cell surface markers expression, and clinical trial results. Three broad sets of functional features that provide utility for future stem cell research and facilitate bioinformatics workflows were identified. These features consisted of the following: common data elements, data visualization and analysis tools, and biomedical ontologies for data integration. Stem cell bioinformatics is a quickly evolving field that generates a growing number of heterogeneous data sets. Further progress in the stem cell research may be greatly facilitated by development of applications for intelligent stem cell data aggregation, sharing and collaboration process.
Language	English
Publishing date	2020-01-28
Publishing country	New Zealand
Document type	Journal Article ; Review
ZDB-ID	2511404-9
ISSN	1178-6957
ISSN	1178-6957
DOI	10.2147/SCCAA.S237361
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Article ; Online: Using big data to promote precision oral health in the context of a learning healthcare system.

Finkelstein, Joseph / Zhang, Frederick / Levitin, Seth A / Cappelli, David

Journal of public health dentistry

2020 Volume 80 Suppl 1, Page(s) S43–S58

Abstract: There has been a call for evidence-based oral healthcare guidelines, to improve precision dentistry and oral healthcare delivery. The main challenges to this goal are the current lack of up-to-date evidence, the limited integrative analytical data sets, ... ...

Abstract	There has been a call for evidence-based oral healthcare guidelines, to improve precision dentistry and oral healthcare delivery. The main challenges to this goal are the current lack of up-to-date evidence, the limited integrative analytical data sets, and the slow translations to routine care delivery. Overcoming these issues requires knowledge discovery pipelines based on big data and health analytics, intelligent integrative informatics approaches, and learning health systems. This article examines how this can be accomplished by utilizing big data. These data can be gathered from four major streams: patients, clinical data, biological data, and normative data sets. All these must then be uniformly combined for analysis and modelling and the meaningful findings can be implemented clinically. By executing data capture cycles and integrating the subsequent findings, practitioners are able to improve public oral health and care delivery.
MeSH term(s)	Big Data ; Delivery of Health Care ; Humans ; Learning Health System ; Oral Health
Language	English
Publishing date	2020-01-06
Publishing country	United States
Document type	Journal Article ; Review
ZDB-ID	410719-6
ISSN	1752-7325 ; 0022-4006
ISSN (online)	1752-7325
ISSN	0022-4006
DOI	10.1111/jphd.12354
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Article: Decoding Intracranial EEG With Machine Learning: A Systematic Review.

Mirchi, Nykan / Warsi, Nebras M / Zhang, Frederick / Wong, Simeon M / Suresh, Hrishikesh / Mithani, Karim / Erdman, Lauren / Ibrahim, George M

Frontiers in human neuroscience

2022 Volume 16, Page(s) 913777

Abstract: Advances in intracranial electroencephalography (iEEG) and neurophysiology have enabled the study of previously inaccessible brain regions with high fidelity temporal and spatial resolution. Studies of iEEG have revealed a rich neural code subserving ... ...

Abstract	Advances in intracranial electroencephalography (iEEG) and neurophysiology have enabled the study of previously inaccessible brain regions with high fidelity temporal and spatial resolution. Studies of iEEG have revealed a rich neural code subserving healthy brain function and which fails in disease states. Machine learning (ML), a form of artificial intelligence, is a modern tool that may be able to better decode complex neural signals and enhance interpretation of these data. To date, a number of publications have applied ML to iEEG, but clinician awareness of these techniques and their relevance to neurosurgery, has been limited. The present work presents a review of existing applications of ML techniques in iEEG data, discusses the relative merits and limitations of the various approaches, and examines potential avenues for clinical translation in neurosurgery. One-hundred-seven articles examining artificial intelligence applications to iEEG were identified from 3 databases. Clinical applications of ML from these articles were categorized into 4 domains: i) seizure analysis, ii) motor tasks, iii) cognitive assessment, and iv) sleep staging. The review revealed that supervised algorithms were most commonly used across studies and often leveraged publicly available timeseries datasets. We conclude with recommendations for future work and potential clinical applications.
Language	English
Publishing date	2022-06-27
Publishing country	Switzerland
Document type	Systematic Review
ZDB-ID	2425477-0
ISSN	1662-5161
ISSN	1662-5161
DOI	10.3389/fnhum.2022.913777
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Article ; Online: Triptolide, a HSP90 middle domain inhibitor, induces apoptosis in triple manner.

Zhang, Frederick Zhehao / Ho, Derek Hoi-Hang / Wong, Roger Hoi-Fung

Oncotarget

2018 Volume 9, Issue 32, Page(s) 22301–22315

Abstract: Triptolide (TL) is a potent anti-tumor, anti-inflammatory and immunosuppressive natural compound. Mechanistic studies revealed that TL inhibits tumor growth and triggers programmed cell death. Studies further suggested that TL inhibits heat shock ... ...

Abstract	Triptolide (TL) is a potent anti-tumor, anti-inflammatory and immunosuppressive natural compound. Mechanistic studies revealed that TL inhibits tumor growth and triggers programmed cell death. Studies further suggested that TL inhibits heat shock response in cancer cells to induce apoptosis. HSP90β is the major component of heat shock response and is overexpressed in different types of cancers. Given almost all identified HSP90β inhibitors are either N or C-terminal inhibitors, small molecules attacking cysteine(s) in the middle domain might represent a new class of inhibitors. In the current study, we showed that TL inhibits HSP90β in triple manner. Characterization suggests that TL inhibits ATPase activity by preventing ATP binding thus blunts the chaperone activity. TL disrupts HSP90β-CDC37 (co-chaperone) complex through middle domain Cys366 of HSP90β and causes kinase client protein degradation. At the cellular level, the TL-mediated decrease in CDK4 protein levels in HeLa cells causes reduced phosphorylation of Rb resulting in cell cycle arrest at the G1 phase. Furthermore, our results demonstrated that TL triggers programmed cell death in an HSP90β-dependent manner as knockdown of HSP90β further sensitized TL-mediated cell cycle arrest and apoptotic effect. Surprisingly, our data showed that TL is the first drug to be reported to induce site-specific phosphorylation of HSP90β to drive apoptosome formation in the early phase of the treatment. In summary, our study established that TL is a novel middle domain HSP90β inhibitor with bi-phasic multi-mechanistic inhibition. The unique regulatory mechanism of TL on HSP90β makes it an effective inhibitor.
Language	English
Publishing date	2018-04-27
Publishing country	United States
Document type	Journal Article
ZDB-ID	2560162-3
ISSN	1949-2553 ; 1949-2553
ISSN (online)	1949-2553
ISSN	1949-2553
DOI	10.18632/oncotarget.24737
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Article ; Online: The BioGRID database: A comprehensive biomedical resource of curated protein, genetic, and chemical interactions.

Oughtred, Rose / Rust, Jennifer / Chang, Christie / Breitkreutz, Bobby-Joe / Stark, Chris / Willems, Andrew / Boucher, Lorrie / Leung, Genie / Kolas, Nadine / Zhang, Frederick / Dolma, Sonam / Coulombe-Huntington, Jasmin / Chatr-Aryamontri, Andrew / Dolinski, Kara / Tyers, Mike

Protein science : a publication of the Protein Society

2020 Volume 30, Issue 1, Page(s) 187–200

Abstract: The BioGRID (Biological General Repository for Interaction Datasets, thebiogrid.org) is an open-access database resource that houses manually curated protein and genetic interactions from multiple species including yeast, worm, fly, mouse, and human. The ...

Abstract	The BioGRID (Biological General Repository for Interaction Datasets, thebiogrid.org) is an open-access database resource that houses manually curated protein and genetic interactions from multiple species including yeast, worm, fly, mouse, and human. The ~1.93 million curated interactions in BioGRID can be used to build complex networks to facilitate biomedical discoveries, particularly as related to human health and disease. All BioGRID content is curated from primary experimental evidence in the biomedical literature, and includes both focused low-throughput studies and large high-throughput datasets. BioGRID also captures protein post-translational modifications and protein or gene interactions with bioactive small molecules including many known drugs. A built-in network visualization tool combines all annotations and allows users to generate network graphs of protein, genetic and chemical interactions. In addition to general curation across species, BioGRID undertakes themed curation projects in specific aspects of cellular regulation, for example the ubiquitin-proteasome system, as well as specific disease areas, such as for the SARS-CoV-2 virus that causes COVID-19 severe acute respiratory syndrome. A recent extension of BioGRID, named the Open Repository of CRISPR Screens (ORCS, orcs.thebiogrid.org), captures single mutant phenotypes and genetic interactions from published high throughput genome-wide CRISPR/Cas9-based genetic screens. BioGRID-ORCS contains datasets for over 1,042 CRISPR screens carried out to date in human, mouse and fly cell lines. The biomedical research community can freely access all BioGRID data through the web interface, standardized file downloads, or via model organism databases and partner meta-databases.
MeSH term(s)	Animals ; COVID-19/genetics ; COVID-19/virology ; Databases, Factual ; Humans ; Mice ; Protein Interaction Mapping ; Proteins/genetics ; SARS-CoV-2/genetics ; SARS-CoV-2/pathogenicity ; User-Computer Interface
Chemical Substances	Proteins
Keywords	covid19
Language	English
Publishing date	2020-11-23
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	1106283-6
ISSN	1469-896X ; 0961-8368
ISSN (online)	1469-896X
ISSN	0961-8368
DOI	10.1002/pro.3978
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Article: The BioGRID database: A comprehensive biomedical resource of curated protein, genetic and chemical interactions

Protein sci

Abstract	The BioGRID (Biological General Repository for Interaction Datasets, thebiogrid.org) is an open-access database resource that houses manually curated protein and genetic interactions from multiple species including yeast, worm, fly, mouse, and human. The ~1.93 million curated interactions in BioGRID can be used to build complex networks to facilitate biomedical discoveries, particularly as related to human health and disease. All BioGRID content is curated from primary experimental evidence in the biomedical literature, and includes both focused low-throughput studies and large high-throughput datasets. BioGRID also captures protein post-translational modifications and protein or gene interactions with bioactive small molecules including many known drugs. A built-in network visualization tool combines all annotations and allows users to generate network graphs of protein, genetic and chemical interactions. In addition to general curation across species, BioGRID undertakes themed curation projects in specific aspects of cellular regulation, for example the ubiquitin-proteasome system, as well as specific disease areas, such as for the SARS-CoV-2 virus that causes COVID-19 severe acute respiratory syndrome. A recent extension of BioGRID, named the Open Repository of CRISPR Screens (ORCS, orcs.thebiogrid.org), captures single mutant phenotypes and genetic interactions from published high throughput genome-wide CRISPR/Cas9-based genetic screens. BioGRID-ORCS contains datasets for over 1,042 CRISPR screens carried out to date in human, mouse and fly cell lines. The biomedical research community can freely access all BioGRID data through the web interface, standardized file downloads or via model organism databases and partner meta-databases. This article is protected by copyright. All rights reserved.
Keywords	covid19
Publisher	WHO
Document type	Article
Note	WHO #Covidence: #866167
Database	COVID19

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Article ; Online: The BioGRID interaction database: 2019 update.

Oughtred, Rose / Stark, Chris / Breitkreutz, Bobby-Joe / Rust, Jennifer / Boucher, Lorrie / Chang, Christie / Kolas, Nadine / O'Donnell, Lara / Leung, Genie / McAdam, Rochelle / Zhang, Frederick / Dolma, Sonam / Willems, Andrew / Coulombe-Huntington, Jasmin / Chatr-Aryamontri, Andrew / Dolinski, Kara / Tyers, Mike

Nucleic acids research

2018 Volume 47, Issue D1, Page(s) D529–D541

Abstract: The Biological General Repository for Interaction Datasets (BioGRID: https://thebiogrid.org) is an open access database dedicated to the curation and archival storage of protein, genetic and chemical interactions for all major model organism species and ... ...

Abstract	The Biological General Repository for Interaction Datasets (BioGRID: https://thebiogrid.org) is an open access database dedicated to the curation and archival storage of protein, genetic and chemical interactions for all major model organism species and humans. As of September 2018 (build 3.4.164), BioGRID contains records for 1 598 688 biological interactions manually annotated from 55 809 publications for 71 species, as classified by an updated set of controlled vocabularies for experimental detection methods. BioGRID also houses records for >700 000 post-translational modification sites. BioGRID now captures chemical interaction data, including chemical-protein interactions for human drug targets drawn from the DrugBank database and manually curated bioactive compounds reported in the literature. A new dedicated aspect of BioGRID annotates genome-wide CRISPR/Cas9-based screens that report gene-phenotype and gene-gene relationships. An extension of the BioGRID resource called the Open Repository for CRISPR Screens (ORCS) database (https://orcs.thebiogrid.org) currently contains over 500 genome-wide screens carried out in human or mouse cell lines. All data in BioGRID is made freely available without restriction, is directly downloadable in standard formats and can be readily incorporated into existing applications via our web service platforms. BioGRID data are also freely distributed through partner model organism databases and meta-databases.
MeSH term(s)	Animals ; CRISPR-Cas Systems ; Data Curation ; Databases, Factual ; Drug Discovery ; Genes ; Humans ; Mice ; Protein Interaction Mapping
Language	English
Publishing date	2018-11-05
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural ; 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/gky1079
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